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Adverse Birth Outcomes Due to Exposure to Household Air Pollution from Unclean Cooking Fuel among Women of Reproductive Age in Nigeria

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  • Jamie Roberman

    (Public Health & Tropical Medicine, College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD 4811, Australia)

  • Theophilus I. Emeto

    (Public Health & Tropical Medicine, College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD 4811, Australia
    Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD 4811, Australia)

  • Oyelola A. Adegboye

    (Public Health & Tropical Medicine, College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD 4811, Australia
    Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD 4811, Australia)

Abstract

Exposure to household air pollution (HAP) from cooking with unclean fuels and indoor smoking has become a significant contributor to global mortality and morbidity, especially in low- and middle-income countries such as Nigeria. Growing evidence suggests that exposure to HAP disproportionately affects mothers and children and can increase risks of adverse birth outcomes. We aimed to quantify the association between HAP and adverse birth outcomes of stillbirth, preterm births, and low birth weight while controlling for geographic variability. This study is based on a cross-sectional survey of 127,545 birth records from 41,821 individual women collected as part of the 2018 Nigeria Demographic and Health Survey (NDHS) covering 2013–2018. We developed Bayesian structured additive regression models based on Bayesian splines for adverse birth outcomes. Our model includes the mother’s level and household characteristics while correcting for spatial effects and multiple births per mother. Model parameters and inferences were based on a fully Bayesian approach via Markov Chain Monte Carlo (MCMC) simulations. We observe that unclean fuel is the primary source of cooking for 89.3% of the 41,821 surveyed women in the 2018 NDHS. Of all pregnancies, 14.9% resulted in at least one adverse birth outcome; 14.3% resulted in stillbirth, 7.3% resulted in an underweight birth, and 1% resulted in premature birth. We found that the risk of stillbirth is significantly higher for mothers using unclean cooking fuel. However, exposure to unclean fuel was not significantly associated with low birth weight and preterm birth. Mothers who attained at least primary education had reduced risk of stillbirth, while the risk of stillbirth increased with the increasing age of the mother. Mothers living in the Northern states had a significantly higher risk of adverse births outcomes in 2018. Our results show that decreasing national levels of adverse birth outcomes depends on working toward addressing the disparities between states.

Suggested Citation

  • Jamie Roberman & Theophilus I. Emeto & Oyelola A. Adegboye, 2021. "Adverse Birth Outcomes Due to Exposure to Household Air Pollution from Unclean Cooking Fuel among Women of Reproductive Age in Nigeria," IJERPH, MDPI, vol. 18(2), pages 1-15, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:2:p:634-:d:479825
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    References listed on IDEAS

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    1. Matinga, Margaret Njirambo & Annegarn, Harold J. & Clancy, Joy S., 2013. "Healthcare provider views on the health effects of biomass fuel collection and use in rural Eastern Cape, South Africa: An ethnographic study," Social Science & Medicine, Elsevier, vol. 97(C), pages 192-200.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    3. Peter C. Smith, 2002. "Discussion on the paper by Stone," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(3), pages 405-434, October.
    4. Evelyn L. Rhodes & Robert Dreibelbis & Elizabeth Klasen & Neha Naithani & Joyce Baliddawa & Diana Menya & Subarna Khatry & Stephanie Levy & James M. Tielsch & J. Jaime Miranda & Caitlin Kennedy & Will, 2014. "Behavioral Attitudes and Preferences in Cooking Practices with Traditional Open-Fire Stoves in Peru, Nepal, and Kenya: Implications for Improved Cookstove Interventions," IJERPH, MDPI, vol. 11(10), pages 1-17, October.
    5. Umlauf, Nikolaus & Adler, Daniel & Kneib, Thomas & Lang, Stefan & Zeileis, Achim, 2015. "Structured Additive Regression Models: An R Interface to BayesX," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i21).
    6. Helene Roth & Stefan Lang & Helga Wagner, 2015. "Random intercept selection in structured additive regression models," Working Papers 2015-02, Faculty of Economics and Statistics, Universität Innsbruck.
    7. Emmanuel Olorunleke Adewuyi & Asa Auta & Vishnu Khanal & Olasunkanmi David Bamidele & Cynthia Pomaa Akuoko & Kazeem Adefemi & Samson Joseph Tapshak & Yun Zhao, 2018. "Prevalence and factors associated with underutilization of antenatal care services in Nigeria: A comparative study of rural and urban residences based on the 2013 Nigeria demographic and health survey," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-21, May.
    8. Brezger, Andreas & Lang, Stefan, 2006. "Generalized structured additive regression based on Bayesian P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 967-991, February.
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    Cited by:

    1. Nuno Canha & Evangelia Diapouli & Susana Marta Almeida, 2021. "Integrated Human Exposure to Air Pollution," IJERPH, MDPI, vol. 18(5), pages 1-6, February.
    2. Joshua Epuitai & Katherine E. Woolley & Suzanne E. Bartington & G. Neil Thomas, 2022. "Association between Wood and Other Biomass Fuels and Risk of Low Birthweight in Uganda: A Cross-Sectional Analysis of 2016 Uganda Demographic and Health Survey Data," IJERPH, MDPI, vol. 19(7), pages 1-14, April.

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