Association of Anaemia with Indoor Air Pollution Among Older Indian Adult Population: Multilevel Modelling Analysis of Nationally Representative Cross-Sectional Study

Association of Anaemia with Indoor Air Pollution Among Older Indian Adult Population: Multilevel Modelling Analysis of Nationally Representative Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of Anaemia with Indoor Air Pollution Among Older Indian Adult Population: Multilevel Modelling Analysis of Nationally Representative Cross-Sectional Study Pritam Halder, Madhur Verma, Saumyarup Pal, Amit Kumar Mishra, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4167764/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Introduction- Anaemia is a disease of public health importance with multi-causal pathways. Previous literature suggests the role of indoor air pollution (IAP) on haemoglobin levels, but this has been studied less due to logistic constraints. A high proportion of the population in developing countries, including India, still depends on unclean fuel, which exacerbates IAP. The objective was to study the association between anaemia and IAP among the older Indian adult population ( > 45 years) as per gender. Methods- Our study analysed the nationally representative dataset of the Longitudinal Ageing Study in India (LASI 2017–18, Wave-1). Bivariate analysis and logistic regression were used to depict the association of anaemia (outcome variable) with IAP (explanatory variable). Multivariable logistic regression was conducted by adjusting for covariates as per their models. P value male (47.60%)). The adjusted likelihood of having anaemia was 19% higher (aOR 1.19; 1.09-1.31) among participants exposed to unclean/solid fuel. The adjusted odds were significantly higher among participants exposed to pollution-generating sources (aOR 1.30; 1.18-1.43), and household indoor smoking (aOR 1.17 (1.07-1.29. The adjusted odds of having anaemia were significantly higher (aOR 1.27; 1.16-1.39) among participants exposed to IAP, which was higher in males (aOR 1.36; 1.15-1.61) than females (aOR 1.21; 1.09-1.35). Conclusion- This study established the positive association of anaemia with indoor air pollution among older Indian adults through a nationally representative large dataset. The association was higher among men. Further research is recommended to understand detailed causation and to establish temporality. It is a high time to implement positive intervention nationally to decrease solid/ unclean fuel usage, vulnerable ventilation, indoor smoking, IAP and health hazards associated with these. Anaemia Air Pollution Indoor Air Pollution Modelling LASI Figures Figure 1 Figure 2 Introduction Anaemia is essentially a homeostatic imbalance in the hemoglobin concentration (<12 g/dL in women and 13 g/dL in men) whereby the production of erythrocytes is outpaced by destruction or loss of erythrocytes. It leads to poor health, economic loss and social burden [1]. It is the result of a wide variety of causes that can be isolated, but often they coexist. Globally, the most common cause of nutrient deficiency anaemia is due to iron deficiency, although other conditions, such as folate, vitamin B12 and vitamin A deficiencies, chronic inflammation, parasitic infections, and inherited disorders can cause anaemia [2]. Prevalence of anaemia is higher in developing countries, south Asian countries contributing 37.5% of global anaemia [3]. Poverty, inadequate diet, diseases, pregnancy/lactation and poor access to health services are some of the key factors contributing to the high burden [4]. Globally prevalence of anaemia was 12-17% among older adults ( > 65 years); 40% hospitalised and 47% patients in nursing home had anaemia [5]. Given that the world's population is getting older due to demographic shifts, the overall burden of disease related to anaemia among the elderly is probably only going to increase [6]. The emergence and advancement of anaemia may be influenced by common environmental exposures, such as air pollution. Numerous extremely common chronic diseases, such as respiratory, mental, and cardiovascular conditions, are recognised to be at risk due to ambient air pollution [7]. In developing nations, ambient air pollution has had a severe negative influence on public health [8]. Industrialisation in these areas, propelled by economic expansion, has resulted in significant rises in air pollution levels, frequently an order of magnitude more than those found in industrialised nations. The latter has exacerbated bad health outcomes and increased the risk to human life [9]. There are few studies on the relationship between air pollution and anaemia, and the majority of the earlier studies concentrated on the population of children or short-term exposures [10,11,12]. There were very few study showing among older adults ( > 45 years) [13,14]. Hence, we have conducted this study to show the association of anaemia with indoor air pollution among older Indian adults ( > 45 years). Objective- To determine the association with anaemia with Indoor air pollution- among older Indian population ( > 45 years). among male and female older Indian population ( > 45 years). Methodology Data source: LASI-1 st wave 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 from all the states and union territories. It was developed to fill the information vacuum regarding thorough and internationally comparable survey data on India's ageing population. The study, which is the biggest of its kind in the world and the first of its kind in India, 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 [15]. The funding agencies were National Institute on Ageing, the Government of India's Ministry of Health and Family Welfare, and the United Nations Population Fund. The University of Southern California, the International Institute for Population Sciences, and the Harvard T.H. Chan School of Public Health were the contributors. Study Population: Over 73,000 adult Indians were surveyed. Out of them, 24862 participants were included for the present study. Details of study flow and sample selection with missing data handling (row wise complete deletion) were documented in Figure 1. Study Variables Outcome variable- The outcome variable of choice was anaemia. Self-reported anaemia prevalence was obtained by questioning ‘In the past 2 years, have you had anaemia?’ Answering ‘yes’ was considered as anaemia present. Explanatory variables- Participants exposed to indoor air pollution (IAP) was the explanatory variable of choice. IAP includes contamination of the air from physical, chemical, and biological sources. A distinct component on 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 opened fire was taken as the higher pollution generating source. 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 ‘Indoor Air Pollution’: exposed (Participants using unclean/ solid fuel for cooking and others by utilising 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 ’indoor air pollution’ were considered as explanatory variables. Covariates- Age group (45-64, > 65 years), 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), physical activity (everyday, once per week, 1-3 times per week, once per month, never), self-rated health (excellent, very good, good, fair, poor,), tobacco abuse (no, yes), alcohol abuse (no, yes) and multimorbidity were taken as other explanatory variables. Following chronic morbidities were included- hypertension, diabetes, cancer, chronic lung diseases (e.g.- chronic obstructive pulmonary disease, asthma, chronic bronchitis, other chronic lung problems), chronic heart disease (e.g.- congestive heart failure, myocardial infarction, heart attack, other chronic heart diseases), stroke, musculoskeletal disorder (MSD e.g.- rheumatism, arthritis, osteoporosis, other chronic joint or bone disorders), dyslipidaemia (high cholesterol), thyroid disorders, Chronic renal failure, visual impairment and hearing impairment. Interviewer asked related question about chronic health conditions/ morbidities with dichotomous answers (no/ yes)- “Has any health professional ever diagnosed you with the following chronic conditions or diseases?” Participants having at least two chronic health conditions were described as multimorbidity. Statistical analysis- Data was analysed in Stata version 17 (StataCorp. 2017. Stata Statistical Software: Release 17. College Station, TX: StataCorp LP.). The characteristics of the participants were described as mean (standard deviation) for continuous variable frequencies and percentages for categorical variables. Individual sample weights were considered during the analysis. A univariate logistic regression was conducted between the outcome variable and each explanatory variable. Variables with P-value <0.2 were included to build a final model using multivariable logistic regression after assessing the multicollinearity among explanatory variables using the VIF (Variance inflation factor), and variables > 5 indicate a high correlation and were omitted. (Self-related health and marital status had VIF>5. (Supplementary Table S1)) Hence, all the explanatory variables except these two were included in the final association. P-value <0.05 were considered as statistically significant. Ethical statement- Being a secondary analysis of a dataset freely available in the public domain, ethical approval for the present study was not deemed necessary. However, the ethical approval to conduct LASI was given by the Indian Council of Medical Research's (ICMR) Central Ethics Committee on Human Research (CECHR) [15]. Results The mean (SD) age of the participants were 59.71 (10.66) years. Around 47.18% of participants were illiterate, which was higher in females (68.98%). Around 65.25% of participants resided in the rural area. Almost two-thirds of the participants were married. Only 2.22% of participants had health insurance. More than half of the participants (51.16%) were unemployed, which was higher among females (71.43%). Almost one-fourth (23.61%) of the participants exercised every day. Multimorbidity was 36.19 overall, higher among females (56.44%). Around 36.19% and 17.76% of participants had a history of tobacco abuse and alcohol abuse, respectively, which was more in men in both cases. (Table 1) Unclean/ solid fuel usage was 34.91% overall. Higher vulnerable ventilation was seen among 16.48% participants. Around 46.86% and 25.09% participants were exposed to pollution generating source and household indoor smoking, respectively. Almost half (50.38%) of the participants were exposed to the indoor air pollution. All the above were higher among females. (Table 2) Self-reported anaemia was present among 2537 (3.89%) participants. Anaemia was more prevalent among female (5.02%) than male (2.58%) participants. The adjusted odds of having anaemia was 19% significantly higher (AOR 1.19 (1.09-1.31)) among participants using unclean/ solid fuel than clean fuel; which was higher among male (AOR 1.41 (1.20-1.67)). The adjusted odds of having anaemia was significantly higher (AOR 1.30 (1.18-1.43)) among participants exposed to pollution generating source, which was higher in male (1.58 (1.33-1.88)). The adjusted odds was significantly higher (AOR 1.17 (1.07-1.29)) among participants exposed to household