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. 2021 Jul 21;16(7):e0254001. doi: 10.1371/journal.pone.0254001

Viewing the US presidential electoral map through the lens of public health

Tymor Hamamsy 1,2,*, Michael Danziger 3, Jonathan Nagler 1,4, Richard Bonneau 1,2,5,6
Editor: M Harvey Brenner7
PMCID: PMC8294501  PMID: 34288913

Abstract

Health, disease, and mortality vary greatly at the county level, and there are strong geographical trends of disease in the United States. Healthcare is and has been a top priority for voters in the U.S., and an important political issue. Consequently, it is important to determine what relationship voting patterns have with health, disease, and mortality, as doing so may help guide appropriate policy. We performed a comprehensive analysis of the relationship between voting patterns and over 150 different public health and wellbeing variables at the county level, comparing all states, including counties in 2016 battleground states, and counties in states that flipped from majority Democrat to majority Republican from 2012 to 2016. We also investigated county-level health trends over the last 30+ years and find statistically significant relationships between a number of health measures and the voting patterns of counties in presidential elections. Collectively, these data exhibit a strong pattern: counties that voted Republican in the 2016 election had overall worse health outcomes than those that voted Democrat. We hope that this strong relationship can guide improvements in healthcare policy legislation at the county level.

Introduction

Healthcare is one of the top priorities for voters in the United States [1, 2]. In some 2020 polls, a substantial percentage of Democratic voters indicated it was their top priority [3]. This party-specific preference was reflected in the Democratic primary candidates debates, in which candidates devoted more time to healthcare than any other topic [4]. While still important to Republican voters, it fell behind the issues of terrorism, the economy, social security, immigration, and the military, with an 18 point gap between Democrat and Republican voters on the issue of health care costs and a 13 point gap on the issue of Medicare reform [2]. Prior to the 2016 election, surveys revealed how polarized voters were on health policy issues like the Affordable Care Act (ACA) [5].

In the present study, counties are categorized as “Republican” or “Democrat”, referring to the political party that won the majority of votes in the county in the presidential election for that year (i.e. 2012 or 2016). Likewise, when states are referred to as being “Republican” or “Democrat”, unless otherwise stated, it is the political party that won the state in the presidential election for that year that is being referenced. "Battleground states” are the tightly contested states of the 2016 Presidential election: Nevada, Colorado, Virginia, Florida, Michigan, Minnesota, Wisconsin, Iowa, New Hampshire, Ohio, and Pennsylvania.

An important prerequisite to understanding the mechanisms driving this significant difference in topical focus is the systematic examination of demographic differences between Republican and Democratic voters [6]. Recent work has demonstrated a contrasting economic realities between Republican and Democratic districts, showing divergence between their economic fortunes and wealth trajectories from 2008 to 2018 [7]. For example, the median household income in districts that voted majority Republican in the midterm elections declined from $55,000 to $53,000, while that of districts that voted majority Democrat experienced an increase from $54,000 to $61,000. Given these divergent economic realities for Republican and Democrat districts, it is not surprising that a recent analysis found that Democratic counties in the 2020 presidential election represented 70% of US GDP [8].

Just as the economy varies greatly by county, so too do health, disease, and mortality. The relationship between voting and health is broad, and previous studies have touched on the effects of health on voter participation, the relationship between life expectancy and voting patterns, and the relationship between health behaviors and voting patterns. Regarding health and voter participation, a study of 30 European countries found that health does have an effect on turnout and that this effect is largest among the elderly [9]. Similarly, a review of 17 studies examining the relationship between voting and health across the US and Europe demonstrated lower voter participation was consistently related to poor self-rated health [10]. Given its relationship to turnout, many analysts have stressed the importance of studying the correlation between health and partisanship in political science research [11]. Many studies, going back several US elections, have also investigated the relationship between health behaviors and voting. Health behavior research is essential because of its fundamental relationship to public health and mortality. For example, a landmark study from 2009 found that smoking and high blood pressure, both of which are preventable, were responsible for the largest number of deaths in the US, and a number of other dietary/lifestyle factors for chronic disease contributed significantly to the number of deaths in the US [12].

The importance of health behaviors to overall health and mortality have made them a popular topic to study alongside voting patterns. One study examining the association between health behavior and the Republican vote share in the 2008 and 2012 Presidential elections found that the Republican vote share was associated with higher odds of flu vaccination and cigarette smoking, but lower odds of avoiding fat/calories, fast/processed foods, and eating fruits and vegetables [13]. Other research found that liberal state ideology was related to lower adult smoking rates, and that this relationship could not be entirely explained by different state anti-smoking policies of the more liberal states [14]. A 2014 study demonstrated that at the state level, there are associations between voting patterns and adolescent vaccination for human papillomavirus (HPV), tetanus-containing (Tdap), and meningococcal (MCV4) vaccinations [15].

While state-level studies are useful, and while there are geographical units in the U.S. smaller than counties (e.g., zip codes), much of the public-health data collection in the U.S., and therefore research, is done at the county-level. And since there are voting data at the county level, an analysis of partisanship and public health at the county level allows for a more granular and nuanced investigation than an analysis at the district or state level. Furthermore, counties are a more natural geographical unit of analysis. As a result, much of the research investigating the relationship between public health and voting has been done at the county level, and we chose to do so as well.

In the 2012 election, it was found that higher county-level obesity prevalence rates were associated with higher support for the Republican Party Presidential candidate [16]. Earlier research also sought to quantify the extent to which county community health was associated with voting changes between the presidential elections of 2012 and 2016. This earlier research focused on the following variables: physically unhealthy days, mentally unhealthy days, percent food insecure, the teen birth rate, the primary care physician visit rate, the age-adjusted mortality rate, the violent crime rate, average health care costs, the percent diabetic, and the percent overweight or obese in a county [17]. Another county-level study in 2016 looked at Medicare claims data and found that counties with high levels of chronic opioid use were more likely to have voted Republican in the 2016 election, but also that much of that association could be explained by socioeconomic county-level factors [18], while another study used an aggregate measure of well-being from Gallup surveys to relate wellbeing to county level voting, and found that in 2016, counties that shifted from Democrat to Republican had lower wellbeing than those that did not [19].

The association between life expectancy, mortality, and partisanship in the US has previously been studied. Early studies found that the vote for Reagan (Republican) in 1980 was associated with lower mortality in states, and concluded that voting conservative was associated with lower mortality [20]. A later study followed 32,830 participants over a number of years, and found that conservatives and moderates had a greater risk of mortality than liberals, suggesting that party affiliation/political ideology was an associated predictor of mortality [21]. Recently, studies examining county-level trends in mortality rates for the different major causes of death showed strong geographical trends (i.e. regional, spatial patterns); one study found that geographic patterns varied meaningfully by cause of death, and there were clear geographic regions with elevated mortality [22].

Disparities have been demonstrated in life expectancy among U.S. counties over the period from 1980 to 2014 [23]. Part of these geographical differences and county-level inequalities are due to deaths of despair (drug overdose, alcoholic liver disease, and suicide deaths), highlighted as a driving factor in the rising midlife morbidity and mortality among white non-Hispanic Americans [24, 25]. Previous studies have shown strong associations between voting patterns, mortality, health, and disease in the 2016 presidential election. Strong associations have additionally been demonstrated between counties that flipped Republican in 2016 (i.e. those that voted Democrat in 2012) and the rising midlife mortality among white non-Hispanic Americans. This demographic was key to Donald Trump’s victory [26]. It has been shown that Trump outperformed Romney (i.e. Trump’s vote share for a county in 2016 exceeded Romney’s vote share in the 2012 Presidential election) in counties with high drug, alcohol, and suicide mortality rates [27]. A strong association has been shown between life expectancy and both the proportion of votes in a county that went Republican in 2016 as well as the Republican margin shift from 2012 to 2016. This highlights the diverging life expectancies of Republican and Democratic counties and the possible impact of life expectancy on voting behavior [28].

Investigating the relationship between voting patterns at the county level and health, disease, and mortality in the US is important for framing future narratives around healthcare reform. While previous studies have looked at the relationship between voting patterns and life expectancy, mortality risk, and public health variables individually, we performed a comprehensive analysis of the relationship between voting patterns and over 150 different public health and wellbeing variables. Our analysis compares counties in all states, including those in battleground states, and counties in states that flipped from Republican to Democrat from 2012 to 2016, investigating both the relationship of health and wellbeing with the voting margin shifts from 2012 to 2016 as well as overall voting proportions. We believe that investigating associations with shifts in voting and focusing on the battleground and states that flipped can provide discontinuities that allow higher-resolution exploration of associations between political and health outcomes. In addition to comparing recent values of these variables with county-level voting patterns, we examined the dynamics of different public health and wellbeing variables over the last 30+ years. We believe that examining these changes over time can both shed light on a changing electorate and elucidate healthcare trends in counties. Additionally, is our belief that this type of comprehensive exploratory analysis, including broader sets of public health variables than previous studies, can better indicate a clear partisan relationship to the variables examined. relationship between voting patterns in the US and public health, healthcare, life expectancy and mortality rates at the county level. We hope that highlighting these relationships permits better focus of healthcare legislative efforts for counties and that it can inform policy better tailored to the needs of a given locale.

Materials and methods

In order to show the relationship between voting patterns at the county level and more than 150 different public health, mortality, and life expectancy variables, data from a number of different publicly available sources were aggregated and aligned at the county level. The health and wellbeing data as well as their sources include: diabetes, physical inactivity, and obesity crude rates from the Centers for Disease Control and Prevention diabetes surveillance atlas [29]; mortality rate data from the Global Burden of Disease, including county level mortality rates for a number of respiratory diseases, infectious diseases, cardiovascular diseases, cancers, deaths of despair, and mortality risk at different ages as well as life expectancy [30]; healthcare cost data from the Centers for Medicare and Medicaid Services, including variables such as costs per capita and costs broken down by imaging/drugs/hospice/procedures/dialysis [31]; Medicaid-relevant data collected from the American Community Survey (ACS), including variables about Medicaid usage at the county level [32]; disability-related data also collected from the ACS; Insurance/Uninsurance rate information collected from the Small Area Health Insurance Estimates (SAHIE) [33]; and a number of public health and demographic variables were collected from the County Health Rankings resource, including health behavioral information (i.e. smoking, drinking, and food indices), access to healthcare, and demographic information, among other data [34]. All of these variables were categorized into the following groups: social, physical and economic environment; respiratory diseases; life expectancy and mortality; insurance and healthcare cost; infectious diseases; health outcomes; health behaviors; demographic; deaths of despair; clinical care; cardiovascular diseases; and cancers.

Political voting data at the county level for presidential voting in 2012 and 2016 were collected from the MIT Election project [35]. The margin shift was calculated by taking the difference in the Republican margin (Republican percentage of total vote minus Democratic percentage of total vote) from 2012 to 2016. We define Republican or Democratic counties as those that voted in favor of the Republican or Democratic candidate in 2016. Whenever feasible, data from years as close to 2016 as possible were used (while 2016 data are available for most sources, the GBD data are from 2014).

Pearson correlations and confidence intervals for the correlations between a selection of the collected public health-related variables, the percentage of voters in the county that voted for Donald Trump or Hillary Clinton, and the Republican margin shift were computed from 2012 to 2016 (Table 1). Correlations for counties from all states, counties from battleground states (defined as states that could be reasonably won by either party), and counties from states that “flipped” from Democrat in 2012 to Republican in 2016, are presented. The battleground states are: Nevada, Colorado, Virginia, Florida, Michigan, Minnesota, Wisconsin, Iowa, New Hampshire, Ohio, and Pennsylvania. The flipped states are: Michigan, Pennsylvania, Wisconsin and Maine. In S1 Table, the same data as Table 1 are presented with the inclusion of the remaining public health-related variables collected. In S2 Table, we use the same data and structure as S1 Table, except that the correlations are now weighed correlations, where the weights are equal to the base 10 logarithm of the population of the county.

Table 1. Pearson correlations between different public health-related variables with the percentage of voters in the county that voted for Donald Trump or Hilary Clinton, and the Republican margin shift (from 2012 to 2016).

