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 Table of Contents  
Year : 2015  |  Volume : 3  |  Issue : 5  |  Page : 19-26

Exposure to biomass fuel and low child birth weight – Findings of Pakistan Demographic and Health Survey 2006–2007

1 Department of Community Medicine, Dow University of Health Sciences, Karachi, Pakistan
2 Department of Surgery, Civil Hospital Karachi, Dow University of Health Science, Karachi, Pakistan
3 Department of Biochemistry, Dow University of Health Sciences, Karachi, Pakistan

Date of Web Publication29-Oct-2015

Correspondence Address:
Mubashir Zafar
Department of Community Medicine, Dow University of Health Sciences, Karachi
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2347-9019.168569

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Objectives: Polluted biomass fuel important contributor for low birth weight (LBW). Exposure to biomass fuel during pregnancy lead to LBW, the objective of this study was to determine the association of exposure to biomass fuel and LBW. Methods: In total, 10,023 ever married women were interviewed reported 41,094 births for last 5 years from Pakistan Demographic Health Survey 2006–07. Logistic regression models were used to assess the association between biomass exposure and birth weight after adjusting for demographic, maternal, and child characteristics. Odds ratios (ORs), 95% confidence interval (CI) and P value were calculated. Results: The proportion of LBW was 35.4% (n = 1777) in common high polluted biomass fuel (wood), 36.3% (n = 282) in less common high polluted biomass fuel (electricity, cylinder gas, and biogas) and 29.5% (n = 805) in common low polluted biomass fuel (natural gas). Infants born to common high polluted biomass fuel (wood) users were 28% more likely to have LBW (OR: 1.28, 95% CI: 1.02–1.61, P = 0.03) compared with infants born to common low polluted biomass fuel (natural gas) users while significant positive association of less common high polluted biomass fuel with LBW (OR: 1.41, 95% CI: 1.07–1.84, P value 0.01) after adjusting for potential confounders. Conclusion: Biomass fuel exposure during pregnancy was significantly associated with LBW of child. There is need for reduce the exposure of polluted biomass fuel and replace with low biomass fuel to reduced burden of LBW.

Keywords: Birth weight, natural gas, Pakistan Demographic Health Survey, pregnancy, propensity

How to cite this article:
Ahmed Z, Zafar M, Khan NA, Qureshi MS. Exposure to biomass fuel and low child birth weight – Findings of Pakistan Demographic and Health Survey 2006–2007. Int J Health Syst Disaster Manage 2015;3, Suppl S1:19-26

How to cite this URL:
Ahmed Z, Zafar M, Khan NA, Qureshi MS. Exposure to biomass fuel and low child birth weight – Findings of Pakistan Demographic and Health Survey 2006–2007. Int J Health Syst Disaster Manage [serial online] 2015 [cited 2021 Oct 22];3, Suppl S1:19-26. Available from: https://www.ijhsdm.org/text.asp?2015/3/5/19/168569

  Introduction Top

Low birth weight (LBW) is a significant prognosticator of high infant morbidity and mortality.[1],[2],[3] LBW can also leads to postnatal stunting,[4] micronutrient deficits,[5] compromised psychomotor development,[6] high rates of morbidity,[7] and chronic diseases in later age.[8] In Pakistan, reported LBW rate is approximately 32%, which varies between different geographical regions.[9] In urban areas the rate of LBW ranges from 12% to 42%[1],[2] and in rural areas it has been reported as high as 31%.[10] Smoking is the foremost in etiology of LBW during pregnancy in developed countries followed by low maternal weight gain.[11] Women in Pakistan have very low prevalence of smoking (3.5%);[12] commonly used methods of smoking are inhaled (hookah) and chewing tobacco which are common in rural areas.[13] However, it is estimated that up to 53% of households in rural areas use wood alone as cooking fuel whereas up to 70% use woods, animal dung, and agricultural waste in combination.[14] Fetal growth retardation owing to smoking is caused by placental hypoxia due to the presence of carbon monoxide in the smoke. Furthermore, hypoxia depresses metabolic process which leads to disturbance in the amino acid transport system.[15] Polyaromatic hydrocarbons and tobacco smoke from air pollution transferred from mother to fetus through placenta and cause growth retardation.[16] In the similar way, smoke released from burning of wood exposed by pregnant women caused intrauterine growth retardation by a mechanism similar to smoking.[17],[18] Higher risks for LBW associated with outdoor air pollution have been reported in other settings.[19],[20],[21],[22]

