Problem statement
Maternal health care remains a critical issue globally, particularly in low-income countries According to the World Health Organization (WHO), the global maternal mortality ratio (MMR) decreased from 380 deaths per 100,000 live births in 1990 to 210 in 2013 Despite this progress, the MMR in developing regions is still 14 times higher than in developed areas While there has been a general decline in maternal deaths worldwide, the target set by the Millennium Development Goal 5—to reduce the MMR by three-quarters between 1990 and 2015—has not yet been met.
Maternal deaths can be attributed to both direct and indirect causes Direct causes arise from complications during pregnancy, delivery, and the postpartum period, including hemorrhage, infection, obstructed labor, unsafe abortion, ectopic pregnancy, and anesthesia-related issues In contrast, indirect causes stem from pre-existing conditions or diseases not directly related to obstetrics, such as hepatitis, anemia, malaria, heart disease, and tetanus According to the World Health Organization (WHO) in 2005, direct causes account for approximately 80% of maternal mortality rates (MMR), highlighting their significant impact on maternal health.
Antenatal care and delivery services, introduced by WHO in the safe motherhood package in 1994, play a crucial role in preventing complications during pregnancy These services provide pregnant women and their families with essential information about their health and the development of their unborn child By monitoring the weight and growth of the baby, antenatal care can help prevent low birth weights through improved maternal nutrition Additionally, regular check-ups allow for the early detection of risks and danger signs, enabling timely interventions such as tetanus immunization, which is vital for the health of both mother and baby Effective management of high blood pressure during pregnancy also significantly enhances maternal health and boosts infant survival rates.
Delivery care is crucial in reducing maternal deaths, as highlighted by the WHO, which recommends childbirth in health facilities attended by skilled health staff to ensure safe deliveries and healthy babies Proper hygiene and adequate medical equipment in these facilities can significantly decrease complications during labor, such as hemorrhage and obstructed labor Moreover, the presence of skilled health professionals ensures safe delivery and effective emergency management when needed.
Vietnam has made significant strides in improving maternal health as part of its commitment to Millennium Development Goal 5, evidenced by a notable decline in the maternal mortality ratio (MMR) According to the World Bank, the MMR in Vietnam decreased from 81 deaths per 100,000 live births in 2000 to 54 per 100,000 in 2015 Access to antenatal care, crucial for the health of pregnant women and their babies, has also improved, with a 2014 Multiple Indicator Cluster Survey (MICS 5) indicating that 95.8% of women aged 15-49 with a live birth in the last two years received antenatal care at least once Despite these advancements, significant disparities in maternal mortality and the utilization of maternal health care persist among different ethnic groups, regions, and living environments.
Maternal mortality rates in mountainous areas are over three times higher than in lowland regions, highlighting significant health disparities The MICS5 data reveals that women in rural areas have fewer prenatal care visits compared to their urban counterparts, particularly in terms of having more than four visits Ethnic minority groups face even greater challenges, with only 1% receiving one visit and 32.7% having at least four visits, in stark contrast to 99.2% and 82.1% of the Kinh group These growing disparities in health outcomes and access to maternal healthcare present substantial challenges in recent years.
Recent studies on maternal health care utilization in Vietnam have primarily examined the impact of demographic and socioeconomic factors (Sepheri et al., 2008; Tran et al., 2011; Goland et al., 2012; Malqvist et al., 2012).
Demographic factors influencing the utilization of health services include younger age and low birth order, while separated or unmarried status and unintended pregnancies are associated with lower usage rates Furthermore, socio-economic factors significantly impact access to maternal health care, with a woman's education level emerging as the most crucial determinant according to various studies.
Lower household income significantly affects the likelihood of utilizing maternal health care services (Sepheri et al 2008, Goland et al 2012) Research highlights disparities in maternal health care access between ethnic majority and minority groups (Malqvist et al 2012, Malqvist et al 2013) Additionally, equity issues exist between rural and urban areas, as noted by Tran et al (2011), while Sepheri et al (2008) emphasize regional disparities in the availability and accessibility of maternal health care in Vietnam.
