Where Do the Rural Poor Deliver When High Coverage of Health Facility Delivery Is Achieved? Findings from a Community and Hospital Survey in Tanzania

<div><p>Introduction</p><p>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.</p><p>Methods</p><p>District population characteristics were obtained from a household <i>community survey</i> (<i>n = 463</i>). A <i>Hospital survey</i> collected data on women who delivered in this facility (<i>n = 1072</i>). 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.</p><p>Results</p><p>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.</p><p>Discussion</p><p>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.</p></div

Association between covariates. Study population from hospital survey compared to the study population from community survey.

<p>*Adjusted Wald test.</p><p>Association between covariates. Study population from hospital survey compared to the study population from community survey.</p

Data flow for the community and hospital surveys.

<p>Data flow for the community and hospital surveys.</p

Odds ratios of belonging to hospital population compared to District population by socio-economic quintiles, with respective 95% confidence intervals.

<p>Iringa District, Tanzania. 2009–2012.</p

Distribution of deliveries by facility caseload in Iringa District in 2012 (based on HMIS data).

<p>*District Hospital.</p><p>Distribution of deliveries by facility caseload in Iringa District in 2012 (based on HMIS data).</p

Socio-demographic characteristics of women who delivered at District hospital compared to women from the community of provenance.

<p>*adjusted for cluster design.</p><p>Iringa District, Tanzania. 2009–2012.</p><p>Socio-demographic characteristics of women who delivered at District hospital compared to women from the community of provenance.</p

Delivery caseload in first-line health facilities (dispensaries and health centres).

<p>* NA: data not available.</p><p>Iringa and Ludewa districts. 2012.</p

Friction coefficients used to estimate walking time in slope and land use rasters.

<p>Friction coefficients were applied to each raster cell to estimate the time needed to cross the cells on foot according to surface characteristics.</p><p>Friction coefficients used to estimate walking time in slope and land use rasters.</p

Catchment area estimated by network analysis.

<p>The areas around health facilities represent a 2 hours’ catchment divided in consecutive intervals for walking speed and for multimodal transport in Iringa and Ludewa Districts. (A, D) Current scenario with all delivery sites; (B, E) reduced number of delivery sites using walking speed; (C, F) reduced number of delivery sites using multimodal transport (vehicular and walking speed). Restriction: non-passing areas (lakes, swamps, etc.). Scaled cost: areas beyond 2 hours’ travel time.</p

Catchment area estimated by raster analysis.

<p>The areas around health facilities represent a 2 hours’ catchment divided in 20 minutes’ intervals. (A) Iringa District current scenario with all delivery sites; (B) Iringa District proposed scenario with reduced number of delivery sites; (C) Ludewa District current scenario with all delivery sites; (D) Ludewa District proposed scenario with reduced number of delivery sites. The grey shades delimit the areas that will loose accessibility within 2 hours by a 40% reduction of delivery sites.</p