The authors have declared that no competing interests exist.
In many low- and middle-income countries (LMICs), including Bangladesh, socioeconomic inequalities in access to maternity care remain a substantial public health concern. Due to the paucity of research, we attempted to determine the factors affecting the facility delivery, quantify wealth-related inequality, and identify potential components that could explain the inequality.
We used the latest Bangladesh Demographic and Health Survey (BDHS 2017–18) data in this study. We utilized logistic regression to investigate the associated factors of facility delivery. The concentration curves (CC), concentration index (CIX) and decomposition of CIX techniques were used to analyze the inequality in-facility delivery.
Women living in the urban areas, age at first birth after (18–24 years ≥25 years), being overweight/obese, having secondary and higher-level education of the women and their husband, seeking four or more ANC, coming from more affluent households, and women with high enlightenment were significant determinants of facility delivery. The concentration curve was below the line of equality, and the relative concentration index (CIX) was 0.205 (p <0.001), indicating that women from wealthy groups were disproportionately more prevalent to facility delivery. The decomposition analysis reveals that wealth status of women (57.40%), age at first birth (10.24%), husband’s education (8.96%), husband’s occupation (7.35%), education of women (7.13%), women’s enlightenment (6.15%), residence (8.64%) and ANC visit (6.84%) are the most major contributors to the inequalities in utilizing facility delivery.
The study demonstrates a clear disparity in the use of facility delivery among Bangladeshi women; hence, immediate action is required to lower the inequalities, with a special emphasis on the contributing factors.
Every day, an estimated 810 maternal deaths occur worldwide, with 94% of these deaths occurring in low- and -middle-income nations (LMICs), despite the fact that these deaths are avoidable. The global maternal mortality ratio (MMR or the number of maternal deaths per 100,000 live births) decreased by approximately 38% between 2000 and 2017 [
The Millennium Development Goals (MDGs) 1990–2015 emphasized the critical need to halve infant and maternal mortality. This has significantly decreased the global maternal mortality rate (MMR) to 38% by 2015 [
In 2017, 295,000 women died because of complications related to pregnancy, low rates of facility-based delivery and a lack of skilled birth attendants (SBAs) during pregnancy or childbirth. These are the key reasons for high maternal death rates in these regions which could possibly be averted by shifting birth to a health facility [
In addition, unsafe home delivery practices are the cause of 35% of all antepartum, intrapartum and postpartum hemorrhage [
Investigating to what extent the socioeconomic inequalities exist in facility delivery might help in identifying the primary causes of these lower utilization and thus guide relevant stakeholders on how to alleviate these inequalities. Additionally, it is essential to examine the determinants of such inequality so that policymakers can develop evidence-based policy interventions. Few research analyzes the socioeconomic factors of inequalities in utilizing facility delivery among women in Bangladesh using a nationally representative sample from the 2017–2018 Bangladesh Demographic and Health Survey (BDHS) data. The principal objectives of this study are three-fold: (i) to analyze the factors of facility delivery in Bangladesh using the most recent round nationally representative survey; (ii) to measure the socio-economic inequality in the use of facility delivery; (iii) to identify the primary components that explain socioeconomic inequality in facility delivery through decomposition analysis.
The study utilized secondary data from the most recent Demographic and Health Surveys (DHS) (BDHS 2017–18). Demographic and Health Surveys are periodic surveys conducted to ascertain the population’s health status. A DHS survey provides a comprehensive picture of the study population including the overall status of mother and child health as well as a variety of other health care theme areas. The dataset has been kept free to access for the academics and researchers for their use from the internet. All protocols for DHS surveys were ethically approved by the Institutional Review Board and country-specific review bodies. The final report contains a full overview of the survey strategy, methodology, sampling, and questionnaires [
Place of delivery (0 = Home, 1 = Facility) was the outcome variable in our analyses. The place of delivery was considered ’facility’ if a woman gave their last birth in a government hospital, district hospital, maternal and child welfare center (MCWC), Upazila health complex, health and family welfare center, private hospital/clinic, private medical college/hospital, rural health center, basic health unit, primary health care center and outreach clinic, or in a clinic run by family planning association. It was considered ’home delivery’ if a woman gave birth at the respondent’s own or relative’s/neighbor’s home.
