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Exposure to household air pollution during first 3 years of life and IQ level among 6–8-year-old children in India – A cross-sectional study
Journal Pre-proofs Exposure to Household Air Pollution During First 3 Years of Life and IQ Level among 6-8-Year-Old Children in India - A Cross-Sectional Study Ajith Brabhukumr, Prabhjot Malhi, Khaiwal Ravindra, P.V.M. Lakshmi PII: DOI: Reference:
S0048-9697(19)35102-2 https://doi.org/10.1016/j.scitotenv.2019.135110 STOTEN 135110
To appear in:
Science of the Total Environment
Received Date: Revised Date: Accepted Date:
1 August 2019 2 October 2019 20 October 2019
Please cite this article as: A. Brabhukumr, P. Malhi, K. Ravindra, P.V.M. Lakshmi, Exposure to Household Air Pollution During First 3 Years of Life and IQ Level among 6-8-Year-Old Children in India - A Cross-Sectional Study, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.135110
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Exposure to Household Air Pollution During First 3 Years of Life and IQ Level among
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6-8-Year-Old Children in India - A Cross-Sectional Study
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Ajith Brabhukumr1, Prabhjot Malhi2, Khaiwal Ravindra3, PVM Lakshmi4
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1Resident
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2Professor
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3Additional
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Doctor (Community Medicine), Department of Community Medicine and School of Public Health, PGIMER, Chandigarh (Child Psychology), Department of Paediatrics, PGIMER, Chandigarh
Professor (Environment Health), Department of Community Medicine and School of Public Health, PGIMER, Chandigarh
4Professor
(Epidemiology), Department of Community Medicine and School of Public Health, PGIMER, Chandigarh
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Corresponding Author: Dr. P.V.M. Lakshmi, Additional Professor of Epidemiology,
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Department of Community Medicine and School of Public Health, Post Graduate Institute of
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Medical Education & Research, Chandigarh Email: [email protected] Mobile
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number: +919914208225
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Running title: Association Between Exposure to Household Air Pollution Due to Solid
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Biomass Smoke During First 3 Years of Life and its association with IQ
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Acknowledgment: We greatly acknowledge the support from the State School Authority of
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Haryana and all the stakeholders who participated in this study. RK would like to thank
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Indian Council of Medical Research (ICMR), Ministry of Health and Family Welfare, for
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funding the linked study on ‘Assessment of Impact of PMUY’ via letter number No.
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58/11/NTF-LPG/2019-NCD-II dated 28/05/19.
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Funding statement: This research received no specific grant from any funding agency in the
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public, commercial or not-for-profit sectors.
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Competing interests: There are no competing interests.
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1
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ABSTRACT:
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Many illnesses have been attributed to the exposure of solid biomass smoke but the effect on
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intelligence has largely been unexplored. The study aims to examine the effect of exposure to
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solid biomass smoke during the first 3 years of life on intelligence among 6-8-year-old
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children. Children aged 6-8 years were enrolled from a primary school and their houses were
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visited to collect data on socio-economic status and household exposure assessment.
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Households using LPG as cooking fuel were considered as the unexposed group. All the
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children were tested for their Intelligence Quotient (IQ) using Malin's Intelligence Scale for
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Indian Children (MISIC). The mean IQ was calculated as the average of Verbal and
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Performance score. Potential confounders were adjusted using multivariate general linear
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model. About 45% of children had average or above-average IQ while the rest had below-
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average IQ. The mean scores for the arithmetic component of IQ were found to be
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significantly lower among solid biomass fuel users as compared to LPG users after adjusting
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for confounders. The mean IQ of LPG users were 5.58 points higher (95% CI: 0.46 – 10.1)
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for the arithmetic component as compared to solid biomass users. Children living in the
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houses using solid biomass fuel for cooking have lower IQ as compared to the children living
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in the houses using LPG for cooking for arithmetic component even after adjusting for
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potential confounders.
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Keywords: Solid Biomass Fuel, Children, Intelligence Quotient, Arithmetic component,
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PMUY
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INTRODUCTION
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The recent WHO report highlights that 4.3 million premature deaths could be associated with
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the use of solid biomass fuel for cooking. According to the WHO report, about 3 billion
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people burn solid biomass for cooking and heating which include wood, animal dung, and
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crop waste. Most of these people are poor, and live in developing countries (WHO. 2011).
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The Lancet Commission on pollution and health and Global Burden of Disease estimate also
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identify household air pollution as a major risk factor for human health (Landrigan et al.,
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2018; Cohen et al., 2017). Though outdoor air pollution has long been known to cause
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several diseases including respiratory, cardiovascular and neurological conditions there are
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limited studies linking the illnesses caused by indoor or household air pollution (HAP). The
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health effects of solid biomass smoke include respiratory illness in children and adults,
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structural birth defects, low birth weight and infant mortality, nutritional deficiencies,
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interstitial lung disease, Chronic Obstructive Pulmonary Disease (COPD), pulmonary
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tuberculosis, lung cancer, nasopharyngeal and laryngeal cancers, cardiovascular diseases,
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cataracts and exacerbation of health effects of HIV infection (Fullerton et al., 2008; Kaur-
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Sidhu et al., 2019).
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Several toxic substances which are released during combustion could be responsible for a
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range of physical and mental deficits in children. There are studies linking nitrogen dioxide in
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indoor air pollution with mental retardation and Attention Deficit Hyperactivity Disorder
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(ADHD) (Morales et al., 2009). Exposure to second-hand tobacco smoke is a well-known
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factor that has detrimental effects on children’s cognition (Chen et al., 2013). Though there
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are some studies on the effect of outdoor air pollution (Gonzalez et al., 2015), urban
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vehicular pollution (Garcidueñas et al., 2016) and environmental tobacco smoke
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(Jedrychowski et al., 2014), on cognitive and academic performance of students, the
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association of HAP (Vrijheid et al., 2012) and the cognitive performance has not yet been 3
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fully explored. Further, most of the studies have assessed the effects of nitrogen dioxide
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(NO2) and particulate matter (PM) on the cognitive disorders in children exposed during both
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the prenatal and postnatal period. Few studies have also focussed on the elemental
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composition of PM that affects the developing brain of the children (Bellinger, 2018). Studies
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focussing on comparison of different types of fuels (LPG, Solid biomass fuel) and their
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cognitive effects on children have not been studied previously.
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Physical and mental development is more rapid in infants under one year of age than at any
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other age and hence they are highly vulnerable to the effects of HAP toxic substances that
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can interfere with the biological systems. Developmental and neurological toxins present in
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HAP are likely of greater concern to the developing fetus because of the even more rapid
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physical and brain development that occurs in-utero. Adding to this risk, the infants are
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almost always carried by their mothers or allowed to roam around or play around them
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wherever they work, which mostly involve cooking in rural areas of India. This practice
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exposes the child to harmful HAP, which can be detrimental to their development. Studies
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have also shown that women exposed to solid biomass smoke suffer more from health and
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respiratory illness compared to women using other fuels, due their poorly ventilated kitchen
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in their house (Sukhsohale et al., 2013).
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Thus, exposure to solid biomass fuel during fetal life and during early life can produce
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children with neurological deficits, which can, in turn, affect the society, economy, and
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growth of the country to a large extent since less intelligent children by being less productive,
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can further hinder development in developing countries or Least Developed Countries
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(LDCs). Hence, the World Health Organization (WHO, 2018) also stress that efforts should
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be made to prevent the HAP exposure to improve their health. In India, 72.2% of population
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reside in rural areas with widespread use of solid fuel, and 86.5% of the rural people use solid
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biomass fuel for cooking (Census of India, 2011; Ravindra et al., 2019a). Further, cooking 4
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with solid biomass fuel also alter the thermal comfort of rural household and could
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synergistically add to adverse health impact of HAP (Ravindra et al., 2019b) Thus,
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intervention on the usage of solid biomass fuel gains significance in the step towards
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prevention of health problems due to their use and betterment of health of the most
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vulnerable sections of the society, i.e. the women and the children.
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Hence, there is a need to look into the association between household air pollution due to
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solid biomass smoke and its effects. Thus, the current study aims to investigate the
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association of HAP during first three years of life with cognitive development at the age of 6-
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8 years.
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MATERIALS AND METHODS
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Participants and Recruitment:
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A cross-sectional study was conducted from July 2012 to June 2013. Children from two
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villages (Kheri and Samlehri), where the Department of Community Medicine and School of
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Public Health, PGIMER is providing community-based health services were selected for the
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study. The estimated sample size was 108, assuming an anticipated effect size (Cohen’s f2)
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for multiple regression as 0.15, with the power of the study as 80%, 95% Confidence Interval
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and the number of predictors as 7. The study participants included 6-8-year-old children.
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Eligible study participants were recruited from the primary school in the area after verifying
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their date of birth, address and obtaining consent from the concerned authorities (school
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principal) and the parents. Children who had an acute illness at the time of enrolment were
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contacted again after recovery from illness. There was no non-response in the study.
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Measurement of exposure variables:
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Socio-economic status was calculated using Udai-Pareek scale, a standardized scale for
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Indian rural setting. The questionnaire was filled by the interviewer based on the interview of
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the mother and observation of the household by the investigator (Pareek & Trivedi, 1964).
