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Predictors of drinking water boiling and bottled water consumption in rural China: A hierarchical modeling approach Alasdair Cohen, Q Zhang, Qing Luo, Yong Tao, John Colford, and Isha Ray Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 20 May 2017 Downloaded from http://pubs.acs.org on May 25, 2017
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Predictors of drinking water boiling and bottled water consumption in rural China: A hierarchical modeling approach Alasdair Cohen1,2*, Qi Zhang3, Qing Luo3, Yong Tao3, John M. Colford, Jr.2, Isha Ray4
1. Department of Environmental Science, Policy and Management, University of California at Berkeley, Berkeley, California, United States of America 2. School of Public Health, University of California at Berkeley, Berkeley, California, United States of America 3. National Center for Rural Water Supply Technical Guidance, Chinese Center for Disease Control and Prevention, Beijing, China 4. Energy and Resources Group, University of California at Berkeley, Berkeley, California, United States of America
*Corresponding author: Alasdair Cohen; Department of Environmental Science, Policy, and Management; University of California, Berkeley; 130 Mulford Hall #3114; Berkeley, CA 94720-3114; Telephone: 510 643 4554; Fax: 510 643 5438; E-mail:
[email protected] Acknowledgements We thank Gemei Zhong, and the Guangxi CCDC staff, as well as Zhenbo Yang at UNICEF-China, and Benjamin Arnold, the ERG Water Group, Alan Hubbard, Thomas Rath, Vincent Resh, Jeff Romm, and Kirk Smith. Funding and support for this research was provided by the U.S. Environmental Protection Agency (www.epa.gov, STAR Fellowship #91744201-0) and the International Fund for Agricultural Development, a specialized United Nations agency (www.ifad.org). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Funding and support for data collection and water sample analyses were provided by the Chinese Center for Disease Control and Prevention (www.chinacdc.cn).
Supporting Information The following supporting information is available: A completed STROBE checklist (pages S2-3); additional information on the Multidimensional Poverty Assessment Tool (MPAT) indicators and household survey (pages S4-5); details of the process used to identify MPAT-derived covariates and a conceptual framework for model construction (pages S6-9); MPAT indicator results (pages S10-11); covariate blocks and adjustments for all three hierarchical models (pages S12-14) as well as associated sensitivity analyses (pages S15-17); additional demographic and socioeconomic results (pages S18-22); and summary tables of key variables associated with HWT and bottled water use as well as related information (pages S23-25).
Competing Financial Interests Declaration The authors declare that they have no actual or potential competing financial interests.
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Abstract ........................................................................................................................................... 3 1. INTRODUCTION AND OBJECTIVES............................................................................................... 4 2. MATERIALS AND METHODS ........................................................................................................ 6 2.1 Site Selection, Survey Instruments, and Data Collection...................................................... 6 2.2 Selection of Model Covariates: Water-related ..................................................................... 6 2.3 Selection of Model Covariates: MPAT-derived ..................................................................... 7 2.4 Hierarchical Model Construction and Sensitivity Analyses .................................................. 7 2.5 Ethics and Reporting ............................................................................................................. 8 3. RESULTS....................................................................................................................................... 9 3.1 Household survey and MPAT indicator results..................................................................... 9 3.2 Model results ...................................................................................................................... 11 3.3 Key demographic, socioeconomic, and water-quality results ............................................ 14 4. DISCUSSION ............................................................................................................................... 17 4.1 The potential benefits of promoting boiling with electric kettles ...................................... 17 4.2 Bottled water and the costs of convenience ...................................................................... 17 4.3 Limitations........................................................................................................................... 18 4.4 Policy implications .............................................................................................................. 19 6. REFERENCES .............................................................................................................................. 20
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Abstract Approximately two billion people drink unsafe water. Boiling is the most commonly used household water treatment (HWT) method globally and in China. HWT can make water safer, but sustained adoption is rare and bottled water consumption is growing. To successfully promote HWT, an understanding of associated socioeconomic factors is critical. We collected survey data and water samples from 450 rural households in Guangxi Province, China. Covariates were grouped into blocks to hierarchically construct modified Poisson models and estimate risk ratios (RR) associated with boiling methods, bottled water, and untreated water. Female-headed households were most likely to boil (RR=1.36, p months/year Column HWT categories are mutually exclusive | Total n excludes missing data (not adjusted with sample weights)
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In Table 1, we see that, across HWT methods, the highest proportion of households who believe their drinking water quality is good or very good are in the group that does not treat their water. Using the data summarized in Table 1 to calculate RRs without any adjustment, we find that if a household perceives its drinking water quality to be good or very good it is 27% less likely to boil its water (using any method) than to drink untreated water (unadjusted RR=0.73, 0.62-0.85, p
