Bio

Richard Haarburger is a postdoctoral scholar in the Department of Medicine (Primary Care and Population Health) at Stanford University, working in the lab of Pascal Geldsetzer. He studies questions at the intersection of epidemiology, health policy, and applied econometrics, with a focus on causal inference in large real-world health datasets.

His current work uses quasi-experimental and survival analysis methods to evaluate how preventive interventions (e.g. herpes zoster vaccinations) affect neurological outcomes such as dementia incidence at the population level. He also develops empirical strategies for dealing with challenges common in observational health data, including treatment effect heterogeneity, incomplete outcome follow-up, and competing risks.

Richard’s broader research interests include impact evaluation methods, causal machine learning, and the health and economic consequences of new technologies. During his PhD in quantitative economics, he worked on measurement bias in health surveys, high-dimensional forecasting, and heterogeneity in technology adoption.

Professional Education

  • PhD, Georg-August-University Göttingen, Economics (2023)
  • M.A., Georg-August-University Göttingen, International Economics (2018)
  • B.A., Georg-August-University Göttingen, Economics (2015)

Stanford Advisors

Contact

  • Academic
    rhaarb@stanford.edu
    University - Scholar Department: School of Medicine Position: Postdoctoral Scholar

Additional Info

  • Mail Code: 5151

Lab Affiliations

All Publications

  • Interviewer biases in medical survey data: The example of blood pressure measurements. PNAS nexus Geldsetzer, P., Chang, A. Y., Meijer, E., Sudharsanan, N., Charu, V., Kramlinger, P., Haarburger, R. 2024; 3 (3): pgae109

    Abstract

    Health agencies rely upon survey-based physical measures to estimate the prevalence of key global health indicators such as hypertension. Such measures are usually collected by nonhealthcare worker personnel and are potentially subject to measurement error due to variations in interviewer technique and setting, termed "interviewer effects." In the context of physical measurements, particularly in low- and middle-income countries, interviewer-induced biases have not yet been examined. Using blood pressure as a case study, we aimed to determine the relative contribution of interviewer effects on the total variance of blood pressure measurements in three large nationally representative health surveys from the Global South. We utilized 169,681 observations between 2008 and 2019 from three health surveys (Indonesia Family Life Survey, National Income Dynamics Study of South Africa, and Longitudinal Aging Study in India). In a linear mixed model, we modeled systolic blood pressure as a continuous dependent variable and interviewer effects as random effects alongside individual factors as covariates. To quantify the interviewer effect-induced uncertainty in hypertension prevalence, we utilized a bootstrap approach comparing subsamples of observed blood pressure measurements to their adjusted counterparts. Our analysis revealed that the proportion of variation contributed by interviewers to blood pressure measurements was statistically significant but small: ∼0.24--2.2% depending on the cohort. Thus, hypertension prevalence estimates were not substantially impacted at national scales. However, individual extreme interviewers could account for measurement divergences as high as 12%. Thus, highly biased interviewers could have important impacts on hypertension estimates at the subdistrict level.

    View details for DOI 10.1093/pnasnexus/pgae109

    View details for PubMedID 38525305

    View details for PubMedCentralID PMC10959064