Measuring geographical disparities in England at the time of COVID-19: results using a composite indicator of population vulnerability

COVID-19 vaccine coverage effectiveness among elderly with geographical information system mapping: what about Indonesia?

Background

The elderly are the next priority after health workers and public service workers get the COVID-19 vaccine to control morbidity and even mortality in the elderly who have a risk factor that is up to 60 times more severe than children.

Objectives

This study aimed to spatially analyze the effectiveness of COVID-19 vaccine coverage among the elderly in Indonesia with geographic information system (GIS) mapping and to analyze the relationship between COVID-19 vaccine coverage in the elderly with the COVID-19 cure rate.

Design

This quantitative study used secondary data on COVID-19 vaccination coverage in the elderly group of Central Java Province, Indonesia in 2021.

Methods

Data were analyzed using a simple linear correlation test to test the relationship between variables with a 1,774,396 elderly sample size, then distributed using mapping of COVID-19 vaccination coverage using a GIS.

Results

The relationship between COVID-19 vaccine dose-2 elderly coverage cure rate showed a strong relationship (r = 0.677) and a positive pattern. The coefficient value with a determination of 0.459 means that the regression line equation obtained can explain 45.90% of the variation in the COVID-19 cure rate. There was a significant relationship between COVID-19 vaccine elderly coverage and the COVID-19 cure rate (p-value = 0.005).

Conclusion

Clinicians and public health workers should continue to encourage elderly vaccination at all recommended doses for eligible individuals.

'COVID-19 is just another way to die…': a qualitative longitudinal study of frontline COVID-19 response governance across Syria

INTRODUCTION

Governance of COVID-19 responses has been challenging for all countries. Syria has been particularly challenged due to protracted multiparty conflict and debilitated health systems fragmented across different areas of control. To improve response governance, better understanding of frontline response policy implementation across the country is needed. This study thus explored perspectives of COVID-19 response governance among frontline healthcare providers over time and across major areas of control.

METHODS

We used a qualitative longitudinal study design, conducting five rounds of remote semistructured interviews in Arabic (ie, approximately eight interviews each in March 2020, July 2020, September 2020, December 2020 and September 2021) with 14 purposively sampled public and private healthcare providers in the three main areas of control (ie, opposition-controlled area, Autonomous Administration-controlled area and al-Assad government-controlled area (GCA)). We conducted integrative thematic analysis in Arabic within and across geography and time.

RESULTS

Almost all participants across all areas and rounds expressed distrust of local health authorities and dissatisfaction with COVID-19 response governance. This was most apparent in initial rounds and in GCA. Response planning was identified as insufficient, non-participatory and non-transparent. Limited infrastructure and resources were the main challenges across time, though anticipated rapid virus spread and health systems' collapse did not occur and participant optimism increased over time. Public adherence to prevention measures varied-initially weak due to general scepticism, increasing after first cases were confirmed and then fluctuating with case numbers and challenges of insecurity and misinformation. Perceptions of COVID-19 vaccination varied, with low uptake and hesitancy attributed to misinformation, disinformation and disinterest. Suggested improvements to COVID-19 response governance focused on strengthening health systems' capacity and coordination.

CONCLUSION

This is a unique longitudinal study of COVID-19 responses. Addressing transparency and misinformation should be a first step to improving public engagement and trust and thus response governance for health emergencies in Syria.

Evaluation of concordance in estimation of excess mortality due to COVID-19 pandemic

BACKGROUND

The World Health Organization (WHO) kept track of COVID-19 data at country level daily during the pandemic that included the number of tests, infected cases and fatalities. This daily record was susceptible to change depending on the time and place and impacted by underreporting. In addition to reporting cases of excess COVID-19-related deaths, the WHO also provided estimates of excess mortality based on mathematical models.

OBJECTIVE

To evaluate the WHO reported and model-based estimate of excess deaths to determine the degree of agreement and universality.

METHODOLOGY

Epidemiological data gathered from nine different countries between April 2020 and December 2021 are used in this study. These countries are India, Indonesia, Italy, Russia, United Kingdom, Mexico, the United States, Brazil and Peru and each of them recorded more than 1.5 million deaths from COVID-19 during these months. Statistical tools including correlation, linear regression, intraclass correlation and Bland-Altman plots are used to assess the degree of agreement between reported and model-based estimates of excess deaths.

