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WASH-related 
Mortality Trends in 
Sub-Saharan Africa 
August 28, 2014
2 
Contextual Factors 
& Initial Conditions 
{IWRM} 
Policy (mix) 
Institutions 
Water Law 
Organizations 
Process 
Integration/Coordination 
Decentralization 
Participation 
WRM 
IWRM 
Water resources 
endowment/distribution/level 
of development 
Level of economic 
development Nation/RB 
Political factors (Democracy 
National/local levels, 
Decentralization, Governance) 
Social/cultural factors (local 
experience with self 
governance and service 
provision) 
etc 
Mohamed Ait Kadi – Zaragoza 0708
Improved Water: Total (Urban  Rural) 
UNICEF and WHO. 2012. Progress on drinking water and sanitation: 2012 update. 
Available at: http://www.wssinfo.org/fileadmin/user_upload/resources/JMP-report-2012-en.pdf Accessed on 28 Jan 2013.
Some Good News! 
Childhood mortality rates are declining  the 
rate of decline is accelerating. 
Across 21 regions of the world, rates of 
neonatal, post-neonatal, and childhood 
mortality are declining, and there is evidence 
of accelerating declines from 2000 to 2010 
compared with 1990 to 2000 (including Sub- 
Saharan Africa). 
Rajaratnam et al. (2010). “Neonatal, post-neonatal, childhood, and under-5 mortality 
for 187 countries, 1970–2010: a systematic analysis of progress towards 
Millennium Development Goal 4.” The Lancet. May 24.
Water  sanitation-related mortality 
1950-2050, by world region
WASH-related Mortality– China, India, Brazil,  
Nigeria (1950-2050) 
0.25 
0.20 
0.15 
0.10 
0.05 
0.00 
1950 
1960 
1970 
1980 
1990 
2000 
2010 
2020 
2030 
2040 
2050 
WSH Mortality Rate (%) 
Year 
China 
India 
Brazil 
Nigeria
Emerging Threshold Countries in SSA 
Source: Radalet 2010 
o 17 emerging countries: dramatic 
changes in economic growth, 
poverty reduction, and political 
accountability since the mid-1990s. 
o Avg. annual GDP growth of 3.2%, 
between 1996 and 2008 (all 17 
emerging countries) 
o 6 threshold countries: considerable 
but less dramatic change
WASH Mortality Categories 
Moderate Mortality; 
Insufficient Decline 
High Mortality 
Low Mortality 
High Mortality 
Stagnant Moderate decline 
Moderate Mortality; 
Sufficient Decline
High Mortality 
High Mortality 
Stagnant Moderate decline 
o Highest WASH mortality rates, both 
at baseline and in projections 
o Stagnant progress in reducing 
WASH mortality in some counties 
(mostly fragile and oil states) 
o Moderate decline in other 
countries (mostly threshold and 
emerging economies)
Moderate Mortality, 
Insufficient Decline (Little Progress) 
o Moderate WASH mortality rates, 
for the Sub-Saharan Africa region 
o Progress is insufficient to achieve 
South Asian levels of WASH 
mortality by 2050 
Moderate Mortality; 
Insufficient Decline
Moderate Mortality, 
Sufficient Decline (Good Progress) 
o Moderate WASH mortality rates, 
for the Sub-Saharan Africa region 
o Progress is sufficient to achieve 
South Asian levels of WASH 
mortality by 2050. 
o Mostly threshold and emerging 
economies 
Moderate Mortality; 
Sufficient Decline
Low Mortality 
o Historically low WASH mortality 
rates, comparable to South Asia 
o By 2050, WASH mortality projected 
to be near zero 
Low Mortality
WASH Modeling Framework 
Population 
Growth 
Economic 
Growth 
WASH 
Coverage 
WASH-Related 
Mortality 
Economic 
Benefits 
- 
+ 
- + 
That’s good! That’s good!
Baseline assumptions: representative countries 
Population
Baseline assumptions: representative countries 
GDP per Capita
Best and Worst Case Assumptions: 
Nigeria 
Base Best Worst 
Avg. Annual GDP Growth 4.1% 4.5% 3.7% 
Coverage 
Piped Water Coverage GDP Elasticity 9.5 14.5 4.5 
Piped Water Coverage % Urban Elasticity 0.35 0.6 0.1 
Improved Water Coverage GDP Elasticity 4.5 9 0 
Improved Water Coverage % Urban Elasticity 0.3 0.5 0.1 
Improved Sanitation Coverage GDP Elasticity 8 14 2 
Improved Sanitation Cov. % Urban Elasticity 0.45 0.8 0.1 
Mortality 
WSH Mortality GDP Elasticity -0.2 -0.3 -0.1 
WSH Mortality Piped WatSan Elasticity -0.02 -0.03 -0.01 
WSH Mortality Log Piped WatSan Elasticity -0.5 -0.7 -0.3 
WSH Mortality Improved Water Elasticity -0.0275 -0.045 -0.01
Results …
WASH Mortality Categories: 
Representative Countries 
Moderate Mortality; 
Insufficient Decline 
High Mortality 
Low Mortality 
High Mortality 
Stagnant 
Democratic Rep. 
of Congo 
Moderate decline 
Ethiopia 
Moderate Mortality; 
Sufficient Decline 
Nigeria 
Kenya South Africa
WASH Mortality Categories 
Moderate Mortality; 
Insufficient Decline 
High Mortality 
Low Mortality 
High Mortality 
Stagnant 
Burundi 
Central African Rep. 
