Abstract

Introduction

Retirement represents a significant life transition and is associated with individual health outcomes. Previous studies on the health effects of retirement have yielded inconsistent conclusions. This study aimed to estimate the impact of retirement on the body mass index (BMI) and BMI-defined overweight and obesity.

Methods

Between 2012 and 2015, this study included 88,471 public sector participants from the Chinese Hypertension Survey (CHS) while excluding those working in the private sector. By utilizing the fuzzy regression discontinuity design (FRDD), this research evaluated the direct impact of retirement on the BMI, as well as on the overweight and obesity rates. Additionally, the study examined variations in the effects of retirement among groups stratified by sex and educational attainment.

Results

The fully adjusted model suggested that retirement did not have a significant impact on the BMI or overweight and obesity rates of the overall population. Notably, retirement significantly impacted the male participants, resulting in an increase in their BMI of 2.18 (95% CI 0.23鈥�4.13), but did not affect the BMI of the female participants. Furthermore, individuals with lower educational backgrounds experienced more pronounced BMI changes upon retirement.

Conclusions

On average, retirement has no significant impact on the BMI and overweight and obesity rates. Retirement leads to an increase in the BMI among men but does not affect that in women. When considering adjustments to existing retirement policies, the differential health effects of retirement across various populations should be taken into account.

Introduction

As the average life expectancy increases and population aging intensifies, several countries consider raising the retirement age as part of pension system reforms. On the one hand, a higher retirement age may result in a greater labor supply and improve the financial status of public pension systems. On the other hand, the association between retirement and health should also be given due attention when adjusting the retirement age. If retirement can improve the overall health status of the population, then delaying retirement may place additional pressure on the health care funding system and impact individual well-being. For individuals, retirement signifies a major transition and turning point in life, reshaping aspects such as the economic status, lifestyle, social interactions, the social status, and psychological stress, thereby impacting health in multiple ways [1,2,3].

There is a growing literature focused on the relationship between retirement and health, but the findings are mixed. For example, based on the Survey of Health, Aging and Retirement in Europe (SHARE) dataset, Coe et al. found that retirement could reduce the probability of a self-reported poor health [4]. Likewise, some researchers using the same dataset found that retirement could enhance mental and subjective health [5, 6], thereby affirming the positive health effects of retirement. However, several longitudinal studies have emphasized that retirement, especially involuntary or forced, often impairs individual鈥檚 health [7, 8]. Even within the same cohort, the results on the health effects of retirement can vary. Using the Health and Retirement Study (HRS) dataset, several studies have found that early retirement has a negative impact on the self-reported health status and mental health [9]. However, using the same HRS dataset, other researchers have discovered that retirement positively influences subjective health in both men and women, which clearly contradicts the notion that retirement detrimentally impacts health [10]. In the same study, the health effects of retirement can be opposite for different subpopulations. A study in the United States showed that the BMI trajectory slope significantly increased for blue-collar workers after retirement, while there was no change for white-collar employees [11].

Existing studies on the relationship between retirement and health have explored various outcomes, with the most extensively studied ones including subjective health (e.g., self-reported health indicators) [12], mental health [13,14,15] the incidence or prevalence of certain chronic diseases [16], and healthcare utilization [17,18,19] Some studies have also focused on objective physiological indicators or physical examination indices; however, these studies are often limited to specific regions (e.g., Shanghai, China [20], or southern Germany [21]) or particular populations (e.g., women [22]), with nationally representative evidence remaining relatively limited. Unlike the former, these objective indicators (such as blood pressure, BMI, etc.) are often revisable risk factors for some diseases, facilitating the design of targeted prevention strategies at an early stage. Additionally, objective indicators better avoid biases due to subjective reporting by participants in surveys. BMI, a commonly used physical examination index, is often used to define obesity and overweight. Additionally, the BMI has a high predictive value for the risk of certain chronic diseases, such as heart disease, hypertension, type 2 diabetes, and gallstones, and overall health conditions [23]. Therefore, clarifying the impact of retirement on the BMI is highly important for preventing adverse health outcomes in retirees and enhancing overall health levels. Since poor health status may be a significant reason for retirement, we believe that it is crucial to consider the endogeneity of retirement, especially the reverse causality problem, in studying the health effects of retirement.

Consequently, we adopted a regression discontinuity design, a quasiexperimental design method, to maximally overcome the endogeneity problem of retirement. Our aim was to explore the impact of retirement on the BMI and BMI-defined overweight and obesity. The study population consisted of participants from a nationwide representative study in China.

