Family Planning (Core)


Welcome to the programmatic area on family planning (FP) core indicators within MEASURE Evaluation’s Family Planning and Reproductive Health Indicators Database. This is one of the subareas found in the family planning section of the database. All indicators for this area include a definition, data requirements, data source(s), purpose, issues and—if relevant—gender implications. FP is the use of contraception to delay or prevent pregnancy, and is a key preventive health service with well-documented effects on improving maternal and child health outcomes. Program monitoring and evaluation is more methodologically advanced for FP than for other areas of reproductive health as a result of over 50 years of dedicated effort to strengthen FP programs and services. Key indicators to monitor and evaluate FP can be found in the links at left.    Full Text Program evaluation is more methodologically advanced for family planning (FP) than for any other area of reproductive health, thanks to 40+ years of dedicated effort and strong funding support for this work. Several factors spurred the development of evaluation methodologies. The demographic concern that underscored the early FP programs translated to monitoring of results in quantitative terms: numbers of new acceptors and continuing users. The problem of the "unequal weight" of different methods (e.g., one condom acceptor versus one vasectomy acceptor) gave rise to the index of couple-years of protection (CYP). Critics and skeptics of FP unwittingly strengthened evaluation efforts. The hot debate originating during the 1970s on the relative contribution of FP programs versus that of socio-economic development impelled researchers to develop methods of demonstrating the independent effect of FP. The FP program effort scores (Lapham and Mauldin, 1984) played a useful role in this research. Although this public debate has subsided, the challenge remains to demonstrate causality (i.e., the program interventions have impact) in FP program evaluation. The World Fertility Survey (conducted from 1972 to 1984) focused primarily on the determinants of fertility, with relatively little attention to FP and related programmatic issues. The Contraceptive Prevalence Survey, first piloted in 1975 in El Salvador, was designed to produce more programmatically useful results with shorter turn-around time. This survey tool later evolved into the Demographic and Health Survey (DHS) and the Reproductive Health Survey (RHS), both national-level representative surveys of women (and increasingly of men) of reproductive age in developing countries (Robey et al., 1992). The DHS and RHS have become the most widely utilized sources of data for FP program evaluation, because of the quality of the information, standardization of items in the core questionnaire, availability of repeat measurement over time, and possibility of cross-national comparisons. Both surveys extend beyond FP to cover related issues of maternal health/safe pregnancy and child health. A number of factors facilitate the job of evaluating FP programs. First, the "intermediate outcome" -- the desired behavioral change at the population level -- is a single measurable behavior: use of a contraceptive method (aggregated to a measure of contraceptive prevalence). Second, despite the sensitive nature of FP in many countries (especially in the early years of programs), women are willing and able to report contraceptive use with a high degree of accuracy, assuming the interviewer creates good rapport and the question is clearly worded. Third, the intermediate outcome of contraceptive prevalence is strongly (inversely) correlated with a key long-term outcome: fertility (except in countries with high levels of abortion). In short, FP evaluators are blessed with a single, measurable, and valid outcome variable. Other areas of reproductive health (with the possible exception of breastfeeding) present a greater methodological challenge. Nonetheless, FP program evaluation does have a few problems of its own. Methodological Challenges of Evaluating FP Programs Contraceptive methods vary in terms of their use-effectiveness, duration of action, and likelihood of continuation. Although all modern methods can protect against pregnancy, some do so better than others. Thus, the program with a higher percentage of users of long-term methods will generally be more effective in pregnancy prevention than those in which users opt for less effective methods will be. The measure -- CYP -- was designed to address this issue, but it has certain problems of its own (discussed below). Large-scale survey data yield the most reliable estimates of contraceptive use, but they have limitations. Valuable as DHS/RHS data are for tracking national trends, they have three major limitations. First, such surveys are conducted only once every three to five years (if that often). Second, they do not yield precise results for geographical sub-areasin most countries, which is the level at which program managers generally need their information. Third, these large-scale surveys are very expensive to conduct and analyze, a fact that has caused some countries to question the feasibility of continuing to fund them, especially if donor funding is unavailable. Given these limitations, program statistics, such as number of acceptors and CYP, are widely used to monitor FP programs on a routine basis. Demonstrating the impact of FP programs on contraceptive use requires more than the simple tracking of contraceptive prevalence over time in a given country. Many working in FP would like to think that "if contraceptive prevalence increases, the program must be successful." However, factors other than the program may have contributed to these increases. Controlled field experiments to demonstrate what would have happened in the absence of the FP program are not feasible in evaluating ongoing, national level programs. The single largest methodological challenge for evaluating FP programs in the past decade has been the issue of establishing attribution. (For a full description of the issue and proposed methods of addressing it, see Evaluating Family Planning Programs with Adaptations for Reproductive Health (Bertrand, Magnani and Rutenberg, 1996), Chapter IV). Evaluation methodology is far more advanced for FP than for other areas of reproductive health, in part because a single, valid outcome indicator measurable through DHS-type studies is available. Yet, definitively establishing cause-and-effect is still relatively rare in the evaluation of FP programs. The long-term outcome variable for FP programs is no longer clear-cut. Prior to the 1994 ICPD, one of the primary goals of many FP programs -- especially in Asia -- was to reduce fertility, an indicator that is reliable and relatively easy to measure. (All indicators pertaining specifically to fertility are included in the Fertility section of this database.)  Whereas many governments worldwide continue to track fertility as a desired outcome of FP programs, a number of programs are repositioning FP within the larger context of reproductive health as a reproductive right or health intervention. Although the field has moved away from a single-minded focus on fertility, an alternative, standardized indicator that reflects the health and reproductive rights aspects has yet to surface. Almost all of the indicators in this section were taken directly from the Handbook of Indicators for Family Planning Program Evaluation (Bertrand, Magnani, and Knowles, 1994), suggesting little change in the basic indicators for evaluating outputs and outcome in FP programs since that time. However, we have reduced the total number of FP indicators, retaining those which have proven most useful for program evaluation in field settings. FP Integration The evidence is building that integrating FP into existing reproductive health programs is a cost-effective, client-centered way to increase client access to information and services.  Promoting integration is one of the Global Health Initiatives goals and more projects are exploring opportunities to design integration programs to reach high-priority, underserved groups. FP integration is reflected in this database as stand-alone technical areas (i.e. Population-Health-Environment, FP/HIV, and FP/Maternal and Child Health) as well as being included as a vital indicator within other reproductive health technical areas (i.e. Postabortion Care, Reproductive Health in Emergency Settings, and Obstetric Fistula).