Many studies have overlooked community factors in assessing maternity health care utilization, with Sepheri et al (2008) being one of the few to estimate the impacts of poverty rates Ignoring these community effects can lead to biased conclusions regarding the influences on disadvantaged women's health care access (Singh et al 2014) Community beliefs and norms play a crucial role in shaping women's health care-seeking behaviors, while economic development within the community can enhance access to health services and empower women in decision-making, fostering positive attitudes towards health service use (Stephenson et al 2006) Key community-level indicators include the poverty rate, the proportion of women with higher education, and the percentage of women delivering in health facilities, with higher poverty rates negatively impacting the likelihood of receiving antenatal care (Gage & Calixte).
2006, Sepehri et al 2008, Ononokpono et al 2013, Singh et al 2014,) and facility delivery
Research indicates that a higher level of education among women and an increased preference for facility-based deliveries are positively correlated with the utilization of maternal health care services.
Ononokpono et al 2013, Singh et al 2014)
This study emphasizes the importance of examining the determinants of health care service utilization at individual, household, and community levels Utilizing the 2014 Vietnam Multiple Indicator Cluster Survey (MICS 5), it employs the Poisson Model to assess the influence of social determinants on prenatal care visits and the Multinomial Logistic Model to analyze the relationship between social factors and the selection of delivery care providers.
Research objectives
This thesis research aims to achieve two primary objectives: first, it analyzes the demand for prenatal health care by examining the factors influencing the number of antenatal care visits among women over the past two years, utilizing data from MICS 5 Second, it explores the decision-making process regarding the choice of delivery service facilities for pregnant women in Vietnam.
Research questions
To investigate the above objectives, the following questions need to be answered thoroughly:
Question 1: What are the determinants of the demand for antenatal care visits?
Question 2: What are the determinants of the choice on delivery care provider?
Structure
This paper is structured to explore the demand for health care and the selection of health care providers, with a focus on the influence of social determinants on maternal health care utilization Chapter 2 outlines the theoretical framework and methodology, utilizing the Multiple Indicator Cluster Survey 2013-2014 (MICS5) to analyze the factors affecting prenatal health care demand and the choice of delivery facilities The findings and discussions are detailed in Chapter 4, while Chapter 5 concludes the study and addresses the implications for health policy.
The role of maternity health care
Motherhood is a rewarding journey for women, yet it can also present significant health challenges during pregnancy, childbirth, and the postpartum period These issues can adversely affect both maternal and infant health, with three-quarters of maternal deaths occurring during childbirth and the postpartum phase Fortunately, proper antenatal and delivery care can effectively mitigate these risks and prevent complications.
Antenatal care (ANC) was introduced in the early 1900s to promote the health of pregnant women and their unborn children, allowing for the timely detection of potential complications ANC enhances women's understanding of fetal development and their own health, helping to prevent adverse outcomes like low birth weight through improved nutrition Women receive vital information about pregnancy and delivery risks, with the World Health Organization (WHO) recommending at least one visit to a skilled health provider or a minimum of four ANC visits overall WHO guidelines for ANC include assessments of maternal and fetal health, such as measurements of body weight and height, blood pressure monitoring, urine and blood tests, as well as medical provisions like tetanus vaccinations and iron and folate supplements, alongside health education and counseling.
The role of health facilities in childbirth is crucial for ensuring the safety of mothers and the delivery of healthy infants Proper medical technology and hygienic conditions can significantly reduce the risk of complications and infections, thereby decreasing maternal and infant morbidity and mortality Skilled birth attendants, including midwives, doctors, and nurses, are essential in this process, as they are trained to manage normal childbirth and address complications effectively The World Health Organization (WHO) recommends that countries with high maternal mortality rates ensure that at least 60% of deliveries are assisted by skilled birth attendants Between 2005 and 2010, it was reported that 69% of women giving birth received care from skilled staff, highlighting progress in improving maternal health services.
Overview of maternal health and health care in Vietnam
Vietnamese culture, particularly in the northern region, is significantly shaped by Confucianism, which emphasizes the importance of sons in family lineage and ancestral worship In this tradition, sons inherit family resources and bear the responsibility of caring for family members, enhancing the status of women when they give birth to male children However, this cultural expectation places considerable pressure on women, especially those with daughters, leading to a pronounced preference for sons and contributing to an increasing sex ratio at birth.
