DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2020 No. 171

Kerry L.D. MacQuarrie

Courtney Allen

Alison Gemmill

August 2020

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

International Development.

Fertility and Family Planning Characteristics of 

Contraceptive Clusters in Burundi

DHS Working Paper No. 171 

Fertility and Family Planning Characteristics of 

Contraceptive Clusters in Burundi 

Kerry L.D. MacQuarrie1

Courtney Allen2

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 appreciation to Kristin Bietsch, Avenir Health, for discussions 

that shaped the authors’ approach to the scope of this study and to Michelle Weinberger, Nicole Bellows, 

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

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

earlier draft of this paper.

Editor: Kerry Aradhya 

Document Production: Natalie Shattuck & Chris Gramer 

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., Courtney Allen, and Alison Gemmill. 2020. Fertility and Family Planning 

Characteristics of Contraceptive Clusters in Burundi. DHS Working Paper No. 171. Rockville, Maryland, 

USA: ICF. 
iii 

CONTENTS 

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

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

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

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

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

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

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

2.2 Sequence and Cluster Analysis .......................................................................................... 4 

2.3 Regression Analyses .......................................................................................................... 4 

2.3.1 Demographic, fertility, and family planning covariates........................................... 4 

2.3.2 Socio-economic control factors .............................................................................. 5 

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

3.1 Identifying Discrete Contraceptive Clusters ........................................................................ 7 

3.2 Sample Description ............................................................................................................. 9 

3.3 Demographic, Fertility, and Family Planning Attributes of Clusters .................................. 11 

3.3.1 Quiet Calendar ..................................................................................................... 13 

3.3.2 Family Builder 1 and Family Builder 2 ................................................................. 13 

3.3.3 Modern Mother ..................................................................................................... 14 

3.3.4 Consistently Covered Mother ............................................................................... 15 

3.3.5 Traditional Mother ................................................................................................ 15 

3.4 Age, Unmet Need, and Cluster Membership .................................................................... 16 

4 CONCLUSIONS ............................................................................................................................. 19 

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

v 

TABLES 

Table 1 Analytic sample profile ............................................................................................. 10

Table 2 Demographic, fertility and family planning factors associated with cluster 

membership. Odds ratios from logistic regressions ................................................. 12

vii 

FIGURES 

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

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

Figure 2 Sequence index plots of each Burundi contraceptive cluster .................................... 8

Figure 3 Prevalence (percent distribution) of contraceptive clusters by age at start of 

the calendar sequences ........................................................................................... 16

Figure 4 Prevalence (percent distribution) of current need for family planning among 

women in each contraceptive cluster ....................................................................... 17

ix 

ABSTRACT 

This study sought to identify discrete subpopulations of women in Burundi based on patterns in their 

contraceptive and pregnancy experience. Toward this aim, we do not use typical cross-sectional measures, 

but instead put to new use retrospective, longitudinal data from Demographic and Health Surveys (DHS) 

contraceptive calendars. Specifically, we apply sequence and cluster analysis of these longitudinal data to 

identify discrete clusters that characterize women’s dynamic contraceptive and pregnancy behaviors over 

the previous 5 years. We further supplement the sequence data of the clusters with additional demographic, 

fertility, and family planning data from elsewhere in the survey and use multivariate logistic regression 

analysis to create rich descriptions of women in these clusters. 

We identify six clusters, with three clusters (85% of women) characterized by no use of contraception and 

three clusters (16%) characterized by contraceptive use. The six clusters are: (1) Quiet Calendar (42%), 

characterized by women who do not experience pregnancy or use any methods of contraception; (2) Family 

Builder 1 (25%) and (3) Family Builder 2 (18%), which are both characterized by women who did not use 

any method and experienced two pregnancies; (4) Modern Mother (8%), which is characterized by women 

who adopted short-term modern methods toward the end of year 2 after a period of non-use and one 

pregnancy; (5) Consistently Covered Mother (6%), characterized by women who adopted long-acting 

reversible contraception or permanent methods after a period of non-use and one pregnancy; and (6) 

Traditional Mother (2%), characterized by those who adopted traditional methods at the end of year 2 after 

non-use and one pregnancy. 

Factors most consistently associated with cluster membership are need for family planning, lifetime 

experience of contraceptive use, marital status, pregnancy experience, and age. The number of children 

ever born at the start of a woman’s calendar sequence is associated with membership in only half of the 

clusters: Quiet Calendar, Modern Mother, and Consistently Covered Mother. Cluster membership is seldom 

differentiated by socio-economic variables. 

The Quiet Calendar cluster stands apart from the other clusters: Along with no use of contraception over 

the past 5 years, Quiet Calendar women are unmarried and have no children or pregnancies, no need for 

family planning, and no history of contraceptive use. The Family Builder 1 and Family Builder 2 clusters, 

which appear similar, deviate from one another in terms of levels of unmet need and lifetime experience 

with contraception. 