Methods

Data source

The sample for our research originated from the Chinese Hypertension Survey (CHS), encompassing 262 counties across 31 provinces of mainland China between 2012 and 2015. To summarize, through a stratified multistage random sampling technique, the CHS successfully secured a representative cross-section of the Chinese population aged 15 years and above. Comprehensive information regarding the CHS design, methodology, and participant demographics has been extensively discussed in other publications [24, 25].

Retirement definition and inclusion and exclusion criteria

The official retirement policy was instituted in China in 1978 and has remained unchanged ever since [15]. At present, the statutory retirement ages are set at 60 years for men, 55 years for female civil servants, and 50 years for other female workers. These retirement ages are uniform across all provinces and are compulsory for public sector employees but not for those in the private sector. Given that the statutory retirement ages in China coincide with pension eligibility, employees in the private sector may be inclined to retire at these ages to qualify for pension benefits [26].

In the CHS questionnaire, retirement status was ascertained using two queries. The first one was regarding the employment status, with options including employed, retired, student, and unemployed, and the second one inquired about the occupation, with retirees indicating their preretirement profession. Individuals were categorized as retired based on their self-reports of retirement (from the first question). Participants employed in the private sector, who accounted for 82.3% of the full CHS participants, were identified based on their responses to the second question. We excluded them from the analysis due to the less stringent enforcement of the mandatory retirement age in this sector.

Variable measurement

All variables in this study were collected using a standardized questionnaire uniformly developed and designed by the CHS project team, which has been detailed in several previous CHS studies [27, 28].

Outcomes

The outcome variables of this study were the body mass index (BMI) and the BMI-defined states of obesity and overweight. The BMI is calculated as the ratio of weight in kilograms to the square of height in meters (i.e., $\:\text{B}\text{M}\text{I}=\text{w}\text{e}\text{i}\text{g}\text{h}\text{t}\left(\text{k}\text{g}\right)/{\text{h}\text{e}\text{i}\text{g}\text{h}\text{t}\left(\text{m}\right)}^{2}$). In the Chinese Hypertension Survey (CHS), the weight and height measurements were conducted by well-trained interviewers using uniformly provided Omron V-BODY HBF-371 body composition analyzers. The BMI is extensively utilized for categorizing overweight and obesity in adults. The World Health Organization (WHO) defines obesity as a BMI of 30听kg/m² or above, a criterion primarily based on data from Caucasian populations, which may not be entirely applicable to Asian, particularly Chinese, populations [22]. Accordingly, following the guidelines for the prevention and control of overweight and obesity in Chinese adults, as well as the recommendations from the Working Group on Obesity in China (WGOC), we defined overweight as a body mass index (BMI) of 24听kg/m² to less than 28听kg/m² and obesity as a BMI of 28听kg/m² or higher [29].

Covariates

In the Chinese Hypertension Survey (CHS), we collected personal information of the participants through face-to-face structured questionnaires. The covariates in this study included three sets of variables. The first set comprised sociodemographic factors, including sex, age, education level, and marital status. The second set comprised health behavior variables, such as smoking and drinking habits. The third set consisted of medical history variables, including histories of myocardial infarction, stroke, and hypertension. To estimate the effects, we sequentially incorporated these three groups of covariates into the RDD model to check the robustness of the results. Individuals with at least a high school education were defined as highly educated.

Statistical analysis

Descriptive statistics

Initially, we calculated descriptive statistics for all variables. Specifically, we described the distribution characteristics of the full sample and the samples within the optimal bandwidth (including those younger and older than the age threshold). For categorical variables, we used frequencies and percentages for descriptive statistics. For continuous variables, their medians and interquartile ranges (IQRs) were calculated to describe their distribution features.

Estimation of the health effects of retirement

Regression discontinuity design

Given that individuals slightly below and slightly above the statutory retirement age share very similar characteristics, a quasiexperimental design could be implemented using the statutory retirement age as a threshold to determine the impact of retirement on the BMI and the prevalence of overweight and obesity, thereby reinforcing the causal relationship between them. Some studies have shown that being overweight or obese is a significant factor influencing individual decisions to retire, particularly increasing the risk of early retirement [30]. To mitigate the issue of reverse causality when estimating the health effects of retirement, we employed a regression discontinuity design (RDD). In observational studies, the RDD is considered one of the quasiexperimental approaches closest to a randomized controlled trial, thereby effectively minimizing estimation biases caused by endogeneity issues, such as reverse causality [22, 31].