RESULTS

The WHO-derived mathematical model for estimating excess deaths due to COVID-19 was found to be appropriate for only four of the nine chosen countries, namely Italy, United Kingdom, the United States and Brazil. The other countries showed proportional biases and significantly high regression coefficients.

CONCLUSION

The study revealed that, for some of the chosen nations, the mathematical model proposed by the WHO is practical and capable of estimating the number of excess deaths brought on by COVID-19. However, the derived approach cannot be applied globally.

Spatiotemporal pattern of COVID-19 mortality and its relationship with socioeconomic and environmental factors in England

This paper investigated the spatiotemporal pattern of COVID-19 mortality and its socioeconomic and environmental determinants in the first and second wave of the pandemic in England. The COVID-19 mortality rates for middle super output areas from March 2020 to April 2021 were used in the analysis. SaTScan was used in the analysis of spatiotemporal pattern of COVID-19 mortality and geographically weighted Poisson regression (GWPR) was used to investigate the association with socioeconomic and environmental factors. The results show that there was significant spatiotemporal variation in hotspots of COVID-19 deaths with the hotspots moving from regions where the COVID-19 outbreak initiated and then spread to other parts of the country. The GWPR analysis revealed that age composition, ethnic composition, deprivation, care home and pollution were all related to COVID-19 mortality. Althoughthe relationship varied over space the association with these factors was fairly consistent over the first and second wave.

Association of socio-economic deprivation with COVID-19 incidence and fatality during the first wave of the pandemic in Italy: lessons learned from a local register-based study

BACKGROUND

COVID-19 has been characterised by its global and rapid spread, with high infection, hospitalisation, and mortality rates worldwide. However, the course of the pandemic showed differences in chronology and intensity in different geographical areas and countries, probably due to a multitude of factors. Among these, socio-economic deprivation has been supposed to play a substantial role, although available evidence is not fully in agreement. Our study aimed to assess incidence and fatality rates of COVID-19 across the levels of socio-economic deprivation during the first epidemic wave (March-May 2020) in the Italian Province of Foggia, Apulia Region.

METHODS

Based on the data of the regional active surveillance platform, we performed a retrospective epidemiological study among all COVID-19 confirmed cases that occurred in the Apulian District of Foggia, Italy, from March 1st to May 5th, 2020. Geocoded addresses were linked to the individual Census Tract (CT) of residence. Effects of socio-economic condition were calculated by means of the Socio-Economic and Health-related Deprivation Index (SEHDI) on COVID-19 incidence and fatality.

RESULTS

Of the 1054 confirmed COVID-19 cases, 537 (50.9%) were men, 682 (64.7%) were 0-64 years old, and 338 (32.1%) had pre-existing comorbidities. COVID-19 incidence was higher in the less deprived areas (p < 0.05), independently on age. The level of socio-economic deprivation did not show a significant impact on the vital status, while a higher fatality was observed in male cases (p < 0.001), cases > 65 years (p < 0.001), cases having a connection with a nursing home (p < 0.05) or having at least 1 comorbidity (p < 0.001). On the other hand, a significant protection for healthcare workers was apparent (p < 0.001).

CONCLUSIONS

Our findings show that deprivation alone does not affect COVID-19 incidence and fatality burden, suggesting that the burden of disease is driven by a complexity of factors not yet fully understood. Better knowledge is needed to identify subgroups at higher risk and implement effective preventive strategies.

Public health emergency preparedness for infectious disease emergencies: a scoping review of recent evidence

BACKGROUND

The COVID-19 pandemic continues to demonstrate the risks and profound health impacts that result from infectious disease emergencies. Emergency preparedness has been defined as the knowledge, capacity and organizational systems that governments, response and recovery organizations, communities and individuals develop to anticipate, respond to, or recover from emergencies. This scoping review explored recent literature on priority areas and indicators for public health emergency preparedness (PHEP) with a focus on infectious disease emergencies.

METHODS

Using scoping review methodology, a comprehensive search was conducted for indexed and grey literature with a focus on records published from 2017 to 2020 onward, respectively. Records were included if they: (a) described PHEP, (b) focused on an infectious emergency, and (c) were published in an Organization for Economic Co-operation and Development country. An evidence-based all-hazards Resilience Framework for PHEP consisting of 11 elements was used as a reference point to identify additional areas of preparedness that have emerged in recent publications. The findings were analyzed deductively and summarized thematically.

