Association of poor housing conditions with COVID-19 incidence and mortality across US counties

Poor housing conditions have been linked with worse health outcomes and infectious disease spread. Since the relationship of poor housing conditions with incidence and mortality of COVID-19 is unknown, we investigated the association between poor housing condition and COVID-19 incidence and mortality in US counties. We conducted cross-sectional analysis of county-level data from the US Centers for Disease Control, US Census Bureau and John Hopkins Coronavirus Resource Center for 3135 US counties. The exposure of interest was percentage of households with poor housing conditions (one or greater of: overcrowding, high housing cost, incomplete kitchen facilities, or incomplete plumbing facilities). Outcomes were incidence rate ratios (IRR) and mortality rate ratios (MRR) of COVID-19 across US counties through 4/21/2020. Multilevel generalized linear modeling (with total population of each county as a denominator) was utilized to estimate relative risk of incidence and mortality related to poor housing conditions with adjustment for population density and county characteristics including demographics, income, education, prevalence of medical comorbidities, access to healthcare insurance and emergency rooms, and state-level COVID-19 test density. We report incidence rate ratios (IRRs) and mortality ratios (MRRs) for a 5% increase in prevalence in households with poor housing conditions. Across 3135 US counties, the mean percentage of households with poor housing conditions was 14.2% (range 2.7% to 60.2%). On April 21st, the mean (SD) number of cases and deaths of COVID-19 were 255.68 (2877.03) cases and 13.90 (272.22) deaths per county, respectively. In the adjusted models standardized by county population, with each 5% increase in percent households with poor housing conditions, there was a 50% higher risk of COVID-19 incidence (IRR 1.50, 95% CI: 1.38-1.62) and a 42% higher risk of COVID-19 mortality (MRR 1.42, 95% CI: 1.25-1.61). Results remained similar using earlier timepoints (3/31/2020 and 4/10/2020). Counties with a higher percentage of households with poor housing had higher incidence of, and mortality associated with, COVID-19. These findings suggest targeted health policies to support individuals living in poor housing conditions should be considered in further efforts to mitigate adverse outcomes associated with COVID-19.

Association of Poor Housing Conditions with COVID-19 Incidence and Mortality Across US Counties

COVID-19 mortality among kidney transplant candidates is strongly associated with social determinants of health.

Making a judgment only based on formal national reports can be misleading. We aimed to assess the relationship between countries' development indicators and reported coronavirus disease 2019 (Covid-19)-related incidences and death. Covid-19 related incidence and death cases were extracted from the updated Humanitarian Data Exchange Website on October 8, 2021. Univariable and multivariable negative binomial regression were utilized to investigate the relationship between development indicator and incidence and mortality from Covid-19 by calculating the Incidence rate ratio (IRR), mortality rate ratio (MRR), and fatality risk ratio (FRR). Very high human development index (HDI) compared with low HDI (IRR:3.56; MRR:9.04), the proportion of physicians (IRR:1.20; MRR:1.16), besides extreme poverty (IRR:1.01; MRR:1.01) were independently correlated with the mortality and incidence rate of Covid-19. Very high HDI and population density were inversely correlated with the fatality risk (FRRs of 0.54 and 0.99). The cross-continental comparison shows Europe and the North Americas, had significantly higher incidence and mortality rates with IRR of 3.56 and 1.84 as well as MRRs of 6.65 and 3.62, respectively. Also, they inversely correlated with the fatality (FRR:0.84 and 0.91, respectively). A positive correlation between the fatality rate ratio based on countries' development indicators and the reverse for the incidence and mortality rate was found. Developed countries with sensitive healthcare systems can diagnose infected cases as soon as possible. Also, the mortality rate of Covid-19 will be accurately registered and reported. Due to more access to diagnostic tests, patients are diagnosed at the initial stages and will have a better opportunity to receive treatment. This leads to higher reports of incidence/and/or mortality rates and lower fatality of COVID-19. In conclusion, more Covid-19 incidence and mortality cases in developed countries can result from a more comprehensive care system and a more accurate recording procedure.