One study from Guatemala [23] and another from Zimbabwe [24] showed that associations of wood fuel with LBW, leading to 63–120 g reduction in mean birth weight among exposed group compared with nonexposed. There are multiple factors; demographic, nutritional, reproductive, and socioeconomic factors associated with LBW and some studies suggest no association of ambient air with LBW.[25],[26] In South Asian region, most of the populations residing in rural and underdeveloped areas have not access to low polluted fuel like natural gas but use high polluted fuel like wood. The potential effects of these high polluted fuels are not very well understood, therefore, the aim of this study was to determine the risk of LBW and birth weight differences among biomass fuel exposed women during prenatal period in comparison with other fuel sources.

  Methods Top

We carried out secondary data analyses using the data of Pakistan Demographic Health Survey (PDHS). The National Institute of Population Studies conducted this national survey during the years 2006 and 2007. Information of households regarding socioeconomic, demographic, and health status of population were gathered from a nationwide representative sample.

The survey take up a stratified, two-stage, cluster sample design. The first stage involved selecting 972 clusters with probability proportional to the size of four provinces (Punjab, Sindh, Khyber Pakhtoonkha [former North-West Frontier Province], and Baluchistan). In urban areas, the clusters were picked from a frame, made up of 26,800 enumeration blocks, each containing about 200–250 households. The frame for rural areas comprises of the list of 50,588 villagescomputed in the 1998 population census. The second stage involved picking households in each cluster, 105 households were carefully chosen by systematic random sampling technique. This means, a total of 102,060 households were chosen. Out of 105 sampled households, ten households in each cluster were selected by means of a systematic random sampling technique. To maximize representativeness of the sample, a national household weighing factor was used.

The Long Household Questionnaire was administered by trained data collector after translation into local language of area in order to minimize language barrier. A total of 10,601 ever married women aged 12–49 were recognized after interviewed of 9255 households with Long Household Questionnaire. This way, 10,023 were successfully interviewed, producing a response rate of 95%.

Child's birth weight was an outcome variable and mothers were inquired for all birth during the 5 years foregoing survey. Child's birth weight was recorded at the time of birth either by health card if she had or from recall. “Size of baby” was also recorded at the time of birth and classify into five groups such as: “Very large,” “larger than average,” “average,” “smaller than average,” and “very small.” These groups combine into two categories for analysis such as “greater than or equal to average size" (≥2500 g) and “less than average size" (birth weight <2500 g) at birth, respectively.

The survey used a 12-fold classification of biomass cooking fuel which was a main predictor variable; natural gas, wood, coal, liquid petroleum gas grass, straw, animal night soil, agricultural waste, charcoal, electricity, biogas, kerosene, and others (never cooked food). For our analysis, biomass fuel divided into five categories namely; common high polluted biomass fuel (straw wood), less common high polluted biomass fuel straw, animal night soil, agricultural waste, kerosene, coal, and charcoal), common low polluted biomass fuel (natural gas), less common low pollution biomass fuel (electricity, cylinder gas and biogas), and others.

