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Research Article
Revised

The Maximum CPR Model: a demographic tool for family planning policy

[version 2; peer review: 2 approved]
PUBLISHED 19 Feb 2020
Author details Author details

Abstract

The Maximum CPR Model (MCM) allows demographers, policy makers, and family planning advocates to determine a country’s highest potential contraceptive prevalence rate (CPR), based on an ideal number of children, demographic life events, and population structure. Understanding the highest potential level of CPR achievable under current circumstances in a population leads to realistic expectations and appropriate policy implementation. Countries with a large gap between current CPR and maximum CPR can focus on removing blocks to contraceptive use, while countries where the maximum potential CPR is near the actual CPR may need to shift their focus to demand generation or postpartum family planning programs. With a focus on equality of access to family planning, MCM produces CPR for all women, regardless of marital status. This paper details the mathematical construction of the MCM. A version of the model is available online for easy use by non-technical audiences in English and French.

Keywords

Contraception, Family Planning, Goal Setting, Advocacy

Revised Amendments from Version 1

We thank the reviewers for their comments and have addressed them in the revised version of our article. This new version includes more information on the assumptions made when creating the model and discusses which assumptions and defaults the users of the model may wish to alter.  A table of the Maximum CPR and survey CPR for 61 countries is included.  We have also produced code (available in our Zenodo repository) which allows users to calculate the maximum CPR for a single country using parity specific birth intervals.

See the authors' detailed response to the review by Philip Anglewicz
See the authors' detailed response to the review by John Ross and Jane T Bertrand

Introduction

The Maximum CPR Model (MCM) allows demographers, policy makers, and family planning advocates to determine a country’s highest potential contraceptive prevalence rate (CPR), based on an ideal number of children, key demographic life events, and structure of the population. Understanding the highest potential level of CPR achievable under current circumstances in a population leads to realistic expectations and appropriate policy implementation. Countries with a large gap between current CPR and maximum CPR can focus on removing blocks to contraceptive use, while countries where the maximum potential CPR is near the actual CPR may need to shift their focus to demand generation or postpartum family planning programs. With a focus on equality of access to family planning, MCM produces CPR for all women, regardless of marital status. This paper details the mathematical construction of the MCM. A version of the model is available online for easy use by non-technical audiences (Bietsch & Sonneveldt, 2020)1, with default data preloaded from countries’ most recent Demographic and Health Surveys (DHS)2 (Figure 1).

967aed09-3368-4523-b857-510b702cb6bf_figure1.gif

Figure 1.The online maximum CPR Model.

An interactive version of this plot can be viewed here: https://track20.shinyapps.io/maximum_cpr/.

The paper is structured as follows: the underlying equations and assumptions of the MCM, the model’s policy applications, especially regarding goal setting for Family Planning 2020 and beyond, and the relationship between the MCM and other models and measures of contraceptive use.

Methods

Maximum contraceptive prevalence calculations

The MCM finds the highest level of current use of contraception possible in a population if all women of reproductive age are using contraception when sexually active, unless they are actively trying to conceive3, already pregnant, postpartum infecund (some women use family planning while postpartum, which is also incorporated into the model4), or infecund, while trying to achieve a given level of fertility, based on the ideal number of children5. The model is composed of two pieces: the reproductive life course and the distribution of women into reproductive stages. At each stage of a woman’s life the model estimates what proportion of that period she would need contraception. Combining the life course with the population distribution data, we can estimate the maximum CPR for the population.

Five periods of the reproductive life course

In this model, the reproductive life course runs from ages 15 to 49 and is separated into 5 time periods:

  1. P1: Time between age 15 and first sex

  2. P2: Time between first sex and first birth

  3. P3: Time between first birth and last birth

  4. P4: Time between last birth and becoming infecund/menopausal6

  5. P5: Time between becoming infecund/menopausal and age 49

The reproductive life course can be summarized as:

ReproductiveLife=i=15Pi(1)

Contraceptive use varies by period. In a population, women are distributed among the five stages. Therefore, the maximum CPR of a population is a function of the distribution of the population and the maximum contraceptive use at each stage of the reproductive life course.

