| 要約: | Anaemia, a deficiency of hemoglobin (Hb) or red blood cells, is a global public health crisis. It is especially prevalent in low- and middle-income countries (LMICs) like India, which has one of the highest burdens. While air pollution epidemiological studies in LMICs have focused mainly on fine particulate matter (PM _2.5 ), 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) in India from 2015–2016. We first created a database of the air quality index (AQI) at a 1 km × 1 km spatial scale as a proxy for multi-pollutant exposure. We then combined this AQI data 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, 15–49 years. We also examined the effect modification by various individual and socio-demographic factors, and performed analyses for single, two-, and three-pollutant models. The average AQI across India increased by 22%, from 122 in 2007–149 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 Hb levels decreased by 0.038 g dL ^−1 (95% CI: 0.036, 0.040). Effect modification was observed for several factors, including wealth index, daily iron intake, body mass index, 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 PM and gaseous pollutants, and hence, in addition to the focus on PM _2.5 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.
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