Have Paved Trails and Protected Bike Lanes Led to More Bicycling in Atlanta?: A Generalized Synthetic-Control Analysis

Garber, Michael D.; Flanders, W. Dana; Watkins, Kari; Lobelo, Felipe; Kramer, Michael R.; McCullough, Lauren E.

doi:10.1097/ede.0000000000001483

Cited by 7 publications

(1 citation statement)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Methods in this family include the original synthetic control method 5 ; its several variations and extensions, including the generalized synthetic control 6,7 ; and the classic difference-in-differences design. In the pages of Epidemiology , authors have used these methods to evaluate state-level gun policies, 8 city-level indoor-dining policies on COVID-19 rates, 9 subnational vaccine policy, 10 built-environment effects on bicycling, 11 and wildfire impacts on hospitalizations, 12 to name some examples.…”

mentioning

confidence: 99%

Precision and Weighting of Effects Estimated by the Generalized Synthetic Control and Related Methods: The Case of Medicaid Expansion

Garber

2023

Epidemiology

Self Cite

View full text Add to dashboard Cite

T he study of the impact of Medicaid expansion on cardiovascular disease mortality by Nianogo and colleagues 1 in this issue joins a growing body of epidemiologic research using methods for group-level longitudinal data to estimate effects of social policies on population health. 2 Nianogo et al. use the generalized synthetic control method for their analyses, 3 a member of a family of methods used for estimating effects of treatments (e.g., interventions, actions, events) in time-series cross-sectional data, also known as "panel" data. 3,4 In its usual structure in this setting, time-series cross-sectional data contain repeated observations for multiple units, a subset of which was exposed to a binary treatment during the observation period, such that data are available for treated units before and during the treatment period and for never-treated units for the same time periods. Methods in this family include the original synthetic control method 5 ; its several variations and extensions, including the generalized synthetic control 6,7 ; and the classic difference-in-differences design. In the pages of Epidemiology, authors have used these methods to evaluate state-level gun policies, 8 city-level indoor-dining policies on COVID-19 rates, 9 subnational vaccine policy, 10 built-environment effects on bicycling, 11 and wildfire impacts on hospitalizations, 12 to name some examples.The use of synthetic control and related methods by epidemiologists to evaluate health effects of above-the-individual actions represents one-way quantitative causal-inference methods can help answer questions in social epidemiology. 13,14 Nianogo et al.'s study of Medicaid expansion is a clear example. The passage of the Affordable Care Act in 2010, the 2012 Supreme Court decision stating that Medicaid expansion is up to the states, and the subsequent state-by-state decisions to expand Medicaid coverage or not are each the result of power dynamics within social and political institutions. Studying Medicaid expansion's effects on health in (sub)populations thus naturally falls within the realm of social epidemiology, using any of the field's various definitions. [15][16][17] Nianogo and colleagues' main finding is that Medicaid expansion at the state level prevented, on average, about 4.3 cardiovascular disease (CVD) deaths per 100,000 adults aged 45 to 64 years old per treated year, a figure that, as they note, corroborates previous research on the topic. 18 They build on that literature by estimating effects by race and ethnicity, an important endeavor given people of color are more likely to have their eligibility for Medicaid affected by Medicaid expansion, 19 and Black individuals in particular bear a persistently high burden of CVD mortality. [20][21][22]

show abstract