The mediating role of health status in the relationship between indoor air pollution and life satisfaction among older adults in India

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Methods It utilizes a mediation analysis approach, drawing on data from 1 st wave of the Longitudinal Ageing Study in India (LASI) carried out in 2017-18, involving a cohort of 29,517 individuals aged 60 and older. The analysis proceeds through three models: first, examining the direct association of indoor air pollution with life satisfaction; second, assessing the impact of pollution on health; and third, integrating both to evaluate the mediation effect. Results Findings indicate a significant negative direct association of pollution with life satisfaction, with an association strength of -0.55(95%CI:-0.75 to -0.35, p < 0.001), and on health, with an association strength of -0.05(95%CI:-0.07 to -0.05, p < 0.001). Additionally, the mediation analysis, supported by the Sobel-Goodman Mediation Test (Z = -4.298, p < 0.001), reveals that health mediates 12.9% of the total impact of indoor pollution on life satisfaction. Conclusions These results underscore the critical role of health as a mediator in this relationship. Interventions aimed at reducing indoor air pollution could significantly enhance the well-being of older adults by improving their health. Indoor air pollution Household air pollution Life satisfaction Quality of life Health Older adults Figures Figure 1 Introduction Indoor air pollution remains a pressing global health issue, disproportionately affecting low- and middle-income countries. For cooking and heating, some 2400 million people globally lack access to clean cooking facilities and instead rely on solid fuels, including wood, manure, crop waste, kerosene, charcoal, and coal (Balmes, 2019 ). These practices contribute to poor indoor air quality, posing significant health risks, particularly to vulnerable groups such as women, children, and older adults (Ali et al., 2021 ). Findings based on the GBD Study-2019 shows that India alone lost around 600,000 individuals due to indoor air pollution(Dhimal et al., 2021 ). Older persons are more vulnerable to the negative consequences of indoor pollution because they often spend more time indoors, where they are exposed to more dangerous contaminants (Kankaria et al., 2014 ). A substantial body of research has demonstrated a correlation between exposure to air pollution and a reduction in subjective well-being, indicating that higher levels of pollutants can adversely affect individuals' perception of their own happiness and life satisfaction. For instance, research using cross-sectional and panel data from Chinese residents has shown that air pollution is correlated with a decline in life satisfaction (Liu et al., 2023 ). Similarly, exposure to local air pollutants and noise has been found to significantly reduce the subjective well-being of the German population (Rehdanz & Maddison, 2008 ). A study on European residents reported significant impacts of sulfur dioxide exposure on life satisfaction, further corroborating these findings (Ferreira et al., 2013 ). According to a regional study conducted across the BRICS nations, using solid fuels may have a detrimental effect on life satisfaction because it lowers the possibility that people will be in a healthy state (Hassan et al., 2022 ). While the correlation between ambient air pollution and life satisfaction has been extensively studied, there is a relative dearth of research exploring these relationships through mediation mechanisms. Mediation analysis offers a nuanced approach to understanding the pathways through which independent variables can influence dependent variables indirectly through one or more intervening variables (Baron & Kenny, 1986 ). For instance, Luechinger ( 2010 ) found that the impacts of air pollution on well-being were mediated by sensory perceptions (Luechinger, 2010 ). Further studies by Levinson ( 2012 ) and Sanduijav et al. ( 2021 ) also suggest that ambient air pollution significantly affects psychological well-being, which in turn influences overall life satisfaction (Levinson, 2012 ; Sanduijav et al., 2021 ). Additionally, Smyth et al. ( 2008 ) explored the indirect effects of environmental factors on happiness, suggesting complex interactions between perceived air quality and mental health (Smyth et al., 2008 ). This indirect pathway is particularly significant considering the high levels of pollution observed in many developing countries, where indoor air quality is a critical health issue. According to Tian et al. ( 2022 ), air pollution not only diminishes life satisfaction directly but also indirectly by exacerbating health issues that limit daily activities and overall quality of life (Tian et al., 2022 ). The research by Hassan et al. ( 2022 ) supports this view, highlighting how energy poverty and the use of solid fuels can negatively impact life satisfaction through deteriorating health conditions (Hassan et al., 2022 ). In India, few studies have explored the direct effects of indoor air pollution on health among older adults (Dakua et al., 2022 ). However, the potential mediating role of health in these relationships remains largely unexamined. This gap in the existing literature is particularly alarming because, according to recent data, the percentage of households with older persons exposed to indoor pollution is on the rise, standing at roughly 14% (IIPS and ICF., 2021). Therefore, this study seeks to fill this gap by employing nationally representative data to model the self-reported life satisfaction of older adults as a function of their demographic characteristics, health conditions, and exposure to indoor air pollution. This study intends to add to the body of knowledge on the effects of the environment on health while also informing policy measures that have the potential to considerably progress the quality of life for older individuals in India through a thorough analysis of these links. We may make great progress toward accomplishing the health and well-being-related Sustainable Development Goals by enhancing indoor air quality. Materials and Methods Data Source: Data has been collected from the first wave of LASI, which was conducted during 2017-18. LASI is a study that includes all Indian adults, as well as older men and women aged 45 years and above, and their spouses residing in the same household, regardless of their age. LASI Wave 1 collected data from all 37 states and union territories using a specific method to select households. All individuals aged 45 years and above, as well as their spouses, were interviewed face-to-face within the selected households. This study includes information from 29,517 complete cases aged 60 years and above from all states. More details on sampling design can be seen in the official report of LASI wave 1(International Institute for Population Sciences (IIPS), 2020). Outcome Variable: Life Satisfaction : We constructed an additive composite scale based on the following questions asked by the respondent (Supplementary File 1). In most ways, my life is close to ideal The conditions of my life are excellent. I am satisfied with my life So far, I have got the important things I want in life. If I could live my life again, I would change almost nothing. It ranges from 5–35; the higher the score, the better the life satisfaction. The scale is found to be internally consistent as the standardized scale reliability coefficient is 0.90. Independent Variable: Indoor Pollution : A binary variable was constructed for indoor pollution based on its sources, i.e., type of unclean cooking fuels (kerosene, charcoal, crop residue, wood/shrub, dung cake, and other sources) and indoor smoking. Mediating Variable: Health : We used self-reported health to measure the health conditions of older adults. As per the available literature (Sanduijav et al., 2021 ; Tian et al., 2022 ), indoor air pollution was found to be highly associated with the life satisfaction of older adults, and health might mediate this effect. Therefore, we used health as a mediator for this study, and we hypothesize that health mediates the association between indoor pollution and the life satisfaction of older adults. The respondents were asked, “Overall, how is your health in general? Would you say it is very good, good, fair, poor, or very poor?”