DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2020 No. 168

Nazmul Alam

Manir Hossain Mollah

Sharin Shahjahan Naomi

August 2020

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

International Development.

Prevalence and Determinants of Adolescent Fertility:

Analysis of 2014 Bangladesh Demographic Health Survey

DHS Working Paper No. 168

Prevalence and Determinants of Adolescent Fertility:

Analysis of 2014 Bangladesh Demographic Health Survey

Nazmul Alam1

Manir Hossain Mollah1

Sharin Shahjahan Naomi1

ICF

Rockville, Maryland, USA

August 2020

1Asian University for Women, 20/A M.M. Ali Road, Chittagong 4000, Bangladesh

Corresponding author: Nazmul Alam, Asian University for Women, 20/A M.M. Ali Road, Chittagong 

4000, Bangladesh; phone: +88 01720281109; fax: +880-31-285-4988; email: nazmul.alam@auw.edu.bd
Acknowledgments: We would like to extend our heartfelt gratitude to ICF International and USAID for 

the opportunity to be part of the 2020 DHS Fellows Program. We are greatly indebted to our facilitators, 

Dr. Shireen Assaf and Ms. Christina Juan, for their guidance and patience during this process. Our 

reviewers, Christina Whang and Sara Riese at ICF, are appreciated for their valuable insights about this 

work. We are also grateful to our DHS Program co-fellows for their support, feedback, and humor. This has 

been an opportunity for growth, and we see ourselves as ambassadors of The DHS Program in Bangladesh 

and beyond. We also want to extend our gratitude to our institution, the Asian University for Women, for 

allowing us to participate in the 2020 DHS Fellows Program. 

Editor: Diane Stoy 

Document Production: Joan Wardell 

This study was implemented with support from 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. For additional information about The DHS Program contact: 

DHS Program, ICF, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: +1-301-572-0950; 

fax: +1-301-572-0999; fax: +1-301-407-6501; email: reports@dhsprogram.com; internet: 

www.dhsprogram.com. 

Recommended citation: 

Alam, Nazmul, Manir Hossain Mollah, and Sharin Shahjahan Naomi. 2020. Prevalence and Determinants 

of Adolescent Fertility: Analysis of 2014 Bangladesh Demographic Health Survey. DHS Working Paper 

No. 168. Rockville, Maryland, USA: ICF. 
iii

CONTENTS

TABLES AND FIGURES........................................................................................................................v

ABSTRACT...........................................................................................................................................vii

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

1.2 Research Questions......................................................................................................2

1.3 Conceptual Framework.................................................................................................2

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

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

2.2 Variables .......................................................................................................................3

2.2.1 Dependent variables ........................................................................................3

2.2.2 Independent variables......................................................................................3

2.3 Statistical Analysis ........................................................................................................3

2.4 Results ..........................................................................................................................4

2.5 Discussion...................................................................................................................10

2.6 Conclusions.................................................................................................................11

REFERENCES......................................................................................................................................13

v

TABLES AND FIGURES

Table 1 Percent distribution of ever-married women age 15-19 according to 

background characteristics ..................................................................................5

Table 2 Percentage of adolescent fertility by background characteristics adjusted with 

all women factors .................................................................................................7

Table 3 Unadjusted and adjusted logistic regression of adolescent fertility of evermarried women age 15-19 (n=1976) ...................................................................9

Figure 1 Conceptual framework that shows the linkages among the independent, 

intermediate, and dependent variables................................................................2

vii

ABSTRACT

Bangladesh has one of the highest rates of adolescent fertility in the world, which contributes to negative 

maternal health outcomes and population growth. This study was conducted to assess the prevalence and 

determinants of adolescent fertility in Bangladesh using data from the 2014 Bangladesh Demographic and 

Health Survey. For this study, 2,023 ever-married women age 15-19 were included in the analysis. This 

study was guided by a conceptual framework of the relationship of selected independent and intermediate 

variables to the dependent variable, which is adolescent fertility.

