Disparities in influenza mortality and transmission related to sociodemographic factors within Chicago in the pandemic of 1918

Did the 1918 influenza pandemic cause a 1920 baby boom? Demographic evidence from neutral Europe

In 1919-20, the European countries that were neutral in the First World War saw a small baby bust followed by a small baby boom. The sparse literature on this topic attributes the 1919 bust to individuals postponing conceptions during the peak of the 1918-20 influenza pandemic and the 1920 boom to recuperation of those conceptions. Using data from six large neutral countries of Europe, we present novel evidence contradicting that narrative. In fact, the subnational populations and maternal birth cohorts whose fertility was initially hit hardest by the pandemic were still experiencing below-average fertility in 1920. Demographic evidence, economic evidence, and a review of post-pandemic fertility trends outside Europe suggest that the 1920 baby boom in neutral Europe was caused by the end of the First World War, not by the end of the pandemic.

Capturing the implications of residential segregation for the dynamics of infectious disease transmission

Occupational and residential segregation and other manifestations of social and economic inequity drive of racial and socioeconomic inequities in infection, severe disease, and death from a wide variety of infections including SARS-CoV-2, influenza, HIV, tuberculosis, and many others. Despite a deep and long-standing quantitative and qualitative literature on infectious disease inequity, mathematical models that give equally serious attention to the social and biological dynamics underlying infection inequity remain rare. In this paper, we develop a simple transmission model that accounts for the mechanistic relationship between residential segregation on inequity in infection outcomes. We conceptualize segregation as a high-level, fundamental social cause of infection inequity that impacts both who-contacts-whom (separation or preferential mixing) as well as the risk of infection upon exposure (vulnerability). We show that the basic reproduction number, ℛ 0 , and epidemic dynamics are sensitive to the interaction between these factors. Specifically, our analytical and simulation results and that separation alone is insufficient to explain segregation-associated differences in infection risks, and that increasing separation only results in the concentration of risk in segregated populations when it is accompanied by increasing vulnerability. Overall, this work shows why it is important to carefully consider the causal linkages and correlations between high-level social determinants - like segregation - and more-proximal transmission mechanisms when either crafting or evaluating public health policies. While the framework applied in this analysis is deliberately simple, it lays the groundwork for future, data-driven explorations of the mechanistic impact of residential segregation on infection inequities.

The complex link between socioeconomic deprivation and COVID-19. Evidence from small areas of Catalonia

This ecological study assesses the association between the incidence rate of COVID-19 confirmed cases and socioeconomic deprivation in the Catalan small areas for the first six waves of the pandemic. The association is estimated using Poisson regressions and, in contrast to previous studies, considering that the relationship is not linear but rather depends on the degree of deprivation. The results show that the association between deprivation and incidence varied between waves, not only in intensity but also in its sign. Although it was insignificant in the first, third and fourth waves, the association was positive and significant in the second, becoming significantly negative in the fifth and sixth waves. Interestingly, the evidence suggests that the link between both magnitudes was not homogeneous throughout the distribution of deprivation, the pattern also varying between waves. The results are discussed in view of the role of non-pharmacological interventions and vaccination, as well as potential biases (for example that associated with differences between population groups in the propensity to be tested in each wave).

Global pattern and determinant for interaction of seasonal influenza viruses

BACKGROUND

The prevalence of different types/subtypes varies across seasons and countries for seasonal influenza viruses, indicating underlying interactions between types/subtypes. The global interaction patterns and determinants for seasonal influenza types/subtypes need to be explored.

METHODS

Influenza epidemiological surveillance data, as well as multidimensional data that include population-related, environment-related, and virus-related factors from 55 countries worldwide were used to explore type/subtype interactions based on Spearman correlation coefficient. The machine learning method Extreme Gradient Boosting (XGBoost) and interpretable framework SHapley Additive exPlanation (SHAP) were utilized to quantify contributing factors and their effects on interactions among influenza types/subtypes. Additionally, causal relationships between types/subtypes were also explored based on Convergent Cross-mapping (CCM).

RESULTS

A consistent globally negative correlation exists between influenza A/H3N2 and A/H1N1. Meanwhile, interactions between influenza A (A/H3N2, A/H1N1) and B show significant differences across countries, primarily influenced by population-related factors. Influenza A has a stronger driving force than influenza B, and A/H3N2 has a stronger driving force than A/H1N1.

CONCLUSION

The research elucidated the globally complex and heterogeneous interaction patterns among influenza type/subtypes, identifying key factors shaping their interactions. This sheds light on better seasonal influenza prediction and model construction, informing targeted prevention strategies and ultimately reducing the global burden of seasonal influenza.

Ozone as an environmental driver of influenza

Under long-standing threat of seasonal influenza outbreaks, it remains imperative to understand the drivers of influenza dynamics which can guide mitigation measures. While the role of absolute humidity and temperature is extensively studied, the possibility of ambient ozone (O3) as an environmental driver of influenza has received scant attention. Here, using state-level data in the USA during 2010-2015, we examined such research hypothesis. For rigorous causal inference by evidence triangulation, we applied 3 distinct methods for data analysis: Convergent Cross Mapping from state-space reconstruction theory, Peter-Clark-momentary-conditional-independence plus as graphical modeling algorithms, and regression-based Generalised Linear Model. The negative impact of ambient O3 on influenza activity at 1-week lag is consistently demonstrated by those 3 methods. With O3 commonly known as air pollutant, the novel findings here on the inhibition effect of O3 on influenza activity warrant further investigations to inform environmental management and public health protection.

Reconstructed influenza A/H3N2 infection histories reveal variation in incidence and antibody dynamics over the life course

Humans experience many influenza infections over their lives, resulting in complex and varied immunological histories. Although experimental and quantitative analyses have improved our understanding of the immunological processes defining an individual's antibody repertoire, how these within-host processes are linked to population-level influenza epidemiology remains unclear. Here, we used a multi-level mathematical model to jointly infer antibody dynamics and individual-level lifetime influenza A/H3N2 infection histories for 1,130 individuals in Guangzhou, China, using 67,683 haemagglutination inhibition (HI) assay measurements against 20 A/H3N2 strains from repeat serum samples collected between 2009 and 2015. These estimated infection histories allowed us to reconstruct historical seasonal influenza patterns and to investigate how influenza incidence varies over time, space and age in this population. We estimated median annual influenza infection rates to be approximately 18% from 1968 to 2015, but with substantial variation between years. 88% of individuals were estimated to have been infected at least once during the study period (2009-2015), and 20% were estimated to have three or more infections in that time. We inferred decreasing infection rates with increasing age, and found that annual attack rates were highly correlated across all locations, regardless of their distance, suggesting that age has a stronger impact than fine-scale spatial effects in determining an individual's antibody profile. Finally, we reconstructed each individual's expected antibody profile over their lifetime and inferred an age-stratified relationship between probability of infection and HI titre. Our analyses show how multi-strain serological panels provide rich information on long term, epidemiological trends, within-host processes and immunity when analyzed using appropriate inference methods, and adds to our understanding of the life course epidemiology of influenza A/H3N2.

Influenza-Associated Outcomes and Healthcare Utilization by Race and Ethnicity in the USA: a Retrospective Cohort Study Using the National Inpatient Sample Database

BACKGROUND

The influenza virus continues to be a public health concern every season. We aimed to evaluate influenza-associated outcomes and healthcare utilization by race and ethnicity.

METHODS

We conducted a retrospective cohort study using the National Inpatient Sample across 2019 and 2020. Influenza pneumonia was selected as the principal diagnosis. Outcomes included mortality, use of respiratory support ventilation, length of stay, and total hospitalization charge. Regression models were adjusted for age, gender, Charlson Comorbidity Index, hospitals' region, bed size, teaching status, insurance status, and median income.

