This course will examine current conditions and trends in water and sanitation services in low and middle income countries. Within it we will take a critical look at the underlying political, economic, social, and technical reasons why almost a billion people lack access to improved water supplies and almost 2 billion still do not have improved sanitation services.
Video 5.2: Estimating the Economic Value of Health Benefits
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来自 University of Manchester 的课程
发展中国家的水供应和卫生政策
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从本节课中
Exploring Health Benefits and Climate Change Issues
与讲师见面
Prof Dale WhittingtonProfessor
Manchester Business School
Dr Duncan ThomasLecturer
Alliance Manchester Business School
Economists often prefer to express the outcomes or consequences of water
and sanitation interventions in terms of
economic benefits measured in monetary units.
In this video, I'll illustrate this approach
for just one component of the total benefits.
This is the mortality risk to households.
It's useful to think of two types of benefits
that households receive from a piped water and sanitation project.
Health benefits and non-health benefits.
The health benefits can be subdivided
into mortality reductions and morbidity reductions.
In addition, positive externalities may spill over and
benefit households who are not using the intervention.
The main non-health benefits to households are
increased income from having more, cleaner and cheaper
water, time savings from not having to collect
water, and aesthetic, or quality of life benefits.
A household can receive the health benefits from non-piped water
and sanitation systems, but only some of the non-health benefits.
For instance, if they still have to spend time carrying water back to the home.
In this video I will focus on the
mortality risk reduction component of the health benefits.
The economic benefits from mortality reduction will
be a function of four uncertain parameters.
The first is the baseline diarrhea incidents in the target population.
The second is the reduction in baseline
diarrhea incidence due to the WASH intervention.
The third is the case fatality rate.
This means the proportion of the cases of diarrhea that lead to death.
The fourth is the value of a statistical life.
This is a measure based on how much people are willing to pay ex
ante, that is, before they get sick, for a specified reduction in mortality risk.
The first step in the calculation is to estimate the baseline burden of disease.
By burden of disease, I mean the baseline incidence of
WASH related diseases in the target population before the WASH intervention.
Where do this data comes from?
Up to date data on burden disease are typically not easy to obtain.
This paper summarizes some of the global statistics by the burden of disease
in a specific target population may be
quite different from a county wide estimate.
This figure is from the Fisher paper.
It shows diarrhea episodes per child per year in selected low income countries.
The range is about 2 to 4 episodes per child per year.
The second parameter is the effectiveness of
the intervention at reducing the incidence of
WASH-related diseases in the population if a
specified number of individuals use the intervention.
You can think of the effectiveness of the
intervention as the percentage reduction in baseline incidence.
So, if an intervention is 90% effective, you have 10% of the number of cases left.
We'll assume, for purposes of illustration, that 100% of
the people in the target population use the intervention.
The instance after the intervention, labeled here Inc subscript after, is 1
minus Eff times the baseline incidence,
that is the instance before the intervention.
The change in the number of cases due to the intervention is the number
of cases in the target population before
minus the number of cases after the intervention.
To calculate the change in the number of cases, you multiply the number of
people in the target population times the
effectiveness of the intervention, times the baseline incidence.
Where does this parameter for effectiveness come from?
For this parameter we go back to the
Fewtrell paper that we discussed in the previous video.
Here's that figure again.
We said a good estimate for the reduction in risk from
baseline conditions due to a large intervention was around 30 to 40%.
In these calculations, I'll use 30% for the mean case.
The third step is to calculate the change in
the number of deaths due to the WASH-related diseases.
You can think of this as the number of lives saved by the intervention.
This is done by multiplying the number of cases avoided by the case fatality rate.
Where does this parameter come from?
This figure and the next show some
estimates of diarrhea mortality rates in selected countries.
The diarrhea mortality rate is not the same as the case fatality rate.
The mortality rate is the number of deaths due to
a specific cause for a population during a specific time period.
The case fatality rate is the proportion
of deaths within a designated population of cases.
You can estimate the case fatality rate from
estimates of the diarrhea mortality rate in baseline incidents.
I'll show you how to do this in a minute.
This figure shows that the diarrhea mortality rates
in Ecuador, Egypt, Brazil and China are very low.
These data are from the WHO's global burden of disease estimates.
The WHO global burden of disease report estimates
the number of deaths by different causes in population.
The mortality rate due to a specific cause of death, such as
diarrhea, is calculated from the estimates of the number of deaths in population.
In many countries, death certificates with a cause of death are not available.
So, these estimates of the number of deaths
due to a specific cause are themselves imprecise.