Family Planning Program Effort Index


This indicator is a score measuring the strength of the national family planning (FP) program of a given country on four dimensions (policies, services, evaluation, and method access). The score has a potential range of 0-300 points, based on 1-10 points for each of 30 items.

Data Requirements:

Responses to a detailed questionnaire from selected key informants (representatives of the ministry of health, IPPF affiliate or other NGO; international consultants familiar with that country; and other informed individuals).

Data Sources:

A questionnaire designed explicitly for this purpose, completed by an average of 10-15 individuals per country. The items have remained constant over the multiple rounds of data collection.


The purpose of the Family Planning Program Effort Index is to assess the strength of FP programs worldwide and to measure changes over time. It attempts to measure the effort (input) that goes into the FP program, not the results achieved. These data have been collected in eight different cycles from 1972 to 2014, spanning over four decades. The questionnaire relates to 30 measures of effort. Researchers convert the responses to these questions to individual scores (ranging from 1-10) for each of the 30 items, using an established set of rules.

The Family Planning Program Effort Index serves several important purposes:

  1. It allows for cross-national comparisons of FP programs at different points over several years;
  2. It traces the evolution of the FP program in a given country over time; and
  3. It measures FP program input, independent of outcomes (such as contraceptive prevalence or fertility).

As such, it has been used to analyze the relative importance of FP program effort versus social and economic factors in the decline of fertility rates worldwide (Lapham and Mauldin, 1984; Mauldin and Ross, 1991; Ross and Mauldin, 1996; Ross and Stover, 2001).

This index is useful primarily to researchers, donor agency representatives, and those interested in understanding FP programs in the international context. Although it indicates areas of strength and weakness for a national program, it has not been the tool of preference for program managers at the country level in identifying ways to improve programs. Indeed, in contrast to other widely used measures and tools (CYP, contraceptive prevalence, DHS and RHS surveys, situation analysis) which are widely known to family planning program managers throughout the developing
world, the Family Planning Program Effort Index is used primarily for research purposes.

Nonetheless, the Index represents a valuable source of information to the international reproductive health community. It is the only source of data that purports to measure inputs using a standard set of questions across countries and over time. The results for the Family Planning Program Effort Index have generally coincided with qualitative assessments of "how good a family planning program is" in a given country. For example, the scores indicate that in 1972 a quarter of the world's population lived in countries with very weak or no FP programs. As of 1999, no country in the world fell in this category. 

Most of the indicators in this database are included to encourage evaluators and researchers to collect the necessary data to use them. By contrast, the data collection for the Family Planning Program Effort Index is conducted by a small team of researchers based in the United States, using a standardized instrument across countries.

The index is useful to the international reproductive health community because it offers data of this nature for secondary use, not as a type of information routinely collected by program managers for the purpose of program improvement.

Detailed Family Planning Effort data by country and round from 1972 to 2014 can be found here, on the Track20 website: 


A major criticisms that has been leveled against this index is that the responses of the key informants are biased by their knowledge of key outcomes (contraceptive use and fertility decline). That is, if contraceptive prevalence is high, the respondents unconsciously give high scores to the availability of methods, assuming that the two go hand in hand. In short, although the index claims to measure inputs, the responses may be biased by a knowledge of outcomes.

Number of first-time users of modern contraception


The number of persons who accept for the first time in their lives any (program) contraceptive method; to be reported for a defined reference period (e.g., one year).

Data Requirements:

Counts of persons accepting any (program) method for the first time in their lives during a one-year period

Data Sources:

Service statistics

Evaluators can obtain this indicator from survey data as well (e.g., from the "calendar" used in the DHS or other data collection tools for obtaining contraceptive histories retrospectively). Moreover, surveys allow one to include non-program methods. However, surveys are rarely used to produce data on first-time users, and total current use rather than "new use" is likely to be of greater interest to those interpreting the data.

Program personnel (including evaluation staff) can disaggregate service statistics by key variables (age, sex, parity, place of residence, ethnicity, or other factors judged relevant in the country context) to obtain a sociodemographic profile of the client population. This information is useful in tracking changes in the composition of the client population over time and in determining whether programs intended to reach certain subgroups are effectively doing so.


This indicator measures the ability of the program to attract new clients from an untapped segment of the population to its services. The measure eliminates the problem of counting as "first-time users" those clients who switch from one source to another for reasons of convenience or cost. As an indicator, it may also reflect the success of special communication programs or other interventions (e.g., social marketing projects) aimed at increasing service utilization among those previously missed by the program. However, in this latter case, one must be mindful that some of the first-time users might have obtained the same or another method from an alternate source (e.g., the unsubsidized pharmacy sector) if the special intervention had not taken place.

