Changes in Neighborhood Socioeconomic Deprivation, Obesity and Diabetes

很少有纵向研究评估由于运动引起的社区SES变化与肥胖作为心血管危险因素之间的关系。因此，我们在达拉斯心脏研究（DHS）中研究了7年来社区社会经济剥夺变化对体重变化的影响。测定2000- 2002年体重（kg），随访7年（N=1835）。地理编码基线和后续家庭住址与达拉斯县的人口普查街区群体相关联。建立了街区组级邻里剥夺指数（NDI）（得分越高=社会经济剥夺越严重）。采用随机效应的重复测量线性混合模型来确定相对于NDI变化的权重变化。使用Heckmans校正因子（HCF）来调整基于年龄、性别、种族、教育、收入、就业、婚姻状况和住房所有权的非随机迁移到NDI较高地区的机会。在7年的研究期间，49%的国土安全部人口在达拉斯县内迁移。黑人比白人或西班牙裔更有可能运动（p<0.01），但运动者与非运动者的基线体重指数或腰围没有差异（p< 0.05）。调整HCF、性别、种族和时间更新的变量（年龄、吸烟、教育、收入、体育活动、居住时间）后，与NDI保持不变或迁移到较低NDI地区的人相比，迁移到NDI较高地区的人体重增加更多（每单位NDI增加0.690+/-0.29 kg, p=0.02）。在搬家的人群中，NDI变化对体重增加的影响随着时间的推移而增加；对于生活在新社区大于中位数4年的人，每单位NDI增加的平均体重增加为0.910+/-0.43 kg (p=0.03)，但对于生活在新社区小于或等于中位数4年的人，体重增加不显著（0.550+/-0.39 kg, p=0.2）。因此，在DHS参与者中，搬到社会经济剥夺程度较高的社区与体重增加有关。在经济和政策因素减少社区社会经济剥夺之前，需要做更多的工作来确定减少其对心血管健康不利影响的个人或社区干预措施。

Few longitudinal studies have evaluated the relationship between change in neighborhood SES due to moving and obesity as a cardiovascular risk factor. Therefore, we examined the impact of change in neighborhood socioeconomic deprivation with moving on weight change over 7 years in the Dallas Heart Study (DHS). Body weight (kg) was measured in 2000-02 and at 7-year follow up (N=1835). Geocoded baseline and follow-up home addresses were linked to census block groups in Dallas County. A block group-level neighborhood deprivation index (NDI) was created (higher scores = more socioeconomic deprivation). Repeated measures linear mixed modeling with random effects was used to determine weight change relative to NDI change. Heckmans correction factor (HCF) was used to adjust for the non-random chance of moving to an area of higher NDI based on age, sex, race, education, income, employment, marital status, and home ownership. 49% of the DHS population moved within Dallas County during the 7-year study period. Blacks were more likely to move than whites or Hispanics (p<0.01), but there were no differences in baseline body mass index or waist circumference in movers vs. non-movers (p>0.05 for both). Adjusting for HCF, sex, race, and time-updated covariates (age, smoking, education, income, physical activity, length of residence), those who moved to areas of higher NDI gained more weight compared to those who remained at the same NDI or moved to lower NDI (0.690+/-0.29 kg per 1-unit NDI increase, p=0.02). Among those who moved, the impact of change in NDI on weight gain increased with time in the new neighborhood; mean weight gain per 1-unit NDI increase was 0.910+/-0.43 kg (p=0.03) for those living in the new neighborhood greater than median of 4 years, but not significant for those living in the new neighborhood less than or equal to median 4 years (0.550+/-0.39 kg, p=0.2). Thus, moving to a neighborhood of higher socioeconomic deprivation was associated with weight gain among DHS participants. Until economic and policy factors reduce neighborhood socioeconomic deprivation, more work is needed to identify individual or community interventions that reduce its adverse effects on cardiovascular health. In addition, we examined the relationship between neighborhood-level socioeconomic deprivation and prevalent diabetes in the DHS. Diabetes was defined by self-report, use of anti-hyperglycemic medication, or fasting glucose>=126 mg/dl. Logistic regression modeling was used to determine odds of prevalent diabetes for those in the highest versus lowest NDI tertile. In DHS, diabetes prevalence was 5%, 13%, and 16% across NDI tertiles (p0.001). In modeling diabetes, we found a significant interaction between race and NDI (p=0.03); therefore, models were race-stratified. White, Hispanic, and black DHS participants in neighborhoods in the highest NDI tertile were up to seven times more likely to have diabetes than those living in the lowest tertile. In Whites and Hispanics, higher deprivation remained associated with a greater likelihood of diabetes after adjustment for age, sex, smoking, and education and was only attenuated after adjusting for income. In contrast, adjustment for confounders attenuated the relationship between NDI and diabetes among blacks. Residing in socioeconomically deprived neighborhoods is associated with prevalent diabetes among whites and Hispanics in DHS. These data suggest racial/ethnic disparities in cardio-metabolic risk