Effects of indoor air pollution due to solid fuel combustion on physical growth of children under 5 in Sri Lanka: A descriptive cross sectional study.

Solid fuel combustion is an important risk factor of morbidity. This study was conducted to determine the effect of indoor air pollution (IAP) due to solid fuel combustion on physical growth in 262 Sri Lankan children under five. Exposure was defined by the type of fuel used for cooking. Pollutant levels were measured in a subsample of households. "High" exposure group (households using biomass fuel/kerosene oil for cooking) comprised 60% of the study population; the prevalence of wasting was 19.7% and underweight was 20.4% in the entire population where 68% were from the high exposure group. Children from the "high" exposure group had significantly lower mean z-scores for weight-for-height (p = 0.047), height-for-age (p = 0.004) and weight-for-age (p = 0.001) as compared to the "low" exposure group (children of households using liquefied petroleum gas and/or electricity) after adjusting for confounders. Z-scores of weight-for-age, height-for-age and weight-for-height were negatively correlated with CO (p = 0.001, 0.018, 0.020, respectively) and PM2.5 concentrations (p<0.001,p = 0.024 p = 0.008, respectively). IAP due to combustion of biomass fuel leads to poor physical growth.

Introduction

Solid fuel combustion for cooking releases air borne hazardous chemicals leading to indoor air pollution (IAP). IAP is an important risk factor of the global burden of disease [1]. Although a wide range of adverse health effects have been described due to burning of unprocessed biomass (wood, animal dung, crop residues, and grasses) and coal, a significant percentage of the world’s population is still using them as the main cooking fuel. Until recently IAP has been a neglected research entity. However, awareness about IAP has increased significantly recently and IAP has been identified as an important etiological agent in many disease conditions.

Compared to any other period of life, the rate of physical growth during infancy is at its maximum. During the first year of life, weight increases by about 200% and height by about 50% [2]. After infancy, growth progresses at a slower rate until puberty, when the second peak of growth is observed. However, the rate of growth during puberty is much less compared to growth observed during infancy. Any irreversible detrimental effect on growth during early childhood will have a permanent impact on the ultimate physical growth attained by an individual.

Growth is a complex interaction between genetics and the environment. In an optimum environment, an individual’s maximum growth potential is determined by genes. Nutrition is the most important environmental factor influencing physical growth. Recurrent infections, chronic diseases like poorly controlled asthma, psycho-social deprivation, congenital abnormalities, and parental substance abuse including smoking are well known risk factors of poor physical growth [3]. Though exposure to toxins is an identified risk factor for poor physical growth, little was known about indoor air pollution until recently. Results of studies carried out earlier have suggested a causal relationship between adverse physical growth and indoor air pollution [4, 5].

Unprocessed solid fuel and coal account for about 80% of the total household fuel needs in developing countries in Southeast Asia and sub-Saharan Africa [6]. Biomass fuels remain at the lower end of the energy ladder in combustion efficiency and cleanliness [7]. Hazardous air pollutants in smoke released from biomass combustion include respirable particulate matter, carbon monoxide (CO), nitrogen oxides, formaldehyde, benzene, 1–3 butadiene, polycyclic aromatic hydrocarbons (such as benzo[a]pyrene), and many other toxic organic compounds [8].

Young children are specifically affected by IAP as they spend a significant time in the kitchen with their mothers. In south India, higher prevalences of underweight and stunting at six months of age were reported among children living in households that burned biomass fuels but the association with wasting was weak [9].

Emerging evidence suggests that IAP is a major risk factor of physical growth retardation during the early years of life [10, 11]. However, most studies have concluded that further evidence is required to confirm a definite causal relationship between IAP and physical growth retardation. In this study, we investigated the effects of IAP due to solid fuel combustion on physical growth of children under five years, living in a suburban area of Sri Lanka.

Materials and methods

Study setting

A descriptive cross sectional study was conducted as a part of an ongoing study in which the adverse health effects of IAP on children from intrauterine life up to five years of age were studied. The study was conducted over a 2-year period from 2012 to 2014 in the Ragama Medical Officer of Health (MOH) area in the Gampaha district of Sri Lanka. The Ragama MOH area is a suburban area situated approximately 25 kilometers from the capital city of Colombo. It comprises a mixed, multi-ethnic and multi-religious community.

Study population

Initially, 262 children under five, permanently resident in the Ragama MOH area, were selected for the study. There was a large scale study going on to assess the birth outcomes of the solid fuel combustion related air pollution. There, the pregnant females were recruited from the antenatal clinics and the air quality levels were monitored. Children living in those households where the air quality measurements were done for the main study were recruited for this study. Children from households using biomass or kerosene as the main source of cooking fuel were considered as the ‘high exposure group’. Children from households using Liquefied Petroleum Gas (LPG) or electricity for cooking comprised the ‘low exposure group’.

Inclusion and exclusion criteria

All the children whose mother was enrolled in the main study and whose parent or guardian consented for the child to participate in the study were recruited. Children with any diagnosed chronic illness, born prematurely (before 36 weeks of gestational age), with congenital abnormalities or having a recorded history of birth insults were excluded.

Data collection

The houses of selected children were visited by a research assistant. The purpose and the nature of the study were explained to the mother/parents/guardians. An interviewer administered questionnaire was used to collect socio-demographic data and the principal source of cooking fuel. The age of the children was confirmed by checking the child health and development record, a record maintained by the routine public health services monitoring the health of the child, which contains the date of birth.

Anthropometric measurements

Weight and height of children were measured according to standard guidelines of the World Health Organization (WHO) [12]. Length was measured in children under 2 years and height in older children to the first decimal in centimeters. Length was measured using an Infantometer with precautions taken to keep the child in the Frankfort plane and knees extended. To ensure the accuracy of measurements two examiners were involved to keep the baby in the correct position.