"Program method" refers to methods made available through established family planning programs: pill, IUD, implant, injection, condom (male and female), spermicides/foam/jelly, diaphragm, tubal ligation, sterilization (male and female), vaginal ring, patch, sponge, and lactational amenorrhea method (LAM), if used under program supervision. Thus, a young woman who formerly obtained condoms from the pharmacy is not a first-time user. By contrast, a client who to date has depended on withdrawal is a first-time user, because withdrawal is not a program method.

The Number of first-time users of modern contraception, defined as first-time use in the life of the individual, reduces the ambiguity associated with the more general term "new acceptor" and avoids a duplication of cases that may result when substitution occurs.

Couple-years of protection (CYP)


The estimated protection provided by family planning (FP) services during a one-year period, based upon the volume of all contraceptives sold or distributed free of charge to clients during that period

The CYP is calculated by multiplying the quantity of each method distributed to clients by a conversion factor, to yield an estimate of the duration of contraceptive protection provided per unit of that method (Wishik and Chen, 1973; Stover, Bertrand, and Shelton, 2000). The CYPs for each method are then summed over all methods to obtain a total CYP figure.

The EVALUATION Project undertook an extensive review of the literature and empirical data on a number of the variables that form the underlying assumptions for the calculation of CYP. USAID issued a slightly modified set of conversion factors, which the USAID system used since 1997. Recently, in 2011, the RESPOND Project updated the  CYP conversion factors, which are endorsed by USAID and are posted on the Agency's website.  The updated factors are as follows:

MethodCYP Per Unit
Oral Contraceptives 15 cycles per CYP
Condoms (male and female) 120 units per CYP
Monthly Vaginal Ring/Patch 15 units per CYP
Vaginal Foaming Tablets 120 units per CYP
Depo Provera Injectable 4 doses (ml) per CYP
Noristerat Injectable  6 doses per CYP
Cyclofem Monthly Injectable 13 doses per CYP
Copper-T 380-A IUD 4.6 CYP per IUD inserted (3.3 for 5 year IUD, e.g. LNG-IUS)
3 Year Implant (e.g. Implanon) 2.5 CYP per implant
4 Year Implant (e.g. Sino-Implant) 3.2 CYP per implant
5 Year Implant (e.g. Jadelle) 3.8 CYP per implant
Emergency Contraceptive Pills 20 doses per CYP
Standard Days Method 1.5 CYP per trained adopter
Lactational Amenorrhea Method (LAM) 4 active users per CYP (or .25 CYP per user)

Sterilization (male and female)*

- Global

- India, Bangladesh, Nepal

- Other Asian Countries

- Latin America and the Caribbean

- Africa


10 CYP

13 CYP

10.3 CYP

10.5 CYP

9.3 CYP

* Note: Because of marked differences in CYP for sterilization by country and by region (based on differences in median age at sterilization), countries should use the median value for their region (assuming their data on age at sterilization conform to those of the region).  For more specific data on CYPs and sterilization, consult with national DHS and CDC reproductive health survey records which may provide a historical calculation based on a specific country's context.

Programs wishing to use country-specific statistics are referred to the Stover, Bertrand, and Shelton (2000) report for the appropriate CYP.

Data Requirements:

Quantities of pills, condoms, and spermicides distributed to clients; numbers of IUDs and NORPLANT implants inserted; number of injections administered; number of sterilization operations performed; number of trained, confirmed clients of natural FP; number of LAM clients during the reference period.

If targeting and/or linking to inequity, outlets can be classified by location (poor/not poor) and CYP can be disaggregated by location.

Data Sources:

Service statistics or logistics management information system


CYP measures the volume of program activity. Program managers and donor agencies use it to monitor progress in the delivery of contraceptive services at the program and project levels. Because USAID and IPPF generally require the organizations they support to report CYP, this measure is currently one of the most widely used indicators of output in international FP programs.

This indicator has several advantages:


The principal disadvantages of the indicator are that:

Regarding the calculation of CYP for long-term methods, most programs "credit" the entire amount to the calendar year in which the client accepted the method. For example, if an FP program performed 100 voluntary surgical contraception procedures in a given year, it would credit all 1000 CYP (100 procedures x 9 years/each) to that calendar year, even though the protection from those procedures would in fact be realized over that and the next nine years. An alternative approach is to "annualize" this projection, allocating it over a nine-year period. The same principle applies to IUDs and the NORPLANT implant. Although the first approach (of crediting the full amount of CYP in the calendar year of acceptance) has been harshly criticized, it represents current practice in most programs that report CYP, probably because it is easier to apply.

Ideally, CYP should be based on the volume of contraceptives delivered to clients who will presumably use them, not on those delivered to facilities where they may remain unused in cartons or on shelves. However, in some projects such as social marketing, it may be impossible to monitor the exact numbers reaching the hands of clients. Rather, the only means of calculating CYP is to base it on the volume of contraceptives delivered to the retailers in question. Given that retailers are unlikely to stock products that move slowly, it is probable that (after an initial shipment) most contraceptives sold to retailers will make their way into consumers' hands. However, in those instances where the calculation of CYP is based on the volume of products delivered to retailers, not directly to the clients or customers themselves, those preparing the CYP report should clarify this detail to the users of the information.

 Illustrative Computation:  CYP, based upon conversion factors given in text




Oral contraceptives 5,022  334.8
IUDs 87  400.2
Condoms 62,810  523.4
Vaginal tablets 3,900  32.5
Tubal ligations (globally) 13  130.0
DepoProvera 1,277  319.3
Total  1740.2


The RESPOND Project technical meeting. New Developments in the Calculation and Use of CYP and Their Implications for Evaluation of Family Planning Programs.  September 8, 2011.

Method mix


The percent distribution of contraceptive users (or alternatively, of first-time users) by method in a defined period (e.g., in the past 12 months). 

