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Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00757-8. [PMID: 38642570 DOI: 10.1016/s0140-6736(24)00757-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/07/2024] [Accepted: 04/12/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Detailed, comprehensive, and timely reporting on population health by underlying causes of disability and premature death is crucial to understanding and responding to complex patterns of disease and injury burden over time and across age groups, sexes, and locations. The availability of disease burden estimates can promote evidence-based interventions that enable public health researchers, policy makers, and other professionals to implement strategies that can mitigate diseases. It can also facilitate more rigorous monitoring of progress towards national and international health targets, such as the Sustainable Development Goals. For three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has filled that need. A global network of collaborators contributed to the production of GBD 2021 by providing, reviewing, and analysing all available data. GBD estimates are updated routinely with additional data and refined analytical methods. GBD 2021 presents, for the first time, estimates of health loss due to the COVID-19 pandemic. METHODS The GBD 2021 disease and injury burden analysis estimated years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries using 100 983 data sources. Data were extracted from vital registration systems, verbal autopsies, censuses, household surveys, disease-specific registries, health service contact data, and other sources. YLDs were calculated by multiplying cause-age-sex-location-year-specific prevalence of sequelae by their respective disability weights, for each disease and injury. YLLs were calculated by multiplying cause-age-sex-location-year-specific deaths by the standard life expectancy at the age that death occurred. DALYs were calculated by summing YLDs and YLLs. HALE estimates were produced using YLDs per capita and age-specific mortality rates by location, age, sex, year, and cause. 95% uncertainty intervals (UIs) were generated for all final estimates as the 2·5th and 97·5th percentiles values of 500 draws. Uncertainty was propagated at each step of the estimation process. Counts and age-standardised rates were calculated globally, for seven super-regions, 21 regions, 204 countries and territories (including 21 countries with subnational locations), and 811 subnational locations, from 1990 to 2021. Here we report data for 2010 to 2021 to highlight trends in disease burden over the past decade and through the first 2 years of the COVID-19 pandemic. FINDINGS Global DALYs increased from 2·63 billion (95% UI 2·44-2·85) in 2010 to 2·88 billion (2·64-3·15) in 2021 for all causes combined. Much of this increase in the number of DALYs was due to population growth and ageing, as indicated by a decrease in global age-standardised all-cause DALY rates of 14·2% (95% UI 10·7-17·3) between 2010 and 2019. Notably, however, this decrease in rates reversed during the first 2 years of the COVID-19 pandemic, with increases in global age-standardised all-cause DALY rates since 2019 of 4·1% (1·8-6·3) in 2020 and 7·2% (4·7-10·0) in 2021. In 2021, COVID-19 was the leading cause of DALYs globally (212·0 million [198·0-234·5] DALYs), followed by ischaemic heart disease (188·3 million [176·7-198·3]), neonatal disorders (186·3 million [162·3-214·9]), and stroke (160·4 million [148·0-171·7]). However, notable health gains were seen among other leading communicable, maternal, neonatal, and nutritional (CMNN) diseases. Globally between 2010 and 2021, the age-standardised DALY rates for HIV/AIDS decreased by 47·8% (43·3-51·7) and for diarrhoeal diseases decreased by 47·0% (39·9-52·9). Non-communicable diseases contributed 1·73 billion (95% UI 1·54-1·94) DALYs in 2021, with a decrease in age-standardised DALY rates since 2010 of 6·4% (95% UI 3·5-9·5). Between 2010 and 2021, among the 25 leading Level 3 causes, age-standardised DALY rates increased most substantially for anxiety disorders (16·7% [14·0-19·8]), depressive disorders (16·4% [11·9-21·3]), and diabetes (14·0% [10·0-17·4]). Age-standardised DALY rates due to injuries decreased globally by 24·0% (20·7-27·2) between 2010 and 2021, although improvements were not uniform across locations, ages, and sexes. Globally, HALE at birth improved slightly, from 61·3 years (58·6-63·6) in 2010 to 62·2 years (59·4-64·7) in 2021. However, despite this overall increase, HALE decreased by 2·2% (1·6-2·9) between 2019 and 2021. INTERPRETATION Putting the COVID-19 pandemic in the context of a mutually exclusive and collectively exhaustive list of causes of health loss is crucial to understanding its impact and ensuring that health funding and policy address needs at both local and global levels through cost-effective and evidence-based interventions. A global epidemiological transition remains underway. Our findings suggest that prioritising non-communicable disease prevention and treatment policies, as well as strengthening health systems, continues to be crucially important. The progress on reducing the burden of CMNN diseases must not stall; although global trends are improving, the burden of CMNN diseases remains unacceptably high. Evidence-based interventions will help save the lives of young children and mothers and improve the overall health and economic conditions of societies across the world. Governments and multilateral organisations should prioritise pandemic preparedness planning alongside efforts to reduce the burden of diseases and injuries that will strain resources in the coming decades. FUNDING Bill & Melinda Gates Foundation.
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Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00367-2. [PMID: 38582094 DOI: 10.1016/s0140-6736(24)00367-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/15/2024] [Accepted: 02/22/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation.
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Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00476-8. [PMID: 38484753 DOI: 10.1016/s0140-6736(24)00476-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/08/2023] [Accepted: 03/06/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020-21 COVID-19 pandemic period. METHODS 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5-65·1] decline), and increased during the COVID-19 pandemic period (2020-21; 5·1% [0·9-9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98-5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50-6·01) in 2019. An estimated 131 million (126-137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7-17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8-24·8), from 49·0 years (46·7-51·3) to 71·7 years (70·9-72·5). Global life expectancy at birth declined by 1·6 years (1·0-2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67-8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4-52·7]) and south Asia (26·3% [9·0-44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING Bill & Melinda Gates Foundation.
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4
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Fong WLE, Nguyen VG, Burns R, Boukari Y, Beale S, Braithwaite I, Byrne TE, Geismar C, Fragaszy E, Hoskins S, Kovar J, Navaratnam AMD, Oskrochi Y, Patel P, Tweed S, Yavlinsky A, Hayward AC, Aldridge RW. The incidence of COVID-19-related hospitalisation in migrants in the UK: Findings from the Virus Watch prospective community cohort study. J Migr Health 2024; 9:100218. [PMID: 38559897 PMCID: PMC10978526 DOI: 10.1016/j.jmh.2024.100218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 08/11/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Background Migrants in the United Kingdom (UK) may be at higher risk of SARS-CoV-2 exposure; however, little is known about their risk of COVID-19-related hospitalisation during waves 1-3 of the pandemic. Methods We analysed secondary care data linked to Virus Watch study data for adults and estimated COVID-19-related hospitalisation incidence rates by migration status. To estimate the total effect of migration status on COVID-19 hospitalisation rates, we ran mixed-effect Poisson regression for wave 1 (01/03/2020-31/08/2020; wildtype), and mixed-effect negative binomial regressions for waves 2 (01/09/2020-31/05/2021; Alpha) and 3 (01/06/2020-31/11/2021; Delta). Results of all models were then meta-analysed. Results Of 30,276 adults in the analyses, 26,492 (87.5 %) were UK-born and 3,784 (12.5 %) were migrants. COVID-19-related hospitalisation incidence rates for UK-born and migrant individuals across waves 1-3 were 2.7 [95 % CI 2.2-3.2], and 4.6 [3.1-6.7] per 1,000 person-years, respectively. Pooled incidence rate ratios across waves suggested increased rate of COVID-19-related hospitalisation in migrants compared to UK-born individuals in unadjusted 1.68 [1.08-2.60] and adjusted analyses 1.35 [0.71-2.60]. Conclusion Our findings suggest migration populations in the UK have excess risk of COVID-19-related hospitalisations and underscore the need for more equitable interventions particularly aimed at COVID-19 vaccination uptake among migrants.
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Affiliation(s)
- Wing Lam Erica Fong
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Vincent G Nguyen
- Institute of Health Informatics, University College London, London NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
| | - Rachel Burns
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Yamina Boukari
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Sarah Beale
- Institute of Health Informatics, University College London, London NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Isobel Braithwaite
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Thomas E Byrne
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Cyril Geismar
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Ellen Fragaszy
- Institute of Health Informatics, University College London, London NW1 2DA, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Jana Kovar
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Annalan MD Navaratnam
- Institute of Health Informatics, University College London, London NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Youssof Oskrochi
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Parth Patel
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Sam Tweed
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Alexei Yavlinsky
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London NW1 2DA, UK
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5
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Navaratnam AMD, O'Callaghan C, Beale S, Nguyen V, Aryee A, Braithwaite I, Byrne TE, Fong WLE, Fragaszy E, Geismar C, Hoskins S, Kovar J, Patel P, Shrotri M, Weber S, Yavlinsky A, Aldridge RW, Hayward AC. Eyeglasses and risk of COVID-19 transmission-analysis of the Virus Watch Community Cohort study. Int J Infect Dis 2024; 139:28-33. [PMID: 38008351 DOI: 10.1016/j.ijid.2023.10.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/20/2023] [Accepted: 10/27/2023] [Indexed: 11/28/2023] Open
Abstract
OBJECTIVES The importance of SARS-CoV-2 transmission via the eyes is unknown, with previous studies mainly focusing on protective eyewear in healthcare settings. This study aimed to test the hypothesis that wearing eyeglasses is associated with a lower risk of COVID-19. METHODS Participants from the Virus Watch prospective community cohort study responded to a questionnaire on the use of eyeglasses and contact lenses. Infection was confirmed through data linkage, self-reported positive results, and, for a subgroup, monthly capillary antibody testing. Multivariable logistic regression models, controlling for age, sex, income, and occupation, were used to identify the odds of infection depending on frequency and purpose of eyeglasses or contact lenses use. RESULTS A total of 19,166 participants responded to the questionnaire, with 13,681 (71.3%, CI 70.7-72.0) reporting they wore eyeglasses. Multivariable logistic regression model showed a 15% lower odds of infection for those who reported using eyeglasses always for general use (odds ratio [OR] 0.85, 95% 0.77-0.95, P = 0.002) compared to those who never wore eyeglasses. The protective effect was reduced for those who said wearing eyeglasses interfered with mask-wearing and was absent for contact lens wearers. CONCLUSIONS People who wear eyeglasses have a moderate reduction in risk of COVID-19 infection, highlighting that eye protection may make a valuable contribution to the reduction of transmission in community and healthcare settings.
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Affiliation(s)
| | - Christopher O'Callaghan
- Infection, Immunity & Inflammation Department, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Sarah Beale
- Institute of Health Informatics, University College London, London, UK; Institute of Epidemiology and Health Care, University College London, London, UK
| | - Vincent Nguyen
- Institute of Health Informatics, University College London, London, UK
| | - Anna Aryee
- Institute of Health Informatics, University College London, London, UK
| | | | - Thomas E Byrne
- Institute of Health Informatics, University College London, London, UK
| | | | - Ellen Fragaszy
- Institute of Health Informatics, University College London, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Cyril Geismar
- Institute of Health Informatics, University College London, London, UK
| | - Susan Hoskins
- Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Parth Patel
- Institute of Health Informatics, University College London, London, UK
| | - Madhumita Shrotri
- Institute of Health Informatics, University College London, London, UK
| | - Sophie Weber
- Institute of Health Informatics, University College London, London, UK
| | - Alexei Yavlinsky
- Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
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Burns R, Wyke S, Eyre MT, Boukari Y, Sørensen TB, Tsang C, Campbell CNJ, Beale S, Zenner D, Hargreaves S, Campos-Matos I, Harron K, Aldridge RW. COVID-19 vaccination coverage for half a million non-EU migrants and refugees in England. Nat Hum Behav 2024; 8:288-299. [PMID: 38049560 PMCID: PMC10896718 DOI: 10.1038/s41562-023-01768-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 10/23/2023] [Indexed: 12/06/2023]
Abstract
Despite evidence suggesting that some migrants are at risk of under-immunization and have experienced severe health inequities during the pandemic, data are limited on migrants' COVID-19 vaccine coverage globally. Here we linked data from non-European Union migrants and resettled refugees to the national COVID-19 vaccination dataset in England. We estimated patterns in second and third dose delays and overdue doses between 12 December 2020 and 20 April 2022 by age, visa type and ethnicity. Of the 465,470 linked records, 91.8% (427,073/465,470) of migrants received a second dose and 51.3% (238,721/465,470) received a third. Refugees had the highest risk of delayed second (adjusted odds ratio 1.66; 95% confidence interval 1.55-1.79) and third dose (1.55; 1.43-1.69). Black migrants were twice as likely to have a second dose delayed (2.37; 2.23-2.54) than white migrants, but this trend reversed for the third dose. Older migrants (>65 years) were four times less likely to have received their second or third dose compared with the general population in England aged >65 or older. Policymakers, researchers and practitioners should work to understand and address personal and structural barriers to vaccination for diverse migrant populations.
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Affiliation(s)
- Rachel Burns
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.
| | - Sacha Wyke
- Department of Health and Social Care, Office for Health Improvement and Disparities, London, UK
| | - Max T Eyre
- Department of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Yamina Boukari
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Tina B Sørensen
- Immunisation and Vaccine Preventable Diseases, UK Health Security Agency, London, UK
| | - Camille Tsang
- Immunisation and Vaccine Preventable Diseases, UK Health Security Agency, London, UK
| | - Colin N J Campbell
- Immunisation and Vaccine Preventable Diseases, UK Health Security Agency, London, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Dominik Zenner
- Global Public Health Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Infection and Population Health Department, Institute of Global Health, University College London, London, UK
| | - Sally Hargreaves
- Institute for Infection and Immunity, St George's University of London, Cranmer Terrace London, London, UK
| | - Ines Campos-Matos
- Department of Health and Social Care, Office for Health Improvement and Disparities, London, UK
- UK Health Security Agency, London, UK
| | - Katie Harron
- UCL Great Ormond Street, Institute of Child Health, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
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Burns R, Wyke S, Eyre MT, Boukari Y, Sørensen TB, Tsang C, Campbell CNJ, Beale S, Zenner D, Hargreaves S, Campos-Matos I, Harron K, Aldridge RW. Author Correction: COVID-19 vaccination coverage for half a million non-EU migrants and refugees in England. Nat Hum Behav 2024; 8:399. [PMID: 38374444 PMCID: PMC10896711 DOI: 10.1038/s41562-024-01845-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Affiliation(s)
- Rachel Burns
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.
| | - Sacha Wyke
- Department of Health and Social Care, Office for Health Improvement and Disparities, London, UK
| | - Max T Eyre
- Department of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Yamina Boukari
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Tina B Sørensen
- Immunisation and Vaccine Preventable Diseases, UK Health Security Agency, London, UK
| | - Camille Tsang
- Immunisation and Vaccine Preventable Diseases, UK Health Security Agency, London, UK
| | - Colin N J Campbell
- Immunisation and Vaccine Preventable Diseases, UK Health Security Agency, London, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Dominik Zenner
- Global Public Health Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Infection and Population Health Department, Institute of Global Health, University College London, London, UK
| | - Sally Hargreaves
- Institute for Infection and Immunity, St George's University of London, Cranmer Terrace London, London, UK
| | - Ines Campos-Matos
- Department of Health and Social Care, Office for Health Improvement and Disparities, London, UK
- UK Health Security Agency, London, UK
| | - Katie Harron
- UCL Great Ormond Street, Institute of Child Health, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
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8
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Burns R, Wyke S, Boukari Y, Katikireddi SV, Zenner D, Campos-Matos I, Harron K, Aldridge RW. Linking migration and hospital data in England: linkage process and evaluation of bias. Int J Popul Data Sci 2024; 9:2181. [PMID: 38476270 PMCID: PMC10929707 DOI: 10.23889/ijpds.v9i1.2181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024] Open
Abstract
Introduction Difficulties ascertaining migrant status in national data sources such as hospital records have limited large-scale evaluation of migrant healthcare needs in many countries, including England. Linkage of immigration data for migrants and refugees, with National Health Service (NHS) hospital care data enables research into the relationship between migration and health for a large cohort of international migrants. Objectives We aimed to describe the linkage process and compare linkage rates between migrant sub-groups to evaluate for potential bias for data on non-EU migrants and resettled refugees linked to Hospital Episode Statistics (HES) in England. Methods We used stepwise deterministic linkage to match records from migrants and refugees to a unique healthcare identifier indicating interaction with the NHS (linkage stage 1 to NHS Personal Demographic Services, PDS), and then to hospital records (linkage stage 2 to HES). We calculated linkage rates and compared linked and unlinked migrant characteristics for each linkage stage. Results Of the 1,799,307 unique migrant records, 1,134,007 (63%) linked to PDS and 451,689 (25%) linked to at least one hospital record between 01/01/2005 and 23/03/2020. Individuals on work, student, or working holiday visas were less likely to link to a hospital record than those on settlement and dependent visas and refugees. Migrants from the Middle East and North Africa and South Asia were four times more likely to link to at least one hospital record, compared to those from East Asia and the Pacific. Differences in age, sex, visa type, and region of origin between linked and unlinked samples were small to moderate. Conclusion This linked dataset represents a unique opportunity to explore healthcare use in migrants. However, lower linkage rates disproportionately affected individuals on shorter-term visas so future studies of these groups may be more biased as a result. Increasing the quality and completeness of identifiers recorded in administrative data could improve data linkage quality.