Chad 
DRC 
Guinea Bissau 
Niger 
Somalia 
Moderate decline 
Angola 
Burkina Faso 
Ethiopia 
Liberia 
Mali 
Sierra Leone 
Moderate Mortality; 
Sufficient Decline 
Benin 
Cameroon 
Comoros 
Congo 
Cote d'Ivoire 
Ghana 
Guinea 
Madagascar 
Malawi 
Mauritania 
Mozambique 
Nigeria 
Rwanda 
Sudan 
Tanzania 
Togo 
Uganda 
Zambia 
Zimbabwe 
Botswana 
Equatorial 
Guinea 
Eritrea 
Gambia 
Kenya 
Lesotho 
Namibia 
Senegal 
Swaziland 
Cape Verde 
Gabon 
Mauritius 
Seychelles 
South Africa
WASH Mortality Rate by Country Grouping
WASH Mortality Rate: Projections
WASH-related Deaths by country grouping
Total WASH-Related Deaths: Projections
Population without Improved WS coverage 
(Representative Countries)
Model Overview 
Collect secondary data 
Assemble country-level data sets and estimate empirical relationships 
using regression analysis 
Coverage with water 
 sanitation services 
WASH-related 
mortality 
Other control 
variables 
Assess associations with 
WASH-related mortality rates 
Assess associations with piped 
and improved water and 
sanitation coverage 
Adjust coverage estimates for 
piped water + sewer 
Simulate outcomes (1990-2050) 
WASH-related 
mortality rates 
Piped service + improved 
water coverage 
Calculate WASH-related 
deaths (1990-2050) 
Calculate WTP to avoid 
mortality (1990-2050) 
Sensitivity analyses 
on model 
parameters
Baseline assumptions: representative countries 
Improved Water Coverage
Economic  Population Projections 
 Drivers of coverage and WASH-related 
mortality
Population by country grouping
GDP by country grouping
GDP per Capita, by country grouping
Projections: Wash-mortality Rates, 
Wash-related Death
Projections by Country Groupings
WASH Mortality Rate, by category
Best/Worst Cases
Best/Worst Cases
Oil and Mineral Exporters
WASH Mortality Rate, 2008 
Oil and Mineral Exporters
WASH Mortality Rate, 2008 
Oil and Mineral Exporters
WASH Deaths, 2008 
Oil and Mineral Exporters

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WASH Related Mortality Trends in Sub-Saharan Africa. By Dale Whittington.

  • 1. WASH-related Mortality Trends in Sub-Saharan Africa August 28, 2014
  • 2. 2 Contextual Factors & Initial Conditions {IWRM} Policy (mix) Institutions Water Law Organizations Process Integration/Coordination Decentralization Participation WRM IWRM Water resources endowment/distribution/level of development Level of economic development Nation/RB Political factors (Democracy National/local levels, Decentralization, Governance) Social/cultural factors (local experience with self governance and service provision) etc Mohamed Ait Kadi – Zaragoza 0708
  • 3. Improved Water: Total (Urban Rural) UNICEF and WHO. 2012. Progress on drinking water and sanitation: 2012 update. Available at: http://www.wssinfo.org/fileadmin/user_upload/resources/JMP-report-2012-en.pdf Accessed on 28 Jan 2013.
  • 4. Some Good News! Childhood mortality rates are declining the rate of decline is accelerating. Across 21 regions of the world, rates of neonatal, post-neonatal, and childhood mortality are declining, and there is evidence of accelerating declines from 2000 to 2010 compared with 1990 to 2000 (including Sub- Saharan Africa). Rajaratnam et al. (2010). “Neonatal, post-neonatal, childhood, and under-5 mortality for 187 countries, 1970–2010: a systematic analysis of progress towards Millennium Development Goal 4.” The Lancet. May 24.
