DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2020 No. 173

Kerry L. D. MacQuarrie

Christina Juan

Alison Gemmill

August 2020

This document was produced for review by the United States Agency for

International Development.

Attributes Associated with Women’s Contraceptive 

Profiles in Burundi: Knowledge, Attitudes, and Interactions 

with Media and Health Services

DHS Working Paper No. 173 

Attributes Associated with Women’s Contraceptive Profiles 

in Burundi: Knowledge, Attitudes, and Interactions with 

Media and Health Services 

Kerry L.D. MacQuarrie1

Christina Juan2

Alison Gemmill3

ICF 

Rockville, Maryland, USA 

August 2020 

1 The DHS Program, Avenir Health 

2 The DHS Program, ICF 

3 Johns Hopkins Bloomberg School of Public Health 

Corresponding author: Kerry L.D. MacQuarrie, The DHS Program, 530 Gaither Road, Suite 500, 

Rockville, MD 20850, USA; phone: 301-572-0282; fax: 301-407-6501; email: kerry.macquarrie@icf.com 
Acknowledgments: The authors extend their gratitude to Michelle Weinberger, Nicole Bellows, and Priya 

Emmart of Avenir Health and to Minki Chatterji, National Institute on Drug Abuse, National Institutes of 

Health, and Mengjia Liang, United Nations Population Fund (UNFPA), for helpful comments on an earlier 

draft of this paper.

Editor: Kerry Aradhya 

Document Production: Chris Gramer and Natalie Shattuck 

The DHS Working Papers series is a prepublication series of papers reporting on research in progress that 

is based on Demographic and Health Surveys (DHS) data. This research is carried out with support provided 

by the United States Agency for International Development (USAID) through The DHS Program (#720-

0AA-18C00083). 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: 

MacQuarrie, Kerry L. D., Christina Juan, and Alison Gemmill. 2020. Attributes Associated with Women’s 

Contraceptive Profiles in Burundi: Knowledge, Attitudes, and Interactions with Media and Health Services. 

DHS Working Paper No. 173. Rockville, Maryland, USA: ICF. 
iii 

CONTENTS 

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

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

ABSTRACT ................................................................................................................................................. ix 

ACRONYMS AND ABBREVIATIONS ........................................................................................................ xi 

1 INTRODUCTION .............................................................................................................................. 1 

2 METHODS ....................................................................................................................................... 3 

2.1 Data ..................................................................................................................................... 3 

2.2 Sequence and Cluster Analysis .......................................................................................... 3 

2.3 Regression Analyses .......................................................................................................... 3 

2.4 Limitations ........................................................................................................................... 5 

3 RESULTS ........................................................................................................................................ 7 

3.1 Contraceptive Clusters ........................................................................................................ 7 

3.2 Sample Description ............................................................................................................. 7 

3.3 Compiling Composite Profiles of Each Cluster ................................................................... 9 

3.3.1 Quiet Calendar ....................................................................................................... 9 

3.3.2 Family Builder 1 ................................................................................................... 12 

3.3.3 Family Builder 2 ................................................................................................... 13 

3.3.4 Modern Mother ..................................................................................................... 13 

3.3.5 Consistently Covered Mother ............................................................................... 14 

3.3.6 Traditional Mother ................................................................................................ 15 

4 CONCLUSIONS ............................................................................................................................. 17 

REFERENCES ............................................................................................................................................ 19 

v 

TABLES 

Table 1 Analytic sample profile ............................................................................................... 8

Table 2 Knowledge, attitudes, and media and health service interactions associated 

with cluster membership. Odds ratios from logistic regressions .............................. 11

vii 

FIGURES 

Figure 1 Representative sequence (medoid) and proportion of each Burundi 

contraceptive cluster .................................................................................................. 7

ix 

ABSTRACT 

This study aims to identify patterns in women’s contraceptive and pregnancy experience using retrospective 

calendar data from the 2016-17 Burundi Demographic and Health Survey (DHS). We apply sequence and 

cluster analysis of longitudinal data to identify discrete clusters that characterize women’s contraceptive 

and pregnancy behaviors over the previous five years. We further pair clusters with data on knowledge, 

attitudes, and women’s interactions with media and health services to create composite profiles of women 

in these clusters. 

Of six clusters, three are characterized by contraceptive use and three are characterized by the absence of 

contraceptive use. Media exposure and attitudes regarding sex preference, wife beating, and self-efficacy 

largely do not explain cluster membership. Contraceptive knowledge is positively associated with two 

clusters and negatively associated with a third. Clusters also differ in their members’ fertility desires, 

contraceptive intentions, and interactions with health services. Two “Family Builder” clusters are both 

characterized by the presence (but not timing) of multiple pregnancies in their calendar histories but differ 

in that women with high contraceptive knowledge, intentions to use contraception, and well-articulated 

family size ideals are characteristic of one cluster (Family Builder 1) and low contraceptive knowledge, no 

use of contraception, and vague family size preferences are characteristic of the other (Family Builder 2). 

These results can guide reproductive health programs as they target social and behavioral change and other 

interventions to the unique subpopulations they seek to serve. 

Key words: Burundi, contraceptive histories, cluster and sequence analysis, social and behavior change, 

health services

xi 

ACRONYMS AND ABBREVIATIONS 

DHS Demographic and Health Survey(s) 

EC emergency contraception 

IUD intra-uterine (contraceptive) device 

LAM lactational amenorrhea method 

LAPM long-acting and permanent method 

LARC long-acting, reversible contraception 

OR odds ratio 

PAM partitioning around medoids 

PSU primary sampling units 

SBC social and behavior change 

SDM standard days method 

UNFPA United Nations Population Fund 

USAID United States Agency for International Development 

1 

1 INTRODUCTION 

Researchers have tried to make data on contraception useful to reproductive health programs by applying 

market segmentation, with the aim that the reproductive health programs can better tailor their messages 

and services to target potential and existing family planning clients. The purpose of market segmentation is 

to identify distinct subpopulations who have different needs, attitudes, and behaviors around contraception. 

