The evolving roles of US political partisanship and social vulnerability in the COVID-19 pandemic from February 2020–February 2021

The COVID-19 pandemic has had intense, heterogeneous impacts on different communities and geographies in the United States. We explore county-level associations between COVID-19 attributed deaths and social, demographic, vulnerability, and political variables to develop a better understanding of the evolving roles these variables have played in relation to mortality. We focus on the role of political variables, as captured by support for either the Republican or Democratic presidential candidates in the 2020 elections and the stringency of state-wide governor mandates, during three non-overlapping time periods between February 2020 and February 2021. We find that during the first three months of the pandemic, Democratic-leaning and internationally-connected urban counties were affected. During subsequent months (between May and September 2020), Republican counties with high percentages of Hispanic and Black populations were most hardly hit. In the third time period –between October 2020 and February 2021– we find that Republican-leaning counties with loose mask mandates experienced up to 3 times higher death rates than Democratic-leaning counties, even after controlling for multiple social vulnerability factors. Some of these deaths could perhaps have been avoided given that the effectiveness of non-pharmaceutical interventions in preventing uncontrolled disease transmission, such as social distancing and wearing masks indoors, had been well-established at this point in time.

Decreasing Survey Response Rates in the Time of COVID-19: Implications for Analyses of Population Health and Health Inequities.

Objectives. To examine whether, and if so how, US national and state survey response rates changed after the onset of the COVID-19 pandemic. Methods. We compared the change in response rates between 2020 and 2019 of 6 (3 social and economic, 3 health focused) major US national surveys (2 with state response rates). Results. All the ongoing surveys except 1 reported relative decreases (∼29%) in response rates. For example, the household response rate to the US Census American Community Survey decreased from 86.0% in 2019 to 71.2% in 2020, and the response rate of the US National Health Interview Survey decreased from 60.0% to 42.7% from the first to the second quarter of 2020. For all surveys, the greatest decreases in response rates occurred among persons with lower income and lower education. Conclusions. Socially patterned decreases in response rates pose serious challenges and must be addressed explicitly in all studies relying on data obtained since the onset of the pandemic. Public Health Implications. Artifactual reduction of estimates of the magnitude of health inequities attributable to differential response rates could adversely affect efforts to reduce these inequities. (Am J Public Health. 2023;113(6):667-670. https://doi.org/10.2105/AJPH.2023.307267).

The evolving roles of US political partisanship and social vulnerability in the COVID-19 pandemic from February 2020-February 2021.

The COVID-19 pandemic has had intense, heterogeneous impacts on different communities and geographies in the United States. We explore county-level associations between COVID-19 attributed deaths and social, demographic, vulnerability, and political variables to develop a better understanding of the evolving roles these variables have played in relation to mortality. We focus on the role of political variables, as captured by support for either the Republican or Democratic presidential candidates in the 2020 elections and the stringency of state-wide governor mandates, during three non-overlapping time periods between February 2020 and February 2021. We find that during the first three months of the pandemic, Democratic-leaning and internationally-connected urban counties were affected. During subsequent months (between May and September 2020), Republican counties with high percentages of Hispanic and Black populations were most hardly hit. In the third time period -between October 2020 and February 2021- we find that Republican-leaning counties with loose mask mandates experienced up to 3 times higher death rates than Democratic-leaning counties, even after controlling for multiple social vulnerability factors. Some of these deaths could perhaps have been avoided given that the effectiveness of non-pharmaceutical interventions in preventing uncontrolled disease transmission, such as social distancing and wearing masks indoors, had been well-established at this point in time.

Relationship of political ideology of US federal and state elected officials and key COVID pandemic outcomes following vaccine rollout to adults: April 2021-March 2022.

Background: Scant research, including in the United States, has quantified relationships between the political ideologies of elected representatives and COVID-19 outcomes among their constituents. Methods: We analyzed observational cross-sectional data on COVID-19 mortality rates (age-standardized) and stress on hospital intensive care unit (ICU) capacity for all 435 US Congressional Districts (CDs) in a period of adult vaccine availability (April 2021-March 2022). Political metrics comprised: (1) ideological scores based on each US Representative's and Senator's concurrent overall voting record and their specific COVID-19 votes, and (2) state trifectas (Governor, State House, and State Senate under the same political party control). Analyses controlled for CD social metrics, population density, vaccination rates, the prevalence of diabetes and obesity, and voter political lean. Findings: During the study period, the higher the exposure to conservatism across several political metrics, the higher the COVID-19 age-standardized mortality rates, even after taking into account the CD's social characteristics; similar patterns occurred for stress on hospital ICU capacity for Republican trifectas and US Senator political ideology scores. For example, in models mutually adjusting for CD political and social metrics and vaccination rates, Republican trifecta and conservative voter political lean independently remained significantly associated with an 11%-26% higher COVID-19 mortality rate. Interpretation: Associations between the political ideologies of US federal elected officials and state concentrations of political party power with population health warrant greater consideration in public health analyses and monitoring dashboards. Funding: This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural Solutions.