In many families, male members are viewed as the primary breadwinners and decision-makers, while female members often face vulnerability, particularly when their lives are dictated by their parents After marriage, women typically reside with their husband's family, leading to their income being controlled by in-laws and spouses The strong influence of Confucianism and existing hierarchies restrict women's autonomy and independent decision-making, particularly regarding their health For instance, the childbirth experiences of mothers and mothers-in-law significantly impact the maternity care of younger women, potentially hindering their access to essential maternal health services.
Vietnam's two-child policy, introduced in the late 1980s, limited the number of children per household and included family planning measures such as free birth control devices and abortion facilities Non-compliance resulted in penalties, including fines and career repercussions for government employees This led some women to conceal pregnancies and neglect maternal healthcare, as additional children imposed financial and time constraints The revised 2009 Population Ordinance now allows couples to choose their childbearing timing while permitting one or two children, resulting in a decline in the total fertility rate from 2.55 in 2001 to 1.99 in 2011, indicating the policy's effectiveness in stabilizing population growth However, challenges remain, including ineffective contraceptive methods, a high abortion rate among youth, and a lack of knowledge about contraception, compounded by societal preferences for sons and financial constraints.
2.2.3 Maternal mortality ratio and maternal health care in Vietnam
In Vietnam, the government advises that pregnant women have a minimum of three prenatal visits to monitor and mitigate health risks for both mother and baby Prenatal care includes essential assessments such as blood pressure measurement, urine and blood testing, and monitoring weight and height The national guidelines also emphasize the importance of delivering in health facilities, as proper medical care and hygienic conditions significantly reduce complications during and after childbirth In cases where a Caesarean section is necessary, it should be conducted by skilled obstetricians to ensure a safe delivery Additionally, during the postpartum period, at least two health checkups are recommended for both mother and child.
The maternal mortality ratio (MMR) measures the number of women who die from pregnancy and childbirth-related causes within 42 days post-delivery, per 100,000 live births According to World Bank data, Vietnam has significantly improved its MMR over the past 15 years, decreasing from 81 per 100,000 in 2000 to 54 per 100,000 in 2015, successfully achieving the Millennium Development Goal 5 target of 58.3 per 100,000 live births Despite this progress, Vietnam still lags behind developed Asian countries like Singapore, Malaysia, and Thailand To ensure sustainable population growth, Vietnam must intensify efforts to further reduce its MMR.
Figure 1: MMR in Vietnam in the period of 2000 – 2015
Figure 2: MMR of the Asian countries in the period of 2000 – 2015
Maternal mortality ratio (per 100,000 live births)
Lao PDR Malaysia Indonesia Thailand Cambodia Brunei Darussalam Singapore
To reduce maternal and infant mortality rates, the WHO recommends that each pregnant woman should have a minimum of four prenatal care visits, or at least one visit with professional health staff Antenatal care is essential for educating women and their families about potential risks during pregnancy and childbirth Vietnam has made significant strides in antenatal care, with 95.8% of pregnant women having at least one prenatal visit in 2014, an increase from 1997 However, only 73.7% of women receive more than four visits, highlighting an ongoing challenge for the country Additionally, there is a notable disparity in maternal health care utilization based on ethnicity, residence, and region Women in rural areas have fewer prenatal visits compared to those in urban areas, and ethnic minority groups face greater barriers, with only 32.7% having at least four visits compared to 82.1% of the Kinh group.
Figure 3: Percentage of women having at least 1 visit and at least 4 visits during pregnancy
Source: Ministry of Planning and Investment, MICS4
At least 4 times by any providers At least 1 visit by skilled health worker
Figure 4: The percentage of the women taking antenatal care visits by residence in 2011 and 2014
Figure 5: The percentage of the women taking antenatal care visits by ethnicity in 2011 and 2014
Antenatal care visits by residence
Antenatal care visits by ethinicity
The demand for health care
Economists became interested in the health seeking behavior in the late 1960s and investigating the factors influencing the behavior The major contributions were by Grossman
(1972), Rosenstock (1974), Thaddeus and Maine (1994) and Andersen (1995)
Grossman (1972) posited that individuals seek good health rather than health care itself when making purchasing decisions He developed a demand model for health that highlights the positive utility derived from consumption goods and the negative utility associated with time spent being sick, denoted as 𝑡 𝑠 (𝐻) (Zweifel et al 2009).
