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. 2023 Jun 20;23:320. doi: 10.1186/s12905-023-02469-y

Spatial pattern and influential factors for early marriage: evidence from Bangladesh Demographic Health Survey 2017-18 data

Md Arif Billah 1,✉, Md Mostaured Ali Khan 2, Syed Manzoor Ahmed Hanifi 1, M Mofizul Islam 3, Md Nuruzzaman Khan 4
PMCID: PMC10283256  PMID: 37340425

Abstract

Background

Early marriage is highly prevalent in Bangladesh. It is linked with a range of adverse outcomes, including maternal and child mortality. However, research on regional variations and factors associated with early marriage is scarce in Bangladesh. This study aimed to explore the geographical variations and predictors of early marriage in Bangladesh.

Methods

Data of women aged 20–24 in the Bangladesh Demographic and Health Survey 2017–18 were analysed. The occurrence of early marriage was the outcome variable. Explanatory variables were several individual-, household- and community-level factors. Geographical hot spots and cold spots of early marriage were first determined using Global Moran’s I statistic. Multilevel mixed-effect Poisson regression was used to determine the association of early marriage with individual-, household-, and community-level factors.

Results

Almost 59% of women aged 20–24 reported they were married before reached 18. The hotspots of early marriage were mainly concentrated in Rajshahi, Rangpur and Barishal, and the cold spots were in Sylhet and Chattogram divisions. The prevalence of early marriage was lower among higher educated (adjusted prevalence ratio (aPR): 0.45; 95% CI: 0.40, 0.52), and non-Muslim women (aPR: 0.89; 95% CI: 0.79, 0.99) than their counterparts. Higher community-level poverty was significantly associated with early marriage (aPR, 1.16; 95% CI: 1.04, 1.29).

Conclusion

The study concludes that promoting girls’ education, awareness-building programs about the adverse effects of early marriage and proper application of the child marriage restraint act, particularly in disadvantaged communities are recommended.

Keywords: Early marriage, Spatial analysis, Influential factors, Multi-level modelling, Bangladesh

Introduction

Early marriage – marriage before the age of 18 years – is a form of sexual violence that is highly prevalent in low- and middle-income countries (LMICs), including Bangladesh [1]. Early marriage violates sexual and reproductive health and rights, although often neglected in LMICs [1–7]. Approximately 37% of marriages in LMICs occur at an early age, of which 28% are in South Asian countries [2, 5]. The rate is even higher in Bangladesh, where around 51% of all first marriages occur before girls reach 18 [8]. This high prevalence ranked the country 8th in the world and 1st among South Asian countries in terms of the rates of child marriage [9]. The rate has dropped over the years, albeit slightly and not as much as one would expect from the progress that Bangladesh has made in other sectors, including increasing girls’ enrolment in education and improving the status of women in society [10].

Marriage before age 18, the transition period from childhood to adulthood, could have consequences on women’s well-being [11]. This includes but is not limited to drop-out from education and occupation, intimate partner violence, hostile marital relationships and increased divorce and separation rates [12, 13]. Moreover, around half of the women who experience early marriage get their first pregnancy before they reach age 20 [9]. Childbearing may be risky for maternal and child health, as women at this stage may not be physically ready for conception and delivery [14, 15]. Pregnancy complications, preterm birth, low birth weight, stillbirths and maternal and child mortality are some of the common adverse outcomes of early marriage [16–20]. Along with physical immaturity, higher malnutrition, lower use of maternal healthcare services, risky fertility behaviour and unwanted pregnancy are also highly prevalent among early-aged married women [21–24]. These are also associated with increased occurrences of child morbidity and mortality. Early marriage, therefore, presents a challenge to Bangladesh and other LMICs to achieve the Sustainable Development Goals (SDGs) 3, health and well-being for all, by 2030 [25, 26]. Bangladesh aims to drastically reduce early marriage by 2041 [8, 9, 27]. To achieve this goal, Bangladesh needs evidence-based policies and programs. Understanding multidimensional factors associated with early marriage is critical for that.

However, studies thus far conducted in LMICs primarily focused on individual-level factors. These studies indicate that early marriage is more common among women from low socioeconomic tiers, with low levels of education [16, 28–38] and/or from conservative families [36, 39–45]. Besides, existing evidence suggests a significant role of community-level factors such as cultural and societal norms in early marriage [7, 14, 32, 34, 37, 38, 41, 43, 44, 46–49]. These predictors vary across regions in most LMICs, including Bangladesh [10, 33, 45, 50] and the strengths of associations are usually stronger for rural than urban women [37, 41, 51, 52]. The possible reasons for this variation are different cultural and social norms, education- and poverty-level across the communities [42, 48, 53] and indicates a hierarchical effect of the individual- and household-and community-level factors. However, to our knowledge, the association between early marriage occurrence and community-level predictors has not yet been explored in Bangladesh and most other LMICs, although a study presented regional-level estimates and likelihood of early marriage [36]. However, estimates of larger regions often fail to represent the distinctiveness of early marriage in the smallest geographical spaces. Therefore, this study aimed to determine the hot spots and cold spots of early marriage and the predictors of early marriage in Bangladesh.

Methods

Data source and study population

We analysed nationally representative Bangladesh Demographic Health Survey 2017-18 data. The National Institute of Population Research and Training (NIPORT) conducted this survey as part of the Demographic and Health Survey (DHS) program. The Ministry of Health and Family Welfare of Bangladesh supervised the data collection procedure. The target population for this survey was women of reproductive age (15–49 years) of the selected households. The households were selected using a two-stage stratified random sampling procedure. In the first stage, 675 primary sampling units (PSUs) were selected randomly from the list of 293,579 PSUs generated by the Bangladesh Bureau of Statistics (BBS) as part of the 2011 national census. Data was collected from 672 of these PSUs and the remaining three PSUs were excluded due to flooding. Geographical locations were also recorded using Global Positioning System (GPS). In the second phase, 30 households were randomly selected from each of the selected PSUs. This resulted in a list of 20,160 households, of which 19,457 households were interviewed. A total of 20,127 ever-married women between 15 and 49 years of age were interviewed based on the inclusion criteria of the BDHS survey, with a non-response rate of < 1%. The selected respondents were interviewed on reproductive health and maternal and child health indicators. Details of the sampling strategy have been published elsewhere [54].

