Data & Analysis > In-Depth Analysis > Overview: Opportunities for Growth
Overview: Opportunities for Growth

Track20 serves as a resource to the global family planning community, providing data and analysis for a wide range of stakeholders on progress and opportunities in family planning. In this section, we highlight some of the key analyses we have been doing to help donors, countries, and other partners think about potential opportunities in and across countries.

When assessing potential opportunities for family planning, it is important to consider a wide range of areas related to demand for contraception, availability and access to services, quality and equity, and the enabling environment. The “Track20 Country Opportunity Briefs” bring together a wide range of data sources to allow for exploration of these key areas. Each brief looks in-depth at a single country, while, using the tabs to the left, you can explore each key area across countries.

Download Opportunity Brief

This section is meant to provide an overview of key data and population segmentations to spark conversations about prioritization and potential impact within and across countries. This should be seen as a starting place for discussions rather than an exhaustive analysis. Additional analysis and examination of other contextual factors beyond what is shown here should be considered in any discussions or decision-making on programming and investment for family planning.

Demand for contraception

In a country with very little demand for family planning, large scale-up of family planning services is likely to have limited impact on uptake of contraception and risks wasting resources (e.g. training providers who then do not have FP clients, so lose those skills). However, in a country where there is a lot of unmet demand, scaling up services could have a substantial impact on increasing access to and uptake of family planning. Understanding this balance is important to help identify opportunities and limitations and prioritize resources to ensure effective family planning programs.

What does the ‘demand curve’ tell us?

The maximum contraceptive prevalence ‘demand curve’ was developed by Track20 to allow for a simple assessment of this balance; the concept has also been built into the FP Goals model. The curve represents the likely maximum mCPR that could be reached in a country given their level of demand. The curve was created by fitting an exponential curve to the maximum of all available DHS survey data on ideal number of children and mCPR.

The ‘demand curve’ concept is most applicable for countries where women have higher fertility intentions, and, in fact, for countries with an ideal number of children below 3, we assume that demand is not a constraint to growth1. For countries with an ideal number of children below 3, we refer to the gap between where a country sits and the curve as the ‘potential use gap’- an estimate of the maximum mCPR growth that a country could expect to achieve within current levels of demand. While the curve is constructed using ideal number of children, an indicator that measures fertility desires, it is representing a wider set of social constructs that may be influencing the motivation to use, or not use, contraception.

1However, there could be other factors limiting growth in these countries that could requite demand-side interventions- such as addressing limited knowledge and information, or myths about contraceptive use. Rather, in these contexts, underlying social norms- ones that are not directly related to contraceptive use- are likely not playing a limiting role.

How does this vary across countries?

In the map below, countries are shaded based on the size of their ‘potential use gap’ at the time of their most recent DHS survey2.

2n this map we are looking at the potential use gap at the time of the latest survey in each country, when available the gap has been calculated based on mCPR for all women- however for Bangladesh and Egypt only married women data is available. For countries with older surveys, the calculated gap might not reflect the current situation.

Published July 2017  

Availability of methods

Ensuring availability of contraceptives- both in terms of commodities as well as qualified service providers- is critical to ensuring that women have access to a full range of contraceptive methods. Unfortunately, data on method availability tends to be more limited than data on contraceptive use. However, by looking across data sources, we can start to paint a full picture of method availability in countries.

Access to specific methods (FPE/NCIFP)

The Family Planning Effort Index (FPE) and the more recent National Composite Index on Family Planning (NCIFP) both include a set of questions about the extent to which the entire population has ready access to each contraceptive method. This data can help show where efforts may be needed to expand access in order to ensure women have access to a full range of methods. This could be achieved through policy changes such as task-sharing, investing in additional training of health care workers, or other interventions.

Contraceptive Stock Outs (Core Indicator 10 & 11)

(note this section has been taken from the 2015-2016 FP2020 Progress Report)

Stock-outs refer to the temporary unavailability of family planning commodities at a health facility or store where they are supposed to be available.

Stock-outs have an impact on contraceptive prevalence and method choice, and reducing contraceptive stock-outs is a critical measure of FP2020’ success. FP2020 stock-out indicators were adopted in 2015 after a consultative process led by the RHSC that resulted in the harmonization of various methods of measuring stock-outs. FP2020 indicators reflect the availability of family planning commodities at the facility level at a point of time (the day of the survey), and measure stock-outs by method (Core Indicator 10) as well as stock-outs for a range of methods (Core Indicators 11a and 11b).

Published July 2017  

Access: PPFP

When thinking about the potential impact of scaling up PPFP interventions on national levels of contraceptive use, it is important to take several factors into consideration: what proportion of women in the country are post-partum in any given year and what baseline PPFP uptake looks like in the country. Places where a large proportion, or absolute number, of women of reproductive age area estimated to be post-partum and not using modern contraception present the largest opportunities for investments in PPFP to lead to growth in mCPR or modern users.

To learn more about levels of PPFPP uptake in countries, and see how this differs based on if women deliver at home or in a facility, visit our special analysis section on Post-Partum Family Planning.

Percent of all women of reproductive age who are post-partum, by PPFP use

In this graph, the total height of the bar shows the proportion of all women of reproductive age in the country who are post-partum.1 The bar is then segmented by use of a PPFP method—overall, or, if you select the option below, you can split this based on where women deliver2. In countries where there is a large segment of post-partum non-modern users who delivered at home, community-based interventions may need to be prioritized, as these women could not be reached through facility-based PPFP integration.

Countries have been organized from largest to smallest % of WRA who are post-partum and not using modern FP. Those countries where post-partum non-users are a large proportion of the WRA offer the greatest potential impact on mCPR by doing PPFP interventions. However, country context must be taken into consideration- increasing PPFP uptake may be harder in some settings than others. In addition, we would never expect PPFP uptake to each 100%- based on existing data, the highest uptake is in Indonesia, which reaches about 70%. Therefore, not all the ‘non-users’ shown in the graph below have the potential to become users.

1This is calculated using UNPD WPP (2015 Revision) projections, as the number of births in 2016 divided by the total number of women of reproductive age in 2016, as a proxy for the % of WRA who will give birth in the year.

2This has been done based on secondary analysis of the latest DHS or MICS survey in each country (see sources tab)- for countries with a calendar this shows modern PPFP uptake at 6 months, for countries without a calendar, this shows the % of women who delivered in the last 6 months who are currently using a modern method.

Number of women of reproductive age who are post-partum, by PPFP use

In this graph, the total height of the bar shows number of women of reproductive age in the country who are post-partum.1 The bar is then segmented by use of a PPFP method—overall, or, if you select the option below, you can split this based on where women deliver2. In countries where there is a large segment of post-partum non-modern users who delivered at home, community-based interventions may need to be prioritized, as these women could not be reached through facility-based PPFP integration.

Countries have been organized from largest to smallest number of WRA who are post-partum and not using modern FP. Those countries where there are the largest absolute number of post-partum non-users offer the greatest potential impact on increasing modern contraceptive users by doing PPFP interventions. However, country context must be taken into consideration- increasing PPFP uptake may be harder in some settings than others. In addition, we would never expect PPFP uptake to each 100%- based on existing data, the highest uptake is in Indonesia, which reaches about 70%. Therefore, not all the ‘non-users’ shown in the graph below have the potential to become users.

1This is calculated using UNPD WPP (2015 Revision) projections, as the number of births in 2016, as a proxy for the # of WRA who will give birth (and thus be post-partum) in the year.

2This has been done based on secondary analysis of the latest DHS or MICS survey in each country (see sources tab)- for countries with a calendar this shows modern PPFP uptake at 6 months, for countries without a calendar, this shows the % of women who delivered in the last 6 months who are currently using a modern method.

Published July 2017  

Access: Youth

When thinking about the potential impact of scaling up youth interventions on national levels of contraceptive use, it is important to take several factors into consideration: what proportion of women in the country are young, what proportion of young women are married, unmarried sexually active, or unmarried and not sexually active, and what current mCPR and unmet need is for each of these groups. Places where a large proportion, or absolute number, of women of reproductive age area estimated to be young, in need of contraception and not using modern contraception present the largest opportunities for investments in youth to lead to growth in mCPR or modern users.

For this opportunity analysis, the focus is on the potential impact on increasing overall contraceptive use. There may be many important investments to be made in young people that do not directly increase contraceptive use, at least in the short term.

To learn more about contraceptive use and non-use among young people visit our special analysis section on Youth.

Percent of all women of reproductive age who are young (15-24) split by use and need

In this graph, the total height of the bar shows the proportion of all women of reproductive age in the country who are young (15-24).1 The bar is then segmented by use and need for modern contraception2. In many countries, a large proportion of young women do not have a need for modern contraception- either because they are already using, they are pregnant or want a child soon, or they are unmarried and not sexually active.

Countries have been organized from largest to smallest % of WRA who are young women with an unmet need for modern contraception. The next graph breaks this group down further- by age and marital status- to help think about what interventions might be best placed to reach these women. Those countries where young women with an unmet need for modern contraception make up a large proportion of all WRA in the country offer the greatest potential impact on mCPR by doing youth-focused interventions.

1This is calculated using UNPD WPP (2015 Revision) projections (by sex and age) for 2016.

2This has been done based on secondary analysis of the latest DHS or MICS survey in each country (see sources tab).

Percent of all women of reproductive age who are young women with an unmet need for modern contraception, split by age and marital status

In this graph, the total height of the bar shows the proportion of all women of reproductive age in the country who are young women with an unmet need for modern contraception (15-24).1,2 The bar is then segmented by age (15-19 v 20-24) and marital status (married v unmarried sexually active)2 where data permits.

When thinking about what interventions for young people can best meet this unmet need, it is important to consider which segments make up the largest proportion of unmet need. In many countries, the largest segment of unmet need is among married 20-24-year-olds, indicating a need to include married youth in youth FP programming. It is important to ensure that efforts match to need in a country. There are also other country contextual factors that are important to take into consideration, beyond what this data can show.

Countries have been organized from largest to smallest % of WRA who are young women with an unmet need for modern contraception. Those countries where young women with an unmet need for modern contraception make up a large proportion of all WRA in the country offer the greatest potential impact on mCPR by doing youth-focused interventions.

1This is calculated using UNPD WPP (2015 Revision) projections (by sex and age) for 2016.

2This has been done based on secondary analysis of the latest DHS or MICS survey in each country (see sources tab).

Number of young women (15-24), split by use and need

In this graph, the total height of the bar shows the total number of young women (15-24) in the country.1 The bar is then segmented by use and need for modern contraception2. In many countries, a large number of young women do not have a need for modern contraception- either because they are already using, they are pregnant or want a child soon, or they are unmarried and not sexually active.

Countries have been organized from largest to smallest number of young women with an unmet need for modern contraception. The next graph breaks this group down further- by age and marital status- to help think about what interventions might be best placed to reach these women. Those countries where there are large numbers of young women with an unmet need for modern contraception offer the greatest potential impact increasing modern users through youth-focused interventions.

1This is calculated using UNPD WPP (2015 Revision) projections (by sex and age) for 2016.

2This has been done based on secondary analysis of the latest DHS or MICS survey in each country (see sources tab).

Number of young women with an unmet need for modern contraception, split by age and marital status

In this graph, the total height of the bar shows the total number of young women with an unmet need for modern contraception (15-24).1,2 The bar is then segmented by age (15-19 v 20-24) and marital status (married v unmarried sexually active)2 where data permits.

When thinking about what interventions for young people can best meet this unmet need, it is important to consider which segments make up the largest number of young women with an unmet need. In many countries, the largest segment of unmet need is among married 20-24-year-olds, indicating a need to include married youth in youth FP programming. It is important to ensure that efforts match to need in a country. There are also other country contextual factors that are important to take into consideration, beyond what this data can show.

Countries have been organized from largest to smallest number of young women with an unmet need for modern contraception. Those countries where there are large numbers of young women with an unmet need for modern contraception offer the greatest potential impact increasing modern users through youth-focused interventions.

1This is calculated using UNPD WPP (2015 Revision) projections (by sex and age) for 2016.

2This has been done based on secondary analysis of the latest DHS or MICS survey in each country (see sources tab).

Published July 2017  

Quality

Ensuring high-quality contraceptive services are available is a critical part of delivering rights-based family planning services. Measuring quality of family planning services is complex and multi-dimensional—for this section, we are limiting our focus to FP2020 Core Indicator 14, the Method Information Index. However, we recognize that there are many other important aspects of quality that must be examined in countries.

The Method Information Index

Core Indicator 14, the Method Information Index, serves as a proxy for quality of counseling and reflects the extent to which women are informed about side effects and alternate methods. The MII is a summary measure constructed from three questions asked of current contraceptive users about the occasion when they obtained their current method:

  • Were you informed about other methods?
  • Were you informed about side effects?
  • Were you told what to do if you experienced side effects?

The MII value is the percentage of respondents answering “yes” to all three questions.

The graph below shows the Method Information Index from the latest survey available in each country1. Use the drop-down to select seeing the overall MII score, or to explore the score for individual methods (Implants, IUDs, Injections, and Pills).

1Based on the latest DHS or PMA2020 survey in each country, see data sources page for details.

Published July 2017  

Data Source

Note: For PPFP and Youth, values have been calculated by Track20 using the full datasets.

Country

Demand

Method

Availability

Stock-Outs

PPFP

PPFP Definition

Youth

MII

Afghanistan

2015 DHS

FPE 2014

 

2015 DHS

Standard Definition

2015 DHS

DHS 2015-16

Bangladesh

2014 DHS

FPE 2014

2014 DHS

Standard Definition

2014 DHS

Benin

2011-12 DHS

FPE 2014

UNFPA  2015

2011-12 DHS

Standard Definition

2011-12 DHS

Bhutan

201 MICS

Alternative Definition

2010 MICS

Bolivia

2008 DHS

FPE 2014

UNFPA  2015

2008 DHS

Standard Definition

2008 DHS

Burkina Faso

2010 DHS

PMA2020 

2015

2010 DHS

Standard Definition

2010 DHS

PMA2020

R2 2015

Burundi

2010 DHS

FPE 2014

UNFPA  2015

2010 DHS

Standard Definition

2010 DHS

Cambodia

2014 DHS

FPE 2014

2014 DHS

Standard Definition

2014 DHS

DHS 2014

Cameroon

2011 DHS

FPE 2014

2014 MICS

Alternative Definition

2014 MICS

CAR

1994-95 DHS

UNFPA  2015

2010 MICS

Alternative Definition

2010 MICS

Chad

2014-15 DHS

FPE 2014

UNFPA  2015

2014-15 DHS

Alternative Definition

2014-15 DHS

DHS 2014-15

Comoros

2012 DHS

DHS 2012

Alternative Definition

2012 DHS

DHS 2012

Congo

2011-12 DHS

FPE 2014

UNFPA  2015

2011-12 DHS

Alternative Definition

2011-12 DHS

Côte d'Ivoire

2011-12 DHS

FPE 2014

UNFPA  2015

2011-12 DHS

Alternative Definition

2011-12 DHS

Djibouti

2006 MICS

Alternative Definition

2006 MICS

DPR Korea

DR Congo

2013-14 DHS

FPE 2014

UNFPA  2015

2013-14 DHS

Alternative Definition

2013-14 DHS

DHS 2013-14

Egypt

2014 DHS

FPE 2014

2014 DHS

Standard Definition

2014 DHS

DHS 2014

Eritrea

2010 EPHS

FPE 2014

Ethiopia

2011 DHS

FPE 2014

UNFPA  2015

2011 DHS

Standard Definition

2011 DHS

PMA2020

R3 2015

Gambia

2013 DHS

FPE 2014

UNFPA  2016

2013 DHS

DHS 2013

Ghana

2014 DHS

FPE 2014

PMA2020 

2015

2014 DHS

Standard Definition

2014 DHS

PMA2020

R4 2015

Guinea

2012 DHS

2012 DHS

Alternative Definition

2012 DHS

DHS 2012

Guinea-Bissau

2015 MICS

Alternative Definition

2014 MICS

Haiti

2012 DHS

FPE 2014

UNFPA  2015

2012 DHS

Alternative Definition

2012 DHS

DHS 2012

Honduras

2011-12 DHS

FPE 2014

UNFPA  2015

2011-12 DHS

Standard Definition

2011-12 DHS

India

2005-06 DHS

FPE 2014

2005-06 DHS

Standard Definition

2005-06 DHS

Indonesia

2012 DHS

FPE 2014

PMA2020 

2015

2012 DHS

Standard Definition

2012 DHS

PMA2020

R1 2015

Iraq

FPE 2014

2011 MICS

Alternative Definition

MICS

Kenya

2014 DHS

FPE 2014

UNFPA  2015

2014 DHS

Standard Definition

2014 DHS

DHS 2014

Kyrgyzstan

2012 DHS

FPE 2014

2012 DHS

Standard Definition

2012 DHS

DHS 2012

Lao PDR

UNFPA  2015

2011-12 LSIS

Alternative Definition

2011-12 LSIS

(note small

samples for

UMSA - 85)

Lesotho

2014 DHS

FPE 2014

2014 DHS

Standard Definition

2014 DHS

DHS 2014

Liberia

2013 DHS

FPE 2014

UNFPA  2015

2013 DHS

Standard Definition

2013 DHS

DHS 2013

Madagascar

2008-09 DHS

FPE 2014

2008-09 DHS

Standard Definition

2008-09 DHS%

Malawi

2015-16 DHS

FPE 2014

2015-16 DHS

Standard Definition

2015-16 DHS

DHS 2015-16

Mali

2012-13 DHS

FPE 2014

2012-13 DHS

Standard Definition

2012-13 DHS

DHS 2012-13

Mauritania

2000-01 DHS

FPE 2014

UNFPA  2015

2011 MICS

Alternative Definition

2011 MICs

Mongolia

FPE 2014

2013-14 MICS

Alternative Definition

MICS 2013-14

Mozambique

2011 DHS

FPE 2014

UNFPA   2015

2011 DHS

Standard Definition

2011 DHS

Myanmar

2015-16 DHS

FPE 2014

UNFPA   2015

2015-16 DHS

Standard Definition

2015-16 DHS

DHS 2016

Nepal

2011 DHS

FPE 2014

UNFPA  2015

2011 DHS

Standard Definition

2011 DHS

Nicaragua

2001 DHS

FPE 2014

2001 DHS

Alternative Definition

2001 DHS

Niger

2012 DHS

FPE 2014

UNFPA   2015

2012 DHS

Standard Definition

2012 DHS

DHS 2012

Nigeria

2013 DHS

FPE 2014

UNFPA   2015

2013 DHS

Standard Definition

2013 DHS

DHS 2013

Pakistan

2012-13 DHS

FPE 2014

2012-13 DHS

Standard Definition

2013 DHS

DHS 2012-13

Papua New Guinea

FPE 2014

UNFPA  2015

Philippines

2013 DHS

FPE 2014

2013 DHS

Alternative Definition

2013 DHS

DHS 2013

Rwanda

2014-15 DHS

FPE 2014

UNFPA   2015

2014-15 DHS

Standard Definition

2014-15 DHS

DHS 2014-15

Sao Tome and Principe

2008-09 DHS

UNFPA   2015

2014 MICS

Alternative Definition

2008-09 DHS

Senegal

2014 DHS

FPE 2014

UNFPA  2015

2014 DHS

Standard Definition

2015 DHS

DHS 2014

Sierra Leone

2013 DHS

UNFPA  2015

2013 DHS

Standard Definition

2013 DHS

DHS 2013

Solomon Islands

2007 DHS

2007 DHS

Standard Definition

2007 DHS

Somalia

2006 MICS

Alternative Definition

2006 MICS

South Africa

1998 DHS

FPE 2014

1998 DHS

South Sudan

FPE 2014

2010 MICS

Alternative Definition

2010 MICS

Sri Lanka

1987 DHS

FPE 2014

1987 DHS?

State of Palestine

2014 MICS

Alternative Definition

2014 MICS

Sudan

1989-90 DHS

UNFPA   2015

2014 MICS

Alternative Definition

2014 MICS

Tajikistan

2012 DHS

FPE 2014

2005 MICS

Alternative Definition

2005 MICS

DHS 2012

Tanzania

2015-16 DHS

FPE 2014

SPA  2014.5

2015-16 DHS

Standard Definition

2015-16 DHS

DHS 2015

Timor-Leste

2009-10 DHS

FPE 2014

2009-10 DHS

Standard Definition

2009-10 DHS

Togo

2013-14 DHS

FPE 2014

UNFPA  2015

2013-14 DHS

Alternative Definition

2013-14 DHS

DHS 2013-14

Uganda

2011 DHS

FPE 2014

UNFPA  2015

2011 DHS

Standard Definition

2011 DHS

PMA2020

R3 2015

Uzbekistan

1996 DHS

FPE 2014

1996 DHS

Alternative Definition

1996 DHS

Viet Nam

2002 DHS

FPE 2014

2013-14 MICS

Alternative Definition

2010-11 MICS

Western Sahara

Yemen

FPE 2014

2013 DHS

Standard Definition

2013 DHS

DHS 2013

Zambia

FPE 2014

RH Survey /

SARA   2015

2013-14 DHS

Standard Definition

2013-14 DHS

DHS 2013-14

Zimbabwe

FPE 2014

DTTU Report 

2016

2015 DHS

Standard Definition

2015 DHS

DHS 2015