Association of Long-Term Trajectories of Neighborhood Socioeconomic Status With Weight Change in Older Adults. Dong Zhang et al. JAMA Netw Open. 2021; 4(2):e2036809. doi:10.1001/jamanetworkopen.2020.36809
Key Points
Question What is the association between neighborhood socioeconomic status (SES) change and weight change among older adults?
Findings In this cohort study of 126 179 US adults, long-term improvement in neighborhood SES was associated with lower risk for excessive weight gain and excessive weight loss, while long-term neighborhood SES decline was associated with higher risks for these outcomes. There was a dose-dependent association, with larger changes in risk observed with larger neighborhood changes.
Meaning This study found that sustained neighborhood changes were associated with significant differences in weight outcomes among older adults.
Abstract
Importance Studying long-term changes in neighborhood socioeconomic status (SES) may help to better understand the associations between neighborhood exposure and weight outcomes and provide evidence supporting neighborhood interventions. Little previous research has been done to examine associations between neighborhood SES and weight loss, a risk factor associated with poor health outcomes in the older population.
Objective To determine whether improvements in neighborhood SES are associated with reduced likelihoods of excessive weight gain and excessive weight loss and whether declines are associated with increased likelihoods of these weight outcomes.
Design, Study, and Participants This cohort study was conducted using data from the National Institutes of Health-AARP (formerly known as the American Association of Retired Persons) Diet and Health study (1995-2006). The analysis included a cohort of 126 179 adults (aged 50-71 years) whose neighborhoods at baseline (1995-1996) were the same as at follow-up (2004-2006). All analyses were performed from December 2018 through December 2020.
Exposures Living in a neighborhood that experienced 1 of 8 neighborhood SES trajectories defined based on a national neighborhood SES index created using data from the US Census and American Community Survey. The 8 trajectory groups, in which high, or H, indicated rankings at or above the sample median of a specific year and low, or L, indicated rankings below the median, were HHH (ie, high in 1990 to high in 2000 to high in 2010), or stable high; HLL, or early decline; HHL, or late decline; HLH, or transient decline; LLL, or stable low; LHH, or early improvement; LLH, or late improvement; and LHL, or transient improvement.
Main Outcomes and Measures Excessive weight gain and loss were defined as gaining or losing 10% or more of baseline weight.
Results Among 126 179 adults, 76 225 (60.4%) were men and the mean (SD) age was 62.1 (5.3) years. Improvements in neighborhood SES were associated with lower likelihoods of excessive weight gain and weight loss over follow-up, while declines in neighborhood SES were associated with higher likelihoods of excessive weight gain and weight loss. Compared with the stable low group, the risk was significantly reduced for excessive weight gain in the early improvement group (odds ratio [OR], 0.87; 95% CI, 0.79-0.95) and for excessive weight loss in the late improvement group (OR, 0.89; 95% CI, 0.80-1.00). Compared with the stable high group, the risk of excessive weight gain was significantly increased for the early decline group (OR, 1.19; 95% CI, 1.08-1.31) and late decline group (OR, 1.13; 95% CI, 1.04-1.24) and for excessive weight loss in the early decline group (OR, 1.15; 95% CI, 1.02-1.28). The increases in likelihood were greater when the improvement or decline in neighborhood SES occurred early in the study period (ie, 1990-2000) and was substantiated throughout the follow-up (ie, the early decline and early improvement groups). Overall, we found a linear association between changes in neighborhood SES and weight outcomes, in which every 5 percentile decline in neighborhood SES was associated with a 1.2% to 2.4% increase in the risk of excessive weight gain or loss (excessive weight gain: OR, 1.01; 95% CI, 1.00-1.02 for women; OR, 1.02; 95% CI, 1.01-1.03 for men; excessive weight loss: OR, 1.02; 95% CI, 1.01-1.03 for women; OR, 1.02; 95% CI, 1.01-1.03 for men; P for- trend < .0001).
Conclusions and Relevance These findings suggest that changing neighborhood environment was associated with changes in weight status in older adults.
In this large cohort study of older US adults, we found that, consistent with our hypothesis, participants in neighborhoods with declines in SES were at higher risk of excessive weight gain and loss, while those in neighborhoods with improvements in SES were at lower risk of these outcomes. Moreover, our results showed dose-dependent associations, in which larger improvements and declines were associated with larger differences in risk of adverse weight outcomes.
Several previous investigations on changes in neighborhood SES and weight outcomes reported findings similar to ours. In the Dallas Heart Study (DHS), a population-based cohort study in Dallas County, Texas, Powell-Wiley et al6 reported that moving to more disadvantaged neighborhoods was associated with larger weight gain over 7 years of follow up compared with moving to similar or more advantaged neighborhoods. In another DHS study, Leonard et al4 characterized neighborhood SES using property appraisal values and found that a 1-SD improvement in neighborhood conditions was associated with 0.7 kg less weight gain, and the association appeared stronger among nonmovers than movers. Additionally, a longitudinal analysis5 among California mothers found that moving to a census tract with a lower poverty level was associated with a 50% reduction in the odds of obesity. Overall, these findings and ours suggest that improvements in neighborhood conditions were associated with lower obesity, while residents in deteriorating neighborhoods may be at higher risk for obesity and related chronic conditions.