For each method, this indicator is calculated as:

(Number of users of a specific method/Total number of contraceptive users) x 100

Data Requirements:

Number of users (or acceptors) by method

Data Sources:

Service statistics (program-based) or DHS-type surveys (population-based)


The method mix provides a profile of the relative level of use of different contraceptive methods. A broad method mix suggests that the population has access to a range of different contraceptive methods. Conversely, method mix can signal: (1) provider bias in the system, if one method is strongly favored to the exclusion of others; (2) user preferences; or (3) both.

Regarding what constitutes a desirable method mix, practitioners generally feel that a program should respond to the changing needs of the population at different stages in the reproductive life cycle, and offer reversible methods for those who desire to space pregnancies and permanent methods for those who have completed their desired family size. Thus, programs offering no permanent methods or overemphasizing permanent methods are subject to criticism. Yet within the category of reversible methods, the distribution of acceptors by type of contraceptive will vary by availability of specific methods, costs, local preferences, and other factors, and thus make it difficult to generalize regarding desirable method mix.


Method mix often changes in response to the introduction of a new method in-country, to non-availability of methods due to stockout, to increased need for a method that also protects against sexually transmitted infections (i.e., condoms), and to user preferences. Data on method mix can signal these changes, but do not provide insight into the reasons for the change. Evaluators can use qualitative methods to better understand the clients' motivations for switching methods.

Because of the problems of monitoring the number of current users based on service statistics, method mix is generally based on acceptors, not on current users, when measured at the program level. The two yield different distributions, since user data reflects the accumulation of long-acting methods from previous years.

Similarly, one expects some discrepancy on method mix calculated from program statistics versus surveys, even in programs with reliable data. (The reason is that program- based statistics reflect activity in the calendar year under study, whereas the survey results include continuing users of long-acting methods who adopted them in previous years and have not needed or chosen to return to the clinic in the calendar year under study). In addition, survey data may include folk methods, non- program methods (e.g., withdrawal), and program methods also available from non-program sources (e.g., pills from pharmacies).

In the case of method mix, the question is not which source of data is better: program- versus population- based. Both are used in forecasting the future contraceptive needs of a country. Many evaluators consider survey data more reliable for assessing preferences for specific methods, because they include clients from both the public and private sector, in addition to those using a non-program method such as withdrawal. However, one must be mindful that in survey data (e.g., the DHS or RHS) the coefficient of variation may be large, and thus affect the stability of the estimate, especially where the percentage using a specific method is very low. Finally, survey data and service statistics sometimes differ, a situation that can arise from inflated service statistics, wastage in the system, or the sale of products outside the intended area for the program (e.g., across borders).

Gender Implications:

Contraceptive method mix can be one indication of gender balance in contraceptive responsibility within a country or program. Globally, vasectomy, though safer and less costly, is much less widely available and used than female sterilization is. Nearly a third of all contraceptors rely on female sterilization, while only seven percent rely on vasectomy. In India, the ratio of 35 female sterilizations to 1 vasectomy suggests that the program is heavily biased towards female responsibility for contraception. In many parts of sub-Saharan Africa, vasectomy remains virtually unknown. The condom, rhythm, and withdrawal also require male participation or responsibility. Family planning programs have historically been poor at involving men in programs, interventions, and discussions.  Encouraging greater gender equity in contraceptive practice is one goal of the efforts to involve men as partners in family planning and reproductive health.

Furthermore, varying gender constraints (e.g. domestic, child/elder care responsibilities, masculine gender roles that inhibit positive health seeking behaviors) and opportunities in-country affect men and women’s ability to access specific family planning methods and/or distribution points and may have significant implications on the actual method mix available to clients in the public sector.      

Contraceptive prevalence rate (CPR)


The percent of women of reproductive age who are using (or whose partner is using) a contraceptive method at a particular point in time, almost always reported for women married or in sexual union

Generally, the measure includes all contraceptive methods (modern and traditional), but it may include modern methods only.

The indicator is calculated as follows:

(# of women 15-49 using a contraceptive method / total # of women 15-49) x 100

Illustrative Example

The DHS for Peru (2000) yielded the following data on CPR, among women 15-45 years of age:

All women Women currently married or in union
CPR= 12,240/27,843  CPR = 10,764/15,628
=0.440 x 100 = 0.689 x 100
=44.0 = 68.9


Data Requirements:

The total number of women of reproductive age, by marital status; and of these, the number that are currently using a contraceptive method

Data Sources:

Population-based surveys


The CPR provides a measure of population coverage of contraceptive use, taking into account all sources of supply and all contraceptive methods; it is the most widely reported measure of outcome for family planning programs at the population level.

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 in union 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 not infecund, pregnant, or amenhorreic. The numerator should reflect the number of contraceptive users from that population. (Note: We include this point for informational purposes only.) 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).

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.)

The DHS and RHS are currently the main sources for obtaining national level estimates of prevalence. ("DHS" is used in this database to mean "DHS-type surveys:" the actual DHS, the RHS surveys conducted with technical support from CDC, and other large-scale national surveys conducted by the countries themselves under other auspices). Evaluators 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 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.

Poverty and Equity Considerations:

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

A study of public family planning service usage found that use s from the wealthiest quintile outnumbered those from the poorest quintile in 13 of the 20 developing countries examined, and that the contraceptive prevalence rate is significantly higher amongst 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/AIDS and other sexually-transmitted disease that help contribute to poverty incidence in developing countries.


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

Eastwood R & Lipton M. 1998. “Impact of Changes in Human Fertility on Poverty". Sussex, United Kingdom. University of Sussex Department of Economics.

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.

