COVID-19 and deprivation amplification: An ecological study of geographical inequalities in mortality in England

Global trends in COVID-19 incidence and case fatality rates (2019-2023): a retrospective analysis

Objectives

Analyzing and comparing COVID-19 infection and case-fatality rates across different regions can help improve our response to future pandemics.

Methods

We used public data from the WHO to calculate and compare the COVID-19 infection and case-fatality rates in different continents and income levels from 2019 to 2023.

Results

The Global prevalence of COVID-19 increased from 0.011 to 0.098, while case fatality rates declined from 0.024 to 0.009. Europe reported the highest cumulative infection rate (0.326), with Africa showing the lowest (0.011). Conversely, Africa experienced the highest cumulative case fatality rates (0.020), with Oceania the lowest (0.002). Infection rates in Asia showed a steady increase in contrast to other continents which observed initial rises followed by decreases. A correlation between economic status and infection rates was identified; high-income countries had the highest cumulative infection rate (0.353) and lowest case fatality rate (0.006). Low-income countries showed low cumulative infection rates (0.006) but the highest case fatality rate (0.016). Initially, high and upper-middle-income countries experienced elevated initial infection and case fatality rates, which subsequently underwent significant reductions.

Conclusions

COVID-19 rates varied significantly by continent and income level. Europe and the Americas faced surges in infections and low case fatality rates. In contrast, Africa experienced low infection rates and higher case fatality rates, with lower- and middle-income nations exceeding case fatality rates in high-income countries over time.

Regional variation of COVID-19 admissions, acute kidney injury and mortality in England - a national observational study using administrative data

BACKGROUND

This study explores regional variations in COVID-19 hospitalization rates, in-hospital mortality, and acute kidney injury (AKI) in England. We investigated the influence of population demographic characteristics, viral strain changes, and therapeutic advances on clinical outcomes.

METHODS

Using hospital episode statistics, we conducted a retrospective cohort study with 749,844 admissions in 337,029 adult patients with laboratory-confirmed COVID-19 infection (March 1, 2020, to March 31, 2021). Multivariable logistic regression identified factors predicting AKI and mortality in COVID-19 hospitalized patients.

RESULTS

London had the highest number of COVID-19 admissions (131,338, 18%), followed by the North-west region (122,683, 16%). The North-west had the highest population incidence of COVID-19 hospital admissions (21,167 per million population, pmp), while the South-west had the lowest (9,292 admissions pmp). Patients in London were relatively younger (67.0 ± 17.7 years) than those in the East of England (72.2 ± 16.8 years). The shortest length of stay was in the North-east (12.2 ± 14.9 days), while the longest was in the North-west (15.2 ± 17.9 days). All eight regions had higher odds of death compared to London, ranging from OR 1.04 (95% CI 1.00, 1.07) in the South-west to OR 1.24 (95% CI 1.21, 1.28) in the North-west. Older age, Asian ethnicity, emergency admission, transfers from other hospitals, AKI presence, ITU admission, social deprivation, and comorbidity were associated with higher odds of death. AKI incidence was 30.3%, and all regions had lower odds of developing AKI compared to London. Increasing age, mixed and black ethnicity, emergency admission, transfers from other providers, ITU care, and different levels of comorbidity were associated with higher odds of developing AKI.

CONCLUSIONS

London exhibited higher hospital admission numbers and AKI incidence, but lower odds of death compared to other regions in England.

TRIAL REGISTRATION

Registered on National Library of Medicine website ( www.

CLINICALTRIALS

gov ) with registration number NCT04579562 on 8/10/2020.

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.

What is missing from how we measure and understand the experience of poverty and deprivation in population health analyses?

BACKGROUND

Explaining why some populations are healthier than others is a core task of epidemiology. Socioeconomic position (SEP), encompassing a broad range of exposures relating to economic circumstances, social class and deprivation, is an important explanation, but lacks a comprehensive framework for understanding the range of relevant exposures it encompasses.

METHODS

We reviewed existing literature on experiential accounts of poverty through database searching and the identification of relevant material by experts. We mapped relevant concepts into a complex systems diagram. We developed this diagram through a process of consultation with academic experts and experts with direct experience of poverty. Finally, we categorized concepts on the basis of whether they have previously been measured, their importance to the causal flow of the diagram, and their importance to those consulted, creating a list of priorities for future measurement.

RESULTS

There are a great many aspects of SEP which are not frequently measured or used in epidemiological research and, for some of these, work is needed to better conceptualize and develop measures. Potentially important missing aspects include stigma, social class processes, access to education, sense of lost potential, neighbourhoods, fairness and justice, emotional labour, masking poverty, being (in)visible, costs, and experiences of power.

CONCLUSIONS

Analyses seeking to understand the extent to which SEP exposures explain differences in the health of populations are likely to benefit from a comprehensive understanding of the range and inter-relationships between different aspects of SEP. More research to better conceptualize and measure these aspects is now needed.

The ethnic density effect as a contextual influence in ecological disease models: Establishing its quantitative expression

Research suggests higher neighbourhood ethnic minority density to be associated with lessened chances of ethnic group illness. We focus on the density effect on psychosis, arguing that (at higher ethnic concentrations) it acts as a contextual influence attenuating the compositional influence whereby minority ethnicity is associated with higher psychosis risk. In terms of ecological disease regression, the ethnic density effect will then be apparent in nonlinear impacts of minority concentration. Contextual effects may also be evident in spatially varying regression coefficient models for psychosis. Nonlinearity or heterogeneity may be associated with other contextual processes where geography modifies demography (e.g. deprivation amplification). We illustrate these issues with an analysis of psychosis prevalence in 4835 London neighbourhoods. The data are collected in primary care (during 2019/20) using clinical diagnosis (e.g. based on referrals to specialists or psychosis hospitalisation), and refer to patients currently under care: such care may extend retrospectively over several years. The data offer a complete population perspective in contrast to survey data, which typically offer limited geographic perspectives. We consider impacts on psychosis prevalence of non-white ethnicity, as well as those of deprivation, social fragmentation and urbanicity. We find evidence suggesting nonlinear impacts of non-white ethnicity on psychosis (essentially flat risk above a threshold concentration), but find no evidence for deprivation amplification.