Sex differences in the relationship between household solid fuel use and incident stroke and the mediating role of depressive symptoms in middle-aged and older Chinese adults

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However, the sex-specific relationship between solid fuel use and incident stroke in older Chinese adults, as well as the potential mediating role of depressive symptoms, remains insufficiently explored. Methods This longitudinal study used data from the China Health and Retirement Longitudinal Study (CHARLS), including 13928 Chinese participants aged 45 years or older free of stroke at baseline. Logistic regression models were used to assess the relationship between solid fuel use, depressive symptoms, and the risk of stroke. To quantify the potential mediation role of depressive symptoms in the pathway from solid fuel use to new-onset stroke, a mediation analysis was performed. Results Of the 13928 adults (mean age of 58, 47.26% male), 917 (6.58%) participants documented the incident stroke. Solid fuel use was significantly associated with the increased incident stroke risk in the total population (OR = 1.27, 95% CI: 1.08–1.51) and female populations (OR = 1.40, 95% CI: 1.11–1.78) in the fully adjusted model. In addition, depressive symptoms could explain the pathway, with the significant mediating proportions up to 16.2%, regardless of whether the depressive symptoms presented as general depression or severe depression. The results of the stratified analysis also indicate that this mediating effect is present only among the female group. Conclusion Household solid fuel use significantly increased the risk of incident stroke, and depressive symptoms played a mediating role in the relationship. These findings highlight the need for integrated public health interventions in the areas of environmental pollution and mental health, with particular attention to women. Household solid fuel use Incident stroke Depressive symptoms Mediating effect Cohort study Figures Figure 1 Figure 2 Introduction Stroke remains a leading cause of disability and death among adults globally[ 1 ]. The global economic burden of stroke, both direct and indirect, amounts to approximately USD 890 billion annually, accounting for 0.66% of global GDP[ 2 ]. Despite substantial improvements in the management of traditional risk factors such as hypertension, diabetes, and smoking, the global incidence and mortality of stroke have not been effectively curbed[ 3 , 4 ]. According to the Lancet Commission on Stroke (2024), global stroke mortality is projected to increase by approximately 50%, rising from 6.6 million deaths in 2020 to 9.7 million deaths in 2050[ 5 ]. In China, the prevalence of stroke reached 26 million in 2021, marking a 104.26% increase compared to 1990[ 6 ]. Moreover, studies have shown that women bear a significantly greater burden of stroke, characterized by higher incidence, disability, and mortality rates[ 7 – 9 ]. With population aging and changing risk profiles, identifying modifiable factors and understanding sex differences have become key priorities in stroke prevention. Household solid fuels typically include coal, firewood, crop residues, and animal dung–based biomass. Although access to clean energy has improved globally, solid fuels remain a primary household energy source for many families, particularly in rural areas of low- and middle-income countries[ 10 ]. In China, the overall use of solid fuels has shown a declining trend; however, in economically disadvantaged rural areas, these fuels remain widely used due to their low cost and ease of access[ 11 ]. The combustion of solid fuels is often incomplete and releases substantial amounts of combustion-related pollutants, including fine particulate matter with an aerodynamic diameter below 2.5 µm (PM₂․₅), inhalable particulate matter below 10 µm (PM₁₀), carbon monoxide (CO), nitrogen oxides (NO), and polycyclic aromatic hydrocarbons[ 12 ]. These pollutants can accumulate in the indoor environment and are considered an important source of household air contamination[ 13 ]. Existing evidence suggests that exposure to such indoor pollutants is associated with multiple adverse health outcomes, including chronic obstructive pulmonary disease, cognitive impairment, and neurodevelopmental abnormalities in children[ 14 – 17 ]. In addition, long-term use of solid fuels has been linked to an elevated risk of adverse cardiovascular outcomes, whereas a transition to cleaner household energy sources has been associated with a lower cardiovascular risk profile[ 18 ]. However, the sex-specific relationship between indoor air pollution and stroke risk in older Chinese adults remains unclear, and the underlying mechanisms require further exploration, which is crucial for developing effective public health interventions. Depression has become an increasingly prevalent and severe health issue among the elderly, with its incidence showing a consistent upward trend[ 19 ]. Research indicates that, while the global population has increased by 44.63% over the past 30 years, the number of elderly individuals suffering from depression has surged by 116.12%, with this increase primarily concentrated among those aged 60 to 74[ 20 ]. Furthermore, the prevalence of depression is significantly higher in women than in men[ 21 ]. Air pollution from ambient and household sources has been shown to increase the risk of psychological and psychiatric disorders in adults, particularly anxiety and depression[ 22 , 23 ]. It alters brain structure and function, leading to changes in neural structures, including increased inflammation, oxidative stress, and abnormalities in neurotransmitters and neuromodulators[ 24 ]. In addition, previous studies have also shown that recurrent or persistent depressive symptoms notably elevate the risk of stroke, indicating that depression may be a significant risk factor for stroke[ 25 ]. Individuals with depression commonly experience reduced physical activity[ 26 ], poor medication adherence[ 27 , 28 ], and unhealthy eating habits[ 29 , 30 ], all of which may further exacerbate the risk of adverse cardiovascular health. Therefore, it is rational to speculate that depressive symptoms may mediate the relationship between solid fuel use and stroke. To address these gaps, we used longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) to prospectively examine the association between household solid fuel use and incident stroke in middle-aged and older Chinese adults. In addition, we also aimed to quantify the mediation role of depressive symptoms in the pathway. This work may contribute to providing new insights into how environmental factors influence stroke risk through mental health, therefore encouraging the integration of pollution reduction and mental health interventions as key components of stroke prevention strategies. Methods Data source and study design This study used data from the CHARLS, a nationally representative survey collecting high-quality longitudinal information on Chinese households and individuals. Data were obtained through face-to-face interviews supported by an integrated information system. The survey encompassed demographics, health status, healthcare access, insurance coverage, family structure, and health-related behaviors. The baseline survey (2011–2012) included 17,708 participants from 150 counties across 28 provinces, followed by biennial waves from 2013 to 2018. All data are publicly available on the CHARLS website ( http://charls.pku.edu.cn/ ). Ethical approval was granted by the Peking University Biomedical Ethics Committee (IRB00001052–11015), and written informed consent was obtained from all participants. This prospective study used the data from CHARLS 2011–2018. Figure 1 shows the screening flowchart for the study sample. Among the original cohort of 17708 participants from the 2011–2012 baseline survey, the following exclusion criteria were applied: (1) individuals with missing data on indoor air pollution (n = 243); (2) individuals aged under 45 years (n = 759); (3) participants with missing data on depressive symptoms or with prevalent stroke at baseline (n = 2778); Finally, a total of 13928 eligible participants were included for the final analysis. Assessment of depressive symptoms Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale (CESD-10), a validated tool commonly applied to identify depressive status. Participants reported the frequency of depressive feelings during the past week on a 4-point scale: ‘rarely or none of the time (< 1 day)’, ‘some or a little of the time (1–2 days)’, ‘occasionally or a moderate amount of time (3–4 days)’, and ‘most or all of the time (5–7 days)’. Each item was scored from 0 to 3, yielding a total score between 0 and 30, where higher scores represent greater depressive symptom severity. A cut-off point of 10 was defined as depressive symptoms[ 31 ], and scores of 20 were classified as severe depressive symptoms. Solid fuel use measurements In the baseline survey, participants reported their households’ main energy sources for cooking and heating. According to self-reported data, crop residues, wood, and coal were categorized as solid fuels, whereas electricity, liquefied petroleum gas, natural gas, marsh gas, and solar energy were considered clean fuels due to their lower emissions of pollutants. Households using solid fuels for either cooking or heating were defined as the exposure group, while those relying exclusively on clean fuels for both purposes were classified as the clean-fuel group. Covariates Covariates considered in this study included sociodemographic factors (age, sex, marital status, education level, and residential area), health-related behaviors (smoking and alcohol consumption), body mass index (BMI), and major comorbidities. Residential area was classified as urban or rural, and marital status as married or unmarried. Education was grouped into three levels: below lower secondary, upper secondary or vocational training, and tertiary education. Smoking status was categorized as current, former, or never smoker, and alcohol consumption was defined as drinking alcohol within the past year. BMI (kg/m²) was calculated as weight divided