Title of PhD project
Uncertainty around water coverage estimates and the costs of piped water supply
Lead: Ian Ross (Faculty of Infectious and Tropical Diseases, firstname.lastname@example.org)
Oliver Cumming (email@example.com)
Satoru Komatsu (firstname.lastname@example.org)
Hirotsugu Aiga (TMGH, email@example.com)
Brief description of project
Background: Understanding and framing of uncertainty regarding water, sanitation and hygiene (WASH) interventions is not as developed as it is for health interventions. For example, WHO/UNICEF publish a “Grade of Confidence” for immunisation coverage estimates,1 amongst other areas of health. However, the WHO/UNICEF Joint Monitoring Programme (JMP) for WASH does not include any information about uncertainty of their national estimates of WASH coverage. While the JMP recently considered alternative methods,2 they currently only report point estimates from a linear regression on survey data over time. Similarly, for costs of interventions, WHO publishes country-specific costs for different aspects of inpatient and outpatient health services.3 For WASH, however, cost estimates are disparate and not centrally collated.4 Uncertainty about costs also hampers financial planning, especially for networked water systems.5
PhD aim: To characterise uncertainty around national coverage estimates for water supply, and around the costs of piped water supply in low- and middle-income countries (L&MICs).
To undertake a review of approaches used by the UN to characterise uncertainty about coverage of health interventions in sustainable development goal (SDG) targets (e.g. immunisation, family planning in SDG 3)
To estimate the “model uncertainty” of JMP water supply coverage estimates, by (i) replicating regressions from published JMP data to estimate 95% confidence intervals; (ii) assessing the level of confidence in whether a country is on- or off-track for SDG targets etc.
To undertake a systematic review of cost estimates for piped water supply interventions in L&MICs, with a focus on how uncertainty and scale are characterised.
To estimate the “price tag” of achieving at-home piped water for all in L&MICs, taking into account different aspects of uncertainty.
WHO/UNICEF (2022) WHO UNICEF Immunization Coverage Estimates 2021 revision, https://www.who.int/docs/default-source/immunization/immunization-coverage/wuenic_notes.pdf?sfvrsn=88ff590d_6
WHO/UNICEF (2015) Meeting report: WHO/UNICEF JMP Task Force on Methods, https://washdata.org/report/jmp-methods-task-force-report-final
Stenberg, K. et al. (2018) Econometric estimation of WHO-CHOICE country-specific costs for inpatient and outpatient health service delivery. Cost Eff Resour Alloc 16, 11. https://doi.org/10.1186/s12962-018-0095-x
Hutton, G. & Varughese, M. (2016). The Costs of Meeting the 2030 Sustainable Development Goal Targets on Drinking Water, Sanitation, and Hygiene. World Bank. https://openknowledge.worldbank.org/handle/10986/23681
Borgomeo, E. et al. (2018). Risk, Robustness and Water Resources Planning Under Uncertainty. Earth's Future, 6, 468– 487. https://doi.org/10.1002/2017EF000730
The role of LSHTM and NU in this collaborative project
The main discipline for this PhD is health economics, but it will draw on inter-disciplinary expertise across the two institutions. On the NU side, Satoru Komatsu (HSS) is a development economist who has published on WASH, while Hirotsugu Aiga (TMGH) has a background in environmental health epidemiology including WASH and other aspects of environmental health. On the LSHTM side, Ian Ross (ITD) is a health economist specialising in WASH economic evaluation, while Oliver Cumming (ITD) is a WASH epidemiologist. The lead supervisor will be Ian Ross, with other supervisors playing a supporting role overall, but with more in-depth engagement in the PhD objectives most aligned with their interests and expertise.
Particular prior educational requirements for a student undertaking this project
Ideally the student would have a Master's level economics qualification, but could also be another Master's-level qualification with a strong statistical component (e.g. public health, engineering).
Skills we expect a student to develop/acquire whilst pursuing this project
Uncertainty modelling, systematic review methods, costing.