Key words: clusters, contraceptive calendar, fertility, family planning, Burundi 

xi 

ACRONYMS AND ABBREVIATIONS 

DHS Demographic and Health Survey 

EC emergency contraception 

IUD intra-uterine (contraceptive) device 

LAM lactational amenorrhea method 

LAPM long-acting and permanent methods 

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 

Fertility remains high in Burundi, having only gradually declined from 6.9 children per woman in 1987 to 

5.5 children per woman in 2016-17 (Ministère à la Présidence chargé de la Bonne Gouvernance et du Plan 

- MPBGP et al. 2017). Almost a quarter (23%) of married women ages 15-49 use a modern method—a 

small and gradual increase from 18% in 2010. Similarly, the demand for family planning has modestly 

increased from 54% in 2010 to 58% in 2016-17. The modern contraceptive method mix is dominated by 

injectables (49%) and implants (26%) (Avenir Health 2018). Burundi is one of 69 priority countries that 

has made commitments toward FP2020 (Brown et al. 2014), but sluggish growth in contraceptive use 

suggests that more effort is needed to meet the country’s goals for increased contraceptive prevalence and 

reduced unmet need. 

The Demographic and Health Surveys (DHS) Program administers a contraceptive calendar as a part of 

most surveys. These contraceptive calendars are a rich source of data on contraceptive behavior and 

pregnancy experience covering the 5 years preceding the survey (Croft, Bradley, and Allen 2018). Calendar 

data have become the basis for much research on fertility behavior, including the calculation of 

discontinuation rates and other contraceptive dynamics (Ali, Cleland, and Shah 2012; Curtis and 

Hammerslough 1995), postpartum family planning adoption (Moore et al. 2015), the analysis of birth 

intervals (Baschieri and Hinde 2007; Moultrie, Sayi, and Timæus 2012), and perinatal mortality and 

pregnancy terminations (Bradley, Winfrey, and Croft 2015; MacQuarrie et al. 2018). These data also 

provide a comprehensive view of women’s journeys with contraceptive use over time. 

Market segmentation researchers have tried to make cross-sectional data useful to reproductive health 

programs by identifying distinct, homogenous groups within a population (i.e., market segments) that have 

different needs and inclinations to use contraceptive services. The aim of market segmentation is to help 

tailor messages and services to distinct groups of potential and existing family planning clients who have 

different motivations to use or avoid services. Segmentation methods use latent class analysis approaches 

on current status from cross-sectional data and tend to prioritize attitudes over behavior. For example, 

several studies have found trust, whether in health systems or in peers (Dabney et al. 2019; Dalglish et al. 

2018), to be a motivating factor, while others form segments based on positive views of contraception or 

gender attitudes (Camber Collective 2014, 2015; Dalglish et al. 2018; Trasi 2018; Wang et al. 2009). 

Underutilized behavioral data on reproductive experiences can complement such attitudinal data. 

Longitudinal data, such as those in DHS calendars, provide more insight on the nuances of contraceptive 

behavior and dynamics over time than current status measures can (Curtis and Hammerslough 1995; 

Finnegan 2019; MacQuarrie, Mallick, and Kishor 2016; MacQuarrie et al. 2014). Although latent class 

analysis and similar statistical techniques typically used in market segmentation are usually ill-suited to 

longitudinal data, other statistical methods can be used to identify patterns and subgroups using 

longitudinal, behavioral data (Abbott 1995; Aisenbrey and Fasang 2010; Dias and Willekens 2005; 

Feldman, Masyn, and Conger 2009), due to the availability of new statistical packages (Dias and Cortinhal 

2008; Furnas 2016; Gabadinho, Ritschard, Mueller, et al. 2011; Gemmill 2019; Genolini et al. 2015; Studer 

2013). We apply these methods to calendar data in Burundi. 
2 

This study identifies discrete subpopulations of women in Burundi based on patterns in their contraceptive 

and pregnancy experience. Toward this aim, we do not use typical cross-sectional measures, but instead put 

to new use retrospective, longitudinal data from DHS contraceptive calendar. Specifically, we apply 

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

dynamic contraceptive and pregnancy behaviors over the previous 5 years. We supplement the sequence 

data of the clusters with additional demographic, fertility, and family planning data that were collected 

elsewhere in the survey to create a rich description of women in these clusters. This is believed to be the 

first such application of these methods to nationally representative longitudinal contraceptive data. 
3 

2 DATA AND METHODS 

2.1 Data 

This study uses contraceptive calendar data from the 2016-17 Burundi DHS survey. The Burundi DHS, like 

other DHS surveys, is a household survey collecting data on numerous health and demographic indicators 

and is representative at the national and subnational levels. Households are selected through a multi-stage, 

clustered sampling process in which primary sampling units (PSUs) are selected based on probability 

proportional to size and households are randomly selected within the selected PSUs. All eligible women in 

each household are selected for interview. Additional details on the design of the survey can be found in 

the survey final report (Ministère à la Présidence chargé de la Bonne Gouvernance et du Plan - MPBGP et 

al. 2017). The 2016-17 Burundi DHS survey achieved a response rate of 98.8% among eligible women. 