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Affiliation(s)
- Rachel Burns
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, 222 Euston Road, London NW1 2DA, United Kingdom
| | - Sacha Wyke
- UK Health Security Agency, 61 Colindale Ave, London NW9 5EQ United Kingdom
| | - Yamina Boukari
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, 222 Euston Road, London NW1 2DA, United Kingdom
| | - Sirinivasa Vittal Katikireddi
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Berkeley Square, 99 Berkeley Street, Glasgow, G3 7HR, United Kingdom
| | - Dominik Zenner
- Global Public Health Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Yvonne Carter Building, 58 Turner Street, London E1 2AB, United Kingdom
- Infection and Population Health Department, Institute of Global Health, University College London
| | - Ines Campos-Matos
- UK Health Security Agency, 61 Colindale Ave, London NW9 5EQ United Kingdom
- Office for Health Improvement and Disparities, Department of Health and Social Care, 39 Victoria Street, London SW1H 0EU, United Kingdom
| | - Katie Harron
- UCL Great Ormond Street, Institute of Child Health, University College London, 30 Guilford Street, London WC1N 1EH, United Kingdom
| | - Robert W. Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, 222 Euston Road, London NW1 2DA, United Kingdom
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9
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Pathak N, Zhang CX, Boukari Y, Burns R, Menezes D, Hugenholtz G, French RS, Gonzalez-Izquierdo A, Mathur R, Denaxas S, Hayward A, Sonnenberg P, Aldridge RW. Sexual and reproductive health and rights of migrant women attending primary care in England: A population-based cohort study of 1.2 million individuals of reproductive age (2009-2018). J Migr Health 2024; 9:100214. [PMID: 38327760 PMCID: PMC10847991 DOI: 10.1016/j.jmh.2024.100214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 01/16/2024] [Accepted: 01/16/2024] [Indexed: 02/09/2024] Open
Abstract
Background Evidence on the sexual and reproductive health and rights (SRHR) of migrants is lacking globally. We describe SRHR healthcare resource use and long-acting reversible contraceptives (LARCs) prescriptions for migrant versus non-migrant women attending primary care in England (2009-2018). Methods This population-based observational cohort study, using Clinical Practice Research Datalink (CPRD) GOLD, included females living in England aged 15 to 49. Migration was defined using a validated codelist. Rates per 100 person years at risk (pyar) and adjusted rate ratios (RRs) were measured in migrants versus non-migrants for consultations related to all-causes, six exemplar SRHR outcomes, and LARC prescriptions. Proportions of migrants and non-migrants ever prescribed LARC were calculated. Findings There were 25,112,116 consultations across 1,246,353 eligible individuals. 98,214 (7.9 %) individuals were migrants. All-cause consultation rates were lower in migrants versus non-migrants (509 vs 583/100pyar;RR 0.9;95 %CI 0.9-0.9), as were consultations rates for emergency contraception (RR 0.7;95 %CI 0.7-0.7) and cervical screening (RR 0.96;95 %CI 0.95-0.97). Higher rates of consultations were found in migrants for abortion (RR 1.2;95 %CI 1.1-1.2) and management of fertility problems (RR 1.39;95 %CI 1.08-1.79). No significant difference was observed for chlamydia testing and domestic violence. Of 1,205,258 individuals eligible for contraception, the proportion of non-migrants ever prescribed LARC (12.2 %;135,047/1,107,894) was almost double that of migrants (6.91 %;6,728/97,364). Higher copper intrauterine devices prescription rates were found in migrants (RR 1.53;95 %CI 1.45-1.61), whilst hormonal LARC rates were lower for migrants: levonorgestrel intrauterine device (RR 0.63;95 %CI 0.60-0.66), subdermal implant (RR 0.72;95 %CI 0.69-0.75), and progesterone-only injection (RR 0.35;95 %CI 0.34-0.36). Interpretation Healthcare resource use differs between migrant and non-migrant women of reproductive age. Opportunities identified for tailored interventions include access to primary care, LARCs, emergency contraception and cervical screening. An inclusive approach to examining health needs is essential to actualise sexual and reproductive health as a human right.
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Affiliation(s)
- Neha Pathak
- Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute for Global Health, University College London, London, WC1E 6JB, UK
- Guy's & St Thomas's NHS Foundation Trust, London, SE1 9RT, UK
| | - Claire X. Zhang
- Institute of Health Informatics, University College London, London, NW1 2DA, UK
- National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, OX3 7LF, UK
| | - Yamina Boukari
- Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Rachel Burns
- Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Dee Menezes
- Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Gregory Hugenholtz
- Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Rebecca S French
- Faculty of Public Health & Policy, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Rohini Mathur
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, NW1 2DA, UK
- BHF Data Science Center, Health Data Research UK, London, NW1 2DA, UK
| | - Andrew Hayward
- Inclusion Health, UK Health Security Agency, London, UK
- Institute of Epidemiology and Healthcare, University College London, London, WC1E 7HB, UK
| | - Pam Sonnenberg
- Institute for Global Health, University College London, London, WC1E 6JB, UK
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10
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Zhang CX, Lewer D, Aldridge RW, Hayward AC, Cornaglia C, Trussell P, Lillford-Wildman C, Castle J, Gommon J, Campos-Matos I. Small numbers, big impact: making a utilitarian case for the contribution of inclusion health to population health in England. J Epidemiol Community Health 2023; 77:816-820. [PMID: 37734936 DOI: 10.1136/jech-2023-220849] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/28/2023] [Indexed: 09/23/2023]
Abstract
Inclusion health groups make up a small proportion of the general population, so despite the extreme social exclusion and poor health outcomes that these groups experience, they are often overlooked in public health investment and policy development. In this paper, we demonstrate that a utilitarian argument can be made for investment in better support for inclusion health groups despite their small size. That is, by preventing social exclusion, there is the potential for large aggregate health benefits to the whole population. We illustrate this by reframing existing published mortality estimates into population attributable fractions to show that 12% of all-cause premature deaths (95% CI 10.03% to 14.29%) are attributable to the circumstances of people who experience homelessness, use drugs and/or have been in prison. We also show that a large proportion of cause-specific premature deaths in the general population can be attributed to specific inclusion health groups, such as 43% of deaths due to viral hepatitis (95% CI 30.35% to 56.61%) and nearly 4000 deaths due to cancer (3844, 95% CI 3438 to 4285) between 2013 and 2021 attributed to individuals who use illicit opioids. Considering the complexity of the inclusion health policy context and the sparseness of evidence, we discuss how a shift in policy framing from 'inclusion health vs the rest of the population' to 'the impact of social exclusion on broader population health' makes a better case for increased policy attention and investment in inclusion health. We discuss the strengths and limitations of this approach and how it can be applied to public health policy, resource prioritisation and future research.
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Affiliation(s)
- Claire X Zhang
- Addictions & Inclusion Directorate, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Dan Lewer
- Institute of Epidemiology & Health Care, University College London, London, UK
- UCL Collaborative Centre for Inclusion Health, University College London, London, UK
- Bradford Institute for Health Research, Bradford, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London, UK
| | - Andrew C Hayward
- Institute of Epidemiology & Health Care, University College London, London, UK
- UCL Collaborative Centre for Inclusion Health, University College London, London, UK
| | - Carlotta Cornaglia
- Addictions & Inclusion Directorate, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Peta Trussell
- Addictions & Inclusion Directorate, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Charlotte Lillford-Wildman
- Addictions & Inclusion Directorate, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Joanna Castle
- Addictions & Inclusion Directorate, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Jake Gommon
- Addictions & Inclusion Directorate, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Ines Campos-Matos
- Addictions & Inclusion Directorate, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
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Humphrey A, Fagan L, Carruthers E, Yuan JM, Ogunlana K, Alfred J, Nagasivam A, Stevenson K, Aldridge RW, Stevenson F, Williams S, Burns R. Perspectives on registration to primary care from inclusion health groups in England: a mixed-method study. Lancet 2023; 402 Suppl 1:S53. [PMID: 37997096 DOI: 10.1016/s0140-6736(23)02074-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 08/10/2023] [Accepted: 09/22/2023] [Indexed: 11/25/2023]
Abstract
BACKGROUND Although everyone living in the UK is entitled to access free primary care within the National Health Service (NHS), evidence shows that people in need of health care are wrongly being refused access. This study aimed to explore the perspectives of individuals from inclusion health groups on primary care registration and accessibility. METHODS This was a mixed-methods study. From Oct 5, 2022, to Feb 20, 2023, we surveyed 49 people (36 [73%] men; 12 [24%] women) and interviewed 25 other (14 [56%] men; 11 [44%] women) who were service users of the University College London Hospital Find & Treat mobile service. This service included people with lived experience of homelessness, asylum seeking, addiction, selling sex, and irregular immigration. We recruited these participants through hostels for people with ongoing addiction and complex needs, initial asylum accommodation centres, and day shelters. Our research team included two peer researchers. FINDINGS Of those surveyed, 25 (51%) perceived their access to primary health-care services as good, and 17 (35%) reported obstacles to going to the general practitioner (GP). Participants described multiple barriers to registering for GP surgeries, including a lack of understanding and poor communication with NHS services, a fear of discrimination, and a lack of digital access that prevents information seeking and access to services. Respondents also reported using emergency services instead of primary care because they were more immediately accessible without previous registration. Facilitators to GP registration included one-on-one support and outreach work that helps people navigate into services and know their rights, and the use of specialist GP services, which are perceived as more accepting, especially for people experiencing homelessness. INTERPRETATION The barriers to registration identified are related to both individual and group level characteristics and produce both similar and divergent needs between different inclusion health groups. The need for additional support during registration was clear, and our work highlights the requirement for interventions to improve access to primary care for underserved groups, as well as coordinated policy action. One-on-one support in particular, either outreach or provided in services where inclusion health groups spend time, appears to be a key facilitator to ensuring comprehensive and fast access to GP services. FUNDING National Institute for Health and Care Research (NIHR).
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Affiliation(s)
- Ada Humphrey
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK; London School of Hygiene & Tropical Medicine, London, UK.
| | - Lucy Fagan
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Elspeth Carruthers
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK; UK Health Security Agency, London, UK; Whittington Health NHS Trust, London, UK
| | - Jin-Min Yuan
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Kemi Ogunlana
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Janet Alfred
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Ahimza Nagasivam
- School of Public Health, Health Education England and Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Kerrie Stevenson
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Fiona Stevenson
- Department of Primary Care and Population Health, University College London, London, UK
| | - Sitira Williams
- Limitless Research Ltd, Park Lane Business Centre, Nottingham, UK
| | - Rachel Burns
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
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12
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Carruthers E, Dobbin J, Fagan L, Humphrey A, Nagasivam A, Stevenson K, Yuan JM, Aldridge RW, Burns R. Interventions to improve access to primary care for inclusion health groups in England: a scoping review. Lancet 2023; 402 Suppl 1:S32. [PMID: 37997073 DOI: 10.1016/s0140-6736(23)02081-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 08/16/2023] [Accepted: 09/22/2023] [Indexed: 11/25/2023]
Abstract
BACKGROUND Everyone in England has the right to primary care without financial charges. Nevertheless, evidence shows that barriers remain for inclusion health populations such as vulnerable migrants, people experiencing homelessness, Gypsy, Roma, and Traveller (GRT) communities, and people who sell sex. There is little evidence for what works to improve access. This study was a scoping review of interventions to improve access to mainstream primary care for inclusion health groups in England. METHODS In this scoping review, we searched databases (Embase, Medline, APA PsychInfo, the Cochrane Collaboration Library, Web of Science and CINAHL) and grey literature sources, including the National Health Service and National Institute for Clinical Excellence, for articles published in English between Jan 1, 2010, and Dec 31, 2020, with no limit on study design. Data were extracted according to inclusion criteria, including interventions taking place in England and targeting people with insecure immigration status, people who sell sex, people experiencing homelessness, and GRT communities. Results were presented in a narrative synthesis. FINDINGS 39 studies describing one or more interventions were included: four peer-reviewed articles (one randomised trial, two quality improvement projects, and one mixed-methods study protocol) and 25 grey literature items (38 interventions in total). Interventions mostly targeted people with insecure immigration status (17/38, 45%), and a majority (12/38, 32%) took place in London. The most common types of intervention were training, education, and resources (such as leaflets or websites) for patients or staff (25/38, 66%), and most interventions targeted GP registration processes (28/38, 74%). Interventions commonly involved voluntary and community sector organisations (16/38, 42%). Most interventions were not evaluated to understand their effectiveness (23/38, 61%). Sources with evaluations identified staff training, direct patient advocacy, and involvement of people with lived experience as effective elements. INTERPRETATION Interventions to improve access to primary care for inclusion health groups in England were heterogeneous, commonly undertaken at community level, and developed to serve local inclusion health groups. Considerations for policymakers and practitioners include groups and geographical areas less commonly included in interventions, the elements of positive practice identified in evaluations, and the need for evaluation of future interventions. FUNDING National Institute for Health and Care Research (NIHR 202050).
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Affiliation(s)
- Elspeth Carruthers
- Whittington Health NHS Trust, Whittington Hospital, London, UK; Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.
| | - Joanna Dobbin
- Department of Primary Care and Population Health, University College London, London, UK
| | - Lucy Fagan
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Ada Humphrey
- Institute for Global Health Innovation, Imperial College, London, UK
| | - Ahimza Nagasivam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK; School of Public Health, Health Education England, London, UK
| | - Kerrie Stevenson
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jin-Min Yuan
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Rachel Burns
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
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13
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Lam J, Aldridge RW, Blackburn R, Harron K. Recording and analysing ethnicity in public health research: a bibliographical review and focus group discussions with young migrants and refugees in the UK. Lancet 2023; 402 Suppl 1:S63. [PMID: 37997107 DOI: 10.1016/s0140-6736(23)02092-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/16/2023] [Accepted: 09/22/2023] [Indexed: 11/25/2023]
Abstract
BACKGROUND The ethnicity data gap hinders public health research from addressing ethnic health inequity in the UK, especially for under-served young, migrant populations. We aimed to review how ethnicity was captured, reported, analysed, and theorised within policy-relevant research. METHODS For this bibliographical review, we reviewed a selection of the 1% most highly cited population health papers reporting UK ethnicity data in MEDLINE and Web of Science databases between Jan 1, 1946, and July 31, 2022, and extracted how ethnicity was recorded and analysed. We included cross-sectional, longitudinal cohort studies, and randomised trials using only UK populations, which were peer-reviewed, were written in English, and reported ethnicity and any health-related outcomes. We held three focus groups with ten participants aged 18-25 years, from Nigeria, Turkistan, Syria, Yemen, and Iran to help us shape and interpret our findings, and asked "How should ethnicity be asked inclusively, and better recorded?" and "Does ethnicity change over time or context? If so, why?". We consolidated feedback from our focus groups into a co-created poster with recommendations for researchers studying ethnicity and health. Written informed consent was obtained for focus group participation. FINDINGS Of 44 papers included in the review, 19 (43%) used self-reported ethnicity, but the number of ethnic categories provided varied. Of 27 papers that aggregated ethnicity, 13 (48%) provided justification. Only eight (18%) explicitly theorised how ethnicity related to health. The focus groups agreed that (1) ethnicity should not be prescribed by others (individuals could be asked to describe their ethnicity in free-text, which researchers could synthesise to extract relevant dimensions of ethnicity for their research) and (2) Ethnicity changes over time and context according to personal experience, social pressure, and nationality change. The lived experience of ethnicity of migrants and non-migrants is not fully interchangeable, even if they share the same ethnic category. INTERPRETATION Researchers should communicate clearly how ethnicity is operationalised in their studies, with appropriate justification for clustering and analysis that is meaningfully theorised. Our study was limited by its non-systematic nature. Implementing the recommendation to capture ethnicity via free text remains challenging in administrative data systems. FUNDING UCL Engagement Beacon Bursary.