Introduction: The use of face masks in public was one of several COVID-19 non-pharmaceutical interventions adopted to mitigate the pandemic in Portugal. The aim of this study was to evaluate the impact of lifting the mask mandate on the April 22, 2022 on COVID-19 incidence and mortality in mainland Portugal and in the Azores. As a secondary objective, we aimed to evaluate the evolution of COVID-19 cases in a setting without a mask mandate (Azores islands) and in a setting with a mask mandate (Madeira islands).Methods: Surveillance data on laboratory-confirmed COVID-19 cases and COVID-19 deaths were used to conduct an interrupted time series analysis to estimate changes in daily incidence and deaths during a mask mandate period (28th March – 21st April 2022) and during a post-mask mandate period (22nd April – 15th May 2022), in mainland Portugal and the Azores. In a second phase, for each group of islands, we fitted a negative binomial regression model, with daily COVID-19 incident cases as the primary outcome of interest, and relative frequency of Omicron BA.5 lineage as explanatory variable.Results: Significant changes in trends were observed for the overall incidence rate and COVID-19 deaths; increasing trends were observed for COVID-19 incidence and deaths in the post mandate period [5.3% per day; incidence rate ratio (IRR): 1.053; 95% confidence interval (CI): 1.029 - 1.078] and [3.2% per day; mortality rate ratio (MRR): 1.032; 95% CI: 1.003 - 1.062], respectively. For every unit increase in the percentage of Omicron BA.5 lineage there was a 1.5% increase per day (IRR: 1.015; 95% CI: 1.006 - 1.024) in COVID-19 incidence rate in the Azores islands, while for Madeira islands an increase of 0.05% COVID-19 cases per day was observed (IRR: 1.005; 95% CI: 1.000 - 1.010).Conclusion: Lifting the mask mandate in Portugal was associated with an increase in COVID-19 incidence and deaths, thus highlighting the positive effect of face mask policies in preventing respiratory virus transmission and saving lives.

The objective of this study is to assess the associations of race/ethnicity and severe housing problems with COVID-19 death rates in the US throughout the first three waves of the COVID-19 pandemic in the US. We conducted a cross-sectional study using a negative binomial regression model to estimate factors associated with COVID-19 deaths in 3063 US counties between March 2020 and July 2021 by wave and pooled across all three waves. In Wave 1, counties with larger percentages of Black, Hispanic, American Indian and Alaska Native (AIAN), and Asian American and Pacific Islander (AAPI) residents experienced a greater risk of deaths per 100,000 residents of +22.82 (95% CI 15.09, 30.56), +7.50 (95% CI 1.74, 13.26), +13.52 (95% CI 8.07, 18.98), and +5.02 (95% CI 0.92, 9.12), respectively, relative to counties with larger White populations. By Wave 3, however, the mortality gap declined considerably in counties with large Black, AIAN and AAPI populations: +10.38 (95% CI 4.44, 16.32), +7.14 (95% CI 1.14, 13.15), and +3.72 (95% CI 0.81, 6.63), respectively. In contrast, the gap increased for counties with a large Hispanic population: +13 (95% CI 8.81, 17.20). Housing problems were an important predictor of COVID-19 deaths. However, while housing problems were associated with increased COVID-19 mortality in Wave 1, by Wave 3, they contributed to magnified mortality in counties with large racial/ethnic minority groups. Our study revealed that focusing on a wave-by-wave analysis is critical to better understand how the associations of race/ethnicity and housing conditions with deaths evolved throughout the first three COVID-19 waves in the US. COVID-19 mortality initially took hold in areas characterized by large racial/ethnic minority populations and poor housing conditions. Over time, as the virus spread to predominantly White counties, these disparities decreased substantially but remained sizable.

The objective of this study was to compare the effect of diabetes and pathologies potentially related to diabetes on the risk of infection and death from COVID-19 among people from Highly-Developed-Country (HDC), including Italians, and immigrants from the High-Migratory-Pressure-Countries (HMPC). Among the population with diabetes, whose prevalence is known to be higher among immigrants, we compared the effect of body mass index among HDC and HMPC populations. A population-based cohort study was conducted, using population registries and routinely collected surveillance data. The population was stratified into HDC and HMPC, according to the place of birth; moreover, a focus was set on the South Asiatic population. Analyses restricted to the population with type-2 diabetes were performed. We reported incidence (IRR) and mortality rate ratios (MRR) and hazard ratios (HR) with 95% confidence interval (CI) to estimate the effect of diabetes on SARS-CoV-2 infection and COVID-19 mortality. Overall, IRR of infection and MRR from COVID-19 comparing HMPC with HDC group were 0.84 (95% CI 0.82–0.87) and 0.67 (95% CI 0.46–0.99), respectively. The effect of diabetes on the risk of infection and death from COVID-19 was slightly higher in the HMPC population than in the HDC population (HRs for infection: 1.37 95% CI 1.22–1.53 vs. 1.20 95% CI 1.14–1.25; HRs for mortality: 3.96 95% CI 1.82–8.60 vs. 1.71 95% CI 1.50–1.95, respectively). No substantial difference in the strength of the association was observed between obesity or other comorbidities and SARS-CoV-2 infection. Similarly for COVID-19 mortality, HRs for obesity (HRs: 18.92 95% CI 4.48–79.87 vs. 3.91 95% CI 2.69–5.69) were larger in HMPC than in the HDC population, but differences could be due to chance. Among the population with diabetes, the HMPC group showed similar incidence (IRR: 0.99 95% CI: 0.88–1.12) and mortality (MRR: 0.89 95% CI: 0.49–1.61) to that of HDC individuals. The effect of obesity on incidence was similar in both HDC and HMPC populations (HRs: 1.73 95% CI 1.41–2.11 among HDC vs. 1.41 95% CI 0.63–3.17 among HMPC), although the estimates were very imprecise. Despite a higher prevalence of diabetes and a stronger effect of diabetes on COVID-19 mortality in HMPC than in the HDC population, our cohort did not show an overall excess risk of COVID-19 mortality in immigrants.