by height squared and categorized as < 18.5, 18.5–24.9, 25.0–29.9, and ≥ 30. Physician-diagnosed comorbidities were self-reported and included hypertension, diabetes, and heart disease. Details on missing values and covariate proportions are provided in Table S1 . Statistical analysis For study participant characteristics, we used median ( P 25 –P 75 ) to describe continuous variables and frequency (percentage) to describe categorical variables. The Wilcoxon rank-sum test (continuous data) and chi-square test (categorical data) were performed to make comparisons between two groups. Multivariate logistic regression models were fitted to investigate the total and sex-specific associations of solid fuel use with incident stroke, depressive symptoms with incident stroke, and solid fuel use with depressive symptoms (yes/no). All results were presented as ORs and 95% confidence intervals (CIs). The basic model (model 1) was adjusted for age and sex, marital status, place of residence, and education level; while model 2 additionally adjusted for smoking, alcohol consumption, body mass index, history of hypertension, diabetes, and heart disease. Mediation analysis was used to explore the potential mediating role of depressive symptoms on the association between solid fuel use and incident stroke. Estimates and 95% confidence intervals (95% CI) were then calculated for each pathway using the 'mediation' R package. Briefly, the total effect between solid fuel use and incident stroke was split into the direct effect of solid fuel use→ incident stroke and solid fuel use → depressive symptoms → incident stroke. A schematic representation of the mediation analysis hypothesis is shown below ( Fig. 2 ) . Reverse causal associations can be largely avoided due to the clear temporal sequence between exposure, mediator, and outcome in this study. To measure the association of depressive symptoms between solid fuel use and incident stroke, the mediating proportion of depressive symptoms was added to the result table. The adjustment variables for the model 2 were kept as above. To validate the robustness of the results, we conducted two sensitivity analyses: 1. Excluding participants with missing covariate values to account for potential bias introduced by missing data; 2. Excluding cases diagnosed within the preceding two years to reduce reverse causality effects. Statistical analyses were performed using the SAS version 9.4 (SAS Institute) and R software (version 4.2.1). All statistical tests were two-sided, and P < 0.05 was considered statistically significant. Results Table 1 summarizes the baseline characteristics of 13928 participants, including 4355 clean fuel users and 9573 solid fuel users. The median age of participants was 58.0 years, with 47.3% male and 52.7% female. Compared to clean fuel users, solid fuel users tended to have lower educational attainment, were more likely to live in rural areas or be smokers, and had higher CESD-10 scores (all P < 0.001). Table 1 Baseline characteristics of the study population level Overall Clean fuel Solid fuel P value N 13928 4355 9573 Age (years) 58 (51, 65) 56 (50, 63) 58 (52, 65) < 0.0001 Sex (%) 0.8014 Men 6583 (47.26) 2051 (47.10) 4532 (47.34) Women 7345 (52.74) 2304 (52.90) 5041 (52.66) Marital status (%) 0.2118 Married 12227 (87.79) 3846 (88.31) 8381 (87.55) Unmarried 1701 (12.21) 509 (11.69) 1192 (12.45) Educational level (%) < 0.0001 Less than lower secondary 12313 (88.40) 3424 (78.62) 8889 (92.85) Upper secondary & vocational training 1362 (9.78) 720 (16.53) 642 (6.71) Tertiary 253 (1.82) 211 (4.85) 42 (0.44) CESD-10 score 7.00 (3.00, 12.00) 5.00 (3.00, 9.00) 8.00 (4.00, 13.00) < 0.0001 Body mass index (%) < 0.0001 30 592 (4.98) 224 (6.53) 368 (4.35) Area of residence (%) < 0.0001 Rural 8548 (61.37) 1437 (33.00) 7111 (74.28) Urban 5380 (38.63) 2918 (67.00) 2462 (25.72) Smoke status (%) < 0.0001 Never 8476 (60.86) 2788 (64.03) 5688 (59.42) Ever 1163 (8.35) 363 (8.34) 800 (8.36) Current 4287 (30.78) 1203 (27.63) 3084 (32.22) Alcohol intake (%) 0.3964 Yes 9332 (76.86) 2888 (77.36) 6444 (76.64) No 2809 (23.14) 845 (22.64) 1964 (23.36) Hypertension (%) 3664 (26.43) 1185 (27.27) 2479 (26.05) 0.1385 Diabetes (%) 847 (6.14) 301 (6.95) 546 (5.76) 0.0075 Heart disease (%) 1839 (13.26) 562 (12.94) 1277 (13.40) 0.4747 The chi-square test is used to compare categorical variables, and the rank-sum test is used to compare continuous variables. Table 2 shows that 917 incident stroke events occurred during the follow-up period, out of 13,928 participants, with an incidence of 6.58%. Compared to participants using clean fuels (248 events, incidence 5.69%), those using solid fuels (669 events, incidence 6.99%) had a higher risk of stroke. In Model 1, after adjusting for age, sex, marital status, place of residence, and education, solid fuel use was associated with a 26% increased risk of stroke (OR = 1.26, 95% CI: 1.07–1.49). This association remained statistically significant and virtually unchanged after full adjustment for smoking, alcohol consumption, BMI, and a history of hypertension, diabetes, and heart disease, with an OR of 1.27 (95% CI: 1.08–1.51). However, subgroup analyses by sex revealed significant heterogeneity in the association between household solid fuel use and stroke risk. Among men, the OR in model 2 was 1.11 (95% CI: 0.88–1.40), not reaching statistical significance. In contrast, among women, the incidence was 4.99% for clean-fuel users (n = 115) and 6.96% for those using solid fuels (n = 351). After full adjustment for the covariates, the OR for women was 1.43 (95% CI: 1.13–1.81), indicating a significant association. Table 2 Association between household solid fuel use and the risk of stroke Cases Incidence (%) OR1 (95% CI) OR2 (95% CI) Total population 917 6.58 Clean fuel 248 5.69 Ref (1.00) Ref (1.00) Solid fuel 669 6.99 1.26 (1.07, 1.49) 1.27 (1.08, 1.51) Men 451 6.85 Clean fuel 133 6.48 Ref (1.00) Ref (1.00) Solid fuel 318 7.02 1.11 (0.88, 1.40) 1.14 (0.90, 1.45) Women 466 6.34 Clean fuel 115 4.99 Ref (1.00) Ref (1.00) Solid fuel 351 6.96 1.43 (1.13, 1.81) 1.40 (1.11, 1.78) OR1 (95% CI) adjusted for age and sex, marital status, place of residence, and education level; OR2 (95% CI) adjusted for age, sex, marital status, place of residence, education level, smoking, alcohol consumption, body mass index, history of hypertension, diabetes, and heart disease. The association between household solid fuel use and incident stroke risk was further evaluated through sensitivity analyses to assess the robustness of our primary findings ( Table S2 ). First, after excluding participants with missing covariate data, we observed a consistent positive association in the overall population (OR = 1.23, 95% CI: 1.01–1.50). Sex-stratified analyses revealed a significant association among women (OR = 1.35, 95% CI: 1.03–1.77), whereas no significant association was found among men (OR = 1.10, 95% CI: 0.83–1.46). Second, after excluding stroke incidents occurring within the first two years of follow-up, the fully adjusted model still showed a statistically significant positive association in the overall population (OR = 1.36, 95% CI: 1.14–1.64). This trend was consistent in sex-stratified analyses, with women exhibiting a stronger association (OR = 1.53, 95% CI: 1.19–1.99), while men continued to show no significant association (OR = 1.19, 95% CI: 0.92–1.54). The results of the mediation analysis examining the effect of household solid fuel use on stroke risk through depressive symptoms are summarized in Table 3 . When depressive symptoms were modeled as a continuous CESD-10 score, solid fuel use was associated with higher CESD-10 scores (β = 1.73, SE = 0.12). A one-point increase in CESD-10 was linked to higher odds of incident stroke (OR = 1.02, 95% CI: 1.01–1.04). The mediation proportion was 16.2% ( P < 0.05), suggesting that depressive symptoms accounted for a modest but statistically significant portion of the total association between household solid fuel use and incident stroke. In addition, household solid fuel use was associated with higher CESD-10 scores in women (β = 1.89, SE = 0.17), with a significant mediation proportion of 14.9%. Table 3 Mediating role of depressive symptoms in the association between household solid fuel use and the risk of stroke Solid fuel use to depression (Beta, SE/OR, 95% CI) Depression to stroke (OR, 95% CI) Mediation proportion (%) Depression: CESD-10 score Total population 1.73 (0.12) 1.02 (1.01, 1.04) 16.2 * Men 1.54 (0.16) 1.02 (1.00, 1.03) 12.5 Women 1.89 (0.17) 1.03 (1.01, 1.04) 14.9* Depression: CESD-10 score ≥ 10 Total population 1.84 (1.69, 2.02) 1.27 (1.10, 1.46) 11.8* Men 1.89 (1.65, 2.17) 1.26 (1.02, 1.55) 15.4 Women 1.82 (1.62, 2.05) 1.25 (1.02, 1.52) 7.6* Depression: CESD-10 score ≥ 20 Total population 2.18 (1.79, 2.67) 1.38 (1.08, 1.75) 5.4* Men 2.17 (1.53, 3.16) 1.47 (0.96, 2.18) 6.1 Women 2.17 (1.71, 2.77) 1.30 (0.95, 1.74) 3.9 Models adjusted for age, sex, marital status, place of residence, education level, smoking, alcohol consumption, body mass index, history of hypertension, diabetes, and heart disease. * For statistical significance. When we defined the presence of depressive symptoms as a CESD-10 score ≥ 10, solid fuel use was significantly associated with an increased risk of depressive symptoms (OR = 1.84, 95% CI: 1.69–2.02). The presence of depressive symptoms (CESD-10 ≥ 10) was associated with an increased risk of stroke (OR = 1.27, 95% CI: 1.10–1.46), and the mediation proportion was 11.8% ( P < 0.05). In sex-stratified analyses, the indirect effect was statistically significant in women (mediation proportion = 7.6%, P < 0.05), but not in men (mediation proportion = 15.4%). Similar results were obtained when defining the severe depressive symptoms with the cut-off value of 20 score of CESD-10. Discussion Based on the data of the ten thousand Chinese population, our findings indicate a significant association between household solid fuel use and incident stroke risk in middle-aged and older Chinese adults. In addition, depressive symptoms may explain up to 16.2% of the relationship between household solid fuel use and incident stroke in the total population. Furthermore, additional analysis revealed significant