Standing height was measured using a stadiometer. The child was made to stand up straight against the backboard with both feet flat on the platform. Heels were kept together with toes approximately 600 apart. To ensure the accuracy of measurements, the occiput, the shoulder blades, the buttocks and heels were kept in contact with the backboard, while the head was placed in the Frankfort plane.

Weight was recorded to the first decimal in kilograms. Measurements were taken with minimum clothing. A beam balance scale was used for children who could not stand. For older children a calibrated digital scale was used, as using a beam balance scale in the field for older children was not practical. Both weighing instruments were standardized against standard weights at the onset of each session. Out of 262 children initially recruited, anthropometric measurements were taken in 240 children. Some children did not cooperate to measure length/height. Hence, data analysis is based on 240 children.

Interpretation of growth parameters

Based on international normograms, individual z-scores for each child for each anthropometric index (height-for-age, weight-for-age and weight-for-height) were obtained using WHO “Anthro” software. The mean z-scores for exposed and control group children were calculated separately for each anthropometric index. In addition, each child was categorized as either normal, stunted, wasted or underweight, based on the WHO classification [13]. A height-for-age z-score between—2 to—3 SD was categorized as moderate stunting and below -3 SD as severe stunting. A weight-for-age z-score between– 2 to -3 SD was taken as moderate underweight and less than -3 SD as severe underweight. Similarly weight-for-height z-score between -2 to -3 SD were taken as moderate wasting and severe wasting when below -3 SD [13]. Data were entered into an EPIDATA database and analyzed using SPSS version 16 software. Anthropometric measurements were processed using WHO “Anthro” software.

Air quality measurements

Air quality measurements were taken from a subsample of households included in the study. Air pollution levels in the kitchen during cooking of the lunch meal, the main meal of the household, were monitored using air quality measuring equipment. PM2.5, Carbon Dioxide (CO2) and Carbon Monoxide (CO) concentrations were measured using two real time monitors. While lunch was prepared, air quality measurements from the kitchen were taken for two consecutive hours with minute-to-minute recording. For accuracy of recordings, machines were calibrated and air sampling probes were mounted according to the guidelines provided by the manufacturer.

DustTrak II monitor (DUSTTRAK™) was used to measure PM2.5 levels. TSI’s Q-trak monitor was used to measure CO2 and CO levels. Its sensors display real-time, simultaneous CO2 and CO concentrations. The zero calibration was done before installing the instruments. The instruments were installed as given in the guidelines; in some instances, slight modifications were done to suit the available space in the kitchen. The monitor receiver inlet was kept 145 cm above the floor, 100 cm from the cooking stove and at least 150 cm away from windows and doors opening outwards. The maximum deviation from these standard specifications was less than 10cm. A standard measuring tape was used to measure distances. Recordings of air quality measurements were extracted daily and entered into the database.

Data analysis

Data were entered into EPIDATA database and analyzed using SPSS version 20 software. Categorical data were analyzed using chi square tests, odds ratios and their 95% confidence intervals.

Z-scores of the anthropometric indices were obtained from the WHO Anthro software [14]. Anthropometric indices were compared between different categories of sociodemographic variables and exposure status using the independent sample t-test. Significant variables were then included in multiple linear regression analyses. Exposure levels were compared using Mann Whitney U statistic.

The association between z-scores of anthropometric indices and air pollution levels (PM2.5, CO and CO2) were assessed using the Pearson product moment correlation coefficient.

All analyses were done using SPSS version 20 [15].

Ethics considerations

The nature and procedures involved in the study were explained to the parents or the guardians of eligible children. Informed written consent was obtained prior to data collection. Confidentiality of the information was ensured. Children found to have problems requiring specialized care were referred to the Colombo North Teaching Hospital at Ragama, Sri Lanka. All mothers in households using solid fuel for cooking were advised on measures to mitigate IAP. Ethics clearance was obtained from the Ethics Review Committee of the Faculty of Medicine, University of Kelaniya, Sri Lanka (P025/04/2011).

Results

Socio-demographic characteristics of the study population

Of the 262 children initially recruited, 155 (59%) belonged to the high exposure group, and 107 (41%) comprised the low exposure group. The socio-demographic characteristics of children are given in Table 1. There was no reported change in the primary cooking fuel in the study population households from the time of recruitment of the mothers to the recruitment of children. 100% of the low exposure group children’s households were using liquefied petroleum gas for cooking as the primary source of fuel. Out of the high exposure group households, 94% of the households were using firewood as the primary cooking fuel.

Table 1

Socio-demographic characteristics of the study population.

Characteristic	High exposure group1	Low exposure group2	p-value3
	n (%)	n (%)
Sex Male	84 (54.2)	58 (54.2)	0.550
Female	71 (45.8)	49 (45.8)
Age group <3 years	83 (54.6)	62 (60.2)	0.225
≥3years	69 (45.4)	41 (39.8)
Ethnicity Sinhala	149 (96.1)	97 (90.7)	0.061
Other	6 (3.9)	10 (9.3)
Father’s education4 Up to O/L	111 (72.5)	64 (59.8)	0.022
Above O/L	42 (27.5)	43 (40.2)
Mother’s education4 Up to O/L	105 (68.6)	57 (53.3)	0.009
Above O/L	48 (31.4)	50 (46.7)
Monthly family income (SLR5) ≤ 20000	41 (26.8)	17 (15.9)	0.026
> 20000	112 (73.2)	90 (84.1)
Mother employed Yes	3 (2.0)	3 (3.0)	0.471
No	147 (98.0)	97 (97.0)
Having a sibling Yes	93 (60.4)	49 (46.2)	0.017
No	61 (39.6)	57 (53.8)
Pre-schooling Yes	61 (39.6)	42 (39.6)	0.550
No	93 (60.4)	64 (60.4)
Smoker living at home Yes	38 (25.2)	25 (23.6)	0.445
No	113 (74.8)	81 (76.4)

1 High exposure group refers to children living in households using biomass as the major type of cooking fuel

2Low exposure group refers to children living in households using LP gas or electricity as the major type of cooking fuel

3based on chi square test

4 O/L refers to General Certificate of Education Ordinary Level (11 years of formal schooling)

5 SLR refers to Sri Lankan Rupees (1 USD≈150 SLR).

Source: adapted from Ranatunge et al. [16].