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The questionnaire developed by Sri Ramachandra Medical College and Research Institute,
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Chennai for household exposure assessment was adapted for the study (Balakrishnan et al.,
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2004). The information about the family profile, components to assess the socio-economic
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status of the family, housing characteristics, kitchen characteristics, the main source of fuel
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used for cooking, the time spent for cooking, usage of incense sticks and mosquito coils in
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house, the source of lighting in the home, presence of smokers in home and the number of
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hours smokers smoke inside the house, the birth history and birth order of the child etc. were
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collected using the questionnaire (Supplementary Annexure 1). Details on air pollutant levels
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in houses using solid biomass and LPG as fuels have been discussed in detail in other studies
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(Ravindra, 2019; Ravindra et al., 2019c; Sidhu et al., 2017). The kitchen characteristics with
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particular focus on ventilation of the kitchen were recorded. Ventilation was graded based on
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the number and size of the windows. Ventilation was graded as good if there are two or more
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windows of adequate size present, presence of exhaust fan or chimneys and open-air outdoor
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kitchen, moderate if only one window was present and poor if no window was present or
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presence of windows of very small size. Only those children from households where cooking
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fuel or housing characteristics have not changed over the past 3-5 years were included in the
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study.
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Measurement of outcome variable:
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Those using LPG as cooking fuel were considered as the unexposed group. All the children
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were tested for their IQ using Malin's Intelligence Scale for Indian Children (MISIC) (Malin.,
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1971). MISIC is the Indian adaptation of the Weschler Intelligence Scale for Children
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(WISC). Picture arrangement present in WISC was excluded in this version and some items
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were adapted to suit the Indian culture. The scale consists of two major components, the
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Verbal score, and Performance score. The Verbal score has the components of Information,
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Comprehension, Arithmetic, Similarities, Vocabulary, and Digit Span. The Performance
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score has the components of Picture completion, Block design, Object assembly, Coding and
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Maze. The mean IQ was calculated as the average of Verbal and Performance score. The
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scores were calculated as percentiles according to the age of the child as shown in Figure 1.
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The person who administered the IQ test was trained and certified for conducting an IQ test
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by a qualified child psychologist.
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Analysis:
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The main fuel used for cooking i.e. either solid biomass or LPG were taken as exposure
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variables, and IQ was taken as the outcome variable. Independent t-tests were done to test
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whether the mean IQ scores are different between solid biomass and LPG users. The
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covariates included in the study were socio-economic status, age and gender of the child,
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exposure to tobacco smoke inside home, ventilation and type of kitchen, anthropometric
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measurements (Height and Weight), presence of pallor, use of mosquito coils and incense
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sticks, source of lighting in the house, mode of delivery, place of delivery, and the
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educational status of the mother. The confounders which were found to be significantly
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associated with the outcome and the exposure in the bivariate analysis or known confounders
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according to the literature were adjusted by using multivariate general linear model using
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SPSS version 17.
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RESULTS
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The socio-demographic characteristics, birth history and health characteristics of the
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respondents are shown in Table 1. There were more females (55%), compared to males
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(45%). The mean age of the respondents was 7 years (SD: 0.742 years). Three fourth of the
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respondents belong to either middle or lower socio-economic status. Most of the mothers
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were literate (78%). Most of the respondents were born by normal vaginal delivery (84%)
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and two-thirds of them were born in hospitals (68%). Nearly three-fourths of the children
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belong to the birth order 1 or 2. Almost all of the respondents were exclusively breastfed
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(95%). All of the respondents were fully immunized except one. Only 8% of the respondents
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had pallor. The mean height of the respondents was 116.6cm (SD: 3.2cm). The mean weight
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of the respondents was 18.7 kg (SD: 2.1kg).
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The household and kitchen characteristics of the respondents are shown in Table 2. Nearly
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two-thirds of the respondents live in pucca houses. About 75% of the households had less
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than 5 rooms. Only 18% of the households had good ventilation. About 43% of the
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respondents use oil or candle for lighting. In 58% of the households, there was someone in
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the family who smoke tobacco inside the house. Fifty-five percent of the households burn
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either mosquito repellant coils or incense sticks in their house.
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About 40% of the households have an indoor kitchen with or without partition from the rest
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of the house. Among the households surveyed 16% of the kitchens do not have either
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window or opening. Less than a quarter of kitchens have good ventilation. Many of the solid
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biomass stoves (71%) are of fixed type. More than half of the stoves were of simple type
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(55%). Most of the stoves (95%) were made of mud. Households very rarely (1%) used solid
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biomass stove for heating purposes. One-fourth (25%) of households used solid biomass
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stove for cooking food for their cattle.
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There were no significant differences in the distribution of sex, age group, birth order,
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literacy of the mother, place of delivery, mode of delivery, exclusive breastfeeding practices
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and immunization history of the respondents among LPG and non-LPG users. The
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socioeconomic status of the respondents was associated with the type of cooking fuel used.
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Most of the respondents who use solid biomass fuel belong to the low socioeconomic status
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(54%) whereas most of the LPG users belong to either middle or upper socioeconomic status
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(83%).
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There was no association with the number of rooms/ doors and the use of mosquito coils/
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incense sticks with the type of fuel used for cooking. There was a statistically significant
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association between the type of house, ventilation of the house and smoking inside the house
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with main fuel used for cooking. About 91% of LPG users had pucca houses, while only 46%
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of solid biomass users had a pucca house (p-value: 0.001). About one-third of the households
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using solid biomass fuel had poor ventilation as compared to only 8% households in the LPG
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users (p-value: 0.005). Only one-third of LPG users had someone in the family who smoke
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indoors as compared to 66% among solid biomass users (p-value: 0.009).
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The kitchen characteristics of the LPG users and the solid biomass fuel users were
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significantly different. An indoor kitchen was present in 70% of LPG users whereas it was
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present in only 30% of the users of solid biomass fuel. The ventilation of the kitchen was
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poor in 32% of solid biomass fuel users whereas it was 17% among LPG users.
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There was no association of the socio-demographic and household characteristics with the IQ
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of the respondents except for age. The mean IQ of the respondents increased with increase in
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the age of the respondents. The mean IQ of the respondents along with SD according to
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socio-demographic, household and kitchen characteristics were given in Table 3 and Table 4.
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About 45% of children had average or above-average IQ while the rest fell under borderline
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and below average.
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The mean IQ of the various components of IQ of solid biomass fuel users and LPG users was
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compared and the results are presented in Table 5. Though there was no significant difference
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in the mean of the overall IQ of the respondents between LPG and solid biomass fuel users,
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there were significant differences in the mean scores of arithmetic, digit span and maze
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components of IQ. The mean scores obtained in the solid biomass fuels in these components
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were significantly lower than the mean scores obtained by the LPG users. Multivariate
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analysis was used to adjust for confounders. The confounders used for adjustment were age
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and sex of the respondent, mother’s literacy, socioeconomic status, smoking inside the house,
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oil and candle use for lighting, use of a coil or incense sticks, ventilation of the house, type of
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house, type of kitchen and ventilation of kitchen. After adjusting for confounders, the mean
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score obtained for the arithmetic component was significantly lower among solid biomass
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users as compared to LPG users. The results of the multivariate analysis were presented in
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Table 6. The mean IQ of LPG users was 5.58 points higher (95% CI: 0.46 – 10.1) for the
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arithmetic component of IQ as compared to solid biomass users.
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Thus, it can be concluded that the IQ of the children measured at the age of 6-8 was
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significantly lower among children who were exposed to solid biomass fuel smoke as
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compared to those who were not exposed, especially for the arithmetic IQ.
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DISCUSSION
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The IQ of the children exposed to the smoke of solid biomass fuel used for cooking is on
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average 5.5 points lower for the arithmetic component of IQ as compared to those who were
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exposed to smoke of LPG fuel used for cooking. An earlier study had shown the 10
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neuropsychological impact on exposure to carbon monoxide from kerosene stoves, in which
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digit span along with few other components in Wechsler’s intelligence scale were found to be
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affected (Amitai et al., 1998). A similar study on Carbon monoxide exposure from wood
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smoke had shown negative co-relation with digit span among three tests, Coding, Symbol
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Search and Digit Span in Wechsler scale, that were administered. Other cognition function
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affected were visuospatial integration, short-term and long-term memory recall and fine
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motor performance from other cognition tests (Dix-Cooper et al., 2012).
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The present study has shown the impact of solid biomass fuels including the commonly used
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cow dung and wood, on the arithmetic function (p<0.033) among the 11 tests in Malin’s scale
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which is the Indian adaptation of Wechsler’s scale, after adjusting for confounders like socio-
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economic class, type of house, ventilation of house, type of kitchen, ventilation of kitchen
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and exposure to tobacco smoke inside the house. Biological plausibility exists between
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outdoor air pollution and cognition and similar pathway may exist for indoor air pollution too
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(Chen et al., 2009). Studies have also shown extra-pulmonary translocation of inhaled metal
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particles to brain through olfactory pathways may affect the IQ of the children (Oberdörster
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et al., 2004). Previous studies have shown association between NO2 exposure during both
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prenatal and postnatal period and impaired neurodevelopment in children (Sentís et al., 2017;
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Loftus et al., 2018). Exposure to fine PM during fetal life also led to structural alterations in
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the brain (Guxens et al., 2018). Studies have also shown that urban air pollution may lead to
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cognitive disorders in children (Annavarapu and Kathi., 2016; Pujol et al., 2016; Lubczyńska
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et al., 2017). This study thereby establishes the impact of exposure to solid biomass fuel on
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the cognitive functioning of brain to some extent.