Overall, the adolescent fertility rate was 31%. Significantly higher odds of adolescent fertility were found 

among women in the Sylhet Division (adjusted odds ratio (AOR) = 3.0; 95% confidence interval (CI): 1.6–

6.1) and the Chittagong Division (AOR = 1.8; 95% CI: 1.8–2.7) compared to the Barisal Region. Compared 

to women in the lowest wealth quintile, women in all other quintiles had lower odds of adolescent fertility, 

with the lowest odds found among women in the richest quintile (AOR = 0.3; 95% CI: 0.2–0.6). Women 

who married at age 14-17 had 60% lower odds of adolescent fertility compared to the women who married

at age 10-13. A substantial proportion of married adolescents were either pregnant or had at least one child 

before their twentieth birthday. There are several modifiable factors such as marriage at early age, 

geographic inequality, and income inequalities, which can have significant influence on adolescent fertility 

in Bangladesh. Policies and interventions to address these factors can reduce adolescent fertility and help 

to achieve improved health, psychosocial wellbeing, and greater women’s empowerment in Bangladesh.

Key words: adolescent fertility, geographic inequality, employment, contraception, determinants, 

Bangladesh
1

1 INTRODUCTION

Remarkable global progress has been made in reducing early marriage, adolescent fertility, and maternal 

mortality over the past few decades, although adolescent fertility remains a social and public health concern

(Austrian et al. 2020; UNFPA 2015; Wang and Qiao 2020). The fact that every year approximately 12 

million girls age 15-19 give birth in low- and middle-income countries is evidence of this grave concern 

(Darroch et al. 2016). Adolescent pregnancy and childbearing have deleterious consequences at individual, 

societal, and global levels (Alemayehu, Haidar, and Habte 2010; Birchall 2018). Adolescent motherhood 

hampers a girl’s health, wellbeing, and educational progress, and also prevents her from realizing her full 

potential (Chen et al. 2007; Ganchimeg et al. 2014; Hossain et al. 2015; Islam and Gagnon 2014; JaénSánchez et al. 2020).

Adolescent fertility is the consequence of complex relationships with multiple factors. Global studies have 

found that high pregnancy rates among adolescent girls are associated with lack of education, less decision 

making power in household and family planning, less reproductive knowledge, poverty, and the experience 

of early marriage (Acharya and Sureender 1996; Alemayehu, Haidar, and Habte 2010; Angeles, Guilkey, 

and Mroz 2005; Chen et al. 2007). The age difference between spouses is also an influential factor in 

determining early pregnancy (Barbieri, Hertrich, and Grieve 2005; MacQuarrie 2016). Adolescent fertility 

rates often are higher in countries where early marriage is prevalent (Cherry, Byers, and Dillon 2009; 

UNICEF 2016). Girls who marry at young ages are more likely to experience multiple pregnancies, 

recurrent miscarriage, termination of pregnancy, and delivery complications (King 2003). One-third of 

married teenage girls in Bangladesh become mothers or are pregnant by their 18th birthday (UNICEF 

2016). Adolescent mothers in Bangladesh face the dual challenge of progressing through the stages of 

adolescence and also adapting to the maternal role.

Adolescent fertility has been a widely discussed issue in Bangladesh with the scientific literature

highlighting the social, economic, and reproductive health consequences (Ferdousi 2014; Islam et al. 2017; 

Jisun 2016; Kamal 2012; Nahar and Min 2008). However, there has been limited progress because of

inadequate attention from policymakers and lack of coordinated efforts by government, nongovernment, 

and community-based organizations(Islam et al. 2017). In the presence of strict laws against early marriage, 

the government’s commitment remains vital in undertaking holistic approaches that promote social 

mobilization and other structural interventions including poverty alleviation, gender equity, and girls 

education that prevent early marriage and reduce the high rate of adolescent pregnancy in Bangladesh. Such 

approaches must be based on scientific insights and evidence.

Making further improvement in lowering adolescent pregnancy rates is a priority for Bangladesh in 

achieving the Sustainable Development Goals (SDG) targets such as Goal 3 on good health and wellbeing, 

and Goal 5 on gender equality of girls and women (Datta and Rabbany 2016). This study examines the 

factors associated with adolescent pregnancy by using data collected by the nationally representative 2014 

