Association Between Income Inequality and County-Level COVID-19 Cases and Deaths in the US

Exploring the Association Between Minimum Wage Policy, Income Inequality, and Obesity Rates in US Counties

OBJECTIVE

To examine the interaction between minimum wage policy, income inequality, and obesity rates among U.S. counties, and how this relationship is shaped by policy, place, and racial/ethnic composition in a county.

METHODS

We used the County Health Rankings Data for obesity ratio (measured by Body Mass Index ≥ 30 kg/m2) in US counties and combined it with the American Community Survey to include the Gini coefficient (GC) and population characteristics. The analytical sample included 3129 counties between 2015 and 2019. We ran several sets of regression analyses, controlling for county characteristics, access to healthy foods, and minimum wage categories as a policy influencer on the obesity ratio.

RESULTS

In total, 31.7% of the population were obese, with wide variations across counties; during this time, counties' average GC was 0.442. Our findings showed that in the lack of any other predictors, GC has a positive association with the county obesity ratio (OLS 0.147, CI 0.122-0.173). Counties with minimum wage between $7.26-$9.0 and $9 + had lower obesity ratios by - 0.6 and - 2.8 percentage points, respectively, and counties with lower access to healthy foods had higher obesity ratio (Coeff = 0.022, CI 0.019-0.025).

CONCLUSIONS

Income inequality is positively associated with the obesity ratio in counties. Access to healthy foods and state minimum wage policy predict obesity rates, with a lack of healthy foods increasing the ratio, while a higher minimum wage reduces it.

Death from COVID-19 in contexts of social deprivation in Mexico

Introduction

Poverty is one of the macro factors that has been little studied in terms of its effect on death from COVID-19 since most studies have focused only on investigating whether the pandemic increased poverty or not. With that on mind, the present study aims to analyze how the social deprivations that comprise the measurement of municipal poverty in interaction with health comorbidities and sociodemographic characteristics, increased the probability of death from COVID-19.

Methods

The study is cross-sectional and covers daily reports on the conditions of COVID-19 in the Mexican population for almost 2 years. Using data from the National Epidemiological Surveillance System and the National Council for Evaluation of the Social Development Policy (N = 5,387,981), we employ a Generalized Linear Mixed Model (GLMM), specifically a binomial generalized linear mixed model.

Results

The findings indicate that, besides comorbidities, sociodemographic traits, and clinical aspects, living in a municipality where one or more of the social deprivations exist increases the probability of death. Specifically, in those municipalities where there is deprivation in education, social security, and food, as well as deprivation due to access to health services and deprivation in household services, the probability of death was greater.

Discussion

Living in a municipality with one or more of the social deprivations that compose poverty generated a greater probability of death. Each one of them or together, shows that poverty is a substantial factor for a pandemic like COVID-19 to worsen contagion and death, becoming a circle from which it is difficult to escape.

National Variation in Black Immigrant Preterm Births and the Role of County-Level Social Factors

Preterm birth rates among Black individuals continue to be inequitably high in the USA. Black immigrants appear to have a preterm birth advantage over US-born counterparts. This national cross-sectional study of singleton non-Hispanic Black individuals in the USA from 2011 to 2018 aimed to investigate if the Black immigrant preterm birth advantage varied geographically and how this advantage associated with county-level social drivers of health. Generalized linear mixed models explored the odds of preterm birth (< 37 weeks) by birthing person's nativity, defined as US- versus foreign-born. In county-level analyses, five measures were explored as possible sources of structural risk for or resilience against preterm birth: percent of residents in poverty, percent uninsured, percent with more than a high school education, percent foreign-born, and racial polarization. County-level immigrant advantage among foreign-born compared to US-born Black individuals was defined by a disparity rate ratio (RR); RR < 1 indicated a county-level immigrant preterm birth advantage. Linear regression models at the level of counties quantified associations between county-level factors and disparity RRs. Among 4,072,326 non-Hispanic Black birthing individuals, immigrants had 24% lower adjusted odds of preterm birth compared to US-born Black individuals (aOR 0.77, 95% CI 0.76-0.78). In county-level analyses, the immigrant advantage varied across counties; disparity RRs ranged from 0.13 to 2.82. County-level lack of health insurance and education greater than high school were both associated with immigrant preterm birth advantage. Future research should explore policies within counties that impact risk of preterm birth for both US-born and immigrant Black individuals.

A National Study of the Associations between Hormonal Modulators in Hydraulic Fracturing Fluid Chemicals and Birth Outcomes in the United States of America: A County-Level Analysis

BACKGROUND

Risk of preterm birth (PTB) and low birth weight (LBW) due to hydraulic fracturing (HF) exposure is a growing concern. Regional studies have demonstrated links, but results are often contradictory among studies.

OBJECTIVES

This is the first US national study to our knowledge linking fracturing fluid ingredients to the human hormone pathways targeted-estrogen, testosterone, or other hormones (e.g., thyroid hormone)-to assess the effect of HF ingredients on rates of PTB and LBW.

METHODS

We constructed generalized linear regression models of the impact of HF well density and hormone targeting chemicals in HF fluids (2001-2018) on the county-level average period prevalence rates of PTB and LBW (2015-2018) with each outcome measured in separate models. Our data sources consisted of publicly available datasets, including the WellExplorer database, which uses data from FracFocus, the March of Dimes Peristats, the US Census Bureau, the US Department of Agriculture, and the Centers for Disease Control and Prevention. We conducted additional stratified analyses to address issues of confounding. We used stratification to address issues regarding outcomes in rural vs. urban communities by assessing whether our models achieved similar results in nonmetro counties, as well as farming and mining counties. We also stratified by the year of the HF data to include HF data that was closer to the time of the birth outcomes. We also added covariate adjustment to address other important factors linked to adverse birth outcomes, including the proportion of the population belonging to various racial and ethnic minority populations (each modeled as a separate variable); education (bachelor's degree and high school); use of fertilizers, herbicides, and insecticides, acres of agricultural land per square mile; poverty; insurance status; marital status; population per square mile; maternal care deserts; and drug deaths per 100,000 people.

RESULTS

We found that the density of HF wells in a county was significantly associated with both PTB and LBW rates (percentage of live births) in our fully adjusted models. We report the results from our more restrictive stratified analysis with a subset including only the 2014-2018 data, because this resulted in the most meaningful time frame for comparison. Across all models, the magnitude of effect was highest for wells with ingredients that include estrogen targeting chemicals (ETCs), testosterone targeting chemicals (TTCs) and other hormone targeting chemicals (OHTCs), and, finally, all wells grouped regardless of chemical type. For every unit increase in well density per square mile of wells that use chemicals that include an ETC, we observed a 3.789-higher PTB rate (95% CI: 1.83, 5.74) compared with counties with no ETC wells from 2014 to 2018 and likewise, we observed a 1.964-higher LBW rate (95% CI: 0.41, 3.52). Similarly, for every unit increase in well density per square mile of wells that use TTC, we observed a 3.192-higher PTB rate (95% CI: 1.62, 4.77) compared with counties with no TTC wells. Likewise, for LBW, we found a 1.619-higher LBW rate (95% CI: 0.37, 2.87). We also found that an increase in well density per square mile among wells that use chemicals that include an OHTC resulting in a 2.276-higher PTB rate (95% CI: 1.25, 3.30) compared with counties with no OHTC wells, and for LBW, we found a 1.244-higher LBW rate (95% CI: 0.43, 2.06). We also explored the role of HF well exposure in general (regardless of the chemicals used) and found that an increase in total well density (grouped regardless of hormonal targeting status of the chemicals used) resulted in a 1.228-higher PTB rate (95% CI: 0.66, 1.80) compared with counties with no wells, and for LBW, we found a 0.602-higher LBW rate (95% CI: 0.15, 1.05) compared with counties with no wells. We found similar results in our primary analysis that used all data without any exclusions and the statistical significance did not change.

DISCUSSION

Our findings reinforce previously identified regional associations between HF and PTB and LBW, but on a national scale. Our findings point to dysregulation of hormonal pathways underpinning HF exposure risk on birth outcomes, which warrants further exploration. Future research must consider the specific ingredients used in HF fluids to properly understand the differential effects of exposure. https://doi.org/10.1289/EHP12628.

The Differential Experience of COVID-19 on Asian American Subgroups: The Los Angeles Pandemic Surveillance Cohort Study

Data from Asian Americans (AsA) are commonly aggregated in research studies and reporting, obscuring the significant differences across AsA subgroups. We investigated the differential experience of AsA subgroups in COVID-19 testing, vaccination, engagement in risky and protective behaviors and mental health status against this infectious disease. We surveyed a representative sample of the Los Angeles County population (N = 5500) in April 2021 as part of the Los Angeles Pandemic Surveillance Cohort Study and focused on participants who self-identified as AsA (N = 756). There were significant differences across the AsA subgroups, with Koreans, Asian Indians, and Other Asians living in areas with higher COVID-19 mortality rates, and Asian Indians demonstrating the lowest proportion of COVID-19 vaccination. Vietnamese and Koreans had a higher proportion of becoming unemployed during the pandemic. Although the AsA sample on average demonstrated better outcomes than other racial and ethnic groups, the apparent advantages were heterogenous and due to specific subgroups of AsAs rather than AsAs as a whole. The observed differences in COVID-19 measures across AsA subgroups underscore the need to disaggregate AsA data to identify and reduce existing disparities.

Social trust and COVID-19 mortality in the United States: lessons in planning for future pandemics using data from the general social survey

BACKGROUND

The United States has lost many lives to COVID-19. The role of social capital and collective action has been previously explored in the context of COVID-19. The current study specifically investigates the role of social trust at the county level and COVID-19 mortality in the US, hypothesizing that counties with higher social trust will have lower COVID-19 mortality rates.

METHODS

We used cross-sectional data from the General Social Survey (GSS). We collected COVID-19 mortality data from the COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University until October 31, 2021. We obtained county characteristics from the 2019 American Community Survey and supplemented this data source with additional publicly available county-level data, such as measures of income inequality and political leanings. We measured social trust as a single item from the GSS and calculated mean social trust in a county by pooling responses from 2002 to 2018. We then modeled the relationship between mean social trust and COVID-19 mortality.

RESULTS

Results indicate that counties with higher social trust have lower COVID-19 mortality rates. Higher values of mean social trust at the county level are associated with a decrease in COVID-19 mortality (b= -0.25, p-value < 0.001), after adjustment for confounding. The direction of association is consistent in a sensitivity analysis.