A systematic literature search was performed to identify the predictor variables. A list of data (respondents’ involvement in deciding on their healthcare, decision on large household purchases, and decision on visits to family or relatives) was used to measure respondents’ decision -making power. The enlightenment level of mothers was measured using educational attainment, newspaper/magazines reading, radio listening, and television watching while wife beating was quantified by aggregating responses from women and categorizing them as low or high. The following items were also used: “beating justified if wife goes out of home without telling husband”, “beating justified if wife neglects the children”, “beating justified if wife argues with husband”, “beating justified if wife refuses to have sex with husband”, and “beating justified if wife burns the food” for the analysis of the study. Using Principal Component Analysis (PCA), the factors were distilled into a more generalised set of weights that score between 0 and 100 to “women’s enlightenment” and “decision making power”. The standardized z-scores were used to differentiate between overall low, medium, and high scores [
SL. No. | Variables | Construction |
---|---|---|
1 | Place of Residence | Rural, Urban |
2 | Division | Barishal, Chattogram, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur, Sylhet |
3 | Age of the mother | 15–24, 25–34, 35–49 |
4 | Age at first Birth | <18, 18–24, ≥25 |
5 | Mother’s BMI | Underweight, Normal, Overweight/Obese |
6 | Mother’s Education | No education, Primary, Secondary, Higher |
7 | Mother’s Employment status | Working, Not Working |
8 | Number of ANC Visits | Nil, 1–3, ≥4 |
9 | Decision-Making Power | Low, High |
10 | Mother’s Enlightenment | Low, Medium, High |
11 | Wife Beating | Low, High |
12 | Husband’s Education | No education, Primary, Secondary, Higher |
13 | Husband’s Occupation | Agricultural, Business, Job/Services, Others |
14 | Household Wealth Status | Poorest, Poorer, Middle, Richer, Richest |
The background characteristics of the study population were summarized using descriptive statistics while weighted prevalence with 95% CI was presented. Unadjusted regression tests were used to determine the relationship between the predictor variable and the delivery location. After controlling for confounding variables, multivariate logistic regression was used to determine the net influence of predictor variables on the outcome variable (facility delivery vs. home delivery), where only the significant (at <0.05 level) variables in the unadjusted model were included in the adjusted regression model. The findings section presents the factors that are statistically significant at 0.05 level in the adjusted model. This article presents both unadjusted/crude odds ratios (cOR) and adjusted odds ratios (AOR) but for results interpreting authors only used the adjusted model. The statistical program Stata/MP 16 (StataCorp, College Station, Texas, USA) was used to conduct all analyses.
The concentration curve (CC) and concentration index (CIX) in their relative formulation (with no correction), were used to investigate the inequality in terms of utilizing facility delivery across analyzable socio-economic characteristics of the population (women) [
The CIX takes a value between − 1 and + 1. When the institutional delivery is equally distributed across socio-economic groups, CIX takes the value of 0. A positive value of CIX implies that the use of institutional delivery is concentrated among the higher socio-economic groups (pro-rich). When institutional delivery is distributed equally across socioeconomic classes, CIX equals zero. A positive number for CIX indicates that institutional delivery is more prevalent among the more affluent socioeconomic categories (pro-rich). In contrast, a negative value of CIX indicates that institutional delivery is primarily used by lower socioeconomic groups (pro-poor). The calculation of CIX was done by using “convenient covariance” formula described by O’Donnell et al. [
Here
The relative CIX was decomposed to determine the portion of inequality owing to the inequality in the underlying determinants. The findings were analyzed and interpreted using the technique described by Wagstaff et al. and O’Donnell et al. The contribution of each determinant of facility delivery to overall wealth-related inequality is determined as the product of the determinant’s sensitivity to facility delivery (elasticity) and the degree of wealth-related inequality in that determinant (CIX of determinant). The residual is the portion of the CIX that is not explained by the determinants.