The distribution of children in the two exposure groups were significantly different by maternal education (p = 0.009), paternal education (p = 0.022), family income (p = 0.026) and having a sibling (p = 0.017).

Prevalence of nutritional status by study group

There were no differences in the prevalence of severe wasting, severe stunting and severe underweight between the two exposure groups. The prevalence of underweight was significantly higher among children in the high exposure group; 20.4% vs 8.2% (p = 0.007). The prevalence of stunting and wasting was higher in the high exposure group as compared to the low exposure group, but the differences were not statistically significant (Table 2).

Table 2

Prevalence of nutritional status by study group.

Nutritional status	Group			p-value *
	Entire study population % (n = 240)	Low exposure group % (n = 98)	High exposure group % (n = 142)
Wasting1	17.1 (n = 41)	13.3 (n = 13)	19.7 (n = 28)	0.128
Severe Wasting2	4.2 (n = 10)	2.0 (n = 2)	5.6 (n = 8)	0.149
Stunting3	10.4 (n = 25)	8.3 (n = 8)	12.0 (n = 17)	0.233
Severe Stunting4	1.3 (n = 3)	1.0 (n = 1)	1.4 (n = 2)	0.637
Underweight5	15.4 (n = 37)	8.2 (n = 8)	20.4 (n = 29)	0.007
Severe Underweight6	1.7 (n = 4)	1.0 (n = 1)	2.1 (n = 3)	0.460

* Significance based on comparison of high and low exposure groups using chi-square test.

1 refers to both moderate and severe wasting where the weight-for-height z-score is below -2SD

2refers to severe wasting where the weight-for-height z-score is below -3SD

3refers to both moderate and severe stunting where the height-for-age z-score is below -2SD

4refers to severe stunting where the height-for-age z-score is below -3SD

5refers to both moderate and severe underweight where the weight-for-age z-score is below -2SD

6refers to severe underweight where the weight-for-age z-score is below -3SD.

The mean z-scores of the high exposure group were significantly lower compared to the low exposure group for all three growth parameters; weight-for-age (-1.132 vs. -0.432; p<0.001), height-for-age (-0.63 vs. 0.008; p = 0.001) and weight-for-height (-0.998 vs. -0.636; p = 0.032) (Table 3). Table 3 gives the associations between anthropometric indices and socio-demographic characteristics of children.

Table 3

Association between anthropometric indices and socio-demographic characteristics of children.

Characteristic	Anthropometric parameters
	Weight-for-age z-score			Height-for-age z-score			Weight-for-height z-score
	Mean	SD1	p-value2	Mean	SD	p-value	Mean	SD	p-value
Group
High exposure3 (n = 132)	-1.132	1.132	<0.001	-0.640	1.274	0.001	-0.998	1.269	0.032
Low exposure4 (n = 90)	-0.435	1.172		0.008	1.606		-0.636	1.291
Sex Male (n = 131)	-0.868	1.173	0.101	-0.516	1.248	0.216	-0.900	1.308	0.501
Female (n = 109)	-0.678	1.189		-0.207	1.655		-0.788	1.266
Age <3 years (n = 134)	-0.642	1.277	0.032	-0.321	1.600	0.429	-0.692	1.375	0.036
≥3years (n = 105)	-0.971	1.024		-0.471	1.219		-1.045	1.148
Monthly Family Income (SLR)5
≤ 20,000 (n = 51)	-0.962	1.152	0.217	-0.515	1.631	0.416	-0.971	1.147	0.463
>20,000 (n = 171)	-0.733	1.192		-0.329	1.400		-0.823	1.325
Father’s education
Up to O/L6 (n = 148)	-0.916	1.124	0.013	-0.537	1.318	0.011	-0.935	1.254	0.173
Above O/L (n = 74)	-0.512	1.264		-0.030	1.648		-0.693	1.346
Mother’s education
Up to O/L (n = 134)	-0.972	1.113	0.002	-0.623	1.378	0.001	-0.937	1.222	0.214
Above O/L (n = 88)	-0.480	1.237		0.035	1.483		-0.724	1.383
Having a sibling
Yes (n = 131)	-0.990	1.142	0.003	-0.490	1.432	0.181	-1.057	1.245	0.006
No (n = 109)	-0.531	1.186		-0.238	1.470		-0.601	1.300

1 Standard deviation

2based on chi square test

3High exposure group refers to children living in households using biomass as the major type of cooking fuel

4Low exposure group refers to children living in households using LP gas or electricity as the major type of cooking fuel

5 SLR refers to Sri Lankan Rupees (1 USD≈150 SLR).

6 O/L refers to General Certificate of Education Ordinary Level (11 years of formal schooling).

The mean weight-for-age z-scores were significantly lower in children whose parents were less educated (-0.916 vs -0.512; p = 0.013 for father’s education and -0.972 vs -0.480; p = 0.002 for mother’s education); the mean height-for-age z-scores were significantly lower in children whose parents were less educated compared to children of more educated parents (-0.537 vs -0.030; p = 0.011 for father’s education and -0.623 vs -0.035; p = 0.001 for mother’s education). Older children had significantly lower weight-for-height (-1.05 vs -0.69; p = 0.036) and weight-for-age (-0.97 vs -0.64; p = 0.032) z-scores compared to younger children. Children with siblings had a significantly lower mean z-score for weight-for-age (-0.99 vs -0.53; p = 0.003) and weight-for-height (-1.06 vs -0.60; p = 0.006) as compared to children without siblings.