CONCLUSIONS

Our findings underscore the importance of investment in social capital and social trust. We believe these findings can be applied beyond the COVID-19 pandemic, as they demonstrate the potential for social trust as a method for emergency preparedness.

Socio-economic conditions affect health-related quality of life, during recovery from acute SARS-CoV-2 infection : Results from the VASCO study (VAriabili Socioeconomiche e COVID-19), on the "Surviving-COVID" cohort, from Bergamo (Italy)

BACKGROUND

Recovery from acute COVID-19 may be slow and incomplete: cases of Post-Acute Sequelae of COVID (PASC) are counted in millions, worldwide. We aimed to explore if and how the pre-existing Socio-economic-status (SES) influences such recovery.

METHODS

We analyzed a database of 1536 consecutive patients from the first wave of COVID-19 in Italy (February-September 2020), previously admitted to our referral hospital, and followed-up in a dedicated multidisciplinary intervention. We excluded those seen earlier than 12 weeks (the conventional limit for a possible PASC syndrome), and those reporting a serious complication from the acute phase (possibly accounting for symptoms persistence). We studied whether the exposition to disadvantaged SES (estimated through the Italian Institute of Statistics's model - ISTAT 2017) was affecting recovery outcomes, that is: symptoms (composite endpoint, i.e. at least one among: dyspnea, fatigue, myalgia, chest pain or palpitations); Health-Related-Quality-of-Life (HRQoL, as by SF-36 scale); post-traumatic-stress-disorder (as by IES-R scale); and lung structural damage (as by impaired CO diffusion, DLCO).

RESULTS

Eight-hundred and twenty-five patients were included in the analysis (median age 59 years; IQR: 50-69 years, 60.2% men), of which 499 (60.5%) were previously admitted to hospital and 27 (3.3%) to Intensive-Care Unit (ICU). Those still complaining of symptoms at follow-up were 337 (40.9%; 95%CI 37.5-42.2%), and 256 had a possible Post-Traumatic Stress Disorder (PTSD) (31%, 95%CI 28.7-35.1%). DLCO was reduced in 147 (19.6%, 95%CI 17.0-22.7%). In a multivariable model, disadvantaged SES was associated with a lower HRQoL, especially for items exploring physical health (Limitations in physical activities: OR = 0.65; 95%CI = 0.47 to 0.89; p = 0.008; AUC = 0.74) and Bodily pain (OR = 0.57; 95%CI = 0.40 to 0.82; p = 0.002; AUC = 0.74). We did not observe any association between SES and the other outcomes.

CONCLUSIONS

Recovery after COVID-19 appears to be independently affected by a pre-existent socio-economic disadvantage, and clinical assessment should incorporate SES and HRQoL measurements, along with symptoms. The socioeconomic determinants of SARS-CoV-2 disease are not exclusive of the acute infection: this finding deserves further research and specific interventions.

Tracking the Uneven Outcomes of COVID-19 on Racial and Ethnic Groups: Implications for Health Policy

The socioeconomic shocks of the first COVID-19 pandemic wave disproportionately affected vulnerable groups. But did that trend continue to hold during the Delta and Omicron waves? Leveraging data from the Johns Hopkins Coronavirus Resource Center, this paper examines whether demographic inequalities persisted across the waves of COVID-19 infections. The current study utilizes fixed effects regressions to isolate the marginal relationships between socioeconomic factors with case counts and death counts. Factors include levels of urbanization, age, gender, racial distribution, educational attainment, and household income, along with time- and state-specific COVID-19 restrictions and other time invariant controls captured via fixed effects controls. County-level health outcomes in large metropolitan areas show that despite higher incidence rates in suburban and exurban counties, urban counties still had disproportionately poor outcomes in the latter COVID-19 waves. Policy makers should consider health disparities when developing long-term public health regulatory policies to help shield low-income households from the adverse effects of COVID-19 and future pandemics.

Social-Economic Backgrounds to US County-Based COVID-19 Deaths: PLS-SEM Analysis

A complex interplay of social, economic, and environmental factors drove the COVID-19 epidemic. Understanding these factors is crucial in explaining the racial disparities observed in COVID-19 deaths. This research investigated various hypotheses, including ecological, racial, demographic, economic, and political party factors, to determine their impact on COVID-19 deaths. The study utilized data from the National Center for Health Statistics (NCHS), specifically focusing on COVID-19 deaths categorized by race and Hispanic origin in US counties, with over 100 recorded deaths as of July 11, 2022.

METHOD

To analyze the data, the study employed partial least squares (PLS) as the statistical approach, considering the presence of multicollinearity in the county-level socioeconomic data. SmartPLS4 software was utilized to illustrate paths depicting variance and covariance and to conduct significance tests. The analysis encompassed overall COVID-19 deaths and deaths among White, Black, and Hispanic Americans, utilizing the same latent variables and paths.

RESULTS

The results revealed that the number of residents aged 65 years or older in a county was the most influential predictor of COVID-19 deaths, irrespective of race. Economic factors emerged as the second strongest predictors. However, when considering each racial group separately, distinct factors aligned with the five hypotheses emerged as significant contributors to COVID-19 deaths. Furthermore, the diagrams illustrating the relationships between these factors (covariates) varied among racial groups, indicating that the underlying social influences differed across races.

DISCUSSION

In light of these findings, it becomes evident that a "one-size-fits-all" approach to prevention strategies is suboptimal. Instead, targeted prevention efforts tailored to specific racial and social classes at high risk of COVID-19 death could have provided more precise messaging and necessitate direct engagement.

Higher unemployment benefits are associated with reduced drug overdose mortality in the United States before and during the COVID-19 pandemic

OBJECTIVES

Overdose mortality rates in the United States remain critical to population health. Economic , such as unemployment, are noted risk factors for drug overdoses. The COVID-19 pandemic exacerbated economic hardship; as a result, the US government enacted income protection programs in conjunction with existing unemployment insurance (UI) to dampen COVID-19-related economic consequences. We investigate whether UI, operationalized as the weekly benefit allowance (WBA) replacement rate, is negatively associated with drug-related overdoses.

METHODS

Data from the pooled 2014-2020 Detailed Restricted Mortality files for all counties from the Centers for Disease Control and Prevention, restricted to people ≥18 years of age, aggregated at the county-quarter level (n = 89,914). We included any fatal drug, opioid, and stimulant overdose. We modeled the association between WBA replacement rate (e.g., a greater proportion of weekly earnings replaced by UI) on each county-level age-adjusted mortality outcome using separate linear regression models during 2014-2020, pre-COVID (2014-2018), and post-COVID (2019-2020). We conducted sensitivity analyses using multi-level linear regression models.

RESULTS

Results indicated that a more robust WBA replacement rate any drug (Risk Difference [RD]: -0.06, 95 % Confidence Interval [CI]: -0.08, -0.05), opioid (RD: -0.04, 95 % CI: -0.06, -0.03), and stimulant (RD: -0.03, 95 % CI: -0.04, -0.02) across the entire study period (2014-2020). A more robust WBA replacement rate was associated with fewer fatal drug, opioid and stimulant overdoses in the pre-COVID-19 period and on fatal any drug and stimulant overdoses in the COVID-19 period.

CONCLUSIONS

Findings support the notion that income protection policies, such as robust UI, can have a supportive role in preventing fatal drug overdoses, calling for a broader discussion onthe role of the safety net programs to buffer drug-related harms.

Moral economy and moral capital: A new approach to understanding health systems

Healthcare systems can be considered moral economies in which moral capital in the form of expectations toward norms, values, and virtues are exchanged and traded. Moral capital, as a concept, is an extension of Bourdieu's forms of symbolic, and in particular, cultural capital. This research set out to identify forms of moral capital evident in the accounts of health professionals and patients within the distinctive healthcare systems of Germany, New Zealand, and the Unites States. Here, we provide an overview of 15 forms of moral capital that were identified. An important form of moral capital is equality. The global coronavirus pandemic has illuminated inequalities in healthcare systems across the world. We suggest considering moral capital as a useful tool to reform healthcare systems and make the provision of healthcare a more equitable enterprise.

Excess deaths associated with COVID-19 by rurality and demographic factors in the United States

PURPOSE

To estimate percent excess deaths during the COVID-19 pandemic by rural-urban residence in the United States and to describe rural-urban disparities by age, sex, and race/ethnicity.

METHODS

Using US mortality data, we used overdispersed Poisson regression models to estimate monthly expected death counts by rurality of residence, age group, sex, and race/ethnicity, and compared expected death counts with observed deaths. We then summarized excess deaths over 6 6-month time periods.

FINDINGS

There were 16.9% (95% confidence interval [CI]: 16.8, 17.0) more deaths than expected between March 2020 and February 2023. The percent excess varied by rurality (large central metro: 18.2% [18.1, 18.4], large fringe metro: 15.6% [15.5, 15.8], medium metro: 18.1% [18.0, 18.3], small metro: 15.5% [15.3, 15.7], micropolitan rural: 16.3% [16.1, 16.5], and noncore rural: 15.8% [15.6, 16.1]). The percent excess deaths were 20.2% (20.1, 20.3) for males and 13.6% (13.5, 13.7) for females, and highest for Hispanic persons (49% [49.0, 49.6]), followed by non-Hispanic Black persons (28% [27.5, 27.9]) and non-Hispanic White persons (12% [11.6, 11.8]). The 6-month time periods with the highest percent excess deaths for large central metro areas were March 2020-August 2020 and September 2020-February 2021; for all other areas, these time periods were September 2020-February 2021 and September 2021-February 2022.

CONCLUSION

Percent excess deaths varied by rurality, age group, sex, race/ethnicity, and time period. Monitoring excess deaths by rurality may be useful in assessing the impact of the pandemic over time, as rural-urban patterns appear to differ.

Wealth Redistribution to Extend Longevity in the US

Importance

The US is unique among wealthy countries in its degree of wealth inequality and its poor health outcomes. Wealth is known to be positively associated with longevity, but little is known about whether wealth redistribution might extend longevity.

Objective

To examine the association between wealth and longevity and estimate the changes in longevity that could occur with simulated wealth distributions that were perfectly equal, similar to that observed in Japan (among the most equitable of Organisation for Economic Co-operation and Development [OECD] countries), generated by minimum inheritance proposals, and produced by baby bonds proposals.

Design, Setting, and Participants

This longitudinal cohort study analyzed the association between wealth and survival among participants in the Health and Retirement Study (1992-2018), a nationally representative panel study of middle-aged and older (≥50 years) community-dwelling, noninstitutionalized US adults. The data analysis was performed between November 15, 2022, and September 24, 2023.