RESULTS

We identified 73,098 individuals hospitalized with influenza pneumonia; 39,807 and 33,291 were admitted in 2019 and 2020, respectively. The sample included 49,829 (68%) White, 11,356 (15.5%) Black, 7526 (10%) Hispanic, 1860 (2.5%) Asian/Pacific, and 617 (0.84%) Native American patients. In-hospital mortality rates and respiratory support (non-invasive ventilation and invasive mechanical ventilation) in 2019 and 2020 were not significantly different across all the races. In 2019 and 2020, the adjusted odds ratios of in-patient mortality were not significantly different. Asians had higher odds of receiving NIV in 2019 but not in 2020 compared to White patients (adjusted odds ratio (aOR) 1.67, p value 0.04). The adjusted odds ratios for receiving IMV were not significantly different between the races in 2019 and 2020.

CONCLUSIONS

This study contributes valuable insight into influenza-associated outcomes and healthcare utilization patterns among diverse racial and ethnic groups. Disparities in healthcare utilization were observed among younger (< 65 years) individuals of Black and Hispanic ethnicity.

State variation in neighborhood COVID-19 burden across the United States

BACKGROUND

A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 infection burden has been distributed across neighborhoods, a key determinant of both risk and resilience. Without more spatially resolute data, efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities will remain difficult to quantify and intervene on.

METHODS

We leveraged spatially-referenced data from 21 states collated through the COVID Neighborhood Project to examine the distribution of COVID-19 cases across neighborhoods and states in the U.S. We also linked the COVID-19 case data with data on the neighborhood social environment from the National Neighborhood Data Archive. We then estimated correlations between neighborhood COVID-19 burden and features of the neighborhood social environment.

RESULTS

We find that the distribution of COVID-19 at the neighborhood-level varies within and between states. The median case count per neighborhood (coefficient of variation (CV)) in Wisconsin is 3078.52 (0.17) per 10,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (CV) is 810.98 (0.84) per 10,000 population. We also find that correlations between features of the neighborhood social environment and burden vary in magnitude and direction by state.

CONCLUSIONS

Our findings underscore the importance that local contexts may play when addressing the long-term social and economic fallout communities will face from COVID-19.

What can we learn from historical pandemics? A systematic review of the literature

What are the insights from historical pandemics for policymaking today? We carry out a systematic review of the literature on the impact of pandemics that occurred since the Industrial Revolution and prior to Covid-19. Our literature searches were conducted between June 2020 and September 2023, with the final review encompassing 169 research papers selected for their relevance to understanding either the demographic or economic impact of pandemics. We include literature from across disciplines to maximise our knowledge base, finding many relevant articles in journals which would not normally be on the radar of social scientists. Our review identifies two gaps in the literature: (1) the need to study pandemics and their effects more collectively rather than looking at them in isolation; and (2) the need for more study of pandemics besides 1918 Spanish Influenza, especially milder pandemic episodes. These gaps are a consequence of academics working in silos, failing to draw on the skills and knowledge offered by other disciplines. Synthesising existing knowledge on pandemics in one place provides a basis upon which to identify the lessons in preparing for future catastrophic disease events.

Social inequalities and the COVID-19 pandemic

Social inequality has been identified as an important determinant of the outcome of infectious diseases and the recent SARS-CoV-2 pandemic has vividly reminded us that there are no "equal opportunity infectors". In a recent article, Chakrabarty et al. (2023) reported the finding of a cross-country comparison of COVID-19 cases and social deprivation, using up-to-date statistical modelling. These results add to the extensive evidence showing that vulnerable populations are consistently at higher risk of contracting the infection and to suffer from more severe symptoms, whatever the spatial scale used (from the country to the neighborhood). Spatial clustering of socially deprived groups, preexisting pathologies and hotspots of COVID-19 cases and deaths indicate that the SARS-CoV-2 should be seen as a syndemic, where both the infection dynamics and the outcome of the disease strongly depend on the three-way interaction between the virus, preexisting pathologies, and the socioeconomic environment.

Socioeconomic mortality differences during the Great Influenza in Spain

Despite being one of the deadliest viruses in history, there is limited information on the socioeconomic factors that affected mortality rates during the Great Influenza Pandemic. In this study, we use occupation-province level data to investigate the relationship between influenza excess mortality rates and occupation-related status in Spain. We obtain three main results. Firstly, individuals in low-income occupations experienced the highest excess mortality, pointing to a notable income gradient. Secondly, professions that involved more social interaction were associated with a higher excess of mortality, regardless of income. Finally, we observe a substantial rural mortality penalty, even after controlling for income-related occupational groups. Based on this evidence, it seems that the high number of deaths was caused by not self-isolating. Some individuals did not quarantine themselves because they could not afford to miss work. In rural areas, home confinement was likely more limited because their inhabitants did not have immediate access to information about the pandemic or fully understand its impact due to their limited experience handling influenza outbreaks.

Spatial distribution and driving factors of the associations between temperature and influenza-like illness in the United States: a time-stratified case-crossover study

BACKGROUND

Several previous studies investigated the associations between temperature and influenza in a single city or region without a national picture. The attributable risk of influenza due to temperature and the corresponding driving factors were unclear. This study aimed to evaluate the spatial distribution characteristics of attributable risk of Influenza-like illness (ILI) caused by adverse temperatures and explore the related driving factors in the United States.

METHODS

ILI, meteorological factors, and PM_2.5 of 48 states in the United States were collected during 2011-2019. The time-stratified case-crossover design with a distributed lag non-linear model was carried out to evaluate the association between temperature and ILI at the state level. The multivariate meta-analysis was performed to obtain the combined effects at the national level. The attributable fraction (AF) was calculated to assess the ILI burden ascribed to adverse temperatures. The ordinary least square model (OLS), spatial lag model (SLM), and spatial error model (SEM) were utilized to identify driving factors.

RESULTS

A total of 7,716,115 ILI cases were included in this study. Overall, the temperature was negatively associated with ILI risk, and lower temperature gave rise to a higher risk of ILI. AF ascribed to adverse temperatures differed across states, from 49.44% (95% eCI: 36.47% ~ 58.68%) in Montana to 6.51% (95% eCI: -6.49% ~ 16.46%) in Wisconsin. At the national level, 29.08% (95% eCI: 27.60% ~ 30.24%) of ILI was attributable to cold. Per 10,000 dollars increase in per-capita income was associated with the increment in AF (OLS: β = -6.110, P = 0.021; SLM: β = -5.496, P = 0.022; SEM: β = -6.150, P = 0.022).

CONCLUSION

The cold could enhance the risk of ILI and result in a considerable proportion of ILI disease burden. The ILI burden attributed to cold varied across states and was higher in those states with lower economic status. Targeted prevention programs should be considered to lower the burden of influenza.

Effect of Rapid Urbanization in Mainland China on the Seasonal Influenza Epidemic: Spatiotemporal Analysis of Surveillance Data From 2010 to 2017

BACKGROUND

The world is undergoing an unprecedented wave of urbanization. However, the effect of rapid urbanization during the early or middle stages of urbanization on seasonal influenza transmission remains unknown. Since about 70% of the world population live in low-income countries, exploring the impact of urbanization on influenza transmission in urbanized countries is significant for global infection prediction and prevention.

OBJECTIVE

The aim of this study was to explore the effect of rapid urbanization on influenza transmission in China.

METHODS

We performed spatiotemporal analyses of province-level influenza surveillance data collected in Mainland China from April 1, 2010, to March 31, 2017. An agent-based model based on hourly human contact-related behaviors was built to simulate the influenza transmission dynamics and to explore the potential mechanism of the impact of urbanization on influenza transmission.

RESULTS

We observed persistent differences in the influenza epidemic attack rates among the provinces of Mainland China across the 7-year study period, and the attack rate in the winter waves exhibited a U-shaped relationship with the urbanization rates, with a turning point at 50%-60% urbanization across Mainland China. Rapid Chinese urbanization has led to increases in the urban population density and percentage of the workforce but decreases in household size and the percentage of student population. The net effect of increased influenza transmission in the community and workplaces but decreased transmission in households and schools yielded the observed U-shaped relationship.

