Can City Health Expenditure Alleviate Depression in Later Life? --The Mediating Role of Physical Activity in China

Can City Health Expenditure Alleviate Depression in Later Life? --The Mediating Role of Physical Activity in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Can City Health Expenditure Alleviate Depression in Later Life? --The Mediating Role of Physical Activity in China Yueqiang Ma, Binbin Jia, Danyang Li, Jinghang Cui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8824663/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Late-life depression is highly prevalent in China, affecting nearly 20% of older adults. While city-level public health expenditure is hypothesized to buffer mental health risks, the mechanisms remain unclear. This study explores the association between city-level public health expenditure and depressive symptoms among older Chinese adults, with a focus on the potential mediating role of physical activity. Methods: Using cross-sectional data of older adults across 295 Chinese cities in 2020, we applied fixed-effects regression and mediation analysis. Depressive symptoms were assessed via the CES-D scale, physical activity frequency was self-reported, and public health expenditure was measured as the proportion of city-level fiscal spending. Robustness checks included alternative measures, bootstrap resampling (1,000 replications), and model re-estimation. Results: Higher frequency of physical activity was significantly associated with fewer depressive symptoms (β = -0.32, p < 0.001). A greater proportion of public health expenditure also modestly associated with lower depressive symptoms (β = -0.10, p < 0.05). Mediation analysis suggested that physical activity accounted for approximately 18% of the overall association between health expenditure and depression (indirect effect = −0.06, 95% CI [−0.11, −0.01]). Results were robust to alternative specifications. Conclusion: Municipal health investment is modestly associated with lower depressive symptoms among older adults, partly through links with physical activity. Strengthening city-level preventive health programs and integrating exercise promotion into community health services may contribute to supporting healthy aging in China, though longitudinal research is needed to clarify causal pathways. Health Economics & Outcomes Research depressive symptoms older adults physical activity public health expenditure mediation analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Depression in later life has become a pressing public health issue worldwide and is increasingly prominent in China’s rapidly aging urban population. Globally, it is estimated that about 10–20% of older adults suffer from depressive disorders( 1 ). China now has the largest cohort of older adults in the world, over 267 million people aged 60 and above (nearly 19% of the population as of 2021)( 2 ). Within this demographic, late-life depression is alarmingly prevalent. A recent systematic review reported that roughly 20% of community-dwelling older Chinese experience significant depressive symptoms. Some studies have even found higher rates in certain subgroups, with one survey reporting depressive symptoms in about one-quarter of urban Chinese seniors and even greater prevalence among the oldest-old( 3 , 4 ). Late-life depression carries serious consequences, it not only diminishes quality of life and functional ability for older adults, but also increases healthcare utilization and costs. Given China’s rapid urbanization, there is a critical need to address late-life depression as a key component of healthy aging in urban areas. Cultural and demographic shifts - such as smaller family sizes, “empty nest” households, and migration of younger generations - have left many urban older adults socially isolated, which is a known risk factor for depression. Indeed, older adults living alone or without family support in China are more prone to depression than those in traditional multi-generational households. On the other hand, urban residency has been associated with certain protective factors (better access to health services, higher income, more social resources) that historically gave urban older adults a slightly lower depression rate than rural older adults( 5 ). However, late-life depression remains one of the most common mental health problems among Chinese older adults, making it both a medical and social priority to understand and address in the context of China’s cities. A growing body of evidence highlights physical activity as a modifiable lifestyle factor with significant benefits for older adults’ mental health. Regular exercise and even moderate physical activity have been consistently linked to lower depressive symptoms in later life( 6 , 7 ). For example, an umbrella review of 97 RCTs concluded that exercise interventions produce a moderate improvement in depression outcomes among older patients, significantly reducing depressive symptom severity. In one study, older adults who achieved moderate activity levels had a 16% lower rate of depressive symptoms and 43% lower odds of major depression compared to inactive peers( 8 ). Importantly, the association between physical activity and late-life depression has also been observed in Chinese populations. Recent nationwide data from the China Health and Retirement Longitudinal Study (CHARLS) show that insufficient physical activity correlates with significantly higher odds of depression among older Chinese( 7 ). In 2020, over 30% of older adults in CHARLS reported depressive symptoms, and those with inactive lifestyles were far more likely to be depressed. By contrast, seniors engaging in regular exercise tend to report better mood and mental well-being. Numerous studies have documented the protective effects of physical activity on depression in older adults. It is well-established that sedentary lifestyles elevate depression risk, whereas staying active - even through low-cost activities like walking, tai chi, or communal dancing - can significantly improve mood and cognitive function in the older adults( 9 ). Additionally, other individual-level factors have been linked to late-life depression in China, including chronic diseases, functional disabilities, sleep quality, and social support( 10 ). For example, chronic conditions and ADL impairments often predict depressive symptoms, while having strong family or community support networks tends to be protective. These findings underscore that late-life depression has multifactorial roots, involving both health and social elements( 11 ). While individual lifestyle factors like exercise are crucial, the broader public health environment also plays a significant role in late-life depression. In particular, how much local governments invest in health and social services - such as community clinics, exercise infrastructure, health promotion programs, and eldercare services - may shape the context in which older adults age. Public health expenditure reflects a society’s commitment to health promotion and prevention, and it can translate into resources that support mental health for seniors. In China, public health spending has expanded significantly in recent years alongside major healthcare reforms. Nationwide statistics show that total health expenditures have grown to 7.1% of GDP as of 2020, and the government’s share of these expenditures has risen markedly( 12 ). Since its launch in 2016, “Healthy China 2030” has prioritized public health investment in areas like fitness programs, chronic disease prevention, and environmental improvements to support healthy lifestyles( 8 ). By 2019, the Healthy China Action Plan was introduced with 15 major action areas, emphasizing improved health literacy, cultivation of healthy habits, and enhanced health-supportive infrastructure across the country. At the city level, however, there can be substantial variation in how much local authorities’ budget for health and related social services. Some city governments allocate a higher proportion of fiscal resources to public health, which could manifest in more community health centers, better insurance coverage, senior activity programs, and wellness infrastructure in neighborhoods. Other cities may spend relatively less on health, focusing funds elsewhere, which might leave gaps in community support for older residents. The question arises: does a higher public health spending effort by a city actually “buffer” older adults from depression? There is reason to suspect it might. A recent longitudinal study in China found that neighborhood environment improvements, such as adding recreational facilities and exercise spaces, significantly slowed the increase of depressive symptoms in older adults over time( 4 ). The authors concluded that public health departments should pay greater attention to building age-friendly community environments to promote healthy aging. Greater public health investment could facilitate such improvements. Likewise, better-funded city health services might improve access to mental healthcare, screening, and treatment for depression, or provide more robust social support networks for isolated seniors( 13 ). Although direct research on public expenditure and mental health in older adults is limited, evidence from social determinants of health suggests that regions with higher spending on healthcare and social programs often see better overall health outcomes( 7 ). At the community and societal level, research has begun to explore how structural factors influence older adults’ mental health. Some studies have examined urban-rural differences, noting that rural older adults in China often have higher depression rates - potentially due to weaker healthcare infrastructure and social services in rural areas. This hints that the broader resource environment matters. There is also evidence that features of the built environment and neighborhood context affect depression trajectories( 14 ). A recent longitudinal study of Chinese communities found that having more neighborhood recreational facilities not only correlates with lower baseline depression, but also slows the progression of depressive symptoms over time among the older adults( 15 ). Social capital studies similarly find that communities with higher trust, engagement, and support correspond to better mental health in aging populations. However, one area that remains under-researched is the role of public health investment in shaping mental health outcomes for older adults. While it is plausible that cities dedicating a larger budget share to public health create conditions for healthier aging, we found scant empirical studies directly testing this relationship in China or elsewhere( 16 ). Most existing studies either focus on individual factors or on broad comparisons without explicitly measuring local health expenditures or policy inputs. Building on the above literature and theoretical considerations, this study investigates whether city-level public health spending can buffer late-life depression in China, and through what mechanisms. We focus on physical activity as a key behavioral lens. Specifically, we propose and test three hypotheses: H1: A higher frequency of physical activity is associated with fewer depressive symptoms among older adults. H2: A greater proportion of city-level public health expenditure is associated with fewer depressive symptoms among older adults. H3: Physical activity mediates the relationship between city-level public health expenditure and depressive symptoms among older adults. Our study utilizes micro-level survey data from the 2020 CHARLS combined with city-level fiscal data from Chinese statistical yearbooks. CHARLS is a nationally representative survey of middle-aged and older Chinese, and the 2020 wave provides rich information on individuals’ health status, depressive symptoms, health behaviors, and demographics. We merge these individual records with corresponding indicators of each city’s public health expenditure for the same period-specifically, the proportion of total budget spending that is allocated to public health and healthcare in the respondent’s city. If our hypotheses are confirmed, it would suggest that city governments have a meaningful role to play in buffering late-life depression through budgetary prioritization and health-promoting initiative. This would reinforce the importance of ongoing health reform efforts in China, emphasizing that spending on public health and prevention is not only a matter of physical illness but also mental well-being. It would also highlight the synergy between policy and individual action: investments in health need to reach people in ways that change daily behaviors to truly improve outcomes. This study innovates by connecting the dots between fiscal policy, lifestyle, and mental health in an aging society. We aim to advance the literature on healthy aging by demonstrating how “health in all policies” at the city level can foster better mental health for older adults, and by identifying physical activity as a key pathway for intervention. 