Political Determinants of Health: Mayoral Governance and Deaths of Despair in Urban American Peer Counties

Abstract

Deaths of despair (15+ years) are described for two peer county groups. Each county experienced competitive, transitional, or monopoly governance based on the duration of one political party’s control. Highest mortality among non-Hispanic Black (Black) people occurred in monopolies, affecting all age-sex-specific groups from 15–19 to 65–69. Transition to Democratic or Republican party monopolies produced similar overall trends. Governance by the same political party produced different rates depending on the length of one-party control. Black: White mortality rate ratios were reduced or reached 1.0 in competitive counties, and 51,801 fewer Black deaths might have occurred in monopoly counties based on competitive rates. The association between duration of one-party control and death rates was statistically significant and independent of state gun law strength. The data generate a hypothetical framework for structural racism based on political privilege/political monopoly rather than racial privilege/political affiliation. Analytic epidemiologic research is needed for testing.

Keywords

despair race inequities urban politics

Introduction

The idea of a syndrome comprising deaths from alcohol, substance abuse, and self-harm was developed during the second decade of the 21st century (King et al., 2022). It gained traction from two influential papers by Case and Deaton (2015, 2017), who coined the term, “deaths of despair” (Case & Deaton, 2017, 2020; King et al., 2022). Prominent theories about the origins of this syndrome included economic disruption (leading to hopelessness) (Case & Deaton, 2020; King et al., 2022) and the availability of lethal intoxicants, with lower educational attainment associated with higher vulnerability (Case & Deaton, 2020; Cutler & Lleras-Muney, 2010; King et al., 2022).

Recent strategies for building national resilience to this syndrome have included better access and coordination of health care, behavioral health, and social services; improving pain treatment; and harm reduction (for example, expanded access to naloxone and clean needle programs) (Auerbach & Miller, 2018). Nonetheless, deaths of despair remain a significant and increasing source of mortality in the United States (King et al., 2022; Mejia et al., 2024). The present report, therefore, aims to generate new hypotheses by describing racial inequities in deaths of despair in two groups of populous urban counties in the United States, each group being internally matched for 19 socioeconomic factors, including income inequality. Also, instead of broad, national policies, the present report will focus more closely on local politics, in particular, mayoral governance at the county seat.

Unlike state or national contests, U.S. mayoral elections focus more closely on local issues (Das et al., 2022). The local power to tax, issue bonds, and annex/zone land (Farmer, 2018; Gordon, 2008) provides county governments with significant autonomy to shape local employment, educational offerings, environmental exposures, safety infrastructure, and local and regional services (Farmer, 2018; Gordon, 2008). Local resources may also be extensive. As of 2015, the 100 largest U.S. cities had average budgets of $2.145 billion (Ballotpedia, n.d.). Primary centers of urban county government (county seats) may therefore have far-reaching influence on the infrastructure of smaller governmental units and neighborhoods within their purview (Baerwald, 1977). Moreover, local political forces are upstream from socio-economic determinants, and as noted by Navarro (2011), “It is not inequalities that kill people. It is the people who produce and reproduce inequalities through their public and private interventions that kill people. . . .”.

Political competition is a key aspect of local politics and may be pertinent to Navarro’s warning. Vibrant political competition has been found to rein in strident single-issue groups, temper conflict, and aggregate disparate interests (Parry et al., 2022). One-party dominance may reduce government efficiency (Gamm & Kousser, 2010), produce neglect of education and transportation policy in favor of hot-button issues important to narrow segments of the electorate (Burke, 2020), diminish bipartisan cooperation (Barrilleaux et al., 2002; Hare & Poole, 2014), decrease state spending on welfare programs in some instances (Holbrook & Van Dunk, 1993; Terry, 2016), and decrease representation of lower socioeconomic groups (Gamm & Kousser, 2021). Political scientists have also shown that non-competitive gubernatorial elections are associated with higher infant mortality, poorer educational outcomes, and shorter life expectancy, partly because expenditures for social services decrease when politicians are uncontested (Gamm & Kousser, 2021). Similarly, state-level analysis has shown that states with systems of political accountability and checks and balances are associated with more favorable health outcomes (Pacheco & LaCombe, 2022).

The present report will also account for steadily evolving concepts pertaining to “Deaths of despair”. For example, the term, “Despair” itself has been characterized as a hypothesis in need of testing by some (Shanahan et al., 2019), and the original Case-Deaton definition (Case & Deaton, 2015, 2017) has been modified by Dwyer-Lindgren et al. (2018), who broadened the syndrome to include assault (King et al., 2022). Under the Dwyer-Lindgren definition, the constellation of causes may be better described as “Harm to self or others” (Dwyer-Lindgren et al., 2018). This is more consistent with psychological theory than a definition based on self-harm alone (Eisner et al., 2021; Feinberg et al., 1982; Hiroto, 1974; National Academies of Sciences, Engineering, and Medicine, 1994; Rotter, 1966; Seligman, 1974; Weiss et al., 1985; Zuroff, 1980). While it was initially proposed that prolonged exposure to adversity produces learned helplessness, it was later recognized that helplessness is not learned. Instead, it is passive and ingrained (Seligman, 1974). Passivity and anxiety, in turn, map well to symptoms of depression, a significant risk factor for self-harm and suicide (Eisner et al., 2021; National Academies of Sciences, Engineering, and Medicine, 1994; Weiss et al., 1985). Also, a perceived external locus of control (people who feel that they do not control their own lives (Hiroto, 1974) is an important psychological factor that has long been understood to interact positively with helplessness (Lefcourt, 1966; Rotter, 1966; Zuroff, 1980). In contrast, others may perceive that violent behavior and/or violent ideation constitutes a valid response to prolonged, uncontrollable, and inescapable adversity (Feinberg et al., 1982).

Adopting Ecosocial Theory (Krieger, 2012), we expect that governance by one political party for extended periods could be associated with harm to non-dominant groups based, in part, on political privilege, whether or not the politically privileged are of the same race as non-dominant residents (Hyra, 2017). Further, we compare socially matched communities with differing exposure to political monopoly, thereby seeking to describe results pertinent to the observation that social determinants of health (Cockerham et al., 2017) have political roots (Dawes, 2020). A list of public internet sites used to compile the duration of mayoral terms and political affiliations is shown in Supplemental Table 1.

Method

Study Design

The method of using peer county analyses to identify modifiable determinants of inequities adversely affecting a racial minority has been previously reported (Levine et al., 2007). Detection of differences within peer county groups other than socioeconomic factors is facilitated as counties are matched for social and economic factors.

Setting and Data

Data for 2001 to 2023 were from all decedents ages 15 to 85+ years issued death certificates in America’s two most populous peer county groups (Group 1, n = 34 counties; Group 2, n = 32 counties) as defined by the Robert Wood Johnson Foundation (RWJ) (County Health Rankings and Roadmaps, 2017, 2023), with the exception of Collin County, TX in Group 2 as information about mayoral political party affiliations was not available. RWJ peer county classifications are based on matching for the following: median household income, population growth, percent high school graduates, receipt of government financial assistance, population density, single-parent households, Gini index of income inequality, population mobility, median home value, overall poverty, percent children, housing stress, elderly poverty, percent elderly, percent foreign-born, population size, percent owner-occupied housing units, unemployment, and sex ratio (County Health Rankings and Roadmaps, 2017, 2023). The RWJ classification for peer group one represents the most populous and established 34 major urban centers/cities across 20 states and the District of Columbia – with 19 having been among the most densely populated cities in the United States as early as the 1900 Census (U.S. Census Bureau, 1906): Orleans, LA (New Orleans); Fulton, GA (Atlanta); Suffolk, MA (Boston); Cook, IL (Chicago); St. Louis City, MO; Philadelphia, PA; Essex, NJ (Newark); Union, NJ (Elizabeth); Milwaukee, WI; District of Columbia; Wayne, MI (Detroit); Cuyahoga, OH (Cleveland); Hudson, NJ (Jersey City); Providence, RI; and New York, Kings, and Queens, New York City); these will be called Historic Metropolitan Peer Counties. Peer group two comprises less populous and later emerging places, with only five of the 33 counties being among the most densely populated in 1900 (San Francisco, CA; Richmond, NY (Staten Island); Allegheny, PA (Pittsburgh); Ramsey, MN (St. Paul); and Alexandria City, VA); these will be called Emerging Urban Peer Counties.

Thus, peer group one counties are larger, long-established, and globally recognized metropolitan centers. These will be called Historic Metropolitan Peer Counties. Peer group two includes smaller, newer, or rapidly expanding urban areas. These will be called Emerging Urban Peer Counties.

Exposure to the lack of political competition is based on the duration of governance in years by a single political entity at the county seat during the observation period (2001–2023). We used a modification of Sørensen’s definition (one-party victory for consecutive terms totaling at least 24 years with margins of 60% or more (Sørensen, 2014). Instead, we classified counties having 24 or more years of one-party governance throughout the observation period (2001–2020) = Monopoly; having <24 years as of both 2001 and 2020 = Competitive; or Transitional (<24 years in 2001 and 24 or more years as of 2020). In part, our rationale was that one-party control may be maintained without an election if the chief executive leaves before completing a term. In addition, the 60% criterion is a rare and moving target in local U.S. elections, partly because the percentage of voters who vote is both variable and small. A study conducted during the observation period found that the median turnout was 20% of registered voters in urban mayoral elections, with turnouts varying from 6% to 52% (Jurjevich, 2018; Jurjevich et al., 2016), so achieving 60% of the vote could have represented anywhere from 3.6% to 31.2% of registered voters in those communities.