Bangladesh Demographic Health Survey (BDHS). The analysis examines the influence of demographic, 

social, economic, and reproductive health factors and will enhance understanding of the role of 

determinants in adolescent fertility in Bangladesh. The study findings will provide an evidence base for 

modifiable factors that can guide interventions potentially to increase age at first birth and reduce adolescent

fertility in Bangladesh.
2

1.2 Research Questions

1. What is the level of adolescent fertility in Bangladesh?

2. What are the independent and intermediate factors that determine adolescent fertility in Bangladesh?

1.3 Conceptual Framework

The conceptual framework explains the relationship of sociodemographic factors (independent variables) 

that affect adolescent fertility through the intermediate variables (knowledge and practices related to 

reproductive health and women’s status). Sociodemographic variables are typically nonmodifiable 

determinants, while the intermediate variables are generally modifiable. Independent variables influence 

the intermediate variables involved with adolescent fertility in a society. This conceptual framework aids 

in understanding how sociodemographic variables affect adolescent fertility. Data analyses and 

interpretation of the study findings have been guided by this conceptual framework to help understand the 

role of modifiable and nonmodifiable variables on adolescent fertility.

Figure 1 Conceptual framework that shows the linkages among the independent, intermediate, and 

dependent variables

Independent variables

Sociodemographic characteristics

Division (region)

Place of residence

Education

Family wealth

Husband’s education

Knowledge and practice

Knowledge of conception

Heard of family planning on TV

Contraceptive use and intention

Dependent variable

Adolescent fertility

Women’s status

 Health-care decision

 Currently working

 Age at marriage

 Spousal age gap

Intermediate variables
3

2 DATA AND METHODS

2.1 Data

This study uses data from the 2014 BDHS, which is the seventh DHS survey conducted in Bangladesh. The 

survey provides information on fertility, contraception and family planning methods, maternal and child 

health, and community-level data on the accessibility and availability of health services. All ever-married 

women age 15-49 in the selected households were eligible to be interviewed. The survey was designed to 

produce representative results for the country, urban, and rural areas, and for each of the seven 

administrative divisions. Details about the survey methodology are available in the DHS final report 

(National Institute of Population Research Training, Mitra Associates, and ICF International 2016). The 

2014 BDHS interviewed 17,863 women age 15-49. Our study sample included 2,023 ever-married women 

age 15-19.

2.2 Variables

2.2.1 Dependent variables

The dependent variable was adolescent fertility, defined as women age 15-19 who were pregnant or had at 

least one child at the time of interview. Applying all women factors as described below will allow this 

variable to be representative of all women and not only the ever-married women.

2.2.2 Independent variables

Independent and explanatory variables were: (a) sociodemographic characteristics of the woman and 

husband; (b) knowledge and practice; and (c) women’s status. Sociodemographic characteristics included 

division (region), place of residence (urban/rural), family wealth quintile, and woman’s and husband’s 

education level (no education, incomplete primary, complete primary, incomplete secondary, complete 

secondary, and higher). Knowledge and practice variables included knowledge of conception, heard about 

family planning on television in the past few months, and contraceptive use and intention (uses modern 

method, traditional method, non-user but intends to use, and does not intend to use). Knowledge of 

conception was recoded as correct knowledge if the respondent answered that the fertile period is in the 

middle of the women’s cycle and incorrect knowledge for any other answer. Variables that described the 

women’s status included health-care decisions on women’s health care (woman alone, woman and her 

husband jointly, husband alone, someone else, and other), working status (currently working or not), age at 

marriage, and spousal age gap. 

2.3 Statistical Analysis

Descriptive analysis was used to assess the sociodemographic characteristics of the study population for 

the ever-married sample in the survey. The proportions of adolescent fertility by the background variables 

were estimated for all women by using all-women factors that are described below. Unadjusted and adjusted 

logistic regression models were fit to determine the factors associated with adolescent fertility. The adjusted 

multivariate logistic models were fit with adjustment of all the independent and intermediate variables

except for the husband’s education, which was found to be highly correlated with the woman’s education. 
4

P-values and 95% confidence intervals are reported to indicate the statistical significance of the odds ratios 

(OR).

The analysis considered the multistage sampling design of the survey and sampling weights. In addition, 

the “all-women factor” was used to adjust the sample weights in this ever-married women sample to make 

estimates for all women for all analyses except Table 1, which describes the variables among ever-married 

women. All-women factors are used for adjustments of sampling weight for each woman, multiplying the 

weight variable for the woman by her appropriate all-women factor to inflate the number of cases from the 

number of ever-married women to the number of all women. For this analysis, the regional all-women 

factor was used to consider the geographical distribution and to be consistent with using one all-women 

factor throughout the analysis.

