Association of Long-Term Trajectories of Neighborhood Socioeconomic Status With Weight Change in Older Adults

This cohort examines the association of changes in neighborhood socioeconomic status with excessive weight gain and loss among older adults.


Introduction
Neighborhood environment is a critical factor associated with health outcomes, including obesity. 1,2 A 2020 systematic review 2 of longitudinal studies found that declines in measures of neighborhood socioeconomic status (neighborhood SES) were associated with higher weight gain and obesity risk.
However, several gaps exist in current literature; to our knowledge, most studies used 1-time snapshots of neighborhood SES and failed to capture neighborhood change, and almost all studies focused on obesity or mean weight change while ignoring weight loss, a unique risk factor associated with morbidity and mortality in the older population. 3 Neighborhoods change over time. Thus, characterizing long-term neighborhood trajectories is crucial for assessing the cumulative and changing exposure to environmental factors among individuals in those neighborhoods. Moreover, because it is challenging to conduct large experiments examining changing neighborhood conditions, studying improvements, declines, and fluctuations that naturally occur in the neighborhood offers an alternative approach for understanding the association between neighborhood exposure and health outcomes and providing meaningful information for developing future interventions. Only a few studies, to our knowledge, have examined the association of changes in neighborhood SES with weight outcomes in adults, and these studies reported mixed findings: 2 studies 4,5 found that neighborhood improvements were associated with reduced weight gain and a lower risk for obesity, a study by Powell-Wiley et al 6 found that declines in neighborhood SES were associated with increased weight gain, and 2 studies 7,8 reported null findings. The inconsistency in previous studies warrants further investigations of neighborhood change and weight outcomes.
Although moderate weight loss in overweight populations is beneficial, an evolving literature has consistently found that excessive weight loss is associated with health decline and increased mortality, particularly in the older population. [9][10][11][12] Neighborhood changes may influence dietary options, social interactions, and health status of residents, all of which have been associated with unintentional weight loss. 13 Although studies from 2011, 14 2006, 15 and 2002 16 examined the association of neighborhood SES with weight loss in adults, none of these studies focused on changes in neighborhood SES as an exposure.
In a large cohort of older adults, we examined the association of trajectories of neighborhood SES between 1990 and 2010 with weight change over 10 years of follow-up. We hypothesized that improvements in neighborhood SES would be associated with lower likelihoods of excessive weight gain and weight loss while declines would be associated with higher likelihoods of these weightassociated outcomes.

Study Population
The National Institutes of Health-AARP (formerly known as the American Association of Retired Persons) Diet and Health Study was established in 1995 by recruiting AARP members aged 50 to 71 years from 6 US states (ie, California, Florida, Louisiana, New Jersey, North Carolina, and Pennsylvania) and 2 metropolitan areas (ie, Atlanta, Georgia, and Detroit, Michigan). 17   separately. The first PCA component was used to generate year-specific national percentile rankings for all census tracts. Next, we created 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: HHH (ie, high in 1990 to high in 2000 to high 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.

Neighborhood SES Trajectories
To assess the dose-dependent association, we derived a continuous variable of neighborhood

Weight Change
Excessive weight gain or loss were defined as gaining or losing 10% or more of baseline weight.

Statistical Analysis
We used multinomial logistic regression models to calculate odds ratios (OR) and 95% CIs. The outcome variable included 3 categories: gaining 10% or more of baseline weight, losing 10% or more of baseline weight, and gaining or losing less than 10% of baseline weight (ie, the reference group).
We conducted separate analyses for the initially advantaged neighborhoods (ie, HHH, HLL, HHL, and HLH groups) and initially disadvantaged neighborhoods (ie, LLL, LHH, LLH, and LHL groups). In each analysis, the stable group (ie, HHH or LLL groups) served as the reference.
We considered a series of models. Model 1 was adjusted for age and sex. Model 2 was additionally adjusted for race/ethnicity and education. In model 3 (the main model), we further included neighborhood SES in 1990. In model 4, we additionally adjusted for lifestyle factors (ie, physical activity, dietary quality, smoking status, and alcohol consumption status) and self-rated health, which are likely mediators of the association between neighborhood SES and weight outcomes. In all models, we used robust variance estimation to account for clustering of participants in census tracts. Finally, we conducted restricted cubic splines analysis to examine the dosedependent association between changes in neighborhood SES and weight outcomes. [22][23][24] Because results from the spline models indicated a significant linear association, we calculated the OR and 95% CI for every 5-percentile change in neighborhood SES.
Hypothesis testing was 2-sided with a significance level of α < .05. All analyses were performed using SAS statistical software version 9.4 (SAS Institute) from December 2018 through December 2020.

Results
Among 126 179 individuals, 76 225 (60.4%) were men and the mean (SD) age was 62. Compared with individuals in the stable high group (ie, HHH), those in the decline groups (ie, HLL, or early decline; HHL, or late decline; and HLH, or transient decline) had lower mean neighborhood SES and higher mean poverty level in 1990. Conversely, when compared with individuals in the stable low group (ie, LLL), those in the improvement groups (ie, LHH, or early improvement; LLH, or late improvement; and LHL, or transient improvement) had a higher mean neighborhood SES and had a lower mean poverty level in 1990.
Baseline participant characteristics according to trajectory groups are presented in Table 1.
Most participants lived in stable neighborhoods (50 076 individuals [39.6%] in the HHH group and 46 136 individuals [36.6%] in the LLL groups). Participants in the decline groups, compared with those in the HHH group, were more likely to be women and report current smoking, but less likely to have a college education, be married, or report excellent health. Participants in the improvement groups, compared with participants in the LLL group, were more likely to be men, married and White; have a college education; and report excellent health. They were less likely to be current smokers or have diabetes at baseline and had higher mean (SD) alcohol consumption.
The associations between neighborhood SES trajectories and excessive weight gain are presented in Table 2 The associations between neighborhood SES trajectories and excessive weight loss are presented in Table 3. Overall, a decline in neighborhood SES was associated with a higher likelihood of excessive weight loss, while an improvement in neighborhood SES was associated with a lower Finally, we examined the dose-dependent association between changes in neighborhood SES and excessive weight gain ( Figure 1) and loss (Figure 2) among individuals living in neighborhoods that did not experience substantial fluctuations in SES from 1990 to 2010. We found a significant linear association between neighborhood SES change and weight gain and loss (P for trend < .0001) for men and women. Every 5-percentile decline in neighborhood SES was associated with 1.2% to 2.4% increase in the risk of excessive weight gain or loss (excessive weight gain: OR, 1.01; 95% CI,

Discussion
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 al 6 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 al 4 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 analysis 5 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. 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 disorders 25,26 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.

Limitations
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.