Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Analytical utility of the JMP school water, sanitation and hygiene global monitoring data

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: LMICs included in the analyses.
Fig. 2: Primary school toilet conditions across female and male enrolment.
Fig. 3: Primary school water conditions across female and male enrolment.
Fig. 4: Secondary school toilet conditions across female and male enrolment.
Fig. 5: Secondary school water conditions across female and male enrolment.

Data availability

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 info@washdata.org.

Code availability

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.

References

  1. Transforming our world: the 2030 Agenda for Sustainable Development. United Nations, Department of Economic and Social Affairs https://sdgs.un.org/2030agenda (2015).

  2. About the JMP. JMP https://washdata.org/how-we-work/about-jmp (2019).

  3. Do you know all 17 goals? United Nations, Department of Economic and Social Affairs https://sdgs.un.org/goals (2021).

  4. Delivering the promise: Safe water and sanitation for all by 2030: The SDG 6 Global Acceleration Framework: In Brief (UN Water, 2020).

  5. Progress on household drinking water, sanitation and hygiene: five years into the SDGs (WHO and UNICEF, 2021).

  6. 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).

    Article  Google Scholar 

  7. 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).

    Article  Google Scholar 

  8. 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

  9. JMP Progress on drinking water, sanitation and hygiene in schools: 2000–2021 data update (WHO and UNICEF, 2022).

  10. 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).

    Article  Google Scholar 

  11. 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).

    Article  Google Scholar 

  12. 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).

    Article  Google Scholar 

  13. 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).

    Article  CAS  Google Scholar 

  14. 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).

    Article  Google Scholar 

  15. 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).

    Article  Google Scholar 

  16. 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).

    Article  Google Scholar 

  17. Adukia, A. Sanitation and education. Am. Econ. J.: Appl. Econ. 9, 23–59 (2017).

    Google Scholar 

  18. 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).

  19. 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).

    Article  Google Scholar 

  20. 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).

  21. 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).

    Article  Google Scholar 

  22. 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).

    Article  Google Scholar 

  23. Grant, M., Lloyd, C. & Mensch, B. Menstruation and school absenteeism: evidence from rural malawi. Comp. Educ. Rev. 57, 260–284 (2013).

    Article  Google Scholar 

  24. 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).

    Article  Google Scholar 

  25. 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).

    Article  Google Scholar 

  26. 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).

    Article  Google Scholar 

  27. 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).

    Article  Google Scholar 

  28. Schools. JMP https://washdata.org/monitoring/schools (2018).

  29. Hutton, G., Haller, L. & Bartram, J. Global cost-benefit analysis of water supply and sanitation interventions. J. Water Health 5, 481–502 (2007).

    Article  Google Scholar 

  30. Song, L., Appleton, S. & Knight, J. Why do girls in rural China have lower school enrollment? World Dev. 34, 1639–1653 (2006).

    Article  Google Scholar 

  31. Mahmud, S. & Amin, S. Girls’ schooling and marriage in rural Bangladesh. Res. Sociol. Educ. 15, 71–99 (2006).

    Article  Google Scholar 

  32. Drèze, J. & Kingdon, G. G. School participation in rural India. Rev. Dev. Econ. 5, 1–24 (2001).

    Article  Google Scholar 

  33. Iddrisu, A. M. The effect of poverty, household structure and child work on school enrolment. J. Educ. Pract. 5, 145–156 (2014).

    Google Scholar 

  34. Daoud, J. I. Multicollinearity and regression analysis. J. Phys. Conf. Ser. 949, 012009–012015 (2017).

    Article  Google Scholar 

  35. Farrar, D. E. & Glauber, R. R. Multicollinearity in regression analysis: the problem revisited. Rev. Econ. Stat. 49, 92–107 (1967).

    Article  Google Scholar 

  36. Keller, K. R. I. Investment in primary, secondary, and higher education and the effects on economic growth. Contemp. Econ. Policy 24, 18–34 (2006).