All States All States All States Battle States Battle States Battle States Flip States Flip States Flip States
Variable % Trump 2016 % Clinton 2016 Rep. margin change % Trump 2016 % Clinton 2016 Rep. margin change % Trump 2016 % Clinton 2016 Rep. margin change category
Asthma -0.21 (-0.25 to -0.18) 0.26 (0.23 to 0.29) -0.05 (-0.08 to -0.01) 0.03 (-0.05 to 0.11) -0.01 (-0.09 to 0.07) 0 (-0.08 to 0.08) -0.23 (-0.34 to -0.1) 0.24 (0.11 to 0.35) 0.18 (0.06 to 0.3) Respiratory diseases
Chronic obstructive pulmonary 0.46 (0.43 to 0.49) -0.42 (-0.45 to -0.39) 0.26 (0.22 to 0.29) 0.34 (0.26 to 0.4) -0.3 (-0.37 to -0.23) 0.12 (0.04 to 0.2) 0.17 (0.05 to 0.29) -0.19 (-0.31 to -0.06) 0.47 (0.37 to 0.57) Respiratory diseases
Coal workers pneumoconiosis 0.1 (0.06 to 0.13) -0.09 (-0.13 to -0.06) 0.07 (0.03 to 0.1) 0.09 (0.01 to 0.17) -0.06 (-0.14 to 0.02) 0.12 (0.04 to 0.19) 0.22 (0.1 to 0.34) -0.19 (-0.31 to -0.07) 0.17 (0.04 to 0.29) Respiratory diseases
Interstitial lung disease -0.26 (-0.3 to -0.23) 0.26 (0.22 to 0.29) -0.07 (-0.1 to -0.03) -0.24 (-0.32 to -0.17) 0.26 (0.19 to 0.34) -0.1 (-0.18 to -0.03) -0.3 (-0.41 to -0.18) 0.27 (0.14 to 0.38) -0.07 (-0.2 to 0.06) Respiratory diseases
Mortality risk, age 0–5 0.07 (0.04 to 0.11) 0.02 (-0.02 to 0.05) 0.01 (-0.03 to 0.05) -0.08 (-0.16 to 0) 0.16 (0.08 to 0.24) -0.17 (-0.25 to -0.09) -0.1 (-0.22 to 0.03) 0.15 (0.02 to 0.27) 0.26 (0.14 to 0.38) Life expectancy and Mortality
Mortality risk, age 25–45 0.11 (0.07 to 0.14) -0.02 (-0.06 to 0.01) 0.08 (0.04 to 0.11) -0.02 (-0.1 to 0.06) 0.09 (0.01 to 0.17) -0.05 (-0.13 to 0.03) 0.06 (-0.07 to 0.18) 0 (-0.13 to 0.13) 0.47 (0.37 to 0.57) Life expectancy and Mortality
Mortality risk, age 45–65 0.16 (0.13 to 0.19) -0.06 (-0.1 to -0.03) 0.15 (0.11 to 0.18) 0.01 (-0.07 to 0.08) 0.08 (0 to 0.15) 0.01 (-0.07 to 0.09) 0.07 (-0.06 to 0.19) -0.02 (-0.14 to 0.11) 0.47 (0.36 to 0.56) Life expectancy and Mortality
Mortality risk, age 5–25 0.21 (0.18 to 0.25) -0.13 (-0.17 to -0.1) 0.11 (0.07 to 0.14) 0.1 (0.02 to 0.18) -0.04 (-0.12 to 0.04) 0.03 (-0.05 to 0.11) 0.26 (0.14 to 0.38) -0.22 (-0.34 to -0.1) 0.58 (0.49 to 0.66) Life expectancy and Mortality
Mortality risk, age 65–85 0.25 (0.21 to 0.28) -0.17 (-0.2 to -0.13) 0.2 (0.16 to 0.23) -0.02 (-0.1 to 0.06) 0.07 (-0.01 to 0.15) 0.12 (0.04 to 0.2) 0.16 (0.03 to 0.28) -0.13 (-0.26 to -0.01) 0.43 (0.32 to 0.53) Life expectancy and Mortality
prct_male_medicaid -0.16 (-0.19 to -0.12) 0.21 (0.18 to 0.24) 0.19 (0.16 to 0.22) -0.25 (-0.32 to -0.17) 0.28 (0.21 to 0.35) 0.2 (0.13 to 0.28) 0.03 (-0.09 to 0.16) -0.03 (-0.16 to 0.1) 0.53 (0.43 to 0.61) Insurance and Healthcare cost
prct_female_medicaid -0.15 (-0.19 to -0.12) 0.21 (0.18 to 0.24) 0.22 (0.18 to 0.25) -0.23 (-0.31 to -0.16) 0.27 (0.19 to 0.34) 0.23 (0.15 to 0.3) 0.04 (-0.09 to 0.17) -0.04 (-0.16 to 0.09) 0.53 (0.43 to 0.61) Insurance and Healthcare cost
Uninsured %: < = 138% of Poverty 0.25 (0.22 to 0.28) -0.2 (-0.24 to -0.17) -0.37 (-0.4 to -0.34) 0.17 (0.09 to 0.25) -0.1 (-0.18 to -0.02) -0.44 (-0.5 to -0.37) -0.13 (-0.26 to -0.01) 0.11 (-0.02 to 0.23) -0.12 (-0.24 to 0.01) Insurance and Healthcare cost
Uninsured %: < = 400% of Poverty 0.19 (0.15 to 0.22) -0.13 (-0.17 to -0.1) -0.34 (-0.37 to -0.31) 0.07 (-0.01 to 0.15) 0 (-0.08 to 0.08) -0.41 (-0.47 to -0.34) -0.13 (-0.26 to -0.01) 0.12 (-0.01 to 0.24) 0.04 (-0.09 to 0.16) Insurance and Healthcare cost
Uninsured %: All Incomes 0.22 (0.18 to 0.25) -0.15 (-0.19 to -0.12) -0.24 (-0.27 to -0.2) 0.12 (0.04 to 0.2) -0.04 (-0.12 to 0.04) -0.29 (-0.36 to -0.22) 0.02 (-0.11 to 0.14) -0.03 (-0.16 to 0.1) 0.31 (0.19 to 0.42) Insurance and Healthcare cost
Part B Drugs Actual Costs -0.33 (-0.36 to -0.29) 0.33 (0.3 to 0.36) -0.29 (-0.32 to -0.26) -0.29 (-0.36 to -0.21) 0.32 (0.25 to 0.39) -0.37 (-0.44 to -0.3) -0.44 (-0.54 to -0.33) 0.48 (0.38 to 0.57) -0.47 (-0.56 to -0.36) Insurance and Healthcare cost
Emergency Department Visits -0.39 (-0.42 to -0.36) 0.4 (0.37 to 0.43) -0.29 (-0.32 to -0.25) -0.41 (-0.47 to -0.34) 0.44 (0.38 to 0.5) -0.39 (-0.46 to -0.32) -0.49 (-0.58 to -0.38) 0.52 (0.42 to 0.61) -0.39 (-0.49 to -0.27) Insurance and Healthcare cost
Actual Per Capita Costs -0.07 (-0.11 to -0.04) 0.14 (0.1 to 0.17) -0.12 (-0.15 to -0.08) 0 (-0.08 to 0.08) 0.05 (-0.03 to 0.12) -0.1 (-0.18 to -0.02) -0.1 (-0.23 to 0.02) 0.16 (0.04 to 0.28) -0.16 (-0.28 to -0.03) Insurance and Healthcare cost
Percent Male 0.18 (0.14 to 0.21) -0.22 (-0.25 to -0.18) 0.2 (0.16 to 0.23) 0.13 (0.05 to 0.2) -0.16 (-0.24 to -0.08) 0.39 (0.32 to 0.46) 0.32 (0.2 to 0.43) -0.33 (-0.44 to -0.21) 0.52 (0.42 to 0.61) Insurance and Healthcare cost
HIV AIDS -0.31 (-0.34 to -0.27) 0.38 (0.35 to 0.41) -0.21 (-0.24 to -0.17) -0.1 (-0.18 to -0.03) 0.16 (0.08 to 0.23) -0.19 (-0.27 to -0.11) -0.4 (-0.5 to -0.29) 0.46 (0.35 to 0.55) -0.25 (-0.37 to -0.13) Infectious diseases
Lower respiratory infections 0.13
(0.1 to 0.17)		 -0.05 (-0.08 to -0.01) 0.04 (0 to 0.07) -0.01 (-0.08 to 0.07) 0.07 (-0.01 to 0.15) -0.1 (-0.18 to -0.02) 0.08 (-0.05 to 0.2) -0.03 (-0.16 to 0.1) -0.02 (-0.15 to 0.11) Infectious diseases
Meningitis -0.17 (-0.21 to -0.14) 0.27 (0.23 to 0.3) -0.16 (-0.19 to -0.13) -0.25 (-0.32 to -0.18) 0.31 (0.24 to 0.38) -0.34 (-0.41 to -0.26) -0.27 (-0.39 to -0.15) 0.34 (0.23 to 0.45) -0.07 (-0.2 to 0.05) Infectious diseases
Tuberculosis -0.36 (-0.39 to -0.33) 0.44 (0.41 to 0.46) -0.24 (-0.27 to -0.21) -0.34 (-0.41 to -0.27) 0.41 (0.35 to 0.48) -0.38 (-0.44 to -0.31) -0.45 (-0.54 to -0.34) 0.5 (0.4 to 0.59) -0.19 (-0.31 to -0.06) Infectious diseases
Years of Potential Life Lost Rate 0.17 (0.13 to 0.2) -0.09 (-0.13 to -0.06) 0.21 (0.18 to 0.25) 0.05 (-0.03 to 0.14) 0.01 (-0.07 to 0.1) 0.07 (-0.01 to 0.15) 0.14 (0.02 to 0.27) -0.09 (-0.21 to 0.04) 0.39 (0.28 to 0.49) Health Outcomes
Physically Unhealthy Days 0.04 (0.01 to 0.08) 0.02 (-0.01 to 0.06) 0.12 (0.09 to 0.16) -0.19 (-0.26 to -0.11) 0.25 (0.18 to 0.33) -0.05 (-0.13 to 0.03) 0.07 (-0.06 to 0.19) -0.05 (-0.17 to 0.08) 0.37 (0.25 to 0.47) Health Outcomes
Mentally Unhealthy Days 0.02 (-0.01 to 0.06) 0.04 (0 to 0.07) 0.1 (0.07 to 0.14) -0.23 (-0.31 to -0.16) 0.31 (0.24 to 0.38) -0.11 (-0.19 to -0.03) 0.12 (-0.01 to 0.24) -0.08 (-0.21 to 0.05) 0.25 (0.13 to 0.37) Health Outcomes
Life Expectancy -0.23 (-0.26 to -0.19) 0.15 (0.12 to 0.19) -0.23 (-0.26 to -0.19) -0.08 (-0.16 to 0) 0.01 (-0.07 to 0.09) -0.13 (-0.21 to -0.05) -0.19 (-0.31 to -0.06) 0.15 (0.02 to 0.27) -0.45 (-0.55 to -0.34) Health Outcomes
Life Expectancy (White) -0.42 (-0.46 to -0.38) 0.33 (0.29 to 0.38) -0.38 (-0.42 to -0.34) -0.37 (-0.46 to -0.27) 0.28 (0.17 to 0.37) -0.38 (-0.47 to -0.28) -0.29 (-0.45 to -0.11) 0.23 (0.05 to 0.4) -0.71 (-0.79 to -0.6) Health Outcomes
