Temporal analysis of social determinants associated with COVID-19 mortality (original) (raw)
Free related PDFsRelated papers
Free PDF
Journal of Health Economics and Outcomes Research
Background: The United States continues to account for the highest proportion of the global Coronavirus Disease-2019 (COVID-19) cases and deaths. Currently, it is important to contextualize COVID-19 fatality to guide mitigation efforts. Objectives: The objective of this study was to assess the ecological factors (policy, health behaviors, socio-economic, physical environment, and clinical care) associated with COVID-19 case fatality rate (CFR) in the United States. Methods: Data from the New York Times’ COVID-19 repository and the Centers for Disease Control and Prevention Data (01/21/2020 - 02/27/2021) were used. County-level CFR was modeled using the Spatial Durbin model (SDM). The SDM estimates were decomposed into direct and indirect impacts. Results: The study found percent positive for COVID-19 (0.057% point), stringency index (0.014% point), percent diabetic (0.011% point), long-term care beds (log) (0.010% point), premature age-adjusted mortality (log) (0.702 % point), incom...
Free PDF
2020
ObjectiveTo determine the association between chronic exposure to fine particulate matter (PM2.5), sociodemographic aspects, and health conditions and COVID-19 mortality in Colombia.MethodsEcological study using data at the municipality level, as units of analysis. COVID-19 data were obtained from official reports up to and including July 17th, 2020. PM2.5 long-term exposure was defined as the 2014-2018 average of the estimated concentrations at municipalities obtained from the Copernicus Atmospheric Monitoring Service Reanalysis (CAMSRA) model. We fit a logit-negative binomial hurdle model for the mortality rate adjusting for sociodemographic and health conditions.ResultsEstimated mortality rate ratios (MRR) for long-term average PM2.5 were not statistically significant in either of the two components of the hurdle model (i.e., the likelihood of reporting at least one death or the count of fatal cases). We found that having 10% or more of the population over 65 years of age (MRR=3....
Free PDF
2021
Efficient and equitable vaccination distribution is a priority for effectively outcompeting the transmission of COVID-19 globally. A recent study from the Centers for Disease Control and Prevention (CDC) identified that US counties with high social vulnerability according to metrics such as poverty, unemployment, low income, and no high school diploma, have significantly lower rates of vaccination compared to the national average1. Here, we build upon this analysis to consider associations between county-level vaccination rates and 68 different demographic, socioeconomic, and environmental factors for 1,510 American counties with over 228 million individuals for which vaccination data was also available. Our analysis reveals that counties with high levels of uninsured individuals have significantly lower COVID-19 vaccination rates (Spearman correlation: −0.264), despite the fact that the CDC has mandated that all COVID-19 vaccines are free and cannot be denied to anyone based upon h...
Free PDF
Spatial Exploration of Social Vulnerability and COVID-19-Related Health Outcomes in Mississippi
Southeastern Geographer, 2022
The COVID-19 pandemic has caused more than 48 million cases and 800,000 deaths in the United States. Mississippi (MS) is one of the hardest-hit states with a high incidence and mortality compared to the US national average. This paper explores the relationship of MS county-level COVID19-related incidence and mortality (through December 2, 2021) with the Center for Disease Control’s Social Vulnerability Index (CDC SVI). The CDC SVI consists of four major subthemes: [1] socio-economic status, [2] household composition and disability, [3] minority status and language, and finally, [4] housing type and transportation. We found that the overall SVI ranking has a statistically significant association with reported COVID-19 cumulative mortality at the county level. Among the SVI subthemes, subtheme 1 (socio-economic status) and subtheme 2 (household composition and disability) showed a significant relationship with incidence and mortality (p < 0.05). The results of our analysis will assist in understanding the spatial relationship between CDC SVI themes and the health effects of COVID-19 in MS and the surrounding areas.
Free PDF
Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina
International Journal of Environmental Research and Public Health
Disparities and their geospatial patterns exist in morbidity and mortality of COVID-19 patients. When it comes to the infection rate, there is a dearth of research with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs). This work aimed to assess the temporal–geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina. We used the spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR) as global models and the geographically weighted regression model (GWR) as a local model. The data were retrieved from multiple sources including USAFacts, U.S. Census Bureau, and the Population Estimates Program. The percentage of males and the unemployed population were positively associated with geodistributions of COVID-19 infection (p values < 0.05) in global models throughout the time. The percentage of the white population and the obesity rate show...
