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Background: Maternal mortality is the subject of the United Nations’ fifth Millennium Development Goal, which is to reduce the maternal mortality ratio by three quarters from 1990 to 2015. The giant strides made by western countries in dropping of their maternal mortality ratio were due to the recognition given to skilled attendants at delivery. In Ghana, nine in ten mothers receive antenatal care from a health professional whereas only 59 and 68% of deliveries are assisted by skilled personnel in 2008 and 2010 respectively. This study therefore examines the determinants of skilled birth attendant at delivery in rural southern Ghana. Methods: This study comprises of 1874 women of reproductive age who had given birth 2 years prior to the study whose information were extracted from the Dodowa Health and Demographic Surveillance System. The univariable and multivariable associations between exposure variables (risk factors) and skilled birth attendant at delivery were explored using logistic regression. Results: Out of a total of 1874 study participants, 98.29% of them receive antenatal care services during pregnancy and only 68.89% were assisted by skilled person at their last delivery prior to the survey. The result shows a remarkable influence of maternal age, level of education, parity, socioeconomic status and antenatal care attendance on skilled attendants at delivery. Conclusion: Although 69% of women in the study had skilled birth attendants at delivery, women from poorest households, higher parity, uneducated, and not attending antenatal care and younger women were more likely to deliver without a skilled birth attendants at delivery. Future intervention in the study area to bridge the gap between the poor and least poor women, improve maternal health and promote the use of skilled birth at delivery is recommended.
Data for this study were extracted from Dodowa Health and Demographic Surveillance System (DHDSS) site database. The DHDSS is located in the south-eastern part of Ghana and operates within the boundaries of the Shai-Osudoku and Ningo-Prampram districts [28]. The DHDSS site lies between latitude 5°45′ south and 6°05′ north and longitude 0°05′ east and 0°20′ west with a land area of 1528.9 sq km. It is about 41 km from the national capital, Accra [28, 29]. The two districts cover a population of 115,754 people in 380 communities in 23,647 households covering a total land area of 1442 sq km. The inhabitants are predominantly subsistence farmers, fishermen and petty traders [29]. Road networks in the DHDSS are usually inaccessible during the wet seasons, making access to health and other services a challenge. The DHDSS visits every household in the demographic surveillance area twice in a year to collect data on demographic, migration and other health indicators [29]. Health care service in the DHDSS is delivered by hospitals, health centres, CHPS zones, private facilities, clinics, maternity homes, mission clinics and quasi government clinics. The study population comprised women of reproductive age (15–49 years) who were resident in the DHDSS from 1st January 2011 to 31st December 2011 who had given birth not more than 1 year prior to the study. The outcome variable for this study is skilled birth attendant (SBA) at delivery which is binary recorded as: 1 “Skilled person” and 0 “No Skilled person”. From the questionnaire and data available, we selected 8 exposure variables which were based on available literature and has the potential to influence the place of delivery. These exposure variables includes: maternal age, education, parity, first live birth or not, marital status, ANC attendance, and wealth index. The wealth index is a proxy measure of a household’s long term standard of living; it’s based on social status, assets ownership, and availability of utilities, among others. The index measures were combined into a wealth index, using weights derived through principal component analysis (PCA) [30]. The proxies from the PCA were divided into five quintiles; poorest, very poor, poor, less poor and least poor. Maternal ages at delivery were calculated using the mothers’ and babies’ birthdates. To ensure the assumption of independence of observations, an initial assessment of clustering at household level was carried out since women from the same household may have same or similar health seeking behaviours. The assessment shows that the assumption of independence was upheld. The univariable associations between each exposure variable (risk factor) and SBA were explored, and those significant at P < 0.05 were entered together into a multiple logistic regression model. Collinearity between all variables and models fitted with and without adjustment was checked using Pearson’s correlation matrix. All analyses were conducted in Stata version 12 and results were presented in the form of tables and summary statistics.
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