Analyzing the myriad ways in which structural racism systemically generates health inequities requires engaging with the profound challenges of conceptualizing, operationalizing, and analyzing the very data deployed-i. e., racialized categories-to document racialized health inequities. This essay, written in the aftermath of the January 6, 2021 vigilante anti-democratic white supremacist assault on the US Capitol, calls attention to the two-edged sword of data at play, reflecting long histories of support for and opposition to white supremacy and scientific racism. As illustrated by both past and present examples, including COVID-19, at issue are both the non-use (Edge #1) and problematic use (Edge #2) of data on racialized groups. Recognizing that structural problems require structural solutions, in this essay I propose a new two-part institutional mandate regarding the reporting and analysis of publicly-funded work involving racialized groups and health data and documentation as to why the proposed mandates are feasible. Proposal/part 1 is to implement enforceable requirements that all US health data sets and research projects supported by government funds must explicitly explain and justify their conceptualization of racialized groups and the metrics used to categorize them. Proposal/part 2 is that any individual-level health data by membership in racialized groups must also be analyzed in relation to relevant data about racialized societal inequities. A new opportunity arises as US government agencies re-engage with their work, out of the shadow of white grievance politics cast by the Trump Administration, to move forward with this structural proposal to aid the work for health equity.

Correction: Variation in racial/ethnic disparities in COVID-19 mortality by age in the United States: A cross-sectional study.

[This corrects the article DOI: 10.1371/journal.pmed.1003402.].

COVID-19 and Overall Mortality Inequities in the Surge in Death Rates by Zip Code Characteristics: Massachusetts, January 1 to May 19, 2020.

Objectives. To address evidence gaps in COVID-19 mortality inequities resulting from inadequate race/ethnicity data and no socioeconomic data.Methods. We analyzed age-standardized death rates in Massachusetts by weekly time intervals, comparing rates for January 1 to May 19, 2020, with the corresponding historical average for 2015 to 2019 stratified by zip code social metrics.Results. At the surge peak (week 16, April 15-21), mortality rate ratios (comparing 2020 vs 2015-2019) were 2.2 (95% confidence interval [CI] = 1.4, 3.5) and 2.7 (95% CI = 1.4, 5.5) for the lowest and highest zip code tabulation area (ZCTA) poverty categories, respectively, with the 2020 peak mortality rate 1.1 (95% CI = 1.0, 1.3) times higher in the highest than the lowest poverty ZCTA. Similarly, rate ratios were significantly elevated for the highest versus lowest quintiles with respect to household crowding (1.7; 95% CI = 1.0, 2.9), racialized economic segregation (3.1; 95% CI = 1.8, 5.3), and percentage population of color (1.8; 95% CI = 1.6, 2.0).Conclusions. The COVID-19 mortality surge exhibited large inequities.Public Health Implications. Using zip code social metrics can guide equity-oriented COVID-19 prevention and mitigation efforts.

COVID-19 and the Unequal Surge in Mortality Rates in Massachusetts, by City/Town and ZIP Code Measures of Poverty, Household Crowding, Race/Ethnicity,and Racialized Economic Segregation

From the Abstract: Despite the paucity of adequate data on race/ethnicity - and no data on socioeconomic position - in US national data on COVID-19 [coronavirus disease 2019] mortality, both investigative journalism and some state and local health departments are beginning to document evidence of the greater mortality burden of COVID-19 on communities of color and low-income communities. To date, such documentation has been in relation to deaths categorized as due to COVID-19. However, in a context when assignment of cause of death to COVID-19 is dynamic and incomplete, given developing scientific evidence, one important strategy for assessing differential impacts of COVID-19 is that of evaluating the overall excess of deaths, as compared to the same time period in prior years. We employ this approach in this working paper and provide a transparent, easy-to-replicate methodology that relies on the reported data (i.e., no model-based estimates or complex modeling assumptions) and that can be readily used by any local or state health agency to monitor the social patterning of excess mortality rates during the COVID-19 pandemic. Key findings are that the surge in excess death rates, both relative and absolute, was evident starting in early April, and was greater in city/towns and ZCTAs [ZIP Code Tabulation Area] with higher poverty, higher household crowding, higher percentage of populations of color, and higher racialized economic segregation.COVID-19 (Disease);Public health case studies;Mortality

COVID-19: US federal accountability for entry, spread, and inequities-lessons for the future.