Total (n/%) | Facility birth (%) | Poorest | Poorer | Middle | Richer | Richest | ||
---|---|---|---|---|---|---|---|---|
Barishal | 518 (10.8) | 41.9 | 8.5 | 5.4 | 6.2 | 3.9 | 2.1 | |
Chattogram | 796 (16.5) | 46.5 | 10.7 | 13.1 | 21.8 | 20.9 | 23.4 | |
Dhaka | 672 (14.0) | 60.3 | 11.9 | 13.7 | 22.1 | 32.6 | 44.3 | |
Khulna | 495 (10.3) | 62.2 | 8.5 | 12.6 | 15.0 | 10.9 | 8.7 | |
Mymensingh | 589 (12.2) | 42.4 | 9.6 | 12.1 | 6.6 | 6.3 | 3.6 | |
Rajshahi | 497 (10.3) | 55.9 | 15.9 | 20.4 | 13.5 | 13.2 | 5.7 | |
Rangpur | 550 (11.4) | 52.0 | 27.4 | 15.5 | 10.3 | 6.3 | 5.6 | |
Sylhet | 697 (14.5) | 41.8 | 7.4 | 7.2 | 4.5 | 5.9 | 6.6 | |
Urban | 1610 (33.4) | 62.5 | 2.7 | 5.1 | 10.6 | 25.4 | 56.2 | |
Rural | 3204 (66.6) | 43.6 | 15.4 | 20.6 | 23.8 | 24.2 | 16.0 | |
15–24 | 2563 (53.2) | 51.1 | 64.3 | 63.2 | 54.4 | 55.9 | 47.9 | |
25–34 | 1974 (41.0) | 49.1 | 32.3 | 33.3 | 39.6 | 40.4 | 46.9 | |
35–49 | 277 (5.8) | 45.1 | 3.3 | 3.5 | 6.0 | 3.7 | 5.3 | |
<18 | 2685 (55.8) | 41.5 | 65.2 | 59.5 | 51.5 | 44.5 | 35.9 | |
18–24 | 1865 (38.7) | 57.5 | 31.1 | 36.7 | 41.2 | 47.2 | 51.6 | |
≥25 | 264 (5.5) | 82.6 | 3.7 | 3.8 | 7.3 | 8.3 | 12.6 | |
Underweight | 762 (15.8) | 39.8 | 21.9 | 17.5 | 12.7 | 10.8 | 6.6 | |
Normal | 3005 (62.4) | 46.1 | 68.1 | 70.7 | 60.9 | 60.8 | 44.3 | |
Overweight/Obese | 1047 (21.7) | 68.4 | 10.0 | 11.8 | 26.4 | 28.3 | 49.1 | |
No education | 312 (6.5) | 3.2 | 7.4 | 4.3 | 4.3 | 2.2 | 1.7 | |
Primary | 1337 (27.8) | 17.8 | 39.6 | 27.1 | 16.1 | 15.1 | 7.3 | |
Secondary | 2308 (47.9) | 50.8 | 45.6 | 58.4 | 60.2 | 53.8 | 44.8 | |
Higher | 857 (17.8) | 28.3 | 7.4 | 10.2 | 19.5 | 28.9 | 46.2 | |
Not Working | 3046 (63.3) | 55.2 | 52.6 | 60.1 | 67.8 | 71.3 | 82.5 | |
Working | 1768 (36.7) | 40.9 | 47.4 | 39.9 | 32.2 | 28.7 | 17.5 | |
Nil | 570 (11.8) | 23.2 | 6.7 | 9.4 | 5.4 | 4.6 | 3.9 | |
1–3 visit | 2009 (41.7) | 39.6 | 50.4 | 46.4 | 39.1 | 34.3 | 19.0 | |
≥ 4 visits | 2235 (46.4) | 66.1 | 43.0 | 44.2 | 55.6 | 61.1 | 77.1 | |
No education | 676 (14.0) | 29.4 | 23.0 | 13.9 | 8.8 | 5.6 | 2.0 | |
Primary | 1637 (34.0) | 38.5 | 46.7 | 43.2 | 27.5 | 23.2 | 10.6 | |
Secondary | 1592 (33.1) | 54.1 | 22.6 | 30.6 | 42.3 | 44.7 | 36.8 | |
Higher | 909 (18.9) | 78.5 | 7.8 | 12.3 | 21.5 | 26.4 | 50.6 | |
Agricultural | 911 (18.9) | 33.5 | 31.1 | 25.5 | 16.7 | 9.0 | 2.3 | |
Business | 1051 (21.8) | 56.4 | 8.5 | 14.7 | 23.6 | 26.0 | 34.7 | |
Job/Services | 2268 (47.1) | 57.1 | 38.1 | 48.8 | 52.1 | 57.0 | 58.4 | |
Others | 584 (12.1) | 36.3 | 22.2 | 11.0 | 7.5 | 8.0 | 4.6 | |
Low | 2625 (54.5) | 50.6 | 27.0 | 56.7 | 52.8 | 51.1 | 54.9 | |
High | 2189 (45.5) | 49.2 | 26.5 | 43.3 | 47.2 | 48.9 | 45.1 | |
Low | 1870 (38.8) | 31.3 | 61.1 | 66.3 | 44.4 | 21.5 | 13.4 | |
Medium | 818 (17.0) | 46.3 | 29.6 | 18.1 | 17.7 | 21.0 | 17.5 | |
High | 2126 (44.2) | 67.7 | 12.7 | 15.6 | 37.9 | 57.5 | 69.1 | |
Low | 3946 (82.0) | 51.7 | 26.3 | 83.3 | 81.2 | 80.7 | 84.2 | |