There are two types of regression discontinuity designs (RDDs): a sharp regression discontinuity design (SRDD) and a fuzzy regression discontinuity design (FRDD). Compared to the SRDD, the FRDD is applicable in scenarios where at the threshold, the probability of participants receiving the treatment jumps from 鈥渁鈥� to 鈥渂鈥� but not directly from 0 to 1 (i.e., 0鈥�<鈥塧鈥�<鈥塨鈥�<鈥�1)听[32]. In this study, because the probability of retirement among the Chinese population significantly increases but does not increase from 0 to 1 upon reaching the statutory retirement age, the fuzzy regression discontinuity design (FRDD) was utilized. The effectiveness of the fuzzy regression discontinuity design (FRDD) relies on two critical assumptions, namely, the local randomization assumption and the covariate continuity assumption. The local randomization assumption necessitates that the assignment variable around the threshold has not been artificially manipulated, ensuring that the distribution of treatment on either side of the threshold is completely random and can constitute a local randomized controlled trial. To validate this assumption, we employed a robust statistical test for the continuity of the assignment variable鈥檚 density, developed by McCrary in 2008 [33]. The covariate continuity assumption requires no discontinuities in covariates at the assignment variable鈥檚 threshold, preventing misattribution of such irregularities to the treatment effect and averting research estimate biases [34]. Drawing on previous studies, we treated all covariates individually as outcomes within the FRDD framework to verify their continuity at the threshold [32, 35].

Guided by several econometric and statistical articles [32, 36], our primary analysis utilized nonparametric local linear regression to evaluate the health impact of retirement, which offers an advantage in minimizing data overfitting risks compared to higher-order polynomial models. The robustness tests included quadratic terms and bias-corrected estimates with robust standard errors in the FRDD framework. A triangular kernel function was employed across all analyses to prioritize samples nearer to the threshold. Utilizing the rdrobust R package, we chose a 48-month optimal bandwidth based on Imbens and Kalyanaraman鈥檚 novel algorithm. For robustness, bandwidth variations of 50%, 80%, 120%, and 150% were also explored to ascertain the sensitivity of our findings to bandwidth adjustments.

Standardized age

Given the relatively limited size of the female civil servant group [37], we adopted age 50 as the RDD threshold for all women, in line with prior research [15]. To assess the health impacts of retirement across the entire population, while accounting for the different statutory retirement ages between men and women, we standardized age in months to ensure a uniform threshold for both sexes in the FRDD analysis. In this analysis, the standardized age (SA) was determined by subtracting the respective statutory retirement age for each sex from the actual age in months, resulting in the SA being defined as age in months minus 720 for men and minus 600 for women. As the SA served as the assignment variable for the FRDD, the threshold for the SA was set at 0.

Model setting

The FRDD model is established using a two-stage least-squares equation. The first-stage regression equation is employed to estimate the impact of age on the retirement behavior:

$$\begin{aligned}{R}_{i}&={\alpha\:}_{0}+{\alpha\:}_{1}{I}_{i}+{\alpha\:}_{2}\psi\:\left({SA}_{i}\right)\\&\quad+{\alpha\:}_{3}{I}_{i}\psi\:\left({SA}_{i}\right)+\gamma\:{\nu\:}_{i}+{\omega\:}_{i}\end{aligned}$$

(1)

The second-stage regression equation is constructed based on the estimated results of the first stage and is utilized to evaluate the impact of the retirement behavior on the outcome:

$$\begin{aligned}{Y}_{i}&={\beta\:}_{0}+{\beta\:}_{1}\widehat{{R}_{i}}+{\beta\:}_{2}\phi\:\left({SA}_{i}\right)\\&\quad+{\beta\:}_{3}{I}_{i}\phi\:\left({SA}_{i}\right)+k{\nu\:}_{i}+{\epsilon\:}_{i}\end{aligned}$$

(2)