Data Sources and Rationale

Death certificate data (county-race-ethnicity-gender-age-specific mortality rates as applicable, standard errors and 95% confidence intervals [CI]) are provided on the public internet site (Wide ranging Online Data for Epidemiologic Research [WONDER]) of the US Centers for Disease Control and Prevention (CDC) (Centers for Disease Control and Prevention, National Center for Health Statistics, 2021). Multiple Cause of Death Files were used to identify deaths of harm to self or others for all people ages 15 to 85+ years for whom deaths of despair were considered underlying or contributory causes of death, thereby averting underestimation (Fedeli et al., 2015). The constellation of causes comprising deaths of harm to self or others included the following: (a) Drug-related – mental and behavioral disorders due to use of opioids, cannabinoids, sedatives, hypnotics, cocaine, other stimulants, hallucinogens, volatile solvents, multiple drug use, and use of other psychoactive substances (F11-F19); Finding of opiate drug (R78.1), cocaine (R78.2), hallucinogen (R78.3), or psychotropic drug in blood; accidental poisoning by and exposure to non-opioid analgesics, antipyretics, antirheumatics; antiepileptic, sedative-hypnotic, anti-parkinsonism, and psychotropic drugs not elsewhere classified; and narcotics, psychodysleptics [hallucinogens] not elsewhere classified; drugs acting on the autonomic nervous system; other and unspecified drugs, medicaments, and biological substances (X40-X44); (b) Alcohol-related – mental and behavioral disorders due to use of alcohol (F10), alcohol-induced pseudo-Cushing syndrome (E24.4), degeneration of nervous system due to alcohol (G31.2), alcohol polyneuropathy (G62.1), alcoholic myopathy (G 72.1), alcoholic cardiomyopathy (I42.6), alcoholic gastritis (K29.2), alcoholic liver disease (K70), alcohol-induced acute pancreatitis (K85.2), alcohol-induced chronic pancreatitis (K86.0), newborn affected by maternal use of alcohol (P04.3 – deaths beyond infancy), fetal alcohol syndrome (dysmorphic) (Q86.0), finding of alcohol in blood (R78.0), accidental poisoning by and exposure to alcohol (X45), poisoning by and exposure to alcohol, undetermined intent (Y15); and (c) Violence – assault (X85-Y09), intentional self-harm (X60-X84), sequelae of intentional self-harm (Y87.0), and sequelae of assault (Y87.1) (Dwyer-Lindgren et al., 2018; King et al., 2022).

In these analyses, we considered each New York City County (four in the Historic Metropolitan Peer Counties (Bronx, Kings (Brooklyn), New York (Manhattan), and Queens), and one in the Emerging Urban Peer Counties (Richmond [Staten Island]) as unique, even though they all have the same mayor. Previous research has shown that different New York City counties have distinctly different environmental and sub-mayoral governance characteristics (Wallace & Wallace, 2000) associated with varying outcomes of mortality (Fry-Johnson et al., 2010; Wallace & Wallace, 2000). Compilation of the duration of single political party governance at the county seat is based on multiple public internet sources (Supplemental Table 1). A table that includes content from the referenced sites may be obtained on request.

Because gun law strength has been observed to reduce deaths from both homicide (Lee et al., 2017) and suicide (Saunders, 2022) during the observation period, ordinary least squares multiple regression (Freemantle, 2000; Glantz, 1992) was used to learn whether duration of one-party mayoral governance was associated with deaths of harm to self or others independently from average state gun law strength as per estimates which approximate the observation period (Chien et al., 2022).

Sign tests were used to compare age (5-year age groups)-race-sex-specific (crude) Black (monopoly):Black (competitive) and White (monopoly):White (competitive) mortality rates. Two-sided probabilities were based on both cumulative probability and the normal approximate z (Freemantle, 2000).

Additional Statistical Methods

Yearly, age-adjusted mortality rates (2001–2023) and 95% Confidence Intervals were presented according to political exposure (Monopoly, Transitional, or Competitive). Mortality rate ratios for non-Hispanic Black:non-Hispanic White people were used to further estimate the magnitude of inequity, while intra-racial rate ratios were used to compare monopoly and competitive county rates. Estimates of excess deaths in monopoly versus competitive counties were made by applying age-race-sex-specific mortality rates from competitive counties to corresponding populations in monopoly counties, subtracting the resulting hypothetical numbers of deaths from the actual numbers of deaths, and obtaining totals.

Results

Table 1 shows the political classification of counties in peer groups one and two as monopoly, transitional or competitive. All monopolies as of 2023, except for Oklahoma City, OK (Republican), have been governed by the Democratic Party. Nonetheless, 11 of 13 Competitive counties in the Historic Metropolitan Peer County group were also governed by the Democratic Party as of 2023 (with one Republican Party and one Independent comprising the rest). Similarly, in the Emerging Urban group, six of nine transitional counties became Democratic monopolies (with two transitioning to Republican and one to Independent monopolies). In comparison, 6 of 10 competitive counties in the Emerging Urban group were governed by the Democratic Party as of 2023, while three were Republican and one was Independent.

Table 1.

Counties and County-Equivalents, Governmental Center (County Seat), and Duration of Mayoral Political Party Affiliation (2001 and 2023).