. The choices were assigned values from 1 to 5 correspondently. Higher values signify better health conditions and vice-versa. Control Variables Previous studies have identified several socio-demographic attributes as potential confounders in the relationship between indoor pollution and the life satisfaction of older adults. Therefore, we included age (60–69, 70–79, and 80 and above), sex (male/female), place of residence (rural/urban), level of education (no schooling, less than 5 years, 5–9 years and 10 & more years of schooling), religion (Hindu/non-Hindu), caste (Scheduled Castes-SC, Scheduled Tribes-ST, Other Backward Classes-OBC and General), marital status (married/unmarried), MPCE quintiles (poorest, poorer, middle, richer, richest), and two dichotomous variables, i.e., history of smoking and alcohol consumptions as control variables in our study. Model specification: Indoor pollution has a direct effect on the life satisfaction of residents, while health acts as a mediator between indoor pollution and life satisfaction for older individuals (Fig. 1 ). To quantify the extent of this mediation, we conducted an analysis using three models. The first model establishes a regression model between the independent variable of indoor pollution and the dependent variable of life satisfaction. The second model establishes regression models between the independent variable of indoor pollution and the mediating variable of health. Finally, the third model establishes a regression model between all three variables: indoor pollution, health, and life satisfaction. Since life satisfaction and health are continuous variables, we estimated the impacts of indoor pollution on life satisfaction (Model 1) and health (Model 2) using a linear regression model, which is frequently employed in empirical research. $$\text{M}\text{o}\text{d}\text{e}\text{l}1: Life Satisfactio{n}_{\text{i}\text{j}}={{\alpha }}_{1}Indoor Pollutio{n}_{\text{i}\text{j}}+{{\beta }}_{1}Contro{l}_{\text{i}\text{j}}+{{\epsilon }}_{1}$$ $$Model2: Healt{h}_{\text{i}\text{j}}={{\alpha }}_{2}Indoor Pollutio{n}_{\text{i}\text{j}}+{{\beta }}_{2}Contro{l}_{\text{i}\text{j}}+{{\epsilon }}_{2}$$ $$Model3: Life Satsfactio{n}_{\text{i}\text{j}}={{\alpha }}_{3}Indoor Pollutio{n}_{\text{i}\text{j}}+{{\gamma }}_{3}Healt{h}_{\text{i}\text{j}}+{{\beta }}_{3}Contro{l}_{\text{i}\text{j}}+{{\epsilon }}_{3}$$ Where pollution is the primary explanatory variable, signifying the emission of air pollution in each household, health is the indicator of the health of the j resident of the I household, and life satisfaction is the dependent variable, representing the life satisfaction of the j resident of the household; The control vector, which was developed from the literature on factors influencing subjective life satisfaction, is made up of all the control variables. The Quintiles of monthly per capita expenditure (MPCE), marital status, education, religion, caste, age, gender, and history of alcohol and tobacco use are among the primary control variables. The regression model's error term is denoted by ε, whereas the equations' regression coefficients are α, β, and γ. The STATA 17 programme was used for all statistical analyses (StataCorp., 2021). Results Our study evaluated the associations between indoor air pollution, health, and life satisfaction among older adults in India. The study sample consists of 29,517 older adults aged 60 years and above. Table 1 summarizes the descriptive statistics of the study variables. The mean life satisfaction score is 23.7 (SD = 7.5), with a range from 5 to 35. About 57.6% of the respondents are exposed to indoor air pollution. The sample is nearly evenly split between males (48.2%) and females (51.8%), with a majority residing in rural areas (67.1%). Most respondents are aged 60–69 years (60.9%), followed by 70–79 years (28.8%), and 80 + years (10.3%). Educational attainment is low, with 53.8% having no education, and only 15.0% having 10 or more years of schooling. The majority are Hindus (74.3%) and belong to Other Backward Classes (OBC) (39.5%). Economic status is evenly distributed across the quintiles of Monthly Per Capita Expenditure (MPCE). Regarding marital status, 63.9% are married. A significant portion of the sample has a history of smoking (38.9%) and alcohol consumption (17.5%). The mean self-reported health score is 3.3 (SD = 0.7), on a scale from 1 to 5. The statistical analysis was performed using three distinct regression models to ascertain the direct and indirect associations of indoor pollution on life satisfaction, mediated by self-reported health status. Table 1 Descriptive Statistics (n = 29,517) Variables Measures Mean/Proportion S.D Min Max Dependent Variable Life Satisfaction Self-reported life conditions 23.7 7.5 5 35 Independent Variable Pollution Yes = 1, No = 0 16991 (57.6) Control Variable Gender Male 14240 (48.2) Female 15277 (51.8) Place of Residence Rural 19792 (67.1) Urban 9725 (33.0) Age 60–69 17983 (60.9) 70–79 8500 (28.8) 80+ 3034 (10.3) Education No education 15879 (53.8) Less than 5 years 3563 (12.1) 5–9 years completed 5646 (19.1) 10 years or more 4429 (15.0) Religion Hindu 21922 (74.3) Non-Hindu 7595 (25.7) Caste SC 4957 (16.8) ST 5084 (17.2) OBC 11657 (39.5) None of the above 7819 (26.5) MPCE quantiles Poorest 6119 (20.7) Poorer 6075 (20.6) Middle 6029 (20.4) Richer 5803 (19.7) Richest 5491 (18.6) Marital Status Married 18870 (63.9) Not Married 10647 (36.1) History of Smoking Yes = 1, No = 0 11490 (38.9) History of Alcohol Consumption Yes = 1, No = 0 5178 (17.5) Mediating Variable Self-Reported Health Self-reported health in general 3.3 0.7 1 5 Direct Association Between Indoor Pollution and Life Satisfaction The first model in Table 2 assessed the direct association of indoor air pollution on life satisfaction. The results indicated a significant negative association, with the pollution variable having a regression coefficient (β) of -0.55, 95% Confidence Interval (CI): -0.75 to -0.35, p < 0.001. This suggests that exposure to indoor air pollution is associated with lower life satisfaction among older adults. The negative coefficient highlights the deleterious effects of poor air quality on well-being by showing that the life satisfaction score drops as the pollution level increases. Table 2 Impact of pollution and health on life satisfaction of older adults of India. Dependent Variable Model 1 Model 2 Model 3 Life Satisfaction % CI p-Value SRH % CI p-Value Life Satisfaction % CI p-Value Independent Variables Pollution -0.55 -0.75 - -0.35 < 0.001 -0.05 -0.07 - -0.03 < 0.001 -0.46 -0.66 - -0.26 < 0.001 Control Variables Gender Male® Female 0.20 -0.02–0.41 0.07 -0.08 -0.10 - -0.06 < 0.001 0.330 0.12–0.54 0.002 Place of Residence Rural Urban 0.51 0.30–0.72 < 0.001 0.01 -0.01–0.02 0.649 0.501 0.30–0.70 < 0.001 Age 60–69® 70–79 0.10 -0.10–0.29 0.326 -0.12 -0.13 - -0.10 < 0.001 0.292 0.10–0.48 0.003 80+ 0.28 -0.02–0.57 0.065 -0.22 -0.24 - -0.19 < 0.001 0.637 0.34–0.93 < 0.001 Education No education® Less than 5 years 0.94 0.67–1.22 < 0.001 -0.01 -0.03–0.02 0.51 0.958 0.68–1.23 < 0.001 5–9 years completed 1.26 1.01–1.50 < 0.001 -0.01 -0.03–0.01 0.327 1.276 1.03–1.52 < 0.001 10 years or more 2.60 2.30–2.90 < 0.001 0.09 0.06–0.12 < 0.001 2.452 2.16–2.75 < 0.001 Religion Hindu Non-Hindu -0.10 -0.30–0.11 0.365 -0.01 -0.03–0.00 0.143 -0.072 -0.28–0.13 0.487 Caste SC® ST 1.15 0.84–1.45 < 0.001 0.19 0.16–0.21 < 0.001 0.841 0.54–1.14 < 0.001 OBC 0.46 0.21–0.71 < 0.001 0.01 -0.01–0.04 0.198 0.436 0.19–0.68 0.001 None of the above 0.97 0.69–1.24 < 0.001 0.03 0.01–0.06 0.011 0.914 0.64–1.19 < 0.001 MPCE quantiles Poorest® Poorer 0.64 0.37–0.90 < 0.001 0.01 -0.01–0.03 0.399 0.621 0.36–0.88 < 0.001 Middle 0.80 0.53–1.07 < 0.001 0.05 0.02–0.07 < 0.001 0.718 0.45–0.98 < 0.001 Richer 0.75 0.48–1.02 < 0.001 0.03 0.01–0.06 0.007 0.696 0.43–0.96 < 0.001 Richest 0.92 0.64–1.20 < 0.001 0.00 -0.02–0.03 0.796 0.912 0.63–1.19 < 0.001 Marital Status Married® Not Married -0.90 -1.10 - -0.70 < 0.001 -0.05 -0.07 - -0.04 < 0.001 -0.807 -1.00 - -0.61 < 0.001 History of Smoking Yes = 1, No = 0 -0.44 -0.64 - -0.24 < 0.001 -0.03 -0.05 - -0.01 0.001 -0.395 -0.59 - -0.20 < 0.001 History of Alcohol Consumption Yes = 1, No = 0 -0.27 -0.52 - -0.02 0.033 -0.03 -0.05 - -0.01 0.011 -0.225 -0.47–0.02 0.075 Mediating Variable Self-Reported Health (SRH) Self-reported health in general 1.658 1.53–1.78 < 0.001 Observations 29,517 29,517 29,517 Association Between Indoor Pollution and Health The second model in Table 2 explored how indoor pollution impacts the health of older adults. The coefficient for pollution in this model was − 0.05 (95% CI: -0.07 to -0.03, p < 0.001), indicating a significant negative impact on health. This finding corroborates the hypothesis that indoor air pollution adversely affects health status, which in turn could influence overall life satisfaction. Mediation of Health in the Association Between Pollution and Life Satisfaction In the third model (Table 2 ), we incorporated health as a mediating variable to understand its role in the relationship between indoor air pollution and life satisfaction. The analysis revealed that when accounting for health, the association strength between pollution and life satisfaction slightly lessened but remained significant, with a coefficient of -0.46 (95% CI: -0.66 to -0.26, p < 0.001). This reduction from the first model (from − 0.55 to -0.46) underscores the mediating role of