Age-Adjusted Mortality 0.2 (0.16 to 0.23) -0.12 (-0.15 to -0.08) 0.21 (0.17 to 0.24) 0.09 (0.01 to 0.16) -0.02 (-0.1 to 0.06) 0.08 (0 to 0.16) 0.17 (0.04 to 0.29) -0.12 (-0.24 to 0.01) 0.44 (0.34 to 0.54) Health Outcomes
diabetes_crude 0.17 (0.14 to 0.2) -0.1 (-0.13 to -0.06) 0.23 (0.2 to 0.26) 0.13 (0.05 to 0.21) -0.07 (-0.15 to 0.01) 0.17 (0.09 to 0.25) 0.39 (0.28 to 0.49) -0.37 (-0.47 to -0.25) 0.38 (0.26 to 0.48) Health Behaviors
obesity_crude 0.16 (0.12 to 0.19) -0.1 (-0.14 to -0.07) 0.28 (0.24 to 0.31) 0.2 (0.12 to 0.27) -0.17 (-0.25 to -0.09) 0.33 (0.25 to 0.39) 0.26 (0.14 to 0.38) -0.26 (-0.38 to -0.14) 0.38 (0.27 to 0.49) Health Behaviors
physical_inactivity_crude 0.36 (0.33 to 0.39) -0.28 (-0.31 to -0.25) 0.3 (0.26 to 0.33) 0.38 (0.31 to 0.45) -0.31 (-0.38 to -0.24) 0.2 (0.13 to 0.28) 0.46 (0.35 to 0.55) -0.43 (-0.53 to -0.32) 0.53 (0.44 to 0.62) Health Behaviors
% Smokers 0.12 (0.09 to 0.16) -0.04 (-0.07 to 0) 0.35 (0.32 to 0.38) -0.03 (-0.11 to 0.05) 0.09 (0.01 to 0.17) 0.14 (0.06 to 0.22) 0.04 (-0.09 to 0.17) -0.04 (-0.16 to 0.09) 0.43 (0.32 to 0.53) Health Behaviors
Food Environment Index 0.06 (0.02 to 0.09) -0.1 (-0.13 to -0.06) 0.06 (0.03 to 0.1) -0.01 (-0.09 to 0.07) -0.02 (-0.1 to 0.06) 0.16 (0.08 to 0.24) 0.17 (0.05 to 0.3) -0.2 (-0.32 to -0.07) -0.19 (-0.31 to -0.06) Health Behaviors
% Excessive Drinking -0.16 (-0.19 to -0.12) 0.11 (0.07 to 0.14) 0.07 (0.03 to 0.1) -0.12 (-0.2 to -0.04) 0.05 (-0.03 to 0.13) 0.18 (0.1 to 0.26) -0.34 (-0.44 to -0.22) 0.3 (0.18 to 0.41) -0.25 (-0.36 to -0.12) Health Behaviors
% Food Insecure -0.14 (-0.17 to -0.1) 0.21 (0.18 to 0.24) -0.07 (-0.11 to -0.04) -0.21 (-0.28 to -0.13) 0.28 (0.21 to 0.35) -0.23 (-0.3 to -0.15) -0.15 (-0.27 to -0.02) 0.17 (0.05 to 0.29) 0.2 (0.08 to 0.32) Health Behaviors
Drug Overdose Mortality Rate 0.15 (0.11 to 0.2) -0.13 (-0.18 to -0.09) 0.3 (0.25 to 0.34) 0.12 (0.02 to 0.21) -0.07 (-0.17 to 0.03) 0.11 (0.01 to 0.21) 0.02 (-0.13 to 0.16) 0.06 (-0.09 to 0.2) 0.05 (-0.1 to 0.19) Health Behaviors
% Insufficient Sleep -0.16 (-0.19 to -0.12) 0.25 (0.22 to 0.28) 0.03 (-0.01 to 0.06) -0.27 (-0.35 to -0.2) 0.36 (0.29 to 0.43) -0.03 (-0.11 to 0.05) 0.09 (-0.04 to 0.22) -0.05 (-0.17 to 0.08) 0.14 (0.02 to 0.26) Health Behaviors
opioid_prescribing_rate 0.13 (0.09 to 0.16) -0.1 (-0.13 to -0.06) -0.02 (-0.05 to 0.02) 0.1 (0.02 to 0.18) -0.04 (-0.12 to 0.04) -0.06 (-0.14 to 0.02) 0.13 (0 to 0.25) -0.13 (-0.25 to 0) 0.16 (0.03 to 0.28) Health Behaviors
Alcohol use disorders -0.23 (-0.26 to -0.19) 0.18 (0.15 to 0.21) -0.02 (-0.06 to 0.01) -0.3 (-0.37 to -0.22) 0.27 (0.2 to 0.34) -0.02 (-0.1 to 0.06) -0.4 (-0.5 to -0.28) 0.4 (0.29 to 0.5) 0.09 (-0.04 to 0.21) Deaths of Despair
Drug use disorders 0.11 (0.07 to 0.14) -0.08 (-0.12 to -0.05) 0.09 (0.06 to 0.12) -0.15 (-0.23 to -0.07) 0.2 (0.12 to 0.27) -0.1 (-0.18 to -0.02) -0.06 (-0.18 to 0.07) 0.11 (-0.02 to 0.24) 0.04 (-0.09 to 0.17) Deaths of Despair
Interpersonal violence -0.29 (-0.32 to -0.26) 0.37 (0.34 to 0.4) -0.14 (-0.17 to -0.1) -0.3 (-0.37 to -0.22) 0.37 (0.3 to 0.43) -0.3 (-0.37 to -0.22) -0.36 (-0.47 to -0.24) 0.42 (0.31 to 0.52) -0.09 (-0.21 to 0.04) Deaths of Despair
% With Access -0.37 (-0.4 to -0.34) 0.32 (0.28 to 0.35) -0.25 (-0.28 to -0.22) -0.41 (-0.48 to -0.35) 0.39 (0.32 to 0.46) -0.21 (-0.28 to -0.13) -0.28 (-0.4 to -0.16) 0.28 (0.16 to 0.4) -0.38 (-0.48 to -0.26) Clinical Care
PCP Rate -0.35 (-0.38 to -0.32) 0.31 (0.28 to 0.35) -0.27 (-0.3 to -0.24) -0.3 (-0.37 to -0.23) 0.27 (0.2 to 0.35) -0.21 (-0.28 to -0.13) -0.35 (-0.46 to -0.23) 0.34 (0.22 to 0.45) -0.38 (-0.48 to -0.26) Clinical Care
Dentist Rate -0.38 (-0.41 to -0.35) 0.34 (0.31 to 0.37) -0.23 (-0.27 to -0.2) -0.35 (-0.42 to -0.28) 0.32 (0.25 to 0.39) -0.24 (-0.31 to -0.16) -0.47 (-0.57 to -0.37) 0.49 (0.39 to 0.58) -0.41 (-0.51 to -0.29) Clinical Care
MHP Rate -0.4 (-0.43 to -0.37) 0.35 (0.32 to 0.39) -0.23 (-0.26 to -0.19) -0.47 (-0.53 to -0.41) 0.46 (0.39 to 0.52) -0.26 (-0.33 to -0.18) -0.52 (-0.61 to -0.42) 0.5 (0.4 to 0.59) -0.35 (-0.46 to -0.23) Clinical Care
Preventable Hosp. Rate 0.13 (0.09 to 0.16) -0.05 (-0.09 to -0.02) 0.14 (0.11 to 0.17) -0.05 (-0.12 to 0.03) 0.08 (0 to 0.16) 0.16 (0.08 to 0.23) -0.02 (-0.15 to 0.1) 0.06 (-0.07 to 0.18) 0.15 (0.03 to 0.27) Clinical Care
% Screened -0.17 (-0.21 to -0.14) 0.16 (0.12 to 0.19) 0.09 (0.06 to 0.13) -0.11 (-0.19 to -0.03) 0.1 (0.02 to 0.18) 0.12 (0.04 to 0.2) -0.01 (-0.14 to 0.12) -0.03 (-0.15 to 0.1) -0.1 (-0.23 to 0.03) Clinical Care
% Vaccinated -0.23 (-0.26 to -0.19) 0.21 (0.18 to 0.25) -0.1 (-0.13 to -0.06) -0.35 (-0.42 to -0.28) 0.35 (0.28 to 0.42) -0.26 (-0.33 to -0.18) -0.34 (-0.45 to -0.22) 0.34 (0.23 to 0.45) -0.51 (-0.6 to -0.41) Clinical Care
Cardiomyopathy & myocarditis -0.2 (-0.23 to -0.16) 0.27 (0.24 to 0.31) -0.05 (-0.08 to -0.01) -0.25 (-0.32 to -0.17) 0.34 (0.27 to 0.41) -0.17 (-0.24 to -0.09) 0 (-0.13 to 0.13) 0.04 (-0.09 to 0.16) -0.09 (-0.21 to 0.04) Cardiovascular diseases
Cardiovascular diseases 0.23 (0.2 to 0.26) -0.14 (-0.17 to -0.11) 0.2 (0.17 to 0.24) 0.06 (-0.01 to 0.14) 0.01 (-0.07 to 0.09) 0.18 (0.11 to 0.26) 0.26 (0.14 to 0.38) -0.22 (-0.34 to -0.09) 0.42 (0.3 to 0.52) Cardiovascular diseases
Hypertensive heart disease -0.13 (-0.16 to -0.09) 0.18 (0.15 to 0.22) -0.13 (-0.17 to -0.1) -0.19 (-0.26 to -0.11) 0.23 (0.15 to 0.3) -0.22 (-0.3 to -0.14) -0.31 (-0.42 to -0.19) 0.32 (0.2 to 0.43) -0.12 (-0.24 to 0.01) Cardiovascular diseases
Ischemic heart disease 0.28 (0.25 to 0.31) -0.2 (-0.23 to -0.16) 0.26 (0.22 to 0.29) 0.14 (0.06 to 0.21) -0.06 (-0.14 to 0.01) 0.24 (0.17 to 0.32) 0.33 (0.21 to 0.44) -0.29 (-0.4 to -0.17) 0.47 (0.36 to 0.56) Cardiovascular diseases
Liver cancer -0.18 (-0.21 to -0.14) 0.25 (0.22 to 0.28) -0.13 (-0.16 to -0.1) -0.28 (-0.35 to -0.2) 0.32 (0.25 to 0.39) -0.12 (-0.2 to -0.04) -0.26 (-0.38 to -0.14) 0.32 (0.2 to 0.43) 0.24 (0.12 to 0.36) Cancers
Malignant skin melanoma 0.54 (0.51 to 0.56) -0.56 (-0.59 to -0.54) 0.15 (0.11 to 0.18) 0.46 (0.4 to 0.52) -0.44 (-0.5 to -0.37) -0.06 (-0.13 to 0.02) 0.34 (0.22 to 0.45) -0.36 (-0.46 to -0.24) 0.14 (0.02 to 0.27) Cancers
Stomach cancer -0.35 (-0.38 to -0.32) 0.44 (0.41 to 0.47) -0.16 (-0.2 to -0.13) -0.47 (-0.53 to -0.4) 0.52 (0.46 to 0.57) -0.13 (-0.21 to -0.05) -0.3 (-0.41 to -0.18) 0.37 (0.25 to 0.47) 0.03 (-0.09 to 0.16) Cancers
Testicular cancer 0.31 (0.28 to 0.34) -0.29 (-0.32 to -0.25) 0.27 (0.24 to 0.3) 0.25 (0.17 to 0.32) -0.23 (-0.31 to -0.16) 0.21 (0.13 to 0.29) 0.42 (0.31 to 0.52) -0.41 (-0.51 to -0.3) 0.52 (0.43 to 0.61) Cancers
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Correlations for counties from all states, counties from battleground states, and counties from states that flipped from Democratic in 2012 to Republican in 2016, are presented.