High | 868 (18.0) | 42.1 | 29.5 | 16.7 | 18.8 | 19.3 | 15.8 |
Utilization of facility birth | ||||||
---|---|---|---|---|---|---|
Total (n/%) | Facility birth | Home birth | P-value | Prevalence (95% CI) | ||
<0.001 | ||||||
Barishal | 518 (10.8) | 41.9 | 58.1 | 39.32(33.75–45.17) | ||
Chattogram | 796 (16.5) | 46.5 | 53.5 | 45.49(42.46–48.55) | ||
Dhaka | 672 (14.0) | 60.3 | 39.7 | 58.27(55.43–61.05) | ||
Khulna | 495 (10.3) | 62.2 | 37.8 | 60.03(55.38–64.51) | ||
Mymensingh | 589 (12.2) | 42.4 | 57.6 | 39.14(34.57–43.91) | ||
Rajshahi | 497 (10.3) | 55.9 | 44.1 | 53.71(49.57–57.81) | ||
Rangpur | 550 (11.4) | 52.0 | 48.2 | 48.5(44.26–52.77) | ||
Sylhet | 697 (14.5) | 41.8 | 58.2 | 38.0(33.33–42.90) | ||
<0.001 | ||||||
Urban | 1610 (33.4) | 62.5 | 37.5 | 63.37(60.65–66.01) | ||
Rural | 3204 (66.6) | 43.6 | 56.4 | 44.99(43.36–46.62) | ||
0.106 | ||||||
15–24 | 2563 (53.2) | 51.1 | 48.9 | 51.42(49.48–53.34) | ||
25–34 | 1974 (41.0) | 49.1 | 50.9 | 48.48(46.29–50.69) | ||
35–49 | 277 (5.8) | 45.1 | 54.9 | 42.08(36.20–48.18) | ||
<0.001 | ||||||
<18 | 2685 (55.8) | 41.5 | 58.5 | 41.80(39.97–43.66) | ||
18–24 | 1865 (38.7) | 57.5 | 42.5 | 57.53(55.25–59.78) | ||
≥25 | 264 (5.5) | 82.6 | 17.4 | 80.67(75.19–85.18) | ||
<0.001 | ||||||
Underweight | 762 (15.8) | 39.8 | 60.2 | 40.27(36.76–43.88) | ||
Normal | 3005 (62.4) | 46.1 | 53.9 | 46.02(44.26–47.80) | ||
Overweight/Obese | 1047 (21.7) | 68.4 | 31.6 | 66.87(63.96–69.64) | ||
<0.001 | ||||||
No education | 312 (6.5) | 24.4 | 75.6 | 26.01(21.44–317) | ||
Primary | 1337 (27.8) | 31.9 | 68.1 | 31.99(29.53–34.56) | ||
Secondary | 2308 (47.9) | 52.9 | 47.1 | 52.81(50.80-54-82) | ||
Higher | 857 (17.8) | 79.5 | 20.5 | 78.41(75.45–81.10) | ||
<0.001 | ||||||
Not Working | 3046 (63.3) | 55.2 | 44.8 | 54.86(53.09–56.62) | ||
Working | 1768 (36.7) | 40.9 | 59.1 | 40.76(38.49–43.07) | ||
<0.001 | ||||||
Nil | 570 (11.8) | 23.2 | 76.8 | 23.53(20.21–27.21) | ||
1–3 visit | 2009 (41.7) | 39.6 | 60.4 | 40.07(37.97–42.20) | ||
≥ 4 visits | 2235 (46.4) | 66.1 | 33.9 | 65.46(63.45–67.42) | ||
<0.001 | ||||||
No education | 676 (14) | 29.4 | 70.6 | 30.03(26.68–33.61) | ||
Primary | 1637 (34) | 38.5 | 61.5 | 38.05(35.73–40.42) | ||
Secondary | 1592 (33.1) | 54.1 | 45.9 | 54.61(52.19–57.01) | ||
Higher | 909 (18.9) | 78.5 | 21.5 | 77.69(74.80–80.33) | ||
<0.001 | ||||||
Agricultural | 911 (18.9) | 33.5 | 66.5 | 34.48(31.52–37.57) | ||
Business | 1051 (21.8) | 56.4 | 43.6 | 57.41(54.35–60.42) | ||
Job/Services | 2268 (47.1) | 57.1 | 42.9 | 56.44(54.39–58.48) | ||
Others | 584 (12.1) | 36.3 | 63.7 | 35.42(31.70–39.31) | ||
<0.001 | ||||||
Poorest | 1061(22.04) | 11.35 | 32.71 | 26.44(23.83–29.24) | ||
Poorer | 996(20.69) | 15.34 | 26.03 | 37.04(34.11–40.07) | ||
Middle | 875(18.18) | 18.21 | 18.14 | 49.92(46.72–53.12) | ||
Richer | 958(19.90) | 24.03 | 15.77 | 60.06(56.96–63.08) | ||