2.4 Results

Among ever-married women age 15-19, the majority (74%) lived in rural areas (Table 1). Half of the study 

subjects had incomplete secondary education (50%), while only 5% of the respondents had no formal 

education. Only 16% of the adolescent women were working at the time of interview. More than threequarters of the adolescent women (81%) had incorrect knowledge of conception. Close to half of the 

respondents (46%) reported using modern contraception methods, while 43% reported using no method but 

intent to use modern contraception to prevent pregnancy. Health-care decisions are made jointly by 40% of 

the adolescent women, while only 9% of women make health-care decisions alone. Approximately 17% of 

adolescent women married at age 10-13, and the majority (73%) married at age 14-17. Nearly half of the 

respondents (46%) had 6 to 10 years of age difference with their partners/husbands and about one-third 

(29%) had 10-plus years of age difference with their partners/husbands.
5

Table 1 Percent distribution of ever-married women age 15-19 according to background 

characteristics

Variables % Frequency

Division

Barisal 6.9 279

Chittagong 19.8 356

Dhaka 34.1 339

Khulna 9.6 276

Rajshahi 11.2 270

Rangpur 12.1 295

Sylhet 6.3 208

Place of residence

Urban 26.4 629

Rural 73.6 1,394

Education

No education 5.1 105

Incomplete primary 14.9 281

Complete primary 11.8 236

Incomplete secondary 50.7 1,037

Complete secondary 8.3 171

Higher 9.2 193

Family wealth

Lowest 19.8 391

Second 19.7 415

Middle 21.7 457

Fourth 22.6 458

Highest 16.1 302

Husband's education 

No education 14.0 282

Incomplete primary 17.4 354

Complete primary 16.0 315

Incomplete secondary 32.5 649

Complete secondary 9.1 176

Higher 10.9 247

Knowledge and practice

Knowledge of conception

No/incorrect knowledge 81.1 1640

Correct knowledge 18.9 383

Heard family planning on TV (N=2021)

No 81.4 1,642

Yes 18.6 379

Contraceptive use and intention

Using modern method 45.7 932

Using traditional method 4.3 85

Non-user - intends to use later 42.5 876

Does not intend to use 7.4 129

Non-user - intends to use later 0.1 1

Women’s status

Health-care decision (N=1981)

Respondent alone 8.5 147

Respondent and husband/partner 40.3 760

Husband/partner alone 35.3 748

Someone else 15.4 315

Other 0.5 11

Currently working

No 83.8 1,731

Yes 16.2 292

Age at marriage

10-13 17.4 316

14-17 73.1 1,518

17-19 9.6 189
6

Spousal age gap (in years) (N=1,978)

≤2 5.8 128

3-5 18.5 370

6-10 46.3 930

>10 29.4 550

Total 100 2,023

The adolescent fertility rate was found to be 31% among women, with 31% in rural areas and 30% in urban 

areas (Table 2). Adolescent fertility was higher in Rajshahi and Rangpur (37 percent each) compared with 

other divisions. Adolescent fertility was very similar across all education levels (between 30-34%), except 

for women with a higher education level who had an adolescent fertility rate of 26%. Adolescent fertility 

decreased with increasing family wealth as shown in Table 2. A large difference was observed in adolescent 

fertility among women who are currently using a modern contraceptive method (35%) and women who do 

not intend to use contraceptives (23%). There were also large differences by decision making for the 

women’s health care. Adolescent fertility was higher when the woman and/or her husband were involved 

in the decision (32-35%) compared to decisions that were made by someone else or others (22% and 12%, 

respectively). Adolescent fertility was highest among women who had the smallest age gap with their 

husband (34% for women with 2 years or less) compared to 30% for women with a 6-10 age gap with their 

husband. There were small differences in adolescent fertility between the categories in the other variables 

and particularly having correct knowledge of contraception, heard of family planning on TV, working 

status, and age at marriage.
7

Table 2 Percentage of adolescent fertility by background characteristics adjusted with all 

women factors

Variables % 95% C.I.