Free PDF
Ultraviolet A Radiation and COVID-19 Deaths in the USA with replication studies in England and Italy
2020
ObjectivesTo determine whether UVA exposure might be associated with COVID-19 deathsDesignEcological regression, with replication in two other countries and pooled estimationSetting2,474 counties of the contiguous USA, 6,755 municipalities in Italy, 6,274 small areas in England. Only small areas in their ‘Vitamin D winter’ (monthly mean UVvitd of under 165 KJ/m2) from Jan to April 2020.ParticipantsThe ‘at-risk’ population is the total small area population, with measures to incorporate spatial infection into the model. The model is adjusted for potential confounders including long-term winter temperature and humidity.Main outcome measuresWe derive UVA measures for each area from remote sensed data and estimate their relationship with COVID-19 mortality with a random effect for States, in a multilevel zero-inflated negative binomial model. In the USA and England death certificates had to record COVID-19. In Italy excess deaths in 2020 over expected from 2015-19.Data sourcesSatellite ...
Free PDF
Delayed Interventions, Low Compliance, and Health Disparities Amplified the Early Spread of COVID-19
medRxiv, 2020
The United States (US) public health interventions were rigorous and rapid, yet failed to arrest the spread of the Coronavirus Disease 2019 (COVID-19) pandemic as infections spread throughout the US. Many factors have contributed to the spread of COVID-19, and the success of public health interventions depends on the level of community adherence to preventative measures. Public health professionals must also understand regional demographic variation in health disparities and determinants to target interventions more effectively. In this study, a systematic evaluation of three significant interventions employed in the US, and their effectiveness in slowing the early spread of COVID-19 was conducted. Next, community-level compliance with a state-level stay at home orders was assessed to determine COVID-19 spread behavior. Finally, health disparities that may have contributed to the disproportionate acceleration of early COVID-19 spread between certain counties were characterized. The ...
Free PDF
MedXriv preprint, 2020
Background: The objective was to document whether and to what extent there was an association between socioeconomic status (SES) and disease outcomes in the last five influenza pandemics. Methods/Principle Findings: The review included studies published in English, Danish, Norwegian and Swedish. Records were identified through systematic literature searches in six databases. Results are summarized narratively and using meta-analytic strategies. We found studies only for the 1918 and 2009 pandemics. Of 14 studies on the 2009 pandemic including data on both medical and social risk factors, after controlling for medical risk factors 8 demonstrated independent impact of SES. A random effect analysis of 46 estimates from 35 studies found a pooled mean odds ratio of 1.4 (95% CI: 1.2-1.7), comparing the lowest to the highest SES, but with substantial effect heterogeneity across studies, reflecting differences in outcome measures and definitions of case and control samples. Analyses by pandemic period (1918 or 2009) and by level of SES measure (individual or ecological) indicate no differences along these dimensions. Studies using healthy controls tend to find low SES associated with worse influenza outcome, and studies using infected controls find low SES associated with more severe outcomes. Studies comparing severe outcomes (ICU or death) to hospital admissions are few but indicate no clear association. Studies with more unusual comparisons (e.g., pandemic vs seasonal influenza, seasonal influenza vs other patient groups) report no or negative associations. Conclusions/Significance: Results show that social risk factors help to explain pandemic outcomes in 1918 and in 2009 although the mechanisms and types of social vulnerabilities leading to disparities in outcomes may differ over time. Studies of the 2009 pandemic also showed that social vulnerability could not always be explained by medical risk factors. To prepare for future pandemics, we must consider social along with medical vulnerability. The protocol for this study has been registered in PROSPERO (ref. no 87922) and has been published.
Free PDF
2021
BackgroundEvidence suggests that the risk of Coronavirus Disease 2019 (COVID-19) varies geographically due to differences in population characteristics. Therefore, the objectives of this study were to identify: (a) geographic disparities of COVID-19 risk in the Greater St. Louis area of Missouri, USA; (b) predictors of the identified disparities.MethodsData on COVID-19 incidence and chronic disease hospitalizations were obtained from the Departments of Health and Missouri Hospital Association, respectively. Socioeconomic and demographic data were obtained from the 2018 American Community Survey while population mobility data were obtained from the SafeGraph website. Choropleth maps were used to identify geographic disparities of COVID-19 risk and its predictors at the ZIP Code Tabulation Area (ZCTA) spatial scale. Global negative binomial and local geographically weighted negative binomial models were used to identify predictors of ZCTA-level geographic disparities of COVID-19 risk....
Free PDF
