UC Berkeley

This dissertation embodies a series of four interrelated chapters, collectively aiming to uncover the role of state health policy (SHP) in population mortality outcomes across the US states and counties over the course of nearly 3 decades, from 1990 to 2018. Chapter 1 introduces necessary background literature on determinants of mortality, its causes and disparities across various sub-populations by socio-economic status, age, sex, place of residence, generational belonging, and health. Based on this evidence, in chapter 2 I propose a conceptual model with a particular focus on isolating assessed causal links between the key indicators, such as population age, health status, and state government ideology. I argue that these three concepts represent a minimal adjustment set to isolate the causal link between SHP and mortality. Additional variables broadly defining area-level socio-economic deprivation, income inequality, residential segregation are considered part of the conceptual model's expanded set that, in principle, ought to augment the precision of the target estimate in a quantitative analysis.

Chapter 3 follows with a detailed description of the method to generate health positivity index scores as well as an alternative, the rate of SHP innovation measure, from a selection of 110 groups of health-related policies, passed into law by states since 1970 through 2018. The main I score index of SHP health positivity takes advantage of the policies' cross-sectional and temporal cumulativity, its thematic relativity with respect to the global maximum in any year, and a double-weighting design to stem over-representation bias. I consider raw and transformed variations of the I score, its decade-lagged variant, and the indicator of the relative rate of new health policy passage. Preliminary testing using one- and two-way repeated measures ANOVA analyses demonstrates medium-to-large effect sizes for the associations between mortality and I score measures at the state level, with notable attenuation in the county group sample, whereas no such association exists for the alternative rate-of-health-policy-innovation indicator. Temporal dependence of the SHP-mortality association is established.

The aim of chapter 4 is to use spatial econometric modeling techniques to arrive at the best possible estimate of the state health policy effect on mortality, as measured by age-standardized death rate, and life expectancy estimates at birth and at age 65. Using mortality data from the United States Mortality Database (USMDB) chapter 4 thus builds upon the previous chapters as follows. First, a set of quantitative indicators representing key concepts presented in chapters 1 and 2 are introduced from secondary sources or are estimated directly on the basis of publicly available secondary data from the US Census Bureau and National Center for Health Statistics. Next, I provide an overview to popular spatial econometric methodology approaches using spatial panel fixed effects designs and submit these variable sets to modeling 3 sex-specific mortality outcomes in various specification combinations, each of which includes one of the measures of state health policy derived in chapter 3. I use evidence from the model diagnostics to arrive at the most optimal model type for each combination of outcome-SHP predictor. My findings indicate that state health policy represented by the two I score measures is the most effective in predicting state-level outcomes. It is estimated that conditional on the socio-economic, political, health and demographic factors SHP could explain up to 30\% of the longevity gap at birth and at age 65 for states on the extreme ends of the longevity spectrum. On the other hand, county group mortality is susceptible to the effects of the parent state health policy only to a limited degree, and is associated more with local socio-economic context. Approximately two-thirds of the SHP-mortality effect is direct and one-third is a by-product of spatial spillovers from the neighboring geography, whereas the reverse is true for the county groups.

The findings of this analysis produce several important contributions. First, the analysis highlights the utility of studying summary indicators of health policy at sub-national aggregate levels as these encompass valuable information that characterizes heterogeneous risk factors that impact the aggregate. Second, the effect of the policy action is inherently spatial, but depends greatly on the scale of aggregation. Longevity in smaller population sub-groups is more sensitive to its local and regional socio-economic and physical environments, with state policy effects percolating more slowly and in a reduced form. Lastly, I find that generalized synthetic measures of state health policy, as applied in the final part of the analysis, are best used in conjunction with summary measures of mortality at coarser geographic resolutions, such as states and regions. Future analyses involving county-level or finer resolution data would benefit from an application of thematic summary components of such measures.