Sample

Data collected from 4,155 women who met the inclusion criteria were analysed in this study. The inclusion criteria were: (i) women aged 20–24 at the time of the survey and (ii) reported their marital status. We included only 20–24 years aged women to reduce the recall bias in reporting marital age. Another important reason for including only 20–24 years aged women was to ensure the generated estimates are relatively current because the older aged group may have married many years back when the country’s socio-demographic conditions were substantially different. BDHS only included ever-married women, although it provided an adjustment factor (all women factor) to consider the unmarried women in the analysis. We adjusted our analysis for that factor by following the DHS guideline [54].

Outcome variable

Women’s age at marriage (with the first spouse in case women reported multiple marriages) was our outcome of interest classified as early marriage (occurred before 18 years) and not early marriage (occurred at 18 years or later). During the survey, women were asked the following question: “How old were you when you first started living with your husband?”. The response recorded was classified as early marriage and not early marriage.

Explanatory variables

Several individual-, household-, and community-level factors were considered explanatory variables. The variables were selected in two stages. In the first stage, using several databases, we conducted a comprehensive search for the relevant papers published in LMICs, particularly in Bangladesh [7, 10, 29, 31, 37, 38, 41, 43–45, 55–57]. The factors that were found to be significantly associated with early marriage in the identified papers were then sorted and their availability was checked in the BDHS data. Finally, the variables that were found available in our data were considered for analysis and classified as individual-, household- and community-level factors as per the socio-ecological model of health [58–62]. Individual-level factors were women’s education (no formal education, primary, secondary and higher), husbands’ education (same categories as women’s education) and mass media exposure (no, yes). Household-level factors were number of family members (small (≤ 4 members) and large (> 4 members)), religion (Muslim, on-Muslim) and wealth index (poorer, poor, middle, rich, richer). The factors that were considered at the community level were the place of residence (urban and rural) and region of residence (Barishal, Chattogram, Dhaka, Khulna, Mymensingh, Rajshahi, Sylhet). In addition, we also examined community-level variables, namely community-level illiteracy and community-level poverty. Since these variables were not directly available in the survey data, we employed a two-step approach to measure them. Firstly, we calculated the proportions of uneducated women and women residing in resource-poor (i.e., women of poorer and poorest wealth quintiles) households at the cluster level. These proportions served as proxies for community-level illiteracy and community-level poverty, respectively. Subsequently, we categorized these cluster level proportions into three distinct categories: low (< 25%), moderate (25–50%), and high (> 50%) for community-level illiteracy; and low (≤ 15%), moderate (1640%), and high (> 40%) for community-level poverty. The detailed calculation procedure for deriving these variables has been previously published [63, 64], and followed that established approach to ensure consistency and comparability.

Statistical analysis

Descriptive statistics – frequency and percentage distribution – were used to describe the characteristics of the respondents. Distribution of early marriage across individual-, household-, and community-level factors was also presented. Utilizing Global Moran’s I statistic, we conducted an analyses to identify regions characterized by relatively high and low prevalence of early marriage, which refer to as “hot spots” and “cold spots”, respectively. This statistical measure allowed us to assess the spatial clustering of early marriage rates.

To computed the Global Moran’s I statistic, we first calculated the mean and variance for the dependent variable, namely early marriage prevalence. Then, we determine the deviation of each feature value from the mean by corresponding subtraction. Subsequently, we multiplied the eviations of all neighbouring features together, resulting in a cross-product ranging from +1 to -1. Deviations for all neighbouring features were then multiplied together to create a cross-product that ranging from +1 to -1. In the context of Moran’s I, a value of +1 indicates strong positive spatial clustering, meaning high values tend to cluster near other high values, and low values cluster near other low values. Conversely, a value of -1 suggests repulsion, where high values repel other high values and tend to be near low values. When the cross-product values balance negative cross-product values, Moran’s I tends to approach 0 [65, 66].

Multilevel mixed-effect Poisson regression with robust variance was used to explore the predictors of early marriage. In BDHS, individuals were nested in households and households were nested within clusters. This hierarchical structure of the data creates dependency among the respondents’ responses, e.g., individual from same household or cluster provided a similar response from a different household or cluster. Moreover, the prevalence of early marriage, the outcome variable, was around 60%. Ordinary logistic regressions do not produce precise results for hierarchical data or when the prevalence of outcome variable is > 10%. Multilevel mixed-effect Poisson regressions with robust variance can address both issues [67, 68]. We developed five models, where Model 1 was the null model used to explore the cluster-level variation of early marriage in Bangladesh. Individual- and household-level factors were considered in models 2 and 3, respectively. Community-level factors were considered in Model 4. Model 5 was the final model that included all individual-, household- and community-level factors. We reported intra-class correlation (ICC), Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) for all models. Results are reported as Prevalence Ratio (PR) with its 95% Confidence Interval (95% CI). All analyses were conducted using the statistical software STATA version 15.1 (Release 15. College Station, TX: Stata Corp LLC.).

Results

Characteristics of the study sample

Of the 4,155 women of age 20–24 years whose data we analysed, approximately 46% completed secondary education (Table 1). Around 38% of the husbands of the participants also completed secondary education. About 16% of women were from households of the poorest wealth quintile. Over 57% of women were from large families. Around two-thirds lived in rural areas and 27% were from the Dhaka division. Around 57% resided in the community with moderate poverty.

Table 1.

Percentage distribution of early marriage by different socioeconomic characteristics of the sample

