Association of frailty with indoor air pollution among older adults and elderly population as per gender and age group: insights from Longitudinal Aging Study in India (LASI-1^st Wave)

Abstract

Background

In recent years indoor air pollution (IAP) has emerged as a critical public health and environmental issue and to highlight the need for coordinated efforts to promote healthy aging and sustainable development, this study determined the association between frailty and IAP among male and female and among older adults (45–59 years) and elderly (≥ 60 years) population in India.

Methods

This study analysed the nationally representative dataset of the Longitudinal Ageing Study in India (LASI 2017–18, Wave-1). Bivariate analysis and logistic regression were conducted to show the association of frailty (outcome variable) with IAP (explanatory variable). Multivariable logistic regression was performed, adjusting for covariates as per three models. P value < 0.05 was considered statistically significant. STATA version 17 was used for analysis.

Results

The results revealed that males have shown a significant association with all the explanatory variables (fuel type, ‘pollution generating source’, ‘vulnerable ventilation’, ‘household indoor smoking’, and ’IAP’) as compared to females, except for the impact of poor ventilation. However, the study reported a higher prevalence of frailty among females. Several factors are significantly associated with frailty in individuals aged ≥ 60 years. Particularly use of unclean or solid fuels was linked to an 11% significant risk of frailty (Adjusted OR: 1.11, 95% CI: 1.03–1.19), while higher pollution-generating sources and exposure to IAP both contributed to an 11-12% higher risk of frailty (Adjusted OR: 1.11, 95% CI:1.03–1.19) and Adjusted OR:1.12,, 95% CI:1.05–1.19) respectively). Additionally, vulnerable ventilation remained a significant factor, with an 11% increased likelihood of frailty (Adjusted OR: 1.11, 95% CI: 1.03–1.20) in this older age group.

Conclusion

Hence, a holistic approach is required for reducing IAP. This can be done by improving household infrastructure, raising awareness about the existing government scheme, promoting early screening for frailty, and enforcing stronger policies. Treating existing government programs like ‘Pradhan Mantri Ujjwala Yojna (PMUY)’, ‘Unnat Jyoti by Affordable LEDs for All (UJALA)’, ‘Pradhan Mantri Sahaj Bijli Har Ghar Yojana - Saubhagya’, ‘Pradhan Mantri Awas Yojana – Gramin (PMAY-G)’, National Clean Air Program (NCAP), The National Tobacco Control Programme (NTCP) and the amended the ‘Cigarettes and other Tobacco Products Act (COTPA), 2020 etc. as base and by addressing the gaps in accessibility and affordability and focusing on vulnerable populations, India can create wholesome living environments. Fostering long-term behavioural changes and implementing targeted interventions will lead the way for improved health and well-being for everyone.

Text box 1. Contributions to the literature
• This study highlights a significant association between indoor air pollution (IAP) and frailty among males, offering a new perspective on gender-specific vulnerability in the Indian context.
• This analysis underscores the critical role of unclean fuel usage, household ventilation, and pollution sources, providing evidence for policy interventions focused on improving household infrastructure to reduce frailty risks.
• By linking existing government programs to frailty reduction, the study recommends holistic approaches, future-focused on accessibility, affordability and behavioural changes to create healthier environments, thus bridging gaps in current research.

Introduction

Population ageing is a global phenomenon. It has been estimated that 2.1 billion people aged 60 or older are expected to be affected by frailty within the next 20 years. This problem has put significant pressure on healthcare systems [1]. Ofori-Asenso et al. estimated that the global incidence of prefrailty and frailty was 150.6 and 43.4 new cases per 1000 person-years, respectively [2]. The global prevalence of frailty varies significantly, with estimates ranging from 4 to 59%, due to disparities in definition and measurement of frailty and population characteristics. The frailty prevalence in India varies considerably across different studies. A study by Nagarkar & Kulkarni, 2024 reported a frailty prevalence of 29.2% among individuals aged 60 and above whereas, Ghosh et al. 2023 have investigated the prevalence of frailty among middle-aged and older adults in India, revealed a frailty prevalence of 30% among adults aged 45 and older, their study has also revealed that the women of this age category are more likely to be frail as compared to their male counterparts [2, 3]. According to Debnath et al.‘s meta-analysis, the pooled prevalence of frailty and prefrailty was 42.3% (95% CI: 34.8−50.1%) and 39.8% (95% CI: 30.4−49.8%), respectively, in India [4]. These studies have indicated that factors such as age, gender, and socioeconomic status impact frailty prevalence and making it a complex health-related issue [5]. As per the widely accepted definition of Frailty, it is a clinical condition with reduced resistance to stressors. It is linked to negative health outcomes such as falls, fractures, disability, hospitalization, and early death. The state of frailty is dynamic. It ranges from healthy to frail. Preventing and diagnosing it early can reduce the disease burden on patients and healthcare systems [6]. Phenotypic frailty and the frailty index are often used in epidemiological investigations. However, a universally recognized standard for quantifying frailty has not been established.

