Indicator 3.9.2 Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services)

Indicator 3.9.2: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services)

Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination

World Health Organization (WHO)

Definition:

The mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services) as defined as the number of deaths from unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe WASH services) in a year, divided by the population, and multiplied by 100,000.

Concepts:

Deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services, expressed per 100,000 population; The included diseases are diarrhoea (GHE code 110 which includes ICD-10 codes A00, A01, A03, A04, A06-A09), acute respiratory infections (GHE code 380 which includes ICD-10 codes H65-H66, J00-J22, P23, and U04) intestinal nematode infections (GHE codes 340, 350 and 360 which include ICD-10 codes B76-B77, and B79) and protein-energy malnutrition (GHE code 550 which includes ICD-10 codes E40-E46).

Mortality rate (deaths per 100,000 population)

Data is compiled mainly from country and other databases directly. To maximize the data for robust estimates, as well as to reduce duplication of data collection to avoid further data reporting burden on countries, complementary data are used from various databases (please refer to section 4.c. for specific data sources).

National statistics offices, Various line ministries and databases covering civil registration with complete coverage and medical certification of cause of death.

Data rely on (a) statistics on WASH services (6.1 and 6.2), which are well assessed in almost all countries, and (b) data on deaths. Data on deaths are also widely available from countries from death registration data or sample registration systems, which are certainly feasible systems. Such data are crucial for improving health and reducing preventable deaths in countries. The main limitation is that not all countries do have such registration systems to date, and data need to be completed with other type of information.

4.c.i. Model

'WHO estimation of health impacts from environmental risks is based on comparative risk assessment (CRA) methods, which are used extensively in burden of disease assessments (Ezzati et al., 2002). This approach estimates the proportional reduction in disease or death that would occur if exposures were reduced to an alternative baseline level bearing a minimum risk (also referred to as theoretical minimum risk), while other conditions remain unchanged. The CRA methodology combines data on exposure, disease burden and the exposure-response relationship to estimate the burden of disease associated with that exposure (Ezzati et al., 2002). For each risk factor (unsafe water, sanitation, or hygiene), the population attributable fraction (PAF) is estimated by comparing current exposure distributions to a counterfactual distribution, for each exposure level, sex and age group:

$\mathrm{P}\mathrm{A}\mathrm{F}=\mathrm{\ }\frac{\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{p}}_{\mathrm{i}}\mathrm{\ }({\mathrm{R}\mathrm{R}}_{\mathrm{i}}-1)}{\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{p}}_{\mathrm{i}}\left({\mathrm{R}\mathrm{R}}_{\mathrm{i}}-1\right)+1}$

Where pi and RR_i are the proportion of the exposed population and the relative risk at exposure level i, respectively, and n is the total number of exposure levels. The joint burden of exposure to unsafe water, sanitation and hygiene was estimated by the following formula (6):

$\mathrm{P}\mathrm{A}\mathrm{F}=1-\mathrm{\ }\prod _{\mathrm{r}=1}^{\mathrm{R}}(1-{\mathrm{P}\mathrm{A}\mathrm{F}}_{\mathrm{r}})$

Where r is the individual risk factor, and R the total of risk factors accounted for in the cluster. Additional details on the methods of estimation are available from various publications (1,7).

This methodology has been used extensively to calculate the health gains from improvements in water supply, as well as sanitation and hygiene and had been published in various documents (Clasen et al., 2014; Prüss-Ustün et al., 2014; Prüss-Ustün et al., 2019)

The following four types of data are required to produce estimates for indicator 3.9.2:

2022-07-07

World Health Organization (WHO)

Indicator 7.1.2: Proportion of population with primary reliance on clean fuels and technology

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