Keywords
mother, baby, care, continuum
Access to maternity care services from qualified health care professionals during pregnancy, labor, and the postnatal period is crucial for the survival and health of the mother and baby. This study aimed to assess the prevalence and factors associated with maternal continuum of care completion in Myanmar.
This study used secondary data from the 2015-2016 Myanmar Demographic and Health Survey. The study included 1455 women aged < 2 years. Bivariate analysis was conducted using one-way ANOVA and chi-square test of independence to explore the unadjusted association between the dependent variable and each independent variable. Multivariable analysis was conducted with ordinal logistic regression to explore the factors associated with the maternity continuum of care completion. Statistical significance was set at p < 0.05.
The prevalence of complete maternity continuum of care was 55.2% (n = 803), partial maternity continuum of care was 31.9% (n = 464), and no maternity continuum of care was 12.9% (n = 188) respectively. After adjusting for covariates, maternal education, region, place of residence, total parity, husband’s education, husband’s occupation, money needed for treatment, timing of first antenatal check, tetanus injection, blood pressure, urine sample taken, blood sample taken, given or bought iron tablets/syrup, and drugs for intestinal parasites during pregnancy were significantly associated with maternal continuum of care.
The prevalence of a complete maternity continuum of care in Myanmar was suboptimal. Important factors include maternal education, urban residency, region, total parity, husbands’ education, and occupation. Improving outcomes in the area requires addressing obstacles such as financial limitations, access to quality health services, and distance. Improving access to maternal healthcare services should be the main goal of policy initiatives, especially for rural areas, lower education levels, and low-income families.
mother, baby, care, continuum
Access to maternity care services from qualified health care professionals during pregnancy, labor, and the postnatal period is crucial for the survival of the mother and baby. To reduce morbidity and mortality, the use of maternity care services within a continuum of care approaches is directly related. There are three main aspects of maternity care services: (i) prenatal care (ANC), (ii) skilled birth attendance (SBA), and (iii) postnatal care (PNC).1
Continuum of maternity care refers to complete and integrated care that women receive from qualified healthcare professionals throughout their pregnancy, labor, and postpartum period.2 This is the collection of high-impact treatment and care for mother and child survival along the care continuum and a method that is intended to keep track of the standard of care given to expectant mothers and their babies.3 It is a simple, cost-effective, and low-technology intervention method that can greatly lower the majority of avoidable maternal and newborn deaths and optimize the ability of both mothers and newborns to experience the best possible quality of life in terms of health.4
There are two key dimensions that should be emphasized for the continuum of maternity care: Time and Place and level of care. The time dimension highlights the importance of linkages among the packages of maternity care service provision over time during pregnancy, childbirth, and the postpartum period. The place or level of care dimension includes home and primary, secondary, and tertiary levels of care in healthcare deliveries.2
Antenatal care (ANC) offers a chance to determine and lower the risk factors for mothers who are at a high risk of obstetric problems. ANC is the initial point of contact between expectant mothers and health services. It shapes their attitudes toward seeking care and connects them to a system of referrals when necessary. Unfavorable pregnancy outcomes are more likely for expectant mothers who do not receive antenatal care (ANC) or receive insufficient visits. Attending ANC increases a woman’s likelihood of giving birth in a medical facility and receiving postnatal care (PNC).5
Skilled attendants can effectively lower maternal mortality and efficiently manage birth problems.2 Early access to emergency obstetric care and high-quality newborn care, including resuscitation, which can be guaranteed by institutional delivery, are essential during labor and the first few days after giving birth to prevent maternal and perinatal mortality.6 In a similar vein, the postnatal period is critical to the health of the mother and her child, and PNC assists in identifying and addressing postpartum issues.7 In underdeveloped nations, postpartum fatalities account for over 60% of all maternal deaths.8 Reaching 90% of mothers and newborns with PNC will prevent between 10% and 27% of neonatal mortality.9
While routine ANC visits promote institutional delivery and PNC services, they do not always avoid difficulties at birth or during the postnatal period. Additionally, it can increase survival rates and guarantee access to emergency obstetric treatment.10 To improve triage and timely referral of high-risk women, the WHO advised eight antenatal visits in 2016 as opposed to four visits based on the focused ANC (FANC) model.11 This recommendation was made in light of data showing that the FANC model was likely linked to a higher incidence of cesarean sections and perinatal mortality.12
Globally, 295,000 women die from pregnancy- and childbirth-related complications.13 In South-East Asia region, almost one-third of all maternal and child fatalities worldwide occur each year.14 In 2013, the maternal mortality rate was 140 cases per 100,000 live births. Many studies have indicated that as many as 250 per 100,000 live births occurred in Myanmar, making it one of the South-East Asian countries with the highest MMR. In order For Myanmar to meet the Sustainable Development Goal of having a maternal mortality rate of less than 70 per 100,000 live births by 2030, World Health Organization (WHO) recommends that the country increase its yearly decrease rate from 3.7% to 5.5%.5
Nationwide surveys have shown that maternal deaths in Myanmar were most frequent during the postnatal period (42 days after delivery) and among mothers with home births. The most common cause of death is postpartum hemorrhage, which indicates that the immediate postpartum period is the most vulnerable period for Myanmar mothers.5
In Myanmar, the recommended number of antenatal visits in Myanmar is yet to be achieved, with only 18% of mothers receiving eight visits, while 59% received four. Significant maternal deaths have been documented in recent years by the Maternal Death Surveillance and Response System of Myanmar, even in cases in which women had four prenatal appointments. The results of the study indicated that even in women who had sufficient prenatal care, there was still a chance of maternal mortality.5 Therefore, it is important to determine whether four prenatal visits are necessary to ensure institutional delivery and PNC. This study focused on women and assessed the continuum of maternal health care utilization.
This study defined the continuum of care (CoC) for maternal health as the continuous utilization of three essential maternal services: antenatal care, skilled birth attendance at either home or health facility, and early postnatal care within 48 hours for the mother. Women who had received all three services were considered to have received complete care. Those who received only one or two services were considered to receive partial care, and those who did not use all three required maternity services were considered to have no maternal CoC at all.
Secondary data from the to 2015-2016 Myanmar Demographic and Health Survey (MDHS) were used, which was a cross-sectional and nationally representative household survey conducted in 14 States and Regions and the Nay Pyi Taw territory. A two-stage stratified sampling approach was employed for the MDHS survey. Every state and region was stratified into rural and urban areas. A total of 422 clusters, comprising 123 urban and 319 rural areas, were selected to represent 30 sample strata from 14 States and Regions, and the Nay Pyi Taw territory. Thirty households were selected from each cluster in the MDHS survey, yielding 13,260 households. A total of 12,500 households with 12,885 women of reproductive age were surveyed.