India, one of the middle-income countries, has been ranked third in poor air quality among the 134 countries assessed in the recent World Air Quality Report [7]. Although the mortality rates due to household air pollution decreased in the 1990s. Still, 17.8% of total deaths in India are attributed to air pollution, mainly due to ambient particulate matter and household air pollution [8].

Recent studies have reported that indoor air pollution (IAP) is a significant risk factor for frailty among older adults [9]. The studies by Cao, 2022 identified coal and wood as significant risk factors associated with increased odds of phenotypic frailty and mild cognitive impairment (MCI) among older adults [10]. Furthermore, Shin and Choi, 2021 identified particulate matters (PM 2.5 and PM10) as a significant risk factor for frailty [11]. As per their findings each 1 g/m ³ increase in PM 2.5 and PM 10 concentrations is associated with higher odds of frailty. Small sample sizes or regional biases were important limitations of these studies. A similar study done using data from the Longitudinal Aging Study in India (LASI) found that older adults using solid cooking fuels faced a higher risk of poor self-reported health compared to those using cleaner fuels [12]. However, the study only explored the association of IAP with self-reported health among older adults, not considering various frailty components as taken in the present study. Further, the study has not provided specific prevalence rates of self-reported health (SRH) issues due to indoor air pollution (IAP) for males and females, different age groups (like those above 60 years) separately.

Thus, from the above literatures it is concluded that there is a considerable health burden due to IAP and the older population are vulnerable to frailty due to indoor air pollution. Therefore, coordinated efforts are needed to address IAP, as environmental challenge and a pressing public health issue to support healthy aging and sustainable development.

In view of this, we conducted the present study with the aim to explore how IAP affects frailty within the Indian population and understand how this association varies by gender and across different age groups, specifically among older adults (45–59 years) and the elderly (60 years and above) for the informed decision by the policy makers and gender and age specific public health interventions. To achieve the aim of our study we analysed data from the first wave of the Longitudinal Aging Study in India (LASI). This nationally representative survey provides a wealth of information about the psychological, social, economic, and health dimensions of aging in the country.

Methods

The current analysis used LASI-1st wave data from 35 Indian states and union territories (UTs), except for Sikkim. It is a longitudinal survey with a national representation that intends to collect detailed information on the psychological, social, economic, and health aspects of ageing in India. It was created to close the knowledge gap about comprehensive and globally comparable survey data on ageing in India. The National Institute on Ageing, the Ministry of Health and Family Welfare (GOI), and the United Nations Population Fund all provided funding for the study. The University of Southern California, the International Institute for Population Sciences, and the Harvard T.H. Chan School of Public Health are working together on it. The demography, health, economy, and social factors are just a few of the important topics it focuses on.

Sampling method

LASI covered all 35 Indian states and union territories between 2017 and 2019. Selecting primary sampling units (PSUs), secondary sampling units (SSUs), and households are the three steps in the sampling process in rural regions. In urban settings, choosing Census Enumeration Blocks (CEB) is a step that is added before moving forward. The probability proportional to size sampling method was used. Face-to-face interviews employing computer-assisted personal interviewing (CAPI) techniques are used to acquire the data. By reducing data entry mistakes, this technique guarantees high-quality data collection. This study evaluates the scientific evidence in the context of variables like demographics, household economic status, chronic health conditions, symptom-based health conditions, functional health, mental health (cognition and depression), biomarkers, healthcare utilisation, family and social networks, social welfare programmes, employment, retirement, satisfaction, and life expectations. The survey intends to follow a representative sample of the older adult population every two years for the following 25 years, with a revised sample size to account for attrition due to death, migration, non-reachable and non-response [13].

