br Methods br Results In there

Methods
Results In 1860 there were 1100 counties within the boundaries of states legally permitting slavery (Upper South: 621; Deep South: 479), and in 2010 these same states consisted of 1,347 counties or county equivalents (716 and 624 for the Upper and Deep South, respectively). Spatial interpolation produced estimates of all variables in 2010 county boundaries, which were highly consistent with source geography (e.g. see supplement Figure S1). In 1860, slavery varied greatly throughout the South, ranging from 0–95% of the total population (mean 29%, median 25.4%) (see supplement, Figure S2). To better understand the relation between 1860 slave concentration and both confounding and intervening variables, Table 1 summarizes all covariates across quintiles of slave concentration. Counties with higher slave concentrations were characterized by larger populations, higher land value and higher manufacturing output in 1860; higher Black, but not White, illiteracy in 1930; larger Black-White gaps in median home value in 1930; higher Black poverty in 1970, and a higher number of cumulative lynchings between 1877 and 1950. The proportion of Blacks in each county in 1930 and 1970 is highly correlated with the 1860 slave proportion (r = 0.88 and 0.82 respectively). The spatial distribution of slavery and the relative decline in heart disease mortality by race are displayed in Fig. 1. There are slower declines for both Blacks and Whites in counties with higher rates of slavery particularly in the Deep South states of Alabama, Mississippi, and Louisiana; there is less apparent association in North and South Carolina and Virginia. Table 2 summarizes conventional multivariable (but aspatial) modeled association between slavery and heart disease mortality decline. Because the dependent variable is percent decline, the interpretation of the coefficients is the change in the magnitude of decline (i.e., the ‘pace of progress’) in heart disease mortality due to each NVP-TNKS656 in an explanatory variable. For example, in unadjusted models, Blacks in counties with the highest 1860 slave concentration experienced a 21% slower rate of decline than Blacks in counties with the lowest 1860 slave concentration (β -0.21, SE 0.014). With adjustment for confounders, this association is attenuated to 17.4% slower decline for Blacks in highest versus lowest slave concentration counties. For Whites, the association between slavery and heart disease decline is also significant, but much smaller in magnitude and in the opposite direction. For instance in the confounder adjusted model, contemporary declines in heart disease mortality were 5.2% faster (β 0.052, SE 0.014) for Whites in counties with a history of highest compared to lowest slave concentration. Finally, in models adjusting for hypothesized inequality-generating processes including Black-White disparities in educational and economic opportunity, and county cumulative incidence of lynchings, the association between slave concentration and heart disease mortality decline was approximately halved, although still significant, for Blacks; it was attenuated somewhat less for Whites. There was evidence of significant autocorrelation of residuals in all models (Moran\'s I range from 0.19 to 0.33, p < 0.001), and Lagrange multiplier tests supported spatial lag econometric models as the best fitting solution. Two additional modifications were made to models reported in Table 3. First, based on significant interaction terms, we stratified on region (Deep South versus Upper South) to further address geographic heterogeneity. Second, while the slavery-heart disease association was linear in original models, after geographic stratification there was a significant quadratic relationship between slave concentration and heart disease decline. Therefore, coefficients for slave concentration and slave concentration squared are reported in Table 3. However, to facilitate interpretation, model-predicted and confounder-adjusted percent decline by region and race are graphically summarized in Fig. 2.