The contraceptive calendar is a retrospective history of more than 5 years in which monthly event data 

including pregnancies, births, terminations, episodes of contraceptive use, and non-use of contraception are 

recorded (Croft, Bradley, and Allen 2018). These data were collected for all women of reproductive age 

(age 15-49) in the 2016-17 Burundi DHS (n=17,269). We placed three restrictions on the data, namely 

restrictions on the period of observation, the number of states (see explanation of states below), and the age 

of the women in the sample. 

First, we omit the month of the interview and the two prior months, since some women who recently became 

pregnant may not yet have recognized that they were pregnant. We also omit months at the beginning of 

the calendar so that we observe exactly 59 months of data for each woman in her calendar sequence, with 

month 1 being the earliest point in the woman’s calendar (approximately 5 years before the interview) and 

month 59 being the most recent month (three months before the interview). 

Next, we condense the state codes in the calendar sequence into five possible states: 

1. No use of contraception 

2. Use of a short-term modern method of contraception 

3. Use of a long-acting or permanent method (LAPM) of contraception 

4. Use of a traditional method of contraception 

5. Pregnancy, birth, or termination. 

Short-term modern methods are pills, injectables, condoms, the lactational amenorrhea method (LAM), 

emergency contraception (EC), and the Standard Days Method (SDM). LAPM consist of two long-acting, 

reversible (LARC) methods—intrauterine devices (IUDs) and implants—plus two permanent methods—

female and male sterilization. Traditional methods include periodic abstinence/rhythm, withdrawal, and 

other traditional or folkloric methods. 

Third, we exclude women who were younger than age 15 at the start of their calendar sequences. Prior 

research indicates that girls this age are neither sexually active nor biologically fecund and seldom use 

contraception (MacQuarrie, Mallick, and Allen 2017; Pullum, Croft, and MacQuarrie 2018), so their 

calendar sequences would not have included the states most of interest to this analysis. These restrictions 

provide us with a weighted analytic sample of 13,293 women in Burundi. 
4 

2.2 Sequence and Cluster Analysis 

In the first stage of analysis, we use sequence and cluster analysis to identify primary clusters of 

contraceptive use. We conduct 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). All analyses are weighted to account for sampling probability, non-response, and the complex 

sampling design. 

Briefly, we use Optimal Matching to calculate distances in our dissimilarity matrix, which (unlike Hamming 

distance-matching procedures) allows for states to be inserted and deleted as well as substituted in 

computing distances, or differences between pairs of sequences. This flexibility allows us to better account 

for the sporadic timing of pregnancies in the sequences by potentially aligning these events over time. To 

measure pairwise distances between sequences, we use a constant cost matrix that assumes uniform costs 

for all substitutions, insertions, or deletions. Constant costs assume that the costs of each transition between 

states, for example from using a short-term method to discontinuing it or from not using a method to 

becoming pregnant, are equal. Finally, we conduct cluster analysis on the dissimilarity matrix to group 

together women whose calendar sequences exhibit similar patterns of contraceptive use and pregnancy 

experience using a k-medoid (i.e., a partitioning around medoids, or PAM) clustering algorithm. These 

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

metrics (Gemmill 2019; Studer 2013). 

These quality metrics led us to a final solution with six clusters in Burundi. These clusters grouped women 

solely on the basis of the contraceptive behaviors and pregnancy experiences observed in their 59-month 

calendar sequences. We use medoid plots and sequence index plots to visually describe the sequences in 

each cluster. Further methodological details have been described previously (MacQuarrie et al. 2019). 

2.3 Regression Analyses 

In the second stage of our analysis, we estimated multivariable logistic regression models separately for 

each of the six clusters to further elaborate on the demographic, fertility, and family planning experiences 

of women in those clusters. These covariates, collected elsewhere in the survey, complement the data 

contained in the calendar sequences upon which the clusters are identified. These covariates describe a 

combination of experiences preceding, during, and immediately following the calendar sequence (at the 

time of the survey). No causal direction is implied. Regression models are used to identify associated 

features only. Models also include socio-economic controls. 

Regression analyses are conducted in Stata ME 16. All analyses are weighted to account for sampling 

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

2.3.1 Demographic, fertility, and family planning covariates 

The covariates in the regression models are age, marital status, number of children, experience of 

unintended pregnancy, need status, and lifetime use of family planning. 
5 

Age at the start of the calendar sequence is expressed in completed years and then grouped into six fiveyear age groups: age 15-19, 20-24, 25-29, 30-34, 35-39, and 40-44. Age 30-34 serves as the reference 

category. 

Marital status is a current status measure categorized on the basis of marital status at the time of interview 

because a complete marital history is not collected for all women in DHS surveys. Marriage includes both 

formal and informal unions as reported in response to the question, “Are you married or living with a man 

as if married?” This variable has three categories: never in union, currently in union, and formerly in union 

(i.e., divorced, separated, or widowed). Never in union serves as the reference category. 

