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 Table of Contents  
Year : 2020  |  Volume : 5  |  Issue : 4  |  Page : 165-170

Relationship between diabetes mellitus and indoor air pollution: An exploratory analysis

1 North Shore Medical Center, Salem MA, USA
2 Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, USA
3 Environmental Health and Water Science, Texas A and M School of Public Health, TX, USA
4 Department of Data Science, Prasanna School of public Health Manipal Academy of Higher Education, Manipal, Karnataka, India
5 Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Alabama, USA

Date of Submission09-Jun-2020
Date of Decision12-Jul-2020
Date of Acceptance20-Jul-2020
Date of Web Publication31-Dec-2020

Correspondence Address:
Prof. Nalini Sathiakumar
School of Public Health, University of Alabama Birmingham, 1665 University Blvd., Birmingham, AL 35294
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jncd.jncd_38_20

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Context: Diabetes is one of the leading causes of morbidity and mortality worldwide. India has the second highest number of individuals with diabetes in the world and these numbers are predicted to reach 120 million by 2045. Environmental exposure to particulate matter and nitrogen dioxide has been identified as a risk factor for diabetes However, to date, no published studies have examined the association of indoor air pollution (IAP) and diabetes in developing countries such as India, where traditional biomass fuels are still used for cooking and heating.
Aims: To evaluate the association between IAP and diabetes mellitus.
Settings and Design: The data collected through India's third National Family Health Survey (NFHS-3) in 2005–2006 were queried and analyzed.
Materials and Methods: This study examined the association between IAP and diabetes among women aged 45 years and above using data from the NFHS-3.
Statistical Analysis: Multivariable regression analysis was used to determine the relationship between diabetes and type of fuel, calculate adjusted odds ratios (OR) and the 95% confidence interval (CI) after adjusting for confounders.
Results: Less than 1/3 of the 9,502 (28%) participants were overweight or obese and 304 women reported having diabetes. A statistically significant association between solid fuel use and diabetes in women >45 years of age was observed (OR: 1.59; 95% CI: 1.08–2.34).
Conclusions: This study is the first attempt to determine the relationship between diabetes and IAP; more robust population-based cohort studies are needed to further explore this association.

Keywords: Diabetes, India, indoor air pollution, particulate matter

How to cite this article:
Mishra S, McClure LA, Golla V, Guddattu V, Lungu C, Sathiakumar N. Relationship between diabetes mellitus and indoor air pollution: An exploratory analysis. Int J Non-Commun Dis 2020;5:165-70

How to cite this URL:
Mishra S, McClure LA, Golla V, Guddattu V, Lungu C, Sathiakumar N. Relationship between diabetes mellitus and indoor air pollution: An exploratory analysis. Int J Non-Commun Dis [serial online] 2020 [cited 2022 Aug 8];5:165-70. Available from: https://www.ijncd.org/text.asp?2020/5/4/165/305995

  Introduction Top

Diabetes is one of the leading causes of morbidity and mortality worldwide and has become one of the topmost global health concerns in the past two decades.[1],[2] The International Diabetes Federation (IDF) estimated that globally 425 million adults in the age group of 20–79 years had diabetes in 2017 and the number will increase to 629 million by 2045.[3] The global health-care expenditure for diabetes has also increased tremendously from USD 232 billion in 2007 to USD 727 billion in 2017.[3] Diabetes accounts for 10.7% of all-cause mortality in the 20–79 years' age group with an estimated 4 million deaths in 2017. The maximum number of these deaths occurred in China and India.[3] India has the second highest number of individuals with diabetes (>72 million) in the world and these numbers are predicted to increase to 120 million by 2045.[3]

Type 2 diabetes, which accounts for majority of the diabetes cases, is the result of a complex interaction involving genes and environment. The most important established risk factors are old age, overweight/obesity, physical inactivity, genetics, previous history of gestational diabetes, and race/ethnicity.[4] However, the high prevalence of diabetes in Asian countries (like India and China) cannot be explained only on the basis of established risk factors such as overweight and obesity.[5] The obesity rates of the population do not correspond with the prevalence of diabetes in India.[6],[7],[8] Studies have indicated the possibility of other risk factors for diabetes in the Indian population such as susceptibility for early decline in β-cell function and in utero undernutrition and/or low birth weight.[5] Air pollution could also be a contributor to the high diabetes rates in India.

