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Reducing the burden of anaemia in Indian women of reproductive age with clean-air targets

Nature Sustainability volume 5, pages 939–946 (2022)Cite this article

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Abstract

India has one of the highest (53%) global prevalences of anaemia among women of reproductive age (WRA, 15–49 years). Long-term exposure to ambient fine particulate matter (PM2.5), a type of air pollution, may increase the prevalence of anaemia through systemic inflammation. Using a linear mixed model adjusted for potential confounding factors, we show that for every 10 µg m−3 increase in ambient PM2.5 exposure, the average anaemia prevalence among Indian WRA increases by 7.23% (95% uncertainty interval, 6.82–7.63). Among PM2.5 species, sulfate and black carbon are more associated with anaemia than organics and dust. Among sectoral contributors, industry was the greatest, followed by the unorganized, domestic, power, road dust, agricultural waste burning and transport sectors. If India meets its recent clean-air targets, such anaemia prevalence among WRA will fall from 53% to 39.5%, taking 186 districts below the national target of 35%. Our results suggest that the transition to clean energy would accelerate India’s progress towards the ‘anaemia-free’ mission target.

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Fig. 1: Spatial patterns of anaemia prevalence (%) and PM2.5 exposure in India.
Fig. 2: Exposure–response curve of ambient PM2.5 long-term (2007–2016) exposure against anaemia prevalence.
Fig. 3: Effect of PM2.5 exposure on odds ratio of anaemia and Hb level among WRA in India.
Fig. 4: Anaemia prevalence and clean-air targets.

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Data availability

The satellite-based PM2.5 data that support the findings of this study are available in the Code Ocean database (https://codeocean.com/capsule/5860976/tree). The MERRA-2 reanalysis data were retrieved from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/. The model data used in this study are available from the corresponding author on reasonable request. DHS data source: https://www.dhsprogram.com/data/available-datasets.cfm.

Code availability

The code that supports the findings of this study is available in the Code Ocean database (https://codeocean.com/capsule/5860976/tree) and from the corresponding author on reasonable request.

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Acknowledgements

The work is supported by a research grant under the SUPRA scheme from the SERB, Department of Science and Technology, GoI. The exposure database was generated as part of a project under the NCAP funded by the Ministry of Environment, Forest and Climate Change, GoI. S.D. acknowledges financial support for the Institute Chair fellowship and DST-FIST programme (SR/FST/ESII-016/2014) for computing support. S.J. acknowledges the Institute Postdoctoral fellowship from IIT Delhi. S.W. acknowledges the National Natural Science Foundation of China (grant no. 22188102).

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Authors and Affiliations

  1. Centre for Atmospheric Sciences, Indian Institute of Technology (IIT) Delhi, New Delhi, India

    Ekta Chaudhary, Sagnik Dey & Srishti Jain

  2. Centre of Excellence for Research on Clean Air, IIT Delhi, New Delhi, India

    Sagnik Dey

  3. School of Public Policy, IIT Delhi, New Delhi, India

    Sagnik Dey

  4. St. John’s Medical College, Bengaluru, India

    Santu Ghosh & Anura V. Kurpad

  5. TERI, New Delhi, India

    Sumit Sharma, Nimish Singh & Shivang Agarwal

  6. Department of Chemical Engineering, IIT Bombay, Bombay, India

    Kushal Tibrewal & Chandra Venkataraman

  7. Health Effects Institute, Boston, MA, USA

    Aaron J. Cohen

  8. Institute for Health Metrics and Evaluation, Seattle, WA, USA

    Aaron J. Cohen

  9. Boston University School of Public Health, Boston, MA, USA

    Aaron J. Cohen

  10. State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China

    Shuxiao Wang

  11. Department of Chemistry, University College Cork, Cork, Ireland

    Srishti Jain

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Contributions

S.D. and E.C. conceived the study and wrote the initial article. E.C. analysed the data with the help of S.G. S.S., N.S., S.A., S.W., C.V. and K.T. did the model simulations and analysis of the model outputs. A.V.K. and A.J.C. provided a critical review of aspects of the epidemiological analysis and aetiological frameworks. S.J. provided the PM2.5 composition data for validating the model data. All authors provided comments and contributed to the final version of the article.

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Correspondence to Sagnik Dey or Santu Ghosh.

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Supplementary Figs. 1–9, discussion, Tables 1–4 and Notes 1 and 2.

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Chaudhary, E., Dey, S., Ghosh, S. et al. Reducing the burden of anaemia in Indian women of reproductive age with clean-air targets. Nat Sustain 5, 939–946 (2022). https://doi.org/10.1038/s41893-022-00944-2

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