CONCLUSIONS

Our results highlight the complicated effects of urbanization on the seasonal influenza epidemic in China. As the current urbanization rate in China is approximately 59%, further urbanization with no relevant interventions suggests a worrisome increasing future trend in the influenza epidemic attack rate.

An extended period of elevated influenza mortality risk follows the main waves of influenza pandemics

Understanding the extent and evolution of pandemic-induced mortality risk is critical given its wide-ranging impacts on population health and socioeconomic outcomes. We examine empirically the persistence and scale of influenza mortality risk following the main waves of influenza pandemics, a quantitative analysis of which is required to understand the true scale of pandemic-induced risk. We provide evidence from municipal public health records that multiple recurrent outbreaks followed the main waves of the 1918-19 pandemic in eight large cities in the UK, a pattern we confirm using data for the same period in the US and data for multiple influenza pandemics during the period 1838-2000 in England and Wales. To estimate the persistence and scale of latent post-pandemic influenza mortality risk, we model the stochastic process of mortality rates as a sequence of bounded Pareto distributions whose tail indexes evolves over time. Consistently across pandemics and locations, we find that influenza mortality risk remains elevated for around two decades after the main pandemic waves before more rapid convergence to background influenza mortality, amplifying the impact of pandemics. Despite the commonality in duration, there is heterogeneity in the persistence and scale of risk across the cities, suggesting effects of both immunity and socioeconomic conditions.

State Variation in Neighborhood COVID-19 Burden: Findings from the COVID Neighborhood Project

A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 burden has been distributed across neighborhoods, a known geographic unit of both risk and resilience, and is hampering efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities. Using spatially-referenced data from 21 states at the ZIP code or census tract level, we documented how the distribution of COVID-19 at the neighborhood-level varies significantly within and between states. The median case count per neighborhood (IQR) in Oregon was 3,608 (2,487) per 100,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (IQR) was 8,142 (11,031) per 100,000. We also found that the association between features of the neighborhood social environment and burden varied in magnitude and direction by state. Our findings underscore the importance of local contexts when addressing the long-term social and economic fallout communities will face from COVID-19.

Intergenerational coresidence and the Covid-19 pandemic in the United States

This paper investigates the relation between intergenerational coresidence and mortality from Covid-19 in 2020. Using a cross-section of U.S. counties, we show that this association is positive, sizeable, significant, and robust to the inclusion of several demographic and socio-economic controls. Furthermore, using evidence from past, pre-pandemic years, we argue that this positive, sizeable and significant association is somewhat specific to the Covid-19 pandemic.

Relative deprivation, inequality and the Covid-19 pandemic

There is a growing concern that inequalities are hindering health outcomes. This paper's primary objective is to investigate the role of relative deprivation and inequality in explaining the daily spread of the Covid-19 pandemic. For this purpose, we use secondary cross-sectional data across 119 (developed and developing) countries from January 2020 – to April 2021. For the empirical analysis, we use a recent dynamic panel data modelling approach that allows us to identify the role of time-invariant variables such as degree of globalisation, political freedom and income inequality on the dynamics of the pandemic and fatality rates across countries. We find that new cases per million and fatality rates are highly persistent processes. After controlling for time-varying mobility statistics from the Google mobility database and region-specific dummy variables, the two significant factors that explain the severity of Covid-19 spread in a country are per-capita Gross Domestic Product (GDP) and Yitzhaki's relative income deprivation index. Lagged value of new cases per million significantly explains cross-country variations in the daily case fatality rates. A higher proportion of the older population and pollution increased fatality rates while better medical infrastructure reduced it.

Health disparities in past influenza pandemics: A scoping review of the literature

Objective

The COVID-19 pandemic has exacerbated existing health disparities. To provide a historical perspective on health disparities for pandemic acute respiratory viruses, we conducted a scoping review of the public health literature of health disparities in influenza outcomes during the 1918, 1957, 1968, and 2009 influenza pandemics.

Methods

We searched for articles examining socioeconomic or racial/ethnic disparities in any population, examining any influenza-related outcome (e.g., incidence, hospitalizations, mortality), during the 1918, 1957, 1968, and 2009 influenza pandemics. We conducted a structured search of English-written articles in PubMed supplemented by a snowball of articles meeting inclusion criteria.

Results

A total of 29 articles met inclusion criteria, all but one focusing exclusively on the 1918 or 2009 pandemics. Individuals of low socioeconomic status, or living in low socioeconomic status areas, experienced higher incidence, hospitalizations, and mortality in the 1918 and 2009 pandemics. There were conflicting results regarding racial/ethnic disparities during the 1918 pandemic, with differences in magnitude and direction by outcome, potentially due to issues in data quality by race/ethnicity. Racial/ethnic minorities had generally higher incidence, mortality, and hospitalization rates in the 1957 and 2009 pandemics.

Conclusion

Individuals of low socioeconomic status and racial/ethnic minorities have historically experienced worse influenza outcomes during pandemics. These historical patterns can inform current research to understand disparities in the ongoing COVID-19 pandemic and future pandemics.

The impact of mass gatherings on the local transmission of COVID-19 and the implications for social distancing policies: Evidence from Hong Kong

Mass gatherings provide conditions for the transmission of infectious diseases and pose complex challenges to public health. Faced with the COVID-19 pandemic, governments and health experts called for suspension of gatherings in order to reduce social contact via which virus is transmitted. However, few studies have investigated the contribution of mass gatherings to COVID-19 transmission in local communities. In Hong Kong, the coincidence of the relaxation of group gathering restrictions with demonstrations against the National Security Law in mid-2020 raised concerns about the safety of mass gatherings under the pandemic. Therefore, this study examines the impacts of mass gatherings on the local transmission of COVID-19 and evaluates the importance of social distancing policies. With an aggregated dataset of epidemiological, city-level meteorological and socioeconomic data, a Synthetic Control Method (SCM) is used for constructing a 'synthetic Hong Kong' from over 200 Chinese cities. This counterfactual control unit is used to simulate COVID-19 infection patterns (i.e., the number of total cases and daily new cases) in the absence of mass gatherings. Comparing the hypothetical trends and the actual ones, our results indicate that the infection rate observed in Hong Kong is substantially higher than that in the counterfactual control unit (2.63% vs. 0.07%). As estimated, mass gatherings increased the number of new infections by 62 cases (or 87.58% of total new cases) over the 10-day period and by 737 cases (or 97.23%) over the 30-day period. These findings suggest the necessity of tightening social distancing policies, especially the prohibition on group gathering regulation (POGGR), to prevent and control COVID-19 outbreaks.

Geographical variations and district-level factors associated with COVID-19 mortality in Indonesia: a nationwide ecological study

BACKGROUND

Ensuring health equity, especially for vulnerable populations in less developed settings with poor health system is essential for the current and future global health threats. This study examined geographical variations of COVID-19 mortality and its association with population health characteristics, health care capacity in responding pandemic, and socio-economic characteristics across 514 districts in Indonesia.

METHODS

This nationwide ecological study included aggregated data of COVID-19 cases and deaths from all 514 districts in Indonesia, recorded in the National COVID-19 Task Force database, during the first two years of the epidemic, from 1 March 2020 to 27 February 2022. The dependent variable was district-level COVID-19 mortality rate per 100,000 populations. The independent variables include district-level COVID-19 incidence rate, population health, health care capacity, and socio-demographics data from government official sources. We used multivariable ordinal logistic regression to examine factors associated with higher mortality rate.