Source of supply (by method)


The percent distribution of the types of service-delivery points cited by users as the source of their current contraceptive method (if more than one source, then the most recent one)

Data Requirements:

Number of respondents currently using contraception, the type of method used, and the source of supply of their method (most recently).

This indicator is calculated for each service delivery point (SDP) type, so it can be disaggregated by SDP sector (public or private), type (e.g., hospital, family planning [FP] clinic, pharmacy, or community health worker) and location (i.e., poor/not poor, rural/urban, and geographic region). It can also be disaggregated by method.

Data Sources:

Population-based surveys


This indicator is useful to FP program officials because it shows where contraceptive users obtain their supplies and thus allows programs to evaluate their effectiveness and to forecast procurement needs. It is particularly appropriate to countries trying to shift the burden for FP services from the public to the private sector. For example, the DHS-type surveys yield information on the percentage of modern method prevalence accounted for by the private sector.

In most countries, the source of supply will vary substantially by type of method. Permanent methods, IUDs, and implants require a clinic-based facility (including mobile clinics). Pills are available through clinics in addition to commercial and community-based distributor (CBD) outlets. DepoProvera, once a clinic-based method, has been introduced into CBD programs and is available in pharmacies in some countries. Condoms and spermicides can be dispensed from any type of facility. Thus, data on source of supply are particularly useful when classified by method.

"Source of supply" yields two types of information: type of facility and type of sector (public/private). Type of facility generally includes hospital, health center, FP clinic, mobile clinic, pharmacy, field worker, private doctor, and shop, among others. Sector distinguishes between governmental programs and those in the private sector (including the local FP association, commercial retailers, private physicians, and other private providers). Ideally, data on source of supply should yield the percentage of contraceptive use attributable to the government program, the private FP association, the private sector (pharmacies, private doctors), and other relevant sources.


The distinction between public and private is often difficult to make, especially in countries with multiple sources of contraception. The respondent may incorrectly identify a given clinic as a government clinic, when in fact it is private (or she simply may not know if it is public or private). A private physician may in fact be participating in a subsidized program to offer low cost services to specific groups. In response to this problem, the DHS questionnaire provides a line for entering the actual name of the facility. Subsequent to the interview, a member of the research team codes the place mentioned according to the correct classification, based on master lists of service delivery points. To classify those not on the list, researchers can later contact key informants from the area.

Contraceptive Continuation rates


The cumulative probability that acceptors of a contraceptive method will still be using any contraceptive method offered by the program after a specified period of time (e.g., one year)

This is also known as the "all-method" continuation rate.

When using cross-sectional population data, evaluators calculate the continuation rate for each unit-interval of use (e.g., first, second, third month of use, and so forth) as the complement of the ratio of acceptors who discontinue use of a program method of contraception at that duration to the number of women still using at the beginning of the month (i.e., 1 minus the discontinuation rate). Evaluators then cumulate these continuation rates to obtain the probability that acceptors of a contraceptive method will still be using any program method after the specified period of time.

The indicator (CRx) is calculated as:

CRx = x(1-qx)/π

x   = 1
qx = Tx/Nx = conditional probability of discontinuing use during a given interval (e.g., one month, one quarter);
Tx = the number of women discontinuing use during the interval; and
Nx = number of women using at the beginning of the interval.

Note: π signifies that (1- qx ) is multiplied over all intervals from 1 to x.

Illustrative Computation
Continuation rates for reversible methods for durations from 1-12 months, Bangladesh, 1992-97

Tx Nx
qx CR


4422.5 .0659 93.4


4025.9 .0398 89.7


3803.1 .0541 84.8


3502.6 .0257 82.7


3322.9 .0184   81.1


3173.7 .0339 78.4


2998.3 .0229 76.6


2868.5 .0199 75.1


2757.4 .0271 73.0


2579.8 .0232 71.3


2458.0 .0227 69.7


2350.8 .0466 66.5

Source of data: Bangladesh Demographic and Health Survey, 1996/97

Data Requirements:

Information on contraceptive initiation, duration of use (including method switching), and discontinuation during a given reference period (e.g., the 3-5 years prior to a survey). Based on this information, one can calculate the percentage who have continuously used for a specific duration (e.g., 12 months, 18 months), as well as the median duration of use.

Data Sources:

Population-based: surveys with retrospective contraceptive use histories or calendars

Program-based: client records accompanied by a follow- up study of program dropouts. This source is rarely used.


Contraceptive continuation rates provide a useful summary measure of the overall effectiveness of program services in enabling clients to sustain contraceptive use even though they may switch from one method to another. However, the calculation of continuation rates from surveys requires knowledge of life table (survival) analysis by those subregions of the country (much less individual facilities), making this indicator more useful at the national than regional or local level.

Although evaluators can calculate continuation rates from either facility-based or population-based data, facility-based data have a number of limitations; thus, researchers tend to use large-scale surveys to provide more valid measurements of continuation among the intended population (e.g., Blanc, Curtis, and Croft, 1999).


Obtaining continuation rates at the program level is theoretically possible if evaluators use follow-up studies of new acceptors at a specified period of time after adoption of the method (e.g., 12 months). However, this technique is rarely used (except in clinical trials), given the difficulty and expense of locating these acceptors a year later.

The preferred source of data is the "calendar," a data collection format used in cross-sectional surveys such as the DHS. However, such surveys have limitations of their own. They (a) depend upon the accuracy of respondent recall, (b) do not allow linking of respondents to specific service delivery points, and (c) may not capture the full contraceptive history (e.g., when five-year calendar is used).

It is important to note the distinction between discontinuation and failure of a contraceptive method. Discontinuation of contraception may occur because the individual chooses to stop using a selected method or because accidental pregnancy intervenes. As such, method failure is a subset of discontinuation. Method failure necessarily results in discontinuation. However, not all discontinuation is attributable to method failure.