County or County Equivalent (and County Seat)	Duration of Affiliation as of 2001 = 2002–	Duration of Affiliation as of 2023 = 2024–	Mayoral Political Party Affiliations
Historic Metropolitan Peer Counties
Monopoly
Orleans Parish, LA (New Orleans)	130	152	(Republican) 1870–1872; (Democratic) 1872–2023
Fulton County, GA (Atlanta)	123	145	(Republican) 1877–1879; (Democratic) 1879–2023
Suffolk County, MA (Boston)	72	94	(Republican) 1926–1930; (Democratic) 1930–2023
Cook County, IL (Chicago)	70	93	(Republican) 1927–1931; (Democratic) 1931–2023
St. Louis City, MO (St. Louis)	53	75	(Republican) 1943–1948; (Democratic) 1949–2023
Philadelphia County, PA (Philadelphia)	50	72	(Republican) 1948–1952; (Democratic) 1952–2023
Essex County, NJ (Newark)	48	71	(Republican) 1949–1953: (Democratic) 1953–2023
Union County, NJ (Elizabeth)	46	66	(Republican) 1953–1955; (Democratic) 1956–2023
Milwaukee County, WI (Milwaukee)	41	64	(Socialist) 1948–1960; (Democratic) 1960–2023
District of Columbia, DC	40	63	(Independent) 1956–1961; (Democratic) 1961–2023
Wayne County, MI (Detroit)	38	62	(Republican) 1957–1962; (Democratic) 1962–2023
Baltimore City, MD (Baltimore)	34	57	(Republican) 1963–1967; (Democratic) 1967–2023
Jefferson County, AL (Birmingham)	26	49	(Republican) 1967–1975; (Democratic) 1975–2023
Transitional (Competitive to Monopoly)
Harris County, TX (Houston)	19	42	(Republican) 1978–1982; (Democratic) 1982–2023
Sacramento County, CA (Sacramento)	19	41	(Independent) 1983; (Democratic) 1983–2023
Hamilton County, OH (Cincinnati)	18	40	(Charterite)1983–1984; (Democratic) 1984–2023
Hillsborough County, FL (Tampa)	16	38	(Democratic to Republican) (1979–1986); (Democratic) 1986–2023
Cuyahoga County, OH (Cleveland)	13	35	(Republican) 1980–1989; (Democratic) 1989–2023
Richmond City, VA (Richmond)	11	34	(Republican) 1988–1990; (Democratic) 1990–2023
Shelby County, TN (Memphis)	11	33	(Independent) 1982–1991; (Democratic) 1992–2023
Norfolk City, VA (Norfolk)	7	30	(Independent)1992–1994; (Democratic) 1994–2023
Competitive
Los Angeles County, CA (Los Angeles)	1	23	(Republican) 1993–2001; (Democratic) 2001–2023
Hudson County, NJ (Jersey City)	1	23	(Republican) 1992–2001; (Democratic) 2001–2023
Providence County, RI (Providence)	11	22	(Independent) 1991–2002; (Democratic) 2002–2023
Orange County, FL (Orlando)	10	21	(Republican) 1992–2003; (Democratic) 2003–2023
Miami-Dade County, FL (Miami)	7	15	(Republican) 1998–2001; (Democratic) 2001–2009; (Republican) 2009–2017, (Republican) 2017–2023
Dallas County, TX (Dallas)	8	13	(Democratic) 1995–2001; (Independent) 2001–2002; (Democratic) 2002–2007; (Republican) 2007–2011; (Democratic) 2011–2019, (Democratic) 2019–2023
Riverside County, CA (Riverside)	9	12	(Democratic) 1994–2012; (Independent) 2012–2023
Bronx County, NY (New York City)	9	11	(Republican) 1994–2001, (Republican) 2002–2007; (Independent) 2007–2013; (Democratic) 2014–2023.
Kings County, NY (Brooklyn, New York City)	9	11	(Republican) 1994–2001, (Republican) 2002–2007; (Independent) 2007–2013; (Democratic) 2014–2023.
New York County, NY (Manhattan, New York City)	9	11	(Republican) 1994–2001, (Republican) 2002–2007; (Independent) 2007–2013; (Democratic) 2014–2023.
Queens County, NY (New York City)	9	11	(Republican) 1994–2001, (Republican) 2002–2007, (Independent) 2007–2013; (Democratic) 2014–2023.
Duval County, FL (Jacksonville)	2	1	(Republican) 1995–2003, (Republican) 2003–2011; (Democratic) 2011–2015;(Republican) 2015–2023; (Democratic) 2023
Marion County, IN (Indianapolis)	4	8	Democratic (2000–2008); (Republican) 2008–2016; (Democratic) 2016–2023
Emerging Urban Peer Counties
Monopoly
Davidson County, TN (Nashville)	105	127	(American Protective) 1895–1897; (Democratic) 1897–2023
Allegheny County, PA (Pittsburgh)	68	90	(Republican) 1933–1934; (Democratic) 1934–2023
Denver County, CO (Denver)	39	61	(Republican) 1959–1963; (Democratic) 1963–2023
San Francisco, CA (San Francisco)	38	60	(Republican) 1956–1964; (Democratic) 1964–2023
Erie County, NY (Buffalo)	37	59	(Republican) 1962–1965; (Democratic) 1966–2023
Santa Clara County, CA (San Jose)	36	58	(Republican) 1965–1966; (Democratic) 1967–2023
Jefferson County, KY (Louisville)	33	55	(Republican) 1965–1969; (Democratic) 1969–2023
King County, WA (Seattle)	33	55	(Republican) 1964–1969; (Democratic) 1969–2023
Hartford County, CT (Hartford)	31	53	(Republican) 1967–1971; (Democratic) 1971–2023
Monroe County, NY (Rochester)	29	51	(Republican) 1970–1973; (Democratic) 1974–2023
Salt Lake County, UT (Salt Lake City)	26	48	(Independent) 1974–1976; (Democratic) 1976–2023
Alameda County, CA (Oakland)	25	47	(Republican) 1966–1977; (Democratic) 1977–2023
Hennepin County, MN (Minneapolis)	25	47	(Independent) 1976–1977; (Democratic/Democratic Farmer Labor) 1977–2023
Transitional (Competitive to Monopoly)
Alexandria City, VA (Independent City)	23	45	(Independent) 1976–1979; (Democratic) 1979–2023
Oklahoma County, OK (Oklahoma City)	15	37	(Democratic) 1983–1987; (Republican) 1987–2023
Jackson County, MO (Kansas City) (second county seat and center of government)	11	33	(Republican) 1979–1991; (Democratic) 1991–2023
Travis County, TX (Austin)	11	33	(Republican) 1988–1991; (Democratic) 1991–2023
Multnomah County, OR (Portland)	10	32	(Independent) 1985–1992; (Democratic) 1993–2023
Orange County, CA (Santa Ana)	8	30	(Democratic to Republican) 1986–1994; (Democratic) 1994–2023
Pinellas County, FL (Clearwater)	3	25	(Democratic) 1987–1999; (Republican) 1999–2023
Clark County, NV (Las Vegas)	3	25	(Democratic) 1991–1999; (Independent) 1999–2023
Franklin County, OH (Columbus)	2	24	(Republican) 1992–2000; (Democratic) 2000–2023
Competitive
Ramsey County, MN (St. Paul)	1	23	(Republican) 1996–2001; (Democratic) 2001–2023
Maricopa County, AZ (Phoenix)	8	20	(Independent) 1991–1994; (Republican) 1994, (Republican) 1994 1994–2004; (Democratic) 2004–2023
Virginia Beach City, VA (Independent City)	16	15	(Republican)1984–1986; (Democratic) 1986–1988, (Democratic) (1988–2008); (Republican) 2009–2023
Mecklenburg County, NC (Charlotte)	15	15	(Democratic) 1983–1987; (Republican) 1987–1991; (Republican) 1991–1995, (Republican) 1995–2009; (Democratic) 2009–2023
Tarrant County, TX (Fort Worth)	9	13	(Republican) 1991–1995; (Independent) 1995–1996, (Democratic) 1996–2003, (Democratic) 2003–2011; (Republican) 2011–2023
Richmond County, NY (Staten Island, New York City)	9	11	(Democratic) 1990–1993; (Republican) 1994–2001, (Republican) 2002–2007; (Independent) 2007–2013; (Democratic) 2014–2023.
Bexar County, TX (San Antonio)	11 (maximum)	10	(Republican) 1989–1991; (Democratic) 1991–1995, (?) 1995–1997, (Democratic) 1997–2001, (Democratic) 2001–2005, (Democratic) 2005–2009, (Democratic) 2009–2014; (Independent) 2014–2017, (Independent) 2017–2023
Kent County, MI (Grand Rapids)	10	8	(Independent)1984–1991, (Independent) 1992–2003; (Republican) 2004–2015; (Democratic) 2016–2023
San Diego County, CA (San Diego)	10	4	(Democratic) 1986–1992; (Republican) 1992–2000, (Republican) 2000–2005; (Democratic) 2005–2005, (Democratic) 2005–2005; (Republican) 2005–2012; (Democratic) 2012–2013, (Democratic) 2013–2014; (Republican) 2014–2020, (Democratic) 2020–2023
Wake County, NC (Raleigh)	9	5	(Democratic) 1983–1993; (Republican) 1993–1999, (Republican) 1999–2001; (Democratic) 2001–2011; (Independent) 2011–2019; (Democratic) 2019–2023

Figures 1 and 2 show yearly mortality rates for non-Hispanic Black residents according to political exposure: monopoly throughout the observation period; transitional (from competitive in 2001 to monopoly as of 2023); and Competitive throughout the observation period. Both peer groups show that rates are significantly higher in monopoly counties than in competitive counties throughout the observation period. In contrast, rates in transitional counties are initially similar to competitive counties and then over time become significantly higher than competitive counties; they are intermediate between monopoly and competitive counties as of 2023. Supplemental Figures 1 and 2 depict these results using the original, Case-Deaton (Case & Deaton, 2015, 2017) definition of Deaths of Despair. Again, monopoly counties have the highest rates, competitive counties have the lowest, and transitional counties become intermediate.

Figure 1.

Deaths of despair in monopoly, transitional, and competitive counties among non-Hispanic Black residents (15+ years) according to type of local governance. Historic metropolitan peer counties, 2001 to 2023.

Figure 2.

Deaths of despair among non-Hispanic Black residents (15+ years) and 95% confidence intervals in monopoly and competitive counties according to the type of local governance. Emerging urban peer counties, 2001 to 2023.

Figures 3 (Historic Metropolitan) and 4 (Emerging Urban) depict rates according to political party. All Transitional counties among the Historic Metropolitan peer counties became Monopoly Democratic, but in the Emerging Urban peer counties, there was evolution to both Monopoly Democratic and Monopoly Republican. Within Competitive counties in both peer groups, there were control by Democratic, Republican, and Independent mayors as of 2023. In both peer groups, counties with monopoly governance throughout have the highest rates, while those transitioning to monopoly status are intermediate between monopoly and competitive counties. Counties transitioning to monopoly status had similar values regardless of which major political party monopoly was reached, and the same was true of political party classification within the competitive groups. The lowest rates as of 2023 were found for counties with competitive Independent Party governance as of that year in both peer groups.

Figure 3.

Deaths of despair among Black residents (15+ years) according to the type of local governance and political party. Historic metropolitan peer counties, 2001 to 2023.

Figure 4.

Deaths of despair among Black residents (15+ years) according to the type of local governance and political party. Emerging urban peer counties, 2001 to 2023.

Figure 5 depicts sex-specific, Black:White mortality rate ratios in monopoly and competitive counties in the most recent 5-year period for which data are available. In both Historic Metropolitan and Emerging Urban Peer counties, inequities are either reduced or eliminated in competitive compared to monopoly counties.

Figure 5.

Black:White mortality rate ratios in monopoly and competitive peer counties. Deaths of despair. Ages 15+ years. 2019 to 2023.

Table 2 shows comparisons of age (5-year age groups)-race-ethnicity (non-Hispanic)-sex-specific (crude) mortality rates among Black monopoly and competitive county residents and White monopoly and competitive county residents in the Historic Metropolitan and Emerging Urban peer county groups for which reliable rates (at least 20 deaths) (Centers for Disease Control and Prevention, National Center for Health Statistics, 2021) were available. Among Black residents, rates were significantly higher in monopoly counties in all but one of 51 instances (75- to 79-year-old females in Historic Metropolitan Peer Counties); two-sided Cumulative and Normal Approximate z probabilities both <0.001. In contrast, comparable comparisons for White residents were not significantly different in 40 of 56 instances (two-sided Cumulative and Normal Approximate z probabilities both = 0.002).

Table 2.

Age-Race-Ethnicity-Sex-Specific Mortality Rates From Deaths of Despair Among Black Monopoly and Competitive County Residents and White Monopoly and Competitive County Residents in the Historic Metropolitan and Emerging Urban Peer County Groups for Which Reliable Rates Were Available.

