Recent progress in the Joint Monitoring Programme’s Sustainable Development Goal 6 monitoring efforts may help build the quantitative evidence base for driving global action around school water, sanitation and hygiene (WASH) infrastructure. To evaluate the analytical value of the expanding database for generating research evidence, we model the relationships between school WASH conditions and student enrolment within select low- and middle-income countries. Using a series of incrementally adjusted linear regressions, we find that there is sufficient variation in the dataset to detect signals of significance with some consistency, including significant associations between the presence and quality of toilets among primary school students and the quality of toilets among secondary school students, particularly among girls. These findings may suggest that the data are amenable to statistical analysis and that there are interesting relationships between school WASH and education to study further at the global level, as well as potential synergies to harness across goals for advancing sustainable development more effectively. However, given their current incompleteness, the data are unable to support rigorous statistical analyses that can supply high-quality evidence. Based on our study, we offer several recommendations to enhance data utility and guide future analyses.
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The enrolment data are open and may be readily accessed and downloaded (for all countries in one file for each combination of educational stage and sex) at https://data.worldbank.org/ by searching separately for ‘net enrolment’ for boys and girls at the primary and secondary school levels. Similarly, the data for each covariate may be accessed and downloaded (for all countries in one file for each covariate) at https://data.worldbank.org through the search bar. The JMP data are also publicly available and may be accessed and downloaded as individual country files at https://washdata.org/monitoring/schools/country-files-2018. The WASH data used in this analysis were not drawn from the website but were given by the JMP to the authors as a Stata file; the data were subsequently extracted as an Excel file to be used in R. The authors were required to sign a memorandum of understanding which stated that datasets shared by the JMP for specific purposes could not be disclosed to a third party without prior permission from the JMP. We therefore recommend interested parties submit their requests to the JMP at email@example.com.
As the analysis was structured around the Stata file version of the JMP dataset, and given the restricted nature of this compiled version, the authors will provide the code only on reasonable request once the requisite permission for the compiled data is obtained from the JMP. All figures were created using the raw JMP and World Bank data and R packages ggplot266 (with Natural Earth public domain data from the maps package for Fig. 1 (ref. 67) and egg68.
Transforming our world: the 2030 Agenda for Sustainable Development. United Nations, Department of Economic and Social Affairs https://sdgs.un.org/2030agenda (2015).
About the JMP. JMP https://washdata.org/how-we-work/about-jmp (2019).
Do you know all 17 goals? United Nations, Department of Economic and Social Affairs https://sdgs.un.org/goals (2021).
Delivering the promise: Safe water and sanitation for all by 2030: The SDG 6 Global Acceleration Framework: In Brief (UN Water, 2020).
Progress on household drinking water, sanitation and hygiene: five years into the SDGs (WHO and UNICEF, 2021).
Cronk, R., Slaymaker, T. & Bartram, J. Monitoring drinking water, sanitation, and hygiene in non-household settings: priorities for policy and practice. Int. J. Hyg. Environ. Health 218, 694–703 (2015).
Bain, R., Johnston, R., Mitis, F., Chatterley, C. & Slaymaker, T. Establishing sustainable development goal baselines for household drinking water, sanitation and hygiene services. Water 10, 1711–1729 (2018).
JMP Drinking water, sanitation and hygiene in schools: global baseline report 2018 (WHO and UNICEF, 2018); https://washdata.org/sites/default/files/documents/reports/2018-11/JMP%20WASH%20in%20Schools%20WEB%20final.pdf
JMP Progress on drinking water, sanitation and hygiene in schools: 2000–2021 data update (WHO and UNICEF, 2022).
Blanton, E. et al. Evaluation of the role of school children in the promotion of point-of-use water treatment and handwashing in schools and households—Nyanza Province, Western Kenya, 2007. Am. J. Trop. Med. Hyg. 82, 664–671 (2010).
Hunter, P. R. et al. Impact of the provision of safe drinking water on school absence rates in Cambodia: a quasi-experimental study. PLoS ONE 9, 5 (2014).
Talaat, M. et al. Effects of hand hygiene campaigns on incidence of laboratory-confirmed influenza and absenteeism in schoolchildren, Cairo, Egypt. Emerg. Infect. Dis. 17, 619–625 (2011).
O'Reilly, C. E. et al. The impact of a school-based safe water and hygiene programme on knowledge and practices of students and their parents: Nyanza Province, western Kenya, 2006. Epidemiol. Infect. 136, 80–91 (2008).
Freeman, M. C., Clasen, T., Brooker, S. J., Akoko, D. O. & Rheingans, R. The impact of a school-based hygiene, water quality and sanitation intervention on soil-transmitted helminth reinfection: a cluster-randomized trial. Am. J. Trop. Med. Hyg. 89, 875–883 (2013).
Khanna, A., Goyal, R. & Bhawsar, R. Menstrual practices and reproductive problems: a study of adolescent girls in Rajasthan. J. Health Manag. 7, 91–107 (2005).