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Reduced arithmetic function and digit span can profoundly affect the child’s academic
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performance which can affect their future job prospects in the current competitive world.
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Maze test in the performance part of the IQ scale can be related to their analytical function 11
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which when affected can impair their thinking process and future decision making for
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themselves and their dependents. Such decreased productivity of the individuals on a large
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scale can contribute to under-performing population which can have an adverse effect on the
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nation’s productivity as a whole.
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It was also found that factors like literacy of mother, place of delivery, exclusive
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breastfeeding of the child and immunization had no association with the type of fuel used.
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Further, the current study shows that 67.6% had institutional delivery, 95.37% had
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exclusively breastfed their children and 99% of children were fully immunized. These
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improvements can be attributed to improved quality and access to health care in rural areas
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through integrated care through Government of India’s National Rural Health Mission
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(NRHM). Smoking was observed to be prevalent in the community with beedi being the
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more commonly smoked form of tobacco among the rural population.
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Socioeconomic status shows a significant association (p<0.001) with the choice of fuel, with
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people opting for cheaper solid biomass fuel. However, a recent study by Sharma et al.
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(2020) reports that uses of solid biomass fuels may not cheap as compared to LPG. The non-
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availability and the high cost of LPG cylinders coupled with cheap and easy availability of
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solid biomass fuels in villages in India are also seen as a major obstacle towards people’s
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choice to switch to cleaner LPG fuel, despite their willingness to do so. The reasons cited for
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not using kerosene as their source of fuel were non-availability and difficulty in procuring
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kerosene. Recently Government of India has launched several scheme o enhance the use of
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clean fuel such as Pradhan Mantri Ujjwala Yojana (PMUY); Give-it-Up (GIU), and direct
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benefit transfer of LPG (DBTL) as detailed by (Smith et al., 2017; Ravindra and Smith
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(2018). However, it was also observed during the study that free meals provided to
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government school children at their schools under Government of India’s flagship Mid-Day
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Meal program food is commonly prepared using solid biomass fuels. Hence, there is a need
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to extend the scope of clean fuel programs for Mid-Day Meal program.
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Although there was no significant association in the overall IQ between the two groups, the
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mean IQ of the children exposed to solid biomass fuels was less when compared to those
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using LPG as the fuel. The mean total Verbal score comprising the individual scores of
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Information, Comprehension, Arithmetic, Similarities, Vocabulary, and Digit Span were
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found to be decreased insignificantly in the solid biomass group compared with the LPG
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group. Similarly, the Performance score comprising the scores of Picture completion, Block
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design, Object Assembly, Coding, and Maze were also decreased insignificantly in the solid
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biomass group compared with the LPG group.
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The insignificant results found in the current study is most probably due to less sample size
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as the confidence intervals were wider. Thus, a similar study with a large sample size will be
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helpful in better understanding the association of IQ and the main cooking fuel used by the
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households. Despite the small size of the study, this study is one of its kind from South Asian
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countries where solid biomass fuel for cooking is more common and for the first time has
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shown that there is some evidence of association of cooking fuel and cognitive function of
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children in the age of 6 -8 years. Environmental exposures like lead and pesticides were not
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assessed in this study, assuming equal exposure among the study population.
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LIMITATIONS:
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Though outdoor air pollution due to factories and heavy vehicular traffic was not present at
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the place of study and the level of pollution was not taken into consideration, it was assumed
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to have an equal impact on all the children geographically. The level of exposure due to solid
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biomass smoke measured in terms of PM2.5 and PM10 levels could have been a better estimate 13
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of the exposure levels but was not carried due to financial constraints. Lead and pesticide
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exposure to the children was a known factor to affect children’s cognition but was not taken
311
into consideration in this study, assuming to have an equal impact on all the children. Air
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velocities inside homes were not monitored and thus in-home observation approach on the
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basis of number, size, and orientation of windows provides a qualitative assessment of
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ventilation in homes.
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RECOMMENDATIONS:
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More studies with varied methodology with larger sample size and accurate measurements of
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toxic chemicals in the smoke and particulate matters (PM2.5 and PM10) may provide further
318
insight into the effects of solid biomass fuel on neurological development.
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Effective government policies are needed to reduce the use of solid biomass fuels in rural
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areas and switch over to cleaner fuel to increase productivity, decrease morbidity and tackle
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climatic changes. Further, efforts should be made to retain the LPG users, who have opted for
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it addressing various barriers as highlighted by Ravindra et al. (2019).
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Contributorship Statement: AP conducted the field work and wrote the first draft of the
325
manuscript. PVML, PM, and KR helped to develop the intellectual content of the protocol
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and manuscript including review/editing.
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Data Sharing Statement: A MD thesis is available on the topic and can be provided by e-
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mail to Dr. PVML.
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Ethical approval: The protocol was approved by Dissertation Approval Committee of the
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Institute and Departmental Peer Review committee.
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475
Table 1: Association of socio-demographic characteristics, birth history and health
476
characteristics of the respondents with the type of fuel used for cooking. Total (N=108)
LPG (N=24)
Bio-mass (N=84)
Male
49 (45.4%)
14 (58.3%)
35 (41.7%)