In the past decade, the possible role of modifiable environmental factors such as particulate matter (PM) and nitrogen dioxide (NO2), which are indicators of air pollution as risk factors for diabetes, has been investigated.[9],[10] Physiological responses such as oxidative stress, imbalance in autonomic nervous system, and endothelial dysfunction, which are known to influence insulin activity leading to diabetes, are also caused by exposure to air pollutants.[11]

However, no published studies to date have examined the association of indoor air pollution (IAP) and diabetes, in developing countries like India, even though the concentration of PM can be very high indoors where traditional biomass fuels are still used for cooking and heating purposes.[12],[13] The high levels of air pollution and the concurrent increase in diabetes incidence at an alarming rate in India need further introspection, particularly among females who are mainly responsible for cooking and have higher likelihood of exposure to IAP. The current study explored the risk factors associated with diabetes among females in India and, in particular, examined the association between exposure to IAP and diabetes.

  Materials and Methods Top


The ethical approval for this study was obtained from the Institutional Review Board of (University of Alabama at Birmingham).

Study design

Selection and description of participants

For the purpose of this study, data collected through India's third National Family Health Survey (NFHS-3) in 2005–2006 were analyzed. Details of the survey are published elsewhere.[14] Briefly, the NFHS-3 survey was conducted in 109,041 households with 74,369 men and 124,385 women residing in 29 states of India. The eligibility criteria for survey participation was based on age and dwelling. Men 15–54 years and women 15–49 years old who stayed at the house the night before the interview were considered eligible. This included both usual residents and visitors. The response rate was 94.5%.[14] The questionnaires were used to obtain information on reproductive and maternal health, prevalence of disease conditions including diabetes, and other important aspects of household characteristics and socioeconomic status (SES).


Analysis in this study was restricted to women aged 45 years and above. The risk of diabetes increases with age. Women aged 45 and above have higher risk of developing diabetes compared to younger women.[4] Entries with missing data for the presence/absence of diabetes and pregnant women with diabetes (gestational diabetes) were excluded. Survey respondents who were visitors and not members of the households were also excluded from the analysis. The final sample included in the analysis was 9502.

Self-reported presence or absence of diabetes was the primary outcome variable of interest. The primary exposure variables included the indicators for IAP in the NFHS questionnaire. As the data lacked quantitative measurement, IAP was assessed based on variables related to household characteristics that are known to be reliable indicators for IAP.[15] These included (1) type of cooking fuel: solid or biomass fuel (wood, coal/charcoal, agriculture crop, animal dung, and grass/straw) or clean fuel (LPG, electricity, biogas, and kerosene); (2) factors influencing ventilation: location of cooking (separate building, inside house with or without separate kitchen, and outdoor).

The covariates chosen were variables associated with diabetes and/or IAP based on existing literature. These included personal factors (age, body mass index [BMI], smoking, and alcohol intake), sociodemographics (education, occupation, wealth index, and location of residence), and access to health care. Wealth index was used as a measure of SES based on ownership of 33 assets, which included consumer items and dwelling characteristics (e.g. motor vehicle, television, and refrigerator). Each asset was given a score and the individuals were ranked on the basis of their cumulative score and divided into quintiles, where one is the poorest 20% of the households and five is the wealthiest 20% of the households.[16]