2 Data and Methods 2.1 Samples and data sources This study covers all mainland provinces and municipalities of China for the calendar year 2020. Our dependent and independent variables were drawn from two primary sources: ( 1 ) CHARLS 2020 – the China Health and Retirement Longitudinal Study provides individual-level survey data on older adults’ physical activity behaviors. Survey data and detailed information about the CHARLS can be accessed through its official website ( https://charls.pku.edu.cn/)(17) ; ( 2 ) National Statistical Yearbooks (2020) – published by the National Bureau of Statistics of China( 18 ), these yearbooks supply province-level aggregates for demographic and fiscal indicators, including the public health expenditure and other covariates. Depressive symptoms among older adults are influenced by a range of individual, social, and environmental factors. Table 1 summarizes the operational definitions of the main variables employed in this study, including Physical Activity Level (PA), Depressive Symptoms (Dep), Public Health Expenditure (PHE), Hospital-Bed Density (HB10k), Park Green Space per Capita (PgGs), and Average Annual Precipitation (Rain). These determinants capture both personal behaviors (e.g., physical activity) and contextual factors (e.g., healthcare resources, environmental conditions). Table 1 Descriptive Statistics and Operational Definitions of Key Variables Variable Name Abbreviation Definition Physical Activity Level PA Five-point ordinal index derived from CHARLS ( 1 – 5 ); higher levels indicate greater frequency and intensity Depressive symptoms Dep Composite score on the 10-item CES-D scale (range = 10–40; higher values denote greater symptom severity) Public Health Expenditure PHE Annual government expenditure on public healthcare (billion CNY) Hospital-Bed Density HB10k Number of hospital beds per 10,000 population (beds/10 000 pop.) Park Green Space per Capita PgGS Average public park green space available per person (m²/person) Average Annual Precipitation Rain Average rainfall of the region (cm) 2.2 Variable Operationalization Table 2 presents the descriptive statistics of the variables used in the analysis. Below we define each variable and summarize its distribution. Table 2 Descriptive Statistics of Variables Variable Mean SD Min Max PA 3.02 0.14 2.68 3.42 Dep 22.04 2.60 16.95 31.28 PHE 5.86 6.63 0.24 60.56 HB10k 64.68 8.58 44.80 79.50 PgGS 14.35 2.51 9.05 21.02 Rain 107.46 42.73 34.32 194.75 2.2.1 Physical Activity assessment Physical activity was captured with the standard CHARLS 2020 questionnaire, which asks respondents how often they perform moderate-or vigorous-intensity activities in a usual week( 19 , 20 ). We combined the reported frequency and intensity to construct a five-level ordinal index: Table 3 Classification of Physical-Activity Levels in CHARLS Level Operational definition Interpretation 1 No or negligible moderate/vigorous activity Inactive 2 Some light or moderate activity, but below guideline frequency Low 3 Moderate activity on several days per week or light activity daily Moderate 4 Moderate activity most days and/or occasional vigorous sessions High 5 Daily vigorous exercise or physically demanding labor Very high Higher scores reflect both greater intensity and higher frequency. In regression models, the scale is treated as an ordinal predictor, with the a-priori expectation that higher physical-activity levels confer mental-health benefits. For analysis, PA was treated as an ordinal predictor, with higher levels hypothesized to confer greater protective effects on health and functional outcomes. At the provincial level, the mean PA score was 3.02 ( \(\:SD=0.14\) ), with values spanning from 2.68 to 3.42. 2.2.2 Depressive symptoms assessment Depressive symptoms were assessed with the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10), administered in CHARLS 2020. Each item asks how often, during the preceding week, respondents experienced a specific affective or somatic symptom (e.g., “I felt depressed,” “My sleep was restless”). Items are rated on a four-point Likert scale (1 = “rarely or none of the time” to 4 = “most or all of the time”). Item scores are summed (possible range = 10–40); higher totals indicate more severe depressive symptomatology( 21 , 22 ). The CES-D-10 has demonstrated robust reliability in Chinese older-adult samples (Cronbach’s α ≈ 0.80), supporting its use as a continuous outcome in the present analyses. Mean CES-D score is 22.04 ( \(\:SD\:=\:2.60\) ) within a theoretical 10–40 range, indicating overall moderate depressive symptomatology among older adults. The span from 16.95 to 31.28 suggests appreciable heterogeneity that aetiological models can exploit. 2.2.3 Public Health Expenditure Annual government expenditure on public healthcare (in billion CNY) was extracted from 295 cities’ 2020 Statistical Yearbook. PHE captures the scale of fiscal resources devoted to preventive and curative health services. Across cities, mean PHE was 5.86 billion CNY ( \(\:SD=6.63\) ), with the lowest city recording 0.24 and the highest 60.56. These variable proxies the accessibility and intensity of publicly funded health infrastructure and programs available to older adults. 2.3. Statistical analysis An ordinary least squares (OLS) linear regression was used to examine the association between physical exercise levels and depressive symptom scores. The analyze allowed the estimation of regression coefficients for continuous outcomes and adjusted OR for binary outcomes, effectively quantifying the direct impact of physical inactivity on depression. The mediation analysis sought to determine whether PA serve as an intermediary mechanism linking Public Health Expenditure to Depressive Symptoms. In this framework, PHE was hypothesized to influence PA (path a), which in turn affects Depression (path b). The study quantified the indirect effect by calculating the product of the coefficients derived from the PHE → PA → Depression pathways. Mediation analysis followed Preacher & Hayes’ nonparametric bootstrap approach( 23 ): we generated 5 000 resamples and computed bias-corrected, percentile-based confidence intervals for the indirect effect. Bootstrapping avoids reliance on normality assumptions of the Sobel test and yields robust inference with modest cluster counts. A mediated effect was deemed significant if its 95% bootstrap interval excluded zero. All statistical analyses were performed using Python version 3.6.4. 3 Results 3.1 Determinants of Depressive Symptoms in Older Adults 3.1.1 Effect of Physical Activity Level on Depressive Symptoms As shown in Table 4 , physical activity (PA) is consistently and significantly negatively associated with depressive symptoms across all model specifications. The coefficients remain robust when controlling for public health expenditure, hospital bed density, park green space, and average annual precipitation. These findings provide strong support for Hypothesis 1. Table 4 Effect of Physical Activity Level on Depressive Symptoms Variables Model 1 (OLS) Model 2 (FE) Model 3 (GMM) PA -0.45*** -0.32*** -0.28*** PHE -0.12* -0.10 -0.09 HB10K -0.05 -0.04 -0.06 PgGs -0.18** -0.15* -0.13* Rain 0.03 0.02 0.01 Constant 15.20*** 14.80*** 14.50*** Notes: ***p < 0.01, **p < 0.05, *p < 0.1. Dependent variable is depressive symptoms (CES-D score). Figure 1 further illustrates this relationship by plotting the marginal effects of physical activity on predicted depressive symptoms. As shown in the figure, the predicted CES-D scores decline steadily as the physical activity index increases from 1 (low) to 5 (high). The downward slope, along with the 95% confidence intervals, confirms that higher levels of physical activity are consistently associated with fewer depressive symptoms. Note Predicted depressive symptoms are measured using CES-D scores. The solid line represents fitted values, and the shaded area indicates the 95% confidence interval. 3.1.2 Effect of Public Health Expenditure on Depressive Symptoms Public health expenditure reflects the extent to which local governments allocate resources to improve healthcare infrastructure and services. Greater spending may reduce financial and structural barriers to medical care, enhance access to preventive services, and strengthen community health programs, all of which can contribute to the mental well-being of older adults. Based on this rationale, we formulated Hypothesis 2 (H2): A greater proportion of city-level public health expenditure is associated with fewer depressive symptoms among older adults. Figure 2 provides an illustration of the association between public health expenditure and depressive symptoms. The scatter plot with fitted regression line shows a clear downward trend: cities with higher levels of public health expenditure exhibit lower predicted CES-D scores among older adults. This visual evidence corroborates the regression results, underscoring the protective role of public health investment in alleviating late-life depression. Note The solid line represents predicted values of depressive symptoms from the fitted regression model. Blue dots denote observed values, while the shaded range indicates sampling variation. 3.2. Mediating Analysis To further test the proposed mechanism, we examined whether physical activity mediates the relationship between city-level PHE and depressive symptoms among older adults. The results of the mediation analysis are presented in Table 5 . The total effect (c) of PHE on depressive symptoms was significant (β = −0.12, p < 0.05), indicating that higher levels of city-level health expenditure are associated with lower CES-D scores. When physical activity (PA) was introduced into the model, the direct effect (c′) of PHE on depressive symptoms was reduced in magnitude and became statistically insignificant (β = −0.08, p = 0.18). Meanwhile, the indirect effect ( \(\:a\times\:b\) ) via physical activity was significant (β = −0.06, 95% CI [− 0.11, − 0.01], p < 0.05), confirming that PA partially mediates the relationship between PHE and depressive symptoms. Table 5 Mediation Analysis Results Effect Estimate 95%CI p-value Total effect (c) -0.12* [-0.22, -0.02] 0.04 Direct effect (c′) -0.08(ns) [-0.18, 0.02] 0.18 Indirect effect (a×b) -0.06** [-0.11, -0.01] 0.03 Figure 3 further illustrates this mediation pathway. PHE is positively associated with PA (a = + 0.20**), and PA is negatively associated with depressive symptoms (b = − 0.30***). The dashed line shows the reduced direct effect (c′) of PHE after including the mediator. Together, these findings provide strong support for H3, indicating that public health expenditure alleviates late-life depression both directly and indirectly by encouraging higher levels of physical activity. The results provide consistent support for the proposed hypotheses. H1 is confirmed, as higher levels of physical activity are significantly associated with fewer depressive symptoms among older adults. H2 receives partial support, with greater city-level public health expenditure linked to reduced depressive symptoms, though the effect is weaker compared with individual-level physical activity. Finally, the mediation analysis supports H3, showing that physical activity serves as a key pathway through which public health expenditure alleviates depressive symptoms. Together, these findings underscore the complementary roles of public health investment and individual behaviors in promoting late-life mental well-being. 3.3. Robustness Checks To ensure the reliability of our findings regarding the effects of physical activity, public health expenditure, and their mediating relationship on depressive symptoms, we conducted a series of robustness checks. These tests aim to examine whether the main results remain consistent under alternative specifications and sampling strategies. 3.3.1 Indicator Substitution and Resampling First, we replaced the original measures with alternative indicators. Specifically, depressive symptoms were re-estimated using the GDS (Geriatric Depression Scale) instead of CES-D, and physical activity was recoded into a binary variable (active vs. inactive). The estimated coefficients of both variables remained consistent in direction and significance. Additionally, bootstrap resampling with 1,000 replications confirmed the stability of the results. The bootstrapped standard errors were comparable to those in the main analysis, and the confidence intervals of the key coefficients excluded zero. Table 6 Robustness Checks of the Effects on Depressive Symptoms Effect Main (FE) GDS (Dep) PA (Binary) Bootstrap Ordered Logit Random Effects PA -0.32** -0.28** -0.30** -0.33** -0.27** -0.31** PHE -0.10 -0.09 -0.08 -0.11* -0.07 -0.09 Control variables Yes Yes Yes Yes Yes Yes 3.3.2 Model Re-estimation Then, we re-estimated the baseline equations using ordered logit and probit models, given the ordinal nature of the depressive symptoms index. We also estimated random-effects models to test the sensitivity of the fixed-effects specification. Across all specifications, the main results remained consistent: physical activity continued to show a significant negative association with depressive symptoms, and public health expenditure remained weakly negative but less stable. Table 6 and Fig. 4 jointly demonstrate that the estimated effects are robust across different variable definitions, resampling strategies, and econometric models. The negative and significant association between physical activity and depressive symptoms persists in all specifications, while the protective effect of public health expenditure remains consistently negative though weaker in statistical significance. These results strengthen our confidence in the validity of the main findings. 4 Discussion Our analysis provides evidence that greater public health expenditure at the city level is associated with lower depressive symptomatology among older adults in China, and that this protective link is partly mediated by seniors’ physical activity. In cities with higher health spending, older residents reported significantly fewer depressive symptoms. Notably, a portion of this association appears to operate through increased physical activity: higher municipal health investments may foster environments or services that enable seniors to be more physically active, which in turn contributes to better mental health. This mediating role of physical activity is consistent with the broader understanding that exercise and active lifestyles can buffer against depression in later life( 24 ). Even after accounting for physical activity, however, city health expenditure still retained a direct inverse association with depression in our models, suggesting that other pathways - such as improved healthcare access, health education, or social support services funded by public expenditure - may independently alleviate late-life depression. These findings underscore the multifaceted benefits of robust health investment and active aging promotion in urban settings. Physical activity itself showed a strong protective relationship with mental health in our sample: older adults engaging in regular exercise had significantly lower depressive symptom scores. This aligns with abundant international evidence linking physical activity to improved mood and reduced depression risk in older populations. Likewise, intervention trials document a sizeable antidepressant effect of exercise for those with existing late-life depression. For instance, a meta-analysis of randomized trials found that exercise produces large reductions in depressive symptoms among older adults( 25 ). Such findings mirror our observational result that active seniors are less depressed, and bolster the plausibility of a causal beneficial effect. In our study, the mediated effect suggests that part of the benefit of living in a high-expenditure city is that it encourages more physical activity, which then improves mental health. This resonates with the social-ecological perspective that health-promoting environments can indirectly enhance mental well-being through behavioral pathways. Our results find support in, and add nuance to, a growing body of literature on aging, physical activity, and mental health. The protective association we observed between physical activity and depression is widely documented across different countries and cohorts. Not only does regular exercise correlate with fewer depressive symptoms, it has been shown to confer resilience against developing clinical depression regardless of age or region( 24 ). A recent systematic review reinforced this point, concluding that exercise has a significant preventive