As depicted in Table 4, weight-for-age, height-for-age and weight-for-height z-scores were regressed on exposure status, age, sex, monthly family income and parental education to control for potential confounding. Even after adjusting for confounders, high exposure status was a significant predictor of lower mean z-scores in all three anthropometric indices, weight-for-age (p = 0.001), height-for-age (p = 0.004) and weight-for-height (p = 0.04); height-for-age and weight-for-age mean z-scores were less by 0.5 and weight-for-height mean z-score by 0.3 in the high exposure group as compared to the low exposure group after adjusting for other variables. Children under 3 years had a significantly higher mean weight-for-age z-score of 0.338 (p = 0.037) and a mean weight-for-height z-score of 0.312 (p = 0.046) as compared to children three years or older after adjusting for other variables (Table 4). Children whose mothers were less educated had a significantly lower mean height-for-age z-score of 0.426 as compared children whose mothers were more educated after controlling for other confounding variables (p = 0.044) (Table 4).

Table 4

Summary of multiple regression analyses using growth parameters as the dependent variable.

Variable	Height-for-Age		Weight-for-Height		Weight-for-Age
	Regression Coefficient	Significance (95% CI of regression coefficient)	Regression Coefficient	Significance (95% CI of regression coefficient)	Regression Coefficient	Significance (95% CI of regression coefficient)
Constant	0.471		-0.571		-0.191
High exposure1	-0.540	0.004 (-0.907)–(-0.173)	-0.342	0.047 (-0.678)–(-0.005)	-0.510	0.001 (-0.808)—(-0.212)
Father’s education (up to O/L)2	-0.281	0.189 (-0.701)– 0.139	-0.166	0.396 (-0.551)– 0.219	-0.227	0.191 (-0.568)–(0.114)
Mother’s education (up to O/L)3	-0.426	0.044 (-0.841)–(-0.011)	-0.075	0.699 (-0.456)– 306	-0.286	0.096 (-0.623)– 0.051
Family income (< SLR 20000)4	0.015	0.946 (-0.430)– 0.460	-0.050	0.808 (-0.459)– 0.358	-0.052	0.777 (-0.413)– 0.309
Age < 3 years5	0.149	0.417 (-0.212)– 0.510	0.338	0.046 0.007–0.669	0.312	0.037 0.019–0.605
Sex (Male)6	-0.307	0.095 (-0.668)– 0.054	-0.182	0.280 (-0.513)– 0.149	-0.236	0.113 (-0.529)– 0.057

1Reference group is low exposure group using LPG and electricity for cooking.

2Reference group is father’s education above ordinary level

3Reference group is mother’s education above ordinary level

4 Reference group is having monthly family income ≥Sri Lanka rupees (SLR) 20,000 (1 USD≈150 SLR)

5Reference group is children aged ≥3 years

6Reference group is female children.

Air quality levels and anthropometric indices

Air quality was measured in 115 households. Details of the measurements are given in the Table 5. Carbon dioxide and carbon monoxide were measured in parts per million and particulate matter (PM2.5) was measured in milligrams per square meter. Minute to minute data were recorded and the average value of 120 data points (2-hour continuous monitoring) were analyzed. There were significant differences in the concentrations of carbon monoxide (p<0.001) and the PM2.5 (p<0.001) levels between the two groups; the high exposure houses had significantly higher concentrations (as much as 2–3 times more) of pollutants as compared to the low exposure group. Carbon dioxide concentrations were similar during cooking in both groups of houses.

Table 5

Air quality measurements in selected houses.

Exposure		Number of households	Median	Interquartile range	Significance
CO	High exposure	64	1.90 ppm	1.20 ppm -3.57 ppm	<0.001
	Low exposure	51	1.20 ppm	0.85 ppm– 1.5 ppm
PM2.5	High exposure	65	0.58 mg/m3	0.17 mg/m3–1.71 mg/m3	0.881
	Low exposure	56	0.15 mg/m3	0.06mg/m3–0.27 mg/m3
CO2	High exposure	66	547.75 ppm	449.0 ppm– 647.25 ppm	<0.001
	Low exposure	52	538.5 ppm	454.0 ppm– 634.63 ppm

Carbon monoxide and PM2.5 levels were significantly negatively correlated with all three anthropometric indices (Table 6). Carbon dioxide levels were not correlated with any anthropometric index.

Table 6

Correlation between air quality levels and anthropometric indices.

		z-scores of anthropometric measurements
		weight-for-age	height-for-age	weight-for-height
Carbon Monoxide	Pearson Correlation Coefficient	-0.354	-0.251	-0.245
	Significance	0.001	0.018	0.020
	N	89	89	89
Carbon Dioxide	Pearson Correlation Coefficient	0.036	-0.026	0.050
	Significance	0.730	0.802	0.631
	N	94	94	94
Particulate Matter 2.5	Pearson Correlation Coefficient	-0.356	-0.233	-0.272
	Significance	<0.001	0.024	0.008
	N	94	94	94

Exposure time was assessed and there was only one child from low exposure group and 2 children from high exposure group who were spending time near stove more than one hour per day.