RESULTS

The included publications largely aligned with the 11 elements of the all-hazards Resilience Framework for PHEP. In particular, the elements related to collaborative networks, community engagement, risk analysis and communication were frequently observed across the publications included in this review. Ten emergent themes were identified that expand on the Resilience Framework for PHEP specific to infectious diseases. Planning to mitigate inequities was a key finding of this review, it was the most frequently identified emergent theme. Additional emergent themes were: research and evidence-informed decision making, building vaccination capacity, building laboratory and diagnostic system capacity, building infection prevention and control capacity, financial investment in infrastructure, health system capacity, climate and environmental health, public health legislation and phases of preparedness.

CONCLUSION

The themes from this review contribute to the evolving understanding of critical public health emergency preparedness actions. The themes expand on the 11 elements outlined in the Resilience Framework for PHEP, specifically relevant to pandemics and infectious disease emergencies. Further research will be important to validate these findings, and expand understanding of how refinements to PHEP frameworks and indicators can support public health practice.

Informal care, older people, and COVID-19: Evidence from the UK

The negative health effects and mortality caused by the COVID-19 pandemic disproportionately fell upon older and disabled people. Protecting these vulnerable groups has been a key policy priority throughout the pandemic and related vaccination campaigns. Using data from the latest survey of the UK Household Longitudinal Study on COVID-19 we found that people who receive informal care have higher probability of being infected when compared to those not receiving informal care. Further, we found that care recipients who are in the lowest income groups have a higher probability of catching the virus when compared to those in the highest income groups. We also estimated the likelihood of being infected for informal carers versus those who did not provide any care during the pandemic and found no significant differences between these two groups. Our empirical findings suggest that the standard measures introduced with the aim of protecting vulnerable groups, such as closing care homes or prioritising the vaccination of their staff, were not sufficient to avoid the spread of the virus amongst disabled and older people. Informal carers play an important role in the social care sector. As such, protecting vulnerable people by investing in the informal care sector should be a priority for future health policy.

Detecting early signals of COVID-19 outbreaks in 2020 in small areas by monitoring healthcare utilisation databases: first lessons learned from the Italian Alert_CoV project

BackgroundDuring the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections.AimTo develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas.MethodsData were retrieved from the healthcare utilisation (HCU) databases of the Lombardy Region, Italy. We identified eight services suggesting a respiratory infection (syndromic proxies). Count time series reporting the weekly occurrence of each proxy from 2015 to 2020 were generated considering small administrative areas (i.e. census units of Cremona and Mantua provinces). The ability to uncover aberrations during 2020 was tested for two algorithms: the improved Farrington algorithm and the generalised likelihood ratio-based procedure for negative binomial counts. To evaluate these algorithms' performance in detecting outbreaks earlier than the standard surveillance, confirmed outbreaks, defined according to the weekly number of confirmed COVID-19 cases, were used as reference. Performances were assessed separately for the first and second semester of the year. Proxies positively impacting performance were identified.ResultsWe estimated that 70% of outbreaks could be detected early using the proposed approach, with a corresponding false positive rate of ca 20%. Performance did not substantially differ either between algorithms or semesters. The best proxies included emergency calls for respiratory or infectious disease causes and emergency room visits.ConclusionImplementing HCU-based monitoring systems in small areas deserves further investigations as it could facilitate the containment of COVID-19 and other unknown infectious diseases in the future.

National geographical pattern of COVID-19 hospitalization, case fatalities, and associated factors in patients covered by Iran Health Insurance Organization

BACKGROUND

Understanding the Spatio-temporal distribution and interpersonal comparisons are important tools in etiological studies. This study was conducted to investigate the temporal and geographical distribution of COVID-19 hospitalized patients in the Iran Health Insurance Organization (IHIO) insured population (the second largest social health insurance organization) and the factors affecting their case fatality rate (CFR).

METHODS

In this descriptive-analytical cross-sectional study, the demographic and clinical data of all insured of the IHIO who were hospitalized with COVID-19 in hospitals across the country until March 2021 was extracted from the comprehensive system of handling the inpatient documents of this organization. The Excel 2019 and GeoDA software were used for descriptive reporting and geographical distribution of variables. A multiple logistic regression model was used to estimate the Odds Ratio (OR) of death in patients with COVID-19 using STATA 14 software.