heterogeneity in these associations across sexes. These findings provide new empirical evidence for the potential psychological pathways linking solid fuel use and stroke, highlighting the importance of addressing mental health in studies of environmental risk and cerebrovascular disease. The positive association between household solid fuel use and incident stroke in this study was generally consistent with previous studies. Multiple studies have shown that household air pollution from the use of solid fuels has a significant adverse impact on cardiovascular health. For example, a study in the Putuo District of Shanghai found that household use of solid fuels was significantly associated with an increased risk of stroke, hypertension, coronary heart disease, and diabetes[ 32 ]. Similarly, a large-scale epidemiological study covering 11 countries and 467 urban and rural communities found that the use of solid fuels for cooking and heating was closely related to higher mortality rates and an increased risk of cardiovascular diseases, including stroke [ 33 ]. Previous studies have primarily examined the association between household solid fuel use and overall cardiovascular outcomes[ 18 , 34 ], with limited attention to stroke as a specific health outcome. Based on a nationally representative prospective cohort, our study investigated the relationship between household solid fuel use and incident stroke, showing that exposure to solid fuel combustion was associated with a 27% higher risk of stroke among middle-aged and older individuals compared with those unexposed. In the sex-stratified analysis, the association between household solid fuel use and incident stroke remained significant only among women, suggesting that females may be more susceptible to the adverse health effects of such exposure. This finding is consistent with a quantitative study on the impact of air pollution on years lived with disability and years of life lost across the Asia-Pacific region from 1990 to 2019, which showed that women are more vulnerable to household air pollution, while men face a higher mortality risk from exposure to atmospheric particulate matter [ 35 ]. Additionally, another study found that women exposed to a 10 µg/m 3 increase in PM 10 concentration had a significantly higher stroke risk (HR = 2.16, 95% CI: 1.15–4.06), whereas no significant increase was observed in men (HR = 1.07, 95% CI: 0.61–1.86) [ 36 ]. This sex difference may reflect biological susceptibility, as females appear more physiologically vulnerable to the adverse effects of air pollution[ 37 ]. In households relying primarily on solid fuels, greater domestic involvement and longer indoor residence among females can lead to prolonged and higher cumulative exposure, thereby amplifying risk. The higher stroke risk observed in women highlights the importance of sex-sensitive public health interventions, particularly those aimed at reducing exposure to household solid fuel combustion among women, who may bear a disproportionate cerebrovascular and cardiovascular burden from such exposure. On the other hand, the lack of a significant association in men may be attributed to smaller sample sizes and shorter exposure times due to factors such as migration for work, which could have limited the statistical power to detect the association. Future research with larger male cohorts is necessary to further determine whether the absence of a significant association in men reflects a true null effect or is due to statistical limitations. Our study identified a significant mediating effect of depression in the association between household solid fuel use and incident stroke. Combustion of household solid fuels is a major source of household air pollution[ 10 ]. Previous studies have demonstrated that air pollution is associated with adverse mental health outcomes. For example, a meta-analysis of 39 studies reported that long-term exposure to PM 2.5 and NO 2 significantly increased the risk of depression[ 38 ]. Similarly, a prospective cohort of 389000 adults from the UK Biobank found that long-term joint exposure to multiple pollutants—including PM 2.5 , PM 2.5−10 , NO 2 , and NO—was associated with higher risks of incident depression and anxiety, even at relatively low concentrations[ 39 ]. In our study, a robust and consistent positive association was observed between household solid fuel use and depressive symptoms across both continuous and categorical measures of depression. In addition, it is widely recognized that depression constitutes an independent risk factor for stroke[ 40 , 41 ]. However, in previous research, much attention has been given to post-stroke depression, while the emotional and psychological changes prior to stroke have been largely overlooked[ 42 , 43 ]. In our prospective cohort study, higher depressive symptom scores were significantly associated with increased risk of incident stroke, especially for females. Given that depression was linked to both household solid fuel use and stroke risk in our study, we speculate that it may act as an intermediate role. Further mediation analyses found that depression plays a significant mediating role between household solid fuel use and incident stroke, with a stronger association observed in women compared to men. Existing studies have not thoroughly explored whether depression plays a mediating role between air pollution and incident stroke. However, existing studies have demonstrated that depression can act as a mediator across various health conditions. For instance, both depression and social relationships have been reported to mediate the association between frailty and cognitive function[ 44 ]. Similarly, depression has been shown to mediate the link between adverse childhood experiences and chronic lung diseases [ 45 ], and its mediating role between sedentary behavior and social frailty has also been confirmed in previous research[ 46 ]. Unlike previous studies, our research systematically evaluated multiple approaches to assessing depressive symptoms, including both continuous scores and binary classifications using different cutoffs (e.g., CESD-10 ≥ 10 and ≥ 20). The results consistently revealed a robust mediating role of depressive symptoms in the association between household solid fuel use and incident stroke. These findings highlight that, regardless of depression severity, evaluating individuals’ living environments—particularly their exposure to indoor air pollution attributed to solid fuel combustion —is essential for preventing stroke onset. Although the molecular mechanisms by which air pollution affects stroke and depression have been widely studied, the specific pathways remain not fully understood. Current evidence suggests that air pollutants, especially fine PM 2.5 , can directly enter the brain through pathways such as the olfactory, trigeminal, and vagus nerves[ 47 ]. They can also enter the lungs via the respiratory system and subsequently cross the blood-brain barrier through the bloodstream, triggering inflammation and oxidative stress both in the brain and systemically[ 24 ]. These processes can lead to endothelial injury, the release of inflammatory mediators, and thrombosis, which are important mechanisms through which air pollution promotes the occurrence of stroke[ 48 ]. Additionally, persistent inflammation and oxidative stress can disrupt hypothalamic-pituitary-adrenal axis function, inhibit hippocampal neurogenesis, and cause degeneration of dopaminergic neurons[ 39 ]. It can also activate the indoleamine 2,3-dioxygenase pathway[ 49 ], accelerating tryptophan degradation, reducing serotonin synthesis, and disrupting the balance between serotonin and norepinephrine, thereby promoting the onset and progression of depression[ 50 , 51 ]. Notably, depression is not only a psychological disorder but also contributes to an increased risk of stroke through multiple mechanisms, including amplifying systemic inflammation, disrupting neuroendocrine balance, and promoting unhealthy behaviors such as smoking, alcohol consumption, and physical inactivity[ 52 ]. This study employed a prospective cohort design, which helps clarify the temporal relationship between household solid fuel use, depressive symptoms, and the occurrence of stroke, and provides a more reliable basis for exploring their potential causal associations. However, this study has several limitations. First, data on household solid fuel use, depressive symptoms, and stroke outcomes relied on self-reported questionnaires, which may introduce information bias. Second, our exposure assessment was based on participants’ self-reported household fuel type (cooking and heating), which we treated as a proxy for indoor combustion-related pollution. We did not directly measure specific pollutant components (e.g., PM₂.