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

Abstract

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 quasiexperimental 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.birth outcomes, home visiting, maternal-child health, quasi-experimental method Administrative datasets are created for purposes other than research but can have important advantages when utilized in research. These datasets include electronic medical records, insurance claims, state agency records, and community program data. While primary data collection can often be costly and burdensome, use of administrative datasets may allow for exploration of new research questions faster and at lower cost, as these datasets can provide a much larger sample size for a lower cost per participant (Jutte et al., 2011). Use of administrative datasets may also allow for evaluation of interventions after dissemination into the community, and the larger size of administrative datasets may be helpful for examining infrequent events or complex interactions such as the intersectionality of race and gender (Bauer et al., 2021). Developing strategies to effectively utilize administrative datasets, registries, and other existing datasets