CmaxPR=i=P1P5CiDi(2)

  • Ci: Maximum contraceptive use in Pi

    Di: Proportion of reproductive age women in Pi

Contraceptive use by period of the reproductive life course

Period 1: time between age 15 and first sex

We assume that in the first period, from 15 to becoming sexually active7, no one is using contraception8.

CP1=0(3)

This assumption potentially underestimates contraceptive use if women start using contraception in anticipation of coital activities.

Period 2: time between first sex and first birth

If we assume women use contraception until they explicitly wish to conceive, then this period is divisible into three sections: risk of unplanned pregnancy (where contraception could be used), time spent trying to conceive, and pregnancy. To calculate the length of this period, we use the median age at first sex9 and the median age at first birth10.

MP2=(AFBAFS)12(4)

  • MP2: Months in Period 2

    AFB: Median age at first birth (Years)

    AFS: Median age at first sex (Years)

We must take into account that not all pregnancies result in live births. For this model, we assume that 10% of recognized pregnancies end in miscarriage or stillbirth (hence referred to as miscarriage) (American College of Obstetricians & Gynecologists, 2018), and the median length of pregnancy at termination is 3 months11. We do not assume any abortions in this model, as CPR would be at its maximum in the absence of abortions12.

To calculate the total number of pregnancies needed to achieve the ideal number of children, we must take into account the number of miscarriages. To calculate the time in each birth interval lost to miscarriage, we take the ideal number of children, divided by the proportion of pregnancies that result in a live birth to calculate the total number of pregnancies needed to obtain the ideal number of live births.

TP=INC1PPM(5)

  • TP: Total number of pregnancies needed to achieve ideal number of children

    INC: Ideal number of children

    PPM: Proportion of pregnancies ending in miscarriage

In this model, we assume PPM=0.1.

The lifetime number of miscarriages is the difference between number of pregnancies and number of births. The lifetime months spent on non-viable pregnancies is the average time to conception, plus 3 (the median length of pregnancy), multiplied by the number of terminations. The time spent for each birth interval is then the total months divided by the ideal number of children.

TM=TPINC(6)

  • TM: Total number of miscarriages

MTM=(MConceive+MMiscarriage)TM(7)

  • MTM: Lifetime months spent on miscarriages

    MConceive: Months to conceive

    MMiscarriage: Average duration of pregnancy at time of miscarriage

In this model, we assume MMiscarriage=3.

MMBI=MTMINC(8)

  • MMBI: Months spent on miscarriage per birth interval

The time trying to conceive13 varies by country. To calculate, we use the contraceptive calendar included in many DHS. Women who stop using a method are asked the reason for discontinuation, one answer being “discontinued to become pregnant.” For these women, we calculate the average time between discontinuation and pregnancy. Across countries, the time varied from 3 to 12 months14. For countries without calendar data, we use the median value across countries: 6 months.

We assume a length of pregnancy of 9 months.

Therefore, the maximum proportion of time in P2 where women could use contraception is the number of months in P2 not spent trying to conceive, pregnant, or with a miscarriage, divided by the total number of months in P2.

CP2=MP2MConceiveMPregnantMMBIMP2(9)

  • MPregnant: Months pregnant

In this model, we assume MPregnant=9.

Women using in this period are classified as using to space, as no country with a DHS has an ideal number of children of 015.

Period 3: Time between first birth and last birth

P3 is made up of periods of closed birth intervals. P3 is similar to P2 in that it includes a time at risk, time trying to conceive, time pregnant, and time lost to miscarriages. However, it also includes a time of postpartum insusceptibility (PPI) following a birth. While some women do not use family planning at this time, others may take part in postpartum family planning (PPFP)16. The default assumption of the model is to use the current level of postpartum family planning, but users may edit this assumption to better understand how a postpartum family planning program can increase contraceptive use without a change in the ideal number of children.

The length of the birth interval is determined by the average birth interval in the country, and the total length of P3 is the number of birth intervals (the number of ideal children minus one) multiplied by the average birth interval17. Time trying to conceive and pregnant are calculated the same way as they were in P2.

