Determinants of health facility delivery among reproductive age women in low and middle income countries; evidence from most recent demographic health survey: Application of multilevel binary logistic regression model

doi:10.21203/rs.3.rs-2011534/v1

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Determinants of health facility delivery among reproductive age women in low and middle income countries; evidence from most recent demographic health survey: Application of multilevel binary logistic regression model

https://doi.org/10.21203/rs.3.rs-2011534/v1

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Background

High prevalence of maternal mortality in LMICs has been attributed to the low patronage of antenatal care and health facility delivery. Childbirth at health facilities is one of the safest ways to prevent maternal morbidity and mortality. The study aims to identify the determinants of health facility of delivery among reproductive age women in LMICs.

Methods

A total weighted sample of 329,721 women who gave birth during the study period was included in the study. Intra-class Correlation Coefficient, Median Odds Ratio, Proportional Change in Variance, AIC, BIC, and deviance were used for model fitness and comparison. Multilevel multivariable logistic regression model was fitted to identify determinants of health facility delivery in LMICs. Adjusted Odds Ratio with its 95% Confidence Interval was used to declare significant determinants of health facility delivery.

Results

The overall prevalence of health facility delivery was 67.6% and this ranged from 19.6% in Chad to 99.8% in Armenia. In the Multilevel multivariable logistic regression model; age less than 20 (AOR = 0.958; 95%CI: 0.928–0.990), age 20 to 34 (AOR = 0.986; 95%CI: 0.957–1.015), rural women (AOR = 0.668; 95%ci: 0.489–0.913), primary educated women (AOR = 1.545; 95%CI: 1.511–1.583), secondary educated women (AOR = 2.145; 95%CI: 2.087–2.206), higher educated women (AOR = 3.362; 95%CI: 3.167–3.570), middle wealth index (AOR = 1.894; 95%CI: 1.859–1.930), rich wealth index (AOR = 2.012; 95%CI: 1.563–2.123), having media exposure (AOR = 1.311; 95%CI: 1.287–1.336), had 4 and more antenatal care visit (AOR = 2.402; 95%CI: 2.360–2.446), unemployed women (AOR = 0.844; 95%CI: 0.843–0.846), and being Western Africa resident (AOR = 0.103; 95%CI: 0.058–0.181) were significantly associated with health facility delivery.

Conclusion

Maternal age, place of residence, maternal education level, family size, number of children, wealth index, marital status, and antenatal care visits were significant determinants of health facility delivery in LMICs. These findings will be useful for the government and stakeholders in planning, designing, and implementing appropriate interventions.

Health facility delivery

LMICs

DHS

Multilevel analysis

Maternal mortality is a global public health problem, with approximately 810 maternal deaths occurring every day, the vast majority of which are avoidable [1, 2]. There is far too much maternal mortality [2]. Around 295,000 women died during and after pregnancy and childbirth in 2017 [1, 2]. The vast majority of these deaths (94%) occurred in low- and middle-income countries, and the vast majority of them could have been avoided [1]. Sub-Saharan Africa and Southern Asia accounted for approximately 86% (254 000) of the estimated global maternal deaths in 2017. Sub-Saharan Africa alone accounted for nearly two-thirds of maternal deaths (196 000), with Southern Asia accounting for nearly one-fifth (58 000) [1, 2].

Women are killed by complications during and after pregnancy and childbirth. The majority of these complications happen during pregnancy and can be avoided or treated. Other complications may exist prior to pregnancy but worsen during pregnancy, especially if not managed as part of the woman's care [3]. Almost 75% of all maternal deaths are the result of (1) severe bleeding (usually after childbirth), (2) infections (usually after childbirth), (3) high blood pressure during pregnancy (pre-eclampsia and eclampsia), (4) delivery complications, and (5) unsafe abortion. "Indirect maternal deaths" refer to the remaining maternal deaths. These occur when a pregnancy is exacerbated by another condition or disease, such as malaria, diabetes, or heart disease [1]. Maternal and newborn health are inextricably linked. Every year, approximately 2.5 million children die in their first month of life, and another 2.6 million are stillborn [4, 5].

The importance of reducing maternal and child mortality by 75% was emphasized in the Millennium Development Goals (MDGs) (1990–2015). As a result, the global maternal mortality rate (MMR) had dropped by 38% by 2015 [1, 6]. According to the Sustainable Development Goal, MMR should be less than 70 deaths per 100,000 live births by 2030. (SDG). Setting this ambitious SDG target is a great call to all nations, but the MDG era still has unfinished business. The remarkable progress made during the MDG era was not evenly distributed around the world.

he high prevalence of maternal mortality in LMICs has been blamed on low utilization of antenatal care and health facility delivery [7, 8]. Maternal mortality occurs most frequently during childbirth and the first 24 hours after delivery [9, 10]. Unsafe home delivery practices are responsible for 35% of all antepartum, intrapartum, and postpartum hemorrhages, according to studies [11, 12]. Many women in low- and middle-income countries continue to lack access to skilled pregnancy and childbirth care. Childbirth in a medical facility is one of the safest ways to reduce maternal morbidity and mortality [13, 14]. To help protect pregnant women's health, the World Health Organization (WHO) has also recommended at least four antenatal and postnatal care visits [10]. Using SBAs and health facility delivery, it is possible to avoid approximately 80% of MMR in LMICs [15]. Maternal mortality can be reduced by using facility-based delivery services, family planning, and antenatal and postnatal care [1, 6, 16].

