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Bio

Richard Haarburger is a postdoctoral scholar in general medicine with a background in economics. During his PhD, he worked on addressing measurement biases and data gaps, handling high-dimensional data, and quantifying the implications of heterogeneous technology adoption. During his time as a scientific trainee at the Joint Research Centre of the European Commission, he conducted policy research on Europe's competitiveness in industrial automation technologies and the increasing adoption of AI in manufacturing.

At Stanford, he applies causal inference methods to research questions in population health and epidemiology. His research interests include impact evaluation methods, causal machine learning, and the impact of AI on healthcare and the economy.

Professional Education

  • Doctor of Philosophy, Georg August Universitat Gottingen (2023)
  • Master of Arts, Georg August Universitat Gottingen (2018)
  • Bachelor of Arts, Georg August Universitat Gottingen (2015)
  • 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

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