The Birth’s Record (BR) file from the MDHS dataset was used and included all women who had children under 2 years old for this analysis. This population was chosen because the MDHS only collected information about postnatal check-ups from mothers giving birth to their last child two years preceding the survey to minimize recall bias. A total of 1455 participants were included in this analysis.
Dependent variable: The maternity continuum of care (CoC) was used as the dependent variable. We computed maternity CoC based on three criteria: receiving at least four antenatal care visits, delivery with skilled birth attendants (doctors, nurses, midwives, and lady health visitors), and receiving postnatal care within 48 hours after delivery. If a mother received all of these services, we categorized them as a complete maternal continuum of care. If a mother received only one or two services, we categorized it as a partial maternal continuum of care. If a mother did not receive all of these services, we categorized them as having no maternal continuum of care.
Independent variables: Sociodemographic characteristics (age, education, occupation, region, place of residence, total parity, status of wanted or unwanted for last child, and relationship to the household head), husband characteristics and decision making on respondents’ health care access (husband’s education, husband’s occupation, and decision making on respondents’ health care access), getting medical help (getting permission to go, getting money needed for treatment, distance to health facility, and not wanting to go alone), and components of antenatal care (timing of first antenatal check, tetanus injections, blood pressure taken, urine sample taken, blood sample taken, counseling about pregnancy complications, given or bought iron tablets/syrup, and drugs for intestinal parasites during pregnancy) were used as independent variables for this analysis.
SPSS version 25 was used for data analysis in this study. First, bivariate analysis was conducted to determine the associations between the dependent variable and each independent variable using one-way ANOVA and the chi-square test of independence. Multivariable analysis with ordinal logistic regression was performed to explore the factors associated with the maternity continuum of care completion. Model 1 was adjusted for sociodemographic characteristics of participants. Model 2 was adjusted for sociodemographic characteristics of the participants and husband characteristics and decision making on respondents’ health care access. Model 3 was adjusted for sociodemographic characteristics of the participants, husband characteristics, decision making on respondents’ health care access, and getting medical help. Model 4 was adjusted for sociodemographic characteristics of the participants, husband characteristics, and decision-making on respondents’ health care access, getting medical help, and components of antenatal care. The quality of the model fit was evaluated by examining the values of Cox and Snell R-Square and Nagelkerke R-Square to find the best model with suitable predictors. Adjusted odds ratios (AOR) with a 95% confidence interval (CI) were computed, and a significance level of 0.05 was considered statistically significant.
The original 2015-2016 Myanmar Demographic and Health Survey was carried out after obtaining ethical approval from the Ethics Review Committee on Medical Research, including Human Subjects in the Department of Medical Research. The secondary data used in this analysis were obtained from the Demographic and Health Survey (DHS) Program. All DHS respondents provide informed consent prior to data collection in which consent was obtained in written form.
This study included 1455 participants in the final analysis. Among the participants, the prevalence of complete maternity continuum of care was 55.2% (n = 803), partial maternity continuum of care was 31.9% (n = 464), and no maternity continuum of care was 12.9% (n = 188) respectively. Table 1 describes the sociodemographic characteristics of the participants, based on the maternal continuum of care. There were statistically significant associations between maternal continuum of care and education, occupation, region, place of residence, total parity, status of wanted or unwanted for the last child, and relationship to the household head, whereas the age of the participants showed no association. The Mothers with higher educational attainment had a higher percentage of complete maternity continuum of care (86.1%), while the illiterate mothers had a higher percentage of no maternal continuum of care (36.8%). Regarding occupation of the participants, the complete maternity continuum of care was the highest among the mothers working as clerks and skilled manuals (80%), followed by professional/technical/managerial positions (68.5%), and sales (67.4%). In terms of region, the mothers living in socioeconomically developed areas, such as Yangon, Mandalay, and Naypyitaw, reported high percentages of complete maternal continuum of care (74.2%, 69%, and 67.6%, respectively), while the lowest percentage of complete maternal continuum of care was reported in Chin State (27.3%). Related to the place of residence, the mothers from the urban areas had a higher percentage of complete maternity continuum of care (79.9%) than the mothers from the rural areas (46.6%). Moreover, the primiparous mothers (66.4%) and the mothers who wanted their last child (56%) showed higher percentage of complete maternity continuum of care. As for the relationship to the household head, the mothers who were daughters (62.9%) and daughters-in-law (61.7%) had high percentage of complete maternity continuum of care.
