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Article Reference Troff document (with manpage macros)The consequences of foster care versus institutional care in early childhood on adolescent cardiometabolic and immune markers: Results from a randomized controlled trial
OBJECTIVE: Children exposed to institutional rearing often exhibit problems across a broad array of developmental domains. We compared the consequences of long-term, high-quality foster care versus standard institution-based care, which began in early childhood on cardiometabolic and immune markers assessed at the time of adolescence. METHODS: The Bucharest Early Intervention Project is a longitudinal investigation of children institutionalized during early childhood (ages 6 to 30 months at baseline) who were subsequently randomized to either high-quality foster care or continued institutional care. At the age of 16 years, 127 respondents participated in a biomarker collection protocol, including 44 institutionalized children randomly assigned to receive care as usual, 41 institutionalized children randomized to be removed from institutional care and placed in high-quality foster care in infancy, and a control group of 42 demographically matched children raised in biological families. Outcomes included body mass index (BMI), systolic and diastolic blood pressure, C-reactive protein, interleukin (IL)-6, IL-8, IL-10, tumor necrosis factor α, glycosylated hemoglobin A1c, and Epstein-Barr virus antibody titers. RESULTS: Early institutional rearing was not associated with differences in cardiometabolic or immune markers. Randomization to foster care and age of placement into foster care were also unrelated to these markers, with the exception of BMI z-score, where children assigned to care as usual had lower BMI z-scores relative to children assigned to foster care (-0.23 versus 0.08, p = .06), and older age at placement was associated with lower BMI (β = -0.07, p = .03). CONCLUSIONS: The impact of institutional rearing on measures of cardiometabolic health and immune system functioning is either absent or not evident until later in development. These findings provide new insights into the biological embedding of adversity and how it varies developmentally and across regulatory systems and adversity type.
Located in Retired Persons / Natalie Slopen, Sc.D. / Natalie Slopen Publications
Article Reference Troff document (with manpage macros)Flies Without Borders - Lessons from Chennai on Improving India's Municipal Public Health Services
India’s cities face key challenges to improving public health outcomes. First, unequally distributed public resources create insanitary conditions, especially in slums – threatening everyone’s health, as suggested by poor child growth even amongst the wealthiest. Second, devolving services to elected bodies works poorly for highly technical services like public health. Third, services are highly fragmented. This paper examines the differences in the organisation and management of municipal services in Chennai and Delhi, two cities with sharply contrasting health indicators. Chennai mitigates these challenges by retaining professional management of service delivery and actively serving vulnerable populations − while services in Delhi are quite constrained. Management and institutional issues have received inadequate attention in the public health literature on developing countries, and the policy lessons from Chennai have wide relevance.
Located in MPRC People / Monica Das Gupta, Ph.D. / Monica Das Gupta Publications
Article Reference Troff document (with manpage macros)Tree-based Machine Learning Methods for Survey Research
Predictive modeling methods from the field of machine learning have become a popular tool across various disciplines for exploring and analyzing diverse data. These methods often do not require specific prior knowledge about the functional form of the relationship under study and are able to adapt to complex non-linear and non-additive interrelations between the outcome and its predictors while focusing specifically on prediction performance. This modeling perspective is beginning to be adopted by survey researchers in order to adjust or improve various aspects of data collection and/or survey management. To facilitate this strand of research, this paper (1) provides an introduction to prominent tree-based machine learning methods, (2) reviews and discusses previous and (potential) prospective applications of tree-based supervised learning in survey research, and (3) exemplifies the usage of these techniques in the context of modeling and predicting nonresponse in panel surveys.
Located in MPRC People / Frauke Kreuter, Ph.D. / Frauke Kreuter Publications
Article Reference Troff document (with manpage macros)Between Privilege and Oppression: An Intersectional Analysis of Active Transportation Experiences Among Washington D.C. Area Youth
The use of active transportation (AT), such as walking, cycling, or even public transit, as a means of transport offers an opportunity to increase youth physical activity and improve health. Despite the well-known benefits of AT, there are environmental and social variables that converge on the AT experiences of low-income youth and youth of color (YOC) that have yet to be fully uncovered. This study uses an intersectional framework, largely focusing on the race-gender-class trinity, to examine youth AT within a context of transportation inequity. Theoretically guided by the Ecological Model of Active Transportation, focus groups were completed with two groups of girls (15 participants) and two groups of boys (nine participants) ranging between the ages of 12-15 years who lived within the Washington D.C. area. This research found race, gender, and class to be inhibitors of AT for both boys and girls, but with more pronounced negative influences on girls.
Located in MPRC People / Jennifer D. Roberts, Dr.P.H., M.P.H. / Jennifer D. Roberts Publications
Article Reference Troff document (with manpage macros)Using Google Street View to examine associations between built environment characteristics and U.S. health outcomes
Neighborhood attributes have been shown to influence health, but advances in neighborhood research has been constrained by the lack of neighborhood data for many geographical areas and few neighborhood studies examine features of nonmetropolitan locations. We leveraged a massive source of Google Street View (GSV) images and computer vision to automatically characterize national neighborhood built environments. Using road network data and Google Street View API, from December 15, 2017-May 14, 2018 we retrieved over 16 million GSV images of street intersections across the United States. Computer vision was applied to label each image. We implemented regression models to estimate associations between built environments and county  health outcomes , controlling for county-level demographics, economics, and  population density . At the county level, greater presence of highways was related to lower chronic diseases and  premature mortality . Areas characterized by street view images as ‘rural’ (having limited infrastructure) had higher obesity,  diabetes , fair/poor self-rated health, premature mortality, physical distress, physical inactivity and teen birth rates but lower rates of excessive drinking. Analyses at the  census  tract level for 500 cities revealed similar adverse associations as was seen at the county level for neighborhood indicators of less urban development. Possible mechanisms include the greater abundance of services and facilities found in more developed areas with roads, enabling access to places and resources for promoting health. GSV images represents an underutilized resource for building national data on neighborhoods and examining the influence of built environments on community health outcomes across the United States.
Located in MPRC People / Quynh Nguyen, Ph.D., M.S.P.H. / Quynh Nguyen Publications