Shah, V. et al. Effects of menstrual health and hygiene on school absenteeism and drop-out among adolescent girls in rural Gambia. Int. J. Environ. Res. Public Health 19, 3337 (2022).
Adukia, A. Sanitation and education. Am. Econ. J.: Appl. Econ. 9, 23–59 (2017).
Njuguna, V. et al. The Sustainability and Impact of School Sanitation, Water and Hygiene Education in Kenya (International Water and Sanitation Centre and UNICEF, 2008).
Caruso, B. A., Dreibelbis, R., Ogutu, E. A. & Rheingans, R. If you build it will they come? Factors influencing rural primary pupils’ urination and defecation practices at school in western Kenya. J. Water Sanit. Hyg. Dev. 4, 642–653 (2014).
Mooijman, A., Snel, M., Ganguly, S. & Shordt, K. Strengthening water, sanitation and hygiene in schools – a WASH guidance manual with a focus on South Asia (International Water and Sanitation Centre, 2009).
Garn, J. V. et al. A cluster-randomized trial assessing the impact of school water, sanitation, and hygiene improvements on pupil enrollment and gender parity in enrollment. J. Water Sanit. Hyg. Dev. 3, 592–601 (2013).
Trinies, V., Garn, J. V., Chang, H. H. & Freeman, M. C. The impact of a school-based water, sanitation, and hygiene program on absenteeism, diarrhea, and respiratory infection: a matched-control trial in Mali. Am. J. Trop. Med. Hyg. 94, 1418–1425 (2016).
Grant, M., Lloyd, C. & Mensch, B. Menstruation and school absenteeism: evidence from rural malawi. Comp. Educ. Rev. 57, 260–284 (2013).
Dreibelbis, R. et al. Water, sanitation, and primary school attendance: a multi-level assessment of determinants of household-reported absence in Kenya. Int. J. Educ. Dev. 33, 457–465 (2013).
Jasper, C., Le, T.-T. & Bartram, J. Water and sanitation in schools: a systematic review of the health and educational outcomes. Int. J. Environ. Res. Public Health 9, 2772–2787 (2012).
McMichael, C. Water, sanitation and hygiene (WASH) in schools in low-income countries: a review of evidence of impact. Int. J. Environ. Res. Public Health 16, 359 (2019).
Pérez-Foguet, A., Giné-Garriga, R. & Ortego, M. I. Compositional data for global monitoring: the case of drinking water and sanitation. Sci. Total Environ. 590–591, 554–565 (2017).
Schools. JMP https://washdata.org/monitoring/schools (2018).
Hutton, G., Haller, L. & Bartram, J. Global cost-benefit analysis of water supply and sanitation interventions. J. Water Health 5, 481–502 (2007).
Song, L., Appleton, S. & Knight, J. Why do girls in rural China have lower school enrollment? World Dev. 34, 1639–1653 (2006).
Mahmud, S. & Amin, S. Girls’ schooling and marriage in rural Bangladesh. Res. Sociol. Educ. 15, 71–99 (2006).
Drèze, J. & Kingdon, G. G. School participation in rural India. Rev. Dev. Econ. 5, 1–24 (2001).
Iddrisu, A. M. The effect of poverty, household structure and child work on school enrolment. J. Educ. Pract. 5, 145–156 (2014).
Daoud, J. I. Multicollinearity and regression analysis. J. Phys. Conf. Ser. 949, 012009–012015 (2017).
Farrar, D. E. & Glauber, R. R. Multicollinearity in regression analysis: the problem revisited. Rev. Econ. Stat. 49, 92–107 (1967).
Keller, K. R. I. Investment in primary, secondary, and higher education and the effects on economic growth. Contemp. Econ. Policy 24, 18–34 (2006).
Kiran, B. Testing the impact of educational expenditures on economic growth: new evidence from Latin American countries. Qual. Quant. 48, 1181–1190 (2014).
Myrskylä, M., Kohler, H.-P. & Billari, F. C. Advances in development reverse fertility declines. Nature 460, 741–743 (2009).
Ward, J. L. & Viner, R. M. The impact of income inequality and national wealth on child and adolescent mortality in low and middle-income countries. BMC Public Health 17, 8 (2017).
Koolwal, G. & van de Walle, D. Access to water, women’s work, and child outcomes. Econ. Dev. Cult. Change 61, 369–405 (2013).
Freeman, M. C. et al. Assessing the impact of a school-based water treatment, hygiene and sanitation programme on pupil absence in Nyanza Province, Kenya: a cluster-randomized trial. Trop. Med. Int. Health 17, 380–391 (2012).
Swanson, E. World Development Indicators 2007 81 (World Bank Publications, 2007).
Chatterley, C. et al. Institutional WASH in the SDGs: data gaps and opportunities for national monitoring. J. Water Sanit. Hyg. Dev. 8, 595–606 (2018).
Vedachalam, S. et al. Underreporting of high-risk water and sanitation practices undermines progress on global targets. PLoS ONE 12, 20 (2017).
Exley, J. L. R., Liseka, B., Cumming, O. & Ensink, J. H. J. The sanitation ladder, what constitutes an improved form of sanitation? Environ. Sci. Technol. 49, 1086–1094 (2015).