A range of factors hinders the utilization of facility delivery and other services during the pregnancy-postnatal continuum in LMICs. Type of place of residence [17, 18], women's education level [10, 19–21], husband education level [10, 18, 22], wealth index of the household [23–25], sex of household head [10], marital status of women [19, 23], women’s age group [26, 27], women’s current working status [19, 20], women’s occupation [10, 28, 29], husband/partner occupation [28, 29], media exposure [19, 23], number of antenatal care visit [17, 18], family size [22], and number of under-five children [30] are some of the critical determinants for utilization of health facility delivery in LMICs.

Despite efforts by various LMICs to increase childbirth facility utilization, utilization of health facility delivery remains low in some countries [31]. According to the results of a cross-sectional survey conducted in 80 low- and middle-income countries, the use of health facilities for delivery was greater than 90% in 25 of them and less than 40% in 11. Only 54% of Ethiopian women give birth in a health facility [17]. Despite Ghana's free maternal health services policy and Kenya's free maternity services policy for all public hospitals, only 59% of women in Ghana use SBAs [32], while 47.6% of deliveries occur in Kenya [33]. According to studies conducted in Bangladesh, Tanzania, Nigeria, Zambia, Pakistan, and Nepal, 50.5, 64.8, 84.9, 54, and 46% of deliveries occurred at a health facility, respectively [19, 20, 25, 27, 34, 35].

While there are numerous publications on health facility delivery in various LMICs [8, 25, 26, 28, 30, 33, 34, 36, 37], there appears to be a gap in the literature at the LMIC sub-regional level. The only studies conducted at the LMIC level were [31, 38, 39]. While the authors examined place of delivery at the LMIC level, they aggregated the countries into sub-regions, failing to account for variation in prevalence and place of delivery determinants based on the respective regions of the countries included in their analysis. As a result, their research did not take into account regional policies that may influence the prevalence and determinants of place of delivery. With the place of delivery being a major determinant of maternal mortality, it is critical to distinguish the source of variation, the fundamental features of the variation in the occurrence of the place of delivery, and the factors of the place of delivery using appropriate statistical techniques of analysis. Because unsuitable outcomes cause confusion between deduction and involvement, However, there is no suggestion that shows studies done at the LMIC level to determine the main factors of diarrhea prevalence while taking the regional effect into account using the mixed effect model method. As a result, this study will use evidence from a recent demographic health survey and a multilevel binary logistic regression model to identify the determinants of facility of delivery among reproductive-age women in LMICs. Accordingly, this study will provide policymakers and the general populace with information that will help reduce the high prevalence of maternal and neonatal mortality in LMICs, which contribute to the global burden.

Data source

The research looked at aggregate data from the most recent Demographic and Health Surveys (DHS), which were conducted in 54 low-income countries (LMICs) between 2010 and 2021. The DHS is a five-year national study being carried out in a number of LMICs throughout Africa, the Caribbean, Asia, and parts of South Europe and Latin America. The DHS employs consistent procedures in questionnaire design, sampling, data collection, data cleaning, coding, and analyses, enabling cross-country comparability [10]. This study included only women who had given birth in the five years preceding the survey. The data was retrieved from the Measure DHS website.

https://www.dhsprogram.com/data/dataset_admin/login_main.cfm after approval was granted through an online request stating the objective of this study. A weighted total sample of 329,721 women who had given birth in the five years preceding the survey was used for this study.

Study variables and measurements

Dependent variable

The main outcome variable of this study was the place of delivery. The outcome variable was coded as 0 for home and 1 for health facility [10, 28, 39].

Predictor variables

According to the literature, the independent variables in this study were of two types. Individual and societal variables Community-level variables include things like region and residence. Individual-level variables include women's education level, her husband's education level, the household wealth index, the gender of the household head, the marital status of women, women's age group, women's current working status, women's occupation, her husband's occupation, media exposure, the number of antenatal care visits, the size of the family, and the number of under-five children. Table 2 shows the independent variables listed above, and the parameters in Table 2 were collected through face-to-face interviews.

Ethics approval and consent to participate

Hence, the permission to get access to the data was obtained from the DHS program's online request at https://www.dhsprogram.com/data/dataset_admin/login_main.cfm, and the data used were publicly available without any personal identifier.