Female
59 (54.6%)
10 (41.7%)
49 (58.3%)
6
31 (28.7%)
9 (37.5%)
22 (26.2%)
7
49 (45.4%)
10 (41.7%)
39 (46.4%)
8
28 (25.9%)
5 (20.8%)
23 (27.4%)
1
34 (31.5%)
8 (33.3%)
26 (31%)
2
45 (41.7%)
10 (41.7%)
35 (41.7%)
≥3
29 (26.9%)
6 (25%)
23 (21.3%)
Upper
26 (24.1%)
6 (25%)
20 (23.8%)
Middle
33 (30.6%)
14 (58.3%)
19 (19%)
Lower
49 (45.4%)
4 (16.7%)
45 (53.6%)
of Literate
84 (77.8%)
21 (87.5%)
63 (75%)
Illiterate
24 (22.2%)
3 (12.5%)
21 (25%)
Present
9 (8.3%)
1 (4.2%)
8 (9.5%)
Absent
99 (91.7%)
23 (95.8%)
76 (90.5%)
73 (67.6%)
20 (83.3%)
53 (63.1%)
35 (32.4%)
4 (16.7%)
31 (36.9%)
91 (84.3%)
21 (87.5%)
70 (83.3%)
3 (12.5%)
14 (16.7%)
Characteristics Gender
Age
Birth order
Socioeconomic status Literacy mother Pallor
Place delivery
of Hospital
Mode delivery
of Normal vaginal
Home
Caesarean 17 (15.7%) Exclusive breast feeding Fully immunized
477
Yes
103 (95.4%)
24 (100%)
79 (94%)
No
5 (4.6%)
0 (0%)
5 (6%)
Yes
107 (99.1%)
24 (100%)
83 (98.8%)
No
1 (0.9%)
0 (0%)
1 (1.2%)
χ2
df
Pvalue
2.09
1
0.17
1.23
2
0.54
0.07
2
0.96
13.41
2
0.001*
1.69
1
0.27
0.70
1
0.68
3.50
1
0.08
0.24
1
0.76
1.50
1
0.59
0.30
1
1.000
* indicatesstatistical significance at p<0.05
19
479
Table 2: Association of household, kitchen and stove characteristics with the type of fuel
480
used for cooking Total (N=108)
Characteristics
Type of house
Kucha/ semi- 40 (37%) pucca 68 (63%) Pucca 1-2
Number of rooms
3-4 >=5 1-2
Number of doors
3-4 >5
34 (31.5%) 48 (44.4%) 26 (24.1%) 42 (38.9%) 41 (38%)
25 (23.1%) Poor 28 (25.9%) Ventilation of the Moderate 61 house (56.5%) Good 19 (17.6%) Indoor with 30 partition (27.8%) Indoor 12 without (11.1%) partition Separate 50 Type of Kitchen indoor kitchen (46.3%) outside the house Open air 16 kitchen (14.8%) outside the house No. of 0 - 1 57 windows/openings (61.0%)
Biomass (N=84) 2 (8.3%) 38 (45.2%) 22 46 (91.7%) (54.8%) 5 29 (20.8%) (34.5%) 15 33 (62.5%) (39.3%) 4 22 (16.7%) (26.2%) 6 (25%) 36 (42.9%) 12 (50%) 29 (34.5%) 6 (25%) 19 (22.6%) 2 (8.3%) 26 (31%) 13 48 (54.2%) (57.1%) 9 10 (37.5%) (11.9%) 11 19 (45.8%) (22.6%) 6 (25%) 6 (7.1%) LPG (N=24)
7 (29.2%)
43 (51.2%)
0 (0%)
16 (19%)
16 (66.7%)
41 (60.3%)
χ2
df
pvalue
10.90 1
0.001*
4.08
2
0.13
2.75
2
0.25
10.66 2
0.005*
15.51 3
0.001*
0.31
0.63
1
20
2 -3
42 (45.7%) 31 (28.7%) 52 (48.1%) 25 (23.1%) 62 (57.4%) 46 (42.6%) 45 (41.7%)
8 (33.3%) 4 (16.7%) 10 (41.7%) 10 (41.7%) 18 (75%)
16 (66.7%)
27 (39.7%) 27 (32.1%) 42 (50%) 15 (17.9%) 44 (52.4%) 40 (47.6%) 29 (34.5%)
63 (58.3%)
8 (33.3%)
55 (65.5%)
49 (45.4%) Mosquito coils and incense sticks 59 Present (54.6%) * indicatesstatistical significance at p<0.05
8 (33.3%) 16 (66.7%)
41 (48.8%) 43 (51.2%)
Poor Ventilation of the Moderate kitchen Good Oil/candle lighting
for Absent Present
Tobacco smoking Absent inside the house Present Absent
481
6 (25%)
6.40
2
0.041*
3.91
1
0.06
7.94
1
0.009*
1.80
1
0.25
21
Table 3: Association of socio-demographic characteristics with the IQ of the
Characteristics Gender
Age
Birth order
Socio-economic status
Literacy of mother
Pallor
Place of delivery
Mode of delivery
Mean IQ
SD
Male
89.02
10.71
Female
83.40
7.52
6
89.37
8.67
7
87.03
9.82
8
92.93
6.92
1
88.87
10.63
2
90.93
7.24
≥3
87.01
9.41
Upper
91.96
8.97
Middle
90.25
10.61
Lower
87.09
7.54
Literate
89.10
9.58
Illiterate
89.69
7.12
Present
90.91
8.58
Absent
89.08
9.13
Hospital
90.06
9.86
Home
87.43
6.93
Normal vaginal
88.94
9.28
Caesarean
90.80
7.87
89.40
9.16
No
85.75
6.45
Yes
89.22
9.11
No
89.53
-
breast Yes
Exclusive feeding
Fully immunized
t
df
p-value
-0.21
1
0.83
3.994#
2
0.021*
1.70#
2
0.19
2.85#
2
0.06
0.28
1
0.78
0.58
1
0.57
1.38
1
0.17
-0.78
1
0.44
-0.88
1
0.38
0.03
1
0.97
respondents #ANOVA
F statistics* indicates statistical significance at p<0.05
22
Table 4: Association of household, kitchen and stove characteristics with IQ of the respondents Characteristics
Mean IQ score
SD
F
df(between, p-value within)
Type of house
88.43
5.68
0.49
1,106
0.49
89.70 87.46 90.32 89.52 87.62 89.98 90.71 86.15 90.05 91.11 89.85
10.58 8.97 9.82 7.63 8.66 9.25 9.40 7.04 8.43 12.53 11.24
1.01
2,105
0.37
1.14
2,105
0.33
2.33
2,105
0.10
1.65
3,104
0.18
91.38
9.99
89.77
7.32
0.59
1,90
0.45
0.05
2,105
0.95
3.06
1,106
0.08
Kucha/ semipucca Pucca Number of rooms 1-2 3-4 >=5 Number of doors 1-2 3-4 >5 Ventilation of the Poor house Moderate Good Indoor with Type of Kitchen partition Indoor without partition Separate indoor kitchen outside the house Open air kitchen outside the house No. of 0 - 1 windows/openings 2 -3 Ventilation of the Poor kitchen Moderate Good Oil/candle for Absent
84.767 8.21 89.44
9.40
90.93 88.82 89.45 89.27 90.53
8.44 6.29 9.46 11.26 9.26
23
lighting
Present
87.47
8.59
Tobacco smoking Absent inside the house
90.71
10.82
Present
88.17
7.49
Mosquito coils Absent and incense sticks Present
89.98
9.60
88.61
8.63
2.08
1,106
0.15
0.6
1,106
0.44
24
Cooking fuel Components
95% Confidence Interval of the Difference
t
df
pvalue
0.44
-1.71
106
0.09
-12.31
13.25
0.08
106
0.94
82.04
-10.70
-1.64
-2.78
106
0.009*
100.77
99.42
-5.59
8.30
0.39
106
0.70
Vocabulary
72.56
75.29
-7.24
1.77
-1.20
106
0.23
Digit span
85.64
94.21
-15.86
-1.27
-2.40
106
0.023*
Picture completion
103.64
105.42
-9.97
6.42
-0.43
106
0.67
Block design
89.50
97.88
-21.95
5.20
-1.22
106
0.22
Object assembly
81.8
84.13
-9.68
5.05
-0.62
106
0.54
Coding
101.76
107.04
-14.99
4.43
-1.08
106
0.28
Maze
84.45
94.67
-18.21
-2.22
-2.61
106
0.014*
Total verbal
84.30
87.37
-8.46
2.32
-1.17
106
0.25
Total performance
92.23
97.83
-11.64
0.46
-1.83
106
0.07
Full scale IQ
88.27
92.60
-9.83
1.17
-1.61
106
0.12
Biomass
LPG
Mean IQ
Mean IQ
Lower
Upper
Information
82.36
85.13
-5.97
Comprehension
88.60
88.13
Arithmetic
75.87
Similarities
Table 5: Association of IQ components with type of fuel used for cooking * indicatesstatistical significance at p<0.05
25
Table 6: Association of IQ components with the type of fuel used for cooking after adjusting for confounders 95% Mean Components
Difference IQ Scores
Confidence
Std.
Interval for Mean
Error
Difference Lower
Upper
t
pvalue
Information
-0.99
2.19
-5.33
3.35
-0.45
0.65
Comprehension
-3.21
5.62
-14.37
7.96
-0.57
0.57
Arithmetic
-5.58
2.58
-10.71
-0.46
-2.17
0.033*
Similarities
1.08
4.91
-8.68
10.83
0.22
0.83
Vocabulary
-4.81
3.19
-11.14
1.53
-1.52
0.13
Digit span
-7.64
3.95
-15.49
0.20
-1.94
0.06
6.65
6.01
-5.28
18.58
1.11
0.27
Block design
-8.03
9.49
-26.89
10.83
-0.85
0.4
Object assembly
0.94
5.44
-9.87
11.75
0.17
0.86
Coding
-0.57
5.57
-11.65
10.50
-0.10
0.92
Maze
-4.56
4.55
-13.60
4.48
-1.00
0.32
Total verbal
-3.53
2.63
-8.76
1.71
-1.34
0.18
-1.12
4.18
-9.42
7.19
-0.27
0.79
-2.32
2.89
-8.05
3.41
-0.80
0.42
Picture completion
Total performance Full scale IQ
Adjusted for sex, mother’s literacy, socioeconomic status, smoking inside the house, oil and candle use for lighting, use of coils or incense sticks, type of house, ventilation of the house, type of kitchen and ventilation of the kitchen
26
Figure 1: Overview of the methodology
500 501
Visiting primary school
502
Informed consent from principal of the school
503
Research Highlights
504
Selecting eligible children in age group 6-8 years
505 506
There are limited evidence on HAP & Cognitive performance of children
507
Exposure to HAP significantly affects IQ among children
508
Low IQ during childhood predicts poor academic performance during later
Getting address of the eligible children
years of life
509 510
HAP significantly effects the maze, digit span & arithmetic component of IQ
511 512
Visiting the child’s home
Consent for study from the parents
Cognitive defects in children due to HAP exposures could be prevented Data collection from mother using a questionnaire
513 514
Measuring the child’s IQ using MISIC
Analysis of data
27
S0048-9697(19)35102-2 https://doi.org/10.1016/j.scitotenv.2019.135110 STOTEN 135110
To appear in:
Science of the Total Environment
Received Date: Revised Date: Accepted Date:
1 August 2019 2 October 2019 20 October 2019
Please cite this article as: A. Brabhukumr, P. Malhi, K. Ravindra, P.V.M. Lakshmi, Exposure to Household Air Pollution During First 3 Years of Life and IQ Level among 6-8-Year-Old Children in India - A Cross-Sectional Study, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.135110
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© 2019 Published by Elsevier B.V.
1
Exposure to Household Air Pollution During First 3 Years of Life and IQ Level among
2
6-8-Year-Old Children in India - A Cross-Sectional Study
3
Ajith Brabhukumr1, Prabhjot Malhi2, Khaiwal Ravindra3, PVM Lakshmi4
4 5
1Resident
6
2Professor
7 8
3Additional
9 10
Doctor (Community Medicine), Department of Community Medicine and School of Public Health, PGIMER, Chandigarh (Child Psychology), Department of Paediatrics, PGIMER, Chandigarh
Professor (Environment Health), Department of Community Medicine and School of Public Health, PGIMER, Chandigarh
4Professor
(Epidemiology), Department of Community Medicine and School of Public Health, PGIMER, Chandigarh
11 12
Corresponding Author: Dr. P.V.M. Lakshmi, Additional Professor of Epidemiology,
13
Department of Community Medicine and School of Public Health, Post Graduate Institute of
14
Medical Education & Research, Chandigarh Email: [email protected] Mobile
15
number: +919914208225
16
Running title: Association Between Exposure to Household Air Pollution Due to Solid
17
Biomass Smoke During First 3 Years of Life and its association with IQ
18
Acknowledgment: We greatly acknowledge the support from the State School Authority of
19
Haryana and all the stakeholders who participated in this study. RK would like to thank
20
Indian Council of Medical Research (ICMR), Ministry of Health and Family Welfare, for
21
funding the linked study on ‘Assessment of Impact of PMUY’ via letter number No.
