Effect of socioeconomic factors during the early COVID-19 pandemic: a spatial analysis

Abstract

Background Spatial variability of COVID-19 cases may suggest geographic disparities of social determinants of health. Spatial analyses of population-level data may provide insight on factors that may contribute to COVID-19 transmission, hospitalization, and death. Methods Generalized additive models were used to map COVID-19 risk from March 2020 to February 2021 in Orange County (OC), California. We geocoded and analyzed 221,843 cases to OC census tracts within a Poisson framework while smoothing over census tract centroids. Location was randomly permuted 1000 times to test for randomness. We also separated the analyses temporally to observe if risk changed over time. COVID-19 cases, hospitalizations, and deaths were mapped across OC while adjusting for population-level demographic data in crude and adjusted models. Results Risk for COVID-19 cases, hospitalizations, and deaths were statistically significant in northern OC. Adjustment for demographic data substantially decreased spatial risk, but areas remained statistically significant. Inclusion of location within our models considerably decreased the magnitude of risk compared to univariate models. However, percent minority (adjusted RR: 1.06, 95%CI: 1.06, 1.07), average household size (aRR: 1.06, 95%CI: 1.05, 1.07), and percent service industry (aRR: 1.05, 95%CI: 1.04, 1.06) remained significantly associated with COVID-19 risk in adjusted spatial models. In addition, areas of risk did not change between surges and risk ratios were similar for hospitalizations and deaths. Conclusion Significant risk factors and areas of increased risk were identified in OC in our adjusted models and suggests that social and environmental factors contribute to the spread of COVID-19 within communities. Areas in north OC remained significant despite adjustment, but risk substantially decreased. Additional investigation of risk factors may provide insight on how to protect vulnerable populations in future infectious disease outbreaks.

Socioeconomic differences in COVID-19 infection, hospitalisation and mortality in urban areas in a region in the South of Europe

“…A 10 per cent increase in risk of infection, 40 per cent increase in risk of hospitalisation, and 100 per cent increase in risk of death was observed for each decrease of one logarithmic unit in annual AGI (for example, the risk of death with a COVID-19 diagnosis during the study period in a census section with an average AGI of 15,000 € was double that of a census section with 40,000 €). This inverse relationship between socioeconomic level and the entire cascade from COVID-infection to mortality had been pointed out previously in other high-income countries [ 25 , 29 , 30 ]. However, the present study shows a more pronounced relationship between lower level and greater severity of the disease.…”

Socio-Demographic, Health, and Transport-Related Factors Affecting the COVID-19 Outbreak in Myanmar: A Cross-Sectional Study

“…However, we could not find any significant association between the COVID-19 cases and the aging rate, unemployment rate, or average income in Myanmar. Though we could not provide sufficient reasons for this, the difference in the unemployment rate, average income and aging rate between areas was small (Table 1 ) when compared to other reports [ 42 ].…”

Epidemiological Profile and Social Welfare Index as Factors Associated with COVID-19 Hospitalization and Severity in Mexico City: A Retrospective Analysis

“…For example, Foster et al [ 15 ] reported major risk of severe cases (RR 6.02, 95% CI 4.72–7.71) and higher mortality (RR 9.60, 95% CI 4.70–21.44) in individuals living in deprivation areas and had unhealthy life styles in United Kingdom. Likewise, specific data of SES as poverty level, income, education level, household size and ethnicity has been associated with higher probability of mortality and hospitalization rate in more than 10% to almost 80% for USA reports [ 14 , 16 ]…”

“…In addition, people living in conditions of socioeconomic deprivation, such as crowded populations, are associated with lower healthcare service access, low income, increased number of persons per house, and/or unhealthy lifestyles factors, leading to higher rates of SARS-CoV2 infection and possibly worse COVID-19 outcomes, such as the need of hospitalization or the need for mechanical ventilation, understood as a condition of intensive care unit hospitalization. [ 15 , 16 ]. Other socioeconomic factors, such as low-income jobs or unemployment (i.e., deriving in lower opportunities to purchase protective equipment, such as appropriate facemasks), low educational level (deriving in not understanding or not following health recommendations or the correct use of the protective equipment), poor diet quality or the use of highly crowded public transport are linked to COVID-19.…”