Every county was assigned as either Republican or Democratic depending on the majority vote in 2016, and the mean, median, 1st quartile, and 3rd quartile values for different public health-related variables were calculated. The differences in these values for Republican and Democratic counties are presented in Table 2, along with the Student t-test statistics and p values for the mean comparisons. S3 Table presents the same data as Table 2, except we additionally include the remaining public health-related variables that we collected.

Table 2. Quantile and mean comparisons of Republican and Democratic counties across select public-health measures.

Variable % Mean Difference % Median Difference % Top Quartile Difference % Bottom Quartile Difference t-test p value t-test t statistic category
Asthma -15.53% -2.09% -24.20% 6% 1.75E-12 -7.227185 Respiratory diseases
Interstitial lung disease -7.59% -7.41% -6.82% -6% 6.27E-11 -6.661729 Respiratory diseases
Chronic obstructive pulmonary 33.27% 31.82% 31.19% 33% 2.00E-85 22.186229 Respiratory diseases
Coal workers pneumoconiosis 360.40% 50.00% 75.00% 0% 7.99E-10 6.1667217 Respiratory diseases
Mortality risk, age 0–5 -2.31% 11.48% -10.87% 17% 0.252621008 -1.145192 Life expectancy and Mortality
Mortality risk, age 25–45 0.57% 11.03% -8.44% 19% 0.769660064 0.2929605 Life expectancy and Mortality
Mortality risk, age 45–65 3.77% 9.50% -1.84% 13% 0.012094384 2.5172813 Life expectancy and Mortality
Mortality risk, age 65–85 5.05% 6.41% 2.53% 7% 2.98E-13 7.460388 Life expectancy and Mortality
Mortality risk, age 5–25 8.86% 24.67% -0.44% 32% 4.31E-05 4.1224536 Life expectancy and Mortality
Part B Drugs Actual Costs -97.17% -85.75% -91.46% -60% 0.00270063 -3.012516 Insurance and Healthcare cost
Emergency Department Visits -97.13% -81.77% -89.77% -65% 0.002669971 -3.016033 Insurance and Healthcare cost
prct_male_medicaid -12.54% -9.83% -15.64% -5% 2.80E-10 -6.419353 Insurance and Healthcare cost
prct_female_medicaid -11.51% -7.56% -15.69% -5% 2.46E-09 -6.05624 Insurance and Healthcare cost
Actual Per Capita Costs -5.00% -3.64% -7.12% -3% 1.83E-10 -6.464521 Insurance and Healthcare cost
Percent Male 2.11% 2.24% 1.60% 2% 4.33E-20 9.4117126 Insurance and Healthcare cost
Uninsured %: < = 400% of Poverty 2.67% -1.50% 3.01% -2% 0.171888273 1.367543 Insurance and Healthcare cost
Uninsured %: All Incomes 6.38% 1.96% 4.48% 11% 0.005906647 2.7616682 Insurance and Healthcare cost
Uninsured %: < = 138% of Poverty 9.40% 11.25% 12.44% 0% 3.19E-06 4.6948186 Insurance and Healthcare cost
HIV AIDS -58.75% -57.86% -67.85% -45% 3.27E-30 -12.12769 Infectious diseases
Tuberculosis -43.03% -40.74% -46.67% -35% 1.28E-35 -13.4303 Infectious diseases
Meningitis -16.06% -6.98% -25.40% 0% 2.10E-17 -8.782586 Infectious diseases
Lower respiratory infections 2.62% 1.57% -1.82% 9% 0.124374871 1.5385809 Infectious diseases
Life Expectancy (White) -2.28% -2.57% -2.97% -2% 1.34E-21 -9.967263 Health Outcomes
Life Expectancy -1.41% -1.82% -2.21% -1% 1.07E-08 -5.804499 Health Outcomes
Mentally Unhealthy Days -0.59% 0.76% 1.82% -3% 0.399461955 -0.843066 Health Outcomes
Physically Unhealthy Days -0.59% 0.99% -0.48% 1% 0.530765566 -0.627172 Health Outcomes
Years of Potential Life Lost Rate 5.60% 16.53% 0.73% 23% 0.00682826 2.7151188 Health Outcomes
Age-Adjusted Mortality 7.07% 16.06% 1.65% 22% 1.27E-04 3.8574268 Health Outcomes
% Food Insecure -13.59% -4.74% -19.69% 1% 2.84E-12 -7.147556 Health Behaviors
% Insufficient Sleep -5.18% -5.05% -6.50% -4% 4.73E-14 -7.719342 Health Behaviors
% Excessive Drinking -2.24% -4.20% -4.44% 1% 0.031192086 -2.159568 Health Behaviors
% Smokers 4.07% 5.65% 1.45% 10% 0.001194561 3.2560056 Health Behaviors
Food Environment Index 5.89% 2.67% -0.30% 11% 4.28E-08 5.5554343 Health Behaviors
obesity_crude 8.91% 12.63% 2.59% 20% 2.73E-14 7.8114067 Health Behaviors
diabetes_crude 14.08% 21.43% 12.33% 20% 9.06E-13 7.289131 Health Behaviors
Drug Overdose Mortality Rate 14.97% 13.37% 11.53% 15% 6.15E-06 4.5607718 Health Behaviors
opioid_prescribing_rate 16.94% 23.90% 25.76% 13% 3.90E-09 5.9619381 Health Behaviors
physical_inactivity_crude 16.97% 18.88% 11.79% 29% 2.80E-32 12.557282 Health Behaviors
% 65 and over 19.68% 21.38% 17.73% 25% 1.62E-47 15.595938 Demographic
% Non-Hispanic White 50.20% 69.16% 22.38% 102% 2.72E-85 23.428989 Demographic
% Rural 93.91% 233.63% 80.54% 788% 2.51E-66 19.412684 Demographic
Interpersonal violence -38.67% -27.68% -41.59% -9% 3.10E-23 -10.42015 Deaths of Despair
Alcohol use disorders -25.97% -21.51% -19.34% -16% 4.65E-08 -5.545754 Deaths of Despair
Drug use disorders 8.55% 2.25% 16.33% -4% 0.001438594 3.1974805 Deaths of Despair
MHP Rate -52.14% -60.95% -50.92% -66% 7.32E-37 -13.68433 Clinical Care
Dentist Rate -36.14% -41.90% -33.63% -40% 2.84E-35 -13.25697 Clinical Care
PCP Rate -33.90% -37.34% -34.07% -33% 1.03E-32 -12.68707 Clinical Care
% With Access -18.96% -24.06% -18.62% -25% 1.26E-27 -11.43411 Clinical Care
% Vaccinated -6.02% -4.55% -4.08% -8% 3.03E-08 -5.60294 Clinical Care
% Screened -0.95% -2.44% 0.00% -3% 0.305959851 -1.024512 Clinical Care
Preventable Hosp. Rate 5.12% 6.49% 3.77% 13% 0.026039081 2.2310975 Clinical Care
Hypertensive heart disease -22.88% -17.79% -26.35% -14% 2.03E-09 -6.090275 Cardiovascular diseases
Cardiomyopathy & myocarditis -13.50% -15.48% -16.10% -6% 2.64E-11 -6.791812 Cardiovascular diseases
Cardiovascular diseases 7.69% 11.17% 4.65% 15% 2.99E-09 6.0209145 Cardiovascular diseases
Ischemic heart disease 13.42% 16.28% 13.25% 20% 1.94E-16 8.4505235 Cardiovascular diseases
Stomach cancer -20.13% -18.81% -25.95% -13% 1.79E-42 -14.92076 Cancers
Liver cancer -12.99% -12.05% -12.69% -10% 2.68E-20 -9.572363 Cancers
Testicular cancer 16.58% 21.74% 18.52% 25% 9.01E-27 11.261797 Cancers
Malignant skin melanoma 26.76% 27.04% 22.20% 31% 9.65E-98 24.990984 Cancers
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Every county was assigned as either Republican or Democratic depending on the majority vote in 2016, and the mean, median, 1st quartile, and 3rd quartile values for different public health-related variables were calculated. The differences in these values for Republican and Democratic counties are presented in Table 2, along with the Student t-test statistics and p values for the mean comparisons.

The distributions of different public health variables for Republican and Democratic counties are presented in Fig 1. The dynamics of different public health variables over time for counties based on their 2016 political party are compared across the aggregated data (Fig 2). The Republican margin shift was then compared with different public health variables for counties in states that flipped from Democratic in 2012 to Republican in 2016, indicating the 2016 total number of votes and the 2016 election outcome for counties by the size and color of their points, respectively (Fig 3).

Fig 1. Boxplots comparing select public health variables for Democratic and Republican counties.

Fig 1

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As shown, there are higher rates of lifestyle factors like smoking, obesity, and physical inactivity, and chronic diseases that are affected by lifestyle like cardiovascular diseases and diabetes in Republican counties than in Democratic counties. Democratic counties also have higher life expectancy, insurance rates, and lower mortality rates than Republican counties. The percentage of individuals who are food insecure or get insufficient sleep in Democratic counties is higher than in Republican counties.

Fig 2. Boxplots comparing Democratic and Republican counties (defined by 2016 presidential election voting) over time for a number of public health variables.

Fig 2

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For diabetes, obesity, and physical inactivity, there has been a growing divide between Republican and Democratic counties between 2006 and 2016. For mortality risk across every age group, Democratic counties have improved more than Republican counties over the time period from 1980 to 2014.

Fig 3. Scatterplots for all counties in the 4 states that flipped from Democrat to Republican in 2016 (Michigan, Pennsylvania, Wisconsin, Maine), showing the Republican margin shift on the x axis, and different demographic and public health related variables on the y axis.

Fig 3

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Counties are sized by the total number of votes made in the 2016 election, and they are colored by the 2012 and 2016 outcomes. The top row includes variables frequently discussed in the narrative around the electoral shift in these states, including the percentage of Non-Hispanic Whites in the county. There is a clear relationship between the obesity rate, physical inactivity rate, smoking rate, and life expectancy and the Republican margin shift in these states.

In the next part of our analysis, multivariate linear models were built to predict the percentage of voters in the county that voted for Donald Trump or Hillary Clinton, and the Republican margin shift. For every public health variable under consideration, we built a linear model to predict the 3 outcomes, using that variable as well as the following education, socio-economic status, and demographic control variables: "Graduation Rate", "% Some College", "% Non-Hispanic White", "% 65 and over", "Household Income", "% Severe Housing Cost Burden", "% Rural", "Actual Per Capita Costs", and "Percent Male". Before applying these linear models, we normalized all of the covariates to have a standard deviation of one and a mean of zero. S4 Table reports the coefficients for each public health variable under consideration, as well as the standard error for those coefficients, for each predictive model.

As many of the public health variables and control variables that we collected are correlated with one another, the next part of our analysis involved attempting to tease out the most important variables and categories in the relationship between health and voting. Before studying the importance of different variables, we first grouped them into their natural categories. For each category, using all of the variables in the category, we calculated the principal components. Fig 4 shows a plot of the first 2 principal components for 9 different categories of variables, with every county colored to indicate the 2012 to 2016 presidental outcome. For every category, we next applied lasso regression to predict the percentage of voters in the county that voted for Donald Trump or Hillary Clinton, and the Republican margin shift. Lasso regression is a form of linear regression that involves a shrinkage regularization term; this term performs both variable selection as well as regularization, by shrinking some coefficients to 0 [36]. For every category, we used the variables from that category as well as the education, socio-economic status, and demographic control variables as covariates. The variable importance of every variable was calculated, which in lasso regression, is the ranked absolute value of the coefficeints from the final model. Table 3 shows the variable importance of the variables from each category for predicting the percentage of voters in the county that voted for Donald Trump or Hillary Clinton, and the Republican margin shift.

Fig 4. Plot shows the first 2 principal components for 9 different categories of public health related variables.

Fig 4

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Counties are sized by the total number of votes made in the 2016 election, and they are colored by the 2012 and 2016 outcomes.

Table 3. For every category, we applied lasso regression to predict the percentage of voters in the county that voted for Donald Trump or Hillary Clinton, and the Republican margin shift.

Prediction Variable Category Rank in category Overall coefficient
Rep. margin change `Malignant skin melanoma` Cancers 1 0.02653
Rep. margin change `Colon & rectum cancer` Cancers 2 0.02544
Rep. margin change `Lip & oral cavity cancer` Cancers 3 0.01959
Rep. margin change `Tracheal, bronchus, & lung ` Cancers 4 0.01902
Rep. margin change `Multiple myeloma` Cancers 5 0.01751
Rep. margin change `Rheumatic heart disease` Cardiovascular diseases 1 0.02951
Rep. margin change `Aortic aneurysm` Cardiovascular diseases 2 0.02736
Rep. margin change `Ischemic heart disease` Cardiovascular diseases 3 0.01447
Rep. margin change `Ischemic stroke` Cardiovascular diseases 4 0.01022
Rep. margin change Endocarditis Cardiovascular diseases 5 0.00712
Rep. margin change `% Screened` Clinical Care 1 0.01589
Rep. margin change `PCP Rate` Clinical Care 2 0.01524
Rep. margin change `Preventable Hosp. Rate` Clinical Care 3 0.00636
Rep. margin change `% With Access` Clinical Care 4 0.00426
Rep. margin change `% Vaccinated` Clinical Care 5 0.00341
Rep. margin change `Self-harm` Deaths of Despair 1 0.02214
Rep. margin change `Alcohol use disorders` Deaths of Despair 2 0.01750
Rep. margin change `Drug use disorders` Deaths of Despair 3 0.00257
Rep. margin change `Interpersonal violence` Deaths of Despair 4 0.00142
Rep. margin change `% Food Insecure` Health Behaviors 1 0.03853
Rep. margin change `% Smokers` Health Behaviors 2 0.03674
Rep. margin change `% Excessive Drinking` Health Behaviors 3 0.03035
Rep. margin change `Food Environment Index` Health Behaviors 4 0.01850
Rep. margin change opioid_prescribing_rate Health Behaviors 5 0.01282
Rep. margin change Meningitis Infectious diseases 1 0.03231
Rep. margin change Hepatitis Infectious diseases 2 0.02039
Rep. margin change `Diarrheal diseases` Infectious diseases 3 0.01853
Rep. margin change `HIV AIDS` Infectious diseases 4 0.01534
Rep. margin change `Lower respiratory infections` Infectious diseases 5 0.00370
Rep. margin change `Mortality risk, age 45–65` Life expectancy, YLL, and Mortality 1 0.03939
Rep. margin change `Mortality risk, age 25–45` Life expectancy, YLL, and Mortality 2 0.03855
Rep. margin change `Years of Potential Life Lost Rate` Life expectancy, YLL, and Mortality 3 0.01646
Rep. margin change `Mortality risk, age 0–5` Life expectancy, YLL, and Mortality 4 0.01298
Rep. margin change `Age-Adjusted Mortality` Life expectancy, YLL, and Mortality 5 0.01292
Rep. margin change `Other pneumoconiosis` Respiratory diseases 1 0.02393
Rep. margin change Asthma Respiratory diseases 2 0.01047
Rep. margin change `Other chronic respiratory ` Respiratory diseases 3 0.00917
Rep. margin change Asbestosis Respiratory diseases 4 0.00529
Rep. margin change `Interstitial lung disease` Respiratory diseases 5 0.00407
Rep. margin change `% Single-Parent Households` Social, Physical and Economic Environment 1 0.05328
Rep. margin change `Firearm Fatalities Rate` Social, Physical and Economic Environment 2 0.03071
Rep. margin change `% Children in Poverty` Social, Physical and Economic Environment 3 0.01253
Rep. margin change `Injury Death Rate` Social, Physical and Economic Environment 4 0.00925
Rep. margin change `% Homeowners` Social, Physical and Economic Environment 5 0.00901
% Trump 2016 `Acute lymphoid leukemia` Cancers 1 0.03711
% Trump 2016 `Liver cancer` Cancers 2 0.02191
% Trump 2016 `Other pharynx cancer` Cancers 3 0.01915
% Trump 2016 `Nasopharynx cancer` Cancers 4 0.01814
% Trump 2016 `Kidney cancer` Cancers 5 0.01802
% Trump 2016 `Other cardiovascular` Cardiovascular diseases 1 0.01340
% Trump 2016 `Ischemic heart disease` Cardiovascular diseases 2 0.01295
% Trump 2016 `Rheumatic heart disease` Cardiovascular diseases 3 0.01012
% Trump 2016 `Aortic aneurysm` Cardiovascular diseases 4 0.00909
% Trump 2016 `Ischemic stroke` Cardiovascular diseases 5 0.00889
% Trump 2016 `% Screened` Clinical Care 1 0.02656
% Trump 2016 `MHP Rate` Clinical Care 2 0.02017
% Trump 2016 `% Vaccinated` Clinical Care 3 0.01250
% Trump 2016 `% With Access` Clinical Care 4 0.00803
% Trump 2016 `PCP Rate` Clinical Care 5 0.00722
% Trump 2016 `Self-harm` Deaths of Despair 1 0.03750
% Trump 2016 `Alcohol use disorders` Deaths of Despair 2 0.03498
% Trump 2016 `Interpersonal violence` Deaths of Despair 3 0.02354
% Trump 2016 `Drug use disorders` Deaths of Despair 4 0.00690
% Trump 2016 physical_inactivity_crude Health Behaviors 1 0.03454
% Trump 2016 `% Food Insecure` Health Behaviors 2 0.02989
% Trump 2016 `MV Mortality Rate` Health Behaviors 3 0.02536
% Trump 2016 `Food Environment Index` Health Behaviors 4 0.02194
% Trump 2016 `Teen Birth Rate` Health Behaviors 5 0.02165
% Trump 2016 Hepatitis Infectious diseases 1 0.03141
% Trump 2016 `Diarrheal diseases` Infectious diseases 2 0.02719
% Trump 2016 `HIV AIDS` Infectious diseases 3 0.02079
% Trump 2016 `Lower respiratory infections` Infectious diseases 4 0.01920
% Trump 2016 Meningitis Infectious diseases 5 0.00957
% Trump 2016 `Years of Potential Life Lost Rate` Life expectancy, YLL, and Mortality 1 0.09069
% Trump 2016 `Mortality risk, age 5–25` Life expectancy, YLL, and Mortality 2 0.07097
% Trump 2016 `YPLL Rate (White)` Life expectancy, YLL, and Mortality 3 0.04372
% Trump 2016 `Mortality risk, age 0–5` Life expectancy, YLL, and Mortality 4 0.02479
% Trump 2016 `Mortality risk, age 65–85` Life expectancy, YLL, and Mortality 5 0.02457
% Trump 2016 `Chronic obstructive pulmonary ` Respiratory diseases 1 0.04661
% Trump 2016 `Interstitial lung disease` Respiratory diseases 2 0.01623
% Trump 2016 Asthma Respiratory diseases 3 0.01243
% Trump 2016 `Other pneumoconiosis` Respiratory diseases 4 0.00720
% Trump 2016 `Other chronic respiratory ` Respiratory diseases 5 0.00712
% Trump 2016 `% Single-Parent Households` Social, Physical and Economic Environment 1 0.06653
% Trump 2016 `% Severe Housing Problems` Social, Physical and Economic Environment 2 0.03953
% Trump 2016 `Firearm Fatalities Rate` Social, Physical and Economic Environment 3 0.03877
% Trump 2016 `% Homeowners` Social, Physical and Economic Environment 4 0.01254
% Trump 2016 `% Children in Poverty` Social, Physical and Economic Environment 5 0.01000
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This table reports the variable importance for the top variables in each category.

