High ambient air pollution erodes the benefits of using clean cooking fuel in preventing low birth weight in India

India faces a high burden of air pollution-related mortality and morbidity. In 2019, there were an estimated 0 · 61 million deaths and 20·9 disability-adjusted life years (DALYs) attributable to household air pollution (HAP) in India. These accounted for 6.5% of the total deaths and 4·5% of total DALYs in the country [1]. HAP is mainly a rural problem, with almost 57% of rural households relying on biomass solid fuel (such as wood, crop residue, dung cakes, and charcoal) for cooking food and water and space heating [2]. Previous research has established a causal association between HAP and adverse birth outcomes [3], childhood morbidity, and mortality [4–6]. A range of HAP-related adverse birth outcomes have been reported in the literature, such as having a low birth weight (LBW) baby, pre-term birth, and stillbirth [7–11].

Biomass fuel burning results in high levels of particulate matter (PM_2.5) and gaseous air pollutants such as carbon particles, iron, lead, cadmium, silica, phenols and free radicals, carbon monoxide (CO), nitrogen dioxide, sulfur dioxide, formaldehyde, hydrocarbon complexes, and other inorganic and organic substances which include polycyclic aromatic hydrocarbons, volatile organic compounds, and chlorinated dioxins [12]. Fine PM_2.5 (of size less than 2.5 μm in aerodynamic diameter) and CO, two important by-products of incomplete combustion, are absorbed in the maternal blood, cross the placental barrier, and impair fetal tissue growth through hypoxia/oxidative stress [3]. A higher risk of LBW is observed with the use of biomass and coal for cooking during pregnancy, compared to clean fuel (gas and biogas) [13]. LBWs were documented in households that cooked with unclean fuels in houses with or without separate kitchens or outdoors, compared to those using clean fuels [14].

More recent evidence from studies that rely on direct exposure measurement further adds to the evidence [15]. In a study by Balakrishnan et al, the odds of LBW were found to increase by 2% with a 10 μg m⁻³ increase in PM_2.5 emitted from solid fuel use in households. A non-linear relationship between PM_2.5 and birth weight (initial increase in birthweight with higher PM_2·5 followed by a subsequent decrease at the higher exposures) was observed in a multi-country randomized clinical trial [16]. Although limited, more recent evidence also raises the possibility of the influence of ambient air pollution (AAP) on the LBW-HAP risk association [17]. Existing research from India reports a negative association between ambient PM_2.5 exposure and child health outcomes such as birth weight [18] and infant mortality [19]. However, the interacting effect of HAP and AAP on child health remains less explored in India.

India has seen a rapid growth of domestic use of clean cooking fuel (CCF) across the country in the last decade. The Pradhan Mantri Ujjwala Yojana (PMUY) [20], was launched in 2016 to facilitate this transition and reduce the disease burden associated with HAP [1]. However, there is a sizeable inter-state variation in the use of CCF. Rural parts of northern and eastern parts of India still have low proportions of primary users of CCF [21]. The same region, almost the entire Indo-Gangetic plain, also has high ambient PM_2.5 levels [19]. It is important to understand how the dual burden of AAP and HAP influences the health benefits of CCF intervention [22].

With this view, we analyzed the national family health survey (2019–21, NFHS-5) data to assess the effectiveness of transition to CCF in reducing the risk of LBW in the presence of varying levels of AAP.

2.1. Health data

The NFHS-5 is a nationwide survey conducted under the aegis of the Ministry of Health and Family Welfare, Government of India [21]. It adopts a uniform, multi-stage random sample design to have a representative sample at the national, state/union territory, and district levels. Each district is stratified into urban and rural areas. Within each explicit rural sampling stratum, a sample of villages is selected as Primary Sampling Units (PSUs) (also referred to as clusters). A total of, 30,456 PSUs were selected across the country in NFHS-5 drawn from 707 districts. At the household level, information was collected on socioeconomic characteristics and various social determinants of health. The Woman's Questionnaire collected information from all eligible women aged 15–49, on many topics, including children born and birth details. Relevant information from household and woman's questionnaires was used for the present study.

The primary outcome—birth weight was based on reporting by the mother. The information was recorded from a health card in 63.8% of cases and from the mother's recall in the remaining. LBW babies were defined as babies weighing below 2500 g at birth (categorical variable). Our analysis included the most recent full-term, singleton live births from rural households born after 2017. The total sample size in the NHFS-5 was 95374, out of which 39374 cases could not be included in the analysis due to multiple factors (figure 1) and the final sample size included in our analysis for whom information on exposure to HAP and AAP was available was 56000.

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2.2. Exposure data

2.2.1. HAP

The type of cooking fuel reported by each woman was used to assign HAP exposure status to each case. Women who reported the primary use of solid cooking fuels (SCF) (coal, lignite, charcoal, wood, straw/shrubs/grass, agricultural crop, animal dung) were classified as 'exposed' or 'SCF users'. Those reporting primary use of liquefied petroleum gas (LPG), electricity, natural gas, and biogas were classified as 'unexposed' or CCF users. Figure 2(a) provides proportion of women using solid fuels for cooking, by primary sampling unit in the rural area.