RESULTS

During the first 14 months of the COVID-19 outbreak in Iran, 0.72% of the IHIO insured (303,887 individuals) were hospitalized with COVID-19. Hospitalization per 100,000 people varied from 192.51 in East Azerbaijan to 1,277.49 in Yazd province. The overall CFR in hospitalized patients was 14%. Tehran and Kohgiluyeh & BoyerAhmad provinces had the highest and lowest CFR with 19.39% and 5.19%, respectively. The highest odds of death were in those over 80 years old people (OR = 9.65), ICU-admitted (OR = 7.49), Hospitalized in governmental hospitals (OR = 2.08), Being a foreign national (OR = 1.45), hospitalized in November (OR = 1.47) and Residence in provinces such as Sistan & Baluchestan (OR = 1.47) and Razavi Khorasan (OR = 1.66) respectively. Furthermore, the odds of death were lower in females (OR = 0.81) than in males.

CONCLUSIONS

A sound understanding of the primary causes of COVID-19 death and severity in different groups can be the basis for developing programs focused on more vulnerable groups in order to manage the crisis more effectively and benefit from resources more efficiently.

A Multi-Criteria Decision Support and Application to the Evaluation of the Fourth Wave of COVID-19 Pandemic

The COVID-19 pandemic caused important health and societal damage across the world in 2020-2022. Its study represents a tremendous challenge for the scientific community. The correct evaluation and analysis of the situation can lead to the elaboration of the most efficient strategies and policies to control and mitigate its propagation. The paper proposes a Multi-Criteria Decision Support (MCDS) based on the combination of three methods: the Group Analytic Hierarchy Process (GAHP), which is a subjective group weighting method; Extended Entropy Weighting Method (EEWM), which is an objective weighting method; and the COmplex PRoportional ASsessment (COPRAS), which is a multi-criteria method. The COPRAS uses the combined weights calculated by the GAHP and EEWM. The sum normalization (SN) is considered for COPRAS and EEWM. An extended entropy is proposed in EEWM. The MCDS is implemented for the development of a complex COVID-19 indicator called COVIND, which includes several countries' COVID-19 indicators, over a fourth COVID-19 wave, for a group of European countries. Based on these indicators, a ranking of the countries is obtained. An analysis of the obtained rankings is realized by the variation of two parameters: a parameter that describes the combination of weights obtained with EEWM and GAHP and the parameter of extended entropy function. A correlation analysis between the new indicator and the general country indicators is performed. The MCDS provides policy makers with a decision support able to synthesize the available information on the fourth wave of the COVID-19 pandemic.

United, can we be stronger? Did French general practitioners in multi-professional groups provide more chronic care follow-up during lockdown?

Background

Given the importance of the continuous follow-up of chronic patients, we evaluated the performance of French private practice general practitioners (GPs) practicing in multi-professional group practices (MGP) regarding chronic care management during the first Covid-19 lockdown in Spring 2020 compared to GPs not in MGP. We consider two outcomes: continuity of care provision for chronic patients and proactivity in contacting these patients.

Methods

The cross-sectional web questionnaire of 1191 GPs took place in April 2020. We exploit self-reported data on: 1) the frequency of consultations for chronic patients during lockdown compared to their “typical” week before the pandemic, along with 2) GPs’ proactive behaviour when contacting their chronic patients. We use probit and bivariate probit models (adjusted for endogeneity of choice of engagement in MGP) to test whether GPs in MGP had significantly different responses to the Covid-19 crisis compared to those practicing outside MGP.

Results

Out of 1191 participants (response rate: 43.1%), around 40% of GPs were female and 34% were younger than 50 years old. Regression results indicate that GPs in MGP were less likely to experience a drop in consultations related to complications of chronic diseases (− 45.3%). They were also more proactive (+ 13.4%) in contacting their chronic patients compared to their peers practicing outside MGP.

Conclusion

We demonstrate that the MGP organisational formula was beneficial to the follow-up of patients with chronic conditions during the lockdown; therefore, it appears beneficial to expand integrated practices, since they perform better when facing a major shock. Further research is needed to confirm the efficiency of these integrated practices outside the particular pandemic setup.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07937-z.