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Affiliation(s)
- Joseph Lam
- UCL Great Ormond Street Institute of Child Health, Faculty of Population Health Sciences, University College London, London, UK.
| | - Robert W Aldridge
- UCL Institute of Health Informatics, University College London, London, UK
| | - Ruth Blackburn
- UCL Great Ormond Street Institute of Child Health, Faculty of Population Health Sciences, University College London, London, UK
| | - Katie Harron
- UCL Great Ormond Street Institute of Child Health, Faculty of Population Health Sciences, University College London, London, UK
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14
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Patel P, Beale S, Nguyen V, Braithwaite I, Byrne TE, Erica Fong WL, Fragaszy E, Geismar C, Hoskins S, Navaratnam AMD, Shrotri M, Kovar J, Aryee A, Hayward AC, Aldridge RW. Inequalities in access to paid sick leave among workers in England and Wales. Int J Health Plann Manage 2023; 38:1864-1876. [PMID: 37549127 PMCID: PMC10946983 DOI: 10.1002/hpm.3697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 07/20/2023] [Indexed: 08/09/2023] Open
Abstract
BACKGROUND It is poorly understood which workers lack access to sick pay in England and Wales. This evidence gap has been of particular interest in the context of the Covid-19 pandemic given the relationship between presenteeism and infectious disease transmission. METHOD This cross-sectional analysis (n = 8874) was nested within a large community cohort study based across England and Wales (Virus Watch). An online survey in February 2021 asked participants in work if they had access to paid sick leave. We used logistic regression to examine sociodemographic factors associated with lacking access to sick pay. RESULTS Only 66% (n = 5864) of participants reported access to sick pay. South Asian workers (adjusted odds ratio [OR] 1.40, 95% confidence interval [CI] 1.06-1.83) and those from Other minority ethnic backgrounds (OR 2.93, 95% CI 1.54-5.59) were more likely to lack access to sick pay compared to White British workers. Older workers (OR range 1.72 [1.53-1.93]-5.26 [4.42-6.26]), workers in low-income households (OR 2.53, 95% CI 2.15-2.98) and those in transport, trade, and service occupations (OR range 2.03 [1.58-2.61]-5.29 [3.67-7.72]) were also more likely to lack access to sick pay compared respectively to workers aged 25-44, those in high income households and managerial occupations. DISCUSSION Unwarranted age and ethnic inequalities in sick pay access are suggestive of labour market discrimination. Occupational differences are also cause for concern. Policymakers should consider expanding access to sick pay to mitigate transmission of Covid-19 and other endemic respiratory infections in the community, and in the context of pandemic preparation.
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Affiliation(s)
- Parth Patel
- Institute of Health InformaticsUniversity College LondonLondonUK
| | - Sarah Beale
- Institute of Health InformaticsUniversity College LondonLondonUK
- Institute of Epidemiology and Health CareUniversity College LondonLondonUK
| | - Vincent Nguyen
- Institute of Health InformaticsUniversity College LondonLondonUK
| | | | - Thomas E. Byrne
- Institute of Health InformaticsUniversity College LondonLondonUK
| | | | - Ellen Fragaszy
- Institute of Health InformaticsUniversity College LondonLondonUK
- Department of Infectious Disease EpidemiologyLondon School of Hygiene and Tropical MedicineLondonUK
| | - Cyril Geismar
- Institute of Health InformaticsUniversity College LondonLondonUK
| | - Susan Hoskins
- Institute of Epidemiology and Health CareUniversity College LondonLondonUK
| | | | | | - Jana Kovar
- Institute of Epidemiology and Health CareUniversity College LondonLondonUK
| | - Anna Aryee
- Institute of Health InformaticsUniversity College LondonLondonUK
| | - Andrew C. Hayward
- Institute of Epidemiology and Health CareUniversity College LondonLondonUK
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15
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Byrne T, Kovar J, Beale S, Braithwaite I, Fragaszy E, Fong WLE, Geismar C, Hoskins S, Navaratnam AMD, Nguyen V, Patel P, Shrotri M, Yavlinsky A, Hardelid P, Wijlaars L, Nastouli E, Spyer M, Aryee A, Cox I, Lampos V, Mckendry RA, Cheng T, Johnson AM, Michie S, Gibbs J, Gilson R, Rodger A, Abubakar I, Hayward A, Aldridge RW. Cohort Profile: Virus Watch-understanding community incidence, symptom profiles and transmission of COVID-19 in relation to population movement and behaviour. Int J Epidemiol 2023; 52:e263-e272. [PMID: 37349899 PMCID: PMC10555858 DOI: 10.1093/ije/dyad087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/31/2023] [Indexed: 06/24/2023] Open
Affiliation(s)
- Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Ellen Fragaszy
- Institute of Epidemiology and Health Care, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Pia Hardelid
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Linda Wijlaars
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Eleni Nastouli
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
- Francis Crick Institute, London, UK
- University College London Hospital, London, UK
| | | | - Anna Aryee
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Ingemar Cox
- Department of Computer Science, University College London, London, UK
| | - Vasileios Lampos
- Department of Computer Science, University College London, London, UK
| | - Rachel A Mckendry
- London Centre for Nanotechnology and Division of Medicine, University College London, London, UK
| | - Tao Cheng
- SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College London, London, UK
| | - Anne M Johnson
- Centre for Population Research in Sexual Health and HIV, Institute for Global Health, London, UK
| | - Susan Michie
- Centre for Behaviour Change, University College London, London, UK
| | - Jo Gibbs
- Institute for Global Health, University College London, London, UK
| | - Richard Gilson
- Institute for Global Health, University College London, London, UK
| | - Alison Rodger
- Institute for Global Health, University College London, London, UK
- Royal Free London NHS Foundation Trust, London, UK
| | - Ibrahim Abubakar
- Institute for Global Health, University College London, London, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
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16
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Hoskins S, Beale S, Nguyen V, Boukari Y, Yavlinsky A, Kovar J, Byrne T, Fong WLE, Geismar C, Patel P, Johnson AM, Aldridge RW, Hayward A. Deprivation, essential and non-essential activities and SARS-CoV-2 infection following the lifting of national public health restrictions in England and Wales. NIHR Open Res 2023; 3:46. [PMID: 37994319 PMCID: PMC10663878 DOI: 10.3310/nihropenres.13445.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/25/2023] [Indexed: 11/24/2023]
Abstract
Background Individuals living in deprived areas in England and Wales undertook essential activities more frequently and experienced higher rates of SARS-CoV-2 infection than less deprived communities during periods of restrictions aimed at controlling the Alpha (B.1.1.7) variant. We aimed to understand whether these deprivation-related differences changed once restrictions were lifted. Methods Among 11,231 adult Virus Watch Community Cohort Study participants multivariable logistic regressions were used to estimate the relationships between deprivation and self-reported activities and deprivation and infection (self-reported lateral flow or PCR tests and linkage to National Testing data and Second Generation Surveillance System (SGSS)) between August - December 2021, following the lifting of national public health restrictions. Results Those living in areas of greatest deprivation were more likely to undertake essential activities (leaving home for work (aOR 1.56 (1.33 - 1.83)), using public transport (aOR 1.33 (1.13 - 1.57)) but less likely to undertake non-essential activities (indoor hospitality (aOR 0.82 (0.70 - 0.96)), outdoor hospitality (aOR 0.56 (0.48 - 0.66)), indoor leisure (aOR 0.63 (0.54 - 0.74)), outdoor leisure (aOR 0.64 (0.46 - 0.88)), or visit a hairdresser (aOR 0.72 (0.61 - 0.85))). No statistical association was observed between deprivation and infection (P=0.5745), with those living in areas of greatest deprivation no more likely to become infected with SARS-CoV-2 (aOR 1.25 (0.87 - 1.79). Conclusion The lack of association between deprivation and infection is likely due to the increased engagement in non-essential activities among the least deprived balancing the increased work-related exposure among the most deprived. The differences in activities highlight stark disparities in an individuals' ability to choose how to limit infection exposure.
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Affiliation(s)
- Susan Hoskins
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
| | - Sarah Beale
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, England, WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, England, WC1E 7HB, UK
| | - Yamina Boukari
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, England, WC1E 7HB, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, England, WC1E 7HB, UK
| | - Parth Patel
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
| | - Anne M. Johnson
- Institute for Global Health, University College London, London, England, WC1N 1EH, UK
| | - Robert W. Aldridge
- Centre for Public Health Data Science, University College London, London, England, NW1 2DA, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, England, WC1E 7HB, UK
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17
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Geismar C, Nguyen V, Fragaszy E, Shrotri M, Navaratnam AMD, Beale S, Byrne TE, Fong WLE, Yavlinsky A, Kovar J, Hoskins S, Braithwaite I, Aldridge RW, Hayward AC, White PJ, Jombart T, Cori A. Bayesian reconstruction of SARS-CoV-2 transmissions highlights substantial proportion of negative serial intervals. Epidemics 2023; 44:100713. [PMID: 37579586 DOI: 10.1016/j.epidem.2023.100713] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 07/25/2023] [Accepted: 07/31/2023] [Indexed: 08/16/2023] Open
Abstract
BACKGROUND The serial interval is a key epidemiological measure that quantifies the time between the onset of symptoms in an infector-infectee pair. It indicates how quickly new generations of cases appear, thus informing on the speed of an epidemic. Estimating the serial interval requires to identify pairs of infectors and infectees. Yet, most studies fail to assess the direction of transmission between cases and assume that the order of infections - and thus transmissions - strictly follows the order of symptom onsets, thereby imposing serial intervals to be positive. Because of the long and highly variable incubation period of SARS-CoV-2, this may not always be true (i.e an infectee may show symptoms before their infector) and negative serial intervals may occur. This study aims to estimate the serial interval of different SARS-CoV-2 variants whilst accounting for negative serial intervals. METHODS This analysis included 5 842 symptomatic individuals with confirmed SARS-CoV-2 infection amongst 2 579 households from September 2020 to August 2022 across England & Wales. We used a Bayesian framework to infer who infected whom by exploring all transmission trees compatible with the observed dates of symptoms, based on a wide range of incubation period and generation time distributions compatible with estimates reported in the literature. Serial intervals were derived from the reconstructed transmission pairs, stratified by variants. RESULTS We estimated that 22% (95% credible interval (CrI) 8-32%) of serial interval values are negative across all VOC. The mean serial interval was shortest for Omicron BA5 (2.02 days, 1.26-2.84) and longest for Alpha (3.37 days, 2.52-4.04). CONCLUSIONS This study highlights the large proportion of negative serial intervals across SARS-CoV-2 variants. Because the serial interval is widely used to estimate transmissibility and forecast cases, these results may have critical implications for epidemic control.
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Affiliation(s)
- Cyril Geismar
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK; Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Ellen Fragaszy
- Institute of Epidemiology and Health Care, University College London, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK; Institute of Epidemiology and Health Care, University College London, London, UK
| | - Thomas E Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Susan Hoskins
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Peter J White
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Thibaut Jombart
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
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Stevenson K, Ogunlana K, Edwards S, Henderson WG, Rayment-Jones H, McGranahan M, Marti-Castaner M, Fellmeth G, Luchenski S, Stevenson FA, Knight M, Aldridge RW. Interventions to improve perinatal outcomes among migrant women in high-income countries: a systematic review protocol. BMJ Open 2023; 13:e072090. [PMID: 37591637 PMCID: PMC10441090 DOI: 10.1136/bmjopen-2023-072090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 07/28/2023] [Indexed: 08/19/2023] Open
Abstract
INTRODUCTION Women who are migrants and who are pregnant or postpartum are at high risk of poorer perinatal outcomes compared with host country populations due to experiencing numerous additional stressors including social exclusion and language barriers. High-income countries (HICs) host many migrants, including forced migrants who may face additional challenges in the peripartum period. Although HICs' maternity care systems are often well developed, they are not routinely tailored to the needs of migrant women. The primary objective will be to determine what interventions exist to improve perinatal outcomes for migrant women in HICs. The secondary objective will be to explore the effectiveness of these interventions by exploring the impact on perinatal outcomes. The main outcomes of interest will be rates of preterm birth, birth weight, and number of antenatal or postnatal appointments attended. METHODS AND ANALYSIS This protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Protocols guidelines. EMBASE, EMCARE, MEDLINE and PsycINFO, CENTRAL, Scopus, CINAHL Plus, and Web of Science, as well as grey literature sources will be searched from inception up to December 2022. We will include randomised controlled trials, quasi-experimental and interventional studies of interventions, which aim to improve perinatal outcomes in any HIC. There will be no language restrictions. We will exclude studies presenting only qualitative outcomes and those including mixed populations of migrant and non-migrant women. Screening and data extraction will be completed by two independent reviewers and risk of bias will be assessed using the Quality Assessment Tool for Quantitative Studies. If a collection of suitably comparable outcomes is retrieved, we will perform meta-analysis applying a random effects model. Presentation of results will comply with guidelines in the Cochrane Handbook of Systematic Reviews of Interventions and the PRISMA statement. ETHICS AND DISSEMINATION Ethical approval is not required. Results will be submitted for peer-reviewed publication and presented at national and international conferences. The findings will inform the work of the Lancet Migration European Hub. PROSPERO REGISTRATION NUMBER CRD42022380678.
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Affiliation(s)
- Kerrie Stevenson
- Institute of Health Informatics, University College London, London, UK
| | - K Ogunlana
- Institute of Health Informatics, University College London, London, UK
| | - Samuel Edwards
- Warwick Medical School, University of Warwick, Coventry, UK
| | | | | | | | - Maria Marti-Castaner
- Health Department of Public Health, University of Copenhagen, Kobenhavn, Denmark
| | - Gracia Fellmeth
- National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Serena Luchenski
- Collaborative Centre for Inclusion Health, Institute of Epidemiology & Health Care, University College London, London, UK
| | - Fiona A Stevenson
- Institute of Epidemiology & Health Care, University College London, London, UK
| | - Marian Knight
- National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London, UK
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19
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Geismar C, Nguyen V, Fragaszy E, Shrotri M, Navaratnam AMD, Beale S, Byrne TE, Fong WLE, Yavlinsky A, Kovar J, Hoskins S, Braithwaite I, Aldridge RW, Hayward AC. Symptom profiles of community cases infected by influenza, RSV, rhinovirus, seasonal coronavirus, and SARS-CoV-2 variants of concern. Sci Rep 2023; 13:12511. [PMID: 37532756 PMCID: PMC10397315 DOI: 10.1038/s41598-023-38869-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 07/16/2023] [Indexed: 08/04/2023] Open
Abstract
Respiratory viruses that were suppressed through previous lockdowns during the COVID-19 pandemic have recently started to co-circulate with SARS-CoV-2. Understanding the clinical characteristics and symptomatology of different respiratory viral infections can help address the challenges related to the identification of cases and the understanding of SARS-CoV-2 variants' evolutionary patterns. Flu Watch (2006-2011) and Virus Watch (2020-2022) are household community cohort studies monitoring the epidemiology of influenza, respiratory syncytial virus, rhinovirus, seasonal coronavirus, and SARS-CoV-2, in England and Wales. This study describes and compares the proportion of symptoms reported during illnesses infected by common respiratory viruses. The SARS-CoV-2 symptom profile increasingly resembles that of other respiratory viruses as new strains emerge. Increased cough, sore throat, runny nose, and sneezing are associated with the emergence of the Omicron strains. As SARS-CoV-2 becomes endemic, monitoring the evolution of its symptomatology associated with new variants will be critical for clinical surveillance.
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Affiliation(s)
- Cyril Geismar
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Ellen Fragaszy
- Institute of Epidemiology and Health Care, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Thomas E Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Susan Hoskins
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
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20
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Nguyen VG, Yavlinsky A, Beale S, Hoskins S, Byrne TE, Lampos V, Braithwaite I, Fong WLE, Fragaszy E, Geismar C, Kovar J, Navaratnam AMD, Patel P, Shrotri M, Weber S, Hayward AC, Aldridge RW. Comparative effectiveness of different primary vaccination courses on mRNA-based booster vaccines against SARs-COV-2 infections: a time-varying cohort analysis using trial emulation in the Virus Watch community cohort. Int J Epidemiol 2023; 52:342-354. [PMID: 36655537 PMCID: PMC10114109 DOI: 10.1093/ije/dyad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 01/13/2023] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND The Omicron B.1.1.529 variant increased severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in doubly vaccinated individuals, particularly in the Oxford-AstraZeneca COVID-19 vaccine (ChAdOx1) recipients. To tackle infections, the UK's booster vaccination programmes used messenger ribonucleic acid (mRNA) vaccines irrespective of an individual's primary course vaccine type, and prioritized the clinically vulnerable. These mRNA vaccines included the Pfizer-BioNTech COVID-19 vaccine (BNT162b2) the Moderna COVID-19 vaccine (mRNA-1273). There is limited understanding of the effectiveness of different primary vaccination courses on mRNA booster vaccines against SARs-COV-2 infections and how time-varying confounders affect these evaluations. METHODS Trial emulation was applied to a prospective community observational cohort in England and Wales to reduce time-varying confounding-by-indication driven by prioritizing vaccination based upon age, vulnerability and exposure. Trial emulation was conducted by meta-analysing eight adult cohort results whose booster vaccinations were staggered between 16 September 2021 and 05 January 2022 and followed until 23 January 2022. Time from booster vaccination until SARS-CoV-2 infection, loss of follow-up or end of study was modelled using Cox proportional hazard models and adjusted for age, sex, minority ethnic status, clinically vulnerability and deprivation. RESULTS A total of 19 159 participants were analysed, with 11 709 ChAdOx1 primary courses and 7450 BNT162b2 primary courses. Median age, clinical vulnerability status and infection rates fluctuate through time. In mRNA-boosted adults, 7.4% (n = 863) of boosted adults with a ChAdOx1 primary course experienced a SARS-CoV-2 infection compared with 7.7% (n = 571) of those who had BNT162b2 as a primary course. The pooled adjusted hazard ratio (aHR) was 1.01 with a 95% confidence interval (CI) of: 0.90 to 1.13. CONCLUSION After an mRNA booster dose, we found no difference in protection comparing those with a primary course of BNT162b2 with those with a ChAdOx1 primary course. This contrasts with pre-booster findings where previous research shows greater effectiveness of BNT162b2 than ChAdOx1 in preventing infection.