health, suggesting that a portion of the impact of pollution on life satisfaction operates through its effect on health. Mediation Analysis To quantify the extent of mediation by health, we employed the Sobel-Goodman Mediation Test and the finding can be found in Table 3 . The test yielded a Z-value of -4.298 with a p-value < 0.001, confirming the significance of the mediation effect. The proportion of the total effect of pollution on life satisfaction that is mediated by health was calculated to be 12.9%. This indicates that approximately 13% of the effect of indoor pollution on life satisfaction is mediated through its impact on health. The ratio of indirect to direct effect was determined to be 0.148, further illustrating the substantial role health plays in this context. Table 3 Mediation test results Coefficient Std. Error Z p-value Sobel -0.068 0.016 -4.298 < 0.001 Delta -0.068 0.016 -4.298 < 0.001 Monte-Carlo -0.068 0.016 -4.295 < 0.001 Percent of total effect that is mediated: 12.9% Ratio of indirect to direct effect: 0.148 These results collectively highlight the significant negative associations of indoor air pollution with both health and life satisfaction among older adults while also demonstrating the critical mediating role of health between these variables. Several control variables were included in the regression models in Table 2 to account for potential confounding factors. Female respondents reported higher life satisfaction compared to males, with a significant positive coefficient in Model 3 (β = 0.330, 95% CI: 0.12 to 0.54, p = 0.002). Urban residence is associated with higher life satisfaction compared to rural residence (β = 0.501, 95% CI: 0.30 to 0.70, p < 0.001). Higher educational attainment is positively associated with life satisfaction, particularly for those with 10 years or more of schooling (β = 2.452, 95% CI: 2.16 to 2.75, p < 0.001). Economic status, measured by MPCE quintiles, also shows a positive association with life satisfaction, with the richest quintile having the highest life satisfaction scores. Married individuals reported lower life satisfaction than their unmarried counterparts (β = -0.807, 95% CI: -1.00 to -0.61, p < 0.001). Both a history of smoking and alcohol consumption are negatively associated with life satisfaction. Discussion This study offers vital insights into the intricate relationships that older persons in India have between indoor air pollution, health, and life happiness. The significant direct negative association between indoor air pollution and life satisfaction, coupled with the mediation effect of health, highlights the multifaceted impact of environmental factors on elderly well-being. Our research found that the detrimental effects of indoor air pollution on life satisfaction are in line with general worldwide trends. For instance, studies across various countries have demonstrated similar detrimental effects of poor air quality on health and psychological well-being (Ezzati & Kammen, 2002 ; Pope III, 2002 ). The health burden from indoor air pollution is significant in areas like Sub-Saharan Africa and Southeast Asia, where biomass fuel use is common. These conditions frequently result in severe respiratory and cardiovascular disorders, which have a direct negative impact on life quality and longevity (Bonjour et al., 2013 ; Lim et al., 2012 ). Moreover, research from developed countries provides a contrast, showing that improvements in air quality can lead to significant enhancements in population health and well-being. For example, interventions in the United States and Europe that have focused on reducing air pollution levels have been associated with improved life expectancy and reduced disease incidence, which contribute to higher life satisfaction ratings among the affected populations (Smith & Sagar, 2014 ). In the context of our study, health significantly mediates the impact of indoor air pollution on life satisfaction among older adults. This mediation underscores the complexity of how physical health conditions, exacerbated by environmental pollutants, directly contribute to the subjective perception of quality of life. Health mediation suggests that the effects of indoor air pollution on life satisfaction are not completely direct but are filtered through the changes in health status that pollution induces. Particulate matter and hazardous compounds derived from biomass fuels are examples of pollutants that can cause respiratory issues, cardiovascular disorders, and long-term ailments, including bronchitis and asthma (Lim et al., 2012 ; Smith & Sagar, 2014 ). These health issues can severely limit daily activities, increase medical costs, and induce stress and anxiety, which cumulatively decrease life satisfaction. The linkage between indoor air pollution and socio-economic status is critical. In India, lower socio-economic groups are more likely to use traditional cooking fuels due to their lower cost and accessibility despite the higher health risks associated (Smith & Sagar, 2014 ). This leads to a vicious cycle in which low health outcomes are both a cause and an effect of poverty, deepening inequalities and lowering life satisfaction in these areas (Hajat et al., 2014 ). These socio-economic dimensions are crucial for understanding the barriers to adopting cleaner technologies and fuels. Cultural preferences, lack of awareness, and inadequate infrastructure also play significant roles in perpetuating reliance on traditional fuels, suggesting that policy interventions must be multifaceted and culturally sensitive to be effective (Dabadge et al., 2018 ). Our findings underscore the need for integrated public health policies that address both the environmental and social determinants of health. First, there is a clear need for policies that facilitate the transition to cleaner cooking fuels through subsidies, incentives, and infrastructure development. Programs like the Pradhan Mantri Ujjwala Yojana have made strides but must be expanded and combined with educational campaigns that emphasize the health benefits of reduced indoor air pollution (The Hindu, 2018). Second, healthcare interventions should focus on screening, early detection, and treatment of pollution-related health issues among older adults. Establishing community health programs that monitor symptoms and provide treatment at local levels can help mitigate the health impacts more effectively. Third, urban planning and housing regulations should incorporate guidelines to improve indoor air quality. This includes better ventilation systems, the use of non-toxic building materials, and green spaces in urban areas, which have been shown to improve overall air quality and enhance life satisfaction (Lim et al., 2012 ; Prüss-Üstün et al., 2016 ). While our study provides insightful findings, it is not without limitations. The cross-sectional design restricts our ability to infer causality between indoor air pollution, health, and life satisfaction. A more conclusive knowledge of these correlations may be obtained through longitudinal investigations. Additionally, the reliance on self-reported measures for health and life satisfaction may introduce bias, highlighting the need for objective health indicators in future research. Further research should explore longitudinal data to establish causal relationships and assess the long-term impacts of air quality improvements on health and life satisfaction. Additionally, studying other potential mediators like mental health, social support, and access to health services could provide a deeper understanding of the pathways through which environmental conditions influence life satisfaction. Comparative studies across different cultural and economic settings would also be valuable in generalizing the findings and tailoring interventions accordingly (Hajat et al., 2014 ). Conclusion Overall, by clarifying the mediating role of health in the association between indoor air pollution and life satisfaction among older individuals in India, this study makes a substantial contribution to the body of current literature. The results emphasize the necessity of all-encompassing approaches that address the social and environmental factors that influence the well-being of the elderly. By improving air quality and enhancing health outcomes, we can significantly boost life satisfaction among vulnerable populations, fostering broader public health benefits and contributing to sustainable development goals. Declarations Funding: We did not receive any grant from any funding agency in public, commercial or non-profit sections for conducting this study. Data Availability: Data is publicly available at https://g2aging.org/lasi/download. Competing interests: The authors declare that they have no competing interests. 