Data management and analysis

The data was cleaned using STATA version 17 software. Sample weighting was used for further analysis. Descriptive and inferential analyses were carried out. The descriptive analysis provided data on background characteristics, the country, and the prevalence of health-care delivery.

Multi-level analysis

Because the outcome variable was a binary two-level mixed effect, logistic regression analysis was used. A complex survey design included a primary sampling unit, strata, and women's individual weight (V005). Individual and community-level variables related to skilled birth attendants were investigated separately in the bi-variable, multi-level logistic regression model, and variables that were statistically significant at a p-value of 0.20 in the bi-variable, multi-level mixed-effects logistic regression analysis were considered for the final individual and community-level model adjustments. In the multivariable multilevel analysis, variables with p-values less than or equal to 0.05 were declared significant determinants of the place of delivery.

Model building

There were four models installed. The first model was a null model with no exposure variables, which was used to assess random effects at the community level and examine variation in communities. Model I was the multivariable model that was adjusted for individual-level variables, while Model II was adjusted for community-level factors. In Model III [17], potential candidate variables from both individual and community-level variables were fitted to the outcome variable.

Parameter estimation method

Fixed effects (an association measure) were used to estimate the association between the likelihood of place of delivery and explanatory variables at both the community and individual levels, and the results were expressed as odds ratios with a 95% confidence interval. As measures of variation, community-level variance with standard deviation, intracluster correlation coefficient (ICC), proportional change in community variance (PCV), and median odds ratio (MOR) were used (random-effects). The median odds ratio (MOR) is intended to translate area-level variance into the commonly used odds ratio (OR) scale, which has a consistent and intuitive interpretation. When two areas are chosen at random, the MOR is defined as the median value of the odds ratio between the highest and lowest risk areas. The MOR can be defined as the increased risk that an individual (in the median) would face if they relocated to a higher-risk area. It is computed by; $MOR = exp[\surd (2\times Var)\times 0.6745]$ [17]. Where Var is the area level variance and 0.6745 is the 75th percentile of the normal distribution's cumulative distribution function with a mean of 0 and a variance of 1. where the proportional change in variance is calculated as [18, 19].

$$PCV=\frac{var\left(null model\right)-var\left(full model\right)}{var\left(null model\right)}$$

The model's assumptions were tested using the intra-class coefficient of correlation (ICC) and the likelihood ratio (LR) test [17]. The ICC is used to assess the degree of similarity between clusters in terms of place of delivery prevalence.

Characteristics of dependent and independent variables

A total of 329,721 households were included in nine countries. The overall prevalence of health facility delivery was 67.6%, and this ranged from 19.6% in Chad to 99.8% in Armenia (Table 1). The majority of the women were from Pakistan: 19,787 (0.06%), while Tajikistan had the fewest: 992 (0.003%). The majority of the women, 219,798 (66.7%), were rural residents. A high number of women, 113,982 (34.6%), were uneducated, and 271,605 (82.4%) were married. Three out of every five women (60.1%) had four or more antenatal care visits. Greater than half of women, 182,568 (55.4%), have media access, and 151,595 (46.0%) of women were obtained from poor households (Table 2).

Determinants of place of delivery among reproductive age women

The random effects model results

The multilevel logistic regression model III had the lowest AIC and BIC, the highest log-likelihood ratio, and the lowest deviance because the models were nested. The ICC value for the empty model was 26.9% [p-value less than 0.001], indicating that cluster variability explained approximately 26.9% of the total variability of place of delivery in LMICs while individual variation explained the remaining 73.1% of the total variability. Furthermore, the MOR was 3.37 (95% CI: 1.79–4.95), indicating that if we randomly selected two women from different clusters, a woman from a high place of delivery was 3.37 times more likely than a woman from a low place of delivery to use a health facility for delivery. This demonstrated the existence of significant heterogeneity in health-care delivery across communities. In the full model, the community variance (community variance = 0.47; P-value less than 0.001) remained significant but decreased (model adjusted for both individual and community-level factors). Even after accounting for some contextual risk factors, approximately 4.7% of the total variance in health facility delivery that can be attributed to context-level factors remained significant. The proportional change in variance (PCV) in this model was 70.90%, indicating that both community and individual level variables explained 70.90% of the community variance observed in the null model (Table 3).

The fixed effects model results

Model III, the best-fit model, had a lower AIC, BIC, deviance, and a higher log-likelihood ratio test. The fixed effects were interpreted using Model III, which was adjusted for both individual and community-level factors. As a result, in the multilevel multivariable analysis, maternal age, place of residence, maternal education level, family size, number of children, wealth index, marital status, husband education level, husband occupation, current working status, maternal occupation, media exposure, and number of antenatal care visits were significant determinants of place of delivery in LMICs.