    Article  Google Scholar 

  37. Kiran, B. Testing the impact of educational expenditures on economic growth: new evidence from Latin American countries. Qual. Quant. 48, 1181–1190 (2014).

    Article  Google Scholar 

  38. Myrskylä, M., Kohler, H.-P. & Billari, F. C. Advances in development reverse fertility declines. Nature 460, 741–743 (2009).

    Article  Google Scholar 

  39. 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).

    Article  Google Scholar 

  40. Koolwal, G. & van de Walle, D. Access to water, women’s work, and child outcomes. Econ. Dev. Cult. Change 61, 369–405 (2013).

    Article  Google Scholar 

  41. 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).

    Google Scholar 

  42. Swanson, E. World Development Indicators 2007 81 (World Bank Publications, 2007).

  43. 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).

    Article  Google Scholar 

  44. Vedachalam, S. et al. Underreporting of high-risk water and sanitation practices undermines progress on global targets. PLoS ONE 12, 20 (2017).

    Article  Google Scholar 

  45. 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).

    Article  CAS  Google Scholar 

  46. 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).

    Article  Google Scholar 

  47. 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).

    Article  Google Scholar 

  48. 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).

    Article  Google Scholar 

  49. 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).

    Article  Google Scholar 

  50. 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).

    Article  Google Scholar 

  51. 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).

    Article  Google Scholar 

  52. Spears, D. Exposure to open defecation can account for the Indian enigma of child height. J. Dev. Econ. 146, 17 (2020).

    Article  Google Scholar 

  53. World Bank Open Data https://data.worldbank.org/ (World Bank, 2019).

  54. Gelman, A. & Hill, J. Data Analysis Using Regression and Hierarchical/Multilevel Models Vol. 1 (Cambridge Univ. Press, 2007).

  55. Fertility rate, total (births per woman) https://data.worldbank.org/indicator/SP.DYN.TFRT.iN (World Bank, 2018).

  56. 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

  57. Duflo, E., Dupas, P. & Kremer, M. Education, HIV, and early fertility: experimental evidence from Kenya. Am. Econ. Rev. 105, 2757–2797 (2015).

    Article  Google Scholar 

  58. Osili, U. O. & Long, B. T. Does female schooling reduce fertility? Evidence from Nigeria. J. Dev. Econ. 87, 57–75 (2008).

    Article  Google Scholar 

  59. Sen, A. Development as Freedom (Oxford Univ. Press, 1999).

  60. 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).

    Article  Google Scholar 

  61. Progress on Drinking Water and Sanitation: 2014 Update (WHO and UNICEF, 2014).

  62. Beckman, P. J. & Gallo, J. Rural education in a global context. Glob. Educ. Rev. 2, 1–4 (2015).

    Google Scholar 

  63. 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).

    Article  Google Scholar 

  64. RStudio: Integrated Development for R v.1.2.1335 (RStudio, 2018); http://www.rstudio.com/

  65. Robitzsch, A. & Grund, S. miceadds: Some additional multiple imputation functions, especially for ‘mice’. R package version 3.9.0 (2020).

  66. Wickham, H. ggplot2: Elegant graphics for data analysis. R package version 3.3.2 (2016).

  67. 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).

  68. 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).

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

Authors

Contributions

L.C.H. and J.K. conceptualized the idea. L.C.H. curated the data and performed the formal analysis. L.C.H. acquired funding and was responsible for the methodology. L.C.H. and J.K were responsible for the resources. J.K. supervised the project. L.C.H. and J.K. wrote the original draft and were also responsible for the review and editing of the manuscript.

Corresponding authors

Correspondence to Leigh C. Hamlet or Jessica Kaminsky.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Sustainability thanks Radu Ban, Guy Howard, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Tables 1–5, Figures 1–14 and References.

Reporting Summary.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41893-022-01005-4

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing