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Given that effective home visiting (HV) programs targeting at-risk families impact different outcomes and associations between risk factors and outcomes may vary across populations, program implementers should evaluate population-specific risk-outcome associations in order to select interventions that are most likely to benefit families in target communities. We used data collected in a rural community in upstate New York (i.e., Elmira) and three standard statistical methods (i.e., bivariate, multivariate, and cumulative risk analyses) to assess associations between maternal socio-demographic risk factors and outcomes typically targeted with HV interventions. With the results, we illustrated how program implementers could use population-specific analyses of data collected prior to the implementation of HV interventions to select interventions that may be most likely to benefit families in a target community. For example, our multivariate results suggested that lower socioeconomic families in Elmira were particularly at-risk for child maltreatment, poor family economic self-sufficiency, and poor child academic achievement, indicating that it may be particularly beneficial to implement HV programs that have been shown to affect these outcomes (e.g., Nurse Family Partnership and Parents as Teachers) in Elmira. We encourage program implementers to conduct similar population-specific analyses to help select evidence-based HV interventions for their target communities.

An illustration of how program implementers can use population-specific analyses to facilitate the selection of evidence-based home visiting programs