indoor smoking, which was higher in female (1.21 (1.08-1.35)). The adjusted odds of having anaemia was significantly higher (AOR 1.27 (1.16-1.39)) among participants exposed to indoor air pollution, which was higher in male (1.36 (1.15-1.61)) than female (1.21 (1.09-1.35)). (Table 3, 4) Females had 1.87 (1.68-2.09) time higher adjusted odds of having anaemia. With increase in the educational status, the odds of having anaemia decreased. Participants residing in urban area had 32% lower adjusted odds of having anaemia. The adjusted odds of having anaemia was highest (AOR 1.37 (1.19-1.57)) among poorest participants. Participants having multimorbidity (AOR 1.86 (1.71-2.03)) and history of tobacco abuse (AOR 1.23 (1.12-1.35)) had significantly higher odds of having anaemia. (Figure 2) Table 1: Socio-demographic characteristics of the adults aged > 45 years included in the Longitudinal Aging Study in India (2017-18) Variable Overall Male Female p-value N (row %) N (row %) N (row %) Total participants 65295 (100) 30452 (46.64%) 34843 (53.36%) Age (years) a 59.71 (10.66) 60.07 (10.67) 59.39 (10.64) - Age group (years) b 45-59 34095 (52.22) 15438 (45.28) 18657 (54.72) 60 31200 (47.78) 15014 (48.12) 16186 (51.88) Education b (minimum) Illiterate 30,807 (47.18) 9,556 (31.02) 21,251 (68.98) <0.001 Less than primary 7,431 (11.38) 4,051 (54.51) 3,380 (45.49) Primary completed 8,564 (13.12) 4,697 (54.85) 3,867 (45.15) Middle completed 6,230 (9.54) 3,864 (62.02) 2,366 (37.98) Secondary school 5,796 (8.88) 3,781 (65.23) 2,015 (34.77) Higher secondary 2,795 (4.28) 1,907 (68.23) 888 (31.77) Diploma/ Graduate 3,672 (5.62) 2,596 (70.70) 1,076 (29.30) Residence b Rural 42605 (65.25) 20030 (47.01) 22575 (52.99) 0.008 Urban 22690 (34.75) 10422 (45.93) 12268 (54.07) Marital Status b Unmarried 851 (1.30) 479 (56.29) 372 (43.71) <0.001 Married/ in live -in 48992 (75.03) 26860 (54.83) 22132 (45.17) Widow/ separated/ divorced 15452 (23.66) 3113 (20.15) 12339 (79.85) MPCE quintile b Poorest 12,705 (19.46) 6,878 (45.86) 12,705 (54.14) 0.147 Poorer 13,169 (20.17) 7,071 (46.31) 13,169 (53.69) Middle 13,147 (20.13) 7,017 (46.63) 13,147 (53.37) Richer 13,189 (20.20) 6,961 (47.22) 13,189 (52.78) Richest 13,085 (20.04) 6,916 (47.15) 13,085 (52.85) Health insurance b No 63844 (97.78) 29268 (45.84) 34576 (54.16) <0.001 Yes 1451 (2.22) 1184 (81.60) 267 (18.40) Occupation b Unemployed 33403 (51.16) 9,544 (28.57) 23,859 (71.43) <0.001 Professional and semi-professional 1554 (2.38) 1,167 (75.10) 387 (24.90) Clerical and skilled 16417 (25.14) 10,852 (66.10) 5,565 (33.90) Unskilled 13921 (21.32) 8,889 (63.85) 5,032 (36.15) Physical activity b Everyday 15,413 (23.61) 9,420 (61.12) 5,993 (38.88) <0.001 More than once / week 4,511 (6.91) 2,571 (56.99) 1,940 (43.01) Once / week 2,367 (3.63) 1,278 (53.99) 1,089 (46.01) 1-3 times /month 3,227 (4.94) 1,618 (50.14) 1,609 (49.86) Never 39,777 (60.92) 15,565 (39.13) 24,212 (60.87) Self-rated health b Excellent 2,512 (3.90) 1,459 (58.08) 1,053 (41.92) <0.001 Very good 12,332 (19.14) 6,424 (52.09) 5,908 (47.92) Good 25,260 (39.21) 11,776 (46.62) 13,484 (53.38) Fair 17,795 (27.63) 7,647 (42.97) 10,148 (57.03) Poor 6,517 (10.12) 2,722 (41.77) 3,795 (58.23) Multimorbidity b No 40843 (62.55) 19800 (48.48) 21043 (51.52) <0.001 Yes 24542 (37.45) 10652 (43.56) 13800 (56.44) Tobacco abuse b No 41664 (63.81) 13759 (33.02) 27905 (66.98) <0.001 Yes 23631 (36.19) 16693 (70.64) 6938 (29.36) Alcohol abuse b No 53696 (82.24) 20275 (37.76) 33421 (62.24) <0.001 Yes 11599 (17.76) 10177 (87.74) 1422 (12.26) *a= mean (SD), b= N (%) Table 2: Distribution of Indian population as per indoor air pollution Characteristics Overall N= 65295 Male N= 30452 Female N= 34843 N % N % N % Fuel type Clean 42502 65.09 19937 46.91 22565 53.09 Unclean/ Soild 22793 34.91 10515 46.13 12278 53.87 Vulnerable ventilation Lower 54535 83.52 25421 46.6 29114 53.39 Higher 10760 16.48 5031 46.76 5729 53.24 Pollution generating source No 34700 53.14 16199 46.68 18501 53.32 Yes 30595 46.86 14253 46.59 16342 53.41 Household* Indoor Smoking No 48912 74.91 22086 45.15 26826 54.85 Yes 16383 25.09 8366 51.07 8017 48.93 Indoor Air Pollution* No 32397 49.62 14794 45.66 17603 54.34 Yes 32898 50.38 15658 47.60 17240 52.40 *p-value<0.05= significant Anemia and IAP need more decription in results. The two variables do deserve one-one dedicated table Table 3: Univariate and multivariable logistic regression of anaemia with indoor air pollution among Indian population Characteristics Anemic Univariate Multivariable Crude odds ratio (95% Confidence interval) Adjusted odds ratio (95% Confidence interval) Model-1 Adjusted odds ratio (95% Confidence interval) Model-2 Adjusted odds ratio (95% Confidence interval) Model-3 Overall ( > 45 years) a Fuel type Clean Reference Reference Reference Reference Unclean/ Soild 1.27 (1.17-1.38)* 1.13 (1.03-1.24)* 1.21 (1.10-1.33)* 1.19 (1.09-1.31)* Vulnerable ventilation Lower Reference Reference Reference Reference Higher 1.03 (0.93-1.15) 0.99 (0.86-1.10) 1.01 (0.90-1.12) 1.01 (0.94-1.12) Pollution generating source No Reference Reference Reference Reference Yes 1.39 (1.28-1.51)* 1.24 (1.13-1.37)* 1.33 (1.21-1.46)* 1.30 (1.18-1.43)* Household Indoor Smoking No Reference Reference Reference Reference Yes 1.23 (1.12-1.34)* 1.21 (1.10-1.32)* 1.23 (1.12-1.34)* 1.17 (1.07-1.29)* Indoor Air Pollution Unexposed Reference Reference Reference Reference Exposed 1.34 (1.24-1.45)* 1.24 (1.13-1.35)* 1.31 (1.19-1.43)* 1.27 (1.16-1.39*) Model 1-Adjusted for age group, gender, education, residence, mpce quintile, health insurance and occupation. Model 2- Model 1+ physical activity and multimorbidity. Model 3- Model 2+ tobacco and alcohol abuse. * p-value<0.05= significant Table 4: Multivariable logistic regression of anaemia with indoor air pollution among Indian population as per gender Characteristics Anemic Univariate Multivariable Crude odds ratio (95% Confidence interval) Adjusted odds ratio (95% Confidence interval) Model-1 Adjusted odds ratio (95% Confidence interval) Model-2 Adjusted odds ratio (95% Confidence interval) Model-3 Male Fuel type Clean Reference Reference Reference Reference Unclean/ Soild 1.65 (1.43-1.90)* 1.37 (1.16-1.60)* 1.47 (1.25-1.73)* 1.41 (1.20-1.67)* Vulnerable ventilation Lower Reference Reference Reference Reference Higher 1.08 (0.89-1.29) 0.97 (0.80-1.17) 0.99 ((0.82-1.20) 0.99 (0.82-1.19) Pollution generating source No Reference Reference Reference Reference Yes 1.84 (1.59-2.13)* 1.57 (1.32-1.86)* 1.66 (1.40-1.97)* 1.58 (1.33-1.88)* Household Indoor Smoking No Reference Reference Reference Reference Yes 1.28 (1.10-1.49)* 1.17 (1.01-1.37)* 1.21 (1.03-1.41)* 1.01 (0.85-1.19) Indoor Air Pollution Unexposed Reference Reference Reference Reference Exposed 1.71 (1.47-1.98)* 1.44 (1.22-1.69)* 1.52 (1.29-1.80)* 1.36 (1.15-1.61)* Female Fuel type Clean Reference Reference Reference Reference Unclean/ Soild 1.11 (1.01-1.23)* 1.03 (0.92-1.15) 1.10 (0.98-1.23) 1.09 (0.98-1.23) Vulnerable ventilation Lower Reference Reference Reference Reference Higher 1.01 (0.89-1.15) 0.99 (0.87-1.13) 1.01 (0.89-1.15) 1.01 (0.89-1.16) Pollution generating source No Reference Reference Reference Reference Yes 1.23 (1.12-1.36)* 1.12 (1.01-1.25)* 1.20 (1.07-1.35)* 1.20 (1.07-1.34)* Household Indoor Smoking No Reference Reference Reference Reference Yes 1.28 (1.15-1.42)* 1.22 (1.9-1.36)* 1.23 (1.10-1.37*) 1.21 (1.08-1.35)* Indoor Air Pollution Unexposed Reference Reference Reference Reference Exposed 1.23 (1.12-1.36)* 1.16 (1.04-1.29)* 1.22 (1.09-1.36)* 1.21 (1.09-1.35)* Model 1-Adjusted for age group, education, residence, mpce quintile, health insurance and occupation. Model 2- Model 1+ physical activity and multimorbidity. Model 3- Model 2+ tobacco and alcohol abuse. * p-value<0.05= significant Discussion This study is one of the very few study to show the association of anaemia with indoor air pollution among older Indian adult population with a significantly large nationally representative dataset. There are several key findings of the study. First, the adjusted likelihood of having anaemia was 19% significantly higher (AOR 1.19 (1.09-1.31)) among participants using unclean/ solid fuel than clean fuel. The adjusted odds of having anaemia was significantly higher among participants exposed to pollution generating source (AOR 1.30 (1.18-1.43)), household indoor smoking (AOR 1.17 (1.07-1.29. The adjusted odds of having anaemia was significantly higher (AOR 1.27 (1.16-1.39)) among participants exposed to indoor air pollution. Similar results were describes by studies conducted by Kelly et al. and Elbarbary et al. [13,14]. The potential cause might be due to prolonged exposure to indoor pollution which might cause oxidative stress, inflammation, decrease absorption of iron and changes in blood parameters, such as haemoglobin levels. Pollutants present in indoor air pollution might cause hemolysis. Breathing in tiny particles (PM2.5) and gaseous pollutants can make your body more inflamed and affect how your bone marrow works [16,17], especially if you already have conditions like diabetes or obesity. Most studies have looked at how these pollutants affect inflammation in the short term [18,19,20], but recent research suggests they could keep causing inflammation over a long time [21,22,23]. This could lead to a chain reaction in your body: it might make your body produce less of a hormone called erythropoietin, make your blood cells less responsive to this hormone, and increase the levels of a protein that controls iron in your body [24,25]. All of these things combined could mean your body makes fewer red blood cells, leading to anemia, especially in older people. Study by Mehta et al. revealed that increase in each 10 μg/m³ PM 2.5 exposure, the average prevalence of anaemia increased by 1.90% (1.43- 2.36) and the average hemoglobin concentration decreased by 0.07 gm/dL (0.05-0.09) in ecological analysis at district level. Individual level analysis produced that the odds of having anaemia was 1.09 (95% CI 1.06, 1.11) time higher with increase in each 10 μg/m³ PM 2.5 exposure in ambient air [26]. With increase in the educational status, the odds of having anaemia decreased. This might be due to with increase in the education status, the participants became more aware about there health status, which leads to early detection, prevention and treatment of anaemia. Participants residing in urban area had 32% lower adjusted odds of having anaemia. Factors which might contribute to this were higher education, awareness, improved nutrition, better maternal education and early access to healthcare infrastructure. The adjusted odds of having anaemia was highest (AOR 1.37 (1.19-1.57)) among poorest participants. This might be due to lesser awareness, education, prevention, access to healthcare inability to pay the out-of-pocket expenditure due to treatment. Participants having multimorbidity (AOR 1.86 (1.71-2.03)) and history of tobacco abuse (AOR 1.23 (1.12-1.35)) had significantly higher odds of having anaemia. This might be due to shared risk factors (inflammation. Poor nutrition), interaction with iron metabolism, challenging and stigma is accessing healthcare services. Similar results were documented by various studies [27,28]. There are certain Strengths and limitations of the study. The