A longitudinal study of convergence between Black and White COVID-19 mortality: A county fixed effects approach

Flexible scan statistic with a restricted likelihood ratio for optimized COVID-19 surveillance

Disease surveillance remains important for early detection of new COVID-19 variants. For this purpose, the World Health Organization (WHO) recommends integrating of COVID-19 surveillance with other respiratory diseases. This requires knowledge of areas with elevated risk, which in developing countries is lacking from the routine analyses. Focusing on Ghana, this study employed scan-statistic cluster analysis to uncover the spatial patterns of incidence and Case Fatality Rates (CFR) of COVID-19 based on reports covering the four pandemic waves in Ghana between 12 March 2020 and 28 February 2022. Applying flexible spatial scan statistic with restricted likelihood ratio, we examined the incidence and CFR clusters before and after adjustment for covariates. We used distance to the epicentre, proportion of the population aged ≥ 65, male proportion of the population and urban proportion of the population as the covariates. We identified 56 significant spatial clusters for incidence and 26 for CFR for all four waves of the pandemic. The Most Likely Clusters (MLCs) of incidence occurred in the districts in south-eastern Ghana, while the CFR ones occurred in districts in the central and the northeastern parts of the country. These districts could serve as sites for sentinel or genomic surveillance. Spatial relationships were identified between COVID-19 incidence covariates and the CFR. We observed closeness to the epicentre and high proportions of urban populations increased COVID-19 incidence, whiles high proportions of those aged ≥ 65 years increased the CFR. Accounting for the covariates resulted in changes in the distribution of the clusters. Both incidence and CFR due to COVID-19 were spatially clustered, and these clusters were affected by high proportions of the urban population, high proportions of the male population, high proportions of the population aged ≥ 65 years and closeness to the epicentre. Surveillance should target districts with elevated risk. Long-term control measures for COVID-19 and other contagious diseases should consider improving quality healthcare access and measures to reduce growth rates of urban populations.

Evolving patterns of COVID-19 mortality in US counties: A longitudinal study of healthcare, socioeconomic, and vaccination associations

The COVID-19 pandemic emphasized the need for pandemic preparedness strategies to mitigate its impacts, particularly in the United States, which experienced multiple waves with varying policies, population response, and vaccination effects. This study explores the relationships between county-level factors and COVID-19 mortality outcomes in the U.S. from 2020 to 2023, focusing on disparities in healthcare access, vaccination coverage, and socioeconomic characteristics. We conduct multi-variable rolling regression analyses to reveal associations between various factors and COVID-19 mortality outcomes, defined as Case Fatality Rate (CFR) and Overall Mortality to Hospitalization Rate (OMHR), at the U.S. county level. Each analysis examines the association between mortality outcomes and one of the three hierarchical levels of the Social Vulnerability Index (SVI), along with other factors such as access to hospital beds, vaccination coverage, and demographic characteristics. Our results reveal persistent and dynamic correlations between various factors and COVID-19 mortality measures. Access to hospital beds and higher vaccination coverage showed persistent protective effects, while higher Social Vulnerability Index was associated with worse outcomes persistently. Socioeconomic status and vulnerable household characteristics within the SVI consistently associated with elevated mortality. Poverty, lower education, unemployment, housing cost burden, single-parent households, and disability population showed significant associations with Case Fatality Rates during different stages of the pandemic. Vulnerable age groups demonstrated varying associations with mortality measures, with worse outcomes predominantly during the Original strain. Rural-Urban Continuum Code exhibited predominantly positive associations with CFR and OMHR, while it starts with a positive OMHR association during the Original strain. This study reveals longitudinal persistent and dynamic factors associated with two mortality rate measures throughout the pandemic, disproportionately affecting marginalized communities. The findings emphasize the urgency of implementing targeted policies and interventions to address disparities in the fight against future pandemics and the pursuit of improved public health outcomes.

