Child Drowning

homes and estimated 26,000 drowning deaths in children 0-4 years old in 2005. This figure was 20 per
cent larger than the GBD estimate for the entire 0-14 age group. 18 If the proportion of under-five
drowning in India compared to the rest of childhood is similar to the proportion in the other LMICs
surveyed, this would result in an undercount of the GBD estimates of at least 50 per cent. The GBD 2008
estimate increased 47.7 per cent and the additional information from the MDS may have been a factor.
Similar discrepancies were found with other causes of death in the MDS compared to GBD estimates,
leading to questions concerning the accuracy and precision of the GBD methodology for estimating
cause-specific mortality. For example, the GBD estimated 5,000 malaria deaths among children 0-4 year
olds in India, whereas the MDS estimated a figure 11 times as large, 55,000 deaths. 19
Drowning estimates that use imputation, modeling and expert adjustment have outputs where the
estimate is composed of ‘virtual deaths’. These virtual deaths are determined by an algorithm rather
than by counting actual deaths. As virtual deaths, they exist in models and other outputs, but they do
not exist as actual children who have drowned. The deaths cannot be physically verified as real deaths.
Unless the virtual drowning is corroborated by measurements of actual drowning where age, sex and
cause can be determined, they are physically unverifiable estimates. In the absence of reliable data that
facilitates estimates based on a count of actual deaths, the use of virtual death estimation serves a
policy purpose by allowing planners to prioritize causes of death. However, they may lead to
unintended policy consequences if the estimates produced are not accurate.
There is a clear need for reliable data to underpin policy formulation. Given the choice between making
policy decisions without any data versus using the best available data adjusted by modeling and expert
review, the latter is the preferred process. However, recognizing the great potential for unintended
adverse consequences in using such data, it is important to validate the modeled and adjusted outputs
with actual measurements. There are a number of ways in which this can be done with mortality
estimates. Some examples are:
Introducing a special drowning module (or an injury module with a drowning sub-module)
into an already planned large-scale nationally representative survey such as a national
household health survey. These are usually conducted once every five years in many LMICs.
The costs would be minimized by inclusion in an already planned survey. The findings
would help validate external estimates made for the country as well as provide useful
injury and drowning mortality data to national policymakers. This is often done in HICs to
collect specific information on a particular disease or condition.
Use the mortality sample from the most recent national census or inter-census in a
country, which provides data every five years. The homes identified in the census as having
deaths could be visited and a verbal autopsy conducted to allow specific causes of death to
be determined. The costs would be minimized by using an already determined mortality
sample. The mortality by cause data linked to the other social and demographic data from
the census would add significant information for policymakers broadly across sectors. A
modified version of this was used for the Million Death Study (MDS).
In some countries, another possibility would be to establish nationally representative
community surveillance systems that measure incidence of death in the community by
specific cause. This is being done with the Disease Surveillance Points (DSP) system in
China.
18 Jagnoor J. et al. (2011). ‘Unintentional injury deaths among children younger than 5 years of age in India: A nationally
representative 19 study’; Injury Prevention, 17: 151-155.
Neeraj, Dhingra et al. (November 2010). ‘Adult and child malaria mortality in India: A nationally representative mortality
survey’; The Lancet, 376 (9754): 1768–1774.
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