Where you live affects your weight more than you think.

Where you live affects your weight more than you think.

New evidence shows where you live can influence weight

New evidence shows that moving to different neighborhoods can influence one's weight toward the local average. This means that access to food, neighborhood layout, and zip code can have just as much of an impact on your body as personal choices.

Study:Product of our environment? Influence of place of residence on body mass index.Image source: YARUNIV Studio/Shutterstock.com

The influence of place of residence on body weight

In a recent study in Social Science & MedicineResearchers examined the influence of place of residence on the body weight of people in Australia. They focused less on individual characteristics and more on the environment.

The researchers found that place of residence can explain about 15.5% of weight differences, showing a strong “place effect.” In some cases, where people lived influenced how much money was spent on food by up to 50%.

Why place of residence could be important for body weight

Obesity is a major health problem worldwide, associated with higher risks of cardiovascular disease, diabetes and several types of cancer. Over the past few decades, overweight and obesity rates have risen sharply in the United States, Europe and Australia. In Australia, for example, obesity rates rose from 24.6% in 2007-08 to 31.7% in 2022.

However, the burden of obesity is not evenly distributed: in some regions the proportion of obese people is less than 15%, while in others it is more than 40%. These striking differences raise an important question: Are weight differences primarily due to personal factors such as income, diet or exercise, or do local environmental characteristics have a greater influence?

Understanding the influence of individual and environmental factors has important policy implications. When individual characteristics dominate, interventions could focus on behavior change; if place of residence is more important, policymakers would need to address food choices, ability to walk, or regional disadvantaged environments.

Long-term data sets show location effects on BMI

The authors tracked people who moved between areas and tested whether their weight trended toward the average weight of their new location. They also compared groups of regions defined by body mass index (BMI), deprivation, food access and density. They also examined whether place of residence influences diet and physical activity, as previous research has shown strong associations between food environments, opportunities for exercise and weight outcomes.

The study uses survey data from a nationally representative longitudinal study collected annually since 2001. Because BMI was first collected in 2006 and COVID-19 disrupted weight and exercise patterns beginning in 2020, the authors limited the main sample to the period 2006 to 2019. This resulted in 99,801 observations from 15,620 adults.

Individuals with extremely high BMI values, pregnant women, or those with missing data were excluded. Movers were defined as those who changed their two-digit zip code once during the study; People who never moved served as a comparison group.

The main method is a dynamic event study where the “event” is moving to a new area. The model tracked weight for several years before and after the move to estimate how much of a change in weight could be attributed to characteristics of the target area.

This design allowed the authors to adapt the analysis to pre-existing weight trends. They also conducted analyzes comparing weight differences between groups. To study behavior, they replaced BMI with measures of food spending behavior, spending on convenience foods, and frequency of physical activity.

Moving influences weight toward the local BMI average

The event study showed a 15.5% approximation to the average BMI of the new place of residence, suggesting that place of residence accounts for about one-sixth of the geographic weight differences. Women showed significantly stronger local effects than men, suggesting that the environment may have a greater influence on women.

Removing BMI outliers weakened the effect slightly, while excluding very young and very old adults strengthened it. Looking at a limited time window around the move showed that the effects of place of residence are stronger in the first few years after a move than immediately after the move.

Diagnostic testing confirmed that basic assumptions, including linear effects, absence of pre-trends, and stable effects over time, were met. Robustness tests showed consistent results when BMI was calculated logarithmically, as a category, or with different area representations. The effects remained even when adjusting for covariates or examining samples with long moves or work-related moves. Only moves shorter than 100 km had no significant effects.

The analysis of the differences showed that the place of residence made a different difference when comparing different areas. Place of residence explained more than 20% of the differences between areas with high BMI and almost 30% of the differences between areas with different levels of access to fruit and vegetable shops. Analyzes showed strong effects of place of residence on food expenditure and restaurant visits, but no significant influence on physical activity.

Where you live is important, but personal factors are even more important

The study shows a clear influence of place of residence on weight, with place of residence explaining around 15% of BMI variation in Australia. Larger contributions emerged when comparing regions with different food outlets or densities, confirming previous research linking local environment to weight impacts. These findings suggest that access to healthy foods could be improved and public spaces could be upgraded to complement individual weight loss strategies.

A major strength of the study is the event study design, which captures weight trends before and after moves and thoroughly tests the underlying assumptions. Extensive robustness checks across multiple subgroups and model specifications increase confidence in the results.

However, Australia's unique population distribution, characterized by sparsely populated interior regions and high coastal concentrations, may limit transmissibility and some remote regions may not have sufficient data.

Overall, although residential factors are clearly important, individual characteristics explain most weight differences. Policy efforts could benefit from combining targeted environmental improvements with support for individual behavior change.

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