We recently published a paper in Current Epidemiology Reports on the use of registry data in injury epidemiology. Injury data are frequently captured in registries that form a census of 100% of known cases that meet specified inclusion criteria. We reviewed study designs commonly used with data extracted from injury registries: (1) Description, (2) Ecologic (with Ecologic Cohort as a particularly informative sub-type), (3) Case-control (with location-based and culpability studies as salient subtypes), (4) Case-only (including case-case and case-crossover subtypes), and (5) Outcomes. Epidemiologic analyses of registry data rest on the understanding that over time an underlying, unenumerated, dynamic population cohort generates cases and data from these cases are recorded into a register. Seeing the registry as the result of an underlying cohort aids in designing studies using the registry data.

Understanding registry data as being derived from a cohort study aides in study design.

Different design choices in analysis of these registries’ data affect the results’ interpretation.  When using registry data the key first step for a researcher is to choose which counterfactual (if any) within which unit of analysis is of interest – that is, what does the researcher imagine could be changed and at what level of organization (e.g. person, neighborhood, state, etc.) to prevent injuries or improve injured parties’ outcomes.  Working from this hypothetical counterfactual, units might be individual people (e.g. when studying characteristics of the injured party or an at-fault party) interventions on individual people (e.g. when studying treatments received in post-injury care) or individual places (e.g. when studying the physical environment at the location of the injury event).  Analytic units could also be groups of people or places, (e.g. when studying states included in an ecological cohort).  The choice of counterfactual and unit of analysis is fundamental to the scientific process, impacting the conceptualization of the underlying at-risk population, the comparison of interest, whether selected variables should be considered confounders, mediators or effect modifiers, and the interpretation of any estimated effects.