RESULTS

Of total 5,539,333 reported COVID-19 cases, 148,034 (2.7%) died, and 5,391,299 (97.4%) were recovered. The district-level mortality rate ranged from 0 to 284 deaths per 100,000 populations. The top five districts with the highest mortality rate were Balikpapan (284 deaths per 100,000 populations), Semarang (263), Madiun (254), Magelang (250), and Yogyakarta (247). A higher COVID-19 incidence (coefficient 1.64, 95% CI 1.22 to 1.75), a higher proportion of ≥ 60 years old population (coefficient 0.26, 95% CI 0.06 to 0.46), a higher prevalence of diabetes mellitus (coefficient 0.60, 95% CI 0.37 to 0.84), a lower prevalence of obesity (coefficient -0.32, 95% CI -0.56 to -0.08), a lower number of nurses per population (coefficient -0.27, 95% CI -0.50 to -0.04), a higher number of midwives per population (coefficient 0.32, 95% CI 0.13 to 0.50), and a higher expenditure (coefficient 0.34, 95% CI 0.10 to 0.57) was associated with a higher COVID-19 mortality rate.

CONCLUSION

COVID-19 mortality rate in Indonesia was highly heterogeneous and associated with higher COVID-19 incidence, different prevalence of pre-existing comorbidity, healthcare capacity in responding the pandemic, and socio-economic characteristics. This study revealed the need of controlling both COVID-19 and those known comorbidities, health capacity strengthening, and better resource allocation to ensure optimal health outcomes for vulnerable population.

Unequal lives: a sociodemographic analysis of COVID-19 transmission and mortality in India

OBJECTIVES

Existing socio-economic inequalities shape, in very particular and measurable ways, the differential impact that a disease has on different sections of the same society. This is particularly true of COVID-19, which has rapidly exhausted the public health system in India, and magnified the gradient of vulnerability in an underserved populace. Using publicly available data, we have aimed to deconstruct this gradient into individual variables of inequality and quantify their impact on the transmission and mortality outcomes of COVID-19 in India.

STUDY DESIGN

Sociodemographic analysis.

METHODS

We quantify doubling times and case fatality ratios for all districts in India, then correlate them to 20 variables of socio-economic vulnerability and demographic structure. Variables that exhibit persistent correlation are then analysed using multivariate beta regression models to validate their impact on COVID-19 outcomes in India.

RESULTS

The transmission of COVID-19 in India is enhanced by the lack of access to indoor latrines, drainage facilities, electricity, and proximate sources of drinking water. Transmission is slowed by the presence of an elderly population. Fatality rates relate negatively to an area's medical infrastructure and the presence of a college-educated populace.

CONCLUSIONS

An interactive matrix of social inequalities, cultural practices, and behavioural patterns determines the path of COVID-19 through a community. Specific variables exhibit patterns of persistent vulnerability; others indicate a resistance to infection and mortality. This body of evidence, when incorporated into policy design, may lead to localised, need-sensitive models of intervention, both for preventive measures and medical care.

Unequal excess mortality during the Spanish Flu pandemic in the Netherlands

A century after the Spanish Flu, the COVID-19 pandemic has brought renewed attention to socioeconomic and occupational differences in mortality in the earlier pandemic. The magnitude of these differences and the pathways between occupation and increased mortality remain unclear, however. In this paper, we explore the relation between occupational characteristics and excess mortality among men during the Spanish Flu pandemic in the Netherlands. By creating a new occupational coding for exposure to disease at work, we separate social status and occupational conditions for viral transmission. We use a new data set based on men's death certificates to calculate excess mortality rates by region, age group, and occupational group. Using OLS regression models, we estimate whether social position, regular interaction in the workplace, and working in an enclosed space affected excess mortality among men in the Netherlands in the autumn of 1918. We find some evidence that men with occupations that featured high levels of social contact had higher mortality in this period. Above all, however, we find a strong socioeconomic gradient to excess mortality among men during the Spanish Flu pandemic, even after accounting for exposure in the workplace.

Nutritional management of individuals with obesity and COVID-19: ESPEN expert statements and practical guidance

The COVID-19 pandemics has created unprecedented challenges and threats to patients and healthcare systems worldwide. Acute respiratory complications that require intensive care unit (ICU) management are a major cause of morbidity and mortality in COVID-19 patients. Among other important risk factors for severe COVID-19 outcomes, obesity has emerged along with undernutrition-malnutrition as a strong predictor of disease risk and severity. Obesity-related excessive body fat may lead to respiratory, metabolic and immune derangements potentially favoring the onset of COVID-19 complications. In addition, patients with obesity may be at risk for loss of skeletal muscle mass, reflecting a state of hidden malnutrition with a strong negative health impact in all clinical settings. Also importantly, obesity is commonly associated with micronutrient deficiencies that directly influence immune function and infection risk. Finally, the pandemic-related lockdown, deleterious lifestyle changes and other numerous psychosocial consequences may worsen eating behaviors, sedentarity, body weight regulation, ultimately leading to further increments of obesity-associated metabolic complications with loss of skeletal muscle mass and higher non-communicable disease risk. Therefore, prevention, diagnosis and treatment of malnutrition and micronutrient deficiencies should be routinely included in the management of COVID-19 patients in the presence of obesity; lockdown-induced health risks should also be specifically monitored and prevented in this population. In the current document, the European Society for Clinical Nutrition and Metabolism (ESPEN) aims at providing clinical practice guidance for nutritional management of COVID-19 patients with obesity in various clinical settings.

Racial Disparities in Mortality During the 1918 Influenza Pandemic in United States Cities

Against a backdrop of extreme racial health inequality, the 1918 influenza pandemic resulted in a striking reduction of non-White to White influenza and pneumonia mortality disparities in United States cities. We provide the most complete account to date of these reduced racial disparities, showing that they were unexpectedly uniform across cities. Linking data from multiple sources, we then examine potential explanations for this finding, including city-level sociodemographic factors such as segregation, implementation of nonpharmaceutical interventions, racial differences in exposure to the milder spring 1918 "herald wave," and racial differences in early-life influenza exposures, resulting in differential immunological vulnerability to the 1918 flu. While we find little evidence for the first three explanations, we offer suggestive evidence that racial variation in childhood exposure to the 1889-1892 influenza pandemic may have shrunk racial disparities in 1918. We also highlight the possibility that differential behavioral responses to the herald wave may have protected non-White urban populations. By providing a comprehensive description and examination of racial inequality in mortality during the 1918 pandemic, we offer a framework for understanding disparities in infectious disease mortality that considers interactions between the natural histories of particular microbial agents and the social histories of those they infect.

Estimation of Ebola’s spillover infection exposure in Sierra Leone based on sociodemographic and economic factors

Zoonotic diseases spread through pathogens-infected animal carriers. In the case of Ebola Virus Disease (EVD), evidence supports that the main carriers are fruit bats and non-human primates. Further, EVD spread is a multi-factorial problem that depends on sociodemographic and economic (SDE) factors. Here we inquire into this phenomenon and aim at determining, quantitatively, the Ebola spillover infection exposure map and try to link it to SDE factors. To that end, we designed and conducted a survey in Sierra Leone and implement a pipeline to analyze data using regression and machine learning techniques. Our methodology is able (1) to identify the features that are best predictors of an individual’s tendency to partake in behaviors that can expose them to Ebola infection, (2) to develop a predictive model about the spillover risk statistics that can be calibrated for different regions and future times, and (3) to compute a spillover exposure map for Sierra Leone. Our results and conclusions are relevant to identify the regions in Sierra Leone at risk of EVD spillover and, consequently, to design and implement policies for an effective deployment of resources (e.g., drug supplies) and other preventative measures (e.g., educational campaigns).

Impact of first UK COVID-19 lockdown on hospital admissions: Interrupted time series study of 32 million people

Background

Uncontrolled infection and lockdown measures introduced in response have resulted in an unprecedented challenge for health systems internationally. Whether such unprecedented impact was due to lockdown itself and recedes when such measures are lifted is unclear. We assessed the short- and medium-term impacts of the first lockdown measures on hospital care for tracer non-COVID-19 conditions in England, Scotland and Wales across diseases, sexes, and socioeconomic and ethnic groups.