Demographic information about other predictors; present age of mother, child gender, birth order, area of residence, educational status of mother and father, iron supplement received during pregnancy, mother ever had terminated pregnancy, mother ever used any contraception method, and wealth index was also obtained. Wealth index was used to access the economic status of household based on standard set of household goods and resources. Household goods and resources were used with which classify the economic status of household. Scoring was done on this basis and divided into quintiles. So, five categories of wealth index such as poorest, poor, middle, rich, and the richest merge into binary variable for analysis as poor or nonpoor. Similarly, mother and father education had also five categories merged as literate or illiterate. Information on pregnancy care (iron supplements during pregnancy) was collected only for the last birth during the 5 years foregoing period, therefore analysis of effects of cooking fuel on birth weight was limited only to the last births.

We carried out all analysis by using SAS system (version 9.1).[27] Checked missing pattern of all predictors and predicted variable in PDHS data using PROC MI nimpute = 0. Missing values were found as child birth weight (951), biomass (5), mother received iron supplement during pregnancy (845), father's education (34), birth order (846), and mother ever had terminated pregnancy (10). Missing values pattern was an arbitrary pattern. We carried out multiple imputations by PROC MI with MCMC to estimate five sets of imputed values including all variables, as well as sample weight variable. Each imputed dataset was then analyzed separately by SAS PROC LOGITIC using logistic regression method. Estimates obtained from the multiple imputations were pooled to obtain a single set of results by SAS PROC MIANALYZE.

To justify the complex survey design, we involved individual sampling weights at national level and clustering variables (primary sampling unit) for model estimation via survey option. A design based point estimate and more robust estimates of standard error was obtained than other analytic methods that overlook survey design features. Descriptive analysis was made by computing proportion and summary statistics for predictors and predicted variable with missing values before and after imputation. Multivariable logistic regression model were built using PROC SURVEYLOGISTIC procedure along with an automatic variable called _IMPUTATION_ during statistical modeling to assess the association of biomass cooking fuel and birth weight. Only covariates with a P < 0.25 in univariate analysis were included in the final multivariate model. Adjusted odds ratios and 95% confidence intervals (95% CI) were calculated for original and multiple imputation data and presented. A P < 0.05 was used as significant.

  Results Top

Sample distribution

Complete 10,023 women were interviewed; their mean age was 29 ± 6.46. A total of 41,094 births reported during the last 5 years. The final data analyzed was 10,023 births. Of the total children, 6063 (60.5%) were of LBW with 951 (9.5%) missing values while after imputation 6691 (66.8%) were LBW. Off those LBW children's, 1777 (35.4%) in common high polluted biomass fuel (wood), 282 (36.3%) in less common high polluted biomass fuel (electricity, cylinder gas, and biogas) and 805 (29.9%) in common low polluted biomass fuel (natural gas) categories. Regarding birth order, 5573 (55.6%) children were higher birth order (more than 2) with missing values 846 (8.4%) but 6083 (60.7%) after imputation. Among eligible women, 6193 (61.8%) resided in rural areas, 3992 (39.8%) belonged to poor family, 6665 (66.5%) were illiterate, and 6049 (60.4%) taken iron supplements during pregnancy. Nearly one fourth (23.0%) of the mothers experienced terminated pregnancy and nearly half (46.6%) used contraceptive procedure at least once in their life. The proportion of different forms of biomass fuel were; common high polluted biomass fuel (50.1%), less common high polluted biomass fuel (7.7%), common low polluted biomass fuel (26.8%), less common low polluted biomass fuel (11.7%), and others (3.5%) with (0.04%) missing values. A similar percentage of type of biomass fuel was observed after imputation. Sample distribution of ever married women by child's birth weight, biomass, and other predictors' variables with missing values before and after imputation has been summarized in [Table 1].
Table 1: Distribution of respondents by categories with missing values before and after imputation

