Estimating family planning coverage from contraceptive prevalence using national household surveys

A.J.D. Barros, T. Boerma, A.R. Hosseinpoor, M.C. Restrepo-Méndez, K.L.M. Wong, C.G. Victora

Research output: Contribution to journalArticle (Academic Journal)peer-review

14 Citations (Scopus)
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Contraception is one of the most important health interventions currently available and yet, many women and couples still do not have reliable access to modern contraceptives. The best indicator for monitoring family planning is the proportion of women using contraception among those who need it. This indicator is frequently called demand for family planning satisfied and we argue that it should be called family planning coverage (FPC). This indicator is complex to calculate and requires a considerable number of questions to be included in a household survey.

We propose a model that can predict FPC from a much simpler indicator – contraceptive use prevalence – for situations where it cannot be derived directly.

Using 197 Multiple Indicator Cluster Surveys and Demographic and Health Surveys from 82 countries, we explored least-squares regression models that could be used to predict FPC. Non-linearity was expected in this situation and we used a fractional polynomial approach to find the best fitting model. We also explored the effect of calendar time and of wealth on the models explored.

Given the high correlation between the variables involved in FPC, we managed to derive a relatively simple model that depends only on contraceptive use prevalence but explains 95% of the variability of the outcome, with high precision for the estimated regression line. We also show that the relationship between the two variables has not changed with time. A concordance analysis showed agreement between observed and fitted results within a range of ±9 percentage points.

We show that it is possible to obtain fairly good estimates of FPC using only contraceptive prevalence as a predictor, a strategy that is useful in situations where it is not possible to estimate FPC directly.
Original languageEnglish
Article number29735
Number of pages9
JournalGlobal Health Action
Issue number1
Early online date9 Nov 2015
Publication statusPublished - Dec 2015

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Export Date: 12 January 2017


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