biggest strength lies the large nationally representative dataset which increased its generalisability. We have established the positive association between anaemia and indoor air pollution among older adults which was noble of its kind. This study not only showed the prevalence of exposure of indoor air pollution throughout the lifetime with stratified details but also unveil the curtain from the association between anaemia and indoor air pollution which was further stratified into detailed classification as per gender. Despite these there were some limitations. Due to its cross-sectional nature temporality could not be established. We were not able to calculate the degree of exposure to pollutants. We were not able to eliminate the effect of external air pollution. Due to self-reporting type of documentation of data, the actual prevalence of anaemia might be higher. Due to the self-reporting style, there were higher probability of recall bias and social desirability bias, which could not be eliminated. There are key policy implications and recommendations emerging from the study. Owing to the limited access to clean fuel and compulsion to use biomass owing to financial hardship in the rural areas, IAP is becoming a serious public health hazard in the nation. Nonetheless, the Indian government has made a number of efforts to improve it, including 'Pradhan Mantri Ujjwala Yojana' (PMUY) [29] and the National Programme on Improved Chula (NPIC) [30]. Due to the pandemic's devastating effects and decimation of rural people's income and way of life, studies must be done in order to adequately examine and reconsider the price of LPG and subsidies for the poor. More attention should be paid to educating the public about the negative impacts of using unclean biomass energy sources, indoor air pollution, and the necessity of a functional kitchen ventilation system, among other things. In unavoidable circumstances, substitutes such enhanced chullha powered by biomass with chimneys and cookstoves can be recommended, as indicated by a previous study, which may result in reduced indoor air pollution [31]. We recommend proper clinical or community-based trial to establish the temporality, causation and natural history of association of anaemia with indoor air pollution among older adults. Conclusion This study establishes convincing statistical evidence regarding positive association of anaemia with indoor air pollution among older Indian adults through a nationally representative large dataset. Interestingly, the association appears to be more pronounced among men. These findings might raise awareness and assist individuals in avoiding the negative effects of using solid/unclean fuels, inadequate ventilation, and indoor smoking. Implementing measures such as upgrading residential stoves with chimneys, providing access to clean cooking fuel, and enhancing home ventilation systems can significantly reduce exposure to indoor air pollution. The study highlights the urgency of initiating programs aimed at improving the accessibility and availability of clean fuel and technologies. By doing so, the nation can make strides toward achieving Sustainable Development Goal 7: Ensuring universal access to affordable, reliable, and modern energy services. Further evidence-based research is recommended to understand detailed causation and to establish temporality. Abbreviations LASI- Longitudinal Aging Study in India, PM 2.5- Particulate matter 2.5, LPG- Liquefied Petroleum Gas, MPCE- Monthly per capita expenditure, IAP- Indoor air pollution, AOR- Adjusted odds ratio. Declarations Ethics approval and consent to participate- Being a secondary analysis of a dataset freely available in the public domain, ethical approval for the present study was not deemed necessary. However, the ethical approval to conduct LASI was given bythe Indian Council of Medical Research's (ICMR) Central Ethics Committee on Human Research (CECHR) [15]. Consent for publication- Not applicable. Availability of data and materials- The study utilizes nationally representative LASI survey data, which is publicly accessible and can be obtained by registering at https://iipsindia.ac.in/sites/default/files/LASI_DataRequestForm_0.pdf. The processed data can be provided by the authors upon request by Dr. Pritam Halder (corresponding author). Competing interests - Authors have no conflict of interest. Funding - This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors contribution- Dr. Pritam Halder- Conceptualization, Methodology, Resources, Data Curation, Writing- Review and editing, Visualization, Supervision. , Dr. Madhur Verma- Resources, Data Curation, Writing- Review and editing, Visualization, Supervision, Dr. Saumyarup Pal- Conceptualization, Methodology, Resources, Data Curation, Writing- Review and editing, Dr. Amit Kumar Mishra- Resources, Data Curation, Writing- Review and editing, Visualization, Supervision, Dr. Trideep Jyoti Deori- Resources, Data Curation, Writing- Review and editing, Visualization, Supervision , Dr. Riya Biswas- Resources, Data Curation, Writing- Review and editing, Visualization, Supervision , Dr. Jaya Tiwari- Conceptualization, Methodology, Resources, Data Curation, Writing- Review and editing , Dr. Anshul Mamgai- Resources, Data Curation, Writing- Review and editing, Visualization, Supervision , Shivani Rathor- Conceptualization, Methodology, Resources, Data Curation, Writing- Review and editing, Dr. Manish Chandra Prabhakar- Conceptualization, Methodology, Resources, Data Curation, Writing- Review and editing. Acknowledgement- We want to convey our sincere gratitude towards the participants and Indian Council of Medical Research References Government of India. Anemia Mukt Bharat Training Tool Kit. New, Delhi. 2018. https://anemiamuktbharat.info/wp-content/uploads/2019/11/English _Anemia-MuktBharat-Training-Modules Folder_Lowress.pdf [Internet]. [cited 2024 Feb 10]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697587/. Bagla P, Bagla P. World Health Organization. Anaemia Policy Brief [Internet]. 2014 http://thousanddays.org/tdayscontent/uploads/Anameia-Policy-Brief.pdf . Stevens GA, Finucane MM, De-Regil LM, Paciorek CJ, Flaxman SR, Branca F, et al. Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995–2011: a systematic analysis of population-representative data. Lancet Glob Health. 2013;1(1):e16–25. 9789241564960_eng.pdf [Internet]. [cited 2024 Feb 10]. https://iris.who.int/bitstream/handle/10665/177094/9789241564960_eng.pdf?sequence=1 . Gaskell H, Derry S, Andrew Moore R, McQuay HJ. Prevalence of anaemia in older persons: systematic review. BMC Geriatr. 2008;8(1):1. Vanasse GJ, Berliner N, the American Society of Hematology American Society of Hematology Education Program. Anemia in elderly patients: An emerging problem for the 21st century. Hematology/the Education Program of. Hematology 2010, 2010, 271–275. [Internet]. [cited 2024 Feb 10]. https://ashpublications.org/hematology/article/2010/1/271/95939/Anemia-in-Elderly-Patients-An-Emerging-Problem-for . Cohen AJ, Ross Anderson H, Ostro B, Pandey KD, Krzyzanowski M, Künzli N, et al. The global burden of disease due to outdoor air pollution. J Toxicol Environ Health A. 2005;68(13–14):1301–7. Mannucci PM, Franchini M. Health Effects of Ambient Air Pollution in Developing Countries. Int J Environ Res Public Health. 2017;14(9):1048. Huang YCT. Outdoor air pollution: A global perspective. J. Occup. Environ. Med. 2014, 56, S3–S7. [Internet]. [cited 2024 Feb 10]. https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health . Mittal H, Roberts L, Fuller GW, O’Driscoll S, Dick MC, Height SE, et al. The effects of air quality on haematological and clinical parameters in children with sickle cell anaemia. Ann Hematol. 2009;88(6):529–33. Hong CH, Falvey C, Harris TB, Simonsick EM, Satterfield S, Ferrucci L, et al. Anemia and risk of dementia in older adults: findings from the Health ABC study. Neurology. 2013;81(6):528–33. Honda T, Pun VC, Manjourides J, Suh H. Anemia prevalence and hemoglobin levels are associated with long-term exposure to air pollution in an older population. Environ. Int. 2017, 101, 125–132. [Internet]. [cited 2024 Feb 10]. https://pubmed.ncbi.nlm.nih.gov/28153527/ . Kelly FJ, Fussell JC. Air pollution and public health: emerging hazards and improved understanding of risk. Environ Geochem Health. 2015;37(4):631–49. Elbarbary M, Honda T, Morgan G, Guo Y, Guo Y, Kowal P, et al. Ambient Air Pollution Exposure Association with Anaemia Prevalence and Haemoglobin Levels in Chinese Older Adults. IJERPH. 2020;17(9):3209. International Institute for Population Sciences (IIPS) NP for, Health Care of Elderly (NPHCE), MoHFW HTHCS of, (USC) PH (HSPH) and the U of SC. Longitudinal Ageing Study in India (LASI) wave 1, 2017–18, India report. 2020. https://www.iipsindia.ac.in/sites/default/files/LASI_India_Report_2020_compressed.pdf . [Internet]. [cited 2023 Oct 4]. https://www.iipsindia.ac.in/lasi [Internet]. [cited 2023 Oct 4]. https://www.iipsindia.ac.in/lasi. Dubowsky SD, Suh H, Schwartz J, Coull BA, Gold DR. (2006). Diabetes, obesity, and hypertension may enhance associations between air pollution and markers of systemic inflammation. Environmental health perspectives, 114(7), 992–998. [Internet]. [cited 2024 Mar 26]. https://pubmed.ncbi.nlm.nih.gov/16835049/ . Marchini T, Zirlik A, Wolf D. Pathogenic Role of Air Pollution Particulate Matter in Cardiometabolic Disease: Evidence from Mice and Humans. Antioxid Redox Signal. 2020;33(4):263–79. Gao N, Xu W, Ji J, Yang Y, Wang ST, Wang J, et al. Lung function and systemic inflammation associated with short-term air pollution exposure in chronic obstructive pulmonary disease patients in Beijing, China. Environ Health. 2020;19(1):12. Dauchet L, Hulo S, Cherot-Kornobis N, Matran R, Amouyel P, Edmé JL, Giovannelli J. (2018). Short-term exposure to air pollution: associations with lung function and inflammatory markers in non-smoking, healthy adults. Environment international, 121, 610–619. [Internet]. [cited 2024 Mar 26]. https://pubmed.ncbi.nlm.nih.gov/30312964/ . Watanabe M, Noma H, Kurai J, Sano H, Hantan D, Ueki M, et al. Effects of Short-Term Exposure to Particulate Air Pollutants on the Inflammatory Response and Respiratory Symptoms: A Panel Study in Schoolchildren from Rural Areas of Japan. Int J Environ Res Public Health. 2016;13(10):983. Adami G, Pontalti M, Cattani G, Rossini M, Viapiana O, Orsolini G, Fassio A. (2022). Association between long-term exposure to air pollution and immune-mediated diseases: a population-based cohort study. RMD open, 8(1), e002055. [Internet]. [cited 2024 Mar 26]. https://pubmed.ncbi.nlm.nih.gov/35292563/ . Viehmann A, Hertel S, Fuks K, Eisele L, Moebus S, Möhlenkamp S, et al. Long-term residential exposure to urban air pollution, and repeated measures of systemic blood markers of inflammation and coagulation. Occup Environ Med. 2015;72(9):656–63. Xu Z, Wang W, Liu Q, Li Z, Lei L, Ren L, Wu S. (2022). Association between gaseous air pollutants and biomarkers of systemic inflammation: a systematic review and meta-analysis. Environmental Pollution, 292, 118336. [Internet]. [cited 2024 Mar 26]. https://pubmed.ncbi.nlm.nih.gov/34634403/ . Marques O, Weiss G, Muckenthaler MU. (2022). The role of iron in chronic inflammatory diseases: from mechanisms to treatment options in anemia of inflammation. Blood, The Journal of the American Society of Hematology, 140(19), 2011–2023. [Internet]. [cited 2024 Mar 26]. https://pubmed.ncbi.nlm.nih.gov/35994752/ . Abiri B, Vafa M. (2020). Iron deficiency and anemia in cancer patients: the role of iron treatment in anemic cancer patients. Nutrition and cancer, 72(5), 864–872. [Internet]. [cited 2024 Mar 26]. https://pubmed.ncbi.nlm.nih.gov/31474155/ . Mehta U, Dey S, Chowdhury S, Ghosh S, Hart JE, Kurpad A. The Association Between Ambient PM2.5 Exposure and Anemia Outcomes Among Children Under Five Years of Age in India. Environ Epidemiol. 