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Introduction. Globally, health facility delivery is encouraged as a single most important strategy in preventing maternal and neonatal morbidity and mortality. However, access to facility-based delivery care remains low in many less developed countries. This study assesses facilitators and barriers to institutional delivery in three districts of Tanzania. Methods. Data come from a cross-sectional survey of random households on health behaviours and service utilization patterns among women and children aged less than 5 years. The survey was conducted in 2011 in Rufiji, Kilombero, and Ulanga districts of Tanzania, using a closed-ended questionnaire. This analysis focuses on 915 women of reproductive age who had given birth in the two years prior to the survey. Chi-square test was used to test for associations in the bivariate analysis and multivariate logistic regression was used to examine factors that influence institutional delivery. Results: Overall, 74.5% of the 915 women delivered at health facilities in the two years prior to the survey. Multivariate analysis showed that the better the quality of antenatal care (ANC) the higher the odds of institutional delivery. Similarly, better socioeconomic status was associated with an increase in the odds of institutional delivery. Women of Sukuma ethnic background were less likely to deliver at health facilities than others. Presence of couple discussion on family planning matters was associated with higher odds of institutional delivery. Conclusion: Institutional delivery in Rufiji, Kilombero, and Ulanga district of Tanzania is relatively high and significantly dependent on the quality of ANC, better socioeconomic status as well as between-partner communication about family planning. Therefore, improving the quality of ANC, socioeconomic empowerment as well as promoting and supporting inter-spousal discussion on family planning matters is likely to enhance institutional delivery. Programs should also target women from the Sukuma ethnic group towards universal access to institutional delivery care in the study area. © 2014Exavery et al.; licensee BioMed Central Ltd.
Data for this study come from a cross-sectional household survey that was conducted in 2011 in Rufiji, Kilombero, and Ulanga districts of Tanzania using two existing and ongoing Health and Demographic Surveillance System (HDSS) platforms, namely, Rufiji HDSS located in Rufiji district, and Ifakara HDSS which occupies portions of Kilombero and Ulanga districts. The data were collected using a closed-ended questionnaire and sought to obtain information on health seeking behaviors and service utilization patterns by women and children of less than five years of age. The main purpose of the survey was to provide baseline estimates for the Connect Project, which is currently being implemented in the three districts using the HDSS platforms. More details about the Connect Project can be found in [22,23] and [24]. In brief, Connect Project tests the hypothesis that introducing a new cadre of paid community health worker, known as Community Health Agent (CHA), into the system, with the necessary supporting operations, including improvement of emergency referral, reduces child mortality, including newborn mortality, improves key maternal health outcomes, and thus accelerates progress towards (or beyond) Millennium Development Goals 4 and 5. Since the HDSS platforms are longitudinal, population-based health and vital events registration systems which monitor demographic events such as births, deaths, pregnancies, and migrations of the individuals in the study area, they were envisioned as suitable forms to monitor the outcomes and impact of the CHAs. Households for the main survey were selected randomly from a list of all households (sampling frame) under surveillance by the Rufiji, and Ifakara HDSS. Selection of these households was accomplished using probability proportional to size (PPS) technique. Since these households came from villages with unequal number of households, PPS was the ideal method to use to ensure that each village is represented in the sample. In each of the households sampled, all women of reproductive age (15‒49 years) were eligible for interviews. A woman over 49 years of age was interviewed only if she wholly took care of at least one child less than five years of age in order to obtain information on health and health service utilization pattern for the child. The current analysis focused on 915 women of reproductive age whose last birth occurred in the two years prior to the survey. Therefore, data pertaining to this population were extracted from the parent database for analysis to answer the current research questions. The outcome variable was place of delivery for births that occurred in the two years prior to the survey. This variable was binary, with one category for health facility or institutional delivery and the other for non-facility delivery. Non-facility delivery referred to all births that occurred at home, in farms or on a way to a facility/birth before arrival. Institutional delivery was coded as ‘1’ and non-facility delivery was coded as ‘0’ for computational reasons. Several explanatory variables were considered. Household socioeconomic status was included, resulting from Principal Component Analysis (PCA) of household assets [25]. Five wealth quintiles were constructed based on ownership of a toilet, toilet type, and source of drinking water. The quintiles ranged from the poorest (Q1) to the wealthiest (Q5) such that the higher the quintile the wealthier the woman’s household. Other assets such as household building material were unfortunately unavailable for the PCA. Other explanatory variables included were education, age, marital status, ethnicity, religion, gravidity, pregnancy intentions, district of residence and type of residence (rural or urban). Gravidity was considered a proxy for fertility. Household headship, inter-spousal communication or discussion about family planning matters were also included in the analysis. Moreover, we included ANC score of 15 health services whose utilization status during pregnancy was available. Each of these had a ‘yes’ or ‘no’ response to whether a woman was weighed, had her blood pressure measured, height measured, urine sampled, blood sampled, abdomen measured, heart rate of the baby assessed, given an injection in the arm to prevent tetanus (TT), counseled on financial preparation for delivery, counseled on breastfeeding immediately after delivery, counseled on danger signs during delivery, counseled on family planning, counseled on identifying emergency transport options, counseled on danger signs of pregnancy, and counseled and tested for HIV. The scores ranged from 0, if a woman received none of the services, to 15 if she received all of these eservices. We assumed that the bigger the score the better the quality of ANC. The other variable included was the number of ANC visits a woman made during pregnancy. The sample was first analyzed descriptively to obtain frequency distribution of the women across several characteristics. Bivariate analysis was then conducted by cross-tabulating place of delivery against each of the explanatory variables. The explanatory variables were categorical, and those which were not were categorized and therefore the degree of association between each pair of variables cross-tabulated was tested using Chi-Square (χ2). Multivariate analysis was performed using logistic regression to assess factors associated with institutional delivery. Beforehand, an assessment of clustering at household level was carried out to check whether the assumption of independence of observations holds. This was prompted by the fact that during data collection, the interview included all eligible women from the same household for households which had more than one. The assumption was that women from the same household may have the same or similar health behaviours. The assessment ultimately showed that the observations were independent of one another because there was no significant evidence of clustering at household level. In performing the logistic regression analysis, a variable was retained in the multivariate model if the log likelihood ratio test showed that its presence improved the overall model [26]. In this case, ANC score of the ANC services was treated as a continuous variable in order to optimize its predictive power. The level of significance was set at 5%. The entire process of data analysis was carried out using STATA (version 11) statistical software. Ethical approval for the main survey was granted by the Medical Research Coordinating Committee (MRCC) of the National Institute for Medical Research (NIMR) in Tanzania. During the survey, participation was voluntary and each woman signed (or provided a thumb print if she was illiterate) a statement of an informed consent after which she was interviewed. For legal reasons, an assent was sought for participants less than 18 years of age. Data storage and processing were all handled securely within the Ifakara Health Institute where the Connect Project is based.
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