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Introduction: Despite a proposed connection between neighborhood environment and obesity, few longitudinal studies have examined the relationship between change in neighborhood socioeconomic deprivation, as defined by moving between neighborhoods, and change in body weight. The purpose of this study is to examine the longitudinal relationship between moving to more socioeconomically deprived neighborhoods and weight gain as a cardiovascular risk factor., Methods: Weight (kilograms) was measured in the Dallas Heart Study (DHS), a multiethnic cohort aged 18-65 years, at baseline (2000-2002) and 7-year follow-up (2007-2009, N=1,835). Data were analyzed in 2013-2014. Geocoded addresses were linked to Dallas County, TX, census block groups. A block group-level neighborhood deprivation index (NDI) was created. Multilevel difference-in-difference models with random effects and a Heckman correction factor (HCF) determined weight change relative to NDI change., Results: Forty-nine percent of the DHS population moved (263 to higher NDI, 586 to lower NDI, 47 within same NDI), with blacks more likely to move than whites or Hispanics (p<0.01), but similar baseline BMI and waist circumference were observed in movers versus non-movers (p>0.05). Adjusting for HCF, sex, race, and time-varying covariates, those who moved to areas of higher NDI gained more weight compared to those remaining in the same or moving to a lower NDI (0.64 kg per 1-unit NDI increase, 95% CI=0.09, 1.19). Impact of NDI change on weight gain increased with time (p=0.03)., Conclusions: Moving to more-socioeconomically deprived neighborhoods was associated with weight gain among DHS participants., (Published by Elsevier Inc.)

Objective: The aim of this study is to examine a relationship between neighborhood-level socioeconomic deprivation and weight change in a multi-ethnic cohort from Dallas County, Texas and whether behavioral/psychosocial factors attenuate the relationship., Methods: Non-movers (those in the same neighborhood throughout the study period) aged 18-65 (N=939) in Dallas Heart Study (DHS) underwent weight measurements between 2000 and 2009 (median 7-year follow-up). Geocoded home addresses defined block groups; a neighborhood deprivation index (NDI) was created (higher NDI=greater deprivation). Multi-level modeling determined weight change relative to NDI. Model fit improvement was examined with adding physical activity and neighborhood environment perceptions (higher score=more unfavorable perceptions) as covariates. A significant interaction between residence length and NDI was found (p-interaction=0.04); results were stratified by median residence length (11 years)., Results: Adjusting for age, sex, race/ethnicity, smoking, and education/income, those who lived in neighborhood >11 years gained 1.0 kg per one-unit increment of NDI (p=0.03), or 6 kg for those in highest NDI tertile compared with those in the lowest tertile. Physical activity improved model fit; NDI remained associated with weight gain after adjustment for physical activity and neighborhood environment perceptions. There was no significant relationship between NDI and weight change for those in their neighborhood ≤11 years., Conclusions: Living in more socioeconomically deprived neighborhoods over a longer time period was associated with weight gain in DHS., (Published by Elsevier Inc.)

Background: In 2002, West Nile virus (WNV) first appeared in Texas. Surveillance data were retrospectively examined to explore the temporal and spatial characteristics of the Texas equine WNV epidemic in 2002. Using Geographic Information Systems (GIS) and the Spatial and Space-Time Scan (SaTScan) statistics, we analyzed 1421 of the reported equine WNV cases from six contiguous state Health Service Regions (HSRs), comprising 158 counties, in western, northern, central and eastern Texas., Results: Two primary epidemic peaks occurred in Epidemiological (Epi) week 35 (August 25 to 31) and Epi week 42 (October 13 to 19) of 2002 in the western and eastern part of the study area, respectively. The SaTScan statistics detected nine non-random spatio-temporal equine case aggregations (mini-outbreaks) and five unique high-risk areas imbedded within the overall epidemic., Conclusion: The 2002 Texas equine WNV epidemic occurred in a bi-modal pattern. Some "local hot spots" of the WNV epidemic developed in Texas. The use of GIS and SaTScan can be valuable tools in analyzing on-going surveillance data to identify high-risk areas and shifts in disease clustering within a large geographic area. Such techniques should become increasingly useful and important in future epidemics, as decisions must be made to effectively allocate limited resources.