Number of children is the number of children ever born (whether currently alive or not) at the start of the 

calendar sequence. This variable precedes the contraceptive and pregnancy experiences that define the 

cluster, so for certain clusters characterized by pregnancies and births, the number of children ever born at 

the time of the survey could exceed the value on this variable. After testing for sensitivity of the variable 

expressed as a continuous or categorical variable, we opted to express number of children as a categorical 

variable to ease interpretation. Women with 1-2 children, 3-4 children, or 5 or more children are compared 

with the reference category (i.e., women with no children at the start of the calendar sequence). 

Experience of unintended pregnancy describes women’s experience at any point during the 5-year 

calendar or in the three months between the end of the calendar sequence and the time of the interview. 

Pregnancies are categorized as well-timed (i.e., wanted then), mistimed (i.e., wanted later), or unwanted 

(i.e., did not want any more) and are compared against the reference category of women who did not 

experience a pregnancy during this time period. 

Need for family planning is a current status measure describing, at the time of the survey, whether women 

have no need, met need, or unmet need for family planning. Use of either traditional or modern methods 

constitutes met need. This formulation of need for family planning follows the revised algorithm now 

standard for DHS surveys (Bradley et al. 2012). No need serves as the reference category. 

Lifetime experience of contraceptive use is a dichotomous variable expressing whether women report 

having ever used family planning. Women who are currently or have ever used a modern or traditional 

method of family planning are compared with women who have never used family planning. 

2.3.2 Socio-economic control factors 

Regression models also include a common set of socio-economic variables as controls. These are 

rural/urban residence, educational attainment (i.e., none, primary, and secondary or higher), and household 

wealth quintile (based on an inventory of assets and housing materials in the household’s possession at the 

time of the survey). 

7 

3 RESULTS 

3.1 Identifying Discrete Contraceptive Clusters 

We identify six distinct clusters, illustrated by their medoid—the most illustrative sequence for each cluster 

(Figure 1). The three most common clusters, including 85% of women, are characterized by no use of 

contraception while the remaining three clusters are characterized by contraceptive use. The six clusters 

are: (1) Quiet Calendar (42%), 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 both 

characterized by women who did not use any method and experienced two pregnancies, but vary 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 one pregnancy; (5) 

Consistently Covered Mother (6%), characterized by women who adopted LAPMs after a period of nonuse and one pregnancy; and (6) Traditional Mother (2%), characterized by those who adopted traditional 

methods at the end of year 2 after non-use and one pregnancy. 

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

cluster 

Sequence index plots show the individual sequences for every woman in each cluster. The sequence index 

plots in Figure 2 show little deviation in sequences among members in each cluster when compared with 

the medoids presented in Figure 1. In aggregate, Quiet Calendar women spent an average of 56 months of 

the 59 months in a non-use state, only 2.5 months in a state of pregnancy, and less than 1 month using shortterm modern methods, LAPMs, or traditional methods. 
8 

Figure 2 Sequence index plots of each Burundi contraceptive cluster 

Women in the Family Builder 1 cluster spent an average of 37 months not using contraception and nearly 

16.5 months in a state of pregnancy. Similarly, women in the Family Building 2 cluster spent an average 

of42 months and 14 months in these respective states. In both of these clusters, contraceptive use of any 

kind, combined, spanned less than six months on average. 
9 

This contrasts sharply with the three clusters characterized by contraceptive use. A total of 36 months, on 

average, was spent using short-term modern methods in the Modern Mother cluster, 38 months using 

LAPMs in the Consistently Covered Mother cluster, and 34 months using traditional methods in the 

Traditional Mother cluster. The average time spent using no contraception in these three clusters ranges 

from 11 months (Consistently Covered Mother) to 13.5 months (Traditional Mother), and the average time 

in pregnancy ranges from 7 months to 10 months, respectively, with the remaining time spent using other 

contraceptive methods. 

3.2 Sample Description 

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

models. The sample is relatively young, with the highest proportion (24%) age 15-19 and the lowest 

proportion (9%) age 40-44 at the start of the calendar sequence. 

Most women are currently married (72%). About one-third of the sample had no children at the start of their 

calendars (34%). The remaining two-thirds are more or less evenly distributed (19% to 24%) across the 

other categories of 1-2, 3-4, or 5 or more children. 

Women have most commonly experienced a well-timed pregnancy (43%) during or since their calendar 

sequences, followed by experiencing no pregnancy (33%). Less common is experiencing an unintended 

pregnancy, with mistimed pregnancies (17%) exceeding unwanted pregnancies (8%). About half of the 

sample currently has no need for family planning, and one-quarter each experiences unmet need and met 

need for family planning. The majority (56%) has never used family planning. 

The sample is largely rural (88%) and has either no education (45%) or primary education only (37%). The 

sample is evenly distributed across household wealth quintiles. 