Discussion

Physical growth of a child is predetermined by genetic factors to a certain extent. However, twin studies have revealed that genetics is not the only determinant of physical growth of a child. It has been shown that monozygotic twins achieve different adult heights depending on the environments they are brought up [17]. Physical growth can be described as an outcome of a complex interaction between genetic and non-genetic factors. Important non-genetic factors influencing physical growth are nutrition, infections/diseases, psycho-social well-being, physical activity and environmental factors [17].

IAP has been increasingly implicated as a preventable cause of morbidity and mortality among children. IAP is also considered an important environmental risk factor for poor physical growth in children [18]. Children under five stay indoors most of the time and are likely to stay near the stove, while the mother prepares meals. Hence, children under five are more likely to be exposed to hazards of indoor air pollution, resulting from combustion of biomass fuel. High minute ventilation, immature immune systems and vigorous physical activities make children more vulnerable to adverse health effects of IAP than adults.

There are only a very few studies that have addressed effects of IAP on physical growth. Weight-for-age and weight-for-height are affected by acute changes in a child’s nutrition and environment, while height-for-age indicates chronic effects [19]. According to our study, all three anthropometric indices (weight-for-age, height-for-age and weight-for-height) were significantly affected by IAP. Even when adjustments were made for confounding factors, IAP was a significant predictor of poor physical growth in children under five. Our results indicate that IAP has both acute and chronic effects on physical growth of children.

Moderate underweight was significantly higher among children in the high exposure group. Moderate stunting was also higher in the high exposure group compared to the low exposure group, but not statistically significant. The rate of weight increase during childhood is several folds faster than length/height [2]. Therefore, an adverse influence on growth will be evident early in weight than in length/height. That is the basis of monitoring weight monthly and length once in three months during first two years. This probably explains the finding of our study, where stunting was not associated with IAP; the findings of a study amongst older children may be different as stunting takes a longer time to manifest with chronic exposure. A study from Bangladesh also reported similar observations as the early life prevalence of underweight is higher than stunting [20].

Although z-scores of anthropometric indices were associated with exposure status after adjusting for other variables, IAP was not directly associated with the prevalence of severe forms of wasting, stunting or underweight. This suggests that effects of IAP on growth may not be as strong as malnutrition or recurrent infections. However, as households with higher IAP are likely to have other adverse conditions for growth like poor parental education, lower income, malnutrition, infections, and worm infestations, IAP would contribute significantly to the cumulative effects on physical growth. In the high exposure group, weight-for-height z-scores were significantly less in older children compared to younger children. This suggests that the effects of IAP may accumulate over the years.

CO and PM2.5 are two well-known pollutants present in smoke from biomass combustion [21]. Combustion of biomass in an open stove without a chimney emits particulate matter (PM) concentrations between 2000 to 15 000 μg/m3, a level many times higher than the worst outdoor settings [22, 23]. Release of these pollutants is minimal with LP gas and electricity. In our study, all three anthropometric indices were negatively correlated with CO and PM2.5 levels.

The exact mechanism of how IAP adversely affects physical growth is not explained. Smoke released from biomass combustion contains many hazardous substances such as respirable particulate matter, CO, nitrogen oxides, formaldehyde, benzene, 1–3 butadiene, and polycyclic aromatic hydrocarbons (such as benzo[a]pyrene). These can exert a direct toxic effect on the growth plates and growing tissues or may cause chronic ill health in children resulting in growth retardation. Chronic respiratory conditions like asthma which have a direct adverse effect on growth have been shown to increase with IAP [24, 25].

Parental education, family income, having siblings and gender can influence physical growth. Even when effects of these confounding factors were eliminated through regression analysis the effects of IAP on physical growth remained significant. Therefore, in lower socio-economic conditions, IAP may act along with other risk factors to hamper physical growth of children. If it is not possible to provide efficient fuels like LP gas and electricity to all households, at least attempts should be made to improve the cook stoves to minimize IAP. In addition, mothers should be educated to keep children away from stoves during cooking.

Limitations

We were able to measure air quality levels in only a subsample of households due to limited resources which is a limitation of our study. Further, we were unable to measure outdoor air pollution levels that may have had an effect on indoor air pollution levels. As the study population was under 5 children who generally stay indoors most of the time during the day, we assumed that exposure to outdoor air pollution will have a minimal effect. All children were from the same geographic area and would likely have been exposed to the same levels of outdoor pollution.

We measured air pollution levels over a two-hour period during the preparation of the lunch meal, the main meal that is cooked in most households. Based on the construction of houses and air circulation within houses it is possible that air pollution levels may have been higher in areas other than in the kitchen where children may have been and beyond the time after we stopped monitoring pollutant levels. We acknowledge this as a limitation and it is likely that it may have affected our results. We were only able to measure CO, CO2 and PM2.5.; this limited our ability to assess interactions with other pollutants.

Conclusion

Biomass combustion results in significant IAP compared to LP gas or electricity. IAP was significantly negatively associated with physical growth of children. Parents should be advised to keep children away from the stove while cooking.

29 Dec 2020

PONE-D-20-37412

Effects of indoor air pollution due to solid fuel combustion on physical growth of children under 5

PLOS ONE

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Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

**********

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Reviewer #1: Yes

Reviewer #2: No

**********

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Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Dec-10-2020

Comments to the manuscript PONE-D-20-37412:

In the present manuscript, the authors used a population-based descriptive cross sectional study to address the association between solid fuel combustion-derived indoor air pollution and the physical growth in children less than five years old in Ragama MOH, Sri Lanka. The exposure was defined by cooking fuel types and pollutant levels were measured in a household subsample. They reported that the high-exposure children had significantly lower mean z-scores for weight-for-height, height-for-age and weight-for-age, and the scores were negatively correlated with indoor CO and PM2.5 concentrations related to cooking. They concluded that indoor air pollution by biomass fuel combustion leads to poor physical growth. The study is of significance; however, there are methodological concerns about the research as presented.