After controlling for other individual and community-level factors, the odds of health facility delivery among women under the age of 20 were 92.8% lower (AOR = 0.958; 95% CI: 0.928–0.99) than among women over the age of 34. The odds of health facility delivery among rural resident women were lower by 66.8% (AOR = 0.668; 95% CI: 0.489–0.913) as compared to urban residents. The likelihood of health facility delivery among women having primary education, secondary education, and higher education were 1.545 (AOR = 1.545; 95%CI: 1.511–1.583), 2.145 (AOR = 2.145; 95%CI: 2.087–2.206), and 3.362 (AOR = 3.362; 95%CI: 3.167–3.570) times higher as compared to women having no formal education, respectively. The odds of health facility delivery among women whose husband’s education level was primary, secondary, or higher were 1.395 (AOR = 1.395, 95%CI: 1.361–1.430). 1.684 (AOR = 1.684; 95%CI: 1.640–1.729) and 1.823 (AOR = 1.823; 95%CI: 1.743–1.907) times higher as compared to women whose husbands had no formal education, respectively. The likelihood of health-care delivery was lower among reproductive-age women with 4 to 8 and more than 8 family members by 0.862 (AOR = 0.862; 95%CI: 0.836–0.889) and 0.929 (AOR = 0.929; 95%CI: 0.895–0.964), respectively, when compared to less than 4 family size. The likelihood of health facility delivery among reproductive-age women having 2 to 3 and more children was lower by 0.859 (AOR = 0.959; 95%CI: 0.842–0.876) and 0.865 (AOR = 0.865; 95%CI: 0.833–0.898) as compared to women having only one child, respectively. The odds of health facility delivery among women from households with middle and rich wealth status were 1.894 (AOR = 1.894, 95%CI: 1.859–1.929) and 2.012 (AOR = 2.012, 95%CI: 1.563–2.123), respectively, as compared to those from households with poor wealth status. The odds of health facility delivery among reproductive age women who are married and living with a partner were 1.376 (AOR = 1.376; 95%CI: 1.340–1.413) and 1.832 (AOR = 1.832; 95%CI: 1.699–1.975) times higher as compared to separated women, respectively. The odds of health facility delivery among reproductive age women who are widowed and divorced were 0.897 (0.790–1.019) and 0.891 (0.799–0.992) times lower as compared to separated women, respectively. The odds of health facility delivery among reproductive-age women whose husbands had occupations were 1.340 (AOR = 1.340; 95% CI: 1.284–1.399) times higher as compared with those whose husbands had no occupation. The odds of health facility delivery among women who have work currently were increased by 33.4% (AOR = 1.334; 95% CI: 1.291–1.378) as compared to those who do not have work currently. The likelihood of health facility delivery among women who had no occupation was lower by 0.844 (AOR = 0.844; 95%CI: 0.843–0.846) as compared to women who had an occupation. The odds of health facility delivery among women who had media access were 1.311 (AOR = 1.311, 95%CI: 1.287–1.336) times higher as compared to women who had no media access. The likelihood of health facility delivery among women who had 4 or more antenatal care visits was 2.402 (AOR = 2.402; 95%CI: 2.360–2.446) times higher as compared to women who had less than 4 antenatal care visits (Table 3).

We investigated the prevalence and determinants of place of delivery among reproductive-age women using data from the DHS of 54 LMICs and multilevel binary logistic regression analysis. Overall, the prevalence of health-care delivery was 67.6%. The figure was significantly higher than in Ethiopia (54%) [17], Bangladesh (51%) [19], East Africa (76.32) [23], and Nepal (46%) [27]. The difference could be attributed to differences in the study period. Maternal age, place of residence, maternal education level, family size, number of children, wealth index, marital status, husband education level, husband occupation, current working status, maternal occupation, media exposure, number of antenatal care visits, and region were significant determinants of place of delivery in LMICs in the multilevel logistic regression analysis.

This study found that as women's ages increased, so did their likelihood of using health facilities for delivery. Studies [20, 28, 40] back up this finding. This could be explained by the fact that as women get older, they gain more experience and knowledge about the advantages of skilled birth attendants from previous pregnancies [10]. Our research also found that a woman's and her husband's educational status influences the utilization of health facility delivery services. Studies in East Africa [28], Nigeria [22], Bangladesh [19], South-Asia [9], and Sub-Saharan Africa [10] all supported this finding. One possible explanation is that educated women and husbands are aware of the benefits of health care delivery due to their exposure to newspapers, mass media, and various social media platforms. Furthermore, educated women and husbands are more likely to seek health care and have access to it. Furthermore, education empowers women by providing them with the information they need to make informed reproductive health decisions, which in our study was health facility delivery, in order to protect their own and their babies' health. Education also provides women with some control over health-related decisions [41]. However, in LMICs, women's union autonomy is eroded. Male partners play an important role in women's reproductive health decision-making because they are revered as the family heads who make the final household decisions, including those affecting childbirth [42, 43]. However, the fact that the likelihood of using a health facility for delivery increased with a partner's level of education in our study suggests that the more educated a woman's partner is, the more likely they are to support her in her reproductive health decision-making.

