Central Data Catalog

Citation Information

Type Journal Article - Reproductive health
Title Taking stock: protocol for evaluating a family planning supply chain intervention in Senegal
Volume 13
Issue 1
Publication (Day/Month/Year) 2016
URL https://preview-reproductive-health-journal.biomedcentral.com/articles/10.1186/s12978-016-0163-7
In Senegal, only 12 % of women of reproductive age in union (WRAU) were using contraceptives and another 29 % had an unmet need for contraceptives in 2010–11. One potential barrier to accessing contraceptives is the lack of stock availability in health facilities where women seek them. Multiple supply chain interventions have been piloted in low- and middle-income countries with the aim of improving contraceptive availability in health facilities. However, there is limited evidence on the effect of these interventions on contraceptive availability in facilities, and in turn on family planning use in the population. This evaluation protocol pertains to a supply chain intervention using performance-based contracting for contraceptive distribution that was introduced throughout Senegal between 2012 and 2015.

This multi-disciplinary research project will include quantitative, qualitative and economic evaluations. Trained researchers in the different disciplines will implement the studies separately but alongside each other, sharing findings throughout the project to inform each other’s data collection. A non-randomised study with stepped-wedge design will be used to estimate the effect of the intervention on contraceptive stock availability in health facilities, and on the modern contraceptive prevalence rate among women in Senegal, compared to the current pull-based distribution model used for other commodities. Secondary data from annual Service Provision Assessments and Demographic and Health Surveys will be used for this study. Data on stock availability and monthly family planning consultations over a 4-year period will be collected from 200 health facilities in five regions to perform time series analyses.

Related studies