1. Indoor air pollution is strongly spatiotemporally dependent. A child may be hurt more severely by indoor air pollution as the cooking lasts longer even if the pollution is comparable or lighter. Analysis with the absence of exposure time will, therefore, compromise the results.

2. In the high-exposure group, unprocessed biomass, which includes wood, grasses, crop residues or animal dung, was used for cooking. Coal and kerosene were also used. In the low-exposure group, they used liquefied petroleum gas or electricity, with the former producing particles, SO2, NOx, CO, non-methane total hydrocarbons (incomplete combustion), etc., at a lesser extent. Since different fuel combustion produces different air pollutants, which may exert different harmful effects on health, the authors need to present in detail the situation of households using different fuels. Besides, they should pay attention to the history of fuels used, because the type of fuels might be changed during the study period.

3. Air quality was measured in 115 out of 242 households. Why were the measurements not taken from all households? Similarly, analysis was performed based on data from the subgroups (e.g., Table 7). Comparisons conducted with smaller sample sizes would potentially bias the results.

4. Inclusion and exclusion criteria were not presented. Was the child with premature birth, obesity or other abnormal conditions (prenatally and postnatally) included? Are there any twins?

5. Description for data analysis is not sufficient. How to compare the anthropometric parameters, e.g., values of weight-for-age z-scores expressed as mean and SD (Table 03), between the two groups? There is a lack of description regarding confounding factors in Method session. The authors should give the reason why they chose exposure status, age, sex, monthly family income and parental education as the confounders. Multicollinearity should be considered for tightly correlated variables if any. Did they consider the co-effects of outdoor-level air pollution, which may not be negligible especially in developing countries?

6. There are strong relationships of different toxic pollutants to one another with regard to air pollution on health. There are no models of multi-pollutants to reflect the texture of air pollutants. The average concentrations of CO, CO2 and PM2.5 for both groups should be given. What did the authors evaluate the temperature and humidity for?

7. The authors may have to evaluate the weakness of the study in the Discussion session.

8. Other points:

(1) “Any detrimental effect on growth during early childhood is irreversible” (Line 51): This may not be true because some effects are temporary and reversible.

(2) form → from (Line 92).

(3) ‘exposed group’ (Line 96) and “control group (Line 97): It is more appropriate to use “high exposure group” and “low exposure group”, as used by the authors in other places.

(4) Statements such as “… to collect socio-demographic data and the principal source of cooking” (Line 102) should go to the Method session.

(5) “PM2.5 and Carbon Monoxide (CO) concentrations were measured …” (Line 144-145): It should be PM2.5, CO2 and CO to be measured.

(6) I suggest the sublevel headings be used for the Results session.

(7) “Stunting and wasting were …” (Line 200-201)：→ The prevalence (or ratio) of stunting and wasting was…

(8) “P” whether in uppercase (as in Table 3) or in lowercase (as in Table 2) should be consistent.

(9): Table 1 (Page 9): “Family income” : Is it monthly income ? (indicated in Line 242)

(10): “Entire study population” column in Table 2 (Page 10): “n = 1.7” ?

(11): Line 213 and 214: “vs” → “vs.”.

(12): Line 235: “… to children three …”→ “… to children of three …”; “(Tables 4 and 6)” → (Tables 4-6).

(13): Line 306-308: Lacking reference(s).

(14): “the findings of a study amongst older children may be different”: No supportive data.

(15): “However, as households with higher IAP … , IAP would contribute … on physical growth” (Line 327-330): The authors give insufficient rationales that IAP contributed significantly because IAP is not definitely correlated with other adverse conditions.

(16): Line 336: “mg/m3” → μg/m3

(17): “respirable particulate matter” (Line 341): Do they refer to “inhalable particulate matter”? If so, “PM2.5” that follows can be omitted.

(18): I suggest the name of country be added in the title and deleted in “Key Words”.

(19): Line 371-373: Since it is not necessary to repeat the funding, the Acknowledgements session can be deleted.

Reviewer #2: This is an important study on the health impact of indoor air pollution on child growth. On the whole, the study is designed appropriately, the data collected with adequate care, and the manuscript well written. My main comments concern the choices that the authors have made in their analysis, presentation, and write-up.

1. As this is an observational study, the authors may wish to change the title to reflect that appropriately.

2. The supplementary information is another article on the effect of indoor air pollution on childhood respiratory diseases published by the authors. Is this intentional or a mistake?

3. The choice of covariates in the regression is not clear to me. Why did the authors not use all the covariates in the regression analysis?

4. The results in tables 4-6 can probably be presented more succinctly (in one table).

5. Did the authors run regressions with the quantitative measures of air pollutants as the independent variable(s) rather than exposure group? I would be interested in seeing the results for these regressions as well.

6. The manuscript does not interpret the regression estimates comprehensively (sign, significance and size). The size of the effect should probably be described in the results section.

7. In the discussion, the authors refer to the results of the correlation between severe anthropometric failure and exposure group after discussing the results of the regressions. If the authors wish to establish the (absence of a) relationship between severe anthropometric failure and exposure group, they might want to run regressions with severe stunting, severe wasting, and severe underweight as the dependent variable.