Methods

We used OpenSAFELY (for England), EAVEII (Scotland), and SAIL Databank (Wales) to extract weekly hospital admission rates for cancer, cardiovascular and respiratory conditions (excluding COVID-19) from the pre-pandemic period until 25/10/2020 and conducted a controlled interrupted time series analysis. We undertook stratified analyses and assessed admission rates over seven months during which lockdown restrictions were gradually lifted.

Findings

Our combined dataset included 32 million people who contributed over 74 million person-years. Admission rates for all three conditions fell by 34.2% (Confidence Interval (CI): -43.0, -25.3) in England, 20.9% (CI: -27.8, -14.1) in Scotland, and 24.7% (CI: -36.7, -12.7) in Wales, with falls across every stratum considered. In all three nations, cancer-related admissions fell the most while respiratory-related admissions fell the least (e.g., rates fell by 40.5% (CI: -47.4, -33.6), 21.9% (CI: -35.4, -8.4), and 19.0% (CI: -30.6, -7.4) in England for cancer, cardiovascular-related, and respiratory-related admissions respectively). Unscheduled admissions rates fell more in the most than the least deprived quintile across all three nations. Some ethnic minority groups experienced greater falls in admissions (e.g., in England, unscheduled admissions fell by 9.5% (CI: -20.2, 1.2) for Whites, but 44.3% (CI: -71.0, -17.6), 34.6% (CI: -63.8, -5.3), and 25.6% (CI: -45.0, -6.3) for Mixed, Other and Black ethnic groups respectively). Despite easing of restrictions, the overall admission rates remained lower in England, Scotland, and Wales by 20.8%, 21.6%, and 22.0%, respectively when compared to the same period (August-September) during the pre-pandemic years. This corresponds to a reduction of 26.2, 23.8 and 30.2 admissions per 100,000 people in England, Scotland, and Wales respectively.

Interpretation

Hospital care for non-COVID diseases fell substantially across England, Scotland, and Wales during the first lockdown, with reductions persisting for at least six months. The most deprived and minority ethnic groups were impacted more severely.

Funding

This work was funded by the Medical Research Council as part of the Lifelong Health and Wellbeing study as part of National Core Studies (MC_PC_20030). SVK acknowledges funding from the Medical Research Council (MC_UU_00022/2), and the Scottish Government Chief Scientist Office (SPHSU17). EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE - The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. BG has received research funding from the NHS National Institute for Health Research (NIHR), the Wellcome Trust, Health Data Research UK, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme.

Health-related and socio-economic burden of the COVID-19 pandemic in Vienna

Previous pandemics have rarely affected everyone equally and, so far, the COVID-19 pandemic is no exception. Emerging evidence has shown that incidence rate, hospitalisation rate, and mortality due to COVID-19 are higher among people in lower socio-economic position (SEP). In addition, first investigations indicate that not everyone is equally affected by this pandemic's collateral public health damage. Using a stratified random sample of 1,004 participants living in Vienna, a Central European city with approximately 1.9 million inhabitants, this study analysed the distribution of 10 adverse health-related and socio-economic outcomes attributable to the COVID-19 pandemic across socio-economic strata. To this end, we estimated differences in the incidence rate of these outcomes by SEP and each of its indicators using zero-inflated Poisson and logistic regression models, adjusted for age and gender. Data were collected during first lockdown measures between 27 April and 17 May 2020. Differences in the incidence rate between the two lowest and two highest SEP groups were clearly visible. Participants in the lowest SEP category had a 32.96% higher incidence rate (IRR = 1.333 [95% CI: 1.079-1.639]), and participants in the second lowest SEP category had a 44.69% higher incidence rate (IRR = 1.447 [95% CI: 1.190-1.760]) compared with participants in the highest SEP category. In sum, 6 out of 10 adverse COVID-19-related outcomes were, to a greater or lesser extent, disproportionately experienced by Viennese residents in lower SEP. Inequalities were most visible between income groups and for the outcomes job loss, worsening of the financial situation, and worse mental health. These results strengthen and extend the current evidence on the unequally distributed burden of the COVID-19 pandemic. In light of effect heterogeneity across SEP indicators, we encourage future investigators to pay increased attention to their operationalisation of SEP. Such awareness will help to correctly identify those in most urgent need of supportive polices.

Compact cities and the Covid-19 pandemic: Systematic review of the associations between transmission of Covid-19 or other respiratory viruses and population density or other features of neighbourhood design

Living in compact neighbourhoods that are walkable, well connected, with accessible green space can benefit physical and mental health. However, the pandemic raises concern up to what extent features of compact neighbourhood design affect transmission of viral respiratory infections. We conducted a systematic review to identify, appraise and synthesise evidence reporting associations between transmission of respiratory viruses, including Covid-19, and dwelling or population density or other features of neighbourhood design. Twenty-one studies met our inclusion criteria. These studies used different measures of neighbourhood design, contributing to inconsistent findings. Whereas no convincing conclusion can be drawn here, the outcome of this review indicates that robust, global evidence is warranted to inform future policies and legislation concerned with compact neighbourhood design and transmission of respiratory and viral infection.

Pandemic inequity in a megacity: a multilevel analysis of individual, community and healthcare vulnerability risks for COVID-19 mortality in Jakarta, Indonesia

INTRODUCTION

Worldwide, the 33 recognised megacities comprise approximately 7% of the global population, yet account for 20% COVID-19 deaths. The specific inequities and other factors within megacities that affect vulnerability to COVID-19 mortality remain poorly defined. We assessed individual, community-level and healthcare factors associated with COVID-19-related mortality in a megacity of Jakarta, Indonesia, during two epidemic waves spanning 2 March 2020 to 31 August 2021.

METHODS

This retrospective cohort included residents of Jakarta, Indonesia, with PCR-confirmed COVID-19. We extracted demographic, clinical, outcome (recovered or died), vaccine coverage data and disease prevalence from Jakarta Health Office surveillance records, and collected subdistrict level sociodemographics data from various official sources. We used multilevel logistic regression to examine individual, community and subdistrict-level healthcare factors and their associations with COVID-19 mortality.

RESULTS

Of 705 503 cases with a definitive outcome by 31 August 2021, 694 706 (98.5%) recovered and 10 797 (1.5%) died. The median age was 36 years (IQR 24-50), 13.2% (93 459) were <18 years and 51.6% were female. The subdistrict level accounted for 1.5% of variance in mortality (p<0.0001). Mortality ranged from 0.9 to 1.8% by subdistrict. Individual-level factors associated with death were older age, male sex, comorbidities and age <5 years during the first wave (adjusted OR (aOR)) 1.56, 95% CI 1.04 to 2.35; reference: age 20-29 years). Community-level factors associated with death were poverty (aOR for the poorer quarter 1.35, 95% CI 1.17 to 1.55; reference: wealthiest quarter) and high population density (aOR for the highest density 1.34, 95% CI 1.14 to 2.58; reference: the lowest). Healthcare factor associated with death was low vaccine coverage (aOR for the lowest coverage 1.25, 95% CI 1.13 to 1.38; reference: the highest).

CONCLUSION

In addition to individual risk factors, living in areas with high poverty and density, and low healthcare performance further increase the vulnerability of communities to COVID-19-associated death in urban low-resource settings.

Addressing the socioeconomic divide in computational modeling for infectious diseases

The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics. Here, the authors provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models.

History Repeating-How Pandemics Collide with Health Disparities in the United States

Across the United States, public health responses to the COVID-19 pandemic have fallen short. COVID-19 has exacerbated longstanding public health shortfalls in disadvantaged communities. Was this predestined? Understanding where we are today requires reflection on our longer journey. Disparities cataloged during COVID-19 reflect the same unequal host exposure and susceptibility risks that shaped previous pandemics. In this review, we provide historical context to better understand current events and to showcase forgotten lessons which may motivate future action to protect our most vulnerable citizens.