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Univariate analysis

Women from households using common high polluted biomass fuel (wood) were 26% more likely (OR: 1.26, 95% CI: 1.08, 1.48) to give birth to a LBW baby associated with those using common low polluted biomass fuel (natural gas). This association slightly remained unchanged after multiple imputations (OR: 1.26, 95% CI: 1.08, 1.47). Similarly, statistically significant positive association of exposure to less common high polluted biomass fuel (straw, animal dung, crop residues, kerosene, coal, and charcoal) with the risk of LBW child was found (OR: 1.42, 95% CI: 1.12, 1.81) while this slightly attenuated after imputations (OR: 1.39, 95% CI: 1.10, 1.74). Mothers who did not receive iron supplements during pregnancy were 22% more likely (OR: 1.22, 95% CI: 1.08, 1.39) to give birth to a LBW child as compared with those who received iron supplements. This association remained consistent and significant (OR: 1.23, 95% CI: 1.10, 1.39) after imputation. Among other maternal factors, lower educational status and never used any contraception procedure, were statistically significant associated with LBW. The results of univariate analysis for type of biomass fuel and other control variables with LBW using original and multiple imputation information are described in [Table 2].
Table 2: The relationship between exposure to biomass fuel and LBW: A univariate analysis

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Multivariate analysis

In multivariable analysis, women from households using common high polluted biomass fuel (wood) were 26% more likely to have LBW child (OR: 1.26, 95% CI: 1.00, 1.59) compared with those using common low polluted biomass fuels (natural gas) after adjusting for current age of mother, birth order of child, iron supplementation during pregnancy, type of residence, income status, father's education, mother's education, ever used any contraception procedure, and ever had terminated pregnancy. This significant positive association also remained fairly similar (OR: 1.28, 95% CI: 1.02, 1.61) when the multivariable model was ran using imputed data. Similarly, women from households using less common high polluted biomass fuel (straw, animal dung, crop residues, kerosene, coal, and charcoal) were more likely 44% (OR: 1.44, 95% CI: 1.09, 1.91) to give birth to a LBW child after adjusting for current age of mother, birth order of child, iron supplementation during pregnancy, type of residence, income status, father education, mother education, ever used any contraception procedure, and ever had terminated pregnancy. Other factors such as if mother received iron supplements during pregnancy, never used any contraception procedure, and ever had a terminated pregnancy also had statistically significant association with child being LBW [Table 3].
Table 3: A multivariate analysis of association between exposure to biomass fuel and LBW

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  Discussion Top

This study suggests that children born in households using high polluted biomass fuels are more likely to be LBW compared to those children who were born in households using low polluted biomass fuels. This relationship remain significant after adjusting for area of residence, mother's education, father's education, mother received iron supplement during pregnancy, mother ever had terminated pregnancy, and mother ever used any contraception method. The result of this study remained unchanged after multiple imputations of missing data.

The findings of this study are consistent with earlier studies of Pakistan because prevalence of LBW is consistent with previous study also signifies that removal of high pollution biomass fuel (wood) exposure from the population has the ability to decrease the risk of LBW from the recent 19% rate to 14.4%. Furthermore, LBW was found 1.7 times more among biomass users compared to natural gas users.[28],[29] In addition, a study from Pakistan conclude that exposure to wood for cooking during pregnancy was found 1.86 times more chance to reduced mean birth weight of infants than natural gas for cooking. More frequent cooking and increased time expended in the kitchen during fuel burning for cooking was associated with increase the risk of LBW in wood users as compared to natural gas users.[30]

Similar associations between biomass fuel and LBW have been reported in other part of the world with consistent findings. Largest share of births (20%) has been reported in India,[31] among these births, rate of LBW estimated as high as 30–40%.[32] Third Indian National Family Health Survey shows that children born in households consuming gas, kerosene, coal, and biomass as a cooking fuel experienced significantly higher odds of LBW (P < 0.05). The odd of LBW was 1.51 times more chance in primarily using kerosene (95% CI: 1.08–2.12), 1.24 times in using biomass (95% CI: 1.04–1.48), and 1.57 times in households using coal (95% CI: 1.03–2.41) as compared to natural gas.[33] A similar finding reported in Zimbabwe, children born from mothers using high pollution biomass fuels are lighter than using electricity or gas. This finding remain consistent in either group where birth weight was recorded from a health card (120 g lighter), and from recall (180 g lighter).[24]

Though the primary mechanism of these associations remains unclear, nevertheless earlier studies have suggested that biomass burns incompletely, thus releasing high levels of noxious chemicals leads to increase risk of LBW. These chemicals included carbon dioxide, suspended particulate matter, nitrogen dioxide, ozone, carbon monoxide, formaldehyde, and polycyclic aromatic hydrocarbons.[34] In another study, LBW was also associated with pollutants in the air such as SO2, CO, and PM10.[35]

Maternal factors like mother received iron supplements during pregnancy, previous miscarriages, mother education, and never used any contraception method may also contributing LBW. This remains similar in earlier studies,[36] we adjusted these factors, as well as type of residence but the apparent relationship between high pollution biomass fuel and LBW remain unchanged.

The strength of this study is that we used a large nationally representative data. Additionally, multiple imputation technique was used to deal missing values and using “size of baby” at time of birth as an alternate for child's birth weight which represent a larger sample. It will increase in power for our study results statistically. The main limitations of this study are absence of temporality of association due to study design of PDHS, so it was not possible to determine if predictor (type of biomass fuel) occurred prior to the predicted (size of baby), although there is no reason to believe that being pregnant or giving birth to a LBW child would lead to the use of high polluted fuel. Misclassification bias of both predictor and predicted was another limitation of this study. Information regarding type of cooking fuel used, we depend on single question in the PDHS questionnaire. Therefore, time trends in fuel use and duration of fuel used were not determined which may have led to exposure misclassification. The main predicted variable that is child's birth weight was not obtainable for most of births and we used “size of baby” as an alternate may also have caused some misclassification bias. However, we checked the potential of misclassification by limiting the analysis on those who had birth weight information available both with birth card, as well as mother's recall. We did not find any statistically significant misclassification (into LBW or normal birth weight) when mother's recall reported weight was compared against birth card reported weight (data not shown).

Information regarding time interval for using biomass fuel, time of pregnancy when exposure had occurred and household using more than one type of fuel were not present in the data. Women using biomass fuel had higher rates of still birth and abortions.[37] Meanwhile only live births were included so the percentage of pregnancies that might lead to LBW child and have died is not known. It is not possible to conduct analysis separately because we did not have information. In the analysis only those births weight and size of baby were included from last 5 years (index pregnancy).

Regardless of these limitations, various studies shows association between LBW and biomass fuel exposure which provide valid issue for public health policy maker to make the policy which will reduced the exposure of biomass fuel especially pregnant mothers.[38],[39] Biomass fuel have caused various health effects, there is a need awareness campaign for general public regarding the risk of exposure to biomass fuel. Due to financial constraints, replacement of cleaner fuels is not feasible, well designed stoves should be promoted which may improve incineration and aeration by use of chimneys.

  Conclusion Top

During pregnancy exposure to the biomass fuels has a harmful positive association with LBW of child. There is a need of taking measures either through using low polluted fuel and/or high quality well-designed stoves with proper ventilation.