₅, PM₁₀, carbon monoxide, or polycyclic aromatic hydrocarbons), nor were we able to comprehensively capture key household factors such as ventilation conditions for different uses, stove efficiency, frequency of use, indoor smoking, or the use of air purification devices. This limitation may introduce some degree of exposure misclassification. Accordingly, future studies should incorporate more refined exposure assessment, including objective monitoring of indoor pollutant concentrations and systematic characterization of household energy-use behaviors, to strengthen causal inference. Third, although several covariates were controlled for in the analysis, unaccounted confounders could not be ruled out. Fourth, our mediation analysis relies on a presumed causal pathway in which household solid fuel use increases indoor combustion-related pollution, which may contribute to depressive symptoms and ultimately raise stroke risk. We did not use genetic or quasi-experimental methods to confirm causality. Still, evidence from Mendelian randomization suggests that air pollution can causally increase stroke risk[ 53 , 54 ], lending biological plausibility to this pathway. Conclusion This study showed a significant positive association between household solid fuel use and the risk of incident stroke in middle-aged and older Chinese adults. In addition, depressive symptoms were found to mediate the pathway. Furthermore, compared to men, women exhibited greater sensitivity to the impact. Our findings suggest that public health policies aiming to reduce the stroke burden should implement an integrated approach, simultaneously promotes household clean energy transition, mitigates indoor air pollution, and strengthens community-based mental health services, especially for women. Declarations Ethics approval and consent to participate This study was carried out based on data extracted from the CHARLS public database, and all methods were performed according to the relevant guidelines and regulations. Written informed consent was obtained from all participants or their legal agents before the commencement of any study process. The ethics approval for the collection of CHARLS data has been approved by the Peking University Biomedical Ethics Review Committee (IRB00001052-11015). The use of CHARLS data was approved by the Human Research Ethics Committee of the University of Newcastle (H-2015-0290). Consent for publication Not applicable. Availability of data and materials The datasets used and analyzed in the current study are available upon reasonable request from the official CHARLS application. Competing interests The authors declare no competing interests. Funding The Jinshan Hospital Affiliated to Fudan University Youth Scientific Research Initiation Fund (grant numbers: JYQN-LC-202310). Authors' contributions CS, BL, NR and YS designed the study concepts; CS and NR participated in the drafting or revising of manuscript; CS, NR and YS performed statistical analysis and interpretation; CS integrated the entire study; BL and YZ edited the manuscript and approved the final version. All authors approved the submission of the final manuscript. Clinical trial number Not applicable Acknowledgements The authors thank the China Health and Retirement Longitudinal Study (CHARLS) team for providing data. References Feigin VL, Brainin M, Norrving B, Martins S, Sacco RL, Hacke W, et al. 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1","display":"","copyAsset":false,"role":"figure","size":121142,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the selection of study participants from CHARLS.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7872823/v1/e796b8a188c4041ba7632d8b.jpg"},{"id":95909383,"identity":"0d467492-c1de-4b15-8137-086a3d591201","added_by":"auto","created_at":"2025-11-14 10:08:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":223350,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesized direct and indirect pathways linking solid fuel use to incident stroke through depressive symptoms. The total effect is decomposed into a natural direct effect (NDE, solid fuel use → stroke) and a natural indirect effect (NIE, solid fuel use → depression → stroke). All models were adjusted for age, sex, marital status, place of residence, education level, smoking, alcohol consumption, body mass index, history of hypertension, diabetes, and heart disease.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7872823/v1/c27637c7fe8a81771e8ac3e0.jpg"},{"id":104739385,"identity":"7d941419-1d8b-4d4a-bac1-f42807abeddf","added_by":"auto","created_at":"2026-03-16 16:05:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1171618,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7872823/v1/f2864ee0-51a2-4d4f-88f4-432c0685d436.pdf"},{"id":95909371,"identity":"9badce38-8bd9-4a60-810a-35f4a21237ae","added_by":"auto","created_at":"2025-11-14 10:08:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17837,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7872823/v1/9b97c6b4b8c844cae8b43f00.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sex differences in the relationship between household solid fuel use and incident stroke and the mediating role of depressive symptoms in middle-aged and older Chinese adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke remains a leading cause of disability and death among adults globally[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The global economic burden of stroke, both direct and indirect, amounts to approximately USD 890\u0026nbsp;billion annually, accounting for 0.66% of global GDP[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite substantial improvements in the management of traditional risk factors such as hypertension, diabetes, and smoking, the global incidence and mortality of stroke have not been effectively curbed[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. According to the Lancet Commission on Stroke (2024), global stroke mortality is projected to increase by approximately 50%, rising from 6.6\u0026nbsp;million deaths in 2020 to 9.7\u0026nbsp;million deaths in 2050[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In China, the prevalence of stroke reached 26\u0026nbsp;million in 2021, marking a 104.26% increase compared to 1990[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, studies have shown that women bear a significantly greater burden of stroke, characterized by higher incidence, disability, and mortality rates[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. With population aging and changing risk profiles, identifying modifiable factors and understanding sex differences have become key priorities in stroke prevention.\u003c/p\u003e\u003cp\u003eHousehold solid fuels typically include coal, firewood, crop residues, and animal dung\u0026ndash;based biomass. Although access to clean energy has improved globally, solid fuels remain a primary household energy source for many families, particularly in rural areas of low- and middle-income countries[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In China, the overall use of solid fuels has shown a declining trend; however, in economically disadvantaged rural areas, these fuels remain widely used due to their low cost and ease of access[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The combustion of solid fuels is often incomplete and releases substantial amounts of combustion-related pollutants, including fine particulate matter with an aerodynamic diameter below 2.5 \u0026micro;m (PM₂․₅), inhalable particulate matter below 10 \u0026micro;m (PM₁₀), carbon monoxide (CO), nitrogen oxides (NO), and polycyclic aromatic hydrocarbons[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These pollutants can accumulate in the indoor environment and are considered an important source of household air contamination[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Existing evidence suggests that exposure to such indoor pollutants is associated with multiple adverse health outcomes, including chronic obstructive pulmonary disease, cognitive impairment, and neurodevelopmental abnormalities in children[\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In addition, long-term use of solid fuels has been linked to an elevated risk of adverse cardiovascular outcomes, whereas a transition to cleaner household energy sources has been associated with a lower cardiovascular risk profile[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, the sex-specific relationship between indoor air pollution and stroke risk in older Chinese adults remains unclear, and the underlying mechanisms require further exploration, which is crucial for developing effective public health interventions.\u003c/p\u003e\u003cp\u003eDepression has become an increasingly prevalent and severe health issue among the elderly, with its incidence showing a consistent upward trend[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Research indicates that, while the global population has increased by 44.63% over the past 30 years, the number of elderly individuals suffering from depression has surged by 116.12%, with this increase primarily concentrated among those aged 60 to 74[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, the prevalence of depression is significantly higher in women than in men[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Air pollution from ambient and household sources has been shown to increase the risk of psychological and psychiatric disorders in adults, particularly anxiety and depression[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. It alters brain structure and function, leading to changes in neural structures, including increased inflammation, oxidative stress, and abnormalities in neurotransmitters and neuromodulators[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In addition, previous studies have also shown that recurrent or persistent depressive symptoms notably elevate the risk of stroke, indicating that depression may be a significant risk factor for stroke[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Individuals with depression commonly experience reduced physical activity[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], poor medication adherence[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and unhealthy eating habits[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], all of which may further exacerbate the risk of adverse cardiovascular health. Therefore, it is rational to speculate that depressive symptoms may mediate the relationship between solid fuel use and stroke.\u003c/p\u003e\u003cp\u003eTo address these gaps, we used longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) to prospectively examine the association between household solid fuel use and incident stroke in middle-aged and older Chinese adults. In addition, we also aimed to quantify the mediation role of depressive symptoms in the pathway. This work may contribute to providing new insights into how environmental factors influence stroke risk through mental health, therefore encouraging the integration of pollution reduction and mental health interventions as key components of stroke prevention strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData source and study design\u003c/h2\u003e\u003cp\u003eThis study used data from the CHARLS, a nationally representative survey collecting high-quality longitudinal information on Chinese households and individuals. Data were obtained through face-to-face interviews supported by an integrated information system. The survey encompassed demographics, health status, healthcare access, insurance coverage, family structure, and health-related behaviors. The baseline survey (2011\u0026ndash;2012) included 17,708 participants from 150 counties across 28 provinces, followed by biennial waves from 2013 to 2018. All data are publicly available on the CHARLS website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://charls.pku.edu.cn/\u003c/span\u003e\u003cspan address=\"http://charls.pku.