Technically speaking, CPR is a ratio, not a rate. (Prevalence is measured by a ratio and incidence by a rate.) For a given year, contraceptive prevalence measures the percentage of women of childbearing age who use a form of contraception. To obtain a true contraceptive use rate, the denominator should reflect the population at risk (of pregnancy), i.e., sexually active women who are fecund and neither pregnant nor amenorrheic. The numerator should reflect the number of contraceptive users from that population. The international population community uses the term “contraceptive prevalence rate” as defined above; thus, this database endorses this practice to assure consistency.

The convention in reporting contraceptive prevalence is to base this calculation on women married or in sexual union (even though most DHS-type surveys ask questions of contraceptive use to women of reproductive age, regardless of their marital status). In countries with relatively little sexual activity outside marriage for women, basing prevalence estimates on women in sexual union captures the population at risk of pregnancy. However, in countries with the widespread practice of sexual activity outside of marriage or stable sexual unions, a prevalence estimate based on women in union only would ignore a considerable percentage of current users. Thus, researchers and program evaluators generally report percentage of sexually active unmarried women using contraception, if appropriate, in addition to contraceptive prevalence, because method mix is very different for those married versus unmarried (in/not in a stable union). Sexually active unmarried women are considered to have had sexual activity within the previous one month (four weeks) to data collection (Short Fabic and Jadhav, 2019).

Whereas evaluators may theoretically derive the CPR from service statistics on numbers of current users and estimates of the population at risk, current practice is to rely upon population-based sample surveys in order to minimize the problems associated with maintaining a running count of current users and with obtaining accurate population estimates. (The problems include incomplete data, double-counting of users who enter the service delivery system at more than one point, purposeful inflation of service statistics, and poor quality of data due to other activities competing for the attention of those recording the information, to name the primary ones.)

Main sources for obtaining national level estimates of contraceptive prevalence include the Demographic and Health Survey (DHS), Fertility and Family Surveys (FFS), Reproductive Health Surveys (RHS), Multiple Indicator Cluster Surveys (MICS), Performance Monitoring for Action (PMA) surveys, and other large-sale national surveys conducted by the countries themselves. Evaluators and researchers may also use smaller scale and/or more focused surveys to estimate the CPR as long as they use probability sampling methods, the essential ingredient for obtaining scientifically sound estimates. Evaluators and researchers may also obtain CPR by adding relevant questions to surveys on other topics (e.g., health program prevalence or coverage surveys), assuming appropriate sampling methods and sample sizes. CPR can also be derived using model-based estimates and projections, as done by the United Nations Population Division.

(Excerpted from: Becker L, Wolf J, Levine R (2006)
Measuring commitment to health. Center for Global Development.)

A study of public family planning service use found that users from the wealthiest quintile outnumbered those from the poorest quintile in 13 of the 20 low-income countries examined, and that the CPR was significantly higher among the wealthiest quintile in all 20 countries. However, the study also found that countries with a higher CPR had less disparity than those in which a smaller percentage used contraceptives, indicating that increasing the CPR could contribute to reducing inequity (Karim, et al., 2004).

The existing literature on the subject makes it clear that contraceptive prevalence is the single most important proximate determinant of total fertility, a fact that can be demonstrated using empirical evidence (Shah, 2006). Eastwood and Lipton have demonstrated a causal link between lower fertility rates and overall poverty rates at the macro-level and it is not unreasonable to hypothesize that increases in contraceptive prevalence will contribute to poverty reduction in the long term (Eastwood & Lipton, 1998). Other poverty-reduction effects may occur because some forms of contraception also prevent HIV and other sexually transmitted diseases that contribute to poverty.

Eastwood R & Lipton M. 1998. “Impact of Changes in Human Fertility on Poverty”. Sussex, United Kingdom. University of Sussex Department of Economics. http://sro.sussex.ac.uk/id/eprint/26937/

Karim A, et al. 2004.  “Equity of Family Planning in Developing Countries”. Arlington, VA, 2004. DELIVER Project. John Snow Inc.

Shah I. 2006. “Levels and Trends in Contraceptive Use”. Geneva. World Health Organization.  http://www.gfmer.ch/Endo/Course2003/PDF/Contraceptive_use.pdf

Short Fabic M & Jadhav A. 2019. “Standardizing measurement of contraceptive use among unmarried women”. Global Health: Science and Practice, 7(4):564-574. https://doi.org/10.9745/GHSP-D-19-00298