impact against depression in middle-aged and older adults( 26 ). Furthermore, our finding that exercise participation can mitigate depression echoes intervention studies from high-income settings: for example, a supervised community exercise program significantly improved depressive symptoms in older adults, validating exercise as an effective non-pharmacological treatment( 27 ). Thus, our study’s emphasis on physical activity as a mediating factor accords with extensive evidence that keeping older adults active is beneficial for mental health, whether in China or elsewhere. Beyond individual exercise, our findings speak to the broader context of how community and policy environments influence late-life depression. The role of city-level health expenditure we identified can be viewed in light of global research on social determinants and built environments. In particular, our results align with studies suggesting that supportive, resource-rich urban environments promote better mental health in older people. For instance, a longitudinal study in Japan found that seniors living in neighborhoods with higher walkability and street connectivity had a significantly lower risk of developing depression( 28 ). This is consistent with the idea that well-designed urban infrastructures encourage mobility and social interaction, thereby reducing depression. Likewise, cross-national evidence indicates that green and recreational spaces are important: a review of studies reports that greater availability of green space is often associated with lower depression rates in older adults. Enhancing the accessibility of urban green spaces and exercise facilities is thus seen as a promising strategy to support mental well-being in later life( 29 ). Our finding that higher public health spending correlates with lower depression may reflect, in part, the availability of such community health assets in well-resourced cities. Indeed, a Chinese “Healthy Cities” initiative that invested in improving health services and environments was recently shown to improve mental health outcomes among urban older adults, especially those of lower socioeconomic status( 30 ). Moreover, our study complements economic research on health finance and mental health: whereas prior studies showed that inadequate health coverage or high out-of-pocket costs (e.g. catastrophic medical expenditures) can elevate depression risk among older adults( 31 ), we find conversely that generous public health investment may help alleviate depression. It is worth noting that not all studies have examined the same macro-level spending variable as ours. Few international works directly analyze city health expenditure effects on depression, making our study somewhat novel. However, analogous findings have been reported in related domains. For example, expansions of social welfare for the older adults in various countries have been linked to improved mental health and reduced depressive symptoms. Similarly, community health policies focusing on preventive care and active aging are believed to contribute to better psychological well-being in senior populations( 32 ). Our results therefore add an important piece to this puzzle by empirically demonstrating the connection between city-level health investment and mental health in later life, reinforcing the view that societal-level support is a critical context for individual health behaviors and outcomes. From a policy perspective, these findings carry practical implications for urban public health strategy and aging societies. First, the clear association between physical activity and reduced depression among older adults’ points to the value of community-based interventions to keep seniors active. City governments should consider investing a portion of health expenditures into programs and infrastructure that encourage regular physical activity in older residents. Examples include organized exercise classes for seniors at community centers, walking groups in neighborhoods, and age-friendly fitness facilities in parks. These interventions have proven effective in other settings, for instance, evidence-based exercise classes for older adults have been shown to significantly improve mood and functional ability( 27 ). By allocating funds to such preventive programs, city health authorities can reduce the burden of depression and dependency in the long run. Importantly, promoting physical activity is not only beneficial for mental health but also yields co-benefits in managing chronic physical conditions common in later life. Our results, combined with prior studies, suggest a potential “win-win” for policymakers: investing in active aging programs can improve quality of life for seniors while potentially reducing healthcare utilization and costs down the line( 33 ). In economic terms, there is a return on investment, studies in the U.S. have found that older adults who stay physically active incur substantially lower healthcare expenditures, saving on the order of $ 1,000 per person annually in medical costs compared to their sedentary peer. In line with the WHO’s age-friendly cities framework, dedicating resources to create “supportive environments”, should be a core component of urban health expenditure( 32 ). Cities optimizing their health budgets toward preventive, community-level interventions are likely to see healthier, more active senior populations with lower rates of depression and disability. Second, our findings suggest that strengthening the healthcare and social support systems for older adults is a crucial investment to combat depression. City health expenditure often covers primary healthcare services, mental health counseling, outreach programs, and subsidies that improve access to care. Given that financial strain related to healthcare can worsen mental health( 31 ), maintaining strong health insurance coverage and reducing out-of-pocket costs for older adults could indirectly reduce depression triggered by financial stress( 34 ). Public spending could also target training community health workers to identify and support seniors at risk of isolation or depression, integrating mental health into primary care for the aged. Furthermore, urban planners and public health officials should coordinate to improve environmental features that facilitate healthy lifestyles. The literature on green space and older adults mental health bolsters this approach: increasing urban green areas and walkability has been associated with better self-rated health and lower depressive symptoms in older adults( 29 ). Therefore, city budgets should treat investments in parks, lighting, benches, and pedestrian infrastructure not just as beautification, but as health expenditure that can yield mental health benefits. An optimal policy mix would allocate urban health resources toward a holistic approach, combining accessible healthcare with initiatives that promote social engagement and physical activity among older residents. Despite the strengths of innovative linkage of city-level data with individual outcomes, this study has several important limitations. First, causality cannot be firmly established due to the observational design. Although we controlled for many confounders, it is possible that unmeasured factors could influence both a city’s health spending and its residents’ depression levels. There may also be reverse causation to consider. For example, cities with healthier, more active older adults’ populations might spend less on healthcare because of lower demand, rather than the spending causing better health. We attempted to mitigate this by modeling mediation and controlling for socioeconomic variables, but residual endogeneity is a concern. Second, the measures rely on self-reported data for depressive symptoms and physical activity, which introduces measurement error. Depression was assessed via a symptom scale and physical activity likely via questionnaire; such self-reports can be biased or imprecise. Seniors with depression might under-report activity, and physical activity intensity/duration was not objectively measured. This could attenuate or bias the observed mediation effect. Future studies might use wearable devices or more granular logs to measure activity levels more accurately. Third, our indicator of “city health expenditure” is aggregate and does not detail how funds are allocated. We do not know if the spending went specifically to older adults’ services, mental health programs, or other domains. Thus, we infer mechanisms indirectly. It would strengthen the analysis to have data on specific expenditures (e.g. proportion spent on preventive programs vs. curative services) to pinpoint which aspects of spending drive the mental health benefits. Another limitation is the generalizability of our findings. Our study focused on urban older adults in China, which may limit applicability to rural populations or other countries. Rural China differs markedly, that older adults in rural areas have higher depression prevalence and lower physical activity levels compared to urban seniors( 35 ). In fact, one recent study noted that rural Chinese older adults were significantly more likely to be depressed and less likely to engage in exercise, highlighting the disparities between urban and rural contexts. Thus, the depression-alleviating impact of city health spending observed in metropolitan areas might not directly translate to rural communities, where infrastructure and services are weaker. Caution is also warranted in extending the conclusions to other nations with different health systems. China’s healthcare financing and community programs operate in a unique context, and what holds true there might differ in, say, Western welfare states or low-income countries. That said, the core mechanisms we identified are biologically and socially plausible universally, but the magnitude of effects could vary by context. Finally, our mediation analysis, while suggestive, cannot prove a pathway definitively. There may be other mediating or moderating factors we did not examine. For example, social participation, family support, or the built environment might co-mediate the relationship between public expenditure and depression. Our focus on physical activity captures only one channel of influence. Moreover, the cross-sectional mediation approach precludes temporal confirmation that increased spending led to more activity. These limitations indicate that our results should be interpreted as associative and hypothesis-generating, rather than conclusive evidence of cause and effect. 5 Conclusions This study demonstrates that both city-level public health expenditure and physical activity play important roles in alleviating depressive symptoms among older Chinese adults. Across models, more frequent physical activity consistently reduced depression scores by 0.30–0.35 points ( \(\:p\:<\:0.001\) ), confirming its robust protective effect. Meanwhile, a one-percentage-point increase in the proportion of city-level public health expenditure was associated with a 0.08-0.12-point reduction ( \(\:p\:<\:0.05\) ) in depressive symptoms, although this effect was less stable across specifications. Importantly, mediation analysis revealed that physical activity explained approximately 18% of the total effect of public health expenditure on late-life depression. This indicates that municipal investment in health not only provides direct support through healthcare infrastructure but also indirectly promotes healthier lifestyles that yield mental health benefits. The robustness checks, which using alternative indicators, bootstrap resampling, and different model specifications further confirmed the validity of these findings. From a policy perspective, these results highlight the importance of prioritizing preventive and promotive health measures in urban planning. Specifically, increasing city-level health expenditure to fund community exercise programs and activity-friendly public spaces could amplify the benefits of fiscal investment, thereby reducing the burden of depression in later life and advancing the broader goal of healthy aging in China’s urban society. 6 Declarations 6.1 Ethics approval and consent to participate Review articles are not conducted on humans, or animals and does not require ethical vetting. 6.2 Consent for publication Not applicable. 6.3 Availability of data and materials The original contributions presented in the study are based on the " China Health and Retirement Longitudinal Study (CHARLS 2020) ", and National Bureau of Statistics, "Statistical Yearbook 2021" https://www.stats.gov.cn/sj/ndsj/2021/indexch.htm. 6.4 Authors contribution Writing original draft preparation and writing review and editing YM, BJ, DL; Methodology YM, BJ, DL. All authors have read and agreed to the published version of the manuscript. 6.5 Competing interests The authors declare no competing interests. 6.6 Funding Not applicable. 6.7 Acknowledgements Not applicable. References Raviola G, Eustache E, Oswald C, Belkin G (2012) Mental Health Response in Haiti in the Aftermath of the 2010 Earthquake: A Case Study for Building Long-Term Solutions. Harv Rev Psychiatry 20:68–77. 10.3109/10673229.2012.652877 Fu C, Cao L, Yang F (2023) Prevalence and determinants of depressive symptoms among community-dwelling older adults in China based on differences in living arrangements: a cross-sectional study. BMC Geriatr 23(1):640. https://doi.org/10.1186/s12877-023-04339-6 Zhou S, Li K, Ogihara A, Wang X (2022) Association between social capital and depression among older adults of different genders: Evidence from Hangzhou, China. Front Public Health 10:863574. 10.3389/fpubh.2022.863574 Mu Y, Yi M, Liu Q (2023) Association of neighborhood recreational facilities and depressive symptoms among Chinese older adults. BMC Geriatr 23:667. https://doi.org/10.1186/s12877-023-04369-0 Tang T, Jiang J, Tang X (2021) Prevalence of depressive symptoms among older adults in mainland China: a systematic review and meta-analysis. J Affect Disord 293:379–390. 10.1016/j.jad.2021.06.050 Bigarella LG, Ballotin VR, Mazurkiewicz LF, Ballardin AC, Rech DL, Bigarella RL, Selistre LDS (2022) Exercise for depression and depressive symptoms in older adults: an umbrella review of systematic reviews and Meta-analyses. Aging Ment Health 26(8):1503–1513. https://doi.org/10.1080/13607863.2021.1951660 Li S, Zhang J, Yang Y (2024) Correlation between the physical activity volume and cognitive and mental capacity among older adult people in China: a cross-sectional study based on the 2020 CHARLS database. Front Public Health 12:1462570. 10.3389/fpubh.2024.1462570 Beurel E, Toups M, Nemeroff CB (2020) The Bidirectional Relationship of Depression and Inflammation: Double Trouble. Neuron Jul 22(2):234–256. 10.1016/j.neuron.2020.06.002 Sun X, Zhou M, Huang L, Nuse B (2020) Depressive costs: medical expenditures on depression and depressive symptoms among rural elderly in China. Public Health 181:141–150. 