22
58/11/NTF-LPG/2019-NCD-II dated 28/05/19.
23
Funding statement: This research received no specific grant from any funding agency in the
24
public, commercial or not-for-profit sectors.
25
Competing interests: There are no competing interests.
26
1
27
ABSTRACT:
28
Many illnesses have been attributed to the exposure of solid biomass smoke but the effect on
29
intelligence has largely been unexplored. The study aims to examine the effect of exposure to
30
solid biomass smoke during the first 3 years of life on intelligence among 6-8-year-old
31
children. Children aged 6-8 years were enrolled from a primary school and their houses were
32
visited to collect data on socio-economic status and household exposure assessment.
33
Households using LPG as cooking fuel were considered as the unexposed group. All the
34
children were tested for their Intelligence Quotient (IQ) using Malin's Intelligence Scale for
35
Indian Children (MISIC). The mean IQ was calculated as the average of Verbal and
36
Performance score. Potential confounders were adjusted using multivariate general linear
37
model. About 45% of children had average or above-average IQ while the rest had below-
38
average IQ. The mean scores for the arithmetic component of IQ were found to be
39
significantly lower among solid biomass fuel users as compared to LPG users after adjusting
40
for confounders. The mean IQ of LPG users were 5.58 points higher (95% CI: 0.46 – 10.1)
41
for the arithmetic component as compared to solid biomass users. Children living in the
42
houses using solid biomass fuel for cooking have lower IQ as compared to the children living
43
in the houses using LPG for cooking for arithmetic component even after adjusting for
44
potential confounders.
45
Keywords: Solid Biomass Fuel, Children, Intelligence Quotient, Arithmetic component,
46
PMUY
2
48
INTRODUCTION
49
The recent WHO report highlights that 4.3 million premature deaths could be associated with
50
the use of solid biomass fuel for cooking. According to the WHO report, about 3 billion
51
people burn solid biomass for cooking and heating which include wood, animal dung, and
52
crop waste. Most of these people are poor, and live in developing countries (WHO. 2011).
53
The Lancet Commission on pollution and health and Global Burden of Disease estimate also
54
identify household air pollution as a major risk factor for human health (Landrigan et al.,
55
2018; Cohen et al., 2017). Though outdoor air pollution has long been known to cause
56
several diseases including respiratory, cardiovascular and neurological conditions there are
57
limited studies linking the illnesses caused by indoor or household air pollution (HAP). The
58
health effects of solid biomass smoke include respiratory illness in children and adults,
59
structural birth defects, low birth weight and infant mortality, nutritional deficiencies,
60
interstitial lung disease, Chronic Obstructive Pulmonary Disease (COPD), pulmonary
61
tuberculosis, lung cancer, nasopharyngeal and laryngeal cancers, cardiovascular diseases,
62
cataracts and exacerbation of health effects of HIV infection (Fullerton et al., 2008; Kaur-
63
Sidhu et al., 2019).
64
Several toxic substances which are released during combustion could be responsible for a
65
range of physical and mental deficits in children. There are studies linking nitrogen dioxide in
66
indoor air pollution with mental retardation and Attention Deficit Hyperactivity Disorder
67
(ADHD) (Morales et al., 2009). Exposure to second-hand tobacco smoke is a well-known
68
factor that has detrimental effects on children’s cognition (Chen et al., 2013). Though there
69
are some studies on the effect of outdoor air pollution (Gonzalez et al., 2015), urban
70
vehicular pollution (Garcidueñas et al., 2016) and environmental tobacco smoke
71
(Jedrychowski et al., 2014), on cognitive and academic performance of students, the
72
association of HAP (Vrijheid et al., 2012) and the cognitive performance has not yet been 3
73
fully explored. Further, most of the studies have assessed the effects of nitrogen dioxide
74
(NO2) and particulate matter (PM) on the cognitive disorders in children exposed during both
75
the prenatal and postnatal period. Few studies have also focussed on the elemental
76
composition of PM that affects the developing brain of the children (Bellinger, 2018). Studies
77
focussing on comparison of different types of fuels (LPG, Solid biomass fuel) and their
78
cognitive effects on children have not been studied previously.
79
Physical and mental development is more rapid in infants under one year of age than at any
80
other age and hence they are highly vulnerable to the effects of HAP toxic substances that
81
can interfere with the biological systems. Developmental and neurological toxins present in
82
HAP are likely of greater concern to the developing fetus because of the even more rapid
83
physical and brain development that occurs in-utero. Adding to this risk, the infants are
84
almost always carried by their mothers or allowed to roam around or play around them
85
wherever they work, which mostly involve cooking in rural areas of India. This practice
86
exposes the child to harmful HAP, which can be detrimental to their development. Studies
87
have also shown that women exposed to solid biomass smoke suffer more from health and
88
respiratory illness compared to women using other fuels, due their poorly ventilated kitchen
89
in their house (Sukhsohale et al., 2013).
90
Thus, exposure to solid biomass fuel during fetal life and during early life can produce
91
children with neurological deficits, which can, in turn, affect the society, economy, and
92
growth of the country to a large extent since less intelligent children by being less productive,
93
can further hinder development in developing countries or Least Developed Countries
94
(LDCs). Hence, the World Health Organization (WHO, 2018) also stress that efforts should
95
be made to prevent the HAP exposure to improve their health. In India, 72.2% of population
96
reside in rural areas with widespread use of solid fuel, and 86.5% of the rural people use solid
97
biomass fuel for cooking (Census of India, 2011; Ravindra et al., 2019a). Further, cooking 4
98
with solid biomass fuel also alter the thermal comfort of rural household and could
99
synergistically add to adverse health impact of HAP (Ravindra et al., 2019b) Thus,
100
intervention on the usage of solid biomass fuel gains significance in the step towards
101
prevention of health problems due to their use and betterment of health of the most
102
vulnerable sections of the society, i.e. the women and the children.
103
Hence, there is a need to look into the association between household air pollution due to
104
solid biomass smoke and its effects. Thus, the current study aims to investigate the
105
association of HAP during first three years of life with cognitive development at the age of 6-
106
8 years.
107
MATERIALS AND METHODS
108
Participants and Recruitment:
109
A cross-sectional study was conducted from July 2012 to June 2013. Children from two
110
villages (Kheri and Samlehri), where the Department of Community Medicine and School of
111
Public Health, PGIMER is providing community-based health services were selected for the
112
study. The estimated sample size was 108, assuming an anticipated effect size (Cohen’s f2)
113
for multiple regression as 0.15, with the power of the study as 80%, 95% Confidence Interval
114
and the number of predictors as 7. The study participants included 6-8-year-old children.
115
Eligible study participants were recruited from the primary school in the area after verifying
116
their date of birth, address and obtaining consent from the concerned authorities (school
117
principal) and the parents. Children who had an acute illness at the time of enrolment were
118
contacted again after recovery from illness. There was no non-response in the study.
119
Measurement of exposure variables:
5
120
Socio-economic status was calculated using Udai-Pareek scale, a standardized scale for
121
Indian rural setting. The questionnaire was filled by the interviewer based on the interview of
122
the mother and observation of the household by the investigator (Pareek & Trivedi, 1964).
123
The questionnaire developed by Sri Ramachandra Medical College and Research Institute,
124
Chennai for household exposure assessment was adapted for the study (Balakrishnan et al.,
125
2004). The information about the family profile, components to assess the socio-economic
126
status of the family, housing characteristics, kitchen characteristics, the main source of fuel
127
used for cooking, the time spent for cooking, usage of incense sticks and mosquito coils in
128
house, the source of lighting in the home, presence of smokers in home and the number of
129
hours smokers smoke inside the house, the birth history and birth order of the child etc. were
130
collected using the questionnaire (Supplementary Annexure 1). Details on air pollutant levels
131
in houses using solid biomass and LPG as fuels have been discussed in detail in other studies
132
(Ravindra, 2019; Ravindra et al., 2019c; Sidhu et al., 2017). The kitchen characteristics with
133
particular focus on ventilation of the kitchen were recorded. Ventilation was graded based on
134
the number and size of the windows. Ventilation was graded as good if there are two or more
135
windows of adequate size present, presence of exhaust fan or chimneys and open-air outdoor
136
kitchen, moderate if only one window was present and poor if no window was present or
137
presence of windows of very small size. Only those children from households where cooking
138
fuel or housing characteristics have not changed over the past 3-5 years were included in the
139
study.