Racial and Ethnic Disparities in Excess All-Cause Mortality in the First Year of the COVID-19 Pandemic

Research on the COVID-19 pandemic in the United States has consistently found disproportionately high mortality among ethnoracial minorities, but reports differ with respect to the magnitude of mortality disparities and reach different conclusions regarding which groups were most impacted. We suggest that these variations stem from differences in the temporal scope of the mortality data used and difficulties inherent in measuring race and ethnicity. To circumvent these issues, we link Social Security Administration death records for 2010 through 2021 to decennial census and American Community Survey race and ethnicity responses. We use these linked data to estimate excess all-cause mortality for age-, sex-, race-, and ethnicity-specific subgroups and examine ethnoracial variation in excess mortality across states and over the course of the pandemic's first year. Results show that non-Hispanic American Indians and Alaska Natives experienced the highest excess mortality of any ethnoracial group in the first year of the pandemic, followed by Hispanics and non-Hispanic Blacks. Spatiotemporal and age-specific ethnoracial disparities suggest that the socioeconomic determinants driving health disparities prior to the pandemic were amplified and expressed in new ways in the pandemic's first year to disproportionately concentrate excess mortality among racial and ethnic minorities.

Dying together: A convergence analysis of fatalities during COVID-19

Governments implemented countermeasures to mitigate the spread of the COVID-19 virus. This had a severe effect on the economy. We examine convergence patterns in the evolution of COVID-19 deaths across countries. We aim to investigate whether countries that implemented different measures managed to limit the number of COVID-19 deaths. We extend the most recent macro-growth convergence methodology to examine convergence of COVID-19 deaths. We combine a long memory stationarity framework with the maximal clique algorithm. This provides a rich and flexible club formation strategy that goes beyond the stationary/non stationary approach adopted in the previous literature. Our results suggest that strict measures (even belated) or an aggressive vaccination scheme can confine the spread of the disease while maintaining the strictness of the measures steady can lead to a burst of the virus. Finally, we observe that fiscal measures did not have an effect on the containment of the virus.

The relationship between staying at home during the pandemic and the number of conceptions: A national panel data analysis

hether the COVID-19 pandemic has changed fertility patterns is still an open question, as social isolation for long periods can impact the number of conceptions in many ways. We combine administrative data on all recent births in Brazil with daily data on individual location to estimate the relationship between the share of individuals staying close to their homes in each week and the number of conceptions in that same week, comparing municipalities with different social isolation patterns during the first semester of 2020. We find that conceptions unequivocally decline when social isolation increases. The effect is stronger for women who are between 21 and 25 years old and more educated, as well as for richer, larger, and more urban municipalities. COVID-19 is likely to change fertility across countries depending on the behavior of the population and on the lock-down measures implemented to fight the pandemic.

COVID-19 and All-Cause Mortality by Race, Ethnicity, and Age Across Five Periods of the Pandemic in the United States

Racial/ethnic and age disparities in COVID-19 and all-cause mortality during 2020 are well documented, but less is known about their evolution over time. We examine changes in age-specific mortality across five pandemic periods in the United States from March 2020 to December 2022 among four racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic Asian) for ages 35+. We fit Gompertz models to all-cause and COVID-19 death rates by 5-year age groups and construct age-specific racial/ethnic mortality ratios across an Initial peak (Mar-Aug 2020), Winter peak (Nov 2020-Feb 2021), Delta peak (Aug-Oct 2021), Omicron peak (Nov 2021-Feb 2022), and Endemic period (Mar-Dec 2022). We then compare to all-cause patterns observed in 2019. The steep age gradients in COVID-19 mortality in the Initial and Winter peak shifted during the Delta peak, with substantial increases in mortality at working ages, before gradually returning to an older age pattern in the subsequent periods. We find a disproportionate COVID-19 mortality burden on racial and ethnic minority populations early in the pandemic, which led to an increase in all-cause mortality disparities and a temporary elimination of the Hispanic mortality advantage at certain age groups. Mortality disparities narrowed over time, with racial/ethnic all-cause inequalities during the Endemic period generally returning to pre-pandemic levels. Black and Hispanic populations, however, faced a younger age gradient in all-cause mortality in the Endemic period relative to 2019, with younger Hispanic and Black adults in a slightly disadvantageous position and older Black adults in a slightly advantageous position, relative to before the pandemic.