2021;5(1):e125. Al-kassab-Córdova A, Mendez-Guerra C, Quevedo-Ramirez A, Espinoza R, Enriquez-Vera D, Robles-Valcarcel P. Rural and urban disparities in anemia among Peruvian children aged 6–59 months: a multivariate decomposition and spatial analysis. Rural and Remote Health. 2022; 22: 6936. https://doi.org/10.22605/RRH6936 [Internet]. [cited 2024 Feb 10]. https://www.rrh.org.au/journal/article/6936 . Abate TW, Getahun B, Birhan MM, Aknaw GM, Belay SA, Demeke D, et al. The urban–rural differential in the association between household wealth index and anemia among women in reproductive age in Ethiopia, 2016. BMC Women’s Health. 2021;21(1):311. Ranjan R, Singh S. Household Cooking Fuel Patterns in Rural India: Pre- and Post-Pradhan Mantri Ujjwala Yojana. Indian J Hum Dev. 2020;14(3):518–26. Hanbar RD, Karve P. National Programme on Improved Chulha (NPIC) of the Government of India: an overview. Energy Sustain Dev. 2002;6(2):49–55. Adane MM, Alene GD, Mereta ST. Biomass-fuelled improved cookstove intervention to prevent household air pollution in Northwest Ethiopia: a cluster randomized controlled trial. Environ Health Prev Med. 2021;26(1):1. Additional Declarations No competing interests reported. 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07:20:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4167764/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4167764/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54040466,"identity":"1d56c1f3-fac5-48f1-b38d-25a5c67ba6e7","added_by":"auto","created_at":"2024-04-03 17:35:02","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":260715,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart showing participants selection process in this study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4167764/v1/35e32e8af22c7afe726fc542.jpeg"},{"id":54040464,"identity":"fb9b86a3-71e0-4cba-b3f7-47c88f358f74","added_by":"auto","created_at":"2024-04-03 17:35:02","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":975280,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariable logistic regression of anemia with indoor air pollution and various determinants among Indian population (Model 3)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4167764/v1/8f722592aa642de15a865803.jpeg"},{"id":54040827,"identity":"03d1cd1e-c5ed-4493-93ad-302f424b2312","added_by":"auto","created_at":"2024-04-03 17:43:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":911278,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4167764/v1/8fca0152-b42c-48ba-b9ac-5e307dd94d3a.pdf"},{"id":54040465,"identity":"d725a15e-6d07-49d6-83bc-27592af0e13b","added_by":"auto","created_at":"2024-04-03 17:35:02","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":21943,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryLASIIAPAnemia.docx","url":"https://assets-eu.researchsquare.com/files/rs-4167764/v1/9113e83fca8563a8a8d63f39.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Anaemia with Indoor Air Pollution Among Older Indian Adult Population: Multilevel Modelling Analysis of Nationally Representative Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnaemia is essentially a homeostatic imbalance in the hemoglobin concentration (\u0026lt;12 g/dL in women and 13 g/dL in men) whereby the production of erythrocytes is outpaced by destruction or loss of erythrocytes. It leads to poor health, economic loss and social burden\u0026nbsp;[1]. It is the result of a wide variety of causes that can be isolated, but often they coexist. Globally, the most common cause of nutrient deficiency anaemia is due to iron deficiency, although other conditions, such as folate, vitamin B12 and vitamin A deficiencies, chronic inflammation, parasitic infections, and inherited disorders can cause anaemia\u0026nbsp;[2].\u003c/p\u003e\n\u003cp\u003ePrevalence of anaemia is higher in developing countries, south Asian countries contributing 37.5% of global anaemia\u0026nbsp;[3]. Poverty, inadequate diet, diseases, pregnancy/lactation and poor access to health services are some of the key factors contributing to the high burden\u0026nbsp;[4]. Globally prevalence of anaemia was 12-17% among older adults (\u003cu\u003e\u0026gt;\u003c/u\u003e65 years); 40% hospitalised and 47% patients in nursing home had anaemia\u0026nbsp;[5]. Given that the world\u0026apos;s population is getting older due to demographic shifts, the overall burden of disease related to anaemia among the elderly is probably only going to increase\u0026nbsp;[6].\u003c/p\u003e\n\u003cp\u003eThe emergence and advancement of anaemia may be influenced by common environmental exposures, such as air pollution. Numerous extremely common chronic diseases, such as respiratory, mental, and cardiovascular conditions, are recognised to be at risk due to ambient air pollution\u0026nbsp;[7]. In developing nations, ambient air pollution has had a severe negative influence on public health\u0026nbsp;[8]. Industrialisation in these areas, propelled by economic expansion, has resulted in significant rises in air pollution levels, frequently an order of magnitude more than those found in industrialised nations. The latter has exacerbated bad health outcomes and increased the risk to human life\u0026nbsp;[9]. There are few studies on the relationship between air pollution and anaemia, and the majority of the earlier studies concentrated on the population of children or short-term exposures\u0026nbsp;[10,11,12]. There were very few study showing among older adults (\u003cu\u003e\u0026gt;\u003c/u\u003e45 years)\u0026nbsp;[13,14]. Hence, we have conducted this study to show the association of anaemia with indoor air pollution among older Indian adults (\u003cu\u003e\u0026gt;\u003c/u\u003e45 years).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective-\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the association with anaemia with Indoor air pollution-\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eamong older Indian population (\u003cu\u003e\u0026gt;\u003c/u\u003e45 years).\u003c/li\u003e\n \u003cli\u003eamong male and female older Indian population (\u003cu\u003e\u0026gt;\u003c/u\u003e45 years).\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Methodology","content":"\u003cp\u003eData source: LASI-1\u003csup\u003est\u003c/sup\u003e wave 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 from all the states and union territories. It was developed to fill the information vacuum regarding thorough and internationally comparable survey data on India\u0026apos;s ageing population. The study, which is the biggest of its kind in the world and the first of its kind in India, 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\u0026nbsp;[15]. The funding agencies were National Institute on Ageing, the Government of India\u0026apos;s Ministry of Health and Family Welfare, and the United Nations Population Fund. The University of Southern California, the International Institute for Population Sciences, and the Harvard T.H. Chan School of Public Health were the contributors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudy Population: Over 73,000 adult Indians were surveyed. Out of them,\u0026nbsp;24862\u0026nbsp;participants were included for the present study. Details of study flow and sample selection with missing data handling (row wise complete deletion) were documented in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Variables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome variable- The\u003c/strong\u003e outcome variable of choice was anaemia. Self-reported anaemia prevalence was obtained by questioning \u0026lsquo;In the past 2 years, have you had anaemia?\u0026rsquo; Answering \u0026lsquo;yes\u0026rsquo; was considered as anaemia present.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExplanatory variables-\u0026nbsp;\u003c/strong\u003eParticipants exposed to indoor air pollution (IAP) was the explanatory variable of choice. IAP includes contamination of the air from physical, chemical, and biological sources. A distinct component on 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\u0026nbsp;other purposes:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(i)\u0026rdquo; What is your main source of cooking fuel?\u0026rdquo; and\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(ii) \u0026ldquo;What are those other sources of fuel used for other purposes (such as boiling water for bathing, lighting, etc.)?\u0026rdquo; (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). \u0026lsquo;Fuel type\u0026rsquo; was generated considering LPG, Biogas, and Electric methods as clean fuels and the rest as unclean or solid fuels. \u0026lsquo;Pollution generating source\u0026rsquo; was generated from type of oven used: (iii) \u0026ldquo;In this household, is food mostly cooked on a mechanical stove, on a traditional Chullah or over an open fire?\u0026rdquo; (Responses: Mechanical Stove/Improved cook stove, Traditional chullah, Open fire, Other, please specify). Traditional Chullah and opened fire was taken as the higher pollution generating source. Next two questions were about place of cooking and ventilation: (iv) \u0026ldquo;Is the cooking usually done in the house, in a separate building, or outdoors?\u0026rdquo; (Responses: In the house, In a separate building, Outdoors, Other, please specify); (v) \u0026ldquo;Is the cooking mainly done under a traditional chimney, exhaust fan, electric chimney or near window/door?\u0026rdquo; (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 \u0026lsquo;Household Indoor Smoking\u0026rsquo;: (vi) \u0026ldquo;Does any usual member of your household smoke inside the home?\u0026rdquo;(Responses: Yes, No). Thus, all six factors were used to generate \u0026lsquo;Indoor Air Pollution\u0026rsquo;: exposed (Participants using unclean/ solid fuel for cooking and others by utilising traditional chullah or open fire and inhouse cooking without any ventilation system along with presence of indoor smoking.) and non-exposed participants. Thus \u0026lsquo;fuel type\u0026rsquo;, \u0026lsquo;pollution generating source\u0026rsquo;, \u0026lsquo;vulnerable ventilation\u0026rsquo;, \u0026lsquo;household indoor smoking\u0026rsquo; and \u0026rsquo;indoor air pollution\u0026rsquo; were considered as explanatory variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates-\u0026nbsp;\u003c/strong\u003eAge group (45-64, \u003cu\u003e\u0026gt;\u003c/u\u003e65 years), 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,\u0026nbsp;Widow/ separated/ divorced), MPCE (monthly per capita expenditure- poorest, poorer, middle, richer, richest) quintile, health insurance (no, yes), occupation (unemployed, professional and semi-professional- \u0026lsquo;legislators and senior officials, professionals, technicians and associate professionals\u0026rsquo;, clerical and skilled- \u0026lsquo;clerks, service workers and shopkeepers, skilled agriculture and fishery workers, craft and related trade worker, plant and machine operator\u0026rsquo;, unskilled), physical activity (everyday, once per week, 1-3 times per week, once per month, never), self-rated health (excellent, very good, good, fair, \u0026nbsp;poor,), tobacco abuse (no, yes), alcohol abuse (no, yes) and multimorbidity were taken as other explanatory variables. Following chronic morbidities were included- hypertension, diabetes, cancer, chronic lung diseases (e.g.