| Variables | Maternal continuum of care | Total (n = 1455) | P-valuea | ||
|---|---|---|---|---|---|
| Complete (n = 803) | Partial (n = 464) | No (n = 188) | |||
| n (%) | n (%) | n (%) | n (%) | ||
| Age (Mean ± SD) | 29.69 ± 6.285 | 29.13 ± 6.442 | 29.05 ± 6.603 | 29.43 ± 6.379 | 0.221b |
| Education | <0.001 | ||||
| Primary | 288 (46.7) | 233 (37.8) | 96 (15.6) | 617 (100.0) | |
| Secondary | 348 (66.0) | 151 (28.7) | 28 (5.3) | 527 (100.0) | |
| Higher | 118 (86.1) | 19 (13.9) | 0 (0.0) | 137 (100.0) | |
| No education | 49 (28.2) | 61 (35.1) | 64 (36.8) | 174 (100.0) | |
| Occupation | <0.001 | ||||
| Not working | 360 (55.2) | 202 (31.0) | 90 (13.8) | 652 (100.0) | |
| Professional/Technical/Managerial | 37 (68.5) | 15 (27.8) | 2 (3.7) | 54 (100.0) | |
| Clerk and skilled manual | 60 (80.0) | 13 (17.3) | 2 (2.7) | 75 (100.0) | |
| Sales | 151 (67.4) | 57 (25.4) | 16 (7.1) | 224 (100.0) | |
| Agricultural | 57 (33.3) | 81 (47.4) | 33 (19.3) | 171 (100.0) | |
| Services and unskilled manual | 138 (49.5) | 96 (34.4) | 45 (16.1) | 279 (100.0) | |
| Region | <0.001 | ||||
| Kachin | 58 (62.4) | 30 (32.3) | 5 (5.4) | 93 (100.0) | |
| Kayah | 75 (59.5) | 42 (33.3) | 9 (7.1) | 126 (100.0) | |
| Kayin | 59 (53.2) | 31 (27.9) | 21 (18.9) | 111 (100.0) | |
| Chin | 18 (27.3) | 35 (53.0) | 13 (19.7) | 66 (100.0) | |
| Mon | 50 (61.7) | 20 (24.7) | 11 (13.6) | 81 (100.0) | |
| Rakhine | 40 (42.6) | 29 (30.9) | 25 (26.6) | 94 (100.0) | |
| Shan | 40 (46.0) | 32 (36.8) | 15 (17.2) | 87 (100.0) | |
| Sagaing | 53 (44.2) | 43 (35.8) | 24 (20.0) | 120 (100.0) | |
| Taninthayi | 66 (55.5) | 35 (29.4) | 18 (15.1) | 119 (100.0) | |
| Bago | 49 (52.1) | 31 (33.0) | 14 (14.9) | 94 (100.0) | |
| Magway | 54 (54.5) | 38 (38.4) | 7 (7.1) | 99 (100.0) | |
| Mandalay | 69 (69.0) | 27 (27.0) | 4 (4.0) | 100 (100.0) | |
| Yangon | 69 (74.2) | 15 (16.1) | 9 (9.7) | 93 (100.0) | |
| Ayeyarwaddy | 53 (54.1) | 36 (36.7) | 9 (9.2) | 98 (100.0) | |
| Naypyitaw | 50 (67.6) | 20 (27.0) | 4 (5.4) | 74 (100.0) | |
| Place of residence | <0.001 | ||||
| Urban | 299 (79.9) | 68 (18.2) | 7 (1.9) | 374 (100.0) | |
| Rural | 504 (46.6) | 396 (36.6) | 181 (16.7) | 1081 (100.0) | |
| Total parity | <0.001 | ||||
| 1 time | 361 (66.4) | 146 (26.8) | 37 (6.8) | 544 (100.0) | |
| 2 times | 223 (58.1) | 119 (31.0) | 42 (10.9) | 384 (100.0) | |
| 3 times | 110 (50.2) | 83 (37.9) | 26 (11.9) | 219 (100.0) | |
| 4 times | 60 (41.1) | 55 (37.7) | 31 (21.2) | 146 (100.0) | |
| ≥ 5 times | 49 (30.2) | 61 (37.7) | 52 (32.1) | 162 (100.0) | |
| Wanted or unwanted (last child) | 0.050 | ||||
| Wanted then | 741 (56.0) | 421 (31.8) | 161 (12.2) | 1323 (100.0) | |
| Wanted later | 33 (47.8) | 24 (34.8) | 12 (17.4) | 69 (100.0) | |
| Wanted no more | 29 (46.0) | 19 (30.2) | 15 (23.8) | 63 (100.0) | |
| Relationship to the household head | 0.001 | ||||
| Head | 24 (54.5) | 14 (31.8) | 6 (13.6) | 44 (100.0) | |
| Wife | 368 (49.5) | 257 (34.6) | 118 (15.9) | 743 (100.0) | |
| Daughter | 215 (62.9) | 96 (28.1) | 31 (9.1) | 342 (100.0) | |
| Daughter-in-law | 153 (61.7) | 68 (27.4) | 27 (10.9) | 248 (100.0) | |
| Others | 43 (55.1) | 29 (37.2) | 6 (7.7) | 78 (100.0) | |
Table 2 presents husbands’ characteristics and decision-making on respondents’ healthcare access by maternal continuum of care. There were statistically significant associations between maternal continuum of care and husbands’ education, occupation, and decision-making on respondents’ health care access. The mothers whose husbands had higher educational attainment had a higher percentage of complete maternity continuum of care (90.7%), whereas the mothers of illiterate husbands had a higher percentage of no maternal continuum of care (26.1%). Regarding husband’s occupations, the complete maternity continuum of care was the highest among the mothers whose husbands were employed in professional/technical/managerial positions (76.1%), followed by sales (71.9%) and clerks and skilled manuals (69.6%). In terms of decision making on health care access, the mothers who could make self-decisions showed the highest percentage of complete maternity continuum of care (58.1%).
| Variables | Maternal continuum of care | Total (n = 1455) | P-valuea | ||
|---|---|---|---|---|---|
| Complete (n = 803) | Partial (n = 464) | No (n = 188) | |||
| n (%) | n (%) | n (%) | n (%) | ||
| Husband’s education | <0.001 | ||||
| Primary | 236 (45.2) | 186 (35.6) | 100 (19.2) | 522 (100.0) | |
| Secondary | 404 (63.0) | 197 (30.7) | 40 (6.2) | 641 (100.0) | |
| Higher | 98 (90.7) | 10 (9.3) | 0 (0.0) | 108 (100.0) | |
| No education | 65 (35.3) | 21 (38.6) | 48 (26.1) | 184 (100.0) | |
| Husband’s occupation | <0.001 | ||||
| Professional/Technical/Managerial | 86 (76.1) | 23 (20.4) | 4 (3.7) | 113 (100.0) | |
| Clerk and skilled manual | 220 (69.6) | 77 (24.4) | 19 (6.0) | 316 (100.0) | |
| Sales | 64 (71.9) | 18 (20.2) | 7 (7.9) | 89 (100.0) | |
| Agricultural | 142 (43.0) | 133 (40.3) | 55 (16.7) | 330 (100.0) | |
| Services and unskilled manual | 291 (47.9) | 213 (35.1) | 103 (17.0) | 607 (100.0) | |
| Decision making on respondent's health care access | 0.042 | ||||
| Respondent alone | 336 (58.1) | 176 (30.4) | 66 (11.4) | 578 (100.0) | |
| Respondent and partner | 366 (56.0) | 203 (31.0) | 85 (13.0) | 654 (100.0) | |
| Partner alone | 77 (43.3) | 71 (39.9) | 30 (16.9) | 178 (100.0) | |
| Someone else/others | 24 (53.3) | 14 (31.1) | 7 (15.6) | 45 (100.0) | |
Table 3 summarizes the medical help provided by the maternal care continuum. There were statistically significant associations between maternal continuum of care and getting permission to go, getting money needed for treatment, distance to health facilities, and not wanting to go alone. If the mothers got permission to go was not a big problem, they were more likely to complete maternity continuum of care compared to the mothers with big problem of getting permission to go (56.7% vs. 24.6%). Similarly, if the mothers get money needed for treatment were not a big problem, they had a higher chance of completing maternal continuum of care than the mothers with big problem of getting money needed for treatment (62.6% vs. 42.4%). Moreover, if the distance to health facility was not a big problem, the mothers were more likely to complete maternity continuum of care compared to their counterparts (60.4% vs. 38.7%). In addition, if the mothers did not want to go alone was not a big problem, they had a higher opportunity to complete maternal continuum of care than the mothers with big problems (58.8% vs. 47%).
| Variables | Maternal continuum of care | Total (n = 1455) | P-valuea | ||
|---|---|---|---|---|---|
| Complete (n = 803) | Partial (n = 464) | No (n = 188) | |||
| n (%) | n (%) | n (%) | n (%) | ||
| Getting permission to go | <0.001 | ||||
| Not a big problem | 787 (56.7) | 438 (31.5) | 165 (11.9) | 1390 (100.0) | |
| Big problem | 16 (24.6) | 26 (40.0) | 23 (35.4) | 65 (100.0) | |
| Getting money needed for treatment | <0.001 | ||||
| Not a big problem | 577 (62.6) | 262 (28.4) | 83 (9.0) | 922 (100.0) | |
| Big problem | 226 (42.4) | 202 (37.9) | 105 (19.7) | 533 (100.0) | |
| Distance to health facility | <0.001 | ||||
| Not a big problem | 670 (60.4) | 333 (30.0) | 108 (9.7) | 1111 (100.0) | |
| Big problem | 133 (38.7) | 131 (38.1) | 80 (23.3) | 344 (100.0) | |
| Not wanting to go alone | <0.001 | ||||
| Not a big problem | 595 (58.8) | 304 (30.0) | 113 (11.2) | 1012 (100.0) | |
| Big problem | 208 (47.0) | 160 (36.1) | 75 (16.9) | 443 (100.0) | |
Table 4 shows the components of antenatal care according to the maternal continuum of care. There were statistically significant associations between maternal continuum of care and timing of the first antenatal check, tetanus injections, blood pressure taken, urine sample taken, blood sample taken, counseling about pregnancy complications, given or bought iron tablets/syrup, and drugs for intestinal parasites during pregnancy. The mothers who underwent first antenatal check in first trimester of pregnancy had a higher percentage of complete maternity continuum of care (60.7%). Regarding the health services provided in antenatal care, the mothers who completed the second dose of tetanus injection (61%), took blood pressure (57.4%), took urine samples (63.2%), took blood samples (63.3%), received counseling about pregnancy complications (58.3%), took iron tablets/syrup (58.6%), and took drugs for intestinal parasites (61.5%) had higher percentage of complete maternal continuum of care compared to their counterparts.
| Variables | Maternal continuum of care | Total (n = 1455) | P-valuea | ||
|---|---|---|---|---|---|
| Complete (n = 803) | Partial (n = 464) | No (n = 188) | |||
| n (%) | n (%) | n (%) | n (%) | ||
| Timing of 1 st antenatal check | <0.001 | ||||
| First trimester | 450 (60.7) | 194 (26.2) | 97 (13.1) | 741 (100.0) | |
| Second trimester | 325 (54.6) | 208 (35.0) | 62 (10.4) | 595 (100.0) | |
| Third trimester | 28 (23.5) | 62 (52.1) | 29 (24.4) | 119 (100.0) | |
| Tetanus injections during pregnancy | <0.001 | ||||
| TT1 completed | 81 (43.3) | 70 (37.4) | 36 (19.3) | 187 (100.0) | |
| TT2 completed | 692 (61.0) | 344 (30.3) | 98 (8.6) | 1134 (100.0) | |
| No Tetanus injections | 30 (22.4) | 50 (37.3) | 54 (40.3) | 134 (100.0) | |
| Blood pressure taken during pregnancy | <0.001 | ||||
| Yes | 792 (57.4) | 435 (31.5) | 152 (11.0) | 1379 (100.0) | |
| No | 11 (14.5) | 29 (38.2) | 36 (47.4) | 76 (100.0) | |
| Urine sample taken during pregnancy | <0.001 | ||||
| Yes | 634 (63.2) | 262 (26.1) | 107 (10.7) | 1003 (100.0) | |
| No | 169 (37.4) | 202 (44.7) | 81 (17.9) | 452 (100.0) | |
| Blood sample taken during pregnancy | <0.001 | ||||
| Yes | 661 (63.3) | 279 (26.7) | 104 (10.0) | 1044 (100.0) | |
| No | 142 (34.5) | 185 (45.0) | 84 (20.4) | 411 (100.0) | |
| Counseling about pregnancy complications during pregnancy | <0.001 | ||||
| Yes | 692 (58.3) | 356 (30.0) | 139 (11.7) | 1187 (100.0) | |
| No | 111 (41.4) | 108 (40.3) | 49 (18.3) | 268 (100.0) | |
| Given or bought iron tablets/syrup during pregnancy | <0.001 | ||||
| Yes | 790 (58.6) | 427 (31.7) | 131 (9.7) | 1348 (100.0) | |
| No | 13 (12.1) | 37 (34.6) | 57 (53.3) | 107 (100.0) | |
| Drugs for intestinal parasites during pregnancy | <0.001 | ||||
| Yes | 580 (61.5) | 290 (30.8) | 73 (7.7) | 943 (100.0) | |
| No | 223 (43.6) | 174 (34.0) | 115 (22.5) | 512 (100.0) | |
Table 5 presents the results of the multivariable regression analysis for maternal continuum of care using ordinal logistic regression. We selected Model 4, which had the highest values of Cox and Snell R-Square (0.366) and Nagelkerke R-Square (0.429) among all models. After adjusting for covariates, maternal education, region, place of residence, total parity, husband’s education, husband’s occupation, money needed for treatment, timing of first antenatal check, tetanus injection, blood pressure, urine sample taken, blood sample taken, given or bought iron tablets/syrup, and drugs for intestinal parasites during pregnancy were significantly associated with maternal continuum of care.