Health stock, denoted as H, fluctuates over time, leading to a depreciation of health capital at a rate of 𝛿 Nevertheless, individuals have the opportunity to enhance their health capital through investments represented by I In a two-period model, the current state of health can be articulated accordingly.
In his 1972 study, Grossman identified two primary reasons for individual demand for health: first, health is viewed as an investment commodity, contributing to future productivity and well-being; second, it serves as a consumption commodity, providing immediate benefits Key variables in this analysis include utility (U), health capital stock (H), the depreciation rate of health (𝛿), wage rate (w), consumption goods (X), medical services consumed (M), health investment (I), sick time (𝑡𝑠), and time invested in health (𝑡𝐼).
Basing on the function 𝐼(𝑀, 𝑡 𝐼 ) and 𝑡 𝑠 (𝐻 1 ) from (1.1) and (1.2), Grossman constructed the demand function for medical services in investment model: ln 𝑀 = 𝑐𝑜𝑛𝑠𝑡 − (1 + 𝛼 𝑀 (𝜀 − 1))𝑙𝑛𝑝 + (1 + 𝛼 𝑀 (𝜀 − 1))𝑙𝑛𝑤 − (1 − 𝜀)𝛼 𝐸 𝐸 (1.3)
Where, 𝛼 𝑀 is the production elasticity of medical services and 𝛼 𝐸 is the effectiveness of education E; 𝜀 is the marginal efficiency of health capital 𝐻 1
From the utility function (1.1) including sick time and consumption good, he constructed the demand function for medical services in consumption model: ln 𝑀 = 𝑐𝑜𝑛𝑠𝑡 − (1 + 𝛼 𝑀 (𝜅 − 1))𝑙𝑛𝑝 + (1 − 𝜅)(1 − 𝛼 𝑀 )𝑙𝑛𝑤 − (1 − 𝜅)𝛼 𝐸 𝐸 − 𝜅𝑙𝑛𝜆
The demand for health services is influenced by various factors, including the price of medical services, wage rates, education, and wealth Specifically, a decrease in the price of medical services leads to a reduction in the optimal quantity of health services demanded, while an increase in wage rates results in a higher quantity demanded Additionally, in a multi-period model, age is incorporated into the demand functions, as the depreciation rate is positively correlated with age (Zweifel et al 2009).
The Health Belief Model, introduced by Rosenstock in 1974, explains individual health-seeking behavior by emphasizing the role of personal beliefs about health issues, perceived benefits and barriers, and cues to action Key factors influencing health-promoting behavior include the perception of serious health risks, which significantly increases the likelihood of taking action Individuals assess the benefits of action against potential barriers, such as inconvenience or side effects; if the perceived benefits outweigh the barriers, health-promoting behaviors are more likely to occur Additionally, cues to action, both internal (like symptoms) and external (such as information from friends or media), play a crucial role in prompting individuals to engage in healthier behaviors This model has been effectively utilized to design interventions aimed at modifying health-related behaviors by addressing its various components.
Thaddeus and Maine (1994) introduced the three delay theory to highlight barriers to timely maternal health care utilization The first phase involves delays in seeking care, influenced by individual and family decisions, women's status, prior experiences with the healthcare system, and financial or opportunity costs The second phase pertains to delays in accessing health facilities, which are affected by facility availability, distance, transportation costs, and infrastructure Finally, the third phase addresses delays in receiving adequate care, stemming from a lack of necessary equipment and qualified health staff.
The Andersen Behavioral Model of Health Services Utilization (1995) is instrumental in analyzing health service usage in both developed and developing nations (Thind et al 2008) This model identifies three key factors influencing an individual's access to and use of healthcare: predisposing, enabling, and need factors Predisposing factors include demographic characteristics (age, gender, marital status), social structure (education, occupation, ethnicity), and health beliefs that shape individuals' perceptions and attitudes towards healthcare systems Enabling factors, both personal (income, health insurance, travel, and waiting times) and organizational (availability of health facilities), assess the actual ability to obtain services Finally, need factors encompass the self-assessment of health status and evaluations made by healthcare professionals, serving as direct indicators of health service utilization.