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2.2.2. AAP

AAP was assigned in terms of average ambient PM_2.5 exposure during pregnancy derived for each PSU. Due to inadequate coverage of India's ground monitoring network, particularly in rural areas, we used a satellite-derived PM_2.5 dataset from a published study Katoch et al [23]. This dataset was generated by converting daily aerosol optical depth (AOD) at 1 km² resolution, retrieved from the Moderate Resolution Imaging Spectroradiometer into surface PM_2.5 concentrations. To address missing data in the daily AOD due to retrieval challenges and cloud cover, the XGBoost machine learning technique was employed. This technique utilized various predictor variables including temperature, cloud fraction, relative humidity, albedo, boundary layer height, zonal and meridional wind, and total columnar water. Subsequently, the gap-filled AOD data was employed in the Random Forest Machine Learning Technique to predict daily PM_2.5 at the same 1 km² resolution. In addition to the predictor variables utilized for AOD gap-filling, supplementary variables such as the nearest fire incident distance from the measured location, distance to major and minor roads, elevation, land use variables, and population density were also used in this model. The performance of the dataset was evaluated against coincident measurements from the Central Pollution Control Board (CPCB) ground monitoring network, which comprised of continuous ambient air quality monitoring stations (CAAQMS) and manual monitors. CAAQMS measures and reports PM_2.5 every 15 min across 144 cities spanning 24 states and union territories. The real-time data is disseminated through the CPCB portal (https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing) in real-time. In contrast, manual monitoring occurs twice a week for a 24-hour cycle, resulting in a maximum of 104 measurement days per year at the respective sites. During the time period considered in this study, the surface PM_2.5 reported an R² of 0.85 and RMSE of 23.3 μg m⁻³ on a daily scale against measurements from the reference-grade monitors maintained by the CPCB (appendix A). More details on the distribution of CPCB ground-based monitors are given in Katoch et al [23].

2.3. Exposure attribution

2.4. Covariates

2.5. Statistical model

3.1. Risk of LBW by type of cooking fuel

3.2. Effect modification by ambient PM_2.5 exposure

The stratified analysis by categories/levels of ambient PM_2.5 exposure shows a heterogeneous picture (figure 3). In the lowest Decile of ambient PM_2.5 exposure (below 37 μg m⁻³), the aOR of having an LBW baby with CCF use was 0.83 (95% CI:0.81–0.85). A gradient between aOR and ambient PM_2.5 exposure was observed. For the second, third, and fourth Deciles of ambient PM_2.5 exposure, the aORs did not change much. Afterwards, for every 10th percentile increase in ambient PM_2.5 exposure, the aOR increased gradually reaching the value of 1 for the ninth Decile. When in-utero ambient PM_2.5 exposure exceeded 93 μg m⁻³, the aOR exceeded 1 indicating that the benefit of switching to CCF was outweighed by the negative effect of AAP.

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3.3. Sensitivity analysis—

Our national-level analysis shows a reduced risk of LBW among CCF users. However, the stratified analysis (by levels of ambient PM_2.5 exposure) shows that AAP acts as an effect modifier of the risk association between LBW and the type of cooking fuel. The protective effect of CCF declined with increasing levels of ambient PM_2.5 levels.

There is limited evidence on the combined influence of HAP and AAP on birth weight in India. In a prospective cohort study from Beijing, China [17], the researchers explored the joint impact of indoor air pollution index and ambient PM_2.5 on fetal growth parameters (measured as abdominal circumference, head circumference, femur length, and estimated fetal weight). The risk ratios of foetal undergrowth were higher with exposure to 'higher ambient PM_2.5 and indoor air pollution' and 'higher ambient PM_2.5 and no indoor air pollution'; compared to lower levels of both. Our study results are in line with these findings. At the lowest level of ambient PM_2.5, the protective efficacy of CCFs was observed to be highest (aOR = 0.86). As the in-utero exposure to ambient PM_2.5 increased gradually from Decile I to X, the odds of LBW neared the value 1 (indicative of lesser efficacy of using CCFs for health benefits). It must be noted that the lowest Decile of PM_2.5 level observed in this study is higher than the air quality guideline set by the World Health Organization [25].

Previous studies across the globe have documented a negative association between ambient PM_2.5 levels and birth weight [26]. The heightened risk of LBW due to AAP is likely to have negated the advantage of the use of CCF. The possibility of fuel stacking (simultaneous use of clean and SCFs is considered as fuel stacking) also needs to be considered while interpreting the findings. The high and moderate AAP clusters in our analysis belong to energy-poor states in India [27]. In these areas, the transition to CCFs has been a relatively recent phenomenon, and many households still rely primarily on SCFs. It is known that transitioning away from traditional cooking fuels is a gradual process [27]. Therefore, even among primary users of CCF, a high level of stacking is likely [28] and can erode the health benefits [29]. Further investigations are needed to understand how personal exposures and associated health outcomes are affected based on the level of stacking in a household. The information gaps on stacking need urgent attention. The indicator on the primary use of cooking fuel used in existing surveys needs to be complemented with indicators that reflect the stacking level.

