The politics hypothesis and racial disparities in infants' health in the United States

Abstract

We propose the politics hypothesis-i.e., the hypothesis that political forces comprise either a powerful predecessor of the social determinants of health or are essential social determinants of health themselves. We examine the hypothesis that political actors like presidents, their ideology, and institutions like the political parties they represent shape overall and race-specific health outcomes. Using census and Vital Statistics data among many other sources, we apply both theory- and data-driven statistical methods to assess the role of the president's party and the president's political ideology as predictors of overall and race-specific infant mortality in the United States, 1965-2010. We find that, net of trend, Republican presidencies and socially-conservative ideology of U.S. presidents are strongly associated with slower declines of infant mortality rates, overall and for white and black infants, compared to Democratic and socially-liberal presidents in the U.S. Approximately half (46%) of the white-black infant mortality gap, about 20,000 additional infant deaths, and most if not all the infant mortality rate gap between the U.S. and the rest of the developed world, can be attributed to the 28 years of Republican administrations during the study period. These findings are consistent with the politicization of public health and the conceptualization of politics as a powerful predecessor, in the causal chain, of the social determinants of health. Understanding the political ideological and institutional contexts in which health policies and healthcare and welfare programs are implemented, as well as how governments construct culture and social psychology, provide a more comprehensive framework for understanding and improving population patterns of disease, mortality, and entrenched racial disparities in health in the U.S.

Keywords: Health gradient; Infant mortality; Infants' health; Political parties; Politics hypothesis; Presidents; Racial disparities in health; Social determinants of health.

Fig. 1

LAR output along sequence of models. Fig. 1. The dark lines (the first ones from left to right of X-axis) refer to variables included in the final model—i.e., the one that minimizes Mallows' Cp statistic, which is a parameter of prediction error (Fig. S2, and Tables S2a–S2c in the Appendix). From left to right of the X-axis, variables are selected into the model in order of importance. In this case, their effects on IMRs are gradually manipulated in the direction of prediction accuracy. The Y-axis refers to the size of coefficients (standardized). Different sets of variables were used in the models (e.g., if models were race-specific, then race-specific variables were used accordingly, if available). The variables were also chosen to diminish multicollinarity (see Tables S6a–S6c in the Appendix). In order of importance, the predicting variables included were: IMR for all races: (1) president's party, (2) PB, (3) Percent rural population, (4) LBW, (5) alcohol consumption, (6) income share of top 5%, (7) abortion ratio, (8) Federal per capita expenditure in maternal/child care, (9) percent with high school, (10) women/men income ratio, and (11) income share of bottom 20%. IMR for whites: (1) president's party, (2) PB, (3) percent whites with high school, (4) abortion ratio, (5) alcohol consumption, (6) federal per capita expenditure in maternal/child care, (7) income share of bottom 20% for whites, (8) LBW, (9) income share of bottom 20% for whites, (10) percent rural population, (11) women/men income ratio, (12) cigarette consumption, and (13) total expenditure maternal/child care as percent of GDP. IMR for blacks: (1) LBW, (2) percent rural population, (3) president's party, (4) abortion percent, (5) alcohol consumption, (6) income share of top 5% for blacks, (7) recession years, (8) total expenditure maternal/child care as percent of GDP, (9) CPI, (10) PB, (11) women/men income ratio, (12) income share of bottom 20% for blacks, (13) Percent black households with females without husband, and (14) percent blacks with high school.

Fig. 2

Baseline structural equation model Fig. 2. Mediation analyses were run using this structural equation model, which accounts for the correlated error of the rotating variables terms (error parameters not shown) (Tables S6a–S6c; Appendix).

Fig. 3

SEM mediation analysis parameter estimates Fig. 3. Each data point represents the standardized indirect (X-axis) and standardized direct (Y-axis) effects of the president's party on IMR, WIMR, or BIMR retrieved from a model each time a given rotating variable was selected into the model. The variables that mediate the effect of the president's party on IMRs can be identified as the president's party indirect effects increase while direct effects decrease. These variables appear in clusters located at the lower-right quadrant of the scatterplots. Standardized coefficients are comparable within panels but not between panels, as coefficients are standardized using the standard deviation of the respective dependent variable (and SD_IMR$\ne$ SD_WMR$\ne$SD_BMR).