Using probabilistic record linkage and propensity-score matching to identify a community-based comparison population.

In retrospective cohort studies of interventions disseminated to communities, it is challenging to find comparison groups with high-quality data for evaluation. We present one methodological approach as part of our study of birth outcomes of second-born children in a home visiting (HV) program targeting first-time mothers. We used probabilistic record linkage to link Connecticut's Nurturing Families Network (NFN) HV program and birth-certificate data for children born from 2005 to 2015. We identified two potential comparison groups: a propensity-score-matched group from the remaining birth certificate sample and eligible-but-unenrolled families. An analysis of interpregnancy interval (IPI) is presented to exemplify the approach. We identified the birth certificates of 4822 NFN families. The propensity-score-matched group had 14,219 families (3-to-1 matching) and we identified 1101 eligible-but-unenrolled families. Covariates were well balanced for the propensity-score-matched group, but poorly balanced for the eligible-but-unenrolled group. No program effect on IPI was found. By combining propensity-score matching and probabilistic record linkage, we were able to retrospectively identify relatively large comparison groups for quasi-experimental research. Using birth certificate data, we accessed outcomes for all of these individuals from a single data source. Multiple comparison groups allow us to confirm findings when each method has some limitations. Other researchers seeking community-based comparison groups could consider a similar approach.