Characteristics Total respondents,
N (%)g 		Early marriageh
n (%)
Prevalence of early marriage 58.85 (57.21–60.47)
Women’s age (in years)a
 20 876 (21.08) 482 (55.01)
 21 867 (20.85) 527 (60.76)
 22 858 (20.65) 494 (57.54)
 23 746 (17.94) 454 (60.89)
 24 809 (19.47) 489 (60.47)
Women’s educationb
 No formal education 145 (3.49) 109 (75.03)
 Primary 908 (21.84) 666 (73.32)
 Secondary 1905 (45.78) 1368 (71.80)
 Higher 1202 (28.90) 303 (25.20)
Husband’s educationa, f
 No formal education 423 (10.53) 305 (72.16)
 Primary 1289 (32.09) 844 (65.49)
 Secondary 1527 (38.02) 877 (57.42)
 Higher 777 (19.35) 327 (42.07)
Religiona
 Muslim 3828 (92.11) 2282 (59.61)
 Non-Muslim 328 (7.89) 164 (49.99)
Wealth indexc
 Poorest 679 (16.32) 504 (74.24)
 Poorer 769 (18.49) 491 (63.90)
 Middle 821 (19.74) 495 (60.36)
 Richer 938 (22.55) 523 (55.74)
 Richest 952 (22.90) 432 (45.41)
Family sizea
 Small (≤ 4 members) 1768 (42.56) 1106 (62.56)
 Large (> 4 members) 2387 (57.44) 1339 (56.10)
Place of residenced
 Urban 1275 (30.68) 696 (54.57)
 Rural 2881 (69.32) 1750 (60.73)
Divisione
 Barisal 209 (5.03) 135 (64.63)
 Chattogram 814 (19.59) 440 (54.07)
 Dhaka 1139 (27.41) 656 (57.57)
 Khulna 391 (9.41) 241 (61.62)
 Mymensingh 306 (7.37) 197 (64.39)
 Rajshahi 520 (12.50) 364 (70.11)
 Rangpur 433 (10.41) 290 (66.96)
 Sylhet 344 (8.27) 122 (35.36)
Community-level illiteracyb
 < 25 (low) 966 (23.21) 495 (51.26)
 25 to 50 (Moderate) 2396 (57.59) 1412 (58.95)
 > 50 (high) 799 (19.20) 538 (67.36)
Community-level povertyc
 > 41.0 (High poverty) 1820 (43.77) 1185 (65.09)
 16–41 (Moderate poverty) 1029 (24.75) 611 (59.39)
 < = 15 (Low poverty) 560 (13.45) 255 (45.54)
 Middle or rich community 749 (18.02) 395 (52.70)
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aEstimates were adjusted or weighted by all women factor-total

bEstimates were adjusted or weighted by all women factor-education

cEstimates were adjusted or weighted by all women factor-wealth quintile

dEstimates were adjusted or weighted by all women factor-urban/rural

eEstimates were adjusted or weighted by all women factor-regional

fThere were some missing values in husband’s education (n = 4019)

gColumn percentage

hRow percentage

Distribution of the prevalence of early marriage

The prevalence of early marriage was 58.85% (95%CI: 57.21–60.47). However, the reported prevalence was not consistent across women’s individual-, household- and community-level factors (Table 1). The prevalence of early marriage was substantially higher among women who completed up to secondary or less education (72–75%) and among Muslim women (60%). Around three-quarters of the early married women reported their husbands did not receive a formal education. With around 74% of early marriage, the prevalence was very high among women of poor households and it declined to 45% among women from households of the richest wealth quintiles. Early marriage was higher among rural women and those who live in the Rangpur, Mymensingh, Khulna and Barishal divisions. The prevalence was also found higher in communities with a high and moderate-level of poverty (Table 1).

Cluster-wise variation of early marriage in Bangladesh

Global Moran’s I value (0.482, p < 0.001) indicates a significant positive autocorrelation, and the Getis-Ord General G statistics reveal the presence of high clustering of early marriage in Bangladesh (z-score = 6.82, p < 0.001). The hot spots are located mainly in the Rangpur, Mymensingh and Rajshahi divisions. The cold spots are located in Sylhet and Chattogram divisions (Fig. 1). We also reported difference in hot spot and cold spot across socio-economic level of the respondents (result not shown in the table or figure).

Fig. 1.

Fig. 1

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Hot-spot and cold-spots of early marriage in Bangladesh

Factors associated with early marriage in Bangladesh

The results of the Poisson regression model for the predictors of early marriage are presented in Table 2. In the final model, we found the likelihood of early marriage declined by 9% (aPR: 0.91; 95% CI: 0.83–0.99) and 55% (aPR: 0.45; 95% CI: 0.40, 0.52) among secondary and higher educated women, respectively, as compared to the women who received no formal education. The likelihood of early marriage also declined by around 6–14% among women whose husbands received primary to higher education as compared to the women whose husbands received no formal education. Early marriage was 11% lower (aPR: 0.89; 95% CI: 0.79, 0.99) among Muslim women than non-Muslim women. As compared to the women in the Sylhet division, the prevalence of early marriage was higher among women in the Rajshahi (aPR: 1.63; 95% CI: 1.45, 1.83), Rangpur (aPR: 1.59; 95% CI: 1.41, 1.78), Barishal (aPR: 1.54; 95% CI: 1.35, 1.74), Khulna (aPR: 1.52; 95% CI: 1.35, 1.73), Mymensingh (aPR: 1.46; 95% CI: 1.29, 1.64), Dhaka (aPR: 1.40; 95% CI: 1.23, 1.59) and Chattogram (aPR: 1.38; 95% CI: 1.22, 1.56) divisions. The prevalence of early marriage was also higher among women residing in the community with moderate (aPR: 1.17; 95% CI: 1.03, 1.32) to a higher level of poverty (aPR: 1.16; 95% CI: 1.04, 1.29) as compared to the women in the community with a lower level of poverty.

Table 2.

Determinants (multilevel Poisson model) and fitted model of early marriage among the women 20–24 years in Bangladesh, 2017/18a