Data and code availability

The analysis and code from this manuscript can be found at the following link: https://github.com/tymor22/Health-and-Politics/. All of the data analyzed in this manuscript is available at the following link: https://zenodo.org/record/3936108#.Xyc5O_hKh_Q with the DOI number 10.5281/zenodo.3936108. The R programming language was used to conduct all of the data cleaning, modelling, analysis, and plotting.

Results

Our analysis covers 3,156 counties from all 50 states and Washington DC, of which 2650 went Republican and 506 went Democratic in 2016. These counties exhibit significantly different demographics: the average “% 65 and older” in Republican counties was 19.68% higher compared to Democratic counties; the average “% Non-Hispanic White” in Republican counties was 50.20% higher; the average “% Rural” in Republican counties was 93.91% higher; and the average “% with Some College” in Republican counties was 8.51% lower. These demographic differences are also driving healthcare differences. For example, Republican counties received 52% of Medicare funding (of which patients over 65 account for 85% [37]) in 2017, compared to 50.5% of spending in 2007. Additionally, the total number of non-elderly individuals with preexisting conditions in states that voted Republican in 2016 was 74.3 million, compared to 59.4 million in Democratic states. However, the average percentage of non-elderly people with preexisting conditions in Republican states was 50% compared to 51% in Democratic states.

Table 1 shows Pearson correlations between different public health-related variables and the percentage of voters in counties who voted for Trump in 2016, for Clinton in 2016, and the Republican margin change from 2012 to 2016. These correlations were calculated for all states, for 2016 battleground states, and for states that flipped in 2016. For counties in all states, the percentage of votes for Trump had a correlation with the life expectancy of whites of -.42, and a correlation with physical inactivity of .36. Some of the variables that are highly correlated with the Republican margin shift in flipped states include mortality risk across all age groups, the percentage of people in the counties on Medicaid across all ages/sex groups, and the overall uninsured rate.

The median Republican county had 11% fewer residents who completed some college and the bottom and top quartiles were 6% and 11% less respectively. Table 2 shows percent differences between the 1st quartile, median, 3rd quartile and means for different health-related variables as well as t-statistics and p-values. The median Republican county had a 17% higher “injury death rate” and a 26% higher “% Disconnected Youth” rate. The median Republican county had a 50% higher rate of coal workers’ pneumoconiosis, 25% higher rate of chronic respiratory diseases, and a 32% higher rate of chronic obstructive pulmonary disease. The mortality risk was higher in every age group for the median and bottom quartile counties, and lower for the age groups less than 65. The healthcare costs per capita were higher in Democratic counties (including Imaging Costs per Capita, Procedures Per Capita Actual Costs, Tests Per Capita Actual Costs, and Actual Costs per capita). There were consistently higher Medicaid participation rates in Democratic counties. The median uninsured % and uninsured % among individuals with less than 138% of the poverty line were higher in Republican counties by 2% and 11%, respectively. The insurance rates were much higher in Democratic counties for those with and without a high school education. The median Republican county had 2% lower life expectancy overall and 3% lower life expectancy among whites. Cancer rates were higher in most Republican counties than Democratic counties, with the exceptions of prostate, liver, and stomach cancers.

The median Republican county had a 13% higher obesity rate, a 21% higher diabetes rate, a 19% higher physical inactivity rate, a 24% higher opioid prescribing rate, and a 6% higher smoking rate. Republican counties are older, with the median Republican county having 21% more individuals in the % 65 and over demographic. They are also whiter, with the median Republican county having a 69% greater rate in the % Non-Hispanic White demographic. Republican counties are more rural (median % rural rate is 234% higher for Republican counties), and access to care decreases in these counties accordingly: the primary care physician rate (ratio of population to primary care physicians) was 37% lower in the median Republican counties. Some of these health behavior, life expectancy, and health insurance rate differences presented in Table 2 are visualized in Fig 1.

Fig 2 shows the dynamics of healthcare and mortality in Democratic and Republican counties over time, visualizing the rates of different diseases and mortality over time. Over the past 10+ years, life expectancy has changed at different rates, and has improved faster in Democratic counties. Since 2008, health behavior measures and chronic diseases such as physical inactivity, diabetes, and obesity have become notably worse in Republican counties. While the mortality risk across all age groups has decreased overall since 1980, the mortality risk is now higher in the median Republican county compared to the median Democratic county for all age groups. S1 Fig clearly shows the growing differences between several health and life expectancy measures in the median Republican and Democratic counties over time.

Much of the 2016 election media narrative was focused on the rural, white, over 65 voters who supported Trump. Fig 3 shows health and demographic variables that are strongly correlated with the Republican margin shift in counties in the states that flipped from Democratic to Republican in 2016. We can also see that obesity, diabetes, physical inactivity and smoking are all highly correlated with the Republican margin shift. There is a strong negative correlation between the life expectancy of whites and the Republican margin shift in these flipped states.

S4 Table, reports coefficients for each variable from a multivariate linear model that includes each variable as well as education, socio-economic and demographic control variables (there are strong correlations between health, education, socioeconomic status and county demographics, and it is important that we include these and other demographic variables in our model). Among public health variables with the biggest positive coefficient for predicting the Republican Margin change are Medicaid variables across different age groups and sexes, the percentage of smokers, the percentage of excessive drinkers, and the obesity rate; while some of the largest negative coefficients were the uninsured rates, malignant skin melanoma, and Part B Drugs Actual Costs. Several of the health behavior variables that were highly correlated with the voting outcomes in univariate models had directional changes when we added control variables.

S2 Fig shows a plot of the correlation matrix clustered for a selection of public health and control variables. Fig 4 shows the first 2 principal components of different public health categories plotted for counties; there is a clear pattern of counties clustering based on their Republican margin shift and the percentage of people that voted for Trump across every public health category. The clustering is striking for many of the categories, especially Health Behaviors, Clinical Care, Cancers, and Life expectancy, Years of Life Lost, and Mortality. The first 2 principal components explain 48% of the variance of the dataset. The variables most correlated with the first principal component are mortality related variables, including mortality risk, age 45–65, age-adjusted mortality, years of potential life lost rate, mortality risk, age 25–45, mortality risk, age 0–5, and age-adjusted mortality (white). The variables that are most correlated with the second PC include cancer-related variables, such as chronic lymphoid leukemia, malignant skin melanoma, leukemia, non-Hodgkin lymphoma. Table 3 shows the importance of the different variables in each category when they are all included in a lasso regression model to predict voting in the county, along with control variables. The most important variables when predicting the percentage that voted for Trump among clinical care variables was the % Screened, and the MHP Rate; among Deaths of Despair variables was the self-harm rate, and the alcohol use disorder rate; among Health Behaviors was the physical inactivity rate, among the life expectancy, YLL, and Mortality category, was `Years of Potential Life Lost Rate`and `Mortality risk, age 5–25`, and among Respiratory diseases was `Chronic obstructive pulmonary`. Noticeable differences in the important variables when predicting the Republican margin change were the `Malignant skin melanoma`among Cancers, the PCP Rate among Clinical Care, the % Food Insecure and `% Smokers`among Health Behaviors, and the `Mortality risk, age 45–65`, and `Mortality risk, age 25–45`among Life expectancy, YLL, and Mortality variables.

Most of the states that participated in Medicaid expansion (though not all) voted Democratic in 2016. States that expanded Medicaid improved the insurance rates of their states and tended to have higher insured rate changes than states that did not, although there are a few exceptions (such as Florida and Idaho) that experienced large changes over this period without expansion. Medicaid expansion was particularly impactful on the insurance rates of individuals making less than 138% of the poverty line. S3 Fig shows insurance rate changes from 2008 to 2017 (capturing the impact of the ACA) for counties in States that did and did not implement Medicaid expansion. Each point represents a county. For this group, Medicaid expansion directly improved the insurance rates of states.

Discussion

In this retrospective cohort study, we found statistically significant relationships between a number of health measures and the political voting patterns of counties in 2016 and over the last three decades. By calculating the median difference between counties that voted Democratic or Republican in the 2016 election, we found that residents of counties that voted Republican in the last presidential election had increased median incidence cardiovascular disease (11% median difference), diabetes (21%), obesity (13%), self-harm (22%), decreased median life expectancy (2%), and physical activity (19%) compared to residents of counties that voted Democrat. Collectively, these data indicates that counties that voted Republican in the 2016 election had very different health outcomes than those that voted Democratic, and generally had a greater proportion of their residents in poor health.

Fig 2 shows that counties that voted Republican in 2016 had increases in negative health outcomes such as diabetes and obesity concomitantly with decreases in life expectancy compared to counties that voted Democratic in 2016. This indicates that these counties have experienced an overall worsening in quality of health over time. It is important to note that these are not necessarily counties that have voted Republican in previous elections.

We have examined the relationship between state voting outcome and Medicaid expansion. We showed that states that expanded Medicaid (at the time of this writing) were more likely to be Democrat and had improved health measures, including improved access to care, better glucose monitoring in diabetes, better hypertension control, reductions in rates of major postoperative morbidity, and reductions in preventable hospitalizations, compared to those that did not [38–40]. The 14 states that have not expanded Medicaid (Alabama, Florida, Georgia, Kansas, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, South Dakota, Tennessee, Texas, Wisconsin, Wyoming) have overall lower median insured rates among those making 138% below the federal poverty level compared to states that expanded Medicaid. In future research, it will be important to study patterns between health policy and political party affiliation more comprehensively, as health policies have a direct impact on public health.

Appropriate resource allocation is a crucial driver of healthcare outcomes and we hope that the data presented can be used to guide policy decisions at a tractable level. 52% of Medicare funds are allocated to counties that voted Republican in 2016 and 55.5% of individuals with preexisting conditions live in states that voted Republican that same year. These data indicate that, contrary to the policies of the Trump administration and attempts by Republican legislators to cut Medicare and other entitlement spending [41], these programs should actually be expanded and better targeted to counties that require them. For example, programs aimed at improving access to care and adherence to treatment in older patients with chronic illnesses (e.g. diabetes or chronic obstructive pulmonary disease) who live in specific rural communities would require a different allocation of resources, but has the potential to decrease morbidity and mortality. If the $237 billion annual healthcare expenditure for diabetes were directed more pointedly towards prevention, screening, and optimizing treatment, it is likely outcomes would improve and spending will decrease over time as the disease is caught earlier and treated more effectively before it can cause major morbidity.