Déjà vu all over again: racial, ethnic and age disparities in mortality from influenza 1918-19 and COVID-19 in the United States

Background

Examination of the mortality patterns in the United States among racial, ethnic, and age groups attributed to the 1918–19 influenza pandemic revealed stark disparities, causes for which could have been addressed and rectified this past century. However, these disparities have been amplified during the current COVID-19 pandemic.

We have ignored the lessons of the past, and were destined to repeat its failings.

Objectives

Compare and contrast mortality patterns by age, race, and ethnicity attributable to the 1918–19 influenza pandemic in the United States with corresponding patterns during the COVID-19 pandemic.

Methods

This is a retrospective study, establishing mortality rates according to age, race and ethnicity attributable to the 1918–19 influenza pandemic in the United States and to the current COVID-19 pandemic, using mortality data published by the U.S. Public Health Service and the Centers for Disease Control and Prevention. Negative binomial regression models were used to establish rate ratios, that is, ratios of mortality rates across the various racial/ethnic groups, and associated 95% confidence intervals.

Results

Mortality patterns by age differ significantly between the 1918–19 influenza pandemic and the COVID-19 pandemic: with infant and young adult (25–40 years old) mortality substantially higher in the former. Disparities in mortality between racial and ethnic groups are amplified in the COVID-19 pandemic compared to the 1918–19 experience.

Conclusions

As we evaluate our nation's response to COVID-19 and design public policy to prepare better for coming pandemics, we cannot ignore the stark disparities in mortality rates experienced by different racial and ethnic groups. This will require a sustained resolve by society and government to delineate and remedy the causative factors, through science devoid of political interpretation and exploitation.

Disparities in co-payments for influenza vaccine among the elderly, during the COVID-19 pandemic in Japan

INTRODUCTION

Seasonal influenza vaccination for the elderly is highly recommended during the COVID-19 pandemic. In Japan, the amount of subsidy for influenza differs among municipalities. Thus, we investigated the amount of and variation in subsidy for influenza vaccination for the elderly in 2020.

METHODS

This was an ecological study of 1,922 municipalities in Japan. The amount of subsidy for influenza vaccines for the elderly in each municipality was surveyed through websites or via telephone. Geographic and financial data for municipalities and prefectures were obtained from the open data. The amount of co-payment for the influenza vaccine and the geographical and financial status of each municipality were compared, according to the aging rate. Univariate logistic regression analysis was performed to explore factors related to the free influenza vaccine.

RESULTS

Municipalities with higher aging rates tended to have higher median co-payments for vaccines in 2020. (0 yen vs 1000 yen, p < 0.001) In addition, they tended to have worse financial conditions and lower per capita incomes. A similar trend was observed in the analysis by prefecture, i.e., a higher influenza mortality rate in prefectures with a higher aging rate. Despite having lower incomes, municipalities and prefectures with higher aging populations had higher mortality rates from influenza and higher co-payments for influenza vaccination.

CONCLUSIONS

In Japan, there is a disparity among elderly people; areas with an aging population have higher co-payments for influenza vaccines despite lower incomes, suggesting that the government needs to implement corrective measures to reduce this disparity.

Interventions on Socioeconomic and Racial Inequities in Respiratory Pandemics: a Rapid Systematic Review

Purpose of Review

Racial and socioeconomic inequities in respiratory pandemics have been consistently documented, but little official guidance exists on effective action to prevent these. We systematically reviewed quantitative evaluations of (real or simulated) interventions targeting racial and socioeconomic inequities in respiratory pandemic outcomes.

Recent Findings

Our systematic search returned 10,208 records, of which 5 met inclusion criteria, including observational (n = 1), randomized trial (n = 1), and simulation (n = 3) studies. Interventions studied included vaccination parity, antiviral distribution, school closure, disinfection, personal protective equipment, and paid sick leave, with a focus on Black (n = 3) and/or Latinx (n = 4) or low-SES (n = 2) communities. Results are suggestive that these interventions might be effective at reducing racial and/or SES disparities in pandemics.

Summary

There is a dearth of research on strategies to reduce pandemic disparities. We provide theory-driven, concrete suggestions for incorporating equity into intervention research for pandemic preparedness, including a focus on social and economic policies.

Sociodemographic determinants of intraurban variations in COVID-19 incidence: the case of Barcelona

BACKGROUND

Intraurban sociodemographic risk factors for COVID-19 have yet to be fully understood. We investigated the relationship between COVID-19 incidence and sociodemographic factors in Barcelona at a fine-grained geography.

METHODS

This cross-sectional ecological study is based on 10 550 confirmed cases of COVID-19 registered during the first wave in the municipality of Barcelona (population 1.64 million). We considered 16 variables on the demographic structure, urban density, household conditions, socioeconomic status, mobility and health characteristics for 76 geographical units of analysis (neighbourhoods), using a lasso analysis to identify the most relevant variables. We then fitted a multivariate Quasi-Poisson model that explained the COVID-19 incidence by neighbourhood in relation to these variables.

RESULTS

Neighbourhoods with: (1) greater population density, (2) an aged population structure, (3) a high presence of nursing homes, (4) high proportions of individuals who left their residential area during lockdown and/or (5) working in health-related occupations were more likely to register a higher number of cases of COVID-19. Conversely, COVID-19 incidence was negatively associated with (6) percentage of residents with post-secondary education and (7) population born in countries with a high Human Development Index.

CONCLUSION

Like other historical pandemics, the incidence of COVID-19 is associated with neighbourhood sociodemographic factors with a greater burden faced by already deprived areas. Because urban social and health injustices already existed in those geographical units with higher COVID-19 incidence in Barcelona, the current pandemic is likely to reinforce both health and social inequalities, and urban environmental injustice all together.

Changing Disparities in Coronavirus Disease 2019 (COVID-19) Burden in the Ethnically Homogeneous Population of Hong Kong Through Pandemic Waves: An Observational Study

BACKGROUND

Disparities were marked in previous pandemics, usually with higher attack rates reported for those in lower socioeconomic positions and for ethnic minorities.

METHODS

We examined characteristics of laboratory-confirmed coronavirus disease 2019 (COVID-19) cases in Hong Kong, assessed associations between incidence and population-level characteristics at the level of small geographic areas, and evaluated relations between socioeconomics and work-from-home (WFH) arrangements.

RESULTS

The largest source of COVID-19 importations switched from students studying overseas in the second wave to foreign domestic helpers in the third. The local cases were mostly individuals not in formal employment (retirees and homemakers) and production workers who were unable to WFH. For every 10% increase in the proportion of population employed as executives or professionals in a given geographic region, there was an 84% (95% confidence interval [CI], 1-97%) reduction in the incidence of COVID-19 during the third wave. In contrast, in the first 2 waves, the same was associated with 3.69 times (95% CI, 1.02-13.33) higher incidence. Executives and professionals were more likely to implement WFH and experienced frequent changes in WFH practice compared with production workers.

CONCLUSIONS

Consistent findings on the reversed socioeconomic patterning of COVID-19 burden between infection waves in Hong Kong in both individual- and population-level analyses indicated that risks of infections may be related to occupations involving high exposure frequency and WFH flexibility. Contextual determinants should be taken into account in policy planning aiming at mitigating such disparities.