Sample size

There were 73,396 adult Indians surveyed in LASI. In this analysis, participants below 45 years were excluded. Because the LASI- first wave was conducted with the focus on participants aged 45 years and above. While selecting the primary participants (45 years and above), their spouses were also selected from the same household. This led to a disproportionate distribution of participants aged < 45 years because of nationally representative data. Thus, participants aged < 45 years were excluded (6790). After eliminating missing data by row-wise deletion and excluding outliers using the STATA command “bacon”, the final sample size was 55,572 in our study [14]. Simple implementation process and consistent data availability across variables are the strengths of complete case analysis using row row-wise deletion method. While it could lead to biased results if the missing data is not completely at random. It may also reduce the sample size, leading to information loss in cases of improper data collection and a lack of responses [15,16,17].

Outcome variable

The outcome variable of interest was frailty, which consisted of following five components- (1) self-reported exhaustion, (2) unintentional weight loss, (3) weak grip strength, (4) self-reported low physical activity, and (5) slow walking time. Thus, the range was 0 to 5. Participants were categorised as having ‘no frailty’ (score 0 to 2) and ‘frailty’ (score ≥ 3) [18] (Table 1).

Table 1 Components of frailty used in LASI/ longitudinal aging study in India from 2017 to 2018 [18]

Explanatory variables

Indoor air pollution (IAP)

Participants exposed to IAP were the explanatory variable of choice. IAP includes contamination of the air from physical, chemical, and biological sources. A distinct component of IAP was surveyed as part of the LASI study. Six questions from the LASI survey were used to calculate IAP. There were two questions concerning the fuel utilised for cooking and other purposes: (i) “What is your main source of cooking fuel?” and (ii) “What are those other sources of fuel used for other purposes (such as boiling water for bathing, lighting, etc.)?” (Responses: Liquefied Petroleum Gas (LPG), Biogas, Kerosene, Electric, Charcoal/Lignite/Coal, Crop residue, Wood/Shrub, Dung cake, do not cook at home, Other, please specify). ‘Fuel type’ was generated considering LPG, Biogas, and Electric methods as clean fuels and the rest as unclean or solid fuels. ‘Pollution generating source’ was generated from type of oven used: (iii) “In this household, is food MOSTLY cooked on a mechanical stove, on a traditional Chullah or over an open fire?” (Responses: Mechanical Stove/Improved cook stove, Traditional chullah, Open fire, Other, please specify). Traditional Chullah and open fire were taken as the higher pollution-generating sources. Next two questions were about place of cooking and ventilation: (iv) “Is the cooking usually done in the house, in a separate building, or outdoors?” (Responses: In the house, In a separate building, Outdoors, Other, please specify); (v) “Is the cooking mainly done under a traditional chimney, exhaust fan, electric chimney or near window/door?” (Responses: Traditional chimney, Electric chimney, Exhaust fan, Near window/door, None). No ventilation with in-house cooking was considered as vulnerable ventilation. Next question was on ‘Household Indoor Smoking’: (vi) “Does any usual member of your household smoke inside the home?” (Responses: Yes, No). Thus, all six factors were used to generate ‘IAP’: exposed (Participants using unclean/ solid fuel for cooking and others by utilizing traditional chullah or open fire and inhouse cooking without any ventilation system along with presence of indoor smoking.) and non-exposed participants. Thus ‘fuel type’, ‘pollution generating source’, ‘vulnerable ventilation’, ‘household indoor smoking’ and ’IAP’ were considered as explanatory variables [16].