The limitations of this study are as follows. First, we cannot attribute voting behavior to individual Democratic and Republican voters. Voter turnout in the United States is low (55.7% in the 2016 election) compared to other developed countries and varies between demographics. Therefore, the available data do not allow us the infer whether the results in a given county reflect the true preferences of its residents. Similarly, the majority of lower socioeconomic individuals do not vote, which skews the data towards those who can, who may also be healthier on average. As such, these data do not indicate the healthcare differences between individual voters and should not be understood as a reflection of individual party member preferences. Similarly, these data does not adequately account for independent and third-party voters.

There are additional healthcare access variables besides insurance rates that we do not include in this study. While we include access variables like the PCP rate, we cannot completely capture the quality and breadth of healthcare available in individual counties. For example, the number of healthcare facilities, the training of healthcare personnel, and the quality of both will vary widely. This distinction is especially relevant between rural and urban healthcare settings. Because we cannot adequately control for the quantity and quality of healthcare and access, we cannot conclude that Republican counties would have worse outcomes if they had the same resources. Republican counties tend to be much more rural, leading to demographic differences. As younger people tend to move from rural to densely populated areas, the age makeup of Republican and Democratic counties will also vary, affecting the health rates of those counties.

As our multivariate analysis found, the public health of counties is strongly correlated with the education level, socio-economic status and demographics of counties, making it hard to quantify the independent relationship between each public health variable and voting. Any multivariate analysis with highly correlated variables can be very fragile, and this is certainly true in our case. As previously mentioned, counties that voted Democratic in the 2020 election accounted for 70% of the US GDP. The socioeconomic status of counties will have a major impact on both healthcare access and the health of those living in those counties.

We found strong relationships between recent county voting patterns and health outcomes. These outcomes stem from both individual mechanisms (like the aforementioned lower priority of health issues of Republican voters) as well as institutional aggregate measures (e.g. ACA and Medicare expansion choices falling along party lines). Polarization and partisanship are increasing in the US, and our work suggests that it is in the public interest to further study the mechanisms that link partisanship to health outcomes in an attempt to decouple political affiliation and health in the future.

Supporting information

S1 Table. Pearson correlations between all of the public health-related variables we collected with the percentage of voters in the county that voted for Donald Trump or Hillary Clinton, and the Republican margin shift (from 2012 to 2016).

Correlations for counties from all states, counties from battleground states, and counties from states that flipped from Democratic in 2012 to Republican in 2016 are presented.

(DOCX)

Click here for additional data file. (80.5KB, docx)
S2 Table. Weighted Pearson correlations (weighted by the log 10 of the county population) between all of the public health-related variables we collected with the percentage of voters in the county that voted for Donald Trump or Hillary Clinton, and the Republican margin shift (from 2012 to 2016).

(DOCX)

Click here for additional data file. (46.6KB, docx)
S3 Table. Quantile and mean comparisons of Republican and Democratic counties across all of the public-health measures we collected.

Every county was assigned as either Republican or Democratic depending on the majority vote in 2016, and the mean, median, 1st quartile, and 3rd quartile values for different public health-related variables were calculated. The differences in these values for Republican and Democratic counties are presented in S3 Table, along with the Student t-test statistics and p values for the mean comparisons.

(DOCX)

Click here for additional data file. (37.1KB, docx)
S4 Table. This table reports the coefficients for each public health variable under consideration, as well as the standard error for those coefficients, when predicting the percentage of voters in the county that voted for Donald Trump or Hillary Clinton, and the Republican margin shift.

Each linear model included education, socio-economic status, and demographic control variables for the county.

(DOCX)

Click here for additional data file. (31.4KB, docx)
S1 Fig. Percent differences in the median value of Republican and Democratic counties for select life expectancy, mortality, and health behavior measures.

The median Republican county has experienced sustained increases in mortality risk across every age group compared to the median Democratic county between 1980 and 2014; this manifests itself in worse life expectancy for the median Republican counties over time. Diabetes, obesity, physical inactivity, and uninsurance rates in the median Republican counties are higher than in the median Democratic counties between 2006 and 2017, and this difference is growing.

(EPS)

Click here for additional data file. (29.4KB, eps)
S2 Fig. This is a clustered plot of the correlation matrix for a select group of public health, education, socio-economic and demographic variables.

(TIFF)

Click here for additional data file. (2.5MB, tiff)
S3 Fig. Boxplots of insurance rate changes between 2008 and 2017 for counties in states.

Boxplots are filled by whether the state expanded Medicaid, and state names are colored by the 2016 political party. States that expanded Medicaid experienced higher insurance rate changes during this time period, indicating the positive impact of the policy.

(EPS)

Click here for additional data file. (53.1KB, eps)

Acknowledgments

We thank Kevin Aslett for discussions and a careful reading of the manuscript.

Data Availability

The data underlying this study are available on Zenodo (DOI: 10.5281/zenodo.3936108).

Funding Statement

RB and TH acknowledge support from the following sources: NIH R01DK103358, Simons Foundation, NSF- IOS-1546218, R35GM122515, NSF CBET- 1728858, NIH R01AI130945. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

Decision Letter 0

M Harvey Brenner

3 Dec 2020

PONE-D-20-17485

Viewing the US presidential electoral map through the lens of public health.

PLOS ONE

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Reviewer #1: This is an interesting and important study. But it needs much more work. Here are my concerns:

There is no literature review. Many of the references are newspaper or other non-scholarly sources. A literature review of peer-reviewed sources is necessary to establish the contribution of this paper. My guess is that many observers are aware that Trump's support is concentrated in rural areas and among white men of lower socio-economic status. These are some of you most robust findings. But what may not be understood is the distribution and prevalence of specific health conditions among Trump supporters. You find a high prevalence of melanoma and COPD for example. I did not know this. Such a focus would, I suspect, be a contribution to the literature but this should be established.

There is no clear hypothesis or purpose of the paper. This could be improved with a better sense of how this work contributes to the literature.

In its present form, the paper seems a bit of a data dump. Much of the data shown in the paper must be pared back. I suggest limiting Table 1 to the most important findings. The remainder can go to an appendix. The same for Table 2. In fact, perhaps Table 2 could be eliminated and discussed in several paragraphs.

The statistical analysis concerns me. A more rigorous approach would employ use of factor analysis or principle component analysis to determine which groups of variables are most important. Use of correlations may be adequate but a statistical analysis of those correlations would be better. I strongly suggest considering further multivariate work.

It does seem a bit odd that so many Trump supporters are in relatively ill health and dependent on public finance of health services yet oppose expansion and other aspects of Medicare, Medicaid and the like. You offer no explanation of this. I think some speculation is called for. Why do they oppose policies that are seemingly in their interest? Perhaps rejection of the "elites" is more important or maybe a sense of independence and self dignity, in spite of material circumstances explains the findings. I certainly don't know but some further exploration should be strongly considered. This might help establish a better understanding of the relationship between health and voting patterns.

You also show that higher insurance rates do not necessarily improve health. This is important and should be underscored. Socio-economic status is often more important. Why? Explain.

Reviewer #2: The authors are examining the relationship of county-level data on morbidity, disability, mortality, life expectancy, health care insurance, and other community social and well-being indicators to data on the county-level political voting outcomes in the presidential elections 2012 and 2016. Unfortunately, the authors do not explain why they chose to work with county-level data, other than the reference to geographical trends in voting patterns. Yet, many readers located without and within the USA may lack understanding of a county's role in government, election boards , registration of political affiliation, certification of election results, -- or even the distinction between the popular and the electoral college vote. Readers will better appreciate this research if the authors provide more detail about their choice to focus on county level data from a public health perspective.

The authors create a problem by waiting until the Methods and Materials section, page 5 to define their county political affiliation variable. There we learn that a county is classified as Democrat or Republican depending upon which political party garnered the greater number of popular votes in favor of its presidential candidate in that election year, But we do not learn this until page 5, so many passages in the abstract and introductory text, referring to Republican or Democrat states/districts/counties/voters , can be confusing before getting to the Methods section. Examples of ambiguous or confusing passages follow:

Paragraph 1 of the Introduction cites articles on the priorities of voters registered as Republican or Democrat -- omits registration as Independent -- and refers to them as 'Republican and Democratic voters'. Do the authors consider the words 'Democrat' and 'Democratic' interchangeable?

Paragraph 2 goes on to cite published data on median household income differences between Republican and Democratic districts. Can we assume the authors mean the median income in districts where in some unspecified election year the Republican (or Democrat) candidate won? Or. do the authors mean that these are districts where the election boards have a greater number of citizens registering as Republican (or Democrat) than those registering with another political affiliation? Or, do they mean the unlikely possibility that the income level and political preference is known for each person who votes in an election?

The paragraph next states 'Republican states' have experienced relative wage stagnation. Are these 'Republican states' those with Republican governors? Or, are they ones with a Republican majority in the state Senate? Or, do they have more residents registered as Republicans than those registered with a different party affiliation.? Or, did that state's delegate to the electoral college vote for the Republican presidential candidate in the most recent election year? Perhaps a Republican candidate for president won the majority of a state's popular votes in a recent election? It just is not clear.

The third paragraph of the Introduction states the major causes of death show 'geographical trends'. The word 'geographical has a wide range of meaning, What is meant by this study? Is a state categorized by the extent to which its territory is composed of mountains, plains, dry deserts, forests, lakes, or with coastline? Does 'geographical' refer to how the state is populated, e.g. urban, peri-urban, small town, rural? Or, are states categorized by location and direction, e.g., Border states. East Coast, West Coast, Middle, Upper Middle, South, North, or by some other 'geographical' distinction?

The authors sometimes use terms that might need more explanation. For example, page 3, the authors say "It has been shown that Trump over-performed in counties with high drug. alcohol. and suicide rates." Is there wide-spread understanding of the phrase 'over-performed'? A similar question could be raised about the term 'battleground states' (see Introduction paragraph 4), though the authors do define this term further on in the Methods section.

There is an egregious summary statement at the end of the abstract, and repeated twice near the end of the article, but by that time there has been sufficient explanation that there is less chance for the reader to misinterpret. The offending but cogent statement is "Collectively, this data exhibits a strong pattern: counties that voted Republican in the 2016 election are 'sicker' than those that voted Democrat." The authors are referring to the finding that counties where the majority of votes were for the Republican presidential candidate, were also the counties where a higher percentage of residents were in poor physical health, by a number of public health measures, compared to counties where the candidate of the Democrats won the majority vote. (Whether the ailing residents actually voted and who they voted for, we do not know.)

The authors must be aware that another, more pejorative meaning of 'sicker' is "morally unsound or corrupt" (Webster). If this article is accepted, it will be published after a hotly contested national presidential election where weeks later emotions continue to run high and misinterpretations can feed the existing political' and cultural fracture. The summary statement of the authors is a headline=grabbing description of this study's collective findings, but it does not belong in a scientific article because of the possible derogatory interpretation of a cultural and political group. when more accurate and precise wording is possible.

Indeed, the authors do clearly define their variables in the Methods section and appropriately label scatter plots and the supplemental box plot figures in the Analysis section of the article. (Note also that the authors provide a comprehensive list of relevant references and available sources of data used in this research,.) But, by waiting until page 5 in the manuscript to define their terms, the authors allow misunderstanding to take root in the Abstract and introductory sections so that it is possible for someone to quote a passage out of context and undercut belief in an unbiased science. A good copy editor should be able to flag all these instances where the reader could misinterpret the terms and associated results, and improve clarity.

In general, the ample data analysis in this study supports the conclusion that those counties where the Republican presidential candidate won a majority of the popular vote (i.e. Republican counties) are counties that have a greater proportion of residents considered to be ailing or in poor health, and not doing as well economically as residents of those counties where the candidate of the Democrats garnered the majority of votes for President. This study is very complete in showing the correlations between a large number of a health and community well-being variables commonly used in the public health field and the voting patters of the two main political parties. It is the consistency of patterns in those collective findings that makes the study's finding convincing.

To their credit, the research team took two additional steps in analysis that enhance the readers' understanding of the relationship between voting patterns and health status at the county level. The first additional step was to examine the data for discontinuities as revealed by a positive or negative shift in in the majority votes for a political party's presidential candidate, 2016-2012. The second step was to examine trends in a county's well-being and health variables over time. This addresses questions of whether conditions in the county are better now or worse than before, Is there more or less of a particular dynamic now, and if so, how does that trend relate to voting patterns in the presidential election?

The authors do cite some limitations on this research study, but could further strengthen this article by adding a brief discussion about two other possible limitations on the interpretations of their results, specifically, acknowledging the possible role of additional health care access variables other than insurance, and the influence that demographic change in rural areas and small towns of the USA has on the assessment of health status. Both could influence interpretations drawn from the data analysis.

In the USA, over time, more and more people 20-39yrs have moved away from small towns and rural areas, leaving older family members, and fewer younger families and children remaining in the area. There has been little replacement, or in-migration to offset the county's loss. A population with this changed age structure has more chronic illness than a population that maintains a more normal age distribution. Such demographic change has been accompanied by decaying downtown business areas, fewer jobs, and less innovation in those same small towns and rural areas, changes which breed deaths of despair. Drug addiction, and suicide are responses to less economic hope and few support structures. Is it possible that the finding that those voting for the Republican presidential candidate are 'sicker', is just a twist on the well-known fact that Trump won the vote of older, conservative, and rural Americans in 2016 and were chiefly responsible for his success in the electoral college vote?