$\:{R}_{i}$ represents the retirement behavior, with a value of 0 or 1. $\:{I}_{i}$ is a dummy variable that is assigned a value of 1 if the actual age of an individual in months exceeds their respective statutory retirement age, and a value of 0 is assigned otherwise. $\:\psi\:\left({SA}_{i}\right)$ and $\:\phi\:\left({SA}_{i}\right)$ are polynomial functions of the standardized age and are linear in the main analysis. $\:{Y}_{i}$ represents the outcomes of this study, namely, the BMI, overweight status, and obesity status. $\:{\nu\:}_{i}$ denotes the covariates included in our study. $\:{\omega\:}_{i}$ and $\:{\epsilon\:}_{i}$ are the error terms in the two regression equations, respectively. The estimated value of $\:{\beta\:}_{1}$ reflects the impact of retirement on the outcomes. We developed four FRDD models, which incorporated covariates incrementally. Model 1 was not adjusted for any covariates. Model 2 was adjusted for demographic variables (i.e., marital status and education level). Model 3 was further adjusted for health behavior variables (i.e., smoking and drinking habits). Model 4, the fully adjusted model, was built upon Model 3 by additionally adjusting it for medical history variables (i.e., the history of myocardial infarction, stroke, and hypertension).

All statistical analyses were performed using R software (version 4.2.0), and two-tailed p values of <鈥�0.05 were considered to be statistically significant. The FRDD estimation was conducted with the help of the rdrobust R package.

Results

Descriptive statistics

As shown in Table听1, 88,471 participants were included in our study. Among all the participants, the median age was 488 months, with an interquartile range of 367鈥�664 months, and women accounted for 44.9% of the participants. Within the optimal bandwidth, there were 8,324 participants, of whom 4,177 were above the retirement threshold age, while 4,147 were below it. Among the participants within the optimal bandwidth, 39.5% were retired, 96.0% were married, 58.6% had received at least a senior high school education, 41.5% were overweight and 13.8% were obese. The median BMI was 24.44听kg/m², with an interquartile range of 22.48鈥�26.57听kg/m².

Table 1 Descriptive statistics of participants included in this study

Fuzzy regression discontinuity design validity tests

Table S2 displays the outcomes of the McCrary test for assessing data manipulation. The null hypothesis of a smooth density distribution at the threshold was not rejected, indicating no evident manipulation of the assignment variable near this point. In the FRDD analysis, covariates were examined as outcome variables to verify their continuity at the threshold, and the findings are detailed in Table S3. Across all covariates, no significant discontinuities were observed at the threshold.

Results of the first-stage regression estimation of FRDD

Figure听1 displays the SA-driven shifts in the retirement rates across the general population and sex-specific groups. Notable discontinuities at the SA threshold, observable in all categories, underscored the effective enforcement of the statutory retirement ages within our sample. This effect was particularly pronounced among males and was attributed to their less complex retirement age criteria compared with those of females. Table S1, showing the FRDD first-stage regression findings, further validated these rate discontinuities at the threshold. The implementation of the retirement policy markedly increased the retirement rates as follows: 0.22 (95% CI 0.18鈥�0.26) overall; 0.23 (95% CI 0.17鈥�0.29) for men; 0.22 (95% CI 0.16鈥�0.27) for women; 0.26 (95% CI 0.19鈥�0.33) for those with lower education; and 0.19 (95% CI 0.14鈥�0.25) for the highly educated (see Table S1).

Fig. 1

Retirement rates corresponding to different standardized ages in months

FRDD based on the entire population

The results of the FRDD estimation are shown in Fig. 2. Overall, based on the estimates from all models, retirement did not significantly impact the BMI, overweight rate, or obesity rate of the general population. According to the results from the fully adjusted model (Model 4) for the overall population, retirement was associated with a slight increase of 1.07 (95% CI -0.28-2.42; P value: 0.119) in the BMI and an increase of 0.17 (95% CI -0.04-0.39; P value: 0.108) in the overweight rate. The effect of retirement on the obesity rate was 鈭掆��0.01 (95% CI -0.15-0.14; P value: 0.921), which is close to zero. From Model 1 to Model 4, the estimates of the health effects of retirement based on the FRDD showed minimal variation, indicating the robustness of these results.

Fig. 2

The impact of retirement on BMI and overweight and obesity rate

Heterogeneity analysis of different subgroups

We also explored whether the impact of retirement on the BMI and its related outcomes varied across several subgroups. As illustrated in Table S4, significant differences were observed in the effects of retirement between males and females. In males, retirement led to a notable increase of 2.18 (95% CI 0.23鈥�4.13; P value: 0.028) in the BMI and an increase of 0.29 (95% CI -0.02-0.60; P value: 0.067) in the overweight rate. However, the impact of retirement on the BMI of females was negligible (point estimate 0.03, 95% CI -1.88-1.94; P value: 0.976). Regarding educational levels, the increasing effect of retirement on the BMI was more pronounced for participants with low education levels (point estimate 1.54, 95% CI -0.36-3.43; P value: 0.112).