 
10 

Table 1 Analytic sample profile 

Percentage Weighted n 

Contraceptive 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 

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 

Marital status at time of survey Never in union 17.3 2,293 

Currently in union 71.6 9,512 

Formerly in union 11.2 1,487 

 

Number of children at start of calendar 

sequence 

0 34.3 4,563 

1-2 23.9 3,172 

3-4 19.2 2,555 

5+ 22.6 3,003 

Experienced unintended pregnancy 

during or since calendar sequence No pregnancy 33.1 4,395 

Well-timed pregnancy 42.6 5,661 

Mistimed pregnancy 16.7 2,221 

Unwanted pregnancy 7.6 1,016 

 

Need for family planning at time of 

survey 

No need 54.9 7,292 

Unmet need 22.9 3,040 

Met need 22.3 2,960 

Ever used family planning No 55.9 7,429 

Yes 44.1 5,864 

 

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 

Total 100.0 13,293 

 
11 

3.3 Demographic, Fertility, and Family Planning Attributes of Clusters 

The sequence and cluster analysis defined the six clusters described above on the basis of contraceptive use 

and pregnancy experience in women’s 59-month calendar sequences, alone. Table 2 presents the results of 

separate logistic regression models, which further elaborate on the demographic, fertility, and family 

planning experiences associated with membership in each of the six clusters. 

Ever use of family planning is included in the regression models for the first three clusters only. It is omitted 

from the last three cluster models (those characterized by contraceptive use) because ever use of family 

planning is, by definition, a characteristic of membership in these clusters.
12 

Table 2 Demographic, fertility, and family planning factors 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 

Age at the start of calendar sequence 

(ref: 30-34) 

15-19 0.86 0.282 1.35 0.011 0.85 0.182 0.98 0.907 1.70 0.022 0.21 0.000 20-24 0.54 0.000 1.32 0.006 0.95 0.635 1.08 0.667 1.97 0.000 0.63 0.082 25-29 0.65 0.000 1.24 0.015 1.02 0.796 0.99 0.943 1.30 0.071 0.77 0.209 35-39 1.48 0.001 0.85 0.103 0.98 0.866 0.74 0.037 0.84 0.288 0.85 0.477 40-44 4.20 0.000 0.24 0.000 0.84 0.314 0.51 0.001 0.45 0.000 1.25 0.426 

Marital status (ref: never in union) 

Currently in union 0.12 0.000 6.09 0.000 1.07 0.745 3.20 0.000 7.14 0.000 7.02 0.001 Formerly in union 0.39 0.000 3.10 0.000 1.01 0.975 2.39 0.005 4.17 0.001 0.70 0.677 

Number of children at start of 

calendar sequence (ref: none) 

1-2 0.54 0.000 1.03 0.765 1.09 0.267 1.55 0.008 4.05 0.000 0.84 0.535 3-4 0.58 0.000 1.09 0.409 0.87 0.181 1.53 0.027 5.83 0.000 0.89 0.704 5+ 0.53 0.000 1.09 0.457 0.93 0.578 1.70 0.011 6.12 0.000 1.04 0.905 

Experience of unintended pregnancy 

during or since calendar sequence (ref: no pregnancy) Well-timed pregnancy 0.06 0.000 60.52 0.000 89.93 0.000 0.88 0.364 0.48 0.000 0.86 0.453 Mistimed pregnancy 0.02 0.000 105.80 0.000 94.58 0.000 0.55 0.001 0.40 0.000 1.35 0.215 Unwanted pregnancy 0.03 0.000 101.30 0.000 84.08 0.000 0.67 0.041 0.38 0.000 1.19 0.506 

Need for family planning at time of 

survey (ref: no need) 

Unmet need 0.91 0.294 0.81 0.002 1.65 0.000 1.32 0.087 0.72 0.110 0.98 0.962 Met need 0.10 0.000 0.79 0.002 0.58 0.000 9.68 0.000 6.78 0.000 10.24 0.000 

Ever used family planning (ref: no) 

Yes 0.20 0.000 1.37 0.000 0.73 0.000 

Residence (ref: urban) 

Rural 1.15 0.241 1.25 0.007 0.85 0.133 0.62 0.001 1.19 0.320 0.77 0.239 

Education (ref: none) 

Primary 1.03 0.703 1.03 0.680 1.04 0.460 0.92 0.414 0.87 0.214 1.40 0.072 Secondary or higher 1.82 0.000 1.03 0.810 0.68 0.005 0.75 0.067 0.87 0.544 1.80 0.012 

Household wealth index (ref: poorest) 

Poorer 0.91 0.328 1.10 0.239 1.07 0.436 0.84 0.170 1.01 0.925 1.14 0.657 

Middle 0.86 0.112 1.05 0.558 1.03 0.746 0.79 0.073 1.48 0.007 1.08 0.780 Richer 0.92 0.379 0.99 0.842 1.09 0.305 0.72 0.011 1.29 0.111 1.89 0.008 

Richest 1.21 0.104 0.89 0.240 0.83 0.106 1.10 0.536 1.27 0.257 1.74 0.038 

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

3.3.1 Quiet Calendar 

Women in the Quiet Calendar cluster are typically not in their 20s at the start of their calendar sequences; 

rather they are either adolescents (age 15-19) or older than age 35, are never married, and have no children. 

After controlling for socio-economic variables such as residence, education, and wealth, currently and 

formerly married women have 88% and 61% lower odds (p<0.001), respectively, of being a member of the 

Quiet Calendar cluster than do never married women (Table 2). In other words, never married women have 

higher odds of belonging to the Quiet Calendar cluster. This pattern contrasts with that in most other 

profiles, in which either currently or formerly married women have higher odds of membership. 