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Affiliation(s)
- Vincent Grigori Nguyen
- Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Alexei Yavlinsky
- Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Thomas E Byrne
- Institute of Health Informatics, University College London, London, UK
| | - Vasileios Lampos
- Department of Computer Science, University College London, London, UK
| | | | | | - Ellen Fragaszy
- Institute of Health Informatics, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Cyril Geismar
- Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | | | - Parth Patel
- Institute of Health Informatics, University College London, London, UK
| | - Madhumita Shrotri
- Institute of Health Informatics, University College London, London, UK
| | - Sophie Weber
- Institute of Health Informatics, University College London, London, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London, UK
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21
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Beale S, Yavlinsky A, Hoskins S, Nguyen V, Byrne T, Fong WLE, Kovar J, Van Tongeren M, Aldridge RW, Hayward A. Between-occupation differences in work-related COVID-19 mitigation strategies over time: Analysis of the Virus Watch Cohort in England and Wales. Scand J Work Environ Health 2023:4092. [PMID: 37066842 DOI: 10.5271/sjweh.4092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023] Open
Abstract
OBJECTIVES COVID-19 mitigations have had a profound impact on workplaces, however, multisectoral comparisons of how work-related mitigations were applied are limited. This study aimed to investigate (i) occupational differences in the usage of key work-related mitigations over time and (ii) workers' perceptions of these mitigations. METHODS Employed/self-employed Virus Watch study participants (N=6279) responded to a mitigation-related online survey covering the periods of December 2020-February 2022. Logistic regression was used to investigate occupation- and time-related differences in the usage of work-related mitigation methods. Participants' perceptions of mitigation methods were investigated descriptively using proportions. RESULTS Usage of work-related mitigation methods differed between occupations and over time, likely reflecting variation in job roles, workplace environments, legislation and guidance. Healthcare workers had the highest predicted probabilities for several mitigations, including reporting frequent hand hygiene [predicted probability across all survey periods 0.61 (95% CI 0.56-0.66)] and always wearing face coverings [predicted probability range 0.71 (95% CI 0.66-0.75) - 0.80 (95% CI 0.76-0.84) across survey periods]. There were significant cross-occupational trends towards reduced mitigations during periods of less stringent national restrictions. The majority of participants across occupations (55-88%) agreed that most mitigations were reasonable and worthwhile even after the relaxation of national restrictions; agreement was lower for physical distancing (39-44%). CONCLUSIONS While usage of work-related mitigations appeared to vary alongside stringency of national restrictions, agreement that most mitigations were reasonable and worthwhile remained substantial. Further investigation into the factors underlying between-occupational differences could assist pandemic planning and prevention of workplace COVID-19 transmission.
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Affiliation(s)
- Sarah Beale
- Institute of Epidemiology and Health Care, University College London, London, UK, WC1E 7HB.
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22
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Beale S, Hoskins S, Byrne T, Fong WLE, Fragaszy E, Geismar C, Kovar J, Navaratnam AMD, Nguyen V, Patel P, Yavlinsky A, Johnson AM, Van Tongeren M, Aldridge RW, Hayward A. Differential Risk of SARS-CoV-2 Infection by Occupation: Evidence from the Virus Watch prospective cohort study in England and Wales. J Occup Med Toxicol 2023; 18:5. [PMID: 37013634 PMCID: PMC10068189 DOI: 10.1186/s12995-023-00371-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 03/21/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND Workers across different occupations vary in their risk of SARS-CoV-2 infection, but the direct contribution of occupation to this relationship is unclear. This study aimed to investigate how infection risk differed across occupational groups in England and Wales up to April 2022, after adjustment for potential confounding and stratification by pandemic phase. METHODS Data from 15,190 employed/self-employed participants in the Virus Watch prospective cohort study were used to generate risk ratios for virologically- or serologically-confirmed SARS-CoV-2 infection using robust Poisson regression, adjusting for socio-demographic and health-related factors and non-work public activities. We calculated attributable fractions (AF) amongst the exposed for belonging to each occupational group based on adjusted risk ratios (aRR). RESULTS Increased risk was seen in nurses (aRR = 1.44, 1.25-1.65; AF = 30%, 20-39%), doctors (aRR = 1.33, 1.08-1.65; AF = 25%, 7-39%), carers (1.45, 1.19-1.76; AF = 31%, 16-43%), primary school teachers (aRR = 1.67, 1.42- 1.96; AF = 40%, 30-49%), secondary school teachers (aRR = 1.48, 1.26-1.72; AF = 32%, 21-42%), and teaching support occupations (aRR = 1.42, 1.23-1.64; AF = 29%, 18-39%) compared to office-based professional occupations. Differential risk was apparent in the earlier phases (Feb 2020-May 2021) and attenuated later (June-October 2021) for most groups, although teachers and teaching support workers demonstrated persistently elevated risk across waves. CONCLUSIONS Occupational differences in SARS-CoV-2 infection risk vary over time and are robust to adjustment for socio-demographic, health-related, and non-workplace activity-related potential confounders. Direct investigation into workplace factors underlying elevated risk and how these change over time is needed to inform occupational health interventions.
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Affiliation(s)
- Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK.
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK.
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Department of Infectious Disease Epidemiology, London, School of Hygiene and Tropical Medicine , Keppel Street, London, WC1E 7HT, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Anne M Johnson
- Institute for Global Health, University College London, London, WC1N 1EH, UK
| | - Martie Van Tongeren
- Division of Population Health, Health Services Research & Primary Care, University of Manchester, Manchester, M13 9NT, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
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23
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Johnson L, Potter LC, Beeching H, Bradbury M, Matos B, Sumner G, Wills L, Worthing K, Aldridge RW, Feder G, Hayward AC, Pathak N, Platt L, Story A, Sultan B, Luchenski SA. Interventions to improve health and the determinants of health among sex workers in high-income countries: a systematic review. Lancet Public Health 2023; 8:e141-e154. [PMID: 36334613 PMCID: PMC10564624 DOI: 10.1016/s2468-2667(22)00252-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 09/09/2022] [Accepted: 09/12/2022] [Indexed: 01/27/2023]
Abstract
Many sex worker populations face high morbidity and mortality, but data are scarce on interventions to improve their health. We did a systematic review of health and social interventions to improve the health and wider determinants of health among adult sex workers in high-income countries. We searched MEDLINE, Embase, PsycINFO, CINAHL, the Cochrane Library, Web of Science, EthOS, OpenGrey, and Social Care Online, as well as the Global Network of Sex Work Projects and the Sex Work Research Hub for studies published between Jan 1, 2005 and Dec 16, 2021 (PROSPERO CRD42019158674). Quantitative studies reporting disaggregated data for sex workers were included and no comparators were specified. We assessed rigour using the Quality Assessment Tool for Quantitative Studies. We summarised studies using vote counting and a narrative synthesis. 20 studies were included. Most reported findings exclusively for female sex workers (n=17) and street-based sex workers (n=11). Intervention components were divided into education and empowerment (n=14), drug treatment (n=4), sexual and reproductive health care (n=7), other health care (n=5), and welfare (n=5). Interventions affected a range of mental health, physical health, and health behaviour outcomes. Multicomponent interventions and interventions that were focused on education and empowerment were of benefit. Interventions that used peer design and peer delivery were effective. An outreach or drop-in component might be beneficial in some contexts. Sex workers who were new to working in an area faced greater challenges accessing services. Data were scarce for male, transgender, and indoor-based sex workers. Co-designed and co-delivered interventions that are either multicomponent or focus on education and empowerment are likely to be effective. Policy makers and health-care providers should improve access to services for all genders of sex workers and those new to an area. Future research should develop interventions for a greater diversity of sex worker populations and for wider health and social needs.
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Affiliation(s)
- Luke Johnson
- Department of Primary Care, Population Sciences and Medical Education, University of Southampton, Southampton, UK; Collaborative Centre for Inclusion Health, Department of Epidemiology and Public Health, University College London, London, UK.
| | - Lucy C Potter
- Centre for Academic Primary Care, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Molly Bradbury
- Collaborative Centre for Inclusion Health, Department of Epidemiology and Public Health, University College London, London, UK
| | - Bella Matos
- Department of Psychology, The American University of Paris, Paris, France
| | - Grace Sumner
- Collaborative Centre for Inclusion Health, Department of Epidemiology and Public Health, University College London, London, UK
| | - Lorna Wills
- Collaborative Centre for Inclusion Health, Department of Epidemiology and Public Health, University College London, London, UK
| | - Kitty Worthing
- Centre for Primary Care and Public Health, Queen Mary University, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Gene Feder
- Centre for Academic Primary Care, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Andrew C Hayward
- Collaborative Centre for Inclusion Health, Department of Epidemiology and Public Health, University College London, London, UK
| | - Neha Pathak
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK; Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Lucy Platt
- Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, UK
| | - Al Story
- Collaborative Centre for Inclusion Health, Department of Epidemiology and Public Health, University College London, London, UK; Find & Treat, University College London Hospital, London, UK
| | - Binta Sultan
- Collaborative Centre for Inclusion Health, Department of Epidemiology and Public Health, University College London, London, UK; Find & Treat, University College London Hospital, London, UK
| | - Serena A Luchenski
- Collaborative Centre for Inclusion Health, Department of Epidemiology and Public Health, University College London, London, UK
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24
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Serisier A, Beale S, Boukari Y, Hoskins S, Nguyen V, Byrne T, Fong WLE, Fragaszy E, Geismar C, Kovar J, Yavlinsky A, Hayward A, Aldridge RW. A case-crossover study of the effect of vaccination on SARS-CoV-2 transmission relevant behaviours during a period of national lockdown in England and Wales. Vaccine 2023; 41:511-518. [PMID: 36496282 PMCID: PMC9721283 DOI: 10.1016/j.vaccine.2022.11.073] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/28/2022] [Accepted: 11/29/2022] [Indexed: 12/11/2022]
Abstract
BACKGROUND Studies of COVID-19 vaccine effectiveness show increases in COVID-19 cases within 14 days of a first dose, potentially reflecting post-vaccination behaviour changes associated with SARS-CoV-2 transmission before vaccine protection. However, direct evidence for a relationship between vaccination and behaviour is lacking. We aimed to examine the association between vaccination status and self-reported non-household contacts and non-essential activities during a national lockdown in England and Wales. METHODS Participants (n = 1154) who had received the first dose of a COVID-19 vaccine reported non-household contacts and non-essential activities from February to March 2021 in monthly surveys during a national lockdown in England and Wales. We used a case-crossover study design and conditional logistic regression to examine the association between vaccination status (pre-vaccination vs 14 days post-vaccination) and self-reported contacts and activities within individuals. Stratified subgroup analyses examined potential effect heterogeneity by sociodemographic characteristics such as sex, household income or age group. RESULTS 457/1154 (39.60 %) participants reported non-household contacts post-vaccination compared with 371/1154 (32.15 %) participants pre-vaccination. 100/1154 (8.67 %) participants reported use of non-essential shops or services post-vaccination compared with 74/1154 (6.41 %) participants pre-vaccination. Post-vaccination status was associated with increased odds of reporting non-household contacts (OR 1.65, 95 % CI 1.31-2.06, p < 0.001) and use of non-essential shops or services (OR 1.50, 95 % CI 1.03-2.17, p = 0.032). This effect varied between men and women and different age groups. CONCLUSION Participants had higher odds of reporting non-household contacts and use of non-essential shops or services within 14 days of their first COVID-19 vaccine compared to pre-vaccination. Public health emphasis on maintaining protective behaviours during this post-vaccination time period when individuals have yet to develop full protection from vaccination could reduce risk of SARS-CoV-2 infection.
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Affiliation(s)
- Aimee Serisier
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Sarah Beale
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK; Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK.
| | - Yamina Boukari
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Vincent Nguyen
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK; Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Cyril Geismar
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK; Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Robert W Aldridge
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
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25
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Beale S, Burns R, Braithwaite I, Byrne T, Lam Erica Fong W, Fragaszy E, Geismar C, Hoskins S, Kovar J, Navaratnam AMD, Nguyen V, Patel P, Yavlinsky A, Van Tongeren M, Aldridge RW, Hayward A. Occupation, Worker Vulnerability, and COVID-19 Vaccination Uptake: Analysis of the Virus Watch prospective cohort study. Vaccine 2022; 40:7646-7652. [PMID: 36372668 PMCID: PMC9637514 DOI: 10.1016/j.vaccine.2022.10.080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 10/28/2022] [Accepted: 10/29/2022] [Indexed: 11/09/2022]
Abstract
BACKGROUND Occupational disparities in COVID-19 vaccine uptake can impact the effectiveness of vaccination programmes and introduce particular risk for vulnerable workers and those with high workplace exposure. This study aimed to investigate COVID-19 vaccine uptake by occupation, including for vulnerable groups and by occupational exposure status. METHODS We used data from employed or self-employed adults who provided occupational information as part of the Virus Watch prospective cohort study (n = 19,595) and linked this to study-obtained information about vulnerability-relevant characteristics (age, medical conditions, obesity status) and work-related COVID-19 exposure based on the Job Exposure Matrix. Participant vaccination status for the first, second, and third dose of any COVID-19 vaccine was obtained based on linkage to national records and study records. We calculated proportions and Sison-Glaz multinomial 95% confidence intervals for vaccine uptake by occupation overall, by vulnerability-relevant characteristics, and by job exposure. FINDINGS Vaccination uptake across occupations ranged from 89-96% for the first dose, 87-94% for the second dose, and 75-86% for the third dose, with transport, trade, service and sales workers persistently demonstrating the lowest uptake. Vulnerable workers tended to demonstrate fewer between-occupational differences in uptake than non-vulnerable workers, although clinically vulnerable transport workers (76%-89% across doses) had lower uptake than several other occupational groups (maximum across doses 86%-96%). Workers with low SARS-CoV-2 exposure risk had higher vaccine uptake (86%-96% across doses) than those with elevated or high risk (81-94% across doses). INTERPRETATION Differential vaccination uptake by occupation, particularly amongst vulnerable and highly-exposed workers, is likely to worsen occupational and related socioeconomic inequalities in infection outcomes. Further investigation into occupational and non-occupational factors influencing differential uptake is required to inform relevant interventions for future COVID-19 booster rollouts and similar vaccination programmes.
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Affiliation(s)
- Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK.
| | - Rachel Burns
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK
| | - Martie Van Tongeren
- Centre for Occupational and Environmental Health, University of Manchester, Manchester M13 9PL, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London NW1 2DA, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
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Hoskins S, Beale S, Nguyen V, Boukari Y, Yavlinsky A, Kovar J, Byrne T, Fragaszy E, Fong WLE, Geismar C, Patel P, Navaratnam AMD, van Tongeren M, Johnson AM, Aldridge RW, Hayward A. Relative contribution of essential and non-essential activities to SARS-CoV-2 transmission following the lifting of public health restrictions in England and Wales. Epidemiol Infect 2022; 151:e3. [PMID: 36475452 PMCID: PMC9990391 DOI: 10.1017/s0950268822001832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 10/24/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022] Open
Abstract
PURPOSE We aimed to understand which non-household activities increased infection odds and contributed greatest to SARS-CoV-2 infections following the lifting of public health restrictions in England and Wales. PROCEDURES We undertook multivariable logistic regressions assessing the contribution to infections of activities reported by adult Virus Watch Community Cohort Study participants. We calculated adjusted weighted population attributable fractions (aPAF) estimating which activity contributed greatest to infections. FINDINGS Among 11 413 participants (493 infections), infection was associated with: leaving home for work (aOR 1.35 (1.11-1.64), aPAF 17%), public transport (aOR 1.27 (1.04-1.57), aPAF 12%), shopping once (aOR 1.83 (1.36-2.45)) vs. more than three times a week, indoor leisure (aOR 1.24 (1.02-1.51), aPAF 10%) and indoor hospitality (aOR 1.21 (0.98-1.48), aPAF 7%). We found no association for outdoor hospitality (1.14 (0.94-1.39), aPAF 5%) or outdoor leisure (1.14 (0.82-1.59), aPAF 1%). CONCLUSION Essential activities (work and public transport) carried the greatest risk and were the dominant contributors to infections. Non-essential indoor activities (hospitality and leisure) increased risk but contributed less. Outdoor activities carried no statistical risk and contributed to fewer infections. As countries aim to 'live with COVID', mitigating transmission in essential and indoor venues becomes increasingly relevant.