MP3=MP3BI(INC1)(10)

MP3BI=MBI(11)

  • MP3: Months in Period 3

  • MP3BI: Months in Period 3, per birth interval

    MBI: Average birth interval (months)

The length of PPI is the median duration from the DHS. To estimate PPFP use, DHS microdata is used to calculate18 the percent of women who are currently using family planning out of women who are postpartum infecund (either are practicing postpartum abstinence or are postpartum amenorrheic)19.

MCPPI=CPPIMPPI(12)

  • MCPPI: Months contracepting during postpartum period

    CPPI: Percent of postpartum women using family planning

    MPPI: Average time postpartum infecund (months)

For the entirety of P3, the maximum proportion of time where women could use contraception is the number of months in P3 not spent postpartum infecund and not using contraception, trying to conceive, pregnant, or with a miscarriage, divided by the total number of months in P3.

CP3=[MP3BI(MPPIMCPPI)MConceiveMPregnantMMBI][INC1]MP3BI[INC1](13)

Which simplifies to:

CP3=MP3BI(MPPIMCPPI)MConceiveMPregnantMMBIMP3BI(14)

Women using in this period are classified as using to space.

Period 4: Time between last birth and becoming infecund/menopausal

While some women continue childbearing in their 50s, the DHS and other surveys assume most childbearing ends at 49. Ideally, we would calculate a median age at infecundity/menopause to close this period, but because most surveys do not have an age group that surpasses 50% infecund/menopausal, this calculation is impossible. For P4, we assume that women remain fecund to 50. We will account for infecundity in the next period and in the population distribution. We assume that a woman’s last birth is also her last pregnancy, since she has reached the ideal number of children. Therefore, time lost to miscarriage is not included in Period 4.

MP4=(50ALB)12(15)

  • MP4: Months in Period 4

    ALB: Age at last birth (years)

To calculate the expected age at last birth, we use the age at first birth, the number of birth intervals to achieve desired family size, and the average birth interval length.

ALB=AFB+[MBI(INC1)]12(16)

After the last birth, there is only postpartum insusceptibility and risk. Therefore, the maximum proportion of time where women could use contraception is the number of months in P4 not spent postpartum infecund and not using contraception divided by the total number of months in P4.

CP4=MP4(MPPIMCPPI)MP4(17)

Women using in this period are classified as using to limit.

Period 5: Time between becoming infecund/menopausal and age 49

As with P1, we do not believe women who are infecund or menopausal will use family planning.

CP5=0(18)

Summary: Contraceptive use by period of the reproductive life course

We now have equations defining maximum contraceptive use during the 5 periods of the reproductive life course, Table 1 summarizes the equations.

Table 1. Contraceptive use at each stage of the reproductive life course.

DescriptionMaximum contraceptive use
Time between age 15 and first sexCP1=0
Time between first sex and first birthCP2=MP2MConceiveMPregnantMMBIMP2
Time between first birth and last birthCP3=MP3BI(MPPIMCPPI)MConceiveMPregnantMMBIMP3BI
Time between last birth and
becoming infecund/menopausal
CP4=MP4(MPPIMCPPI)MP4
Time between becoming
infecund/menopausal and age 49
CP5=0

Population distribution

We distribute the population of women of reproductive age into the following groups20.

D1549={DNS=PercentofwomenwhohaveneverhadsexD0=PercentofwomenwhohavehadsexbutnotfirstbirthD1=Percentofwomenatparity1D2=Percentofwomenatparity2D3=Percentofwomenatparity3D4=Percentofwomenatparity4D5=Percentofwomenatparity5D6=Percentofwomenatparity6D7=Percentofwomenatparity7D8=Percentofwomenatparity8D9=Percentofwomenatparity9+DI=Percentofwomenwhoareinfecund(19)

Infecundity is determined using the same classifications as described in the DHS definition of Unmet Need (Bradley et al., 2012).

The population is collapsed into the following groups shown in Table 2, corresponding with the periods of the reproductive life course.

Table 2. Population distribution in the reproductive life course.