Women with any number of children at the start of their calendar sequences are significantly less likely to 

belong to the Quiet Calendar cluster than are women with no children, with similarly lower odds (42% to 

47% lower odds, p<0.001) across each category. As with marital status, this pattern is unique to the Quiet 

Calendar cluster. 

Women who have experienced any pregnancy during or following their calendar sequences, whether 

mistimed/unwanted or well-timed, have significantly lower odds of belonging to the Quiet Calendar cluster 

(94% to 98% lower odds, p<0.001). Quiet Calendar women are likely to have no need for family planning, 

as evidenced by 90% lower odds (p<0.001) of membership among women with met need and no 

significantly different odds among women with unmet need. Similarly, women who have never used family 

planning in their lifetimes have 80% lower odds of being in the Quiet Calendar cluster than do women who 

have used family planning (p<0.001). 

Quiet Calendar membership is associated with having at least a secondary education; however, neither 

residence nor wealth appear to be an indicator of membership in this cluster. 

In sum, the Quiet Calendar cluster lives up to its name, with constituent women having few reproductive 

experiences and no use of or need for family planning. 

3.3.2 Family Builder 1 and Family Builder 2 

Family Builder 1 and 2 resemble one another in that their representative sequences are characterized by no 

use of contraception and the experience of two pregnancies. They differ only in terms of the timing of the 

pregnancies within their sequences: Women in Family Builder 1 had their pregnancies more recently than 

Family Builder 2 women. In spite of their similarities, women in these clusters differ along several other 

dimensions (Table 2). 

The Family Builder 1 cluster consists of young women. The odds of membership are 24% to 35% higher 

for those younger than age 30 (p<0.05) and 76% lower for those age 40-44 (p<0.001) compared with the 

middle age group. Currently married women have six times the odds and formerly married women have 

three times the odds of belonging to the Family Builder 1 cluster compared with never married women 

(p<0.001). Neither age nor marital status is associated with membership in the Family Builder 2 cluster. 

Women in the Family Builder 1 cluster are likely to have experienced unintended pregnancies during or 

since their calendar sequence. The odds ratio of being a member of this cluster is between 101 and 106 

times higher (p<0.001) for women with an unintended pregnancy than for women with no pregnancy and 
14 

60.5 times higher (p<0.001) for those with a well-timed pregnancy. Similar to women in Family Builder 1, 

women in Family Builder 2 are more likely to have experienced a well-timed or unintended pregnancy. 

However, the differences in timing are not as stark. Women who experienced well-timed pregnancies have 

90 times the odds (p<0.001) of belonging to the Family Builder 2 cluster when compared with women with 

no pregnancy; this is similar to women who experienced an unintended pregnancy, whose odds of 

membership range from 84 to 95 times higher (p<0.001), depending on whether the pregnancy was 

mistimed or unwanted. 

The Family Builder 1 cluster further consists of women who have no current need for family planning, as 

indicated by about 20% lower odds (p<0.01) of cluster membership than women with either met or unmet 

need. In contrast, the odds of belonging to the Family Builder 2 cluster are lower (OR=0.58, p<0.001) 

among women with met need but 65% higher (p<0.001) among those with unmet need (compared with 

those with no need). Women who have used contraception at some point in their lifetimes have 37% higher 

odds (p<0.001) of being in the Family Building 1 cluster and 27% lower odds (p<0.001) of being in the 

Family Builder 2 cluster. 

Rural residents have increased odds (OR=1.25, p<0.01) of Family Builder 1 membership compared with 

urban residents, while women with secondary or higher education have reduced odds (OR=0.68, p<0.01) 

of Family Builder 2 membership compared with women with less education. For both Family Builder 

clusters, membership appears to be unrelated to the number of children women had at the start of their 

calendar sequences. 

Need for family planning, use of family planning, and experience of unintended pregnancies are the clearest 

dimensions by which membership in the Family Builder 1 and 2 clusters differ from one another. 

3.3.3 Modern Mother 

In general, the Modern Mother cluster consists of women who were unlikely to be older at the start of their 

calendar sequences and are likely to reside in urban areas, be currently or formerly married, and have had 

children at the start of their calendar sequences. Women are unlikely to be in this cluster if they had 

experienced an unintended pregnancy and have currently met their need for family planning. 

Women older than age 35 have reduced odds (p<0.05) of being in the Modern Mother cluster compared 

with women age 30-34. Meanwhile, currently married women have more than three times the odds 

(p<0.001) of belonging to the Modern Mother cluster and formerly married women nearly two and a half 

times the odds (p<0.01) compared with never married women. 

A pregnancy recorded in a Modern Mother’s calendar sequence is typically not her first. Women with either 

1-2 children or 3-4 children at the start of their calendar sequences have about 1.5 times the odds (p<0.05) 

of belonging to the Modern Mother cluster and women with 5 or more children have 1.7 times the odds 

(p<0.05) of being in this cluster, compared with women with no children. Women who have experienced a 

mistimed or unwanted pregnancy since the start of their calendar sequences (but not a well-timed one) have 

33% to 45% lower odds (p<0.05) of being in the Modern Mother cluster than women with no pregnancy in 

the past five years. Although having met need is associated with lower odds of membership in the Quiet 

Calendar and both Family Builder clusters, women with met need have 9.7 times the odds (p<0.001) of 

being in the Modern Mother cluster when compared with their counterparts with no need. Women with 
15 

rural residence have lower odds (p<0.001) of Modern Mother cluster membership than women with urban 

residence—the only socio-economic control to show an association with cluster membership. 

3.3.4 Consistently Covered Mother 

Overall, the Consistently Covered Mother cluster consists of currently and, to a lesser extent, formerly 

married women who have several children, have not experienced a well-timed or unintended pregnancy 

during or since their calendar sequence, and have had their current family planning needs met. 

Compared with women age 30-34, women younger than age 24 have increased odds (p<0.05) of 

membership in the Consistently Covered Mother cluster and women older than age 40 have reduced odds 

(p<0.001) of cluster membership. Currently and formerly married women have between four and seven 

times the odds (p<0.001)of cluster membership compared with never married women. The odds of 

membership in the Consistently Covered Mother cluster increases steadily with the number of children 

born, ranging from four times higher among women with 1-2 children to more than six times higher among 

those with 5 or more (p<0.001) children, when compared with women with no children. 

Although LAPM use followed a pregnancy in the representative sequence for this cluster, women who had 

experienced any pregnancy, whether mistimed/unwanted or well-timed, have 52% to 62% lower odds 

(p<0.001) of belonging to the Consistently Covered cluster than women with no pregnancy, when 

controlling for other factors in the model. 

Having met need at the time of the survey is associated with 6.8 times higher odds (p<0.001) of membership 

in the Consistently Covered Mother cluster than was having no need, although women with unmet need do 

not differ significantly from women with no need regarding cluster membership. With the exception of 

being in the middle wealth quintile, residence, education, and wealth are not indicative of cluster 

membership. 

3.3.5 Traditional Mother 

In general, the Traditional Mother cluster consists of non-adolescent women who are currently married and 

whose need for family planning is being met. Adolescent women age 15-19 have 79% lower odds (p<0.001) 

of membership in the Traditional Mother cluster than do women in the middle age group. Currently married 

women have seven times the odds of belonging to the Traditional Mother cluster compared with never 

married women, though no association is detected for formerly married women. The odds of belonging to 

the Traditional Mother cluster are 10.2 times higher (p<0.001) for women with a met need for family 

planning than for those who have no need. 

As with the Family Builder clusters, membership in the Traditional Mother cluster does not appear to differ 

noticeably among women with no children, few children, or many children. Cluster membership is also not 

associated with experience of unintended pregnancy, and Traditional Mother is the only cluster for which 

no association is found with this variable. With the exception of being in the richer wealth quintile, socioeconomic controls also show no relationship with cluster membership. 
16 

3.4 Age, Unmet Need, and Cluster Membership 

As shown in Table 2, the Quiet Calendar cluster has a distinctive age pattern when compared with other 

clusters. The bivariate distribution of clusters across age groups, presented in Figure 3, further illustrates 

this finding. It shows that the Quiet Calendar cluster is commonly experienced by both adolescent and older 

women. In contrast, in the Family Building clusters and those characterized by contraceptive use, the 

prevalence of membership increases with age until it begins to decrease again in a middle (Family Builder) 

or older (all three contraceptive clusters) age group. 

Figure 3 Prevalence (percent distribution) of contraceptive clusters by age at start of the calendar 

sequences 

Unmet need is a significant factor for predicting membership in each cluster, as shown in Table 2. The 

results of bivariate analysis of need status, distributed across the six clusters (Figure 4), complements the 

results of the multivariate analysis presented in Table 2. They reiterate that women with no need for family 

planning are concentrated in the Quiet Calendar cluster and are least likely to belong to the clusters 

characterized by contraceptive use. Unmet need is highest in the two Family Builder clusters, while met 

need is most prevalent in the three contraception clusters. Although total levels of need are similar in the 

two Family Builder clusters, unmet need is substantially greater in the Family Builder 2 cluster than in the 

Family Building 1 cluster. 

0

10

20

30

40

50

60

70

80

90

100

15-19 20-24 25-29 30-34 35-39 40-44 Total

Percent

Age at start of calendar sequence

Traditional Mother

Consistently Covered Mother

Modern Mother

Family Builder 2

Family Builder 1

Quiet Calendar
17 

Figure 4 Prevalence (percent distribution) of current need for family planning among women in each 

contraceptive cluster 

85.3

37.5 39.7

22.2 18.6

12

3.1

29

15

69.4 70.4 78.6

11.6

33.5

45.3

8.4 11 9.5

0

10

20

30

40

50

60

70

80

90

100

Quiet

Calendar

Family

Builder 1

Family

Builder 2

Consistently

Covered

Mother

Modern

Mother

Traditional

Mother

Percent

Unmet need

Met need

No need

19 

4 CONCLUSIONS 

This study identified six clusters of reproductive behavior in Burundi, three of which are characterized by 

contraceptive use and three of which are characterized by the absence of contraceptive use. This study 

further identified demographic, fertility, and family planning factors associated with membership in these 

clusters. 