Variables Null model Individual-level model Household-level model Community-level model Final model
aPR (95% CI) aPR (95% CI) aPR (95% CI) aPR (95% CI)
Women’s education
 No formal education (reference) 1.00 1.00
 Primary 0.97 (0.88, 1.06) 0.93 (0.85, 1.02)
 Secondary 0.97 (0.88, 1.06) 0.91 (0.83, 0.99)*
 Higher 0.49 (0.42, 0.56)*** 0.45 (0.40, 0.52)***
Husbands’ education
 No formal education (reference) 1.00 1.00
 Primary 0.93 (0.88, 0.99)* 0.94 (0.89, 0.99)*
 Secondary 0.89 (0.83, 0.95)*** 0.92 (0.86, 0.98)*
 Higher 0.85 (0.77, 0.95)** 0.86 (0.77, 0.97)*
Media exposure
 No (reference) 1.00 1.00
 Yes 1.01 (0.96, 1.07) 1.02 (0.96, 1.07)
Family size
 Small (reference) 1.00 1.00
 Large 0.91 (0.87, 0.95)*** 0.97 (0.93, 1.00)
Wealth index
 Poorer (reference) 1.00 1.00
 Poor 0.89 (0.83, 0.96)** 0.94 (0.87, 1.01)
 Middle 0.88 (0.82, 0.94)*** 1.00 (0.93, 1.08)
 Rich 0.80 (0.74, 0.86)*** 1.01 (0.93, 1.11)
 Richer 0.72 (0.66, 0.78)*** 1.06 (0.95, 1.18)
Religion
 Muslim (reference) 1.00 1.00
 Non-Muslim 0.82 (0.73, 0.92)*** .89 (0.79, 0.99)*
Place of residence
 Urban (reference) 1.00 1.00
 Rural 0.94 (0.88, 1.02) .97 (0.90, 1.04)
Region
 Sylhet (reference) 1.00 1.00
 Barishal 1.47 (1.29, 1.68)*** 1.54 (1.35, 1.74)***
 Chattogram 1.36 (1.20, 1.54)*** 1.38 (1.22, 1.56)***
 Dhaka 1.40 (1.22, 1.59)*** 1.40 (1.23, 1.59)***
 Khulna 1.45 (1.27, 1.65)*** 1.52 (1.35, 1.73)***
 Mymensingh 1.40 (1.24, 1.59(*** 1.45 (1.29, 1.64)***
 Rajshahi 1.58 (1.40, 1.78)*** 1.63 (1.45, 1.83)***
 Rangpur 1.48 (1.31, 1.67)*** 1.59 (1.41, 1.78)***
Community-level illiteracy
 < 25 (Low) (reference) 1.00 1.00
 25–50 (Moderate) 1.04 (0.96, 1.12) 0.97 (0.90, 1.04)
 > 50 (High) 1.11 (1.01, 1.22)* 0.97 (0.89, 1.06)
Community-level poverty
 Middle or rich (reference) 1.00 1.00
 <= 15 (Low poverty) 0.89 (0.78, 1.02) 0.94 (0.83, 1.07)
 16–41 (Moderate poverty) 1.11 (1.00, 1.24) 1.16 (1.04, 1.29)**
 > 41 (High poverty) 1.16 (1.02, 1.30)* 1.17 (1.03, 1.32)*
Constant 58.85 (0.829)*** 74.30 (68.03, 81.14)*** 74.15 (70.53, 77.96)*** 38.61 (33.37, 44.66)*** 51.91 (43.52, 61.93)***
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The first model contains only individual-level determinants (women’s education, husbands’ education, media exposure)

The second model contains only family-level determinants (family size, religion, and wealth index)

The third model contains only community-level determinants (place of residence and division, community-level literacy, and poverty)

The final model includes both individual- and community-level determinants (women’s education, husbands’ education, media exposure, family size, religion, wealth index, place of residence, division, community-level literacy and poverty)

PR Prevalence rate, CI Confidence interval

The asterisk indicates the significance level * at p > 0.05, ** at p > 0.01 and *** at p > 0.001

aAll the estimates were adjusted/weighted by all women factor considering complex survey design with linearized error variance

Model selection

Table 3 presents the AIC, BIC and ICC values of the five models that were run separately for assessing the predictors of early marriage. The best model is one which has the lowest AIC, BIC, and ICC values. According to these markers, final model, the combined model, was the best. We found around 20% variation in early marriage across the clusters before adjusting for the individual-, household- and community-level factors. The variation was reduced to 7% after adjustments in the final model.

Table 3.

Intra class correlation (ICC), variances for random intercepts, Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC) for early marriage, Bangladesh 2017–18

Models Prevalence of early marriage (ref: No)
Statistics Intra class coefficient Variance of random intercept Akaike Information Criteria Bayesian Information Criteria
Null model 0.20 1.38 (0.56) 9514.23 8247.12
Individual-level model 0.14 1.20 (0.30) 8925.36 7415.36
Household-level model 0.11 1.14 (0.28) 7256.32 7058.36
Community-level model 0.09 1.11 (0.26) 6125.36 6982.32
Final model 0.07 0.90 (0.21) 4561.36 4156.36
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Discussion

This study aimed to explore the hot spots and cold spots of early marriage and the predictors of early marriage in Bangladesh. We found hot spots of early marriage were located mostly in the Mymensingh, Rangpur and Rajshahi divisions and cold spots were located mostly in the Sylhet division. The likelihood of early marriage declined with increasing levels of education of women as well as their husbands. The prevalence of early marriage was higher among Muslim than non-Muslim women. There were higher likelihoods of early marriage among women in all except the Sylhet division, and among women who resided in the community with moderate to higher poverty. We generated these results by using advanced statistical modelling in the most recent nationally representative survey data. Therefore, the findings are robust and should be used to refer to the present situation of Bangladesh as well as develop relevant policies and programs.

We found around 59% occurrence of early marriage. The hot spots of early marriage in Bangladesh are mainly located in parts of the Mymensingh, Rajshahi, and Rangpur divisions. This finding is novel and so we could not compare this finding with that in the existing literature. However, previous studies reported a higher prevalence of early marriage in the Rajshahi and Rangpur divisions [50]. This variation could be due to differences in socio-cultural factors such as area-level norms and traditions, education levels [4, 41, 50], employment and economic conditions [4, 41, 50, 69]. Also, the existing misconception and prejudice about early marriage vary across regions [70].

Administrative failure to reduce early marriage could be another important reason for the divisional variation. Bangladesh has four layers of administrative structure (division, district, upazila, and union parishad), in which union parishad and upazila are at the bottom. Although policies and programs are taken mainly on the divisional levels, upazila and union parishad administrations play critical roles in preventing early marriage. They operate awareness-building programs and impose relevant laws at the root level to prevent early marriage [71]. It is also common in Bangladesh that upazila and union parishad administrations stop early marriages on the day of occurrence by reaching the victims’ homes. They usually do so upon receiving information through their official channels (e.g., hotlines) as well as other sources [71, 72]. Some upazila and union parishads have been able to declare their jurisdictions free from child marriage [72]. It is likely that inadequate efforts by upazila and union parishads are made in areas identified as hot spots. Comparing administration-level activities in early marriage prevention in hot spots and cold spots may provide useful information about future approaches to intervention. It is also crucial to develop local-level policies and programs instead of the current top-down approach.

An important observation of this study was that the prevalence of early marriage decreases with increasing education levels. This observation is consistent both for women and their husbands. Each additional year of education may delay marriage [73]. This observation is similar to the existing literature [28, 30, 44, 48, 74]. Educated people are usually aware of the adverse effects of early marriage and may have the ability to fight against societal norms, culture and religious misunderstandings. Education may also give girls and boys decision-making abilities [14, 41, 56, 75].

