What have we learned about socioeconomic inequalities in the spread of COVID-19? A systematic review

Benita, Francisco; Rebollar-Ruelas, Leonardo; Alfaro, Édgar David Gaytán

doi:10.1016/j.scs.2022.104158

Cited by 21 publications

(12 citation statements)

References 94 publications

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“…Our results also show an inconclusive impact of IMD on transmission, though lean towards higher reproduction numbers seen in more deprived areas. In their systematic review of socioeconomic COVID-19 impacts, Benita et al (2022) [17] list only nine UK-specific studies, and report a global trend of mixed and inconclusive findings as to the impact of poverty metrics on COVID-19 infection. The trend we have shown in Fig 1B, of differences in case incidence by deprivation quantiles seeming pronounced in some time periods, before reversing in others is seen in multiple other countries [18,19].…”

Section: Discussionmentioning

confidence: 99%

The impact of health inequity on spatial variation of COVID-19 transmission in England

Rawson,

Hinsley,

Sonabend

et al. 2024

PLoS Comput Biol

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Considerable spatial heterogeneity has been observed in COVID-19 transmission across administrative areas of England throughout the pandemic. This study investigates what drives these differences. We constructed a probabilistic case count model for 306 administrative areas of England across 95 weeks, fit using a Bayesian evidence synthesis framework. We incorporate the impact of acquired immunity, of spatial exportation of cases, and 16 spatially-varying socio-economic, socio-demographic, health, and mobility variables. Model comparison assesses the relative contributions of these respective mechanisms. We find that spatially-varying and time-varying differences in week-to-week transmission were definitively associated with differences in: time spent at home, variant-of-concern proportion, and adult social care funding. However, model comparison demonstrates that the impact of these terms is negligible compared to the role of spatial exportation between administrative areas. While these results confirm the impact of some, but not all, static measures of spatially-varying inequity in England, our work corroborates the finding that observed differences in disease transmission during the pandemic were predominantly driven by underlying epidemiological factors rather than aggregated metrics of demography and health inequity between areas. Further work is required to assess how health inequity more broadly contributes to these epidemiological factors.

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Section: Discussionmentioning

confidence: 99%

The impact of health inequity on spatial variation of COVID-19 transmission in England

Rawson,

Hinsley,

Sonabend

et al. 2024

PLoS Comput Biol

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“…They also enable developed countries to prepare adequate medical and human resources to quickly respond to an outbreak ( 27 ). Furthermore, these factors ensure that developed countries can provide financial support to the population, increasing their willingness to comply with intervention policies ( 28 ). Thus, developed countries generally have better conditions to implement intervention policies.…”

Section: Discussionmentioning

confidence: 99%

Assessing the impact of local context and priorities regarding domestic disease outbreaks and imported risk on early pandemic response: Cross-continental comparisons

Kuo

Wen

2023

Front. Public Health

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IntroductionContainment and closure policies are effective measures used in the early stages of a highly transmissible global pandemic such as COVID-19 to mitigate the spread and reduce transmissions. However, these policies can have negative impacts on the economy and personal freedom. Governments must carefully consider the necessity of increasing their stringency. Local contexts and priorities regarding domestic disease outbreaks and the risk of imported cases from other countries may vary among different countries, and could influence the decision to increase containment measures. Thus, this study aimed to differentiate the impacts of these affecting factors on the stringency of governmental containment measures through cross-continental comparisons.MethodsThis study utilized a zero/one inflated beta (ZOIB) regression model to investigate how domestic epidemic, imported risk, and local context affect government responses to a pandemic. We used a country’s weekly confirmed case and death numbers as a measure of its domestic threat. The imported risk was measured using a combination of weekly new cases in each country and the air passenger traffic between countries.ResultsThe findings indicate that domestic case numbers are a primary concern for governments when deciding to increase policy stringency. Countries with higher development levels tend to implement stricter policies as they can better handle the negative impacts. Additionally, there is an interaction between case numbers and development level, with countries at the second or third highest development level focusing more on domestic outbreaks than imported risks, while those at the highest level have similar concerns for both.ConclusionsWe concluded that most countries adjust policies’ stringency majorly based on the variation of domestic case number rather than the other pandemic factors and the countries with a high development level tend to implement strict policies since their socio-economical condition could afford such policies. These insights can aid policymakers in improving containment and closure policies for future pandemics.

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“…Studies have shown that the spread of COVID-19 is strongly influenced by socioeconomic structures in the population, socioeconomically disadvantaged areas being more severely affected ( 36 , 37 ). Besides studies analyzing socioeconomic factors, only a few studies have specifically examined the age distribution of the infected population over time ( 38 , 39 ).…”

Section: Introductionmentioning

confidence: 99%

Exploration of the COVID-19 pandemic at the neighborhood level in an intra-urban setting

Schmitz

Lakes

Manafa

et al. 2023

Front. Public Health

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The COVID-19 pandemic represents a worldwide threat to health. Since its onset in 2019, the pandemic has proceeded in different phases, which have been shaped by a complex set of influencing factors, including public health and social measures, the emergence of new virus variants, and seasonality. Understanding the development of COVID-19 incidence and its spatiotemporal patterns at a neighborhood level is crucial for local health authorities to identify high-risk areas and develop tailored mitigation strategies. However, analyses at the neighborhood level are scarce and mostly limited to specific phases of the pandemic. The aim of this study was to explore the development of COVID-19 incidence and spatiotemporal patterns of incidence at a neighborhood scale in an intra-urban setting over several pandemic phases (March 2020–December 2021). We used reported COVID-19 case data from the health department of the district Berlin-Neukölln, Germany, additional socio-demographic data, and text documents and materials on implemented public health and social measures. We examined incidence over time in the context of the measures and other influencing factors, with a particular focus on age groups. We used incidence maps and spatial scan statistics to reveal changing spatiotemporal patterns. Our results show that several factors may have influenced the development of COVID-19 incidence. In particular, the far-reaching measures for contact reduction showed a substantial impact on incidence in Neukölln. We observed several age group-specific effects: school closures had an effect on incidence in the younger population (< 18 years), whereas the start of the vaccination campaign had an impact primarily on incidence among the elderly (> 65 years). The spatial analysis revealed that high-risk areas were heterogeneously distributed across the district. The location of high-risk areas also changed across the pandemic phases. In this study, existing intra-urban studies were supplemented by our investigation of the course of the pandemic and the underlying processes at a small scale over a long period of time. Our findings provide new insights for public health authorities, community planners, and policymakers about the spatiotemporal development of the COVID-19 pandemic at the neighborhood level. These insights are crucial for guiding decision-makers in implementing mitigation strategies.

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What have we learned about socioeconomic inequalities in the spread of COVID-19? A systematic review

Cited by 21 publications

References 94 publications

The impact of health inequity on spatial variation of COVID-19 transmission in England

The impact of health inequity on spatial variation of COVID-19 transmission in England

Assessing the impact of local context and priorities regarding domestic disease outbreaks and imported risk on early pandemic response: Cross-continental comparisons

Exploration of the COVID-19 pandemic at the neighborhood level in an intra-urban setting

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