A large majority of women (85%) belong to one of the non-contraception clusters, with a minority (16%) 

belonging to one of the three contraceptive clusters. These findings are consistent with estimates of current 

contraceptive use in other studies (Avenir Health 2018; Ba et al. 2019; Cahill et al. 2018). Factors most 

consistently associated with cluster membership are need for family planning, lifetime experience of 

contraceptive use, marital status, pregnancy experience, and age. The number of children ever born at the 

start of a woman’s calendar sequence is associated with membership in only half of the clusters: Quiet 

Calendar, Modern Mother, and Consistently Covered Mother. Cluster membership is seldom differentiated 

by socio-economic variables. 

The Quiet Calendar stands apart from the other clusters in many ways: Along with no use of contraception 

over the past 5 years, Quiet Calendar women are unmarried, have no children or pregnancies, no need for 

family planning, and no history of contraceptive use. The size of this cluster (42%) may be a surprise in a 

high fertility setting such as Burundi. Although we understand that fertility desires and associated 

behavioral patterns are dynamic (e.g., Bernardi, Mynarska, and Rossier 2015; Speizer and Lance 2015)—

and this study’s own data indicate both dynamism within most clusters and that women likely move between 

clusters at different points in their lives—the Quiet Calendar cluster is an exception. The steady lack of 

activity over an extended period of time (five years) is striking. It suggests that health programs may need 

to reconsider ways to reach women with no imminent need for family planning or maternal health services 

to make sure they still have the access they need for general preventive and curative health care. 

Two of the non-contraception clusters are characterized by family building, defined as the use of no 

contraception and the experience of two pregnancies. The Family Builder 1 and 2 clusters appear at first 

glance to be nearly identical, differing only by the timing of the pregnancies. This similarity raises the 

question as to whether their identification as separate clusters is an anomaly of the statistical procedures 

used and based on sampling variance in the times at which women were interviewed relative to their 

pregnancies, or whether they are indeed programmatically meaningful, distinct groups of women. The 

results of regression analyses in this study support the latter conclusion. 

Women in Family Builder 1—the cluster in which the experience of two pregnancies is more recent—

experience unintended pregnancies to a greater extent than women in Family Builder 2. They also tend to 

have no unmet need for family planning and have lifetime experience using contraception. In contrast, 

Family Builder 2 women generally experience unmet need and have never used contraception. Family 

Builder 2 is characterized by women who have less experience with contraception, either are not using or 

do not intend to use contraception, and have current unmet need for family planning. These findings 

comport with those of additional analyses indicating that Family Builder 1 women articulate more clearly 

defined fertility intentions than do Family Builder 2 women (MacQuarrie, Juan, and Gemmill 2020) and 

are more likely than Family Builder 2 women to participate in joint contraceptive decision making (Juan, 
20 

Allen, and MacQuarrie 2020). In sum, Family Builder 1 women are inclined to use contraception at points 

in their lives when they need it but are presently focused on having children, although they have struggled 

with planning the timing of childbearing. 

The three contraceptive clusters are each characterized by the type of contraception used—short-term 

modern methods, LAPMs, or traditional methods. Interestingly, women in these clusters are not segmented 

based on other dimensions of contraceptive behavior, such as discontinuation, experimentation or switching 

among methods, timing of method adoption, or interruptions in or continuity of use. 

In all three contraceptive clusters, contraceptive use follows the experience of pregnancy. This reiterates 

findings from another study indicating that women’s pregnancy experiences predict contraceptive behavior 

in Burundi (Bakibinga et al. 2016). The degree of pregnancies in these clusters and the two Family Builder 

clusters both reflects the high fertility in Burundi (Gerland, Biddlecom, and Kantorová 2017; Ministère à 

la Présidence chargé de la Bonne Gouvernance et du Plan - MPBGP et al. 2017) and highlights the potential 

of maternal health services and postpartum family planning (Rutaremwa and Kabagenyi 2018; Track20 

2018). 

Women in these three contraceptive clusters are currently meeting their need for family planning and 

(particularly for women in the Modern Mother and Consistently Covered Mother clusters) are unlikely to 

have experienced an unintended pregnancy. Although Modern Mother and Consistently Covered Mother 

women have had children prior to their calendar sequences (especially Consistently Covered Mothers), this 

is not so for Traditional Mothers, suggesting that life course factors also differentiate membership in 

contraceptive clusters (CT Innovation Lab 2018; Wang et al. 2009). 

This study took an innovative approach to segmenting women, not based on current contraceptive status 

and attitudes, but based on their contraceptive and pregnancy behaviors over the past five years. This 

represents a new use of DHS calendar data. This study also elaborated on the demographic, family planning, 

and fertility attributes associated with women in each of six discrete clusters of women in Burundi, 

providing reproductive health programs with unique insights on the clientele they seek to serve. 
21 

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