In our study, urban women in their reproductive years had a higher prevalence and probability of choosing a health facility as the place of delivery than rural women. This study agrees with the previous studies [10, 20, 27, 30]. In most LMICs countries, there are large disparities in the placement of health facilities, including those providing skilled delivery services, to the benefit of urban areas [44, 45], which reflects the higher health facility utilization found in our study among urban women. As a result, because women in rural areas are disadvantaged in terms of health facility availability, they are likely to travel long distances to access skilled delivery services in urban areas, which have a plethora of these facilities in the sub-region [46]. The distance becomes a major issue that discourages them from using health facilities for delivery because roads from rural areas are usually in poor condition, in addition to the high cost of transportation fares to urban areas, which most women cannot afford.

In our study, the prevalence and likelihood of giving birth at a health facility as the place of delivery increased as the women's wealth status increased. This study supports the findings of studies [19, 25, 44, 47]. Financial constraints on access and utilization of health services are prevalent in LMICs, preventing many people, particularly the poor, from using the services. This was evident in our finding, where the prevalence and odds of using health facilities for delivery were higher for women who did not consider the cost of treatment to be a major issue, though the difference was not statistically significant. While interventions to ensure people's financial health protection in LMICs have been largely effective, the majority of those who benefit from such interventions, which include health insurance, are those in the highest income quintiles, leaving out the poor [48, 49]. Policies to improve health facility utilization in LMIC countries must therefore not only be properly designed but also implemented with a focus on meeting the needs of the poor, who require them the most. The use of media among women was discovered to be a facilitating factor for health facility delivery in this study. This is most likely because mothers who used media were more likely to receive updated maternal and child health (MNCH) and related information, potentially increasing awareness of the importance of health facility delivery [50].

This study discovered that women who had 4 or more antenatal care visits had a significantly higher likelihood of having a health facility delivery than women who had fewer than 4 antenatal care visits. Studies in Ethiopia [17], Sub-Saharan Africa [47], Bangladesh [40], and Tanzania [20] all supported this finding. One possible explanation is that women in the ANC follow-up received health education about the benefits of health facility delivery. As a result, women will exhibit behavioral changes toward health-care delivery during ANC follow-up.

Access to media was also an important factor in health facility utilization in this study. Individuals with media access (reading newspapers or magazines, listening to the radio, and watching television) had a higher chance of giving birth in a healthcare setting than their non-exposure counterparts, which is consistent with previous research][19, 51, 52]. Individuals with access to local media were also more likely to use health-care services [53]. This simple fact may explain how individuals can quickly obtain various health messages, information about maternal health risk factors, and promotions of institutional delivery via multiple media programs on radio or television [54]. According to the findings of this study, broadcasting the importance of adopting health facility delivery on television, radio, and newspapers may aid in the achievement of maternal and child health-related goals in LMICs [19]. Our research revealed that working women had a higher chance of delivering a child in a health facility compared to their nonworking counterparts. This is consistent with the study [22]. Due to financial constraints, nonworking women are unable to attend proper ANC visits and do not use health facility delivery. This finding revealed that the more live children and family size in the household, the lower the mothers' hospital delivery rate, which was statistically important. This is in agreement with the studies in Ethiopia [30], and Nigeria [22]. This is because having a bigger family may have a negative impact on the household’s economic situation.

In this study, married women and women who live with their partners were more likely than separated women to give birth in a health facility. This study is related to the studies [20, 23, 28] According to theories that link marital status, pregnancy, and birth readiness, separated women face a lack of or a reduced level of psychosocial support and relationship stability. Pregnant women who are not married may be unplanned and/or unwanted. On the contrary, social acceptance of separated status is low because illegitimate births are still stigmatized in many countries. As a result, separated women may be fundamentally different from married women, who may be less empowered, self-isolated, or lack motivation to seek health care [55–57]. All of these factors may be increasing the likelihood of home birth among currently separated mothers. Finally, these findings will be useful for the government and stakeholders in planning, designing, and implementing appropriate interventions, as well as addressing barriers to improving utilization of health facilities, thereby contributing to the reduction of maternal mortality in LMICs.

Strengths and limitations

This study's dataset was derived from nationally representative surveys, which is one of its strengths. Furthermore, this study was founded on an appropriate model (multilevel analysis) that takes the hierarchical nature of the DHS data into account when estimating parameters. Furthermore, because this was a multi-country study, policymakers and program planners will be thinking about better medical decision-making. However, because the data and surveys are cross-sectional, the findings of this study do not establish a cause-and-effect relationship between the outcome variable and the independent variables. Cultural differences in attitudes toward health-care facility utilization across LMICs may have influenced this result. The quality of this study may be influenced by social desirability bias and recall bias.

