DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2020 No. 172

Christina Juan

Courtney Allen

Kerry L. D. MacQuarrie

August 2020

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

International Development.

Associations between Women’s Current Contraceptive 

Method Decision Making and their Reproductive Calendar 

Histories in Burundi

DHS Working Paper No. 172 

Associations between Women’s Current Contraceptive 

Method Decision Making and their Reproductive Calendar 

Histories in Burundi 

Christina Juan1

Courtney Allen1

Kerry L. D. MacQuarrie2 

ICF 

Rockville, Maryland, USA 

August 2020 

1

ICF, The Demographic and Health Surveys (DHS) Program 

2

Avenir Health, The DHS Program 

Corresponding author: Christina Juan, International Health and Development, ICF, 530 Gaither Road, 

Suite 500, Rockville, MD 20850, USA; phone: +1 301-572-0332; email: Christina.Juan@icf.com 
Acknowledgments: The authors thank our research and analysis team director, Thomas Pullum, for 

discussing questions regarding data management including weighting. We are also grateful to our colleague 

Sara Riese for her internal review and feedback for our paper. 

Editor: Kerry Aradhya 

Document Production: Joan Wardell 

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; phone: +1 301-407-6500; fax: +1 301-

407-6501; email: info@DHSprogram.com; internet: www.DHSprogram.com. 

Recommended citation: 

Juan, Christina, Courtney Allen, and Kerry L. D. MacQuarrie. 2020. Associations between Women’s Current 

Contraceptive Method Decision Making and their Reproductive Calendar Histories in Burundi. DHS 

Working Paper No. 172. Rockville, Maryland, USA: ICF. 
iii 

CONTENTS 

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

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

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

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

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

1 BACKGROUND ......................................................................................................................... 1 

2 DATA AND METHODS ............................................................................................................. 3

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

2.2 Methods ........................................................................................................................ 4 

2.2.1 Contraceptive calendar data and clusters ....................................................... 4 

2.2.2 Decision-making indicators and covariates ..................................................... 6 

2.2.3 Analyses ........................................................................................................... 8

3 RESULTS .................................................................................................................................. 9 

4 DISCUSSION ........................................................................................................................... 17 

REFERENCES ...................................................................................................................................... 21 

APPENDIX ............................................................................................................................................ 25 

v 

TABLES 

Table 1 Marital control, fertility, and family planning covariates ....................................... 7 

Table 2 Profile of analytical sample of women, Burundi DHS 2016-17 ............................ 9 

Table 3 Bivariate results of associations among clusters, covariates, and decisionmaking power to use or not use contraception, Burundi DHS 2016-17 ............ 11 

Table 4 Adjusted relative risks from multivariable multinomial logistic regression 

examining clusters and decision-making power to use or not use 

contraception, Burundi DHS 2016-17 ................................................................ 13

vii 

FIGURES 

Figure 1 Conceptual framework ......................................................................................... 4 

Figure 2 Representative sequences within each Burundi cluster ...................................... 6 

Figure 3 Forest plot of women’s decision-making power to use or not use 

contraception, after accounting for other variables of interest, Burundi DHS 

2016-17 .............................................................................................................. 15 

Appendix Figure Sequence index plot of each Burundi contraceptive cluster .............................. 25 

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 Working Papers 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 United States Agency 

for International Development. 

It is hoped that the DHS Working Papers 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 

In a recent study of contraceptive calendar data from Demographic and Health Surveys (DHS) in Burundi 

and Nepal, the researchers created clusters of women based on their contraceptive behavior and pregnancy 

experiences over a 5-year calendar period (MacQuarrie, Juan, Allen, Zweimueller, and Gemmill 2019). In 

Burundi, the following six contraceptive clusters were created: Quiet Calendar, Family Builder 1, Family 

Builder 2, Modern Mother, Consistently Covered Mother, and Traditional Mother. 

Here, we carried out multivariable, multinomial logistic regression analysis to examine the associations of 

these clusters with current decision-making power to use or not use contraception. We also examined how 

other important factors, including experiences of marital control in terms of suspicion and isolation, 

intimate partner violence, employment status, fertility intentions, exposure to family planning messages, 

and family planning discussions with a health provider in a health facility may impact women’s current 

contraceptive decision-making behaviors, after accounting for their 5-year reproductive calendar histories. 

We found that women in three of the six clusters—Family Builder 1, Modern Mother, and Consistently 

Covered Mother—had higher relative risks of deciding jointly whether or not to use contraception with 

their partners or husbands than of not making a decision about contraception, when compared with women 

in the Quiet Calendar cluster. Our findings suggest that using clusters based on patterns of key events in a 

woman’s reproductive life gives greater insight into her reproductive journey and into the effects of those 

patterns on her current ability to make decisions—either jointly or solely—for her health and well-being. 

Key words: gender, contraceptive calendar, decision making, longitudinal data, Burundi 

xiii 

ACRONYMS AND ABBREVIATIONS 

CI confidence interval 

DHS Demographic and Health Surveys 

IPV intimate partner violence 

LAPM long-acting or permanent method 

PBT pregnancy, birth, or termination 

RR relative risk 

USAID United States Agency for International Development 

1 

1 BACKGROUND 

Burundi is a small, landlocked East African country where 77% of the population lives in rural areas. Its 

total fertility rate of 5.5 is the fourth highest in the world, and modern contraceptive use among all women 

in the country remains low (15%) (MPBGP et al. 2017). Modern contraceptive methods were not promoted 

in Burundi until the early 1980s (MPBGP et al. 2017), when the rate of use was less than 1% (Nzokirishaka 

and Itua 2018). Since then, modern contraceptive prevalence has slowly risen; however, uptake of methods 

for postpartum family planning still remains very low, and the country’s long-standing civil conflict has led 

to low service utilization (Rutaremwa and Kabagenyi 2018). 