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G., Aryee, M., Atkinson, C., Bacchus, L. J., Bahalim, A. N., Balakrishnan, K., Balmes, J., Barker-Collo, S., Baxter, A., Bell, M. L., … Ezzati, M. (2012). A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet , 380 (9859), 2224–2260. https://doi.org/10.1016/S0140-6736(12)61766-8 Liu, Y., Wang, Z.-S., & Fang, X.-G. (2023). Impact of Air Pollution on Residents’ Health in China: Moderating Effect Analysis Based on a Hierarchical Linear Model. Atmosphere , 14 (2), 334. https://doi.org/10.3390/atmos14020334 Luechinger, S. (2010). Life satisfaction and transboundary air pollution. Economics Letters , 107 (1), 4–6. Pope III, C. A. (2002). Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. JAMA , 287 (9), 1132. https://doi.org/10.1001/jama.287.9.1132 Prüss-Üstün, A., Wolf, J., Corvalán, C. F., Bos, R., & Neira, M. P. (2016). Preventing disease through healthy environments: a global assessment of the burden of disease from environmental risks . World Health Organization. Rehdanz, K., & Maddison, D. (2008). Local environmental quality and life-satisfaction in Germany. Ecological Economics , 64 (4), 787–797. https://EconPapers.repec.org/RePEc:eee:ecolec:v:64:y:2008:i:4:p:787-797 Sanduijav, C., Ferreira, S., Filipski, M., & Hashida, Y. (2021). Air pollution and happiness: Evidence from the coldest capital in the world. Ecological Economics , 187 , 107085. Smith, K. R., & Sagar, A. (2014). Making the clean available: Escaping India’s Chulha Trap. Energy Policy , 75 , 410–414. https://doi.org/10.1016/j.enpol.2014.09.024 Smyth, R., Mishra, V., & Qian, X. (2008). The environment and well-being in urban China. Ecological Economics , 68 (1–2), 547–555. StataCorp. (2021). Stata Statistical Software: Release 17 . TX: StataCorp LP. The Hindu. (2018, December 18). Data | How effective has the Pradhan Mantri Ujjwala Yojana been. The Hindu . https://www.thehindu.com/data/data-how-effective-has-the-pradhan-mantri-ujjwala-yojana-been/article30338388.ece Tian, X., Zhang, C., & Xu, B. (2022). The Impact of Air Pollution on Residents’ Happiness: A Study on the Moderating Effect Based on Pollution Sensitivity. International Journal of Environmental Research and Public Health , 19 (12), 7536. https://doi.org/10.3390/ijerph19127536 Supplementary File 1 Supplementary File 1 is not available with this version Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4642687","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324006511,"identity":"e95f2693-6559-4986-b69a-ce90005e5f4f","order_by":0,"name":"Mihir Adhikary","email":"data:image/png;base64,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","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mihir","middleName":"","lastName":"Adhikary","suffix":""},{"id":324006512,"identity":"a70412a1-b4c0-4fea-b0f2-e36859d64328","order_by":1,"name":"Amit Goyal","email":"","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":false,"prefix":"","firstName":"Amit","middleName":"","lastName":"Goyal","suffix":""},{"id":324006513,"identity":"2efa428d-0d1a-4689-af97-615fdf2a390a","order_by":2,"name":"Shamrin Akhtar","email":"","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shamrin","middleName":"","lastName":"Akhtar","suffix":""}],"badges":[],"createdAt":"2024-06-26 12:18:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4642687/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4642687/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-24256-0","type":"published","date":"2025-10-08T15:57:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60710472,"identity":"d29f6b9d-ec87-4e9b-adda-72c77b61367a","added_by":"auto","created_at":"2024-07-19 20:05:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":18930,"visible":true,"origin":"","legend":"\u003cp\u003eMediating role of health.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4642687/v1/be0195c0306c5b3fb3020ee2.png"},{"id":93419730,"identity":"01a00cfa-77e3-4260-a0f8-d96d55381c38","added_by":"auto","created_at":"2025-10-13 16:06:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":920630,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4642687/v1/ba102cf9-2068-4b73-bb7e-f1103e7933a8.pdf"},{"id":60710473,"identity":"01765e3e-88fe-4aff-beba-a8dc1033af3b","added_by":"auto","created_at":"2024-07-19 20:05:37","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":256099,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4642687/v1/3b8bae694159b09051c0dd43.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"The mediating role of health status in the relationship between indoor air pollution and life satisfaction among older adults in India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIndoor air pollution remains a pressing global health issue, disproportionately affecting low- and middle-income countries. For cooking and heating, some 2400\u0026nbsp;million people globally lack access to clean cooking facilities and instead rely on solid fuels, including wood, manure, crop waste, kerosene, charcoal, and coal (Balmes, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These practices contribute to poor indoor air quality, posing significant health risks, particularly to vulnerable groups such as women, children, and older adults (Ali et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFindings based on the GBD Study-2019 shows that India alone lost around 600,000 individuals due to indoor air pollution(Dhimal et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Older persons are more vulnerable to the negative consequences of indoor pollution because they often spend more time indoors, where they are exposed to more dangerous contaminants (Kankaria et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA substantial body of research has demonstrated a correlation between exposure to air pollution and a reduction in subjective well-being, indicating that higher levels of pollutants can adversely affect individuals' perception of their own happiness and life satisfaction. For instance, research using cross-sectional and panel data from Chinese residents has shown that air pollution is correlated with a decline in life satisfaction (Liu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, exposure to local air pollutants and noise has been found to significantly reduce the subjective well-being of the German population (Rehdanz \u0026amp; Maddison, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). A study on European residents reported significant impacts of sulfur dioxide exposure on life satisfaction, further corroborating these findings (Ferreira et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). According to a regional study conducted across the BRICS nations, using solid fuels may have a detrimental effect on life satisfaction because it lowers the possibility that people will be in a healthy state (Hassan et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile the correlation between ambient air pollution and life satisfaction has been extensively studied, there is a relative dearth of research exploring these relationships through mediation mechanisms. Mediation analysis offers a nuanced approach to understanding the pathways through which independent variables can influence dependent variables indirectly through one or more intervening variables (Baron \u0026amp; Kenny, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). For instance, Luechinger (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found that the impacts of air pollution on well-being were mediated by sensory perceptions (Luechinger, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Further studies by Levinson (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Sanduijav et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also suggest that ambient air pollution significantly affects psychological well-being, which in turn influences overall life satisfaction (Levinson, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sanduijav et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, Smyth et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) explored the indirect effects of environmental factors on happiness, suggesting complex interactions between perceived air quality and mental health (Smyth et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis indirect pathway is particularly significant considering the high levels of pollution observed in many developing countries, where indoor air quality is a critical health issue. According to Tian et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), air pollution not only diminishes life satisfaction directly but also indirectly by exacerbating health issues that limit daily activities and overall quality of life (Tian et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The research by Hassan et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) supports this view, highlighting how energy poverty and the use of solid fuels can negatively impact life satisfaction through deteriorating health conditions (Hassan et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn India, few studies have explored the direct effects of indoor air pollution on health among older adults (Dakua et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the potential mediating role of health in these relationships remains largely unexamined. This gap in the existing literature is particularly alarming because, according to recent data, the percentage of households with older persons exposed to indoor pollution is on the rise, standing at roughly 14% (IIPS and ICF., 2021). Therefore, this study seeks to fill this gap by employing nationally representative data to model the self-reported life satisfaction of older adults as a function of their demographic characteristics, health conditions, and exposure to indoor air pollution.\u003c/p\u003e \u003cp\u003eThis study intends to add to the body of knowledge on the effects of the environment on health while also informing policy measures that have the potential to considerably progress the quality of life for older individuals in India through a thorough analysis of these links. We may make great progress toward accomplishing the health and well-being-related Sustainable Development Goals by enhancing indoor air quality.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source:\u003c/h2\u003e \u003cp\u003eData has been collected from the first wave of LASI, which was conducted during 2017-18. LASI is a study that includes all Indian adults, as well as older men and women aged 45 years and above, and their spouses residing in the same household, regardless of their age. LASI Wave 1 collected data from all 37 states and union territories using a specific method to select households. All individuals aged 45 years and above, as well as their spouses, were interviewed face-to-face within the selected households. This study includes information from 29,517 complete cases aged 60 years and above from all states. More details on sampling design can be seen in the official report of LASI wave 1(International Institute for Population Sciences (IIPS), 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOutcome Variable:\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLife Satisfaction\u003c/span\u003e: We constructed an additive composite scale based on the following questions asked by the respondent (Supplementary File 1).\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn most ways, my life is close to ideal\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe conditions of my life are excellent.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eI am satisfied with my life\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSo far, I have got the important things I want in life.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIf I could live my life again, I would change almost nothing.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIt ranges from 5\u0026ndash;35; the higher the score, the better the life satisfaction. The scale is found to be internally consistent as the standardized scale reliability coefficient is 0.90.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIndependent Variable:\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIndoor Pollution\u003c/span\u003e: A binary variable was constructed for indoor pollution based on its sources, i.e., type of unclean cooking fuels (kerosene, charcoal, crop residue, wood/shrub, dung cake, and other sources) and indoor smoking.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMediating Variable:\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHealth\u003c/span\u003e: We used self-reported health to measure the health conditions of older adults. As per the available literature (Sanduijav et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), indoor air pollution was found to be highly associated with the life satisfaction of older adults, and health might mediate this effect. Therefore, we used health as a mediator for this study, and we hypothesize that health mediates the association between indoor pollution and the life satisfaction of older adults.\u003c/p\u003e \u003cp\u003eThe respondents were asked, \u0026ldquo;Overall, how is your health in general? Would you say it is very good, good, fair, poor, or very poor?\u0026rdquo;. The choices were assigned values from 1 to 5 correspondently. Higher values signify better health conditions and vice-versa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eControl Variables\u003c/h2\u003e \u003cp\u003ePrevious studies have identified several socio-demographic attributes as potential confounders in the relationship between indoor pollution and the life satisfaction of older adults. Therefore, we included age (60\u0026ndash;69, 70\u0026ndash;79, and 80 and above), sex (male/female), place of residence (rural/urban), level of education (no schooling, less than 5 years, 5\u0026ndash;9 years and 10 \u0026amp; more years of schooling), religion (Hindu/non-Hindu), caste (Scheduled Castes-SC, Scheduled Tribes-ST, Other Backward Classes-OBC and General), marital status (married/unmarried), MPCE quintiles (poorest, poorer, middle, richer, richest), and two dichotomous variables, i.e., history of smoking and alcohol consumptions as control variables in our study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModel specification:\u003c/h2\u003e \u003cp\u003eIndoor pollution has a direct effect on the life satisfaction of residents, while health acts as a mediator between indoor pollution and life satisfaction for older individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To quantify the extent of this mediation, we conducted an analysis using three models. The first model establishes a regression model between the independent variable of indoor pollution and the dependent variable of life satisfaction. The second model establishes regression models between the independent variable of indoor pollution and the mediating variable of health. Finally, the third model establishes a regression model between all three variables: indoor pollution, health, and life satisfaction. Since life satisfaction and health are continuous variables, we estimated the impacts of indoor pollution on life satisfaction (Model 1) and health (Model 2) using a linear regression model, which is frequently employed in empirical research.\u003c/p\u003e \u003cp\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{M}\\text{o}\\text{d}\\text{e}\\text{l}1: Life Satisfactio{n}_{\\text{i}\\text{j}}={{\\alpha }}_{1}Indoor Pollutio{n}_{\\text{i}\\text{j}}+{{\\beta }}_{1}Contro{l}_{\\text{i}\\text{j}}+{{\\epsilon }}_{1}$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$Model2: Healt{h}_{\\text{i}\\text{j}}={{\\alpha }}_{2}Indoor Pollutio{n}_{\\text{i}\\text{j}}+{{\\beta }}_{2}Contro{l}_{\\text{i}\\text{j}}+{{\\epsilon }}_{2}$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equc\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$Model3: Life Satsfactio{n}_{\\text{i}\\text{j}}={{\\alpha }}_{3}Indoor Pollutio{n}_{\\text{i}\\text{j}}+{{\\gamma }}_{3}Healt{h}_{\\text{i}\\text{j}}+{{\\beta }}_{3}Contro{l}_{\\text{i}\\text{j}}+{{\\epsilon }}_{3}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere pollution is the primary explanatory variable, signifying the emission of air pollution in each household, health is the indicator of the health of the j resident of the I household, and life satisfaction is the dependent variable, representing the life satisfaction of the j resident of the household; The control vector, which was developed from the literature on factors influencing subjective life satisfaction, is made up of all the control variables. The Quintiles of monthly per capita expenditure (MPCE), marital status, education, religion, caste, age, gender, and history of alcohol and tobacco use are among the primary control variables. The regression model's error term is denoted by ε, whereas the equations' regression coefficients are α, β, and γ. The STATA 17 programme was used for all statistical analyses (StataCorp., 2021).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOur study evaluated the associations between indoor air pollution, health, and life satisfaction among older adults in India. The study sample consists of 29,517 older adults aged 60 years and above. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the descriptive statistics of the study variables. The mean life satisfaction score is 23.7 (SD\u0026thinsp;=\u0026thinsp;7.5), with a range from 5 to 35. About 57.6% of the respondents are exposed to indoor air pollution. The sample is nearly evenly split between males (48.2%) and females (51.8%), with a majority residing in rural areas (67.1%). Most respondents are aged 60\u0026ndash;69 years (60.9%), followed by 70\u0026ndash;79 years (28.8%), and 80\u0026thinsp;+\u0026thinsp;years (10.3%). Educational attainment is low, with 53.8% having no education, and only 15.0% having 10 or more years of schooling. The majority are Hindus (74.3%) and belong to Other Backward Classes (OBC) (39.5%). Economic status is evenly distributed across the quintiles of Monthly Per Capita Expenditure (MPCE). Regarding marital status, 63.9% are married. A significant portion of the sample has a history of smoking (38.9%) and alcohol consumption (17.5%). The mean self-reported health score is 3.3 (SD\u0026thinsp;=\u0026thinsp;0.7), on a scale from 1 to 5. The statistical analysis was performed using three distinct regression models to ascertain the direct and indirect associations of indoor pollution on life satisfaction, mediated by self-reported health status.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics (n\u0026thinsp;=\u0026thinsp;29,517)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean/Proportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS.D\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent \u003c/p\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLife Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-reported life conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16991 (57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"25\" rowspan=\"26\"\u003e \u003cp\u003eControl Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14240 (48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15277 (51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlace of Residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19792 (67.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9725 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17983 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8500 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3034 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15879 (53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLess than 5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3563 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026ndash;9 years completed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5646 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 years or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4429 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21922 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Hindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7595 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4957 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5084 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11657 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone of the above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7819 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPCE quantiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6119 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6075 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6029 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5803 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5491 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18870 (63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10647 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistory of Smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11490 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistory of Alcohol Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5178 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediating Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-Reported Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-reported health in general\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDirect Association Between Indoor Pollution and Life Satisfaction\u003c/p\u003e \u003cp\u003eThe first model in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e assessed the direct association of indoor air pollution on life satisfaction. The results indicated a significant negative association, with the pollution variable having a regression coefficient (β) of -0.55, 95% Confidence Interval (CI): -0.75 to -0.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. This suggests that exposure to indoor air pollution is associated with lower life satisfaction among older adults. The negative coefficient highlights the deleterious effects of poor air quality on well-being by showing that the life satisfaction score drops as the pollution level increases.\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\u003eImpact of pollution and health on life satisfaction of older adults of India.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c3\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLife Satisfaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSRH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLife Satisfaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent \u003c/p\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.75 - -0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.07 - -0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.66 - -0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"25\" rowspan=\"26\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u0026reg;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u0026ndash;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.10 - -0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.12\u0026ndash;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlace of Residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRural\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30\u0026ndash;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.01\u0026ndash;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.30\u0026ndash;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u0026ndash;69\u0026reg;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.10\u0026ndash;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.13 - -0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.10\u0026ndash;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u0026ndash;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.24 - -0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.34\u0026ndash;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo education\u0026reg;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLess than 5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u0026ndash;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03\u0026ndash;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.68\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026ndash;9 years completed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u0026ndash;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03\u0026ndash;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03\u0026ndash;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 years or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.30\u0026ndash;2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u0026ndash;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.16\u0026ndash;2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHindu\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Hindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.30\u0026ndash;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03\u0026ndash;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.28\u0026ndash;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSC\u0026reg;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u0026ndash;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u0026ndash;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.54\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u0026ndash;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.01\u0026ndash;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.19\u0026ndash;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone of the above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u0026ndash;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u0026ndash;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.64\u0026ndash;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPCE quantiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoorest\u0026reg;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u0026ndash;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.01\u0026ndash;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.36\u0026ndash;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u0026ndash;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.45\u0026ndash;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u0026ndash;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.43\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64\u0026ndash;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.02\u0026ndash;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.63\u0026ndash;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarried\u0026reg;\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.10 - -0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.07 - -0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.00 - -0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistory of Smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.64 - -0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.05 - -0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.59 - -0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistory of Alcohol \u003c/p\u003e \u003cp\u003eConsumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.52 - -0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.05 - -0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.47\u0026ndash;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediating Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-Reported Health \u003c/p\u003e \u003cp\u003e(SRH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-reported health \u003c/p\u003e \u003cp\u003ein general\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.53\u0026ndash;1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e29,517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e29,517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e29,517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssociation Between Indoor Pollution and Health\u003c/p\u003e \u003cp\u003eThe second model in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e explored how indoor pollution impacts the health of older adults. The coefficient for pollution in this model was \u0026minus;\u0026thinsp;0.05 (95% CI: -0.07 to -0.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a significant negative impact on health. This finding corroborates the hypothesis that indoor air pollution adversely affects health status, which in turn could influence overall life satisfaction.\u003c/p\u003e \u003cp\u003eMediation of Health in the Association Between Pollution and Life Satisfaction\u003c/p\u003e \u003cp\u003eIn the third model (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), we incorporated health as a mediating variable to understand its role in the relationship between indoor air pollution and life satisfaction. The analysis revealed that when accounting for health, the association strength between pollution and life satisfaction slightly lessened but remained significant, with a coefficient of -0.46 (95% CI: -0.66 to -0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This reduction from the first model (from \u0026minus;\u0026thinsp;0.55 to -0.46) underscores the mediating role of health, suggesting that a portion of the impact of pollution on life satisfaction operates through its effect on health.\u003c/p\u003e \u003cp\u003eMediation Analysis\u003c/p\u003e \u003cp\u003eTo quantify the extent of mediation by health, we employed the Sobel-Goodman Mediation Test and the finding can be found in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The test yielded a Z-value of -4.298 with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, confirming the significance of the mediation effect. The proportion of the total effect of pollution on life satisfaction that is mediated by health was calculated to be 12.9%. This indicates that approximately 13% of the effect of indoor pollution on life satisfaction is mediated through its impact on health. The ratio of indirect to direct effect was determined to be 0.148, further illustrating the substantial role health plays in this context.\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\u003eMediation test results\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=\"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\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSobel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonte-Carlo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePercent of total effect that is mediated: 12.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eRatio of indirect to direct effect: 0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese results collectively highlight the significant negative associations of indoor air pollution with both health and life satisfaction among older adults while also demonstrating the critical mediating role of health between these variables.\u003c/p\u003e \u003cp\u003eSeveral control variables were included in the regression models in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e to account for potential confounding factors. Female respondents reported higher life satisfaction compared to males, with a significant positive coefficient in Model 3 (β\u0026thinsp;=\u0026thinsp;0.330, 95% CI: 0.12 to 0.54, p\u0026thinsp;=\u0026thinsp;0.002). Urban residence is associated with higher life satisfaction compared to rural residence (β\u0026thinsp;=\u0026thinsp;0.501, 95% CI: 0.30 to 0.70, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher educational attainment is positively associated with life satisfaction, particularly for those with 10 years or more of schooling (β\u0026thinsp;=\u0026thinsp;2.452, 95% CI: 2.16 to 2.75, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Economic status, measured by MPCE quintiles, also shows a positive association with life satisfaction, with the richest quintile having the highest life satisfaction scores. Married individuals reported lower life satisfaction than their unmarried counterparts (β = -0.807, 95% CI: -1.00 to -0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Both a history of smoking and alcohol consumption are negatively associated with life satisfaction.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study offers vital insights into the intricate relationships that older persons in India have between indoor air pollution, health, and life happiness. The significant direct negative association between indoor air pollution and life satisfaction, coupled with the mediation effect of health, highlights the multifaceted impact of environmental factors on elderly well-being.\u003c/p\u003e \u003cp\u003eOur research found that the detrimental effects of indoor air pollution on life satisfaction are in line with general worldwide trends. For instance, studies across various countries have demonstrated similar detrimental effects of poor air quality on health and psychological well-being (Ezzati \u0026amp; Kammen, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Pope III, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The health burden from indoor air pollution is significant in areas like Sub-Saharan Africa and Southeast Asia, where biomass fuel use is common. These conditions frequently result in severe respiratory and cardiovascular disorders, which have a direct negative impact on life quality and longevity (Bonjour et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lim et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moreover, research from developed countries provides a contrast, showing that improvements in air quality can lead to significant enhancements in population health and well-being. For example, interventions in the United States and Europe that have focused on reducing air pollution levels have been associated with improved life expectancy and reduced disease incidence, which contribute to higher life satisfaction ratings among the affected populations (Smith \u0026amp; Sagar, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the context of our study, health significantly mediates the impact of indoor air pollution on life satisfaction among older adults. This mediation underscores the complexity of how physical health conditions, exacerbated by environmental pollutants, directly contribute to the subjective perception of quality of life. Health mediation suggests that the effects of indoor air pollution on life satisfaction are not completely direct but are filtered through the changes in health status that pollution induces. Particulate matter and hazardous compounds derived from biomass fuels are examples of pollutants that can cause respiratory issues, cardiovascular disorders, and long-term ailments, including bronchitis and asthma (Lim et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Smith \u0026amp; Sagar, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These health issues can severely limit daily activities, increase medical costs, and induce stress and anxiety, which cumulatively decrease life satisfaction.