Hospital Variation in Cesarean Delivery: A Multilevel Analysis
Andres I. Vecino-Ortiz, MEcon, PhDc1, *, David Bardey, PhD2,3 , Ramon Castano-Yepes, PhD3,4
1. The unit of clustering: Hospitals within a single insurance network in Colombia and individuals covered by this insurance network who gave birth at in-network hospitals
2. The hypothesized effects: To explore the amount of unexplained variance between hospitals in their utilization of cesarean section while accounting for reimbursement rates and individual health factors. They hypothesized that hospitals would account for the majority of the variation in cesarean section utilization.
3. Level at which the exposure is measured: Hospital level (complexity of patient base; physician reimbursement schedule; region; public hospital; teaching hospital) and individual level (mother age; mother income; users per insurance contract; education; previous births; type of admission; gender of newborn; cesarean)
4. Statistical model used to estimate the effect: Multi-level regression model and an alternative variance decomposition to explain the proportion of the variance explained by the region. They chose this method because they could generate an error term for each level of analysis, as compared to the single error term from logistic regression analysis. 5. Describe whether there are any other appropriate statistical models: Because the authors were interested in estimating an error term at each level of clustering, the multi-level model appears to be the best choice. The GEE model gives an average coefficient in the presence of clustering, so it only pertains to the entire model.