In this study, we found a higher occurrence of early marriage among Muslim women than their non-Muslim counterparts which is consistent with previous studies in Bangladesh [10, 34, 42, 43, 56, 76]. The reason should be traditional norms of giving marriage early, mostly soon after the girls experience menstruation, that comes from misinterpretations of the religious doctrines [7, 14, 32, 34, 37, 38, 41, 43, 44, 46–49]. Also, preference for finding suitable brides/grooms within the same caste still prevails among Hindu communities, which constitute the lion part of the non-Muslim people in Bangladesh. This caste system together with a relatively small population makes the size of the eligible young people’s pool relatively smaller which subsequently reduces the chance of child marriage among the Hindu communities.

With growing access to the internet and social media, Bangladeshi communities are heavily influenced by western cultures. As such, a significant change has been noticed among school-going girls, including pre-marital sexual relationships [77–79]. Sexual relationships outside marriage are strictly prohibited in Islam and may put a girl in an extremely vulnerable situation [80]. If a young female is involved in an affair or is exposed to premarital sex, parents have no choice but to favour an immediate marriage, even if she is under-aged. This cultural change and fear about their children’s’ pre-marital relationship may motivate some parents to marry off their girls even if they are below 18.

As found in this study, community-level poverty is a cause of early marriage. We could not make any direct comparison of this finding because of the lack of relevant studies. There are several possible reasons for such associations. Poverty and education are intricately connected. A community where most people are relatively poor may not be aware of the adverse effects of early marriage. Early marriage may be a common social norm. Even if a small subset of parents is unwilling to early marriage, they may prefer to follow the community norms due to societal pressure. Moreover, in poorer households, parents are usually less interested to invest for their daughters as much they like to do for their sons. The reason could be community-level perception and tradition that girls are ultimately for grooms’ families. Sons stay with parents lifelong. Consequently, some parents may consider investing in their daughters’ education has little value and prefer to marry off as early as possible. This may also save expenditures associated with rearing, educating, and supporting until their marriages. Besides, girls’ marriage at a later age may necessitate providing dowry during marriage, although by law, it is completely prohibited in Bangladesh [7, 48, 81].

Strengths and limitations

This study has several strengths. We demonstrated the variation in early marriage at the small geographic clusters using nationally representative data and robust statistical procedures. We also have identified influential factors of early marriage at the individual-, family- and community-level and developed the most parsimonious model that included all three layers of factors. Multilevel Poisson regression added further strength for hierarchical data when the prevalence of the outcome variable is relatively high. Besides, a large sample of data reduces variance and help predicts values that are close to the population parameters. The cross-sectional nature of the study design, however, may impact causality. Also, data on the unrevealed exposures that maybe intricately connected to early marriage, such as parental characteristics, familial and social circumstances, and psychosocial incentives for early marriage were not available.

Conclusion

Our study found Mymensingh, Rangpur and Rajshahi divisions and some parts of the Barishal division had relatively high prevalence of early marriage. Women and their husbands who received relatively low levels of education, from Muslim families and moderate or high community-level poverty were more likely than their counterparts to experience early marriage. The findings are likely to be robust and suitable for informing evidence-based policies and programmes. Promoting girls’ and boys’ education, mandatory registration of all marriages, strict enforcement of the legal age of marriage, raising community awareness of the adverse effects of child marriage, and improving employment opportunities for women are some of the pragmatic programmes that can reduce the relatively high rate of early marriage in Bangladesh.

Acknowledgements

We are thankful to MEASURE DHS for the data support and also grateful to icddr,b is also grateful to the Governments of Bangladesh, Canada, Sweden and the UK for providing core/unrestricted support for its operations and research, where the data for this study was analysed. The authors also acknowledge the support of Health System and Population Studies Division, icddr,b and Department of Population Science, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh Bangladesh.

Abbreviations

LMICs

Low- and Middle-Income Countries

DHS

Demographic Health Survey

BDHS

Bangladesh Demographic Health Survey

AIC

Akaike Information Criteria

BIC

Bayesian Information Criteria

ICC

Intra-Class Correlation Coefficient

aPR

Adjusted Prevalence Ratio

CI

Confidence Interval

SDG

Sustainable Development Goal

NIPORT

National Institute of Population Research and Training

PSU

Primary Sampling Unit

GPS

Global Positioning System

Authors’ contributions

MAB and MNK developed the study concepts. MAB, MMAK and MNK analysed the data. MAB and MMAK drafted the manuscript. MNK, MMI, SMAH critically reviewed the manuscript. All authors have seen and approved the final version of the paper.

Funding

The study has no external or institutional fund.

Availability of data and materials

The dataset can download after registering with the MEASURE DHS. Necessary information is available at: http://dhsprogram.com/data/Using-DataSets-for-Analysis.cfm.