Age (years 45–59, ≥ 60), gender (male, female), minimum education (illiterate, less than primary. primary completed, middle completed, secondary school, higher secondary, and Diploma/ graduate), residence (rural, urban), marital status (unmarried, married/ in live-in, Widow/ separated/ divorced), MPCE (monthly per capita expenditure- poorest, poorer, middle, richer, richest ) quintile, health insurance (no, yes), occupation (unemployed, professional and semi-professional- ‘legislators and senior officials, professionals, technicians and associate professionals’, clerical and skilled- ‘clerks, service workers and shopkeepers, skilled agriculture and fishery workers, craft and related trade worker, plant and machine operator’, unskilled), living arrangement (living alone, with spouse and/ or family, with spouse and children, with children and others, with others only), self-rated health (excellent, very good, good, fair, poor), tobacco abuse (no, yes) and alcohol abuse (no, yes) were taken as other explanatory variables [19, 20].

The present study has removed data of participants below the age of 45 years. We have included the participants for this study after adjusting missing data by row wise deletion and excluding BMI outliers. Details were provided in Fig. 1. Thus, this secondary study of LASI-first wave data included information from 55,572 participants.

Fig. 1

Flowchart showing the participants’ selection process used in LASI/ Longitudinal Aging Study in India from 2017 to 2018. BMI = Body Mass Index, LASI = Longitudinal Aging Study in India

Statistical analysis

Data analysis was conducted in Stata version 17 (Stata Corp, 2017. Stata Statistical Software: Release 17. College Station, TX: Stata Corp LP.). Baseline characteristics of the study participants were described as mean (standard deviation) for continuous variables and frequencies and percentages for categorical variables. Individual-level sample weights provided in the LASI dataset were considered during the analysis. Univariate logistic regression was conducted between the outcome variable and each explanatory variable. To avoid multicollinearity among explanatory variables, VIF (Variance Inflation Factor) was applied. VIF > 5 indicates a high correlation between a given explanatory variable and other explanatory variables in the model, which might create problems with the regression analysis. Variables with VIF > 5 (marital status, self-rated health, and living arrangement) were excluded from final association (supplementary table S1). P-values < 0.05 were considered as statistically significant. P-value < 0.2 was taken for further multiple logistic regression [15,16,17]. Association was calculated in overall population and was further categorised as per gender and age groups. We have documented classification accuracy and pseudo-R² to select the best fit model [21,22,23].

Table 2 Various background characteristics (both unweighted and weighted) of Indian population aged ≥ 45 years, 45–59 years and ≥ 60 years used in LASI/ Longitudinal Aging Study in India from 2017 to 2018

Results

The findings from the data analysis regarding the prevalence of frailty were broken down into the following categories:

Socio-demographic factors

The prevalence of frailty was 21.31% among the individuals with a lack of education, higher among illiterate females (22.44%) and the elderly (31.37%). In contrast, the individuals with higher education levels (graduates/diploma) showed a lower prevalence of frailty (7.15%), with females at 8.41% and the elderly at 14.16%. Frailty prevalence was found to be higher in rural areas (17.94%), particularly among rural females (20.11%) and the elderly (28.27%). The lowest MPCE quintile exhibited frailty prevalence at 18.22%, with females at 20.73% and the elderly at 28.85% (Table 2) (Fig. 2).

Fig. 2

Subgroup analysis in the association using multivariable logistic regression between- (used in LASI/ Longitudinal Aging Study in India from 2017 to 2018). Outcome variable: Frailty. Explanatory variable: A. Fuel type. B. Pollution generating source. C. Vulnerable ventilation. D. Household indoor smoking. E. Indoor air pollution. Covariates: Age-group, Gender, Minimum education, Residence, MPCE quintile, Occupation, Tobacco abuse, Alcohol abuse

Table 3 Distribution of Indian population (both unweighted and weighted) as per indoor air pollution and frailty aged ≥ 45 years, 45–59 years and ≥ 60 years used in LASI/ Longitudinal Aging Study in India from 2017 to 2018

Health insurance and employment status

The prevalence of frailty was observed to be higher among those without health insurance (17.39%), especially among women (19.80%) and the elderly (27.52%). Unemployed individuals showed a prevalence of 26.23%, with females (30.84%) and the elderly (34.45%) at higher risk (Table 2).

Behavioral factors

Tobacco and Alcohol consumption were taken as behavioural factors. Frailty prevalence among individuals with tobacco consumption was observed as 17.38%, with higher rates in females (22.90%) and, on the contrary, it was highly prevalent among the individuals above 60 years who did not consume tobacco (27.46%). Alcohol abstinence was linked to an overall frailty prevalence of 18.08%, with females at 17.71% and the elderly at 28.35% (Table 2).

