Where do the rural poor deliver when high coverage of health facility delivery is achieved? Findings from a community and hospital survey in Tanzania

Introduction: As part of maternal mortality reducing strategies, coverage of delivery care among sub-Saharan African rural poor will improve, with a range of facilities providing services. Whether high coverage will benefit all socio-economic groups is unknown. Iringa rural District, Southern Tanzania, with high facility delivery coverage, offers a paradigm to address this question. Delivery services are available in first-line facilities (dispensaries, health centres) and one hospital. We assessed whether all socio-economic groups access the only comprehensive emergency obstetric care facility equally, and surveyed existing delivery services. Methods: District population characteristics were obtained from a household community survey (n=463). A Hospital survey collected data on women who delivered in this facility ( n=1072). Principal component analysis on household assets was used to assess socio-economic status. Hospital population socio-demographic characteristics were compared to District population using multivariable logistic regression. Deliveries’ distribution in District facilities and staffing were analysed using routine data. Results: Women from the hospital compared to the District population were more likely to be wealthier. Adjusted odds ratio of hospital delivery increased progressively across socio-economic groups, from 1.73 for the poorer (p=0.0031) to 4.53 (p<0.0001) for the richest. Remarkable dispersion of deliveries and poor staffing were found. In 2012, 5505/7645 (72%) institutional deliveries took place in 68 first-line facilities, the remaining in the hospital. 56/68 (67.6%) first-line facilities reported ≤100 deliveries/year, attending 33% of deliveries. Insufficient numbers of skilled birth attendants were found in 42.9% of facilities. Discussion: Poorer women remain disadvantaged in high coverage, as they access lower level facilities and are under-represented where life-saving transfusions and caesarean sections are available. Tackling the challenges posed by low caseloads and staffing on first-line rural care requires confronting a dilemma between coverage and quality. Reducing number of delivery sites is recommended to improve quality and equity of care. Iringa District (formerly Iringa rural) is within Iringa Region, in the Southern Highlands of Tanzania. The population according to the 2012 census was 254,023 [18]. It is mostly rural, with 85% relying on subsistence farming. The District includes 122 villages. Health services in 2012 were available in 73 facilities, of which 66 dispensaries, 6 health centres and one diocesan District hospital [19]. The majority of health facilities are public, with only 27% run by private non-profit organizations. Baseline information on the District population including socio-economic data was obtained from a cross-sectional household survey carried out in October 2009 (Community survey). Objective of the survey was to collect information on access to health services in the area, as part of a health system strengthening programme. A representative sample of the District population was obtained through two-stage cluster sampling. Thirty villages were randomly chosen in the first stage with probability proportional to size. Twenty-five households were selected in each village in the second stage through random systematic sampling. Women with a delivery in the last five years were included in analysis. The socio-economic profile of women accessing the only comprehensive emergency obstetric care facility was obtained from a cross-sectional survey of women discharged from the District hospital Maternity Ward between October 2011 to May 2012 (hospital survey). The survey was conducted as part of a development programme, on access, quality and equity of maternal services. Interviews at discharge were conducted in Swahili by female trained interviewers using a pretested structured questionnaire. For women who had died, information was collected from relatives. In the hospital survey, only women who had delivered in the hospital and lived within the District were included in analysis. Women from outside the District (who have travelled beyond their District hospital) may belong to a higher socio-economic group, therefore creating bias in the analysis. As validation of collected data, records collected were matched with hospital Maternity registers. Data entry and cleaning was carried out using Epidata software (version 3.1) by a principal investigator, and analysis was carried out using STATA (version 9) software. Characteristics of women from the two surveys were compared (Dataset S1). Variables examined were age (at index delivery for the community survey), parity, education, sex of household head, type of delivery and socio-economic status. Proportions and 95% CI were estimated for both populations taking study design into account. After merging of data sets, bivariate and multivariable analysis were performed. Crude odds ratios for belonging to the hospital population were produced, with a 95% confidence interval. Multivariable logistic regression including all variables significant in bivariate analysis was performed to estimate adjusted odds ratios of belonging to the hospital compared to the District population. Svyset commands were used to account for clustered design. Socio-economic status (SES) was assessed based on durable household possessions (bicycle, radio, mobile phone) and housing characteristics (non-grass roof, non-mud floor and electricity) as applied by Bernard et al [20] in rural Tanzania. Principal Component Analysis was used to define weights to each variable and to construct a household socio-economic score for the community and for the hospital population respectively [21]. Although the score from the first principal component does not give information on absolute level of wealth, it can be used for comparison across different settings, provided that calculation is based on the same variables [21]. We classified the district population into five SES groups (1–5), from poorest to richest by dividing the community household socio-economic score into quintiles. We thus applied the quintile cut-off values derived from the community sample to the hospital population socio-economic score to create five comparable SES categories across the two settings. Details of available services and the distribution of deliveries within them in 2012 were obtained using the routine District Health Management Information System (HMIS, MTUHA in Tanzania), through the District Medical Office. Annual reports are compiled by each health facility on a national standardized form (F005), and sent to the District Medical Office yearly. The annual reports for 2012 for the facilities of Iringa District were examined, and data on deliveries was collected (Dataset S2). Reported data was cross checked with data at facility level during supervision visits. Data on health facility staffing in 2012 was obtained from the Human Resources Information System, available in the District Medical Office (Dataset S2). The information was validated and where necessary updated during health facility supervision visits. Skilled birth attendants (SBA) are accredited health professionals with the necessary skills to manage childbirth and to identify, manage and refer complications in women and newborn [22], [23]. In Tanzania, clinicians (medical officers, assistant medical officers, clinical officers), and enrolled and registered nurses are classified as skilled birth attendants; lower cadres such as nursing assistants are not [24]. Ethical clearance for the study was obtained from the National Institute for Medical Research, Dar es Salaam, Tanzania. Participants to both community and hospital surveys provided signed informed consent.