Assessing delivery practices of mothers over time and over space in Uganda, 2003–2012

Abstract

BackgroundIt 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.MethodsWe 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.ResultsWe 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.ConclusionsOur 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12982-016-0049-8) contains supplementary material, which is available to authorized users.

Evaluation of a community-based intervention to improve maternal and neonatal health service coverage in the most rural and remote districts of Zambia

“…The small sample design of LQAS is based on the binomial distribution [ 26 ]. Prior studies suggest that LQAS is an efficient sampling design used when one wants to identify general program coverage or indeed communities having inadequate service coverage [ 27 – 29 ]. Therefore, in the context of the poorest and most remote districts of Zambia, LQAS was considered a suitable sampling design for evaluating the intervention because of the small sample size it requires for each cluster.…”

“…The segmentation sampling approach, advocated in survey guidelines was used as more rigorous second-stage sampling technique [ 29 , 30 ]. Once a reference house was selected, the next closest house was selected for an interview.…”

Determinants of home delivery in Nepal – A disaggregated analysis of marginalised and non-marginalised women from the 2016 Nepal Demographic and Health Survey

“…The women who had no education and basic education had greater odds of having a home delivery than those who had higher education. Other studies too have shown that institutional delivery is higher among educated people and those with better income status than the uneducated and poor [39][40][41]. This might be because educated women comprehend better about the potential risk associated with home delivery and have better idea about the service availability.…”

“…In both groups, economic status was also an important predictor of home delivery. Poverty is one of the key factors associated with low utilisation of health care services in Nepal and in other developing countries [39,40]. But very few women, both from marginalised and nonmarginalised (close to 2%) reported cost as the reason for not delivering in health facility.…”

Is development aid to strengthen health systems during protracted conflict a useful investment? The case of South Sudan, 2011–2015

“…Although some countries are carrying out these surveys semiannually, 88 others do so annually or biennially. 89 Fragile countries experiencing conflict may have longer intervals due the higher costs and logistical challenges of carrying out a national survey in a humanitarian setting.…”