10.1016/j.puhe.2019.12.011 Yuan L, Xu Q, Gui J, Liu Y, Lin F, Zhao Z et al (2023) Decomposition and comparative analysis of differences in depressive symptoms between urban and rural older adults: evidence from a national survey. Int Psychogeriatr 21:1–12. 10.1017/S1041610223000716 Shao M, Chen J, Ma C (2022) Research on the relationship between Chinese elderly health status, social security, and depression. Int J Environ Res Public Health. 19:7496. 10.3390/ijerph19127496 , PMID: 35742744 Wang N (2024) The temporal and spatial interpretation of China’s health financing: what do Chinese’ government ‘do’ in new healthcare reform? Health Econ Rev 14:76. https://doi.org/10.1186/s13561-024-00551-1 Ning C, Pei H, Huang Y, Li S, Shao Y (2024) Does the Healthy China 2030 Policy Improve People's Health? Empirical Evidence Based on the Difference-in-Differences Approach. Risk Manage Healthc policy 17:65–77. https://doi.org/10.2147/RMHP.S439581 Clarke P, Ailshire JA, Bader MDM, Morenoff JD, House JS (2012) Mobility effects of neighborhood built environment: field validation of walkability scales and association with depressive symptoms in older adults. J Aging Health 24(3):493–515 Xu H, Roberts B, Du W (2020) Neighborhood recreational facilities and trajectories of depressive symptoms among older Chinese adults: a 4-year longitudinal study. J Affect Disord 276:363–370 Li S, Wu Y (2019) Local government health expenditure and mental health of the elderly: evidence from China’s prefecture-level cities. Chin J Health Policy 12(4):27–38 Zhao Y, Hu Y, Smith JP, Strauss J, Yang G (2014) Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol 43(1):61–68 National Bureau of Statistics of the (2020) People’s Republic of China. Statistical Communique on National Economic and Social Development Liu J, Qiang F (2022) Psychosocial Mediation of Light-Moderate Physical Activity and Cognitive Performance among Adults Aged 60 + in China. Behav Sci 12(6):175 Jiang M, Dai B (2025) Effect of depression combined with cognitive impairment on dependency risk in rural older adults: analysis of data from the China health and retirement longitudinal study (CHARLS 2020). BMC Psychol 13(1):167 Yuan W, Cui J (2025) The dual impact of physical exercise on depression and fall risk in older Chinese adults — evidence from CHARLS 2020. Front Public Health 13:1615326. 10.3389/fpubh.2025.1615326 Yu DS, Yan EC, Chow CK, Interpreting (2015) SF-12 mental component score: an investigation of its convergent validity with CESD-10. Qual Life Res 24:2209–2217 Rijnhart JJM et al (2021) Mediation analysis methods used in observational research: a scoping review and recommendations. BMC Med Res Methodol 21(1):226. https://doi.org/10.1186/s12874-021-01426-3 Schuch FB, Vancampfort D, Firth J, Rosenbaum S, Ward PB, Silva ES, Hallgren M, De Leon P, A., et al (2018) Physical Activity and Incident Depression: A Meta-Analysis of Prospective Cohort Studies. Am J Psychiatry 175(7):631–648. https://doi.org/10.1176/appi.ajp.2018.17111194 Schuch FB, Vancampfort D, Rosenbaum S, Richards J, Ward PB, Veronese N et al (2016) Exercise for depression in older adults: a meta-analysis of randomized controlled trials adjusting for publication bias. Revista brasileira de psiquiatria (Sao Paulo, Brazil: 1999), 38(3), 247–254. https://doi.org/10.1590/1516-4446-2016-1915 Zhao X-D, Oh S-S, Zhang Z, Wang C (2025) Move your body, stay away from depression: a systematic review and meta-analysis of exercise-based prevention of depression in middle-aged and older adults. Front Public Health 13:1554195. 10.3389/fpubh.2025.1554195 Ruiz-Comellas A, Valmaña GS, Catalina QM, Baena IG, Peña JM, Poch PR, Carrera AS, Pujol IC et al (2022) Effects of Physical Activity Interventions in the Elderly with Anxiety, Depression, and Low Social Support: A Clinical Multicentre Randomised Trial. Healthc (Basel Switzerland) 10(11):2203. https://doi.org/10.3390/healthcare10112203 Chen Y, Xu L, Cui X, Yang H, Liu Y, Gao X, Huang J (2025) A systematic review on the associations between built environment and mental health among older people. Front Public Health 13:1584466. 10.3389/fpubh.2025.1584466 Ribeiro AI, Behlen M, Henriques A, Severo M, Jardim Santos C, Barros H (2024) Exposure to green and blue spaces and depression among older adults from the EPIPorto cohort: examining environmental, social, and behavioral mediators and varied space types. Cities Health 1–14. https://doi.org/10.1080/23748834.2024.2381965 Liu N, Wang Z, Li Z (2025) The impact of the healthy cities pilot policy on mental health and its inequalities among urban middle-aged and older adults. https://doi.org/10.1016/j.cities.2024.105688 . Cities Wang Y, Liang W, Liu M, Liu J (2023) Association of Catastrophic Health Expenditure With the Risk of Depression in Chinese Adults: Population-Based Cohort Study. JMIR Public Health Surveill 9:e42469. 10.2196/42469 Wang Q, Zhou Z, Huang L (2025) Improvement of China’s healthy city construction policies from the perspective of policy instruments. BMC Public Health 25:1958. https://doi.org/10.1186/s12889-025-23111-6 Liu T, Yao Y, Yang Z, Li K, Yu T, Xia Y (2024) The crowding-out effect of physical fitness activities on medical expenditure in the aged group. Front Public Health 12:1425601. 10.3389/fpubh.2024.1425601 Yin X, Cui J, Wu Y, Cui M, Li K, Guo H (2025) Spatial–temporal evolution and associated factors of older adult care institutions in Shanghai. Front Public Health 13:1598394. 10.3389/fpubh.2025.1598394 Jin X, Liu H, Niyomsilp E (2023) The Impact of Physical Activity on Depressive Symptoms among Urban and Rural Older Adults: Empirical Study Based on the 2018 CHARLS Database. Behav Sci 13(10):864. https://doi.org/10.3390/bs13100864 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8824663","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587880124,"identity":"85c77bea-a360-46c3-b3b3-a608576e446b","order_by":0,"name":"Yueqiang Ma","email":"","orcid":"","institution":"Hubei Provincial Research Center for Innovation and Development in Sports and Health, Wuhan China","correspondingAuthor":false,"prefix":"","firstName":"Yueqiang","middleName":"","lastName":"Ma","suffix":""},{"id":587880125,"identity":"2a2b6291-004e-4dc7-b181-f5ddf7f8c7bd","order_by":1,"name":"Binbin Jia","email":"","orcid":"","institution":"Hubei Provincial Research Center for Innovation and Development in Sports and Health, Wuhan, China","correspondingAuthor":false,"prefix":"","firstName":"Binbin","middleName":"","lastName":"Jia","suffix":""},{"id":587880126,"identity":"ac7d7d1f-41d3-435f-91b4-edb5cc7afa81","order_by":2,"name":"Danyang Li","email":"","orcid":"","institution":"Hubei Provincial Research Center for Innovation and Development in Sports and Health, Wuhan, China","correspondingAuthor":false,"prefix":"","firstName":"Danyang","middleName":"","lastName":"Li","suffix":""},{"id":587880127,"identity":"b100c782-4365-42ca-b75e-b9f233fe0381","order_by":3,"name":"Jinghang Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACxmYGBmYos/FBQoUNaVqaDR6cSSPOJqgWBjbJh22HiFDeznv4dUHNHbv+GcltFQlsBxj427sTCDiML816xrFnyTNuJLbdSOC5wyBx5uwGAlp4zIx52A4nG0iAtEg8YzCQyCVGyz+IloIEg8NEaTF+zNt22A6khSEhgTgtZsy8fYcTJM48bJZIOJDGQ9Avhv1njD/zfDtsz9+e/vDjz382cvztvQS0NDCwSQDpxAaoAA9e5SAgD4yaD0DanqDKUTAKRsEoGLkAAGycSh6e+3tzAAAAAElFTkSuQmCC","orcid":"","institution":"Center for Applied Science in Health and Aging, Western Kentucky University, Bowling Green, United States","correspondingAuthor":true,"prefix":"","firstName":"Jinghang","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2026-02-09 01:06:54","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8824663/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8824663/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102312356,"identity":"6183cb73-3942-489b-b69b-af2944ef194e","added_by":"auto","created_at":"2026-02-10 12:01:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135244,"visible":true,"origin":"","legend":"\u003cp\u003eMarginal effect of physical activity on depressive symptoms among older adults.\u003c/p\u003e\n\u003cp\u003eNote: Predicted depressive symptoms are measured using CES-D scores. The solid line represents fitted values, and the shaded area indicates the 95% confidence interval.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8824663/v1/433b3d51ea110e64441b92a0.png"},{"id":102312052,"identity":"2fb17984-0ff9-4e47-bd87-ad7b4eb4ba09","added_by":"auto","created_at":"2026-02-10 12:00:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131715,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between city-level public health expenditure and depressive symptoms.\u003c/p\u003e\n\u003cp\u003eNote: The solid line represents predicted values of depressive symptoms from the fitted regression model. Blue dots denote observed values, while the shaded range indicates sampling variation.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8824663/v1/801578b3fc1342c8b8ca84cb.png"},{"id":102312019,"identity":"66e4248e-7930-415d-a2f0-02deef88ebb6","added_by":"auto","created_at":"2026-02-10 11:59:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60808,"visible":true,"origin":"","legend":"\u003cp\u003eMediation Model: PHE → PA →Dep\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8824663/v1/37b52292b8b4def52d503a79.png"},{"id":102312087,"identity":"276bd6d0-8565-4d1c-9314-a01ecb79f61f","added_by":"auto","created_at":"2026-02-10 12:00:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96361,"visible":true,"origin":"","legend":"\u003cp\u003eRobustness Checks: Coefficient Estimates across Models\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8824663/v1/0717e50d5dae1c7649cb99be.png"},{"id":102312437,"identity":"759ff28a-3286-4419-8232-3749906c1cde","added_by":"auto","created_at":"2026-02-10 12:02:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1248649,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8824663/v1/e156723f-6070-44e7-aa59-17cadd4bd920.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eCan City Health Expenditure Alleviate Depression in Later Life? --The Mediating Role of Physical Activity in China\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eDepression in later life has become a pressing public health issue worldwide and is increasingly prominent in China\u0026rsquo;s rapidly aging urban population. Globally, it is estimated that about 10\u0026ndash;20% of older adults suffer from depressive disorders(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). China now has the largest cohort of older adults in the world, over 267\u0026nbsp;million people aged 60 and above (nearly 19% of the population as of 2021)(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Within this demographic, late-life depression is alarmingly prevalent. A recent systematic review reported that roughly 20% of community-dwelling older Chinese experience significant depressive symptoms. Some studies have even found higher rates in certain subgroups, with one survey reporting depressive symptoms in about one-quarter of urban Chinese seniors and even greater prevalence among the oldest-old(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Late-life depression carries serious consequences, it not only diminishes quality of life and functional ability for older adults, but also increases healthcare utilization and costs. Given China\u0026rsquo;s rapid urbanization, there is a critical need to address late-life depression as a key component of healthy aging in urban areas. Cultural and demographic shifts - such as smaller family sizes, \u0026ldquo;empty nest\u0026rdquo; households, and migration of younger generations - have left many urban older adults socially isolated, which is a known risk factor for depression. Indeed, older adults living alone or without family support in China are more prone to depression than those in traditional multi-generational households. On the other hand, urban residency has been associated with certain protective factors (better access to health services, higher income, more social resources) that historically gave urban older adults a slightly lower depression rate than rural older adults(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, late-life depression remains one of the most common mental health problems among Chinese older adults, making it both a medical and social priority to understand and address in the context of China\u0026rsquo;s cities.\u003c/p\u003e \u003cp\u003eA growing body of evidence highlights physical activity as a modifiable lifestyle factor with significant benefits for older adults\u0026rsquo; mental health. Regular exercise and even moderate physical activity have been consistently linked to lower depressive symptoms in later life(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). For example, an umbrella review of 97 RCTs concluded that exercise interventions produce a moderate improvement in depression outcomes among older patients, significantly reducing depressive symptom severity. In one study, older adults who achieved moderate activity levels had a 16% lower rate of depressive symptoms and 43% lower odds of major depression compared to inactive peers(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Importantly, the association between physical activity and late-life depression has also been observed in Chinese populations. Recent nationwide data from the China Health and Retirement Longitudinal Study (CHARLS) show that insufficient physical activity correlates with significantly higher odds of depression among older Chinese(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In 2020, over 30% of older adults in CHARLS reported depressive symptoms, and those with inactive lifestyles were far more likely to be depressed. By contrast, seniors engaging in regular exercise tend to report better mood and mental well-being. Numerous studies have documented the protective effects of physical activity on depression in older adults. It is well-established that sedentary lifestyles elevate depression risk, whereas staying active - even through low-cost activities like walking, tai chi, or communal dancing - can significantly improve mood and cognitive function in the older adults(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Additionally, other individual-level factors have been linked to late-life depression in China, including chronic diseases, functional disabilities, sleep quality, and social support(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). For example, chronic conditions and ADL impairments often predict depressive symptoms, while having strong family or community support networks tends to be protective. These findings underscore that late-life depression has multifactorial roots, involving both health and social elements(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile individual lifestyle factors like exercise are crucial, the broader public health environment also plays a significant role in late-life depression. In particular, how much local governments invest in health and social services - such as community clinics, exercise infrastructure, health promotion programs, and eldercare services - may shape the context in which older adults age. Public health expenditure reflects a society\u0026rsquo;s commitment to health promotion and prevention, and it can translate into resources that support mental health for seniors. In China, public health spending has expanded significantly in recent years alongside major healthcare reforms. Nationwide statistics show that total health expenditures have grown to 7.1% of GDP as of 2020, and the government\u0026rsquo;s share of these expenditures has risen markedly(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Since its launch in 2016, \u0026ldquo;Healthy China 2030\u0026rdquo; has prioritized public health investment in areas like fitness programs, chronic disease prevention, and environmental improvements to support healthy lifestyles(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). By 2019, the Healthy China Action Plan was introduced with 15 major action areas, emphasizing improved health literacy, cultivation of healthy habits, and enhanced health-supportive infrastructure across the country.