Prenatal care plays a crucial role in reducing maternal and infant mortality by providing essential measurements and counseling that help women understand their health and that of their babies This proactive approach allows for the identification of potential risks, ensuring safer pregnancies and childbirths while also minimizing postpartum complications Research has explored the relationship between maternal health care and health outcomes, as well as the factors influencing the utilization of these services The following discusses the key determinants affecting the demand for prenatal care visits and the selection of delivery locations based on previous studies.
Determinants of health and well-being are classified into three levels: individual, household, and community Individual factors encompass education level, maternal age, marital status, religion, and ethnicity Household characteristics include the size of the household and its wealth At the community level, key elements comprise the place of residence, regional variations, poverty rates, and illiteracy rates.
Numerous studies emphasize the critical role of maternal education in the utilization of maternal health care services Women with higher educational levels are more likely to access adequate antenatal care (ANC) (Arthur 2012, Bbaale 2011, Navaneetham & Dharmalingam 2002) Educated women tend to possess greater decision-making power regarding health matters and are more inclined to seek quality health care beyond their homes (Navaneetham & Dharmalingam 2002) Interestingly, there is no significant difference in maternal health care utilization among educated women when comparing primary and secondary education levels (Navaneetham & Dharmalingam 2002) Furthermore, a study on the impact of the National Health Insurance (NHI) introduction in Taiwan revealed that while educational attainment had an insignificant effect on ANC prior to NHI, it showed a significant positive correlation afterward (Chen et al 2003) This discrepancy remains unclear Overall, educated women are generally more aware of the importance of maternal care and tend to have more frequent antenatal care visits compared to their less-educated counterparts.
Marital status also is a key determinant of the use of maternal health care Sepehri et al
Research indicates that marital status influences prenatal care utilization, with married women more likely to access maternal care compared to their unmarried counterparts Sepehri et al (2008) found that while marital status affects the number of prenatal visits, it does not significantly impact the choice of delivery location This disparity can be attributed to the stigma single mothers face in Vietnam, where childbirth is traditionally viewed as a shared responsibility between parents Supporting this, Chen et al (2003) demonstrated in a Taiwanese study that married women benefit from their husbands' support, leading to increased access to maternal healthcare services.
The age of expectant mothers significantly influences their utilization of maternal health services; as mothers get older, the likelihood of them seeking healthcare decreases.
Research indicates that maternal health knowledge and experience significantly influence women's health-seeking behaviors (Chen et al., 2003) Tsawe & Susuman (2014) found that women aged 15-39 are more likely to attend regular health check-ups compared to those over 40 However, some studies, such as one conducted in Turkey, suggest that age does not significantly impact attendance at antenatal care (ANC) services.
Research indicates that the number of children a mother has negatively impacts her use of maternal health care services, with higher-order births leading to decreased utilization due to time and resource constraints (Navaneetham & Dharmalingam, 2002) First-time mothers are more likely to seek antenatal care, often due to their lack of experience However, previous negative experiences with maternal services can deter women from utilizing antenatal care in subsequent pregnancies (Arthur, 2012) This is supported by Tsawe & Susuman (2014), who noted that the quality of maternal health services directly influences women's willingness to seek care Additionally, policies such as the two-child limit and associated penalties may further discourage mothers with more than two children from accessing necessary maternal health care (Sepehri et al., 2008).
A study by Wado et al (2013) in Southwestern Ethiopia revealed a significant link between pregnancy intention and the use of antenatal care, while the relationship with delivery care remained unclear The researchers suggested that women experiencing unwanted pregnancies may not adequately prepare emotionally or financially for childbirth, leading to less attention to their health and that of their unborn child Additionally, these women often recognize their pregnancies later, resulting in missed early antenatal care visits Specifically, the study found that women with unintended pregnancies detected their condition about a month later than those with intended pregnancies, underscoring the strong association between pregnancy intention and maternal care utilization.