Within the field of reproductive health and family planning, particular attention is placed on access to and 

uptake of modern contraceptive methods. Rutaremwa and Kabagenyi (2018) found that family planning 

uptake among postpartum women in Burundi increased with education and exposure to media yet was 

unaffected by wealth, contributing to evidence of strong cultural expectations for women to keep bearing 

children (Rutaremwa and Kabagenyi 2018). Because there are long-held cultural and social norms around 

family formation, fertility, and sexual and reproductive health, it is critical to examine women’s behavior 

in navigating these norms, such as through their decision making around these topics. Contraceptive 

decision making encapsulates behavior beyond modern contraceptive method uptake, such as the decision 

to use a traditional method or not use a method at all. Women’s agency or empowerment is critical for 

uptake and continued use of contraception in low- and middle-income countries (Bhatti and Jeffery 2012; 

Santhya et al. 2010), and some studies suggest that women’s agency can be a necessary precondition for, 

or product of, contraceptive use (James-Hawkins et al. 2018). Low past and current rates of contraceptive 

use are associated with a woman’s inferior decision-making power when compared with that of her husband 

(Hindin 2000), and empowered women are more likely than their less-empowered counterparts to use 

contraception (OlaOlorun and Hindin 2014). 

A woman’s agency to use or not use contraception is affected by power dynamics between her and her 

partner as well as those perpetuated by society. Women may have less autonomy to limit childbearing in a 

patriarchal society (Bogale et al. 2011; Hogan, Berhanu, and Hailemariam 1999). Some measures of agency, 

such as women’s education and participation in the formal economy, have been linked to lower levels of 

fertility (Richards and Bass 2019) and reproductive empowerment (Gammage, Joshi, and Rodgers 2020). 

In Burundi, a husband’s leadership role within the family is considered very important, so empowerment 

for a woman could present itself as the woman and the man making decisions together (Ngenzebuke, De 

Rock, and Verwimp 2018). 

Joint decision making may depend on spousal communication—an important part of family planning 

interventions. Couples who discuss fertility intentions have greater use of contraception (Bawah 2002; 

Kulczycki 2008; Yue, O’Donnell, and Sparks 2010). Where couples disagree on fertility preferences or 

desires, men’s power in a relationship may contribute to greater unmet need (Mason and Smith 2000). 

Despite the influence that partners may have on decisions, women commonly use family planning covertly, 

indicating that men and women do not always make decisions as a unit; instead, some women make 

decisions individually (Biddlecom and Fapohunda 1998; Yue, O’Donnell, and Sparks 2010). 
2 

A wealth of literature has examined decision making and contraceptive use, and research shows that any 

level of decision-making power has a positive relationship with contraceptive use. However, capturing 

agency through decision-making indicators can be context-specific (James-Hawkins et al. 2018; Malhotra 

and Schuler 2005; Yount et al. 2016), and scholars have not agreed on a defined measurement of agency 

(Yount et al. 2016). Scant literature has examined women’s response to the question, “Whose decision is it 

to (not) use contraception?” This question is a direct measurement of women’s perceived agency to use or 

not use contraception, and we focused on this question in our analysis. 

Reproductive health and family planning health providers serve adolescent girls and women in all stages of 

their reproductive journeys, from around the time of menarche through menopause. However, current 

evidence, policies, and programming inadequately capture the nuances of a woman’s reproductive health 

and family planning journey. This is because insights often rely on cross-sectional data, which create a 

snapshot of needs at a specific moment of time without fully accounting for changes in those needs. 

Because of the limitations of the cross-sectional nature of Demographic and Health Surveys (DHS) data, 

we examined 5-year retrospective contraceptive calendars found in some DHS surveys that capture 

contraceptive and pregnancy events. A recent study used these calendar data from the 2016-17 Burundi 

DHS to cluster women based on the sequences of their reproductive experiences (MacQuarrie, Juan, Allen, 

Zweimueller, and Gemmill 2019). These dynamic clusters represented the most common sequences that 

women of reproductive age in Burundi may experience, offering rich insight into their unique reproductive 

journeys. Using these clusters, it also became possible to analyze a woman’s perceived agency to use or not 

use contraception with a lens of women’s journeys to their present state. 

This study examined whether the contraceptive decision making of women in Burundi was associated with 

distinct patterns in their contraceptive and pregnancy histories and in their experiences with intimate partner 

violence (IPV). This was accomplished using a specific measure of decision making and the distinct clusters 

identified from the study of 5-year retrospective calendars capturing reproductive events—an innovative 

application of these data (MacQuarrie, Juan, Allen, Zweimueller, and Gemmill 2019). The results of this 

study contribute to our understanding of contraceptive behavior and women’s agency, and examines 

whether retrospective contraceptive calendar data is predictive of decision making. 
3 

2 DATA AND METHODS 

2.1 Data 

This study used data from the 2016-17 Burundi DHS survey—a nationally representative, household survey 

of 17,269 women age 15-49 and 7,552 men age 15-59. The DHS survey included data on a wide range of 

sexual and reproductive health, population, maternal and child health, and nutrition indicators collected by 

ICF International in collaboration with the Burundi government and seven other organizations. We selected 

this country for our analysis to gain further understanding of the six contraceptive clusters that were 

analyzed in the study by MacQuarrie et al. (2019). 

A total of 5,388 women were chosen for our study. To reach this sample size, we first restricted our analysis 

to women who had both completed the DHS survey and were included in the recent study in which the six 

contraceptive clusters were created based on 59 months of data (n = 13,184) (MacQuarrie, Juan, Allen, 

Zweimueller, and Gemmill 2019). MacQuarrie et al. (2019) omitted the most recent 3 months of data as 

some events may not become known by the respondent (e.g., a pregnancy) and reported as a result; 

additionally, the authors excluded women who were younger than age 15 at the start of their 5-year 

retrospective contraceptive calendar, because adolescent girls and young women are generally neither 

sexually active nor biologically fecund during the period of their 5-year calendar histories (MacQuarrie, 

Mallick, and Allen 2017). We further restricted our sample to women who were currently married or in a 

union who reported not being pregnant at the time of the survey. Because of their pregnancy status, these 

women were not asked about their decision-making power to use or not use contraception. Women who 

were not currently married or in a union were not asked the questions about marital control that were 

pertinent to our conceptual framework. Lastly, we restricted our analysis to the subpopulation of women 

who were also selected to participate in the domestic violence module of the survey. 

Conceptual Framework 

We developed a conceptual framework (Figure 1) to inform our analysis. 
4 

Figure 1 Conceptual framework 

We hypothesized that a woman’s membership in a cluster (based on her 5-year calendar history of 

experiences related to births, pregnancies, terminations, contraceptive method use, and method type) 

impacts her current capacity to make or participate in decisions related to contraception. We investigated 

these relationships, accounting for socioeconomic factors such as current employment status. We further 

hypothesized that experiences of marital control and IPV impact a woman’s ability to decide whether or 

not to use contraception, so we accounted for these covariates. Women who experience IPV and marital 

control would signal a lower risk of feeling empowered to decide whether or not to use a contraceptive 

method, for instance. Lastly, we controlled for other variables that could affect decision making, including 

fertility intentions, exposure to family planning messages, and family planning counseling discussions with 

a health provider. Women who access the health system more frequently and have greater exposure to family 

planning messages feel more empowered to decide whether or not to use a contraceptive method. 

2.2 Methods 

2.2.1 Contraceptive calendar data and clusters 

The analysis uses variables from a previous study that examined contraceptive calendar data to create 

clusters, also referred to as contraceptive profiles in the previous study (MacQuarrie, Juan, Allen, 

Zweimueller, and Gemmill 2019). For this study, we will refer to these contraceptive profiles as clusters. 

The DHS contraceptive calendar is a month-by-month history of life events in terms of births, pregnancies, 

Current decisionmaking power to 

use/not use a 

contraceptive 

method

Clusters based 

on 5-year 

reproductive 

calendar 

histories

Family planning 

messages and 

health provider 

counseling

Fertility 

intentions

Intimate 

partner violence

Marital control

Socioeconomic 

factors (e.g., 

age, wealth, 

residence, 

employment)
5 

terminations, contraceptive use, and other experiences pertaining to reproductive and sexual outcomes. The 

calendar provides information related to the year of the survey plus the five full calendar years preceding 

the survey. To produce the six primary clusters, sequence and cluster analysis of the analytical sample was 

carried out using the TraMineR and WeightedCluster packages in R (Gabadinho et al. 2011; Studer 2013). 

For instance, Optimal matching, a sequence analysis method, was used to account for the timing of 

pregnancies and births in the sequences. The cluster analysis also helped to group women in the sample 

together based on similar sexual and reproductive health events they experienced during the contraceptive 

calendar. These events were grouped into five states: 1) no contraceptive use; 2) traditional method use; 

3) short-term modern method use; 4) use of a long-acting or permanent method (LAPM); and 5) pregnancy, 

birth, or termination (PBT). 

For context and because our analysis built upon the results of the study by MacQuarrie et al. (2019), Figure 

2 shows how the population of that study was distributed across the six contraceptive clusters based on 59 

months of calendar data: Quiet Calendar, Family Builder 1, Family Builder 2, Modern Mother, Consistently 

Covered Mother, and Traditional Mother. This figure shows six medoids, or representative sequences, that 

correspond to each contraceptive cluster. As these clusters were created using the 5-year contraceptive 

calendar data, each medoid represents 5 years of data; each color indicates a state within that representative 

sequence. 

Women in the Quiet Calendar cluster (42% of the analytical sample) were characterized by their nonuse of 

contraception over their 59-month calendar sequences. Family Builder 1 was the second largest cluster, 

representing a quarter of women. In this cluster, women were characterized by experiencing two distinct 

PBT states (as indicated in blue) among states of contraceptive nonuse (indicated in green) over the 5-year 

reporting period. The next most common sequence was within the Family Builder 2 cluster (18%). Family 

Builder 1 and Family Builder 2 differed in that women within the Family Builder 1 cluster reported a 

contraceptive nonuse state 5 years before the survey, followed by a PBT state, and the women in the Family 

Builder 2 cluster reported the opposite (a PBT state followed by a nonuse state). The Modern Mother cluster 

(8% of the sample) was characterized by women who eventually used a short-term modern contraceptive 

method after experiences of nonuse and PBT. Lastly, 8% of women in the sample belonged to the 

Consistently Covered Mother (6%) and Traditional Mother (2%) clusters, which were characterized by 

LAPM and traditional method use, respectively, after experiences of nonuse and PBT. 
6 

Figure 2 Representative sequences within each Burundi cluster 

Note: LAPM: long-acting or permanent method 

Although the contraceptive clusters offer insight into the most typical patterns of events women experience 

in Burundi, we understand that they are only representative sequences; consequently, we understand that 

women assigned to the medoids displayed in Figure 2 also experience other states of contraceptive behavior 

and pregnancy-related episodes that are more diverse and nuanced. As such, the appendix shows sequence 

index plots, illustrating the diversity of experiences in each cluster. The sequence index plots, to be 

interpreted as women’s 5-year event calendars horizontally, exhibit the variation of states and sequences 

that occur at an individual level. Some women in the Quiet Calendar cluster, for example, experienced the 

PBT state, although this was not the main pattern across the women in this cluster. 

2.2.2 Decision-making indicators and covariates 

To create a measure of decision-making power to use or not use contraception, we created an indicator that 

describes whether a woman has no, sole, or joint—with her partner or husband—decision-making power—

a three-category variable. In our analysis, this variable served as our outcome variable of interest. A woman 

was considered to have any contraceptive decision-making power if she reported either solely or jointly 

deciding to use or not use contraception. This variable was constructed based on two separate survey 

questions regarding who the decision maker is for using or not using contraception, depending on whether 

the respondent was using contraception. Possible responses were 1) mainly the respondent, 2) mainly the 

husband or partner, 3) the respondent and husband/partner jointly, and 4) other. 

We reflected on our conceptual framework (Figure 1) and conducted bivariate and multinomial logistic 

regression analysis to examine the associations among our three-category outcome of interest (on decision 

making) and the six clusters (Quiet Calendar, Family Builder 1, Family Builder 2, Modern Mother, 

Consistently Covered Mother, and Traditional Mother) —our main exposure variable. 
7 

We controlled for demographic and socioeconomic factors, fertility, and family planning using the 

following covariates: age, education, current work status, wealth, residence, region, fertility intentions, 

exposure to family planning messages, and whether the woman had discussed family planning with a health 

provider. To examine fertility intentions, we created a binary variable by grouping women into two 

categories: “no” if they stated clearly that they either did not want children or were unable to have children 

(i.e., they were infecund), and “yes” if they wanted children or were undecided. The undecided women 

were included in the “yes” category, assuming that some of these women would eventually decide they 

wanted children. 

We also examined experiences of marital control and IPV. Regarding marital control, we created two 

separate indicators to examine whether a woman’s partner or husband was suspicious or isolating. Items 

were categorized as 1) suspicious if the husband or partner was jealous, accused the respondent of being 

unfaithful, or insisted on knowing where the respondent was at all times. They were categorized as isolating

if the husband or partner did not permit the respondent to meet her female friends or tried to limit the 

respondent’s contact with family. Using data from numerous DHS surveys in sub-Saharan Africa, previous 

research on IPV and marital control using factor analysis identified that specific items pointed to spousal 

violence (i.e., IPV) perpetrated against married and in-union women by their husbands or cohabitating 

partners (MacQuarrie, Winter, and Kishor 2014). In terms of IPV, we also included an indicator regarding 

sexual violence and another one to examine experiences of emotional violence, which we defined as 

respondents who had been humiliated, threatened with harm, or insulted or made to feel bad by their 

partners or husbands. For marital control and IPV indicators, respondents who were married or in a union 

as well as those who had participated in the domestic violence module of the DHS survey were part of this 

subsample. 

Table 1 lists the indicators and the corresponding definitions used to calculate them for this study. 

Table 1 Marital control, fertility, and family planning covariates 

Indicator Definition 

Marital control and violence 

Emotional violence experienced Whether respondent experienced emotional violence by husband/partner in the past 12 

months 

Sexual violence experienced Whether respondent experienced sexual violence by husband/partner in the past 12 

months 

Marital suspicion experienced Whether respondent ever experienced any of the following types of marital control 

exercised by husband or partner: 1) the partner was jealous, 2) the partner accused 

respondent of being unfaithful, or 3) the partner insisted on knowing where the 

respondent was at all times. 

Marital isolation experienced Whether respondent ever experienced any of the following types of marital suspicion 

exercised by husband or partner: 1) the partner did not permit respondent to meet 

female friends, or 2) the partner tried to limit respondent’s contact with family 

Fertility 

Fertility intentions Women who wanted children or were undecided versus those who did not want children 

or were infecund 

Family planning 

Current contraceptive method use Modern or traditional contraceptive method use 

Exposure to family planning messages Exposure to family planning messages in the past few months (e.g., through radio, TV, 

newspaper/magazine) 

Discussed family planning use Whether the respondent visited a health facility and was told about family planning by a 

health provider in the past 12 months 
8 

2.2.3 Analyses 

We carried out bivariable statistical analyses using chi-square tests and fitted multinomial logistic 

regression to model the trichotomous outcome variable of jointly, solely, or not deciding whether to use or 

not use contraception. Our statistical analyses accounted for complex survey design using the svy command 

in Stata/MP16.0 and the weights from the domestic violence module were used. All statistics presented are 

weighted. Sensitivity analysis was carried out with regression models among the subgroup of women who 

decided to use a contraceptive method and the subgroup who decided not to use a contraceptive method, 

separately, compared with our main outcome variable of interest on contraceptive decision making. 
9 

3 RESULTS 

Table 2 presents sample descriptive statistics for the women in our study. The distribution of contraceptive 

clusters corresponded to that in the study by MacQuarrie et al. (2019); the majority of women belonged to 

the Family Builder 1 cluster, followed by the Quiet Calendar cluster, with the lowest proportion of women 

in the Traditional Mother cluster. The majority of women (88%) reported currently working, and 90% of 

the women lived in rural areas in Burundi. Half of the sample was not educated, and nearly 40% had 

completed primary education. 

Table 2 Profile of analytical sample of women, Burundi DHS 2016-17 

Characteristic N (weighted) % 

Cluster 

Family Builder 1 1,493 31.9 

Family Builder 2 981 21.0 

Traditional Mother 141 3.0 

Modern Mother 480 10.3 

Quiet Calendar 1,238 26.5 

Consistently Covered Mother 343 7.3 

Age group 

20-24 689 14.7 

25-29 1,073 22.9 

30-34 1,016 21.7 

35-39 821 17.6 

40-44 604 12.9 

45-49 474 10.1 

Highest education 

None 2,346 50.2 

Primary 1,817 38.9 

Secondary or higher 513 11.0 

Work status 

Currently not working 579 12.4 

Currently working 4,098 87.6 

Wealth 

Lowest 952 20.4 

Second 1,001 21.4 

Middle 932 19.9 

Fourth 916 19.6 

Highest 876 18.7 

Residence 

Urban 470 10.1 

Rural 4,206 89.9 

Continued... 

 
10 

Table 2—Continued

Characteristic N (weighted) % 

Region 

Bubanza 237 5.1 

Bujumbura Rural 253 5.4 

Bururi 128 2.7 

Cankuzo 142 3.0 

Cibitoke 250 5.4 

Gitega 387 8.3 

Karusi 277 5.9 

Kayanza 310 6.6 

Kirundo 354 7.6 

Makamba 280 6.0 

Muramvya 179 3.8 

Muyinga 360 7.7 

Mwaro 160 3.4 

Ngozi 440 9.4 

Rutana 198 4.2 

Ruyigi 239 5.1 

Bujumbura Mairie 250 5.3 

Rumonge 231 4.9 

Decision-making power to use or 

not use contraception 

None 522 11.2 

Sole 1,071 22.9 

Joint 3,083 65.9 

Ever experienced emotional 

violence by partner or husband 

No 3,606 77.1 

Yes 1,070 22.9 

Ever experienced sexual violence 

by partner or husband 

No 3,530 75.5 

Yes 1,146 24.5 

Marital suspicion 

No 3,312 70.8 

Yes 1,364 29.2 

Marital isolation 

No 4,006 85.7 

Yes 670 14.3 

Desire more children 

No or infecund 2,270 48.5 

Yes or unsure 2,406 51.5 

Current contraceptive method use 

Not using 3,163 67.6 

Modern method 1,218 26.1 

Traditional method 295 6.3 

 

Exposure to family planning 

messages 

No 3,232 69.1 

Yes 1,445 30.9 

Discussed family planning with 

health worker 

No 2,882 61.6 

Yes 1,794 38.4 

Total 4,676 100.0 

Table 3 displays associations among the clusters, covariates, and decision-making power to use or not use 

contraception, based on results of bivariate analysis. Across all clusters, joint decision making was more 

common than sole or no decision making to use or not use contraception. For instance, there is strong 
11 

statistical evidence that more women in the Family Builder 1 cluster made decisions jointly than made sole 

decisions about whether to use or not use contraception (68% versus 22%; p<0.001). 

Results also showed that a significantly higher proportion of both women who had ever experienced 

emotional violence and women who had ever experienced sexual violence by their partners or husbands 

decided jointly to use a form of contraception, and that there was strong statistical evidence of an association 

between these forms of violence and current contraceptive decision making (p<0.001 for both). The same 

was true for women who experienced marital suspicion and those who experienced marital isolation, with 

strong evidence an association between these forms of marital control and current contraceptive decision 

making (p<0.001 for both). Among women who reported current method use, there was strong statistical 

evidence of an association between current method use and current contraceptive decision making where 

88% who were using traditional methods and 82% who were using modern methods reported joint decision 

making over sole or no decision making (p<0.001 for both). 

Table 3 Bivariate results of associations among clusters, covariates, and decision-making 

power to use or not use contraception, Burundi DHS 2016-17 

Decision-making power to use or not use contraception 

None Sole Joint Total 

p Characteristic % % % % 1

Weighted 

n 

Cluster Family Builder 1 10.1 21.9 68.0 100.0 1,493 

*** 

Family Builder 2 13.5 25.8 60.7 100.0 981 

Traditional Mother 7.2 6.8 85.9 100.0 141 

Modern Mother 4.1 16.2 79.7 100.0 480 

Quiet Calendar 15.5 28.2 56.3 100.0 1,238 

Consistently Covered Mother 5.2 15.5 79.3 100.0 343 

Marital control and violence 

Ever experienced emotional violence 

by partner or husband 

No 11.2 20.5 68.3 100.0 3,606 *** Yes 11.0 31.0 58.0 100.0 1,070 

Ever experienced sexual violence by 

partner or husband No 10.8 21.1 68.1 100.0 3,530 *** Yes 12.2 28.5 59.2 100.0 1,146 

 

Marital suspicion 

No 10.7 20.4 68.9 100.0 3,312 *** Yes 12.3 28.9 58.8 100.0 1,364 

Marital isolation No 11.0 21.7 67.3 100.0 4,006 *** Yes 12.4 29.9 57.7 100.0 670 

 

Fertility

Desire more children 

No or infecund 11.5 25.5 63.0 100.0 2,270 ** Yes or unsure 10.9 20.4 68.7 100.0 2,406 

Family planning Current method use Not using 14.0 28.3 57.7 100.0 3,163 

Modern method 4.7 13.3 82.0 100.0 1,218 *** 

Traditional method 6.8 4.9 88.3 100.0 295 

 

Exposure to family planning messages 

No 11.5 25.6 63.0 100.0 3,232 *** Yes 10.5 16.9 72.6 100.0 1,445 

Continued... 

 
12 

Table 3—Continued

Decision-making power to use or not use contraception 

None Sole Joint Total 

p Characteristic % % % % 1

Weighted 

n 

Discussed family planning with health 

worker No 11.3 23.0 65.7 100.0 2,882 

Yes 11.0 22.8 66.3 100.0 1,794 0.94 

Age 

20-24 11.4 19.3 69.3 100.0 689 

** 

25-29 10.2 21.2 68.6 100.0 1,073 

30-34 9.3 23.5 67.2 100.0 1,016 

35-39 12.0 21.9 66.1 100.0 821 

40-44 14.4 26.6 59.0 100.0 604 

45-49 11.3 27.8 60.9 100.0 474 

Highest education None 11.9 26.6 61.5 100.0 2,346 

Primary 10.7 20.5 68.8 100.0 1,817 *** 

Secondary or higher 9.2 14.5 76.3 100.0 513 

Work status 

Currently not working 16.6 17.4 66.0 100.0 579 *** Currently working 10.4 23.7 65.9 100.0 4,098 

Wealth Lowest 11.1 30.4 58.6 100.0 952 

*** 

Second 10.9 24.1 65.0 100.0 1,001 

Middle 12.1 20.4 67.4 100.0 932 

Fourth 12.6 19.6 67.8 100.0 916 

Highest 9.0 19.4 71.6 100.0 876 

 

Residence 

Urban 8.9 19.6 71.5 100.0 470 

Rural 11.4 23.3 65.3 100.0 4,206 0.05 

Region Bubanza 17.9 21.8 60.3 100.0 237 

*** 

Bujumbura Rural 13.6 18.2 68.1 100.0 253 

Bururi 30.4 23.8 45.8 100.0 128 

Cankuzo 6.4 16.8 76.8 100.0 142 

Cibitoke 14.3 22.1 63.6 100.0 250 

Gitega 14.3 17.1 68.6 100.0 387 

Karusi 9.1 31.4 59.5 100.0 277 

Kayanza 1.9 29.8 68.3 100.0 310 

Kirundo 9.4 25.0 65.6 100.0 354 

Makamba 6.8 15.0 78.2 100.0 280 

Muramvya 5.3 18.4 76.2 100.0 179 

Muyinga 15.1 27.4 57.4 100.0 360 

Mwaro 13.8 26.5 59.7 100.0 160 

Ngozi 2.5 29.9 67.6 100.0 440 

Rutana 14.3 21.7 64.0 100.0 198 

Ruyigi 19.8 7.0 73.2 100.0 239 

Bujumbura Mairie 8.8 20.8 70.5 100.0 250 

Rumonge 11.8 30.5 57.7 100.0 231 

 

Total (weighted N) 4,676 

Notes: 

1

 p-value indicates statistical strength of the association with the covariate. 

*p<0.05; **p<0.01; ***p<0.001 

Results of multivariable multinomial logistic regression for decision-making ability to use or not use 

contraception are displayed in Table 4. The analysis compared women who reported sole decision making 

with those who did not make a decision, and women who reported joint decision making with those who 

did not make a decision. Results are presented as relative risks (RRs). 
13 

Table 4 Adjusted relative risks from multivariable multinomial logistic regression examining 

clusters and decision-making power to use or not use contraception, Burundi DHS 

2016-17 

Decision-making power to use or not use contraception 

Sole vs. none Joint vs. none 

Characteristic RR1 [95% CI] RR1 [95% CI] 

Cluster Quiet Calendar (ref) Family Builder 1 1.24 [0.89 - 1.73] 1.56** [1.17 - 2.08] 

Family Builder 2 1.10 [0.78 - 1.54] 1.20 [0.88 - 1.64] 

Traditional Mother 1.24 [0.43 - 3.58] 1.61 [0.69 - 3.75] 

Modern Mother 1.53 [0.83 - 2.81] 2.26** [1.33 - 3.84] 

Consistently Covered Mother 1.36 [0.72 - 2.55] 2.14** [1.31 - 3.49] 

Ever experienced emotional violence by 

partner or husband 

No (ref) 

Yes 1.26 [0.91 - 1.75] 0.85 [0.64 - 1.13] 

Ever experienced sexual violence by 

partner or husband No (ref) Yes 1.10 [0.81 - 1.51] 0.82 [0.62 - 1.09] 

Marital suspicion 

No (ref) 

Yes 1.07 [0.78 - 1.47] 0.74* [0.56 - 0.98] 

Marital isolation No (ref) Yes 1.02 [0.70 - 1.48] 1.00 [0.71 - 1.42] 

Desire more children 

No or infecund (ref) 

Yes or unsure 0.86 [0.65 - 1.14] 1.18 [0.93 - 1.48] 

Current contraceptive method use None (ref) Modern 0.93 [0.63 - 1.37] 2.90*** [2.07 - 4.06] 

Traditional 0.33** [0.14 - 0.76] 2.66** [1.48 - 4.78] 

Exposure to family planning messages 

No (ref) 

Yes 0.73* [0.54 - 0.98] 1.06 [0.81 - 1.37] 

Discussed family planning with a health worker No (ref) Yes 1.05 [0.81 - 1.37] 1.00 [0.80 - 1.26] 

Age 

20-24 (ref) 

25-29 0.70 [0.40 - 1.24] 0.66 [0.40 - 1.07] 

30-34 0.83 [0.49 - 1.39] 0.70 [0.44 - 1.10] 

35-39 0.98 [0.59 - 1.62] 0.77 [0.50 - 1.20] 

40-44 0.67 [0.41 - 1.10] 0.62* [0.40 - 0.96] 

45-49 0.69 [0.40 - 1.21] 0.55* [0.34 - 0.90] 

Highest education None (ref) Primary 0.97 [0.74 - 1.27] 1.15 [0.90 - 1.46] 

Secondary or higher 0.70 [0.42 - 1.18] 1.16 [0.75 - 1.77] 

Work status 

Currently not working (ref) 

Currently working 1.98*** [1.37 - 2.85] 1.68** [1.20 - 2.37] 

Continued... 
14 

Table 4—Continued

Decision-making power to use or not use contraception 

Sole vs. none Joint vs. none 

Characteristic RR1 [95% CI] RR1 [95% CI] 

Wealth Lowest (ref) Second 0.94 [0.66 – 1.35] 1.13 [0.82 – 1.56] 

Middle 0.77 [0.54 – 1.10] 1.03 [0.75 – 1.41] 

Fourth 0.75 [0.53 – 1.05] 1.01 [0.73 – 1.40] 

Highest 1.36 [0.85 – 2.17] 1.39 [0.90 – 2.15] 

Residence 

Urban (ref) 

Rural 1.16 [0.65 – 2.06] 1.00 [0.61 – 1.65] 

Region Bubanza (ref) Bujumbura Rural 1.27 [0.53 – 3.05] 1.48 [0.70 – 3.12] 

Bururi 0.72 [0.29 – 1.77] 0.47 [0.22 – 1.01] 

Cankuzo 1.88 [0.88 – 4.00] 3.41*** [1.84 – 6.32] 

Cibitoke 1.31 [0.60 – 2.83] 1.47 [0.71 – 3.04] 

Gitega 1.01 [0.44 – 2.31] 1.35 [0.68 – 2.68] 

Karusa 2.69* [1.23 – 5.86] 1.54 [0.74 – 3.23] 

Kayanza 11.97*** [4.78 – 29.96] 7.76*** [3.09 – 19.52] 

Kirundo 1.89 [0.91 – 3.95] 1.98* [1.09 – 3.62] 

Makamba 2.01 [0.69 – 5.86] 3.60** [1.38 – 9.41] 

Muramvya 2.82* [1.15 – 6.89] 3.65** [1.57 – 8.46] 

Muyinga 1.48 [0.76 – 2.85] 0.96 [0.53 – 1.73] 

Mwaro 1.64 [0.75 – 3.57] 1.19 [0.59 – 2.41] 

Ngozi 10.48*** [4.47 – 24.55] 6.28*** [2.86 – 13.78] 

Rutana 1.20 [0.60 – 2.40] 1.19 [0.63 – 2.24] 

Ruyigi 0.33** [0.14 – 0.75] 1.19 [0.66 – 2.15] 

Bujumbura Mairie 2.52 [0.98 – 6.48] 1.77 [0.78 – 4.01] 

Rumonge 2.16 [0.93 – 4.99] 1.38 [0.70 – 2.75] 

Observations 5,388 5,388 

 

Notes: 

1

 The p-value indicates statistical strength of association of the covariate. 