Declarations

Ethics approval and consent to participate

The data of this study were obtained from MEASURE DHS Archive, and originally collected by the Macro, Calverton, USA. ORC Macro Institutional Review Board reviewed and approved the data collection procedure. Informed consent was obtained from each participant prior to enrolment.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Rahman A. Child victims of sexual exploitation in Bangladesh. Glob Bioeth. 2005;18(1):37–44. doi: 10.1080/11287462.2005.10800864. [DOI] [Google Scholar]
  • 2.Child marriage: child marriage is a violation of human rights, but is all too common. https://data.unicef.org/topic/child-protection/child-marriage/. Accessed 3 Dec 2022.
  • 3.Yount KM, Crandall A, Cheong YF, Osypuk TL, Bates LM, Naved RT, Schuler SR. Child marriage and intimate partner violence in rural Bangladesh: a longitudinal multilevel analysis. Demography. 2016;53(6):1821–1852. doi: 10.1007/s13524-016-0520-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Ame KR. Overcoming the curse of early marriage in Bangladesh. Asian J Women’s Stud. 2013;19(4):150–163. doi: 10.1080/12259276.2013.11666169. [DOI] [Google Scholar]
  • 5.Child marriage. https://data.unicef.org/topic/child-protection/child-marriage/. Accessed 12 Aug 2022.
  • 6.Rahman A. Child victims of sexual exploitation in Bangladesh. Glob Bioeth. 2014;18(1):37–44. doi: 10.1080/11287462.2005.10800864. [DOI] [Google Scholar]
  • 7.Ame KR. Overcoming the curse of early marriage in Bangladesh. AJWS. 2013;19(4):150–163. [Google Scholar]
  • 8.Ending child marriage: a profile of progress in Bangladesh. https://data.unicef.org/resources/ending-child-marriage-a-profile-of-progress-in-bangladesh/.
  • 9.UNICEF . Ending child marriage: a profile of progress in Bangladesh. New York: UNICEF; 2020. [Google Scholar]
  • 10.Biswas RK, Khan JR, Kabir E. Trend of child marriage in Bangladesh: a reflection on significant socioeconomic factors. Child Youth Serv Rev. 2019;104:104382. doi: 10.1016/j.childyouth.2019.06.017. [DOI] [Google Scholar]
  • 11.Fan S, Koski A. The health consequences of child marriage: a systematic review of the evidence. BMC Public Health. 2022;22(1):1–7. doi: 10.1186/s12889-022-12707-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.John NA, Edmeades J, Murithi L, Barre I. Child marriage and relationship quality in Ethiopia. Cult Health Sex. 2019;21(8):853–866. doi: 10.1080/13691058.2018.1520919. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Islam MM. Child marriage, marital disruption, and marriage thereafter: evidence from a national survey. BMC Womens Health. 2022;22(1):485. doi: 10.1186/s12905-022-02088-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Psaki SR, Melnikas AJ, Haque E, Saul G, Misunas C, Patel SK, Ngo T, Amin S. What are the drivers of child marriage? A conceptual framework to guide policies and programs. J Adolesc Health. 2021;69(6S):S13–S22. doi: 10.1016/j.jadohealth.2021.09.001. [DOI] [PubMed] [Google Scholar]
  • 15.Mitiku Y, Kiffle D, Siyoum D, Birlie B. Determinants of time to first marriage among rural women in Ethiopia. Biomed Stat Inform. 2018;3(1):1. doi: 10.11648/j.bsi.20180301.11. [DOI] [Google Scholar]
  • 16.Trommlerova SK. When children have children: the effects of child marriages and teenage pregnancies on early childhood mortality in Bangladesh. Econ Hum Biol. 2020;39:100904. doi: 10.1016/j.ehb.2020.100904. [DOI] [PubMed] [Google Scholar]
  • 17.Lee KH, Chowdhury AI, Rahman QS, Bari S, El Arifeen S, Gurley ES. Prevalence of child marriage in rural Bangladesh and associations with adverse pregnancy outcomes. Am J Trop Med Hyg. 2019;101:22–22. [Google Scholar]
  • 18.Huang H, Wei Y, Xia Y, Wei L, Chen X, Zhang R, Su L, Rahman ML, Rahman M, Qamruzzaman Q, et al. Child marriage, maternal serum metal exposure, and risk of preterm birth in rural Bangladesh: evidence from mediation analysis. J Expo Sci Env Epid. 2021;31(3):571–580. doi: 10.1038/s41370-021-00319-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Kamal SM, Hassan CH. Child marriage and its association with adverse reproductive outcomes for women in Bangladesh. Asia Pac J Public Health. 2015;27(2):NP1492–1506. doi: 10.1177/1010539513503868. [DOI] [PubMed] [Google Scholar]
  • 20.Kamal SMM, Ulas E. Child marriage and its impact on fertility and fertility-related outcomes in South Asian countries. Int Sociol. 2020;36(3):362–377. doi: 10.1177/0268580920961316. [DOI] [Google Scholar]
  • 21.Sezgin AU, Punamäki RL. Impacts of early marriage and adolescent pregnancy on mental and somatic health: the role of partner violence. Arch Womens Ment Health. 2020;23(2):155–166. doi: 10.1007/s00737-019-00960-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Yaya S, Odusina EK, Bishwajit G. Prevalence of child marriage and its impact on fertility outcomes in 34 sub-Saharan African countries. BMC Int Health Hum Rights. 2019;19(1):33. doi: 10.1186/s12914-019-0219-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Onagoruwa A, Wodon Q. Measuring the impact of child marriage on total fertility: a study for fifteen countries. J Biosoc Sci. 2018;50(5):626–639. doi: 10.1017/S0021932017000542. [DOI] [PubMed] [Google Scholar]
  • 24.Marphatia AA, Ambale GS, Reid AM. Women’s marriage age matters for public health: a review of the broader health and social implications in South Asia. Front Public Health. 2017;5:269. doi: 10.3389/fpubh.2017.00269. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.UN . Transforming our world: the 2030 agenda for sustainable development (A/RES/70/1) New York: United Nations General Assembly; 2015. [Google Scholar]
  • 26.Goals-3: ensure healthy lives and promote well-being for all at all ages. https://sdgs.un.org/goals/goal3. Accessed 14 Nov 2022.
  • 27.UNICEF. Plan of Action launched to eliminate child marriage in Bangladesh. 2018.
  • 28.Amin S, Asadullah MN, Hossain S, Wahhaj Z. Eradicating child marriage in the commonwealth: is investment in girls’ education sufficient? Round Table. 2017;106(2):221–223. doi: 10.1080/00358533.2017.1299461. [DOI] [Google Scholar]