The choice of health care provider
In the late 1980s, Gertler, Locay, and Sanderson introduced the concept of health-seeking behavior in choosing healthcare providers They posited that the decision-making process occurs in two stages: first, individuals determine whether to seek healthcare, and second, they select the provider that offers the greatest utility The utility function for an individual receiving care from a specific provider is foundational to understanding this behavior.
In which, 𝑈 𝑖𝑗 is the utility of the individual i after receiving health care from provider j, ℎ 𝑖𝑗 is expected health status of the individual after receiving health care from provider j and
𝐶 𝑗 is other consumption expenditure after paying provider j
The health status after receiving health care from provider j for an individual i depends on the quality of provider j’s health care
Where ℎ 0 is the health status before receiving health care from provider j
The quality of health care varies significantly among providers and individuals This variation is influenced by the distinct characteristics of health care providers (𝑍𝑖) and the individual patients (𝑋𝑖).
After receiving healthcare, the remaining consumption expenditure \( C_j \) represents the leftover income \( Y_i \) after settling payments to healthcare provider \( j \) The price \( P_{ij} \) for this alternative includes both direct costs, such as consultation and medication fees, as well as indirect costs, like transportation expenses and waiting time.
Assuming that the individual has j alternatives and would like to maximize the utility so the utility maximization is expressed as:
Where 𝑈 ∗ is the maximum utility and 𝑈 1 , … , 𝑈 𝑗 is the individual utility with alternative of health care provider 1, …., j
Researchers have been unable to directly measure individual utility; instead, they focus on analyzing the characteristics of both the alternatives and the individual (Train, 2009) Consequently, the utility function for an individual is formulated accordingly.
In which, 𝑉 𝑖𝑗 is observed characteristics and 𝜀 𝑖𝑗 is unobserved characteristics
The characteristics influencing healthcare choices include observable factors such as gender, age, education, income, and insurance, alongside unobservable elements like perceptions of healthcare quality and preferred medical administration Similarly, healthcare providers are assessed based on observable traits such as pricing and proximity to patients' homes, while unobservable factors include the provider's reputation and prestige.
Childbirth in health facilities significantly reduces maternal mortality and morbidity rates, yet many women in developing countries still opt for home births To understand this choice, previous studies have explored various determinants, focusing on the individual characteristics of women as well as the household and community factors influencing their decision regarding delivery care providers.
Numerous studies emphasize the crucial role of prenatal care visits in influencing healthcare decisions According to Stephenson et al (2006), adequate prenatal care significantly impacts the choice of facility-based delivery by educating expectant mothers about the advantages of institutional care and available services Similarly, Sepehri et al (2008) support this notion, stating that timely and sufficient prenatal visits enhance awareness regarding the necessity of care during delivery.
Educational attainment significantly influences the choice of childbirth location Research by Celik & Hotchkiss (2000) indicates that women with higher education levels are more inclined to opt for facility-based deliveries instead of traditional home births This trend suggests that educated women are more empowered to make informed decisions regarding their healthcare Additionally, they are better equipped to understand the advantages of facility-based delivery, leading to safer childbirth experiences.
With respect to the birth order, the previous studies reported that it has strong association with the choice of delivery at health facilities Navaneetham & Dharmalingam
Research from 2002 indicates that women having their first child are more likely to deliver in healthcare institutions compared to those with subsequent births This discrepancy is attributed to various factors; some researchers argue that women with higher parity often encounter time and resource limitations due to larger family sizes Conversely, negative experiences from previous childbirths may lead these women to underestimate the necessity of facility-based deliveries.
Research on the impact of maternal age on delivery method choices presents mixed findings According to Celik & Hotchkiss (2000), the age of women at their last childbirth does not significantly influence their preferred delivery location.
On contrary, Stephenson et al (2006) argued that the age of the interviewed women had significant association with the choice of facility delivery in the study of six Africa countries
Women aged 40-49 and 30-39 are more likely to give birth in health facilities compared to those aged 20-29 However, this trend is not significant in certain countries, including Burkina Faso, Ivory Coast, and Ghana.
Marital status significantly influences the choice of birth delivery location, with mixed outcomes observed According to Stephenson et al (2006), women in polygamous marriages or those who are separated are less likely to give birth in health institutions for their most recent childbirth across all six studied regions.