*p<0.05; **p<0.01; ***p<0.001. 

CI = confidence interval; ref = reference; RR = relative risk. 

After accounting for IPV, marital control, fertility intentions, and other covariates that we would expect to 

impact contraceptive decision-making power, the results highlighted the importance of 5-year reproductive 

calendar histories to predict decision making in the majority of the clusters. The relative risk of making a 

joint decision with a partner or husband was 1.6 times higher for women in the Family Builder 1 cluster 

than for women in the Quiet Calendar cluster (RR: 1.56; p<0.001; 95% CI: 1.17–2.08). Following a similar 

trend, the relative risk of joint decision-making power was more than two times higher for women in the 

Modern Mother and Consistently Covered Mother clusters than for women in the Quite Calendar cluster 

(RR: 2.3 and 2.1, respectively). There was no statistical evidence of a difference in terms of contraceptive 

decision making among women in the Family Builder 2 and Traditional Mother clusters when compared 

with women in the Quiet Calendar cluster. In terms of sole decision-making capacity, there was no statistical 

evidence of its association with any of the clusters. 

Our multivariable multinomial regression model illustrated statistical evidence of associations of marital 

suspicion, current contraceptive method use, age, current employment, and region with contraceptive 

decision-making power. Women experiencing marital suspicion had a 26% reduced relative risk of jointly 

deciding whether or not to use contraception when compared with women who had not experienced marital 

suspicion (RR: 0.74; p = 0.04; 95% CI: 0.56–0.98). Women using traditional methods had a 67% reduced 
15 

relative risk of solely deciding whether or not to use contraception when compared with women who did 

not have any decision-making power to use or not use contraception (RR: 0.33; p = 0.01; 95% CI: 0.14–

0.76). Current use of modern methods and current use of traditional methods, as reported at the time of the 

survey, both served as strong predictors of joint contraceptive decision making (RR: 2.90 and 2.66, 

respectively). In addition, women’s work status was important in predicting both sole and joint decision 

making. The relative risk of making a sole decision regarding contraception was nearly two times higher 

for women who were working than for women who were not working (RR: 1.98; p<0.001; 95% CI: 1.37–

2.85). A similar positive relationship was found between current employment status and joint decision 

making. Lastly, living in certain regions in Burundi appeared to predict either type of decision making (sole 

or joint); however, caution must be made when interpreting these relative risk and confidence interval 

results, as sample sizes for each category were small. 

Figure 3 is a forest plot of women’s decision-making power to use or not use contraception in relation to 

the clusters, after accounting for other variables of interest. This figure illustrates the adjusted relative risks 

from the multinomial logistic regression analysis. 

Figure 3 Forest plot of women’s decision-making power to use or not use contraception, after accounting 

for other variables of interest, Burundi DHS 2016-17 

There was evidence that women in the Family Builder 1, Modern Mother, and Consistently Covered Mother 

clusters had a higher relative risk than women in the Quiet Calendar cluster of jointly deciding (with their 
16 

partners or husbands) whether to use contraception, after adjusting for other factors. Other clusters did not 

show statistical evidence of an association with any type of decision-making power. When examining 

whether women who decided to use contraception were different from those who decided not to use it, the 

sensitivity analysis conducted did not change the results. The clusters behaved similarly whether examining 

only the subgroup who decided to use or only the subgroup who decided not to use contraception. Therefore, 

we believe that the results were robust. 
17 

4 DISCUSSION 

In a novel application of 5-year longitudinal reproductive calendar data from the Burundi DHS, sequence 

and cluster analyses had been used to identify six contraceptive clusters among women (MacQuarrie, Juan, 

Allen, Zweimueller, and Gemmill 2019). Our study was a first attempt to test and prove the robustness of 

these clusters to inform research, policy, and programs and to elucidate women’s current contraceptive 

decision-making power. Given deviations and variations from the original six clusters in our sample of 

women (appendix figure), we also examined the nuanced journeys of these women. We assessed whether 

factors such as marital control and IPV impact women’s current contraceptive decision-making abilities, 

and we included a variable on current method use to account for the lag in time between the 5-year calendar 

data histories (in the form of clusters) and current decision-making abilities (at the time of the survey). 

Even after accounting for marital control and violence, fertility, family planning, and socioeconomic 

factors, we found that three of the six clusters—Family Builder 1, Modern Mother, and Consistently 

Covered Mother—showed strong statistical evidence of higher relative risk of jointly deciding whether or 

not to use contraception with their partners or husbands than for not deciding about contraception, when 

compared with women in the Quiet Calendar cluster. These clusters have a lasting relationship with 

women’s current decision-making power, even after controlling for many salient factors, conveying the 

importance of examining women’s reproductive journeys. The outcome variable in our study represents 

current contraceptive decision making at the time of the survey. While a woman’s decision-making power 

is likely fluid and intertwined with her reproductive and contraceptive experiences, information about her 

past decision-making process through the 5-year calendar, and how it may have influenced cluster 

membership, is unknown given the unavailability of data. Although cross-sectional data are more readily 

available, we propose that using calendar data to add depth of understanding to women’s experiences is a 

crucial—and underutilized—step in examining current contraceptive decision-making behaviors. This 

study contributes to the need to examine retrospective histories to understand reproductive empowerment. 