140
Measurement of outcome variable:
141
Those using LPG as cooking fuel were considered as the unexposed group. All the children
142
were tested for their IQ using Malin's Intelligence Scale for Indian Children (MISIC) (Malin.,
143
1971). MISIC is the Indian adaptation of the Weschler Intelligence Scale for Children
6
144
(WISC). Picture arrangement present in WISC was excluded in this version and some items
145
were adapted to suit the Indian culture. The scale consists of two major components, the
146
Verbal score, and Performance score. The Verbal score has the components of Information,
147
Comprehension, Arithmetic, Similarities, Vocabulary, and Digit Span. The Performance
148
score has the components of Picture completion, Block design, Object assembly, Coding and
149
Maze. The mean IQ was calculated as the average of Verbal and Performance score. The
150
scores were calculated as percentiles according to the age of the child as shown in Figure 1.
151
The person who administered the IQ test was trained and certified for conducting an IQ test
152
by a qualified child psychologist.
153
Analysis:
154
The main fuel used for cooking i.e. either solid biomass or LPG were taken as exposure
155
variables, and IQ was taken as the outcome variable. Independent t-tests were done to test
156
whether the mean IQ scores are different between solid biomass and LPG users. The
157
covariates included in the study were socio-economic status, age and gender of the child,
158
exposure to tobacco smoke inside home, ventilation and type of kitchen, anthropometric
159
measurements (Height and Weight), presence of pallor, use of mosquito coils and incense
160
sticks, source of lighting in the house, mode of delivery, place of delivery, and the
161
educational status of the mother. The confounders which were found to be significantly
162
associated with the outcome and the exposure in the bivariate analysis or known confounders
163
according to the literature were adjusted by using multivariate general linear model using
164
SPSS version 17.
165
RESULTS
7
166
The socio-demographic characteristics, birth history and health characteristics of the
167
respondents are shown in Table 1. There were more females (55%), compared to males
168
(45%). The mean age of the respondents was 7 years (SD: 0.742 years). Three fourth of the
169
respondents belong to either middle or lower socio-economic status. Most of the mothers
170
were literate (78%). Most of the respondents were born by normal vaginal delivery (84%)
171
and two-thirds of them were born in hospitals (68%). Nearly three-fourths of the children
172
belong to the birth order 1 or 2. Almost all of the respondents were exclusively breastfed
173
(95%). All of the respondents were fully immunized except one. Only 8% of the respondents
174
had pallor. The mean height of the respondents was 116.6cm (SD: 3.2cm). The mean weight
175
of the respondents was 18.7 kg (SD: 2.1kg).
176
The household and kitchen characteristics of the respondents are shown in Table 2. Nearly
177
two-thirds of the respondents live in pucca houses. About 75% of the households had less
178
than 5 rooms. Only 18% of the households had good ventilation. About 43% of the
179
respondents use oil or candle for lighting. In 58% of the households, there was someone in
180
the family who smoke tobacco inside the house. Fifty-five percent of the households burn
181
either mosquito repellant coils or incense sticks in their house.
182
About 40% of the households have an indoor kitchen with or without partition from the rest
183
of the house. Among the households surveyed 16% of the kitchens do not have either
184
window or opening. Less than a quarter of kitchens have good ventilation. Many of the solid
185
biomass stoves (71%) are of fixed type. More than half of the stoves were of simple type
186
(55%). Most of the stoves (95%) were made of mud. Households very rarely (1%) used solid
187
biomass stove for heating purposes. One-fourth (25%) of households used solid biomass
188
stove for cooking food for their cattle.
8
189
There were no significant differences in the distribution of sex, age group, birth order,
190
literacy of the mother, place of delivery, mode of delivery, exclusive breastfeeding practices
191
and immunization history of the respondents among LPG and non-LPG users. The
192
socioeconomic status of the respondents was associated with the type of cooking fuel used.
193
Most of the respondents who use solid biomass fuel belong to the low socioeconomic status
194
(54%) whereas most of the LPG users belong to either middle or upper socioeconomic status
195
(83%).
196
There was no association with the number of rooms/ doors and the use of mosquito coils/
197
incense sticks with the type of fuel used for cooking. There was a statistically significant
198
association between the type of house, ventilation of the house and smoking inside the house
199
with main fuel used for cooking. About 91% of LPG users had pucca houses, while only 46%
200
of solid biomass users had a pucca house (p-value: 0.001). About one-third of the households
201
using solid biomass fuel had poor ventilation as compared to only 8% households in the LPG
202
users (p-value: 0.005). Only one-third of LPG users had someone in the family who smoke
203
indoors as compared to 66% among solid biomass users (p-value: 0.009).
204
The kitchen characteristics of the LPG users and the solid biomass fuel users were
205
significantly different. An indoor kitchen was present in 70% of LPG users whereas it was
206
present in only 30% of the users of solid biomass fuel. The ventilation of the kitchen was
207
poor in 32% of solid biomass fuel users whereas it was 17% among LPG users.
208
There was no association of the socio-demographic and household characteristics with the IQ
209
of the respondents except for age. The mean IQ of the respondents increased with increase in
210
the age of the respondents. The mean IQ of the respondents along with SD according to
211
socio-demographic, household and kitchen characteristics were given in Table 3 and Table 4.
9
212
About 45% of children had average or above-average IQ while the rest fell under borderline
213
and below average.
214
The mean IQ of the various components of IQ of solid biomass fuel users and LPG users was
215
compared and the results are presented in Table 5. Though there was no significant difference
216
in the mean of the overall IQ of the respondents between LPG and solid biomass fuel users,
217
there were significant differences in the mean scores of arithmetic, digit span and maze
218
components of IQ. The mean scores obtained in the solid biomass fuels in these components
219
were significantly lower than the mean scores obtained by the LPG users. Multivariate
220
analysis was used to adjust for confounders. The confounders used for adjustment were age
221
and sex of the respondent, mother’s literacy, socioeconomic status, smoking inside the house,
222
oil and candle use for lighting, use of a coil or incense sticks, ventilation of the house, type of
223
house, type of kitchen and ventilation of kitchen. After adjusting for confounders, the mean
224
score obtained for the arithmetic component was significantly lower among solid biomass
225
users as compared to LPG users. The results of the multivariate analysis were presented in
226
Table 6. The mean IQ of LPG users was 5.58 points higher (95% CI: 0.46 – 10.1) for the
227
arithmetic component of IQ as compared to solid biomass users.
228
Thus, it can be concluded that the IQ of the children measured at the age of 6-8 was
229
significantly lower among children who were exposed to solid biomass fuel smoke as
230
compared to those who were not exposed, especially for the arithmetic IQ.
231 232
DISCUSSION
233
The IQ of the children exposed to the smoke of solid biomass fuel used for cooking is on
234
average 5.5 points lower for the arithmetic component of IQ as compared to those who were
235
exposed to smoke of LPG fuel used for cooking. An earlier study had shown the 10
236
neuropsychological impact on exposure to carbon monoxide from kerosene stoves, in which
237
digit span along with few other components in Wechsler’s intelligence scale were found to be
238
affected (Amitai et al., 1998). A similar study on Carbon monoxide exposure from wood
239
smoke had shown negative co-relation with digit span among three tests, Coding, Symbol
240
Search and Digit Span in Wechsler scale, that were administered. Other cognition function
241
affected were visuospatial integration, short-term and long-term memory recall and fine
242
motor performance from other cognition tests (Dix-Cooper et al., 2012).
243
The present study has shown the impact of solid biomass fuels including the commonly used
244
cow dung and wood, on the arithmetic function (p<0.033) among the 11 tests in Malin’s scale
245
which is the Indian adaptation of Wechsler’s scale, after adjusting for confounders like socio-
246
economic class, type of house, ventilation of house, type of kitchen, ventilation of kitchen
247
and exposure to tobacco smoke inside the house. Biological plausibility exists between
248
outdoor air pollution and cognition and similar pathway may exist for indoor air pollution too
249
(Chen et al., 2009). Studies have also shown extra-pulmonary translocation of inhaled metal
250
particles to brain through olfactory pathways may affect the IQ of the children (Oberdörster
251
et al., 2004). Previous studies have shown association between NO2 exposure during both
252
prenatal and postnatal period and impaired neurodevelopment in children (Sentís et al., 2017;
253
Loftus et al., 2018). Exposure to fine PM during fetal life also led to structural alterations in
254
the brain (Guxens et al., 2018). Studies have also shown that urban air pollution may lead to
255
cognitive disorders in children (Annavarapu and Kathi., 2016; Pujol et al., 2016; Lubczyńska
256
et al., 2017). This study thereby establishes the impact of exposure to solid biomass fuel on
257
the cognitive functioning of brain to some extent.
258
Reduced arithmetic function and digit span can profoundly affect the child’s academic
259
performance which can affect their future job prospects in the current competitive world.
260
Maze test in the performance part of the IQ scale can be related to their analytical function 11
261
which when affected can impair their thinking process and future decision making for
262
themselves and their dependents. Such decreased productivity of the individuals on a large
263
scale can contribute to under-performing population which can have an adverse effect on the
264
nation’s productivity as a whole.
265
It was also found that factors like literacy of mother, place of delivery, exclusive
266
breastfeeding of the child and immunization had no association with the type of fuel used.
267
Further, the current study shows that 67.6% had institutional delivery, 95.37% had
268
exclusively breastfed their children and 99% of children were fully immunized. These
269
improvements can be attributed to improved quality and access to health care in rural areas
270
through integrated care through Government of India’s National Rural Health Mission
271
(NRHM). Smoking was observed to be prevalent in the community with beedi being the
272
more commonly smoked form of tobacco among the rural population.