Static Socio-Ecological COVID-19 Vulnerability Index and Vaccine Hesitancy Index for England

BACKGROUND

Population characteristics can be used to infer vulnerability of communities to COVID-19, or to the likelihood of high levels of vaccine hesitancy. Communities harder hit by the virus, or at risk of being so, stand to benefit from greater resource allocation than their population size alone would suggest. This study reports a simple but efficacious method of ranking small areas of England by relative characteristics that are linked with COVID-19 vulnerability and vaccine hesitancy.

METHODS

Publicly available data on a range of characteristics previously linked with either poor COVID-19 outcomes or vaccine hesitancy were collated for all Middle Super Output Areas of England (MSOA, n=6790, excluding Isles of Scilly), scaled and combined into two numeric indices. Multivariable linear regression was used to build a parsimonious model of vulnerability (static socio-ecological vulnerability index, SEVI) in 60% of MSOAs, and retained variables were used to construct two simple indices. Assuming a monotonic relationship between indices and outcomes, Spearman correlation coefficients were calculated between the SEVI and cumulative COVID-19 case rates at MSOA level in the remaining 40% of MSOAs over periods both during and out with national lockdowns. Similarly, a novel vaccine hesitancy index (VHI) was constructed using population characteristics aligned with factors identified by an Office for National Statistics (ONS) survey analysis. The relationship between the VHI and vaccine coverage in people aged 12+years (as of 2021-06-24) was determined using Spearman correlation. The indices were split into quintiles, and MSOAs within the highest vulnerability and vaccine hesitancy quintiles were mapped.

FINDINGS

The SEVI showed a moderate to strong relationship with case rates in the validation dataset across the whole study period, and for every intervening period studied except early in the pandemic when testing was highly selective. The SEVI was more strongly correlated with case rates than any of its domains (rs 0·59 95% CI 0.57-0.62) and outperformed an existing MSOA-level vulnerability index. The VHI was significantly negatively correlated with COVID-19 vaccine coverage in the validation data at the time of writing (rs -0·43 95% CI -0·46 to -0·41). London had the largest number and proportion of MSOAs in quintile 5 (most vulnerable/hesitant) of SEVI and VHI concurrently.

INTERPRETATION

The indices presented offer an efficacious way of identifying geographical disparities in COVID-19 risk, thus helping focus resources according to need.

FUNDING

Funder: Integrated Covid Hub North East.

AWARD NUMBER

n/a.

GRANT RECIPIENT

Fiona Matthews.

Factors that most expose countries to COVID-19: a composite indicators-based approach

Studies carried out in different countries correlate social, economic, environmental, and health factors with the number of cases and deaths from COVID-19. However, such studies do not reveal which factors make one country more exposed to COVID-19 than other. Based on the composite indicators approach, this research identifies the factors that most impact the number of cases and deaths of COVID-19 worldwide and measures countries' exposure to COVID-19. Three composite indicators of exposure to COVID-19 were constructed through Principal Component Analysis, Simple Additive Weighting, and k-means clustering. The number of cases and deaths from COVID-19 is strongly correlated ( R  > 0.60) with composite indicator scores and moderately concordant ( K  > 0.4) with country clusters. Factors directly or indirectly associated with the age of the population are the ones that most expose countries to COVID-19. The population of countries most exposed to COVID-19 is 12 years older on average. The proportion of the elderly population in these countries is at least twice that of countries less exposed to COVID-19. Factors that can increase the population's life expectancy, such as Gross Domestic Product per capita and the Human Development Index, are four times and 1.3 times higher in more exposed countries to COVID-19. Providing better living conditions increases both the population's life expectancy and the country's exposure to COVID-19.

HIV Infection Prevalence Significantly Intersects With COVID-19 Infection At the Area Level: A US County-Level Analysis

BACKGROUND

Limited empirical evidence exists about the extent to which the current HIV epidemic intersects with COVID-19 infections at the area/geographic level. Moreover, little is known about how demographic, social, economic, behavioral, and clinical determinants are jointly associated with these infectious diseases.

SETTING

Contiguous US counties (N = 3108).

