A scoping review of the role of policy in mitigating childhood obesity in underserved populations using the RE‐AIM framework

Abstract

BackgroundChildhood obesity is an escalating crisis in the United States. Health policy may impact this epidemic which disproportionally affects underserved populations.AimThe aim was to use the reach, effectiveness, adoption, implementation, and maintenance (RE‐AIM) framework to assess health policy impact on preventing or treating school‐aged children (5 > 18 years) with obesity in underserved populations.MethodsA scoping review of 842 articles was conducted. Twenty‐four articles met the inclusion criteria and underwent data extraction.ResultsTwelve studies included subgroup analysis, with four suggesting an impact of policy on at‐risk groups. None of the 24 studies fully applied the RE‐AIM framework. Policies positively impacted childhood obesity in 12 studies across the sample.Linking Evidence to ActionOur review revealed inconsistent evidence for the effectiveness of policy on childhood obesity, perhaps due to the lack of focus on the social determinants of health. In addition, many studies did not evaluate the outcomes for underserved populations. Therefore, we propose more attention to social determinants in future legislation and evaluation of policy effectiveness on underserved populations. Findings identify an urgent need for the design, implementation, and evaluation of policies specifically directed to address the inequities of racism, social injustices, and social determinants of health that impact childhood obesity in the United States. Future work needs to identify who was reached by the policy, who benefitted from the policy, and how policies were implemented to address obesity‐related health disparities. Nurses should advocate for the evaluation of childhood obesity policies, particularly in underserved populations, to determine effectiveness. Nurses, particularly those trained in population and community health and research, should advocate for policy research that considers inequities rather than controls for these variables. Multi‐layered interventions can then be tailored to sub‐populations and evaluated more effectively.