Demand for emergency caesarean deliveryat Kenyatta National Hospital: free maternal health care program

Type Thesis or Dissertation - Master of Science in Health Economics and Policy
Title Demand for emergency caesarean deliveryat Kenyatta National Hospital: free maternal health care program
Author(s)
Publication (Day/Month/Year) 2016
URL http://erepository.uonbi.ac.ke/bitstream/handle/11295/100001/Akech_Demand for Emergency Caesarean​Delivery at Kenyatta National Hospital- Free Maternal Health Care Program.pdf?sequence=1&isAllowed=y
Abstract
The Millennium Development Goal number five - to improve maternal health status by
reducing maternal mortality and morbidity rate (MMR) by 75% by the year 2015.
Unfortunately, Kenya is among the countries with MMR of more than 100 among other 23
countries in sub-Saharan Africa (Ministry of Health, 2013a). Locally, a number of initiatives
are being implemented to reduce MMR. Notably: the jubilee manifesto of free maternal care
in public hospitals and beyond- zero campaign by her Excellency the first lady – Margret
Kenyatta. As a result, there has been an increased demand for caesarian section due to the large
number of women seeking care in government hospitals despite the challenges of inadequate
resource allocation. Globally, Caesarian Section (CS) rates currently stands at 15% with the
rates progressively increasing in developed and developing countries (Lauer et al., 2010).
The rate of caesarian section in Kenya is 6% though it is proportionately higher in private
hospitals. This high rate has been correlated with higher rates of maternal morbidity and
mortality. As country, there is an urgent need to come up with policies and practices that can
help in reducing or maintaining this rate. With this in mind, a cross-sectional retrospective
study was undertaken at The Kenyatta National Hospital (KNH) to determine the demand for
emergency caesarean delivery from 1st January, 2014 to 31st December, 2014 using purposive
and systematic random sampling of 371 files at KNH registry.
Data was entered into stata software from where binary logistic regression analysis was
conducted to make inferences about the demand for emergency caesarian section. Prior to this,
the researcher sought for the approval from Research Committee of University of Nairobi and
Kenyatta National Hospital. The results were presented in pictorial diagrams of tables, bar
graphs, and pie charts

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