DescriptionDistribution
by period
Population
distribution
Never sexually activeDP1DNS
Nulliparous spacersDP2D0
Parous spacersDP3p=1INC1Dp
LimitersDP4p=INC9Dp
InfecundDP5DI

As the ideal number of children changes, D1 - D9 shift between P3 and P4. DP3 will include parous, fecund women with at least one less child than the ideal number of children, and DP4 will include women at the parity of the ideal number of children and above. Note that in this model, ideal number of children is measured at the population level and is rounded to a whole number.

This model employs averages and medians at the population level. In many instances within a population, women at the same parity will have different ideal numbers of children. We assume that if a population was to achieve a fertility level of the average ideal number of children of its reproductive age women and had the maximum level of contraceptive use, on average, women below this parity would be using to space, and women above would be using to limit.

MCM

The maximum CPR of a population can thus be defined as the summation of the maximum CPR at each period of the reproductive life course multiplied by the proportion of the population of women of reproductive age in each period.

CmaxPR=i=P1P5CiDi(20)

The CPR can be separated into CPR for spacing and limiting as follows:

CmaxPRspacing=i=P2P3CiDi(21)

CmaxPRLimiting=CP4DP4(22)

Results and discussion

Policy implications of the MCM

Understanding the highest potential level of CPR achievable under current circumstances in a population leads to realistic expectations and appropriate policy implementation. The following sections detail the policy relevant outputs and inputs of the MCM. While data from a recent survey can be used to estimate the current Maximum CPR, users may also create alternative scenarios by changing input values. To facilitate scenario creations for non-technical audiences, the authors have developed an online tool (in English and French) to implement and visualize the MCM21.

Outputs of MCM

Users of the MCM may find the model helpful for both understanding current situations and alternative scenarios. Policy makers looking to set contraceptive use goals can begin with the default data from a recent DHS and compare their country’s current CPR to the maximum CPR. Countries with large gaps between the current CPR and maximum CPR may be interested in exploring why such a gap exists, if there are barriers preventing women from accessing or using contraception22. If only a small gap exists between the current CPR and maximum CPR, countries will need to explore the inputs of the model and see if any would be appropriate for policy interventions. Table 3 shows the maximum CPR (based off default data) and survey CPR for the countries included in the online model.

Table 3. Maximum CPR with Default Data and Survey CPR.

Country and Survey YearSurvey
CPR
Maximum
CPR
Afghanistan 201515.332.2
Albania 2017-1833.246.7
Angola 2015-1613.328.5
Armenia 2015-1636.738.3
Bangladesh 201444.262.3
Benin 2017-1814.432.9
Bolivia 200841.356.8
Burkina Faso 201015.325.4
Burundi 2016-1717.931.7
Cambodia 201438.544.4
Cameroon 201123.726.9
Chad 2014-155.411.1
Colombia 201564.966.9
Comoros 201213.729.9
Congo 2011-1244.343.5
Congo Democratic Republic 2013-1419.328.9
Cote d'Ivoire 2011-1219.737.3
Dominican Republic 201355.159.9
Egypt 201440.852.2
Ethiopia 201625.336.9
Gabon 201233.640.4
Gambia 20137.118.8
Ghana 201422.845.3
Guatemala 2014-1539.445.4
Guinea 20128.517.8
Haiti 2016-1724.151.8
Honduras 2011-1248.854.1
India 2015-1640.851.7
Indonesia 201245.751.0
Jordan 2017-1828.435.3
Kenya 201442.650.1
Kyrgyz Republic 201224.433.4
Lesotho 201448.954.3
Liberia 201321.736.8
Madagascar 2008-0931.736.6
Malawi 2015-1646.048.3
Maldives 2016-1713.336.0
Mali 2012-139.928.5
Mozambique 201127.432.3
Myanmar 2015-1631.644.6
Namibia 201350.253.2
Nepal 201640.859.2
Niger 201212.516.7
Nigeria 201316.031.0
Pakistan 2017-1821.836.6
Peru 201251.563.3
Philippines 201733.640.7
Rwanda 2014-1530.946.0
Sao Tome and Principe 2008-0930.751.8
Senegal 201719.928.2
Sierra Leone 201322.131.2
South Africa 201648.257.9
Tajikistan 201721.336.8
Tanzania 2015-1632.441.6
Timor-Leste 201616.125.1
Togo 2013-1419.342.0
Turkey 201349.647.5
Uganda 201630.338.1
Yemen 201320.538.5
Zambia 2013-1435.143.8
Zimbabwe 201548.650.1

By allowing maximum CPR to be separated into CPRSpacing and CPRLimiting, countries can better plan for the different types of contraceptive counseling needed by women who are interested in spacing or limiting births23.