The estimates of the mortality rates in this figure show
that very few people in these countries are dying of diarrhea.
Even Bangladesh looks pretty good.
For Ethiopia, the mortality rate is estimated
at about 150 deaths per 100,000 people.
That's a 0.15% chance of dying from diarrhea per year in Ethiopia.
But note that the mortality rates are an average over all age cohorts.
Mortality rates for children under five years of age will be considerably higher.
This next figure shows estimates of diarrhea mortality rates by region.
The average diarrhea mortality rate for Africa stands out.
Is much larger than for other regions.
It is about 115 per 100,000, compared to South Asia which is about 65 per 100,000.
Now, let's look at the relationship between the diarrhea
mortality rate, the baseline incidence and the case fatality rate.
Assume that the baseline incidence is 1.5 cases of diarrhea per person per year.
So, in a population of a 100,000 there
will be a 150,000 episodes of diarrhea per year.
If the diarrhea mortality rate is a 150 per 100,000, in
a population of 100,000, a 150 people will die of diarrhea.
The case fatality rate is then 150 deaths divided
by 150,000 episodes, or 1 death per 1000 episodes.
The fourth step is to multiply the number of deaths avoided by the value
of the mortality risk reduction as judged
by the members of the target population themselves.
The mortality reduction benefits, from an economist's perspective, are equal
to the value of the statistical life, multiplied by the
case fatality rate, times the size of the par, target
population, times the effectiveness of
the intervention, times the baseline incidence.
I should emphasize though that the size of the target population is
the only parameter in this equation that is known with much certainty.
Our calculations depend on four uncertain parameters,
baseline diarrhea incidence, reduction in baseline diarrhea incidence
due to the WASH intervention, case fatality
rate and the value of a statistical life.
Now I want to show what happens to the final
result as we make different assumptions about these four parameters.
This table shows you the assumptions I'll make for the parameters.
For each parameter I'll make a low, mean and high assumption.
In the baseline incidence column you'll see, I'm assuming
0.5, 1, and 1.5 cases of diarrhea per year.
Note that these estimates are an average of both adults and children and are a
little different that then two to four episodes
per year for children that we saw earlier.
For the effectiveness of the intervention column, I assume 10, 30,
and 50% reduction in risk as a result of the intervention.
For the case fatality rate column, I assume 4,
8, and 12 fatalities per 10,000 cases of diarrhea.
For the VSL or the value statistical life
I assume US $10,000, US $60,000 and US $100,000.
And I assume a target population of 100,000 people in a medium size city.
In this table, we calculate the numerous cases
of diarrhea avoided as a result of the intervention.
For the mean case, that's the 30% reduction.
The second row here, in this table, the number of cases of diarrhea avoided is
30,000 but this varies from 5,000 for the low case to 75,000 for the high case.
This table shows the results for step three.
The calculation of the number of deaths avoided per year due to the intervention.
For the mean case, the first row here, the result is 24 deaths per year.
This varies from 2 deaths per year for the low
case to 90 deaths per year for the high case.
Notice how the range of uncertainty is expanding.
2 deaths per year from a population of a
100,00 is very different from 90 deaths per year.
This table shows step four, the calculation of the economic
value of the mortality risk reduction due to the intervention.
The total economic value of the mortality risk
reduction varies from US $20,000 to US $9 million.
That's a huge range.
But I want to focus on the benefits at the household level.
That's the blue numbers in the right most column here.
The high estimate is equivalent of US $38 per household per month.
This is a huge benefit for a poor household in a low income country.
But the low estimate of US $0.08 per household per
month is not a large benefit, even for a poor household.
If we are trying to understand household behavior, and how
a household thinks about the economic value of an intervention.
The difference between these three cases is very large.
To wrap up, I want you to think about what
these calculations mean for understanding baseline or status quo conditions.
One lesson is that the economic outcomes of a water and sanitation intervention
will depend on the timing and sequencing of investments, and on local conditions.
And inevitably will be subject to a high level of uncertainty.
This is because the uncertainty in the estimates increases
as you multiply several uncertain perimeters in a row.
In other words, this means that uncertainty is not
additive, it's multiplicative, and the range of uncertainty expands rapidly.
This finding resonates with Nobel
Laureate Douglass North's observation that, quoting,
we should be very tentative about how we understand the world.
That doesn't mean you don't do things.
You've got to do things, but you've got to recognize that you may be wrong.
We will return to the implications of this insight in our second follow up MOOC.
To remind you, that's where we'll focus on specific policy interventions in
the water and sanitation sector, the
questions, what works, what should be done.