Exposure

Household wealth on study entry, calculated as the sum of all assets minus the value of debts and classified into deciles.

Main Outcomes and Measures

Weibull survival models were used to estimate the association between per-person wealth decile and survival, adjusting for age, sex, marital status, household size, and race and ethnicity. Changes in longevity that might occur under alternative wealth distributions were then estimated.

Results

The sample included 35 164 participants (mean [SE] age at study entry, 59.1 [0.1] years; 50.1% female and 49.9% male [weighted]). The hazard of death generally decreased with increasing wealth, wherein participants in the highest wealth decile had a hazard ratio of 0.59 for death (95% CI, 0.53-0.66) compared with those in the lowest decile, corresponding to a 13.5-year difference in survival. A simulated wealth distribution of perfect equality would increase populationwide median longevity by 2.2 years (95% CI, 2.2-2.3 years), fully closing the mortality gap between the US and the OECD average. A simulated minimum inheritance proposal would increase populationwide median longevity by 1.7 years; a simulated wealth distribution similar to Japan's would increase populationwide median longevity by 1.2 years; and a simulated baby bonds proposal would increase populationwide median longevity by 1.0 year.

Conclusions and Relevance

These findings suggest that wealth inequality in the US is associated with significant inequities in survival. Wealth redistribution policies may substantially reduce those inequities and increase population longevity.

Is income inequality linked to infectious disease prevalence? A hypothesis-generating study using tuberculosis

We study the association between infectious disease incidence and income inequality. We hypothesize that random social mixing in an income-unequal society brings into contact a) susceptible and infected poor and b) the infected-poor and the susceptible-rich, raising infectious disease incidence. We analyzed publicly available, country-level panel data for a large cross-section of countries between 1995 and 2013 to examine whether countries with elevated levels of income inequality have higher rates of pulmonary Tuberculosis (TB) incidence per capita. A "negative control" using anemia and diabetes (both non-communicable diseases and hence impervious to the hypothesized mechanism) is also applied. We find that high levels of income inequality are positively associated with tuberculosis incidence. All else equal, countries with income-Gini coefficients 10% apart show a statistically significant 4% difference in tuberculosis incidence. Income inequality had a null effect on the negative controls. Our cross-country regression results suggest that income inequality may create conditions where TB spreads more easily, and policy action to reduce income inequities could directly contribute to a reduced TB burden.

Causal Assessment of Income Inequality on Self-Rated Health and All-Cause Mortality: A Systematic Review and Meta-Analysis

Policy Points Income is thought to impact a broad range of health outcomes. However, whether income inequality (how unequal the distribution of income is in a population) has an additional impact on health is extensively debated. Studies that use multilevel data, which have recently increased in popularity, are necessary to separate the contextual effects of income inequality on health from the effects of individual income on health. Our systematic review found only small associations between income inequality and poor self-rated health and all-cause mortality. The available evidence does not suggest causality, although it remains methodologically flawed and limited, with very few studies using natural experimental approaches or examining income inequality at the national level.

CONTEXT

Whether income inequality has a direct effect on health or is only associated because of the effect of individual income has long been debated. We aimed to understand the association between income inequality and self-rated health (SRH) and all-cause mortality (mortality) and assess if these relationships are likely to be causal.

METHODS

We searched Medline, ISI Web of Science, Embase, and EconLit (PROSPERO: CRD42021252791) for studies considering income inequality and SRH or mortality using multilevel data and adjusting for individual-level socioeconomic position. We calculated pooled odds ratios (ORs) for poor SRH and relative risk ratios (RRs) for mortality from random-effects meta-analyses. We critically appraised included studies using the Risk of Bias in Nonrandomized Studies - of Interventions tool. We assessed certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation framework and causality using Bradford Hill (BH) viewpoints.

FINDINGS

The primary meta-analyses included 2,916,576 participants in 38 cross-sectional studies assessing SRH and 10,727,470 participants in 14 cohort studies of mortality. Per 0.05-unit increase in the Gini coefficient, a measure of income inequality, the ORs and RRs (95% confidence intervals) for SRH and mortality were 1.06 (1.03-1.08) and 1.02 (1.00-1.04), respectively. A total of 63.2% of SRH and 50.0% of mortality studies were at serious risk of bias (RoB), resulting in very low and low certainty ratings, respectively. For SRH and mortality, we did not identify relevant evidence to assess the specificity or, for SRH only, the experiment BH viewpoints; evidence for strength of association and dose-response gradient was inconclusive because of the high RoB; we found evidence in support of temporality and plausibility.

CONCLUSIONS

Increased income inequality is only marginally associated with SRH and mortality, but the current evidence base is too methodologically limited to support a causal relationship. To address the gaps we identified, future research should focus on income inequality measured at the national level and addressing confounding with natural experiment approaches.

Differential associations of mask mandates on COVID-19 infection and mortality by community social vulnerability

BACKGROUND

The COVID-19 pandemic in the United States has disproportionately impacted communities deemed vulnerable to disease outbreaks. Our objectives were to test (1) whether infection and mortality decreased in counties in the most vulnerable (highest) tercile of the Social Vulnerability Index (SVI), and (2) whether disparities between terciles of SVI were reduced, as the length of mask mandates increased.

METHODS

Using the New York Times COVID-19 and the Centers for Disease Control and Prevention SVI and mask mandate datasets, we conducted negative binomial regression analyses of county-level COVID-19 cases and deaths from 1/2020-11/2021 on interactions of SVI and mask mandate durations.

RESULTS

Mask mandates were associated with decreases in mid-SVI cases (IRR: 0.79) and deaths (IRR: 0.90) and high-SVI cases (IRR: 0.89) and deaths (IRR: 0.88). Mandates were associated with the mitigation of infection disparities (Change in IRR: 0.92) and mortality disparities (Change in IRR: 0.85) between low and mid-SVI counties and mortality disparities between low and high-SVI counties (Change in IRR: 0.84).

DISCUSSION

Mask mandates were associated with reductions in COVID-19 infection and mortality and mitigation of disparities for mid and high-vulnerability communities.

CONCLUSIONS

Ongoing COVID-19 response efforts may benefit from longer-standing infection control policies, particularly in the most vulnerable communities.

Pandemics, Income Inequality, and Refugees: The Case of COVID-19

Refugees are more vulnerable to COVID-19 due to factors such as low standard of living, accommodation in crowded households, difficulty in receiving health care due to high treatment costs in some countries, and inability to access public health and social services. The increasing income inequalities, anxiety about providing minimum living conditions, and fear of being unemployed compel refugees to continue their jobs, and this affects the number of cases and case-related deaths. The aim of the study is to analyze the impact of refugees and income inequality on COVID-19 cases and deaths in 95 countries for the year 2021 using Poisson regression, Negative Binomial Regression, and Machine Learning methods. According to the estimation results, refugees and income inequalities increase both COVID-19 cases and deaths. On the other hand, the impact of income inequality on COVID-19 cases and deaths is stronger than on refugees.

Disparities in access to COVID-19 testing in Los Angeles County

COVID-19 has disparately impacted low-income persons and racial and ethnic minorities-primarily Black and Hispanic populations. Our objective is to quantify disparities in access to COVID-19 testing and identify barriers to testing during the winter 2020-2021 surge in COVID-19 infections in Los Angeles County. An online survey was administered between December 2020 and January 2021 through which respondents were asked about their use of COVID testing and the barriers to testing they experienced. Our sample of 1,984 was reweighted to match the demographics of Los Angeles County. Despite similar testing rates to White residents, Hispanic residents were more likely to report testing positive. Persons with an annual income of $20,000 or less were less likely to receive a test than those with an income of $100,000 or more. Barriers to testing were more prevalent among racial/ethnic minorities and low-income persons. White respondents and high-income persons were more likely to report the ability to take time off work to await test results. Rates of testing were not commensurate with the rates of infection across racial/ethnic groups, which may be explained by higher rates of reported barriers to testing among Black and Hispanic residents. These findings may inform policies that address structural barriers to testing that disproportionately impact racial/ethnic minorities and low-income populations.

"We're Here to Take Care of Our Community": Lessons Learned From the U.S. Federal Health Center Covid-19 Vaccine Program

Equitable access to vaccination is crucial to mitigating the disproportionate impact of Covid-19 on low-income communities and people of color in the United States. As primary care clinics for medically underserved patients, Federally Qualified Health Centers (FQHCs) emerged as a success story in the national effort to vaccinate the U.S. public against Covid-19. In February 2021, the Federal Health Center Covid-19 Vaccine Program began allocating vaccine supply directly to FQHCs in an effort to improve vaccine equity. This qualitative study documents how FQHCs in two states successfully mitigated barriers to vaccine access, responded to patient concerns about vaccination, and worked to maintain and grow community trust in a climate of uncertainty and fear during early vaccine roll-out to the general population. Using a socio-ecological model, we show how FQHCs intervened at multiple levels to advance vaccine equity, revealing valuable lessons for health promotion practice in primary care settings or underserved communities. Our findings provide descriptive context for existing quantitative evidence showing FQHCs' greater success in vaccinating people of color, and foreground valuable and innovative strategies for trustworthy health communication practices and equitable resource allocation to medically underserved patients and populations.

COVID-19 vaccines should be evaluated from the societal perspective

The COVID-19 pandemic demonstrates the importance of valuing vaccines from a broad societal perspective (SP), as opposed to a narrower health-payer perspective (HPP). COVID-19's catastrophic global impacts extend not only to its health-related effects, but also to the profound macroeconomic losses caused by lockdowns required for disease control, leading to the worst global economic crisis in a century. COVID-19 vaccination (CV) has been the central policy tool for resolving this economic crisis, and it has been hypothesized that this macroeconomic benefit alone justifies the cost of CV many times over. Yet HPP-based vaccine valuations are wholly insensitive to this enormous benefit, not allowing it to influence the allocation of given health budgets nor the determination of the magnitudes of such budgets, thereby risking inadequate societal spending on CV. HPP allocates given health budgets to maximize only health, giving no weight to macroeconomic outcomes, causing allocative inefficiency by not allowing welfare-improving trade-offs of health for wealth. HPP assumes health budgets are optimal, not scrutinizing whether their scale adequately reflects the macroeconomic benefits of health spending, thereby risking productive inefficiency by foregoing health spending increases such as on CV that could raise both population-level health and wealth. These allocative and productive inefficiencies in turn distort for-profit R&D incentives, risking dynamic inefficiency. And since the socio-economic and health burdens of COVID-19 are disproportionately borne by the worse off, HPP's failure to promote optimal levels of societal investment in CV may disproportionately burden the worse off as well, exacerbating inequality. Vaccine valuations from the societal perspective allow the allocation and determination of health budgets to reflect macroeconomic and distributional values, thereby promoting allocative, productive, and dynamic efficiency, as well as equity. These considerations of efficiency and equity support evaluating CV, and to ensure a level playing field, all vaccines, from a societal perspective.