Segmentation methods group women into different homogenous profiles (i.e., segments) that characterize 

their needs and inclinations to use particular services. 

A study in the Philippines contrasts “young intenders” with older “ready to limit” women (Wang et al. 

2009). In Niger, one study differentiated groups of women who “trust family planning and the health 

system” from those who “accept limiting” (Dalglish et al. 2018), while another segmented women into 

“healthy proactives,” “traditional autonomists,” and “modern elites” (Camber Collective 2014, 2015). The 

(Re)solve project combines segmentation and behavioral design to, among other goals, develop 

interventions for adolescent girls based on their trust among peers and (mis)perceptions of pregnancy risk 

(Dabney et al. 2019). These various approaches, which often integrate qualitative data, generally focus on 

attitudes to form their segments and do not analyze women’s behaviors. 

A broad literature demonstrates that attitudes are associated with the use of modern contraception 

(Alemayehu et al. 2016; Ankomah, Anyanti, and Oladosu 2011; Grindlay et al. 2018; Nanda, Schuler, and 

Lenzi 2013; Samandari, Speizer, and O’Connell 2010; Walcott et al. 2015; Yue, O’Donnell, and Sparks 

2010). These attitudes include those toward contraception generally as well as those toward the efficacy 

and safety of specific methods, self-efficacy, gender, and the role of men. One meta-analysis found that 

supportive attitudes increase the odds of contraceptive use by an average of 10% to 90%, depending on the 

specific attitude (Rosen et al. 2019). As a result, social and behavior change (SBC) interventions seek both 

to take advantage of supportive attitudes and target services to a clientele inclined to use them and to shift 

attitudes and social norms (Ayanore, Pavlova, and Groot 2017; Kaggwa, Diop, and Storey 2008; Sharan 

and Valente 2002; Storey et al. 1999; Wegs et al. 2016). This same meta-analysis indicated that SBC 

interventions work to increase modern contraceptive use both directly through their mass media and 

interpersonal communication activities and indirectly through their effect on attitudes and partner 

communication (Rosen et al. 2019). 

In contrast to the strong focus on attitudes in most segmentation analysis, a recent study has developed 

methods to identify discrete groups of women based on behavioral data from contraceptive calendars in 

Demographic and Health Surveys (DHS) in Burundi and Nepal (MacQuarrie, Allen, and Gemmill 2020; 

MacQuarrie et al. 2019). DHS contraceptive calendars are a rich source of data on contraceptive behavior 

and pregnancy experience covering the five years preceding the survey (Croft, Bradley, and Allen 2018). 

Although these behavioral data complement attitudinal data well, the groups of women identified through 

these methods have not to date been described in terms of their attitudinal attributes. 

This study aims to identify patterns in women’s contraceptive and pregnancy experience using the dynamic 

experiences captured in retrospective, longitudinal calendar data. To do this, we apply sequence and cluster 

analysis of longitudinal data to identify discrete clusters that characterize women’s contraceptive and 

pregnancy behaviors over the previous five years. We further pair the clusters with data on knowledge, 

attitudes, and women’s interactions with media and health services to create rich, composite profiles of 

women in these clusters. 

3 

2 METHODS 

2.1 Data 

This study uses data from the 2016-17 Burundi DHS survey, which is a nationally representative survey 

that applied a multi-stage, clustered sampling process with a response rate of 98.8% among eligible women 

(Ministère à la Présidence chargé de la Bonne Gouvernance et du Plan - MPBGP et al. 2017). Specifically, 

this study uses contraceptive calendar data from the DHS survey. A contraceptive calendar is a retrospective 

history that records monthly event data on reproductive and contraceptive experiences (Croft, Bradley, and 

Allen 2018), which we organized into five possible states: (1) no use of contraception; (2) use of a shortterm, modern method of contraception; (3) use of a long-acting or permanent method (LAPM) of 

contraception; (4) use of a traditional method of contraception; and (5) pregnancy, birth, or termination. 

The short-term, modern methods are pills, injectables, male or female condoms, lactational amenorrhea 

method (LAM), emergency contraception (EC), and the Standard Days Method (SDM). The LAPMs are 

intrauterine devices (IUDs), implants, and female and male sterilization. Traditional methods include 

periodic abstinence/rhythm, withdrawal, and other traditional or folkloric methods. 

2.2 Sequence and Cluster Analysis 

We arrange the five states into calendar sequences that are exactly 59 months long for each woman, with 

month 1 being the earliest point in the woman’s calendar (approximately five years before the interview) 

and month 59 being the most recent month. Our analytic sample consists of 13,293 women age 15-44 at the 

start of their calendar sequence. Prior to the analysis presented here, we used sequence and cluster analysis 

to group the women into six distinct contraceptive clusters based solely on patterns of behavior observed 

in their 59-month calendar sequences. Methodological details of the sequencing and clustering procedures 

used to identify the six clusters are documented elsewhere (MacQuarrie, Allen, and Gemmill 2020; 

MacQuarrie et al. 2019). 

Briefly, we conducted sequence and cluster analysis in R using the TraMineR and WeightedCluster 

packages (Gabadinho, Ritschard, Mueller, et al. 2011; Gabadinho, Ritschard, Studer, et al. 2011; Studer 

2013). We used Optimal Matching to calculate distances in our dissimilarity matrix. This matching 

parameter allows for insertions and deletions as well as substitutions in computing distances, which better 

accounts for the sporadic timing of pregnancies by potentially aligning them within women’s sequences. 

We assumed uniform costs for all substitutions, insertions, and deletions so used a constant cost matrix to 

measure pairwise distances between sequences. Finally, we used a k-medoid (i.e., partitioning around 

medoids, or PAM) clustering algorithm to group women’s sequences into six clusters. These parameters 

and the number of clusters in the final solution were guided by scores on a series of quality metrics 

(Gemmill 2019; Studer 2013). 

2.3 Regression Analyses 

We estimate separate multivariable logistic regression models to identify women’s attributes associated 

with membership in each of the identified clusters. Our outcome variable is a dichotomous measure of 

membership in each cluster. For our regression models, we select seven covariates that describe knowledge 

and attitudes and six covariates that describe media exposure and interactions with the health system. These 
4 

variables align with conceptual frameworks describing how SBC interventions may influence contraceptive 

behavior (Rosen et al. 2019). In particular, the frameworks predict that SBC interventions that engage 

people through mass media initiatives and interpersonal communication (typically with providers) 

influence contraceptive behavior, both directly and indirectly, through increasing knowledge and shifting 

attitudes and norms (Rosen et al. 2019). In the regression models, contraceptive knowledge is categorized 

as low (0-9 methods), medium (10-11 methods), or high (12-14 methods) based roughly on the tercile 

distribution of the number of contraceptive methods known. Other knowledge variables, including correct 

knowledge of the fertility cycle and postpartum return to fecundity, were explored but excluded from the 

final, more parsimonious model because of their weaker bivariate associations. 

We measure the ideal number of children and sex preference for children based on questions regarding how 

many girls, boys, and children of either sex a respondent would ideally like to have. We categorize women 

as having son preference if they preferred more boys than girls, daughter preference if they preferred more 

girls than boys, and gender balanced/no preference if they expressed no preference or preferred an equal 

number of girls and boys. 

Attitudes accepting wife beating is a dichotomous variable set to one if women reported that wife beating 

was justified in at least one of five scenarios and zero if they reported that wife beating was not justified in 

any of the scenarios. Intolerance of wife beating (zero) is interpreted as the gender equitable perspective. 

Attitudes toward protective self-efficacy is an index of the number of scenarios, ranging from 0 to 2, in 

which a respondent believed that a woman is justified in taking action to protect her sexual health. The two 

scenarios are (1) if a wife is justified in refusing sex with her husband if he has sex with other women and 

2) if a wife is justified in insisting on using a condom if her husband has signs of a sexually transmitted 

infection. A higher index score is interpreted as the gender equitable perspective, demonstrating higher 

levels of support for protective self-efficacy. 

We measure fertility desires based on whether a woman wanted a child within two years (i.e., soon), wanted 

a child after two or more years, wanted a child but was unsure of the timing, or wanted no more children 

(or was sterilized or infecund). We also include a measure of intention to use contraception (currently using 

contraception, intends to use in the future, does not intend to use). 

Another measure we include is whether a woman owned a mobile phone or had accessed the internet in the 

past month. We measure if women reported hearing family planning messages on the radio, on tv, in 

newspapers/magazines, or via mobile phone in the past few months. A separate measure capturing general 

exposure to ratio, tv, or newspapers was tested but excluded from the final analysis. 

We include a variable describing whether a woman visited a health facility or was visited at home by a 

health worker in the past 12 months and whether family planning was discussed during that visit. The health 

insurance variable captures whether women were covered by any type of health insurance. 

Problems seeking medical advice is based on women’s responses to whether any of the following presented 

a “big problem” when she is sick and wants to seek medical advice or treatment: getting permission to go, 

getting money needed for treatment, distance to the health facility, and not wanting to go alone. The variable 

is categorized as a dichotomous variable comparing women who experienced one or more of these problems 

with women who experienced none of them. 
5 

Multivariable models also control for age at the start of the calendar sequence, residence, education, and 

household wealth quintile. Regression analyses are conducted in Stata ME 16 and results are presented in 

odds ratios (ORs) to ease interpretation. All analyses are weighted to account for sampling probability and 

non-response, and svyset commands are used to account for the complex sampling design. 

2.4 Limitations 

This study has some limitations. First, the regression models are not intended to describe causal 

relationships. Many of the covariates in these models assess factors at the time of the survey, whereas the 

clusters are defined by behaviors over the past five years. The models are employed to describe the attributes 

of women constituting each of the identified clusters while controlling for a range of current factors. 

Second, this study does not contain any measures of spousal communication or power dynamics. Such 

factors are often the target of SBC interventions and are seen as one possible pathway that influences 

modern contraceptive use (Assaf and Davis 2019; Nanda, Schuler, and Lenzi 2013; Raj et al. 2016; Yue, 

O’Donnell, and Sparks 2010). However, measures of spousal communication around family planning are 

not available in the 2016-17 Burundi DHS survey. We also exclude measures of household or contraceptive 

decision making. This topic is outside the scope of the current study and is explored elsewhere (Juan, Allen, 

and MacQuarrie 2020). 

7 

3 RESULTS 

3.1 Contraceptive Clusters 

We identify six distinct clusters from women’s contraceptive calendar sequences in Burundi. These clusters 

are illustrated by their medoid—the most illustrative sequence for each cluster—in Figure 1. They comprise 

(1) Quiet Calendar (42% of women), characterized by women who did not experience pregnancy or use 

any methods of contraception; (2) Family Builder 1 (25%) and (3) Family Builder 2 (18%), which are 

similar in that they are both characterized by women who did not use any method and experienced two 

pregnancies, but varied in terms of timing during the calendar sequence; (4) Modern Mother (8%), 

characterized by women who adopted short-term modern methods toward the end of year 2 after a period 

of non-use and a pregnancy; (5) Consistently Covered Mother (6%), characterized by women who adopted 

LAPMs after a period of non-use and a pregnancy; and (6) Traditional Mother (2%), characterized by those 

who adopted traditional methods at the end of year 2 after non-use and a pregnancy. Additional details about 

these clusters are reported elsewhere (MacQuarrie, Allen, and Gemmill 2020; MacQuarrie et al. 2019). 

Figure 1 Representative sequence (medoid) and proportion of each Burundi contraceptive cluster 

3.2 Sample Description 

Table 1 presents the characteristics of women in the analytic sample, using the factors in the regression 

models. As described previously, Quiet Calendar is the most common cluster (42%) and Traditional Mother 

is the least common cluster (2%). The sample is relatively young, with the proportion declining from 24% 

in the youngest of the five age groups (age 15-19) to 9% in the oldest age group (age 40-44) at the start of 

the calendar sequence. The sample is largely rural (88%), with most women having either no education 

(45%) or primary education only (37%). The sample is evenly distributed across household wealth quintiles. 
8 

Table 1 Analytic sample profile 

Percent Weighted n 

Contraceptive Profile Cluster 

Quiet calendar 41.5 5,521 

Family builder 1 24.9 3,308 

Family builder 2 18.1 2,400 

Modern mother 7.6 1,007 

Consistently covered mother 5.6 750 

Traditional mother 2.3 308 

Socio-economic factors 

Age at the start of calendar sequence 15-19 24.2 3,219 

20-24 22.6 3,006 

25-29 18.3 2,431 

30-34 14.6 1,941 

35-39 11.5 1,533 

40-44 8.8 1,165 

 

Residence Urban 12.5 1,666 

Rural 87.5 11,627 

 

Highest education level No education 44.8 5,955 

Primary 36.8 4,896 

Secondary or higher 18.4 2,441 

Household wealth quintile Poorest 20.3 2,696 

Poorer 20.2 2,688 

Middle 20.1 2,671 

Richer 18.9 2,513 

Richest 20.5 2,725 

 

Knowledge and Attitudinal Factors 

Contraceptive knowledge (# of methods 

known) 

Low (0-9) 29.2 3,881 

Medium (10-11) 26.6 3,538 

High (12-14 44.2 5,874 

Ideal number of children 

0 1.5 195 

1-2 8.3 1,110 

3-4 62.4 8,292 

5+ 25.7 3,420 

Non-numeric response 2.1 276 

Sex preference for children 

Balanced or no preference 55.3 7,352 

Son preference 30 3,993 

Daughter preference 14.7 1,948 

Attitudes accepting wife-beating 

In no scenario 38.6 5,132 

In at least one scenario 61.4 8,161 

Attitudes accepting self-efficacy (# of 

scenarios) 

0 14.1 1,880 

1 32.5 4,314 

2 53.4 7,100 

Intention to use contraception in the future 

Using 22.3 2,960 

Intends to use 41.4 5,503 

Does not intend to use 36.3 4,829 

Continued... 
9 

Table 1 Analytic sample profile (continued)

Percent Weighted n 

Interactions with media and health 

systems 

Access to internet or mobile phone No 73.3 9,748 

Yes 26.7 3,545 

 

Heard FP media messages in last few 

months No 68.3 9,082 

Yes 31.7 4,211 

 

Visited with health facility or fieldworker in 

last 12 months No visit 18.5 2,455 

Visited 48.5 6,445 

Discussed FP at visit 33 4,393 

 

Covered by health insurance No 76.8 10,211 

Yes 23.2 3,082 

 

Problems seeking medical advice when 

sick None 28.8 3,830 

One or more 71.2 9,463 

 

Total 100.0 13,293 

3.3 Compiling Composite Profiles of Each Cluster 

The regression analyses identify which of the women’s attributes related to knowledge, attitudes, and health 

system and media exposure) are associated with cluster membership (Table 2). Combined, the results of 

these analyses provide composite profiles that richly described and further differentiated the clusters (as the 

clusters were originally based only on data from the calendar histories). 

3.3.1 Quiet Calendar 

Overall, Quiet Calendar women tend to be on the very young or older tails of the age spectrum, be educated, 

and own mobile phones. However, they do not have high contraceptive knowledge nor much interaction 

with health services. Although they prefer a small family size and prefer sons, they do not have defined 

plans for having a child (or another child) and do not intend to use contraception. 

Table 2 shows a unique pattern of association between age and membership in the Quiet Calendar cluster 

not found in any of the other five clusters. Compared with women in the middle age group (age 30-34), 

both older women (OR=2.5–10.5, p<0.001) and adolescent women (OR=1.5, p<0.001) have higher odds of 

belonging to the Quiet Calendar cluster, while women in their 20s have lower odds (OR=0.6, p<0.001). 

Among the other socio-economic factors, neither residence nor wealth is associated with Quiet Calendar 

cluster membership. However, education is associated with membership in this cluster: Women with 

secondary or higher education have higher odds (OR=3.7, p<0.001) of being in this cluster than do women 

without education. Nonetheless, women with medium or high knowledge of contraceptive methods have 

30% lower odds (OR=0.7, p<0.001) of belonging to the Quiet Calendar cluster than do women who have 

low contraceptive knowledge; this was the only cluster to demonstrate a negative association with 

contraceptive knowledge. 
10 

Multiple attitudinal factors are associated with Quiet Calendar membership. Gender attitudes, however, are 

not. Women whose ideal family included many children have lower odds of being in the Quiet Calendar 

cluster than do women who reported 1-2 children as their ideal number of children. Women with a 

preference for sons have 15% higher odds (p<0.05) of being in the Quiet Calendar cluster than women who 

preferred gender balance or who had no preference in the sex composition of their children. Sex preference 

is not a factor in membership in any other cluster model. 

When compared with women who wanted a child within two years, women who wanted a child but were 

unsure of the timing have more than four times the odds of being in the Quiet Calendar cluster, while those 

who wanted to delay children or wanted no more children have 65% lower odds (p<0.001). Women who 

are using contraception (OR=0.04, p<0.001) or intended to use contraception (OR=0.47, p<0.001) have 

lower odds of being in the Quiet Calendar cluster than women who do not intend to use contraception. 

Quiet Calendar women are no more likely than women non-members to have heard family planning 

messages in the media. This is in spite of their mobile phone and internet access: Women who had a mobile 

phone or access to the internet have 29% higher odds (p<0.001) of being in the Quiet Calendar cluster. 
11 

Table 2 Knowledge, attitudes, and media and health service interactions associated with cluster membership. Odds ratios from logistic 

regressions 

Quiet Calendar 

(42%) 

Family Builder 1 

(25%) 

Family Builder 2 

(18%) 

Modern Mother 

(8%) 

Consistently 

Covered Mother 

(6%) 

Traditional Mother 

(2%) 

Odds 

ratio p-value 

Odds 

ratio p-value 

Odds 

ratio p-value 

Odds 

ratio p-value 

Odds 

ratio p-value 

Odds 

ratio p-value 

Socio-economic factors 

Age at the start of calendar sequence (ref: 30-34)

15-19 1.54 <0.001 1.15 0.171 1.00 0.979 0.56 <0.001 0.46 <0.001 0.14 <0.001 20-24 0.60 <0.001 1.42 <0.001 1.27 0.007 0.79 0.106 1.06 0.691 0.43 <0.001 25-29 0.59 <0.001 1.36 <0.001 1.23 0.008 0.85 0.193 1.03 0.852 0.61 0.012 35-39 2.47 <0.001 0.58 <0.001 0.67 <0.001 0.92 0.574 1.14 0.375 0.96 0.843 40-44 10.54 <0.001 0.09 <0.001 0.24 <0.001 0.82 0.314 0.95 0.815 1.77 0.046 

Residence (ref: urban)

Rural 1.05 0.616 1.19 0.064 0.93 0.476 0.61 0.001 1.28 0.131 0.80 0.290 

Education (ref: none)

Primary 1.10 0.130 0.99 0.900 1.04 0.512 0.85 0.119 0.78 0.025 1.24 0.274 Secondary or higher 3.68 <0.001 0.64 <0.001 0.51 <0.001 0.56 <0.001 0.50 0.002 1.16 0.550 

Household wealth index (ref: poorest)

Poorer 0.89 0.154 1.10 0.228 1.06 0.497 0.81 0.085 1.01 0.974 1.05 0.857 Middle 0.91 0.276 1.02 0.845 1.00 0.960 0.76 0.039 1.53 0.005 1.02 0.938 Richer 0.88 0.162 0.98 0.780 1.11 0.233 0.69 0.007 1.32 0.097 1.71 0.030 Richest 0.91 0.422 0.97 0.802 0.95 0.663 1.06 0.752 1.18 0.428 1.56 0.123 

Knowledge and attitudes 

Contraceptive knowledge (ref: Low (<10 methods))

Medium (10-11 methods) 0.70 <0.001 1.14 0.051 1.11 0.168 1.22 0.175 1.19 0.265 1.99 0.003 

High (12-14 methods) 0.70 <0.001 1.20 0.002 0.95 0.419 1.43 0.006 1.23 0.082 1.83 0.005 

Ideal number of children (ref: 1-2)

0 0.95 0.855 1.15 0.531 1.01 0.961 0.69 0.400 1.09 0.832 1.10 0.886 

3-4 0.68 <0.001 1.31 0.005 1.11 0.283 0.88 0.346 1.18 0.323 1.26 0.370 

5+ 0.46 <0.001 1.79 <0.001 1.22 0.059 0.75 0.069 0.99 0.933 1.05 0.868 

Non-numeric response 0.44 <0.001 1.23 0.329 1.83 0.001 1.15 0.710 0.64 0.359 0.72 0.593 

Sex preference for children (ref: gender balanced/no preference)

Son preference 1.15 0.030 0.97 0.622 0.96 0.478 1.08 0.436 0.92 0.423 0.71 0.035 

Daughter preference 1.12 0.147 0.96 0.529 1.01 0.930 0.97 0.788 0.77 0.055 1.13 0.503 

Attitudes accepting wife beating in at least one scenario 

Yes 1.07 0.161 0.95 0.317 0.89 0.040 1.26 0.007 1.07 0.530 0.88 0.349 

Attitudes accepting self-efficacy (# of scenarios, ref: none)

1 1.04 0.687 1.01 0.913 0.82 0.031 1.46 0.015 1.10 0.548 1.21 0.501 

2 1.04 0.684 1.03 0.666 0.82 0.030 1.26 0.113 1.05 0.730 1.44 0.188 

Fertility desires (ref: wants within 2 years)

Wants after 2+ years 0.35 <0.001 3.15 <0.001 1.28 0.009 0.64 0.002 0.54 <0.001 1.11 0.653 

Wants, unsure timing 4.21 <0.001 0.29 <0.001 0.30 <0.001 0.35 <0.001 0.21 <0.001 0.45 0.061 

Wants no more/sterilized/infecund 0.35 <0.001 2.70 <0.001 1.39 <0.001 0.71 0.015 0.86 0.318 0.70 0.096 

Intention to use contraception in the future 

(ref: does not intend to use)

Using 0.04 <0.001 1.30 <0.001 0.43 <0.001 29.96 <0.001 21.58 <0.001 32.98 <0.001 

Intends to use 0.47 <0.001 1.24 <0.001 1.01 0.888 5.53 <0.001 4.20 <0.001 5.17 <0.001 

Interactions with health services and media 

Access to mobile phone or internet (ref: no)

Yes 1.29 0.002 0.86 0.034 0.82 0.012 0.91 0.508 1.55 0.001 1.00 0.997 

Heard family planning media messages in last few months (ref: no)

Yes 1.10 0.160 0.97 0.483 0.90 0.084 1.16 0.094 0.77 0.012 1.28 0.092 

Visited with health facility or fieldworker in last 12 months (ref: no 

visit)Visited 0.34 <0.001 2.04 <0.001 2.14 <0.001 1.35 0.038 1.26 0.150 1.08 0.725 Discussed family planning at visit 0.18 <0.001 2.92 <0.001 2.41 <0.001 1.37 0.042 1.32 0.115 0.94 0.800 

Covered by health insurance (ref: no)

Yes 0.77 <0.001 1.10 0.101 0.98 0.737 1.22 0.030 1.00 0.965 1.31 0.075 

Problems seeking medical advice when sick (ref: none)

One or more 1.27 <0.001 0.93 0.183 0.94 0.305 0.89 0.229 0.92 0.435 1.06 0.718 

Observations 13,293 13,293 13,293 13,293 13,293 13,293 
12 

Membership in the Quiet Calendar cluster is also associated with little interaction with health services. 

Having visited a health facility or visited with a health fieldworker in the past 12 months (OR=0.34, 

p<0.001), having discussed family planning with a health worker (OR=0.18, p<0.001), and having health 

insurance (OR=0.77, p<0.001) are each negatively associated with membership in the Quiet Calendar 

cluster. Meanwhile, women who encountered one or more problems seeking medical advice when sick have 

1.3 times the odds of belonging to the Quiet Calendar cluster when compared with women who did not 

have such access problems. This factor is not associated with membership in any of the other cluster models. 

3.3.2 Family Builder 1 

The Family Builder 1 cluster generally consists of women who are younger than age 30, are not welleducated, and do not own a mobile phone or have internet access, but who have visited recently with a 

health worker. Cluster membership is also associated with high contraceptive knowledge, having a larger 

ideal family size, having a desire to avoid or delay pregnancy, and current use or intention to use 

contraception. 

Compared with women in the middle age group, younger women have higher odds and older women have 

lower odds of being in this cluster than do women age 30-34. Women with secondary or higher education 

have 36% lower odds of cluster membership than women with no education. Women who had a mobile 

phone or internet access have 14% lower odds (p<0.001) of being in the Family Builder 1 cluster compared 

with women who lacked this access, the opposite direction as for the Quiet Calendar. 

Women with high contraceptive knowledge have 20% higher odds (p<0.01) of belonging to the Family 

Builder 1 cluster than women with low contraceptive knowledge. Compared with women who believed that 

1-2 was the ideal number of children, women whose ideal was 3-4 children have 31% higher odds and those 

whose ideal was 5 or more have 79% higher odds of being in the Family Builder 1 cluster. 

Fertility desires have a strong, significant association with cluster membership. Women who wanted no 

more children have 3.15 times the odds (p<0.001) and those who wanted to delay birth by two or more 

years have 2.7 times the odds (p<0.001) of being in the Family Builder 1 cluster when compared with 

women who wanted a child soon. Women who were currently using or who intended to use contraception 

have 24% to 30% higher odds (p<0.001) of being in the Family Builder 1 cluster than do women who did 

not intend to use contraception. 

Women who had visited a health facility or visited with a health fieldworker in the past 12 months have 

more than two times the odds of membership in the Family Builder 1 compared with women who had no 

visit. The same women have nearly three times the odds of membership if they had discussed family 

planning during that visit. 

Factors related to residence, wealth, sex preference, gender attitudes, family planning media messages, 

health insurance, and problems seeking medical advice are not associated with membership in the Family 

Builder 1 cluster. 
13 

3.3.3 Family Builder 2 

The Family Builder 1 and Family Builder 2 clusters resemble one another in terms of the elements in their 

calendar sequences, though they differ in the timing of these sequences. Although they share some 

similarities, Table 2 highlights differences between the two clusters. 

Like Family Builder 1, the Family Builder 2 cluster consists of younger women who are not well-educated 

and who lacked mobile phones or internet access. Also like in Family Builder 1, having visited with a health 

worker in the past 12 months is positively associated with membership in the Family Builder 2 cluster 

(OR=2.14, p<0.001), with the magnitude of the association being larger for women who discussed family 

planning during that visit (OR=2.41, p<0.001). The Family Builder 1 and 2 clusters are similar in their 

members’ fertility desires; however, the magnitude of the effect is smaller for Family Builder 2. For the 

Family Builder 2 cluster, the odds of membership are slightly higher among women who wanted to delay 

another birth by two or more years (OR=1.28, p<0.001) or have no more children (OR=1.39, p<0.001); for 

the Family Builder 1 cluster, the odds of membership are 2.7-3.2 times higher for these women. 

The Family Builder 2 cluster differs from the Family Builder 1 cluster in terms of contraceptive knowledge 

and intentions, ideal family size, and gender attitudes. Women who provided a non-numeric response when 

asked about their ideal family size have 83% higher odds of being in the Family Builder 2 cluster than 

women who preferred 1-2 children. In contrast to the model for Family Builder 1, the ideal number of 

children is not otherwise associated with cluster membership. Unlike Family Builder 1, women who are 

using contraception have 57% lower odds (p<0.001)—not higher odds—of belonging to the Family Builder 

2 cluster than women who did not intend to use contraception. Contraceptive knowledge is not a factor in 

cluster membership for the Family Builder 2 cluster. 

Women belonging to the Family Builder 2 cluster tend to be mixed in their gender attitudes. Those who 

believed wife beating to be acceptable—the gender inequitable perspective—have 11% lower odds 

(p<0.05) of cluster membership than women who found wife beating unacceptable. However, the other 

gender equity variable operates in the opposing direction: Women who believed that a woman was justified 

in protecting her sexual health in at least one scenario—the gender equitable perspective—also have 18% 

lower odds (p<0.05) of belonging to this cluster than women who found protective self-efficacy 

unacceptable. This finding is robust in that several alternate specifications of these variables and the 

omission of either of these variables does not change the finding. 

Residence, household wealth, sex preference, health insurance, and problems seeking medical advice are 

not indicative of membership in this cluster, as is also the case with Family Builder 1. 

3.3.4 Modern Mother 

The Modern Mother cluster consists of women who had high contraceptive knowledge, wanted to 

delay/avoid pregnancy, were using or intended to use contraception, and had had a recent visit with a health 

worker. The women in this cluster tend to be older than age 20, live in urban areas, and lack secondary or 

higher education. 

Several socio-economic factors are associated with membership in the Modern Mother cluster—one of the 

three clusters characterized by contraceptive use. Women in the Modern Mother cluster are unlikely to be 
14 

adolescents. Women age 15-19 at the start of the calendar sequence have 44% lower odds (p<0.001) of 

cluster membership than women age 30-34. Compared with urban women, women who resided in rural 

areas also have 39% reduced odds (p=0.001) of being in this cluster—the only cluster for which residence 

is a factor. Similar to the Family Builder clusters, women with secondary or higher education also have 

lower odds of cluster membership. There is some indication that women in the richer wealth quintile have 

lower odds of belonging to the Modern Mother cluster than do the poorest women, but wealth is otherwise 

not a factor. 

Contraceptive knowledge, fertility desires, and contraceptive intentions are associated with membership in 

the Modern Mother cluster. Ideal number of children and sex preference are not associated with membership 

in this cluster, nor in any other cluster characterized by contraceptive use. Women with high contraceptive 

knowledge (12-14 methods) have higher odds of membership (OR=1.43, p<0.01), similar to Family Builder 

1 and Traditional Mother clusters. Women who wanted to delay, wanted to avoid, or were unsure of the 

timing of having another child have lower odds of being in the Modern Mother cluster than do women who 

wanted a child soon, ranging from 29% lower for women who wanted no more children (OR=0.71, p<0.05) 

to 65% lower for women who were unsure (OR=0.35, p<0.001). The odds of being in the Modern Mother 

cluster are much higher among women who intended to use (OR=5.53, p<0.001) or were currently using 

(OR=30, p<0.001) contraception when compared with women not using contraception. 

Gender attitudes are also mixed in this cluster, but in the opposite direction as in the Family Builder 2 

cluster. The odds of Modern Mother membership are higher among those who found wife beating 

acceptable (OR=1.26, p<0.01)—the gender inequitable perspective—and among those who thought 

protective self-efficacy was acceptable in one scenario (OR=1.46, p<0.05)—the gender equitable

perspective. 

As for factors related to media and health service interactions, women who visited with a health worker or 

discussed family planning during a health worker visit in the past 12 months have 35% to 37% higher odds 

of cluster membership than do those with no health worker visit. When compared with women who lacked 

health insurance coverage, women with health insurance also have 22% higher odds of being in the Modern 

Mother cluster—one of two clusters for which health insurance is a factor. Mobile phone or internet access 

and family planning messages in the media are not associated with membership, nor are problems seeking 

medical advice when sick. 

3.3.5 Consistently Covered Mother 

Women in the Consistently Covered Mother cluster tend to not be adolescents, lack education, not have a 

mobile phone or internet access, and not have heard family planning media messages in the past few 

months. The cluster consists of women who were more apt to want a child soon, but who were using or 

intended to use contraception. 

Age, education, and hearing family planning media messages are negatively associated with cluster 

membership, while mobile phone/internet access and contraceptive intentions are positively associated. 

Like in the Modern Mother cluster, women age 15-19 (and no other age group) have 54% lower odds of 

cluster membership (p<0.001) than do women age 30-34. Women with either a primary education or a 

secondary or higher education also have lower odds (22% and 50%, respectively) of being in the 

Consistently Covered Mother cluster compared with women with no education. 
15 

The odds of cluster membership are 23% lower (p<0.05) among women who had heard family planning 

media messages and 55% higher among those with a mobile phone or internet access (p=0.001). This is the 

only cluster for which hearing family planning media messages in the past few months is associated with 

cluster membership. 

Regarding fertility desires, women who wanted to delay or avoid pregnancy have 46% to 79% lower odds 

of belonging to the Consistently Covered Mother cluster than do women in the reference category. In other 

words, women who wanted a child soon have greater odds of being in this cluster—a finding similar to that 

in the Modern Mother model. In spite of these desires, women who intended to use contraception have 4.2 

times the odds and women who were currently using contraception have 21.6 times the odds of being in 

this cluster compared with women who do not intend to use contraception. 

In contrast to the other clusters characterized by contraceptive use, women in this cluster do not necessarily 

have high contraceptive knowledge. Ideal number of children, sex preference, gender attitudes, health 

insurance, and problems seeking medical advice are not associated with membership. 

3.3.6 Traditional Mother 

Contraceptive knowledge and intentions are the only attitudinal factors associated with membership in the 

Traditional Mother cluster. Women with medium or high contraceptive knowledge have nearly twice the 

odds (ORs=1.83-1.99, p<0.01) of being in the Traditional Mother cluster than do women with low 

knowledge. In a pattern similar to the Modern Mother and Consistently Covered Mother clusters, women 

who intended to use contraception have more than five times the odds and those who were using 

contraception have nearly 33 times the odds of being in the Traditional Mother cluster (p<0.001). 

Although sex preference is not generally associated with membership in any other cluster model, women 

who had a preference for sons have 29% lower odds (p<0.05) of being members of the Traditional Mother 

cluster compared with women who had no preference regarding sex composition. Membership in this 

cluster is not otherwise associated with attitudes toward ideal family size, gender, or fertility desires. 