- chronic obstructive pulmonary disease, asthma, chronic bronchitis, other chronic lung problems), chronic heart disease (e.g.- congestive heart failure, myocardial infarction, heart attack, other chronic heart diseases), stroke, musculoskeletal disorder (MSD e.g.- rheumatism, arthritis, osteoporosis, other chronic joint or bone disorders), dyslipidaemia (high cholesterol), thyroid disorders, Chronic renal failure, visual impairment and hearing impairment. Interviewer asked related question about chronic health conditions/ morbidities with dichotomous answers (no/ yes)- \u0026ldquo;Has any health professional ever diagnosed you with the following chronic conditions or diseases?\u0026rdquo; Participants having at least two chronic health conditions were described as multimorbidity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis-\u003c/strong\u003e Data was analysed in Stata version 17 (StataCorp. 2017. Stata Statistical Software: Release 17. College Station, TX: StataCorp LP.). The characteristics of the participants were described as mean (standard deviation) for continuous variable frequencies and percentages for categorical variables. Individual sample weights were considered during the analysis. A univariate logistic regression was conducted between the outcome variable and each explanatory variable. Variables with P-value \u0026lt;0.2 were included to build a final model using multivariable logistic regression after assessing the multicollinearity among explanatory variables using the VIF (Variance inflation factor), and variables \u0026gt; 5 indicate a high correlation and were\u0026nbsp;omitted. (Self-related health and marital status had VIF\u0026gt;5.\u003cstrong\u003e\u0026nbsp;(Supplementary Table S1))\u003c/strong\u003e Hence, all the explanatory variables except these two were included in the final association.\u0026nbsp;P-value \u0026lt;0.05 were considered as statistically significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement-\u003c/strong\u003e Being a secondary analysis of a dataset freely available in the public domain, ethical approval for the present study was not deemed necessary. However, the ethical approval to conduct LASI was given by\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ethe Indian Council of Medical Research\u0026apos;s (ICMR) Central Ethics Committee on Human Research (CECHR) [15].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean (SD) age of the participants were 59.71 (10.66) years. Around 47.18% of participants were illiterate, which was higher in females (68.98%). Around 65.25% of participants resided in the rural area. Almost two-thirds of the participants were married. Only 2.22% of participants had health insurance. More than half of the participants (51.16%) were unemployed, which was higher among females (71.43%). Almost one-fourth (23.61%) of the participants exercised every day. Multimorbidity was 36.19 overall, higher among females (56.44%). Around 36.19% and 17.76% of participants had a history of tobacco abuse and alcohol abuse, respectively, which was more in men in both cases. (Table 1)\u003c/p\u003e\n\u003cp\u003eUnclean/ solid fuel usage was 34.91% overall. Higher vulnerable ventilation was seen among 16.48% participants. Around 46.86% and 25.09% participants were exposed to pollution generating source and household indoor smoking, respectively. Almost half (50.38%) of the participants were exposed to the indoor air pollution. All the above were higher among females. (Table 2)\u003c/p\u003e\n\u003cp\u003eSelf-reported anaemia was present among 2537 (3.89%) participants. Anaemia was more prevalent among female (5.02%) than male (2.58%) participants. The adjusted odds of having anaemia was 19% significantly higher (AOR 1.19 (1.09-1.31)) among participants using unclean/ solid fuel than clean fuel; which was higher among male (AOR 1.41 (1.20-1.67)). The adjusted odds of having anaemia was significantly higher (AOR 1.30 (1.18-1.43)) among participants exposed to pollution generating source, which was higher in male (1.58 (1.33-1.88)). The adjusted odds was significantly higher (AOR 1.17 (1.07-1.29)) among participants exposed to household indoor smoking, which was higher in female (1.21 (1.08-1.35)). The adjusted odds of having anaemia was significantly higher (AOR 1.27 (1.16-1.39)) among participants exposed to indoor air pollution, which was higher in male (1.36 (1.15-1.61)) than female (1.21 (1.09-1.35)). (Table 3, 4)\u003c/p\u003e\n\u003cp\u003eFemales had 1.87 (1.68-2.09) time higher adjusted odds of having anaemia. With increase in the educational status, the odds of having anaemia decreased. Participants residing in urban area had 32% lower adjusted odds of having anaemia. The adjusted odds of having anaemia was highest (AOR 1.37 (1.19-1.57)) among poorest participants. Participants having multimorbidity (AOR 1.86 (1.71-2.03)) and history of tobacco abuse (AOR 1.23 (1.12-1.35)) had significantly higher odds of having anaemia. (Figure 2)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Socio-demographic characteristics of the adults aged \u003cu\u003e\u0026gt;\u003c/u\u003e45 years included in the Longitudinal Aging Study in India (2017-18)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (row %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (row %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (row %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"top\"\u003e\n \u003cp\u003eTotal participants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"bottom\"\u003e\n \u003cp\u003e65295 (100)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e30452 \u0026nbsp;(46.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"top\"\u003e\n \u003cp\u003e34843 (53.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003csup\u003ea\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"bottom\"\u003e\n \u003cp\u003e59.71 (10.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e60.07 (10.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"top\"\u003e\n \u003cp\u003e59.39 (10.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.175895765472312%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group (years)\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.82410423452768%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"top\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"bottom\"\u003e\n \u003cp\u003e34095 (52.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e15438 (45.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"top\"\u003e\n \u003cp\u003e18657 (54.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003e\u0026gt;\u003c/u\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e31200 (47.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e15014 (48.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"top\"\u003e\n \u003cp\u003e16186 (51.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.175895765472312%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003csup\u003eb\u003c/sup\u003e (minimum)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.82410423452768%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"top\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"bottom\"\u003e\n \u003cp\u003e30,807 (47.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e9,556 (31.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"bottom\"\u003e\n \u003cp\u003e21,251 (68.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" rowspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eLess than primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e7,431 (11.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,051 (54.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,380 (45.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary completed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e8,564 (13.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,697 (54.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,867 (45.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle completed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,230 (9.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,864 (62.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,366 (37.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary school\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,796 (8.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,781 (65.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,015 (34.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eHigher secondary\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,795 (4.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,907 (68.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e888 (31.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eDiploma/ Graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,672 (5.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,596 (70.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,076 (29.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.175895765472312%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.82410423452768%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"top\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"bottom\"\u003e\n \u003cp\u003e42605 (65.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e20030 (47.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"top\"\u003e\n \u003cp\u003e22575 (52.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e22690 (34.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e10422 (45.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"top\"\u003e\n \u003cp\u003e12268 (54.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.175895765472312%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.82410423452768%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"top\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"bottom\"\u003e\n \u003cp\u003e851 (1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e479 (56.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"top\"\u003e\n \u003cp\u003e372 (43.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eMarried/ in live -in\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e48992 (75.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e26860 (54.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"top\"\u003e\n \u003cp\u003e22132 (45.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eWidow/ separated/ divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e15452 (23.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e3113 (20.