According to our findings, many LMICs are unable to meet the SDG targets for reducing maternal mortality and improving reproductive-age women's health. Some of these interventions include the development and implementation of harmonized social health insurance schemes, the availability and accessibility of birth centers, the training and deployment of SBAs, the involvement of men in pregnancy and childbirth events, the use of mass media for health education and awareness, and community-based mobilization for maternal healthcare. Special consideration could be given to poor women, unemployed women, rural women, uneducated women, and those who use fewer antenatal care visits when implementing these interventions.

AOR: Adjusted Odds Ratio; CI: Confidence Interval; DHS: Demographic Health Survey; ICC: Intra-class Correlation Coefficient; LLR: Log-likelihood Ratio; MGDs: Millennium Development Goals; LMICs: Low and Middle Income Countries; MMR: Maternal Mortality Ratio; MOR: Median Odds Ratio; Ref.: Reference Category; SBA: Skilled Birth Attendance; SDG: Sustainable Development Goal; WHO: World Health Organization.

Authors’ contributions

AA wrote the proposal, analyzed the data, and did the manuscript writing. YA accredited the proposal with revisions, analysis of the data, and manuscript writing. Both authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Ethics approval

This study was based on an examination of publicly available secondary data that had been stripped of all identifiers. The MEASURE DHS Project Phase 3 was reviewed and approved by the Institutional Review Board (IRB) of ICF Macro in Fairfax, Virginia, USA. That approval covers the DHSs from 2010 to 2018. ICF Macro's IRB met the requirements of the US Department of Health and Human Services for "Protection of Human Subjects" (45 CFR 46). The authors were granted permission to use the data by ICF Macro. The complete list of ethical approvals can be found at http://dhsprogram.com, and the data can be found at https://www.dhsprogram.com/data/dataset_admin/login_main.cfm. We confirm that all methods were carried out in accordance with the relevant guidelines and regulations.

Consent to participate

Most importantly, the informed consent statement emphasizes that participation is voluntary; that the respondent may refuse to answer any question, decline any biomarker test, or terminate participation at any time; and that the respondent's identity and information will be kept strictly confidential. In addition, written informed consent was obtained from a parent or guardian for participants under 16 years old.

Availability of data and materials

The data used in this article were available on http://dhsprogram.com.

Funding

The authors have no support or funding to report.

Acknowledgments

The authors are grateful to the DHS program for giving us permission to use the data for our purpose. The manuscript was edited and proofread for language by Assafaw Kelebu (MSc.), Department of English Language and Literature, Mekdela Amba University.

Consent for publication

Not applicable

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Table 1. DHS years of study and study participants of health facility delivery in LMICs.

Region	Country	DHS year	Weighted number of reproductive women	Place of delivery
				Home		Health facility
				Frequency	%	Frequency	%
Eastern Africa	Burundi	2016/17	7573	1160	15.3	6413	84.7
	Comoros	2012	1637	490	30.1	1145	69.9
	Ethiopia	2016	6514	4127	63.4	2387	36.6
	Kenya	2014	6512	2760	42.4	3752	57.6
	Malawi	2015/16	10783	726	6.7	10057	93.3
	Rwanda	2019/20	7377	1739	23.6	5638	76.4
	Tanzania	2015/16	4050	518	12.8	3532	87.2
	Uganda	2016	4696	1408	30.0	3288	70.0
Middle Africa	Angola	2015	5686	3090	54.3	2596	45.7
	Cameroon	2018	4803	1549	32.3	3254	67.7
	Chad	2014/15	10139	8150	80.4	1989	19.6
	Congo	2011/12	5358	712	13.3	4646	86.7
	Congo DR.	2013/14	10298	3131	30.4	7167	69.6
	Gabon	2012	2837	464	16.4	2373	83.6
Northern Africa	Egypt	2014	15727	2425	15.4	13302	84.6
Southern Africa	Lesotho	2014	2238	543	24.3	1695	75.7
	South Africa	2016	5278	922	17.5	4356	82.5
	Zimbabwe	2015	8149	2166	26.6	5983	73.4
	Namibia	2013	1621	273	16.8	1348	83.2
	Zambia	2018	5231	821	15.7	4410	84.3
Western Africa	Benin	2017	7738	1278	16.5	6460	83.5
	Burkina Faso	2010	10117	2695	26.6	7422	73.4
	Cote d’Ivoire	2011/12	4505	2031	45.1	2474	54.9
	Gambia	2019/20	4701	911	19.4	3790	80.6
	Guinea	2018	4857	2442	50.3	2415	49.7
	Liberia	2019	2696	524	19.4	2172	80.6
	Mali	2018	5662	1929	34.1	3733	65.9
	Mauritania	2019/20	4872	1274	26.1	3598	73.9

Table 1. DHS years of study and study participants of health facility delivery in LMICs (Continued).

