India, a nation grappling with rapid urbanisation and industrial growth, faces a formidable challenge with air pollution. In 2023, it ranked as the third most polluted country globally, a stark reminder of the silent killer that permeates its skies. This pervasive issue, particularly fine particulate matter (PM2.5), is a profound public health crisis waiting to happen, especially for the most vulnerable among us.
A new study from the Indian Institute of Technology (IIT) Delhi, Asian Institute of Technology (AIT), Thailand, University College Dublin, Ireland, International Institute for Population Sciences, Mumbai, and University of St Andrews, Scotland, sheds light on how this invisible threat impacts newborns even before they take their first breath. The study links in-utero (within the womb) exposure to PM2.5 with adverse birth outcomes like low birth weight and preterm birth across India.
Did You Know? PM2.5 particles are so tiny – less than 2.5 micrometres in diameter – that they are about 30 times smaller than the width of a human hair. This small size allows them to enter your lungs and even your bloodstream easily. |
The researchers uncovered a concerning reality: approximately 13% of children in their study sample were born prematurely, and 17% were born with low birth weight. As ambient PM2.5 concentrations increased during pregnancy, so did the likelihood of these adverse outcomes. Specifically, mothers exposed to higher levels of PM2.5 had 1.4 times higher odds of delivering a low birth weight baby (under 2,500 grams or about 5.5 pounds) and 1.7 times higher odds of a preterm birth (born before 37 weeks of pregnancy) compared to those with lower exposure.
To put this into perspective, a mere 10 micrograms per cubic meter (µg/m³) increase in PM2.5 exposure was associated with a 5% rise in low-birth-weight cases and a 12% increase in preterm births. The study also highlighted that the risk of adverse birth outcomes showed a discernible upward trend, particularly after PM2.5 exposure levels crossed 40 µg/m³ for low birth weight and a rapid, exponential increase after 50 µg/m³ for preterm birth.
Beyond air pollution, the study also identified other significant factors at play. Climatic elements like rainfall and temperature were found to have a notable association with adverse birth outcomes. For instance, higher rainfall was significantly linked to both low birth weight and preterm birth, potentially due to increased risk of waterborne diseases and disruptions to healthcare access during monsoon seasons.
Elevated temperatures were also associated with higher odds of low birth weight, likely due to maternal heat stress. The use of solid fuels for cooking, a common practice in many Indian households, was also associated with higher odds of low birth weight, underscoring the impact of indoor air pollution.
Maternal characteristics played a role too: teenage mothers and those with lower education levels or who were underweight faced higher risks. Children born at home or in rural areas also had higher rates of adverse birth outcomes, pointing to disparities in healthcare access and environmental conditions. Geographically, the study revealed that children in the northern districts of India, particularly the upper Gangetic region encompassing states such as Punjab, Delhi, Uttar Pradesh, Bihar, and Haryana, were more susceptible to the adverse effects of air pollution. This region consistently exhibited high concentrations of PM2.5, which aligns with the higher prevalence of low birth weight and preterm birth observed there.
To arrive at these detailed conclusions, the researchers employed data and analytical techniques. They drew population and health data from the National Family Health Survey (NFHS) 2019-21, a large-scale national survey that collects comprehensive information on maternal and child health. For air pollution data, they utilised global PM2.5 data derived from remote sensing, specifically from the Atmospheric Composition Analysis Group. This data, which has a fine spatial resolution, combines information from multiple satellite sensors and is calibrated against ground-based observations to ensure accuracy.
To calculate a mother's in-utero exposure, they matched the PM2.5 levels to the child's date of birth and pregnancy duration, creating a 3-kilometre buffer around survey cluster points to account for privacy displacements. Climate data, including rainfall and temperature, was sourced from reputable datasets such as the Climate Hazards Group and ERA5-Land, and processed in a manner similar to the PM2.5 data.
The scientific backbone of their analysis involved various statistical and geospatial models. They began with basic statistical tests, such as the Chi-square test, to identify initial associations. Then, they used multivariate logistic regression to calculate odds ratios (AORs), which tell us how much more likely an outcome is when a certain factor is present, while controlling for other influences. To understand the geographical patterns, they used Bivariate Local Indicators of Spatial Association (LISA), which helped them identify hotspots where high pollution levels coincided with high rates of adverse birth outcomes.
Finally, to identify the best model for capturing complex spatial relationships, they compared several geospatial regression models: Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), Multiscale Geographically Weighted Regression (MGWR), Spatial Lag Model (SLM), and Spatial Error Model (SEM). The MGWR model proved to be the most effective, showing the best fit for the actual scenario and accurately capturing how the relationship between pollution and birth outcomes varies across different locations and scales.
This study is the first to measure preterm birth at the district level in India, using detailed calendar data from the NFHS. It also uniquely integrates satellite-derived air pollution data with large-scale survey data to establish a robust link between in-utero PM2.5 exposure and adverse birth outcomes in India. The comprehensive adjustment for various confounding factors, including environmental, socioeconomic, maternal, and child characteristics, strengthens the confidence in their findings.
However, the researchers acknowledge certain limitations. They assumed that mothers did not change their residence during pregnancy, which could introduce some spatial uncertainty. The study also couldn't include all potential confounding factors due to data limitations or underreporting. Relying on self-reported birth weight and pregnancy duration might introduce biases, and the cross-sectional nature of the study means it can't definitively prove cause-and-effect relationships. Future research, ideally with longitudinal designs, could investigate seasonal effects and explore the biological mechanisms underlying the relationship between air pollution and fetal development.
By precisely identifying vulnerable regions, particularly the Northern districts, the study provides crucial evidence for targeted interventions. It underscores the need to intensify the National Clean Air Program, pushing for stricter emission standards and enhanced air quality monitoring. Integrating air quality data with existing health surveillance systems can help pinpoint at-risk populations more accurately.
Beyond pollution control, the findings underscore the importance of promoting clean cooking fuels and energy-efficient technologies to mitigate indoor air pollution, a significant contributor to PM2.5 exposure. Furthermore, the study emphasises the critical need for climate adaptation strategies, such as developing heat action plans and improving water management, to mitigate the effects of extreme temperatures and irregular rainfall on maternal and neonatal health. Ultimately, this work serves as a powerful call to action, urging public health initiatives to raise awareness among pregnant women about the risks of air pollution and climate change, ensuring a healthier start for India's future generations.
This article was written with the help of generative AI and edited by an editor at Research Matters.