Unmet need for family planning


The number or percent of women currently married or in union who are fecund and who desire to either terminate or postpone childbearing, but who are not currently using a contraceptive method

The total number of women with an unmet need for family planning (FP) consists of two groups of women: (a) those with an unmet need for limiting, and (b) those with an unmet need for spacing.

Women with an unmet need for limiting are those who desire no additional children and who do not currently use a contraceptive method. Women with an unmet need for spacing are those who desire to postpone their next birth by a specified length of time (for example, for at least two years from the date of a survey) and who do not currently use a contraceptive method.

The indicator is calculated as follows:

UL+ US = U

U   = the number or percent of women with unmet need for FP;
UL = the number or percent of women with an unmet need for limiting; and
US = the number or percent of women with an unmet need for spacing.

Note: The actual calculation of unmet need is fairly involved, as depicted in the United Nations Population Division's 2009 Metadata on Unmet Need. A common misconception is that unmet need is measured simply by asking women if/when they want to become pregnant and if they are using contraception. In fact, calculating unmet need is extremely complex and is measured using more than 15 different survey questions. In the past, definitions of unmet need also used information from the contraceptive calendar and other questions that were not included in every survey – which led to unmet need being calculated inconsistently.  The definition of unmet need was recently revised in 2012, as explained in Revising Unmet Need for Family Planning. The revised definition of unmet need produces similar, although slightly higher, levels of unmet need compared with the original definition. In contrast to the original definition, the revised definition can be applied consistently to compare estimates across countries and to reliably measure trends over time.

Illustrative Computation
Estimate of unmet need for FP, Peru, 2000 (expressed as the percentage of women currently married or in union).

U = UL + US
= 6.7 + 3.5
= 10.2
Source of data: Peru Demographic and Health Survey, 2000

Data Requirements:

Responses to survey questions on:

Note: The use of the information in the final two items in the computation of the indicator is explained below.

Data Sources:

Population-based survey


This indicator provides information on the size of an extremely important population sub-group for FP program management: women at risk of pregnancy with an apparent need for FP services based upon their expressed desire to limit or space future births, but who do not use contraception. Such women have an "unmet demand" or "unmet need" for FP and are the logical primary audience of program efforts.

The indicator may also be interpreted as the number of additional clients who would be using contraception (over and above the number of current users) if all women at risk of pregnancy and desiring to either terminate or postpone childbearing were to adopt contraception.

The indicator follows from the breakdown of total demand for FP services into two components: "met demand" and "unmet demand" (or "unmet need"). Met demand consists of women with demand for FP who are using a contraceptive method to achieve their reproductive goals; unmet need, or unmet demand, consists of women with an apparent demand for FP who are not using contraception.

Following the procedure proposed by Westoff and Ochoa (1991), women are considered to be at risk of pregnancy in the present indicator if they are:

However, the following categories of women are not considered to have an unmet need for FP, and thus, when computing the indicator, evaluators should exclude:

A 2003 policy brief from Population Reference Bureau outlines the policy and program implications for determining unmet need.


Although unmet need for contraception has been a central indicator for monitoring the progress of FP programs for the past 25 years, its measurement contains numerous assumptions and imprecisions (Cleland, Harbison and Shah, 2014). One challenge pertains to reporting unmet need for married women, unmarried women, and all women of reproductive age. The MDG5 indicator is restricted to married or cohabiting women.  However, refining the population of interest refinement is particularly relevant for countries in which a significant share of childbearing occurs outside of recognized marriages/unions.

Some researchers have argued that the definition of unmet need should be broadened to include women using: (1) traditional contraceptive methods (on the grounds of high failure rates for such methods); (2) a theoretically effective method incorrectly or sporadically; and (3) a method that is unsafe or unsuitable for them (Foreit, 1992; Dixon-Mueller and Germain, 1992). The RHS modified the calculation of unmet need to include traditional contraceptive methods in countries where they are in widespread use (e.g., Eastern Europe, Turkey, Mauritius). The adoption of these alternative definitions would raise significantly the estimated numbers of women with unmet need for FP in many developing country settings.  Because of inconsistent collection of calendar data within and across countries over time, it raises problems with trend interpretation with measuring unmet need.

A related indicator, the satisfaction of demand for FP services, consists of the percentage of total demand for FP at any time that is being satisfied by current contraceptive use. Thus:

Satisfaction of demand for FP = contraceptive prevalence rate (CPR) / (CPR + unmet need)

Using an example from the Kenya 2008-2009 DHS, if unmet need for currently married women is 25.6 and CPR is 45.5, the percent of demand for FP satisfied is 64%:

(45.5 / (45.5 + 25.6)) x 100 = 64%

Another concern about this indicator is that it does not decrease linearly when FP programs improve and desired fertility decreases; initial improvements in FP programs can actually increase demand for contraception, which often causes demand to exceed the existing supply. As a result, unmet need will rise until supply shifts in response to demand (Bongaarts, 1991). This curvilinear relationship between unmet need and program strength (as proxied by CPR), which has been noted in the literature from early in the formulation of the unmet need concept, renders it necessary to use unmet need and contraceptive prevalence measures jointly to effectively capture the intersection of FP policies and practices.  In the longer term, unmet need declines and this progressive satisfaction of need through, for instance, better access to services of higher quality, remains the main driving force behind increasing CPR (and both falling fertility and reduced recourse to abortion) (Becker, et al., 2006).