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Ethical approval was granted by the Peking University Biomedical Ethics Committee (IRB00001052\u0026ndash;11015), and written informed consent was obtained from all participants.\u003c/p\u003e\u003cp\u003eThis prospective study used the data from CHARLS 2011\u0026ndash;2018. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the screening flowchart for the study sample. Among the original cohort of 17708 participants from the 2011\u0026ndash;2012 baseline survey, the following exclusion criteria were applied: (1) individuals with missing data on indoor air pollution (n\u0026thinsp;=\u0026thinsp;243); (2) individuals aged under 45 years (n\u0026thinsp;=\u0026thinsp;759); (3) participants with missing data on depressive symptoms or with prevalent stroke at baseline (n\u0026thinsp;=\u0026thinsp;2778); Finally, a total of 13928 eligible participants were included for the final analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssessment of depressive symptoms\u003c/h3\u003e\n\u003cp\u003eDepressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale (CESD-10), a validated tool commonly applied to identify depressive status. Participants reported the frequency of depressive feelings during the past week on a 4-point scale: \u0026lsquo;rarely or none of the time (\u0026lt;\u0026thinsp;1 day)\u0026rsquo;, \u0026lsquo;some or a little of the time (1\u0026ndash;2 days)\u0026rsquo;, \u0026lsquo;occasionally or a moderate amount of time (3\u0026ndash;4 days)\u0026rsquo;, and \u0026lsquo;most or all of the time (5\u0026ndash;7 days)\u0026rsquo;. Each item was scored from 0 to 3, yielding a total score between 0 and 30, where higher scores represent greater depressive symptom severity. A cut-off point of 10 was defined as depressive symptoms[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and scores of 20 were classified as severe depressive symptoms.\u003c/p\u003e\n\u003ch3\u003eSolid fuel use measurements\u003c/h3\u003e\n\u003cp\u003eIn the baseline survey, participants reported their households\u0026rsquo; main energy sources for cooking and heating. According to self-reported data, crop residues, wood, and coal were categorized as solid fuels, whereas electricity, liquefied petroleum gas, natural gas, marsh gas, and solar energy were considered clean fuels due to their lower emissions of pollutants. Households using solid fuels for either cooking or heating were defined as the exposure group, while those relying exclusively on clean fuels for both purposes were classified as the clean-fuel group.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eCovariates considered in this study included sociodemographic factors (age, sex, marital status, education level, and residential area), health-related behaviors (smoking and alcohol consumption), body mass index (BMI), and major comorbidities. Residential area was classified as urban or rural, and marital status as married or unmarried. Education was grouped into three levels: below lower secondary, upper secondary or vocational training, and tertiary education. Smoking status was categorized as current, former, or never smoker, and alcohol consumption was defined as drinking alcohol within the past year. BMI (kg/m\u0026sup2;) was calculated as weight divided by height squared and categorized as \u0026lt;\u0026thinsp;18.5, 18.5\u0026ndash;24.9, 25.0\u0026ndash;29.9, and \u0026ge;\u0026thinsp;30. Physician-diagnosed comorbidities were self-reported and included hypertension, diabetes, and heart disease. Details on missing values and covariate proportions are provided in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eFor study participant characteristics, we used median (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e25\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026ndash;P\u003c/em\u003e\u003csub\u003e\u003cem\u003e75\u003c/em\u003e\u003c/sub\u003e) to describe continuous variables and frequency (percentage) to describe categorical variables. The Wilcoxon rank-sum test (continuous data) and chi-square test (categorical data) were performed to make comparisons between two groups.\u003c/p\u003e\u003cp\u003eMultivariate logistic regression models were fitted to investigate the total and sex-specific associations of solid fuel use with incident stroke, depressive symptoms with incident stroke, and solid fuel use with depressive symptoms (yes/no). All results were presented as ORs and 95% confidence intervals (CIs). The basic model (model 1) was adjusted for age and sex, marital status, place of residence, and education level; while model 2 additionally adjusted for smoking, alcohol consumption, body mass index, history of hypertension, diabetes, and heart disease.\u003c/p\u003e\u003cp\u003eMediation analysis was used to explore the potential mediating role of depressive symptoms on the association between solid fuel use and incident stroke. Estimates and 95% confidence intervals (95% CI) were then calculated for each pathway using the 'mediation' R package. Briefly, the total effect between solid fuel use and incident stroke was split into the direct effect of solid fuel use\u0026rarr; incident stroke and solid fuel use \u0026rarr; depressive symptoms \u0026rarr; incident stroke. A schematic representation of the mediation analysis hypothesis is shown below \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Reverse causal associations can be largely avoided due to the clear temporal sequence between exposure, mediator, and outcome in this study. To measure the association of depressive symptoms between solid fuel use and incident stroke, the mediating proportion of depressive symptoms was added to the result table. The adjustment variables for the model 2 were kept as above.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo validate the robustness of the results, we conducted two sensitivity analyses: 1. Excluding participants with missing covariate values to account for potential bias introduced by missing data; 2. Excluding cases diagnosed within the preceding two years to reduce reverse causality effects.\u003c/p\u003e\u003cp\u003eStatistical analyses were performed using the SAS version 9.4 (SAS Institute) and R software (version 4.2.1). All statistical tests were two-sided, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline characteristics of 13928 participants, including 4355 clean fuel users and 9573 solid fuel users. The median age of participants was 58.0 years, with 47.3% male and 52.7% female. Compared to clean fuel users, solid fuel users tended to have lower educational attainment, were more likely to live in rural areas or be smokers, and had higher CESD-10 scores (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eBaseline characteristics of the study population\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClean fuel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSolid fuel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (51, 65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (50, 63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (52, 65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6583 (47.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2051 (47.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4532 (47.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7345 (52.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2304 (52.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5041 (52.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2118\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12227 (87.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3846 (88.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8381 (87.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1701 (12.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e509 (11.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1192 (12.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational level (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than lower secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12313 (88.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3424 (78.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8889 (92.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper secondary \u0026amp; vocational training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1362 (9.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e720 (16.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e642 (6.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e253 (1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e211 (4.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCESD-10 score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.00 (3.00, 12.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.00 (3.00, 9.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.00 (4.00, 13.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody mass index (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e792 (6.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140 (4.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e652 (7.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18.5\u0026ndash;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7455 (62.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2065 (60.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5390 (63.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3050 (25.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e999 (29.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2051 (24.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e592 (4.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e224 (6.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e368 (4.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArea of residence (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8548 (61.