8. The limitations of the study should be described in the discussion.

**********

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Reviewer #1: No

Reviewer #2: No

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10 Apr 2021

Responses to reviewer comments

Reviewer comment – Reviewer 01

Comment 01

Indoor air pollution is strongly spatiotemporally dependent. A child may be hurt more severely by indoor air pollution as the cooking lasts longer even if the pollution is comparable or lighter. Analysis with the absence of exposure time will, therefore, compromise the results.

Response to comment 01

We agree with the reviewer’s comment. Exposure time was assessed and there was only one child from the low exposure group and 2 children from high exposure group who spent time near stove more than one hour during the exposure measurement. Furthered the maximum time spent near the stove per day by a child was 2 hours (one child)(revised in the manuscript line 282-283).

As suggested by the reviewer, indoor air pollution is spatiotemporally dependent. We were only able to measure exposure for a two hour period during the cooking of lunch, the main meal of the family. We have mentioned this as a limitation (line numbers 354 to 358 in revised manuscript).

Comment 02

In the high-exposure group, unprocessed biomass, which includes wood, grasses, crop residues or animal dung, was used for cooking. Coal and kerosene were also used. In the low-exposure group, they used liquefied petroleum gas or electricity, with the former producing particles, SO2, NOx, CO, non-methane total hydrocarbons (incomplete combustion), etc., at a lesser extent. Since different fuel combustion produces different air pollutants, which may exert different harmful effects on health, the authors need to present in detail the situation of households using different fuels. Besides, they should pay attention to the history of fuels used, because the type of fuels might be changed during the study period.

Response to comment 02

We monitored the use of different fuels during the study. The mothers of these children were recruited during their pregnancy and continuously followed. Since, recruitment of these mothers there was no change in the principal cooking fuel. 100% of the low exposure group households were using liquid petroleum gas for cooking as the primary source of fuel. In the high exposure group households, 94% of the households were using firewood as the primary cooking fuel. As we present baseline results of the study on children under 5, any change in fuel is unlikely to have affected the results.(Revised in the manuscript line 191-195)

Comment 03

Air quality was measured in 115 out of 242 households. Why were the measurements not taken from all households? Similarly, analysis was performed based on data from the subgroups (e.g., Table 7). Comparisons conducted with smaller sample sizes would potentially bias the results.

Response to comment 03

Due to the limited availability of resources, we measured the air quality levels in a subsample of the cohort. That was a limitation of the research and included in the limitation section. (Revised in the manuscript line 353-355)

Comment 04

Inclusion and exclusion criteria were not presented. Was the child with premature birth, obesity or other abnormal conditions (prenatally and postnatally) included? Are there any twins?

Response to comment 04

Inclusion and exclusion criteria have been included.(Revised in the manuscript line 99-103)

Comment 05

Description for data analysis is not sufficient. How to compare the anthropometric parameters, e.g., values of weight-for-age z-scores expressed as mean and SD (Table 03), between the two groups? There is a lack of description regarding confounding factors in Method session. The authors should give the reason why they chose exposure status, age, sex, monthly family income and parental education as the confounders. Multicollinearity should be considered for tightly correlated variables if any. Did they consider the co-effects of outdoor-level air pollution, which may not be negligible especially in developing countries?

Response to comment 05

Z scores for anthropometric measurements were obtained from WHO Anthro software. Mean and SD was calculated for those values. Comparisons between the high and low exposure groups was done using independent sample t-tests. The anthropometric parameters for different socio-demographic groups were also compared using the independent sample t-test.

The reason why age, sex, monthly family income and parental education were considered were included as potential confounders as they were significant on bivariate analysis (unadjusted). In the multiple regression model, multicollinearity between independent variables would have been considered in the final output.

We did not consider co-effects of outdoor air pollution as we did not measure. We have included it as a limitation. (Revised in the manuscript line 354-355)

Comment 06

There are strong relationships of different toxic pollutants to one another with regard to air pollution on health. There are no models of multi-pollutants to reflect the texture of air pollutants. The average concentrations of CO, CO2 and PM2.5 for both groups should be given. What did the authors evaluate the temperature and humidity for?

Response to comment 06

We were able to measure only CO, CO2 and PM2.5. We have included a statement as a limitation (Revised in the manuscript line 364-365) The air quality measurements were obtained with the temperature and humidity being automatically measured by the testing instrument. As we did not observe any significant variation in those measurements, we did not analyze those data. Therefore, that part was not included in the manuscript. Data presented in the table 06 are related to the sub sample of households (115) where 55% belonged to high exposure group. An additional table has been included (Table 05)

Comment 07

The authors may have to evaluate the weakness of the study in the Discussion session.

Response to comment 07

Included in the revised manuscript giving the limitations as a subsection

Comment 08

“Any detrimental effect on growth during early childhood is irreversible” (Line 51): This may not be true because some effects are temporary and reversible.

Response to comment 08

The meaning of “detrimental effect” defined here is about irreversible effects and the sentence is revised in the manuscript

Comment 09

form → from (Line 92).

Response to comment 09

Revised the error

Comment 10

‘exposed group’ (Line 96) and “control group (Line 97): It is more appropriate to use “high exposure group” and “low exposure group”, as used by the authors in other places.

Response to comment 10

Corrected in the manuscript

Comment 12

Statements such as “… to collect socio-demographic data and the principal source of cooking” (Line 102) should go to the Method session.

Response to comment 12

It is in the methods section

Comment 13

“PM2.5 and Carbon Monoxide (CO) concentrations were measured …” (Line 144-145): It should be PM2.5, CO2 and CO to be measured.

Response to comment 13

This has been corrected in the revised manuscript.

Comment 14

I suggest the sublevel headings be used for the Results session.