within areas of higher socioeconomic deprivation in Dallas County. We also examined the relationship between neighborhood deprivation, blood pressure change, and incident hypertension in DHS. After adjusting for covariates, including moving status and residential self-selection, we observed significant associations between residing in the more deprived neighborhoods and 1) increasing blood pressure over time and 2) incident hypertension. In the fully adjusted model of continuous blood pressure change, significant relationships were seen for both medium and high deprivation. In the fully adjusted model of incident hypertension, participants in areas of high deprivation had 1.69 higher odds of developing HTN (OR 1.69; 95% CI 1.02, 2.82), as defined by 2017 hypertension guidelines. Results varied based on definition of hypertension used (pre-2017 vs. 2017 guidelines). These findings highlight the potential impact of adverse neighborhood conditions on cardiometabolic outcomes, such as hypertension. Researchers measuring relationships between neighborhoods and health have also begun using property appraisal data as a source of information about neighborhoods. Economists have developed a rich tool kit to understand how neighborhood characteristics are quantified in appraisal values. This tool kit principally relies on hedonic (implicit) price models and has much to offer regarding the interpretation and operationalization of property appraisal data-derived neighborhood measures. We developed a theoretically informed hedonic-based neighborhood measure using residuals of a hedonic price regression applied to appraisal data in Dallas County, TX. We examined its reliability in different types of neighborhoods and correlation with other neighborhood measures (i.e., raw neighborhood appraisal values, census block group poverty, and observed property characteristics). We also examined the association between all neighborhood measures and body mass index. The hedonic-based neighborhood measure was correlated in the expected direction with block group poverty rate and observed property characteristics. The neighborhood measure and average raw neighborhood appraisal value, but not census block group poverty, were associated with individual body mass index. Therefore, we demonstrated how to leverage the implicit valuation of neighborhoods contained in publicly available appraisal data. Our findings suggest that researchers should proceed with a careful use of appraisal values utilizing theoretically informed methods such as the one we have developed. The few available population-based longitudinal studies examining the link between change in neighborhood condition and weight change to date have only examined neighborhood changes generated by residential mobility. Applying a difference-in-difference analytic framework to DHS data , we evaluated the relationship between changes in neighborhood condition based on property appraisal data and weight change for both movers and non-movers over an approximate seven-year follow-up period. We employed a measure of neighborhood condition based on property appraisal data to capture temporally consistent measures of change in neighborhood condition regardless of residential mobility. We observed an inverse relationship between weight change and change in neighborhood condition which was more pronounced for non-movers (1.9 fewer kilograms gained per 1-standard deviation improvement in neighborhood condition) than for movers (1.5 fewer kilograms gained per 1-standard deviation improvement in neighborhood condition). Finally, our research findings that demonstrate a relationship between change in neighborhood socioeconomic status, as measured by NDI, and weight change over time support the need to examine how neighborhood socioeconomic environment relates to the individual variability seen in weight loss and weight maintenance interventions targeting adults. Therefore, we are working in a multidisciplinary group of experts as a part of the Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Project to identify variables, like NDI, that should be measured across future weight loss/maintenance interventions in adults to systematically examine the impact of neighborhood environment on weight-related outcomes.