Association of Severe COVID-19 and Persistent COVID-19 Symptoms With Economic Hardship Among US Families

Importance

Little is known about the association of severe COVID-19 and post-COVID-19 conditions with household finances.

Objective

To examine associations between COVID-19 outcomes, pandemic-related economic hardship, and prepandemic socioeconomic status among families in the US.

Design, Setting, and Participants

This cohort study used data from the Panel Study of Income Dynamics (PSID), a nationally representative, longitudinal study. Data included 6932 families active in the PSID in both 2019 and 2021.

Exposures

Ordinal exposure categories were defined based on whether the reference person or spouse or partner reported a positive COVID-19 diagnosis and (1) persistent COVID-19 symptoms, (2) previous severe COVID-19, or (3) previous moderate, mild, or asymptomatic COVID-19. Families with no history of COVID-19 served as the reference group.

Main Outcomes and Measures

Outcomes included whether a resident family member was laid off or furloughed, lost earnings, or had any financial difficulties due to the COVID-19 pandemic.

Results

In this cohort study of 6932 families (772 Hispanic families [weighted, 13.5%; 95% CI, 12.4%-14.6%], 2725 non-Hispanic Black families [weighted, 13.1%; 95% CI, 12.3%-14.1%], and 3242 non-Hispanic White families [weighted, 66.8%; 95% CI, 65.2%-68.3%]), close to 1 in 4 (2222 [weighted, 27.0%; 95% CI, 25.6%-28.6%]) reported income below 200% of the US Census Bureau poverty threshold. In survey-weighted regression models adjusted for prepandemic sociodemographic characteristics and experiences of economic hardship, the odds of reporting pandemic-related economic hardship were 2.0 to 3.7 times higher among families headed by an adult with persistent COVID-19 symptoms (laid off or furloughed: adjusted odds ratio [AOR], 1.98 [95% CI, 1.37-2.85]; lost earnings: AOR, 2.86 [95% CI, 2.06-3.97]; financial difficulties: AOR, 3.72 [95% CI, 2.62-5.27]) and 1.7 to 2.0 times higher among families headed by an adult with previous severe COVID-19 (laid off or furloughed: AOR, 1.69 [95% CI, 1.13-2.53]; lost earnings: AOR, 1.99 [95% CI, 1.37-2.90]; financial difficulties: AOR, 1.87 [95% CI, 1.25-2.80]) compared with families with no history of COVID-19. Families headed by an adult with persistent COVID-19 symptoms had increased odds of reporting financial difficulties due to the pandemic regardless of prepandemic socioeconomic status (families with lower income: AOR, 3.71 [95% CI, 1.94-7.10]; families with higher income: AOR, 3.74 [95% CI, 2.48-5.63]). Previous severe COVID-19 was significantly associated with financial difficulties among families with lower income (AOR, 2.59 [95% CI, 1.26-5.31]) but was not significantly associated with financial difficulties among those with high income (OR, 1.56 [95% CI, 0.95-2.56]).

Conclusions and Relevance

This cohort study suggests that persistent COVID-19 symptoms and, to a lesser extent, previous severe COVID-19 were associated with increased odds of pandemic-related economic hardship in a cohort of US families. The economic consequences of COVID-19 varied according to socioeconomic status; families with lower income before the pandemic were more vulnerable to employment disruptions and earnings losses associated with an adult family member's COVID-19 illness.

Health Care Access and Cardiovascular Risk Factor Management Among Working-Age US Adults During the Pandemic

BACKGROUND

Low-income working-age US adults disproportionately experienced health care disruptions at the onset of the coronavirus disease 2019 pandemic. Little is known about how health care access and cardiovascular risk factor management changed as the pandemic went on or if patterns differed by state Medicaid expansion status.

METHODS

Cross-sectional data from the behavioral risk factor surveillance system were used to compare self-reported measures of health care access and cardiovascular risk factor management among US adults aged 18 to 64 years in 2021 (pandemic) to 2019 (prepandemic) using multivariable Poisson regression models. We assessed differential changes between low-income (<138% federal poverty level) and high-income (>400% federal poverty level) working-age adults by including an interaction term for income group and year. We then evaluated changes among low-income adults in Medicaid expansion versus nonexpansion states using a similar approach.

RESULTS

The unweighted study population included 80 767 low-income and 184 136 high-income adults. Low-income adults experienced improvements in insurance coverage (relative risk [RR], 1.10 [95% CI, 1.08-1.12]), access to a provider (RR, 1.12 [95% CI, 1.09-1.14]), and ability to afford care (RR, 1.07 [95% CI, 1.05-1.09]) in 2021 compared with 2019. While these measures also improved for high-income adults, gains in coverage and ability to afford care were more pronounced among low-income adults. However, routine visits (RR, 0.96 [95% CI, 0.94-0.98]) and cholesterol testing (RR, 0.93 [95% CI, 0.91-0.96]) decreased for low-income adults, while diabetes screening (RR, 1.01 [95% CI, 0.95-1.08]) remained stable. Treatment for hypertension (RR, 1.05 [95% CI, 1.02-1.08]) increased, and diabetes-focused visits and insulin use remained stable. These patterns were similar for high-income adults. Across most outcomes, there were no differential changes between low-income adults residing in Medicaid expansion versus nonexpansion states.

CONCLUSIONS

In this national study of working-age adults in the United States, measures of health care access improved for low- and high-income adults in 2021. However, routine outpatient visits and cardiovascular risk factor screening did not return to prepandemic levels, while risk factor treatment remained stable. As many coronavirus disease-era safety net policies come to an end, targeted strategies are needed to protect health care access and improve cardiovascular risk factor screening for working-age adults.

US County-Level Variation in Preterm Birth Rates, 2007-2019

Importance

Preterm birth is a leading cause of preventable neonatal morbidity and mortality. Preterm birth rates at the national level may mask important geographic variation in rates and trends at the county level.

Objective

To estimate age-standardized preterm birth rates by US county from 2007 to 2019.

Design, Setting, and Participants

This serial cross-sectional study used data from the National Center for Health Statistics composed of all live births in the US between 2007 and 2019. Data analyses were performed between March 22, 2022, and September 29, 2022.

Main Outcomes and Measures

Age-standardized preterm birth (<37 weeks' gestation) and secondarily early preterm birth (<34 weeks' gestation) rates by county and year calculated with a validated small area estimation model (hierarchical bayesian spatiotemporal model) and percent change in preterm birth rates using log-linear regression models.

Results

Between 2007 and 2019, there were 51 044 482 live births in 2383 counties. In 2007, the national age-standardized preterm birth rate was 12.6 (95% CI, 12.6-12.7) per 100 live births. Preterm birth rates varied significantly among counties, with an absolute difference between the 90th and 10th percentile counties of 6.4 (95% CI, 6.2-6.7). The gap between the highest and lowest counties for preterm births was 20.7 per 100 live births in 2007. Several counties in the Southeast consistently had the highest preterm birth rates compared with counties in California and New England, which had the lowest preterm birth rates. Although there was no statistically significant change in preterm birth rates between 2007 and 2019 at the national level (percent change, -5.0%; 95% CI, -10.7% to 0.9%), increases occurred in 15.4% (95% CI, 14.1%-16.9%) of counties. The absolute and relative geographic inequalities were similar across all maternal age groups. Higher quartile of the Social Vulnerability Index was associated with higher preterm birth rates (quartile 4 vs quartile 1 risk ratio, 1.34; 95% CI, 1.31-1.36), which persisted across the study period. Similar patterns were observed for early preterm birth rates.

Conclusions and Relevance

In this serial cross-sectional study of county-level preterm and early preterm birth rates, substantial geographic disparities were observed, which were associated with place-based social disadvantage. Stability in aggregated rates of preterm birth at the national level masked increases in nearly 1 in 6 counties between 2007 and 2019.

Sociodemographic, clinical and laboratory characteristics and risk factors for mortality of hospitalized COVID-19 patients at alternate care site: a Latin American experience

BACKGROUND

The establishment of Alternate Care Sites (ACS) helped the most severely impacted countries expand their response capability. The aim of this study was to evaluate the clinical characteristics and risk factors associated with the mortality of hospitalized COVID-19 patients at Alternate Care Site in Mexico City.

PATIENTS AND METHODS

A monocentric cohort study was conducted at Mexico City's Temporary Unit COVID-19 (UTC-19). Sociodemographic, clinical, laboratory and treatment variables were included in the analysis.

RESULTS

A total of 4865 patients were included, with a mean age of 49.33 years ± SD 15.28 years (IQR 38 to 60 years); 50.53% were women. 63.53% of the patients presented at least one comorbidity, the most frequent being: obesity (39.94%), systemic arterial hypertension (25.14%), and diabetes mellitus (21.52%). A total of 4549 patients (93.50%) were discharged due to improvement, 64 patients (1.31%) requested voluntary discharge, 39 patients (0.80%) were referred to another unit, and 213 patients (4.37%) died. Factors that were independently and significantly associated with death included male gender (odds ratio [OR], 1.60), age ≥ 50 years (OR 14.75), null or low schooling (OR 3.47), have at least one comorbidity (OR 3.26), atrial fibrillation (OR 22.14). In the multivariate analysis, the lymphopenia ≤ 1 × 103/μL (OR 1.91), and having required steroid treatment (OR 2.85), supplemental oxygen with high-flow nasal cannula (OR 3.12) or invasive mechanical ventilation (OR 42.52), was significantly associated with an increased risk of death.

CONCLUSIONS

This study identified the clinical characteristics and risk factors for mortality of hospitalized COVID-19 patients at ACS in Mexico City.KEY MESSAGESAn Alternate Care Site (ACS) is any building or structure that is temporarily converted or constructed for healthcare use during a public health emergency.Factors associated with death included male gender, age over 50 years, and lower educational attainment (elementary school or less).The findings corroborate the utility of the CALL score as a predictor of mortality; lymphopenia ≤1 × 103/μL was the most relevant biomarker.