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"top\"\u003e\n \u003cp\u003e12339 (79.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.175895765472312%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMPCE quintile\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.82410423452768%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"bottom\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"bottom\"\u003e\n \u003cp\u003e12,705 (19.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,878 (45.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"bottom\"\u003e\n \u003cp\u003e12,705 (54.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"bottom\"\u003e\n \u003cp\u003ePoorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e13,169 (20.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e7,071 (46.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e13,169 (53.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"bottom\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e13,147 (20.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e7,017 (46.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e13,147 (53.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"bottom\"\u003e\n \u003cp\u003eRicher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e13,189 (20.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,961 (47.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e13,189 (52.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"bottom\"\u003e\n \u003cp\u003eRichest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e13,085 (20.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,916 (47.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e13,085 (52.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.175895765472312%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth insurance\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.82410423452768%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"bottom\"\u003e\n \u003cp\u003e63844 (97.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e29268 (45.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"top\"\u003e\n \u003cp\u003e34576 (54.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e1451 (2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e1184 (81.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"top\"\u003e\n \u003cp\u003e267 (18.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.175895765472312%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.82410423452768%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"bottom\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"bottom\"\u003e\n \u003cp\u003e33403 (51.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e9,544 (28.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"bottom\"\u003e\n \u003cp\u003e23,859 (71.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"bottom\"\u003e\n \u003cp\u003eProfessional and semi-professional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e1554 (2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,167 (75.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e387 (24.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"bottom\"\u003e\n \u003cp\u003eClerical and skilled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e16417 (25.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e10,852 (66.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,565 (33.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"bottom\"\u003e\n \u003cp\u003eUnskilled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e13921 (21.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e8,889 (63.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,032 (36.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.175895765472312%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.82410423452768%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"top\"\u003e\n \u003cp\u003eEveryday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"bottom\"\u003e\n \u003cp\u003e15,413 (23.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e9,420 (61.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,993 (38.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eMore than once / week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e4,511 (6.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,571 (56.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,940 (43.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eOnce / week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,367 (3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,278 (53.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,089 (46.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003e1-3 times /month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,227 (4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,618 (50.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,609 (49.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e39,777 (60.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e15,565 (39.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e24,212 (60.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.175895765472312%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-rated health\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.82410423452768%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"top\"\u003e\n \u003cp\u003eExcellent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,512 (3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,459 (58.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"bottom\"\u003e\n \u003cp\u003e1,053 (41.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eVery good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e12,332 (19.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,424 (52.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,908 (47.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e25,260 (39.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e11,776 (46.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e13,484 (53.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eFair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e17,795 (27.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e7,647 (42.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e10,148 (57.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,517 (10.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e2,722 (41.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e3,795 (58.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.175895765472312%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultimorbidity\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.82410423452768%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"bottom\"\u003e\n \u003cp\u003e40843 (62.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e19800 (48.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"top\"\u003e\n \u003cp\u003e21043 (51.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e24542 (37.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e10652 (43.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"top\"\u003e\n \u003cp\u003e13800 (56.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.175895765472312%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTobacco abuse\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.82410423452768%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"top\"\u003e\n \u003cp\u003e41664 (63.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e13759 (33.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"top\"\u003e\n \u003cp\u003e27905 (66.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e23631 (36.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e16693 (70.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"top\"\u003e\n \u003cp\u003e6938 (29.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.175895765472312%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol abuse\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.82410423452768%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.130081300813007%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"bottom\"\u003e\n \u003cp\u003e53696 (82.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.349593495934958%\" valign=\"bottom\"\u003e\n \u003cp\u003e20275 (37.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.3739837398374%\" valign=\"bottom\"\u003e\n \u003cp\u003e33421 (62.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.17117117117117%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"bottom\"\u003e\n \u003cp\u003e11599 (17.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44144144144144%\" valign=\"bottom\"\u003e\n \u003cp\u003e10177 (87.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.36036036036036%\" valign=\"bottom\"\u003e\n \u003cp\u003e1422 (12.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e*a= mean (SD), b= N (%)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Distribution of Indian population as per indoor air pollution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN= 65295\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN= 30452\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN= 34843\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFuel type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19937\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnclean/ Soild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVulnerable ventilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e29114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePollution generating source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e18501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e46.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHousehold*\u003c/p\u003e\n \u003cp\u003eIndoor Smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIndoor Air Pollution*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e50.