Factors affecting home delivery among women living in remote areas of rural zambia: A cross-sectional, mixed-methods analysis

Purpose: Access to skilled care and facilities with capacity to provide emergency obstetric and newborn care is critical to reducing maternal mortality. In rural areas of Zambia, 42% of women deliver at home, suggesting persistent challenges for women in seeking, reaching, and receiving quality maternity care. This study assessed the determinants of home delivery among remote women in rural Zambia. Methods: A household survey was administered to a random selection of recently delivered women living 10 km or more from their catchment area health facility in 40 sites. A subset of respondents completed an in-depth interview. Multiple regression and content analysis were used to analyze the data. Results: The final sample included 2,381 women, of which 240 also completed an interview. Households were a median of 12.8 km (interquartile range 10.9, 16.2) from their catchment area health facility. Although 1% of respondents intended to deliver at home, 15.3% of respondents actually delivered at home and 3.2% delivered en route to a facility. Respondents cited shorter than expected labor, limited availability and high costs of transport, distance, and costs of required supplies as reasons for not delivering at a health facility. After adjusting for confounders, women with a first pregnancy (adjusted OR [aOR]: 0.1, 95% CI: 0.1, 0.2) and who stayed at a maternity waiting home (MWH) while awaiting delivery were associated with reduced odds of home delivery (aOR 0.1, 95% CI: 0.1, 0.2). Being over 35 (aOR 1.3, 95% CI: 0.9, 1.9), never married (aOR 2.1, 95% CI: 1.2, 3.7), not completing the recommended four or more antenatal visits (aOR 2.0, 95% CI: 1.5, 2.5), and not living in districts exposed to a large-scale maternal health program (aOR 3.2, 95% CI: 2.3, 4.5) were significant predictors of home delivery. After adjusting for confounders, living nearer to the facility (9.5–10 km) was not associated with reduced odds of home delivery, though the CIs suggest a trend toward significance (aOR 0.7, 95% CI: 0.4, 1.1). Conclusion: Findings highlight persistent challenges facing women living in remote areas when it comes to realizing their intentions regarding delivery location. Interventions to reduce home deliveries should potentially target not only those residing farthest away, but multigravida women, those who attend fewer antenatal visits, and those who do not utilize MWHs.