Longitudinal trends (2011-2020) of premature mortality and years of potential life loss (YPLL) and associated covariates of the 62 New York State counties

BACKGROUND

New York State (NYS) is the 27^th largest state and the 4^th most populous state in the U.S., with close to 20 million people in 62 counties. Territories with diverse populations present the best opportunity to study health outcomes and associated covariates, and how these differ across different populations and groups. The County Health Ranking and Roadmaps (CHR&R) ranks counties by linking the population's characteristics and health outcomes and contextual factors in a synchronic approach.

METHODS

The goal of this study is to analyze the longitudinal trends in NYS counties of age-adjusted premature mortality rate and years of potential life loss rate (YPLL) from 2011-2020 using (CHR&R) data to identify similarities and trends among the counties of the state. This study used a weighted mixed regression model to analyze the longitudinal trend in health outcomes as a function of the time-varying covariates and clustered the 62 counties according to the trend over time in the covariates.

RESULTS

Four clusters of counties were identified. Cluster 1, which represents 33 of the 62 counties in NYS, contains the most rural counties and the least racially and ethnically diverse counties. Clusters 2 and 3 mirror each other in most covariates and Cluster 4 is comprised of 3 counties (Bronx, Kings/Brooklyn, Queens) representing the most urban and racial and ethnic diverse counties in the state.

CONCLUSION

The analysis clustered counties according to the longitudinal trends of the covariates, and by doing so identified clusters of counties that shared similar trends among the covariates, to later examine trends in the health outcomes through a regression model. The strength of this approach lies in the predictive feature of what is to come for the counties by understanding the covariates and setting prevention goals.

A Study of Cumulative COVID-19 Mortality Trends Associated with Ethnic-Racial Composition, Income Inequality, and Party Inclination among US Counties

This research analyzes the association between cumulative COVID-19 mortality and ethnic-racial composition, income inequality, and political party inclination across counties in the United States. The study extends prior research by taking a long view-examining cumulative mortality burdens over the first 900 days of the COVID-19 pandemic at five time points (via negative binomial models) and as trajectories of cumulative mortality trends (via growth curve models). The analysis shows that counties with a higher Republican vote share display a higher cumulative mortality, especially over longer periods of the pandemic. It also demonstrates that counties with a higher composition of ethnic-racial minorities, especially Blacks, bear a much higher cumulative mortality burden, and such an elevated burden would be even higher when a county has a higher level of income inequality. For counties with a higher proportion of Hispanic population, while the burden is lower than that for counties with a higher proportion of Blacks, the cumulative COVID-19 mortality burden still is elevated and compounded by income inequality, at any given time point during the pandemic.

The polarization of politics and public opinion and their effects on racial inequality in COVID mortality

Evidence from the early months of the COVID-19 pandemic in the U.S. indicated that the virus had vastly different effects across races, with black Americans faring worse on dimensions including illness, hospitalization and death. New data suggests that our understanding of the pandemic's racial inequities must be revised given the closing of the gap between black and white COVID-related mortality. Initial explanations for inequality in COVID-related outcomes concentrated on static factors-e.g., geography, urbanicity, segregation or age-structures-that are insufficient on their own to explain observed time-varying patterns in inequality. Drawing from a literature suggesting the relevance of political factors in explaining pandemic outcomes, we highlight the importance of political polarization-the partisan divide in pandemic-related policies and beliefs-that varies over time and across geographic units. Specifically, we investigate the role of polarization through two political factors, public opinion and state-level public health policies, using fine-grained data on disparities in public concern over COVID and in state containment/health policies to understand the changing pattern of inequality in mortality. We show that (1) apparent decreases in inequality are driven by increasing total deaths-mostly among white Americans-rather than decreasing mortality among black Americans (2) containment policies are associated with decreasing inequality, likely resulting from lower relative mortality among Blacks (3) as the partisan disparity in Americans who were "unconcerned" about COVID increased, racial inequality in COVID mortality decreased, generating the appearance of greater equality consistent with a "race to the bottom'' explanation as overall deaths increased and substantively swamping the effects of containment policies.