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p-value\u0026lt;0.05= significant\u003c/p\u003e\n\u003cp\u003eAnemia and IAP need more decription in results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe two variables do deserve one-one dedicated table\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e \u003cstrong\u003eUnivariate and multivariable logistic regression of anaemia with indoor air pollution among Indian population\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.644549763033176%\" colspan=\"2\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.35545023696683%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.62473794549266%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"73.37526205450733%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.62473794549266%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude odds ratio (95% Confidence interval)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.78616352201258%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted odds ratio (95% Confidence interval)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModel-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.68972746331237%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted odds ratio (95% Confidence interval)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModel-2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.89937106918239%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted odds ratio (95% Confidence interval)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModel-3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (\u003cu\u003e\u0026gt;\u003c/u\u003e45 years)\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFuel type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003eUnclean/ Soild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.678571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e1.27 (1.17-1.38)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e1.13 (1.03-1.24)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e1.21 (1.10-1.33)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.357142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e1.19 (1.09-1.31)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVulnerable ventilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.678571428571427%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.03 (0.93-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.99 (0.86-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e1.01 (0.90-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.357142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e1.01 (0.94-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePollution generating source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.678571428571427%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.39 (1.28-1.51)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.24 (1.13-1.37)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e1.33 (1.21-1.46)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.357142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e1.30 (1.18-1.43)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHousehold\u003c/p\u003e\n \u003cp\u003eIndoor Smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.678571428571427%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.23 (1.12-1.34)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.21 (1.10-1.32)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e1.23 (1.12-1.34)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.357142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e1.17 (1.07-1.29)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIndoor Air Pollution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnexposed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003eExposed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.678571428571427%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.34 (1.24-1.45)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.24 (1.13-1.35)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e1.31 (1.19-1.43)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.357142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e1.27 (1.16-1.39*)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eModel 1-Adjusted for age group, gender, education, residence, mpce quintile, health insurance and occupation.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModel 2- Model 1+ physical activity and multimorbidity.\u003c/p\u003e\n \u003cp\u003eModel 3- Model 2+ tobacco and alcohol abuse.\u003c/p\u003e\n \u003cp\u003e*\u003cstrong\u003ep-value\u0026lt;0.05= significant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e \u003cstrong\u003eMultivariable logistic regression of anaemia with indoor air pollution among Indian population as per gender\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"648\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude odds ratio (95% Confidence interval)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted odds ratio (95% Confidence interval)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModel-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted odds ratio (95% Confidence interval)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModel-2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted odds ratio (95% Confidence interval)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModel-3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 98.7654%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFuel type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnclean/ Soild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.65 (1.43-1.90)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.37 (1.16-1.60)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.47 (1.25-1.73)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.41 (1.20-1.67)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVulnerable ventilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.08 (0.89-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.97 (0.80-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99 ((0.82-1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99 (0.82-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePollution generating source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.84 (1.59-2.13)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.57 (1.32-1.86)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.66 (1.40-1.97)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.58 (1.33-1.88)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHousehold\u003c/p\u003e\n \u003cp\u003eIndoor Smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.28 (1.10-1.49)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.17 (1.01-1.37)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21 (1.03-1.41)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01 (0.85-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIndoor Air Pollution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnexposed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExposed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.71 (1.47-1.98)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.44 (1.22-1.69)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.52 (1.29-1.80)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.36 (1.15-1.61)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 98.7654%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFuel type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnclean/ Soild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.11 (1.01-1.23)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03 (0.92-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.10 (0.98-1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.09 (0.98-1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVulnerable ventilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.01 (0.89-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.99 (0.87-1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01 (0.89-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01 (0.89-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePollution generating source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.23 (1.12-1.36)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.12 (1.01-1.25)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.20 (1.07-1.35)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.20 (1.07-1.34)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHousehold\u003c/p\u003e\n \u003cp\u003eIndoor Smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.28 (1.15-1.42)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.22 (1.9-1.36)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.23 (1.10-1.37*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21 (1.08-1.35)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIndoor Air Pollution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnexposed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExposed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.23 (1.12-1.36)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.16 (1.04-1.29)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.22 (1.09-1.36)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21 (1.09-1.35)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eModel 1-Adjusted for age group, education, residence, mpce quintile, health insurance and occupation.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModel 2- Model 1+ physical activity and multimorbidity.\u003c/p\u003e\n \u003cp\u003eModel 3- Model 2+ tobacco and alcohol abuse.\u003c/p\u003e\n \u003cp\u003e*\u003cstrong\u003ep-value\u0026lt;0.05= significant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is one of the very few study to show the association of anaemia with indoor air pollution among older Indian adult population with a significantly large nationally representative dataset. There are several key findings of the study. First, the adjusted likelihood of having anaemia was 19% significantly higher (AOR 1.19 (1.09-1.31)) among participants using unclean/ solid fuel than clean fuel.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe adjusted odds of having anaemia was significantly higher among participants exposed to pollution generating source (AOR 1.30 (1.18-1.43)), household indoor smoking (AOR 1.17 (1.07-1.29. The adjusted odds of having anaemia was significantly higher (AOR 1.27 (1.16-1.39)) among participants exposed to indoor air pollution. Similar results were describes by studies conducted by Kelly et al. and Elbarbary et al.\u0026nbsp;[13,14]. The potential cause might be due to prolonged exposure to indoor pollution which might cause oxidative stress, inflammation, decrease absorption of iron and changes in blood parameters, such as haemoglobin levels. Pollutants present in indoor air pollution might cause hemolysis. Breathing in tiny particles (PM2.5) and gaseous pollutants can make your body more inflamed and affect how your bone marrow works\u0026nbsp;[16,17], especially if you already have conditions like diabetes or obesity. Most studies have looked at how these pollutants affect inflammation in the short term\u0026nbsp;[18,19,20], but recent research suggests they could keep causing inflammation over a long time\u0026nbsp;[21,22,23]. This could lead to a chain reaction in your body: it might make your body produce less of a hormone called erythropoietin, make your blood cells less responsive to this hormone, and increase the levels of a protein that controls iron in your body\u0026nbsp;[24,25]. All of these things combined could mean your body makes fewer red blood cells, leading to anemia, especially in older people.\u003c/p\u003e\n\u003cp\u003eStudy by Mehta et al. revealed that increase in each 10 \u0026mu;g/m\u0026sup3; PM 2.5 exposure, the average prevalence of anaemia increased by 1.90% (1.43- 2.36) and the average hemoglobin concentration decreased by 0.07 gm/dL (0.05-0.09) in ecological analysis at district level. Individual level analysis produced that the odds of having anaemia was 1.09 (95% CI 1.06, 1.11) time higher with increase in each 10 \u0026mu;g/m\u0026sup3; PM 2.5 exposure in ambient air\u0026nbsp;[26].