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Affiliation(s)
- Susan Hoskins
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Yamina Boukari
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Annalan M. D. Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
| | - Martie van Tongeren
- Centre for Occupational and Environmental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, Greater Manchester, UK
| | - Anne M. Johnson
- Institute for Global Health, University College London, London, WC1N 1EH, UK
| | - Robert W. Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, WC1E 7HB, UK
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27
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Luchenski SA, Dawes J, Aldridge RW, Stevenson F, Tariq S, Hewett N, Hayward AC. Hospital-based preventative interventions for people experiencing homelessness in high-income countries: A systematic review. EClinicalMedicine 2022; 54:101657. [PMID: 36311895 PMCID: PMC9597099 DOI: 10.1016/j.eclinm.2022.101657] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 08/15/2022] [Accepted: 08/29/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND People experiencing homelessness have significant unmet needs and high rates of unplanned care. We aimed to describe preventative interventions, defined in their broadest sense, for people experiencing homelessness in a hospital context. Secondary aims included mapping outcomes and assessing intervention effectiveness. METHODS We searched online databases (MEDLINE, Embase, PsycINFO, HMIC, CINAHL, Web of Science, Cochrane Library) from 1999-2019 and conducted backward and forward citation searches to 31 December 2020 (PROSPERO CRD42019154036). We included quantitative studies in emergency and inpatient settings measuring health or social outcomes for adults experiencing homelessness in high income countries. We assessed rigour using the "Quality Assessment Tool for Quantitative Studies" and summarised findings using descriptive quantitative methods, a binomial test, a Harvest Plot, and narrative synthesis. We used PRISMA and SWiM reporting guidelines. FINDINGS Twenty-eight studies identified eight intervention types: care coordination (n=18); advocacy, support, and outreach (n=13); social welfare assistance (n=13); discharge planning (n=12); homelessness identification (n=6); psychological therapy and treatment (n=6); infectious disease prevention (n=5); and screening, treatment, and referrals (n=5). The evidence strength was weak (n=16) to moderate (n=10), with two high quality randomised controlled trials. We identified six outcome categories with potential benefits observed for psychosocial outcomes, including housing (11/13 studies, 95%CI=54.6-98.1%, p=0.023), healthcare use (14/17, 56.6-96.2%, p=0.013), and healthcare costs (8/8, 63.1-100%, p=0.008). Benefits were less likely for health outcomes (4/5, 28.3-99.5%, p=0.375), integration with onward care (2/4, 6.8-93.2%, p=1.000), and feasibility/acceptability (5/6, 35.9-99.6%, p=0.219), but confidence intervals were very wide. We observed no harms. Most studies showing potential benefits were multi-component interventions. INTERPRETATION Hospital-based preventative interventions for people experiencing homelessness are potentially beneficial, but more rigorous research is needed. In the context of high needs and extreme inequities, policymakers and healthcare providers may consider implementing multi-component preventative interventions. FUNDING SL is supported by an NIHR Clinical Doctoral Research Fellowship (ICA-CDRF-2016-02-042). JD is supported by an NIHR School of Public Health Research Pre-doctoral Fellowship (NU-004252). RWA is supported by a Wellcome Clinical Research Career Development Fellowship (206602).
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Affiliation(s)
- Serena A. Luchenski
- Collaborative Centre for Inclusion Health, Institute of Epidemiology and Healthcare, University College London, 1-19 Torrington Place, London WC1E 7HT, United Kingdom
- Corresponding author.
| | - Joanna Dawes
- Collaborative Centre for Inclusion Health, Institute of Epidemiology and Healthcare, University College London, 1-19 Torrington Place, London WC1E 7HT, United Kingdom
| | - Robert W. Aldridge
- Centre for Public Health Data Science, Institute for Health Informatics, University College London, 255 Euston Road, London NW1 2DA, United Kingdom
| | - Fiona Stevenson
- Department of Primary Care and Population Health, Institute of Epidemiology and Healthcare, University College London, Royal Free Hospital, Rowland Hill Street, London NW3 2PF, United Kingdom
| | - Shema Tariq
- Centre for Clinical Research in Infection and Sexual Health, Institute for Global Health, University College London, Mortimer Market Centre, off Capper Street, London WC1E 6JB, United Kingdom
| | - Nigel Hewett
- Pathway, 4th Floor, East, 250 Euston Rd, London NW1 2PG, United Kingdom
| | - Andrew C. Hayward
- Collaborative Centre for Inclusion Health, Institute of Epidemiology and Healthcare, University College London, 1-19 Torrington Place, London WC1E 7HT, United Kingdom
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28
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Miller F, Nguyen DV, Navaratnam AMD, Shrotri M, Kovar J, Hayward AC, Fragaszy E, Aldridge RW, Hardelid P. Prevalence and Characteristics of Persistent Symptoms in Children During the COVID-19 Pandemic: Evidence From a Household Cohort Study in England and Wales. Pediatr Infect Dis J 2022; 41:979-984. [PMID: 36375098 PMCID: PMC9645448 DOI: 10.1097/inf.0000000000003715] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/28/2022] [Indexed: 11/16/2022]
Affiliation(s)
- Faith Miller
- From the Institute for Global Health, University College London, UK
| | | | | | | | - Jana Kovar
- Institute for Health Informatics, University College London, UK
| | | | - Ellen Fragaszy
- Institute for Health Informatics, University College London, UK
| | | | - Pia Hardelid
- Great Ormond Street Institute of Child Health, University College London, UK
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29
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Tinelli M, Wittenberg R, Cornes M, Aldridge RW, Clark M, Byng R, Foster G, Fuller J, Hayward A, Hewett N, Kilmister A, Manthorpe J, Neale J, Biswell E, Whiteford M. The economic case for hospital discharge services for people experiencing homelessness in England: An in-depth analysis with different service configurations providing specialist care. Health Soc Care Community 2022; 30:e6194-e6205. [PMID: 36205443 PMCID: PMC10092708 DOI: 10.1111/hsc.14057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 08/11/2022] [Accepted: 09/12/2022] [Indexed: 06/16/2023]
Abstract
There are long-standing concerns that people experiencing homelessness may not recover well if left unsupported after a hospital stay. This study reports on a study investigating the cost-effectiveness of three different 'in patient care coordination and discharge planning' configurations for adults experiencing homelessness who are discharged from hospitals in England. The first configuration provided a clinical and housing in-reach service during acute care and discharge coordination but with no 'step-down' care. The second configuration provided clinical and housing in-reach, discharge coordination and 'step-down' intermediate care. The third configuration consisted of housing support workers providing in-reach and discharge coordination as well as step-down care. These three configurations were each compared with 'standard care' (control, defined as one visit by the homelessness health nurse before discharge during which patients received an information leaflet on local services). Multiple sources of data and multi-outcome measures were adopted to assess the cost utility of hospital discharge service delivery for the NHS and broader public perspective. Details of 354 participants were collated on service delivery costs (salary, on-costs, capital, overheads and 'hotel' costs, advertising and other indirect costs), the economic consequences for different public services (e.g. NHS, social care, criminal justice, housing, etc.) and health utilities (quality-adjusted-life-years, QALYs). Findings were complex across the configurations, but, on the whole, there was promising evidence suggesting that, with delivery costs similar to those reported for bed-based intermediate care, step-down care secured better health outcomes and improved cost-effectiveness (compared with usual care) within NICE cost-effectiveness recommendations.
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Affiliation(s)
- Michela Tinelli
- Care Policy and Evaluation CentreThe London School of Economics and Political ScienceLondonUK
| | - Raphael Wittenberg
- Care Policy and Evaluation CentreThe London School of Economics and Political ScienceLondonUK
| | - Michelle Cornes
- NIHR Policy Research Unit in Health and Social Care WorkforceLondonUK
| | - Robert W. Aldridge
- Institute of Health Informatics, University College London Department of Epidemiology and Public Health, Institute of Epidemiology and Health CareLondonUK
| | - Michael Clark
- Care Policy and Evaluation CentreThe London School of Economics and Political ScienceLondonUK
| | - Richard Byng
- Community and Primary Care Research Group, Peninsula School of MedicineUniversity of Plymouth, ITTCPlymouthUK
| | - Graham Foster
- Blizard Institute, Queen Mary University of LondonLondonUK
| | - James Fuller
- NIHR Policy Research Unit in Health and Social Care WorkforceLondonUK
| | - Andrew Hayward
- Institute of Health Informatics, University College London Department of Epidemiology and Public Health, Institute of Epidemiology and Health CareLondonUK
| | - Nigel Hewett
- Pathway and the Faculty for Homeless and Inclusion HealthLondonUK
| | - Alan Kilmister
- NIHR Policy Research Unit in Health and Social Care WorkforceLondonUK
| | - Jill Manthorpe
- NIHR Policy Research Unit in Health and Social Care WorkforceLondonUK
| | - Joanne Neale
- National Addiction CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College London, Addictions Sciences BuildingLondonUK
| | - Elizabeth Biswell
- NIHR Policy Research Unit in Health and Social Care WorkforceLondonUK
| | - Martin Whiteford
- Department of Community Nursing and Community HealthGlasgow Caledonian UniversityGlasgowUK
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30
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Geismar C, Nguyen V, Fragaszy E, Shrotri M, Navaratnam AMD, Beale S, Byrne TE, Fong WLE, Yavlinsky A, Kovar J, Braithwaite I, Aldridge RW, Hayward AC, White P, Jombart T, Cori A. Bayesian reconstruction of household transmissions to infer the serial interval of COVID-19 by variants of concern: analysis from a prospective community cohort study (Virus Watch). Lancet 2022; 400 Suppl 1:S40. [PMID: 36929985 PMCID: PMC9691060 DOI: 10.1016/s0140-6736(22)02250-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
BACKGROUND The serial interval is a key epidemiological measure that quantifies the time between an infector's and an infectee's onset of symptoms. This measure helps investigate epidemiological links between cases, and is an important parameter in transmission models used to estimate transmissibility and inform control strategies. The emergence of multiple variants of concern (VOC) during the SARS-CoV-2 pandemic has led to uncertainties about potential changes in the serial interval of COVID-19. We estimated the household serial interval of multiple VOC using data collected by the Virus Watch study. This online, prospective, community cohort study followed-up entire households in England and Wales since mid-June 2020. METHODS This analysis included 5842 symptomatic individuals with confirmed SARS-CoV-2 infection among 2579 households from Sept 1, 2020, to Aug 10, 2022. SARS-CoV-2 variant designation was based upon national surveillance data of variant prevalence by date and geographical region. We used a Bayesian framework to infer who infected whom by exploring all transmission trees compatible with the observed dates of symptoms, given assumptions on the incubation period and generation time distributions using the R package outbreaker2. FINDINGS We characterised the serial interval of COVID-19 by VOC. The mean serial interval was shortest for omicron BA5 (2·02 days; 95% credible interval [CrI] 1·26-2·84) and longest for alpha (3·37 days; 2·52-4·04). The mean serial interval before alpha (wild-type) was 2·29 days (95% CrI 1·39-2·94), 3·11 days (2·28-3·90) for delta, 2·72 days (2·01-3·47) for omicron BA1, and 2·67 days (1·90-3·46) for omicron BA2. We estimated that 17% (95% CrI 5-26) of serial interval values are negative across all variants. INTERPRETATION Most methods estimating the reproduction number from incidence time series do not allow for a negative serial interval by construction. Further research is needed to extend these methods and assess biases introduced by not accounting for negative serial intervals. To our knowledge, this study is the first to use a Bayesian framework to estimate the serial interval of all major SARS-CoV-2 VOC from thousands of confirmed household cases. FUNDING UK Medical Research Council and Wellcome Trust.
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Affiliation(s)
- Cyril Geismar
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Ellen Fragaszy
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK; Institute of Epidemiology and Health Care, University College London, London, UK
| | - Thomas E Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Peter White
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Thibaut Jombart
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
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31
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Burns R, Campos-Matos I, Harron K, Aldridge RW. COVID-19 vaccination uptake for half a million non-EU migrants and refugees in England: a linked retrospective population-based cohort study. Lancet 2022; 400 Suppl 1:S5. [PMID: 36929995 PMCID: PMC9691052 DOI: 10.1016/s0140-6736(22)02215-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
BACKGROUND COVID-19 has highlighted severe health inequities experienced by certain migrants. Despite evidence suggesting that migrants are at risk of under-immunisation, data are limited for migrants' COVID-19 vaccine uptake in England. METHODS We did a retrospective population-based cohort study on COVID-19 vaccination uptake in England. We linked the Million Migrant cohort (which includes non-EU migrants and resettled refugees) to the national COVID-19 vaccination dataset, using a stepwise deterministic matching procedure adapted from NHS Digital, and compared migrants with the general population. For migrants who linked to at least one vaccination record, we estimate temporal trends in first dose uptake and differences in second and third dose uptake and consequent delays between Dec 8, 2020, and April 20, 2022, by age, visa type, and ethnicity. FINDINGS Of the 465 470 migrants who linked to one or more vaccination record, 427 073 (91·8%) received a second dose and 238 721 (51·3%) received a third. Migrants (>30 years) reached 75% first-dose coverage between 1 and 2 weeks after the general population in England, with the gap widening to 6 weeks for younger migrants (16-29 years). Refugees specifically had a higher risk of a delayed second dose (odds ratio 1·75 [95 CI% 1·62-1·88]) and third dose (1·41 [1·31-1·53]). Older migrants (>65 years) were at least four times more likely to have not received their second or third dose compared with those of the same age in England. INTERPRETATION Uptake of the first dose was slower across all age groups for migrants compared with the general population. Refugees and older migrants were more likely to have delayed uptake of COVID-19 vaccines and to not have received their second or third dose. Policymakers, researchers, and practitioners should consider how to best drive uptake of COVID-19 and other routine vaccine doses and understand and address personal and structural barriers to vaccination systems for diverse migrant populations. FUNDING Wellcome Trust and UK Research and Innovation.
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Affiliation(s)
- Rachel Burns
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.
| | - Ines Campos-Matos
- Office for Health Improvement and Disparities, UK Department of Health and Social Care, London, UK
| | - Katie Harron
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK; Institute of Child Health, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
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32
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Cheng T, Chen T, Liu Y, Aldridge RW, Nguyen V, Hayward AC, Michie S. Human mobility variations in response to restriction policies during the COVID-19 pandemic: An analysis from the Virus Watch community cohort in England, UK. Front Public Health 2022; 10:999521. [PMID: 36330119 PMCID: PMC9623896 DOI: 10.3389/fpubh.2022.999521] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/15/2022] [Indexed: 01/26/2023] Open
Abstract
Objective Since the outbreak of COVID-19, public health and social measures to contain its transmission (e.g., social distancing and lockdowns) have dramatically changed people's lives in rural and urban areas globally. To facilitate future management of the pandemic, it is important to understand how different socio-demographic groups adhere to such demands. This study aims to evaluate the influences of restriction policies on human mobility variations associated with socio-demographic groups in England, UK. Methods Using mobile phone global positioning system (GPS) trajectory data, we measured variations in human mobility across socio-demographic groups during different restriction periods from Oct 14, 2020 to Sep 15, 2021. The six restriction periods which varied in degree of mobility restriction policies, denoted as "Three-tier Restriction," "Second National Lockdown," "Four-tier Restriction," "Third National Lockdown," "Steps out of Lockdown," and "Post-restriction," respectively. Individual human mobility was measured with respect to the time period people stayed at home, visited places outside the home, and traveled long distances. We compared these indicators across the six restriction periods and across socio-demographic groups. Results All human mobility indicators significantly differed across the six restriction periods, and the influences of restriction policies on individual mobility behaviors are correlated with socio-demographic groups. In particular, influences relating to mobility behaviors are stronger in younger and low-income groups in the second and third national lockdowns. Conclusions This study enhances our understanding of the influences of COVID-19 pandemic restriction policies on human mobility behaviors within different social groups in England. The findings can be usefully extended to support policy-making by investigating human mobility and differences in policy effects across not only age and income groups, but also across geographical regions.