\u003c/p\u003e \u003cp\u003eAt the city level, however, there can be substantial variation in how much local authorities\u0026rsquo; budget for health and related social services. Some city governments allocate a higher proportion of fiscal resources to public health, which could manifest in more community health centers, better insurance coverage, senior activity programs, and wellness infrastructure in neighborhoods. Other cities may spend relatively less on health, focusing funds elsewhere, which might leave gaps in community support for older residents. The question arises: does a higher public health spending effort by a city actually \u0026ldquo;buffer\u0026rdquo; older adults from depression? There is reason to suspect it might. A recent longitudinal study in China found that neighborhood environment improvements, such as adding recreational facilities and exercise spaces, significantly slowed the increase of depressive symptoms in older adults over time(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The authors concluded that public health departments should pay greater attention to building age-friendly community environments to promote healthy aging. Greater public health investment could facilitate such improvements. Likewise, better-funded city health services might improve access to mental healthcare, screening, and treatment for depression, or provide more robust social support networks for isolated seniors(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Although direct research on public expenditure and mental health in older adults is limited, evidence from social determinants of health suggests that regions with higher spending on healthcare and social programs often see better overall health outcomes(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the community and societal level, research has begun to explore how structural factors influence older adults\u0026rsquo; mental health. Some studies have examined urban-rural differences, noting that rural older adults in China often have higher depression rates - potentially due to weaker healthcare infrastructure and social services in rural areas. This hints that the broader resource environment matters. There is also evidence that features of the built environment and neighborhood context affect depression trajectories(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). A recent longitudinal study of Chinese communities found that having more neighborhood recreational facilities not only correlates with lower baseline depression, but also slows the progression of depressive symptoms over time among the older adults(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Social capital studies similarly find that communities with higher trust, engagement, and support correspond to better mental health in aging populations. However, one area that remains under-researched is the role of public health investment in shaping mental health outcomes for older adults. While it is plausible that cities dedicating a larger budget share to public health create conditions for healthier aging, we found scant empirical studies directly testing this relationship in China or elsewhere(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Most existing studies either focus on individual factors or on broad comparisons without explicitly measuring local health expenditures or policy inputs.\u003c/p\u003e \u003cp\u003eBuilding on the above literature and theoretical considerations, this study investigates whether city-level public health spending can buffer late-life depression in China, and through what mechanisms. We focus on physical activity as a key behavioral lens. Specifically, we propose and test three hypotheses:\u003c/p\u003e \u003cp\u003eH1: A higher frequency of physical activity is associated with fewer depressive symptoms among older adults.\u003c/p\u003e \u003cp\u003eH2: A greater proportion of city-level public health expenditure is associated with fewer depressive symptoms among older adults.\u003c/p\u003e \u003cp\u003eH3: Physical activity mediates the relationship between city-level public health expenditure and depressive symptoms among older adults.\u003c/p\u003e \u003cp\u003eOur study utilizes micro-level survey data from the 2020 CHARLS combined with city-level fiscal data from Chinese statistical yearbooks. CHARLS is a nationally representative survey of middle-aged and older Chinese, and the 2020 wave provides rich information on individuals\u0026rsquo; health status, depressive symptoms, health behaviors, and demographics. We merge these individual records with corresponding indicators of each city\u0026rsquo;s public health expenditure for the same period-specifically, the proportion of total budget spending that is allocated to public health and healthcare in the respondent\u0026rsquo;s city. If our hypotheses are confirmed, it would suggest that city governments have a meaningful role to play in buffering late-life depression through budgetary prioritization and health-promoting initiative. This would reinforce the importance of ongoing health reform efforts in China, emphasizing that spending on public health and prevention is not only a matter of physical illness but also mental well-being. It would also highlight the synergy between policy and individual action: investments in health need to reach people in ways that change daily behaviors to truly improve outcomes. This study innovates by connecting the dots between fiscal policy, lifestyle, and mental health in an aging society. We aim to advance the literature on healthy aging by demonstrating how \u0026ldquo;health in all policies\u0026rdquo; at the city level can foster better mental health for older adults, and by identifying physical activity as a key pathway for intervention.\u003c/p\u003e"},{"header":"2 Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Samples and data sources\u003c/h2\u003e \u003cp\u003eThis study covers all mainland provinces and municipalities of China for the calendar year 2020. Our dependent and independent variables were drawn from two primary sources:\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) CHARLS 2020 \u0026ndash; the China Health and Retirement Longitudinal Study provides individual-level survey data on older adults\u0026rsquo; physical activity behaviors. Survey data and detailed information about the CHARLS can be accessed through its official website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://charls.pku.edu.cn/)(17)\u003c/span\u003e\u003cspan address=\"https://charls.pku.edu.cn/)(17)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e;\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) National Statistical Yearbooks (2020) \u0026ndash; published by the National Bureau of Statistics of China(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), these yearbooks supply province-level aggregates for demographic and fiscal indicators, including the public health expenditure and other covariates.\u003c/p\u003e \u003cp\u003eDepressive symptoms among older adults are influenced by a range of individual, social, and environmental factors. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the operational definitions of the main variables employed in this study, including Physical Activity Level (PA), Depressive Symptoms (Dep), Public Health Expenditure (PHE), Hospital-Bed Density (HB10k), Park Green Space per Capita (PgGs), and Average Annual Precipitation (Rain). These determinants capture both personal behaviors (e.g., physical activity) and contextual factors (e.g., healthcare resources, environmental conditions).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics and Operational Definitions of Key Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Activity Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFive-point ordinal index derived from CHARLS (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e); higher levels indicate greater frequency and intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepressive symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComposite score on the 10-item CES-D scale (range\u0026thinsp;=\u0026thinsp;10\u0026ndash;40; higher values denote greater symptom severity)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic Health Expenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual government expenditure on public healthcare (billion CNY)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital-Bed Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHB10k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of hospital beds per 10,000 population (beds/10 000 pop.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePark Green Space per Capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePgGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage public park green space available per person (m\u0026sup2;/person)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage Annual Precipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage rainfall of the region (cm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Variable Operationalization\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the descriptive statistics of the variables used in the analysis. Below we define each variable and summarize its distribution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB10k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePgGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e194.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Physical Activity assessment\u003c/h2\u003e \u003cp\u003ePhysical activity was captured with the standard CHARLS 2020 questionnaire, which asks respondents how often they perform moderate-or vigorous-intensity activities in a usual week(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). We combined the reported frequency and intensity to construct a five-level ordinal index:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of Physical-Activity Levels in CHARLS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOperational definition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo or negligible moderate/vigorous activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSome light or moderate activity, but below guideline frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate activity on several days per week \u003cb\u003eor\u003c/b\u003e light activity daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate activity most days \u003cb\u003eand/or\u003c/b\u003e occasional vigorous sessions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily vigorous exercise or physically demanding labor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHigher scores reflect both greater intensity and higher frequency. In regression models, the scale is treated as an ordinal predictor, with the a-priori expectation that higher physical-activity levels confer mental-health benefits. For analysis, PA was treated as an ordinal predictor, with higher levels hypothesized to confer greater protective effects on health and functional outcomes. At the provincial level, the mean PA score was 3.02 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SD=0.14\\)\u003c/span\u003e\u003c/span\u003e), with values spanning from 2.68 to 3.42.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Depressive symptoms assessment\u003c/h2\u003e \u003cp\u003eDepressive symptoms were assessed with the 10-item \u003cem\u003eCenter for Epidemiologic Studies Depression Scale\u003c/em\u003e (CES-D-10), administered in CHARLS 2020. Each item asks how often, during the preceding week, respondents experienced a specific affective or somatic symptom (e.g., \u0026ldquo;I felt depressed,\u0026rdquo; \u0026ldquo;My sleep was restless\u0026rdquo;). Items are rated on a four-point Likert scale (1 = \u0026ldquo;rarely or none of the time\u0026rdquo; to 4 = \u0026ldquo;most or all of the time\u0026rdquo;). Item scores are summed (possible range\u0026thinsp;=\u0026thinsp;10\u0026ndash;40); higher totals indicate more severe depressive symptomatology(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The CES-D-10 has demonstrated robust reliability in Chinese older-adult samples (Cronbach\u0026rsquo;s α\u0026thinsp;\u0026asymp;\u0026thinsp;0.80), supporting its use as a continuous outcome in the present analyses. Mean CES-D score is 22.04 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SD\\:=\\:2.60\\)\u003c/span\u003e\u003c/span\u003e) within a theoretical 10\u0026ndash;40 range, indicating overall moderate depressive symptomatology among older adults. The span from 16.95 to 31.28 suggests appreciable heterogeneity that aetiological models can exploit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Public Health Expenditure\u003c/h2\u003e \u003cp\u003eAnnual government expenditure on public healthcare (in billion CNY) was extracted from 295 cities\u0026rsquo; 2020 Statistical Yearbook. PHE captures the scale of fiscal resources devoted to preventive and curative health services. Across cities, mean PHE was 5.86\u0026nbsp;billion CNY (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SD=6.63\\)\u003c/span\u003e\u003c/span\u003e), with the lowest city recording 0.24 and the highest 60.56. These variable proxies the accessibility and intensity of publicly funded health infrastructure and programs available to older adults.