Many countries set goals for family planning as part of the FP2020 Global Initiative (Family Planning 2020, n.d.). As of May 2019, Track20 had collected information from 43 countries and converted goals24 into all women mCPR25. We compare the goals set by countries to the maximum CPR calculated using default data from the most recent DHS for the 37 goal setting countries with data.

In total, 16 countries have FP2020 goals higher than the Maximum CPR given the demographic characteristics in their most recent DHS. Of the 16 countries, 11 have goals within 5 percentage points of the maximum CPR. Some of these goals may be achievable if changes in inputs took place, such as increases in postpartum family planning or declines in ideal number of children. Guinea, Madagascar, Ethiopia, Malawi, and Niger are the five countries with the largest gap between their FP2020 goals and their current maximum CPR: in Malawi there is a 12.5 percentage point gap, and in Niger a 31.2 percentage point gap. While countries want to set ambitious goals to motivate investments and support, setting unrealistically high goals can demoralize policy implementors. As the global community looks forward past the end of the 2020 initiative to 2030 and beyond, the MCM can help policy makers set ambitious targets, both given their current demographic landscape and potential future scenarios.

After calculating the maximum CPR (or several based off different inputs), users can input the maximum CPR or a lower CPR into FamPlan, part of the Spectrum software (Avenir Health, 2019), to calculate a wide range of policy relevant data, such as the costs associated with increasing contraceptive use and the number of unintended pregnancies averted.

Inputs of MCM

Several inputs of the MCM may be of interest for policy makers. These include the ideal number of children, postpartum family planning, age at first sex and birth, and the average birth interval.

  • Ideal number of children: By changing the ideal number of children, the user changes how much of a woman’s reproductive life she will spend on childbearing, how much contraception she would need for spacing, and how much she would need for limiting. Changing the ideal number of children is not a simple policy intervention.

  • Percent of postpartum insusceptible women using family planning: Changing this number will change the use of family planning for spacing (and to a lesser extent, for limiting). Shifting the percent using PPFP allows users to see the impact of either no, some, or a large postpartum family planning program on the overall population’s CPR.

  • Age at first sex and first birth: If a policy goal is to delay the age at first birth, users may want to see the impact of increasing the period between first sex and first birth. If the period becomes significantly larger, users may want to adjust the distribution of women to have a larger share of their population sexually active, but nulliparous.

  • Average Birth Interval: Many health programs stress the importance of appropriately spaced births. The maximum CPR for spacing, will increase as birth intervals increase.

MCM compared to other models and measures of contraceptive use

Proximate Determinants of Fertility. The Proximate Determinants of Fertility Model, originally proposed by Bongaarts (1978) (and a simplification of a framework discussed by Davis & Blake (1956)), shares many similarities to the MCM, with notable differences. The Proximate Determinants of Fertility includes exposure to sexually activity (originally defined as percent married), contraception, induced abortion, lactational infecundability, frequency of intercourse, sterility, spontaneous intrauterine mortality, and duration of the fertile period. The main difference between the two models is the outcome of interest: contraceptive use for the MCM and the total fertility rate (TFR) for the Proximate Determinants of Fertility. Another important difference is that the Proximate Determinants of Fertility (and TFR) is age standardized, while there is no age standardization in MCM, in fact the population distribution is a key input. MCM uses ideal number of children, not TFR, because of a desire for the model to focus on a rights-based approach to family planning: the output should be read as how high can CPR grow while women are achieving their desired family size. While abortion is a key limiting factor of a population’s TFR in Bongaarts’ model, the MCM does not include induced abortion because it assumes CPR will be maximized when abortions are minimized. Lactation infecundability is included in both models, with MCM focusing on postpartum family planning as a keep input. MCM does not include a measure of frequency of intercourse, though Bongaarts says that it is easily demonstrated that coital frequency is not a very important determinant of fertility differences between populations, and does not include coital frequency in his mathematical equations. Intrauterine mortality is included in MCM as a constant (10% of pregnancies), and is discussed in Bongaarts’ model, though not thought to vary much between population. It is also not included in his mathematical equations. In summary, the Maximum CPR follows many assumptions of fertility limitations of the Proximate Determinants of Fertility, with changes to address the goals of MCM’s output.