Age Group	Deaths	Population	Deaths	Population	Black:Black Competitive Rate Ratios (and 95% Confidence Intervals)	Deaths	Population	Deaths	Population	Monopoly White:White Competitive Rate Ratios (and 95% Confidence Intervals)
Historic Metropolitan Peer Counties
Female
15–19	227	825,597	113	712,976	1.7 (1.4, 2.2)***	53	761,542	91	993,940	0.8 (0.5, 1.1)
20–24	397	876,913	179	789,791	2.0 (1.7, 2.4)***	168	915,364	194	1,194,807	1.1 (0.9, 1.4)
25–29	580	1,054,367	279	970,940	1.9 (1.7, 2.2)***	316	1,320,604	367	1,747,756	1.1 (1.0, 1.3)*
30–34	697	1,059,105	322	989,034	2.0 (1.8, 2.3)***	502	1,329,491	507	1,831,121	1.4 (1.2, 1.5)***
35–39	670	901,343	326	855,596	2.0 (1.7, 2.2)***	572	1,134,206	586	1,571,131	1.4 (1.2, 1.5)***
40–44	665	851,102	343	796,721	1.8 (1.6, 2.1)***	538	959,119	554	1,328,003	1.3 (1.2, 1.5)***
45–49	677	819,118	325	773,366	2.0 (1.7, 2.2)***	502	876,165	593	1,245,627	1.2 (1.1, 1.4)***
50–54	873	850,708	405	818,988	2.1 (1.8, 2.3)***	600	950,482	694	1,357,326	1.2 (1.1, 1.4)***
55–59	1,080	881,447	561	860,239	1.9 (1.7, 2.1)***	558	1,033,044	830	1,475,750	1.0 (0.9, 1.1)
60–64	858	883,763	493	811,030	1.6 (1.4, 1.8)***	523	1,085,387	711	1,540,548	1.0 (0.9, 1.2)
65–69	513	756,943	282	657,966	1.6 (1.4, 1.8)***	307	984,120	494	1,437,471	0.9(0.8, 1.0)
70–74	169	590,068	105	511,812	1.4 (1.1, 1.8)***	178	825,495	317	1,278,595	0.9 (0.7, 1.0)
75–79	69	392,307	63	361,840	1.0 (0.7, 1.4)	80	593,547	196	959,407	0.9 (0.7, 1.0)***
80–84	31	267,717	Not Available	247,507	Not Available	64	409,401	107	667,588	1.0 (0.7, 1.3)
Male
15–19	1,526	819,047	690	723,482	2.0 (1.8, 2.1)***	169	764,887	204	1,026,741	1.1 (0.9, 1.4)
20–24	2,499	813,608	1,085	756,401	2.1 (2.0, 2.3)***	441	864,894	594	1,157,254	1.0 (0.9, 1.1)
25–29	3,075	950,289	1,299	931,576	2.3 (2.2, 2.5)***	969	1,290,729	1,078	1,726,500	1.2 (1.1, 1.3)***
30–34	2,945	914,422	1,330	941,175	2.3 (2.1, 2.4)***	1,266	1,368,598	1,523	1,907,751	1.2 (1.1, 1.2)***
35–39	2,303	735,733	1,206	774,057	2.0 (1.9, 2.2)***	1,428	1,185,493	1,616	1,688,229	1.3 (1.2, 1.4)***
40–44	2,132	675,130	1,133	685,696	1.9 (1.8, 2.1)***	1,459	1,000,712	1,590	1,439,696	1.3 (1.2, 1.4)***
45–49	2,080	643,611	1,036	643,521	2.0 (1.9, .2)***	1,347	919,010	1,582	1,337,998	1.2 (1.2,1.3)***
50–54	2,491	687,779	1,187	687,834	2.1 (2.0, 2.2)***	1,550	1,003,661	2,005	1,460,023	1.1 (1.1, 1.2)***
55–59	3,138	708,626	1,630	716,461	1.9 (1.8, 2.1)***	1,789	1,064,281	2,428	1,561,884	1.1 (1.1, 1.1)***
60–64	3,055	679,495	1,682	661,467	1.8 (1.7, 1.9)***	1,562	1,063,664	2,292	1,567,852	1.0 (0.9, 1.1)
65–69	1,975	535,513	1,088	498,153	1.7 (1.6, 1.8)***	1,040	916,566	1,593	1,364,667	1.0 (0.9, 1.1)
70–74	880	387,576	505	351,705	1.6 (0.9, 1.1)***	616	722,198	1,027	1,127,194	0.9 (0.8, 1.0)
75–79	327	241,831	199	223,012	1.5 (1.3, 1.8)***	312	471,384	598	787,944	0.9 (0.8, 1.0)**
80–84	107	145,297	62	136,885	1.6 (1.2, 2.2)***	204	284,198	338	494,573	1.1 (0.9, 1.2)
Emerging Urban Peer Counties
Female
15–19	63	156,953	23	187,494	3.3 (2.0, 5.4)***	46	598,461	57	605,976	0.9 (0.6, 1.2)
20–24	73	165,546	50	195,299	1.7 (1.2, 2.5)**	132	667,924	114	634,212	1.1( 0.9, 1.4)
25–29	96	196,735	74	234,571	1.5 (1.1, 2.1)***	224	955,504	165	808,259	1.1 (0.9, 1.4)
30–34	122	188,503	63	227,124	2.3 (1.7, 3.2)***	299	1,000,343	219	840,739	1.1 (1.0, 1.4)
35–39	99	166,504	68	206,391	1.8 (1.3, 2.5)***	327	896,583	251	793,045	1.2 (1.0, 1.4)*
40–44	67	148,942	58	191,536	1.5 (1.0, 2.1)**	350	769,434	258	726,358	1.3 (1.1, 1.5)***
45–49	85	142,445	53	181,998	2.0 (1.5, 2.9)***	308	735,762	257	729,894	1.2 (1.0, 1.4)**
50–54	106	139,600	55	172,594	2.4 (1.7, 3.3)***	429	813,192	345	789,315	1.2 (1.1, 1.4)**
55–59	147	145,095	68	165,802	2.5 (1.9, 3.3)***	462	898,001	446	875,084	1.0 (0.9, 1.1)
60–64	98	137,526	47	143,761	2.2 (1.5, 3.1)***	406	914,906	404	876,697	1.0 (0.8, 1.1)
65–69	63	110,575	38	111,973	1.7 (1.1, 2.5)**	260	811,011	257	790,499	1.0 (0.8, 1.1)
70–74	32	82,260	Not Available	80,429	Not Available	175	676,962	162	694,174	1.1 (0.9, 1.4)
75–79	Not Available	53,353	Not Available	48,594	Not Available	124	469,757	107	480,261	1.2 (0.9, 1.5)
80–84	Not Available	36,845	Not Available	30,472	Not Available	62	322,466	53	318,494	1.2 (0.8, 1.7)
15–19	260	159,264	200	192,430	1.6 (1.3, 1.9)***	145	621,048	194	650,441	0.8 (0.6, 1.0)**
20–24	392	162,806	301	199,559	1.6 (1.4, 1.9)***	396	667,940	454	703,164	0.9 (0.8, 1.1)
25–29	437	201,018	275	236,081	1.9 (1.6, 2.2)***	664	1,005,238	619	855,351	0.9 (0.8, 1.0)
30–34	411	188,075	297	218,490	1.6 (1.4, 1.9)***	811	1,083,065	780	876,159	0.8 (0.8, 0.9)***
35–39	352	157,550	265	191,832	1.6 (1.4, 1.9)***	933	971,164	776	823,077	1.0 (0.9, 1.1)*
40–44	299	137,783	170	171,592	2.2 (1.8, 2.6)***	769	821,022	739	743,855	0.9 (0.9, 1.0)
45–49	289	129,460	180	160,222	2.0 (1.7, 2.4)***	824	777,629	790	748,932	1.0 (0.9, 1.1)
50–54	373	130,651	190	154,136	2.3 (1.9, 2.8)***	1,074	846,249	945	799,123	1.1 (1.0, 1.2)
55–59	385	132,327	216	148,857	2.0 (1.7, 2.4)***	1,318	902,363	1,224	858,133	1.0 (0.9, 1.1)
60–64	363	120,399	204	125,517	1.9 (1.6, 2.2)***	1,253	890,589	1,123	831,843	1.0 (1.0, 1.1)
65–69	265	88,471	115	87,253	2.2 (1.8, 2.8)***	856	752,109	813	705,290	1.0 (0.9, 1.1)
70–74	129	61,855	65	58,653	1.9 (1.4, 2.5)***	532	586,961	458	603,202	1.2 (1.1, 1.4)***
75–79	47	36,461	25	32,482	1.7 (1.0,2.8)**	347	374,818	320	401,078	1.2 (1.0, 1.4)*
80–84	Not Available	21,905	Not Available	18,795	Not Available	194	230,512	191	244,600	1.1 (0.9, 1.3)

p < .10, **p < .05, ***p < .01.

Figures 6 through 9 depict age-race-ethnicity-sex-specific mortality rates in monopoly and competitive counties from 2019 to 2023. Except for the oldest age groups and for Monopoly Black and Monopoly White females at ages 40 to 44 years, rates are highest among both female and male Black residents from ages 15 to 19 years forward, with rates for males exceeding those for females. Strikingly, however, rates for Black females residing in competitive Historic Metropolitan and Emerging Urban peer counties were similar to or lower than those at comparable ages for White females residing in Historic Metropolitan or Emerging Urban competitive counties.

Figure 6.

Deaths of despair among Black females and White females in monopoly and competitive historic metropolitan peer counties, 2019 to 2023.

Figure 7.

Deaths of despair among Black males and White males in monopoly and competitive historic metropolitan peer counties, 2019 to 2023.

Figure 8.

Deaths of despair among Black females and White females in monopoly and competitive emerging urban peer counties, 2019 to 2023.

Figure 9.

Deaths of despair among Black males and White males in monopoly and competitive emerging urban peer counties, 2019 to 2023.