Association Between Partisan Affiliation of State Governments and State Mortality Rates Before and During the COVID-19 Pandemic

Policy Points The increasing political polarization of states reached new heights during the COVID-19 pandemic, when response plans differed sharply across party lines. This study found that states with Republican governors and larger Republican majorities in legislatures experienced higher death rates during the COVID-19 pandemic-and in preceding years-but these associations often lost statistical significance after adjusting for the average income and health status of state populations and for the policy orientations of the states. Future research may help clarify whether the higher death rates in these states result from policy choices or have other explanations, such as the tendency of voters with lower incomes or poorer health to elect Republican candidates.

CONTEXT

Increasing polarization of states reached a high point during the COVID-19 pandemic, when the party affiliation of elected officials often predicted their policy response. The health consequences of these divisions are unclear. Prior studies compared mortality rates based on presidential voting patterns, but few considered the partisan orientation of state officials. This study examined whether the partisan orientation of governors or legislatures was associated with mortality outcomes during the COVID-19 pandemic.

METHODS

Data on deaths and the partisan orientation of governors and legislators were obtained from the Centers for Disease Control and Prevention and the National Conference of State Legislatures, respectively. Linear regression was used to measure the association between Republican representation (percentage of seats held) in legislatures and (1) age-adjusted, all-cause mortality rates (AAMRs) in 2015-2021 and (2) excess death rates during three phases of the COVID-19 pandemic, controlling for median household income, the prevalence of four risk factors (obesity, chronic obstructive pulmonary disease, heart attack, stroke), and state policy orientation. Associations between excess death rates and the governor's party were also examined.

FINDINGS

States with Republican governors or greater Republican representation in legislatures experienced higher AAMRs during 2015-2021, lower excess death rates during Phase 1 of the COVID-19 pandemic (weeks ending March 28, 2020, through June 13, 2020), and higher excess death rates in Phases 2 and 3 (weeks ending June 20, 2020, through April 30, 2022; p < 0.05). Most associations lost statistical significance after adjustment for control variables.

CONCLUSIONS

Mortality was higher in states with Republican governors and greater Republican legislative representation before and during much of the pandemic. Observed associations could be explained by the adverse effects of policy choices, reverse causality (e.g., popularity of Republican candidates in states with lower socioeconomic and health status), or unmeasured factors that predominate in states with Republican leaders.

Relationships between COVID-19 healthcare outcomes and county characteristics in the U.S. for Delta (B.1.617.2) and Omicron (B.1.1.529 and BA.1.1) variants

Background

COVID-19 is constantly evolving, and highly populated communities consist of many different characteristics that may contribute to COVID-19 health outcomes. Therefore, we aimed to (1) quantify the relationships between county characteristics and severe and non-severe county-level health outcomes related to COVID-19. We also aimed to (2) compare these relationships across time periods where the Delta (B.1.617.2) and Omicron (B.1.1.529 and BA.1.1) variants were dominant in the U.S.

Methods

We used multiple regression to measure the strength of relationships between healthcare outcomes and county characteristics in the 50 most populous U.S. counties.

Results

We found many different significant predictors including the proportion of a population vaccinated, median household income, population density, and the proportion of residents aged 65+, but mainly found that socioeconomic factors and the proportion of a population vaccinated play a large role in the dynamics of the spread and severity of COVID-19 in communities with high populations.

Discussion

The present study shines light on the associations between public health outcomes and county characteristics and how these relationships change throughout Delta and Omicron's dominance. It is important to understand factors underlying COVID-19 health outcomes to prepare for future health crises.

Determinants of differences in RT-PCR testing rates among Southeast Asian countries during the first six months of the COVID-19 pandemic

A positive correlation has been demonstrated between gross domestic product (GDP) per capita and COVID-19 tests per 1000 people. Although frequently used as an indicator of economic performance, GDP per capita does not directly reflect income distribution inequalities and imposed health costs. In this longitudinal ecological study, we aimed to determine if, besides GDP per capita, indicators relating to governance, public health measures enforcement, and health and research investment explain differences in RT-PCR testing rates among countries in Southeast Asia (SEA) during the first six months of the COVID-19 pandemic. Using open-access COVID-19 panel data, we estimated the effect of various indicators (GDP per capita, health expenditure per capita, number of researchers per one million population, corruption perceptions index, stringency index, regional authority index) on daily COVID-19 testing by performing fixed-effects negative binomial regression. After accounting for all indicators, the number of daily confirmed COVID-19 cases, and population density, the model provided a 2019 GDP per capita coefficient of 0.0046330 (95% CI: 0.0040171, 0.0052488; p <0.001), indicating that a rise in 2019 GDP per capita by 100 international dollars is associated with a 46.33% increase in the number of daily tests performed. Additionally, all indicators were significantly associated with the daily number of RT-PCR testing on multivariable analysis. In conclusion, we identified different country-level indicators significantly associated with differences in COVID-19 testing rates among SEA countries. Due to the study's ecological design, we caution on applying our results to the individual level given potential for systematic differences between the included countries. Additional investigation is likewise needed to understand how government expenditure on healthcare may have impacted COVID-19 testing capacity during the initial stages of the pandemic.

Income inequality and its association with COVID-19 cases and deaths: a cross-country analysis in the Eastern Mediterranean region

INTRODUCTION

There is limited evidence on the associations between economic and social disparities in the Eastern Mediterranean region (EMR) with COVID-19 infections and deaths. This study aims to investigate the relationship between income inequalities using Gini coefficients and COVID-19 cases and deaths per million population in the EMR countries.

METHODS

Country-level data on monthly COVID-19 cases and deaths between March 2020 and October 2021, along with data on selected confounders, were collected from publicly available databases. Mixed-effect negative binomial and inverse hyperbolic sine transformation regressions were estimated to examine the association.

RESULTS

The study showed that, in the EMR, a unit increase in Gini coefficient is associated with approximately 7.2% and 3.9% increase in COVID-19 cases and deaths per million population, respectively. The magnitude and direction of the association between income inequality and COVID-19 cases and deaths per-million population still remain the same after excluding four warzone countries from the analysis.

CONCLUSION

This increase in COVID-19 cases and deaths is underpinned by the fact that a large number of the population in the region is living in conditions of poverty, with inadequate housing, comorbidities and limited or virtually no access to essential healthcare services. Healthcare policy-makers across countries in the region need to implement effective interventions in areas of income inequality, where it may be linked to increasing the risk of COVID-19 cases and deaths.

Population Characteristics and Organ Procurement Organization Performance Metrics

Importance

In 2020, the Centers for Medicare & Medicaid Services (CMS) issued a final rule updating the Organ Procurement Organization (OPO) Conditions for Coverage. This rule evaluates OPO performance based on an unadjusted donation rate and an age-adjusted transplant rate; however, neither considers other underlying population differences.

Objective

To evaluate whether adjusting for age and/or area deprivation index yields the same tier assignments as the cause, age, and location consistent (CALC) tier used by CMS.

Design, Setting, and Participants

This retrospective cross-sectional study examined the performance of 58 OPOs from 2018 to 2020 across the entire US. A total of 12 041 778 death records were examined from the 2017 to 2020 National Center for Health Statistics' Restricted Vital Statistics Detailed Multiple Cause of Death files; 399 530 of these met the definition of potential deceased donor. Information about 42 572 solid organ donors from the Scientific Registry of Transplant Recipients was also used. Statistical analysis was performed from January 2017 to December 2020.

Exposure

Area deprivation of donation service areas and age of potential donors.

Main Outcome and Measures

OPO performance as measured by donation and transplant rates.

Results

A total of 399 530 potential deceased donors and 42 572 actual solid donor organs were assigned to 1 of 58 OPOs. Age and ADI adjustment resulted in 19.0% (11 of 58) to 31.0% (18 of 58) reclassification of tier ratings for the OPOs, with 46.6% of OPOs (27 of 58) changing tier ranking at least once during the 3-year period. Between 6.9% (4 of 58) and 12.1% (7 of 58) moved into tier 1 and up to 8.6% (5 of 58) moved into tier 3.

Conclusions and Relevance

This cross-sectional study of population characteristics and OPO performance metrics found that adjusting for area deprivation and age significantly changed OPO measured performance and tier classifications. These findings suggest that underlying population characteristics may alter processes of care and characterize donation and transplant rates independent of OPO performance. Risk adjustment accounting for population characteristics warrants consideration in prospective policy and further evaluation of quality metrics.

Early trends in ECMO mortality during the first quarters of 2019 and 2020: Could we have predicted the onset of the pandemic?

OBJECTIVE

To compare mortality trends in patients requiring Extracorporeal Membrane Oxygenation (ECMO) support between the first quarters of 2019 and 2020 and determine whether these trends might have predicted the severe acute respiratory syndrome coronavirus-2 (SARS)-Cov-2 pandemic in the United States.

METHODS

We analyzed 5% Medicare claims data at aggregate, state, hospital, and encounter levels using MS-DRG (Medicare Severity-Diagnosis Related Group) codes for ECMO, combining state-level data with national census data. Necessity and sufficiency relations associated with change in mortality between the 2 years were modeled using qualitative comparative analysis (QCA). Multilevel, generalized linear modeling was used to evaluate mortality trends.

RESULTS

Based on state-level data, there was a 3.36% increase in mortality between 2019 and 2020. Necessity and sufficiency evaluation of aggregate data at state and institutional levels did not identify any association or combinations of risk factors associated with this increase in mortality. However, multilevel and generalized linear models using disaggregated patient-level data to evaluate institution mortality and patient death, identified statistically significant differences between the first (p = .019) and second (p = .02) months of the 2 years, the first and second quarters (p < .001 and p = .042, respectively), and the first 6 months (p < .001) of 2019 and 2020.

CONCLUSION

Mortality in ECMO patients increased significantly during the first quarter of 2020 and may have served as an early warning of the SARS-Cov-2 pandemic. Granular data shared in real-time may be used to better predict public health threats.

The Effect of COVID-19 on Dual-Eligible Beneficiaries: A Scoping Review

OBJECTIVES

To examine the impact of COVID-19 on clinical health outcomes and health-related social needs among Medicaid-Medicare dual-eligible beneficiaries.

DESIGN

Scoping review.

SETTING AND PARTICIPANTS

Dual eligibles during COVID-19.

METHODS

We performed a comprehensive scoping review including observational studies, clinical trials, and original empirical research studies of PubMed and CINAHL. We generated a list of terms related to programs that both serve dual eligibles and address our desired outcomes. With the assistance of a medical librarian, we identified relevant abstracts published during COVID-19 meeting our inclusion criteria. We performed full-text reviews of relevant abstracts and selected the final studies. We extracted the study population, design, and major findings, then conducted thematic analysis.