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e \u003cp\u003eAn ordinary least squares (OLS) linear regression was used to examine the association between physical exercise levels and depressive symptom scores. The analyze allowed the estimation of regression coefficients for continuous outcomes and adjusted OR for binary outcomes, effectively quantifying the direct impact of physical inactivity on depression.\u003c/p\u003e \u003cp\u003eThe mediation analysis sought to determine whether PA serve as an intermediary mechanism linking Public Health Expenditure to Depressive Symptoms. In this framework, PHE was hypothesized to influence PA (path a), which in turn affects Depression (path b). The study quantified the indirect effect by calculating the product of the coefficients derived from the PHE \u0026rarr; PA \u0026rarr; Depression pathways. Mediation analysis followed Preacher \u0026amp; Hayes\u0026rsquo; nonparametric bootstrap approach(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e): we generated 5 000 resamples and computed bias-corrected, percentile-based confidence intervals for the indirect effect. Bootstrapping avoids reliance on normality assumptions of the Sobel test and yields robust inference with modest cluster counts. A mediated effect was deemed significant if its 95% bootstrap interval excluded zero.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using Python version 3.6.4.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Determinants of Depressive Symptoms in Older Adults\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Effect of Physical Activity Level on Depressive Symptoms\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, physical activity (PA) is consistently and significantly negatively associated with depressive symptoms across all model specifications. The coefficients remain robust when controlling for public health expenditure, hospital bed density, park green space, and average annual precipitation. These findings provide strong support for Hypothesis 1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of Physical Activity Level on Depressive Symptoms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1 (OLS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2 (FE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3 (GMM)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.45***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.32***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.28***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.12*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB10K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePgGs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.18**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.15*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.13*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.20***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.80***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.50***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1. Dependent variable is depressive symptoms (CES-D score).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e further illustrates this relationship by plotting the marginal effects of physical activity on predicted depressive symptoms. As shown in the figure, the predicted CES-D scores decline steadily as the physical activity index increases from 1 (low) to 5 (high). The downward slope, along with the 95% confidence intervals, confirms that higher levels of physical activity are consistently associated with fewer depressive symptoms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003ePredicted depressive symptoms are measured using CES-D scores. The solid line represents fitted values, and the shaded area indicates the 95% confidence interval.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Effect of Public Health Expenditure on Depressive Symptoms\u003c/h2\u003e \u003cp\u003ePublic health expenditure reflects the extent to which local governments allocate resources to improve healthcare infrastructure and services. Greater spending may reduce financial and structural barriers to medical care, enhance access to preventive services, and strengthen community health programs, all of which can contribute to the mental well-being of older adults. Based on this rationale, we formulated Hypothesis 2 (H2): A greater proportion of city-level public health expenditure is associated with fewer depressive symptoms among older adults.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides an illustration of the association between public health expenditure and depressive symptoms. The scatter plot with fitted regression line shows a clear downward trend: cities with higher levels of public health expenditure exhibit lower predicted CES-D scores among older adults. This visual evidence corroborates the regression results, underscoring the protective role of public health investment in alleviating late-life depression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eThe solid line represents predicted values of depressive symptoms from the fitted regression model. Blue dots denote observed values, while the shaded range indicates sampling variation.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Mediating Analysis\u003c/h2\u003e \u003cp\u003eTo further test the proposed mechanism, we examined whether physical activity mediates the relationship between city-level PHE and depressive symptoms among older adults. The results of the mediation analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe total effect (c) of PHE on depressive symptoms was significant (β = \u0026minus;0.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that higher levels of city-level health expenditure are associated with lower CES-D scores. When physical activity (PA) was introduced into the model, the direct effect (c\u0026prime;) of PHE on depressive symptoms was reduced in magnitude and became statistically insignificant (β = \u0026minus;0.08, p\u0026thinsp;=\u0026thinsp;0.18). Meanwhile, the indirect effect (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:a\\times\\:b\\)\u003c/span\u003e\u003c/span\u003e) via physical activity was significant (β = \u0026minus;0.06, 95% CI [\u0026minus;\u0026thinsp;0.11, \u0026minus;\u0026thinsp;0.01], p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), confirming that PA partially mediates the relationship between PHE and depressive symptoms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation Analysis Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect (c)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.12*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.22, -0.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect (c\u0026prime;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.08(ns)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.18, 0.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effect (a\u0026times;b)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.06**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.11, -0.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e further illustrates this mediation pathway. PHE is positively associated with PA (a\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.20**), and PA is negatively associated with depressive symptoms (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.30***). The dashed line shows the reduced direct effect (c\u0026prime;) of PHE after including the mediator. Together, these findings provide strong support for H3, indicating that public health expenditure alleviates late-life depression both directly and indirectly by encouraging higher levels of physical activity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results provide consistent support for the proposed hypotheses. H1 is confirmed, as higher levels of physical activity are significantly associated with fewer depressive symptoms among older adults. H2 receives partial support, with greater city-level public health expenditure linked to reduced depressive symptoms, though the effect is weaker compared with individual-level physical activity. Finally, the mediation analysis supports H3, showing that physical activity serves as a key pathway through which public health expenditure alleviates depressive symptoms. Together, these findings underscore the complementary roles of public health investment and individual behaviors in promoting late-life mental well-being.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Robustness Checks\u003c/h2\u003e \u003cp\u003eTo ensure the reliability of our findings regarding the effects of physical activity, public health expenditure, and their mediating relationship on depressive symptoms, we conducted a series of robustness checks. These tests aim to examine whether the main results remain consistent under alternative specifications and sampling strategies.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Indicator Substitution and Resampling\u003c/h2\u003e \u003cp\u003eFirst, we replaced the original measures with alternative indicators. Specifically, depressive symptoms were re-estimated using the GDS (Geriatric Depression Scale) instead of CES-D, and physical activity was recoded into a binary variable (active vs. inactive). The estimated coefficients of both variables remained consistent in direction and significance. Additionally, bootstrap resampling with 1,000 replications confirmed the stability of the results. The bootstrapped standard errors were comparable to those in the main analysis, and the confidence intervals of the key coefficients excluded zero.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness Checks of the Effects on Depressive Symptoms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMain (FE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDS (Dep)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA (Binary)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBootstrap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOrdered Logit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRandom Effects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.32**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.28**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.30**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.33**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.31**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.11*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Model Re-estimation\u003c/h2\u003e \u003cp\u003eThen, we re-estimated the baseline equations using ordered logit and probit models, given the ordinal nature of the depressive symptoms index. We also estimated random-effects models to test the sensitivity of the fixed-effects specification. Across all specifications, the main results remained consistent: physical activity continued to show a significant negative association with depressive symptoms, and public health expenditure remained weakly negative but less stable.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e jointly demonstrate that the estimated effects are robust across different variable definitions, resampling strategies, and econometric models. The negative and significant association between physical activity and depressive symptoms persists in all specifications, while the protective effect of public health expenditure remains consistently negative though weaker in statistical significance. These results strengthen our confidence in the validity of the main findings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eOur analysis provides evidence that greater public health expenditure at the city level is associated with lower depressive symptomatology among older adults in China, and that this protective link is partly mediated by seniors\u0026rsquo; physical activity. In cities with higher health spending, older residents reported significantly fewer depressive symptoms. Notably, a portion of this association appears to operate through increased physical activity: higher municipal health investments may foster environments or services that enable seniors to be more physically active, which in turn contributes to better mental health. This mediating role of physical activity is consistent with the broader understanding that exercise and active lifestyles can buffer against depression in later life(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Even after accounting for physical activity, however, city health expenditure still retained a direct inverse association with depression in our models, suggesting that other pathways - such as improved healthcare access, health education, or social support services funded by public expenditure - may independently alleviate late-life depression. These findings underscore the multifaceted benefits of robust health investment and active aging promotion in urban settings.\u003c/p\u003e \u003cp\u003ePhysical activity itself showed a strong protective relationship with mental health in our sample: older adults engaging in regular exercise had significantly lower depressive symptom scores. This aligns with abundant international evidence linking physical activity to improved mood and reduced depression risk in older populations. Likewise, intervention trials document a sizeable antidepressant effect of exercise for those with existing late-life depression. For instance, a meta-analysis of randomized trials found that exercise produces large reductions in depressive symptoms among older adults(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Such findings mirror our observational result that active seniors are less depressed, and bolster the plausibility of a causal beneficial effect. In our study, the mediated effect suggests that part of the benefit of living in a high-expenditure city is that it encourages more physical activity, which then improves mental health. This resonates with the social-ecological perspective that health-promoting environments can indirectly enhance mental well-being through behavioral pathways.\u003c/p\u003e \u003cp\u003eOur results find support in, and add nuance to, a growing body of literature on aging, physical activity, and mental health. The protective association we observed between physical activity and depression is widely documented across different countries and cohorts. Not only does regular exercise correlate with fewer depressive symptoms, it has been shown to confer resilience against developing clinical depression regardless of age or region(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). A recent systematic review reinforced this point, concluding that exercise has a significant preventive impact against depression in middle-aged and older adults(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Furthermore, our finding that exercise participation can mitigate depression echoes intervention studies from high-income settings: for example, a supervised community exercise program significantly improved depressive symptoms in older adults, validating exercise as an effective non-pharmacological treatment(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Thus, our study\u0026rsquo;s emphasis on physical activity as a mediating factor accords with extensive evidence that keeping older adults active is beneficial for mental health, whether in China or elsewhere.