Tables 3 and 4 show county values for age-adjusted (15 to 85+ years) rates of harm to self or others among Black residents and Black: White residents’ mortality rate ratios for 2016–2020, the most recent years for which county-specific WONDER data are available. Rates varied widely, with the highest county rate among Historic Metropolitan Peer Counties (St. Louis City, MO = 290.2 per 100,000 population) being 7.0 times that of the lowest in peer group one (Queens County, NY = 41.4), and with the highest value among the Emerging Urban Peer Counties (Jackson County, MO = 150.5) being 4.6 times that of the lowest in that group (Orange County, CA = 32.5). Greatest inequities were found in Washington, DC, among Historic Metropolitan Peer Counties (Black:White Mortality Rate Ratio = 5.6) and San Francisco, CA = 3.2 among the Emerging Urban Peer Counties. Eight Historic Metropolitan Peer Counties (seven competitive (Miami-Dade, Providence, Riverside, Queens, Orange (FL), Bronx, and Duval) and one transitional (Hillsboro) and eight Emerging Urban Peer counties (four transitional (Clark, Travis, Orange (CA), and Pinellas), three competitive (Wake, Tarrant, and Virginia Beach), and one monopoly (Hartford) had ratios of 1.0 or less. Of these sixteen, nine were in the South U.S. Census Region, four in the East, and three in the West, leaving the Midwest unrepresented (Centers for Disease Control and Prevention, National Center for Health Statistics, 2021).

Table 3.

Age-Adjusted (15 to 85+ Years) Mortality per 100,000 Population From Deaths of Despair Among Black Residents and Black:Non-Hispanic White Resident Mortality Rate Ratios. Historic Metropolitan Peer Counties, 2016 to 2020.

Sorted by Age-adjusted Rate of Firearm Homicide (Ages 1 to 85+ years) Among Non-Hispanic Black Residents per 100,000 Population			Sorted by the Mortality Rate Ratio for Non-Hispanic Black:Non-Hispanic White Residents
County	Age-Adjusted Rate	B:W	County	Age-Adjusted Rate	B:W
St. Louis city, MO	290.2	2.2	District of Columbia, DC	161.1	5.6
Baltimore city, MD	189.1	1.8	Cook County, IL	128.7	2.8
District of Columbia, DC	161.1	5.6	New York County, NY	91.6	2.6
Orleans Parish, LA	149.4	1.5	St. Louis city, MO	290.2	2.2
Philadelphia County, PA	133.6	1.1	Essex County, NJ	105.3	2.2
Cook County, IL	128.7	2.8	Richmond city, VA	115.3	2.0
Milwaukee County, WI	121.8	1.7	Fulton County, GA	87.9	1.8
Richmond city, VA	115.3	2.0	Baltimore city, MD	189.1	1.8
Marion County, IN	110.7	1.6	Hudson County, NJ	104.4	1.8
Jefferson County, AL	110.4	1.2	Milwaukee County, WI	121.8	1.7
Norfolk city, VA	105.5	1.2	Wayne County, MI	104.5	1.7
Essex County, NJ	105.3	2.2	Cuyahoga County, OH	101.9	1.7
Wayne County, MI	104.5	1.7	Los Angeles County, CA	62.4	1.7
Hudson County, NJ	104.4	1.8	Marion County, IN	110.7	1.6
Cuyahoga County, OH	101.9	1.7	Orleans Parish, LA	149.4	1.5
Shelby County, TN	100.7	1.2	Sacramento County, CA	89.3	1.4
Hamilton County, OH	100.1	1.4	Hamilton County, OH	100.1	1.4
New York County, NY	91.6	2.6	Kings County, NY	49.6	1.3
Sacramento County, CA	89.3	1.4	Norfolk city, VA	105.5	1.2
Fulton County, GA	87.9	1.8	Union County, NJ	59.5	1.2
Suffolk County, MA	87.7	1.1	Jefferson County, AL	110.4	1.2
Duval County, FL	83.3	0.7	Shelby County, TN	100.7	1.2
Bronx County, NY	76.2	0.8	Harris County, TX	71.8	1.1
Miami-Dade County, FL	75.3	0.9	Suffolk County, MA	87.7	1.1
Dallas County, TX	73.0	1.1	Philadelphia County, PA	133.6	1.1
Harris County, TX	71.8	1.1	Dallas County, TX	73.0	1.1
Providence County, RI	66.7	0.9	Miami-Dade County, FL	75.3	0.9
Los Angeles County, CA	62.4	1.7	Providence County, RI	66.7	0.9
Hillsborough County, FL	62.1	0.8	Riverside County, CA	49.2	0.9
Union County, NJ	59.5	1.2	Queens County, NY	41.4	0.8
Orange County, FL	55.3	0.8	Orange County, FL	55.3	0.8
Kings County, NY	49.6	1.3	Bronx County, NY	76.2	0.8
Riverside County, CA	49.2	0.9	Hillsborough County, FL	62.1	0.8
Queens County, NY	41.4	0.8	Duval County, FL	83.3	0.7

Note. As of this writing, age-adjusted mortality rates for individual counties are not available from CDC WONDER after the year 2020.

Table 4.

Age-Adjusted (15 to 85+ Years) Mortality per 100,000 Population From Deaths of Despair Among Non-Hispanic Black Residents and Non-Hispanic Black:Non-Hispanic White Resident Mortality Rate Ratios. Emerging Urban Peer Counties, 2016–2020.

Sorted by Age-adjusted Rate of Firearm Homicide (Ages 1 to 85+ years) Among Non-Hispanic Black Residents per 100,000 Population			Sorted by the Mortality Rate Ratio for Non-Hispanic Black:Non-Hispanic White Residents
County	Black rate	Black:White	County	B rate	BW
Jackson County, MO	150.5	1.9	San Francisco County, CA	143.9	3.2
San Francisco County, CA	143.9	3.2	Allegheny County, PA	111.3	2.5
Denver County, CO	136.7	1.7	Alameda County, CA	91.4	2.5
Hennepin County, MN	120.6	2.2	Hennepin County, MN	120.6	2.2
Jefferson County, KY	116.3	1.3	Ramsey County, MN	93.1	2.0
Franklin County, OH	113.9	1.4	Kent County, MI	79.5	1.9
Allegheny County, PA	111.3	2.5	Jackson County, MO	150.5	1.9
Multnomah County, OR	110.5	1.4	Erie County, NY	107.9	1.9
Erie County, NY	107.9	1.9	King County, WA	70.4	1.7
Monroe County, NY	105.0	1.7	Denver County, CO	136.7	1.7
Oklahoma County, OK	102.9	1.3	Monroe County, NY	105.0	1.7
Davidson County, TN	102.1	1.3	Santa Clara County, CA	66.6	1.6
Ramsey County, MN	93.1	2.0	Alexandria city, VA	48.4	1.6
Alameda County, CA	91.4	2.5	Franklin County, OH	113.9	1.4
Maricopa County, AZ	89.4	1.3	Multnomah County, OR	110.5	1.4
Clark County, NV	83.1	1.0	Jefferson County, KY	116.3	1.3
Pinellas County, FL	82.1	0.7	Maricopa County, AZ	89.4	1.3
Salt Lake County, UT	80.9	1.1	Oklahoma County, OK	102.9	1.3
Kent County, MI	79.5	1.9	Davidson County, TN	102.1	1.3
Hartford County, CT	74.8	0.9	Mecklenburg County, NC	50.5	1.3
King County, WA	70.4	1.7	San Diego County, CA	64.3	1.2
Santa Clara County, CA	66.6	1.6	Salt Lake County, UT	80.9	1.1
San Diego County, CA	64.3	1.2	Richmond County, NY	64.1	1.1
Richmond County, NY	64.1	1.1	Bexar County, TX	55.6	1.1
Bexar County, TX	55.6	1.1	Wake County, NC	39.4	1.0
Mecklenburg County, NC	50.5	1.3	Clark County, NV	83.1	1.0
Alexandria city, VA	48.4	1.6	Travis County, TX	47.8	0.9
Travis County, TX	47.8	0.9	Hartford County, CT	74.8	0.9
Virginia Beach city, VA	47.1	0.8	Tarrant County, TX	40.0	0.8
Tarrant County, TX	40.0	0.8	Orange County, CA	32.5	0.8
Wake County, NC	39.4	1.0	Virginia Beach city, VA	47.1	0.8
Orange County, CA	32.5	0.8	Pinellas County, FL	82.1	0.7

Table 5 presents the estimated number of excess deaths in monopoly counties based on reductions that would have occurred had age-race-sex-specific mortality rates from competitive counties been applied to monopoly counties. Overall, there were 51,801 excess deaths from 2001 to 2023. In Historic Metropolitan peer counties, 84.6% of the excess (35,239/41,676) occurred in males, while the corresponding figure in Emerging Urban peer counties was 81.5% (8,659/106.25).

Table 5.

Estimated Excess Deaths From Harm to Self or Others in Monopoly Counties. 15 to 85+ Years, 2001 to 2023.