Region	Country	DHS year	Weighted number of reproductive women	Place of delivery
				Home		Health facility
				Frequency	%	Frequency	%
Western Africa	Niger	2012	3921	1533	39.1	2388	60.9
	Nigeria	2018	7468	4591	61.5	2877	38.5
	Senegal	2019	4933	332	6.7	4601	93.3
	Sierra Leone	2019	3805	823	21.6	2982	78.4
	Togo	2013/14	4754	2342	49.3	2412	50.7
	Ghana	2014	3847	1108	28.8	2739	71.2
Central Asia	Kyrgyz Rep	2012	3055	15	0.5	3040	99.5
Central Asia	Tajikistan	2017	992	45	4.5	947	95.5
South – Eastern Asia	Cambodia	2014	5846	965	16.5	4881	83.5
South – Eastern Asia	Philippines	2017	5257	2129	40.5	3128	59.5
Southern Asia	Afghanistan	2015	19015	9909	52.1	9106	47.9
	Bangladesh	2017/18	4918	2477	50.4	2441	49.6
	Indonesia	2017	14721	3885	26.4	10836	73.6
	Maldives	2016/17	2051	110	5.4	1941	94.6
	Nepal	2016	1621	273	16.8	1348	83.2
	Pakistan	2017/18	19787	11805	59.7	7982	40.3
Western Asia	Armenia	2015/16	1251	3	0.2	1248	99.8
Western Asia	Jordan	2017/18	7002	102	1.5	6900	98.5
Central America	Guatemala	2014/15	9007	2795	31.0	6212	69.0
Central America	Honduras	2011/12	8142	1617	19.9	6525	80.1
Southern Europe	Albania	2017/18	2280	33	1.4	2247	98.6
Caribbean	Dominican republic	2013	2655	58	2.2	2597	97.8
	Haiti	2016/17	4205	2598	61.8	1607	38.2
	Myanmar	2015/16	3723	2313	62.1	1410	37.9
	Timor leste	2016	5705	1993	34.9	3712	65.1
Oceania	Papua NG	2016/18	8105	2686	33.1	5419	66.9
Overall			329,721	106, 800	32.4	222,921	67.6

Table 2. Frequency and Percentage distribution of characteristics of respondents in LMICs

Independent variables	Characteristics	Frequency (%)
Age of mother	Less than 20	31618 (9.6)
	20to34	216402 (65.6)
	Greater than34	81701 (24.8)
Types of place of residence	Urban	109923 (33.3)
Types of place of residence	Rural	219798 (66.7)
mother education level	No education	113982 (34.6)
	Primary	95088 (28.8)
	Secondary	95103 (28.8)
	Higher	25548 (7.7)
Family size	Less than 4	39343 (11.9)
	4 to 8	216296 (65.6)
	More than 8	74082 (22.5)
Number of children	Only one	153100 (46.4)
	2 to 3	153099 (46.4)
	More than 3	23522 (7.1)
Sex of household head	Male	282330 (85.6)
Sex of household head	Female	47397 (14.4)
Wealth index	Poor	101811 (30.88)
	Middle	128126 (38.86)
	Rich	99784 (30.26)
Current marital status	Married	271605 (82.4)
	Living with partners	49178 (14.9)
	Widowed	1840 (0.6)
	Divorced	1524 (0.5)
	Separated	5574 (1.7)
Husband/partner’s education level	No education	94973 (28.8)
	Primary	89116 (27.0)
	Secondary	112032 (34.0)
	Higher	33600 (10.2)
Husband/partner’s occupation	Have no any work	14288 (4.3)
	Have any work	315433 (95.7)
Currently working status	No	166440 (50.5)
	Yes	163281 (49.8)
Mother occupation	Have no any work	139690 (42.4)
	Have any work	190031 (57.6)
Media exposure	No	182568 (55.4)
	Yes	147153 (44.6)
Antenatal care visits	Less than 4 visits
	4 and more than 4 visits

Table 3. Multivariable multilevel logistic regression analysis of both individual and community-level factors associated with health facility delivery in LMICs.

Individual and community-level factors	Models
Individual and community-level factors	Null model AOR (95%CI)	Model I AOR (95%CI)	Model II AOR (95%CI)	Model III AOR (95%CI)
Age of mother
Less than 20		0.723 (0.703 – 0.747)		0.958 (0.928 – 0.990)
20 to 34		0.943 (0.925 – 0.961)		0.986 (0.957 – 1.015)
Greater than 34		Ref.		Ref.
Types of place of residence
Urban				Ref.
Rural				0.668 (0.489 – 0.913)
Mother education level
No education		Ref.		Ref.
Primary		1.564 (1.530 – 1.599)		1.545 (1.511 – 1.583)
Secondary		2.336 (2.275 – 2.398)		2.145 (2.087 – 2.206)
Higher		4.848 (4.233 – 4.750)		3.362 (3.167 – 3.570)
Family size
Less than 4		Ref.		Ref.
4 to 8		0.838 (0.808 – 0.868)		0.862 (0.836 – 0.889)
More than 8		0.840 (0.815 – 0.865)		0.929 (0.895 – 0.964)
Number of children
Only one		Ref.		Ref.
2 to 3		0.874 (0.858 – 0.891)		0.859 (0.842 – 0.876)
More than 3		0.886 (0.856 – 0.921)		0.865 (0.833 – 0.898)
Sex of household head
Male		0.974 (0.950 – 0.998)		0.993 (0.968 – 1.019)
Female		Ref.		Ref.
Wealth index
Poor		Ref.		Ref.
Middle		2.124 (2.088 – 2.160)		1.894 (1.859 – 1.930)
Rich		2.916 (2.845 – 2.989)		2.012 (1.563 – 2.123)