Furthermore, members and non-members do not differ in their interactions with health services and media, 

including mobile phone/internet access, hearing family planning media messages, health worker visits, 

health insurance, or problems seeking medical advice. 

As shown in Table 2, age at the start of the calendar sequence shows a positive association with being in 

the Traditional Mother cluster. Compared with women age 30-34, younger women have lower odds of 

cluster membership, while those age 40 and older have higher odds of membership. 

17 

4 CONCLUSIONS 

This study identified six clusters of reproductive behavior in Burundi and used data on women’s 

contraceptive knowledge, attitudes, and interactions with the media and health services to determine 

associations between these factors and membership in the individual clusters. Three of the clusters are 

characterized by contraceptive use, and three are characterized by the absence of contraceptive use. A large 

majority of women in the study (85%) belong to one of the non-contraception clusters, with a minority 

(16%) belonging to one of the three contraception clusters. 

Two of the non-contraception clusters are characterized by family building (i.e., the use of no contraception 

and the experience of two pregnancies). The Family Building 1 and 2 clusters appear at first glance to be 

nearly identical, differing only by the timing of pregnancies. This raises the question as to (1) whether their 

identification as separate clusters was an anomaly of the statistical procedures used and was based on 

variance in when women were interviewed relative to their pregnancies or (2) whether they are, indeed, 

programmatically meaningful, distinct groups of women. The results of our regression analyses support the 

latter conclusion. 

Although women in Family Builder 1 and 2 clusters have some attributes in common (they are generally 

young and have interacted with a health provider recently), they differ in several important ways. In Family 

Builder 1, women tend to have medium to high contraceptive knowledge, are using or intended to use 

contraception, and desired moderate to large families (at least three children). In contrast, women in the 

Family Builder 2 cluster are more likely to provide a non-numeric answer about their ideal number of 

children, suggesting that women who fall into this profile are in a concentrated family building stage but 

may not believe they can exercise much control over their reproductive lives (Coale 1973; Frye and Bachan 

2017; Hayford and Agadjanian 2011; Lloyd and Ivanov 1988; McCarthy and Oni 1987; Trinitapoli and 

Yeatman 2011; Van de Walle 1992). This contrast comports with other analyses that indicate that women in 

the Family Builder 1 cluster have more experience with contraception than do women in Family Builder 2, 

are less likely to experience unmet need, and are more likely to participate in decisions related to 

contraceptive use (Juan, Allen, and MacQuarrie 2020; MacQuarrie, Allen, and Gemmill 2020). 

The three clusters characterized by contraceptive use are each characterized by the type of contraception 

used—short-term, modern methods; LAPMs; and traditional methods. Although women who are members 

of these clusters may be using contraception, some (such as members of the Consistently Covered Mother 

cluster) may want to have another child soon. This illustrates the dynamic nature of fertility desires over 

time (Bernardi, Mynarska, and Rossier 2015; Dasgupta, Zaba, and Crampin 2015; Roy et al. 2003; Speizer 

and Lance 2015) suggesting that, just as women who are not using contraception may need these services 

soon, women who are contracepting may soon need LARC-removal services, antenatal care, or other 

reproductive services in support of their reproductive intentions (Brandi and Fuentes 2020; Staveteig, 

Mallick, and Winter 2015). 

Contraceptive knowledge was positively associated with membership in the Family Builder 1 and 

Traditional Mother clusters and negatively associated with membership in the Quiet Calendar cluster. 

Surprisingly, it was not associated with membership in either cluster defined by the use of modern methods, 
18 

suggesting a need for improved counseling on a range of contraceptive methods, including how they work 

and their efficacy, side effects, and alternatives. 

Gender equitable attitudes are believed to be related to contraceptive use (Nanda, Schuler, and Lenzi 2013; 

Raj et al. 2016; Walcott et al. 2015). However, in this study, we found that gender attitudes were seldom 

associated with membership in clusters based on contraceptive and pregnancy experiences. When they are 

associated with cluster membership (i.e., in the Family Builder 2 and Modern Mother clusters), attitudes 

toward violence and women’s self-efficacy operated in opposite directions, suggesting that gender attitudes 

are more complex and nuanced than typically conceptualized. 

Mass media initiatives are a common component of SBC interventions and have been shown to increase 

contraceptive use (Hutchinson and Meekers 2012; Jah, Connolly, and Ryerson 2018; Rosen et al. 2019; 

Speizer et al. 2014). However, in this study, we found that exposure to family planning messages in the 

media is largely not associated with cluster membership. 

We also found that visiting with a health worker and discussing family planning with that health worker is 

positively associated with membership in the Modern Mother cluster and both Family Builder clusters. For 

Family Builders, these health worker visits may have been related to maternal health care, given that the 

women in these clusters have recent pregnancies in their calendar sequences. Their interactions with health 

services reinforces the importance of counseling about postpartum family planning (and beyond) during 

antenatal care visits. Recent interaction with health services was negatively associated with membership in 

the Quiet Calendar cluster—the largest cluster, encompassing more than 4 in 10 women in Burundi. 

Problems seeking medical care and low contraceptive knowledge are also associated with the Quiet 

Calendar cluster. This presents a formidable challenge to reproductive health programs, and innovative 

approaches may be required to reach hard-to-reach women with information and general health services 

before contraceptive or pregnancy care is needed. 

This study made novel use of DHS calendar data in Burundi to identify six discrete clusters of women based 

on their contraceptive and pregnancy experiences over the past five years, and to determine the attributes 

associated with these clusters. The results can guide reproductive health programs as they target SBC and 

other interventions to the unique subpopulations they seek to serve. 
19 

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