273
Socioeconomic status shows a significant association (p<0.001) with the choice of fuel, with
274
people opting for cheaper solid biomass fuel. However, a recent study by Sharma et al.
275
(2020) reports that uses of solid biomass fuels may not cheap as compared to LPG. The non-
276
availability and the high cost of LPG cylinders coupled with cheap and easy availability of
277
solid biomass fuels in villages in India are also seen as a major obstacle towards people’s
278
choice to switch to cleaner LPG fuel, despite their willingness to do so. The reasons cited for
279
not using kerosene as their source of fuel were non-availability and difficulty in procuring
280
kerosene. Recently Government of India has launched several scheme o enhance the use of
281
clean fuel such as Pradhan Mantri Ujjwala Yojana (PMUY); Give-it-Up (GIU), and direct
282
benefit transfer of LPG (DBTL) as detailed by (Smith et al., 2017; Ravindra and Smith
283
(2018). However, it was also observed during the study that free meals provided to
284
government school children at their schools under Government of India’s flagship Mid-Day
12
285
Meal program food is commonly prepared using solid biomass fuels. Hence, there is a need
286
to extend the scope of clean fuel programs for Mid-Day Meal program.
287
Although there was no significant association in the overall IQ between the two groups, the
288
mean IQ of the children exposed to solid biomass fuels was less when compared to those
289
using LPG as the fuel. The mean total Verbal score comprising the individual scores of
290
Information, Comprehension, Arithmetic, Similarities, Vocabulary, and Digit Span were
291
found to be decreased insignificantly in the solid biomass group compared with the LPG
292
group. Similarly, the Performance score comprising the scores of Picture completion, Block
293
design, Object Assembly, Coding, and Maze were also decreased insignificantly in the solid
294
biomass group compared with the LPG group.
295
The insignificant results found in the current study is most probably due to less sample size
296
as the confidence intervals were wider. Thus, a similar study with a large sample size will be
297
helpful in better understanding the association of IQ and the main cooking fuel used by the
298
households. Despite the small size of the study, this study is one of its kind from South Asian
299
countries where solid biomass fuel for cooking is more common and for the first time has
300
shown that there is some evidence of association of cooking fuel and cognitive function of
301
children in the age of 6 -8 years. Environmental exposures like lead and pesticides were not
302
assessed in this study, assuming equal exposure among the study population.
303 304
LIMITATIONS:
305
Though outdoor air pollution due to factories and heavy vehicular traffic was not present at
306
the place of study and the level of pollution was not taken into consideration, it was assumed
307
to have an equal impact on all the children geographically. The level of exposure due to solid
308
biomass smoke measured in terms of PM2.5 and PM10 levels could have been a better estimate 13
309
of the exposure levels but was not carried due to financial constraints. Lead and pesticide
310
exposure to the children was a known factor to affect children’s cognition but was not taken
311
into consideration in this study, assuming to have an equal impact on all the children. Air
312
velocities inside homes were not monitored and thus in-home observation approach on the
313
basis of number, size, and orientation of windows provides a qualitative assessment of
314
ventilation in homes.
315
RECOMMENDATIONS:
316
More studies with varied methodology with larger sample size and accurate measurements of
317
toxic chemicals in the smoke and particulate matters (PM2.5 and PM10) may provide further
318
insight into the effects of solid biomass fuel on neurological development.
319
Effective government policies are needed to reduce the use of solid biomass fuels in rural
320
areas and switch over to cleaner fuel to increase productivity, decrease morbidity and tackle
321
climatic changes. Further, efforts should be made to retain the LPG users, who have opted for
322
it addressing various barriers as highlighted by Ravindra et al. (2019).
14
324
Contributorship Statement: AP conducted the field work and wrote the first draft of the
325
manuscript. PVML, PM, and KR helped to develop the intellectual content of the protocol
326
and manuscript including review/editing.
327
Data Sharing Statement: A MD thesis is available on the topic and can be provided by e-
328
mail to Dr. PVML.
329
Ethical approval: The protocol was approved by Dissertation Approval Committee of the
330
Institute and Departmental Peer Review committee.
15
331
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Ravindra, K., Agarwal, N., Kaur-Sidhu, M., & Mor, S. (2019b). Appraisal of thermal comfort in rural household kitchens of Punjab, India and adaptation strategies for better health. Environment international, 124, 431-440. Ravindra, K., Singh, T., Mor, S., Singh, V., Mandal, T. K., Bhatti, M. S., ... & Beig, G. (2019c). Real-time monitoring of air pollutants in seven cities of North India during crop residue burning and their relationship with meteorology and transboundary movement of air. Science of The Total Environment, 690, 717-729. Sentís, A., Sunyer, J., Dalmau-Bueno, A., Andiarena, A., Ballester, F., Cirach, M., ... & Lertxundi, A. (2017). Prenatal and postnatal exposure to NO2 and child attentional function at 4–5 years of age. Environment international, 106, 170-177. Sharma, D., Ravindra, K., Kaur, M., Prinja, S., & Mor, S. (2020). Cost Evaluation of Different Household Fuels and Identification of the Barriers for the Choice of Clean Cooking Fuels in India. Sustainable Cities and Society, 101825. Sidhu, M. K., Ravindra, K., Mor, S., & John, S. (2017). Household air pollution from various types of rural kitchens and its exposure assessment. Science of the Total Environment, 586, 419-429. Smith KR (2017) The Indian LPG programmes: globally pioneering initiatives. In: Ganguly A, Debroy B (eds) The Modi years, vol 3. Dr. Syama Prasad Mookerjee Research Foundation, New Delhi Suades-Gonzalez, E., Gascon, M., Guxens, M., & Sunyer, J. (2015). Air pollution and neuropsychological development: a review of the latest evidence. Endocrinology, 156(10), 3473-3482. Sukhsohale, N. D., Narlawar, U. W., & Phatak, M. S. (2013). Indoor air pollution from biomass combustion and its adverse health effects in central India: an exposure-response study. Indian journal of community medicine: official publication of Indian Association of Preventive & Social Medicine, 38(3), 162. Udai Pareek; G Trivedi. Socio-economic Status Scale (Rural). Form and manual Delhi:Manasayan. 1964:32. Vrijheid, M., Martinez, D., Aguilera, I., Bustamante, M., Ballester, F., Estarlich, M., ... & Tardon, A. (2012). Indoor air pollution from gas cooking and infant neurodevelopment. Epidemiology, 23-32. World Health Organisation. Fact sheet: Indoor air pollution and health 2011 [cited 2013 16 Dec]. Available from: http://www.who.int/mediacentre/factsheets/fs292/en/index.html. World Health Organization. (2018). Air pollution and child health: prescribing clean air: summary (No. WHO/CED/PHE/18.01). World Health Organization. https://www.who.int/ceh/publications/air-pollution-child-health/en/
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475
Table 1: Association of socio-demographic characteristics, birth history and health
476
characteristics of the respondents with the type of fuel used for cooking. Total (N=108)
LPG (N=24)
Bio-mass (N=84)
Male