Ten-Year Age Groups	Non-Hispanic Black Population(Monopoly Counties)	Non-Hispanic BlackCompetitive CountyMortality Rates	Actual Non-Hispanic Black Deaths of Despair	Hypothetical Non-Hispanic Black Deaths(Competitive Rates Applied to Monopoly Populations)	Excess Deaths (Actual Minus Hypothetical)
Historic Metropolitan Peer Counties
Female
15–24 years	9,517,727	13.2	2,024	1,256	768
25–34 years	9,513,120	19.7	3,088	1,871	1,217
35–44 years	8,898,877	28.2	4,005	2,507	1,498
45–54 years	8,882,110	39.7	5,122	3,529	1,593
55–64 years	7,496,245	39.5	4,026	2,959	1,067
65–74 years	4,922,138	20.5	1,234	1,011	223
75–84 years	2,782,609	10.0	320	279	41
85+ years	1,160,471	6.9	110	80	30
15–85 Years	6,646,662		19,929	13,492	6,437
Male
15–24 years	9,031,063	103.9	18,292	9,379,	9
25–34 years	7,859,705	118.7	19,088	9,332	9,756
35–44 years	7,136,064	105.6	13,473	7,534	5,939
45–54 years	7,145,691	128.3	14,133	9,171	4,962
55–64 years	5,771,882	163.0	13,515	9,409	4,106
65–74 years	3,360,491	120.5	5,472	4,049	1,423
75–84 years	1,575,584	64.1	1,121	1,010	111
85+ years	458,336	37.8	202	173	29
15–85 years	42,338,816		85,296	50,057	35,239
Total Historic Metropolitan Peer Counties
15–85 years	48,985,478		105,225	63,549	41,676
Emerging Urban Peer Counties
Female
15–24 years	2,766,928	14.1	596	389	207
25–34 years	2,753,783	20.4	844	560	284
35–44 years	2,474,440	24.7	924	610	314
45–54 years	2,322,791	28.8	1,192	670	522
55–64 years	1,860,806	30.5	1,024	567	457
65–74 years	1,137,366	15.9	368	181	187
75–84 years	612,378	12.2	70	75	−5
85+ years	258,059	Not Available	Not Available	Not Available	Not Available
15–85+ years	14,186,551		5,018	3,052	1,966
Male
15–24 years	2,794,764	86.5	4,513	2,417	2,096
25–34 years	2,467,891	94.0	4,132	2,321	1,811
35–44 years	2,219,622	78.5	2,976	1,743	1,233
45–54 years	2,128,664	99.8	3,681	2,125	1,556
55–64 years	1,534,926	116.5	2,891	1,788	1,103
65–74 years	773,122	94.0	1,088	727	361
75–84 years	338,492	Not Available	287	Not Available	Not Available
85+ years	108,807	Not Available	212	Not Available	Not Available
15–85+ years	12,366,288		19,780	11,121	8,659
Total Emerging Urban Peer Counties
	26,552,839		24,798	14,173	10,625
Grand Total
					51,801

Multiple regression analysis with county age-adjusted (15+ years) mortality from deaths from harm to self or others (2001–2020) as the dependent variable and years of single political party governance as of 2020 and mean state gun law index (2001–2018) as independent variables in Historic Metropolitan Peer Counties showed b = 71.73, t = 8.31, and p <.001 for the intercept; b = .32, r = .47, t = 2.90 and p = .01 for duration of one-party governance; and b = −29.90, r = −0.28, t = 1.58, and p = .12 for mean state gun law index. The mean variance inflation factor was 1.02, and the Durbin–Watson test statistic was 1.83. For Emerging Urban Peer Counties, b = 48.88, t = 5.11, and p ≤.001 for the intercept; b = .43, r = 0.40, t = 2.34, and p = .03 for duration of one-party governance; and b = 12.54, r = .10, t = .51, and p = .61 for mean state gun law index. The mean variance inflation factor was 1.01, and the Durbin–Watson test statistic was 2.17.

Discussion

The study generates the hypothesis that political monopolies are associated with high mortality from deaths of despair in these counties (Figures 1 and 2). An additional hypothesis generated is that differences in mortality and racial inequity from deaths of despair may be specific to levels of political competition rather than party affiliation. Among these urban counties, the Democratic party was dominant among all Monopoly counties, and all counties transitioning from Competitive to Monopoly status in peer group 1, Historic Metropolitan Peer Counties (Table 1). In peer group 2, Emerging Urban Peer Counties, however, transitions occurred to both Republican and Democratic Monopoly status (Table 2). Figures 3 and 4 show that the transition from Competitive to Democratic or Republican Monopoly status was similar. Moreover, Monopoly counties in both peer groups may be described as authoritarian (Magaloni & Kricheli, 2010). Mayoral elections are held, but they are token events whose outcome is rarely in doubt, even if the election is nominally non-partisan (Schaffner et al., 2001). Some evidence suggests that increases in markers of despair contribute to decreased voter turnout (Franko & Pacheco, 2025). In particular, the observed transition in these data from Competitive to Monopoly status in both peer groups generates the hypothesis that as monopolies arose in these counties, Black people died at increasing rates from deaths of despair, regardless of whether the Democratic or Republican party was gaining control.

While political monopolies appeared to be important in both peer groups, multiple regression analyses generated the hypothesis that political forces operated differently in each peer group. In peer group two, the duration of one-party governance as of 2020 was key. We hypothesize that just as state political monopolies tend to reduce investment in human capital, particularly as it affects education, transportation, and health (Das et al., 2022), so too may county-level political monopolies. Ecosocial theory (Krieger, 2012) postulates that the poor and underserved are likely to experience the greatest adverse effects.

In contrast, the percent nH Black population was important in peer group one. Nonetheless, while the percentage of nH Black residents stayed the same or declined in Monopoly, Transitional, and Competitive counties, mortality from Deaths of Despair increased (p < .001 in all three groups). As shown by the stability of percent nH Black population over time in the study, however, the observed increases in mortality from Deaths of Despair were not driven by increases in the percentage of nH Black residents. An alternative explanation derives from evidence of intense social pressure to vote for the Democratic Party within the Black community (White & Laird, 2020). Moreover, Black people whose friendship networks are mostly made up of other Blacks are significantly more likely to identify as Democrats (White & Laird, 2020). The latter observation is corroborated in the study results by the significantly higher exposure indices in Monopoly than in Competitive historic metropolitan areas in peer group one. Collectively, the present data generate the hypothesis that a high percent nH Black population may equate with high mortality from deaths of despair, at least in part, because political boundary setting within the Black community results in support for Democratic Party governance.

Political scientists have previously disagreed about the effects of one-party support. White and Laird (2020) argue that the ability of the Black community to define its political boundaries in support of one political party is, “Essential to its existence and its ability to mobilize . . . [and] effectively make demands on government” (p. 173). Cohen (1999), in contrast, points to deep fractures within the Black community and proposes that boundary setting by Black leaders may have paradoxical effects:

. . .as indigenous institutions increasingly involve themselves in the political, social and economic activities of dominant groups, the goal of maintaining and expanding integration into dominant society often becomes the primary concern of such institutions. This may come at the expense of more radical transformation and redistribution of dominant institutional resources. (p. 64)

Future research should explore this issue within the context of differential access to safety resources in the face of an increasingly toxic illicit drug supply, worsening economic worries, increased allostatic load, and poorer access/utilization of mental health and substance abuse programs (Brown et al., 2016; Friedman & Hansen, 2024).

Limitations of the present data include its descriptive nature, which makes the information suitable for generating but not testing hypotheses. Moreover, the data are ecological. It is therefore possible that observed results reflect differences in the characteristics of people residing in counties with different levels of local political monopoly rather than political monopoly alone. The usual limits of death certificate data, including the exclusion of persons who are not legal residents of the United States, also apply (Hennekens & Buring, 1987), as does the inability to address potential confounding by matching variables. The most important limitation, however, may be the slender capacity of widely used epidemiological and biostatistical analyses to account for the vast array of potential confounders, which might be affecting the observed outcomes (Langston et al., 2014). Such confounders range from the multitude of forces shaping governmental structures to the thousands of other social, demographic, and environmental factors which have been identified as part of the public health exposome (Juarez et al., 2014). Shifts in state-level policies have been associated with changes in life expectancy by state (Montez et al., 2020), and therefore, the complex interplay between state and local policies on population health might be aided by exposome analyses – pioneered by geographers and community scientists – which make it possible to simultaneously assess those thousands of factors in scientifically rigorous ways (Juarez et al., 2014; Monnat & Brown, 2017).

Deaths of despair are often discussed as a social problem among rural, non-Hispanic Whites in areas that have experienced substantial economic decline (Lee et al., 2023; Stein et al., 2017). In these areas of declining industry, non-Hispanic Whites with lower education and poor access to resources have been identified as particularly vulnerable to this phenomenon (Lee et al., 2023), although additional analysis suggests that drug overdose is also a disproportionate driver of mortality increases (Masters et al., 2018). In the 1990s and 2000s, this theme was demonstrated as drug-related mortality increased substantially among non-Hispanic Whites (Tilstra et al., 2021). However, post-2010, non-Hispanic Black men and women have exhibited substantial increases in drug-related mortality as the opioid crisis has shifted to heroin and synthetic opioids such as fentanyl (Friedman & Hansen, 2024; Tilstra et al., 2021).

In summary, these results generate the hypothesis that political monopolies in these urban peer counties are associated with increased mortality from deaths of despair among their urban, non-Hispanic Black residents. As with this and all other hypotheses generated by these data, testing by analytic epidemiologic research is required to confirm or deny.