\u003c/p\u003e \u003cp\u003eThe linkage between indoor air pollution and socio-economic status is critical. In India, lower socio-economic groups are more likely to use traditional cooking fuels due to their lower cost and accessibility despite the higher health risks associated (Smith \u0026amp; Sagar, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This leads to a vicious cycle in which low health outcomes are both a cause and an effect of poverty, deepening inequalities and lowering life satisfaction in these areas (Hajat et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These socio-economic dimensions are crucial for understanding the barriers to adopting cleaner technologies and fuels. Cultural preferences, lack of awareness, and inadequate infrastructure also play significant roles in perpetuating reliance on traditional fuels, suggesting that policy interventions must be multifaceted and culturally sensitive to be effective (Dabadge et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings underscore the need for integrated public health policies that address both the environmental and social determinants of health. First, there is a clear need for policies that facilitate the transition to cleaner cooking fuels through subsidies, incentives, and infrastructure development. Programs like the Pradhan Mantri Ujjwala Yojana have made strides but must be expanded and combined with educational campaigns that emphasize the health benefits of reduced indoor air pollution (The Hindu, 2018). Second, healthcare interventions should focus on screening, early detection, and treatment of pollution-related health issues among older adults. Establishing community health programs that monitor symptoms and provide treatment at local levels can help mitigate the health impacts more effectively. Third, urban planning and housing regulations should incorporate guidelines to improve indoor air quality. This includes better ventilation systems, the use of non-toxic building materials, and green spaces in urban areas, which have been shown to improve overall air quality and enhance life satisfaction (Lim et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pr\u0026uuml;ss-\u0026Uuml;st\u0026uuml;n et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile our study provides insightful findings, it is not without limitations. The cross-sectional design restricts our ability to infer causality between indoor air pollution, health, and life satisfaction. A more conclusive knowledge of these correlations may be obtained through longitudinal investigations. Additionally, the reliance on self-reported measures for health and life satisfaction may introduce bias, highlighting the need for objective health indicators in future research.\u003c/p\u003e \u003cp\u003eFurther research should explore longitudinal data to establish causal relationships and assess the long-term impacts of air quality improvements on health and life satisfaction. Additionally, studying other potential mediators like mental health, social support, and access to health services could provide a deeper understanding of the pathways through which environmental conditions influence life satisfaction. Comparative studies across different cultural and economic settings would also be valuable in generalizing the findings and tailoring interventions accordingly (Hajat et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, by clarifying the mediating role of health in the association between indoor air pollution and life satisfaction among older individuals in India, this study makes a substantial contribution to the body of current literature. The results emphasize the necessity of all-encompassing approaches that address the social and environmental factors that influence the well-being of the elderly. By improving air quality and enhancing health outcomes, we can significantly boost life satisfaction among vulnerable populations, fostering broader public health benefits and contributing to sustainable development goals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eWe did not receive any grant from any funding agency in public, commercial or non-profit sections for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eData is publicly available at https://g2aging.org/lasi/download.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Consideration:\u0026nbsp;\u003c/strong\u003eNo ethical approval is required as this paper utilizes anonymized secondary sources of data.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAli, M. 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The environment and well-being in urban China. \u003cem\u003eEcological Economics\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(1\u0026ndash;2), 547\u0026ndash;555.\u003c/li\u003e\n\u003cli\u003eStataCorp. (2021). \u003cem\u003eStata Statistical Software: Release 17\u003c/em\u003e. TX: StataCorp LP.\u003c/li\u003e\n\u003cli\u003eThe Hindu. (2018, December 18). Data | How effective has the Pradhan Mantri Ujjwala Yojana been. \u003cem\u003eThe Hindu\u003c/em\u003e. https://www.thehindu.com/data/data-how-effective-has-the-pradhan-mantri-ujjwala-yojana-been/article30338388.ece\u003c/li\u003e\n\u003cli\u003eTian, X., Zhang, C., \u0026amp; Xu, B. (2022). The Impact of Air Pollution on Residents\u0026rsquo; Happiness: A Study on the Moderating Effect Based on Pollution Sensitivity. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(12), 7536. https://doi.org/10.3390/ijerph19127536\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary File 1","content":"\u003cp\u003eSupplementary File 1 is not available with this version\u003c/p\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":"Indoor air pollution, Household air pollution, Life satisfaction, Quality of life, Health, Older adults","lastPublishedDoi":"10.21203/rs.3.rs-4642687/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4642687/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research examines the link between indoor air pollution and overall contentment in life, considering health status as an intervening variable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt utilizes a mediation analysis approach, drawing on data from 1\u003csup\u003est\u003c/sup\u003e wave of the Longitudinal Ageing Study in India (LASI) carried out in 2017-18, involving a cohort of 29,517 individuals aged 60 and older. The analysis proceeds through three models: first, examining the direct association of indoor air pollution with life satisfaction; second, assessing the impact of pollution on health; and third, integrating both to evaluate the mediation effect.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFindings indicate a significant negative direct association of pollution with life satisfaction, with an association strength of -0.55(95%CI:-0.75 to -0.35, p \u0026lt; 0.001), and on health, with an association strength of -0.05(95%CI:-0.07 to -0.05, p \u0026lt; 0.001). Additionally, the mediation analysis, supported by the Sobel-Goodman Mediation Test (Z = -4.298, p \u0026lt; 0.001), reveals that health mediates 12.9% of the total impact of indoor pollution on life satisfaction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese results underscore the critical role of health as a mediator in this relationship. Interventions aimed at reducing indoor air pollution could significantly enhance the well-being of older adults by improving their health.\u003c/p\u003e","manuscriptTitle":"The mediating role of health status in the relationship between indoor air pollution and life satisfaction among older adults in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 20:05:28","doi":"10.21203/rs.3.rs-4642687/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-06T06:24:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-06T06:04:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-23T06:24:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60163484877920841976294220235399537626","date":"2024-08-20T13:39:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-18T03:28:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193862000826229934048560914650583400225","date":"2024-08-14T16:00:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35530897815545107771454287367180020395","date":"2024-08-14T13:12:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328766368389508648547017263088605482596","date":"2024-08-12T13:18:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-12T13:05:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-28T06:58:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-28T02:00:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-28T01:59:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-06-26T12:17:26+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":"f62d0015-becb-465a-8617-2337b9205ea5","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:01:24+00:00","versionOfRecord":{"articleIdentity":"rs-4642687","link":"https://doi.org/10.1186/s12889-025-24256-0","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-10-08 15:57:48","publishedOnDateReadable":"October 8th, 2025"},"versionCreatedAt":"2024-07-19 20:05:28","video":"","vorDoi":"10.1186/s12889-025-24256-0","vorDoiUrl":"https://doi.org/10.1186/s12889-025-24256-0","workflowStages":[]},"version":"v1","identity":"rs-4642687","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4642687","identity":"rs-4642687","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}