The last decade has produced many public health studies demonstrating the importance of health care access to the health of an individual and a community. Health care costs and insurance to offset those costs are an important part of access, The study team wisely included a health care insurance variable in their analysis and the authors detail those findings in the article . But, ability to pay is not the sole determinant of health care access. The actual presence of facilities and trained health personnel are necessary to provide not only emergency, acute and chronic medical care for physical and mental health crises, but also rehabilitation for stroke, heart attack, fracture, and trauma -- problems frequently seen in counties with an older, rural, or small own population, and decaying infrastructure. However, in recent news reports, we have learned of the closing of more hospitals that provide services to small towns and rural areas, Not only facilities are few in number, fewer trained health professionals are are located there. Nurses, health educators, primary care providers, as well as those in geriatrics, mental health, pulmonology and other medical specialties are mainly located in or near urban areas with large populations.

The authors point out that 'Republican counties' have a higher proportion of residents with behavioral health problems than in 'Democratic counties'. Behavioral health problems can by impacted by access to education. counseling, group support, mentoring practices, and program sponsored rewards and other incentives. Facilities and trained health professionals are needed to promote behavioral health through classes on stopping smoking, weight management, diabetes control and nutrition, stress management, balance and strength raining, grief support, and small group activities designed to foster interchange and reduce social isolation. Yet small towns rand rural areas have limited access to community health or social programs sponsored by health facilities, non-profits, local agencies university or government. Transport itself can be a problem depending on how far away a program is being held. No wonder the emphasis on self-reliance and do-it-yourself. Who and what is there to be of help to most small town and rural folk? The answer is kin (what there is left of them), neighbors and the church.

The question remains: Would the 'Republican counties have a 'sicker' population with more risk factors, (e.g. smoking, obesity, and lack of physical activity), if they had access to the same or similar health care resources as the counties where a Democrat presidential candidate won the the majority vote? Such additional access variables as presence and number of health care facilities, trained health personnel, and transport time to care were outside the focus of this particular research study, but are possible limitation on interpretation of study findings.

The final pages of text pages 45-48, begin to explore a fascinating topic--patterns between health policy and political party affiliation, and the current toxic influence of partisanship on health funding, practices, and outcomes, including a short discussion of issues around the 2020 coronavirus pandemic. In effect, the authors start a second, but connected research question and discussion. What the authors have to say is very worthwhile and needs to be pursued as another research article, and draft article detailing question, methods, analysis and conclusions -- or possibly adapt for an Op-Ed in a major national newspaper. I hope the authors will pursue this inquiry. They have the interest, background knowledge, and have done the groundwork. But, the topic and existing associated text should be considered a discrete inquiry and discussion, separate from this article on the relationship of a county population's health and social well-being and their voting patterns in 2012 and 2016 presidential election.

Recommendation: Accept with revisions.

**********

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Reviewer #1: Yes: Peter Hilsenrath

Reviewer #2: Yes: Dory Storms, ScD, MPH

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PLoS One. 2021 Jul 21;16(7):e0254001. doi: 10.1371/journal.pone.0254001.r002

Author response to Decision Letter 0

12 Jan 2021

Dear Dr. Brenner,

Thank you very much for taking the time to thoroughly review our article and for the helpful feedback. We have attempted to address all of the reviewer comments, and we very much appreciate their careful review.

Regarding the first reviewer’s comments, we have added a more substantial literature review to the introduction, and we clearly state our hypothesis and the purpose of the paper. We have added additional methodological detail and explanations for why we take the univariate approach that we do. We have also added a multivariate analysis for investigating the relationship between public health and voting patterns, controlling for the education levels, socio-economic status, and demographics of counties. We next tried to investigate the importance of different variables and variable categories. First, we performed a principal components analysis for each category of public health variables to see if counties clustered based on voting patterns for different categories. We next applied lasso regression using all of the variables from each category, and calculated their variable importance, finally ranking variables.

Regarding the second reviewer’s comments, we have followed them closely and addressed them all below. We have fixed the language and terms that were unclear/undefined. We now define our political affiliation variable at the beginning of the paper. We removed the problematic summary statement involving “sicker”. We substantially extended our limitation section to reflect the limitations that reviewer 2 suggested, as well as the limitations that come with our univariate approach. We have now removed the material in pages 45-48 in the discussion.

All reviewer comments are addressed in detail below.

Thanks again.

Rich, Mike, Jonathan, and Tymor

_____________________________________

Reviewer #1: This is an interesting and important study. But it needs much more work. Here are my concerns:

There is no literature review. Many of the references are newspaper or other non-scholarly sources. A literature review of peer-reviewed sources is necessary to establish the contribution of this paper. My guess is that many observers are aware that Trump's support is concentrated in rural areas and among white men of lower socio-economic status. These are some of you most robust findings. But what may not be understood is the distribution and prevalence of specific health conditions among Trump supporters. You find a high prevalence of melanoma and COPD for example. I did not know this. Such a focus would, I suspect, be a contribution to the literature but this should be established.

There is no clear hypothesis or purpose of the paper. This could be improved with a better sense of how this work contributes to the literature.

We introduced a more substantial literature review to the paper. There is existing literature that looks at health behavior and voting patterns, life expectancy and voting patterns, as well as deaths of despair and voting patterns. A lot of these studies are great, and our contribution, which we stress in the introduction, is a comprehensive exploratory analysis that includes a broader set of public health variables than previous studies (including COPD and other variables that have not been connected to voting patterns before), and collectively indicate worse health in Republican counties.

In its present form, the paper seems a bit of a data dump. Much of the data shown in the paper must be pared back. I suggest limiting Table 1 to the most important findings. The remainder can go to an appendix. The same for Table 2. In fact, perhaps Table 2 could be eliminated and discussed in several paragraphs.

We reduced Table 1 and Table 2 to some of the most important findings. We create 2 supplementary tables for the appendix, that are just the original Table 1 and Table 2 that include all of the 150+ variables.

Part of the purpose of the paper is to be a bit of a data dump - as we are including a very broad set of public health variables, we hope that this paper can be a resource for people, and help others generate ideas.

The statistical analysis concerns me. A more rigorous approach would employ use of factor analysis or principle component analysis to determine which groups of variables are most important. Use of correlations may be adequate but a statistical analysis of those correlations would be better. I strongly suggest considering further multivariate work.

In table 2, we do include p values and t statistics, and in table 1, all of the correlations are statistically significant- we include 95% confidence intervals.

We agree entirely about the importance of multivariate work, especially as so many of the variables in our study are correlated with one another. We introduce a visualization of the correlation table for all of the variables from table 1 and table 2, as a supplementary figure. Indeed, public health is extremely correlated with the education levels, socioeconomic status, and demographics of counties. We have added a multivariate analysis for investigating the relationship between public health and voting patterns, controlling for the education levels, socio-economic status, and demographics of counties. In order to investigate the importance of different variables and variable categories, we performed a principal components analysis for each category of public health variables, visualizing counties using the first two principal components, and showing clustering of counties based on both the Republican margin shift and the percentage of voters for Trump in the county. We next applied lasso regression using all of the variables in each category as well as the control county variables, and measured the variable importance of variables from each category, ranking the most important variables.

The main intention with this study was to comprehensively explore and describe the public health of counties through the lens of voting patterns, and we therefore do not make any strong claims/inferences from our multivariate analysis. Multivariate analysis with highly correlated variables can be very fragile, and we explain this in our limitations section.

It does seem a bit odd that so many Trump supporters are in relatively ill health and dependent on public finance of health services yet oppose expansion and other aspects of Medicare, Medicaid and the like. You offer no explanation of this. I think some speculation is called for. Why do they oppose policies that are seemingly in their interest? Perhaps rejection of the "elites" is more important or maybe a sense of independence and self dignity, in spite of material circumstances explains the findings. I certainly don't know but some further exploration should be strongly considered. This might help establish a better understanding of the relationship between health and voting patterns.

This is a very interesting point, and there is certainly a long history of people voting against their interests in american politics, especially given the polarized media environment and misinformation on social media. As we removed pages 45-48 to address reviewer 2’s concerns, this section was also removed.

You also show that higher insurance rates do not necessarily improve health. This is important and should be underscored. Socio-economic status is often more important. Why? Explain.

Socioeconomic status is extremely important, and we add a discussion about it to our limitations section, as it is certainly a driver behind healthcare access, quality, and health behavior.

Insurance rates do not necessarily improve health, and we discuss a host of other health access/health quality related variables in the limitation section that address this point.

_____________________________________

Reviewer #2: The authors are examining the relationship of county-level data on morbidity, disability, mortality, life expectancy, health care insurance, and other community social and well-being indicators to data on the county-level political voting outcomes in the presidential elections 2012 and 2016. Unfortunately, the authors do not explain why they chose to work with county-level data, other than the reference to geographical trends in voting patterns. Yet, many readers located without and within the USA may lack understanding of a county's role in government, election boards , registration of political affiliation, certification of election results, -- or even the distinction between the popular and the electoral college vote. Readers will better appreciate this research if the authors provide more detail about their choice to focus on county level data from a public health perspective.

We added an explanation for what counties are and why counties are the best geographical unit for this study.

The authors create a problem by waiting until the Methods and Materials section, page 5 to define their county political affiliation variable. There we learn that a county is classified as Democrat or Republican depending upon which political party garnered the greater number of popular votes in favor of its presidential candidate in that election year, But we do not learn this until page 5, so many passages in the abstract and introductory text, referring to Republican or Democrat states/districts/counties/voters , can be confusing before getting to the Methods section. Examples of ambiguous or confusing passages follow:

Paragraph 1 of the Introduction cites articles on the priorities of voters registered as Republican or Democrat -- omits registration as Independent -- and refers to them as 'Republican and Democratic voters'. Do the authors consider the words 'Democrat' and 'Democratic' interchangeable?

Paragraph 2 goes on to cite published data on median household income differences between Republican and Democratic districts. Can we assume the authors mean the median income in districts where in some unspecified election year the Republican (or Democrat) candidate won? Or. do the authors mean that these are districts where the election boards have a greater number of citizens registering as Republican (or Democrat) than those registering with another political affiliation? Or, do they mean the unlikely possibility that the income level and political preference is known for each person who votes in an election?

The paragraph next states 'Republican states' have experienced relative wage stagnation. Are these 'Republican states' those with Republican governors? Or, are they ones with a Republican majority in the state Senate? Or, do they have more residents registered as Republicans than those registered with a different party affiliation.? Or, did that state's delegate to the electoral college vote for the Republican presidential candidate in the most recent election year? Perhaps a Republican candidate for president won the majority of a state's popular votes in a recent election? It just is not clear.

We added a definition early in the introduction for Democrat and Republilcan counties. We added clarifications for Democrat/Republican districts when they are mentioned, and we added clarifications for what is meant by Democrat and Republican states.

We consistently applied Democratic to avoid confusion, however they are interchangeable.

The third paragraph of the Introduction states the major causes of death show 'geographical trends'. The word 'geographical has a wide range of meaning, What is meant by this study? Is a state categorized by the extent to which its territory is composed of mountains, plains, dry deserts, forests, lakes, or with coastline? Does 'geographical' refer to how the state is populated, e.g. urban, peri-urban, small town, rural? Or, are states categorized by location and direction, e.g., Border states. East Coast, West Coast, Middle, Upper Middle, South, North, or by some other 'geographical' distinction?

We added an explanation for what we mean by geographical trends (regional, spatial patterns).

The authors sometimes use terms that might need more explanation. For example, page 3, the authors say "It has been shown that Trump over-performed in counties with high drug. alcohol. and suicide rates." Is there wide-spread understanding of the phrase 'over-performed'? A similar question could be raised about the term 'battleground states' (see Introduction paragraph 4), though the authors do define this term further on in the Methods section.

We now define battleground states at the beginning of the introduction.

We now reference what out-performed means in the context of that study (counties where Trump did better in 2016 than Romney did in 2012)

There is an egregious summary statement at the end of the abstract, and repeated twice near the end of the article, but by that time there has been sufficient explanation that there is less chance for the reader to misinterpret. The offending but cogent statement is "Collectively, this data exhibits a strong pattern: counties that voted Republican in the 2016 election are 'sicker' than those that voted Democrat." The authors are referring to the finding that counties where the majority of votes were for the Republican presidential candidate, were also the counties where a higher percentage of residents were in poor physical health, by a number of public health measures, compared to counties where the candidate of the Democrats won the majority vote. (Whether the ailing residents actually voted and who they voted for, we do not know.)

The authors must be aware that another, more pejorative meaning of 'sicker' is "morally unsound or corrupt" (Webster). If this article is accepted, it will be published after a hotly contested national presidential election where weeks later emotions continue to run high and misinterpretations can feed the existing political' and cultural fracture. The summary statement of the authors is a headline=grabbing description of this study's collective findings, but it does not belong in a scientific article because of the possible derogatory interpretation of a cultural and political group. when more accurate and precise wording is possible.

We removed all mention of “sicker” and instead say “worse health outcomes”.