  • 29.Bates LM, Maselko J, Schuler SR. Women’s education and the timing of marriage and childbearing in the next generation: evidence from rural Bangladesh. Stud Fam Plann. 2007;38(2):101–112. doi: 10.1111/j.1728-4465.2007.00121.x. [DOI] [PubMed] [Google Scholar]
  • 30.Hahn Y, Islam A, Nuzhat K, Smyth R, Yang H-S. Education, marriage, and fertility: long-term evidence from a female stipend program in Bangladesh. Econ Dev Cult Change. 2018;66(2):383–415. doi: 10.1086/694930. [DOI] [Google Scholar]
  • 31.Raj A, McDougal L, Silverman JG, Rusch ML. Cross-sectional time series analysis of associations between education and girl child marriage in Bangladesh, India, Nepal and Pakistan, 1991–2011. PLoS One. 2014;9(9):e106210. doi: 10.1371/journal.pone.0106210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Nasrullah M, Zakar R, Zakar MZ, Abbas S, Safdar R, Shaukat M, Kramer A. Knowledge and attitude towards child marriage practice among women married as children-a qualitative study in urban slums of Lahore, Pakistan. BMC Public Health. 2014;14:1148. doi: 10.1186/1471-2458-14-1148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Hossain MG, Mahumud RA, Saw A. Prevalence of child marriage among Bangladeshi women and trend of change over time. J Biosoc Sci. 2016;48(4):530–538. doi: 10.1017/S0021932015000279. [DOI] [PubMed] [Google Scholar]
  • 34.Ferdous J, Mallick D. Norms, practices, and gendered vulnerabilities in the lower Teesta basin, Bangladesh. Environ Dev. 2019;31:88–96. doi: 10.1016/j.envdev.2018.10.003. [DOI] [Google Scholar]
  • 35.Islam MA, Dey Barna S. Survival analysis of timing of early marriage among women in Bangladesh: evidence from the 2014 Bangladesh demographic and health survey. Fam Med Prim Care Rev. 2021;23(4):429–436. doi: 10.5114/fmpcr.2021.110356. [DOI] [Google Scholar]
  • 36.Scott S, Nguyen PH, Neupane S, Pramanik P, Nanda P, Bhutta ZA, Afsana K, Menon P. Early marriage and early childbearing in South Asia: trends, inequalities, and drivers from 2005 to 2018. Ann N Y Acad Sci. 2021;1491(1):60–73. doi: 10.1111/nyas.14531. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Mashiur R. Determinates of early marriage in Bangladesh: an evidence of the nationally representative survey. IJSA. 2017;9(1):1–7. [Google Scholar]
  • 38.Razu SR. Determinants of early marriage among women: an experience from rural Bangladesh, vol. 17. 2018.
  • 39.Asadullah MN, Islam KMM, Wahhaj Z. Child marriage, climate vulnerability and natural disasters in coastal Bangladesh. J Biosoc Sci. 2021;53(6):948–967. doi: 10.1017/S0021932020000644. [DOI] [PubMed] [Google Scholar]
  • 40.Saleheen AAS, Afrin S, Kabir S, Habib MJ, Zinnia MA, Hossain MI, Haq I, Talukder A. Sociodemographic factors and early marriage among women in Bangladesh, Ghana and Iraq: an illustration from Multiple Indicator Cluster Survey. Heliyon. 2021;7(5):e07111. doi: 10.1016/j.heliyon.2021.e07111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Kamal SM, Hassan CH, Alam GM, Ying Y. Child marriage in Bangladesh: trends and determinants. J Biosoc Sci. 2015;47(1):120–139. doi: 10.1017/S0021932013000746. [DOI] [PubMed] [Google Scholar]
  • 42.Henry EG, Lehnertz NB, Alam A, Ali NA, Williams EK, Rahman SM, Ahmed S, El Arifeen S, Baqui AH, Winch PJ. Sociocultural factors perpetuating the practices of early marriage and childbirth in Sylhet District, Bangladesh. Int Health. 2015;7(3):212–217. doi: 10.1093/inthealth/ihu074. [DOI] [PubMed] [Google Scholar]
  • 43.Chowdhury FD. The socio-cultural context of child marriage in a Bangladeshi village. Int J Soc Welf. 2004;13(3):244–253. doi: 10.1111/j.1369-6866.2004.00318.x. [DOI] [Google Scholar]
  • 44.Fattah KN, Camellia S. Poverty, dowry and the ‘good match’: revisiting community perceptions and practices of child marriage in a rural setting in Bangladesh. J Biosoc Sci. 2022;54(1):39–53. doi: 10.1017/S0021932020000668. [DOI] [PubMed] [Google Scholar]
  • 45.Talukder A, Hasan MM, Razu SR, Hossain MZ. Early marriage in Bangladesh: a cross-sectional study exploring the associated factors. J Int Women’s Stud. 2020;21(1):68–78. [Google Scholar]
  • 46.Banerjee PR. Dowry in 21st-century India: the sociocultural face of exploitation. Trauma Violence Abuse. 2014;15(1):34–40. doi: 10.1177/1524838013496334. [DOI] [PubMed] [Google Scholar]
  • 47.Biswas SC, Karim S, Rashid SF. Should we care: a qualitative exploration of the factors that influence the decision of early marriage among young men in urban slums of Bangladesh. BMJ Open. 2020;10(10):e039195. doi: 10.1136/bmjopen-2020-039195. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Akter S, Williams C, Talukder A, Islam MN, Escallon JV, Sultana T, Kapil N, Sarker M. Harmful practices prevail despite legal knowledge: a mixed-method study on the paradox of child marriage in Bangladesh. Sex Reprod Health Matters. 2021;29(2):1885790. doi: 10.1080/26410397.2021.1885790. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Bhowmik J, Biswas RK, Hossain S. Child marriage and adolescent motherhood: a nationwide vulnerability for women in Bangladesh. Int J Environ Res Public Health. 2021;18(8):4030. doi: 10.3390/ijerph18084030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Islam MK, Haque MR, Hossain MB. Regional variations in child marriage in Bangladesh. J Biosoc Sci. 2016;48(5):694–708. doi: 10.1017/S0021932016000110. [DOI] [PubMed] [Google Scholar]
  • 51.Uddin J, Pulok MH, Johnson RB, Rana J, Baker E. Association between child marriage and institutional delivery care services use in Bangladesh: intersections between education and place of residence. Public Health. 2019;171:6–14. doi: 10.1016/j.puhe.2019.03.014. [DOI] [PubMed] [Google Scholar]
  • 52.Sultana I, Hossain ME, Hoq MN. Factors affecting on early marriage of women in rural areas, Bangladesh. Asian J Humanity Art Lit. 2015;2(3):149. doi: 10.18034/ajhal.v2i3.703. [DOI] [Google Scholar]
  • 53.Coale AJ. Some relations among cultural traditions, nuptiality and fertility. Pak Dev Rev. 1991;30(4 Pt 1):397–406. doi: 10.30541/v30i4Ipp.397-410. [DOI] [PubMed] [Google Scholar]
