Assessing delivery practices of mothers over time and over space in Uganda, 2003-2012.
BACKGROUND
It is well known that safe delivery in a health facility reduces the risks of maternal and infant mortality resulting from perinatal complications. What is less understood are the factors associated with safe delivery practices. We investigate factors influencing health facility delivery practices while adjusting for multiple other factors simultaneously, spatial heterogeneity, and trends over time.


METHODS
We fitted a logistic regression model to Lot Quality Assurance Sampling (LQAS) data from Uganda in a framework that considered individual-level covariates, geographical features, and variations over five time points. We accounted for all two-covariate interactions and all three-covariate interactions for which two of the covariates already had a significant interaction, were able to quantify uncertainty in outputs using computationally intensive cluster bootstrap methods, and displayed outputs using a geographical information system. Finally, we investigated what information could be predicted about districts at future time-points, before the next LQAS survey is carried out. To do this, we applied the model to project a confidence interval for the district level coverage of health facility delivery at future time points, by using the lower and upper end values of known demographics to construct a confidence range for the prediction and define priority groups.


RESULTS
We show that ease of access, maternal age and education are strongly associated with delivery in a health facility; after accounting for this, there remains a significant trend towards greater uptake over time. We use this model together with known demographics to formulate a nascent early warning system that identifies candidate districts expected to have low prevalence of facility-based delivery in the immediate future.


CONCLUSIONS
Our results support the hypothesis that increased development, particularly related to education and access to health facilities, will act to increase facility-based deliveries, a factor associated with reducing perinatal associated mortality. We provide a statistical method for using inexpensive and routinely collected monitoring and evaluation data to answer complex epidemiology and public health questions in a resource-poor setting. We produced a model based on this data that explained the spatial distribution of facility-based delivery in Uganda. Finally, we used this model to make a prediction about the future priority of districts that was validated by monitoring and evaluation data collected in the next year.
Centre for Complexity Science, University of Warwick, Coventry, CV4 7AL UK.|Management Sciences for Health, USAID STAR-E project, Kampala, Uganda.
Daniel A
DA
Sprague
METRe Group, Department of International Health, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA UK.|Management Sciences for Health, USAID STAR-E project, Kampala, Uganda.
Caroline
C
Jeffery
METRe Group, Department of International Health, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA UK.|Management Sciences for Health, USAID STAR-E project, Kampala, Uganda.
Nadine
N
Crossland
School of Mathematics, University of Manchester, Manchester, M13 9PL UK.
Thomas
T
House
Statistics Department, University of Warwick, Coventry, CV4 7AL UK.
Gareth O
GO
Roberts
METRe Group, Department of International Health, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA UK.|Management Sciences for Health, Kampala, Uganda.
William
W
Vargas
Management Sciences for Health, Kampala, Uganda.|Management Sciences for Health, USAID STAR-E project, Kampala, Uganda.
Joseph
J
Ouma
Management Sciences for Health, Kampala, Uganda.|Management Sciences for Health, USAID STAR-E project, Kampala, Uganda.
Stephen K
SK
Lwanga
METRe Group, Department of International Health, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA UK.|Management Sciences for Health, USAID STAR-E project, Kampala, Uganda.
Joseph J
0000-0002-6575-6592
JJ
Valadez
Early warning system; Ease of access; Facility-based delivery; LQAS; Spatial modelling; Survey; Uganda