Purpose Prior studies have found disparities in child restraint system (CRS) use by individual-level factors like race/ethnicity and income. However, it is unclear if CRS use varies at the community level, which can directly inform effective implementation of resources (e.g., check-points). We determined how rates of CRS use among crash-involved drivers were associated with community-level indicators of race/ethnicity and income.

Methods We utilized the New Jersey Safety and Health Outcomes warehouse—which includes individual-level statewide data on police-reported crashes—to identify all drivers involved in a crash in 2014 who were carrying child occupants < age 8 (n=13,578). We derived each driver’s census tract from residential addresses and determined whether each child occupant was restrained using a CRS. We categorized NJ census tracts by quintile of median household income and proportion of residents who were non-Hispanic white.

Results The proportion of crash-involved drivers who had all child occupants in CRS’s varied substantially by community-level indicators. 58.0% of crash-involved drivers residing in census tracts with the greatest proportion of minority residents had all children in CRS’s compared with 79.1% of drivers residing in tracts with highest proportions of non-Hispanic white residents (p<0.001). Similarly, CRS use was significantly lower among drivers residing in the lowest-income tracts compared with highest-income tracts (60.1% vs. 77.0%, respectively; p<0.001).

Conclusion There appears to be disparities in CRS use across communities, similar to other health behaviors. Future research should aim to better understand the relative contribution of and relationship between individual (e.g., income) and community level (e.g., social norms) influences on CRS use.

Significance of Contributions This project is the first to use statewide crash data to describe community-level CRS use. Results will be utilized to evaluate whether current community-based CRS resources are distributed in an equitable way that prioritizes highest-risk communities.