Implementation Context of Food Insecurity Screening Initiatives in Primary Care: A Multiple Case Study

Food insecurity (hereafter, FI) is an economic condition where access to nutritious food prevents individuals from leading active and healthy lives. FI is associated with poor nutrition, diet-related health conditions and adverse health outcomes. The cost to treat FI related health conditions is roughly $160 billion dollars a year. Federal, state and local food assistance programs remain underutilized because at-risk patients cannot gain access to them. Emerging research points to the unique and critical role primary care providers can play to help FI patients navigate participation barriers. What has evolved are food insecurity screening and referral initiatives in clinical settings where primary care providers act as connecters to food assistance programs. Together, a healthcare organization and food assistance program form a clinical-community partnership to address FI in low-income patient populations. Providers screen for food insecurity during routine patient visits and refer patients to a food assistance partner program that provides immediate and long-term access to food, as well as wrap-around services. Even though the major components for these programs have been identified, standard practices for how these components are implemented have yet to be developed. This limits program upscale and the ability to measure program effectiveness. This study applied an embedded, multiple case-study design and a cross-case analysis to understand implementation facilitators and barriers across two programs implemented across five primary care clinics located in different jurisdictions within Chicago and suburban Cook County. The purpose was to establish a foundation for the development of standard practices. The key take away from this study is that because healthcare organizations have limited financial and human resources to dedicate to food insecurity screening initiatives, primary care practices need to be supported in their ability to implement programs in a feasible way. In this study, the high level of adaptability and trialability of food insecurity screening initiatives allowed each case to implement their program using existing financial and human capital, as well as structural support. Future studies may continue to build on and refine the proposed conceptual model, which is formative in nature and sets the stage for the development of standard program practices

Machine Learning-Augmented Propensity Score-Adjusted Multilevel Mixed Effects Panel Analysis of Hands-On Cooking and Nutrition Education versus Traditional Curriculum for Medical Students as Preventive Cardiology: Multisite Cohort Study of 3,248 Trainees over 5 Years

Background. Cardiovascular disease (CVD) annually claims more lives and costs more dollars than any other disease globally amid widening health disparities, despite the known significant reductions in this burden by low cost dietary changes. The world’s first medical school-based teaching kitchen therefore launched CHOP-Medical Students as the largest known multisite cohort study of hands-on cooking and nutrition education versus traditional curriculum for medical students. Methods. This analysis provides a novel integration of artificial intelligence-based machine learning (ML) with causal inference statistics. 43 ML automated algorithms were tested, with the top performer compared to triply robust propensity score-adjusted multilevel mixed effects regression panel analysis of longitudinal data. Inverse-variance weighted fixed effects meta-analysis pooled the individual estimates for competencies. Results. 3,248 unique medical trainees met study criteria from 20 medical schools nationally from August 1, 2012, to June 26, 2017, generating 4,026 completed validated surveys. ML analysis produced similar results to the causal inference statistics based on root mean squared error and accuracy. Hands-on cooking and nutrition education compared to traditional medical school curriculum significantly improved student competencies (OR 2.14, 95% CI 2.00–2.28, p<0.001) and MedDiet adherence (OR 1.40, 95% CI 1.07–1.84, p=0.015), while reducing trainees’ soft drink consumption (OR 0.56, 95% CI 0.37–0.85, p=0.007). Overall improved competencies were demonstrated from the initial study site through the scale-up of the intervention to 10 sites nationally (p<0.001). Discussion. This study provides the first machine learning-augmented causal inference analysis of a multisite cohort showing hands-on cooking and nutrition education for medical trainees improves their competencies counseling patients on nutrition, while improving students’ own diets. This study suggests that the public health and medical sectors can unite population health management and precision medicine for a sustainable model of next-generation health systems providing effective, equitable, accessible care beginning with reversing the CVD epidemic