LSTM Home > LSTM Research > LSTM Online Archive

Hybrid prevalence estimation: a method to improve intervention coverage estimations

Downloads

Downloads per month over past year

Jeffery, Caroline ORCID: https://orcid.org/0000-0002-8023-0708, Pagano, Marcello, Hemingway, Janet ORCID: https://orcid.org/0000-0002-3200-7173 and Valadez, Joseph (2018) 'Hybrid prevalence estimation: a method to improve intervention coverage estimations'. Proceedings of the National Academy of Sciences of the United States of America.

[img]
Preview
Text
Hybrid prevalence final Revision 4 Oct 18.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (199kB) | Preview
[img] Text
Proof to publish version.pdf - Submitted Version
Restricted to Repository staff only

Download (209kB)

Abstract

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.

Item Type: Article
Subjects: W General Medicine. Health Professions > W 82 Biomedical technology (General)
W General Medicine. Health Professions > W 26.5 Informatics. Health informatics
WA Public Health > WA 20.5 Research (General)
WA Public Health > Health Problems of Special Population Groups > WA 395 Health in developing countries
Faculty: Department: Clinical Sciences & International Health > International Public Health Department
Digital Object Identifer (DOI): https://doi.org/10.1073/pnas.1810287115
Depositing User: Stacy Murtagh
Date Deposited: 18 Dec 2018 12:23
Last Modified: 18 Dec 2018 14:27
URI: http://archive.lstmed.ac.uk/id/eprint/9618

Statistics

View details

Actions (login required)

Edit Item Edit Item