| Variables | Model 1 | Model 2 | Model 3 | Model 4 |
|---|---|---|---|---|
| AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | |
| Education | ||||
| Primary | 2.231 (1.585, 3.140) *** | 2.071 (1.453, 2.952) *** | 1.963 (1.373, 2.806) *** | 1.689 (1.159, 2.463) ** |
| Secondary | 3.459 (2.358, 5.073) *** | 2.881 (1.928, 4.305) *** | 2.638 (1.759, 3.958) *** | 2.199 (1.435, 3.370) *** |
| Higher | 7.318 (3.811, 14.054) *** | 4.276 (2.121, 8.618) *** | 3.828 (1.885, 7.775) *** | 2.999 (1.436, 6.260) ** |
| No education | 1 | 1 | 1 | 1 |
| Occupation | ||||
| Not working | 1.012 (0.751, 1.365) | 0.869 (0.638, 1.183) | 0.829 (0.607, 1.131) | 0.798 (0.576, 1.106) |
| Professional/Technical/Managerial | 0.868 (0.423, 1.780) | 0.604 (0.283, 1.286) | 0.564 (0.263, 1.207) | 0.643 (0.290, 1.424) |
| Clerk and skilled manual | 1.954 (1.014, 3.765) * | 1.526 (0.782, 2.977) | 1.560 (0.794, 3.064) | 1.327 (0.668, 2.635) |
| Sales | 1.264 (0.851, 1.878) | 0.956 (0.627, 1.456) | 0.885 (0.579, 1.353) | 0.865 (0.552, 1.357) |
| Agricultural | 0.761 (0.512, 1.131) | 0.664 (0.428, 1.030) | 0.680 (0.437, 1.059) | 0.671 (0.422, 1.068) |
| Services and unskilled manual | 1 | 1 | 1 | 1 |
| Region | ||||
| Kachin | 0.684 (0.344, 1.360) | 0.584 (0.291, 1.172) | 0.631 (0.311, 1.281) | 0.545 (0.259, 1.149) |
| Kayah | 0.704 (0.370, 1.340) | 0.707 (0.365, 1.367) | 0.681 (0.348, 1.332) | 0.427 (0.210, 0.867) * |
| Kayin | 0.561 (0.294, 1.070) | 0.579 (0.300, 1.116) | 0.566 (0.291, 1.102) | 0.491 (0.241, 1.000) * |
| Chin | 0.212 (0.104, 0.434) *** | 0.170 (0.081, 0.355) *** | 0.220 (0.103, 0.471) *** | 0.182 (0.081, 0.408) *** |
| Mon | 0.659 (0.329, 1.321) | 0.687 (0.339, 1.390) | 0.588 (0.288, 1.200) | 0.496 (0.233, 1.054) |
| Rakhine | 0.303 (0.157, 0.586) *** | 0.322 (0.164, 0.634) *** | 0.322 (0.162, 0.638) *** | 0.290 (0.140, 0.602) *** |
| Shan | 0.437 (0.224, 0.854) * | 0.436 (0.220, 0.864) * | 0.418 (0.209, 0835) * | 0.461 (0.220, 0.969) * |
| Sagaing | 0.299 (0.159, 0.561) *** | 0.260 (0.136, 0.496) *** | 0.233 (0.120, 0.449) *** | 0.213 (0.106, 0.428) *** |
| Taninthayi | 0.514 (0.272, 0.972) * | 0.544 (0.284, 1.040) | 0.541 (0.279, 1.051) | 0.521 (0.257, 1.054) |
| Bago | 0.339 (0.175, 0.654) *** | 0.324 (0.166, 0.631) *** | 0.282 (0.143, 0.559) *** | 0.262 (0.127, 0.540) *** |
| Magway | 0.590 (0.306, 1.136) | 0.559 (0.287, 1.088) | 0.518 (0.264, 1.017) | 0.460 (0.225, 0.943) * |
| Mandalay | 0.781 (0.389, 1.570) | 0.788 (0.387, 1.601) | 0.769 (0.374, 1.584) | 0.788 (0.364, 1.704) |
| Yangon | 0.557 (0.271, 1.146) | 0.520 (0.250, 1.079) | 0.501 (0.239, 1.048) | 0.428 (0.195, 0.937) * |
| Ayeyarwaddy | 0.523 (0.272, 1.005) | 0.544 (0.280, 1.058) | 0.586 (0.299, 1.149) | 0.433 (0.212, 0.883) * |
| Naypyitaw | 1 | 1 | 1 | |
| Place of residence | ||||
| Urban | 2.873 (2.085, 3.960) *** | 2.443 (1.747, 3.415) *** | 2.396 (1.710, 3.356) *** | 2.179 (1.530, 3.101) *** |
| Rural | 1 | 1 | 1 | 1 |
| Total parity | ||||
| 1 time | 2.440 (1.598, 3.726) *** | 2.340 (1.519, 3.605) *** | 2.233 (1.446, 3.450) *** | 2.309 (1.462, 3.646) *** |
| 2 times | 1.864 (1.252, 2.777) ** | 1.862 (1.243, 2.790) ** | 1.755 (1.169, 2.636) ** | 1.635 (1.067, 2.503) * |
| 3 times | 1.613 (1.063, 2.447) * | 1.634 (1.071, 2.493) * | 1.579 (1.034, 2.413) * | 1.353 (0.868, 2.109) |
| 4 times | 1.298 (0.837, 2.014) | 1.321 (0.848, 2.058) | 1.274 (0.815, 1.991) | 1.391 (0.867, 2.230) |
| ≥ 5 times | 1 | 1 | 1 | 1 |
| Wanted or unwanted (last child) | ||||
| Wanted then | 1.077 (0.639, 1.816) | 1.102 (0.647, 1.878) | 1.105 (0.648, 1.885) | 1.346 (0.779, 2.325) |
| Wanted later | 0.880 (0.440, 1.760) | 0.855 (0.425, 1.722) | 0.911 (0.451, 1.840) | 1.087 (0.523, 2.259) |
| Wanted no more | 1 | 1 | 1 | 1 |
| Relationship to the household head | ||||
| Head | 2.110 (0.952, 4.673) | 2.052 (0.921, 4.574) | 1.975 (0.883, 4.416) | 1.847 (0.809, 4.219) |
| Wife | 1.423 (0.854, 2.373) | 1.497 (0.891, 2.515) | 1.495 (0.887, 2.520) | 1.468 (0.850, 2.535) |
| Daughter | 1.502 (0.887, 0.543) | 1.465 (0.858, 2.502) | 1.441 (0.842, 2.467) | 1.400 (0.796, 2.464) |
| Daughter-in-law | 1.621 (0.937, 2.806) | 1.679 (0.961, 2.934) | 1.672 (0.953, 2.932) | 1.562 (0.866, 2.818) |
| Others | 1 | 1 | 1 | 1 |
| Husband’s education | ||||
| Primary | 1.022 (0.725, 1.440) | 0.989 (0.700, 1.397) | 1.001 (0.697, 1.437) | |
| Secondary | 1.505 (1.046, 2.165) * | 1.449 (1.005, 2.089) * | 1.388 (0.944, 2.041) | |
| Higher | 3.677 (1.610, 8.396) ** | 3.522 (1.539, 8.061) ** | 3.376 (1.417, 8.046) ** | |
| No education | 1 | 1 | 1 | |
| Husband’s occupation | ||||
| Professional/Technical/Managerial | 1.940 (1.104, 3.412) * | 1.753 (0.993, 3.094) | 1.910 (1.060, 3.442) * | |
| Clerk and skilled manual | 1.594 (1.147, 2.216) ** | 1.475 (1.058, 2.057) * | 1.448 (1.018, 2.058) * | |
| Sales | 1.469 (0.838, 2.574) | 1.392 (0.792, 2.446) | 1.406 (0.766, 2.581) | |
| Agricultural | 1.389 (1.003, 1.922) * | 1.358 (0.980, 1.883) | 1.284 (0.912, 1.808) | |
| Services and unskilled manual | 1 | 1 | 1 | |
| Decision making on respondent's health care access | ||||
| Respondent alone | 1.290 (0.676, 2.461) | 1.195 (0.622, 2.295) | 1.136 (0.572, 2.256) | |
| Respondent and partner | 1.353 (0.712, 2.571) | 1.240 (0.648, 2.372) | 1.124 (0.568, 2.227) | |
| Partner alone | 0.998 (0.501, 1.990) | 0.969 (0.483, 1.941) | 1.010 (0.486, 2.100) | |
| Someone else/others | 1 | 1 | 1 | |
| Getting permission to go | ||||
| Not a big problem | 1.535 (0.906, 2.603) | 1.423 (0.808, 2.505) | ||
| Big problem | 1 | 1 | ||
| Getting money needed for treatment | ||||
| Not a big problem | 1.275 (0.982, 1.655) | 1.336 (1.017, 1.754) * | ||
| Big problem | 1 | 1 | ||
| Distance to health facility | ||||
| Not a big problem | 1.587 (1.158, 2.174) ** | 1.341 (0.963, 1.869) | ||
| Big problem | 1 | 1 | ||
| Not wanting to go alone | ||||
| Not a big problem | 0.915 (0.685, 1.223) | 0.902 (0.665, 1.224) | ||
| Big problem | 1 | 1 | ||
| Timing of 1 st antenatal check | ||||
| First trimester | 2.921 (1.918, 4.450) *** | |||
| Second trimester | 2.290 (1.502, 3.493) *** | |||
| Third trimester | 1 | |||
| Tetanus injections during pregnancy | ||||
| TT1 completed | 1.121 (0.677, 1.856) | |||
| TT2 completed | 1.756 (1.135, 2.717) * | |||
| No Tetanus injections | 1 | |||
| Blood pressure taken during pregnancy | ||||
| Yes | 3.140 (1.825, 5.405) *** | |||
| No | 1 | |||
| Urine sample taken during pregnancy | ||||
| Yes | 1.524 (1.128, 2.059) ** | |||
| No | 1 | |||
| Blood sample taken during pregnancy | ||||
| Yes | 1.366 (1.003, 1.861) * | |||
| No | 1 | |||
| Counseling about pregnancy complications during pregnancy | ||||
| Yes | 1.128 (0.821, 1.549) | |||
| No | 1 | |||
| Given or bought iron tablets/syrup during pregnancy | ||||
| Yes | 5.469 (3.333, 8.975) *** | |||
| No | 1 | |||
| Drugs for intestinal parasites during pregnancy | ||||
| Yes | 1.725 (1.334, 2.230) *** | |||
| No | 1 | |||
| Cox and Snell R-Square | 0.216 | 0.237 | 0.249 | 0.366 |
| Nagelkerke R-Square | 0.253 | 0.278 | 0.292 | 0.429 |
Regarding educational attainment, the mothers with higher education, secondary education and primary education were almost 3 times, 2.2 times and 1.7 times more likely to complete maternity continuum of care compared to the illiterate mothers (Higher education: AOR = 2.999, 95% CI = 1.436, 6.260; Secondary education: AOR = 2.199, 95% CI = 1.435, 3.370; Primary education: AOR = 1.689, 95% CI = 1.159, 2.463). Compared to the mothers living in Naypyitaw territory, the odds of mothers having complete maternity continuum of care were 81.8% lower in Chin State (AOR = 0.182, 95% CI = 0.081, 0.408), 78.7% lower in Sagaing Region (AOR = 0.213, 95% CI = 0.106, 0.428), 73.8% lower in Bago Region (AOR = 0.262, 95% CI = 0.127, 0.540), 71% lower in Rakhine State (AOR = 0.290, 95% CI = 0.140, 0.602), 57.3% lower in Kayah State (AOR = 0.427, 95% CI = 0.210, 0.867), 57.2% lower in Yangon Region (AOR = 0.428, 95% CI = 0.195, 0.937), 56.7% lower in Ayeyarwaddy Region (AOR = 0.433, 95% CI = 0.212, 0.883), 54% lower in Magway Region (AOR = 0.460, 95% CI = 0.225, 0.943), 53.9% lower in Shan State (AOR = 0.461, 95% CI = 0.220, 0.969), and 50.9% lower in Kayin State (AOR = 0.491, 95% CI = 0.241, 1.000) respectively. Related to the place of residence, the mothers from the urban areas were approximately 2.2 times more likely to complete maternal continuum of care compared to the mothers from the rural areas (AOR = 2.179, 95% CI = 1.530, 3.101). Moreover, the mothers who were pregnant for one time and two times had 2.3 times and 1.6 times higher probability of completing maternity continuum of care compared to the mothers who were pregnant for five times and more (One time: AOR = 2.309, 95% CI = 1.462, 3.646; Two times: AOR = 1.635, 95% CI = 1.067, 2.503).
As for the educational attainment of husbands, the mothers whose husbands had higher education were almost 3.4 times more likely to complete maternal continuum of care compared to the mothers of illiterate husbands (AOR = 3.376, 95% CI = 1.417, 8.046). For the occupation of husbands, the mothers whose husbands were employed in professional/technical/managerial positions and clerk and skill manual had 1.9 times and 1.4 times higher probability of completing maternal continuum of care than the mothers whose husbands were employed as services and unskilled manual (Professional/Technical/Managerial: AOR = 1.910, 95% CI = 1.060, 3.442; Clerk and skilled manual: AOR = 1.448, 95% CI = 1.018, 2.058). Regarding with getting money needed for treatment, the mothers not having a big problem were 1.3 times more likely to complete maternity continuum of care compared to the mothers with a big problem (AOR = 1.336, 95% CI = 1.017, 1.754).