Supplemental Material

sj-docx-1-ccj-10.1177_10439862261448586 – Supplemental material for Political Determinants of Health: Mayoral Governance and Deaths of Despair in Urban American Peer Counties

Supplemental material, sj-docx-1-ccj-10.1177_10439862261448586 for Political Determinants of Health: Mayoral Governance and Deaths of Despair in Urban American Peer Counties by Robert S. Levine, Lisaann S. Gittner, Jeff A. Dennis, Jerry M. Stott, Jaden M. Harris, Frederick Ernst and Dennis Patterson in Journal of Contemporary Criminal Justice

Footnotes

Ethical Considerations

This study received ethical approval from the Baylor College of Medicine IRB (approval for protocol # H-57501) on June 05, 2025. This is an IRB-approved retrospective study. All patient information was de-identified, and patient consent was not required. Participant data will not be shared with third parties. Baylor College of Medicine Institutional Review Board Statement of June 5, 2025: “The information included in the proposed activity will not consist of identifiable information, so no authorization or waiver of authorization by patients for the release of individually identifiable protected health information will be required. This activity does not fall under the regulations for IRB review of human subjects research found at 45 CFR 46.102(e)(5) and 45 CFR 46.102(e)(6) as the information is not considered identifiable information.”

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors received no external financial support for the research, authorship, and/or publication of this article. Access to email services was provided by the Baylor College of Medicine and Texas Tech University; access to the Texas Medical Center Library was provided by the Baylor College of Medicine; Texas Tech University provided access to the University Libraries.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data are available for sharing.

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Robert S. Levine, MD, is a Clinical Professor at the Baylor College of Medicine. He has worked as a public health officer and as a researcher on a variety of topics, including the development of blood pressure among infants, a training model for integrating curative and preventive medicine, and descriptive studies of mortality.

Lisaann S. Gittner, PhD, is a Professor of Public Health at Texas Tech University Health Sciences Center. She has over 30 years of experience across academia, health care, and applied research. Her work examines the social, political and economic determinants of poor outcomes in geographic areas, criminal justice contact among people with serious mental illness, and the ways local governance priorities shape health and justice systems. She has also held senior research leadership roles in the pharmaceutical and health care sectors.

Jeff A. Dennis, PhD, is Assistant Dean for Student Affairs and Associate Professor of Public Health in the Julia Jones Matthews School of Population and Public Health at Texas Tech University Health Sciences Center. His research examines the intersection of public health and the criminal justice system, with a focus on mental health diversion, interagency collaboration, and rural populations.

Jerry M. Stott, PhD, is a doctoral candidate in the Department of Political Science at the University of Utah. He received a Master of Public Administration from the University of Wyoming in 2018. He is the recipient of the 2025 to 2026 Schwartz-Shea and Yanow Dissertation Fellowship for Interpretive Research in the Department of Political Science. His research focus is environmental law, environmental justice, public lands policy, sustainability, and social equity in public administration.

Jaden M. Harris, MA, is a researcher with the Department of Family and Community Medicine at the Baylor College of Medicine.

Frederick Ernst, PhD, is a visiting Assistant Professor at Indiana University, Bloomington. His work has included psychological studies of blood pressure and perspectives on health attitudes. He currently teaches several courses, including Introduction to Psychology and Brain Sciences, Abnormal Psychology, Criminal Behavior, and Health Psychology.

Dennis Patterson, PhD, is a Professor of Political Science at Texas Tech University. He received his Ph.D. in Political Science from the University of California, Los Angeles in 1995, and his areas of specialization include Asian politics and security with an emphasis on Japan and South Korea, as well as Statistical and Formal methods. He is also the author of Improving South Korea–Japan Relations (2023), The Japan that Never Was: Explaining the Rise and Decline of a Misunderstood Country (SUNY Press, 2004), and South Korea’s Rise to International Influence (Lexington Books, 2019), as well as numerous book chapters and peer-reviewed articles. He previously taught at the University of Wisconsin, Milwaukee, and Michigan State University.

References

Auerbach

Miller

B. F.

(2018). Deaths of despair and building a national resilience strategy. Journal of Public Health Management and Practice, 24(4), 297–300. https://doi.org/10.1097/PHH.0000000000000835

Baerwald

T. J.

(1977). County seats in the middle western urban system. The Geographical Bulletin, 14(1), 2. https://digitalcommons.kennesaw.edu/thegeographicalbulletin/vol14/iss1/2

Ballotpedia. (n.d.). Analysis of spending in America’s largest cities. https://ballotpedia.org/Analysis_of_spending_in_America%27s_largest_cities

Barrilleaux

Holbrook

Langer

(2002). Electoral competition, legislative balance, and American state welfare policy. American Journal of Political Science, 46(2), 415–427. https://doi.org/10.2307/3088385

Brown

T. N.

Turner

R. J.

Moore

T. R.

(2016). The multidimensionality of health: Associations between allostatic load and self-report health measures in a community epidemiologic study. Health Sociology Review, 25(3), 272–287. https://doi.org/10.1080/14461242.2016.1184989

Burke

(2020). Nationalization and its consequences for state legislatures. Social Science Quarterly, 102(1), 269–280. https://doi.org/10.1111/ssqu.12926

Case

Deaton

(2015). Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. Proceedings of the National Academy of Sciences, 112(49), 15078–15083. https://doi.org/10.1073/pnas.1518393112

Case

Deaton

(2017). Mortality and morbidity in the 21st century. Brookings Papers on Economic Activity, 2017(1), 397–476. https://doi.org/10.1353/eca.2017.0005

Case

Deaton

(2020). Deaths of despair and the future of capitalism. Princeton University Press.

10.

Centers for Disease Control and Prevention, National Center for Health Statistics. (2021). National Vital Statistics System, mortality 1999-2020 on CDC WONDER online database. http://wonder.cdc.gov/mcd-icd10.html

11.

Chien

L. C.

Holloway

Gakh

Coughenour

(2022). A novel gun law strength index and influential state gun laws for firearm homicide in the United States, 1999-2018. Public Health, 210, 91–98. https://doi.org/10.1016/j.puhe.2022.06.015

12.

Cockerham

W. C.

Hamby

B. W.

Oates

G. R.

(2017). The social determinants of chronic disease. American Journal of Preventive Medicine, 52(1S1), S5–S12. https://doi.org/10.1016/j.amepre.2016.09.010

13.

Cohen

C. J.

(1999). The boundaries of blackness: AIDS and the breakdown of black politics. University of Chicago Press.

14.

County Health Rankings and Roadmaps. (2017). Peer counties methodology. Robert Wood Johnson Foundation. https://www.countyhealthrankings.org/resources/peer-counties-methodology

15.

County Health Rankings and Roadmaps. (2023). County health rankings and roadmaps. Robert Wood Johnson Foundation. https://www.countyhealthrankings.org/

16.

Cutler

D. M.

Lleras-Muney

(2010). Understanding differences in health behaviors by education. Journal of Health Economics, 29(1), 1–28. https://doi.org/10.1016/j.jhealeco.2009.10.003

17.

Das

Sinclair

Webster

S. W.

Yan

(2022). All (mayoral) politics is local. The Journal of Politics, 84(2), 1021–1034. https://doi.org/10.1086/716945

18.

Dawes

D. E.

(2020). The political determinants of health. Johns Hopkins University Press.

19.

Dwyer-Lindgren

Bertozzi-Villa

Stubbs

R. W.

Morozoff

Shirude

Unützer

Naghavi

Mokdad

A. H.

Murray

C. J. L.

(2018). Trends and patterns of geographic variation in mortality from substance use disorders and intentional injuries among US counties, 1980-2014. JAMA, 319(10), 1013–1023. https://doi.org/10.1001/jama.2018.0900

20.

Eisner

Averdijk

Kaiser

Murray

A. L.

Nivette

Shanahan

van Gelder

J.-L.

Ribeaud

(2021). The association of polyvictimization with violent ideations in late adolescence and early adulthood: A longitudinal study. Aggressive Behavior, 47(4), 472–482. https://doi.org/10.1002/ab.21965

21.

Farmer

J. L.

(2018). Connecting theory with practice: The institutional evolvement of U.S. counties functioning as regional service providers. American Review of Public Administration, 48(8), 808–821. https://doi.org/10.1177/0275074017730399

22.

Fedeli

Zoppini

Goldoni

C. A.

Corti

M. C.

Schievano

(2015). Multiple causes of death analysis of chronic diseases: The example of diabetes. Population Health Metrics, 13, Article 21. https://doi.org/10.1186/s12963-015-0056-y

23.

Feinberg

R. A.

Miller

F. G.

Weiss

R. F.

Steigleder

M. K.

Lombardo

J. P.

(1982). Motivational aspects of learned helplessness. The Journal of General Psychology, 106(1), 273–311. https://doi.org/10.1080/00221309.1982.9709895

24.

Franko

Pacheco

(2025). Despair and voter turnout in the United States. Journal of Health Politics, Policy and Law, 50(3), 337–370. https://doi.org/10.1215/03616878-11670168

25.

Freemantle

(2000). StatsDirect—Statistical software for medical research in the 21st century. BMJ, 321(7275), Article 1536. https://doi.org/10.1136/bmj.321.7275.1536

26.

Friedman

Hansen

(2024). Trends in deaths of despair by race and ethnicity from 1999 to 2022. JAMA Psychiatry, 81(7), 731–732. https://doi.org/10.1001/jamapsychiatry.2024.0303

27.

Fry-Johnson

Y. W.

Levine

Rowley

Agboto

Rust

(2010). United States Black:White infant mortality disparities are not inevitable: Identification of community resilience independent of socioeconomic status. Ethnicity & Disease, 20(1 Suppl. 1), S131–S135.

28.

Gamm

Kousser

(2010). Broad bills or particularistic policy? Historical patterns in American state legislatures. American Political Science Review, 104(1), 151–170. https://doi.org/10.1017/S000305540999030X

29.

Gamm

Kousser

(2021). Life, literacy, and the pursuit of prosperity: Party competition and policy outcomes in 50 states. American Political Science Review, 115(4), 1442–1463. https://doi.org/10.1017/S0003055421000617

30.