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Conflicts of interest

There are no conflicts of interest

  References Top

Ismail M, Zaidi K, Maqbool S. Premature and low birth weight neonates and their management at neonatology unit of Shaikh Zayed Hospital Lahore. Pak J Med Res 2003;42:54-7.  Back to cited text no. 1
Khan A, Khattak AA, Ali L. The outcome of low birth weight babies admitted in a tertiary care nursery in Peshawar. Med Channel 2005;11:33-6.  Back to cited text no. 2
Tripathy R, Parida SN, Tripathy SN, Devi PS, Das RN, Swain A. Physical status of newborns and neonatal outcome. Indian J Pediatr 2002;69:1041-5.  Back to cited text no. 3
Fikree FF, Rahbar MH, Berendes HW. Risk factors for stunting and wasting at age six, twelve and twenty-four months for squatter children of Karachi, Pakistan. J Pak Med Assoc 2000;50:341-8.  Back to cited text no. 4
Rasmussen K. Is there a causal relationship between iron deficiency or iron-deficiency anemia and weight at birth, length of gestation and perinatal mortality? J Nutr 2001;131:590S-601S.  Back to cited text no. 5
Tandon A, Kumari S, Ramji S, Malik A, Singh S, Nigam VR. Intellectual psycho-educational and functional status of low birth weight survivors beyond 5 years of age. Indian J Pediatr 2000;67:791-6.  Back to cited text no. 6
Moss W, Darmstadt GL, Marsh DR, Black RE, Santosham M. Research priorities for the reduction of perinatal and neonatal morbidity and mortality in developing country communities. J Perinatol 2002;22:484-95.  Back to cited text no. 7
Barker DJ, Eriksson JG, Forsén T, Osmond C. Fetal origins of adult disease: Strength of effects and biological basis. Int J Epidemiol 2002;31:1235-9.  Back to cited text no. 8
United Nations Childrens Fund. The State of the World's Children. FRH/MSM.96.7.WHO G1UNCFU. Geneva: United Nations Childrens Fund (UNICEF); 2005.  Back to cited text no. 9
World Health Organization. Report from a Symposium Held at the Annual Conference of the International Society for Environmental Epidemiology (ISEE) 2005. Geneva: World Health Organization; 2007. Available from: http://www.hqlibdoc.who.int/publications/2007/9789241505735_eng.pdf?ua=1. [Last accessed on 2015 Feb 28].  Back to cited text no. 10
Kramer MS, Séguin L, Lydon J, Goulet L. Socio-economic disparities in pregnancy outcome: Why do the poor fare so poorly? Paediatr Perinat Epidemiol 2000;14:194-210.  Back to cited text no. 11
Nasir K, Rehan N. Epidemiology of cigarette smoking in Pakistan. Addiction 2001;96:1847-54.  Back to cited text no. 12
Khan IA, Siddiqui MS. Trends and pattern of tobacco consumption in rural women of Pakistan. Pak Armed Forces Med J 2002;52:208-12.  Back to cited text no. 13
Rehfuess E, Mehta S, Prüss-Ustün A. Assessing household solid fuel use: Multiple implications for the Millennium Development Goals. Environ Health Perspect 2006;114:373-8.  Back to cited text no. 14
Sastry BV. Placental toxicology: Tobacco smoke, abused drugs, multiple chemical interactions, and placental function. Reprod Fertil Dev 1991;3:355-72.  Back to cited text no. 15
Perera FP, Jedrychowski W, Rauh V, Whyatt RM. Molecular epidemiologic research on the effects of environmental pollutants on the fetus. Environ Health Perspect 1999;107 Suppl 3:451-60.  Back to cited text no. 16
Kourembanas S. Hypoxia and carbon monoxide in the vasculature. Antioxid Redox Signal 2002;4:291-9.  Back to cited text no. 17
Li N, Sioutas C, Cho A, Schmitz D, Misra C, Sempf J, et al. Ultrafine particulate pollutants induce oxidative stress and mitochondrial damage. Environ Health Perspect 2003;111:455-60.  Back to cited text no. 18
Dejmek J, Selevan SG, Benes I, Solanský I, Srám RJ. Fetal growth and maternal exposure to particulate matter during pregnancy. Environ Health Perspect 1999;107:475-80.  Back to cited text no. 19