Data analysis was done using SAS version 9.3 (Cary, NC). In order to control for differential sampling in certain regions and variation in response rates from one geographical area to another and to maximize representation of the general population, survey weights were used. The weights were calculated and provided by the NFHS authority (IPSS and ORC Macro). Frequency and percentage were used to summarize sociodemographics and household characteristics of the study participants. The indicators of IAP (i.e., type of fuel and location of cooking), the known risk factors, and the demographic factors were tested for association with diabetes using bivariate analysis. In order to assess confounding, the association of the primary exposure variable (type of fuel use) with the covariates was also analyzed. Analyses were done using Chi-square tests. Prevalence odds ratio (OR) and 95% confidence intervals (CI) were calculated. All variables were dichotomized based on either existing evidence or the study hypotheses of their association with diabetes. The type of fuel used for cooking was dichotomized as clean (LPG, electricity, biogas, and kerosene) or solid fuel (wood, coal/charcoal, agriculture crop, animal dung, and grass/straw) as solid fuels are known to pollute significantly more than clean fuels.[17] The location of cooking or kitchen type was classified as outdoor (if food is cooked outside) or indoor (if food is cooked inside house or in a separate building). The categorization was done on the basis of ventilation, as cooking indoors can have higher pollution due to lower ventilation compared to cooking outdoors.[18] The SES of the sample as determined by wealth index was broadly divided into two categories. Those in the top forty percent of the wealth index quintiles considered as rich or richest were grouped into one category and the rest into another category to determine the role of wealth which influences lifestyle-related diseases such as diabetes. The level of education of the participants was dichotomized into <5 years of completed formal education or >5 years. The occupation of the respondents was categorized as active job (jobs which involve a lot of physical activity-skilled and unskilled manual, and agricultural jobs) or a sedentary job (desk based or office-based jobs, professional, clerical, sales, and those not working).

Multivariable modeling was done to determine significant predictors for diabetes in the survey population and to assess the relationship between diabetes and IAP. Variables found significant in bivariate analyses were included in the model. The possibility of multicollinearity between predictor variables was assessed using cross-tabulations. Multivariable regression analysis models were built using SURVEYLOGISTIC procedure in SAS system. Adjusted ORs and the 95% CI were calculated. P < 0.05 was considered statistically significant. The relationship between diabetes and type of fuel and cooking location was examined including all the confounders under study.

  Results Top

The NFHS-3 dataset included responses from 124,385 female participants. After applying the inclusion and exclusion criteria, 9502 female participants were included in the final analysis. The unweighted and weighted characteristics of the study participants are summarized in [Table 1] and [Table 2]. The majority of the participants were married (87%), nonsmokers (96%), nondrinkers (97%), and had received no formal education (59%). Although there were a large number of agricultural (28%) and manual workers (9%), the majority of the female participants were not working (53%). Less than 1/3rd of the participants (28%) were overweight or obese. As per the wealth indices, 48% belonged to the top 40% of the wealth index quintiles. Household characteristics revealed that wood and LPG were the most common fuel types used by the participants (46% and 29%, respectively) and almost 84% of the respondents cooked indoors.
Table 1: Select demographic characteristics of the study sample

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Table 2: Lifestyle, fuel, and kitchen type characteristics of the study sample

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Bivariate analysis showed that diabetes rates were three times higher in overweight/obese women compared to women who were normal weight or underweight (OR: 3.18; 95% CI: 2.52–4.00). The observed association between type of job and diabetes was similar to other reports. Women with sedentary jobs (includes not working women) had significantly higher odds of diabetes (OR: 2.89; 95% CI: 2.17–3.84) compared to women with jobs that involved intense physical activity (e.g., agricultural and manual workers). Diabetes in urban women was two and half times more prevalent than rural women (OR: 2.68; 95% CI: 2.00–3.58). Women in the top 40% category of the wealth index had three times higher rates of diabetes compared to those in the lower 60% category (OR: 3.15; 95% CI: 2.43–4.08). Among behavioral factors, diabetes was also observed more in smokers, but the association was not significant.

Results of the multivariable analysis are shown in [Table 3]. A statistically significant association between solid fuel use and diabetes was observed (OR: 1.59; 95% CI: 1.08–2.34) after controlling for confounding factors (BMI, wealth index, residence location, alcohol, smoking status, education, and occupation).
Table 3: Effects of type of fuel and other variables on diabetes (n=9488.8)