Glantz

S. A.

(1992). Primer of biostatistics (3rd ed.). McGraw-Hill.

31.

Gordon

(2008). Mapping decline: St. Louis and the fate of the American city. University of Pennsylvania Press.

32.

Hare

Poole

K. T.

(2014). The polarization of contemporary American politics. Polity, 46(3), 411–429. https://doi.org/10.1057/pol.2014.10

33.

Hennekens

C. H.

Buring

(1987). Epidemiology in medicine ( Mayrent

S. L.

, Ed.). Little, Brown.

34.

Hiroto

D. S.

(1974). Locus of control of learned helplessness. Journal of Experimental Psychology, 102(2), 187–193. https://doi.org/10.1037/h0035910

35.

Holbrook

T. M.

Van Dunk

(1993). Electoral competition in the American states. American Political Science Review, 87(4), 955–962. https://doi.org/10.2307/2939454

36.

Hyra

D. S.

(2017). Race, class, and politics in the cappuccino city. University of Chicago Press.

37.

Juarez

P. D.

Matthews-Juarez

Hood

D. B.

Levine

R. S.

Kilbourne

B. J.

Langston

M. A.

Al-Hamdan

M. Z.

Crosson

W. L.

Estes

M. G.

Estes

S. M.

Agboto

V. K.

Robinson

Wilson

Lichtveld

M. Y.

(2014). The public health exposome: A population-based, exposure science approach to health disparities research. International Journal of Environmental Research and Public Health, 11(12), 12866–12895. https://doi.org/10.3390/ijerph111212866

38.

Jurjevich

(2018). Who votes for mayor? Unfortunately, not many of us. Medium. https://medium.com/@whovotesformayor/whovotes-851e631fbeaf

39.

Jurjevich

Keisling

Rancik

Gorecki

Hawke

(2016). Who votes for mayor? Portland State University PDX Scholar. https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1167&context=usp_fac

40.

King

Scheiring

Nosrati

(2022). Deaths of despair in comparative perspective. Annual Review of Sociology, 48, 299–317. https://doi.org/10.1146/annurev-soc-03020031757

41.

Krieger

(2012). Methods for the scientific study of discrimination and health: An ecosocial approach. American Journal of Public Health, 102(5), 936–944. https://doi.org/10.2105/AJPH.2011.300544

42.

Langston

M. A.

Levine

R. S.

Kilbourne

B. J.

Rogers

L. G.

Kershenbaum

A. D.

Baktash

S. H.

Coughlin

S. S.

Saxton

A. M.

Agboto

V. K.

Hood

D. B.

Litchveld

M. Y.

Oyana

T. J.

Matthews-Juarez

Juarez

P. D.

(2014). Scalable combinatorial tools for health disparities research. International Journal of Environmental Research and Public Health, 11(10), 10419–10443. https://doi.org/10.3390/ijerph111010419

43.

Lee

J. H.

Wheeler

D. C.

Zimmerman

E. B.

Hines

A. L.

Chapman

D. A.

(2023). Urban–rural disparities in deaths of despair: A county-level analysis 2004–2016 in the US. American Journal of Preventive Medicine, 64(2), 149–156. https://doi.org/10.1016/j.amepre.2022.08.015

44.

Lee

L. K.

Fleegler

E. W.

Farrell

Avakame

Srinivasan

Hemenway

Monuteaux

M. C.

(2017). Firearm laws and firearm homicides: A systematic review. JAMA Internal Medicine, 177(1), 106–119. https://doi.org/10.1001/jamainternmed.2016.7051

45.

Lefcourt

H. M

. (1966). Internal versus external control of reinforcement: A review. Psychological Bulletin, 65(4), 206–220. https://doi.org/10.1037/h0023116

46.

Levine

R. S.

Briggs

N. C.

Hollar

Husaini

B. A.

Hennekens

C. H.

Kilbourne

(2007). Descriptive and analytic epidemiologic studies to identify modifiable determinants of disparities in mortality rates between Blacks and Whites. Ethnicity & Disease, 17(2), 280–283.

47.

Magaloni

Kricheli

(2010). Political order and one-party rule. Annual Review of Political Science, 13, 123–143. https://doi.org/10.1146/annrev.polisci.031908.220529

48.

Masters

R. K.

Tilstra

A. M.

Simon

D. H.

(2018). Explaining recent mortality trends among younger and middle-aged White Americans. International Journal of Epidemiology, 47(1), 81–88. https://doi.org/10.1093/ije/dyx127

49.

Mejia

M. C.

Hennekens

C. H.

Levine

R. S.

Kitsantas

(2024). Deaths of despair: A major and increasing contributor to United States deaths. Advances in Preventive Medicine and Health Care, 7, Article 1062. https://doi.org/10.29011/2688-996X.001062

50.

Monnat

S. M.

Brown

D. L.

(2017). More than a rural revolt: Landscapes of despair and the 2016 presidential election. Journal of Rural Studies, 55, 227–236. https://doi.org/10.1016/j.jrurstud.2017.08.010

51.

Montez

J. K.

Beckfield

Cooney

J. K.

Grumbach

J. M.

Hayward

M. D.

Koytak

H. Z.

Woolf

S. H.

Zajacova

(2020). US state policies, politics, and life expectancy. The Milbank Quarterly, 98(3), 668–699. https://doi.org/10.1111/1468-0009.12469

52.

National Academies of Sciences, Engineering, and Medicine. (1994). Understanding and preventing violence, Volume 4: Consequences and control. The National Academies Press. https://doi.org/10.17226/4422

53.

Navarro

(2011). The importance of politics in policy. Australian and New Zealand Journal of Public Health, 35(4), 313–314. https://doi.org/10.1111/j.1753-6405.2011.00715.x

54.

Pacheco

LaCombe

(2022). The link between democratic institutions and population health in the American states. Journal of Health Politics, Policy and Law, 47(5), 527–554. https://doi.org/10.1215/03616878-9742618

55.

Parry

J. A.

Dowdle

A. J.

Long

A. B.

Kloss

J. R.

(2022). The rule, not the exception: One-party monopolies in the American states. State Politics & Policy Quarterly, 22(2), 226–245. https://doi.org/10.1017/spq.2022.2

56.

Rotter

J. B.

(1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs: General and Applied, 80(1), 1–28. https://doi.org/10.1037/h0092976

57.

Sørensen

R. J.

(2014). Political competition, party polarization, and government performance. Public Choice, 161(3/4), 427–450. https://doi.org/10.1007/s11127-014-0168-0

58.

Saunders

(2022, July 18). Do states with easier access to guns have more suicide deaths by firearm? Kaiser Family Foundation. https://www.kff.org/mental-health/issue-brief/do-states-with-easier-access-to-guns-have-more-suicide-deaths-by-firearm/

59.

Schaffner

B. F.

Streb

Wright

(2001). Teams without uniforms: The nonpartisan ballot in state and local elections. Political Research Quarterly, 54(1), 7–30.

60.

Seligman

M. E. P.

(1974). Depression and learned helplessness. In Friedman

R. J.

Katz

M. M.

(Eds.), The psychology of depression: Contemporary theory and research (pp. 83–113). Winston-Wiley.

61.

Shanahan

Hill

S. N.

Gaydosh

L. M.

Steinhoff

Costello

E. J.

Dodge

K. A.

Harris

K. M.

Copeland

W. E.

(2019). Does despair really kill? A roadmap for an evidence-based answer. American Journal of Public Health, 109(6), 854–858. https://doi.org/10.2105/AJPH.2019.305016

62.

Stein

E. M.

Gennuso

K. P.

Ugboaja

D. C.

Remington

P. L.

(2017). The epidemic of despair among white Americans: Trends in the leading causes of premature death, 1999–2015. American Journal of Public Health, 107(10), 1541–1547. https://doi.org/10.2105/AJPH.2017.303941

63.

Terry

W. C.

(2016). The electoral determinants of state welfare effort in the U.S. South, 1960–2008. State Politics & Policy Quarterly, 16(4), 455–480. https://doi.org/10.1177/1532440016645553

64.

Tilstra

A. M.

Simon

D. H.

Masters

R. K.

(2021). Trends in “deaths of despair” among working-aged White and Black Americans, 1990–2017. American Journal of Epidemiology, 190(9), 1751–1759. https://doi.org/10.1093/aje/kwab065

65.

U.S. Census Bureau. (1906). Twelfth census of the United States—1900, supplementary analysis. U.S. Department of Commerce and Labor. http://www2.census.gov/prod2/decennial/documents/00186079ch01.pdf

66.

Wallace

(2000). Life and death in upper Manhattan and the Bronx: Toward an evolutionary perspective on catastrophic social change. Environment and Planning A: Economy and Space, 32(7), 1245–1266. https://doi.org/10.1068/a32208

67.

Weiss

J. M.

Simson

P. G.

Ambrose

M. J.

Webster

Hoffman

L. J.

(1985). Neurochemical basis of behavioral depression. In Katkin

Manuck

(Eds.), Advances in behavioral medicine (Vol. 1, pp. 233–275). JAI Press.

68.

White

I. K.

Laird

C. N.

(2020). Steadfast Democrats: How social forces shape Black political behavior. Princeton University Press.

69.

Zuroff

D. J.

(1980). Learned helplessness in humans: An analysis of learning processes and the roles of individual and situational differences. Journal of Personality and Social Psychology, 39(1), 130–146. https://doi.org/10.1037/0022-3514.39.1.130

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.16 MB