Early Childhood Education Teacher Well-Being: Performativity as a Means of Coping

Detrimental circumstances (e.g., poverty, homelessness) may affect parents, parenting, and children. These circumstances may lead to children being labeled "at risk" for school failure. To ameliorate this risk, more school and school earlier (e.g., Head Start) is offered. To improve child outcomes, Head Start teachers are expected to bolster children?s academic readiness in a manner that is beneficially warm, circulating warmth in their classrooms to sustain positive teacher-child relationships and the positive climate of the classroom. The Classroom Assessment Scoring System (CLASS; Pianta et al., 2008) is one tool by which these domains of warmth are assessed. There are, however, significant personal and professional stressors with which Head Start teachers contend which the CLASS (Pianta et al., 2008) does not consider in its scoring methods. Uplifting the voices of six Head Start teachers, the present study implemented individual and focus group interviews during the summer and fall months of 2020 during the COVID-19 pandemic, asking (a) What were the stories, histories, and lived experiences of these Head Start teachers with regard to stress and warmth in a time of crisis? and (b) How did these teachers understand and approach the CLASS (Pianta et al., 2008) and its measures of their warmth? Data demonstrated Head Start teachers engaged in a type of performativity to 1) mask their stress, potentially worsening their levels of stress in order to maintain warmth for their students' sake, and 2) outwit the prescribed CLASS (Pianta et al., 2008) observations. Implications and insights are discussed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10643-022-01387-2.

Sociodemographic and geographic disparities in excess fatal drug overdoses during the COVID-19 pandemic in California: A population-based study

Background

The coronavirus disease 2019 (COVID-19) pandemic is co-occurring with a drug addiction and overdose crisis.

Methods

We fit overdispersed Poisson models, accounting for seasonality and secular trends, to estimate the excess fatal drug overdoses (i.e., deaths greater than expected), using data on all deaths in California from 2016 to 2020.

Findings

Between January 5, 2020 and December 26, 2020, there were 8605 fatal drug overdoses—a 44% increase over the same period one year prior. We estimated 2084 (95% CI: 1925 to 2243) fatal drug overdoses were excess deaths, representing 5·28 (4·88 to 5·68) excess fatal drug overdoses per 100,000 population. Excess fatal drug overdoses were driven by opioids (4·48 [95% CI: 4·18 to 4·77] per 100,000), especially synthetic opioids (2·85 [95% CI: 2·56 to 3·13] per 100,000). The non-Hispanic Black and Other non-Hispanic populations were disproportionately affected with 10·1 (95% CI: 7·6 to 12·5) and 13·26 (95% CI: 11·0 to 15·5) excess fatal drug overdoses per 100,000 population, respectively, compared to 5·99 (95% CI: 5.2 to 6.8) per 100,000 population in the non-Hispanic white population. There was a steep, nonlinear educational gradient with the highest rate among those with only a high school degree. There was a strong spatial patterning with the highest levels of excess mortality in the southernmost region and consistently lower levels at progressively more northern latitudes (7·73 vs 1·96 per 100,000).

Interpretation

Fatal drug overdoses disproportionately increased in 2020 among structurally marginalized populations and showed a strong geographic gradient. Local, tailored public health interventions are urgently needed to reduce growing inequities in overdose deaths.

Funding

US National Institutes of Health and Department of Veterans Affairs.

COVID-19 Outbreak Management and Vaccination Strategy in The United States of America

Four months after the first case of COVID-19 was reported in the United States, the SARS-CoV-2 virus had spread to more than 90% of all counties. Although the transmission of the virus can be grossly mitigated through non-pharmaceutical interventions and public health measures, risks of future outbreaks, emergence of more infectious variants, and disruptions to socio-economic life will probably remain until effective vaccines are administered to large portions of the global population. An exceptional collaboration between governments and the scientific community has led to the authorization of eight vaccines globally for full use, four of which were funded and developed in the United States. In this paper, we contextualize epidemiological, political, and economic impacts of the COVID-19 vaccination strategy in the United States of America between 20 January 2020, to 5 May 2021, with a key focus on vaccine hesitancy and public-private partnerships.