Robustness tests

The results of the robustness tests are depicted in Figure S1 and Table S5. While effect estimates decreased as the bandwidth increased, they generally aligned with our baseline results, and their statistical significance remained consistent across different bandwidths, demonstrating bandwidth insensitivity of our results. Furthermore, the congruent outcomes from applying quadratic age terms and bias-corrected estimates with robust standard errors in the FRDD underscored the solidity of our results.

Discussion

This study, based on the CHS dataset, assessed the impact of retirement on the BMI, overweight rate, and obesity rate within a Chinese population. We observed that retirement did not significantly affect the BMI, overweight rate, or obesity rate across the entire population. Furthermore, these findings proved to be robust when adjusting for various covariates, altering the bandwidth size around the age threshold, and utilizing different functional forms for estimation. This research also revealed heterogeneity in the health effects of retirement among subgroups stratified by sex and educational level. Specifically, retirement led to a significant increase in the BMI among males, while its impact on females was negligible. Among those with lower educational levels, the increasing effect of retirement on the BMI was more pronounced than that in the higher educated group.

Retirement may lead to an increase in the BMI or a greater prevalence of overweight and obesity across the entire population; however, these effects are not statistically significant. Given that RDD estimates are contingent upon a specific bandwidth, we employed a range of different bandwidths to verify the robustness of our findings. As the bandwidths widened, the estimated health effects of retirement slightly decreased, and their confidence intervals narrowed; however, their direction and statistical significance remained unchanged. These findings are consistent with those of several previous studies utilizing the RDD [15, 34], thus underscoring the robustness of our results. Conclusions of some previous studies align with our conclusions. One study that assessed the impact of retirement on the health status of Italian adults revealed that the risk of overweight and obesity did not significantly change during the transition to retirement (relative risk reduction 0.96, 95% CI 0.81鈥�1.15) [38]. Similarly, a study employing the RDD estimated that the impact of retirement on the stress levels among older Chinese individuals was close to zero, further supporting the notion that retirement does not have a significant effect on mental health [15]. On the other hand, there are studies that contradict our findings, as they suggest that retirement has significant positive or negative impacts on health [2, 39]. Given the diverse methods employed to control for endogeneity and the inclusion/exclusion criteria for participants in retirement research, it is acceptable that different studies arrive at varied conclusions regarding the health effects of retirement. Moreover, the variability in findings can be attributed to differences in the compulsory nature of retirement policies across countries or regions. Mandatory, nonvoluntary retirement is often associated with a decline in the individual health status [7, 8].

Our findings indicate that the health effects of retirement differ among subgroups stratified by sex and educational level. Retirement led to a significant increase in the BMI and a potential increase in overweight among men but had a negligible impact on women. Some studies corroborate our findings. Utilizing a regression discontinuity design, a nationwide study in China determined that retirement resulted in a significant increase of 2.1听kg in the weight and an increase of 0.92 units in the BMI for men, whereas it had no effect on the weight or BMI of women [23]. Another study, based on the Survey of Health, Aging and Retirement in Europe (SHARE) dataset, revealed that retirement increased the risk of obesity in men from 19 to 30% but had no effect on women [40]. Additionally, our findings revealed that retirement appeared to have a more pronounced effect, although not statistically significant, on the elevation of the BMI in individuals with lower educational levels (point estimate 1.54, 95% CI -0.36-3.43; P value: 0.112) than in those with higher educational attainment (point estimate 0.75, 95% CI -1.19-2.69; P value: 0.450). Several factors could explain our findings in the subgroup analysis. First, compared with that for women, retirement for men may imply a greater disparity in income levels, social activities, and the social status, which in turn could detrimentally affect physical and mental health of men. Previous research has demonstrated that upon retirement, men exhibit a marked decline in social participation [41], concomitant with an increased likelihood of experiencing loneliness [42]. Second, with the loss of work-imposed lifestyle restrictions, men may have more freedom after retirement, which potentially leads to the development of unhealthy dietary habits. A study has confirmed a causal relationship between retirement and a reduction in the consumption of fruits and vegetables by men [23]. Compared with those with higher educational backgrounds, individuals with lower levels of education, who potentially exhibit weaker self-discipline and limited health knowledge, are more susceptible to the negative repercussions of retirement.