Not the great equalizers: Covid-19, 1918-20 influenza, and the need for a paradigm shift in pandemic preparedness

Despite common perceptions to the contrary, pandemic diseases do not affect populations indiscriminately. In this paper, we review literature produced by demographers, historians, epidemiologists, and other researchers on disparities during the 1918-20 influenza pandemic and the Covid-19 pandemic. Evidence from these studies demonstrates that lower socio-economic status and minority/stigmatized race or ethnicity are associated with higher morbidity and mortality. However, such research often lacks theoretical frameworks or appropriate data to explain the mechanisms underlying these disparities fully. We suggest using a framework that considers proximal and distal factors contributing to differential exposure, susceptibility, and consequences as one way to move this research forward. Further, current pandemic preparedness plans emphasize medically defined risk groups and epidemiological approaches. Therefore, we conclude by arguing in favour of a transdisciplinary paradigm that recognizes socially defined risk groups, includes input from the social sciences and humanities and other diverse perspectives, and contributes to the reduction of health disparities before a pandemic hits.

Psychosocial Impact of COVID-19 Pandemic in Elderly Psychiatric Patients: a Longitudinal Study

The study was designed to investigate the impact of the Coronavirus Disease 2019 (COVID-19) pandemic on mental health and perceived psychosocial support for elderly psychiatric patients in a longitudinal design. n = 32 patients with affective or anxiety disorders aged ≥60 years were included. Telephone interviews were conducted in April/May 2020 (T1) and August 2020 (T2). The psychosocial impact (PSI) of the pandemic and psychopathology were measured. Changes between T1 and T2 were examined. Patients' psychosocial support system six months before the pandemic and at T1/T2 was assessed. We found a significant positive correlation between general PSI and depression as well as severity of illness. General PSI differed significantly depending on social contact. Neither general PSI nor psychopathology changed significantly between T1 and T2. At T1, patients' psychosocial support systems were reduced as compared to six months before. Patients reported an increase in psychosocial support between T1 and T2 and high demand for additional support (sports, arts/occupational therapy, physiotherapy, psychotherapy). Elderly psychiatric patients show a negative PSI of the pandemic. They are likely to suffer from an impaired psychosocial situation, emphasizing the importance of developing concepts for sufficient psychosocial support during a pandemic.

Is compulsory home quarantine less effective than centralized quarantine in controlling the COVID-19 outbreak? Evidence from Hong Kong

Faced with the global spread of COVID-19, the Hong Kong government imposed compulsory home quarantine on all overseas arrivals, while cities in mainland China and Macau adopted a more stringent centralized quarantine approach. This study evaluates the effectiveness of compulsory home quarantine as a means of pandemic control. Combining epidemiological data with traditional socioeconomic and meteorological data from over 250 cities, we employ the Synthetic Control Method (SCM) to construct a counterfactual "synthetic Hong Kong". This model simulates the infection trends for a hypothetical situation in which HK adopts centralized quarantine measures, and compares them to actual infection numbers. Results suggest that home quarantine would have been less effective than centralized quarantine initially. However, the infection rate under home quarantine later converges with the counterfactual estimate under centralized quarantine (0.136% vs. 0.174%), suggesting similar efficacy in the later phase of implementation. Considering its minimal reliance on public resources, home quarantine with heightened enforcement may therefore be preferable to centralized quarantine in countries with limited public health resources. Home quarantine as a quarantine alternative balances public protection and individual freedom, while conserving resources, making it a more sustainable option for many cities.

Predicting the Economic Impact of the COVID-19 Pandemic in the United Kingdom Using Time-Series Mining

The COVID-19 pandemic has brought economic activity to a near standstill as many countries imposed very strict restrictions on movement to halt the spread of the virus. This study aims at assessing the economic impacts of COVID-19 in the United Kingdom (UK) using artificial intelligence (AI) and data from previous economic crises to predict future economic impacts. The macroeconomic indicators, gross domestic products (GDP) and GDP growth, and data on the performance of three primary industries in the UK (the construction, production and service industries) were analysed using a comparison with the pattern of previous economic crises. In this research, we experimented with the effectiveness of both continuous and categorical time-series forecasting on predicting future values to generate more accurate and useful results in the economic domain. Continuous value predictions indicate that GDP growth in 2021 will remain steady, but at around −8.5% contraction, compared to the baseline figures before the pandemic. Further, the categorical predictions indicate that there will be no quarterly drop in GDP following the first quarter of 2021. This study provided evidence-based data on the economic effects of COVID-19 that can be used to plan necessary recovery procedures and to take appropriate actions to support the economy.

Geospatial Analysis and Mapping Strategies for Fine-Grained and Detailed COVID-19 Data with GIS

The unprecedented COVID-19 pandemic is showing dramatic impact across the world. Public health authorities attempt to fight against the virus while maintaining economic activity. In the face of the uncertainty derived from the virus, all the countries have adopted non-pharmaceutical interventions for limiting the mobility and maintaining social distancing. In order to support these interventions, some health authorities and governments have opted for sharing very fine-grained data related with the impact of the virus in their territories. Geographical science is playing a major role in terms of understanding how the virus spreads across regions. Location of cases allows identifying the spatial patterns traced by the virus. Understanding these patterns makes controlling the virus spread feasible, minimizes its impact in vulnerable regions, anticipates potential outbreaks, or elaborates predictive risk maps. The application of geospatial analysis to fine-grained data must be urgently adopted for optimal decision making in real and near-real time. However, some aspects related to process and map sensitive health data in emergency cases have not yet been sufficiently explored. Among them include concerns about how these datasets with sensitive information must be shown depending on aspects related to data aggregation, scaling, privacy issues, or the need to know in advance the particularities of the study area. In this paper, we introduce our experience in mapping fine-grained data related to the incidence of the COVID-19 during the first wave in the region of Galicia (NW Spain), and after that we discuss the mentioned aspects.

The association between socioeconomic status and pandemic influenza: Systematic review and meta-analysis

BACKGROUND

The objective of this study is to document whether and to what extent there is an association between socioeconomic status (SES) and disease outcomes in the last five influenza pandemics.

METHODS/PRINCIPLE FINDINGS

The review included studies published in English, Danish, Norwegian and Swedish. Records were identified through systematic literature searches in six databases. We summarized results narratively and through meta-analytic strategies. Only studies for the 1918 and 2009 pandemics were identified. Of 14 studies on the 2009 pandemic including data on both medical and social risk factors, after controlling for medical risk factors 8 demonstrated independent impact of SES. In the random effect analysis of 46 estimates from 35 studies we found a pooled mean odds ratio of 1.4 (95% CI: 1.2-1.7, p < 0.001), comparing the lowest to the highest SES, but with substantial effect heterogeneity across studies,-reflecting differences in outcome measures and definitions of case and control samples. Analyses by pandemic period (1918 or 2009) and by level of SES measure (individual or ecological) indicated no differences along these dimensions. Studies using healthy controls tended to document that low SES was associated with worse influenza outcome, and studies using infected controls find low SES associated with more severe outcomes. A few studies compared severe outcomes (ICU or death) to hospital admissions but these did not find significant SES associations in any direction. Studies with more unusual comparisons (e.g., pandemic vs seasonal influenza, seasonal influenza vs other patient groups) reported no or negative non-significant associations.

CONCLUSIONS/SIGNIFICANCE

We found that SES was significantly associated with pandemic influenza outcomes with people of lower SES having the highest disease burden in both 1918 and 2009. To prepare for future pandemics, we must consider social vulnerability. The protocol for this study has been registered in PROSPERO (ref. no 87922) and has been published Mamelund et al. (2019).

Multivariate Analysis of Risk Factors of the COVID-19 Pandemic in the Community of Madrid, Spain

It has been more than one year since Chinese authorities identified a deadly new strain of coronavirus, SARS-CoV-2. Since then, the scientific work regarding the transmission risk factors of COVID-19 has been intense. The relationship between COVID-19 and environmental conditions is becoming an increasingly popular research topic. Based on the findings of the early research, we focused on the community of Madrid, Spain, which is one of the world's most significant pandemic hotspots. We employed different multivariate statistical analyses, including principal component analysis, analysis of variance, clustering, and linear regression models. Principal component analysis was employed in order to reduce the number of risk factors down to three new components that explained 71% of the original variance. Cluster analysis was used to delimit the territory of Madrid according to these new risk components. An ANOVA test revealed different incidence rates between the territories delimited by the previously identified components. Finally, a set of linear models was applied to demonstrate how environmental factors present a greater influence on COVID-19 infections than socioeconomic dimensions. This type of local research provides valuable information that could help societies become more resilient in the face of future pandemics.