Environmental factors

IAP and fuel use

The individuals using unclean/solid fuels exhibited the prevalence of frailty at 19.90%, higher among females (21.54%) and the elderly (30.84%). Whereas the individuals exposed to pollution-generating sources had a prevalence of frailty at 19.07%, with females at 20.86% and the elderly at 30.24% (Table 2).

Ventilation and smoking

The condition of poor ventilation, termed as ‘vulnerable ventilation’ had frailty prevalence of 20.04%, with females at 22.53% and the elderly at 30.50%. Household indoor smoking was associated with frailty in 17.22% of the population, with females at 19.30% and the elderly at 27.90%.

Association of risk factors with frailty

Overall population (Aged ≥ 45 Years)

In the overall population aged 45 years and above, almost all risk factors, except for household indoor smoking, showed significant associations with the risk of frailty. The unclean/solid fuel use was associated with 9% higher odds of frailty (Adjusted OR: 1.09, 95% CI: 1.03–1.15). Individuals exposed to pollution-generating sources had 6% higher odds of frailty (Adjusted OR: 1.06, 95% CI: 1.01–1.13). Vulnerable ventilation was linked to 14% higher odds of frailty (Adjusted OR: 1.14, 95% CI: 1.07–1.22). Individuals exposed to IAP had 10% higher odds of frailty (Adjusted OR: 1.10, 95% CI: 1.04–1.16), (Table 3).

Table 4 Univariate and multiple logistic regression of frailty and indoor air pollution among the Indian population as per gender and age-group used in LASI/ Longitudinal Aging Study in India from 2017 to 2018

Gender-specific findings

Males using unclean/solid fuel showed 17% higher odds of frailty in comparison to those using clean fuel (Adjusted OR: 1.17, 95% CI: 1.07–1.28). Males exposed to higher pollution-generating sources had 11% higher odds of frailty (Adjusted OR: 1.11, 95% CI: 1.01–1.22). Males with vulnerable ventilation had significantly 18% higher odds of frailty compared to those with no vulnerable ventilation (Adjusted OR:1.18, 95% CI:1.06–1.30). Males exposed to indoor smoking had significantly 10% higher odds of frailty (Adjusted OR: 1.10, 95% CI: 1.01–1.20). furthermore, Males exposed to IAP had 18% significantly higher odds of frailty compared to those unexposed (Adjusted OR: 1.18, 95% CI: 1.08–1.28).

Among females, no significant associations were observed with the frailty for unclean/soiled fuel use, pollution generating sources, household indoor smoking and IAP. However, vulnerable ventilation showed 12% higher odds of frailty (Adjusted OR: 1.12, 95% CI: 1.02–1.21), Table 3.

Age-specific findings

Similarly, on analysing the association of risk factors with frailty across different age sub- groups (i.e. 45–59 years and ≥ 60 years), it was found that for the individual aged between 45 and 59 years vulnerable ventilation was the only significant risk factor, with 22% higher odds of frailty (Adjusted OR: 1.22, 95% CI: 1.09–1.37), compared to those with adequate ventilation.

Whereas several factors showed a significant association with frailty in individuals aged ≥ 60 years. Particularly use of unclean or solid fuels was linked to an 11% significant risk of frailty (Adjusted OR: 1.11, 95% CI: 1.03–1.19), while higher pollution-generating sources and exposure to IAP both contributed to an 11-12% higher risk of frailty (Adjusted OR: 1.11, 95% CI: (1.03–1.19) and Adjusted OR:1.12,, 95% CI:1.05–1.19) respectively). Additionally, vulnerable ventilation remained a significant factor, with an 11% increased likelihood of frailty (Adjusted OR: 1.11, 95% CI: 1.03–1.20) in this older age group Table 4.

These findings highlighted the importance of addressing various socio-economic, behavioural, environmental and household conditions to mitigate the risk of frailty, particularly among older adults. The interpretation and mechanism of the findings have been explained in the discussion section.