This study utilized a cross-sectional, concurrent triangulation mixed-methods design to cross-validate findings.16,17 Data for this analysis were collected for the baseline observation of an impact evaluation ({“type”:”clinical-trial”,”attrs”:{“text”:”NCT 02620436″,”term_id”:”NCT02620436″}}NCT 02620436)40 of a maternity waiting home (MWH) intervention designed from formative research18–21 being implemented in 40 primary health facility catchment areas (HFCAs) within seven districts across Zambia. A quantitative HHS was administered and qualitative IDIs were conducted concurrently to triangulate and corroborate findings (Figure 1). Illustration of the MAHMAZ concurrent mixed-methods study design. Abbreviations: ANC, antenatal care; PNC, postnatal care. Data collection occurred between April and May of 2016 in Choma, Kalomo, and Pemba districts in Southern Province, Nyimba and Lundazi districts in Eastern Province, and Mansa and Chembe districts in Luapula Province. These districts are primarily rural with pockets of peri-urban areas. Long distances, poor road networks, and the cost and lack of transport are documented barriers to accessing maternal health services in rural Zambia.11,22 The average distance from a rural health center (RHC) to the district health office in all study districts, a proxy for a referral hospital which is usually situated next door or just down the road, ranges from 43 (Mansa district) to 85 km (Chembe district).23 A study conducted in Mansa, Lundazi, Nyimba, and Kalomo found the average travel time for women in the poorest quintile to reach a health facility for delivery was 94 minutes, using a variety of transport modes; overall, only 57% of women used motorized transport.13 Generally in Zambia, ambulances are scarce and not equally distributed among the provinces. Only half of the district health offices have vehicles that are suitable for use on the roads and ~30% of rural health facilities use motor bikes or bicycles as a means of transport.8 In the same districts, community members self-reported that having no ambulance, no available transport, and long distances were challenges for pregnant women in the communities.19,21,24 At the time of data collection, all RHCs in the districts offered ANC and over 85% of RHCs offered delivery services.23 Although there are no formal fees for obstetric services and it varies by facility, women are reportedly asked to bring supplies necessary for delivery including clean cloth, soap, disinfectant, a bucket, and baby clothes.19 Each of the seven districts are also target sites for the Saving Mothers, Giving Life (SMGL) initiative, a public– private partnership aimed at accelerating reductions in maternal mortality by improving health systems and addressing the delays in seeking, reaching, and receiving care in Nigeria, Uganda, and Zambia.25 In addition to general health system-strengthening approaches, key activities of SMGL include: 1) community mobilization activities to increase demand through community leaders and volunteers trained in delivering messages and supporting pregnant women, known as Safe Motherhood Action Groups (SMAGs);26 2) infrastructure development and improvements to transport and communication systems to increase access to and availability of services; and 3) strengthening health facility capacity to manage obstetric complications and improve quality of care.25 The first proof-of-concept phase of SMGL was launched in 2012 and targeted Chembe, Kalomo, Lundazi, Mansa, and Nyimba districts. The two additional districts in this study, Pemba and Choma, were not part of SMGL proof-of-concept activities but are included in the SMGL scale-up and scale-out phase, which commenced in 201527 and only started on-the-ground activities after data for this study had already been collected. It is essential that the RHCs that are affiliated with the MWHs have the capacity to manage basic emergency obstetric and neonatal complications, and be physically located within a reasonable travel time to a higher-level referral hospital. As such, the 40 sites in this program are located within 2 hours travel time by vehicle to a comprehensive emergency obstetric and newborn care referral facility, conduct a minimum of 150 deliveries per year, and either 1) have the capacity to provide at least five of seven basic emergency obstetric and newborn care signal functions or 2) have at least one skilled birth attendant on staff, routinely practice active management of third stage labor, and have had no reported stock-outs of oxytocin or magnesium sulfate in the 12 months prior to the study. Two sets of criteria were necessary as consistent data were not available across all districts during the site selection process. Eligibility criteria to participate in the HHS and IDIs included: the respondent had delivered a baby within the past 12 months; was aged 15 or older (guardian available for consent if under 18); and was a resident of the village identified for sampling. If the eligible respondent in the household had died, the household was eligible if a proxy respondent was available and at least 18 years of age. Multi-stage random sampling was used to ensure a representative sample of remote women living in the selected 40 HFCAs across the seven districts. First, to generate a sample frame of clusters (villages), all villages in the 40 selected HFCAs were geocoded and those where the village center was located >10 km from their catchment area health facility by the most direct travel routes using ArcGIS® Online (Esri, Redlands, CA, USA), rounding up to the nearest km, were identified. Ten kilometers was selected for comparability because it is a commonly used measure of distance in the maternal health literature.11,15,28 Because distance was rounded up to the nearest km, some village centers are located between 9.5 and 10 km from their catchment area health facility. From the sample frame, we then randomly selected approximately ten villages per HFCA for inclusion with probability proportional to population size. Second, all eligible households within each selected village were listed through the assistance of community members and village leadership. We then randomly ordered households and approached them for participation until the sample size (approximately six households) for that village was reached. Third, if a household had more than one eligible participant, one respondent was randomly selected by the electronic data capture system. Ten percent of households were also randomly selected to participate in a short IDI immediately after the HHS was completed.40 A local team of enumerators, literate in the local language(s) and in English, were trained in qualitative and quantitative interviewing techniques and human subjects’ protection during 5-day training. The quantitative HHS captured information for each respondent on: household and individual demographics, barriers to accessing facilities for delivery, and service utilization. Enumerators captured survey data on encrypted tablets using SurveyCTO Collect v2.212 (Dobility, Inc, Cambridge, MA, USA). The qualitative IDIs were conducted using a semi-structured interview guide to gain a deeper understanding of the respondents’ perceptions of barriers to accessing maternal care, and decision-making regarding delivery. IDIs were administered to a randomly selected subset of HHS respondents immediately following the respondents’ HHS. IDIs were audio-recorded, translated from the local language into English, and then transcribed verbatim. For this analysis, the primary outcome, delivery location, was captured through women’s responses about where she delivered her most recent child (index child): in a home; at any health facility; on the way to the health post/facility/hospital. Key demographic variables and variables that have been well established in the literature as predictors of delivery location were included in the analysis: age category; maternal education level categorized as none, any primary or more than primary; marital status; wealth quartile; parity; first pregnancy (primigravida); and whether she attended the recommended four or more ANC visits dichotomized as yes or no. A categorical variable was created to control for the distance from the household’s geocoded village center to the village’s catchment area health facility. It is possible, however, that some households may lie closer to or farther from the facility based on their actual proximity to the geocoded village center. For those who delivered at any health facility, self-reported travel time was captured in hours and minutes, converted to hours, and presented categorically. Lastly, a variable was created to account for districts’ exposure to SMGL activities, with those participating in the proof-of-concept phase categorized as “SMGL exposed”. Those in the scale-up and scale-out phase were categorized as “SMGL unexposed” because on-the-ground programmatic activities had not yet commenced at the time of data collection. All quantitative analyses were conducted in SAS v9.4 (SAS Institute Inc., Cary, NC, USA). First, descriptive characteristics for the full study sample and IDI sub-sample were calculated from the HHS; the full sample and IDI sub-sample were compared using a chi-squared test of association. Second, the primary outcome of delivery location (at home, at any health facility, or on the way to a facility) was assessed against key sociodemographic characteristics and potential covariates as described previously in bivariate tables, using a chi-squared test of association. Lastly, multivariate logistic regression models were used to assess the relationship between predictor variables on home delivery, with the most frequent category serving as the reference in the model.29 Predictor variables that were significant at the P=0.05 level were included in the regression model, though intended delivery location was excluded because of small cell size. Self-reported travel time was not included in the model as it was only asked of those who delivered at a facility. All analyses accounted for clustering in the districts using the survey analysis procedures. Quantitative data are presented as mean ± SD or median and interquartile range (IQR). We also present unadjusted ORs and adjusted ORs (aORs), with 95% CI. All qualitative data were systematically coded and analyzed using content analysis in NVivo 10© (QSR International Pty Ltd, Doncaster, Australia).30 The texts were first coded to a theme and then to directionality (positive, negative, neutral); these were then explored during analysis to identify common issues or instances mentioned in the data. Coding themes were identified a priori according to the semi-structured interview guide which contained questions regarding delivery location and barriers to facility delivery. Additional themes were included as they emerged. Quantitative and qualitative findings were then triangulated and we convened a 1-day data meeting with relevant stakeholders from the District and Provincial Health Offices in Zambia, to solicit feedback on the analyses and to better interpret the findings.

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• scott, nancy a.
• henry, elizabeth g.
• kaiser, jeanette l.
• mataka, kaluba
• rockers, peter c.
• fong, rachel m.
• ngoma, thandiwe
• hamer, davidson h.
• munro-kramer, michelle l.
• lori, jody r.