Compared to the mothers who took first antenatal check in third trimester of pregnancy, the mothers receiving first ANC in first trimester and second trimester were approximately 2.9 times and 2.3 times more likely to complete maternal continuum of care (First trimester: AOR = 2.921, 95% CI = 1.918, 4.450; Second trimester: AOR = 2.290, 95% CI = 1.502, 3.493). The odds of completing maternity continuum of care were 1.7 times higher among the mothers who completed second dose of tetanus injections than the mothers who did not receive any tetanus injections (AOR = 1.756, 95% CI = 1.135, 2.717). Similarly, the odds of completing maternal continuum of care were 3.1 times, 1.5 times, 1.4 times, 5.5 times and 1.7 times higher among the mothers who took blood pressure, urine sample, blood sample, iron tablets/syrup and drugs for intestinal parasites during pregnancy compared to their counterparts (Blood pressure: AOR = 3.140, 95% CI = 1.825, 5.405; Urine sample: AOR = 1.524, 95% CI = 1.128, 2.059; Blood sample: AOR = 1.366, 95% CI = 1.003, 1.861; Iron tablets/syrup: AOR = 5.469, 95% CI = 3.333, 8.975; Drugs for intestinal parasites: AOR = 1.725, 95% CI = 1.334, 2.230).
This study analyzed nationally representative data from the 2015 to 2016 Myanmar Demographic and Health Survey to investigate the completion level of maternity care and identify attributing factors associated with maternal CoC completion or dropout. This study found three different prevalence rates for maternal CoC: 55.2% for complete care, 31.9% for partial care, and 12.9% for no care. Additionally, several variables determine the prevalence of maternal continuum of care in Myanmar. The study discovered that important participants’ sociodemographic characteristics to complete the maternal CoC rate include maternal education, region and place of residence, total parity, women’s self-decision to seek healthcare, and relationship to the household head. According to this study, other cue factors also significantly contributed to the completion rate of maternal CoC. These factors include the husband’s education, employment, obtaining essential maternal ANC services throughout pregnancy, and the early timing of the first ANC visit. Women in Myanmar encounter several challenges, including large problems in obtaining permission to go to a health center, financial constraints, and distance to a health facility, all of which might cause them to stop receiving care before the recommended number of visits and services is completed.
According to this study’s findings, 55.2% of participants received completed maternal CoC. This result is less than that of research conducted in the South-East Asia region, including Maldives (59.8%), Cambodia (60.0%), Indonesia (69.7%), and Philippines (69.9%). Nevertheless, according to a previous survey, only 2.8% of women in Afghanistan received full maternal CoC, Timor-Leste (26.1%), Bangladesh (31.5%), Pakistan (35.1%), and Nepal (38.8%), followed behind in SEA regions as well.15
The low rate of the maternal CoC completion in the area is consistent with earlier research in Nepal, which showed that only 46% of expectant mothers received all essential services throughout the continuum of care. They estimated that 87% of pregnant women had at least one antenatal care appointment (ANC); however, there was a notable dropout rate between ANC, SBA delivery, and postnatal care.16 A similar study discovered that many women discontinued prenatal care after their first visit, without access to trained birth attendants or postnatal care.17,18
In accordance with previous studies, this study’s findings indicated women’s education levels, employment, birth order, place of residence, and region. Women with higher education levels have a significantly higher association with all MNCH services than illiterate women.15,19–21 Women living in urban areas were more complete than rural19–21 and working women.15 The results certainly show that first-order newborns had a larger percentage of CoCs in MNCH services when compared to second- and higher-order births. Multiparous women have a higher chance of being dismissed in seeking ANC and institutional delivery.19–24 Women’s prenatal care utilization and continued use of skilled birth attendants are related to residential regions, such as the central and easy geographic areas where women reside.20
Women with unexpected pregnancies completed maternal CoC at a lower rate than women with planned pregnancies, as shown in studies conducted in Ethiopia, Ghana, and Nepal.25–28 Compared to women who become pregnant accidentally, those who become pregnant intentionally are more likely to notice their pregnancy early and may be more careful about their pregnancy status. In addition, women who become pregnant unexpectedly are probably not as emotionally and financially ready for the stress of pregnancy and childrearing.
Interestingly, husband-related factors such as having highly educated and employed husbands29 and discussing services with husbands or family members30 were new factors for promoting the continuum of care.29,31
The quality of prenatal care services and receiving four ANC visits were found to be significant predictors of later use of a trained skilled birth attendant. Similar results showed that skilled birth attendance is positively impacted by quality ANC, which included checked blood pressure, counseling complications of pregnancy, and blood and urine samples, and that this effect is influenced by the care women receive during their ANC visits.18 The World Health Organization (WHO) recommends that at specific stages of pregnancy, women should receive targeted prenatal care appointments that include a range of essential services. Women who receive high-quality ANC are better prepared for pregnancy and are more likely to understand the significance of safe motherhood.17 As many ANC visits are needed to provide this service, both the number of visits and quality of care given to women should be the main goals of future initiatives in Myanmar.
The completeness of maternal health services across the maternal CoC is impacted by women’s unfavorable experiences when receiving care, such as poor quality of care, disagreement to visit health facilities, distance from health service delivery places, financial barriers, and high opportunity costs. Women who reported financial access to treatment had less, not getting permission to visit health facilities, remoteness, and difficulties in transportation from health services had lower, CoC coverage.15,32,33 Moreover, distance from medical facilities increased community members’ out-of-pocket spending on transportation, the cost of returning home via transportation, and the cost of meals and medical bills. These factors create barriers and discourage women from using and continuing their services.32
After adjusting for the covariates in the multivariable regression analysis, maternal education, region, place of residence, birth order, husband’s education, husband’s occupation, no financial constraints, timing of first antenatal check, and quality of prenatal care services were found to be significantly associated with maternal continuum of care. These results are in line with some studies on the regression analysis findings.
Women’s higher education was associated with 2.15 times higher odds ratio values of maternal CoC, according to a cross-sectional study. However, perceptions of distance from the health facility as a major issue, unwanted pregnancy, living in rural regions, and delayed first ANC visits were demonstrated to decrease the chances of maternal CoC, with adjusted odds values of 0.88, 0.87, 0.78, and 0.43, respectively.33 Another study compared women with no education to those with higher education, and the likelihood of completing the continuum of care is nearly five times greater.16 According to the results of the regression analysis, women who have autonomy over their health care decisions, women who wanted pregnancy, and women whose husbands work are factors that were strongly associated with completing the maternity continuum of care, with adjusted odds ratio values of these factors ranged from 3.33 to 4.97.29 Women who had blood pressure checked, urine samples obtained, and blood samples drawn as part of prenatal care had an odds ratio increase in having a trained birth attendant of 30 to 50%.21
This study used nationally representative data from the 2015 to 2016 Myanmar Demographic and Health Survey with a two-stage stratified cluster design and standardized instruments to ensure high external validity. The maternal continuum of care (CoC) was clearly and reproducibly defined (≥4 ANC visits, skilled birth attendance, and PNC within 48 hours), and an a priori stepwise ordinal logistic regression modeling approach enhanced interpretability.