Rationale: The COVID-19 pandemic has disproportionately impacted racial/ethnic minority and socioeconomically disadvantaged groups in the United States, including at-risk populations within Southeastern Pennsylvania. We sought to determine whether neighborhood-level health, demographic, and socioeconomic characteristics in Southeastern Pennsylvania were associated with COVID-19 incidence and mortality at the zip code and municipality level, thereby establishing whether neighborhood-level disparities mirror individual-level ones. Methods: Cumulative zip code- and municipality-level data on COVID-19 cases and deaths were obtained from the public data hubs of 5 counties in Southeast Pennsylvania, and those of individual long-term care facilities (LTCFs) were obtained from the Pennsylvania Department of Health. For corresponding geographic areas, demographic and socioeconomic status variables were obtained from the American Community Survey, and data on the health status and behaviors of local residents were obtained from the Southeastern Pennsylvania Household Health Survey. COVID-19 cases and deaths reported by LTCFs were excluded from area-aggregated counts. Multivariable quasi-Poisson models with offsets for population counts were created to determine whether neighborhood-level variables were associated with COVID-19 incidence and mortality. Before adjusted incidence rate ratios were calculated, such models included individual predictors that were significantly associated (p<0.05) with COVID-19 outcomes and excluded highly collinear terms as determined by having variance inflation factors greater than 3. Results: Among 208 zip codes and municipalities that had complete data, the COVID-19 cumulative incidence through July 24, 2020 ranged from 0 to 331.9 per 10,000 residents, and the COVID-19 mortality rate ranged from 0 to 1.0 per 10,000 residents. Among 45 neighborhood-level variables considered, 5 were independently associated with COVID-19 incidence (p<0.01): 1) the proportion of residents aged 65 years or older (incidence rate ratio [IRR] = 1.341, 95%-CI: 1.147-1.567 for a 10% increase), 2) population density (IRR = 1.002, 95%-CI: 1.001-1.003 for a 100 people/square kilometer increase), 3) the proportion of individuals eating 3 or more servings of fruits/vegetables daily (IRR = 0.891, 95%-CI: 0.836-0.950 for a 10% increase), 4) average median house value (IRR = 0.989, 95%-CI: 0.980-0.994 for a $10,000 USD increase), and 5) the proportion of 2-person households (IRR = 0.997, 95%-CI: 0.995-0.999 for a 10% increase). The proportion of individuals aged 65 years or older was the only factor independently associated with COVID-19 mortality (IRR = 2.59, 95%-CI: 1.55-4.41 for a 10% increase). Conclusions: Neighborhood-level data can help identify specific needs of vulnerable populations and inform policies to address health disparities related to COVID-19.

Background:COVID-19 is an infectious disease that has killed more than 246,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection.Objectives:We evaluated whether greenness is related to COVID-19 incidence and mortality in the United States.Methods:We downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April-May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home order.Results:An increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with higher population density. Discussion: Exposures to NDVI had beneficial impacts on county-level incidence of COVID-19 in the US and may have reduced county-level COVID-19 mortality rates, especially in densely populated counties.

County-level exposures to greenness and associations with COVID-19 incidence and mortality in the United States

County-Level Exposures to Greenness and Associations with COVID-19 Incidence and Mortality in the United States

Associations between nighttime light and COVID-19 incidence and mortality in the United States

EPH207 Factors Associated with COVID-19 Infections and Deaths in the Gulf Cooperation Council (GCC) Countries: A Time Series Cross-Section Analysis

Low education predicts large increase in COVID-19 mortality: the role of collective culture and individual literacy

BackgroundSocially vulnerable communities may be at higher risk for COVID-19 outbreaks in the US. However, no prior studies examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. Therefore, we examined temporal trends among counties with high and low social vulnerability to quantify disparities in trends over time.MethodsWe conducted a longitudinal analysis examining COVID-19 incidence and death rates from March 15 to December 31, 2020, for each US county using data from USAFacts. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention, with higher values indicating more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles, adjusting for rurality, percentage in poor or fair health, percentage female, percentage of smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, daily temperature and precipitation, and proportion tested for COVID-19.ResultsAt the outset of the pandemic, the most vulnerable counties had, on average, fewer cases per 100,000 than least vulnerable SVI quartile. However, on March 28, we observed a crossover effect in which the most vulnerable counties experienced higher COVID-19 incidence rates compared to the least vulnerable counties (RR = 1.05, 95% PI: 0.98, 1.12). Vulnerable counties had higher death rates starting on May 21 (RR = 1.08, 95% PI: 1.00,1.16). However, by October, this trend reversed and the most vulnerable counties had lower death rates compared to least vulnerable counties.ConclusionsThe impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties and back again over time.