Anusha Vable, ScD, MPH
Associate Professor
Family Community Medicine
School of Medicine

I am a Social Epidemiologist and the director of VabLab, a large research team at UCSF that focuses on identifying and advocating for structural solutions to health inequities.

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We employ methods from basic linear regression with interaction terms; to sophisticated approaches for identifying causal effects from epidemiology and econometrics; to sequence analysis, an approach that originated in genetics but has extensions in social science to identify common patterns in variables that unfold over time. Our research focuses on socioeconomic exposures (particularly education, but also occupation and income) and diseases associated with aging (particularly cognitive aging, but also heart disease and diabetes); I have received several R01 and R01-equivalent grants as PI or mPI to support these goals. Methodologically, VabLab has two areas of expertise that may be especially important for equity researchers: [1] Quantile regression: a method to evaluate how exposures impact the entire outcome distribution (vs. the mean, which is typically evaluated). This is important for equity research because the most structurally minoritized individuals are typically in the tails of the outcome distribution. [2] Sequence analysis is a powerful and underutilized method to characterize variables that occur over time or over the lifecourse. Our team has been using sequence analysis to understand educational trajectories from age 14 – 48; employment trajectories from ages 18 – 65; air pollution trajectories from 2000 – 2010; state-level abortion access trajectories from 1970 – 2014, Earned Income Tax Credit trajectories from ages 22 - 48, etc., and how these exposures predict subsequent health outcomes. Sequence analysis is descriptive, not causal, but I argue that for most exposures that cumulate over the lifecourse, we are in the descriptive phase, not the causal phase. Substantively, our team evaluates if socioeconomic exposures and policies are racist / discriminatory (widen inequities) or anti-racist / anti-discriminatory (narrow inequities). We have found that the Korean War GI Bill, which subsidized college education for qualifying veterans, predicted smaller socioeconomic disparities in markers of mental, physical and cognitive health among veterans compared to non-veterans. Similarly, in work led by postdoc Dr. Aayush Khadka, we found that Vietnam War GI Bill eligibility shifted and reshaped the blood pressure distribution to one of lower CVD risk and reduced childhood socioeconomic disparities in blood pressure. We have also found that structurally marginalized groups (women, racial minoritized people, and those from lower socioeconomic backgrounds) seem to benefit more from each year of education than structurally advantaged groups (e.g. high socioeconomic status White men). Our work to date suggests programs and policies to increase educational attainment could be powerful mechanism to reduce racial and socioeconomic health inequities; these consistent findings inform our social change work. Our social change work, informed by our substantive work, focuses on building the evidence base for the state of California to invest in the public education system at 3 levels: [1] Early childhood (0 - 5 years): we know this period is critical and we know it’s a market failure; if it’s a market failure, it’s the government’s responsibility to fix this; they are the only ones who can. [2] K-12 education in California has been underfunded for decades: uneven distribution of buses, nurses, and guidance counselors; they have to fundraise for basic programming like physical education, music, and arts. Especially important for equity is decoupling the funding for public education from property taxes; that is, we need to create a system where the size of your house doesn’t determine the quality of your public education options. [3] Higher education: tuition at UCs and CSU systems used to be free; similarly, the reason 48,000 UC employees went on strike in 2022 has the same root cause: underfunding by the state. To make up for the budgetary shortfall caused by underinvestment by the state, our public universities are squeezing students with ballooning tuition payments and squeezing low-wage workers with barely livable wages in order to stay solvent. Higher education at public universities can become accessible to the public again if the state decides to prioritize it.

The State of California is under-investing in our kids. We know that education is the primary mechanism for opportunity and social mobility in this country, therefore it is vital that we convince the state to adequately fund the education system. I am not talking about a one-time investment, but a massive and systemic course-correction.

Awards

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  • Health Disparities Loan Repayment Program Recipient, National Institutes of Health, 2019-2021
  • Harvey V. Fineberg Fellowship in Cancer Prevention, Harvard T.H. Chan School of Public Health, 2013-2014
  • Mitchell L. & Robin LaFoley Dong Scholarship, Harvard T.H. Chan School of Public Health, 2010-2012
  • "Good Ideas" Contest Winner for suggesting and implementing K writing groups for postdocs, University of California San Francisco, Department of Epidemiology and Biostatistics, 2018
  • Finalist for the Erickson Foundation Award for Excellence in Research on Positive Aging, American Public Health Association, 2016

Education & Training

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  • Postdoctoral fellow Social Epidemiology Stanford Center for Population Health Sciences 2018
  • Postdoctoral fellow Social Epidemiology Harvard T.H. Chan School of Public Health 2016
  • ScD Social Epidemiology Harvard T.H. Chan School of Public Health 2015
  • MPH Epidemiology University of Michigan 2007
  • BA Chemistry Grinnell College 2004

Interests

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  • Social policies
  • Heterogenous treatment effects
  • Health equity
  • Health disparities
  • Educational policies

Websites

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Grants and Projects

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Publications (32)

Top publication keywords:
SpousesEpidemiologic MeasurementsRetirementPeak Expiratory Flow RateSocial MobilityWidowhoodHealth Status DisparitiesSoftware ValidationEpidemiologyEducational StatusAcademic SuccessKorean WarSocial ClassVeteransEpidemiologic Methods

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