Matthew Shupler1, Perry Hystad2, Paul Gustafson3, Sumathy Rangarajan4, Maha Mushtaha4, K G Jayachtria5, Prem K Mony5, Deepa Mohan6, Parthiban Kumar6, Pvm Lakshmi7, Vivek Sagar7,8, Rajeev Gupta9, Indu Mohan9, Sanjeev Nair10, Ravi Prasad Varma10,11, Wei Li12, Bo Hu12, Kai You13, Tatenda Ncube14, Brian Ncube14, Jephat Chifamba14, Nicola West15, Karen Yeates15,16, Romaina Iqbal17, Rehman Khawaja17, Rita Yusuf18, Afreen Khan18, Pamela Seron19, Fernando Lanas19, Patricio Lopez-Jaramillo20, Paul A Camacho21, Thandi Puoane22, Salim Yusuf4, Michael Brauer1. 1. School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada. 2. College of Public Health and Human Sciences, Oregon State University, Corvallis, Oregon, United States. 3. Department of Statistics, University of British Columbia, Vancouver, British Columbia. 4. Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada. 5. St. John's Medical College & Research Institute, Bangalore, India. 6. Madras Diabetes Research Foundation, Chennai, India. 7. School of Public Health, PGIMER, Chandigarh, India. 8. Department of Community Medicine, PGIMER, Chandigarh, India. 9. Eternal Heart Care Centre and Research Institute, Jaipur, India. 10. Health Action By People, Thiruvananthapuram and Medical College, Trivandrum, India. 11. Achutha Menon Centre for Health Science Studies, Trivandrum India. 12. Medical Research & Biometrics Center, National Center for Cardiovascular Diseases, Beijing, China. 13. Shunyi District Center for Disease Prevention and Control, Beijing, China. 14. Department of Physiology, University of Zimbabwe, Harare, Zimbabwe. 15. Pamoja Tunaweza Research Centre, Moshi, Tanzania. 16. Department of Medicine, Queen's University, Kingston, Ontario, Canada. 17. Department of Community Health Science, Aga Khan University Hospital, Karachi, Pakistan. 18. School of Life Sciences, Independent University, Dhaka, Bangladesh. 19. Universidad de La Frontera, Temuco, Chile. 20. Research Department, FOSCAL and Medical School, Universidad de Santander (UDES), Bucaramanga, Colombia. 21. Research Department, FOSCAL and Medical School, Universidad Autonoma de Bucaramanga (UNAB), Colombia. 22. School of Public Health, University of the Western Cape, Bellville, South Africa.
Abstract
INTRODUCTION: Switching from polluting (e.g. wood, crop waste, coal) to clean cooking fuels (e.g. gas, electricity) can reduce household air pollution (HAP) exposures and climate-forcing emissions. While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood. METHODS: We examined longitudinal survey data from 24,172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology (PURE) study. We assessed household-level primary cooking fuel switching during a median of 10 years of follow up (~2005-2015). We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching. RESULTS: One-half of study households (12,369) reported changing their primary cooking fuels between baseline and follow up surveys. Of these, 61% (7,582) switched from polluting (wood, dung, agricultural waste, charcoal, coal, kerosene) to clean (gas, electricity) fuels, 26% (3,109) switched between different polluting fuels, 10% (1,164) switched from clean to polluting fuels and 3% (522) switched between different clean fuels. Among the 17,830 households using polluting cooking fuels at baseline, household-level factors (e.g. larger household size, higher wealth, higher education level) were most strongly associated with switching from polluting to clean fuels in India; in all other countries, community-level factors (e.g. larger population density in 2010, larger increase in population density between 2005-2015) were the strongest predictors of polluting-to-clean fuel switching. CONCLUSIONS: The importance of community and sub-national factors relative to household characteristics in determining polluting-to-clean fuel switching varied dramatically across the nine countries examined. This highlights the potential importance of national and other contextual factors in shaping large-scale clean cooking transitions among rural communities in low- and middle-income countries.
INTRODUCTION: Switching from polluting (e.g. wood, crop waste, coal) to clean cooking fuels (e.g. gas, electricity) can reduce household air pollution (HAP) exposures and climate-forcing emissions. While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood. METHODS: We examined longitudinal survey data from 24,172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology (PURE) study. We assessed household-level primary cooking fuel switching during a median of 10 years of follow up (~2005-2015). We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching. RESULTS: One-half of study households (12,369) reported changing their primary cooking fuels between baseline and follow up surveys. Of these, 61% (7,582) switched from polluting (wood, dung, agricultural waste, charcoal, coal, kerosene) to clean (gas, electricity) fuels, 26% (3,109) switched between different polluting fuels, 10% (1,164) switched from clean to polluting fuels and 3% (522) switched between different clean fuels. Among the 17,830 households using polluting cooking fuels at baseline, household-level factors (e.g. larger household size, higher wealth, higher education level) were most strongly associated with switching from polluting to clean fuels in India; in all other countries, community-level factors (e.g. larger population density in 2010, larger increase in population density between 2005-2015) were the strongest predictors of polluting-to-clean fuel switching. CONCLUSIONS: The importance of community and sub-national factors relative to household characteristics in determining polluting-to-clean fuel switching varied dramatically across the nine countries examined. This highlights the potential importance of national and other contextual factors in shaping large-scale clean cooking transitions among rural communities in low- and middle-income countries.
Entities:
Keywords:
Household air pollution; community factors; longitudinal; multilevel modeling; primary fuel switching
Authors: Ahmed Mushfiq Mobarak; Puneet Dwivedi; Robert Bailis; Lynn Hildemann; Grant Miller Journal: Proc Natl Acad Sci U S A Date: 2012-06-11 Impact factor: 11.205
Authors: Crystal L Weagle; Graydon Snider; Chi Li; Aaron van Donkelaar; Sajeev Philip; Paul Bissonnette; Jaqueline Burke; John Jackson; Robyn Latimer; Emily Stone; Ihab Abboud; Clement Akoshile; Nguyen Xuan Anh; Jeffrey Robert Brook; Aaron Cohen; Jinlu Dong; Mark D Gibson; Derek Griffith; Kebin B He; Brent N Holben; Ralph Kahn; Christoph A Keller; Jong Sung Kim; Nofel Lagrosas; Puji Lestari; Yeo Lik Khian; Yang Liu; Eloise A Marais; J Vanderlei Martins; Amit Misra; Ulfi Muliane; Rizki Pratiwi; Eduardo J Quel; Abdus Salam; Lior Segev; Sachchida N Tripathi; Chien Wang; Qiang Zhang; Michael Brauer; Yinon Rudich; Randall V Martin Journal: Environ Sci Technol Date: 2018-10-01 Impact factor: 9.028