Impact of the COVID-19 pandemic on unmet healthcare needs in Seoul, South Korea: a cross-sectional study

OBJECTIVES

This study investigated the factors influencing unmet healthcare needs of people during the early stage of the COVID-19 pandemic in Seoul, South Korea. The findings help to identify people who have difficulty accessing healthcare services during a pandemic situation.

DESIGN

We conducted a cross-sectional study using a proportionate quota sampling method according to five major districts, sex and age, using an online survey. We analysed the key characteristics of influencing factors of unmet healthcare needs based on the Andersen behavioural model of healthcare utilisation: predisposing factors (eg, sex, age), need factors (eg, health status, illness) and enabling factors (eg, income, efficacy belief).

SETTING

The questionnaire was sent via email and mobile text messages from the end of April to the beginning of May 2020 during the first wave of the COVID-19 pandemic.

PARTICIPANTS

A sample of 813 respondents was used, and the respondent information was anonymised in the analysis process.

RESULTS

For the predisposing factors, sex, age, education level and occupational cluster were associated with unmet needs for healthcare. Chronic diseases and mental health were the influencing factors as an enabling factor that exerted an influence on the unmet need for healthcare in South Korea. Women, younger persons, those with lower education and persons with white-collar jobs were more likely to experience unmet healthcare needs. In addition, the more chronic diseases people had, the more COVID-19 negatively affected them mentally; and the more people felt fear of COVID-19, the higher chances they experienced unmet healthcare needs.

CONCLUSION

Government and policymakers are guided to draw out measures such as health communication and telemedicine to reduce the unmet healthcare needs during the pandemic and to recognise the different influencing factors.

Socioeconomic patterns and COVID-19 outcomes before, during and after the lockdown in Italy (2020)

The objective was to investigate the association between deprivation and COVID-19 outcomes in Italy during pre-lockdown, lockdown and post-lockdown periods using a retrospective cohort study with 38,534,169 citizens and 222,875 COVID-19 cases. Multilevel negative binomial regression models, adjusting for age, sex, population-density and region of residence were conducted to evaluate the association between area-level deprivation and COVID-19 incidence, case-hospitalisation rate and case-fatality. During lockdown and post-lockdown, but not during pre-lockdown, higher incidence of cases was observed in the most deprived municipalities compared with the least deprived ones. No differences in case-hospitalisation and case-fatality according to deprivation were observed in any period under study.

[Social inequalities in the regional spread of SARS-CoV-2 infections]

Hintergrund und Ziel

Ob sozioökonomische Faktoren die Ausbreitung von SARS-CoV‑2 beeinflussen, ist nicht ausreichend beantwortet, da frühere Studien in der Regel kumulative Inzidenzen betrachtet und die zeitliche Entwicklung der Ausbreitung außer Acht gelassen haben. Dieser Beitrag konzentriert sich daher auf die Entwicklung von regionalen Neuinfektionen in Zusammenhang mit sozioökonomischen Faktoren. Ausgehend vom internationalen Forschungsstand präsentieren wir eigene Analysen von Meldedaten aus Deutschland.

Methoden

Diese Studie untersucht regionale Daten gemeldeter COVID-19-Fälle für die 401 Landkreise und kreisfreien Städte (Kreisebene) in Deutschland und vergleicht den zeitlichen Verlauf entlang sozioökonomischer Merkmale der Kreise. Betrachtet werden altersstandardisierte wöchentliche Inzidenzen für den Zeitraum 03.02.2020–28.03.2021. Sozial- und Wirtschaftsindikatoren auf Kreisebene stammen aus der INKAR(Indikatoren und Karten zur Raum- und Stadtentwicklung)-Datenbank (z. B. Einkommen, Beschäftigtenquote, Wohnfläche).

Ergebnisse

Während in der ersten und zu Beginn der zweiten Welle der Pandemie Kreise mit höherem mittleren Haushaltseinkommen höhere Inzidenzen hatten, stiegen sie in Kreisen mit niedrigem Einkommen ab Dezember 2020 deutlich an. Kreise mit einem hohen Anteil an Beschäftigten allgemein und speziell solchen im Produktionssektor hatten gerade in der zweiten und dritten Welle hohe Inzidenzen. Kreise mit einer geringen Wohnfläche je Einwohner hatten ab November 2020 ausgeprägt höhere Inzidenzen.

Schlussfolgerung

Der regionale Verlauf der Pandemie unterscheidet sich nach Sozial- und Wirtschaftsindikatoren. Eine differenzierte Betrachtung dieser Unterschiede könnte Hinweise auf zielgruppenspezifische Schutz- und Teststrategien geben und helfen, soziale Faktoren zu identifizieren, die Infektionen begünstigen.

Zusatzmaterial online

Zusätzliche Informationen sind in der Online-Version dieses Artikels (10.1007/s00103-021-03387-w) enthalten.

Regional variation in COVID-19 positive hospitalisation across Scotland during the first wave of the pandemic and its relation to population density: A cross-sectional observation study

Background

There have been large regional differences in COVID-19 virus activity across the UK with many commentators suggesting that these are related to age, ethnicity and social class. There has also been a focus on cases, hospitalisations and deaths rather than on hospitalisation rates expressed per 100,000 population. The purpose of our study was to examine regional variation in COVID-19 positive hospitalisation rates in Scotland during the first wave of the pandemic and the possibility that these might be related to population density.

Methods and findings

This was a repeated point prevalence study. The number of COVID-19 positive patients hospitalised in the eleven Scottish mainland health boards peaked at 1517 on 19th April, then fell to a low of 243 on 16th August before rising slightly to 262 on 15th September. In July, August and September only four boards had more than 5 hospitalised patients. There was a statistically significant relationship between hospitalisation rates and population density on 97.7% of individual days during the first wave of the pandemic (Pearson’s r 0.62–0.93, with 123 of a possible 174 days having p values <0.001). Multiple linear regression analyses performed on data from the 11 mainland boards across six time points suggest that population density accounted for 70.2% of the variation in hospitalisation rate in April, 72.3% in May, 81.2% in June, 91.0% in July, 91.0% in August, and 88.1% in September. Neither population median age nor median social deprivation score at health board level were statistically significant in the final model for hospitalisation.

Conclusion

There were large differences in crude COVID-19 hospitalisation rates across the 11 mainland Scottish health boards, that were significantly related to population density. Given that lockdown was originally introduced to prevent the NHS from being overwhelmed, we believe our results support a regional rather than a national approach to lifting or reimposing more restrictive measures, and that hospitalisation rates should be part of the decision making process.

Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020

About 8% of the Americans contract influenza during an average season according to the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning for influenza and the prediction of public health. In this study, Spatial autocorrelation analysis and spatial scanning analysis were used to identify the spatiotemporal patterns of influenza-like illness (ILI) prevalence in the United States, during the 2011-2020 transmission seasons. A seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict the influenza incidence of high-risk states. We found the highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. Mississippi was a high-risk state with a higher influenza incidence, and exhibited a high-high cluster with neighboring states. A SARIMA (1, 0, 0) (1, 1, 0)₅₂ model was suitable for forecasting the ILI incidence of Mississippi. The relative errors between actual values and predicted values indicated that the predicted values matched the actual values well. Influenza is still an important health problem in the United States. The spread of ILI varies by season and geographical region. The peak season of influenza was the winter and spring, and the states with higher influenza rates are concentrated in the southeast. Increased surveillance in high-risk states could help control the spread of the influenza.