RESULTS

1100 articles were identified, with 439 deemed relevant. On full text-review, 15 articles met inclusion criteria representing more than 86 million Medicare beneficiaries. No studies were specific only to dual eligibles. Topic areas included in this review include COVID-19 case counts (2 articles), mortality (8 articles), hospitalizations (7 articles), food insecurity (1 article), self-reported mental health (1 article), and social connectedness (2 articles). Dual eligibles had disparate COVID-19-related outcomes from Medicare-only enrollees in 12 of 15 studies. Studies show higher mortality for dual eligibles overall, but this was not true for dual eligibles in nursing homes and assisted living communities. Dual eligibles were more likely to experience food insecurity. More favorably, dual eligibles reported greater social connectedness.

CONCLUSIONS AND IMPLICATIONS

Dual eligibles had different outcomes from Medicare-only recipients in multiple health outcomes and health-related social needs during COVID-19, but studies are limited, particularly in terms of health-related social needs. Future work focusing on outcomes only among dual-eligible beneficiaries, integrated care programs, and fiscal alignment between Medicare and Medicaid plans may help stakeholders address health needs specific to dual eligibles.

Patient-facing job role is associated with SARS-CoV-2 positivity among healthcare workers in long term care facilities in Minnesota, August-December, 2020

OBJECTIVE

Healthcare workers (HCWs) in long-term care facilities (LTCFs) are disproportionately affected by severe acute respiratory coronavirus virus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19). To characterize factors associated with SARS-CoV-2 positivity among LTCF HCWs, we performed a retrospective cohort study among HCWs in 32 LTCFs in the Minneapolis-St Paul region.

METHODS

We analyzed the outcome of SARS-CoV-2 polymerase chain reaction (PCR) positivity among LTCF HCWs during weeks 34-52 of 2020. LTCF and HCW-level characteristics, including facility size, facility risk score for resident-HCW contact, and resident-facing job role, were modeled in univariable and multivariable generalized linear regressions to determine their association with SARS-CoV-2 positivity.

RESULTS

Between weeks 34 and 52, 440 (20.7%) of 2,130 unique HCWs tested positive for SARS-CoV-2 at least once. In the univariable model, non-resident-facing HCWs had lower odds of infection (odds ratio [OR], 0.50; 95% confidence interval [CI], 0.36-0.70). In the multivariable model, the odds remained lower for non-resident-facing HCW (OR, 0.50; 95% CI, 0.36-0.71), and those in medium- versus low-risk facilities experienced higher odds of testing positive for SARS-CoV-2 (OR, 1.47; 95% CI, 1.08-2.02).

CONCLUSIONS

Our findings suggest that COVID-19 cases are related to contact between HCW and residents in LTCFs. This association should be considered when formulating infection prevention and control policies to mitigate the spread of SARS-CoV-2 in LTCFs.

Evolution of COVID-19 Health Disparities in Arizona

COVID-19 burdens are disproportionally high in underserved and vulnerable communities in Arizona. As the pandemic progressed, it is unclear if the initial associated health disparities have changed. This study aims to elicit the dynamic landscape of COVID-19 disparities at the community level and identify newly emerging vulnerable subpopulations. Findings from this study can inform interventions to increase health equity among minoritized communities in the Southwest, other regions of the US, and globally. We compiled biweekly COVID-19 case counts of 274 zip code tabulation areas (ZCTAs) in Arizona from October 21, 2020, to November 25, 2021, a time spanning multiple waves of COVID-19 case growth. Within each biweekly period, we tested the associations between the growth rate of COVID-19 cases and the population composition in a ZCTA including race/ethnicity, income, employment, and age using multiple regression analysis. We then compared the associations across time periods to discover temporal patterns of health disparities. The association between the percentage of Latinx population and the COVID-19 growth rate was positive before April 2021 but gradually converted to negative afterwards. The percentage of Black population was not associated with the COVID-19 growth rate at the beginning of the study but became positive after January 2021 which persisted till the end of the study period. Young median age and high unemployment rate emerged as new risk factors around mid-August 2021. Based on these findings, we identified 37 ZCTAs that were highly vulnerable to future fast escalation of COVID-19 cases. As the pandemic progresses, vulnerabilities associated with Latinx ethnicity improved gradually, possibly bolstered by culturally responsive programs in Arizona to support Latinx. Still communities with disadvantaged social determinants of health continued to struggle. Our findings inform the need to adjust current resource allocations to support the design and implementation of new interventions addressing the emerging vulnerabilities at the community level.

Excess mortality in 22 European countries in 2020 and 2021: relationship with socioeconomic indicators

AIMS

For better understanding of the impact of COVID-19 pandemic on mortality in different countries, we studied the excess mortality from any cause during 2020 and 2021 in 22 European countries, and its relationship with three socioeconomic variables: life expectancy at birth in 2019, per capita income, and low education level.

METHODS

Using an ecological design, we analyzed excess mortality data between January 2020 and December 2021 in 22 European countries, obtained from the EuroMOMO surveillance system. Using weekly Z-score data for each country, we estimated the annual average deviation in mortality during 2020 and 2021 for each country. We analyzed possible relationships between the excess mortality and three independent variables: gross domestic product per capita (GDPpc) in 2020, life expectancy at birth in 2019, and proportion of the population over age 18 years with a lower than secondary education level in 2018.

RESULTS

In the 22 European countries analyzed, the total number of excess deaths in 2020 and 2021 was 800,011 (11%) more than expected, with deaths among those aged 65 years and over accounting for 87.66% of these. Excess mortality was higher in 2020, especially in Spain, UK, Italy, and France. In 2021, excess mortality was highest in Hungary, the Netherlands, France, and Germany. Excess mortality during 2021 was inversely correlated with life expectancy (r =-.489, p = .021) and income level (r =-.550, p = .008).

CONCLUSION

Reducing socioeconomic inequalities among countries not only improves conditions of most disadvantages but also will help to reduce excess of mortality from future pandemics.

Healthcare service disruption in 14 Latin American and Caribbean countries during the COVID-19 pandemic: Analysis of household phone surveys, 2020-2021

Background

The coronavirus 2019 (COVID-19 pandemic) and associated responses have significantly disrupted healthcare. We aimed to estimate the magnitude of and reasons for households reporting healthcare disruption in 14 Latin America and the Caribbean (LAC) region countries from mid-2020 to mid-2021, and its relationship with country contextual factors.

Methods

We used COVID-19 high-frequency phone surveys (HFPS) conducted in 14 LAC countries in three rounds in 2020 and one in 2021. We classified the reasons reported for healthcare disruption into four groups: concerns about contracting COVID-19, healthcare supply constraints, financial reasons, and public health measures (PHMs). We used bivariate and multivariate regressions to examine correlates of reported healthcare disruption with the above groups and country context as control variables.

Results

On average, 20% of households reported a disruption in May-June 2020 (45% to 10% at country level), dropping to 9% in June-July 2020 (31% to 3%) and July-August 2020 (26% to 3%), and declining to 3% in May-July 2021 (11% to 1%). The most common reason reported for disruption was healthcare supply constraints, followed by concerns about contracting COVID-19, PHM, and financial reasons. In multivariable regression analyses, we found that a higher incidence of new COVID-19 cases (regression coefficient (β) = 0.018, P < 0.01), stricter PHM (β = 0.002, P < 0.01), fewer hospital beds per population (β = -0.011, P < 0.01), and lower out-of-pocket health spending (β = -0.0008, P < 0.01) were associated with higher levels of disrupted care. A higher care disruption was associated with a lower gross domestic product (GDP) per person (β = -0.00001, P < 0.01) and lower population density (β = -0.056, P < 0.01).

Conclusions

Healthcare services for households in LAC were substantially disrupted during the COVID-19 pandemic. Findings about supply and financial constraints can inform the recovery of postponed healthcare services, while public health and contextual factors findings can inform future health system resilience efforts in LAC and elsewhere.

Discovering new pathways toward integration between health and sustainable development goals with natural language processing and network science

BACKGROUND

Research on health and sustainable development is growing at a pace such that conventional literature review methods appear increasingly unable to synthesize all relevant evidence. This paper employs a novel combination of natural language processing (NLP) and network science techniques to address this problem and to answer two questions: (1) how is health thematically interconnected with the Sustainable Development Goals (SDGs) in global science? (2) What specific themes have emerged in research at the intersection between SDG 3 ("Good health and well-being") and other sustainability goals?

METHODS

After a descriptive analysis of the integration between SDGs in twenty years of global science (2001-2020) as indexed by dimensions.ai, we analyze abstracts of articles that are simultaneously relevant to SDG 3 and at least one other SDG (N = 27,928). We use the top2vec algorithm to discover topics in this corpus and measure semantic closeness between these topics. We then use network science methods to describe the network of substantive relationships between the topics and identify 'zipper themes', actionable domains of research and policy to co-advance health and other sustainability goals simultaneously.

RESULTS

We observe a clear increase in scientific research integrating SDG 3 and other SDGs since 2001, both in absolute and relative terms, especially on topics relevant to interconnections between health and SDGs 2 ("Zero hunger"), 4 ("Quality education"), and 11 ("Sustainable cities and communities"). We distill a network of 197 topics from literature on health and sustainable development, with 19 distinct network communities - areas of growing integration with potential to further bridge health and sustainability science and policy. Literature focused explicitly on the SDGs is highly central in this network, while topical overlaps between SDG 3 and the environmental SDGs (12-15) are under-developed.

CONCLUSION

Our analysis demonstrates the feasibility and promise of NLP and network science for synthesizing large amounts of health-related scientific literature and for suggesting novel research and policy domains to co-advance multiple SDGs. Many of the 'zipper themes' identified by our method resonate with the One Health perspective that human, animal, and plant health are closely interdependent. This and similar perspectives will help meet the challenge of 'rewiring' sustainability research to co-advance goals in health and sustainability.

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

BACKGROUND

Although many studies have found a high correlation between socio-economic inequalities and risk of COVID-19 deaths, there is a reason to believe that much of this association is the product of differing levels of education.

STUDY DESIGN

We use a multi-level negative binomial regression model for analyzing COVID-19 mortality.

METHODS

We present multivariate models of fortnightly (n = 60) COVID-19 deaths in 3108 US counties for the period January 20, 2020, to May 10, 2022. We model the direct (unmediated) effect of education, controlling for economy, race, geography, lack of vaccination, political orientation (vote Republican), poor health, and lack of preventative health behavior.