Total 30.8 29.1 - 32.5

Division

Barisal 31.4 28.2 - 34.6

Chittagong 26.4 23.8 - 29.1

Dhaka 31.8 27.8 - 35.7

Khulna 31.2 27.5 - 35.0

Rajshahi 36.6 32.7 - 40.5

Rangpur 36.9 32.1 - 41.7

Sylhet 24.5 21.4 - 27.5

Place of residence

Urban 29.5 26.1 - 33.0

Rural 31.2 29.3 - 33.2

Education

No education 34.9 28.7 - 41.0

Incomplete primary 33.5 29.5 - 37.5

Complete primary 31.0 26.4 - 35.6

Incomplete secondary 30.1 27.7 - 32.5

Complete secondary 31.3 26.0 - 36.5

Higher 25.9 20.7 - 31.2

Family wealth 

Lowest 35.1 31.2 - 38.9

Second 32.2 28.8 - 35.6

Husband's education 30.0 26.6 - 33.5

No education 29.0 26.1 - 31.9

Highest 27.1 22.7 - 31.5

Husband's education 

No education 34.1 29.6 - 38.6

Incomplete primary 37.7 34.0 - 41.3

Complete primary 34.1 30.2 - 38.0

Knowledge and practice 27.3 24.5 - 30.1

Complete secondary 27.0 21.9 - 32.1

No/incorrect knowledge 23.9 20.0 - 27.8

Correct knowledge

No/incorrect knowledge 30.3 28.3 - 32.3

Correct knowledge 33.3 29.7 - 37.0

Heard family planning on TV

No 30.9 29.0 - 32.9

Yes 30.4 26.9 - 34.0

Contraceptive method use and intention

Using modern method 34.6 32.2 - 36.9

Does not intend to use 21.2 14.5 - 27.9

Non-user - intends to use later 29.5 27.1 - 32.0

Women’s status 22.8 16.3 - 29.2

Health-care decision

Respondent alone 32.1 25.3 - 38.8

Respondent and husband/partner 34.7 32.3 - 37.1

Husband/partner alone 31.1 28.6 - 33.6

Someone else 22.9 19.2 - 26.7

Other 11.6 0.0 - 27.0

Currently working

No 30.7 28.9 - 32.5

Yes 31.5 25.7 - 37.2

Age at marriage

10-13 32.1 28.5 - 35.6

14-17 30.5 28.3 - 32.7

17-19 29.8 24.0 - 35.6

Spousal age gap (in years)

≤2 34.4 28.1 - 40.7

3-5 30.5 26.7 - 34.4
8

6-10 29.6 27.1 - 32.1

>10 33.3 30.0 - 36.7

In the adjusted logistic models, we see that among the geographic divisions, the highest adjusted odds of 

adolescent fertility were found in the Sylhet Division (AOR = 3.0; 95% CI: 1.6–6.1) and in the Chittagong

Division (AOR = 1.8; 95% CI: 1.8–2.7) when compared to the Barisal Division. Compared to women in 

lowest quintile, women in all other quintiles had lower odds of adolescent fertility, with the lowest odds

found among women in richest quintiles (AOR = 0.3; 95% CI: 0.2–0.6). Women who married at age 14-17 

had 60% lower odds of adolescent fertility and those who married at age 17-19 had 80% lower odds of 

adolescent fertility compared to women who married at age 10-13. Women who use traditional 

contraceptives had 70% lower odds of adolescent fertility, and women who do not intend to use 

contraceptives had 50% lower odds of adolescent fertility compared to women who use modern 

contraceptives. Women for whom decisions about their health care were made by someone else or others 

had lower odds of adolescent fertility compared to when the decision was made by the individual women. 

Place of residence, knowledge of contraception, heard of family planning on television, working status, and 

spousal age gap were not significant predictors of adolescent fertility both in the unadjusted and adjusted 

models.
9

Table 3 Unadjusted and adjusted logistic regression of adolescent fertility of ever-married 

women age 15-19 (n=1976)