Table 3. Multivariable multilevel logistic regression analysis of both individual and community-level factors associated with health facility delivery in LMICs (Continued).

Individual and community-level factors	Models
Individual and community-level factors	Null model AOR (95%CI)	Model I AOR (95%CI)	Model II AOR (95%CI)	Model III AOR (95%CI)
Marital status
Married		1.251 (1.221 – 1.282)		1.376 (1.340 – 1.413)
Living with partner		1.552 (1.444 – 1.669)		1.832 (1.699 – 1.975)
Widowed		0.894 (0.793 – 1.008)		0.897 (0.790 – 1.019)
Divorced		0.832 (0.750 – 0.924)		0.891 (0.799 – 0.992)
Separated				Ref.
Husband education level
No education		Ref.		Ref.
Primary		1.269 (1.240 – 1.299)		1.395 (1.361 – 1.430)
Secondary		1.520 (1.484 – 1.558)		1.684 (1.640 – 1.729)
Higher		1.728 (1.656 – 1.804)		1.823 (1.743 – 1.907)
Husband occupation
Have no any work		Ref.		Ref.
Have any work		1.127 (1.085 – 1.170)		1.340 (1.284 – 1.398)
Current working status
No		Ref.		Ref.
Yes		1.576 (1.530 – 1.625)		1.335 (1.331 – 1.338)
Mother occupation
Have no any work		0.674 (0.653 – 0.695)		0.844 (0.843 – 0.846)
Have any work		Ref.		Ref.
Media exposure
No		Ref.		Ref.
Yes		1.344 (1.321 – 1.368)		1.311 (1.287 – 1.336)
Antenatal care visits
Less than 4 visits		Ref.		Ref.
4 and more visits		2.633 (2.588 – 2.678)		2.402 (2.360 – 2.446)

Table 3. Multivariable multilevel logistic regression analysis of both individual and community-level factors associated with health facility delivery in LMICs (Continued)

Individual and community-level factors	Models
Individual and community-level factors	Null model AOR (95%CI)	Model I AOR (95%CI)	Model II AOR (95%CI)	Model III AOR (95%CI)
Region
East Africa			0.085 (0.048 – 0.149)	0.072 (0.040 – 0.127)
Middle Africa			0.038 (0.022 – 0.068)	0.034 (0.019 – 0.060)
Northern Africa			0.104 (0.058 – 0.183)	0.070 (0.039 – 0.124)
Southern Africa			0.129 (0.072 – 0.228)	0.100 (0.056 – 0.178)
Western Africa			0.073 (0.042 – 0.129)	0.103 (0.058 – 0.181)
Central Asia			0.835 (0.413 – 1.687)	0.386 (0.193 – 0.774)
South-Eastern Asia			0.153 (0.085 – 0.276)	0.106 (0.058 – 0.190)
Southern Asia			0.033 (0.018 – 0.057)	0.029 (0.016 – 0.051)
Western Asia			0.775 (0.426 – 1.409)	0.572 (0.315 – 1.039)
Central America			0.093 (0.053 – 0.165)	0.054 (0.030 – 0.095)
Caribbean			0.052 (0.030 – 0.093)	0.031 (0.018 – 0.053)
Oceania			0.040 (0.023 – 0.071)	0.053 (0.030 – 0.094)
Southern Europe			Ref.	Ref.
Random effects model
Community variance ()	1.62	0.53	0.82	0.47
ICC%	26.9	14.6	18.15	6.4
PCV%	Ref.	67.28	49.38	70.90
MOR (95%CI)	3.37 (1.79 – 4.95)	2.00 (1.44 – 2.55)	2.36 (2.35 – 2.45)	1.91 (1.87 – 1.98)
Model comparison
Log-likelihood ratio	-184716.7	-161938.6	-184668.6	-161895.6
Deviance	369433.5	323877.2	369337.1	323791.2
AIC	369441.5	323931.2	369371.1	323871.2
BIC	369484.3	324220.2	369553.1	324299.4

No competing interests reported.

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Determinants of health facility delivery among reproductive age women in low and middle income countries; evidence from most recent demographic health survey: Application of multilevel binary logistic regression model

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