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1437 (33.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7111 (74.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5380 (38.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2918 (67.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2462 (25.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke status (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8476 (60.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2788 (64.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5688 (59.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1163 (8.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e363 (8.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e800 (8.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4287 (30.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1203 (27.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3084 (32.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol intake (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.3964\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9332 (76.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2888 (77.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6444 (76.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2809 (23.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e845 (22.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1964 (23.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3664 (26.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1185 (27.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2479 (26.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1385\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e847 (6.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e301 (6.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e546 (5.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart disease (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1839 (13.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e562 (12.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1277 (13.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4747\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe chi-square test is used to compare categorical variables, and the rank-sum test is used to compare continuous variables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that 917 incident stroke events occurred during the follow-up period, out of 13,928 participants, with an incidence of 6.58%. Compared to participants using clean fuels (248 events, incidence 5.69%), those using solid fuels (669 events, incidence 6.99%) had a higher risk of stroke. In Model 1, after adjusting for age, sex, marital status, place of residence, and education, solid fuel use was associated with a 26% increased risk of stroke (OR\u0026thinsp;=\u0026thinsp;1.26, 95% CI: 1.07\u0026ndash;1.49). This association remained statistically significant and virtually unchanged after full adjustment for smoking, alcohol consumption, BMI, and a history of hypertension, diabetes, and heart disease, with an OR of 1.27 (95% CI: 1.08\u0026ndash;1.51). However, subgroup analyses by sex revealed significant heterogeneity in the association between household solid fuel use and stroke risk. Among men, the OR in model 2 was 1.11 (95% CI: 0.88\u0026ndash;1.40), not reaching statistical significance. In contrast, among women, the incidence was 4.99% for clean-fuel users (n\u0026thinsp;=\u0026thinsp;115) and 6.96% for those using solid fuels (n\u0026thinsp;=\u0026thinsp;351). After full adjustment for the covariates, the OR for women was 1.43 (95% CI: 1.13\u0026ndash;1.81), indicating a significant association.\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\u003eAssociation between household solid fuel use and the risk of stroke\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncidence (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR1 (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR2 (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClean fuel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef (1.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolid fuel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.26 (1.07, 1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.27 (1.08, 1.51)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClean fuel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef (1.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolid fuel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.11 (0.88, 1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.14 (0.90, 1.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClean fuel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef (1.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolid fuel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.43 (1.13, 1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.40 (1.11, 1.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eOR1 (95% CI) adjusted for age and sex, marital status, place of residence, and education level;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eOR2 (95% CI) adjusted for age, sex, marital status, place of residence, education level, smoking, alcohol consumption, body mass index, history of hypertension, diabetes, and heart disease.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe association between household solid fuel use and incident stroke risk was further evaluated through sensitivity analyses to assess the robustness of our primary findings (\u003cb\u003eTable S2\u003c/b\u003e). First, after excluding participants with missing covariate data, we observed a consistent positive association in the overall population (OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI: 1.01\u0026ndash;1.50). Sex-stratified analyses revealed a significant association among women (OR\u0026thinsp;=\u0026thinsp;1.35, 95% CI: 1.03\u0026ndash;1.77), whereas no significant association was found among men (OR\u0026thinsp;=\u0026thinsp;1.10, 95% CI: 0.83\u0026ndash;1.46). Second, after excluding stroke incidents occurring within the first two years of follow-up, the fully adjusted model still showed a statistically significant positive association in the overall population (OR\u0026thinsp;=\u0026thinsp;1.36, 95% CI: 1.14\u0026ndash;1.64). This trend was consistent in sex-stratified analyses, with women exhibiting a stronger association (OR\u0026thinsp;=\u0026thinsp;1.53, 95% CI: 1.19\u0026ndash;1.99), while men continued to show no significant association (OR\u0026thinsp;=\u0026thinsp;1.19, 95% CI: 0.92\u0026ndash;1.54).\u003c/p\u003e\u003cp\u003eThe results of the mediation analysis examining the effect of household solid fuel use on stroke risk through depressive symptoms are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. When depressive symptoms were modeled as a continuous CESD-10 score, solid fuel use was associated with higher CESD-10 scores (β\u0026thinsp;=\u0026thinsp;1.73, SE\u0026thinsp;=\u0026thinsp;0.12). A one-point increase in CESD-10 was linked to higher odds of incident stroke (OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.01\u0026ndash;1.04). The mediation proportion was 16.2% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that depressive symptoms accounted for a modest but statistically significant portion of the total association between household solid fuel use and incident stroke. In addition, household solid fuel use was associated with higher CESD-10 scores in women (β\u0026thinsp;=\u0026thinsp;1.89, SE\u0026thinsp;=\u0026thinsp;0.17), with a significant mediation proportion of 14.9%.\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\u003eMediating role of depressive symptoms in the association between household solid fuel use and the risk of stroke\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSolid fuel use to depression (Beta, SE/OR, 95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDepression to stroke (OR, 95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMediation proportion (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression:\u003c/p\u003e\u003cp\u003eCESD-10 score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.73 (0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.02 (1.01, 1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.2 *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.54 (0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.02 (1.00, 1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.89 (0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.03 (1.01, 1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.9*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression: CESD-10 score\u0026thinsp;\u0026ge;\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.84 (1.69, 2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.27 (1.10, 1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.8*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.89 (1.65, 2.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.26 (1.02, 1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.82 (1.62, 2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.25 (1.02, 1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.6*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression: CESD-10 score\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.18 (1.79, 2.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.38 (1.08, 1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.4*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.17 (1.53, 3.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.47 (0.96, 2.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.17 (1.71, 2.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.30 (0.95, 1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eModels adjusted for age, sex, marital status, place of residence, education level, smoking, alcohol consumption, body mass index, history of hypertension, diabetes, and heart disease.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e* For statistical significance.