Richest | 924(19.19) | 31.06 | 7.35 | 79.33(76.53–81.88) | ||
0.311 | ||||||
Low | 2625 (54.5) | 50.6 | 49.4 | 49.6(47.69–51.520) | ||
High | 2189 (45.5) | 49.2 | 50.8 | 49.83(47.75–51.91) | ||
<0.001 | ||||||
Low | 1870 (38.8) | 31.3 | 68.7 | 30.87(28.79–33.03) | ||
Medium | 818 (17.0) | 46.3 | 53.7 | 46.80(43.42–50.21) | ||
High | 2126 (44.2) | 67.7 | 32.3 | 66.67(64.66–68.63) | ||
<0.001 | ||||||
Low | 3946 (82.0) | 51.7 | 48.3 | 50.93(49.37–52.49) | ||
High | 868 (18.0) | 42.1 | 57.9 | 44.24(40.99–47.54) |
The results from regression analysis presented in (
Divisions | UOR (95% CI) | P-Value | AOR (95% CI) | P-Value | |
---|---|---|---|---|---|
Barishal (RC) | 1.00 | 1.00 | |||
Chattogram | 1.20 (0.96–1.51) | 0.102 | 0.95 (0.73–1.23) | 0.681 | |
Dhaka | 2.10 (1.67–2.66) | <0.001 | 1.26 (0.95–1.65) | 0.103 | |
Khulna | 2.28 (1.78–2.94) | <0.001 | 1.78 (1.33–2.37) | <0.001 | |
Mymensingh | 1.02 (0.81–1.30) | 0.853 | 1.05 (0.80–1.39) | 0.713 | |
Rajshahi | 1.76 (1.37–2.26) | <0.001 | 1.63 (1.22–2.16) | 0.001 | |
Rangpur | 1.50 (1.18–1.91) | 0.001 | 1.55 (1.17–2.06) | 0.002 | |
Sylhet | 0.99 (0.79–1.25) | 0.961 | 1.02 (0.78–1.33) | 0.911 | |
Urban | 2.16 (1.91–2.44) | <0.001 | 1.18 (1.01–1.37) | 0.037 | |
Rural (RC) | 1.00 | 1.00 | |||
15-24(RC) | 1.00 | 1.00 | |||
25–34 | 0.92 (0.82–1.04) | 0.188 | 0.75 (0.65–0.86) | <0.001 | |
35–49 | 0.79 (0.61–1.01) | 0.059 | 0.68 (0.49–0.92) | 0.014 | |
<18 (RC) | 1.00 | 1.00 | |||
18–24 | 1.9 (1.69–2.15) | <0.001 | 1.42 (1.24–1.64) | <0.001 | |
≥25 | 6.67 (4.81–9.26) | <0.001 | 3.82 (2.62–5.59) | <0.001 | |
Underweight (RC) | 1.00 | 1.00 | |||
Normal | 1.30 (1.10–1.52) | 0.002 | 1.04 (0.86–1.25) | 0.690 | |
Overweight/Obese | 3.28 (2.70–3.98) | <0.001 | 1.77 (1.41–2.22) | <0.001 | |
No education | 1.00 | 1.00 | |||
Primary | 1.46 (1.10–1.93) | 0.009 | 1.06 (0.77–1.46) | 0.733 | |
Secondary | 3.49 (2.66–4.57) | <0.001 | 1.50 (1.07–2.09) | 0.016 | |
Higher | 12.02 (8.84–16.33) | <0.001 | 2.16 (1.44–3.23) | <0.001 | |
Not Working (RC) | 1.00 | 1.00 | |||
Working | 0.56 (0.50–0.63) | <0.001 | 0.67 (0.58–0.77) | <0.001 | |
Nil (RC) | 1.00 | 1.00 | |||
1–3 visit | 2.17 (1.75–2.69) | <0.001 | 1.71 (1.36–2.17) | <0.001 | |
≥ 4 visits | 6.48 (5.23–8.02) | <0.001 | 3.40 (2.68–4.31) | <0.001 | |
No education (RC) | 1.00 | 1.00 | |||
Primary | 1.50 (1.24–1.82) | <0.001 | 1.14 (0.92–1.41) | 0.229 | |
Secondary | 2.83 (2.33–3.43) | <0.001 | 1.32 (1.06–1.65) | 0.014 | |
Higher | 8.78 (6.98–11.04) | <0.001 | 2.23 (1.69–2.92) | <0.001 | |
Agricultural | 0.88 (0.71–1.10) | 0.263 | 0.96 (0.75–1.22) | 0.741 | |
Business | 2.27 (1.85–2.80) | <0.001 | 1.17 (0.92–1.48) | 0.199 | |
Job/Services | 2.34 (1.94–2.82) | <0.001 | 1.21 (0.97–1.49) | 0.087 | |
Others (RC) | 1.00 | 1.00 | |||
Poorest (RC) | 1.00 | 1.00 | |||
Poorer | 1.70(1.41–2.05) | <0.001 | 1.28(1.04–1.57) | 0.021 | |
Middle | 2.89(2.39–3.50) | <0.001 | 1.61(1.29–2.01) | <0.001 | |
