Burden of disease attributable to unsafe drinking water, sanitation, and hygiene in domestic settings: a global analysis for selected adverse health outcomes

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Summary

Background

Assessments of disease burden are important to inform national, regional, and global strategies and to guide investment. We aimed to estimate the drinking water, sanitation, and hygiene (WASH)-attributable burden of disease for diarrhoea, acute respiratory infections, undernutrition, and soil-transmitted helminthiasis, using the WASH service levels used to monitor the UN Sustainable Development Goals (SDGs) as counterfactual minimum risk-exposure levels.

Methods

We assessed the WASH-attributable disease burden of the four health outcomes overall and disaggregated by region, age, and sex for the year 2019. We calculated WASH-attributable fractions of diarrhoea and acute respiratory infections by country using modelled WASH exposures and exposure–response relationships from two updated meta-analyses. We used the WHO and UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene public database to estimate population exposure to different WASH service levels. WASH-attributable undernutrition was estimated by combining the population attributable fractions (PAF) of diarrhoea caused by unsafe WASH and the PAF of undernutrition caused by diarrhoea. Soil-transmitted helminthiasis was fully attributed to unsafe WASH.

Findings

We estimate that 1·4 (95% CI 1·3–1·5) million deaths and 74 (68–80) million disability-adjusted life-years (DALYs) could have been prevented by safe WASH in 2019 across the four designated outcomes, representing 2·5% of global deaths and 2·9% of global DALYs from all causes. The proportion of diarrhoea that is attributable to unsafe WASH is 0·69 (0·65–0·72), 0·14 (0·13–0·17) for acute respiratory infections, and 0·10 (0·09–0·10) for undernutrition, and we assume that the entire disease burden from soil-transmitted helminthiasis was attributable to unsafe WASH.

Interpretation

WASH-attributable burden of disease estimates based on the levels of service established under the SDG framework show that progress towards the internationally agreed goal of safely managed WASH services for all would yield major public-health returns.

Funding

WHO and Foreign, Commonwealth & Development Office.

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Most cited references 56

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A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

Stephen S Lim, Theo Vos, Abraham D. Flaxman … (2012)

The Lancet, 380(9859), 2224-2260

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GRADE: an emerging consensus on rating quality of evidence and strength of recommendations.

Gordon Guyatt, Andrew D Oxman, Gunn E. Vist … (2008)

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Is Open Access

Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

(2022)

Summary Background Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen–drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. Methods We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen–drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drug-resistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. Findings On the basis of our predictive statistical models, there were an estimated 4·95 million (3·62–6·57) deaths associated with bacterial AMR in 2019, including 1·27 million (95% UI 0·911–1·71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27·3 deaths per 100 000 (20·9–35·3), and lowest in Australasia, at 6·5 deaths (4·3–9·4) per 100 000. Lower respiratory infections accounted for more than 1·5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000–1 270 000) deaths attributable to AMR and 3·57 million (2·62–4·78) deaths associated with AMR in 2019. One pathogen–drug combination, meticillin-resistant S aureus, caused more than 100 000 deaths attributable to AMR in 2019, while six more each caused 50 000–100 000 deaths: multidrug-resistant excluding extensively drug-resistant tuberculosis, third-generation cephalosporin-resistant E coli, carbapenem-resistant A baumannii, fluoroquinolone-resistant E coli, carbapenem-resistant K pneumoniae, and third-generation cephalosporin-resistant K pneumoniae. Interpretation To our knowledge, this study provides the first comprehensive assessment of the global burden of AMR, as well as an evaluation of the availability of data. AMR is a leading cause of death around the world, with the highest burdens in low-resource settings. Understanding the burden of AMR and the leading pathogen–drug combinations contributing to it is crucial to making informed and location-specific policy decisions, particularly about infection prevention and control programmes, access to essential antibiotics, and research and development of new vaccines and antibiotics. There are serious data gaps in many low-income settings, emphasising the need to expand microbiology laboratory capacity and data collection systems to improve our understanding of this important human health threat. Funding Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.

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Author and article information

Contributors

Jennyfer Wolf

Journal

Journal ID (nlm-ta): Lancet

Journal ID (iso-abbrev): Lancet

Title: Lancet (London, England)

Publisher: Elsevier

ISSN (Print): 0140-6736

ISSN (Electronic): 1474-547X

Publication date PMC-release: 17 June 2023

Publication date (Print): 17 June 2023

Volume: 401

Issue: 10393

Pages: 2060-2071

Affiliations

[a ]Department of Environment, Climate Change and Health, World Health Organization, Geneva, Switzerland

[b ]Division of Water and Health, Ethiopian Institution of Water Resources, Addis Ababa University, Addis Ababa, Ethiopia

[c ]FI Proctor Foundation, University of California, San Francisco, CA, USA

[d ]UNICEF Middle East and North Africa, Amman, Jordan

[e ]Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA

[f ]Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA

[g ]School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada

[h ]Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

[i ]The Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA

[j ]Gangarose Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA

[k ]Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA, USA

[l ]School of Civil Engineering, University of Leeds, Leeds, UK

[m ]Wellcome Trust Research Laboratory, Division of Gastrointestinal Sciences, Christian Medical College, Vellore, Tami Nadu, India

[n ]Instituto de Investigación Nutricional, Lima, Peru

[o ]School of Medicine, Vanderbilt University, Nashville, TN, USA

[p ]Department of Epidemiology, London School of Hygiene & Tropical Medicine, London, UK

[q ]Department of Disease Control, Faculty of Infectious Tropical Disease, London School of Hygiene & Tropical Medicine, London, UK

Author notes

[* ]Correspondence to: Dr Jennyfer Wolf, Department of Environment, Climate Change and Health, World Health Organization, Geneva 1211, Switzerland wolfj@ 123456who.int

Article

Publisher Item ID: S0140-6736(23)00458-0

DOI: 10.1016/S0140-6736(23)00458-0

PMC ID: 10290941

PubMed ID: 37290458

SO-VID: 652360e1-9ccc-43e2-b3f6-73f721c008d2

License:

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).

Burden of disease attributable to unsafe drinking water, sanitation, and hygiene in domestic settings: a global analysis for selected adverse health outcomes

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Summary

Background

Methods

Findings

Interpretation

Funding

Related collections

UN Sustainable Development Goals

Most cited references 56

A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

GRADE: an emerging consensus on rating quality of evidence and strength of recommendations.

Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

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