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

doi:10.21203/rs.3.rs-4167764/v1

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<0.05 was considered statistically significant. SATA version 17 was used for analysis.

Results- Almost half (50.38%) of the participants were exposed to IAP (female (52.40%) > 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

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).

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].

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)	<0.001
>60	31200 (47.78)	15014 (48.12)	16186 (51.88)	<0.001
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)	0.008
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)	<0.001
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)	<0.001
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)	<0.001
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)	<0.001

*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
Characteristics		N	%	N	%	N	%
Fuel type	Clean	42502	65.09	19937	46.91	22565	53.09
Fuel type	Unclean/ Soild	22793	34.91	10515	46.13	12278	53.87
Vulnerable ventilation	Lower	54535	83.52	25421	46.6	29114	53.39
Vulnerable ventilation	Higher	10760	16.48	5031	46.76	5729	53.24
Pollution generating source	No	34700	53.14	16199	46.68	18501	53.32
Pollution generating source	Yes	30595	46.86	14253	46.59	16342	53.41
Household* Indoor Smoking	No	48912	74.91	22086	45.15	26826	54.85
Household* Indoor Smoking	Yes	16383	25.09	8366	51.07	8017	48.93
Indoor Air Pollution*	No	32397	49.62	14794	45.66	17603	54.34
Indoor Air Pollution*	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
Fuel type	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
Vulnerable ventilation	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
Pollution generating source	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
Household Indoor Smoking	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
Indoor Air Pollution	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
Fuel type	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
Vulnerable ventilation	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
Pollution generating source	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
Household Indoor Smoking	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
Indoor Air Pollution	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
Fuel type	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
Vulnerable ventilation	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
Pollution generating source	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
Household Indoor Smoking	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
Indoor Air Pollution	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

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.

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.

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.

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

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No competing interests reported.

SupplementaryLASIIAPAnemia.docx

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

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