However, not all study results were consistent with ours. An early investigation in the Multi-Ethnic Study of Atherosclerosis7 used latent growth curve models to estimate six 20-year trajectory groups (1980-1999) of neighborhood poverty patterns and found that the trajectory showing substantial reductions in poverty (4.1% of study population) was not associated with BMI. In another study, Kimbro et al8 examined the likelihood of obesity in association with within-individual changes in neighborhood conditions and had null findings. Although it is unclear what specific factors may lead to inconsistent results among these studies, all studies, including ours, differed in a number of ways, including population sociodemographic characteristics, geographic regions, measures of neighborhood SES and weight outcomes used, and statistical model characteristics, including controlling of confounders. We need future studies, including original investigations, meta-analyses, and systematic reviews, to clarify the association between changes in neighborhood SES and weight outcomes, identify population and contextual factors that may modulate the associations, and examine methodological issues that may be associated with changes in the results.
A main distinction between our study and the earlier studies was that we treated weight gain and weight loss as separate outcomes. Weight loss is prevalent among older populations; it has been estimated that 15% to 20% of adults aged 65 years or older experienced a 5% or greater reduction in body weight over a relatively short period of time (ie, 6 months to 1 year), often without an intention to lose weight.13 Unintentional weight loss has been associated with social isolation, poor nutrition, and chronic diseases, such as cancer, gastrointestinal problems, and mental disorders.13 The high prevalence and distinct underlying mechanisms of unintentional weight loss suggest that it should be treated as a unique weight outcome in older populations. Neighborhood environment has been associated with risks for cancer and mental disorders25,26 and is a critical factor associated with shaping social interactions, diet, and physical activity behaviors.27 Indeed, we found that neighborhood declines were associated with a higher risk for excessive weight loss. However, our observational study was not designed to establish causality, and we did not examine the underlying mechanisms of the observed associations. Future studies should focus on pinpointing the specific pathways through which neighborhood environment may affect weight loss. It has been estimated that weight loss was associated with a 22% to 39% increase in mortality risk in healthy older adults and those with chronic conditions.12 Thus, our study results suggest that clinicians and public health officials should pay close attention to weight loss among older adults who live in a neighborhood with declining SES. Moreover, as most of the current research efforts, to our knowledge, focus on obesity, weight loss remains an understudied area and more research is needed to identify modifiable risk factors at the individual and neighborhood levels to inform clinical practices and public health interventions.
Our study measured neighborhood SES at 3 time points, which allowed us to distinguish among changes that occurred early, late, or transiently during the 20-year study period. In most cases, we found that improvements or declines that occurred early tended to be associated with larger increases in risk, suggesting that there may be a lag period for the association of weight with changes in neighborhood SES. Furthermore, the results also indicated that it may require sustained neighborhood changes for a significant association with changes in weight distribution among residents to appear, a potentially important consideration when designing programs aimed at improving neighborhood conditions to promote healthy weight status.
Our study has important strengths, including a large sample size, geographically diverse neighborhoods, and a long follow-up period. Neighborhoods tend to be stable over time. Therefore, it requires a large and diverse population to capture the small fraction of neighborhoods with substantial changes. Another strength of this study is its use of national rankings to assess neighborhood SES, instead of relying on sample-specific measures. This strategy may have reduced the impact of events and trends that are highly specific to the study population. For example, a study that included neighborhoods that, as a whole, experienced deteriorating conditions would characterize a stable neighborhood in this study as an improved neighborhood; the same neighborhood would be characterized as a declined neighborhood in a study that included neighborhoods with largely upward changes in SES. As a result, it may be difficult to generalize the findings from 1 study to others or to the entire country, and the use of national rankings in our current study was associated with reductions in this problem.
This study has several limitations. First, our neighborhood assessments were restricted to the 3 time points when the US Census and ACS were conducted (ie, 1990, 2000, and 2010), while weight status was measured from 1995 to 1996 and 2004 to 2006. The difference in the time frame of exposure and outcome measurements may lead to misclassification, as the actual neighborhood changes may have occurred before 1995 or after 2006. In addition, although we restricted our analysis to individuals who reported living in the same area at both baseline and follow-up, we were not able to identify those who moved out of and back into the baseline neighborhood, which may also lead to exposure misclassification. Also, weight status was reported only at baseline and 10 years later, at follow-up, which did not allow us to assess short-term weight fluctuations. Importantly, gaining or losing weight over a short period of time (ie, several months to years) may be associated with a larger change in health outcomes compared with gradual change in weight over years, and more studies are needed to investigate the association between neighborhood environment and short-term weight change. Additionally, participants in our study were predominantly White and had high SES, as measured by college education or higher; therefore, the results may not be generalizable to other racial/ethnic groups and low SES populations, for whom the association between neighborhood SES and weight may differ from that observed among our participants. The relatively high baseline neighborhood SES has limited our ability to assess the potential association between neighborhood improvement and weight change among residents of disadvantaged communities.
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