  • 54.National Institute of Population Research Training . Bangladesh demographic and health survey 2017–18. Dhaka: NIPORT/ICF; 2020. [Google Scholar]
  • 55.Islam S, Islam MA, Padmadas SS. High fertility regions in Bangladesh: a marriage cohort analysis. J Biosoc Sci. 2010;42(6):705–719. doi: 10.1017/S0021932010000428. [DOI] [PubMed] [Google Scholar]
  • 56.Streatfield PK, Kamal N, Ahsan KZ, Nahar Q. Early marriage in Bangladesh. Asian Popul Stud. 2015;11(1):94–110. doi: 10.1080/17441730.2015.1012785. [DOI] [Google Scholar]
  • 57.Azmi R, Chowdhury MU, Sara HH, Biswas SC, Ahsan S, Mowri SS, Sultana R, Hasan T, Rashid SF. Factors influencing choices for early marriage in urban informal settlements of Bangladesh. Prog Dev Stud. 2021;21(3):298–320. doi: 10.1177/14649934211023003. [DOI] [Google Scholar]
  • 58.Sallis JF, Owen N, Fisher EB, Glanz K, Rimer BK, Viswanath K. Health behavior and health education, vol. null. 2008.
  • 59.Control CfD, Prevention. The social-ecological model: a framework for prevention. 2015.
  • 60.Kilanowski JFPRACF. Breadth of the socio-ecological model. J Agromedicine. 2017;22(4):295–297. doi: 10.1080/1059924X.2017.1358971. [DOI] [PubMed] [Google Scholar]
  • 61.Muhanguzi FK, Bantebya-Kyomuhendo G, Watson C. Social institutions as mediating sites for changing gender norms: nurturing girl’s resilience to child marriage in Uganda. Agenda. 2017;31(2):109–119. doi: 10.1080/10130950.2017.1362897. [DOI] [Google Scholar]
  • 62.Kumari N, Shekhar C. Trend and determinants of early marriage in Rajasthan: evidence from the national family health survey. Child Youth Serv Rev. 2022;(145):106746.
  • 63.Khan MN, Harris M, Loxton D. Modern contraceptive use following an unplanned birth in Bangladesh: an analysis of national survey data. Int Perspect Sex Reprod Health. 2020;46:77–87. doi: 10.1363/46e8820. [DOI] [PubMed] [Google Scholar]
  • 64.Khan MN, Harris ML, Loxton D. Does unintended pregnancy have an impact on skilled delivery care use in Bangladesh? A nationally representative cross-sectional study using Demography and Health Survey data. J Biosoc Sci. 2021;53(5):773–789. doi: 10.1017/S0021932020000528. [DOI] [PubMed] [Google Scholar]
  • 65.Griffith DA. Interpreting Moran eigenvector maps with the Getis-Ord Gi* Statistic. Prof Geogr. 2021;73(3):447–463. doi: 10.1080/00330124.2021.1878908. [DOI] [Google Scholar]
  • 66.Griffith DA, Agarwal K, Chen M, Lee C, Panetti EA, Rhyu K, Venigalla L, Yu X. Geospatial socio-economic/demographic data: the existence of spatial autocorrelation mixtures in georeferenced data—part I. Trans GIS. 2022;26(1):72–87. doi: 10.1111/tgis.12826. [DOI] [Google Scholar]
  • 67.Barros AJD, Hirakata VN. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol. 2003;3(1):21. doi: 10.1186/1471-2288-3-21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Tamhane AR, Westfall AO, Burkholder GA, Cutter GR. Prevalence odds ratio versus prevalence ratio: choice comes with consequences. Stat Med. 2016;35(30):5730–5735. doi: 10.1002/sim.7059. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Amin S. Programs to address child marriage: framing the problem. 2011.
  • 70.Henry EG, Lehnertz NB, Alam A, Ali NA, Williams EK, Rahman SM, Ahmed S, El Arifeen S, Baqui AH, Winch PJ. Sociocultural factors perpetuating the practices of early marriage and childbirth in Sylhet District, Bangladesh. Int Health. 2014;7(3):212–217. doi: 10.1093/inthealth/ihu074. [DOI] [PubMed] [Google Scholar]
  • 71.Government of the People’s Republic of Bangladesh . National action plan to end child marriage 2018–2013. Dhaka: Ministry of Women and Children Affairs; 2018. [Google Scholar]
  • 72.Parveen S, Sarkar NN. Report on child marriage free unions-plan Bangladesh. In: Plan Bangladesh. 2013. p. 7.
  • 73.Field E, Ambrus A. Early marriage, age of menarche, and female schooling attainment in Bangladesh. J Polit Econ. 2008;116(5):881–930. doi: 10.1086/593333. [DOI] [Google Scholar]
  • 74.Amin S, Saha JS, Ahmed JA. Skills-building programs to reduce child marriage in Bangladesh: a randomized controlled trial. J Adolesc Health. 2018;63(3):293–300. doi: 10.1016/j.jadohealth.2018.05.013. [DOI] [PubMed] [Google Scholar]
  • 75.Kamal SM. Decline in child marriage and changes in its effect on reproductive outcomes in Bangladesh. J Health Popul Nutr. 2012;30(3):317–330. doi: 10.3329/jhpn.v30i3.12296. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Uddin ME. Family socio-cultural values affecting early marriage between Muslim and Santal communities in rural Bangladesh. Int J Sociol Soc Policy. 2015;35(3/4):141–164. doi: 10.1108/IJSSP-06-2014-0046. [DOI] [Google Scholar]
  • 77.Acharya DR, Regmi P, Simkhada P, van Teijlingen E. Modernisation and changes in attitudes towards sex and relationships in young people. In: The dynamics of health in Nepal. 2015.
  • 78.Khan M, Ali M, Rahman M, Islam M, Karim M, Hasan M, Jesmin SS. Suicidal behavior among school-going adolescents in Bangladesh: findings of the global school-based student health survey. Soc Psychiatry Psychiatr Epidemiol. 2020;55(11):1491–1502. doi: 10.1007/s00127-020-01867-z. [DOI] [PubMed] [Google Scholar]
  • 79.Murshid NS, Irish A. Understanding teen sex in Bangladesh: results from global school health survey 2014. Child Youth Serv Rev. 2020;119:105619. doi: 10.1016/j.childyouth.2020.105619. [DOI] [Google Scholar]
  • 80.Islam MM, Khan MN, Rahman MM. Factors affecting child marriage and contraceptive use among Rohingya girls in refugee camps. Lancet Reg Health West Pac. 2021;12:100175. doi: 10.1016/j.lanwpc.2021.100175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Patoari MMH. Causes and effects of child marriage in Bangladesh: a case study at Halishahar, Chattogram, Bangladesh. Acad J Interdiscip Stud. 2020;9(2):162–172. doi: 10.36941/ajis-2020-0034. [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The dataset can download after registering with the MEASURE DHS. Necessary information is available at: http://dhsprogram.com/data/Using-DataSets-for-Analysis.cfm.

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