Nganyanyuka, K., Martinez, J., Wesselink, A., Lungo, J. H. & Georgiadou, Y. Accessing water services in Dar es Salaam: are we counting what counts? Habitat Int. 44, 358–366 (2014).
Evans, B. et al. Limited services? The role of shared sanitation in the 2030 Agenda for Sustainable Development. J. Water Sanit. Hyg. Dev. 7, 349–351 (2017).
Bain, R., Johnston, R., Khan, S., Hancioglu, A. & Slaymaker, T. Monitoring drinking water quality in nationally representative household surveys in low- and middle-income countries: cross-sectional analysis of 27 multiple indicator cluster surveys 2014–2020. Environ. Health Perspect. 129, 19 (2021).
Morgan, C., Bowling, M., Bartram, J. & Lyn Kayser, G. Water, sanitation, and hygiene in schools: status and implications of low coverage in Ethiopia, Kenya, Mozambique, Rwanda, Uganda, and Zambia. Int. J. Hyg. Environ. Health 220, 950–959 (2017).
Sommer, M. & Sahin, M. Overcoming the taboo: advancing the global agenda for menstrual hygiene management for schoolgirls. Am. J. Public Health 103, 1556–1559 (2013).
Elledge, M. F. et al. Menstrual hygiene management and waste disposal in low and middle income countries—a review of the literature. Int. J. Environ. Res. Public Health 15, 20 (2018).
Spears, D. Exposure to open defecation can account for the Indian enigma of child height. J. Dev. Econ. 146, 17 (2020).
World Bank Open Data https://data.worldbank.org/ (World Bank, 2019).
Gelman, A. & Hill, J. Data Analysis Using Regression and Hierarchical/Multilevel Models Vol. 1 (Cambridge Univ. Press, 2007).
Fertility rate, total (births per woman) https://data.worldbank.org/indicator/SP.DYN.TFRT.iN (World Bank, 2018).
Breierova, L. & Duflo, E. The Impact of Education on Fertility and Child Mortality: Do Fathers Really Matter Less Than Mothers? Working Paper No. 10513 (National Bureau of Economic Research, 2004); http://www.nber.org/papers/w10513.pdf
Duflo, E., Dupas, P. & Kremer, M. Education, HIV, and early fertility: experimental evidence from Kenya. Am. Econ. Rev. 105, 2757–2797 (2015).
Osili, U. O. & Long, B. T. Does female schooling reduce fertility? Evidence from Nigeria. J. Dev. Econ. 87, 57–75 (2008).
Sen, A. Development as Freedom (Oxford Univ. Press, 1999).
Graham, J. P., Hirai, M. & Kim, S.-S. An analysis of water collection labor among women and children in 24 Sub-Saharan African countries. PLoS ONE 11, 14 (2016).
Progress on Drinking Water and Sanitation: 2014 Update (WHO and UNICEF, 2014).
Beckman, P. J. & Gallo, J. Rural education in a global context. Glob. Educ. Rev. 2, 1–4 (2015).
Bhatia, A., Krieger, N. & Subramanian, S. V. Learning from history about reducing infant mortality: contrasting the centrality of structural interventions to early 20th-century successes in the United States to their neglect in current global initiatives. Milbank Q. 97, 285–345 (2019).
RStudio: Integrated Development for R v.1.2.1335 (RStudio, 2018); http://www.rstudio.com/
Robitzsch, A. & Grund, S. miceadds: Some additional multiple imputation functions, especially for ‘mice’. R package version 3.9.0 (2020).
Wickham, H. ggplot2: Elegant graphics for data analysis. R package version 3.3.2 (2016).
Becker, R. A., Wilks A. R., Brownrigg, R., Minka T. P. & Deckmyn, A. maps: Draw geographical maps. R package version 3.3.0 https://cran.r-project.org/web/packages/maps/index.html (2018).
Auguie, B. egg: Extensions for ‘ggplot2’: Custom geom, custom themes, plot alignment, labelled panels, symmetric scales, and fixed panel size. R package version 0.4.5 (2019).
This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1762114 (received by L.C.H.). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We are also thankful for the support of the ARCS Foundation (L.C.H.). The National Science Foundation and ARCS Foundation were not involved in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. The authors gratefully acknowledge the work of the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene and all the in-country agencies that helped compile the school WASH database. Similarly, we appreciate the World Bank and their partnering in-country agencies for free and open access to global enrolment data. We are indebted to the wonderful consultants of the Centre for Social Science Computation and Research, and the Centre for Statistics and the Social Sciences at the University of Washington. Many thanks to J. Herting for sharing his statistical expertise and S. Chakrabarti for helping to frame the article. Thank you to J. Carlson for assistance with data compilation. Any errors are the sole responsibility of the authors.
The authors declare no competing interests.
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Hamlet, L.C., Kaminsky, J. Analytical utility of the JMP school water, sanitation and hygiene global monitoring data. Nat Sustain (2022). https://doi.org/10.1038/s41893-022-01005-4