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhen we defined the presence of depressive symptoms as a CESD-10 score\u0026thinsp;\u0026ge;\u0026thinsp;10, solid fuel use was significantly associated with an increased risk of depressive symptoms (OR\u0026thinsp;=\u0026thinsp;1.84, 95% CI: 1.69\u0026ndash;2.02). The presence of depressive symptoms (CESD-10\u0026thinsp;\u0026ge;\u0026thinsp;10) was associated with an increased risk of stroke (OR\u0026thinsp;=\u0026thinsp;1.27, 95% CI: 1.10\u0026ndash;1.46), and the mediation proportion was 11.8% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In sex-stratified analyses, the indirect effect was statistically significant in women (mediation proportion\u0026thinsp;=\u0026thinsp;7.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but not in men (mediation proportion\u0026thinsp;=\u0026thinsp;15.4%). Similar results were obtained when defining the severe depressive symptoms with the cut-off value of 20 score of CESD-10.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on the data of the ten thousand Chinese population, our findings indicate a significant association between household solid fuel use and incident stroke risk in middle-aged and older Chinese adults. In addition, depressive symptoms may explain up to 16.2% of the relationship between household solid fuel use and incident stroke in the total population. Furthermore, additional analysis revealed significant heterogeneity in these associations across sexes. These findings provide new empirical evidence for the potential psychological pathways linking solid fuel use and stroke, highlighting the importance of addressing mental health in studies of environmental risk and cerebrovascular disease.\u003c/p\u003e\u003cp\u003eThe positive association between household solid fuel use and incident stroke in this study was generally consistent with previous studies. Multiple studies have shown that household air pollution from the use of solid fuels has a significant adverse impact on cardiovascular health. For example, a study in the Putuo District of Shanghai found that household use of solid fuels was significantly associated with an increased risk of stroke, hypertension, coronary heart disease, and diabetes[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similarly, a large-scale epidemiological study covering 11 countries and 467 urban and rural communities found that the use of solid fuels for cooking and heating was closely related to higher mortality rates and an increased risk of cardiovascular diseases, including stroke [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Previous studies have primarily examined the association between household solid fuel use and overall cardiovascular outcomes[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], with limited attention to stroke as a specific health outcome. Based on a nationally representative prospective cohort, our study investigated the relationship between household solid fuel use and incident stroke, showing that exposure to solid fuel combustion was associated with a 27% higher risk of stroke among middle-aged and older individuals compared with those unexposed.\u003c/p\u003e\u003cp\u003eIn the sex-stratified analysis, the association between household solid fuel use and incident stroke remained significant only among women, suggesting that females may be more susceptible to the adverse health effects of such exposure. This finding is consistent with a quantitative study on the impact of air pollution on years lived with disability and years of life lost across the Asia-Pacific region from 1990 to 2019, which showed that women are more vulnerable to household air pollution, while men face a higher mortality risk from exposure to atmospheric particulate matter [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, another study found that women exposed to a 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase in PM\u003csub\u003e10\u003c/sub\u003e concentration had a significantly higher stroke risk (HR\u0026thinsp;=\u0026thinsp;2.16, 95% CI: 1.15\u0026ndash;4.06), whereas no significant increase was observed in men (HR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 0.61\u0026ndash;1.86) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This sex difference may reflect biological susceptibility, as females appear more physiologically vulnerable to the adverse effects of air pollution[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In households relying primarily on solid fuels, greater domestic involvement and longer indoor residence among females can lead to prolonged and higher cumulative exposure, thereby amplifying risk. The higher stroke risk observed in women highlights the importance of sex-sensitive public health interventions, particularly those aimed at reducing exposure to household solid fuel combustion among women, who may bear a disproportionate cerebrovascular and cardiovascular burden from such exposure. On the other hand, the lack of a significant association in men may be attributed to smaller sample sizes and shorter exposure times due to factors such as migration for work, which could have limited the statistical power to detect the association. Future research with larger male cohorts is necessary to further determine whether the absence of a significant association in men reflects a true null effect or is due to statistical limitations.\u003c/p\u003e\u003cp\u003eOur study identified a significant mediating effect of depression in the association between household solid fuel use and incident stroke. Combustion of household solid fuels is a major source of household air pollution[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Previous studies have demonstrated that air pollution is associated with adverse mental health outcomes. For example, a meta-analysis of 39 studies reported that long-term exposure to PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e significantly increased the risk of depression[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Similarly, a prospective cohort of 389000 adults from the UK Biobank found that long-term joint exposure to multiple pollutants\u0026mdash;including PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and NO\u0026mdash;was associated with higher risks of incident depression and anxiety, even at relatively low concentrations[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In our study, a robust and consistent positive association was observed between household solid fuel use and depressive symptoms across both continuous and categorical measures of depression. In addition, it is widely recognized that depression constitutes an independent risk factor for stroke[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, in previous research, much attention has been given to post-stroke depression, while the emotional and psychological changes prior to stroke have been largely overlooked[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In our prospective cohort study, higher depressive symptom scores were significantly associated with increased risk of incident stroke, especially for females. Given that depression was linked to both household solid fuel use and stroke risk in our study, we speculate that it may act as an intermediate role. Further mediation analyses found that depression plays a significant mediating role between household solid fuel use and incident stroke, with a stronger association observed in women compared to men. Existing studies have not thoroughly explored whether depression plays a mediating role between air pollution and incident stroke. However, existing studies have demonstrated that depression can act as a mediator across various health conditions. For instance, both depression and social relationships have been reported to mediate the association between frailty and cognitive function[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Similarly, depression has been shown to mediate the link between adverse childhood experiences and chronic lung diseases [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and its mediating role between sedentary behavior and social frailty has also been confirmed in previous research[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Unlike previous studies, our research systematically evaluated multiple approaches to assessing depressive symptoms, including both continuous scores and binary classifications using different cutoffs (e.g., CESD-10\u0026thinsp;\u0026ge;\u0026thinsp;10 and \u0026ge;\u0026thinsp;20). The results consistently revealed a robust mediating role of depressive symptoms in the association between household solid fuel use and incident stroke. These findings highlight that, regardless of depression severity, evaluating individuals\u0026rsquo; living environments\u0026mdash;particularly their exposure to indoor air pollution attributed to solid fuel combustion \u0026mdash;is essential for preventing stroke onset.\u003c/p\u003e\u003cp\u003eAlthough the molecular mechanisms by which air pollution affects stroke and depression have been widely studied, the specific pathways remain not fully understood. Current evidence suggests that air pollutants, especially fine PM\u003csub\u003e2.5\u003c/sub\u003e, can directly enter the brain through pathways such as the olfactory, trigeminal, and vagus nerves[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. They can also enter the lungs via the respiratory system and subsequently cross the blood-brain barrier through the bloodstream, triggering inflammation and oxidative stress both in the brain and systemically[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These processes can lead to endothelial injury, the release of inflammatory mediators, and thrombosis, which are important mechanisms through which air pollution promotes the occurrence of stroke[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Additionally, persistent inflammation and oxidative stress can disrupt hypothalamic-pituitary-adrenal axis function, inhibit hippocampal neurogenesis, and cause degeneration of dopaminergic neurons[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. It can also activate the indoleamine 2,3-dioxygenase pathway[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], accelerating tryptophan degradation, reducing serotonin synthesis, and disrupting the balance between serotonin and norepinephrine, thereby promoting the onset and progression of depression[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Notably, depression is not only a psychological disorder but also contributes to an increased risk of stroke through multiple mechanisms, including amplifying systemic inflammation, disrupting neuroendocrine balance, and promoting unhealthy behaviors such as smoking, alcohol consumption, and physical inactivity[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study employed a prospective cohort design, which helps clarify the temporal relationship between household solid fuel use, depressive symptoms, and the occurrence of stroke, and provides a more reliable basis for exploring their potential causal associations. However, this study has several limitations. First, data on household solid fuel use, depressive symptoms, and stroke outcomes relied on self-reported questionnaires, which may introduce information bias. Second, our exposure assessment was based on participants\u0026rsquo; self-reported household fuel type (cooking and heating), which we treated as a proxy for indoor combustion-related pollution. We did not directly measure specific pollutant components (e.g., PM₂.