Response to comment 14

Included in the manuscript

Comment 15

“Stunting and wasting were …” (Line 200-201) → The prevalence (or ratio) of stunting and wasting was.

Response to comment 15

Corrected in the manuscript

Comment 16

“P” whether in uppercase (as in Table 3) or in lowercase (as in Table 2) should be consistent.

Response to comment 16

Converted to lowercase in table 3.

Comment 17

Table 1 (Page 9): “Family income”: Is it monthly income? (indicated in Line 242)

Response to comment 17

Corrected in tables 1 and 3.

Comment 18

“Entire study population” column in Table 2 (Page 10): “n = 1.7”?

Response to comment 18

It was a typing error. Corrected in the revised manuscript

Comment 19

Line 213 and 214: “vs” → “vs.”.

Response to comment 19

Corrected in the manuscript

Comment 20

Line 235: “… to children three …”→ “… to children of three …”; “(Tables 4 and 6)” → (Tables 4-6).

Response to comment 20

Corrected in the manuscript

Comment 21

Line 306-308: Lacking reference(s).

Response to comment 21

Reference given

Comment 22

“the findings of a study amongst older children may be different”: No supportive data.

Response to comment 22

It was mentioned in the discussion where it needs a follow up for a longer time and that has not been assessed in this study. (Revised in the manuscript line 317)

Comment 23

“However, as households with higher IAP … , IAP would contribute … on physical growth” (Line 327-330): The authors give insufficient rationales that IAP contributed significantly because IAP is not definitely correlated with other adverse conditions.

Response to comment 23

In this section, it was meant to address the multifactorial effects of growth which was addressed in this study and not completely addressed in some aspects such as nutrition. Therefore, we discussed the cumulative effect which can be a reinforcing factor for growth faltering.

Comment 24

Line 336: “mg/m3” → μg/m3

Response to comment 24

Corrected in the manuscript

Comment 25

“respirable particulate matter” (Line 341): Do they refer to “inhalable particulate matter”? If so, “PM2.5” that follows can be omitted.

Response to comment 25

Corrected in the manuscript

Comment 26

I suggest the name of country be added in the title and deleted in “Key Words”.

Response to comment 26

Corrected in the manuscript

Comment 27

Line 371-373: Since it is not necessary to repeat the funding, the Acknowledgements session can be deleted.

Response to comment 27

Corrected in the manuscript

Responses to reviewer comment – Reviewer 02

Comment 01

As this is an observational study, the authors may wish to change the title to reflect that appropriately.

Response to comment 01

Corrected in the manuscript

Comment 02

The supplementary information is another article on the effect of indoor air pollution on childhood respiratory diseases published by the authors. Is this intentional or a mistake?

Response to comment 02

It was given intentionally as this is the same study population and the same sociodemographic data are repeated in both.

Comment 03

The choice of covariates in the regression is not clear to me. Why did the authors not use all the covariates in the regression analysis?

Response to comment 03

We have now explained it under data analysis. Only the significant covariates on bivariate analyses were included in the multiple linear regression analyses.(Revised in the manuscript line 170-173)

Comment 04

The results in tables 4-6 can probably be presented more succinctly (in one table).

Response to comment 04

Corrected in the manuscript

Comment 05

Did the authors run regressions with the quantitative measures of air pollutants as the independent variable(s) rather than exposure group? I would be interested in seeing the results for these regressions as well.

Response to comment 05

We did not do this analysis as air pollutant levels were measured in only 114 households.

Comment 06

The manuscript does not interpret the regression estimates comprehensively (sign, significance and size). The size of the effect should probably be described in the results section.

Response to comment 06

This has been done in the revised manuscript.

Comment 07

In the discussion, the authors refer to the results of the correlation between severe anthropometric failure and exposure group after discussing the results of the regressions. If the authors wish to establish the (absence of a) relationship between severe anthropometric failure and exposure group, they might want to run regressions with severe stunting, severe wasting, and severe underweight as the dependent variable.

Response to comment 07

We did not do this analysis as the numbers with severe forms of malnutrition were too few to perform any analysis.

Comment 08

The limitations of the study should be described in the discussion.

Response to comment 08

Added as a separate section. (Revised in the manuscript line 353-365)

Submitted filename: Responses to reviewers.docx

Click here for additional data file.

12 May 2021

Effects of indoor air pollution due to solid fuel combustion on physical growth of children under 5 in Sri Lanka: A descriptive cross sectional study

PONE-D-20-37412R1

Dear Dr. Ranathunga,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Qinghua Sun, MD, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Comments to the revised version of the manuscript (No. PONE-D-20-37412R1):

The authors have answered all the questions well and revised what necessary in the text, including the addition of “Limitations” statement. With the addition of exclusion criteria, i.e., “Children with any diagnosed chronic illness, born prematurely (before 36 weeks of gestational age), with congenital abnormalities or having a recorded history of birth insults were excluded” (P.99-102), it is anticipated that there might be a few children who met the criteria to be excluded. I wonder why none of the children was ruled out. The authors may need to check the data carefully.

Some minor points:

1. “Data were entered … using SPSS version 16 software” (L.159 in “Data Analysis”) and “All analyses were done using SPSS version 20” (L.168): Which version is right?

2. “databaseand” → database and (L.158)

“software.Categorical” → software. Categorical (L.159)

“Ethicsconsiderations” → Ethics considerations (L.169)

“wereusing” (L.185) → were using

“whogenerally” (L.354) → who generally

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

17 May 2021

PONE-D-20-37412R1

Effects of indoor air pollution due to solid fuel combustion on physical growth of children under 5 in Sri Lanka : A descriptive cross sectional study

Dear Dr. Ranathunga:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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