RESULTS

After controlling for correlated risk factors and indirect mechanisms that mediate education's impact on COVID-19 mortality, we find a strong direct (unmediated) correlation between low education and COVID-19 mortality (incidence rate ratio = 1.17; 95% confidence interval: 1.15, 1.20). We theorize that this correlation reflects education's relationship with (1) collective cultures, such as norms of mask wearing, and (2) individual literacy, such as ability to engage with scientific communication.

CONCLUSIONS

Low education is strongly correlated with COVID-19 deaths, with an effect size of a university degree comparable to that of being aged >65 years. If this correlation is indeed causal, then it would imply that low education accounts for between 1 in 10 and 1 in 7 deaths in low-education counties. Education should be conceptualized as a potential high-risk factor for COVID-19 death and be taken into account when attempting to combat COVID-19 in disadvantaged communities. The effect of education cannot be reduced to its impact on vaccination or correlation with poor health or economic status, but it seems likely that low-education communities have collective cultures that expose individuals to greater risks and lack of individual literacy that limits engagement with public health messaging.

Risk factors for COVID-19 mortality among telehealth patients in Bangladesh: A prospective cohort study

BACKGROUND AND OBJECTIVE

Estimating the contribution of risk factors of mortality due to COVID-19 is particularly important in settings with low vaccination coverage and limited public health and clinical resources. Very few studies of risk factors of COVID-19 mortality used high-quality data at an individual level from low- and middle-income countries (LMICs). We examined the contribution of demographic, socioeconomic and clinical risk factors of COVID-19 mortality in Bangladesh, a lower middle-income country in South Asia.

METHODS

We used data from 290,488 lab-confirmed COVID-19 patients who participated in a telehealth service in Bangladesh between May 2020 and June 2021, linked with COVID-19 death data from a national database to study the risk factors associated with mortality. Multivariable logistic regression models were used to estimate the association between risk factors and mortality. We used classification and regression trees to identify the risk factors that are the most important for clinical decision-making.

FINDINGS

This study is one of the largest prospective cohort studies of COVID-19 mortality in a LMIC, covering 36% of all lab-confirmed COVID-19 cases in the country during the study period. We found that being male, being very young or elderly, having low socioeconomic status, chronic kidney and liver disease, and being infected during the latter pandemic period were significantly associated with a higher risk of mortality from COVID-19. Males had 1.15 times higher odds (95% Confidence Interval, CI: 1.09, 1.22) of death compared to females. Compared to the reference age group (20-24 years olds), the odds ratio of mortality increased monotonically with age, ranging from an odds ratio of 1.35 (95% CI: 1.05, 1.73) for ages 30-34 to an odds ratio of 21.6 (95% CI: 17.08, 27.38) for ages 75-79 year group. For children 0-4 years old the odds of mortality were 3.93 (95% CI: 2.74, 5.64) times higher than 20-24 years olds. Other significant predictors were severe symptoms of COVID-19 such as breathing difficulty, fever, and diarrhea. Patients who were assessed by a physician as having a severe episode of COVID-19 based on the telehealth interview had 12.43 (95% CI: 11.04, 13.99) times higher odds of mortality compared to those assessed to have a mild episode. The finding that the telehealth doctors' assessment of disease severity was highly predictive of subsequent COVID-19 mortality, underscores the feasibility and value of the telehealth services.

CONCLUSIONS

Our findings confirm the universality of certain COVID-19 risk factors-such as gender and age-while highlighting other risk factors that appear to be more (or less) relevant in the context of Bangladesh. These findings on the demographic, socioeconomic, and clinical risk factors for COVID-19 mortality can help guide public health and clinical decision-making. Harnessing the benefits of the telehealth system and optimizing care for those most at risk of mortality, particularly in the context of a LMIC, are the key takeaways from this study.

Socioeconomic disparities and concentration of the spread of the COVID-19 pandemic in the province of Quebec, Canada

BACKGROUND

Recent studies suggest that the risk of SARS-CoV-2 infection may be greater in more densely populated areas and in cities with a higher proportion of persons who are poor, immigrant, or essential workers. This study examines spatial inequalities in SARS-CoV-2 exposure in a health region of the province of Quebec in Canada.

METHODS

The study was conducted on the 1206 Canadian census dissemination areas in the Capitale-Nationale region of the province of Quebec. The observation period was 21 months (March 2020 to November 2021). The number of cases reported daily in each dissemination area was identified from available administrative databases. The magnitude of inequalities was estimated using Gini and Foster-Greer-Thorbecke (FGT) indices. The association between transmission and socioeconomic deprivation was identified based on the concentration of transmission in socially disadvantaged areas and on nonparametric regressions relating the cumulative incidence rate by area to ecological indicators of spatial disadvantage. Quantification of the association between median family income and degree of exposure of dissemination areas was supplemented by an ordered probit multiple regression model.

RESULTS

Spatial disparities were elevated (Gini = 0.265; 95% CI [0.251, 0.279]). The spread was more limited in the less densely populated areas of the Quebec City agglomeration and outlying municipalities. The mean cumulative incidence in the subsample made up of the areas most exposed to the pandemic was 0.093. The spread of the epidemic was concentrated in the most disadvantaged areas, especially in the densely populated areas. Socioeconomic inequality appeared early and increased with each successive pandemic wave. The models showed that areas with economically disadvantaged populations were three times more likely to be among the areas at highest risk for COVID-19 (RR = 3.55; 95% CI [2.02, 5.08]). In contrast, areas with a higher income population (fifth quintile) were two times less likely to be among the most exposed areas (RR = 0.52; 95% CI [0.32, 0.72]).

CONCLUSION

As with the H1N1 pandemics of 1918 and 2009, the SARS-CoV-2 pandemic revealed social vulnerabilities. Further research is needed to explore the various manifestations of social inequality in relation to the pandemic.

Lessons Learned from Health Disparities in Coronavirus Disease-2019 in the United States

In the United States, the coronavirus disease-2019 (COVID-19) pandemic has disproportionally affected Black, Latinx, and Indigenous populations, immigrants, and economically disadvantaged individuals. Such historically marginalized groups are more often employed in low-wage jobs without health insurance and have higher rates of infection, hospitalization, and death from COVID-19 than non-Latinx White individuals. Mistrust in the health care system, language barriers, and limited health literacy have hindered vaccination rates in minorities, further exacerbating health disparities rooted in structural, institutional, and socioeconomic inequities. In this article, we discuss the lessons learned over the last 2 years and how to mitigate health disparities moving forward.

Explaining the higher COVID-19 mortality rates among disproportionately Black counties: A decomposition analysis

Background

Why is COVID-19 mortality higher in counties with a disproportionately large (>13.4%) share of Black residents (hereafter "Black counties") relative to others ("non-Black counties")? Existing literature points to six categories of determinants: (1) social distancing, (2) COVID-19 testing, (3) socioeconomic characteristics, (4) environmental characteristics, (5) prevalence of (pre-existing) chronic health conditions, and (6) demographic characteristics. The relative importance of these determinants has not yet been thoroughly examined.

Methods

We built a dataset consisting of 21 sub-indicators across the six categories of determinants for 3108 US counties and their COVID-19 mortality over the period of January 22, 2020-December 31, 2020. Applying the Gelbach's decomposition, we quantified which determinants were most (or least) associated with the COVID-19 mortality disparity between Black and non-Black counties.

Results

We find that COVID-19 death rates were 26 percent higher in Black counties compared to non-Black counties. This disparity was almost completely explained by the six categories of determinants included in our model. Decomposition analyses indicate that county-level demographic and population health characteristics explained most of this disparity. Among all sub-indicators considered, the greater proportion of females and smaller proportion of rural residents in Black counties were the two largest contributors to the COVID-19 mortality gap between Black and non-Black counties. Proportions of diabetic residents, uninsured residents, and the degree of income inequality also significantly contributed to the gap in COVID-19 mortality.

Conclusion

The COVID-19 mortality gap between Black and non-Black counties was largely explained by pre-pandemic differences in demographic and population health characteristics. Policies aiming to reduce the prevalence of chronic conditions and uninsured residents in Black counties would have helped narrow the COVID-19 mortality gap between Black and non-Black counties in 2020.

Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States

BACKGROUND

COVID-19 is an important public health concern due to its high morbidity, mortality and socioeconomic impact. Its burden varies by geographic location affecting some communities more than others. Identifying these disparities is important for guiding health planning and service provision. Therefore, this study investigated geographical disparities and temporal changes of the percentage of positive COVID-19 tests and COVID-19 incidence risk in North Dakota.

METHODS

COVID-19 retrospective data on total number of tests and confirmed cases reported in North Dakota from March 2020 to September 2021 were obtained from the North Dakota COVID-19 Dashboard and Department of Health, respectively. Monthly incidence risks of the disease were calculated and reported as number of cases per 100,000 persons. To adjust for geographic autocorrelation and the small number problem, Spatial Empirical Bayesian (SEB) smoothing was performed using queen spatial weights. Identification of high-risk geographic clusters of percentages of positive tests and COVID-19 incidence risks were accomplished using Tango's flexible spatial scan statistic. ArcGIS was used to display and visiualize the geographic distribution of percentages of positive tests, COVID-19 incidence risks, and high-risk clusters.

RESULTS

County-level percentages of positive tests and SEB incidence risks varied by geographic location ranging from 0.11% to 13.67% and 122 to 16,443 cases per 100,000 persons, respectively. Clusters of high percentages of positive tests were consistently detected in the western part of the state. High incidence risks were identified in the central and south-western parts of the state, where significant high-risk spatial clusters were reported. Additionally, two peaks (August 2020-December 2020 and August 2021-September 2021) and two non-peak periods of COVID-19 incidence risk (March 2020-July 2020 and January 2021-July 2021) were observed.

CONCLUSION

Geographic disparities in COVID incidence risks exist in North Dakota with high-risk clusters being identified in the rural central and southwest parts of the state. These findings are useful for guiding intervention strategies by identifying high risk communities so that resources for disease control can be better allocated to communities in need based on empirical evidence. Future studies will investigate predictors of the identified disparities so as to guide planning, disease control and health policy.

COVID-19 Risk by Workers' Occupation and Industry in the United States, 2020‒2021

Objectives. To assess the risk of COVID-19 by occupation and industry in the United States. Methods. Using the 2020-2021 National Health Interview Survey, we estimated the risk of having had a diagnosis of COVID-19 by workers' industry and occupation, with and without adjustment for confounders. We also examined COVID-19 period prevalence by the number of workers in a household. Results. Relative to workers in other industries and occupations, those in the industry "health care and social assistance" (adjusted prevalence ratio = 1.23; 95% confidence interval = 1.11, 1.37), or in the occupations "health practitioners and technical," "health care support," or "protective services" had elevated risks of COVID-19. However, compared with nonworkers, workers in 12 of 21 industries and 11 of 23 occupations (e.g., manufacturing, food preparation, and sales) were at elevated risk. COVID-19 prevalence rose with each additional worker in a household. Conclusions. Workers in several industries and occupations with public-facing roles and adults in households with multiple workers had elevated risk of COVID-19. Public Health Implications. Stronger workplace protections, paid sick leave, and better health care access might mitigate working families' risks from this and future pandemics. (Am J Public Health. Published online ahead of print April 13, 2023:e1-e10. https://doi.org/10.2105/AJPH.2023.307249).