METHODS

We conducted a cross-sectional analysis and investigated the joint association between new HIV infection prevalence in 2018 and COVID-19 infections (January 22, 2020 and October 7, 2020) and explore the contribution of factors such as income inequality, binge drinking, and socioeconomic deprivation. We used Bayesian multivariate spatial models to estimate the cross-disease correlations between these diseases and identified hotspots, which we defined as a county with a posterior probability greater than 80% of being in the top decile of that disease.

RESULTS

New HIV infection prevalence and COVID-19 infection moderately and significantly intersect [spatial correlation = 0.37, 95% credible interval (CrI) = 0.36-0.37]. Seventy-five counties, mostly in the south, were at elevated burden for HIV and COVID-19 infections. Higher income inequality was positively associated with both COVID-19 (relative risk 1.05, 95% CrI = 1.03-1.07) and HIV infection (relative risk = 1.12, 95% CrI = 1.09-1.15).

CONCLUSIONS

We found that there is a considerable intersection between the current distribution of HIV burden with COVID-19 infections at the area level. We identified areas that federal funding and vaccination campaigns should prioritize for prevention and care efforts.

Spatial spillover and COVID-19 spread in the U.S

BACKGROUND

This research estimates the effects of vulnerability on the spread of COVID-19 cases across U.S. counties. Vulnerability factors (Socioeconomic Status, Minority Status & Language, Housing type, Transportation, Household Composition & Disability, Epidemiological Factors, Healthcare system Factors, High-risk Environments, and Population density) do not only influence an individual's likelihood of getting infected but also influence the likelihood of his/her neighbors getting infected. Thus, spatial interactions occurring among individuals are likely to lead to spillover effects which may cause further virus transmission.

METHODS

This research uses the COVID-19 community index (CCVI), which defines communities likely vulnerable to the impact of the pandemic and captures the multi-dimensionality of vulnerability. The spatial Durbin model was used to estimate the spillover effects of vulnerability to COVID-19 in U.S. counties, from May 1 to December 15, 2020.

RESULTS

The findings confirm the existence of spatial spillover effects; with indirect effects (from neighboring counties) dominating the direct effects (from county-own vulnerability level). This not only validates social distancing as a strategy to contain the spread of the pandemic but also calls for comprehensive and coordinated approach to fight its effects. By keeping vulnerability factors constant but varying the number of reported infected cases every 2 weeks, we found that marginal effects of vulnerability vary significantly across counties. This might be the reflection of both the changing intensity of the pandemic itself but also the lack of consistency in the measures implemented to combat it.

CONCLUSION

Overall, the results indicate that high vulnerability in Minority, Epidemiological factors, Healthcare System Factors, and High-Risk Environments in each county and adjacent counties leads to an increase in COVID-19 confirmed cases.

Geo-clusters and socio-demographic profiles at village-level associated with COVID-19 incidence in the metropolitan city of Jakarta: An ecological study

The Special Capital Region of Jakarta is the epicentre of the transmission of COVID‐19 in Indonesia. However, much remains unknown about the spatial and temporal patterns of COVID‐19 incidence and related socio‐demographic factors explaining the variations of COVID‐19 incidence at local level. COVID‐19 cases at the village level of Jakarta from March 2020 to June 2021 were analyzed from the local public COVID‐19 dashboard. Global and local spatial clustering of COVID‐19 incidence was examined using the Moran's I and local Moran analysis. Socio‐demographic profiles of identified hotspots were elaborated. The association between village characteristics and COVID‐19 incidence was evaluated. The COVID‐19 incidence was significantly clustered based on the geographical village level (Moran's I = 0.174; p = .002). Seventeen COVID‐19 high‐risk clusters were found and dynamically shifted over the study period. The proportion of people aged 20–49 (incidence rate ratio [IRR] = 1.016; 95% confidence interval [CI]: 1.012–1.019), proportion of elderly (≥50 years) (IRR = 1.045; 95% CI = 1.041–1.050), number of households (IRR = 1.196; 95% CI = 1.193–1.200), access to metered water for washing, and the main occupation of the residents were village level socio‐demographic factors associated with the risk of COVID‐19. Targeted public health responses such as restriction, improved testing and contact tracing, and improved access to health services for those vulnerable populations are essential in areas with high‐risk COVID‐19.

