Regression Discontinuity Designs in Health

Abstract

BACKGROUND: Regression discontinuity designs are non-randomized study designs that permit strong causal inference with relatively weak assumptions. Interest in these designs is growing but there is limited knowledge of the extent of their application in health. We aimed to conduct a comprehensive systematic review of the use of regression discontinuity designs in health research. METHODS: We included studies that used regression discontinuity designs to investigate the physical or mental health outcomes of any interventions or exposures in any populations. We searched 32 health, social science, and gray literature databases (1 January 1960 to 1 January 2019). We critically appraised studies using eight criteria adapted from the What Works Clearinghouse Standards for regression discontinuity designs. We conducted a narrative synthesis, analyzing the forcing variables and threshold rules used in each study. RESULTS: The literature search retrieved 7658 records, producing 325 studies that met the inclusion criteria. A broad range of health topics was represented. The forcing variables used to implement the design were age, socioeconomic measures, date or time of exposure or implementation, environmental measures such as air quality, geographic location, and clinical measures that act as a threshold for treatment. Twelve percent of the studies fully met the eight quality appraisal criteria. Fifteen percent of studies reported a prespecified primary outcome or study protocol. CONCLUSIONS: This systematic review demonstrates that regression discontinuity designs have been widely applied in health research and could be used more widely still. Shortcomings in study quality and reporting suggest that the potential benefits of this method have not yet been fully realized.

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