The June 2014 special issue of Studies in Family Planning contains several articles pertaining to issues and challenges with this indicator and goes into detail about various considerations for calculating unmet need.  For instance:

Poverty and Equity Considerations:

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

Despite the nonlinear relationship between reducing unmet need for FP and increasing the CPR, the former is seen as an important strategy for achieving increased contraceptive use and decreasing total fertility. Thus, the relationship between contraceptive prevalence and poverty discussed above also applies to unmet need for FP.

Also, by decreasing unmet need, governments also will decrease unwanted pregnancies, which occur disproportionately among the poor and may have a significant impact on poverty status. An examination of unwanted fertility rates in developing countries found that in more than three-quarters of the countries, the poorest quintile experiences a higher unwanted fertility rate than the wealthiest (Gelband H, et al. 2001). In many cases, the difference between the two is substantial.

Unwanted pregnancies often tend to be higher risk, particularly among women at the extremes of the fertile age spectrum. Unwanted pregnancy is strongly associated with maternal mortality through unsafe abortion and pregnancy complications involved with high-risk factors (Klima, 1998). Some evidence suggests that the children who are the products of unwanted pregnancies experience negative outcomes later in life (Marston & Cleland, 2003). In addition to the mortality effects of unintended pregnancy, it also can limit educational opportunities for the mother, and can strain household finances. All of these factors collectively can impact the poverty status of the whole family in both the short and the long term (Gelband, et al., 2001).

Gender Implications:

A gender-sensitive approach to unmet need would examine which factors lead to unmet need, distinguish between the unmet need of women and men, and include gender-sensitive service-delivery strategies.

1. Factors that lead to unmet need:


2. Unmet need of women and men:


 3. Service-delivery issues:


Ashford, L. 2003. Policy Brief - Unmet Need for Family Planning: Recent Trends and Their Implications for Programs.  Population Reference Bureau and MEASURE DHS+.

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

Bongaarts, J. 1991. "The KAP-Gap and the Unmet Need for Contraception." Population and Development Review 17, 2: 293-313.

Dixon-Mueller R and Germain A. 1992. "Stalking the Elusive 'Unmet Need' for Family Planning." Studies in Family Planning 23, 5:330-335.

Foreit K. 1992. "Unmet Demand for Contraception vs. Unmet Demand for Appropriate Contraception." Paper presented at the 120th Annual Meeting of the American Public Health Association.  Washington DC.

Gelband H, et al. 2001. “The Evidence Base for Interventions to Reduce Maternal and Neonatal Mortality in Low and Middle-Income Countries”. Geneva, Commission on Macroeconomics and Health Working Paper Series. WHO Commission on Macroeconomics and Health.

Kenya National Bureau of Statistics (KNBS) and ICF Macro. 2010. Kenya Demographic and Health Survey 2008-09. Calverton, MD: KNBS and ICF Macro.

Klima C. 1998. "Unintended Pregnancy: Consequences and Solutions for a Worldwide Problem." Journal of Nurse-Midwifery 43.6: 483.

Marston C & Cleland J. 2003.  "Do Unintended Pregnancies Carried to Term Lead to Adverse Outcomes for Mother and Child? An Assessment in Five Developing Countries." Population Studies 57.1: 77-93.

MEASURE DHS.  Peru: Standard DHS, 2000.

United Nations Population Division/DESA: Fertility and Family Planning Section.  World Contraceptive Use 2009: Unmet Need for Family Planning. 

Westoff, CF and Ochoa LH. 1991.  Unmet Need and Demand for Family Planning. Demographic and Health Surveys Comparative Studies 5. Columbia, MD: Institute for Resource Development/Macro International Inc.

Cleland J, Harbison S and Shah IH.  2014.  "Unmet Need for Contraception: Issues and Challenges."  Studies in Family Planning 45(2): 105-122.

Percent of births that are reported as unintended


Percent of births that resulted from pregnancies that were reported to be either unwanted (i.e., they occurred when no children, or no more children, were desired) or mistimed (i.e., they occurred earlier than desired).  In this case, intentions are only measured for pregnancies ending in a live birth.    

This indicator is calculated as:

(# of births reported as unintended / total # of births reported) x 100

Data Requirements:

Percent of births occurring in the last five years that respondents reported were unwanted or mistimed.  In the DHS, a birth is reported as unintended if the respondent answers “No” to the following question: “When you got pregnant with (NAME), did you want to get pregnant at that time?” (MEASURE DHS, 2010).

Data Sources:

Population-based survey


Knowledge of proportion of births reported as unintended is essential for those supporting women’s ability to determine whether and when to have children (Santelli, 2003).  Assessing pregnancy intentions is important in understanding fertility-related behaviors, forecasting fertility, estimating unmet need for contraception, understanding the impact of pregnancy intentions on maternal and child health, designing family planning programs and evaluating their effectiveness, and creating and evaluating community-based programs that prevent unintended pregnancy (Klerman, 2000).


Identifying whether a birth was intended or not can be rather complex with a host of factors affecting a woman’s response.  A major problem with measuring unintended pregnancy is that it measures women’s intentions after a birth has occurred, when the intentions are likely influenced by the presence of the baby (Santelli, 2003). Since retrospectively reported intentions generally become more positive over time, after the mother has bonded with the infant, the data tends to be skewed downwards. Secondly, if the pregnancy of interest was reported as mistimed, the indicator does not specify to what extent it was mistimed.  A pregnancy that occurs a few months earlier than wanted or planned will likely have less of an impact on a woman or couple compared with one that comes several years too soon (Santelli, 2003). Although mistimed and unwanted pregnancies are grouped together in the analysis, they have different effects on women depending on their age and stage in their reproductive life. Furthermore, a pregnancy reported as mistimed does not necessarily imply that the woman was unhappy about it, which leads back to intentions being influenced by the baby’s presence.  Due to conflicting emotional, psychological, and cultural factors, including a male partner’s attitudes and behaviors, it is not uncommon for a woman to feel ambivalent about her child’s birth and therefore fail to form an intention about her pregnancy. 