The United States (US) has been among those nations most severely affected by the first-and subsequent-phases of the pandemic of COVID-19, the disease caused by SARS-CoV-2. With only 4% of the worldwide population, the US has seen about 22% of COVID-19 deaths. Despite formidable advantages in resources and expertise, presently the per capita mortality rate is over 585/million, respectively 2.4 and 5 times higher compared to Canada and Germany. As we enter Fall 2020, the US is enduring ongoing outbreaks across large regions of the country. Moreover, within the US, an early and persistent feature of the pandemic has been the disproportionate impact on populations already made vulnerable by racism and dangerous jobs, inadequate wages, and unaffordable housing, and this is true for both the headline public health threat and the additional disastrous economic impacts. In this article we assess the impact of missteps by the Federal Government in three specific areas: the introduction of the virus to the US and the establishment of community transmission; the lack of national COVID-19 workplace standards and enforcement, and lack of personal protective equipment (PPE) for workplaces as represented by complaints to the Occupational Safety and Health Administration (OSHA) which we find are correlated with deaths 16 days later (ρ = 0.83); and the total excess deaths in 2020 to date already total more than 230,000, while COVID-19 mortality rates exhibit severe-and rising-inequities in race/ethnicity, including among working age adults.

Variation in racial/ethnic disparities in COVID-19 mortality by age in the United States: A cross-sectional study.

BACKGROUND: In the United States, non-Hispanic Black (NHB), Hispanic, and non-Hispanic American Indian/Alaska Native (NHAIAN) populations experience excess COVID-19 mortality, compared to the non-Hispanic White (NHW) population, but racial/ethnic differences in age at death are not known. The release of national COVID-19 death data by racial/ethnic group now permits analysis of age-specific mortality rates for these groups and the non-Hispanic Asian or Pacific Islander (NHAPI) population. Our objectives were to examine variation in age-specific COVID-19 mortality rates by racial/ethnicity and to calculate the impact of this mortality using years of potential life lost (YPLL). METHODS AND FINDINGS: This cross-sectional study used the recently publicly available data on US COVID-19 deaths with reported race/ethnicity, for the time period February 1, 2020, to July 22, 2020. Population data were drawn from the US Census. As of July 22, 2020, the number of COVID-19 deaths equaled 68,377 for NHW, 29,476 for NHB, 23,256 for Hispanic, 1,143 for NHAIAN, and 6,468 for NHAPI populations; the corresponding population sizes were 186.4 million, 40.6 million, 2.6 million, 19.5 million, and 57.7 million. Age-standardized rate ratios relative to NHW were 3.6 (95% CI 3.5, 3.8; p < 0.001) for NHB, 2.8 (95% CI 2.7, 3.0; p < 0.001) for Hispanic, 2.2 (95% CI 1.8, 2.6; p < 0.001) for NHAIAN, and 1.6 (95% CI 1.4, 1.7; p < 0.001) for NHAP populations. By contrast, NHB rate ratios relative to NHW were 7.1 (95% CI 5.8, 8.7; p < 0.001) for persons aged 25-34 years, 9.0 (95% CI 7.9, 10.2; p < 0.001) for persons aged 35-44 years, and 7.4 (95% CI 6.9, 7.9; p < 0.001) for persons aged 45-54 years. Even at older ages, NHB rate ratios were between 2.0 and 5.7. Similarly, rate ratios for the Hispanic versus NHW population were 7.0 (95% CI 5.8, 8.7; p < 0.001), 8.8 (95% CI 7.8, 9.9; p < 0.001), and 7.0 (95% CI 6.6, 7.5; p < 0.001) for the corresponding age strata above, with remaining rate ratios ranging from 1.4 to 5.0. Rate ratios for NHAIAN were similarly high through age 74 years. Among NHAPI persons, rate ratios ranged from 2.0 to 2.8 for persons aged 25-74 years and were 1.6 and 1.2 for persons aged 75-84 and 85+ years, respectively. As a consequence, more YPLL before age 65 were experienced by the NHB and Hispanic populations than the NHW population-despite the fact that the NHW population is larger-with a ratio of 4.6:1 and 3.2:1, respectively, for NHB and Hispanic persons. Study limitations include likely lag time in receipt of completed death certificates received by the Centers for Disease Control and Prevention for transmission to NCHS, with consequent lag in capturing the total number of deaths compared to data reported on state dashboards. CONCLUSIONS: In this study, we observed racial variation in age-specific mortality rates not fully captured with examination of age-standardized rates alone. These findings suggest the importance of examining age-specific mortality rates and underscores how age standardization can obscure extreme variations within age strata. To avoid overlooking such variation, data that permit age-specific analyses should be routinely publicly available.