\u003c/p\u003e\n\u003cp\u003eWith increase in the educational status, the odds of having anaemia decreased. This might be due to with increase in the education status, the participants became more aware about there health status, which leads to early detection, prevention and treatment of anaemia. Participants residing in urban area had 32% lower adjusted odds of having anaemia. Factors which might contribute to this were higher education, awareness, improved nutrition, better maternal education and early access to healthcare infrastructure. The adjusted odds of having anaemia was highest (AOR 1.37 (1.19-1.57)) among poorest participants. This might be due to lesser awareness, education, prevention, access to healthcare inability to pay the out-of-pocket expenditure due to treatment. Participants having multimorbidity (AOR 1.86 (1.71-2.03)) and history of tobacco abuse (AOR 1.23 (1.12-1.35)) had significantly higher odds of having anaemia. This might be due to shared risk factors (inflammation. Poor nutrition), interaction with iron metabolism, challenging and stigma is accessing healthcare services. Similar results were documented by various studies\u0026nbsp;[27,28].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThere are certain Strengths and limitations of the study.\u0026nbsp;\u003c/strong\u003eThe biggest strength lies the large nationally representative dataset which increased its generalisability. We have established the positive association between anaemia and indoor air pollution among older adults which was noble of its kind. This study not only showed the prevalence of exposure of indoor air pollution throughout the lifetime with stratified details but also unveil the curtain from the association between anaemia and indoor air pollution which was further stratified into detailed classification as per gender. Despite these there were some limitations. Due to its cross-sectional nature temporality could not be established. We were not able to calculate the degree of exposure to pollutants. We were not able to eliminate the effect of external air pollution. Due to self-reporting type of documentation of data, the actual prevalence of anaemia might be higher. Due to the self-reporting style, there were higher probability of recall bias and social desirability bias, which could not be eliminated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere are key policy implications and recommendations emerging from the study. Owing to the limited access to clean fuel and compulsion to use biomass owing to financial hardship in the rural areas, IAP is becoming a serious public health hazard in the nation. Nonetheless, the Indian government has made a number of efforts to improve it, including \u0026apos;Pradhan Mantri Ujjwala Yojana\u0026apos; (PMUY) [29] and the National Programme on Improved Chula (NPIC) [30]. Due to the pandemic\u0026apos;s devastating effects and decimation of rural people\u0026apos;s income and way of life, studies must be done in order to adequately examine and reconsider the price of LPG and subsidies for the poor. More attention should be paid to educating the public about the negative impacts of using unclean biomass energy sources, indoor air pollution, and the necessity of a functional kitchen ventilation system, among other things. In unavoidable circumstances, substitutes such enhanced chullha powered by biomass with chimneys and cookstoves can be recommended, as indicated by a previous study, which may result in reduced indoor air pollution [31]. We recommend proper clinical or community-based trial to establish the temporality, causation and natural history of association of anaemia with indoor air pollution among older adults.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study establishes convincing statistical evidence regarding positive association of anaemia with indoor air pollution among older Indian adults through a nationally representative large dataset. Interestingly, the association appears to be more pronounced among men. These findings might raise awareness and assist individuals in avoiding the negative effects of using solid/unclean fuels, inadequate ventilation, and indoor smoking. Implementing measures such as upgrading residential stoves with chimneys, providing access to clean cooking fuel, and enhancing home ventilation systems can significantly reduce exposure to indoor air pollution. The study highlights the urgency of initiating programs aimed at improving the accessibility and availability of clean fuel and technologies. By doing so, the nation can make strides toward achieving Sustainable Development Goal 7: Ensuring universal access to affordable, reliable, and modern energy services. \u0026nbsp;Further evidence-based research is recommended to understand detailed causation and to establish temporality.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLASI- Longitudinal Aging Study in India, PM 2.5- Particulate matter 2.5, LPG- Liquefied Petroleum Gas, MPCE- Monthly per capita expenditure, IAP- Indoor air pollution, AOR- Adjusted odds ratio.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate-\u003c/strong\u003e Being a secondary analysis of a dataset freely available in the public domain, ethical approval for the present study was not deemed necessary. However, the ethical approval to conduct LASI was given bythe Indian Council of Medical Research\u0026apos;s (ICMR) Central Ethics Committee on Human Research (CECHR)\u0026nbsp;[15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication-\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials-\u0026nbsp;\u003c/strong\u003eThe study utilizes nationally representative LASI survey data, which is publicly accessible and can be obtained by registering at https://iipsindia.ac.in/sites/default/files/LASI_DataRequestForm_0.pdf. The processed data can be provided by the authors upon request by Dr. Pritam Halder (corresponding author).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e- Authors have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e-\u0026nbsp;This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution- \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDr. Pritam Halder-\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Resources, Data Curation, Writing- Review and editing, Visualization, Supervision.\u003cstrong\u003e, Dr. Madhur Verma-\u0026nbsp;\u003c/strong\u003eResources, Data Curation, Writing- Review and editing, Visualization, Supervision, \u003cstrong\u003eDr. Saumyarup Pal-\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Resources, Data Curation, Writing- Review and editing, \u003cstrong\u003eDr. Amit Kumar Mishra-\u0026nbsp;\u003c/strong\u003eResources, Data Curation, Writing- Review and editing, Visualization, Supervision,\u0026nbsp;\u003cstrong\u003eDr.\u003c/strong\u003e \u003cstrong\u003eTrideep Jyoti Deori-\u0026nbsp;\u003c/strong\u003eResources, Data Curation, Writing- Review and editing, Visualization, Supervision\u003cstrong\u003e, Dr. Riya Biswas-\u0026nbsp;\u003c/strong\u003eResources, Data Curation, Writing- Review and editing, Visualization, Supervision\u003cstrong\u003e, Dr. Jaya Tiwari-\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Resources, Data Curation, Writing- Review and editing\u003cstrong\u003e, Dr. Anshul Mamgai-\u0026nbsp;\u003c/strong\u003eResources, Data Curation, Writing- Review and editing, Visualization, Supervision\u003cstrong\u003e,\u003c/strong\u003e \u003cstrong\u003eShivani Rathor-\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Resources, Data Curation, Writing- Review and editing,\u003cstrong\u003e\u0026nbsp;Dr. Manish Chandra Prabhakar-\u003c/strong\u003e Conceptualization, Methodology, Resources, Data Curation, Writing- Review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement-\u0026nbsp;\u003c/strong\u003eWe want to convey our sincere gratitude towards the participants and Indian Council of Medical Research\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGovernment of India. 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Environ Health Prev Med. 2021;26(1):1.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anaemia, Air Pollution, Indoor Air Pollution, Modelling, LASI","lastPublishedDoi":"10.21203/rs.3.rs-4167764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4167764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction- \u003c/strong\u003eAnaemia is a disease of public health importance with multi-causal pathways. Previous literature suggests the role of indoor air pollution (IAP) on haemoglobin levels, but this has been studied less due to logistic constraints. A high proportion of the population in developing countries, including India, still depends on unclean fuel, which exacerbates IAP. The objective was to study the association between anaemia and IAP among the older Indian adult population (\u003cu\u003e\u0026gt;\u003c/u\u003e45 years) as per gender.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods- \u003c/strong\u003eOur study analysed the nationally representative dataset of the Longitudinal Ageing Study in India (LASI 2017–18, Wave-1). Bivariate analysis and logistic regression were used to depict the association of anaemia (outcome variable) with IAP (explanatory variable). Multivariable logistic regression was conducted by adjusting for covariates as per their models. P value\u0026lt;0.05 was considered statistically significant. SATA version 17 was used for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults- \u003c/strong\u003eAlmost half (50.38%) of the participants were exposed to IAP (female (52.40%) \u0026gt; male (47.60%)). The adjusted likelihood of having anaemia was 19% higher (aOR 1.19; 1.09-1.31) among participants exposed to unclean/solid fuel. The adjusted odds were significantly higher among participants exposed to pollution-generating sources (aOR 1.30; 1.18-1.43), and household indoor smoking (aOR 1.17 (1.07-1.29. The adjusted odds of having anaemia were significantly higher (aOR 1.27; 1.16-1.39) among participants exposed to IAP, which was higher in males (aOR 1.36; 1.15-1.61) than females (aOR 1.21; 1.09-1.35).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion- \u003c/strong\u003eThis study established the positive association of anaemia with indoor air pollution among older Indian adults through a nationally representative large dataset. The association was higher among men. Further research is recommended to understand detailed causation and to establish temporality. It is a high time to implement positive intervention nationally to decrease solid/ unclean fuel usage, vulnerable ventilation, indoor smoking, IAP and health hazards associated with these.\u003c/p\u003e","manuscriptTitle":"Association of Anaemia with Indoor Air Pollution Among Older Indian Adult Population: Multilevel Modelling Analysis of Nationally Representative Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-03 17:34:58","doi":"10.21203/rs.3.rs-4167764/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvited","content":"","date":"2024-03-28T17:24:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-28T17:19:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2024-03-26T07:19:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3dd9e70a-afb5-4ed5-be40-68814209bfc9","owner":[],"postedDate":"April 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-24T12:40:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-03 17:34:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4167764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4167764","identity":"rs-4167764","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}