  • 5. Water sanitation-related mortality 1950-2050, by world region
  • 6. WASH-related Mortality– China, India, Brazil, Nigeria (1950-2050) 0.25 0.20 0.15 0.10 0.05 0.00 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 WSH Mortality Rate (%) Year China India Brazil Nigeria
  • 7. Emerging Threshold Countries in SSA Source: Radalet 2010 o 17 emerging countries: dramatic changes in economic growth, poverty reduction, and political accountability since the mid-1990s. o Avg. annual GDP growth of 3.2%, between 1996 and 2008 (all 17 emerging countries) o 6 threshold countries: considerable but less dramatic change
  • 8. WASH Mortality Categories Moderate Mortality; Insufficient Decline High Mortality Low Mortality High Mortality Stagnant Moderate decline Moderate Mortality; Sufficient Decline
  • 9. High Mortality High Mortality Stagnant Moderate decline o Highest WASH mortality rates, both at baseline and in projections o Stagnant progress in reducing WASH mortality in some counties (mostly fragile and oil states) o Moderate decline in other countries (mostly threshold and emerging economies)
  • 10. Moderate Mortality, Insufficient Decline (Little Progress) o Moderate WASH mortality rates, for the Sub-Saharan Africa region o Progress is insufficient to achieve South Asian levels of WASH mortality by 2050 Moderate Mortality; Insufficient Decline
  • 11. Moderate Mortality, Sufficient Decline (Good Progress) o Moderate WASH mortality rates, for the Sub-Saharan Africa region o Progress is sufficient to achieve South Asian levels of WASH mortality by 2050. o Mostly threshold and emerging economies Moderate Mortality; Sufficient Decline
  • 12. Low Mortality o Historically low WASH mortality rates, comparable to South Asia o By 2050, WASH mortality projected to be near zero Low Mortality
  • 13. WASH Modeling Framework Population Growth Economic Growth WASH Coverage WASH-Related Mortality Economic Benefits - + - + That’s good! That’s good!
  • 14. Baseline assumptions: representative countries Population
  • 15. Baseline assumptions: representative countries GDP per Capita
  • 16. Best and Worst Case Assumptions: Nigeria Base Best Worst Avg. Annual GDP Growth 4.1% 4.5% 3.7% Coverage Piped Water Coverage GDP Elasticity 9.5 14.5 4.5 Piped Water Coverage % Urban Elasticity 0.35 0.6 0.1 Improved Water Coverage GDP Elasticity 4.5 9 0 Improved Water Coverage % Urban Elasticity 0.3 0.5 0.1 Improved Sanitation Coverage GDP Elasticity 8 14 2 Improved Sanitation Cov. % Urban Elasticity 0.45 0.8 0.1 Mortality WSH Mortality GDP Elasticity -0.2 -0.3 -0.1 WSH Mortality Piped WatSan Elasticity -0.02 -0.03 -0.01 WSH Mortality Log Piped WatSan Elasticity -0.5 -0.7 -0.3 WSH Mortality Improved Water Elasticity -0.0275 -0.045 -0.01
  • 18. WASH Mortality Categories: Representative Countries Moderate Mortality; Insufficient Decline High Mortality Low Mortality High Mortality Stagnant Democratic Rep. of Congo Moderate decline Ethiopia Moderate Mortality; Sufficient Decline Nigeria Kenya South Africa
  • 19. WASH Mortality Categories Moderate Mortality; Insufficient Decline High Mortality Low Mortality High Mortality Stagnant Burundi Central African Rep. Chad DRC Guinea Bissau Niger Somalia Moderate decline Angola Burkina Faso Ethiopia Liberia Mali Sierra Leone Moderate Mortality; Sufficient Decline Benin Cameroon Comoros Congo Cote d'Ivoire Ghana Guinea Madagascar Malawi Mauritania Mozambique Nigeria Rwanda Sudan Tanzania Togo Uganda Zambia Zimbabwe Botswana Equatorial Guinea Eritrea Gambia Kenya Lesotho Namibia Senegal Swaziland Cape Verde Gabon Mauritius Seychelles South Africa
  • 20. WASH Mortality Rate by Country Grouping
  • 21. WASH Mortality Rate: Projections
  • 22. WASH-related Deaths by country grouping
  • 24. Population without Improved WS coverage (Representative Countries)
  • 25. Model Overview Collect secondary data Assemble country-level data sets and estimate empirical relationships using regression analysis Coverage with water sanitation services WASH-related mortality Other control variables Assess associations with WASH-related mortality rates Assess associations with piped and improved water and sanitation coverage Adjust coverage estimates for piped water + sewer Simulate outcomes (1990-2050) WASH-related mortality rates Piped service + improved water coverage Calculate WASH-related deaths (1990-2050) Calculate WTP to avoid mortality (1990-2050) Sensitivity analyses on model parameters
  • 26. Baseline assumptions: representative countries Improved Water Coverage
  • 27. Economic Population Projections Drivers of coverage and WASH-related mortality
  • 29. GDP by country grouping
  • 30. GDP per Capita, by country grouping
  • 33. WASH Mortality Rate, by category
  • 36. Oil and Mineral Exporters
  • 37. WASH Mortality Rate, 2008 Oil and Mineral Exporters
  • 38. WASH Mortality Rate, 2008 Oil and Mineral Exporters
  • 39. WASH Deaths, 2008 Oil and Mineral Exporters