COVID-19 inequalities in England: a mathematical modelling study of transmission risk and clinical vulnerability by socioeconomic status

Abstract

Background: The COVID-19 pandemic resulted in major inequalities in infection and disease burden between areas of varying socioeconomic deprivation in many countries, including England. Areas of higher deprivation tend to have a different population structure-generally younger-which can increase viral transmission due to higher contact rates in school-going children and working-age adults. Higher deprivation is also associated with a higher presence of chronic comorbidities, which were convincingly demonstrated to be risk factors for severe COVID-19 disease. These two major factors need to be combined to better understand and quantify their relative importance in the observed COVID-19 inequalities.

Methods: We used UK Census data on health status and demography stratified by decile of the Index of Multiple Deprivation (IMD), which is a measure of socioeconomic deprivation. We calculated epidemiological impact using an age-stratified COVID-19 transmission model, which incorporated different contact patterns and clinical health profiles by decile. To separate the contribution of each factor, we considered a scenario where the clinical health profile of all deciles was at the level of the least deprived. We also considered the effectiveness of school closures and vaccination of over 65-year-olds in each decile.

Results: In the modelled epidemics in urban areas, the most deprived decile experienced 9% more infections, 13% more clinical cases, and a 97% larger peak clinical size than the least deprived; we found similar inequalities in rural areas. Twenty-one per cent of clinical cases and 16% of deaths in England observed under the model assumptions would not occur if all deciles experienced the clinical health profile of the least deprived decile. We found that more deaths were prevented in more affluent areas during school closures and vaccination rollouts.

Conclusions: This study demonstrates that both clinical and demographic factors synergise to generate health inequalities in COVID-19, that improving the clinical health profile of populations would increase health equity, and that some interventions can increase health inequalities.

Keywords: COVID-19; Comorbidities; Demography; England; Mathematical model; Socioeconomic inequality.

Fig. 1

a Proportion of each geography-specific IMD decile in each age group. b Age- and IMD-specific health prevalence (1, most deprived decile; 10, least deprived). c Age-stratified SEIRD model, specific to IMD decile and geography. Subscript a denotes age-specificity, c clinical parameters, and s subclinical parameters

Fig. 2

Results of mapping underlying health to clinical vulnerability. a The training dataset of age-specific health prevalence and clinical fraction estimates for the general population of England over age 10, and corresponding predictive model, with linear extensions outside the domain [0.21, 0.69]. b Resulting age- and IMD-specific clinical fractions (1, most deprived decile; 10, least deprived)

Fig. 3

Measures of the size of a COVID-19 epidemic in each IMD decile and geography. Solid lines represent crude measures, and dashed lines represent those age-standardised by geography. The most deprived decile is decile 1, and the least is decile 10. a Total infections per 1000 population. b Total clinical cases per 1000 population. c Clinical cases per 1000 population at the clinical peak of the epidemic. d Total deaths per 1000 population. e Infection fatality ratio. f Basic reproduction number, R₀

Fig. 4

Epidemiological burden in counterfactual scenarios. a Total clinical cases per 1000 population, in geography-specific areas of each IMD decile (1, most deprived decile; 10, least deprived), in the counterfactual health prevalence scenario, and in the counterfactual constant age structure scenario. The original model is shown for comparison in pale lines. b Total deaths per 1000 population, in geography-specific areas of each IMD decile, under the same scenarios

Fig. 5

Results of implementing school closures. a R₀ in each IMD- and geography-specific population (1, most deprived decile; 10, least deprived), before (pale lines) and after school closures. b Reductions in R₀ due to school closures. c Crude (solid lines) and age-standardised by geography (dashed lines) reductions in deaths observed per 1000 population after implementing school closures at P = 0.05

Fig. 6

Results of vaccinating the over 65-year-olds. a Deaths prevented per 1000 population, after vaccinating all adults over 65. b Total number of deaths prevented by vaccination in each decile (stratified by urban and rural areas). c Total number of vaccine doses given in each decile (stratified by urban and rural areas) when vaccinating all over 65s