₅, PM₁₀, carbon monoxide, or polycyclic aromatic hydrocarbons), nor were we able to comprehensively capture key household factors such as ventilation conditions for different uses, stove efficiency, frequency of use, indoor smoking, or the use of air purification devices. This limitation may introduce some degree of exposure misclassification. Accordingly, future studies should incorporate more refined exposure assessment, including objective monitoring of indoor pollutant concentrations and systematic characterization of household energy-use behaviors, to strengthen causal inference. Third, although several covariates were controlled for in the analysis, unaccounted confounders could not be ruled out. Fourth, our mediation analysis relies on a presumed causal pathway in which household solid fuel use increases indoor combustion-related pollution, which may contribute to depressive symptoms and ultimately raise stroke risk. We did not use genetic or quasi-experimental methods to confirm causality. Still, evidence from Mendelian randomization suggests that air pollution can causally increase stroke risk[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], lending biological plausibility to this pathway.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study showed a significant positive association between household solid fuel use and the risk of incident stroke in middle-aged and older Chinese adults. In addition, depressive symptoms were found to mediate the pathway. Furthermore, compared to men, women exhibited greater sensitivity to the impact. Our findings suggest that public health policies aiming to reduce the stroke burden should implement an integrated approach, simultaneously promotes household clean energy transition, mitigates indoor air pollution, and strengthens community-based mental health services, especially for women.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was carried out based on data extracted from the CHARLS public database, and all methods were performed according to the relevant guidelines and regulations. Written informed consent was obtained from all participants or their legal agents before the commencement of any study process. The ethics approval for the collection of CHARLS data has been approved by the Peking University Biomedical Ethics Review Committee (IRB00001052-11015). The use of CHARLS data was approved by the Human Research Ethics Committee of the University of Newcastle (H-2015-0290).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed in the current study are available upon reasonable request from the official CHARLS application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Jinshan Hospital Affiliated to Fudan University Youth Scientific Research Initiation Fund (grant numbers: JYQN-LC-202310).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCS, BL, NR and YS designed the study concepts; CS and NR participated in the drafting or revising of manuscript; CS, NR and YS performed statistical analysis and interpretation; CS integrated the entire study; BL and YZ edited the manuscript and approved the final version. All authors approved the submission of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the China Health and Retirement Longitudinal Study (CHARLS) team for providing data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFeigin VL, Brainin M, Norrving B, Martins S, Sacco RL, Hacke W, et al. World stroke organization (WSO): global stroke fact sheet 2022. Int J Stroke: Off J Int Stroke Soc. 2022;17:18\u0026ndash;29. https://doi.org/10.1177/17474930211065917.\u003c/li\u003e\n\u003cli\u003eFeigin VL, Brainin M, Norrving B, Martins SO, Pandian J, Lindsay P, et al. World stroke organization: global stroke fact sheet 2025. 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Stroke. 2012;43:32\u0026ndash;7. https://doi.org/10.1161/STROKEAHA.111.630871.\u003c/li\u003e\n\u003cli\u003eWang Y, Wang R, Peng Z, Li Z, Qi Z, Wu Q, et al. A novel concern from two sample mendelian randomization study: the effects of air pollution exposure on the cardiovascular, respiratory, and nervous system. Ecotoxicol Environ Saf. 2024;284:116871. https://doi.org/10.1016/j.ecoenv.2024.116871.\u003c/li\u003e\n\u003cli\u003eDai L, Jiang S, Zhou P. Causal effects of exposure to air pollution on the risk of neurosurgical multi-system diseases: a worldwide study of mendelian randomization. Int J Med Sci. 2025;22:3565\u0026ndash;80. https://doi.org/10.7150/ijms.115853.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Household solid fuel use, Incident stroke, Depressive symptoms, Mediating effect, Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-7872823/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7872823/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHousehold solid fuel use is associated with a higher risk of a wide spectrum of health disorders. However, the sex-specific relationship between solid fuel use and incident stroke in older Chinese adults, as well as the potential mediating role of depressive symptoms, remains insufficiently explored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis longitudinal study used data from the China Health and Retirement Longitudinal Study (CHARLS), including 13928 Chinese participants aged 45 years or older free of stroke at baseline. Logistic regression models were used to assess the relationship between solid fuel use, depressive symptoms, and the risk of stroke. To quantify the potential mediation role of depressive symptoms in the pathway from solid fuel use to new-onset stroke, a mediation analysis was performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOf the 13928 adults (mean age of 58, 47.26% male), 917 (6.58%) participants documented the incident stroke. Solid fuel use was significantly associated with the increased incident stroke risk in the total population (OR\u0026thinsp;=\u0026thinsp;1.27, 95% CI: 1.08\u0026ndash;1.51) and female populations (OR\u0026thinsp;=\u0026thinsp;1.40, 95% CI: 1.11\u0026ndash;1.78) in the fully adjusted model. In addition, depressive symptoms could explain the pathway, with the significant mediating proportions up to 16.2%, regardless of whether the depressive symptoms presented as general depression or severe depression. The results of the stratified analysis also indicate that this mediating effect is present only among the female group.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eHousehold solid fuel use significantly increased the risk of incident stroke, and depressive symptoms played a mediating role in the relationship. These findings highlight the need for integrated public health interventions in the areas of environmental pollution and mental health, with particular attention to women.\u003c/p\u003e","manuscriptTitle":"Sex differences in the relationship between household solid fuel use and incident stroke and the mediating role of depressive symptoms in middle-aged and older Chinese adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 10:08:12","doi":"10.21203/rs.3.rs-7872823/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-21T09:09:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T00:22:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T21:56:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214003004698376928412279968635552698176","date":"2025-11-09T20:42:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304832134384923086225598978362080293344","date":"2025-11-08T19:55:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260003622637991874373416402115363752180","date":"2025-11-06T21:05:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32486792990636641203818292865411743821","date":"2025-11-06T05:56:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274186514448044127630819180604341165880","date":"2025-11-06T00:01:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T12:03:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90183010784665394799302468427380074350","date":"2025-11-05T11:51:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T20:40:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-01T16:48:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-11-01T16:44:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"54e6da39-79d5-4e1c-8bf7-67dc925d6339","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:02:15+00:00","versionOfRecord":{"articleIdentity":"rs-7872823","link":"https://doi.org/10.1186/s12889-026-26863-x","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-03-12 15:58:24","publishedOnDateReadable":"March 12th, 2026"},"versionCreatedAt":"2025-11-14 10:08:12","video":"","vorDoi":"10.1186/s12889-026-26863-x","vorDoiUrl":"https://doi.org/10.1186/s12889-026-26863-x","workflowStages":[]},"version":"v1","identity":"rs-7872823","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7872823","identity":"rs-7872823","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}