However, the analysis may not have fully incorporated survey weights and complex design features, which could have biased the estimates and confidence intervals. The proportional odds assumption in ordinal regression was not tested, potentially affecting the model validity. As a secondary, cross-sectional analysis of self-reported DHS data, the study is also limited by possible recall bias, residual confounding, and the absence of key variables, such as obstetric complications and facility quality, restricting causal inference.
Based on socioeconomic and demographic characteristics, this study revealed significant variations in MNCH service coverage and completion. Important factors include maternal education, urban residency, region, total parity, husbands’ education, and occupation. Improving MNCH outcomes requires addressing obstacles such as financial limitations, access to quality health services, and distance. Improving access to maternal healthcare services should be the main goal of policy initiatives, especially for rural areas, lower education levels, low-income families, and remote or outreach populations. Interventions in developing countries should be more focused on: (1) health promotion and education, (2) financial assistance through universal health coverage or improved social security and health insurance, (3) technology solutions such as mass media campaigns and mobile reminders, (4) community engagement and women empowerment programs to increase women’s decision-making power and continue seeking health care, and (5) the provision of adequate healthcare infrastructure in geographically difficult regions.
Link: https://doi.org/10.6084/m9.figshare.30741830
| Item | Recommendation | Addressed in your manuscript |
|---|---|---|
| Title and abstract | ||
| 1 | Indicate study design in title/abstract | Title specifies “secondary analysis”; Abstract states use of 2015–2016 MDHS, cross-sectional |
| 2 | Provide informative, balanced abstract | Structured abstract with Introduction, Methods, Results, Conclusion included |
| Introduction | ||
| 3 | Background/rationale | p.1–2, paragraphs on importance of maternal continuum of care, global/MMR context |
| 4 | Objectives | End of Introduction, clear aim: assess prevalence and associated factors of CoC in Myanmar |
| Methods | ||
| 5 | Study design | Clearly stated: secondary analysis of DHS, cross-sectional |
| 6 | Setting | p.6, description of MDHS survey, time period (2015–2016), national coverage |
| 7 | Participants | Inclusion criteria: women with children <2 years; sampling frame described (n=1455) |
| 8 | Variables | Dependent: CoC categories (complete, partial, none); Independent: sociodemographic, husband’s characteristics, barriers, ANC components |
| 9 | Data sources/measurement | DHS datasets (Birth’s Record file), standard DHS questionnaires; variable definitions explained |
| 10 | Bias | Recall bias minimized (restricting to births <2 years); acknowledged in limitations |
| 11 | Study size | Sample size = 1455, derived from DHS dataset |
| 12 | Quantitative variables | Operational definitions of CoC (ANC≥4, SBA, PNC≤48h), recoding explained |
| 13 | Statistical methods | SPSS v25; bivariate (ANOVA, chi-square), multivariable (ordinal logistic regression, stepwise models, AORs, 95% CI); significance threshold 0.05 |
| Results | ||
| 14 | Participants | Flow of sample (DHS total → analytic sample of 1455) described |
| 15 | Descriptive data | Sociodemographic, husband’s, ANC components, tables 1–4 |
| 16 | Outcome data | Prevalence of CoC: complete 55.2%, partial 31.9%, none 12.9% |
| 17 | Main results | Multivariable model results ( Table 5, Model 4, AORs with 95% CIs) |
| 18 | Other analyses | Stratified reporting by sociodemographic factors; stepwise models compared (Cox & Snell, Nagelkerke R2) |
| Discussion | ||
| 19 | Key results | Restated in first paragraph of Discussion |
| 20 | Limitations | Recall bias, limited variables, inability to infer causality, lack of survey weights, etc. |
| 21 | Interpretation | Compared with other countries in SEA and beyond, policy implications discussed |
| 22 | Generalisability | Findings generalized to Myanmar national population, within DHS representativeness limits |
| Other information | ||
| 23 | Ethical approval | MDHS approval described; secondary data access permission noted |
The 2015–2016 Myanmar Demographic and Health Survey (MDHS) was conducted with ethical approval from the Ethics Review Committee on Medical Research, including Human Subjects, Department of Medical Research, Ministry of Health and Sports, Myanmar, and by the Institutional Review Board of ICF International, USA. Specific reference or approval numbers for the MDHS ethical clearance were not publicly reported in the MDHS final report or DHS documentation.
Permission to use the MDHS dataset for this secondary analysis was obtained from the Demographic and Health Survey (DHS). No additional ethical approval was required for this secondary analysis, as the study used anonymized, de-identified, publicly available survey data.
Written informed consent was obtained from all participants by the original MDHS data collection team prior to participation. The DHS Program ensures that all surveys are conducted in accordance with international ethical standards for research involving human participants.
This secondary analysis was conducted in accordance with the principles of the Declaration of Helsinki and adhered to relevant ethical guidelines for the use of human research data.
All data underlying the results of this study are fully available and openly accessible in accordance with F1000Research’s open data policy. The dataset used for the secondary analysis was derived from the 2015–2016 Myanmar Demographic and Health Survey (DHS) and has been deposited on Figshare. No restrictions apply to data access, and the dataset may be reused for verification or further research.
Dataset: Dataset for “Maternal continuum of care completion and associated factors in Myanmar: A secondary analysis of the 2015–2016 Myanmar Demographic and Health Survey”
Figshare: https://doi.org/10.6084/m9.figshare.30741818
Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).
The authors gratefully acknowledge the Demographic and Health Surveys (DHS) Program for granting access to the Myanmar Demographic and Health Survey (2015–2016) dataset used in this study. We also express sincere appreciation to the College of Public Health Sciences, Chulalongkorn University, Bangkok, Thailand, and the School of Global Health, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand for their academic support, mentorship, and technical guidance throughout the research process.
Special thanks are extended to the DHS data collection teams and all the participants who contributed to the original survey. The authors also thank the faculty mentors and colleagues from both institutions for their valuable insights and encouragement during the analysis and manuscript preparation.
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