In our 2025 wrap-up, we recapped last year’s theme of political determinants of health. In this post, we apply that lens to sexual and reproductive health — part of this year’s focus on Health in All Policies.
No doubt about it: politics and policy matter to our health
As we’ve written here many times before, our health outcomes are driven, in part, by the conditions in which we work, live, and play — the social drivers of health. Whether we call them drivers or determinants or “non-medical factors,” these are the aspects of our neighborhoods and lives that help or hinder health. One domain of social determinants is politics and policy.
Sexual and reproductive health (SRH) outcomes include sexually transmitted infections like HIV, birth outcomes like infant mortality, and reproductive choices such as long-acting reversible contraception (LARC).
Mean SRH risk score by state
We previously published the results of our machine-learning analysis of social drivers of SRH, and interactive national scores are available at RTIRarity.io. The risk scores are percentile ranks, 0-100, so lower is better.
The figure at right shows the mean risk of adverse SRH outcomes (a composite of adolescent pregnancies, adolescent births, low birthweight infants, and incidence rates of chlamydia and gonorrhea) by state, as predicted by neighborhood social determinants. We call the percentile-ranked scores Local Social Index (LSI) scores.
Politics isn’t the only driver of SRH outcomes, of course. Some relatively liberal states, such as New Mexico, have worse-than-average SRH outcomes (shown in maroon). And some relatively conservative states, such as Kansas, have better-than-average SRH health outcomes (shown in light green).
We’ve presented work before about some of the policies that affect SRH, such as abortion bans and restricted sex education. Next, we will share some recent findings based on applying our methods to model SRH outcomes at the Census tract level in California and at sub-county areas within Los Angeles County.
California politics and SRH
California leans liberal on average. However, there are counties and precincts that are more conservative, such as the 48th Congressional District, currently represented by Republican Darrell Issa. Availability of publicly funded reproductive health care also varies widely across the state. Nevertheless, as shown in the figure above, CA has a local social index (LSI) of SRH at the low (better) end compared to the rest of the US.
When comparing across states, the cross-state (national) scores are best. When our analysis is focused on a single state, we need within-state scores.
Map of tertiles of SRH risk scores in California. Darker indicates higher risk of adverse SRH outcomes.
California LSI-SRH scores
The map of California shows tertiles of the within-state LSI-SRH scores. Darker shading indicates higher risk of adverse SRH outcomes.
The California model identifies the top 10 predictors of SRH in that state. These are listed below.
- Food insecurity, % of residents, 2019
- Violent crimes per 100k residents, 2018
- 4-year HS graduation rate, % of students, 2010–13, 2015, 2017–18
- Hospital readmission rate, %, 2013–18
- Students who are economically disadvantaged, % 2013–18
- Emergency department visit rate, per 1k, 2013–18
- Unemployment rate, %, 2015–19
- Low social support, %, 2018
- Voting-age citizens who voted in the presidential election, %, 2016
- Students receiving free/reduced-price lunch, %, 2013–18
The bolded predictors indicate the measures that were also identified in the top 10 most important variables in the national model.
What can we take away from this list in terms of policy and political drivers? First, food insecurity is the most important predictor, and that is driven by poverty (especially youth poverty, #5), supermarket access, food prices, and food assistance benefits (such as SNAP and free or reduced price school lunches, #10). Policies that reduce poverty, lower food prices, and increase benefits, and access to supermarkets could potentially have a side effect of reducing the incidence of low birthweight babies.
More directly, voter turnout in 2016 (#9) was also important in predicting SRH outcomes. We know from other research that voter turnout is lower in poorer communities, which face higher barriers to voting. One major barrier may be political disconnection — believing that it doesn’t matter which politicians are elected, having no interest in politics, or not liking any of the candidates. Another is lack of time to become informed about the issues and candidates. When you’re working multiple jobs and/or taking care of family, time can be in short supply.
Map of deciles of LSI-SRH scores within Los Angeles County. Higher risk of adverse SRH outcomes shown in red and orange.
Los Angeles LSI-SRH scores
Sub-county scores for Los Angeles allow us to get more granular still. On this map, we see the highest areas of risk indicated in reds and oranges. Knowing where the risks are in a county like LA, with its roughly 10 million residents (larger than many states!), allows public health practicioners to direct scarce resources where they are most needed.
The list of top 10 predictors in Los Angeles County is different from that of California’s model, as shown below.
- Obesity prevalence rate, %, 2018, 2019
- Asthma prevalence rate, %, 2018, 2019
- Index of Concentration at the Extremes (ICE), non-Hispanic White & Hispanic, 2010-2014
- Children who grew up and lived as adults in a tract with low poverty, %, 1978-2015
- Chance of reaching top 20% income bracket among children born the same year, %, 1978-2015
- Tooth loss prevalence rate, %, 2018
- Sleep insufficiency rate, %, 2018
- Children claimed by two people in the year they were linked to parents, %, 1978-82
- Smoking prevalence rate, %, 2018, 2019
- Renter-occupied household size, 2015-19
Note: ICE (#3) measures spatial polarization in terms of income and ethnicity. This is a compelling and important indicator associated with SRH in Los Angeles.
Other notable predictors in Los Angeles use data from the Opportunity Atlas. Predictors #4 and 5 represent the opportunity for children born between 1978 and 1982 to grow up and live in higher-income neighborhoods and to reach a higher income as adults, when followed up in 2015. Predictor #8 is also a historical measure of divorce rates in the late 70s-early 80s, when some of the parents of today’s children were growing up. These historical and longitudinal measures give a multi-generational picture of some of the drivers of health.
Call to Action
Our in-progress peer-reviewed publications will go into much greater detail on our methods and findings, so stay tuned for those. We also welcome comments on this post.
Most importantly, we hope you’ll spread the word about our work. If there’s ever been a time to focus on the political drivers of health in the US, it is now.
Lisa M. Lines
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Christina Fowler