Legacies of Environmental Injustice on Neighborhood Violence, Poverty and Active Living in an African American Community.

Features of the built environment such as parks and open spaces contribute to increased physical activity in populations, while living in neighborhoods with high poverty, racial/ethnic segregation, presence of neighborhood problems, and violence has been associated with less active living. Our present study examined the factors that may facilitate or hinder the long-term success of built environment interventions aimed at promoting physical activity in communities with a legacy of environmental injustice. The data for this study came from a larger assessment of the impact of a new local park in Newark, NJ. Analysis included all adults from the original study population who self-identified as African American/Black (N=95). To provide an in-depth understanding of how neighborhood social and physical features influence physical activity among African Americans living in high poverty neighborhoods, we analyzed data from two focus groups with a total of 14 participants, and six in-depth interviews held in 2009-2010. Survey results indicated high exposure to violence, and associations between neighborhood features and walking. Self-reported neighborhood walkability was associated with increased walking (P=.01), while increased perception of neighborhood safety was associated with less walking (P=.01). Qualitative results indicated that residents perceived the new park as a positive change, but also expressed concern about the presence of violence and lack of social cohesion among neighbors, with younger generations expressing less optimism than the elderly. Positive changes associated with improvements to the built environment may be limited by social conditions such as neighborhood violence. These mixed findings suggest that policies and initiatives aimed at improving the built environment should address poverty, safety, and social cohesion to ensure more active living communities.

Machine Learning for Social Services: A Study of Prenatal Case Management in Illinois.

OBJECTIVES: To evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services. METHODS: We used administrative data for 6457 women collected by the Illinois Department of Human Services from July 2014 to May 2015 to develop a machine learning model for adverse birth prediction and improve upon the existing paper-based risk assessment. We compared different models and determined the strongest predictors of adverse birth outcomes using positive predictive value as the metric for selection. RESULTS: Machine learning algorithms performed similarly, outperforming the current paper-based risk assessment by up to 36%; a refined paper-based assessment outperformed the current assessment by up to 22%. We estimate that these improvements will allow 100 to 170 additional high-risk pregnant women screened for program eligibility each year to receive services that would have otherwise been unobtainable. CONCLUSIONS: Our analysis exhibits the potential for machine learning to move government agencies toward a more data-informed approach to evaluating risk and providing social services. Overall, such efforts will improve the efficiency of allocating resource-intensive interventions.

Acute responses to opioidergic blockade as a biomarker of hedonic eating among obese women enrolled in a mindfulness-based weight loss intervention trial.

There are currently no commonly used or easily accessible 'biomarkers' of hedonic eating. Physiologic responses to acute opioidergic blockade, indexed by cortisol changes and nausea, may represent indirect functional measures of opioid-mediated hedonic eating drive and predict weight loss following a mindfulness-based intervention for stress eating. In the current study, we tested whether cortisol and nausea responses induced by oral ingestion of an opioidergic antagonist (naltrexone) correlated with weight and self-report measures of hedonic eating and predicted changes in these measures following a mindfulness-based weight loss intervention. Obese women (N = 88; age = 46.7 ± 13.2 years; BMI = 35.8 ± 3.8) elected to complete an optional sub-study prior to a 5.5-month weight loss intervention with or without mindfulness training. On two separate days, participants ingested naltrexone and placebo pills, collected saliva samples, and reported nausea levels. Supporting previous findings, naltrexone-induced cortisol increases were associated with greater hedonic eating (greater food addiction symptoms and reward-driven eating) and less mindful eating. Among participants with larger cortisol increases (+1 SD above mean), mindfulness participants (relative to control participants) reported greater reductions in food addiction symptoms, b = -0.95, SE(b) = 0.40, 95% CI [-1.74, -0.15], p = .021. Naltrexone-induced nausea was marginally associated with reward-based eating. Among participants who endorsed naltrexone-induced nausea (n = 38), mindfulness participants (relative to control participants) reported greater reductions in food addiction symptoms, b = -1.00, 95% CI [-1.85, -0.77], p = .024, and trended toward reduced reward-based eating, binge eating, and weight, post-intervention. Single assessments of naltrexone-induced cortisol increases and nausea responses may be useful time- and cost-effective biological markers to identify obese individuals with greater opioid-mediated hedonic eating drive who may benefit from weight loss interventions with adjuvant mindfulness training that targets hedonic eating.