Roch A. Nianogo

Dr. Nianogo is an Assistant Professor in the department of Epidemiology at the UCLA Fielding School of Public Health. He is a general medicine physician-scientist and epidemiologist. The goal of his research lab, CiPreme (Causal Inference for Preventive Medicine and Epidemiology) is to develop and utilize innovative and rigorous epidemiologic, econometric and causal inference methods, as well as computational modeling and simulation tools for investigating the impact of lifestyle, metabolic and social interventions in preventing chronic diseases and translating such evidence into effective clinical, public health and policy interventions. To this end, Dr. Nianogo has developed interests and expertise that are both methodological and substantive in nature. Methodologically, his work has involved the application of advanced epidemiologic tools including: (1) population impact assessment methods (e.g. g-computation, population attributable fraction), (2) longitudinal analytics methods (e.g. causal mediation analysis, causal survival analysis), (3) quasi-experiments and instrument-based methods (e.g. difference-in-difference, synthetic control methods) and (4) predictive modeling and simulation tools (e.g. machine learning, microsimulation). Substantively, Dr. Nianogo’s work has focused on the prevention and treatment of chronic diseases including cardiovascular diseases (CVD), diabetes, obesity, cancer, Alzheimer's diseases and related dementias as well as the reduction of health disparities.

Dr. Nianogo received his MD in general medicine from the Université de Ouagadougou in Burkina Faso, his MPH in Community Health Sciences at UCLA, his PhD in Epidemiology at UCLA and completed a post-doctoral fellowship in systems science modeling at UCLA.

Center Affiliations


  • Post-doctoral fellowship, Systems science modeling, University of California, Los Angeles
  • PhD, Epidemiology, University of California, Los Angeles
  • MPH, Public Health, University of California, Los Angeles
  • MD, General Medicine, Université de Ouagadougou, Burkina Faso

Selected Courses

  • EPI 200C: Methods III: Analysis
  • EPI 205: Methods for Analyzing Non-Randomized and Quasi-Experimental Studies
  • EPI 206: Systems Science Modeling and Simulation in Epidemiology

Areas of Interest

  • Epidemiologic methods
  • Population Impact Assessment methods
  • Quasi-experiments and Instrument-based methods
  • Predictive Modeling and Simulation tools
  • Chronic Diseases (e.g. heart disease, hypertension, stroke, diabetes, obesity, cancer, Alzheimer’s diseases and other dementias)
  • Cost-effectiveness Analysis and Decision Analysis
  • Evidence Synthesis and Integration
  • Comparative Effectiveness Research
  • Randomized Controlled Trials and Pragmatic Trials
  • Clinical Preventive Services
  • Lifestyle Medicine
  • Complementary and Alternative Medicine (CAM)

Selected Publications

Nianogo RA, Mueller MP, Keeler B, Kreuger K, Nhan LA, Nobari TZ, Crespi CM, Osgood N, Kuo T, Prelip M, Wang MC. Evaluating the impact of community interventions on childhood obesity in populations living in low-income households in Los Angeles: A simulation study. Pediatr Obes. 2022 Nov;17(11):e12954. doi: 10.1111/ijpo.12954. Epub 2022 Jun 28.

Nianogo RA, Rosenwohl-Mack A, Yaffe K, Carrasco A, Hoffmann CM, Barnes DE. Risk Factors Associated With Alzheimer Disease and Related Dementias by Sex and Race and Ethnicity in the US. JAMA Neurol. 2022 Jun 1;79(6):584-591. doi: 10.1001/jamaneurol.2022.0976.

Hoffmann CM*, Nianogo RA*, Yaffe K, Rosenwohl-Mack A, Carrasco A, Barnes DE. Importance of Accounting for Regional Differences in Modifiable Risk Factors for Alzheimer's Disease and Related Dementias: The Case for Tailored Interventions. J Alzheimers Dis. 2022;89(2):563-570. doi: 10.3233/JAD-220278.

Xia T, Zhao F, Nianogo RA. Interventions in hypertension: systematic review and meta-analysis of natural and quasi-experiments. Clin Hypertens. 2022 May 1;28(1):13. doi: 10.1186/s40885-022-00198-2.

Nianogo RA, Arah OA. Forecasting Obesity and Type 2 Diabetes Incidence and Burden: The ViLA-Obesity Simulation Model. Front Public Health. 2022 Apr5;10:818816. doi: 10.3389/fpubh.2022.818816.

Zhao F, Nianogo RA. Medicaid Expansion's Impact on Emergency Department Use by State and Payer. Value Health. 2022 Apr;25(4):630-637. doi:10.1016/j.jval.2021.09.014. Epub 2021 Oct 26.

Nianogo RA, Emeruwa IO, Gounder P, Manuel V, Anderson NW, Kuo T, Inkelas M,Arah OA. Optimal uses of pooled testing for COVID-19 incorporating imperfect test performance and pool dilution effect: An application to congregate settings in Los Angeles County. J Med Virol. 2021 Sep;93(9):5396-5404. doi:10.1002/jmv.27054. Epub 2021 May 27.

Nianogo RA, Arah OA. Investigating the Role of Childhood Adiposity in the Development of Adult Type 2 Diabetes in a 64-year Follow-up Cohort: An Application of the Parametric G-formula Within an Agent-based Simulation Study. Epidemiology. 2019 Nov;30 Suppl 2:S101-S109. doi: 10.1097/EDE.0000000000001062.

Nianogo RA, Wang MC, Basurto-Davila R, Nobari TZ, Prelip M, Arah OA, Whaley SE. Economic evaluation of California prenatal participation in the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) to prevent preterm birth. Prev Med. 2019 Jul;124:42-49. doi: 10.1016/j.ypmed.2019.04.011. Epub 2019 Apr 16.

Nianogo RA, Arah OA. Impact of Public Health Interventions on Obesity and Type 2 Diabetes Prevention: A Simulation Study. Am J Prev Med. 2018 Dec;55(6):795-802. doi:10.1016/j.amepre.2018.07.014. Epub 2018 Oct 19.

PubMed Publications