Role of Savings and Internal Lending Communities (SILCs) in improving household wealth and financial preparedness for birth in rural Zambia

The role of Savings and Internal Lending Communities (SILCs) in improving community-level household wealth, financial preparedness for birth, and utilization of reproductive health services in rural Zambia: a secondary analysis

Background

Savings and Internal Lending Communities (SILCs) are a type of informal microfinance mechanism widely adapted in Zambia. The benefits of SILCs paired with other interventions have been studied in many countries. However, limited studies have examined SILCs in the context of maternal health. This study examined the association between having access to SILCs and: 1) household wealth, 2) financial preparedness for birth, and 3) utilization of various reproductive health services (RHSs).

Methods

Secondary analysis was conducted on baseline and endline household survey data collected as part of a Maternity Waiting Home (MWH) intervention trial in 20 rural communities across seven districts of Zambia. Data from 4711 women who gave birth in the previous year (baseline: 2381 endline: 2330) were analyzed. The data were stratified into three community groups (CGs): CG1) communities with neither MWH nor SILC, CG2) communities with only MWH, and CG3) communities with both MWH and SILC. To capture the community level changes with the exposure to SILCs, different women were randomly selected from each of the communities for baseline and endline data, rather than same women being surveyed two times. Interaction effect of CG and timepoint on the outcome variables – household wealth, saving for birth, antenatal care visits, postnatal care visits, MWH utilization, health facility based delivery, and skilled provider assisted delivery – were examined.

Results

Interaction effect of CGs and timepoint were significantly associated only with MWH utilization, health facility delivery, and skilled provider delivery. Compared to women from CG3, women from CG1 had lower odds of utilizing MWHs and delivering at health facility at endline. Additionally, women from CG1 and women from CG2 had lower odds of delivering with a skilled provider compared to women from CG3.

Conclusion

Access to SILCs was associated with increased MWH use and health facility delivery when MWHs were available. Furthermore, access to SILCs was associated with increased skilled provider delivery regardless of the availability of MWH. Future studies should explore the roles of SILCs in improving the continuity of reproductive health services.

Trial registration

NCT02620436.

Planning for Maternity Waiting Home Bed Capacity: Lessons from Rural Zambia

Background:

Maternity waiting homes (MWH) allow pregnant women to stay in a residential facility close to a health center while awaiting delivery. This approach can improve health outcomes for women and children. Health planners need to consider many factors in deciding the number of beds needed for an MWH.

Objective:

The objective of the study is to review experience in Zambia in planning and implementing MWHs, and consider lessons learned in determining optimal capacity.

Methods:

We conducted a study of 10 newly built MWH in Zambia over 12 months. For this case study analysis, data on beds, service volume, and catchment area population were examined, including women staying at the homes, bed occupancy, and average length of stay. We analyzed bed occupancy by location and health facility catchment area size, and categorized occupancy by month from very low to very high.

Findings:

Most study sites were rural, with 3 of the 10 study sites rural-remote. Four sites served small catchment areas (<9 000), 3 had medium (9 000–11 000), and 3 had large (>11 000) size populations. Annual occupancy was variable among the sites, ranging from 13% (a medium rural site) to 151% (a large rural-remote site). Occupancy higher than 100% was accommodated by repurposing the MWH postnatal beds and using extra mattresses. Most sites had between 26–69% annual occupancy, but monthly occupancy was highly variable for reasons that seem unrelated to catchment area size, rural or rural-remote location.

Conclusion:

Planning for MWH capacity is difficult due to high variability. Our analysis suggests planners should try to gather actual recent monthly birth data and estimate capacity using the highest expected utilization months, anticipating that facility-based deliveries may increase with introduction of a MWH. Further research is needed to document and share data on MWH operations, including utilization statistics like number of beds, mattresses, occupancy rates and average length of stay.