title: Marginal structural models for skewed outcomes: identifying causal relationships in health care utilization.
abstract: Evaluating the impacts of clinical or policy interventions on health care utilization requires addressing methodological challenges for causal inference while also analyzing highly skewed data. We examine the impact of registering with a Family Medicine Group, an integrated primary care model in Quebec, on hospitalization and emergency department visits using propensity scores to adjust for baseline characteristics and marginal structural models to account for time-varying exposures. We also evaluate the performance of different marginal structural generalized linear models in the presence of highly skewed data and conduct a simulation study to determine the robustness of alternative generalized linear models to distributional model mis-specification. Although the simulations found that the zero-inflated Poisson likelihood performed the best overall, the negative binomial likelihood gave the best fit for both outcomes in the real dataset. Our results suggest that registration to a Family Medicine Group for all 3 years caused a small reduction in the number of emergency room visits and no significant change in the number of hospitalizations in the final year.
authors.affiliation: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
authors.forename: Julie
authors.initials: J
authors.lastname: Héroux
authors.forename: Erica E M
authors.initials: EE
authors.lastname: Moodie
authors.forename: Erin
authors.initials: E
authors.lastname: Strumpf
authors.forename: Natalie
authors.initials: N
authors.lastname: Coyle
authors.forename: Pierre
authors.initials: P
authors.lastname: Tousignant
authors.forename: Mamadou
authors.initials: M
authors.lastname: Diop
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003198:Computer Simulation; D004636:Emergency Service, Hospital; D006760:Hospitalization; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008297:Male; D011320:Primary Health Care / Q000706:statistics & numerical data*; D057216:Propensity Score; D011792:Quebec
keywords: causal inference; generalized linear models; health care utilization; marginal structural models; over-dispersion; primary care reform