Richer | 4.39(3.63–3.30) | <0.001 | 2.00(1.58–2.53) | <0.001 | |
Richest | 12.18(9.84–15.05) | <0.001 | 3.87(2.54–4.41) | <0.001 | |
Low (RC) | 1.00 | Not Adjusted in the Final Model | |||
High | 0.94 (0.84–1.06) | 0.309 | |||
Low (RC) | 1.00 | 1.00 | |||
Medium | 1.89 (1.60–2.24) | <0.001 | 1.11 (0.92–1.34) | 0.273 | |
High | 4.60 (4.02–5.26) | <0.001 | 1.56 (1.32–1.85) | <0.001 | |
Low | 1.47 (1.27–1.71) | <0.001 | 1.07 (0.9–1.27) | 0.442 | |
High (RC) | 1.00 | 1.00 |
RC stands for Reference Category, ANC for Antenatal Care, BMI means to Body Mass Index, Divisions means Administrative Regions.
Variables | Elasticity | CIX | Contribution to overall |
||
---|---|---|---|---|---|
Absolute contribution | Percentage contribution | ||||
Barishal (RC) | |||||
Chattogram | -0.012 | 0.075 | -0.001 | -0.450 | |
Dhaka | 0.045 | 0.259 | 0.009 | 3.630 | |
Khulna | 0.041 | 0.036 | 0.001 | 0.717 | |
Mymensingh | -0.003 | -0.204 | 0.001 | 0.264 | |
Rajshahi | 0.041 | -0.072 | -0.003 | -1.419 | |
Rangpur | 0.035 | -0.302 | -0.011 | -6.202 | |
Sylhet | 0.001 | -0.111 | -0.001 | -0.005 | |
Subtotal | |||||
Rural (RC) | |||||
Urban | 0.029 | 0.376 | 0.011 | 4.246 | |
Subtotal | |||||
15–24 years (RC) | |||||
25–34 years | -0.083 | 0.022 | -0.002 | -0.880 | |
35–49 years | -0.015 | -0.008 | 0.001 | 0.056 | |
Subtotal | |||||
No education (RC) | |||||
Primary | -0.001 | -0.259 | 0.002 | 0.906 | |
Secondary | 0.110 | 0.040 | 0.004 | 2.163 | |
Higher | 0.078 | 0.420 | 0.013 | 4.065 | |
Subtotal | |||||
<18 years (RC) | |||||
18–24 years | 0.086 | 0.114 | 0.010 | 4.806 | |
≥ 25 years | 0.044 | 0.343 | 0.015 | 5.436 | |
Subtotal | |||||
Normal (RC) | |||||
Underweight | -0.006 | -0.195 | 0.001 | 0.546 | |
Overweight/Obese | 0.062 | 0.293 | 0.012 | 5.628 | |
Subtotal | |||||
Not working (RC) | |||||
Working | -0.097 | -0.171 | 0.017 | 5.096 | |
Subtotal | |||||
Nil (RC) | |||||
1–3 visit | 0.188 | -0.103 | -0.019 | -9.502 | |
≥ 4 visits | 0.278 | 0.137 | 0.029 | 7.622 | |
Subtotal | |||||
No education (RC) | |||||
Primary | 0.008 | -0.199 | -0.002 | -0.803 | |
Secondary | 0.041 | 0.123 | 0.005 | 2.456 | |
Higher | 0.070 | 0.420 | 0.019 | 7.307 | |
Subtotal | |||||
Others (RC) | |||||
Agricultural | 0.004 | -0.332 | -0.001 | -0.686 | |
Business | 0.036 | 0.204 | 0.007 | 3.609 | |
Job/Services | 0.083 | 0.109 | 0.009 | 4.429 | |
Subtotal | |||||
Poorest (RC) | |||||
Poor | 0.031 | -0.368 | -0.011 | -5.561 | |
Middle | 0.058 | 0.034 | 0.002 | 0.963 | |
Rich | 0.095 | 0.431 | 0.035 | 13.028 | |
Richest | 0.148 | 0.817 | 0.103 | 48.969 | |
Subtotal | |||||
Low (RC) | |||||
High | -0.028 | 0.020 | -0.001 | -0.279 | |
Subtotal | |||||
Low (RC) | |||||
Medium | 0.021 | 0.014 | 0.001 | 0.139 | |
High | 0.095 | 0.306 | 0.019 | 6.010 | |
Subtotal | |||||
High (RC) | |||||
Low | -0.015 | 0.019 | -0.001 | -0.142 | |
Subtotal | |||||
CI: Concentration Index.