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Affiliation(s)
- Tao Cheng
- SpaceTimeLab for Big Data Analytics, Department of Civil, Environmental and Geomatic Engineering, University College London, London, United Kingdom
| | - Tongxin Chen
- SpaceTimeLab for Big Data Analytics, Department of Civil, Environmental and Geomatic Engineering, University College London, London, United Kingdom
| | - Yunzhe Liu
- SpaceTimeLab for Big Data Analytics, Department of Civil, Environmental and Geomatic Engineering, University College London, London, United Kingdom
| | - Robert W. Aldridge
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Vincent Nguyen
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Andrew C. Hayward
- Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Susan Michie
- Centre for Behaviour Change, University College London, London, United Kingdom
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Shrotri M, Fragaszy E, Nguyen V, Navaratnam AMD, Geismar C, Beale S, Kovar J, Byrne TE, Fong WLE, Patel P, Aryee A, Braithwaite I, Johnson AM, Rodger A, Hayward AC, Aldridge RW. Spike-antibody responses to COVID-19 vaccination by demographic and clinical factors in a prospective community cohort study. Nat Commun 2022; 13:5780. [PMID: 36184633 PMCID: PMC9526787 DOI: 10.1038/s41467-022-33550-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 09/22/2022] [Indexed: 12/04/2022] Open
Abstract
Vaccination constitutes the best long-term solution against Coronavirus Disease-2019; however, vaccine-derived immunity may not protect all groups equally, and the durability of protective antibodies may be short. We evaluate Spike-antibody responses following BNT162b2 or ChAdOx1-S vaccination amongst SARS-CoV2-naive adults across England and Wales enrolled in a prospective cohort study (Virus Watch). Here we show BNT162b2 recipients achieved higher peak antibody levels after two doses; however, both groups experience substantial antibody waning over time. In 8356 individuals submitting a sample ≥28 days after Dose 2, we observe significantly reduced Spike-antibody levels following two doses amongst individuals reporting conditions and therapies that cause immunosuppression. After adjusting for these, several common chronic conditions also appear to attenuate the antibody response. These findings suggest the need to continue prioritising vulnerable groups, who have been vaccinated earliest and have the most attenuated antibody responses, for future boosters.
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Affiliation(s)
- Madhumita Shrotri
- Institute of Health Informatics, University College London, London, UK
| | - Ellen Fragaszy
- Institute of Health Informatics, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Vincent Nguyen
- Institute of Health Informatics, University College London, London, UK
| | | | - Cyril Geismar
- Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Thomas E Byrne
- Institute of Health Informatics, University College London, London, UK
| | | | - Parth Patel
- Institute of Health Informatics, University College London, London, UK
| | - Anna Aryee
- Institute of Health Informatics, University College London, London, UK
| | | | - Anne M Johnson
- Institute for Global Health, University College London, London, UK
| | - Alison Rodger
- Institute for Global Health, University College London, London, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London, UK.
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Navaratnam AMD, Shrotri M, Nguyen V, Braithwaite I, Beale S, Byrne TE, Fong WLE, Fragaszy E, Geismar C, Hoskins S, Kovar J, Patel P, Yavlinsky A, Aryee A, Rodger A, Hayward AC, Aldridge RW. Nucleocapsid and spike antibody responses following virologically confirmed SARS-CoV-2 infection: an observational analysis in the Virus Watch community cohort. Int J Infect Dis 2022; 123:104-111. [PMID: 35987470 PMCID: PMC9385348 DOI: 10.1016/j.ijid.2022.07.053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES Seroprevalence studies can provide a measure of SARS-CoV-2 cumulative incidence, but a better understanding of spike and nucleocapsid (anti-N) antibody dynamics following infection is needed to assess the longevity of detectability. METHODS Adults aged ≥18 years, from households enrolled in the Virus Watch prospective community cohort study in England and Wales, provided monthly capillary blood samples, which were tested for spike antibody and anti-N. Participants self-reported vaccination dates and past medical history. Previous polymerase chain reaction (PCR) swabs were obtained through Second Generation Surveillance System linkage data. The primary outcome variables were seropositivity and total anti-N and spike antibody levels after PCR-confirmed infection. RESULTS A total of 13,802 eligible individuals provided 58,770 capillary blood samples. A total of 537 of these had a previous positive PCR-confirmed SARS-CoV-2 infection within 0-269 days of antibody sample date, among them 432 (80.45%) having a positive anti-N result. Median anti-N levels peaked between days 90 and 119 after PCR results and then began to decline. There is evidence of anti-N waning from 120 days onwards, with earlier waning for females and younger age categories. CONCLUSION Our findings suggest that anti-N has around 80% sensitivity for identifying previous COVID-19 infection, and the duration of detectability is affected by sex and age.
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Affiliation(s)
| | - Madhumita Shrotri
- Institute of Health Informatics, University College London, United Kingdom
| | - Vincent Nguyen
- Institute of Health Informatics, University College London, United Kingdom
| | - Isobel Braithwaite
- Institute of Health Informatics, University College London, United Kingdom
| | - Sarah Beale
- Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Thomas E Byrne
- Institute of Health Informatics, University College London, United Kingdom
| | | | - Ellen Fragaszy
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, United Kingdom
| | - Cyril Geismar
- Institute of Health Informatics, University College London, United Kingdom
| | - Susan Hoskins
- Institute of Health Informatics, University College London, United Kingdom
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Parth Patel
- Institute of Health Informatics, University College London, United Kingdom
| | - Alexei Yavlinsky
- Institute of Health Informatics, University College London, United Kingdom
| | - Anna Aryee
- Institute of Health Informatics, University College London, United Kingdom
| | - Alison Rodger
- Institute for Global Health, University College London, London, United Kingdom
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, United Kingdom.
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35
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Zhang CX, Boukari Y, Pathak N, Mathur R, Katikireddi SV, Patel P, Campos-Matos I, Lewer D, Nguyen V, Hugenholtz GC, Burns R, Mulick A, Henderson A, Aldridge RW. Migrants' primary care utilisation before and during the COVID-19 pandemic in England: An interrupted time series analysis. Lancet Reg Health Eur 2022; 20:100455. [PMID: 35789753 PMCID: PMC9243519 DOI: 10.1016/j.lanepe.2022.100455] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Background How international migrants access and use primary care in England is poorly understood. We aimed to compare primary care consultation rates between international migrants and non-migrants in England before and during the COVID-19 pandemic (2015-2020). Methods Using data from the Clinical Practice Research Datalink (CPRD) GOLD, we identified migrants using country-of-birth, visa-status or other codes indicating international migration. We linked CPRD to Office for National Statistics deprivation data and ran a controlled interrupted time series (ITS) using negative binomial regression to compare rates before and during the pandemic. Findings In 262,644 individuals, pre-pandemic consultation rates per person-year were 4.35 (4.34-4.36) for migrants and 4.60 (4.59-4.60) for non-migrants (RR:0.94 [0.92-0.96]). Between 29 March and 26 December 2020, rates reduced to 3.54 (3.52-3.57) for migrants and 4.2 (4.17-4.23) for non-migrants (RR:0.84 [0.8-0.88]). The first year of the pandemic was associated with a widening of the gap in consultation rates between migrants and non-migrants to 0.89 (95% CI 0.84-0.94) times the ratio before the pandemic. This widening in ratios was greater for children, individuals whose first language was not English, and individuals of White British, White non-British and Black/African/Caribbean/Black British ethnicities. It was also greater in the case of telephone consultations, particularly in London. Interpretation Migrants were less likely to use primary care than non-migrants before the pandemic and the first year of the pandemic exacerbated this difference. As GP practices retain remote and hybrid models of service delivery, they must improve services and ensure primary care is accessible and responsive to migrants' healthcare needs. Funding This study was funded by the Medical Research Council (MC_PC 19070 and MR/V028375/1) and a Wellcome Clinical Research Career Development Fellowship (206602).
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Affiliation(s)
- Claire X. Zhang
- Institute of Health Informatics, University College London, 222 Euston Rd, London NW1 2DA, United Kingdom
- Office for Health Improvement and Disparities, Department of Health and Social Care, 39 Victoria Street, London SW1H 0EU, United Kingdom
- National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Headington, Oxford OX3 7LF, United Kingdom
| | - Yamina Boukari
- Institute of Health Informatics, University College London, 222 Euston Rd, London NW1 2DA, United Kingdom
- Office for Health Improvement and Disparities, Department of Health and Social Care, 39 Victoria Street, London SW1H 0EU, United Kingdom
| | - Neha Pathak
- Institute of Health Informatics, University College London, 222 Euston Rd, London NW1 2DA, United Kingdom
- Guy's & St Thomas's NHS Foundation Trust, London SE1 9RT, United Kingdom
| | - Rohini Mathur
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom
| | - Srinivasa Vittal Katikireddi
- MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Berkeley Square, 99 Berkeley Street, Glasgow G3 7HR, United Kingdom
| | - Parth Patel
- Institute of Health Informatics, University College London, 222 Euston Rd, London NW1 2DA, United Kingdom
| | - Ines Campos-Matos
- Office for Health Improvement and Disparities, Department of Health and Social Care, 39 Victoria Street, London SW1H 0EU, United Kingdom
- UK Health Security Agency, Wellington House, 133–155, Waterloo Road, London SE1 8UG, United Kingdom
| | - Dan Lewer
- Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, United Kingdom
| | - Vincent Nguyen
- Institute of Health Informatics, University College London, 222 Euston Rd, London NW1 2DA, United Kingdom
- Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, United Kingdom
| | - Greg C.G. Hugenholtz
- Institute of Health Informatics, University College London, 222 Euston Rd, London NW1 2DA, United Kingdom
| | - Rachel Burns
- Institute of Health Informatics, University College London, 222 Euston Rd, London NW1 2DA, United Kingdom
| | - Amy Mulick
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom
| | - Alasdair Henderson
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom
| | - Robert W. Aldridge
- Institute of Health Informatics, University College London, 222 Euston Rd, London NW1 2DA, United Kingdom
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36
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Aldridge RW, Yavlinsky A, Nguyen V, Eyre MT, Shrotri M, Navaratnam AMD, Beale S, Braithwaite I, Byrne T, Kovar J, Fragaszy E, Fong WLE, Geismar C, Patel P, Rodger A, Johnson AM, Hayward A. SARS-CoV-2 antibodies and breakthrough infections in the Virus Watch cohort. Nat Commun 2022; 13:4869. [PMID: 35982056 PMCID: PMC9387883 DOI: 10.1038/s41467-022-32265-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 07/22/2022] [Indexed: 12/27/2022] Open
Abstract
A range of studies globally demonstrate that the effectiveness of SARS-CoV-2 vaccines wane over time, but the total effect of anti-S antibody levels on risk of SARS-CoV-2 infection and whether this varies by vaccine type is not well understood. Here we show that anti-S levels peak three to four weeks following the second dose of vaccine and the geometric mean of the samples is nine fold higher for BNT162b2 than ChAdOx1. Increasing anti-S levels are associated with a reduced risk of SARS-CoV-2 infection (Hazard Ratio 0.85; 95%CIs: 0.79-0.92). We do not find evidence that this antibody relationship with risk of infection varies by second dose vaccine type (BNT162b2 vs. ChAdOx1). In keeping with our anti-S antibody data, we find that people vaccinated with ChAdOx1 had 1.64 times the odds (95% confidence interval 1.45-1.85) of a breakthrough infection compared to BNT162b2. We anticipate our findings to be useful in the estimation of the protective effect of anti-S levels on risk of infection due to Delta. Our findings provide evidence about the relationship between antibody levels and protection for different vaccines and will support decisions on optimising the timing of booster vaccinations and identifying individuals who should be prioritised for booster vaccination, including those who are older, clinically extremely vulnerable, or received ChAdOx1 as their primary course. Our finding that risk of infection by anti-S level does not interact with vaccine type, but that individuals vaccinated with ChAdOx1 were at higher risk of infection, provides additional support for the use of using anti-S levels for estimating vaccine efficacy.
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Affiliation(s)
- Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Max T Eyre
- Centre of Health Informatics, Computing and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK
- Liverpool School of Tropical Medicine, Liverpool, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alison Rodger
- Institute for Global Health, University College London, London, UK
| | - Anne M Johnson
- Institute for Global Health, University College London, London, UK
| | - Andrew Hayward
- Centre of Health Informatics, Computing and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK
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37
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Hoskins S, Beale S, Nguyen V, Fragaszy E, Navaratnam AM, Smith C, French C, Kovar J, Byrne T, Fong WLE, Geismar C, Patel P, Yavlinksy A, Johnson AM, Aldridge RW, Hayward A. Settings for non-household transmission of SARS-CoV-2 during the second lockdown in England and Wales - analysis of the Virus Watch household community cohort study. Wellcome Open Res 2022; 7:199. [PMID: 36874571 PMCID: PMC9975411 DOI: 10.12688/wellcomeopenres.17981.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/22/2022] [Indexed: 11/20/2022] Open
Abstract
Background: "Lockdowns" to control serious respiratory virus pandemics were widely used during the coronavirus disease 2019 (COVID-19) pandemic. However, there is limited information to understand the settings in which most transmission occurs during lockdowns, to support refinement of similar policies for future pandemics. Methods: Among Virus Watch household cohort participants we identified those infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outside the household. Using survey activity data, we undertook multivariable logistic regressions assessing the contribution of activities on non-household infection risk. We calculated adjusted population attributable fractions (APAF) to estimate which activity accounted for the greatest proportion of non-household infections during the pandemic's second wave. Results: Among 10,858 adults, 18% of cases were likely due to household transmission. Among 10,475 participants (household-acquired cases excluded), including 874 non-household-acquired infections, infection was associated with: leaving home for work or education (AOR 1.20 (1.02 - 1.42), APAF 6.9%); public transport (more than once per week AOR 1.82 (1.49 - 2.23), public transport APAF 12.42%); and shopping (more than once per week AOR 1.69 (1.29 - 2.21), shopping APAF 34.56%). Other non-household activities were rare and not significantly associated with infection. Conclusions: During lockdown, going to work and using public or shared transport independently increased infection risk, however only a minority did these activities. Most participants visited shops, accounting for one-third of non-household transmission. Transmission in restricted hospitality and leisure settings was minimal suggesting these restrictions were effective. If future respiratory infection pandemics emerge these findings highlight the value of working from home, using forms of transport that minimise exposure to others, minimising exposure to shops and restricting non-essential activities.
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Affiliation(s)
- Susan Hoskins
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, Greater London, WC1E 7HT, UK
| | - Annalan M.D. Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Colette Smith
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Clare French
- NIHR Health Protection Research Unit in Behavioural Science and Evaluation, Uinversity of Bristol, Bristol, BS8 2BN, UK
| | - Jana Kovar
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Alexei Yavlinksy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Anne M. Johnson
- Institute for Global Health, University College London, London, WC1N 1EH, UK
| | - Robert W. Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Andrew Hayward
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Virus Watch Collaborative
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, Greater London, WC1E 7HT, UK
- NIHR Health Protection Research Unit in Behavioural Science and Evaluation, Uinversity of Bristol, Bristol, BS8 2BN, UK
- Institute for Global Health, University College London, London, WC1N 1EH, UK
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38
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Yavlinsky A, Beale S, Nguyen V, Shrotri M, Byrne T, Geismar C, Fragaszy E, Hoskins S, Fong WLE, Navaratnam AMD, Braithwaite I, Patel P, Kovar J, Hayward A, Aldridge RW. Anti-spike antibody trajectories in individuals previously immunised with BNT162b2 or ChAdOx1 following a BNT162b2 booster dose. Wellcome Open Res 2022. [DOI: 10.12688/wellcomeopenres.17914.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: The two most common SARS-CoV-2 vaccines in the UK, BNT162b2 (Pfizer-BioNTech) and ChAdOx1 nCoV-19 (Oxford-AstraZeneca), employ different immunogenic mechanisms. Compared to BNT162b2, two-dose immunisation with ChAdOx1 induces substantially lower peak anti-spike antibody (anti-S) levels and is associated with a higher risk of breakthrough infections. To provide preliminary indication of how a third booster BNT162b2 dose impacts anti-S levels, we performed a cross-sectional analysis using capillary blood samples from vaccinated adults participating in Virus Watch, a prospective community cohort study in England and Wales. Methods: Blood samples were analysed using Roche Elecsys Anti-SARS-CoV-2 S immunoassay. We analysed anti-S levels by week since the third dose for vaccines administered on or after 1 September 2021 and stratified the results by second-dose vaccine type (ChAdOx1 or BNT162b2), age, sex and clinical vulnerability. Results: Anti-S levels peaked at two weeks post-booster for BNT162b2 (22,185 U/mL; 95%CI: 21,406-22,990) and ChAdOx1 second-dose recipients (19,203 U/mL; 95%CI: 18,094-20,377). These were higher than the corresponding peak antibody levels post-second dose for BNT162b2 (12,386 U/mL; 95%CI: 9,801-15,653, week 2) and ChAdOx1 (1,192 U/mL; 95%CI: 818-1735, week 3). No differences emerged by second dose vaccine type, age, sex or clinical vulnerability. Anti-S levels declined post-booster for BNT162b2 (half-life=44 days) and ChAdOx1 second dose recipients (half-life=40 days). These rates of decline were steeper than those post-second dose for BNT162b2 (half-life=54 days) and ChAdOx1 (half-life=80 days). Conclusions: Our findings suggest that peak anti-S levels are higher post-booster than post-second dose, but levels are projected to be similar after six months for BNT162b2 recipients. Higher peak anti-S levels post-booster may partially explain the increased effectiveness of booster vaccination compared to two-dose vaccination against symptomatic infection with the Omicron variant. Faster waning trajectories post-third dose may have implications for the timing of future booster campaigns or four-dose vaccination regimens for the clinically vulnerable.
