Estimating inequities in sanitation-related disease burden and estimating the potential impacts of propoor targeting,

Estimating inequities in sanitation-related disease burden and estimating the potential impacts of propoor targeting, 2012.

Richard Rheingans, et al. SHARE.

The objectives of this study are to model for 10 low-income countries in sub-Saharan Africa and South Asia:

  • The distribution of sanitation-related health burden by wealth quintile
  • The distribution of health benefits for targeting different wealth quintile groups
  • The spatial distribution of sanitation-related health burden and benefits


Key results

Although inadequate data and knowledge prevent definitive answers to the questions outlined in the objectives for this study, the results of this modeling exercise based on exisiting information suggest the following:

  • The health burden of poor sanitation falls disproportionately on children living in the poorest households
  • This increased health burden is the result of both greater exposure to infection and increased susceptibility among children in these households
  • The increased exposure among these children is a function of their increased likelihood of having no access to a private facility, having to use shared facilities and being more likely to live in an area with a high density of people without sanitation
  • Children in poor households are more likely to be susceptible (resulting from lower nutritional status) to diarrhoeal diseases and suffer higher mortality
  • Improvements in sanitation for households in the poorest quintile may bring significantly greater health benefits than improvements in the richest quintiles
  • The sanitation-related burden differs between rural and urban settings, but children in poor households in both settings consistently suffer disproportionately
  • While rural populations generally have lower levels of access, the sanitation associated risk may be greater for the urban poor due to the increased likelihood of these households being in areas with a high density of people without sanitation

Conclusions

  • There are important limitations of this study that must be highlighted: (1) the relative importance of the three exposure variables which are modeled as being equal; (2) the susceptibility index contains only three variables (nutritional vulnerability, Vitamin Adose, and ORS treatment); (3) only diarrhoeal mortality is considered in estimating the distribution of health impacts, and the total burden and its distribution would change if other sanitation-related health impacts were included
  • More effective targeting strategies to reach children in the poorest households are required to both protect those children and households most at risk and to maximize the potential impact of sanitation investments. Although the study did not directly consider
    the relative costs associated with reaching the poorest households, the results suggest that targeting the poorest households could yield substantially higher health returns and may also bring greater economic returns
  • Better use of available information on the distribution of sanitation-associated risk and health burden could strengthen planning and resource allocation
  • Current monitoring indicators at the national and global levels fail to incentivise targeting the areas of greatest need and potential greatest impact. Existing limitations in monitoring efforts include a focus on household coverage rather than child coverage; the use of household access, not community level exposure measures; no direct targets for focusing improved access on the poorest; and, in some settings, the under counting of the most vulnerable urban populations
  • Additional information on the relative risk of shared facilities and density of population without sanitation would allow for better identification of priority areas and targeting of interventions.

See the related press release, policy briefing and podcast.

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