Indeed, the authors do clearly define their variables in the Methods section and appropriately label scatter plots and the supplemental box plot figures in the Analysis section of the article. (Note also that the authors provide a comprehensive list of relevant references and available sources of data used in this research,.) But, by waiting until page 5 in the manuscript to define their terms, the authors allow misunderstanding to take root in the Abstract and introductory sections so that it is possible for someone to quote a passage out of context and undercut belief in an unbiased science. A good copy editor should be able to flag all these instances where the reader could misinterpret the terms and associated results, and improve clarity.

In general, the ample data analysis in this study supports the conclusion that those counties where the Republican presidential candidate won a majority of the popular vote (i.e. Republican counties) are counties that have a greater proportion of residents considered to be ailing or in poor health, and not doing as well economically as residents of those counties where the candidate of the Democrats garnered the majority of votes for President. This study is very complete in showing the correlations between a large number of a health and community well-being variables commonly used in the public health field and the voting patters of the two main political parties. It is the consistency of patterns in those collective findings that makes the study's finding convincing.

To their credit, the research team took two additional steps in analysis that enhance the readers' understanding of the relationship between voting patterns and health status at the county level. The first additional step was to examine the data for discontinuities as revealed by a positive or negative shift in in the majority votes for a political party's presidential candidate, 2016-2012. The second step was to examine trends in a county's well-being and health variables over time. This addresses questions of whether conditions in the county are better now or worse than before, Is there more or less of a particular dynamic now, and if so, how does that trend relate to voting patterns in the presidential election?

The authors do cite some limitations on this research study, but could further strengthen this article by adding a brief discussion about two other possible limitations on the interpretations of their results, specifically, acknowledging the possible role of additional health care access variables other than insurance, and the influence that demographic change in rural areas and small towns of the USA has on the assessment of health status. Both could influence interpretations drawn from the data analysis.

We added to our limitations section to reflect these very big limitations.

In the USA, over time, more and more people 20-39yrs have moved away from small towns and rural areas, leaving older family members, and fewer younger families and children remaining in the area. There has been little replacement, or in-migration to offset the county's loss. A population with this changed age structure has more chronic illness than a population that maintains a more normal age distribution. Such demographic change has been accompanied by decaying downtown business areas, fewer jobs, and less innovation in those same small towns and rural areas, changes which breed deaths of despair. Drug addiction, and suicide are responses to less economic hope and few support structures. Is it possible that the finding that those voting for the Republican presidential candidate are 'sicker', is just a twist on the well-known fact that Trump won the vote of older, conservative, and rural Americans in 2016 and were chiefly responsible for his success in the electoral college vote?

We now stress the important differences between rural/urban settings and how changing demographics affect health.

The last decade has produced many public health studies demonstrating the importance of health care access to the health of an individual and a community. Health care costs and insurance to offset those costs are an important part of access, The study team wisely included a health care insurance variable in their analysis and the authors detail those findings in the article . But, ability to pay is not the sole determinant of health care access. The actual presence of facilities and trained health personnel are necessary to provide not only emergency, acute and chronic medical care for physical and mental health crises, but also rehabilitation for stroke, heart attack, fracture, and trauma -- problems frequently seen in counties with an older, rural, or small own population, and decaying infrastructure. However, in recent news reports, we have learned of the closing of more hospitals that provide services to small towns and rural areas, Not only facilities are few in number, fewer trained health professionals are are located there. Nurses, health educators, primary care providers, as well as those in geriatrics, mental health, pulmonology and other medical specialties are mainly located in or near urban areas with large populations.

We now more extensively discuss the issues of healthcare access as well as healthcare quality, and how these are important variables that need to be studied further, and their partial omission from this study is a limitation.

The authors point out that 'Republican counties' have a higher proportion of residents with behavioral health problems than in 'Democratic counties'. Behavioral health problems can by impacted by access to education. counseling, group support, mentoring practices, and program sponsored rewards and other incentives. Facilities and trained health professionals are needed to promote behavioral health through classes on stopping smoking, weight management, diabetes control and nutrition, stress management, balance and strength raining, grief support, and small group activities designed to foster interchange and reduce social isolation. Yet small towns rand rural areas have limited access to community health or social programs sponsored by health facilities, non-profits, local agencies university or government. Transport itself can be a problem depending on how far away a program is being held. No wonder the emphasis on self-reliance and do-it-yourself. Who and what is there to be of help to most small town and rural folk? The answer is kin (what there is left of them), neighbors and the church.

The question remains: Would the 'Republican counties have a 'sicker' population with more risk factors, (e.g. smoking, obesity, and lack of physical activity), if they had access to the same or similar health care resources as the counties where a Democrat presidential candidate won the the majority vote? Such additional access variables as presence and number of health care facilities, trained health personnel, and transport time to care were outside the focus of this particular research study, but are possible limitation on interpretation of study findings.

We now mention in our limitations section an explanation for why behavioral health could differ between Republican and Democrat counties for many of the reasons presented above (i.e less public health education, group support...)

The final pages of text pages 45-48, begin to explore a fascinating topic--patterns between health policy and political party affiliation, and the current toxic influence of partisanship on health funding, practices, and outcomes, including a short discussion of issues around the 2020 coronavirus pandemic. In effect, the authors start a second, but connected research question and discussion. What the authors have to say is very worthwhile and needs to be pursued as another research article, and draft article detailing question, methods, analysis and conclusions -- or possibly adapt for an Op-Ed in a major national newspaper. I hope the authors will pursue this inquiry. They have the interest, background knowledge, and have done the groundwork. But, the topic and existing associated text should be considered a discrete inquiry and discussion, separate from this article on the relationship of a county population's health and social well-being and their voting patterns in 2012 and 2016 presidential election.

We have removed the sections of text referred to on pages 45-48 above.

Attachment

Submitted filename: Response to reviewers.pdf

Click here for additional data file. (98KB, pdf)

Decision Letter 1

M Harvey Brenner

19 Apr 2021

PONE-D-20-17485R1

Viewing the US presidential electoral map through the lens of public health.

PLOS ONE

Dear Dr. Hamamsy,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please address the concerns and recommendations of Reviewer 2. Specifically  (1) the main question, the whole rationale of this paper is too vague. What do the authors hope to accomplish? What difference might the findings make in real life?  In the Abstract, the authors say “it is important to understand the relationship between voting “patterns, health, disease, and mortality.”  Why? How do you measure “understand”?  (2) How much of the associations between voting patterns, health, disease and mortality can be explained by social economic and demographic factors? (3) Since voting patterns are associated with health behaviors and health outcomes, the results of this study might indicate, for example, need for funding of special health education initiatives for rural, older, economically marginal citizens.

Please submit your revised manuscript by 4/5/2021. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

M. Harvey Brenner, PhD

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I remain concerned about too much data. Table 1 is 40 pages long. Maybe much of this should be placed in an appendix. But that is an editor call. I also wonder how much variation the first two principle components accounted for. And what is your interpretation of each of these two? It would be easy to add this.

Finally, I want to emphasize the importance of this work. The polarization in our society is the worst I have seen since the Viet Nam era. The Democrats lost much of the white working class vote and Trump hijacked the Republican party with it. More understanding of their plight seems a national priority. This paper serves that end.

Reviewer #2: I recognize the completeness of the revisions that the authors have made. However, I have more concerns, and they follow in my Comments to the Authors, after first acknowledging the adequate sections of the revised paper. My comments total 26,861 characters or 22,671 excluding spaces, so I will upload my review comments as an attachment.

**********

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Reviewer #1: Yes: Peter Hilsenrath

Reviewer #2: Yes: Dory Storms, ScD

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Attachment

Submitted filename: D. Storms - Review of Revised Voting Patterns and Health, Wellness, Mortality .docx

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PLoS One. 2021 Jul 21;16(7):e0254001. doi: 10.1371/journal.pone.0254001.r004

Author response to Decision Letter 1

12 May 2021

We would like to thank the editor for closely reading this manuscript as well as the reviewer comments and our responses to them. In addition to addressing reviewer comments we have streamlined the overall text and links to the supplemental material. We detail our full response to the review comments below.

Reviewer 1:

We want to thank the reviewer for all of their helpful, productive comments in this process.

We have addressed reviewer #1’s point to include the variance explained of the first 2 principle components, as well as noting the variables that are associated with those principle components. We have also removed the supplementary tables from the manuscript, and into the appendix.

Reviewer 2:

We want to thank the reviewer for their expertise in guiding the process of refining this paper and we appreciate the reviewer’s kind words.

We appreciate their comment regarding the clarity of the paper and have taken steps to improve the general understanding of the goal of our work.

We agree that the majority of tables should be included as an appendix and have modified the manuscript so they are not in the body of the manuscript.

We particularly appreciate the reviewer’s specific comments regarding how to make this work applicable beyond the realm of pure academic interest. We are similarly interested in how this sort of work can have actionable consequences. As such, we have modified the manuscript in particular sections to reflect our belief that these data should guide policy.

Thank you for your consideration.

Attachment

Submitted filename: TymorH-health-disp-coverLetter-rev2.docx

Click here for additional data file. (172.2KB, docx)

Decision Letter 2

M Harvey Brenner

18 Jun 2021

Viewing the US presidential electoral map through the lens of public health.

PONE-D-20-17485R2

Dear Dr. Tymor Hamamsy,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Reviewer #2: PAGE 25, SECOND PARAGRAPH NEEDS CITATION NUMBER. JUST SAYS (CITATION)!

This is a beautiful paper! Clear, careful wording . Your hard work at revising has resulted in a first-class paper with implications for state, county, and national level politicians of both parties for how best to strengthen county public health policy and funding.

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Acceptance letter

M Harvey Brenner

30 Jun 2021

PONE-D-20-17485R2

Viewing the US presidential electoral map through the lens of public health.

Dear Dr. Hamamsy:

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Pearson correlations between all of the public health-related variables we collected with the percentage of voters in the county that voted for Donald Trump or Hillary Clinton, and the Republican margin shift (from 2012 to 2016).

    Correlations for counties from all states, counties from battleground states, and counties from states that flipped from Democratic in 2012 to Republican in 2016 are presented.

    (DOCX)

    Click here for additional data file. (80.5KB, docx)
    S2 Table. Weighted Pearson correlations (weighted by the log 10 of the county population) between all of the public health-related variables we collected with the percentage of voters in the county that voted for Donald Trump or Hillary Clinton, and the Republican margin shift (from 2012 to 2016).

    (DOCX)

    Click here for additional data file. (46.6KB, docx)
    S3 Table. Quantile and mean comparisons of Republican and Democratic counties across all of the public-health measures we collected.

    Every county was assigned as either Republican or Democratic depending on the majority vote in 2016, and the mean, median, 1st quartile, and 3rd quartile values for different public health-related variables were calculated. The differences in these values for Republican and Democratic counties are presented in S3 Table, along with the Student t-test statistics and p values for the mean comparisons.

    (DOCX)

    Click here for additional data file. (37.1KB, docx)
    S4 Table. This table reports the coefficients for each public health variable under consideration, as well as the standard error for those coefficients, when predicting the percentage of voters in the county that voted for Donald Trump or Hillary Clinton, and the Republican margin shift.

    Each linear model included education, socio-economic status, and demographic control variables for the county.

    (DOCX)

    Click here for additional data file. (31.4KB, docx)
    S1 Fig. Percent differences in the median value of Republican and Democratic counties for select life expectancy, mortality, and health behavior measures.

    The median Republican county has experienced sustained increases in mortality risk across every age group compared to the median Democratic county between 1980 and 2014; this manifests itself in worse life expectancy for the median Republican counties over time. Diabetes, obesity, physical inactivity, and uninsurance rates in the median Republican counties are higher than in the median Democratic counties between 2006 and 2017, and this difference is growing.

    (EPS)

    Click here for additional data file. (29.4KB, eps)
    S2 Fig. This is a clustered plot of the correlation matrix for a select group of public health, education, socio-economic and demographic variables.

    (TIFF)

    Click here for additional data file. (2.5MB, tiff)
    S3 Fig. Boxplots of insurance rate changes between 2008 and 2017 for counties in states.

    Boxplots are filled by whether the state expanded Medicaid, and state names are colored by the 2016 political party. States that expanded Medicaid experienced higher insurance rate changes during this time period, indicating the positive impact of the policy.

    (EPS)

    Click here for additional data file. (53.1KB, eps)
    Attachment

    Submitted filename: Response to reviewers.pdf

    Click here for additional data file. (98KB, pdf)
    Attachment

    Submitted filename: D. Storms - Review of Revised Voting Patterns and Health, Wellness, Mortality .docx

    Click here for additional data file. (15.2KB, docx)
    Attachment

    Submitted filename: TymorH-health-disp-coverLetter-rev2.docx

    Click here for additional data file. (172.2KB, docx)

    Data Availability Statement

    The data underlying this study are available on Zenodo (DOI: 10.5281/zenodo.3936108).

    The analysis and code from this manuscript can be found at the following link: https://github.com/tymor22/Health-and-Politics/. All of the data analyzed in this manuscript is available at the following link: https://zenodo.org/record/3936108#.Xyc5O_hKh_Q with the DOI number 10.5281/zenodo.3936108. The R programming language was used to conduct all of the data cleaning, modelling, analysis, and plotting.

    Articles from PLoS ONE are provided here courtesy of PLOS

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