Discussion

Main findings

This detailed study revealed the significant associations between IAP (IAP), socio-demographic factors, and frailty among individuals aged 45 years and older in India. The findings spotlighted a higher prevalence of frailty among illiterate, poor, rural, undeserved females and elderly individuals (> 60 years) without health insurance. the findings of the present study resonate with the results of other studies [8, 10,11,12,13] where educational level, wealth, urban and rural regions were used as socioeconomic indicators to assess the socioeconomic disparities in frailty among older adults in six low- and middle-income countries. The findings accentuate the urgent need to implement robust socioeconomic projects to mitigate the negative health effects caused by poverty, low levels of education, higher exposure to IAP in rural households, and not having health insurance. These initiatives require not only improved literacy rate, awareness, and building better rural healthcare infrastructure but also high living standards and limited household IAP exposures in rural areas. In addition to this, increasing health insurance coverage, especially for underprivileged populations, may be a key tactic in lowering frailty.

Further, the analysis sheds light on the fact that the various indicators of IAP and socio-demographic factors largely contribute to the risk of frailty. Specifically, the use of unclean or solid fuels, exposure to higher pollution-generating sources, poor ventilation within homes, and IAP exposure. These associations remained significant across various statistical models of the current study, emphasizing the robustness of the findings. Interestingly, gender-specific analysis indicated that these associations were significant in males but not in females, except for the impact of poor ventilation.

Interpretation of findings

The above findings aligned with the existing literature that emphasized the detrimental health effects of various indicators of indoor air pollution and socio-demographic factors, particularly in the context of frailty. The use of unclean or solid fuels is prevalent in many low- and middle-income countries, where these fuels are often burned in inefficient stoves that emit high levels of particulate matter, carbon monoxide, and volatile organic compounds, which are known to exacerbate respiratory and cardiovascular conditions [14]. Chronic exposure to these pollutants can lead to systemic inflammation and oxidative stress, both of which are key pathways leading to the development of frailty [14, 15]. The strong association between poor ventilation and frailty highlights the crucial role of adequate air exchange in mitigating the accumulation of indoor pollutants. Poorly ventilated homes trap harmful substances, increasing the risk of respiratory illnesses and exacerbating conditions that contribute to frailty [24].

The gender-specific findings, where significant associations were predominantly observed in males, might be explained by differing exposure patterns and comorbidities. Males might experience higher levels of exposure to environmental hazards due to occupational activities or different lifestyle factors. Additionally, physiological differences between males and females, such as how toxins are metabolized and how inflammation is regulated, might influence the observed associations [25]. In contrast, the higher overall prevalence of frailty in females, despite fewer significant associations with environmental factors, suggests that other socio-economic, lifestyle, health-seeking behaviour, and hormonal changes in females during post-menopause may be at play, causing frailty. For instance, females often have less social life and longer lifespans and may experience cumulative effects of frailty-related risk factors over time. The significant impact of poor ventilation on frailty among females indicated that interventions to improve air quality in homes could be particularly beneficial for this group [Gordon et al., 2017]. Thus, the findings of the study emphasized on the importance of tailoring frailty assessments and interventions based on gender to improve health outcomes in older adults.

Age-specific analysis reveals that older adults (≥ 60 years) are more susceptible to the detrimental effects of environmental exposures, with significant associations for unclean fuel use, high pollution exposure, and IAP. This heightened vulnerability in older adults may be due to age-related declines in physiological resilience and an increased burden of chronic diseases, making them more prone to the adverse effects of environmental stressors.

Mechanism and pathways

Several mechanisms may explain the observed associations between environmental factors and frailty. First, the combustion of solid fuels produces fine particulate matter (PM 2.5 and PM 10, etc.) and other pollutants that contribute to chronic systemic inflammation. This inflammation can damage cells and tissues, impair muscle function, and reduce several physical capabilities, leading to increased frailty [26, 27]. Studies have shown significant associations between inflammatory markers (e.g., IL-6, cystatin C) and frailty in older populations, suggesting that environmental exposures may exacerbate these inflammatory processes [28]. Second, poor ventilation exacerbates the accumulation of indoor pollutants, creating a harmful indoor environment that can further impair respiratory and cardiovascular health [29, 30, 31].