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39
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Beale S, Hoskins S, Byrne T, Fong WLE, Fragaszy E, Geismar C, Kovar J, Navaratnam AM, Nguyen V, Patel P, Yavlinsky A, Johnson AM, Van Tongeren M, Aldridge RW, Hayward A. Workplace contact patterns in England during the COVID-19 pandemic: Analysis of the Virus Watch prospective cohort study. Lancet Reg Health Eur 2022; 16:100352. [PMID: 35475035 PMCID: PMC9023315 DOI: 10.1016/j.lanepe.2022.100352] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Background Workplaces are an important potential source of SARS-CoV-2 exposure; however, investigation into workplace contact patterns is lacking. This study aimed to investigate how workplace attendance and features of contact varied between occupations across the COVID-19 pandemic in England. Methods Data were obtained from electronic contact diaries (November 2020-November 2021) submitted by employed/self-employed prospective cohort study participants (n=4,616). We used mixed models to investigate the effects of occupation and time for: workplace attendance, number of people sharing workspace, time spent sharing workspace, number of close contacts, and usage of face coverings. Findings Workplace attendance and contact patterns varied across occupations and time. The predicted probability of intense space sharing during the day was highest for healthcare (78% [95% CI: 75-81%]) and education workers (64% [59%-69%]), who also had the highest probabilities for larger numbers of close contacts (36% [32%-40%] and 38% [33%-43%] respectively). Education workers also demonstrated relatively low predicted probability (51% [44%-57%]) of wearing a face covering during close contact. Across all occupational groups, workspace sharing and close contact increased and usage of face coverings decreased during phases of less stringent restrictions. Interpretation Major variations in workplace contact patterns and mask use likely contribute to differential COVID-19 risk. Patterns of variation by occupation and restriction phase may inform interventions for future waves of COVID-19 or other respiratory epidemics. Across occupations, increasing workplace contact and reduced face covering usage is concerning given ongoing high levels of community transmission and emergence of variants. Funding Medical Research Council; HM Government; Wellcome Trust.
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Affiliation(s)
- Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Susan Hoskins
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Annalan M.D. Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Anne M. Johnson
- Institute for Global Health, University College London, London WC1N 1EH, UK
| | - Martie Van Tongeren
- Centre for Occupational and Environmental Health, University of Manchester, Manchester M13 9PL, UK
| | - Robert W. Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
| | - Virus Watch Collaborative
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
- Institute for Global Health, University College London, London WC1N 1EH, UK
- Centre for Occupational and Environmental Health, University of Manchester, Manchester M13 9PL, UK
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40
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Fragaszy E, Shrotri M, Geismar C, Aryee A, Beale S, Braithwaite I, Byrne T, Eyre MT, Fong WLE, Gibbs J, Hardelid P, Kovar J, Lampos V, Nastouli E, Navaratnam AM, Nguyen V, Patel P, Aldridge RW, Hayward A. Symptom profiles and accuracy of clinical case definitions for COVID-19 in a community cohort: results from the Virus Watch study. Wellcome Open Res 2022; 7:84. [PMID: 37745779 PMCID: PMC10514573 DOI: 10.12688/wellcomeopenres.17479.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/31/2022] [Indexed: 09/23/2023] Open
Abstract
Background: Understanding symptomatology and accuracy of clinical case definitions for community COVID-19 cases is important for Test, Trace and Isolate (TTI) and future targeting of early antiviral treatment. Methods: Community cohort participants prospectively recorded daily symptoms and swab results (mainly undertaken through the UK TTI system). We compared symptom frequency, severity, timing, and duration in test positive and negative illnesses. We compared the test performance of the current UK TTI case definition (cough, high temperature, or loss of or altered sense of smell or taste) with a wider definition adding muscle aches, chills, headache, or loss of appetite. Results: Among 9706 swabbed illnesses, including 973 SARS-CoV-2 positives, symptoms were more common, severe and longer lasting in swab positive than negative illnesses. Cough, headache, fatigue, and muscle aches were the most common symptoms in positive illnesses but also common in negative illnesses. Conversely, high temperature, loss or altered sense of smell or taste and loss of appetite were less frequent in positive illnesses, but comparatively even less frequent in negative illnesses. The current UK definition had 81% sensitivity and 47% specificity versus 93% and 27% respectively for the broader definition. 1.7-fold more illnesses met the broader case definition than the current definition. Conclusions: Symptoms alone cannot reliably distinguish COVID-19 from other respiratory illnesses. Adding additional symptoms to case definitions could identify more infections, but with a large increase in the number needing testing and the number of unwell individuals and contacts self-isolating whilst awaiting results.
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Affiliation(s)
- Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Anna Aryee
- Centre of Health Informatics, Computing and Statistics, Lancaster University, Lancaster, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Max T. Eyre
- Centre of Health Informatics, Computing and Statistics, Lancaster University, Lancaster, UK
- Liverpool School of Tropical Medicine, Liverpool, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jo Gibbs
- Institute for Global Health, University College London, London, UK
| | - Pia Hardelid
- Population, Policy and Practice Research and Teaching Department, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Vasileios Lampos
- Department of Computer Science, University College London, London, UK
| | - Eleni Nastouli
- Francis Crick Institute, London, UK
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Annalan M.D. Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W. Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Virus Watch Collaborative
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- Institute of Epidemiology and Health Care, University College London, London, UK
- Centre of Health Informatics, Computing and Statistics, Lancaster University, Lancaster, UK
- Liverpool School of Tropical Medicine, Liverpool, UK
- Institute for Global Health, University College London, London, UK
- Population, Policy and Practice Research and Teaching Department, University College London, London, UK
- Department of Computer Science, University College London, London, UK
- Francis Crick Institute, London, UK
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
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41
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Geismar C, Fragaszy E, Nguyen V, Fong WLE, Shrotri M, Beale S, Rodger A, Lampos V, Byrne T, Kovar J, Navaratnam AMD, Patel P, Aldridge RW, Hayward A. Household serial interval of COVID-19 and the effect of Variant B.1.1.7: analyses from prospective community cohort study (Virus Watch). Wellcome Open Res 2022; 6:224. [PMID: 34796276 PMCID: PMC8564743 DOI: 10.12688/wellcomeopenres.16974.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2021] [Indexed: 01/21/2023] Open
Abstract
Introduction: Increased transmissibility of B.1.1.7 variant of concern (VOC) in the UK may explain its rapid emergence and global spread. We analysed data from putative household infector - infectee pairs in the Virus Watch Community cohort study to assess the serial interval of COVID-19 and whether this was affected by emergence of the B.1.1.7 variant. Methods: The Virus Watch study is an online, prospective, community cohort study following up entire households in England and Wales during the COVID-19 pandemic. Putative household infector-infectee pairs were identified where more than one person in the household had a positive swab matched to an illness episode. Data on whether or not individual infections were caused by the B.1.1.7 variant were not available. We therefore developed a classification system based on the percentage of cases estimated to be due to B.1.1.7 in national surveillance data for different English regions and study weeks. Results: Out of 24,887 illnesses reported, 915 tested positive for SARS-CoV-2 and 186 likely ‘infector-infectee’ pairs in 186 households amongst 372 individuals were identified. The mean COVID-19 serial interval was 3.18 (95%CI: 2.55-3.81, sd=4.36) days. There was no significant difference (p=0.267) between the mean serial interval for VOC hotspots (mean = 3.64 days, (95%CI: 2.55 – 4.73)) days and non-VOC hotspots, (mean = 2.72 days, (95%CI: 1.48 – 3.96)). Conclusions: Our estimates of the average serial interval of COVID-19 are broadly similar to estimates from previous studies and we find no evidence that B.1.1.7 is associated with a change in serial intervals. Alternative explanations such as increased viral load, longer period of viral shedding or improved receptor binding may instead explain the increased transmissibility and rapid spread and should undergo further investigation.
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Affiliation(s)
- Cyril Geismar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.,Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Vincent Nguyen
- Institute of Epidemiology and Health Care, University College London, London, UK.,Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Institute of Epidemiology and Health Care, University College London, London, UK.,Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alison Rodger
- Institute for Global Health, University College London, London, UK
| | - Vasileios Lampos
- Department of Computer Science, University College London, London, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Annalan M D Navaratnam
- Institute of Epidemiology and Health Care, University College London, London, UK.,Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
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42
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Pathak N, Zhang CX, Boukari Y, Burns R, Mathur R, Gonzalez-Izquierdo A, Denaxas S, Sonnenberg P, Hayward A, Aldridge RW. Development and Validation of a Primary Care Electronic Health Record Phenotype to Study Migration and Health in the UK. Int J Environ Res Public Health 2021; 18:13304. [PMID: 34948912 PMCID: PMC8707886 DOI: 10.3390/ijerph182413304] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/07/2021] [Accepted: 12/10/2021] [Indexed: 11/16/2022]
Abstract
International migrants comprised 14% of the UK's population in 2020; however, their health is rarely studied at a population level using primary care electronic health records due to difficulties in their identification. We developed a migration phenotype using country of birth, visa status, non-English main/first language and non-UK-origin codes and applied it to the Clinical Practice Research Datalink (CPRD) GOLD database of 16,071,111 primary care patients between 1997 and 2018. We compared the completeness and representativeness of the identified migrant population to Office for National Statistics (ONS) country-of-birth and 2011 census data by year, age, sex, geographic region of birth and ethnicity. Between 1997 to 2018, 403,768 migrants (2.51% of the CPRD GOLD population) were identified: 178,749 (1.11%) had foreign-country-of-birth or visa -status codes, 216,731 (1.35%) non-English-main/first-language codes, and 8288 (0.05%) non-UK-origin codes. The cohort was similarly distributed versus ONS data by sex and region of birth. Migration recording improved over time and younger migrants were better represented than those aged ≥50. The validated phenotype identified a large migrant cohort for use in migration health research in CPRD GOLD to inform healthcare policy and practice. The under-recording of migration status in earlier years and older ages necessitates cautious interpretation of future studies in these groups.
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Affiliation(s)
- Neha Pathak
- Institute of Health Informatics, University College London, 222 Euston Rd., London NW1 2DA, UK; (N.P.); (C.X.Z.); (Y.B.); (R.B.); (A.G.-I.); (S.D.)
- Guy’s & St. Thomas’ NHS Foundation Trust, London SE1 9RT, UK
| | - Claire X. Zhang
- Institute of Health Informatics, University College London, 222 Euston Rd., London NW1 2DA, UK; (N.P.); (C.X.Z.); (Y.B.); (R.B.); (A.G.-I.); (S.D.)
- Office for Health Improvement and Disparities, Department of Health and Social Care, 39 Victoria Street, London SW1H 0EU, UK
| | - Yamina Boukari
- Institute of Health Informatics, University College London, 222 Euston Rd., London NW1 2DA, UK; (N.P.); (C.X.Z.); (Y.B.); (R.B.); (A.G.-I.); (S.D.)
- Office for Health Improvement and Disparities, Department of Health and Social Care, 39 Victoria Street, London SW1H 0EU, UK
| | - Rachel Burns
- Institute of Health Informatics, University College London, 222 Euston Rd., London NW1 2DA, UK; (N.P.); (C.X.Z.); (Y.B.); (R.B.); (A.G.-I.); (S.D.)
| | - Rohini Mathur
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK;
| | - Arturo Gonzalez-Izquierdo
- Institute of Health Informatics, University College London, 222 Euston Rd., London NW1 2DA, UK; (N.P.); (C.X.Z.); (Y.B.); (R.B.); (A.G.-I.); (S.D.)
- Health Data Research UK, London NW1 2BF, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, 222 Euston Rd., London NW1 2DA, UK; (N.P.); (C.X.Z.); (Y.B.); (R.B.); (A.G.-I.); (S.D.)
- Health Data Research UK, London NW1 2BF, UK
| | - Pam Sonnenberg
- Institute for Global Health, University College London, 30 Guilford Street, London WC1N 1EH, UK;
| | - Andrew Hayward
- Institute of Epidemiology & Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, UK;
| | - Robert W. Aldridge
- Institute of Health Informatics, University College London, 222 Euston Rd., London NW1 2DA, UK; (N.P.); (C.X.Z.); (Y.B.); (R.B.); (A.G.-I.); (S.D.)
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Aldridge RW, Pineo H, Fragaszy E, Eyre MT, Kovar J, Nguyen V, Beale S, Byrne T, Aryee A, Smith C, Devakumar D, Taylor J, Katikireddi SV, Fong WLE, Geismar C, Patel P, Shrotri M, Braithwaite I, Patni N, Navaratnam AMD, Johnson A, Hayward A. Household overcrowding and risk of SARS-CoV-2: analysis of the Virus Watch prospective community cohort study in England and Wales. Wellcome Open Res 2021. [DOI: 10.12688/wellcomeopenres.17308.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: Household overcrowding is associated with increased risk of infectious diseases across contexts and countries. Limited data exist linking household overcrowding and risk of COVID-19. We used data collected from the Virus Watch cohort to examine the association between overcrowded households and SARS-CoV-2. Methods: The Virus Watch study is a household community cohort of acute respiratory infections in England and Wales. We calculated overcrowding using the measure of persons per room for each household. We considered two primary outcomes: PCR-confirmed positive SARS-CoV-2 antigen tests and laboratory-confirmed SARS-CoV-2 antibodies. We used mixed-effects logistic regression models that accounted for household structure to estimate the association between household overcrowding and SARS-CoV-2 infection. Results: 26,367 participants were included in our analyses. The proportion of participants with a positive SARS-CoV-2 PCR result was highest in the overcrowded group (9.0%; 99/1,100) and lowest in the under-occupied group (4.2%; 980/23,196). In a mixed-effects logistic regression model, we found strong evidence of an increased odds of a positive PCR SARS-CoV-2 antigen result (odds ratio 2.45; 95% CI:1.43–4.19; p-value=0.001) and increased odds of a positive SARS-CoV-2 antibody result in individuals living in overcrowded houses (3.32; 95% CI:1.54–7.15; p-value<0.001) compared with people living in under-occupied houses. Conclusion: Public health interventions to prevent and stop the spread of SARS-CoV-2 should consider the risk of infection for people living in overcrowded households and pay greater attention to reducing household transmission.
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44
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Corona Maioli S, Bhabha J, Wickramage K, Wood LCN, Erragne L, Ortega García O, Burgess R, Digidiki V, Aldridge RW, Devakumar D. International migration of unaccompanied minors: trends, health risks, and legal protection. Lancet Child Adolesc Health 2021; 5:882-895. [PMID: 34416189 PMCID: PMC7615140 DOI: 10.1016/s2352-4642(21)00194-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 12/16/2022]
Abstract
The global population of unaccompanied minors-children and adolescents younger than 18 years who migrate without their legal guardians-is increasing. However, as data are not systematically collected in any region, if collected at all, little is known about this diverse group of young people. Compared with adult migrants, unaccompanied minors are at greater risk of harm to their health and integrity because they do not have the protection provided by a family, which can affect their short-term and long-term health. This Review summarises evidence regarding the international migration and health of unaccompanied minors. Unaccompanied minors are entitled to protection that should follow their best interests as a primary consideration; however, detention, sometimes under the guise of protection, is a widespread practice. If these minors are provided with appropriate forms of protection, including health and psychosocial care, they can thrive and have good long-term outcomes. Instead, hostile immigration practices persist, which are not in the best interests of the child.