Socioeconomic Patterns of COVID-19 Clusters in Low-Incidence City, Hong Kong

Although coronavirus disease (COVID-19) outbreaks have been relatively well controlled in Hong Kong, containment remains challenging among socioeconomically disadvantaged persons. They are at higher risk for widespread COVID-19 transmission through sizable clustering, probably because of exposure to social settings in which existing mitigation policies had differential socioeconomic effects.

Population Mobility Trends, Deprivation Index and the Spatio-Temporal Spread of Coronavirus Disease 2019 in Ireland

Like most countries worldwide, the coronavirus disease (COVID-19) has adversely affected Ireland. The aim of this study was to (i) investigate the spatio-temporal trend of COVID-19 incidence; (ii) describe mobility trends as measured by aggregated mobile phone records; and (iii) investigate the association between deprivation index, population density and COVID-19 cases while accounting for spatial and temporal correlation. Standardised incidence ratios of cases were calculated and mapped at a high spatial resolution (electoral division level) over time. Trends in the percentage change in mobility compared to a pre-COVID-19 period were plotted to investigate the impact of lockdown restrictions. We implemented a hierarchical Bayesian spatio-temporal model (Besag, York and Mollié (BYM)), commonly used for disease mapping, to investigate the association between covariates and the number of cases. There have been three distinct "waves" of COVID-19 cases in Ireland to date. Lockdown restrictions led to a substantial reduction in human movement, particularly during the 1st and 3rd wave. Despite adjustment for population density (incidence ratio (IR) = 1.985 (1.915-2.058)) and the average number of persons per room (IR = 10.411 (5.264-22.533)), we found an association between deprivation index and COVID-19 incidence (IR = 1.210 (CI: 1.077-1.357) for the most deprived quintile compared to the least deprived). There is a large range of spatial heterogeneity in COVID-19 cases in Ireland. The methods presented can be used to explore locally intensive surveillance with the possibility of localised lockdown measures to curb the transmission of infection, while keeping other, low-incidence areas open. Our results suggest that prioritising densely populated deprived areas (that are at increased risk of comorbidities) during vaccination rollout may capture people that are at risk of infection and, potentially, also those at increased risk of hospitalisation.

Variability in COVID-19 in-hospital mortality rates between national health service trusts and regions in England: A national observational study for the Getting It Right First Time Programme

BACKGROUND

A key first step in optimising COVID-19 patient outcomes during future case-surges is to learn from the experience within individual hospitals during the early stages of the pandemic. The aim of this study was to investigate the extent of variation in COVID-19 outcomes between National Health Service (NHS) hospital trusts and regions in England using data from March-July 2020.

METHODS

This was a retrospective observational study using the Hospital Episode Statistics administrative dataset. Patients aged ≥ 18 years who had a diagnosis of COVID-19 during a hospital stay in England that was completed between March 1st and July 31st, 2020 were included. In-hospital mortality was the primary outcome of interest. In secondary analysis, critical care admission, length of stay and mortality within 30 days of discharge were also investigated. Multilevel logistic regression was used to adjust for covariates.

FINDINGS

There were 86,356 patients with a confirmed diagnosis of COVID-19 included in the study, of whom 22,944 (26.6%) died in hospital with COVID-19 as the primary cause of death. After adjusting for covariates, the extent of the variation in-hospital mortality rates between hospital trusts and regions was relatively modest. Trusts with the largest baseline number of beds and a greater proportion of patients admitted to critical care had the lowest in-hospital mortality rates.

INTERPRETATION

There is little evidence of clustering of deaths within hospital trusts. There may be opportunities to learn from the experience of individual trusts to help prepare hospitals for future case-surges.

Global Population Aging, National Development Level, and Vulnerability to the Pandemic

PURPOSE

This study examined the roles of population aging and national development level in affecting different phases of novel coronavirus disease development with a view to advancing preparedness and corresponding policy.

METHODS

Regression analysis was conducted using multisource data from the World Bank and Johns Hopkins COVID-19 Dashboard.

RESULTS

Population aging is positively associated with confirmed cases of day-10, day-20, and day-30. The positive association between population aging and death does not emerge until day-20. Countries with a higher proportion of older males face higher risks of death.. Countries with a higher proportion of males aged 70-74 years are at the highest risk of confirmed cases. National development level is not associated with confirmed cases, but developing countries face significantly higher risks of death of day-10 and day-20.

CONCLUSION

Prevention and control policies for older adults and underdeveloped areas and sex differences need to be studied.