As seen in our analysis, there was a clear north–south divide in the level of air pollution. The use of SCFs is more prevalent in northern regions of the country, especially along the Indo-Gangetic plain. These regions also have higher ambient PM_2.5 levels. The average ambient PM_2.5 concentration during 2017–2021 in most of the rural PSUs from the Indo-Gangetic plain was above 50 μg m⁻³. HAP significantly contributes (as high as 30%) to AAP in these regions [30]. Universal, complete, and sustained transition to CCF in these areas will not only help in eliminating HAP but will also result in a substantial reduction in AAP levels, rendering additional health benefits. Switching to clean fuel for domestic usage during pregnancy reduces household exposure and thereby lowers the risk of having an LBW baby. Such action will also reduce ambient PM2.5 exposure in varying margins depending on the relative contributions of household emissions across the states and will further lower the LBW risk. Further research is necessary to quantify these benefits.

The present study has a few limitations. It is based on a cross-sectional survey that collects data on current practices of cooking fuels and we assumed that the cooking fuel use was same during the period when the mother was pregnant. Since cooking behaviors are slow to change [27], it is more likely a valid assumption. The study lacks quantitative data on indoor PM_2.5 levels and instead uses proxy measures in the form of reported use of cooking fuel. Households primarily relying on solid fuels for cooking have high levels of PM_2.5 [31]. A study from India [32] modeled household concentrations of PM_2.5 as a function of multiple, independent household level variables available in national household surveys. The measured mean 24-hour concentration of PM_2.5 in SCF using households ranged from 163 μg m⁻³ in the living area to 609 μg m⁻³ in the kitchen area. Comparatively, the PM_2.5 levels are much lower among households with primary use of LPG. A multi-country randomized controlled trial [33] observed substantial exposure reductions in median kitchen PM_2.5 concentrations from 296 to 24 μg m⁻³ when households switched from solid to CCFs in rural settings. In NFHS-5, the information on birth weight was obtained from the health card (in most cases) or based on the mother's recall. This can induce recall bias for outcome measurement. However, sensitivity analysis by outcome measurement method (appendix B) did not show any difference in the risk analysis. Although the analysis was controlled for many known confounders, every possible confounder (e.g. obstetric history, maternal co-morbidity, work address, mobility during pregnancy etc.) could not be incorporated due to the lack of data.

While interpreting the findings of our analysis, it is also important to consider the deviations that occurred during NFHS-5 data collection due to the pandemic [21]. Phase 1 (22 states) of NFHS-5 lasted from July to December 2019. Phase 2 (14 states) started in January 2020, was halted from March to November 2020, and was finally completed in April 2021. Three free LPG cylinders were distributed to PMUY households during the financial year 2020–21. The average annual use of LPG cylinders in these households increased to 4.4 during 2020–21, compared to 2.8 in the previous year [34]. Even with this increase, many of the PMUY households are likely to have relied primarily on solid fuels, as 7–8 cylinders year⁻¹ are typically required for a household of four people to completely shift to LPG-based cooking. Therefore, a sudden change in the category (from primary users of SCFs to primary users of CCFs) seems less likely during this time period. On the other hand, the level of stacking may have increased in non-PMUY households during the pandemic period due to access and affordability issues. This can erode the benefits of CFU. However, only a small proportion of the sample belongs to the abovementioned period. Around 15% of the births included in the analysis occurred during April—December 2020, and 2% occurred in 2021.

Finally, AAP exposure during pregnancy was estimated using satellite-PM_2.5 and it was assumed that the accuracy of the product (evaluated against measurements in urban regions) is the same over urban and rural areas. Actual error in satellite-PM_2.5 in the rural region is not possible to determine unless the ground monitoring network expands to cover rural India in the future.

Switching to CCF for cooking and heating during pregnancy is expected to reduce the risk of LBW babies. Our study shows that this benefit will be lost if AAP levels continue to be high. Cohorts are required to elucidate the causal pathway for such intermediated effect. It will be important to address the issue of HAP and AAP simultaneously, especially in intervention areas where both are prevalent.

The work is supported by a grant from the Clean Air Fund (FT/2023/11/03) and SUPRA scheme from the SERB (SPR/2020/000212). S D acknowledges IIT Delhi for the Institute Chair Fellowship.

The satellite-based PM_2.5 data that support the findings of this study can be available in the portal from the authors upon request.

The data that support the findings of this study are openly available at the following URL/DOI: www.dhsprogram.com/data/available-datasets.cfm.

As this study is based on secondary data available in the public domain for research use, no ethical approval was required from any institutional review board.

RP conceived the study, curated the data, carried out the analysis, wrote the first draft, and edited and revised the text.

EC participated in data analysis.

SD, SG, and SDa provided inputs to carry out the analysis.

SD, AK, and EC provided the exposure assessments used in the analysis.

SD, SG, EC, AK, and SDa edited and revised the text.

The authors declare that they have no competing interests.