Our study, based on a quasiexperimental design, contributes to the expanding body of literature on the relationship between retirement and health. One of the health outcomes in this research was the BMI, which is acknowledged as one of the key variables for assessing the potential health status in individuals [23]. When the BMI is above the normal range, individuals are at a much greater risk of chronic disease or worsening mental health [43, 44]. Given that the BMI is a dynamic metric and that its reasonable fluctuations within a certain range may not have a positive or negative impact on health, our study goes beyond the focus of most previous studies, which solely examined the effect of retirement on the absolute BMI values. We also considered overweight and obesity, as defined by the BMI, as outcomes of our study. The increased prevalence of overweight and obesity, which are independent risk factors for cardiovascular diseases, kidney diseases, diabetes, musculoskeletal disorders, and certain cancers, poses a significant health and economic burden worldwide [45]. In particular, in China, overweight and obesity have become serious public health issues and have been linked to 11.1% of noncommunicable disease (NCD)-related deaths in 2019⁴⁶. As the rates of obesity and overweight have continued to rise since 2016, China has surpassed other countries of the world in terms of the number of obese individuals [22]. The China Health and Nutrition Survey (2020) revealed that the current rates of overweight and obesity among Chinese adults are 34.3% and 16.4%, respectively, with more than 600听million Chinese people suffering from overweight or obesity. In middle-aged and older populations, these proportions are even greater, leading to substantial national health expenditures for the prevention and treatment of noncommunicable diseases (NCDs) [46]. A nationwide study in China found that elevated BMI in female household heads, specifically overweight, was associated with a 26% increased risk of catastrophic health expenditure (aHR 1.26, 95% CI 1.09鈥�1.47) [47]. A global study covering 192 countries and territories, including China, reported that the total global cost of musculoskeletal disorders attributable to high BMI reached $180.7听billion in 2019 [48]. Given the pivotal role of retirement in the life trajectory, the health effects of retirement on the BMI and on the prevalence of overweight and obesity are crucial for the well-being of retirees and the pressure on the national health care system. Therefore, when considering adjustments to existing retirement policies, the aforementioned health impacts of retirement should also be taken into consideration by policymakers in China. Our work, based on a nationally representative dataset, offers a robust association between retirement and health from the perspective of the BMI, with this association exhibiting strong causality.

Currently, the gradual delay of retirement to mitigate labor shortages and alleviate fiscal pressures from pension expenditures is a trend in many countries worldwide. China, as one of the countries with the earliest statutory retirement ages globally, is also considering advancing this policy. Health issues are a focal point in adjusting retirement policies, and our study offers the following public health policy suggestions. Although the impact of retirement on the overall population is not significant, there may be heterogeneous effects hidden within different subgroups. Therefore, increasing the flexibility of the statutory retirement age and introducing separate delayed retirement policies for men and women could be more rational. For women, the currently set statutory retirement age (50 years) is premature. Appropriately raising the retirement age for women could enhance their economic contribution to society and increase their sense of self-identity. For men, policymakers should consider the potential harm that retirement could pose to their health. During the transition to retirement, men should be encouraged to engage more in household chores and public volunteer services, which could help reduce fluctuations in their psychological state and lifestyle behaviors during this period. Finally, increasing health education for retirees across society and promoting physical exercise could increase the likelihood of a healthy transition to retirement [39].

This study has several limitations. First, notwithstanding the strong internal validity of the RDD, its external applicability is potentially limited. As we determined, the impact of retirement mainly applied to individuals near the statutory retirement age, limiting the generalizability of our findings to those who are significantly younger or older. Second, while changes in income might mediate the relationship between retirement and health outcomes, our dataset鈥攖he CHS鈥攍acked information on the economic status of the participants. Future research should therefore incorporate income-related variables to more accurately assess the impact of retirement on health. Third, although the body mass index (BMI) is a widely utilized metric in the assessment of individual and public health, it has limitations, such as the inability to distinguish between fat and muscle mass, as well as not reflecting the distribution of fat throughout the body. Therefore, future research exploring the association between retirement and obesity might consider employing alternative health outcomes (such as the waist-to-hip ratio) for comparison with the findings of this study. Finally, considering the inherent limitations of cross-sectional data, the findings of this study may require validation through large-scale longitudinal studies in the future.

Conclusions

On average, retirement has no significant impact on the BMI, overweight rates, or obesity rates. Retirement leads to an increase in the BMI among men but does not affect the BMI of women. When considering adjustments to existing retirement policies, the differential health effects of retirement across various populations should be taken into account.