Unmet need for family planning. Some may believe that the maximum contraceptive use in a population is the currently level of contraceptive use plus unmet need for family planning (The DHS Program, n.d.), but this is not the case. MCM is a current status measure, taking into account both the reproductive life course and current population distribution. Unmet need is composed of current status for some women, and retrospective status for others. Pregnant and postpartum amenorrheic women can be defined as having unmet need based on the intention status of their current/recent pregnancy. In MCM, they would not be included in the group of potential current contraceptive users. Thus, it is not uncommon for a survey CPR and unmet need, when added together, to surpass the maximum CPR estimated by the model.

The Demand Curve. The Demand Curve (Weinberger et al., 2017) looks at the ideal number of children and current modern contraceptive use in a country to determine if a country should consider increased investments in access or demand interventions. The Demand Curve is an equation calculated off the highest CPR witnessed in countries at give ideal number of children. We have conducted a comparison between the MCM with default data from recent DHS and Demand Curve, and present results in Figure 2.

967aed09-3368-4523-b857-510b702cb6bf_figure2.gif

Figure 2. Comparing demand curve with Maximum CPR Model.

The MCM results are shown in red and are close to the demand line for most countries with an ideal number of children of 3 or higher. We do not expect the results of lower fertility areas to overlap- research of the Demand Curve indicates that for countries with an ideal number of children below 3, there is no curve as in these countries it is assumed that fertility intentions are not limiting mCPR growth. There are numerous cases where the maximum CPR is higher than the demand curve, this is because the demand curve is based on historical observations, while MCM is a theoretical maximum, which most countries never have or will obtain. Figure 2 also shows the range MCM can take for the same ideal number of children: at 4 children, the maximum CPR ranges from 25% to 52%, highlighting the importance of other demographic indicators in determining a country’s room for contraceptive growth.

Conclusions

The MCM combines the reproductive life course and the structure of the population to produce the theoretical maximum current level of contraception for a population. This model is of particular interest to policy makers who can use it to set ambitious but achievable family planning goals. An online version of the model with built in default data, input adaptability, and graphic result displays aims to make demographic information as easily accessible as possible to technical and non-technical audiences. In a spirit of open research, all equations used in the calculations of the model outputs are available in this paper and a technical note on the Track20 website. Additionally, Stata files used to calculate default data are also available online. Technical support for users of the model is available through the Track20 team. The goal of the MCM is to make family planning modeling and goal setting available to all, in the hopes that demographic data will be more frequently included in informed policies.

Data availability

Underlying data

Users can enter their own data into the MCM. Default data for the most recent DHS is calculated by the authors and preloaded into the online model; default data will be updated periodically as new surveys are released.

The datasets used to generate the Maximum CPR Model are available in the MEASURE DHS repository (http://www.measuredhs.com). Access to the dataset requires registration and is granted to those that wish to use the data for legitimate research purposes. A guide for how to apply for dataset access is available at: https://dhsprogram.com/data/Access-Instructions.cfm.

Software availability

An online version of the MCM is available in English: https://track20.shinyapps.io/maximum_cpr/.

A French-language version is available at: https://track20.shinyapps.io/tpc_maximum/.

Source code used to create the model is available: https://github.com/kristinbietsch/MaxCPR/tree/v1.0.1.

Archived source code at time of publication: https://doi.org/10.5281/zenodo.3636183

License: MIT License.

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Bietsch KE and Sonneveldt E. The Maximum CPR Model: a demographic tool for family planning policy [version 2; peer review: 2 approved]. Gates Open Res 2020, 3:1736 (https://doi.org/10.12688/gatesopenres.13094.2)
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Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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