Variables UOR (95% CI) p-value AOR (95% CI) p-value

Division

Barisal Reference category

Chittagong 1.1 (0.8 - 1.6) 0.576 1.8 (1.8 - 2.7) 0.003**

Dhaka 1.2 (0.8 - 1.9) 0.326 1.5 (1.0 - 2.4) 0.068

Khulna 0.9 (0.6 - 1.3) 0.501 1.1 (0.7 - 1.6) 0.638

Rajshahi 1.3 (0.9 - 1.8) 0.221 1.2 (0.8 - 1.8) 0.366

Rangpur 1.4 (0.9 - 2.2) 0.171 1.3 (0.9 - 2.1) 0.192

Sylhet 2.3 (1.3 - 4.1) 0.006** 3.0 (1.6 - 6.1) 0.001**

Place of residence

Urban Reference category

Rural 1.3 (0.9 - 1.8) 0.122 1.0 (0.7 - 1.6) 0.923

Education

No education Reference category

Incomplete primary 1.0 (0.5 - 2.1) 0.990 1.2 (0.5 - 2.8) 0.617

Complete primary 0.8 (0.4 - 1.8) 0.650 1.0 (0.5 - 2.2) 0.952

Incomplete secondary 0.6 (0.3 - 1.1) 0.112 0.8 (0.4 - 1.7) 0.613

Complete secondary 0.5 (0.2 - 1.0) 0.044* 0.9 (0.4 - 1.9) 0.732

Higher 0.3 (0.1 - 0.5) < 0.001*** 0.6 (0.2 - 2.3) 0.161

Family wealth

Lowest Reference category

Second 0.7 (0.4 - 1.1) 0.083 0.6 (0.4 - 1.0) 0.05*

Middle 0.6 (0.4 - 1.0) 0.029* 0.5 (0.3 - 0.9) 0.01**

Fourth 0.5 (0.4 - 0.8) 0.001** 0.5 (0.3 - 0.9) 0.013**

Highest 0.4 (0.2 - 0.6) < 0.001*** 0.3 (0.2 - 0.6) < 0.001***

Knowledge of conception

No/incorrect knowledge Reference category

Correct knowledge 1.0 (0.7 - 1.4) 0.928 1.2 (0.9 - 1.8) 0.248

Heard family planning on TV 

No Reference category

Yes 0.8 (0.6 - 1.1) 0.159 1.1 (0.7 – 1.5) 0.758

Contraception use or intention

Use modern method Reference category

Use traditional methods 0.3 (0.2 - 0.6) < 0.001*** 0.3 (0.1.- 0.5) < 0.001***

Intend to use later 0.8 (0.6 - 1.1) 0.209 0.9 (0.6 - 1.3) 0.504

No intention to use later 0.6 (0.3 - 1.0) 0.05* 0.5 (0.3 - 0.9) 0.031**

Health-care decision

Respondent alone Reference category

Respondent and husband/partner 0.9 (0.5 - 1.5) 0.645 1.0 (0.6 - 1.6) 0.852

Husband/partner alone 0.9 (0.5 - 1.5) 0.570 0.9 (0.6 - 1.5) 0.754

Someone else 0.4 (0.2 - 0.7) 0.001** 0.4 (0.2 - 0.7) 0.001*

Other 0.1 (0.0 - 0.7) 0.023* 0.1 (0.0 - 0.6) 0.008**

Currently working

No Reference category

Yes 0.7 (0.5 - 1.2) 0.195 0.7 (0.4 - 1.1) 0.124

Age at marriage

10-13 Reference category

14-17 0.4 (0.3 - 0.6) < 0.001*** 0.4 (0.3 - 0.7) 0.001**

17-19 0.2 (0.1 - 0.3) < 0.001*** 0.2 (0.1 - 0.4) < 0.001***

Spousal age gap (in years)

≤2 Reference category

3-5 0.8 (0.5 - 1.3) 0.368 0.8 (0.5 - 1.4) 0.404

6-10 0.9 (0.6 - 1.5) 0.717 0.8 (0.5 - 1.3) 0.318

>10 1.4 (0.8 - 2.4) 0.229 1.3 (0.7 - 2.2) 0.397

*p<0.05, **p<0.01, ***p<0.0001
10

2.5 Discussion

Based on the analysis of 2014 BDHS data, the prevalence of adolescent fertility in Bangladesh is substantial. 

One-third of married adolescents were either pregnant or already had at least one child before their twentieth 

birthday, which is substantially higher than in other South Asian countries (Birchall 2018; Ganchimeg et al. 