Existing government initiatives

To tackle the IAP and its health impacts, the Indian government has launched several programs. Over 10.33 crore LPG connections have been provided under the Pradhan Mantri Ujjwala Yojana (PMUY) (2016), aiming to reduce reliance on solid fuels, while the Unnat Chulha Abhiyan (2014) promotes cooking on clean cookstoves [32]. The Saubhagya Scheme (2017) expanded household electrification and helped in cutting down the soot particle emissions from kerosene lamp usage [33]. Additionally, the UJALA program (2015) distributed affordable LED bulbs, tube lights, and fans to lower the indoor air pollution from other oil-burning sources of lights. The Pradhan Mantri Awas Yojana – Gramin (PMAY-G) launched in 2016, improved housing with better ventilation and the National Clean Air Programme (NCAP) (2019) addresses air quality issues. The National Tobacco Control Programme (NTCP) and the amended COTPA Act (2020) target to prevent indoor smoking [34]. However, challenges remain in affordability, behavioural adoption, awareness, and enforcement with these efforts. Therefore, policymakers should focus on subsidized LPG refills, integrating indoor air quality under NCAP, including outdoor/ambient air quality under national NCD targets, and improving rural ventilation with community-driven awareness campaigns and real-time monitoring for sustained impact [35].

Limitations

Certain limitations have been observed in the present study, for instance, being observational study design, it cannot establish direct cause-and-effect relationships between IAP, socio-demographic factors, and frailty. Furthermore, the data are self-reported, like the type of household fuel usage, which might bring some inaccuracies. Other important factors like diet, genetics, and access to healthcare can also affect frailty. These variables have not been considered in this study. Moreover, the findings may not fully capture the diversity of regional differences across India. We were not able to eliminate recall bias and social desirability bias. Application of the row-wise deletion method to eliminate missing data led to potential bias due to non-random distribution of missing data, which could not be eliminated here. Along with these limitations, the study still renders a useful understanding and serves as a sound base for future research.

Recommendations

The outcome of the study throws light on the immediate requirement to combat the issue of IAP due to its strong association to frailty and overall health of older adults and elderly population. However, programs like PMUY and PMAY-G, and UJALA have made a positive impact, but more needs to be done. For the successful adoption of LPG cylinder under PMUY scheme government should implement income-based refill subsidies—Rs. 500 for Antyodaya households, Rs. 300 for BPL mid-tier, Rs. 100 for others. There should be the provision of rural refill network by establishing micro-distribution points in every panchayat, managed by local entrepreneurs or Self-Help Groups (SHGs). Under the PMAY-G scheme, windows or chimneys should be made mandatory in all PMAY-G kitchens, rural masons should be trained in airflow design. In addition to it every PMAY-G house should be handed over with solar panel installation under the PM Surya Ghar Scheme for electricity from clean source of energy, free LPG cylinder under PMUY for clean cooking and later streamlined the continuous delivery of LPG cylinder at subsidised rate and LED bulbs, tubelights and fan for proper lighting and cooling under UJALA scheme. The Ayushman Bharat (PM-JAY) scheme of GOI primarily focuses on hospitalization and critical care rather than preventive health, regular screenings or environmental health risks. To make it more effective, the scheme should integrate routine frailty assessments at primary health centres, collaborate with programs like PMUY, PMAY-G, and UJALA for cleaner household environments.

Furthermore, the standards of indoor air quality should also be included in the National Clean Air Programme (NCAP) and better ventilation designs for rural homes can further reduce harmful exposures. Furthermore, the campaign for public awareness, especially by community leaders and health care workers, can encourage healthier cooking habits. Additionally, making affordable air quality sensors available for real-time monitoring, along with stricter enforcement of anti-smoking laws, will help improve public health outcomes.

Conclusion

Thus, to reduce indoor air pollution (IAP), India must prioritise and upgrade household infrastructure with mandatory ventilation, clean cooking, solar panels and lighting through schemes like PMAY, PMUY, PM Surya Ghar Yojana, UJALA, and NCAP. Grassroots campaigns should raise awareness, while rural health centres must screen for frailty early. Policies enforcing clean indoor air standards are crucial, especially for women, children and low-income families. Gram panchayats and women-led outreach can improve access, while long-term incentives for LPG and electric cooking will ensure lasting change. These actions can turn policies into measurable health benefits.