Revealing the Unequal Burden of COVID-19 by Income, Race/Ethnicity, and Household Crowding: US County Versus Zip Code Analyses.

OBJECTIVE: To overcome the absence of national, state, and local public health data on the unequal economic and social burden of COVID-19 in the United States. DESIGN: We analyze US county COVID-19 deaths and confirmed COVID-19 cases and positive COVID-19 tests in Illinois and New York City zip codes by area percent poverty, percent crowding, percent population of color, and the Index of Concentration at the Extremes. SETTING: US counties and zip codes in Illinois and New York City, as of May 5, 2020. MAIN OUTCOME MEASURES: Rates, rate differences, and rate ratios of COVID-19 mortality, confirmed cases, and positive tests by category of county and zip code-level area-based socioeconomic measures. RESULTS: As of May 5, 2020, the COVID-19 death rate per 100 000 person-years equaled the following: 143.2 (95% confidence interval [CI]: 140.9, 145.5) vs 83.3 (95% CI: 78.3, 88.4) in high versus low poverty counties (≥20% vs <5% of persons below poverty); 124.4 (95% CI: 122.7, 126.0) versus 48.2 (95% CI: 47.2, 49.2) in counties in the top versus bottom quintile for household crowding; and 127.7 (95% CI: 126.0, 129.4) versus 25.9 (95% CI: 25.1, 26.6) for counties in the top versus bottom quintile for the percentage of persons who are people of color. Socioeconomic gradients in Illinois confirmed cases and New York City positive tests by zip code-level area-based socioeconomic measures were also observed. CONCLUSIONS: Stark social inequities exist in the United States for COVID-19 outcomes. We recommend that public health departments use these straightforward cost-effective methods to report on social inequities in COVID-19 outcomes to provide an evidence base for policy and resource allocation.

US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis.

OBJECTIVES: To illustrate the intersections of, and intercounty variation in, individual, household and community factors that influence the impact of COVID-19 on US counties and their ability to respond. DESIGN: We identified key individual, household and community characteristics influencing COVID-19 risks of infection and survival, guided by international experiences and consideration of epidemiological parameters of importance. Using publicly available data, we developed an open-access online tool that allows county-specific querying and mapping of risk factors. As an illustrative example, we assess the pairwise intersections of age (individual level), poverty (household level) and prevalence of group homes (community-level) in US counties. We also examine how these factors intersect with the proportion of the population that is people of colour (ie, not non-Hispanic white), a metric that reflects histories of US race relations. We defined 'high' risk counties as those above the 75th percentile. This threshold can be changed using the online tool. SETTING: US counties. PARTICIPANTS: Analyses are based on publicly available county-level data from the Area Health Resources Files, American Community Survey, Centers for Disease Control and Prevention Atlas file, National Center for Health Statistic and RWJF Community Health Rankings. RESULTS: Our findings demonstrate significant intercounty variation in the distribution of individual, household and community characteristics that affect risks of infection, severe disease or mortality from COVID-19. About 9% of counties, affecting 10 million residents, are in higher risk categories for both age and group quarters. About 14% of counties, affecting 31 million residents, have both high levels of poverty and a high proportion of people of colour. CONCLUSION: Federal and state governments will benefit from recognising high intrastate, intercounty variation in population risks and response capacity. Equitable responses to the pandemic require strategies to protect those in counties at highest risk of adverse COVID-19 outcomes and their social and economic impacts.