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Background: In Uganda and elsewhere, the private sector provides an increasing and significant proportion of maternal and child health services. However, little is known whether private care results in better quality services and improved outcomes compared to the public sector, especially regarding care at the time of birth. Objective: To describe the characteristics of care-seekers and assess newborn care practices and services received at public and private facilities in rural eastern Uganda. Design: Within a community-based maternal and newborn care intervention with health systems strengthening, we collected data from mothers with infants at baseline and endline using a structured questionnaire. Descriptive, bivariate, and multivariate data analysis comparing nine newborn care practices and three composite newborn care indicators among private and public health facilities was conducted. Results: The proportion of women giving birth at private facilities decreased from 25% at baseline to 17% at endline, whereas overall facility births increased. Private health facilities did not perform significantly better than public health facilities in terms of coverage of any essential newborn care interventions, and babies were more likely to receive thermal care practices in public facilities compared to private (68% compared to 60%, p=0.007). Babies born at public health facilities received an average of 7.0 essential newborn care interventions compared to 6.2 at private facilities (p < 0.001).Women delivering in private facilities were more likely to have higher parity, lower socio-economic status, less education, to seek antenatal care later in pregnancy, and to have a normal delivery compared to women delivering in public facilities. Conclusions: In this setting, private health facilities serve a vulnerable population and provide access to service for those who might not otherwise have it. However, provision of essential newborn care practices was slightly lower in private compared to public facilities, calling for quality improvement in both private and public sector facilities, and a greater emphasis on tracking access to and quality of care in private sector facilities.
The UNEST design and package has been described elsewhere (32–34). In brief, the study took place in the Iganga-Mayuge Health and Demographic Surveillance Site (HDSS) located in Iganga and Mayuge districts in the eastern region of Uganda, about 120 km east of the capital city of Kampala. The HDSS serves a population size of 70,000 people, at the time of the study, living in 65 villages, with women of reproductive age comprising 23%. The total fertility rate of the HDSS is 4.3. The population is served by 20 facilities including six private facilities (Fig. 1). The public hospital in Iganga is the only comprehensive emergency obstetric care facility. The public facilities charge no fees for services, although there are often informal costs requested of families. Typically, private facilities consisted of a small clinic with less than five staff who could provide essential care for common conditions. Private facilities are more accessible to the population and sometimes to rural areas than public facilities. Map of the UNEST study area. Villages were randomised to intervention or control arms. Intervention villages had a community health worker who was trained to provide home visits during pregnancy and the first week after delivery, whereas comparison villages received the standard care as delivered by the facilities in the area. Health facility strengthening including training of health workers on essential maternal-newborn care skills and provision of medicine, basic equipment, and supplies was done in all health facilities with a reasonable client load (more than 15–20 per month) for delivery care, independent of ownership and management or whether the facility was located in the intervention or comparison area. Both public and private health facilities were supported by quarterly supervision as part of the health system strengthening. In addition, linkages between community and health facilities were strengthened. A standardised tool was adapted and pretested for data collection. Data collectors were experienced HDSS field staff. The baseline census was done between March and August 2007. Women with infants aged 1–4 months (n=395) in the HDSS were interviewed through visits to all households (35). At endline census, done between August and November 2011, we interviewed all women of childbearing age who had had a live birth in the previous 12 months (n=1,761) (17, 36–38). All analyses used Stata software version 12.1. Univariate and bivariate analyses were used to describe background characteristics of women who delivered in a health facility. The chi-square test was used to compare the difference between the private and public facilities as place of delivery. A multiple logistical regression model was constructed to identify determinants of private facility births using all of the explanatory variables which were significant at bivariate analysis. We checked for multicollinearity between the independent variables, and only included non-collinear variables in the analysis. For this study the effect of treatment – overall and within subgroups – and covariates were reported using odds ratios (ORs). Data on nine essential newborn care practices were collected. These interventions included wrapping the baby immediately after birth using a dry cloth, early skin-to-skin placement, delayed bath at least 6 h after delivery, clean instrument used to cut the umbilical cord, clean device used to tie or clamp the cord, placing nothing on the cord stump, breastfeeding within the first hour after birth; not giving the baby a bottle, and not giving any food or drink other than breast milk. Interventions were combined into composite indicators for thermal care, hygienic cord care, and optimal feeding practices. In addition we assessed how many women received more than one to all nine essential newborn care interventions. Coverage of babies receiving essential newborn care interventions. * χ2prob=0.007 Wealth quintiles were constructed using the Principal Component Analysis based on household assets as used by the Ugandan Bureau of Statistics, including number of sleeping rooms, type of floor material, type of roof material, wall material, type of bed, fuel used for cooking, source of light; and possession of a radio, a sewing machine, an electric flat iron, charcoal flat iron, a bed net, kerosene lamp, kerosene stove, car, tea table, refrigerator, television set, sound stereo, telephone, mattress, wheelbarrow, cell phone, and camera. These gave a Cronbach's alpha of 0.848. Principal component analysis was performed and the first principal component was scored to create an asset index that was used to group all households in the HDSS into wealth quintiles (35). Schooling was assessed using categories of completed education level.
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