2014; Jisun 2016). Although considerable progress has been made, Bangladesh remains one of the leading 

countries in the world with high rates of teenage pregnancy. High prevalence of adolescent fertility is a 

hindrance for women’s status in Bangladesh because of the negative consequences in health, as well as 

social and economic wellbeing. Four plausible explanations for high fertility among adolescents include 

early age at marriage, early first birth, low contraceptive use among teenagers, and short birth interval

(Nahar and Min 2008).

Age at marriage was found to be a significant predictor of adolescent fertility in this study. Adolescent 

women who married at age 14-19 had significantly lower odds of adolescent fertility compared to the 

women who married at age 10-13. Bangladesh has some of the highest rates of child marriage worldwide, 

with more than half of all girls being married before the legal age of 18. Girls are often under pressure from 

both families to give birth to a child early in the marriage (Jaén-Sánchez et al. 2020). Although Bangladesh 

introduced a strict law against early marriage, progress has not been made over the years because it requires 

strong complementary social mobilization and other structural interventions that include poverty 

alleviation, gender equity, and an education program for girls (Shukrana 2020). Adolescent women have a 

lower contraceptive prevalence rate than other women and a higher unmet need for family planning. In 

2014, the prevalence of contraceptive use by any method among adolescent girls age 15–19 was 51.2%, 

compared to 67.7% among women age 25–29 (Hossain et al. 2018; Islam et al. 2017).

Our study findings suggest that adolescent girls who belong to the richest quintile were significantly less 

likely to experience adolescent fertility. Income inequality is a growing source of concern globally. The 

disparity between rich and poor families has influenced overall adolescent health and adolescent fertility

(Viner et al. 2017). Women from richer households are more likely to have better control over the decision 

to use contraception and are more aware of the consequences of early childbearing on their own and their 

children’s health (Angeles, Guilkey, and Mroz 2005; Marchetta and Sahn 2016). Household wealth could 

also reflect greater access to media as a tool for better knowledge and awareness including the decision to 

use contraceptives and reduce adolescent fertility (Acharya and Sureender 1996; Islam and Hasan 2000; 

Rabbi 2012).

There were considerable variations in adolescent fertility rates across geographic divisions in Bangladesh. 

After adjusting for the effect of confounding variables, significantly higher odds of adolescent fertility were 

found in the Sylhet and Chittagong divisions compared to Barisal Region. These two divisions were 

reported as low-performing regions in Bangladesh in terms of contraceptive use and other maternal and 

reproductive health indicators (Hajizadeh, Alam, and Nandi 2014; Islam, Haque, and Hema 2020). The 

significant variations in adolescent fertility in these two divisions may be due to differences in the religious, 

cultural, and program coverage (Mollborn 2010; Rabbi 2012). The Government of Bangladesh has given 

due attention to reducing regional disparities in adolescent fertility by introducing regional family planning 

programs that target the low-performing areas.
11

This study has some limitations, which include using the 2014 BDHS data, which was collected with a 

cross-sectional survey. Causality cannot be ascertained for the predictors we discussed in our study findings. 

However, the DHS data had a large sample size and nationally representative sampling methodology, which 

increase the generalizability of our study findings. In addition, some unmeasured characteristics including 

parental socioeconomic status and cultural influences on fertility after marriage might have a systemic 

relationship with adolescent fertility in Bangladesh.

2.6 Conclusions

This study has provided insights on the magnitude and determinants of adolescent fertility in Bangladesh 

in terms of economic, social, and reproductive factors. This study highlighted that the adolescent girls from 

the richer wealth quintile had lower odds of fertility during their adolescent period. Age at marriage was 

found to be a significant determinant of adolescent fertility. To address the issues of regional disparity, 

family wealth inequality, and child marriage, it is paramount to strengthen structural interventions that target 

individual and societal level change, and raise awareness, poverty alleviation, girls’ education and

employment (WHO 2016). Bangladesh is one of the few developing countries that has achieved most of 

the Millennium Development Goals that include reducing poverty, increasing female education, and 

reducing gender inequality (Ahmed 2013; Datta and Rabbany 2016; United Nations 2015; UNPFA 2015). 

The study findings generated evidence that could be useful in formulating policies and interventions that 

address modifiable factors to reduce adolescent fertility and achieve improved health and psychosocial 

wellbeing for women in Bangladesh.

13

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