Lee BE, Ha EH, Park HS, Kim YJ, Hong YC, Kim H, et al. Exposure to air pollution during different gestational phases contributes to risks of low birth weight. Hum Reprod 2003;18:638-43.  Back to cited text no. 20
Salam MT, Millstein J, Li YF, Lurmann FW, Margolis HG, Gilliland FD. Birth outcomes and prenatal exposure to ozone, carbon monoxide, and particulate matter: Results from the children's health study. Environ Health Perspect 2005;113:1638-44.  Back to cited text no. 21
Wang X, Ding H, Ryan L, Xu X. Association between air pollution and low birth weight: A community-based study. Environ Health Perspect 1997;105:514-20.  Back to cited text no. 22
Boy E, Bruce N, Delgado H. Birth weight and exposure to kitchen wood smoke during pregnancy in rural Guatemala. Environ Health Perspect 2002;110:109-14.  Back to cited text no. 23
Mishra V, Dai X, Smith KR, Mika L. Maternal exposure to biomass smoke and reduced birth weight in Zimbabwe. Ann Epidemiol 2004;14:740-7.  Back to cited text no. 24
Glinianaia SV, Rankin J, Bell R, Pless-Mulloli T, Howel D. Particulate air pollution and fetal health: A systematic review of the epidemiologic evidence. Epidemiology 2004;15:36-45.  Back to cited text no. 25
Maisonet M, Correa A, Misra D, Jaakkola JJ. A review of the literature on the effects of ambient air pollution on fetal growth. Environ Res 2004;95:106-15.  Back to cited text no. 26
SAS Institute I. SAS System for Windows, Release February 08, 2010. Carry, NC: SAS Institute, Inc.; 2010.  Back to cited text no. 27
An evaluation of infant growth: The use and interpretation of anthropometry in infants. WHO Working Group on Infant Growth. Bull World Health Organ 1995;73:165-74.  Back to cited text no. 28
World Bank. Pakistan Strategic Country Environment Assessment. World Bank Report. Vol. 2. Washington DC, USA: World Bank; 2006. Available from: . [Last accessed on 2015 Feb 28].  Back to cited text no. 29
Siddiqui AR, Gold EB, Yang X, Lee K, Brown KH, Bhutta ZA. Prenatal exposure to wood fuel smoke and low birth weight. Environ Health Perspect 2008;116:543-9.  Back to cited text no. 30
Ramji S. Impact of infant and young child feeding and caring practices on nutritional status and health. Indian J Med Res 2009;130:624-6.  Back to cited text no. 31
[PUBMED]  Medknow Journal  
George K, Prasad J, Singh D, Minz S, Albert DS, Muliyil J, et al. Perinatal outcomes in a South Asian setting with high rates of low birth weight. BMC Pregnancy Childbirth 2009;9:5.  Back to cited text no. 32
Epstein MB, Bates MN, Arora NK, Balakrishnan K, Jack DW, Smith KR. Household fuels, low birth weight, and neonatal death in India: The separate impacts of biomass, kerosene, and coal. Int J Hyg Environ Health 2013;216:523-32.  Back to cited text no. 33
Bruce N, Perez-Padilla R, Albalak R. Indoor air pollution in developing countries: A major environmental and public health challenge. Bull World Health Organ 2000;78:1078-92.  Back to cited text no. 34
Lakshmi PV, Virdi NK, Thakur JS, Smith KR, Bates MN, Kumar R. Biomass fuel and risk of tuberculosis: A case-control study from Northern India. J Epidemiol Community Health 2012;66:457-61.  Back to cited text no. 35
Veghari G. Iron supplementation during pregnancy and birth weight in Iran: A retrospective study. Pak J Biol Sci 2009;12:427-32.  Back to cited text no. 36
International Institute for Population Sciences MI. National Family Health Survey (NFHS-3), 2005-06. Vol. I. India: IIPS; 2010.  Back to cited text no. 37
Gouveia N, Bremner SA, Novaes HM. Association between ambient air pollution and birth weight in São Paulo, Brazil. J Epidemiol Community Health 2004;58:11-7.  Back to cited text no. 38
Pokhrel AK, Bates MN, Verma SC, Joshi HS, Sreeramareddy CT, Smith KR. Tuberculosis and indoor biomass and kerosene use in Nepal: A case-control study. Environ Health Perspect 2010;118:558-64.  Back to cited text no. 39


  [Table 1], [Table 2], [Table 3]

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