Hybrid prevalence estimation: a method to improve intervention coverage estimations

Delivering excellent health services requires accurate Health Information Systems (HIS) data. Poor quality data can lead to poor judgments and outcomes. Unlike probability surveys, which are

representative of the population and carry accuracy estimates, HIS do not, but in many countries the HIS is the primary source of data used for administrative estimates. However, HIS are not structured

to detect gaps in service coverage and leave communities exposed to unnecessary health risks.

Here we propose a method to improve informatics by combining HIS and probability survey data to construct a hybrid estimator. This technique provides a more accurate estimator than either data

source alone and facilitates informed decision-making. We use data from vitamin A and polio vaccination campaigns in children from Madagascar and Benin to demonstrate the impact. The

hybrid estimator is a weighted average of two measurements and produces standard errors (SE) and 95% Confidence Intervals (CI) for the hybrid and HIS estimators.

The estimates of coverage proportions using the combined data and the survey estimates differ by no more than 3%, while decreasing the SE by 1-6%; the administrative estimates from the HIS and

combined data estimates are very different, with 3-25 times larger CI, questioning the value of administrative estimates.

Estimators of unknown accuracy may lead to poorly formulated policies and wasted resources. The hybrid estimator technique can be applied to disease prevention services for which population

coverages are measured. This methodology creates more accurate estimators, alongside measured HIS errors, to improve tracking the public’s health.