49 (45.4%)
14 (58.3%)
35 (41.7%)
Female
59 (54.6%)
10 (41.7%)
49 (58.3%)
6
31 (28.7%)
9 (37.5%)
22 (26.2%)
7
49 (45.4%)
10 (41.7%)
39 (46.4%)
8
28 (25.9%)
5 (20.8%)
23 (27.4%)
1
34 (31.5%)
8 (33.3%)
26 (31%)
2
45 (41.7%)
10 (41.7%)
35 (41.7%)
≥3
29 (26.9%)
6 (25%)
23 (21.3%)
Upper
26 (24.1%)
6 (25%)
20 (23.8%)
Middle
33 (30.6%)
14 (58.3%)
19 (19%)
Lower
49 (45.4%)
4 (16.7%)
45 (53.6%)
of Literate
84 (77.8%)
21 (87.5%)
63 (75%)
Illiterate
24 (22.2%)
3 (12.5%)
21 (25%)
Present
9 (8.3%)
1 (4.2%)
8 (9.5%)
Absent
99 (91.7%)
23 (95.8%)
76 (90.5%)
73 (67.6%)
20 (83.3%)
53 (63.1%)
35 (32.4%)
4 (16.7%)
31 (36.9%)
91 (84.3%)
21 (87.5%)
70 (83.3%)
3 (12.5%)
14 (16.7%)
Characteristics Gender
Age
Birth order
Socioeconomic status Literacy mother Pallor
Place delivery
of Hospital
Mode delivery
of Normal vaginal
Home
Caesarean 17 (15.7%) Exclusive breast feeding Fully immunized
477
Yes
103 (95.4%)
24 (100%)
79 (94%)
No
5 (4.6%)
0 (0%)
5 (6%)
Yes
107 (99.1%)
24 (100%)
83 (98.8%)
No
1 (0.9%)
0 (0%)
1 (1.2%)
χ2
df
Pvalue
2.09
1
0.17
1.23
2
0.54
0.07
2
0.96
13.41
2
0.001*
1.69
1
0.27
0.70
1
0.68
3.50
1
0.08
0.24
1
0.76
1.50
1
0.59
0.30
1
1.000
* indicatesstatistical significance at p<0.05
19
479
Table 2: Association of household, kitchen and stove characteristics with the type of fuel
480
used for cooking Total (N=108)
Characteristics
Type of house
Kucha/ semi- 40 (37%) pucca 68 (63%) Pucca 1-2
Number of rooms
3-4 >=5 1-2
Number of doors
3-4 >5
34 (31.5%) 48 (44.4%) 26 (24.1%) 42 (38.9%) 41 (38%)
25 (23.1%) Poor 28 (25.9%) Ventilation of the Moderate 61 house (56.5%) Good 19 (17.6%) Indoor with 30 partition (27.8%) Indoor 12 without (11.1%) partition Separate 50 Type of Kitchen indoor kitchen (46.3%) outside the house Open air 16 kitchen (14.8%) outside the house No. of 0 - 1 57 windows/openings (61.0%)
Biomass (N=84) 2 (8.3%) 38 (45.2%) 22 46 (91.7%) (54.8%) 5 29 (20.8%) (34.5%) 15 33 (62.5%) (39.3%) 4 22 (16.7%) (26.2%) 6 (25%) 36 (42.9%) 12 (50%) 29 (34.5%) 6 (25%) 19 (22.6%) 2 (8.3%) 26 (31%) 13 48 (54.2%) (57.1%) 9 10 (37.5%) (11.9%) 11 19 (45.8%) (22.6%) 6 (25%) 6 (7.1%) LPG (N=24)
7 (29.2%)
43 (51.2%)
0 (0%)
16 (19%)
16 (66.7%)
41 (60.3%)
χ2
df
pvalue
10.90 1
0.001*
4.08
2
0.13
2.75
2
0.25
10.66 2
0.005*
15.51 3
0.001*
0.31
0.63
1
20
2 -3
42 (45.7%) 31 (28.7%) 52 (48.1%) 25 (23.1%) 62 (57.4%) 46 (42.6%) 45 (41.7%)
8 (33.3%) 4 (16.7%) 10 (41.7%) 10 (41.7%) 18 (75%)
16 (66.7%)
27 (39.7%) 27 (32.1%) 42 (50%) 15 (17.9%) 44 (52.4%) 40 (47.6%) 29 (34.5%)
63 (58.3%)
8 (33.3%)
55 (65.5%)
49 (45.4%) Mosquito coils and incense sticks 59 Present (54.6%) * indicatesstatistical significance at p<0.05
8 (33.3%) 16 (66.7%)
41 (48.8%) 43 (51.2%)
Poor Ventilation of the Moderate kitchen Good Oil/candle lighting
for Absent Present
Tobacco smoking Absent inside the house Present Absent
481
6 (25%)
6.40
2
0.041*
3.91
1
0.06
7.94
1
0.009*
1.80
1
0.25
21
Table 3: Association of socio-demographic characteristics with the IQ of the
Characteristics Gender
Age
Birth order
Socio-economic status
Literacy of mother
Pallor
Place of delivery
Mode of delivery
Mean IQ
SD
Male
89.02
10.71
Female
83.40
7.52
6
89.37
8.67
7
87.03
9.82
8
92.93
6.92
1
88.87
10.63
2
90.93
7.24
≥3
87.01
9.41
Upper
91.96
8.97
Middle
90.25
10.61
Lower
87.09
7.54
Literate
89.10
9.58
Illiterate
89.69
7.12
Present
90.91
8.58
Absent
89.08
9.13
Hospital
90.06
9.86
Home
87.43
6.93
Normal vaginal
88.94
9.28
Caesarean
90.80
7.87
89.40
9.16
No
85.75
6.45
Yes
89.22
9.11
No
89.53
-
breast Yes
Exclusive feeding
Fully immunized
t
df
p-value
-0.21
1
0.83
3.994#
2
0.021*
1.70#
2
0.19
2.85#
2
0.06
0.28
1
0.78
0.58
1
0.57
1.38
1
0.17
-0.78
1
0.44
-0.88
1
0.38
0.03
1
0.97
respondents #ANOVA
F statistics* indicates statistical significance at p<0.05
22
Table 4: Association of household, kitchen and stove characteristics with IQ of the respondents Characteristics
Mean IQ score
SD
F
df(between, p-value within)
Type of house
88.43
5.68
0.49
1,106
0.49
89.70 87.46 90.32 89.52 87.62 89.98 90.71 86.15 90.05 91.11 89.85
10.58 8.97 9.82 7.63 8.66 9.25 9.40 7.04 8.43 12.53 11.24
1.01
2,105
0.37
1.14
2,105
0.33
2.33
2,105
0.10
1.65
3,104
0.18
91.38
9.99
89.77
7.32
0.59
1,90
0.45
0.05
2,105
0.95
3.06
1,106
0.08
Kucha/ semipucca Pucca Number of rooms 1-2 3-4 >=5 Number of doors 1-2 3-4 >5 Ventilation of the Poor house Moderate Good Indoor with Type of Kitchen partition Indoor without partition Separate indoor kitchen outside the house Open air kitchen outside the house No. of 0 - 1 windows/openings 2 -3 Ventilation of the Poor kitchen Moderate Good Oil/candle for Absent
84.767 8.21 89.44
9.40
90.93 88.82 89.45 89.27 90.53
8.44 6.29 9.46 11.26 9.26
23
lighting
Present
87.47
8.59
Tobacco smoking Absent inside the house
90.71
10.82
Present
88.17
7.49
Mosquito coils Absent and incense sticks Present
89.98
9.60
88.61
8.63
2.08
1,106
0.15
0.6
1,106
0.44
24
Cooking fuel Components
95% Confidence Interval of the Difference
t
df
pvalue
0.44
-1.71
106
0.09
-12.31
13.25
0.08
106
0.94
82.04
-10.70
-1.64
-2.78
106
0.009*
100.77
99.42
-5.59
8.30
0.39
106
0.70
Vocabulary
72.56
75.29
-7.24
1.77
-1.20
106
0.23
Digit span
85.64
94.21
-15.86
-1.27
-2.40
106
0.023*
Picture completion
103.64
105.42
-9.97
6.42
-0.43
106
0.67
Block design
89.50
97.88
-21.95
5.20
-1.22
106
0.22
Object assembly
81.8
84.13
-9.68
5.05
-0.62
106
0.54
Coding
101.76
107.04
-14.99
4.43
-1.08
106
0.28
Maze
84.45
94.67
-18.21
-2.22
-2.61
106
0.014*
Total verbal
84.30
87.37
-8.46
2.32
-1.17
106
0.25
Total performance
92.23
97.83
-11.64
0.46
-1.83
106
0.07
Full scale IQ
88.27
92.60
-9.83
1.17
-1.61
106
0.12
Biomass
LPG
Mean IQ
Mean IQ
Lower
Upper
Information
82.36
85.13
-5.97
Comprehension
88.60
88.13
Arithmetic
75.87
Similarities
Table 5: Association of IQ components with type of fuel used for cooking * indicatesstatistical significance at p<0.05
25
Table 6: Association of IQ components with the type of fuel used for cooking after adjusting for confounders 95% Mean Components
Difference IQ Scores
Confidence
Std.
Interval for Mean
Error
Difference Lower
Upper
t
pvalue
Information
-0.99
2.19
-5.33
3.35
-0.45
0.65
Comprehension
-3.21
5.62
-14.37
7.96
-0.57
0.57
Arithmetic
-5.58
2.58
-10.71
-0.46
-2.17
0.033*
Similarities
1.08
4.91
-8.68
10.83
0.22
0.83
Vocabulary
-4.81
3.19
-11.14
1.53
-1.52
0.13
Digit span
-7.64
3.95
-15.49
0.20
-1.94
0.06
6.65
6.01
-5.28
18.58
1.11
0.27
Block design
-8.03
9.49
-26.89
10.83
-0.85
0.4
Object assembly
0.94
5.44
-9.87
11.75
0.17
0.86
Coding
-0.57
5.57
-11.65
10.50
-0.10
0.92
Maze
-4.56
4.55
-13.60
4.48
-1.00
0.32
Total verbal
-3.53
2.63
-8.76
1.71
-1.34
0.18
-1.12
4.18
-9.42
7.19
-0.27
0.79
-2.32
2.89
-8.05
3.41
-0.80
0.42
Picture completion
Total performance Full scale IQ
Adjusted for sex, mother’s literacy, socioeconomic status, smoking inside the house, oil and candle use for lighting, use of coils or incense sticks, type of house, ventilation of the house, type of kitchen and ventilation of the kitchen
26
Figure 1: Overview of the methodology
500 501
Visiting primary school
502
Informed consent from principal of the school
503
Research Highlights
504
Selecting eligible children in age group 6-8 years
505 506
There are limited evidence on HAP & Cognitive performance of children
507
Exposure to HAP significantly affects IQ among children
508
Low IQ during childhood predicts poor academic performance during later
Getting address of the eligible children
years of life
509 510
HAP significantly effects the maze, digit span & arithmetic component of IQ
511 512
Visiting the child’s home
Consent for study from the parents
Cognitive defects in children due to HAP exposures could be prevented Data collection from mother using a questionnaire
513 514
Measuring the child’s IQ using MISIC
Analysis of data
27