MEASURE DHS, 2010. Demographic and Health Surveys Model Woman’s Questionnaire.  ICF Macro, Calverton, MD.

Santelli, J., Rochat, R., Hatfield-Timajchy, K., Colley Gilbert, B., Curtis, K., Cabral, R., Hirsch, J., and Schieve, L. 2003. “The Measurement and Meaning of Unintended Pregnancy.”  Perspectives on Sexual and Reproductive Health 35 (2): 94-101.

Klerman, LV. 2000.  “The intendededness of pregnancy: a concept in transition.” Maternal and Child Health Journal 4 (3): 155-162.

Desire for additional children


The number or percent of women (or men) of reproductive age who want to have a (another) child or, conversely, who do not want to have additional children

Data Requirements:

Numbers or percent of respondents reporting that additional children are/are not desired

Data Sources:

Population-based surveys or facility-based data


This indicator is widely used in surveys to identify both: (1) women (or men) with a demand for additional children and (2) those who do not desire additional children and thus have an apparent need/demand for fertility limitation. In the DHS, non-pregnant women married or in union are asked, "Would you like to have a (another) child or would you prefer not to have any (more) children?" Women who are pregnant (or uncertain of their status) at the time of the survey are asked, "After the child you are expecting, would you like to have another child or would you prefer not to have any more children?" On the basis of responses to these questions, evaluators may divide respondents into two categories: those desiring additional children and those desiring to terminate childbearing, with women in the latter category considered as having a "demand for family planning."

Evaluators may also combine responses to this type of question with information on current fecundity and contraceptive use to assess the level of unmet need for family planning. Comparable information may sometimes be available from service statistics of clinic-based family planning programs. Questions similar to those included in the DHS are often asked of (at minimum) new clients in order to determine the appropriateness of different contraceptive methods in relation to reproductive intentions: that is, methods appropriate for limiting versus spacing.


Despite earlier concerns as to the validity of survey questions of this type in predicting actual fertility behavior, several studies have provided rather convincing evidence of strong aggregate-level associations between expressed desires for additional children on the one hand and patterns of current contraceptive use and current and future fertility on the other (Bongaarts, 1990; Westoff, 1991). The indicator is currently viewed as relatively unbiased, because respondents have no obvious reasons to over- or under-report preferences to continue childbearing.

Percent of women of reproductive age who have heard about at least three methods of family planning


Percent of women 15-49 years old who have heard of three or more family planning (FP) methods, modern or traditional 

This indicator is calculated as:

(# of women aged 15-49 who have heard  about at least three methods of FP / # of women aged 15-49 interviewed)  x 100

Data Requirements:

Age of the woman.  Programs may be interested in knowing the woman’s marital status as well as specifically what methods the woman knows about, especially if the program is interested in promoting a particular method or increasing its use.

The question can be posed by an interviewer asking the respondent which of the methods she has heard of, or it can be asked in a written survey.  The method of questioning will depend on the literacy level of the population of women surveyed.

Data Sources:

Population-based survey


Knowledge of methods is related to the acceptability of, and access to, FP. Individuals and couples are more likely to use FP if there are a variety of methods available.  This indicator measures diffusion of knowledge of FP methods, both modern and traditional.  It does not measure behavior change.  Programs may be interested in knowing what methods people have heard about, especially if the program is interested in introducing or promoting a specific method.


This indicator does not measure the respondents’ level of understanding toward various FP methods, use, or attitude toward the methods.  Likewise, the indicator does not ask where a woman may have heard about a specific method, but simply if she has heard of at least three FP methods.


“Flexible Fund Guidance for Grantees: Implementation Plan and Baseline Assessments”. USAID, 2008.

Percent of the population who know of at least one source of modern contraceptive services and/or supplies


“Modern” family planning (FP) methods refer to the following: pill, intrauterine device, implant, injectable, condom (male and female), spermicide, diaphragm, patch, vaginal ring, sponge, and sterilization (tubal ligation and vasectomy).  Sources of modern contraceptives services and/or supplies will vary by location.  They may be public or private and may include health facilities, pharmacies and community-based distributors along with non-traditional sources such as hair stylists, taxi drivers, and shopkeepers.

This indicator is calculated as:

(# of people surveyed/interviewed who know of at least one source of modern contraceptive services and/or supplies / total # of people surveyed/interviewed ) x 100

Data Requirements:

Sex and age disaggregation. Typically this indicator is asked among those aged 15-49.  In addition to knowing the stated source of modern contraceptives services and/or supplies, evaluators may want to ask what methods can be obtained via these sources.  Respondents should not be prompted when asked to name a location where they can obtain modern contraceptives.

For evaluators looking specifically at private sector involvement in FP, a suggested modification to this indicator would be, “Percent of the population who know of at least one private sector source of modern contraceptive services and/or supplies.”

Data Sources:

Population-based survey


Increasing use of modern contraceptives requires user knowledge of where to obtain modern methods. This indicator provides program managers with a basis for assessing whether promotional or awareness-raising activities are required to educate men and women on where they can obtain modern FP methods as well as how successful demand promotion has been.  This indicator also provides information on gender and age differences on where to obtain modern FP methods.


Asking respondents to name a specific location prevents respondents from giving false affirmative answers to please the interviewer.  However, this indicator does not measure knowledge or use of FP services.


 MEASURE Evaluation, USAID. 2007. A Guide for Monitoring and Evaluating Population-Health-Environment Programs. Washington DC: USAID.