Place, Race, and Case: Examining Racialized Economic Segregation and COVID-19 in Louisiana

Early COVID-19 pandemic data suggested racial/ethnic minority and low-income earning people bore the greatest burden of infection. Structural racism, the reinforcement of racial and ethnic discrimination via policy, provides a framework for understanding disparities in health outcomes like COVID-19 infection. Residential racial and economic segregation is one indicator of structural racism. Little attention has been paid to the relationship of infection to relative overall concentrations of risk (i.e., segregation of the most privileged from the most disadvantaged). We used ordinary least squares and geographically weighted regression models to evaluate the relationship between racial and economic segregation, measured by the Index of Concentration at the Extremes, and COVID-19 cases in Louisiana. We found a significant global association between racial segregation and cumulative COVID-19 case rate in Louisiana and variation across the state during the study period. The northwest and central regions exhibited a strong negative relationship indicating greater risk in areas with high concentrations of Black residents. On the other hand, the southeastern part of the state exhibited more neutral or positive relationships indicating greater risk in areas with high concentrations of White residents. Our findings that the relationship between racial segregation and COVID-19 cases varied within a state further support evidence that social and political determinants, not biological, drive racial disparities. Small area measures and measures of polarization provide localized information better suited to tailoring public health policy according to the dynamics of communities at the census tract level, which may lead to better health outcomes.

A person-centered approach to capture health disparities and multidimensional impact of COVID-related stressors

The COVID-19 pandemic has influenced people's lives in diverse ways. The authors utilized latent class analysis (LCA), a person-centered approach, to examine distinct patterns of COVID-related stressors and their associations with alcohol-related, mental health, and quality of life outcomes. Participants were 463 adults who completed the baseline assessment of the National Institute on Alcohol Abuse and Alcoholism COVID-19 Pandemic Impact on Alcohol Study from June 2020 to January 2022. Using cross-sectional data, three analytic methods (continuous sum score, categorical grouping, and LCA) were applied to model 17 COVID-related stressors. Regression analyses indicated higher COVID-related stress and endorsement of four or more COVID-related stressors were generally associated with worse health-related outcomes. LCA revealed four classes: Class 1: Minimal COVID-Related Impact (51.6%); Class 2: Work Interruptions (24.8%); Class 3: Family/Friends Affected by COVID (14.5%); and Class 4: Serious Financial Stress (9.1%). Racial/ethnic minorities were more likely to be in Class 3, whereas individuals with more years of education and higher income were less likely to be in Class 4. Individuals with a history of alcohol use disorder were more likely to be in Classes 2 and 4. Compared with Class 1, Class 4 reported highest levels of perceived stress, problematic alcohol use, anxiety symptoms, depressive symptoms, alcohol craving, loneliness, drinking to cope, and lowest levels of physical, psychological, social, and environment quality of life. COVID-related stressors disproportionately affected minority and vulnerable groups. Individuals who experienced multiple financial stressors had the greatest risk for negative health-related outcomes and may benefit from holistic interventions and community outreach. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Analysis of Road Transport Response to COVID-19 Pandemic in Nigeria and its Policy Implications

The COVID-19 pandemic presents a serious global health challenge to humanity in recent times. It has caused fundamental disruptions to the global transportation system, supply chains, and trade. The impact on the transport sector resulting from lockdowns has led to huge losses in revenue. At the moment there are limited studies of the road transport sector response to the COVID-19 pandemic. This paper fills this gap using Nigeria as a case study area. A mixed method involving both qualitative and quantitative research was employed. Principal Component Analysis and Multiple Criteria Analysis were used to analyze the data. The results suggest that road transport operators strongly (90.7%) believe that 51 adopted new technologies/innovations, processes, and procedures will keep them and passengers safe from the COVID-19 pandemic in Nigeria. A breakdown shows that observing the lockdown directive is perceived by road transport operators as the most effective response to the pandemic. The breakdown continues in descending order thus: COVID-19 safety protocols, environmental sanitation, and promotion of hygiene, information technology, facemask, and social distancing. Others are public enlightenment, palliative, inclusion, and mass media. This indicates that non-pharmaceutical measures are very effective in the fight against the pandemic. This finding leverages support for the application of non-pharmaceutical guidelines in containing the COVID-19 pandemic in Nigeria.

The impact of access to financial services on mitigating COVID-19 mortality globally

The COVID-19 pandemic has disproportionately affected different social and demographic groups, deepening the negative health implications of social and economic inequalities and highlighting the importance of social determinants of health. Despite a deep literature on pandemic-related disparities, specifically regarding social determinants and health outcomes, the influence of the accessibility of financial services on health outcomes during COVID-19 remains largely unexplored. Modeling (pre-omicron) COVID-19 mortality across 142 nations, we assess the impact of national-level usage and access to formal financial services. Two financial access indexes constructed through principal component analysis capture (1) usage of and access to formal financial tools and (2) reliance on alternative and informal financial tools. On average, nations with higher pre-pandemic use of and access to formal financial services had substantially lower population mortality risk from COVID-19, controlling for key population health, demographic, and socioeconomic covariates. The scale of effect is similar in magnitude-but opposite in direction-to major risk factors identified in previous literature, such as lung cancer, hypertension, and income inequality. Findings suggest that financial services deserve greater attention both in the public health literature related to COVID-19 and more broadly in policy discussions about fostering better public health overall.

Resources availability and COVID-19 mortality among US counties

The COVID-19 was declared a pandemic by WHO on 03/2020 has claimed millions of lives worldwide. The US leads all countries in COVID-19-related deaths. Individual level (preexisting conditions and demographics) and county-level (availability of resources) factors have been attributed to increased risk of COVID-19-related deaths. This study builds on previous studies to assess the relationship between county-level resources and COVID-19 mortality among 2,438 US counties. We merged 2019 data from AHA, AHRF, and USA FACTS. The dependent variable was the total number of COVID-19-related deaths. Independent variables included county-level resources: (1) hospital staffing levels (FTE RNs, hospitalists, and intensivists) per 10,000 population; (2) hospital capacity (occupancy rate, proportion of teaching hospitals, and number of airborne infection control rooms per 10,000 population); and (3) macroeconomic resources [per capita income and location (urban/rural)]. We controlled for population 65+, racial/ethnic minority, and COVID-19 deaths per 1,000 population. A negative binomial regression was used. Hospital staffing per 10,000 population {FTE RN [IRR = 0.997; CI (0.995-0.999)], FTE hospitalists [IRR = 0.936; CI (0.897-0.978)], and FTE intensivists [IRR = 0.606; CI (0.516-0.712)]} was associated with lower COVID-19-related deaths. Hospital occupancy rate, proportion of teaching hospitals, and total number of airborne infection control rooms per 10,000 population were positively associated with COVID-19-related deaths. Per capita income and being in an urban county were positively associated with COVID-19-related deaths. Finally, the proportion of 65+, racial/ethnic minorities, and the number of cases were positively associated with COVID-19-related deaths. Our findings suggest that focusing on maintaining adequate hospital staffing could improve COVID-19 mortality.

Race, employment, and the pandemic: An exploration of covariate explanations of COVID-19 case fatality rate variance

We derive a simple asymptotic approximation for the long-run case fatality rate of COVID-19 (alpha and delta variants) and show that these estimations are highly correlated to the interaction between US State median age and projected US unemployment rate (Adj. r2 = 60%). We contrast this to the high level of correlation between point (instantaneous) estimates of per state case fatality rates and the interaction of median age, population density and current unemployment rates (Adj. r2 = 50.2%). To determine whether this is caused by a "race effect," we then analyze unemployment, race, median age and population density across US states and show that adding the interaction of African American population and unemployment explains 53.5% of the variance in COVID case fatality rates for the alpha and delta variants when considering instantaneous case fatality rate. Interestingly, when the asymptotic case fatality rate is used, the dependence on the African American population disappears, which is consistent with the fact that in the long-run COVID does not discriminate on race, but may discriminate on access to medical care which is highly correlated to employment in the US. The results provide further evidence of the impact inequality can have on case fatality rates in COVID-19 and the impact complex social, health and economic factors can have on patient survival.

Association Between County-Level Natality and Income in the US, 2000-2020

Importance

It is generally accepted that birth rates are negatively associated with income. However, less is known about the nature and evolution of natality differences between high- and low-income counties and how these differences are associated with the recent decline in the birth rate overall in the US.

Objective

To quantify the association between county-level income and natality between 2000 and 2020 and explore how natality inequality is associated with recent nationwide natality declines.

Design, Setting, and Participants

This cross-sectional study included all births to US women aged 15 to 44 years among county-year-level observations with at least 100 women of the same age.

Exposures

County-level median household income rank.

Main Outcomes and Measures

Association between natality and income and between natality inequality and national natality trends. Income ventiles were used to rank counties from 1 to 20, with each ventile including 5% of the female population aged 15 to 44 years. A counterfactual simulation was used to estimate natality rates while holding natality inequality constant at its year 2000 value.

Results

A total of 86 679 356 births were aggregated to 65 554 county-year-level observations from 2000 to 2020. The analysis yielded 2 main results. First was the changing nature of inequality. Estimates of the natality income gradient, which reflect the association between county-level natality and county-level income, changed from -0.061 (95% CI, -0.200 to 0.078) in 2000 to -0.572 (95% CI, -0.678 to -0.466) in 2020, reflecting an increase in natality inequality. Intuitively, a negative gradient reflects that natality is lower in higher-income counties compared with lower-income counties. Second, the counterfactual simulation showed that there would have been an additional 3.5 million births (an increase of 4.1%) over the remainder of the study period had natality inequality remained constant at its year 2000 value. While this counterfactual is based on strong assumptions that are unlikely to hold in practice, it suggests that natality may have been higher during this period absent the rise in natality inequality.

Conclusions and Relevance

The findings of this cross-sectional study suggest that natality inequality has increased in recent years and is likely associated with nationwide natality declines. The causes of this inequality remain to be explored in future work.