The average facility delivery per household wealth category is shown in
Using nationally representative surveys in Bangladesh, this study found several socioeconomic inequalities in utilizing facility delivery. According to the findings of our analysis, women with a higher level of education are more likely to utilize facility delivery while women with no education are less likely to do so. This connection between education and facility delivery seems obvious, as educated people are more aware of personal health issues, have stronger self-efficacy, and adhere to self-care [
Besides educational attainment, our study also found that women in urban areas of Bangladesh are more likely to utilize facility delivery than women living in the rural areas. The plausible explanations could be that urban women are more likely to be educated and as a result, are more health-conscious and have better access to health care services than rural women. This would result in a rise of ANC visits which could further facilitate institutional deliveries. In contrast, rural women were more likely to birth at home owing to their lower socioeconomic status and poor number of ANC visits. However, due to governments and different stakeholders multiple measures including financial and other incentives as well as the construction of rural birth centers, inequalities in the use of facility delivery is decreasing noticeably [
It might the fact that low-income households usually spend the lion’s share of their budget on food resulting in difficult trade-offs regarding spending on education and health care. The poor class and even those in the middle-income bracket cannot afford the cost of delivery at the government hospitals let alone at private clinic. Consequently, women are compelled to give birth at home with the help of a Traditional Birth Attendant(TBA) [
Therefore, national policies targeting to income generation and poverty reduction are likely to have a positive effect on improving the utilization of facility delivery in Bangladesh. Decision-making power is an important factor for maternal health related issues which align with the results of some previous studies [
However, according to several studies, joint choice, or effective communication between partners increases facility delivery usage more than individual decisions [
Due to the fact that only 39.7% of deliveries take place in hospitals in Bangladesh [
This study’s strengths and limitations were acknowledged with caution. Firstly, this study used the recent round nationally representative survey data therefore the findings could be generalizable to the entire population of Bangladesh. Secondly, standard statistical methods have been employed to estimate the prevalence and the decomposition of inequality measures. The inherent limitations of a cross-sectional study design limited our ability to infer causality. Some potential factors such as distance to the nearest institution with a birthing facility, cost (direct or indirect) of facility delivery, waiting time, behavior of the healthcare practitioners, and the availability of transportation facilities and awareness of the importance of safe delivery were excluded from this analysis because they were not included in the original DHS data.
Even though the rate if facility delivery births in Bangladesh is low, a considerable percentage of women continue to give birth at home. There are inequalities in the utilization of facility delivery exist in urban and rural areas in Bangladesh, as well as between the wealth class of households. The findings of the study demand the revision of strategies and programs to eliminate the disparities and improve facility delivery service utilization. The factors that are closely correlated to utilizing the facility delivery are educational attainment, number of ANC visits, place of residence, women’s ability to make health-related decisions. Based on these insights, the health policymakers need to consider implementing special intervention programs aiming to reduce inequality and improve the utilization of facility delivery in Bangladesh. This would tremendously contribute to achieving Sustainable Development Goals (SDGs) in line with the government’s own visions.
The authors are grateful to the Demographic and Health Surveys (DHS) Program for providing BDHS data accessibility for conducting the study. The authors also want to give a big thank to Umesh Prasad Bhusal (
PONE-D-22-13608Socioeconomic Inequalities in Utilizing Facility Delivery in Bangladesh: A Decomposition Analysis Using Nationwide 2017-2018 Demographic and Health Survey DataPLOS ONE
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Reviewer #1: Socioeconomic Inequalities in Utilizing Facility Delivery in Bangladesh: A Decomposition Analysis Using Nationwide 2017-2018 Demographic and Health Survey Data.
The authors have chosen appropriate topic for the study as high maternal mortality is one of the challenge before achieving sustainable development. This can be reduced by pushing maternal healthcare and one among them is institutional delivery/facility based delivery. Authors’ efforts are highly appreciated but there are some suggestions for the improvement of the quality of the paper along with expectation about newness in the paper-
1. The research study aimed to examine socio-economic inequality in facility delivery in Bangladesh using latest available cross sectional data for 2017/18. It’s fine. But complexity of framework is missing somewhere as authors included demographic to socio-economic, regional to empowerment & autonomy to programme factors. So, broadly we can say demand and supply side factors. Even with the demand side factors, authors should follow appropriate framework and including so many dimensions seems not needed.
