Cross-sectional analysis reveals a significant link between increased air pollution and anaemia rates in women of reproductive age, highlighting the need for targeted interventions.
Anaemia is a global public health crisis, but is especially prevalent in low- and middle-income countries (LMICs) like India. While air pollution epidemiological studies in LMICs have focused mainly on fine particulate matter (PM2.5), the health risks of other pollutants and their combined effects remain largely unexplored. This study aimed to estimate the association between long-term exposure to multiple air pollutants and anaemia among women of reproductive age (WRA, 15-49 years) in India. We developed a database of the air quality index (AQI) at a 1-km × 1-km spatial scale as a proxy for multi-pollutant exposure and combined it with health data from the fourth round of the National Family Health Survey (2015-2016). Using a logistic regression model adjusted for potential confounders, we conducted a cross-sectional analysis to assess the association between long-term AQI exposure (2007-2016) and anaemia prevalence among WRA. We performed analyses for single, two-, and three-pollutant models. The average AQI across India increased by 22%, from 122 in 2007 to 149 in 2016, with the most significant rise observed in the Indo-Gangetic Plain and western India. For each 10-unit increase in AQI, the odds ratio for anaemia among WRA was 1.061 (95% CI: 1.058, 1.063), and haemoglobin levels decreased by 0.038 g/dL (95% CI: 0.036, 0.040). Effect modification was observed for several factors, including wealth index, daily iron intake, BMI, education, place of residence, and cooking fuel. Our results imply that the anaemia prevalence is higher with a higher AQI, even within a single AQI category. Moreover, the prevalence of anaemia is comparable for exposure to particulate matter and gaseous pollutants, and hence, in addition to the focus on PM2.5 in the clean air action plan, gaseous pollutants also need to be controlled. The effect modification analysis would help prioritize targeted interventions for the more vulnerable group.
No takes yet. Share an insight, caveat, or question.
Khushboo et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: