DHS ANALYTICAL

STUDIES 74

CONTRACEPTIVE USE, METHOD MIX, 

AND METHOD AVAILABILITY: 

A MULTILEVEL ANALYSIS

August 2020

This publication was produced for review by the United States Agency for International Development. It was prepared by

Lindsay Mallick, Shireen Assaf, Michelle Weinberger, and Gbaike Ajayi.

DHS Analytical Study No. 74

Contraceptive Use, Method Mix, and Method Availability: 

A Multilevel Analysis

Lindsay Mallick1,2

Shireen Assaf2,3

Michelle Weinberger1

Gbaike Ajayi2,3

ICF

Rockville, Maryland, USA

August 2020

1 Avenir Health

2 The DHS Program

3

ICF

Corresponding author: Lindsay Mallick, International Health and Development, ICF, 530 Gaither Road, 

Suite 500, Rockville, MD 20850, USA; phone: +1 301-407-6500; fax: +1 301-407-6501; email: 

lindsay.mallick@icf.com
Acknowledgments: The authors are grateful to Apoorva Jadhav and Kristen Wares for providing support 

for this study and conceptual motivation. We express our gratitude to Kerry MacQuarrie and Kristin Bietsch

for their feedback on an earlier draft. Thank you also to Mahmoud Elkasabi, Ruilin Ren, and Thomas 

Pullum for developing the cluster-level weights used in this report, and to Mahmoud Elkasabi for sharing 

the report and accompanying Stata code in advance so that this report could benefit from it. Finally, we 

offer our thanks to Thomas Pullum for his mentorship and statistical support for this project. 

Editor: Diane Stoy

Document Production: Natalie Shattuck

This study was implemented with support from the United States Agency for International Development 

(USAID) through The DHS Program (#720-OAA-18C-00083). The views expressed are those of the 

authors and do not necessarily reflect the views of USAID or the United States Government.

The DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. Additional information about The DHS Program can be obtained 

from ICF, 530 Gaither Road, Suite 500, Rockville, MD 20850 USA; telephone: +1 301-407-6500, fax: 

+1 301-407-6501, email: info@DHSprogram.com, Internet: www.DHSprogram.com.

Recommended citation:

Mallick,Lindsay, Shireen Assaf, Michelle Weinberger, and Gbaike Ajayi. 2020. Contraceptive Use, Method 

Mix, and Method Availability: A Multilevel Analysis. DHS Analytical Studies No. 74. Rockville, Maryland, 

USA: ICF.
iii

CONTENTS

TABLES........................................................................................................................................................ v

FIGURES .................................................................................................................................................... vii

PREFACE.................................................................................................................................................... ix

ABSTRACT.................................................................................................................................................. xi

ACRONYMS AND ABBREVIATIONS ...................................................................................................... xiii

1 BACKGROUND OF THE REPORT ................................................................................................ 1

2 DATA AND METHODS ................................................................................................................... 5

2.1 Data..................................................................................................................................... 5

2.2 Methods .............................................................................................................................. 6

2.2.1 DHS measures....................................................................................................... 6

2.2.2 Service environment measures ............................................................................. 7

2.2.3 National and subnational-level analysis................................................................. 8

2.2.4 Individual and cluster-level analysis in Haiti and Malawi ....................................... 8

3 RESULTS ...................................................................................................................................... 11

3.1 Bangladesh ....................................................................................................................... 11

3.2 Haiti ................................................................................................................................... 15

3.3 Malawi ............................................................................................................................... 19

3.4 Nepal................................................................................................................................. 24

3.5 Senegal ............................................................................................................................. 27

3.6 Tanzania ........................................................................................................................... 31

4 DISCUSSION................................................................................................................................. 35

4.1 Country-specific findings in context .................................................................................. 35

4.2 Contextualizing results across countries........................................................................... 37

4.3 Strengths and limitations................................................................................................... 38

4.4 Recommendations ............................................................................................................ 39

4.5 Conclusions....................................................................................................................... 39

REFERENCES............................................................................................................................................ 41

APPENDIX.................................................................................................................................................. 47

v

TABLES

Table 1 Surveys included in the analysis with sample sizes .................................................. 5

Table 2 Distribution of number of facilities with a mix of methods, by country and 

residence ................................................................................................................... 9

Table 3 Family planning facilities and availability of methods, Bangladesh SPA 2014........ 13

Table 4 Family planning facilities and availability of methods, Haiti SPA 2017-18 .............. 16

Table 5 Family planning facilities and availability of methods, Malawi SPA 2013-14 .......... 21

Table 6 Family planning facilities and availability of methods, Nepal SPA 2015 ................. 25

Table 7 Family planning facilities and availability of methods, Senegal SPA 2017.............. 29

Table 8 Family planning facilities and availability of methods, Tanzania SPA 2014-15....... 33

Appendix Table 1 Percentage and 95% confidence interval of overall modern contraceptive use, 

method distribution, and average deviation among modern users, Bangladesh 

DHS 2014 ................................................................................................................ 48

Appendix Table 2 Percentage and 95% confidence interval of overall modern contraceptive use, 

method distribution, and average deviation among modern users, Haiti 

DHS 2016-17 ........................................................................................................... 48

Appendix Table 3 Background characteristics of women, Haiti DHS 2016-17..................................... 49

Appendix Table 4 Unadjusted odds ratios, adjusted odds ratios, and 95% confidence intervals of 

using modern contraceptives, Haiti DHS 2016-17................................................... 50

Appendix Table 5 Percentage and 95% confidence interval of overall modern contraceptive use, 

method distribution, and average deviation among modern users, Malawi 

DHS 2015-16 ........................................................................................................... 51

Appendix Table 6 Background characteristics of women, Malawi DHS 2015-16................................. 52

Appendix Table 7 Unadjusted odds ratios, adjusted odds ratios, and 95% confidence intervals of 

using modern contraceptives, Malawi DHS 2015-16............................................... 53

Appendix Table 8 Percentage and 95% confidence interval of overall modern contraceptive use, 

method distribution, and average deviation among modern users, Nepal 

DHS 2016 ................................................................................................................ 54

Appendix Table 9 Percentage and 95% confidence interval of overall modern contraceptive use, 

method distribution, and average deviation among modern users, Senegal 

DHS 2017 ................................................................................................................ 54

Appendix Table 10 Percentage and 95% confidence interval of overall modern contraceptive use, 

method distribution, and average deviation among modern users, Tanzania 

DHS 2015-16 ........................................................................................................... 55

vii

FIGURES

Figure 1 Pathways between method availability, contraceptive use, and diversity 

of methods in use....................................................................................................... 4

Figure 2 Method mix by region, Bangladesh ......................................................................... 12

Figure 3 Most recent source of family planning method, Bangladesh................................... 12

Figure 4 Contraceptive prevalence by average deviation and measures of method 

availability, Bangladesh ........................................................................................... 14

Figure 5 Average deviation by measures of method availability, Bangladesh ...................... 14

Figure 6 Method mix by region, Haiti ..................................................................................... 15

Figure 7 Most recent source of family planning method, Haiti............................................... 16

Figure 8 Contraceptive prevalence by average deviation and measures of method 

availability, Haiti ....................................................................................................... 17

Figure 9 Average deviation by measures of method availability, Haiti .................................. 18

Figure 10 Odds ratios of using modern contraception by family planning service 

environment factors, Haiti ........................................................................................ 19

Figure 11 Method mix by region, Malawi ................................................................................. 20

Figure 12 Most recent source of family planning method, Malawi........................................... 20

Figure 13 Contraceptive prevalence by average deviation and measures of method 

availability, Malawi ................................................................................................... 21

Figure 14 Average deviation by measures of method availability, Malawi .............................. 22

Figure 15 Odds ratios of using modern contraception by family planning service 

environment factors, Malawi .................................................................................... 23

Figure 16 Method mix by region, Nepal .................................................................................. 24

Figure 17 Most recent source of family planning method, Nepal............................................. 25

Figure 18 Contraceptive prevalence by average deviation and measures of method 

availability, Nepal..................................................................................................... 26

Figure 19 Average deviation by measures of method availability, Nepal ................................ 26

Figure 20 Method mix by region, Senegal ............................................................................... 27

Figure 21 Most recent source of family planning method, Senegal......................................... 28

Figure 22 Contraceptive prevalence by average deviation and measures of method 

availability, Senegal ................................................................................................. 30

Figure 23 Average deviation by measures of method availability, Senegal ............................ 31

Figure 24 Method mix by region, Tanzania.............................................................................. 32

Figure 25 Most recent source of family planning method, Tanzania ....................................... 32

Figure 26 Contraceptive prevalence by average deviation and measures of method 

availability, Tanzania................................................................................................ 34

Figure 27 Average deviation by measures of method availability, Tanzania........................... 34

ix

PREFACE

The Demographic and Health Surveys (DHS) Program is one of the principal sources of international data 

on fertility, family planning, maternal and child health, nutrition, mortality, environmental health, 

HIV/AIDS, malaria, and provision of health services. 

One of the objectives of The DHS Program is to analyze DHS data and provide findings that will be useful 

to policymakers and program managers in low- and middle-income countries. DHS Analytical Studies serve 

this objective by providing in-depth research on a wide range of topics, typically including several countries 

and applying multivariate statistical tools and models. These reports are also intended to illustrate research 

methods and applications of DHS data that may build the capacity of other researchers. 

The topics in this series are selected by The DHS Program in consultation with the U.S. Agency for 

International Development.

It is hoped that the DHS Analytical Studies will be useful to researchers, policymakers, and survey 

specialists, particularly those engaged in work in low- and middle-income countries.

Sunita Kishor

Director, The DHS Program

xi

ABSTRACT

Access to a range of contraceptives affords women greater opportunity to find a method that meets their 

unique reproductive needs, possibly leading to greater diversity in the method mix and increased

contraceptive use at population levels. A more diverse method mix may also directly correlate with

increased use. However, these relationships have not been concurrently or robustly assessed. We used data 

from six countries with both recent Demographic and Health Surveys (DHS) household surveys and Service 

Provision Assessment (SPA) facility surveys conducted within 2 years of each other. The analysis included

data from women age 15-49 and health facilities that provide family planning (FP) services. We examined

the subnational relationships between modern contraceptive prevalence (mCP), balance of methods in use, 

availability of FP services and mix of methods (at least one short-acting, one long-acting or permanent 

method, and one nonhormonal method) provided. In Haiti and Malawi, where SPA surveys are censuses

that enable linking household and facility data by household clusters, we conducted multilevel, 

multivariable logistic regressions, applying a novel method for approximating level-weights (individualand cluster-level) to estimate the contribution of community-level factors to an individual outcome (use 

of FP). 

The subnational analysis showed that the relationships between mCP with diversity in the method mix and

method availability, as well as relationships between method mix and method availability, vary by country;

many countries showed minimal or no evidence of associations. However, in Senegal, we found expected 

relationships between greater mCP, more facilities with FP, and a more balanced mix. A more balanced mix

was also associated with a greater number of facilities with a mix of methods. Among rural women in Haiti 

and Malawi, after controlling for other factors, there were marginally significant, modest (20%) increases 

in the odds of using FP if a woman lived in a cluster with a high number of facilities that offered a mix of 

methods compared with women living in areas with a low number of facilities with a mix of methods. In 

both countries, the odds of using contraceptives also increased if there was some community distribution

or mobile outreach versus none. In urban Haiti, the odds were nearly twice as high. Although relationships

between method availability, method use, and method mix are complex, method choice should continue to 

be a central tenet of rights-based FP to ensure that women have access to the methods that meet their needs.

KEY WORDS: family planning, contraceptive use, method mix, method skew

xiii

ACRONYMS AND ABBREVIATIONS

AD average deviation

AOR adjusted odds ratio

BLM Banja la Mtsogolo

CI confidence interval

DHS Demographic and Health Survey

DRC Democratic Republic of the Congo

FP family planning 

FPE Family Planning Effort Index

IUD intrauterine device

LAM lactational amenorrhea method

mCP modern contraceptive prevalence

SPA Service Provision Assessment

UOR unadjusted odds ratio

USAID United States Agency for International Development

WHO World Health Organization 

1

1 BACKGROUND OF THE REPORT

Globally, one in ten women of reproductive age, which includes 190 million women, who do not want to 

become pregnant do not use a contraceptive method (UN DESA 2019). Unwanted or unplanned pregnancies 

carry many risks that include unsafe abortion, stillbirth, preterm births, and even neonatal and maternal 

deaths. Approximately 295,000 women died from complications during pregnancy and childbirth in 2017, 

and roughly 2.5 million neonatal deaths occurred in 2018 with 36% of these deaths occurring within 48 

hours of birth (Lawn et al. 2014; WHO et al. 2019). Contraceptive methods offer women and couples the 

opportunity to control the timing and spacing of pregnancies, which can reduce maternal and neonatal 

deaths (Ahmed et al. 2012; Bhutta et al. 2014; Stover and Ross 2010). 

Ensuring that a wide range of contraceptive methods is available is a critical component of ensuring that 

women have their needs met. Since contraceptive needs vary over a woman’s reproductive lifecycle

(MacQuarrie et al. 2019), it is important to offer a variety of methods from which women can choose an 

effective method that meets their needs, whether they are temporarily spacing pregnancies, or limiting

future, further, or any childbearing. Among other factors, limited availability of contraceptive methods 

compels women to choose either ineffective methods or methods that are inadequate for their reproductive 

needs and which contribute to method discontinuation and unplanned pregnancies (Samuel, Fetters, and 

Desta 2016). 

Bruce (1990) identified method choice—the ability to choose from a variety of methods—as a core 

component of quality family planning (FP) services that is essential to the client experience. In their revision 

of Bruce’s framework, Jain and Hardee (2018) continue to acknowledge the importance of method choice 

and expanded it to include both the availability of FP equipment and qualified health-care professionals to 

deliver the method. In recognition of the importance of method availability, FP2020 Core Indicator number 

11 measures the proportion of facilities, by level, with at least three or five modern methods available 

(Track20 n.d.). This indicator, which is usually drawn from facility-based surveys, provides valuable insight 

into the methods women can choose. 

Several studies have found a positive relationship between method availability and the proportion of women 

who are currently using a method, which is also known as contraceptive prevalence (Freedman and 

Berelson 1976; Jain 1989; Ross, Keesbury, and Hardee 2015; Ross et al. 2002; Sutherland, Otterness, and 

Janowitz 2011; Wang et al. 2012). For example, Wang et al. (2012) linked Service Provision Assessment 

(SPA) surveys and Demographic and Health Survey (DHS) data from four East African countries to 

examine the relationship between contraceptive use and the regional availability of contraceptives. They 

found that the odds of a woman using modern contraception increased by 50% with the addition of one 

available method. Ross and Hardee (2013) used DHS data and the National Family Planning Effort Index 

(FPE) to show that high and consistent access to methods was associated with increased use of contraceptive 

methods. In addition, an analysis of national data from 113 countries, which also used the FPE, suggested

that adding one more method to the mix increased the modern contraceptive use by 4 to 8 percentage points 

(Ross and Stover 2013).

While those studies examined the associations between availability and use across national or subnational 

units, others have investigated the association between contraceptive availability and use at more granular 
2

levels. One study linked data from women interviewed in the 2003 Egypt DHS to nearby facilities 

interviewed in the SPA surveys to investigate the relationship between contraceptive use and the quality of 

FP services, which included contraceptive supply. The study found an increase in intrauterine device (IUD)

use by women who lived near facilities with high-quality FP services (Hong, Montana, and Mishra 2006). 

A study from Kenya, linking women to their reported source of care, found that having a consistent stock 

of methods was marginally associated with use of a modern method (Tumlinson et al. 2015). Skiles et al. 

(2015) report a positive association between contraceptive use in Malawi and both access to a reliable stock 

of methods and distance to facilities that offer FP services. Similarly, a study by Shiferaw et al. (2017)

found that Ethiopian women who lived near health facilities with a wide range of contraceptive options had 

greater odds of using contraceptives. In a recent publication, Wang and Mallick (2019) linked women 

interviewed in DHS with SPA and found a relationship between contraceptive use and the number of 

methods available at FP facilities. Their analysis confirms earlier results by Chen and Guilkey (2003), which 

showed the positive correlation between contraceptive use and the number of methods available at a facility 

within 5 km. 

Results of an assessment by Babazadeh et al. (2020) contradict other studies that relate method availability 

and distance with higher method use among women in Kinshasa, the Democratic Republic of Congo (DRC). 

There was no significant change in the percentage of women who used modern contraceptives and lived

close to service delivery points that offered at least three or five or more contraceptive methods. Given the 

high fertility rate in the DRC, demand for FP is low, and there are very limited FP services available across 

the country (Kwete et al. 2018). These findings imply that both demand- and supply-side factors at the 

national and subnational levels can dictate how method availability and FP services interact to influence 

contraceptive use.

In addition to the level of contraceptive use, it is also important to consider the methods used by women. 

Method mix, along with the percent distribution of contraceptive users, is often used as a proxy for method 

choice or availability (Bertrand et al. 2014; Sullivan et al. 2006). Although FP experts do not recognize an 

ideal method mix, this metric can represent the extent to which women have a choice of methods available 

to meet their FP needs (Bertrand et al. 2014; Ho and Wheeler 2018; RamaRao and Jain 2015; Sullivan et 

al. 2006). Method choice may also provide women with an opportunity for switching methods rather than 

discontinuing contraception entirely (Jain 1989), although method availability alone may not be the 

strongest factor that explains discontinuation (Blanc, Curtis, and Croft 1999), since a country’s method mix 

responds to both supply-side and demand-side factors. 

Supply-side factors such as provider bias, government policies, FP programs, and facility characteristics 

can influence method mix. Attitudes toward certain population groups also influence whether providers 

discuss, recommend, and offer contraceptive methods (Calhoun et al. 2013; RamaRao and Jain 2015; Solo 

and Festin 2019; Sullivan et al. 2006). Method-specific bias stems from providers’ lack of knowledge about 

the range of available methods, opinions about certain contraceptive methods, and skill in administering 

modern methods (Lince-Deroche et al. 2020; Rattan et al. 2016; Solo and Festin 2019; Tuoane, Diamond, 

and Madise 2003). Along with provider bias, government policies and FP programs can affect the types of 

contraceptive methods available for women, as evidenced by the prevalence of IUD use in Egypt (Sullivan 

et al. 2006). 
3

Demand-side factors such as the length of time the method has been available in a country, user 

characteristics and preferences, and attributes of the methods can also influence method mix (Sullivan et 

al. 2006). A method recently introduced into a country may not have a sizeable proportion of users compared 

to a method with a longer history of use in the country. Thus, demand may be lower until potential users 

become more familiar with the new method. Properties of methods can also influence the types of 

contraceptives preferred by users. For example, ease of use, convenience, and effectiveness have been 

identified as factors that increased uptake of implants in sub-Saharan Africa (Jacobstein 2018). 

Characteristics of the users and preferences, including knowledge about different methods, desire for 

discretion, demand for spacing or limiting, age, religion, and socioeconomic status are also key factors that 

influence demand for FP methods (Ho and Wheeler 2018; Rattan et al. 2016). 

Supply and demand factors, whether alone or in combination, can cause an imbalance in the method mix 

(Sullivan et al. 2006). A balanced method mix, in which each method has the same proportion of users, is 

not the ideal because all methods are not equally effective or desired (Bertrand et al. 2014; Ross, Keesbury, 

and Hardee 2015). A skewed method mix may also indicate a limited choice of methods. Researchers have 

defined the method mix as skewed if more than half of users rely on a single method (Bertrand et al. 2000; 

Bertrand et al. 2014; Sullivan et al. 2006). Using this definition, Sullivan et al. (2006) analyzed national 

survey data from 96 countries and found that 35% of the countries had a skewed method mix. Nearly half 

of the countries with a skewed mix were located in sub-Saharan Africa where contraceptive prevalence is 

low and traditional contraceptive methods are dominant. In a later analysis of 109 countries, 30% of the 

countries had a skewed method mix with fewer relying on traditional methods (Bertrand et al. 2014). In 

addition, Bertrand and colleagues reported no association between method skew and modern contraceptive 

prevalence. A skewed method mix can exist even in settings with high modern contraceptive prevalence 

(RamaRao and Jain 2015). The “50% rule” shows which method dominates a country’s method mix, but 

does not show the changes in methods over time (Ross, Keesbury, and Hardee 2015).

To address the limitations of the 50% rule, alternative measures of assessing method skew have been used 

(Bertrand et al. 2000; Blanc, Curtis, and Croft 1999). Ross, Keesbury, and Hardee (2015) use the average 

deviation (AD) method, a measure similar to the index of dissimilarity (Shryock, Siegel, and Larmon 1973), 

as a way to assess deviations from a standard distribution or use of all methods. Countries with a high AD 

have a skewed method mix, while those with a low AD value have a more balanced mix. Analysis of the 

national survey data from 123 countries revealed that 15 countries that previously had a skewed method 

mix had moved toward having a more balanced mix. Countries have achieved a less skewed mix by 

replacing traditional methods with modern contraceptive methods. The AD method offers an alternative 

approach to further investigate the relationship between method skew and contraceptive prevalence, at both 

the national and subnational levels.

Although it is possible that a wide range of methods may be available, there is an overall preference for

only one or two forms of FP despite availability. Currently, there is a gap in the research that explores

relationships between contraceptive use, method skew or balance, and method availability in tandem with

an examination of the relationship between method skew or balance and method availability. This may be 

due to a dearth of data from health facilities. Meanwhile, stakeholders may assume that the method mix can 

be a proxy for method availability (Sullivan et al. 2006). Although it is true that using a method that is not 

available would be unlikely, the converse—more method availability signals greater diversity in use—has 

not been proven. 
4

Thus, the balance of method mix deserves further examination to understand if skew may be related to 

supply-side factors, which can reflect limited rights to reproductive health. The SPA surveys provide an 

opportunity to explore such questions by collecting data on the service environment and availability. Both 

SPA and DHS are national surveys drawn from a representative sample and include geospatial coordinates 

data (for facilities in SPA or clusters in DHS). This allows for linkages at the population, subnational, and 

even cluster levels depending on the survey design. Linking these data sources allows for deeper analysis 

on the supply side to examine health facility factors that may be associated with contraceptive prevalence

method mix as gauged through DHS household surveys.

This paper uses the AD method to explore the method mix and contraceptive use in Malawi, Haiti, 

Bangladesh, Nepal, Senegal, and Tanzania. By using health facility surveys conducted in the same 

countries, this report goes beyond looking at data on contraceptive use to relate both modern contraceptive 

prevalence (mCP) and AD to contraceptive method availability at the facility level. The report extends 

existing research by exploring the relationship between contraceptive prevalence and factors such as 

method balance, method availability, and access to FP services at subnational levels, and conducts an 

individual and community-level analysis where data permits. The hypothesized pathways for these

relationships are summarized in Figure 1.

Figure 1 Pathways between method availability, contraceptive use, and diversity of methods in use

Our study attempts to answer the following research questions. 

1. Are there associations between greater availability of methods and a balanced method mix? 

2. Is greater availability of methods associated with a higher contraceptive use?

To answer these questions, our study draws from data from multiple sources of information from both 

household surveys and health facility surveys conducted by the DHS Program. 

Method 

availability at 

facilities

Share of women 

using modern 

contraception 

(mCP)

Diversity of 

methods in use 

(Measured by 

average 

deviation)

Other important factors include demand for contraception, demand for specific 

methods, social norms, provider bias, and government policies.

Source: SPA

Source: DHS

Source: DHS
5

2 DATA AND METHODS

2.1 Data 

This analysis used data from the six countries with recent (within the last decade), closely timed SPA health 

facility and DHS household surveys, which were conducted within 2 years of one another. The countries 

include Bangladesh, Haiti, Malawi, Nepal, Senegal, and Tanzania. In all countries, except Haiti, the SPA 

precedes or is conducted within the same year as the DHS. As described below, this is ideal because the 

dependent variables of interest are DHS-based measures and the independent variables are largely SPAbased.

SPA surveys are conducted with a nationally representative sample or a census of health facilities. Haiti and 

Malawi were census surveys and the remainder were samples. The facilities included formal sector public, 

private (for-profit, not-for-profit, nongovernmental, and faith-based), and mixed private-public facilities. 

Informal outlets such as pharmacies and mobile clinics are usually not surveyed. Included in the survey is 

a facility inventory, which collects information about the services, basic infrastructure, equipment, 

medicines, and guidelines, which can be used to describe the availability of services and the readiness of 

facilities to provide those services. For FP, this includes if FP services are offered, and if a suite of methods 

is offered, prescribed, available, and valid (not expired) at the facility on the day of the survey. 

The DHS households are also drawn from a nationally representative sample of households and are 

representative subnationally as well as by urban and rural residence. Households solicited for participation 

in the survey are selected with a stratified two-stage cluster sampling design. At the first stage, enumeration 

areas are selected within each stratum, and are typically based on the most recent census. In the second 

stage, a sample of households israndomly selected from a household listing. The households selected within 

each enumeration area are then described as clusters. Table 1 shows the year and sample of households, 

women, and facilities in the respective surveys for each country.

Table 1 Surveys included in the analysis with sample sizes

Country DHS survey

Number of 

households 

interviewed

Number of women 

interviewed SPA survey

Number of health 

facilities 

interviewed

Bangladesh1 2014 17,300 17,863 2014 1,524

Haiti 2016-17 13,405 14,371 2017-18 1,007

Malawi 2015-16 26,361 24,562 2013-14 977

Nepal 2016 11,040 12,862 2015 963

Senegal 2017 8,380 16,787 2017 396

Tanzania 2015-16 12,563 13,266 2014-15 1,188

1 The Bangladesh sample included ever-married women; however, the survey only posed questions about contraceptive use to 

currently married women. This sample included 16,858 women.

To facilitate subnational analysis, we used current administrative level-1 units in each country. For example, 

we used provinces, and in Bangladesh we used regions. Although some provinces have been renamed, we 

used the numbered provinces because they had not been renamed at the time of the DHS. In Malawi, where 

district information was available and appropriate (large enough sample size), we used districts 

(administrative level 2 unit). In addition, given the small population in the Zanzibar Archipelago of 

Tanzania, we aggregated the islands into one category (Zanzibar). 
6

These surveys were reviewed and approved by the ICF Review Board and the Ethics Review Committee 

of each country. All data are publicly available from https://www.dhsprogram.com/. The final reports for 

each survey are available online from the same website and provide detailed information about the survey 

design, response rates, and other topics.

2.2 Methods

We conducted a stepwise analysis to understand the relationships between contraceptive use, method mix, 

and method availability for each country separately. The following sections detail the measures used in the 

analysis. 

2.2.1 DHS measures

We first examined contraceptive prevalence and method mix within subnational administrative units (e.g.,

regions and provinces), and presented the overall levels in each country. Modern contraceptive prevalence, 

method mix of modern methods, the average deviation of the method mix, and method source were 

coded as:

Modern contraceptive prevalence (mCP): The proportion of women age 15-49 who are using a modern 

contraceptive method. For all the surveys except the Bangladesh 2014 survey, this includes all women. The 

Bangladesh 2014 survey included only ever-married women, and contraceptive use was only examined 

among currently married women. Modern contraceptive methods include female and male sterilization

IUD, implants, injectables, pills, female and male condoms, foam or jelly, emergency contraceptive pill, 

and the lactational amenorrhea method (LAM). The mCP may also include other modern contraceptive 

methods that are country-specific or less common but were reported by the respondent and identified in the 

dataset as modern methods.

Method mix: Seven categories were produced for this variable among women age 15-49 who were using a 

modern contraceptive method. This includes female sterilization, IUD, implants/Norplant, injectables, pills, 

male condom, and other methods. Other methods included methods in low use in the countries; these were

emergency contraceptive pill, LAM, female condom, standard days method, and male sterilization. In 

Nepal, male sterilization was approximately 13% but was combined with other modern methods to be 

consistent with the other countries in the analysis, where male sterilization was very uncommon.

Average deviation (AD) (balance in method mix): To examine the balance in the distribution of method 

mix, we used the AD method (Ross, Keesbury, and Hardee 2015). This measure can also be expressed as 

the mean deviation from a uniform distribution. The AD was computed by first creating a dummy variable 

for each of the seven methods in the method mix variable. We then subtracted 14.3% (100% divided by 7)

from the percentage of each method available. The value 14.3% would be the percent if all 7 methods were 

distributed evenly (the average value or a uniform distribution). We then sum the absolute value of the

deviations of each method from this average value and divide the sum by seven. The AD was computed for 

the country as a whole and by region within each country. 

Method source: To bring additional context to the source of FP in each country, we examine responses to 

the question, “Where did you obtain (CURRENT METHOD) the last time?” We harmonized responses

across countries. These consolidated categories were government hospital, government health center or 
7

clinic (including FP clinics in Haiti, Nepal, and Senegal), mobile clinic, fieldworker, other public source 

(health post or health huts in Senegal and dispensaries in Tanzania), private, pharmacy, and 

nonmedical/unspecified such as shop, church, friend or relative. In Malawi, we also included a separate 

category for Banja La Mtsogolo (BLM), a FP program run by Marie Stopes International that provides 

outreach services, which are often situated within public facilities. 

2.2.2 Service environment measures 

We examined several service environment variables that reflect geographic access to FP facilities, which 

are defined as the number of facilities that provide FP services and two measures of availability of a range 

of methods. These include percent of facilities with a mix of methods and the number of facilities with a 

mix of methods, as defined below. The number of facilities with a mix of methods incorporates both access 

to facilities with FP and availability of methods. Although the number of facilities alone may moderate the 

relationship between the percent and number of facilities with a mix of methods, we theorize that each of 

these facility-based measures may yield distinct relationships with the dependent variables of mCP and AD. 

Thus, we examined them independently. We present the overall and subnational indicators of the service 

environment for each country separately. 

Number of facilities that provide FP services

To create a measure of geographic access to FP services, we summarized the number of facilities, among 

all facilities interviewed in the SPA survey, that reported providing FP services. In Haiti and Malawi, where 

the SPA surveys are census surveys, this raw number reflects the true number of facilities with FP service. 

However, in the other countries in this study, this number serves as a proxy only because these surveys were 

samples. The measure created proportionately reflects FP services since SPA surveys are sampled to be 

representative at the subnational level. The SPA sampling design accounts for the distribution of facilities 

across subnational levels and samples accordingly. In this sense, this variable is useful for within-country 

analysis to compare relative subnational access. Confidence intervals (95%) are presented in tables for each 

country where the SPA survey was a sample (Bangladesh, Nepal, Senegal, Tanzania).

Availability of mix of method types (percentage)

This variable measures the level of availability of a mix of methods at facilities. This reflects the percentage 

of all facilities that provide FP services with at least one short-acting, one long-acting or permanent, and 

one barrier or nonhormonal method. Short-acting methods include pills, injectables, or the emergency 

contraceptive pill. For this category, the method must be provided and available at the time of the survey. 

Long-acting and permanent methods include implants or IUDs, which the facility states are provided and 

the observer notes are in stock and not expired, or male or female sterilization, which the facility states are 

routinely provided. Barrier or nonhormonal methods include condom (male or female) or cycle beads for 

Standard Days Method.

Number of facilities with a mix of method types

This variable combines the above two variables and summarizes the number of facilities that provide FP

and have at least three types of methods: one short-acting, one long-acting or permanent, and one barrier or 

nonhormonal method. Similar to the number of facilities with FP services, this is also a proxy of the true 
8

number, which reflects a representative proportion of facilities at subnational or national levels in sample 

surveys (Bangladesh, Nepal, Senegal, and Tanzania).

2.2.3 National and subnational-level analysis

We calculated contraceptive prevalence, method mix, and AD at the national and subnational levels. To 

examine the relationship between mCP and AD, a scatter plot was produced for each country for all the 

first-level administrative units (region, division, province), for Malawi by district, presented among urban, 

rural, and total. For the three countries that had above 10 regions, a linear regression of mCP and AD was 

fit to produce a best fit line that described this relationship. This was performed for Haiti, Senegal, and 

Tanzania. The regression was also performed for Malawi at the district level. The results from the linear 

regression would identify if the relationship is significant and in which direction. The R2value would also 

indicate the strength of the relationship. However, the results should be interpreted with caution because 

these analyses do not control for the myriad factors that may influence contraceptive use or the average 

deviation.

2.2.4 Individual and cluster-level analysis in Haiti and Malawi

Given that the SPA was a census in Haiti and Malawi, we conducted an individual and cluster-level analysis 

in Haiti and Malawi to examine the relationship between contraceptive use and the FP service environment 

variables related to method availability and access. We geographically linked the SPA health facility data 

to clusters of women sampled in the DHS household surveys. This method is not recommended when the 

SPA survey is a sample (Burgert and Prosnitz 2014). However, in Haiti and Malawi, the ability to link 

allows for the examination of individual and community factors related to contraceptive use. To link SPA 

data to DHS, we created summary variables that describe all the facilities located within a 5-kilometer 

radius for urban clusters and 10-kilometer radius for rural clusters. These distances account for the 

maximum potential displacement of clusters (Burgert and Prosnitz 2014). Geographically displacing the 

location of the clusters is standard practice in DHS surveys in order to protect the anonymity of survey 

respondents. This method of linking may not be appropriate in highly urbanized areas, such as Port-auPrince, a densely populated metropolitan area (IHSI 2015), where care seekers have access to a large 

number of facilities and where the 5 kilometer radius may not accurately capture the facilities that care 

seekers attend (Wang et al. 2015). Previous research has documented differences in the service environment 

by urban and rural residence (Wang et al. 2015). Therefore, we conducted this analysis separately for urban 

and rural residence and excluded 3,632 urban residents of Port-au-Prince, Haiti. We also excluded 13 rural 

clusters in Haiti within 5 kilometers of Port-au-Prince (653 women) given that their proximity to a large 

number of health facilities is atypical for rural residence.

We created one count variable reflecting the number of facilities with a mix of methods within the relative 

proximity to each cluster, 5 or 10 kilometers. We hypothesized that there may not be a linear relationship 

between proximity to incrementally greater numbers of facilities with FP choices and contraceptive use, 

and we categorized this variable. The categories were none, low, medium, and high. The low, medium, and 

high categories were created with tercile cut points among clusters with any facilities that met the criteria 

(providing FP or providing FP with a mix of methods). The terciles were created for each variable, 

respective to each country’s distribution as well as respective to urban and rural cluster locality. Thus, the 

number of facilities that are included in each low, medium, and high category differ. Table 2 shows the

distribution and highlights the importance of creating categories respective to each country and place of 
9

residence. As discussed in the results section, there were 20% of women in urban Haiti living in a cluster 

that linked to no facilities, 13% in rural Haiti, less than 1% in urban Malawi, and 16% in rural Malawi. 

Table 2 Distribution of number of facilities with a mix of methods, by country and residence 

Number of facilities with a mix of methods

Country Residence None Low Medium High

Haiti Urban 0 1 2-3 4-5

Rural 0 1-2 3 4-8

Malawi Urban 0 1-9 10-17 18-37

Rural 0 1-2 3-4 5-43

We conducted multilevel, multivariable logistic regression to examine the relationships between our 

dependent variable (outcome) of interest—contraceptive use—and individual and community-level factors. 

Multilevel models account for the similarities that may exist among individuals within communities. These 

models partition the variance in the outcome between individuals and their communities and allow for an 

estimation of variation in the outcome explained by the community-level factors. Random intercept models

were fit to allow the intercept to vary across clusters. These multilevel models are among the first to utilize 

recently developed methods for estimating cluster weights as described in a forthcoming publication 

(Elkasabi, Ren, and Pullum 2020). In these models, we applied the Stata code provided by the publication, 

which denormalized the level-1 individual weights and approximated the level-2 weights by equally 

allocating the variation in weights between the individual and cluster levels (α=0.50) (Elkasabi, Ren, and 

Pullum 2020). 

Individual-level factors included education (none, primary, and secondary or higher); wealth quintile; 

employment (not employed, professional employment, and agriculture or other work); age (15-19, 20-29, 

30-39, and 40-49); marital status (never married, currently married, and formerly married); and exposure 

to FP messages in the last few months (yes or no). We did not include parity as a control because it was 

highly correlated with marriage and age.

Community-level variables were department (Haiti) or region (Malawi), population density, and a measure 

of community-level outreach. Population density is a standard variable included in the DHS Geospatial 

Covariate data file. Population density is extracted from the UN-Adjusted Population Density, Gridded

Population of the World, Version 4 (CIESIN and Columbia University 2016). The Geospatial Covariate 

data file provides population density relative to each cluster sampled in DHS surveys (Mayala et al. 2018).

We categorized population density of urban and rural residences separately, by using three quantiles of 

density: low, medium, and high. The outreach variable was calculated at the cluster level using aggregated

responses to the question, “Where did you obtain (CURRENT METHOD) the last time?”. First, a binary 

variable was created to reflect if an individual contraceptive, identified as a woman’s source for FP, was

either community-based or mobile outreach. In Malawi, this included outreach from a mobile clinic, health 

surveillance assistants, community-based distribution agents or door-to-door, and BLM. In Haiti, this 

included a community health worker or a mobile clinic. Next, we calculated the percentage of outreach as 

a source for last FP method for each cluster among women who were using modern contraception. Finally,

we categorized clusters as 0 (no outreach), and among the clusters with any outreach, terciles of low, 

medium, or high outreach, which were calculated separately for urban and rural residence. 
10

To keep the analysis meaningfully consistent across residences and countries, the same reference group was 

chosen across all models. For each independent variable, we chose the baseline or lowest reference group 

within each category (such as lowest wealth or lowest age group), unless the sample size in that category 

was small. In total, our regressions included all women age 15-49, a total of 3,098 women in urban Haiti, 

6,987 in rural Haiti, 4,496 women in urban Malawi, and 20,066 women in rural Malawi. 

All analyses used Stata 16.0 and applied the svy command to account for nonresponse and multistage survey 

design, except for the SPA survey data in Haiti and Malawi. In those two countries, SPA survey data were 

only adjusted for nonresponse because these surveys are census surveys. Therefore, descriptive tables for 

these two countries’ SPA-based statistics do not include confidence intervals.
11

3 RESULTS

Results will be discussed by country in alphabetical order. The results begin with a description of the mCP

and method mix overall and by region. The AD is also presented as a summary measure for the distribution 

of mixed methods. The higher the AD, the more skewed the distribution, with one or two methods 

predominating. A lower AD indicates a more uniform mix of method use. 

We present two sets of scatter plots for each country. The first set describes the relationship between mCP 

and several indicators using DHS and SPA data, at subnational levels overall and by urban and rural 

residence. The indicators include the AD, number of FP facilities, and the percentage of facilities with at 

least three method types (short-acting, long-acting, and nonhormonal or barrier). The second set of scatter 

plots demonstrates the relationships between AD as the dependent variable, and the three above-mentioned 

SPA indicators. In Haiti and Malawi, we present the results of the multilevel, multivariable logistic 

regression that examined the relationship between contraceptive use, individual, and community-level 

factors, and focused on the FP service environment.

3.1 Bangladesh

Just over half (54%) of currently married women age 15-49 in Bangladesh use a modern contraceptive 

method (Appendix Table 1). This ranged from 41% in the Sylhet Division to 63% in Rangpur. Figure 2

shows the method mix in Bangladesh among women who use a modern contraceptive method. The most 

frequently used modern method in Bangladesh for all divisions was the pill, which was used by over 50% 

of women in Chattogram, Dhaka, Rangpur, and Sylhet. Injectables were the second most used method for 

all divisions, except for Sylhet in which there was almost an equal distribution of women who used 

injectables and female sterilization (16%). The overall AD in Bangladesh was 12.7, which ranged from 11.0 

in Sylhet to 15.1 in Barisal. Although Sylhet had the lowest AD score, which reflected the most method 

balance, one method (the pill) dominates the method mix. 

Figure 3 shows the distribution of sources of FP methods. The public sector serves 49% of modern 

contraceptive users with 20% obtaining their method from a fieldworker. Over two-thirds of women receive 

their method from a pharmacy, which is expected since pills dominate the method mix. Refills for pills can 

largely be obtained from pharmacies, whereas for injectables or other long-term methods, attendance at a 

health facility is required.
12

Figure 2 Method mix by region, Bangladesh

Figure 3 Most recent source of family planning method, Bangladesh

Note: Government health centers or clinics includes family planning clinics. Private includes private 

doctors, hospitals, clinics, and other private sources.

Government hospital

5%

Government 

health center or 

clinic

24%

Fieldworker

20%

Other government source

<1%

Private

5%

Pharmacy

38%

Nonmedical/unspecified

8%
13

Table 3 shows the number of facilities by region as well as facilities with a mix of methods. A large number 

(over 200) of facilities that provide FP services were sampled in Dhaka and Chattogram, while there were 

only 91 sampled facilities that provided FP in Sylhet and 103 in Barishal. Although these numbers do not 

represent the true number of facilities, the number of facilities sampled per region is proportionate to the 

true numbers. The number of sampled facilities with a mix of methods ranged from 22 in Sylhet to 114 in 

Dhaka, with the largest percentage of facilities with a mix of methods found in Rangpur (46%).

Table 3 Family planning facilities and availability of methods, Bangladesh SPA 2014

Number of FP 

facilities

Percentage of 

facilities with a mix 

of method types, 

% [95% CI]

Number of FP 

facilities with a mix 

of method types

Total 1,281 31.0 [27.9,34.2] 397

Barishal 103 29.0 [22.8,36.2] 30

Chattogram 240 27.6 [21.5,34.6] 66

Dhaka 296 38.3 [30.6,46.6] 114

Khulna 187 18.5 [14.9,22.8] 35

Rajshahi 170 24.7 [17.4,33.9] 42

Rangpur 194 45.8 [34.8,57.2] 89

Sylhet 91 24.3 [18.0,32.0] 22

Note: CI = confidence interval; FP = family planning.

Figure 4A describes the relationship between mCP and several measures at the overall level and by urban 

and rural clusters. There is no clear pattern observed from the plot of mCP and AD, although the Sylhet 

Division had the lowest mCP and the lowest AD (mCP of 41% and AD of 11.9). Figure 4C shows an 

increase in mCP, with an increase in the percentage of facilities with a mix of method overall and in rural 

areas. However, we cannot test for significance given so few data points. The trend was not observed for 

urban areas. Figures 4B and 4D do not show any clear relationship between mCP and the number of 

facilities or the number of facilities with a mix of methods. 

Figures 5A-5C show no clear relationship between AD and the number of facilities, percent of facilities 

with a mix of methods, and number of facilities with a mix of methods. We do observe that urban facilities 

have a larger percentage of facilities with a mix of methods compared to rural facilities, although there are 

fewer facilities in urban areas compared to the rural areas. 
14

Figure 4 Contraceptive prevalence by average deviation and measures of method availability, 

Bangladesh

Note: A best-fit line is drawn separately for the total, urban, and rural areas.

Figure 5 Average deviation by measures of method availability, Bangladesh

Note: A best-fit line is drawn separately for the total, urban, and rural areas.
15

3.2 Haiti

Less than a quarter (22%) of women age 15-49 in Haiti use a modern contraceptive method (Appendix 

Table 2). This ranged from 16% in Rest-Ouest to 28% in Centre and Nippes. Injectables were the most used 

method in Haiti overall (54%), and for all regions except Aire Metropolitaine (Figure 6). In Aire

Metropolitaine, the most used method was the male condom (46%), followed by injectables (41%). For all 

but three regions (Aire Metropolitaine, Sud-Est, and Sud), injectables were used by more than half of users 

and by approximately two-thirds or more of women in Nord-Ouest and Grand’Anse (66%) and Artibonite 

(68%). The overall AD in Haiti was 15.3, with ranges from 13.8 in Rest-Ouest and Sud-Est to 16.7 in Aire 

Metropolitaine. Despite Aire Metropolitaine not having one method that dominates the mix (over 50%), the 

AD score was the highest. 

Figure 6 Method mix by region, Haiti 
16

Figure 7 shows the source where women obtained their most recent FP method in Haiti. Just over half of 

all women obtain their contraceptives from a government hospital or health facility. An additional 15% 

obtain their method from a fieldworker, 8% from a private medical source, and 11% from a pharmacy.

Figure 7 Most recent source of family planning method, Haiti

Note: Government health centers or clinics includes family planning clinics. Private includes private 

doctors, hospitals, clinics, and other private sources.

Table 4 shows that the largest number of facilities were in Aire Metropolitaine, Ouest, and Artibonite 

regions (all with over 100 facilities). However, the largest percentage of facilities with a mix of methods 

was found in Centre (61% with 38 facilities), and Nippes (61% with 28 facilities). The Ouest Department 

had 117 facilities (the second largest in the country) and only 9% had a mix of FP methods, which left only 

11 facilities that provide FP services with a mix of methods. Aire Metropolitaine, with the largest number 

of facilities with FP services, also had fewer than 25% of facilities with a mix of methods. This meant that 

there were only 32 facilities with this service and method availability in the entire metro area. 

Table 4 Family planning facilities and availability of methods, Haiti SPA 2017-18

Number of 

FP facilities

Percentage of facilities 

with a mix of method 

types, % [95% CI]

Number of FP facilities 

with a mix of method 

types

Total 756 29.3 221

Aire Metropolitaine 139 23.0 32

Ouest 117 9.4 11

Sud-Est 50 42.0 21

Nord 62 40.3 25

Nord’Est 36 44.4 16

Artibonite 102 24.5 25

Centre 38 60.5 23

Sud 64 31.3 20

Grand’Anse 47 27.7 13

Nord-Ouest 75 25.5 19

Nippes 28 60.7 17

Note: CI = confidence interval; FP = family planning; SPA = Service Provision Assessment.

Government 

hospital

19%

Government 

health center or 

clinic

33%

Mobile clinic

<1% 

Fieldworker

15%

Private

8%

Pharmacy

11%

Non-medical/unspecified

14%
17

Figure 8A illustrates that there is no significant relationship between mCP and AD overall and also for rural 

and urban areas. There was also no significant relationship found between mCP and the number of facilities 

or the number of facilities with a mix of methods (Figures 8B and 8D). However, in Figure 8C, there is a 

positive, significant relationship between mCP and the percentage of facilities with a mix of methods overall 

and in rural areas. The larger the percentage of facilities with a mix of methods, the larger the mCP in the 

region. The R-squared value was also relatively high for the total and rural areas (approximately 0.6), which 

indicates that the percentage of facilities with a mix of methods explains most of the variance of mCP at 

the regional level. This significance was not found for urban areas. Like Bangladesh, there is a greater 

availability of methods in urban areas compared with rural, but fewer facilities. 

Figure 8 Contraceptive prevalence by average deviation and measures of method availability, Haiti

Note: A best-fit line is drawn separately for the total, urban, and rural areas.
18

Figures 9A-9C show no significant relationship between AD and the number of facilities or percentages of 

facilities with a mix of methods. Therefore, the percentage of FP facilities with a mix of methods was 

associated with increase in mCP (Figure 8C), but was not associated with use of a more diverse or balanced 

use of methods (Figure 9C). 

Figure 9 Average deviation by measures of method availability, Haiti

Note: A best-fit line is drawn separately for the total, urban, and rural areas.

In Haiti we conducted a multilevel, multivariable logistic regression to examine the determinants of 

contraceptive use according to individual and community-level factors among women aged 15-49. The 

characteristics of the women are described in Appendix Table 3. Two thirds (69%) of urban women and 

42% of rural women had secondary or higher education, and 46% and 58% were currently married, 

respectively. Approximately three-quarters of urban (73%) and rural (79%) women had not had recent 

exposure to FP messaging. Only 20% of urban women and 13% of rural women lived in an area with no 

facilities that provide a mix of methods, while 42% and 37% lived in an area where no woman reported 

obtaining their most recent FP method from community outreach. 

At the individual level, the regressions controlled for wealth, education, employment, marital status, age, 

and exposure to FP messaging, as well as community factors such as the number of facilities that provide 

FP with a mix of methods, community outreach for FP, population density, and department. Figure 10 

presents the odds ratios of using contraception according to the service environment—number of facilities 

with a mix of methods and community outreach, as quantified by the multilevel regression models. After 

controlling for covariates, in rural clusters in Haiti, women who lived in areas with the greatest number of 

health facilities had a marginally significant, 20% increase in the odds of using contraception compared 

with women in clusters with a low number of facilities with mix of methods (p < 0.05; 95% CI: 1.0-1.5). 
19

In both urban and rural areas, women who lived in clusters with low levels of community or outreach 

provision of FP were more likely to use contraceptive methods than women residing in clusters with no 

community or outreach provision. In urban areas, these women had a nearly twofold increase in the odds 

of using FP (adjusted odds ratio (AOR): 1.9; p < 0.001; 95% CI: 1.4-2.4) and in rural women, a 50% 

increase (AOR: 1.5; p < 0.001; 95% CI: 1.2-1.9).

Figure 10 Odds ratios of using modern contraception by family planning service environment factors, Haiti

Note: Models control for wealth, education, employment, marital status, age, exposure to FP messaging, population density, and 

department.

Appendix Table 4 shows the unadjusted odds ratios (UORs) and AORs for the full models. These models 

show a number of highly significant determinants that include marital status and age, as expected. Married 

women in both urban and rural areas were more likely to use FP compared with unmarried women (urban 

odds ratio: 2.6; p < 0.001; 95% CI: 1.9-3.6; rural odds ratio: 4.6; p < 0.001; 95% CI: 3.6-5.8). Women who 

were age 20-29 or 30-39 had two to three times the odds of using FP versus women age 15-19. For example, 

in both urban and rural areas, the odds were 2.6 (p < 0.001; 95% CI: 1.8-3.7) and 2.4 (p < 0.001; 95% CI: 

1.8-3.1), respectively, when comparing women age 20-29 to women age 15-19. Overall, wealth, education, 

employment, and exposure to FP messages were not strong determinants of contraceptive use. 

3.3 Malawi

Just under half (45%) of women age 15-49 use a modern contraceptive method (Appendix Table 5). This 

differed very little by the three regions in Malawi from 43% in the Southern and Northern regions to 48% 

in the Central Region. Half of modern users used injectables, followed by implants (20%) and female 

sterilization (18%). Figure 11 shows that injectables were also the most frequently used method for each 

region. The overall AD in Malawi was 12.9 with few differences by region. 

Figure 12 shows that the majority of women obtain their contraceptive methods from public sources, which 

are either government health centers and clinics (52%) or government hospitals (16%). The “other”

category is almost entirely composed of BLM, a form of outreach in Malawi. However, 90% of BLM 

outreach is situated within public facilities (Weinberger, Bietsch, and Williamson 2017). Only 6% of users 

obtain their method from a private medical source. 
20

Figure 11 Method mix by region, Malawi

Figure 12 Most recent source of family planning method, Malawi

Note: Government health centers and clinics includes family planning clinics. Private includes private 

doctors, hospitals, clinics, and other private sources.

Government 

hospital

16%

Government health 

center or clinic

52%

Mobile clinic

2%

Fieldworker 

<1%

Other government 

source

10%

Private

5%

<1%

Nonmedical/unspecified

7%

Banja La 

Mtsogolo 

8%
21

Table 5 shows the number of facilities nationally and by region with FP services, the percent with facilities 

with a mix of methods, and the total number with a mix of methods. At a national level, 53% of 809 facilities 

have a mix of methods, 65% of 140 facilities in the North Region have a mix of methods, and 46% of 307 

facilities in the Central Region have a mix of methods. Figure 13 demonstrates that there is no evidence of 

any relationships between district level use of modern contraceptives and AD, number of FP facilities, 

aggregate availability of a mix of methods, or number of FP facilities with a mix of methods. Table 5 shows 

the values for each of these measures by region in Malawi.

Table 5 Family planning facilities and availability of methods, Malawi SPA 2013-14

Number of 

FP facilities

Percentage of facilities 

with a mix of method 

types, % [95% CI]

Number of FP facilities 

with a mix of method types

Total 809 53.2 430

North 140 64.9 91

Central 307 46.3 142

South 361 54.5 197

Note: CI = confidence interval; FP = family planning.

Similarly, there was no evidence of an association at the district level between AD and any health facility 

indicator examined such as access to FP facilities, access to facilities with a mix of method, or greater 

proportions of facilities with a mix of methods, as seen in Figure 14. 

Figure 13 Contraceptive prevalence by average deviation and measures of method availability, Malawi

Note: A best-fit line is drawn separately for the total, urban, and rural areas.
22

Figure 14 Average deviation by measures of method availability, Malawi

Note: A best-fit line is drawn separately for the total, urban, and rural areas 

As in Haiti, the SPA is a census in Malawi, which enables a linked analysis to examine cluster-level health 

facility data with contraceptive use at the individual level. Appendix Table 6 shows the background 

characteristics of women analyzed in Malawi. This shows that most of these women were married—58% 

of urban women and 67% of rural women. In urban areas, 59% of women have a secondary or higher 

education whereas in rural areas, only 18% do, while 68% have only primary education. Although both 

urban and rural women commonly work in agriculture or other fields, the proportion is twice as high among 

rural women (63% compared with 29%). Urban-rural differences are also seen in exposure to FPmessaging: 

66% of urban women versus 40% of rural women. There are also notable differences in the FP service 

environment. Fewer than 1% of women in urban areas live in a cluster with no facilities that provide at least 

three types of methods, unlike 16% of rural women. In addition, 12% and 20% of women of urban and rural 

women live in a cluster with no community outreach for provision of FP methods, according to the women’s 

report of FP sources. 

Figure 15 and Appendix Table 7 show the odds of using modern contraceptives among urban and rural 

women by the FP service environment. In rural Malawi, women have 20% greater odds of using modern 

FP if they live in an area with the greatest number of facilities that offer FP with a mix of methods (AOR: 

1.2; p < 0.05; 95% CI: 1.0-1.5). Conversely, among urban women who live in an area with no facilities with 

a mix of methods, there is a 50% increase in the odds of using FP (AOR: 1.5; p < 0.001; 95% CI: 1.2-1.8). 

However, the none category includes only 11 women surveyed in three clusters which were all within one 

district. Nonetheless, urban women in clusters with medium and high availability of methods were more 

likely to use contraceptives, which was significant for women in medium clusters (AOR: 1.3; p < 0.01; 

95% CI: 1.1-1.5). In rural Malawi, women in clusters with low community or outreach provision of FP

were more likely to use a method compared with women in areas where no women reported using
23

community or outreach outlets for their most recent method (AOR 1.3; p < 0.01; 95% CI 1.1-1.5). Together, 

these two variables explained 69% of the between-cluster variation in the use of contraceptives in urban 

areas and 20% in the rural areas. 

As seen in Appendix Table 7, the odds of using FP were 8 to 9 times higher among currently married women 

(urban AOR 8.9; p < 0.001; 95% CI 6.5-12.3; rural AOR 8.3; p < 0.001; 95% CI 6.9-10.0) and 3 to 4 times 

higher among formerly married women (urban AOR 3.6; p < 0.001; 95% CI 2.3-5.7; rural AOR 3.3; p 

< 0.001; 95% CI 2.7-4.2) and compared with never married women. Age was also strongly associated with 

contraceptive use, with women age 30-39 having the highest odds of using contraception of any age group 

compared with women age 15-19 (urban AOR 2.5; p < 0.001; 95% CI 1.7-3.6; rural AOR 2.8; p < 0.001; 

95% CI 2.4-3.3). Employment was significantly associated with contraceptive use in rural, but not urban 

Malawi. The odds were 40% higher among both professionally and agriculturally employed women 

compared with the unemployed women (professional AOR 1.4; p < 0.01; 95% CI 1.1-1.6; agricultural AOR 

1.4; p < 0.001; 95% CI 1.2-1.5). Wealth was also positively associated in rural but not urban areas, as was 

population density. There was no evidence of an association between contraceptive use and either education 

or exposure to FP messaging.

Figure 15 Odds ratios of using modern contraception by family planning service environment factors, 

Malawi

Note: Models controlled for wealth, education, employment, marital status, age, exposure to FP messaging, population density, and 

region.
24

3.4 Nepal

One in three (33%) women age 15-49 in Nepal use a modern contraception method (Appendix Table 8). 

There were few differences in mCP by region with ranges from 29% in Province 4 to 37% in Province 7. 

Overall, the most prevalent method in Nepal among women using a modern method was female sterilization 

(35%). Injectables were the second most used method (21%). Approximately 13% of women in Nepal used 

another modern method, mostly male sterilization (12.8%, results not shown). The method mix differed by 

province, as shown in Figure 16. One-third of modern users in Province 1 used injectables, followed by 

29% of modern users in Province 6. However, in Province 2, only 11% of modern users used injections and

approximately three-fourths (76%) used female sterilization. Provinces 3 and 6 had the lowest percentage 

of female sterilization (13% and 9%, respectively), but the highest use of other modern methods (26% and 

31% respectively), which is predominately male sterilization. The overall AD in Nepal was 7.7, which 

ranged from 7.0 in Province 5 to 17.6 in Province 2. 

Figure 16 Method mix by region, Nepal 
25

As Figure 17 shows, the public sector serves nearly seven of ten (69%) users, while private medical sources 

serve one-fourth of users. Government hospitals provide nearly one-third (32%) of FP methods, which is a 

relatively large share for FP. Female sterilization and long-acting methods are common (Figure 16) and 

require higher levels of care. Government health centers and clinics provide another quarter of methods 

(26%) and private facilities another 17%.

Figure 17 Most recent source of family planning method, Nepal

Note: Government health centers and clinics includes family planning clinics. Private includes private 

doctors, hospitals, clinics, and other private sources.

Table 6 shows the facility variables by region in Nepal. Figure 18 plots the relationships between provinciallevel contraceptive prevalence and AD, number of facilities with FP, percentages of facilities with a mix of 

methods, and number of facilities with a mix of methods. There were too few points to test a relationship, 

although these plots show that contraceptive use does not vary by any of these factors. Figure 19 plots AD 

against the number of facilities with FP, percentages of facilities with a mix of methods, and number of 

facilities with a mix of methods at the provincial level. Although a line of best fit is included, Province 2 is 

an outlier in AD (17.9) and its presence distorts these lines. Among the other provinces, AD varies little and 

there is no apparent evidence of a correlation between AD and FP facility factors.

Table 6 Family planning facilities and availability of methods, Nepal SPA 2015

Number of 

FP facilities

Percentage of facilities 

with a mix of method 

types, % [95% CI]

Number of FP facilities 

with a mix of method 

types

Total 919 23.9 [20.7,27.4] 220

Province 1 158 25.0 [17.4,34.5] 39

Province 2 167 13.9 [8.6,21.6] 23

Province 3 177 25.5 [18.6,34.0] 45

Province 4 119 24.5 [15.2,37.2] 29

Province 5 135 25.2 [18.0,34.2] 34

Province 6 74 25.6 [15.6,39.1] 19

Province 7 89 33.4 [23.4,45.1] 30

Note: CI = confidence interval; FP = family planning.

Government 

hospital

32%

Government health 

center or clinic

26%

Other government 

source

11%

Private

17%

Pharmacy

9%

Non-medical/unspecified

5%
26

Figure 18 Contraceptive prevalence by average deviation and measures of method availability, Nepal

Note: A best-fit line is drawn separately for the total, urban, and rural areas in A and for only the total in B-D.

Figure 19 Average deviation by measures of method availability, Nepal

Note: A best-fit line is drawn separately for the total, urban, and rural areas in A and for only the total in B-D.
27

3.5 Senegal

Only 19% of women age 15-49 in Senegal use a modern contraceptive method (Appendix Table 9). This 

differed greatly by region, from 8% in Matam to 25% in Dakar. Among modern users, injectables were the 

most prevalent method (38%), followed by implants (31%). As shown in Figure 20, the regions fluctuated 

between injectables and implants as the most used method. In Fatick and Matam, more than half of women 

used injectables (50% and 59%, respectively), and in Kolda, 53% of women used implants. The overall AD 

in Senegal was 11.9, which ranged from 9.0 in Dakar to 18.4 in Kolda.

Figure 20 Method mix by region, Senegal 
28

Figure 21 shows that the large majority of women obtain their methods from government facilities, and 

among those, government health posts (60.6%) and other government facilities (3.5%) supply a total of 

64% of all sources. Pharmacies serve 8% of users. 

Figure 21 Most recent source of family planning method, Senegal

Note: Government health centers and clinics includes family planning clinics. Other public sources

include health posts and health huts. Private includes private doctors, hospitals, clinics, and other 

private sources.

Government 

hospital

6%

Government health 

center or clinic

15%

Mobile clinic

1%

Government health post and other

64%

Private

5%

Pharmacy

8%

Non-medical/unspecified

1%
29

Table 7 shows that Thiès had 100 sampled FP facilities, followed by Kolda (70) and Louga (59). Fewer 

than 50 FP facilities were sampled in 9 of the 14 regions in Senegal. The capital, Dakar, had 53 FP facilities, 

and the largest percentage of facilities with a mix of methods (74%). However, this left 39 FP facilities with 

a mix of methods for the entire Dakar Region. Although Thiès had the largest number of FP facilities

sampled, only one-third of these facilities had a mix of methods. Similarly, Kolda had the second largest 

number of FP facilities (70), and the lowest percentage of facilities with a mix of methods (22%).

Table 7 Family planning facilities and availability of methods, Senegal SPA 2017

Number of 

FP facilities

Percentage of facilities 

with a mix of method 

types, % [95% CI]

Number of FP facilities 

with a mix of method 

types

Total 657 43.2 [41.1,45.2] 283

Dakar 53 74.1 [54.0,87.4] 39

Ziguinchor 44 62.3 [55.0,69.1] 27

Diourbel 31 66.8 [59.3,73.5] 21

Saint-Louis 48 47.0 [38.4,55.9] 23

Tambacounda 41 26.0 [16.8,38.0] 11

Kaolack 53 37.5 [36.9,38.0] 20

Thiès 100 33.3 [29.6,37.3] 33

Louga 59 40.9 [34.9,47.2] 24

Fatick 49 49.0 [48.5,49.5] 24

Kolda 70 21.5 [18.9,24.5] 15

Matam 27 53.7 [39.9,66.9] 14

Kaffrine 41 35.8 [35.0,36.5] 15

Kédougou 18 36.7 [32.3,41.2] 7

Sédhiou 24 46.7 [45.7,47.7] 11

Note: CI = confidence interval; FP = family planning.
30

Figure 22A indicates that there is significant negative relationship between mCP and AD. The higher the 

AD, the lower the mCP, which is in the expected direction. The R-squared value also shows that AD 

explains 34% of the variance in mCP. This relationship was not found to be significant when assessed by 

urban and rural areas. Figures 22B and 22D show that mCP was also significantly associated with the 

number of facilities and number of facilities with a mix of methods and that these indicators explained 35% 

and 50% of the variance of mCP, respectively. The association was also in the expected direction with an 

increase in mCP with increasing number of facilities. This relationship was not significant in urban or rural 

areas. In Figure 22C, we see no significant association between mCP and the percentage of facilities with 

a mix of methods. 

Figure 22 Contraceptive prevalence by average deviation and measures of method availability, Senegal

Note: A best-fit line is drawn separately for the total, urban, and rural areas.
31

Figure 23A shows a significant negative relationship between AD and the number of FP facilities, but only 

for urban areas. Here we see that AD decreases (more balanced mix of methods) with an increase in number 

of FP facilities for urban areas. In addition, 66% of the variance in AD was explained by this number at the 

regional level. Therefore, in addition to the positive association of the number of FP facilities with mCP we 

observed in Figure 22A, this indicator also seems to be associated with a greater mix of methods. In Figure 

23B, overall AD was significantly associated with the percentage of facilities with a mix of methods, 

although this was not significant for urban and rural areas. Figure 23C also shows that AD was significantly 

associated with the number of facilities with a mix of methods overall and in urban areas. The relationship 

was in the expected direction with a decrease in AD (larger mix of methods) observed with an increase in 

the number of facilities with a mix of methods. This number also explained 80% of the variance in AD 

overall and 69% of the variance in AD for urban areas at the regional level. 

Figure 23 Average deviation by measures of method availability, Senegal

Note: A best-fit line is drawn separately for the total, urban, and rural areas. 

3.6 Tanzania

Slightly more than a quarter (27%) of women age 15-49 in Tanzania used a modern contraceptive method 

(Appendix Table 10). This ranged from only 9% in Zanzibar to 48% in Lindi. The most common modern 

method used was injectables (37%) followed by implants (21%). As shown in Figure 24, injectables were 

the most used method in most regions with implant usually a close second. The male condom was highly 

used in Dar es Salaam (37%), Iringa (24%), Mbeya (25%) and Mara (22%). The overall AD in Tanzania 

was 8.4. This ranged from 7.4 in Iringa to 14.1 in Lindi. 
32

Figure 25 shows 61% of users receive their method from a public source, with 37% provided by dispensaries 

or clinics; 12% of users obtain their methods from a private source. Despite the dominance of long-acting 

or permanent methods, one-fifth of women obtain their method from a pharmacy. However, it should be 

noted that outreach or mobile clinics were not provided as a response option in this DHS survey.

Figure 24 Method mix by region, Tanzania

Figure 25 Most recent source of family planning method, Tanzania

Note: Government health centers and clinics includes FP clinics or dispensaries. Private includes 

private doctors, hospitals, clinics, and other private sources.

Government 

hospital

11%

Government health 

center or clinic

50% Fieldworker

0%

Private

12%

Pharmacy

21%

Non-medical/unspecified

6%
33

In Tanzania, there are 947 sampled facilities that provide FP, 41% (384) of which have a mix of methods 

available (Table 8). Mbeya had the greatest number of sampled facilities (67), Manyara had the highest 

percentage of facilities with a mix of methods (78% of 22 facilities), and Dar es Salaam had the greatest 

number of sampled facilities with a mix of methods (31). In contrast, Katavi had only 10 sampled facilities 

with FP services among which only 3 had a mix of methods. 

Table 8 Family planning facilities and availability of methods, Tanzania SPA 2014-15

Number of FP 

facilities

Percentage of 

facilities with a mix

of method types, 

% [95% CI]

Number of FP 

facilities with a mix 

of method types

Total 947 40.6 [36.4,44.9] 384

Dodoma 51 21.2 [9.5,40.7] 11

Arusha 37 30.1 [13.6,54.1] 11

Kilimanjaro 42 29.9 [12.9,55.1] 13

Tanga 49 43.7 [23.4,66.4] 21

Morogoro 44 13.9 [10.5,18.2] 6

Pwani 38 53.3 [30.8,74.4] 20

Dar es Salaam 43 71.4 [45.6,88.2] 31

Lindi 35 27.5 [12.4,50.4] 10

Mtwara 31 21.6 [8.3,45.6] 7

Ruvuma 39 38.0 [18.8,61.8] 15

Iringa 30 48.2 [24.6,72.7] 14

Mbeya 67 43.8 [23.5,66.4] 29

Singida 30 49.1 [25.6,73.0] 14

Tabora 46 61.9 [40.4,79.6] 28

Rukwa 30 42.8 [22.6,65.7] 13

Kigoma 39 55.5 [33.5,75.5] 21

Shinyanga 26 54.6 [29.8,77.3] 14

Kagera 41 27.6 [11.2,53.7] 11

Mwanza 45 37.7 [16.9,64.3] 17

Mara 38 33.9 [15.0,60.0] 13

Manyara 22 78.2 [45.2,94.0] 18

Njombe 34 18.1 [6.4,41.5] 6

Katavi 10 30.5 [15.9,50.5] 3

Simiyu 28 45.1 [24.6,67.4] 13

Geita 21 33.6 [15.9,57.6] 7

Zanzibar 30 56.2 [42.9,68.6] 17

Note: CI = confidence interval; FP = family planning.

Figure 26 shows the relationship between contraceptive prevalence and AD, as well as health facility 

measures (number of FP facilities, percent with a mix of methods, and number with a mix of methods), 

overall and by urban and rural residences among the regions. There is no evidence of a correlation between 

contraceptive use and AD (26A), number of facilities (26B), or number of facilities with a mix of methods

(26D). Urban Dar es Salaam stands out as an urban area with an exceptionally large number of facilities,

which is understandable given the population of the capital of Tanzania. Contrary to what was expected, 

there is some evidence of a negative relationship between contraceptive prevalence and the percentage 

availability of a mix of methods (26C) for the total and for rural areas. These relationships were only 

marginally significant (p < 0.05) and do not control for other potentially important factors. The R-squared 

values show that the percent of facilities with a mix of methods explains 21% of the variance in mCP overall 

and 18% in rural areas. Finally, Figure 27 shows no evidence of a correlation between total, urban, or rural 

AD and any factor related to availability of FP services or availability of a mix of methods by region. 
34

Figure 26 Contraceptive prevalence by average deviation and measures of method availability, Tanzania

Note: A best-fit line is drawn separately for the total, urban, and rural areas.

Figure 27 Average deviation by measures of method availability, Tanzania

Note: A best-fit line is drawn separately for the total, urban, and rural areas.
35

4 DISCUSSION

This analysis examined the relationship between contraceptive use (measured by mCP), the diversity of 

method mix (measured by AD), and availability of methods at facilities (measured by three indicators

related to the number and share of facilities with contraceptive methods available) across six countries.

Overall, there are noticeable differences across these measures by country. The mCP ranged from 19% of 

all women in Senegal to 45% in Malawi; AD ranged from 7.7 in Nepal to 15.3 in Haiti, and the percentage 

of facilities with a mix of method types ranged from 24% in Nepal to 53% in Malawi. The analysis explored 

subnational differences within each country. In some countries, there was very little variation in these 

indicators across subnational areas, while other countries demonstrated much more diversity in one or more 

indicators. For example, in Tanzania, there is nearly a 40-percentage point spread in mCP between the 

regions with the lowest and highest level of contraceptive use, while Haiti, Malawi, and Nepal all have 

spreads of fewer than 12 percentage points. 

In terms of method mix, there was a spread of only 2-3 points between the lowest and highest AD scores 

by subregion in Bangladesh, Haiti, and Malawi compared to spreads of 7 in Tanzania, 9 in Senegal, and 12 

in Nepal. The spread between the lowest and highest percent of facilities with a mix of method types ranged 

from a 19-percentage point difference (Malawi) to greater than 50-percentage point difference in Haiti, 

Senegal, and Tanzania. This analysis found that the relationships between contraceptive use, method mix, 

and method availability vary considerably across the countries studied. In most cases, the analysis by 

subnational area found no significant relationships between contraceptive use and either AD or FP services 

or method availability at the facility level, or between AD and services or method availability at the facility 

level. The following section reviews these relationships in each country. 

4.1 Country-specific findings in context

Bangladesh showed a potentially positive relationship between mCP and the percentage of facilities with a 

mix of methods at the regional level. However, too few data points meant that significance could not be 

tested. There was no clear relationship between AD and any of the availability measures. However, in 

Bangladesh, only a small number of facilities offer a mix of methods, and this is coupled with high use of 

short-term methods, and most notably, pills. Sylhet is somewhat of an outlier in the analysis in Bangladesh.

This is the division with the lowest mCP (over 20 percentage points lower than the highest division), one 

method that dominates the mix (the pill), and the lowest percent and number of facilities with a mix of 

methods. Sylhet also has the lowest AD score, which indicates that AD, while assessing balance, is not 

necessarily the opposite of skew. It is important to note that in Bangladesh, the most common source for FP

methods are pharmacies (38% of all users, 48% of pill users), which are not captured by the SPA surveys

(NIPORT/Bangladesh, Mitra Associates, and ICF International 2016). This may attenuate the relationship 

between availability of methods at formal health facilities surveyed in the SPA and contraceptive use.

Haiti showed a positive significant relationship between mCP and the percentage of facilities with a mix of 

methods (overall and rural). There was no clear relationship between AD and any of the availability 

measures. Regression analysis in Haiti showed that women living in the rural areas with the greatest number 

of health facilities with a mix of methods had a marginally significant higher use than women living in 

areas with low numbers of facilities with a mix of methods. Community distribution or outreach (low versus 
36

none) was also significant. Use of injectables dominates the method mix in almost all subnational areas 

with no clear relationship with method availability at facilities. For example, injectables comprise 61% of 

the method mix in both Nord and Nippes, while the percent of facilities with a mix of method types is only 

40% in Nord compared to 61% in Nippes. This may be partly due to a high reliance on community-based 

sources for injectables that are not captured in the facility accessibility measures. These findings only 

somewhat align with another study that examined an earlier round of DHS and SPA data collected in Haiti. 

Wang and Mallick (2019) found that women in both urban and rural areas had greater odds (twice as high 

or 75% higher, respectively) of using FP in areas with high availability compared with areas with low 

availability. However, in this study that used earlier data, the service environment variables were defined 

somewhat differently, which may explain some of the discrepancy. 

In Malawi, no significant findings were found in the association between with mCP or AD and method 

availability measures at the district level. However, regression analysis did highlight significant findings 

for rural women both in terms of facility method mix and community outreach (low versus none). While 

injectables largely dominate the method mix in Malawi, this presents an interesting case with a fairly high 

use of female sterilization relative to other countries in the region as well as relatively high levels of use of 

implants. Mobile outreach services play a key role in providing these methods, and even tubal ligations are

provided by the BLM outreach teams (Chipeta-Khonje et al. 2009; USAID 2012), which may not be 

captured in facility-based availability measures. In agreement with this study, a separate analysis 

(Weinberger, Bietsch, and Williamson 2017) found a significant relationship between proximity to BLM 

outreach sites and mCP. 

In Nepal, there were too few data points at the regional level to test for relationships, although among the 

available data, there were no clear relationships between either CP or AD and method availability measures. 

However, the high use of both male and female sterilization in the country may distort findings because this

influences both the timing of services (sterilization may have occurred much before the time of the Nepal 

survey) and the types of facilities utilized for services (higher-level only). 

Senegal showed multiple significant relationships. There was an overall significant relationship between 

mCP and AD, as well as mCP and both the number of facilities that offer FP and the number of facilities 

that offer a mix of method types. In addition, AD decreased significantly with the number of facilities that 

offer FP in urban areas and was significantly associated with the number of facilities with a mix of methods 

overall and in urban areas. In Senegal, as a low mCP country with a relatively high total fertility rate (4.6 

in 2017 (ANSD/Sénégal and ICF 2018)) that has recently experienced rapid increases in contraceptive use, 

the increased availability of methods may be playing a clearer role in influencing contraceptive use than in 

countries that have already reached higher levels of mCP (Adetunji, Feyisetan, and Starbird 2017). 

In Tanzania, despite wide variations in measures across regions, no clear patterns were seen in the 

relationships of these measures to one another. Further, an unexpected result was seen in which mCP 

decreased as the percentage of facilities with a mix of methods increased (overall and in rural areas).

However, this relationship was only marginally significant. One-fifth of women reported obtaining their 

most recent method from a pharmacy; the situation in Tanzania may be further complicated by the large 

role of mobile outreach services, especially in the provision of implants (Duvall et al. 2014; Wickstrom et 

al. 2013). Facility-based measures of method availability may not capture the full extent of availability, 
37

especially when outreach teams provide services within public facilities, a practice that is supported in 

Tanzania (Jarvis, Wickstrom, and Shannon 2018).

4.2 Contextualizing results across countries 

In summary, across the six countries, no conclusive or consistent relationships between the contraceptive 

use (mCP), the balance of a method mix (AD), and the availability of methods at facilities were found. This 

lack of consistency of findings may not be unexpected given the diversity of contexts across these countries, 

as well as the fact that a wide range of factors beyond availability of methods at facilities on both the supplyside and demand-side influence method choice. This includes familiarity with methods, prevailing social 

norms, contraceptive preferences, provider bias, program and policy structures, and affordability of 

methods, as well as availability of methods outside of facilities (such as provision by community health 

workers, drug shops, or mobile outreach teams as above). The findings of this report highlight a point noted 

in an analysis of 123 countries that found an association between lower AD values and higher contraceptive 

prevalence, and also noted that the same AD can occur in settings with both high and low contraceptive 

prevalence (Ross, Keesbury, and Hardee 2015). The inconclusive relationship is also consistent with a study 

that found no relationship between method skew and mCP (Bertrand et al. 2014).

Only in Haiti and Malawi could analysis be done at the individual level or in a way that controlled for 

factors that are likely to confound the relationships between these measures. In Malawi, the analyses that 

examined bivariate relationships between mCP, AD, and method availability produced no significant 

results, although the regressions were able to identify significant relationships in rural areas. This suggests 

that it is possible that significant relationships may exist in the other countries, but that available data (at 

the subnational, administrative 1 level) is not granular enough to reveal such relationships. Further, it may 

be that the relationship between contraceptive use, method mix, and method availability changes over time 

in a country as contraceptive use increases, and that changes in demand for family planning may also play 

a role (Weinberger, Sonneveldt, and Stover 2017). The findings from Senegal, the lowest mCP country 

included in this analysis, may point to this fact. For the higher prevalence countries, it may be that there is 

enough availability of methods that these measures no longer mediate contraceptive use. Future research 

could examine these relationships over time where there have been multiple rounds of DHS and SPA 

surveys such as Bangladesh, Haiti, Senegal, Tanzania.

Despite the lack of consistent relationships across countries, it would be a misuse of these findings to 

suggest that making a wide range of methods available and accessible is not important. Earlier work using 

more aggregated measures like the FPE have shown relationships between the number of methods available 

and mCP. It was worthwhile to explore the relationship between method availability and balance of method 

mix (measured here through AD), but as has been shown earlier, method skew can exist across many 

contexts and greater method availability may not inherently lead to a more balanced method mix. Although 

researchers may be tempted to use method mix as a proxy for method availability (Sullivan et al. 2006), 

results from this study indicate that this measure of balance in a method mix, the AD, should therefore not 

be used in such a manner without first assessing these assumptions. Future research should continue to 

examine supply-side measures to understand availability. Availability of a mix of methods is indeed a 

prerequisite for a more balanced method mix, and thus measures of availability should continue to be 

monitored. Ensuring women are able to choose from a wide range of methods is critical to a successful FP

program and a rights-based approach. 
38

4.3 Strengths and limitations

A key strength of this paper is the multilevel approach to furthering our understanding of the complex 

relationships between contraceptive use, method mix, and access to FP methods. Community-level factors 

that reflect access to a diverse range of methods is a research area that has not been widely studied at lower 

than subnational levels. Our literature review identified only a small number of countries studied at these 

lower levels (Hong, Montana, and Mishra 2006; Shiferaw et al. 2017; Sullivan et al. 2006; Tumlinson et al. 

2015; Wang and Mallick 2019). The multivariable, multilevel approach is novel. By applying a new method 

to approximate cluster weights, we have more precisely estimated the relationship between communitylevel factors and contraceptive use. 

By linking SPA data to DHS data, while also considering factors such as community distribution or mobile 

outreach distribution at FP facilities, we added to the field of research that accounts for multiple levels 

within the health system. By using SPA data, we can gauge the relationship between supply-side factors 

such as method availability and demand or use factors such as contraceptive use and method mix balance. 

Without such well-timed, geographically linkable data sources, research is typically left to assume that 

demand factors such as method mix balance are related to supply. However, our research shows this is not 

always the case, at least at subnational levels. Our analysis at more granular (cluster) levels in Haiti and 

Malawi reveals that method availability is modestly associated with an increase of contraceptive use,

although there are other, highly relevant determinants of contraceptive use such as age and marital status. 

With our analysis of Haiti and Malawi, we also provide evidence that there is an association that shows that 

at least some level of outreach, especially compared to areas where there is none, can help meet women’s 

demand for FP and can increase contraceptive use. This adds new, novel information to the body of 

literature.

One limitation of this analysis is that for most countries analyzed, we were only able to examine 

relationships at the first administrative unit. When SPA surveys are sample surveys, samples may not 

capture all facilities—especially the lower-level facilities important in rural areas—that are within 

proximity to each DHS cluster, which is a requisite for ecologically linking data (Burgert and Prosnitz 

2014). Further, SPA surveys do not capture all sources where women obtain their FP methods, such as 

pharmacies or community health workers. In these subnational analyses, many important confounders were 

omitted, such as consideration of the facility to population ratio, or population density. However, we 

controlled for population density in our analysis in Haiti and Malawi, and found that in rural Malawi, 

population density does appear to play a role in contraceptive use. 

In the individual-level analysis, where linkage between household and facility level data is possible down 

to the cluster level, we did not include potentially important confounders, such as quality of care, which are 

known to influence or be associated with contraceptive behaviors at both the aggregate and individual levels

(Jain et al. 2019; RamaRao et al. 2003; Staveteig, Mallick, and Winter 2015; Tumlinson et al. 2015). While 

the SPA surveys also include an observation of FP services, there are important limitations to these 

indicators that prohibited our inclusion of the indicators in our analysis, such as biases in the sample of 

clients and limitations to the observation protocol (Mallick, Temsah, and Wang 2019; Mallick, Benedict, et 

al. 2020). In addition, we did not present an analysis of the relationship between provider training and either 

mCP or AD, because in an initial exploration, we found no conclusive evidence of relationships at 

subnational levels (results not shown). Nevertheless, provider counseling, high quality of counseling, and 
39

access to multiple methods may not necessarily yield an increase in contraceptive prevalence at the 

population level. These aspects of quality of care may help current users find a method more suitable for 

their needs and the overall prevalence may remain the same. Future research could use a more focused, 

targeted approach to better understand these relationships at more granular levels. 

Finally, both the DHS and the SPA surveys are cross-sectional surveys that reflect point-in-time 

contraceptive use and point-in-time availability of methods. Since methods may come in and out of stock 

frequently, future research could examine data from health or logistics management information systems 

(Mallick, Temsah, et al. 2020). The more frequent data points could be used to assessthe extent of stockouts, 

with averages in availability calculated over time, to explore how changes in or relative stability of method 

availability over time may influence both contraceptive use and measures of method mix, such as AD. More 

in-depth analysis of the contraceptive calendar may provide further insights, for example, into reasons for 

discontinuation among women who had used a method and stopped in the 5 years before the survey, as 

found in previous research (Bradley, Schwandt, and Khan 2009; Finnegan, Sao, and Huchko 2019; 

Staveteig, Mallick, and Winter 2015), and linking more detailed information about contraceptive behavior

to supply-side factors that are measurable by available data.

4.4 Recommendations 

In addition to the future research noted above, immediate actions can be undertaken based on the results of 

this study. These include ensuring that more facilities provide a variety of FP methods to meet women’s 

diverse contraceptive needs. In addition, a straightforward analysis of the level of outreach by cluster or 

region can help to identify areas where scale-up of outreach can help target FP programs to best meet the 

demand for FP. Finally, additional supply-side factors should be explored to motivate service delivery 

interventions such as strengthening supply chains, task sharing, and provider training. With rights-based 

FP, if method skew is driven by user preference, then efforts can be made to ensure a consistent supply of

common methods, rather than diversifying methods and drawing users away from the methods they prefer. 

4.5 Conclusions

This analysis, which explored the relationships between contraceptive use, method mix, and method 

availability, capitalized on the unique opportunity to join nationally and subnationally representative 

household and health facility data. This paper examined whether method availability also relates to method 

mix. Without a consistent or clear picture across countries, it is important to examine every country’s unique 

context related to the FP environment. Women in Haiti and Malawi showed modest increases in use of 

modern methods in areas with both greater method availability and outreach, but there was no aggregate 

association with method balance. Meanwhile, in Senegal, there were very strong regional relationships 

between greater contraceptive use and a more balanced mix, while more balanced mix was also strongly 

associated with greater availability of methods. This relationship in Senegal, a low-prevalence country, 

suggests that investments in expanding availability of a range of methods are important as countries shift 

from low to high mCP. From this analysis, we recommend that method balance, as assessed by the AD,

cannot be used as a household-level proxy for method availability. Nonetheless, method choice should 

continue to be a central tenet of rights-based FP to ensure that women have access to the methods that meet 

their unique reproductive needs. 

41

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APPENDIX
48

Appendix Table 1 Percentage and 95% confidence interval of overall modern contraceptive use, method distribution, and aver

modern users, Bangladesh DHS 2014

Among women age 15-49 using a modern contraceptive metho

mCP N

Female 

sterilization IUD Implants Injectables Pill

Male 

condom

Total 54.1 [52.7,55.4] 16,858 8.5 [7.7,9.4] 1.2 [0.9,1.6] 3.2 [2.5,3.9] 23.0 [21.1,25.0] 49.9 [48.0,51.9] 11.9 [10.9,

Barishal 54.6 [51.5,57.6] 1,051 5.7 [4.1,7.8] 0.3 [0.1,0.8] 3.4 [2.2,5.2] 31.5 [25.0,38.9] 49.8 [45.8,53.7] 8.1 [5.3,1

Chattogram 47.2 [44.4,50.0] 3,121 7.7 [5.9,10.0] 1.6 [1.0,2.5] 2.6 [1.5,4.5] 25.4 [20.9,30.6] 51.0 [46.5,55.4] 10.3 [8.3,1

Dhaka 54.2 [51.3,57.2] 5,857 7.3 [6.0,8.9] 1.1 [0.6,1.8] 3.3 [2.0,5.5] 19.8 [15.8,24.6] 50.6 [46.0,55.2] 15.7 [13.5,

Khulna 56.4 [54.1,58.7] 1,729 10.9 [8.6,13.7] 0.9 [0.5,1.6] 3.6 [2.1,6.0] 24.5 [20.7,28.7] 46.5 [42.2,50.8] 12.2 [9.9,1

Rajshahi 60.7 [58.2,63.1] 2,007 9.3 [7.4,11.7] 1.1 [0.6,2.0] 3.1 [2.1,4.7] 26.2 [21.3,31.8] 46.0 [42.2,49.7] 12.2 [10.0,

Rangpur 63.0 [60.3,65.7] 1,946 8.2 [6.2,10.8] 1.8 [0.7,4.7] 3.5 [2.4,5.1] 22.5 [18.7,26.9] 52.7 [48.7,56.6] 6.2 [4.6,8

Sylhet 40.9 [36.5,45.4] 1,147 16.3 [13.0,20.3] 0.7 [0.3,1.8] 1.9 [1.0,3.8] 15.8 [12.4,20.0] 52.3 [47.0,57.6] 9.7 [6.7,1

Note: mCP = modern contraceptive prevalence; IUD = intrauterine device; AD = average deviation.

Appendix Table 2 Percentage and 95% confidence interval of overall modern contraceptive use, method distribution, and aver

modern users, Haiti DHS 2016-17

Among women age 15-49 using a modern contraceptive metho

mCP N

Female 

sterilization IUD Implants Injectables Pill

Male 

condom

Total 22.3 [21.2,23.4] 14,371 3.2 [2.5,4.0] 0.3 [0.1,0.6] 6.1 [5.0,7.5] 54.1 [51.0,57.1] 6.3 [5.3,7.5] 28.0 [24.9,

Aire Metropolitaine 24.0 [21.4,26.8] 3,632 0.8 [0.3,2.0] 0.2 [0.1,1.0] 3.0 [1.7,5.1] 40.9 [33.2,49.2] 8.0 [5.8,10.9] 46.0 [37.4,

Rest-Ouest 16.3 [13.7,19.4] 2,285 4.9 [2.3,10.2] 0.6 [0.1,4.5] 6.8 [3.9,11.5] 54.4 [46.7,61.8] 8.6 [5.1,14.3] 22.4 [16.3,

Sud-Est 19.6 [16.1,23.7] 756 3.5 [1.7,7.3] 0 11.4 [5.9,21.0] 40.3 [31.9,49.5] 4.7 [2.5,8.7] 36.6 [25.0,

Nord 23.7 [20.3,27.4] 1,559 3.9 [1.9,7.6] 0.2 [0.0,1.6] 6.7 [3.8,11.7] 61.2 [52.9,68.8] 5.5 [2.7,10.9] 17.7 [12.7,

Nord-Est 27.4 [24.6,30.4] 516 4.5 [2.8,7.3] 0.5 [0.1,3.5] 3.2 [1.4,7.0] 56.0 [45.4,66.1] 5.4 [3.1,9.2] 28.6 [19.2,

Artibonite 22.1 [18.7,25.9] 2,090 2.0 [1.0,4.1] 0 9.6 [5.8,15.6] 68.3 [62.2,73.9] 5.0 [3.2,7.8] 13.1 [9.4,1

Centre 27.8 [24.9,30.8] 918 5.5 [3.2,9.4] 0.7 [0.2,2.5] 7.8 [4.2,14.1] 57.4 [50.4,64.1] 4.4 [2.4,7.8] 22.4 [16.2,

Sud 19.2 [16.5,22.3] 951 6.1 [3.3,10.9] 0 7.3 [3.9,13.4] 48.1 [40.3,56.1] 5.3 [3.2,8.7] 31.7 [25.1,

Grand’Anse 23.3 [20.2,26.6] 532 3.7 [2.0,6.7] 0.6 [0.1,4.5] 2.7 [1.3,5.4] 66.0 [55.3,75.4] 5.4 [2.9,9.9] 19.2 [11.7,

Nord-Ouest 21.5 [17.9,25.6] 703 4.9 [2.9,8.2] 0.2 [0.0,1.8] 3.1 [1.5,6.5] 65.6 [56.5,73.6] 3.9 [2.0,7.4] 20.8 [14.7,

Nippes 27.9 [23.1,33.2] 427 3.1 [1.6,6.0] 0 9.9 [6.1,15.7] 61.3 [52.1,69.8] 6.8 [4.4,10.4] 17.8 [12.5,

Note: mCP = modern contraceptive prevalence; IUD = intrauterine device; AD = average deviation.
49

Appendix Table 3 Background characteristics of women, Haiti DHS 2016-17

Urban Rural

% N % N 

Number of family planning facilities 

with a mix of methods

None 19.8 614 13.4 939 

Low 38.5 1,192 48.2 3,371 

Medium 21.4 663 11.8 824 

High 20.3 629 26.5 1,854 

Community-level outreach

None 41.6 1,289 37.3 2,605 

Low 21.2 656 21.2 1,483 

Medium 19.7 611 21.7 1,513 

High 17.5 543 19.8 1,387 

Wealth quintile

Lowest 0.7 20 28.9 2,022 

Second 5.4 168 30.2 2,112 

Middle 19.2 595 24.2 1,694 

Fourth 43.3 1,342 11.4 799 

Highest 31.4 973 5.2 361 

Education

None 6.8 209 20.1 1,403 

Primary 24.1 748 38.1 2,664 

Secondary + 69.1 2,141 41.8 2,920 

Employment

Not employed 47.1 1,459 43.3 3,023 

Professional 45.2 1,402 45.0 3,143 

Agricultural, other 7.7 238 11.8 822 

Marital status

Never married 45.7 1,415 35.5 2,479 

Currently married 45.7 1,417 57.8 4,037 

Formerly married 8.6 267 6.7 471 

Age

15-19 24.0 743 23.1 1,616 

20-29 35.4 1,098 33.5 2,340 

30-39 25.0 776 25.0 1,747 

40-49 15.6 482 18.4 1,284 

Exposure to family planning media

No 73.1 2,265 79.5 5,554 

Yes 26.9 834 20.5 1,434 

Department

Rest-Ouest 9.5 296 19.1 1,336 

Sud-Est 4.0 124 9.1 633 

Nord 23.0 712 12.1 847 

Nord-Est 8.8 274 3.5 242 

Artibonite 27.1 840 17.9 1,250 

Centre 7.6 234 9.8 684 

Sud 6.5 201 10.7 750 

Grand’Anse 5.2 161 5.3 371 

Nord-Ouest 6.3 195 7.3 508 

Nippes 2.0 61 5.2 366 

Total 100.0 3,098 100.0 6,987 

Note: due to rounding, percentages may not add up to 100.
50

Appendix Table 4 Unadjusted odds ratios, adjusted odds ratios, and 95% confidence intervals of using 

modern contraceptives, Haiti DHS 2016-17

Urban Rural

Variable UOR 95% CI AOR 95% CI UOR 95% CI AOR 95% CI

Number of family planning 

facilities with a mix of methods 

(ref=none)

None 0.8 [0.5,1.1] 0.8 [0.6,1.1] 1.0 [0.7,1.3] 1.1 [0.8,1.6]

Medium 0.8 [0.5,1.3] 0.9 [0.7,1.2] 1.0 [0.8,1.2] 0.9 [0.7,1.2]

High 0.9 [0.7,1.2] 1.1 [0.8,1.5] 1.3* [1.1,1.5] 1.2* [1.0,1.5]

Community-level outreach 

(ref=none)

Low 1.9*** [1.5,2.4] 1.9*** [1.4,2.4] 1.7*** [1.4,2.0] 1.5*** [1.2,1.9]

Medium 1.1 [0.8,1.5] 0.9 [0.7,1.2] 1.3* [1.1,1.6] 1.2* [1.0,1.5]

High 1.1 [0.8,1.4] 0.9 [0.6,1.2] 1.2 [1.0,1.5] 1.1 [0.9,1.4]

Wealth quintile (ref=lowest)

Second 1.4 [0.4,4.4] 1.0 [0.3,3.0] 1.0 [0.8,1.2] 1.1 [0.9,1.3]

Middle 1.5 [0.5,4.4] 1.2 [0.4,3.2] 1.3* [1.0,1.6] 1.4** [1.2,1.8]

Fourth 1.2 [0.4,3.3] 0.9 [0.3,2.4] 1.1 [0.8,1.5] 1.2 [0.8,1.7]

Highest 0.7 [0.3,2.1] 0.6 [0.2,1.5] 1.2 [0.8,1.7] 1.3 [0.8,2.0]

Education (ref=none)

Primary 1.0 [0.6,1.7] 1.0 [0.6,1.6] 1.0 [0.8,1.1] 1.1 [0.9,1.4]

Secondary + 0.8 [0.5,1.3] 0.9 [0.6,1.3] 0.7*** [0.6,0.8] 1.0 [0.8,1.3]

Employment 

(ref=not employed)

Professional 1.8*** [1.4,2.2] 1.1 [1.0,1.4] 2.2*** [1.8,2.5] 1.1 [1.0,1.4]

Agricultural, other 1.2 [0.9,1.6] 0.8 [0.5,1.3] 1.9*** [1.5,2.3] 1.1 [0.9,1.4]

Marital status 

(ref=never married)

Currently married 3.0*** [2.1,4.2] 2.6*** [1.9,3.6] 6.0*** [4.8,7.4] 4.6*** [3.6,5.8]

Formerly married 1.1 [0.6,1.8] 1.0 [0.7,1.5] 1.8** [1.3,2.6] 1.5* [1.0,2.2]

Age(ref=15-19)

20-29 3.8*** [2.7,5.3] 2.6*** [1.8,3.7] 5.5*** [4.4,6.8] 2.4*** [1.8,3.1]

30-39 3.9*** [2.3,6.7] 1.9* [1.1,3.3] 7.0*** [5.4,8.9] 2.1*** [1.6,2.9]

40-49 1.9** [1.2,3.0] 0.9 [0.5,1.3] 3.9*** [2.9,5.3] 1.2 [0.8,1.7]

Exposure to family planning 

media (ref=no)

Yes 1.2 [0.9,1.5] 1.1 [0.9,1.5] 1.2* [1.0,1.4] 1.1 [0.9,1.3]

UN population density

Medium 0.7** [0.5,0.9] 0.9 [0.7,1.2] 1.1 [0.9,1.3] 0.9 [0.8,1.1]

High 0.3*** [0.3,0.4] 0.8 [0.6,1.1] 0.3*** [0.2,0.3] 1.1 [0.9,1.4]

Department (ref=Rest-Ouest)

Sud-Est 1.6 [0.8,3.3] 0.9 [0.4,2.2] 1.0 [0.7,1.4] 1.3 [0.8,2.0]

Nord 1.3 [0.6,2.6] 1.0 [0.5,2.0] 1.5* [1.1,2.1] 1.7** [1.1,2.5]

Nord-Est 2.0* [1.0,4.0] 1.4 [0.6,3.2] 1.4* [1.0,2.1] 1.8* [1.1,2.8]

Artibonite 1.1 [0.5,2.3] 0.9 [0.4,1.8] 1.4* [1.1,1.9] 1.6* [1.1,2.3]

Centre 1.5 [0.8,2.9] 1.3 [0.6,2.9] 1.9*** [1.4,2.6] 2.5*** [1.7,3.6]

Sud 1.3 [0.6,2.8] 1.1 [0.5,2.5] 1.0 [0.7,1.4] 1.4 [0.9,2.0]

Grand’Anse 1.4 [0.7,3.0] 1.0 [0.5,2.2] 1.5* [1.1,2.1] 2.0** [1.3,3.1]

Nord-Ouest 1.3 [0.6,2.7] 1.0 [0.5,2.0] 1.2 [0.9,1.8] 1.6* [1.1,2.5]

Nippes 1.3 [0.6,3.1] 1.0 [0.4,2.4] 1.9*** [1.3,2.6] 2.1** [1.4,3.3]

Note: adjusted odds ratios = AOR; confidence intervals = CI; unadjusted odds ratios = UOR.
51

Appendix Table 5 Percentage and 95% confidence interval of overall modern contraceptive use, method distribution, an

among modern users, Malawi DHS 2015-16

Among women age 15-49 using a modern contraceptive method

mCP N

Female 

sterilization IUD Implants Injectables Pill

Male 

condom

Total 45.2 [44.2,46.1] 24,562 18.4 [17.3,19.6] 1.8 [1.5,2.2] 19.8 [18.7,21.1] 49.8 [48.3,51.4] 3.9 [3.4,4.4] 5.7 [5.1,6.3

Northern Region 42.9 [40.6,45.2] 2,838 17.1 [14.7,20.0] 3.3 [2.3,4.8] 25.8 [22.8,29.1] 40.4 [37.1,43.8] 5.0 [3.7,6.8] 7.8 [6.2,9.8

Central Region 48.0 [46.4,49.6] 10,529 23.6 [21.5,25.8] 1.5 [1.0,2.2] 20.0 [18.1,21.9] 46.0 [43.3,48.8] 3.6 [2.8,4.5] 4.7 [3.9,5.8

Southern Region 43.0 [41.8,44.3] 11,194 13.3 [12.1,14.7] 1.7 [1.3,2.1] 18.2 [16.5,20.0] 56.2 [54.1,58.3] 3.8 [3.2,4.6] 6.1 [5.3,7.1

Note: mCP = modern contraceptive prevalence; IUD = intrauterine device; AD = average deviation.
52

Appendix Table 6 Background characteristics of women, Malawi DHS 2015-16

Urban Rural

% N % N 

Number of family planning facilities 

with a mix of methods

None 0.3 11 15.8 3,174 

Low 33.6 1,511 35.2 7,066 

Medium 35.4 1,593 32.4 6,496 

High 30.7 1,380 16.6 3,331 

Community-level outreach

None 12.0 542 20.4 4,094 

Low 31.9 1,432 26.6 5,345 

Medium 28.1 1,261 27.1 5,448 

High 28.0 1,261 25.8 5,180 

Wealth quintile

Lowest 1.7 77 23.3 4,668 

Second 1.7 77 23.0 4,615 

Middle 4.1 185 22.2 4,449 

Fourth 13.4 604 20.3 4,077 

Highest 79.0 3,553 11.3 2,257 

Education

None 3.4 153 14.1 2,824 

Primary 37.2 1,672 67.6 13,574 

Secondary + 59.4 2,671 18.3 3,669 

Employment

Not employed 41.2 1,850 31.0 6,227 

Professional 29.9 1,346 5.8 1,165 

Agricultural, other 28.9 1,300 63.2 12,674 

Marital status

Never married 30.4 1,366 19.0 3,804 

Currently married 58.1 2,612 67.4 13,518 

Formerly married 11.5 518 13.7 2,744 

Age

15-19 20.4 918 21.7 4,345 

20-29 41.0 1,844 36.2 7,268 

30-39 26.9 1,209 26.8 5,382 

40-49 11.7 524 15.3 3,071 

Exposure to family planning media

No 34.2 1,536 60.2 12,074 

Yes 65.8 2,960 39.8 7,992 

Population density

Low 34.0 1,531 33.4 6,698 

Medium 32.7 1,469 33.5 6,717 

High 33.3 1,497 33.1 6,651 

Region

Northern 11.4 514 11.6 2,325 

Central 44.8 2,016 42.4 8,514 

Southern 43.7 1,967 46.0 9,227 

Total 100.0 4,496 100.0 20,066 

Note: due to rounding, percentages may not add up to 100.
53

Appendix Table 7 Unadjusted odds ratios, adjusted odds ratios, and 95% confidence intervals of 

using modern contraceptives, Malawi DHS 2015-16

Urban Rural

Variable UOR 95% CI AOR 95% CI UOR 95% CI AOR 95% CI

Number of family planning facilities 

with a mix of methods (ref=low)

None 1.3* [1.0,1.6] 1.5*** [1.2,1.8] 1.0 [0.8,1.1] 1.0 [0.8,1.1]

Medium 1.2 [1.0,1.5] 1.3** [1.1,1.5] 1.0 [0.9,1.2] 1.1 [1.0,1.2]

High 1.2* [1.0,1.5] 1.3 [1.0,1.6] 1.2* [1.0,1.4] 1.2* [1.0,1.5]

Community-level outreach 

(ref=none)

Low 1.1 [0.9,1.5] 1.2 [0.9,1.5] 1.3*** [1.1,1.5] 1.3** [1.1,1.5]

Medium 0.9 [0.7,1.2] 1.1 [0.9,1.4] 1.2* [1.0,1.4] 1.1 [1.0,1.3]

High 0.9 [0.6,1.2] 1.0 [0.8,1.3] 1.1 [0.9,1.3] 1.1 [0.9,1.3]

Wealth quintile (ref=lowest)

Second 2.3* [1.2,4.7] 1.6 [0.8,3.3] 1.3*** [1.1,1.4] 1.2** [1.1,1.4]

Middle 1.3 [0.8,2.1] 1.2 [0.7,2.3] 1.2*** [1.1,1.3] 1.2** [1.1,1.4]

Fourth 1.3 [0.8,2.2] 1.1 [0.6,2.1] 1.2** [1.0,1.3] 1.2** [1.1,1.4]

Highest 1.0 [0.6,1.6] 1.2 [0.6,2.1] 1.0 [0.9,1.2] 1.2* [1.0,1.5]

Education (ref=none)

Primary 0.8 [0.5,1.3] 0.9 [0.6,1.5] 0.9** [0.8,0.9] 1.1 [1.0,1.2]

Secondary + 0.5** [0.3,0.8] 0.6 [0.4,1.1] 0.6*** [0.6,0.7] 1.0 [0.8,1.1]

Employment (ref=not employed)

Professional 2.1*** [1.7,2.6] 1.2 [0.9,1.5] 2.2*** [1.9,2.6] 1.4** [1.1,1.6]

Agricultural, other 1.9*** [1.5,2.3] 1.0 [0.8,1.2] 2.0*** [1.8,2.2] 1.4*** [1.2,1.5]

Marital status (ref=never married)

Currently married 14.0*** [10.3,19.0] 8.9*** [6.5,12.3] 16.0*** [13.6,18.9] 8.3*** [6.9,10.0]

Formerly married 6.0*** [3.8,9.3] 3.6*** [2.3,5.7] 6.4*** [5.3,7.8] 3.3*** [2.7,4.2]

Age (ref=15-19)

20-29 5.7*** [4.5,7.2] 2.0*** [1.5,2.6] 6.5*** [5.8,7.4] 2.4*** [2.1,2.8]

30-39 11.4*** [8.5,15.3] 2.5*** [1.7,3.6] 8.6*** [7.6,9.8] 2.8*** [2.4,3.3]

40-49 7.3*** [5.2,10.3] 1.6* [1.1,2.3] 5.5*** [4.8,6.3] 1.8*** [1.5,2.2]

Exposure to family planning media 

(ref=no)

Yes 1.0 [0.9,1.2] 1.0 [0.8,1.2] 1.3*** [1.2,1.4] 1.1 [1.0,1.2]

UN population density

Medium 1.0 [0.8,1.2] 0.9 [0.8,1.1] 1.0 [0.9,1.1] 0.9 [0.8,1.0]

High 1.0 [0.8,1.2] 0.9 [0.7,1.1] 1.0 [0.9,1.1] 1.3** [1.1,1.5]

Region (ref=Northern)

Central 1.5** [1.1,1.9] 1.5*** [1.2,1.8] 1.2** [1.0,1.3] 1.3** [1.1,1.4]

Southern 1.3* [1.0,1.7] 1.3* [1.1,1.6] 1.0 [0.9,1.1] 0.9 [0.8,1.1]

Note: adjusted odds ratios = AOR; confidence intervals = CI; unadjusted odds ratios = UOR.
54

Appendix Table 8 Percentage and 95% confidence interval of overall modern contraceptive use, method distribution, and aver

modern users, Nepal DHS 2016

Among women age 15-49 using a modern contraceptive method

mCP N

Female 

sterilization IUD Implants Injectables Pill

Male 

condom

Total 33.2 [31.9,34.5] 12,862 34.9 [32.3,37.6] 3.3 [2.6,4.1] 7.7 [6.5,9.0] 20.7 [18.9,22.5] 10.7 [9.5,12.0] 9.8 [8.7,1

Province 1 30.9 [27.8,34.2] 2,173 27.3 [19.0,37.5] 2.3 [1.2,4.6] 7.1 [4.4,11.4] 32.3 [25.6,39.8] 18.6 [14.7,23.3] 6.5 [4.5,9

Province 2 35.9 [32.9,39.0] 2,563 75.7 [71.0,79.8] 1.4 [0.6,3.2] 3.3 [2.0,5.6] 11.1 [8.5,14.5] 4.8 [3.2,7.0] 2.6 [1.6,4

Province 3 35.0 [32.4,37.7] 2,732 13.3 [9.1,19.1] 4.6 [2.7,7.8] 9.9 [7.3,13.3] 22.9 [19.3,27.1] 10.8 [8.0,14.4] 12.3 [9.2,1

Province 4 28.7 [25.7,31.9] 1,249 25.7 [20.0,32.5] 5.8 [3.6,9.3] 5.6 [3.7,8.4] 20.2 [16.0,25.1] 11.6 [8.4,15.7] 8.3 [5.7,1

Province 5 30.3 [26.8,34.0] 2,274 33.1 [28.1,38.5] 4.3 [2.8,6.6] 10.9 [7.4,15.7] 18.1 [14.6,22.3] 10.2 [7.7,13.3] 16.2 [13.1,

Province 6 36.3 [33.5,39.2] 724 9.3 [6.4,13.2] 2.8 [1.7,4.6] 9.4 [6.0,14.4] 28.7 [21.9,36.6] 11.3 [7.2,17.1] 8.0 [4.9,1

Province 7 35.9 [32.0,40.1] 1,145 34.0 [28.1,40.5] 2.5 [1.3,4.6] 8.6 [5.6,13.0] 17.2 [13.8,21.3] 10.3 [7.3,14.4] 17.4 [13.7,

Note: mCP = modern contraceptive prevalence; IUD = intrauterine device; AD = average deviation.

Appendix Table 9 Percentage and 95% confidence interval of overall modern contraceptive use, method distribution, and aver

modern users, Senegal DHS 2017

Among women age 15-49 using a modern contraceptive method

mCP N

Female 

sterilization IUD Implants Injectables Pill

Male 

condom

Total 18.9 [17.8,20.0] 16,787 1.3 [0.8,2.1] 7.9 [6.6,9.4] 31.2 [29.2,33.2] 37.6 [35.2,40.1] 15.6 [14.2,17.2] 5.7 [4.3,7

 

Dakar 24.6 [21.9,27.6] 4,640 1.6 [0.6,4.1] 12.2 [9.2,16.0] 23.2 [19.7,27.1] 31.8 [27.2,36.8] 19.2 [16.2,22.5] 11.1 [7.4,1

Ziguinchor 21.1 [18.1,24.5] 607 2.0 [0.7,5.5] 3.6 [1.7,7.6] 49.0 [40.1,57.9] 26.4 [18.5,36.2] 5.8 [3.9,8.5] 12.1 [7.6,1

Diourbel 11.9 [9.9,14.3] 1,864 0 4.1 [2.1,7.8] 30.0 [22.2,39.0] 47.9 [38.4,57.6] 16.8 [12.6,22.1] 1.0 [0.2,4

Saint-Louis 17.2 [14.3,20.5] 1,082 1.4 [0.4,4.7] 5.6 [3.1,9.8] 25.5 [19.7,32.3] 38.6 [31.2,46.6] 26.4 [20.1,33.7] 2.0 [0.8,5

Tambacounda 11.0 [8.1,14.7] 836 1.4 [0.3,5.7] 5.9 [2.6,12.7] 44.7 [31.6,58.6] 39.1 [28.1,51.3] 8.9 [4.9,15.6] 0 

Kaolack 15.9 [13.2,19.2] 1,094 1.2 [0.3,4.9] 7.8 [5.0,12.0] 39.4 [31.8,47.5] 39.1 [32.7,46.0] 10.0 [5.9,16.6] 2.5 [0.9,6

Thies 22.8 [19.4,26.5] 2,198 1.3 [0.5,3.6] 6.7 [4.6,9.7] 30.4 [24.6,36.9] 41.3 [34.0,49.0] 15.3 [12.3,18.9] 3.5 [1.5,8

Louga 14.5 [12.0,17.6] 1,055 2.6 [1.1,6.1] 4.3 [2.0,9.0] 30.1 [23.3,37.8] 40.8 [34.8,47.2] 22.2 [17.1,28.3] 0 

Fatick 19.5 [16.6,22.6] 806 1.2 [0.4,3.6] 3.4 [1.6,7.4] 28.1 [22.2,34.7] 50.1 [43.1,57.0] 15.6 [10.0,23.5] 0.8 [0.2,3

Kolda 18.9 [16.0,22.3] 729 0.6 [0.1,4.0] 1.1 [0.3,4.1] 53.0 [47.5,58.5] 39.9 [34.2,46.0] 3.2 [1.3,8.0] 1.8 [0.5,6

Matam 7.8 [6.1,9.9] 606 1.2 [0.2,8.2] 2.9 [0.8,10.1] 26.0 [18.4,35.5] 58.6 [46.6,69.7] 11.3 [6.5,19.0] 0 

Kaffrine 19.0 [15.4,23.3] 651 0.3 [0.0,2.2] 11.7 [6.1,21.3] 41.8 [32.2,52.0] 41.8 [33.6,50.4] 3.9 [1.9,7.9] 0 

Kédougou 10.7 [6.8,16.4] 158 0 2.1 [0.6,7.1] 41.9 [27.9,57.4] 46.4 [33.8,59.5] 9.6 [3.9,21.5] 0 

Sédhiou 18.4 [15.0,22.3] 460 0 4.3 [2.0,8.9] 58.3 [50.9,65.3] 26.0 [18.8,34.7] 4.8 [2.3,9.6] 6.1 [3.2,1

Note: mCP = modern contraceptive prevalence; IUD = intrauterine device; AD = average deviation.
55

Appendix Table 10 Percentage and 95% confidence interval of overall modern contraceptive use, method distribution, and av

modern users, Tanzania DHS 2015-16

Among women age 15-49 using a modern contraceptive method

mCP N

Female 

sterilization IUD Implants Injectables Pill

Male 

condom

Total 27.1 [25.7,28.6] 13,266 9.4 [8.0,10.9] 2.5 [1.9,3.3] 20.5 [18.6,22.5] 36.6 [34.4,38.8] 15.2 [13.6,17.0] 14.4 [12.6,1

Dodoma 35.6 [27.9,44.2] 572 3.8 [1.6,8.6] 0 26.2 [16.0,39.6] 37.8 [28.7,47.8] 23.6 [14.5,36.0] 5.5 [2.5,11

Arusha 26.2 [19.1,34.8] 508 7.1 [3.4,14.5] 6.9 [2.7,16.8] 22.9 [13.8,35.6] 31.1 [21.0,43.4] 19.2 [11.3,30.8] 12.7 [6.2,2

Kilimanjaro 33.8 [28.9,38.9] 361 9.9 [5.4,17.4] 2.1 [0.6,6.7] 20.5 [13.9,29.1] 39.7 [30.0,50.3] 17.1 [9.6,28.4] 10.1 [5.9,1

Tanga 21.4 [15.8,28.4] 706 4.8 [1.1,18.5] 2.0 [0.5,8.3] 17.4 [10.8,27.0] 40.7 [31.4,50.8] 26.4 [17.7,37.5] 8.6 [4.2,16

Morogoro 40.4 [33.8,47.5] 636 5.2 [2.2,11.9] 2.6 [0.6,11.7] 24.9 [16.7,35.4] 37.5 [27.8,48.2] 16.2 [9.7,25.8] 13.0 [7.3,2

Pwani 33.5 [27.5,40.1] 285 3.5 [1.0,11.2] 3.8 [1.5,9.4] 14.7 [9.1,22.9] 48.5 [37.9,59.3] 20.2 [13.9,28.4] 8.0 [4.2,14

Dar es Salaam 28.7 [25.8,31.8] 1,536 6.5 [3.6,11.4] 1.9 [0.7,4.9] 11.2 [7.8,15.9] 26.2 [21.1,31.9] 15.7 [12.0,20.4] 36.6 [30.8,4

Lindi 48.0 [41.4,54.7] 288 2.3 [0.8,6.3] 1.2 [0.3,5.0] 24.3 [17.0,33.5] 48.0 [38.3,57.9] 20.0 [13.1,29.3] 4.2 [2.2,7

Mtwara 47.1 [40.4,53.8] 412 8.8 [4.2,17.8] 0 11.4 [6.6,18.9] 38.7 [29.7,48.5] 33.2 [25.0,42.5] 7.4 [3.1,16

Ruvuma 46.0 [39.9,52.3] 360 9.8 [5.5,17.0] 1.1 [0.2,4.4] 26.9 [18.5,37.4] 36.7 [26.8,47.8] 13.0 [8.0,20.4] 12.5 [7.9,1

Iringa 26.9 [21.4,33.2] 245 10.0 [4.2,21.9] 7.9 [2.8,20.5] 19.7 [10.9,33.0] 25.5 [18.2,34.5] 12.4 [7.5,19.9] 23.5 [16.9,3

Mbeya 37.2 [29.7,45.5] 828 7.5 [2.4,21.1] 4.0 [1.8,8.9] 27.0 [17.7,39.0] 28.8 [21.4,37.5] 8.1 [4.7,13.5] 24.6 [17.4,3

Singida 31.6 [23.5,40.9] 370 12.5 [5.8,24.8] 1.3 [0.3,5.7] 27.2 [17.6,39.5] 26.9 [18.4,37.5] 18.9 [13.9,25.1] 6.4 [2.9,13

Tabora 20.2 [14.8,26.8] 737 15.7 [9.9,24.0] 1.5 [0.3,6.9] 22.9 [14.9,33.4] 35.3 [26.2,45.7] 12.7 [6.8,22.5] 11.9 [7.2,1

Rukwa 26.7 [22.8,31.0] 288 7.6 [2.3,21.9] 1.7 [0.4,7.1] 19.8 [11.4,31.9] 55.2 [43.3,66.5] 8.9 [5.1,15.1] 5.8 [2.2,14

Kigoma 13.8 [9.6,19.5] 542 13.9 [4.8,33.7] 0.8 [0.1,6.4] 11.3 [5.0,23.7] 53.2 [36.8,69.0] 9.5 [3.9,21.3] 11.4 [4.5,2

Shinyanga 20.1 [15.6,25.5] 504 18.0 [10.6,29.0] 1.0 [0.1,7.4] 21.1 [14.6,29.5] 35.2 [25.9,45.9] 9.2 [3.9,19.9] 12.5 [7.1,2

Kagera 30.8 [22.8,40.2] 612 21.2 [12.7,33.3] 7.6 [3.5,15.6] 14.2 [8.8,22.3] 34.6 [22.2,49.5] 13.4 [6.0,27.2] 5.2 [2.0,13

Mwanza 16.1 [12.2,20.8] 859 15.6 [9.7,24.1] 1.1 [0.1,8.1] 28.7 [17.5,43.2] 44.3 [32.1,57.2] 5.5 [1.3,20.6] 4.9 [1.2,17

Mara 26.0 [22.4,29.9] 523 8.4 [5.2,13.1] 2.5 [0.7,8.4] 14.4 [8.5,23.3] 44.9 [33.2,57.2] 4.5 [1.8,11.0] 21.8 [14.7,3

Manyara 22.5 [17.4,28.7] 394 9.5 [4.9,17.7] 1.9 [0.4,7.8] 25.4 [15.6,38.6] 45.1 [32.0,58.8] 6.0 [2.0,16.4] 9.6 [4.2,20

Njombe 34.2 [28.9,39.9] 203 11.3 [6.2,19.6] 6.5 [2.7,15.2] 15.6 [9.7,24.1] 40.6 [29.2,53.1] 14.4 [8.1,24.3] 10.9 [4.8,2

Katavi 17.5 [11.3,26.1] 130 4.9 [1.6,14.2] 1.5 [0.2,10.2] 34.3 [25.4,44.4] 36.3 [25.8,48.3] 15.5 [8.7,26.0] 7.5 [3.1,17

Simiyu 15.4 [12.7,18.4] 479 20.5 [13.5,30.0] 1.9 [0.4,8.1] 21.6 [14.9,30.2] 32.3 [20.9,46.2] 9.7 [3.3,25.2] 9.6 [5.1,17

Geita 10.6 [7.2,15.2] 485 17.3 [8.1,32.9] 3.3 [0.9,11.6] 29.4 [18.6,43.2] 31.5 [20.5,45.1] 4.0 [1.1,13.4] 9.4 [4.4,19

Zanzibar 8.9 [7.6,10.5] 404 8.2 [4.6,14.2] 1.0 [0.1,6.8] 25.1 [19.4,31.8] 43.4 [36.0,51.1] 17.2 [10.6,26.7] 4.5 [2.3,8

Note: mCP = modern contraceptive prevalence; IUD = intrauterine device; AD = average deviation.