A recent study looking at economic and reproductive empowerment called for addressing this important 

gap in research (Gammage, Joshi, and Rodgers 2020). 

Some of our key findings were expected. Women who belonged to the Modern Mother and Consistently 

Covered Mother clusters had a two times higher relative risk of jointly deciding whether or not to use 

contraception than women in the Quiet Calendar cluster. Both clusters were characterized by use of modern 

methods after a pregnancy event, with Modern Mothers characterized by their use of short-acting methods 

and Consistently Covered Mothers by their use of long-acting reversible contraceptives. This finding may 

reflect the accessibility of sexual and reproductive health services or women’s power to make decisions 

around the postpartum period. 

Another finding from our study that is worth noting is that women experiencing marital suspicion had a 

26% reduced relative risk of jointly deciding whether or not to use contraception when compared with 

women who had not experienced marital suspicion (RR: 0.74; p = 0.04; 95% CI: 0.56–0.98). This confirmed 

our hypothesis given that marital suspicion is defined as the partner or husband being jealous, accusing the 

respondent of being unfaithful, or insisting on knowing where the respondent is at all times. Previous 

research points to control of movement as a hindrance to gender-equitable relationships (Blum, Mmari, and 

Moreau 2017), and this may reduce a woman’s relative risk of making decisions about contraceptive use 
18 

and health care. Beyond the contraceptive clusters, our results offer important insights into the relationship 

between women’s experiences with violence/controlling partnerships and their ability to make decisions. 

What is perhaps less straightforward is the nature of who makes decisions and who is involved in the 

decision-making process. Decision-making power related to contraception varies according to context. Sole 

decision-making behavior is not associated with cluster membership; however, joint decision making is 

associated with cluster membership. A study in Malawi that examined couples’ communication and 

decision-making dynamics also found that joint decisions were common among couples, although they may 

be much more nuanced than sole decisions. For example, one woman in the study cited that a joint decision 

was made about family planning but also stated that her husband made the final decision in terms of going 

to the clinic (Hartmann et al. 2012). Acknowledging that a joint decision was made does not provide a full 

picture of the nature of a woman’s decision-making power. We do not know much beyond the woman’s 

involvement in the decision, such as the process or relative influence of each party. Our findings 

complement other studies in the sub-Saharan context where other reproductive events, such as having a 

large family, may inform a woman’s decision-making power within a household (Ngenzebuke, De Rock, 

and Verwimp 2018), or where having more children may increase a woman’s status in the household and 

thus her decision-making power (Richards and Bass 2019). 

Of the six clusters, three displayed statistical evidence of a relationship with joint contraceptive decisionmaking power, and this has potential programmatic-level implications. Previous studies have shown that 

men’s involvement in their partner’s reproductive behaviors and decisions is influential, so family planning 

programs have been encouraged to target and engage couples, therefore integrating men into discussions 

regarding reproductive health (Biddlecom and Fapohunda 1998; Hartmann et al. 2012). Couples counseling 

programs that address gender and social norms related to relationships should consider integrating healthy 

communications strategies; if there are norms around jealousy and knowing where a spouse is at all times, 

this may impact a woman’s ability to make decisions to use or not use contraception. The relative risk of 

joint decision-making power is at least two times higher among women who use a short- or long-term 

modern method of contraception, which coincides with previous research that has found greater unmet need 

among women who do not know their husband’s preferences (Nzokirishaka and Itua 2018). A woman’s 

perceived ability to make choices about her own reproductive health hinges not only on notions of 

empowerment and agency, but also on incentives around fertility and other family planning factors. 

Our study has limitations worth noting. The original study we built upon (MacQuarrie et al. 2019) excluded 

girls age 15-19 given previous research that little fertility or contraceptive use takes place in the years that 

would have been reflected in the reproductive calendars of these girls (MacQuarrie, Mallick, and Allen 

2017). However, Burundi was one of the countries in the multi-country study that had evidence of fertility 

among very young adolescents (ages 10-14) (MacQuarrie, Mallick, and Allen 2017). The clusters examined 

in our study therefore left out an especially important demographic since young women in Burundi may be 

sexually active, starting families, or using contraception. The inclusion of these women might even have 

changed the clusters that were revealed through the sequence analysis, so further work should be done to 

understand how the calendar events of very young adolescents influence their ability to make decisions 

regarding contraception. In addition, this analysis included current method use as a covariate. Although the 

contraceptive clusters took into account a contraceptive method if the method was used three months before 

the survey, the clusters did not account for any events (including method use) within the three months 

immediately preceding the survey. We felt it was pertinent to include this covariate because the clusters 
19 

represent a multitude of unique experiences and because women may be using methods not typified by their 

clusters. This covariate has a strong relationship with decision-making power to use or not use 

contraception, so including it created a more thorough and robust analysis. 

Finally, we believe that the names of the clusters may be rooted in bias, so clusters should be interpreted 

with care, taking into account the sequences of events that are represented. This study illustrates that the 

two clusters of Family Builders—Family Builder 1 and Family Builder 2—are different. Women who 

belong to the Family Builder 1 cluster are less likely to experience unmet need and more likely to have used 

family planning in their lifetime (MacQuarrie, Allen, and Gemmill 2020). Additionally, women in the 

Family Builder 1 cluster have greater contraceptive knowledge and intend to use family planning in the 

future, while the opposite is true for women who belong to the Family Builder 2 cluster (MacQuarrie, Juan, 

and Gemmill 2020). More research is needed to disentangle and distinguish these two types of women from 

one another to better understand their unique needs and behaviors. Nonetheless, using clusters based on a 

pattern of key events in a woman’s reproductive life gives greater insight into a woman’s journey and its 

effect on her current ability to make important decisions for her own health and well-being.

21 

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25 

APPENDIX 

Appendix Figure Sequence index plot of each Burundi contraceptive cluster 

Note: LAPM = long-acting or permanent method.