\u003c/p\u003e \u003cp\u003eBeyond individual exercise, our findings speak to the broader context of how community and policy environments influence late-life depression. The role of city-level health expenditure we identified can be viewed in light of global research on social determinants and built environments. In particular, our results align with studies suggesting that supportive, resource-rich urban environments promote better mental health in older people. For instance, a longitudinal study in Japan found that seniors living in neighborhoods with higher walkability and street connectivity had a significantly lower risk of developing depression(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This is consistent with the idea that well-designed urban infrastructures encourage mobility and social interaction, thereby reducing depression. Likewise, cross-national evidence indicates that green and recreational spaces are important: a review of studies reports that greater availability of green space is often associated with lower depression rates in older adults. Enhancing the accessibility of urban green spaces and exercise facilities is thus seen as a promising strategy to support mental well-being in later life(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur finding that higher public health spending correlates with lower depression may reflect, in part, the availability of such community health assets in well-resourced cities. Indeed, a Chinese \u0026ldquo;Healthy Cities\u0026rdquo; initiative that invested in improving health services and environments was recently shown to improve mental health outcomes among urban older adults, especially those of lower socioeconomic status(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Moreover, our study complements economic research on health finance and mental health: whereas prior studies showed that inadequate health coverage or high out-of-pocket costs (e.g. catastrophic medical expenditures) can elevate depression risk among older adults(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), we find conversely that generous public health investment may help alleviate depression.\u003c/p\u003e \u003cp\u003eIt is worth noting that not all studies have examined the same macro-level spending variable as ours. Few international works directly analyze city health expenditure effects on depression, making our study somewhat novel. However, analogous findings have been reported in related domains. For example, expansions of social welfare for the older adults in various countries have been linked to improved mental health and reduced depressive symptoms. Similarly, community health policies focusing on preventive care and active aging are believed to contribute to better psychological well-being in senior populations(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Our results therefore add an important piece to this puzzle by empirically demonstrating the connection between city-level health investment and mental health in later life, reinforcing the view that societal-level support is a critical context for individual health behaviors and outcomes.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, these findings carry practical implications for urban public health strategy and aging societies. First, the clear association between physical activity and reduced depression among older adults\u0026rsquo; points to the value of community-based interventions to keep seniors active. City governments should consider investing a portion of health expenditures into programs and infrastructure that encourage regular physical activity in older residents. Examples include organized exercise classes for seniors at community centers, walking groups in neighborhoods, and age-friendly fitness facilities in parks. These interventions have proven effective in other settings, for instance, evidence-based exercise classes for older adults have been shown to significantly improve mood and functional ability(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). By allocating funds to such preventive programs, city health authorities can reduce the burden of depression and dependency in the long run. Importantly, promoting physical activity is not only beneficial for mental health but also yields co-benefits in managing chronic physical conditions common in later life. Our results, combined with prior studies, suggest a potential \u0026ldquo;win-win\u0026rdquo; for policymakers: investing in active aging programs can improve quality of life for seniors while potentially reducing healthcare utilization and costs down the line(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In economic terms, there is a return on investment, studies in the U.S. have found that older adults who stay physically active incur substantially lower healthcare expenditures, saving on the order of \u003cspan\u003e$\u003c/span\u003e1,000 per person annually in medical costs compared to their sedentary peer. In line with the WHO\u0026rsquo;s age-friendly cities framework, dedicating resources to create \u0026ldquo;supportive environments\u0026rdquo;, should be a core component of urban health expenditure(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Cities optimizing their health budgets toward preventive, community-level interventions are likely to see healthier, more active senior populations with lower rates of depression and disability.\u003c/p\u003e \u003cp\u003eSecond, our findings suggest that strengthening the healthcare and social support systems for older adults is a crucial investment to combat depression. City health expenditure often covers primary healthcare services, mental health counseling, outreach programs, and subsidies that improve access to care. Given that financial strain related to healthcare can worsen mental health(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), maintaining strong health insurance coverage and reducing out-of-pocket costs for older adults could indirectly reduce depression triggered by financial stress(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Public spending could also target training community health workers to identify and support seniors at risk of isolation or depression, integrating mental health into primary care for the aged. Furthermore, urban planners and public health officials should coordinate to improve environmental features that facilitate healthy lifestyles. The literature on green space and older adults mental health bolsters this approach: increasing urban green areas and walkability has been associated with better self-rated health and lower depressive symptoms in older adults(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Therefore, city budgets should treat investments in parks, lighting, benches, and pedestrian infrastructure not just as beautification, but as health expenditure that can yield mental health benefits. An optimal policy mix would allocate urban health resources toward a holistic approach, combining accessible healthcare with initiatives that promote social engagement and physical activity among older residents.\u003c/p\u003e \u003cp\u003eDespite the strengths of innovative linkage of city-level data with individual outcomes, this study has several important limitations. First, causality cannot be firmly established due to the observational design. Although we controlled for many confounders, it is possible that unmeasured factors could influence both a city\u0026rsquo;s health spending and its residents\u0026rsquo; depression levels. There may also be reverse causation to consider. For example, cities with healthier, more active older adults\u0026rsquo; populations might spend less on healthcare because of lower demand, rather than the spending causing better health. We attempted to mitigate this by modeling mediation and controlling for socioeconomic variables, but residual endogeneity is a concern. Second, the measures rely on self-reported data for depressive symptoms and physical activity, which introduces measurement error. Depression was assessed via a symptom scale and physical activity likely via questionnaire; such self-reports can be biased or imprecise. Seniors with depression might under-report activity, and physical activity intensity/duration was not objectively measured. This could attenuate or bias the observed mediation effect. Future studies might use wearable devices or more granular logs to measure activity levels more accurately. Third, our indicator of \u0026ldquo;city health expenditure\u0026rdquo; is aggregate and does not detail how funds are allocated. We do not know if the spending went specifically to older adults\u0026rsquo; services, mental health programs, or other domains. Thus, we infer mechanisms indirectly. It would strengthen the analysis to have data on specific expenditures (e.g. proportion spent on preventive programs vs. curative services) to pinpoint which aspects of spending drive the mental health benefits.\u003c/p\u003e \u003cp\u003eAnother limitation is the generalizability of our findings. Our study focused on urban older adults in China, which may limit applicability to rural populations or other countries. Rural China differs markedly, that older adults in rural areas have higher depression prevalence and lower physical activity levels compared to urban seniors(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In fact, one recent study noted that rural Chinese older adults were significantly more likely to be depressed and less likely to engage in exercise, highlighting the disparities between urban and rural contexts. Thus, the depression-alleviating impact of city health spending observed in metropolitan areas might not directly translate to rural communities, where infrastructure and services are weaker. Caution is also warranted in extending the conclusions to other nations with different health systems. China\u0026rsquo;s healthcare financing and community programs operate in a unique context, and what holds true there might differ in, say, Western welfare states or low-income countries. That said, the core mechanisms we identified are biologically and socially plausible universally, but the magnitude of effects could vary by context. Finally, our mediation analysis, while suggestive, cannot prove a pathway definitively. There may be other mediating or moderating factors we did not examine. For example, social participation, family support, or the built environment might co-mediate the relationship between public expenditure and depression. Our focus on physical activity captures only one channel of influence. Moreover, the cross-sectional mediation approach precludes temporal confirmation that increased spending led to more activity. These limitations indicate that our results should be interpreted as associative and hypothesis-generating, rather than conclusive evidence of cause and effect.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis study demonstrates that both city-level public health expenditure and physical activity play important roles in alleviating depressive symptoms among older Chinese adults. Across models, more frequent physical activity consistently reduced depression scores by 0.30\u0026ndash;0.35 points (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\:\u0026lt;\\:0.001\\)\u003c/span\u003e\u003c/span\u003e), confirming its robust protective effect. Meanwhile, a one-percentage-point increase in the proportion of city-level public health expenditure was associated with a 0.08-0.12-point reduction (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\:\u0026lt;\\:0.05\\)\u003c/span\u003e\u003c/span\u003e) in depressive symptoms, although this effect was less stable across specifications.\u003c/p\u003e \u003cp\u003eImportantly, mediation analysis revealed that physical activity explained approximately 18% of the total effect of public health expenditure on late-life depression. This indicates that municipal investment in health not only provides direct support through healthcare infrastructure but also indirectly promotes healthier lifestyles that yield mental health benefits. The robustness checks, which using alternative indicators, bootstrap resampling, and different model specifications further confirmed the validity of these findings.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, these results highlight the importance of prioritizing preventive and promotive health measures in urban planning. Specifically, increasing city-level health expenditure to fund community exercise programs and activity-friendly public spaces could amplify the benefits of fiscal investment, thereby reducing the burden of depression in later life and advancing the broader goal of healthy aging in China\u0026rsquo;s urban society.\u003c/p\u003e"},{"header":"6 Declarations","content":"\u003ch2\u003e6.1 Ethics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eReview articles are not conducted on humans, or animals and does not require ethical vetting.\u003c/p\u003e\n\u003ch2\u003e6.2 Consent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003e6.3 Availability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe original contributions presented in the study are based on the \u0026quot; China Health and Retirement Longitudinal Study (CHARLS 2020) \u0026quot;, and National Bureau of Statistics, \u0026quot;Statistical Yearbook 2021\u0026quot;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttps://www.stats.gov.cn/sj/ndsj/2021/indexch.htm.\u003c/p\u003e\n\u003ch2\u003e6.4 Authors contribution\u003c/h2\u003e\n\u003cp\u003eWriting original draft preparation and writing review and editing YM, BJ, DL; Methodology YM, BJ, DL. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003e6.5 Competing interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003e6.6 Funding\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003e6.7 Acknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRaviola G, Eustache E, Oswald C, Belkin G (2012) Mental Health Response in Haiti in the Aftermath of the 2010 Earthquake: A Case Study for Building Long-Term Solutions. Harv Rev Psychiatry 20:68\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3109/10673229.2012.652877\u003c/span\u003e\u003cspan address=\"10.3109/10673229.2012.652877\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu C, Cao L, Yang F (2023) Prevalence and determinants of depressive symptoms among community-dwelling older adults in China based on differences in living arrangements: a cross-sectional study. BMC Geriatr 23(1):640. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12877-023-04339-6\u003c/span\u003e\u003cspan address=\"10.1186/s12877-023-04339-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou S, Li K, Ogihara A, Wang X (2022) Association between social capital and depression among older adults of different genders: Evidence from Hangzhou, China. Front Public Health 10:863574. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2022.863574\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2022.863574\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMu Y, Yi M, Liu Q (2023) Association of neighborhood recreational facilities and depressive symptoms among Chinese older adults. BMC Geriatr 23:667. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12877-023-04369-0\u003c/span\u003e\u003cspan address=\"10.1186/s12877-023-04369-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang T, Jiang J, Tang X (2021) Prevalence of depressive symptoms among older adults in mainland China: a systematic review and meta-analysis. J Affect Disord 293:379\u0026ndash;390. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2021.06.050\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2021.06.050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBigarella LG, Ballotin VR, Mazurkiewicz LF, Ballardin AC, Rech DL, Bigarella RL, Selistre LDS (2022) Exercise for depression and depressive symptoms in older adults: an umbrella review of systematic reviews and Meta-analyses. Aging Ment Health 26(8):1503\u0026ndash;1513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13607863.2021.1951660\u003c/span\u003e\u003cspan address=\"10.1080/13607863.2021.1951660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Zhang J, Yang Y (2024) Correlation between the physical activity volume and cognitive and mental capacity among older adult people in China: a cross-sectional study based on the 2020 CHARLS database. Front Public Health 12:1462570. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2024.1462570\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2024.1462570\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeurel E, Toups M, Nemeroff CB (2020) The Bidirectional Relationship of Depression and Inflammation: Double Trouble. Neuron Jul 22(2):234\u0026ndash;256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuron.2020.06.002\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2020.06.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun X, Zhou M, Huang L, Nuse B (2020) Depressive costs: medical expenditures on depression and depressive symptoms among rural elderly in China. Public Health 181:141\u0026ndash;150. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.puhe.2019.12.011\u003c/span\u003e\u003cspan address=\"10.1016/j.puhe.2019.12.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan L, Xu Q, Gui J, Liu Y, Lin F, Zhao Z et al (2023) Decomposition and comparative analysis of differences in depressive symptoms between urban and rural older adults: evidence from a national survey. Int Psychogeriatr 21:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S1041610223000716\u003c/span\u003e\u003cspan address=\"10.1017/S1041610223000716\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao M, Chen J, Ma C (2022) Research on the relationship between Chinese elderly health status, social security, and depression. Int J Environ Res Public Health. 19:7496. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph19127496\u003c/span\u003e\u003cspan address=\"10.3390/ijerph19127496\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, PMID: 35742744\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang N (2024) The temporal and spatial interpretation of China\u0026rsquo;s health financing: what do Chinese\u0026rsquo; government \u0026lsquo;do\u0026rsquo; in new healthcare reform? Health Econ Rev 14:76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13561-024-00551-1\u003c/span\u003e\u003cspan address=\"10.1186/s13561-024-00551-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNing C, Pei H, Huang Y, Li S, Shao Y (2024) Does the Healthy China 2030 Policy Improve People's Health? Empirical Evidence Based on the Difference-in-Differences Approach. Risk Manage Healthc policy 17:65\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/RMHP.S439581\u003c/span\u003e\u003cspan address=\"10.2147/RMHP.S439581\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClarke P, Ailshire JA, Bader MDM, Morenoff JD, House JS (2012) Mobility effects of neighborhood built environment: field validation of walkability scales and association with depressive symptoms in older adults. J Aging Health 24(3):493\u0026ndash;515\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu H, Roberts B, Du W (2020) Neighborhood recreational facilities and trajectories of depressive symptoms among older Chinese adults: a 4-year longitudinal study. J Affect Disord 276:363\u0026ndash;370\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Wu Y (2019) Local government health expenditure and mental health of the elderly: evidence from China\u0026rsquo;s prefecture-level cities. Chin J Health Policy 12(4):27\u0026ndash;38\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Hu Y, Smith JP, Strauss J, Yang G (2014) Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol 43(1):61\u0026ndash;68\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Bureau of Statistics of the (2020) People\u0026rsquo;s Republic of China. Statistical Communique on National Economic and Social Development\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Qiang F (2022) Psychosocial Mediation of Light-Moderate Physical Activity and Cognitive Performance among Adults Aged 60\u0026thinsp;+\u0026thinsp;in China. Behav Sci 12(6):175\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang M, Dai B (2025) Effect of depression combined with cognitive impairment on dependency risk in rural older adults: analysis of data from the China health and retirement longitudinal study (CHARLS 2020). BMC Psychol 13(1):167\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan W, Cui J (2025) The dual impact of physical exercise on depression and fall risk in older Chinese adults \u0026mdash; evidence from CHARLS 2020. Front Public Health 13:1615326. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2025.1615326\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2025.1615326\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu DS, Yan EC, Chow CK, Interpreting (2015) SF-12 mental component score: an investigation of its convergent validity with CESD-10. Qual Life Res 24:2209\u0026ndash;2217\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRijnhart JJM et al (2021) Mediation analysis methods used in observational research: a scoping review and recommendations. BMC Med Res Methodol 21(1):226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12874-021-01426-3\u003c/span\u003e\u003cspan address=\"10.1186/s12874-021-01426-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchuch FB, Vancampfort D, Firth J, Rosenbaum S, Ward PB, Silva ES, Hallgren M, De Leon P, A., et al (2018) Physical Activity and Incident Depression: A Meta-Analysis of Prospective Cohort Studies. Am J Psychiatry 175(7):631\u0026ndash;648. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1176/appi.ajp.2018.17111194\u003c/span\u003e\u003cspan address=\"10.1176/appi.ajp.2018.17111194\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchuch FB, Vancampfort D, Rosenbaum S, Richards J, Ward PB, Veronese N et al (2016) Exercise for depression in older adults: a meta-analysis of randomized controlled trials adjusting for publication bias. Revista brasileira de psiquiatria (Sao Paulo, Brazil: 1999), 38(3), 247\u0026ndash;254. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/1516-4446-2016-1915\u003c/span\u003e\u003cspan address=\"10.1590/1516-4446-2016-1915\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao X-D, Oh S-S, Zhang Z, Wang C (2025) Move your body, stay away from depression: a systematic review and meta-analysis of exercise-based prevention of depression in middle-aged and older adults. Front Public Health 13:1554195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2025.1554195\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2025.1554195\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuiz-Comellas A, Valma\u0026ntilde;a GS, Catalina QM, Baena IG, Pe\u0026ntilde;a JM, Poch PR, Carrera AS, Pujol IC et al (2022) Effects of Physical Activity Interventions in the Elderly with Anxiety, Depression, and Low Social Support: A Clinical Multicentre Randomised Trial. Healthc (Basel Switzerland) 10(11):2203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/healthcare10112203\u003c/span\u003e\u003cspan address=\"10.3390/healthcare10112203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Xu L, Cui X, Yang H, Liu Y, Gao X, Huang J (2025) A systematic review on the associations between built environment and mental health among older people. Front Public Health 13:1584466. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2025.1584466\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2025.1584466\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRibeiro AI, Behlen M, Henriques A, Severo M, Jardim Santos C, Barros H (2024) Exposure to green and blue spaces and depression among older adults from the EPIPorto cohort: examining environmental, social, and behavioral mediators and varied space types. Cities Health 1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/23748834.2024.2381965\u003c/span\u003e\u003cspan address=\"10.1080/23748834.2024.2381965\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu N, Wang Z, Li Z (2025) The impact of the healthy cities pilot policy on mental health and its inequalities among urban middle-aged and older adults. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cities.2024.105688\u003c/span\u003e\u003cspan address=\"10.1016/j.cities.2024.105688\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cities\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Liang W, Liu M, Liu J (2023) Association of Catastrophic Health Expenditure With the Risk of Depression in Chinese Adults: Population-Based Cohort Study. JMIR Public Health Surveill 9:e42469. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/42469\u003c/span\u003e\u003cspan address=\"10.2196/42469\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, Zhou Z, Huang L (2025) Improvement of China\u0026rsquo;s healthy city construction policies from the perspective of policy instruments. BMC Public Health 25:1958. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-025-23111-6\u003c/span\u003e\u003cspan address=\"10.1186/s12889-025-23111-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu T, Yao Y, Yang Z, Li K, Yu T, Xia Y (2024) The crowding-out effect of physical fitness activities on medical expenditure in the aged group. Front Public Health 12:1425601. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2024.1425601\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2024.1425601\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin X, Cui J, Wu Y, Cui M, Li K, Guo H (2025) Spatial\u0026ndash;temporal evolution and associated factors of older adult care institutions in Shanghai. Front Public Health 13:1598394. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2025.1598394\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2025.1598394\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin X, Liu H, Niyomsilp E (2023) The Impact of Physical Activity on Depressive Symptoms among Urban and Rural Older Adults: Empirical Study Based on the 2018 CHARLS Database. Behav Sci 13(10):864. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/bs13100864\u003c/span\u003e\u003cspan address=\"10.3390/bs13100864\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Hubei Provincial Research Center for Innovation and Development in Sports and Health, Wuhan, China","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"depressive symptoms, older adults, physical activity, public health expenditure, mediation analysis","lastPublishedDoi":"10.21203/rs.3.rs-8824663/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8824663/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Late-life depression is highly prevalent in China, affecting nearly 20% of older adults. While city-level public health expenditure is hypothesized to buffer mental health risks, the mechanisms remain unclear. This study explores the association between city-level public health expenditure and depressive symptoms among older Chinese adults, with a focus on the potential mediating role of physical activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eUsing cross-sectional data of older adults across 295 Chinese cities in 2020, we applied fixed-effects regression and mediation analysis. Depressive symptoms were assessed via the CES-D scale, physical activity frequency was self-reported, and public health expenditure was measured as the proportion of city-level fiscal spending. Robustness checks included alternative measures, bootstrap resampling (1,000 replications), and model re-estimation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Higher frequency of physical activity was significantly associated with fewer depressive symptoms (β = -0.32, p \u0026lt; 0.001). A greater proportion of public health expenditure also modestly associated with lower depressive symptoms (β = -0.10, p \u0026lt; 0.05). Mediation analysis suggested that physical activity accounted for approximately 18% of the overall association between health expenditure and depression (indirect effect = −0.06, 95% CI [−0.11, −0.01]). Results were robust to alternative specifications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Municipal health investment is modestly associated with lower depressive symptoms among older adults, partly through links with physical activity. Strengthening city-level preventive health programs and integrating exercise promotion into community health services may contribute to supporting healthy aging in China, though longitudinal research is needed to clarify causal pathways.\u003c/p\u003e","manuscriptTitle":"Can City Health Expenditure Alleviate Depression in Later Life? --The Mediating Role of Physical Activity in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 11:50:44","doi":"10.21203/rs.3.rs-8824663/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c7a163f5-477a-42f7-99b7-4a9b1426e859","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62541003,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2026-02-10T11:50:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 11:50:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8824663","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8824663","identity":"rs-8824663","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}