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Affiliation(s)
| | - Jacqueline Bhabha
- Harvard FXB Center for Health and Human Rights, Harvard University, Boston, MA, USA
| | - Kolitha Wickramage
- Migration Health Division, Global Migration Health Research and Epidemiology Unit, International Organization for Migration, Manila, Philippines
| | - Laura C N Wood
- Centre for Child & Family Justice Research, Lancaster University, Lancaster, UK
| | | | | | | | - Vasileia Digidiki
- Harvard FXB Center for Health and Human Rights, Harvard University, Boston, MA, USA
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45
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Byrne T, Patel P, Shrotri M, Beale S, Michie S, Butt J, Hawkins N, Hardelid P, Rodger A, Aryee A, Braithwaite I, Fong WLE, Fragaszy E, Geismar C, Kovar J, Navaratnam AMD, Nguyen V, Hayward A, Aldridge RW. Trends, patterns and psychological influences on COVID-19 vaccination intention: Findings from a large prospective community cohort study in England and Wales (Virus Watch). Vaccine 2021; 39:7108-7116. [PMID: 34728095 PMCID: PMC8498741 DOI: 10.1016/j.vaccine.2021.09.066] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 12/27/2022]
Abstract
BACKGROUND Vaccination intention is key to the success of any vaccination programme, alongside vaccine availability and access. Public intention to take a COVID-19 vaccine is high in England and Wales compared to other countries, but vaccination rate disparities between ethnic, social and age groups has led to concern. METHODS Online survey of prospective household community cohort study participants across England and Wales (Virus Watch). Vaccination intention was measured by individual participant responses to 'Would you accept a COVID-19 vaccine if offered?', collected in December 2020 and February 2021. Responses to a 13-item questionnaire collected in January 2021 were analysed using factor analysis to investigate psychological influences on vaccination intention. RESULTS Survey response rate was 56% (20,785/36,998) in December 2020 and 53% (20,590/38,727) in February 2021, with 14,880 adults reporting across both time points. In December 2020, 1,469 (10%) participants responded 'No' or 'Unsure'. Of these people, 1,266 (86%) changed their mind and responded 'Yes' or 'Already had a COVID-19 vaccine' by February 2021. Vaccination intention increased across all ethnic groups and levels of social deprivation. Age was most strongly associated with vaccination intention, with 16-24-year-olds more likely to respond "Unsure" or "No" versus "Yes" than 65-74-year-olds in December 2020 (OR: 4.63, 95 %CI: 3.42, 6.27 & OR 7.17 95 %CI: 4.26, 12.07 respectively) and February 2021 (OR: 27.92 95 %CI: 13.79, 56.51 & OR 17.16 95 %CI: 4.12, 71.55). The association between ethnicity and vaccination intention weakened, but did not disappear, over time. Both vaccine- and illness-related psychological factors were shown to influence vaccination intention. CONCLUSIONS Four in five adults (86%) who were reluctant or intending to refuse a COVID-19 vaccine in December 2020 had changed their mind in February 2021 and planned to accept, or had already accepted, a vaccine.
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Affiliation(s)
- Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK.
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK.
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Susan Michie
- Centre for Behaviour Change, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Jabeer Butt
- Race Equality Foundation, 27 Greenwood Pl, London NW5 1LB, UK
| | | | - Pia Hardelid
- Department of Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, 30 Guilford St, London WC1N 1EH, UK
| | - Alison Rodger
- Institute for Global Health, University College London, 30 Guilford St, London WC1N 1EH, UK; Royal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK
| | - Anna Aryee
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel St, London WC1E 7HT, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK; Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College, 222 Euston Rd, London NW1 2DA, UK.
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46
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Geismar C, Fragaszy E, Nguyen V, Fong WLE, Shrotri M, Beale S, Rodger A, Lampos V, Byrne T, Kovar J, Navaratnam AMD, Patel P, Aldridge RW, Hayward A. Serial interval of COVID-19 and the effect of Variant B.1.1.7: analyses from prospective community cohort study (Virus Watch). Wellcome Open Res 2021; 6:224. [PMID: 34796276 DOI: 10.12688/wellcomeopenres.16974.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2021] [Indexed: 01/21/2023] Open
Abstract
Introduction: Increased transmissibility of B.1.1.7 variant of concern (VOC) in the UK may explain its rapid emergence and global spread. We analysed data from putative household infector - infectee pairs in the Virus Watch Community cohort study to assess the serial interval of COVID-19 and whether this was affected by emergence of the B.1.1.7 variant. Methods: The Virus Watch study is an online, prospective, community cohort study following up entire households in England and Wales during the COVID-19 pandemic. Putative household infector-infectee pairs were identified where more than one person in the household had a positive swab matched to an illness episode. Data on whether or not individual infections were caused by the B.1.1.7 variant were not available. We therefore developed a classification system based on the percentage of cases estimated to be due to B.1.1.7 in national surveillance data for different English regions and study weeks. Results: Out of 24,887 illnesses reported, 915 tested positive for SARS-CoV-2 and 186 likely 'infector-infectee' pairs in 186 households amongst 372 individuals were identified. The mean COVID-19 serial interval was 3.18 (95%CI: 2.55 - 3.81) days. There was no significant difference (p=0.267) between the mean serial interval for VOC hotspots (mean = 3.64 days, (95%CI: 2.55 - 4.73)) days and non-VOC hotspots, (mean = 2.72 days, (95%CI: 1.48 - 3.96)). Conclusions: Our estimates of the average serial interval of COVID-19 are broadly similar to estimates from previous studies and we find no evidence that B.1.1.7 is associated with a change in serial intervals. Alternative explanations such as increased viral load, longer period of viral shedding or improved receptor binding may instead explain the increased transmissibility and rapid spread and should undergo further investigation.
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Affiliation(s)
- Cyril Geismar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.,Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Vincent Nguyen
- Institute of Epidemiology and Health Care, University College London, London, UK.,Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Institute of Epidemiology and Health Care, University College London, London, UK.,Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alison Rodger
- Institute for Global Health, University College London, London, UK
| | - Vasileios Lampos
- Department of Computer Science, University College London, London, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Annalan M D Navaratnam
- Institute of Epidemiology and Health Care, University College London, London, UK.,Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
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47
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Tweed EJ, Thomson RM, Lewer D, Sumpter C, Kirolos A, Southworth PM, Purba AK, Aldridge RW, Hayward A, Story A, Hwang SW, Katikireddi SV. Health of people experiencing co-occurring homelessness, imprisonment, substance use, sex work and/or severe mental illness in high-income countries: a systematic review and meta-analysis. J Epidemiol Community Health 2021; 75:1010-1018. [PMID: 33893182 PMCID: PMC8458085 DOI: 10.1136/jech-2020-215975] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 03/03/2021] [Accepted: 04/08/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND People affected by homelessness, imprisonment, substance use, sex work or severe mental illness experience substantial excess ill health and premature death. Though these experiences often co-occur, health outcomes associated with their overlap have not previously been reviewed. We synthesised existing evidence on mortality, morbidity, self-rated health and quality of life among people affected by more than one of these experiences. METHODS In this systematic review and meta-analysis, we searched Medline, Embase, and PsycINFO for peer-reviewed English-language observational studies from high-income countries published between 1 January 1998 and 11 June 2018. Two authors undertook independent screening, with risk of bias assessed using a modified Newcastle-Ottawa Scale. Findings were summarised by narrative synthesis and random-effect meta-analysis. RESULTS From 15 976 citations, 2517 studies underwent full-text screening, and 444 were included. The most common exposure combinations were imprisonment/substance use (31% of data points) and severe mental illness/substance use (27%); only 1% reported outcomes associated with more than two exposures. Infections were the most common outcomes studied, with blood-borne viruses accounting for 31% of all data points. Multiple exposures were associated with poorer outcomes in 80% of data points included (sign test for effect direction, p<0.001). Meta-analysis suggested increased all-cause mortality among people with multiple versus fewer exposures (HR: 1.57 and 95% CI: 1.38 to 1.77), though heterogeneity was high. CONCLUSION People affected by multiple exclusionary processes experience profound health inequalities, though there are important gaps in the research landscape. Addressing the health needs of these populations is likely to require co-ordinated action across multiple sectors, such as healthcare, criminal justice, drug treatment, housing and social security. PROSPERO REGISTRATION NUMBER CRD42018097189.
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Affiliation(s)
- Emily J Tweed
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow Institute of Health and Wellbeing, Glasgow, UK
| | - Rachel M Thomson
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow Institute of Health and Wellbeing, Glasgow, UK
| | - Dan Lewer
- Collaborative Centre for Inclusion Health, University College London, London, UK
| | - Colin Sumpter
- Department of Public Health, NHS Forth Valley, Stirling, UK
| | - Amir Kirolos
- Department of Clinical Infection, Microbiology & Immunology, Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
| | - Paul M Southworth
- Department of Public Health, NHS Dumfries and Galloway, Dumfries, UK
| | - Amrit Kaur Purba
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow Institute of Health and Wellbeing, Glasgow, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London, UK
| | - Andrew Hayward
- Collaborative Centre for Inclusion Health, University College London, London, UK
| | - Alistair Story
- Find and Treat Service, University College London Hospitals NHS Foundation Trust, London, UK
| | - Stephen W Hwang
- Centre for Urban Health Solutions, St. Michael's Hospital, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Srinivasa Vittal Katikireddi
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow Institute of Health and Wellbeing, Glasgow, UK
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48
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Beale S, Byrne T, Fragaszy E, Kovar J, Nguyen V, Aryee A, Fong WLE, Geismar C, Patel P, Shrotri M, Patni N, Braithwaite I, Navaratnam A, Johnson AM, Aldridge RW, Hayward AC. Reported exposure to SARS-CoV-2 and relative perceived importance of different settings for SARS-CoV-2 acquisition in England and Wales: Analysis of the Virus Watch Community Cohort. Wellcome Open Res 2021. [DOI: 10.12688/wellcomeopenres.17067.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
We aimed to assess the relative importance of different settings for SARS-CoV-2 transmission in a large community cohort based on perceived location of infection for self-reported confirmed SARS-COV-2 cases. We demonstrate the importance of home, work and education as perceived venues for transmission. In children, education was most important and in older adults essential shopping was of high importance. Our findings support public health messaging about infection control at home, advice on working from home and restrictions in different venues.
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49
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Miranda JJ, Pesantes MA, Lazo-Porras M, Portocarrero J, Diez-Canseco F, Carrillo-Larco RM, Bernabe-Ortiz A, Trujillo AJ, Aldridge RW. Design of financial incentive interventions to improve lifestyle behaviors and health outcomes: A systematic review. Wellcome Open Res 2021; 6:163. [PMID: 34595355 PMCID: PMC8447049 DOI: 10.12688/wellcomeopenres.16947.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/23/2021] [Indexed: 11/20/2022] Open
Abstract
Background: Financial incentives may improve the initiation and engagement of behaviour change that reduce the negative outcomes associated with non-communicable diseases. There is still a paucity in guidelines or recommendations that help define key aspects of incentive-oriented interventions, including the type of incentive (e.g. cash rewards, vouchers), the frequency and magnitude of the incentive, and its mode of delivery. We aimed to systematically review the literature on financial incentives that promote healthy lifestyle behaviours or improve health profiles, and focused on the methodological approach to define the incentive intervention and its delivery. The protocol was registered at PROSPERO on 26 July 2018 ( CRD42018102556). Methods: We sought studies in which a financial incentive was delivered to improve a health-related lifestyle behaviour (e.g., physical activity) or a health profile (e.g., HbA1c in people with diabetes). The search (which took place on March 3 rd 2018) was conducted using OVID (MEDLINE and Embase), CINAHL and Scopus. Results: The search yielded 7,575 results and 37 were included for synthesis. Of the total, 83.8% (31/37) of the studies were conducted in the US, and 40.5% (15/37) were randomised controlled trials. Only one study reported the background and rationale followed to develop the incentive and conducted a focus group to understand what sort of incentives would be acceptable for their study population. There was a degree of consistency across the studies in terms of the direction, form, certainty, and recipient of the financial incentives used, but the magnitude and immediacy of the incentives were heterogeneous. Conclusions: The available literature on financial incentives to improve health-related lifestyles rarely reports on the rationale or background that defines the incentive approach, the magnitude of the incentive and other relevant details of the intervention, and the reporting of this information is essential to foster its use as potential effective interventions.
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Affiliation(s)
- J. Jaime Miranda
- CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, 15074, Peru
- Department of Medicine, School of Medicine, Universidad Peruana Cayetano Heredia, Lima, 15102, Peru
| | - M. Amalia Pesantes
- CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, 15074, Peru
| | - María Lazo-Porras
- CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, 15074, Peru
- Division of Tropical and Humanitarian Medicine, Geneva University Hospitals & University of Geneva, Geneva, 1205, Switzerland
| | - Jill Portocarrero
- CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, 15074, Peru
| | - Francisco Diez-Canseco
- CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, 15074, Peru
| | - Rodrigo M. Carrillo-Larco
- CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, 15074, Peru
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1UA, UK
| | - Antonio Bernabe-Ortiz
- CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, 15074, Peru
| | - Antonio J. Trujillo
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Robert W. Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, NW1 2DA, UK
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50
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Cornes M, Aldridge RW, Biswell E, Byng R, Clark M, Foster G, Fuller J, Hayward A, Hewett N, Kilmister A, Manthorpe J, Neale J, Tinelli M, Whiteford M. Improving care transfers for homeless patients after hospital discharge: a realist evaluation. Health Serv Deliv Res 2021. [DOI: 10.3310/hsdr09170] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Background
In 2013, 70% of people who were homeless on admission to hospital were discharged back to the street without having their care and support needs addressed. In response, the UK government provided funding for 52 new specialist homeless hospital discharge schemes. This study employed RAMESES II (Realist And Meta-narrative Evidence Syntheses: Evolving Standards) guidelines between September 2015 and 2019 to undertake a realist evaluation to establish what worked, for whom, under what circumstances and why. It was hypothesised that delivering outcomes linked to consistently safe, timely care transfers for homeless patients would depend on hospital discharge schemes implementing a series of high-impact changes (resource mechanisms). These changes encompassed multidisciplinary discharge co-ordination (delivered through clinically led homeless teams) and ‘step-down’ intermediate care. These facilitated time-limited care and support and alternative pathways out of hospital for people who could not go straight home.
Methods
The realist hypothesis was tested empirically and refined through three work packages. Work package 1 generated seven qualitative case studies, comparing sites with different types of specialist homeless hospital discharge schemes (n = 5) and those with no specialist discharge scheme (standard care) (n = 2). Methods of data collection included interviews with 77 practitioners and stakeholders and 70 people who were homeless on admission to hospital. A ‘data linkage’ process (work package 2) and an economic evaluation (work package 3) were also undertaken. The data linkage process resulted in data being collected on > 3882 patients from 17 discharge schemes across England. The study involved people with lived experience of homelessness in all stages.
Results
There was strong evidence to support our realist hypothesis. Specialist homeless hospital discharge schemes employing multidisciplinary discharge co-ordination and ‘step-down’ intermediate care were more effective and cost-effective than standard care. Specialist care was shown to reduce delayed transfers of care. Accident and emergency visits were also 18% lower among homeless patients discharged at a site with a step-down service than at those without. However, there was an impact on the effectiveness of the schemes when they were underfunded or when there was a shortage of permanent supportive housing and longer-term care and support. In these contexts, it remained (tacitly) accepted practice (across both standard and specialist care sites) to discharge homeless patients to the streets, rather than delay their transfer. We found little evidence that discharge schemes fired a change in reasoning with regard to the cultural distance that positions ‘homeless patients’ as somehow less vulnerable than other groups of patients. We refined our hypothesis to reflect that high-impact changes need to be underpinned by robust adult safeguarding.
Strengths and limitations
To our knowledge, this is the largest study of the outcomes of homeless patients discharged from hospital in the UK. Owing to issues with the comparator group, the effectiveness analysis undertaken for the data linkage was limited to comparisons of different types of specialist discharge scheme (rather than specialist vs. standard care).
Future work
There is a need to consider approaches that align with those for value or alliance-based commissioning where the evaluative gaze is shifted from discrete interventions to understanding how the system is working as a whole to deliver outcomes for a defined patient population.
Funding
This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 9, No. 17. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Michelle Cornes
- Health and Social Care Workforce Research Unit, King’s College London, London, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London, UK
| | - Elizabeth Biswell
- Health and Social Care Workforce Research Unit, King’s College London, London, UK
| | - Richard Byng
- Clinical Trials and Health Research, University of Plymouth, Plymouth, UK
| | - Michael Clark
- Care Policy and Evaluation Centre, London School of Economics and Political Science, London, UK
| | - Graham Foster
- Blizard Institute, Queen Mary University of London, London, UK
| | - James Fuller
- Health and Social Care Workforce Research Unit, King’s College London, London, UK
| | - Andrew Hayward
- Institute of Health Informatics, University College London, London, UK
| | - Nigel Hewett
- Pathway and the Faculty for Homeless and Inclusion Health, London, UK
| | - Alan Kilmister
- Health and Social Care Workforce Research Unit, King’s College London, London, UK
| | - Jill Manthorpe
- Health and Social Care Workforce Research Unit, King’s College London, London, UK
| | - Joanne Neale
- National Addiction Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Michela Tinelli
- Care Policy and Evaluation Centre, London School of Economics and Political Science, London, UK
| | - Martin Whiteford
- Department of Community Nursing and Community Health, Glasgow Caledonian University, Glasgow, UK
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