In this article, published in BMJ Global Health, we reflect on the value of utilizing a new measure of contraceptive autonomy through its testing in A360’s continuation cohort study in northern Nigeria. Original article published via BMJ.
Abstract:
Introduction Universal access to sexual and reproductive healthcare—including family planning (FP)—is a global priority, yet there is no standard outcome measure to evaluate rights-based FP programme performance at the regional, national or global levels.
Methods We collected a modified version of preference-aligned fertility management (PFM), a newly proposed rights-based FP outcome measure which we operationalised as concordance between an individual’s desired and actual current contraceptive use. We also constructed a modified version (satisfaction-adjusted PFM) that reclassified current contraceptive users who wanted to use contraception but who were dissatisfied with their method as not having PFM. Our analysis used data collected 3.5 months after contraceptive method initiation within an ongoing prospective cohort of married adolescent girls aged 15–19 years in Northern Nigeria. We described and compared prevalence of contraceptive use and PFM in this population.
Results Ninety-seven per cent (n=1020/1056) of respondents were practising PFM 3.5 months after initiating modern contraception, while 93% (n=986/1056) were practising satisfaction-adjusted PFM. Among participants not practising satisfaction-adjusted PFM (n=70), most were using contraception but did not want to be (n=30/70, 43%) or wanted to use contraception but were dissatisfied with their method (n=34/70, 49%), while the remaining 9% (n=6/70) wanted but were not currently using contraception.
Conclusion PFM captured meaningful discordance between contraceptive use desires and behaviours in this cohort of married Nigerian adolescent girls. Observed discordance in both directions provides actionable insights for intervention. PFM is a promising rights-focused FP outcome measure that warrants future field-testing in programmatic and population-based research.