2. In the inequality measurement section, the authors have elaborated much about the advantages of CI and CC, as it is well known and accepted measure of inequality along income/wealth score. The authors are advices to compress it.
3. In line number 233/234, ‘The CIX accepts values between one and one’- this should be corrected as ‘The CIX accepts values between minus one and plus one’.
4. Why two categories for decision making power and wife beating whereas three category for mother’s enlightenments.
5. Line number 195-239, not needed too much elaboration as well as repetition. Be concise with more citation and studies pertaining to it.
6. In table 2; check for residence, decision making power, mother entitlement, wife beating etc as they are some mistake when analysing distribution of facility based delivery on wealth index category.
7. In table 3, check for wealth status.
8. It table 4, possibly, authors have missed to add the information about the variable on which the model is adjusted for as the cOR and AOR seems having differential magnitude of relative risk. So, it would be really helpful for the readers, if authors can add about the adjustment made into the logistic regression.
9. “While Figure 2 illustrates the inequalities in facility delivery based on one's wealth status. Due to the fact that the concentration curve is above the line of equality, facility delivery was disproportionately higher among women from affluent groups. The relative CIX value for facility delivery is shown in Table 5. A positive estimated CIX in suggests that facility delivery was more concentrated among wealthy women than among poorer women”- The statement made based on the figure 2 is incorrect. The CC appears above the line of equality, when the outcome of interest is high among the poor. In the figure 2, the CC is below the line of equality. Even in table 5, the sign of CI value is positive. So, be careful when interpreting such inequality measure. So, please do correct it.
10. Authors are advised to interpret the results of table 5 [decomposition of CI]as it is missing in the manuscript.
11. Figure 1 needs appropriate formatting, look it carefully.
12. Line 356-360 pertaining to women work status and place of delivery. In case of women work, type of work regulates more to access to services rather working or not working status. Think the women who are working but are engaged in agricultural activities to those who are in white collar jobs. So, the findings on those lines are also needs corroboration.
13. Discussion section mostly revolved around education and economic status. Authors should have a thought on it and restructure it.
14. In the acknowledgement section, Lorenz curve is typed which we draw in case of Gini based inequality but when we use Concentration Index, the curve is concentration curve.
14. Proper editing is also needed.
Reviewer #2: The authors attempt to study an important issue in the context of developing countries. This study comprehensively analyzed the factors determining the use of facility delivery in Bangladesh. However, before its acceptance, this study needed major revision to make it more apparent to the readers.
The author provides a strong background for the study; however, it needs to add the following points in this section.
1. The authors should emphasize the relevance of this study in the context of Bangladesh.
2. The evidence of the socioeconomic inequality in the utilization of facility delivery from the previous study is missing in the background. The authors needed to provide an explicit summary of what is already known from the earlier studies and the additional contribution of the current research on this topic.
Data sources:
In the data section, the author needed to provide some information about the data, such as the number of households, the number of women interviewed, and the sample size.
The outcome variable study period is missing from the manuscript. The reference period of the outcome variable must be mentioned in detail of the outcome variable, whether facility delivery is analyzed for the last birth or the birth in some specific years.
Explanatory variable:
The justification for using the explanatory variable in the study is missing. Particularly how the mother's age and wife's Beating are associated with the delivery facility's utilization, these variables are also not interpreted in the results and discussion sections. In addition to the controlled variables, the model can control for any complications before the delivery if any information is given in the data.
In the methods section, a few lines are repeated; for example, lines 211-214 and 221-124 are repeated and also needed to make clearer these sentences.
In the equations, details of each component should be cleared to the readers; for example, what's mean is μ.
Results:
The authors used unadjusted logistic regression, but there is no single sentence about the results in the result section. Moreover, the results from the models and decompositions needed to give more interpretation.
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Socioeconomic Inequalities in Utilizing Facility Delivery in Bangladesh: A Decomposition Analysis Using Nationwide 2017-2018 Demographic and Health Survey Data
PONE-D-22-13608R1
Dear Md. Ashfikur Rahman,
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Reviewer #1: The authors have addressed all the comments and suggestions. The background to data and method sections have improved with the inputs provided by the reviewers. The discussion section has been also strengthened with the required inputs.Now No other comments at this stage.
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PONE-D-22-13608R1
Socioeconomic Inequalities in Utilizing Facility Delivery in Bangladesh: A Decomposition Analysis Using Nationwide 2017-2018 Demographic and Health Survey Data
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