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  Discussion Top

The role of IAP in numerous adverse health effects has been repeatedly demonstrated,[19],[20] but its effect on the risk of developing diabetes is largely unknown. The current study examined the association of IAP with diabetes using data from a nationally representative sample from India. We found that solid fuel use was significantly associated with higher prevalence of diabetes among women aged 45 and above. The study also reemphasized the importance of established risk factors of diabetes such as obesity and sedentary lifestyle. Women who were obese, who had a desk/office-based job, or who were not working in a job were more likely to have diabetes. The rise in prevalence of diabetes in the urban region and with improvement of SES in India has been previously studied.[21],[22] The current study showed similar findings; prevalence of diabetes in urban women and women of higher SES was observed to be significantly higher. It is interesting to note that in Western nations like the U.S., the burden of diabetes is higher for people belonging to lower SES groups.[23] In comparison, in India, diabetes is more prevalent among people of higher SES. It is possible that the adoption of Western dietary patterns (i.e. high-calorie fast foods and intake of sugary drinks) by people belonging to high SES in India[24] might be playing a role in diabetes as has been shown in developed countries like the U.S.[25]

The study has a number of strengths. To the best of our knowledge, this study is the first attempt to determine the relationship between IAP and diabetes. The large sample size is representative of 99% of the Indian population,[14] thus ensuring statistical stability, high power, reduction in the number of spurious associations, and random errors. Second, the survey items were based on standardized and validated measures, which reduces measurement errors. There are also certain limitations to this study, which need consideration when interpreting the results. The study is cross-sectional in design. No temporal relationship can be determined between the observed associations of diabetes with the risk factors. There are possibilities of misclassifications in both outcome and exposure assessment. Based on the survey data, we could not differentiate between type 1 and type 2 diabetes in the study population. However, type 2 is the most prevalent form of diabetes in India.[26] The data provided information on diabetes based on a yes/no question asked to the participants, but did not provide information on family history of diabetes, presence of hypertension, cholesterol levels, and waist circumference. These are established risk factors for diabetes[4],[27] and the lack of information could potentially confound the study results.

The prevalence of diabetes was based on self-reported data. This could have resulted in underreporting because the prevalence of diabetes based on questionnaire data may not be as accurate as clinical diagnosis. However, a number of studies from other countries have shown strong agreement between questionnaire responses and medical records pertaining to diabetes.[28],[29] The study lacks quantitative exposure assessment of IAP in the survey. Considering that personal and area monitoring is the gold standard for assessing exposure to IAP, the use of self-reported survey data cannot be as accurate as true exposure assessment. However, monitoring IAP is expensive and requires training and substantial resources for community-based research. In comparison, surveys are inexpensive, easily administered, and provide reliable data as observed in earlier studies that determined the concordance between questionnaire data and objective measurement of IAP.[18],[30] In addition, a number of studies have convincingly demonstrated the link between adverse health effects and IAP based on survey data.[31],[32] The questionnaire data in the NFHS 3 survey does not provide information on time activity patterns, time spent cooking daily, and the number of years since started cooking. The lack of this information potentially underestimates the true exposure and results in directing risk estimates toward null.

The analysis of the survey data has been done on the assumption that only one type of fuel was used for cooking. In real-world scenarios, people might use one fuel predominantly but might also switch between different types of fuel based on availability, variations in price, and convenience. This blurs the strict classification of the sample into clean and solid fuel users and could lead to errors in risk estimation.

  Conclusion Top

Studies have repeatedly shown significant associations between diabetes and outdoor air pollution and there is no biologically plausible reason to deny the possibility of a similar association with IAP. Considering that the current study is the first attempt to determine the relationship between diabetes and IAP, more robust population-based cohort studies utilizing detailed questionnaires on IAP indicators and quantitative assessment would be needed. Second, instead of self-reporting, diabetes confirmation by clinical tests would further reduce misclassification errors and would have more validity in assessing the true association between diabetes and IAP. It is important to realize that the high prevalence of diabetes in India needs immediate attention. Considering the disease burden and economic costs, controlling diabetes by identifying all potential risk factors should be a national priority.

Financial support and sponsorship

The present work was supported by the University of Alabama at Birmingham International Training and Research in Environmental and Occupational Health program, Grant Number 5 D43 TW05750, from the National Institutes of Health-Fogarty International Center (NIH-FIC). The content is solely the responsibility of the authors and do not necessarily represent the official views of the NIH-FIC.

Conflicts of interest

There are no conflicts of interest.

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  [Table 1], [Table 2], [Table 3]

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