Africa countries However, Sepehri et al (2008) found that marital status had no significant impact in the choice of delivery location
The choice of delivery location is significantly influenced by household characteristics such as wealth index, ethnicity, and religion Research by Stephenson et al (2006) indicates that women from households with a higher income index are more likely to opt for health institution deliveries compared to those with lower income levels This disparity arises because the costs associated with childbirth and transportation often prevent poorer women from accessing necessary health services Additionally, previous studies have highlighted the substantial impact of ethnicity on the decision regarding facility-based deliveries.
Hotchkiss (2000) noted that both urban and rural women experience similar effects related to cultural and economic barriers, as well as inadequate healthcare services Additionally, religion plays a significant role in women's choices regarding childbirth locations A study by Stephenson et al (2006) across six African countries revealed that in Ghana, Muslim women are less likely to give birth in healthcare facilities compared to Catholic women, while Protestant women are more inclined to utilize these services than their Catholic counterparts.
There are also remarkable regional differences in the choice of delivery location Celik
Research by Hotchkiss (2000) indicates that urban women are more inclined to seek facility deliveries compared to their rural counterparts Additionally, Celik & Hotchkiss (2000) highlight that women in Eastern Turkey face greater challenges in accessing facility deliveries than those in the Western and Northern regions, reflecting the disparities in regional advantages Gage (2007) further supports this by noting that these regional differences reveal the unequal accessibility and availability of healthcare facilities.
Conceptual framework
Prenatal care visits and the selection of delivery care providers are influenced by individual, household, and community-level characteristics Key individual factors include education level, media access, employment status, marital status, pregnancy intention, and birth order Household factors encompass wealth index, size, ethnicity, and the religion of the head of the household Additionally, community-level characteristics such as residence location, poverty rate, illiteracy rate, and the proportion of women delivering in health facilities play a significant role in shaping maternal health-seeking behaviors This comprehensive conceptual framework is illustrated in Figure 6.
Figure 6The association between individual level, household level and community level characteristics with the utilization of maternal health care services
Empirical framework
This study aims to assess the impact of social determinants on maternal health care services, specifically focusing on the frequency of prenatal care visits and the selection of delivery facilities To analyze the number of prenatal check-ups, the research employs a Negative Binomial Model, while a Multinomial Logit Model is utilized to evaluate the decision-making process regarding delivery facility choices.
The Poisson Model is utilized for dependent variables that are non-negative integers, employing the Poisson distribution to assess the probability of events occurring k times within a specified timeframe This distribution function effectively captures the likelihood of such occurrences.
With the condition that 𝜆 is non-negative and mean equal to variance 𝐸(𝑌) = 𝑣𝑎𝑟 (𝑌) 𝜆.The meaning is that when X changes, how the expected value of y changes
The Poisson Model has a key limitation: it assumes that the variance equals the mean, which can be unrealistic for count data where this equality often does not hold To address this issue, Negative Binomial regression is a viable alternative, as it allows for variance to differ from the mean This regression method maintains the same mean structure as Poisson regression but introduces an additional parameter to account for over-dispersion In Stata, the command nbreg is utilized to estimate Negative Binomial regression and to test for over-dispersion.
The study examines the number of maternal check-ups attended by pregnant women as the dependent variable, while independent variables are categorized into individual, household, and community level characteristics Notably, the analysis excludes healthcare costs and income due to data limitations, which will be further elaborated in the following section.
3.2.2 Choice of birth delivery facility
The Multinomial Logit Model is utilized to analyze the relationship between categorical variables and other explanatory factors, with the assumption that mothers select the facility that maximizes their utility Each individual's choice of facility, denoted as 𝑌 𝑖, can take on values from 1 to J, representing different alternatives The probabilities associated with choosing each option are represented as 𝑝 𝑖1, 𝑝 𝑖2, …, 𝑝 𝑖𝐽, while the logit function provides a mathematical framework for these choices.
Log-likelihood function: log 𝐿 = ∑ 𝑁 𝑖=1 ∑ 𝐽 𝑖=1 𝑦 𝑖𝑗 ln 𝑝 𝑖𝑗 where 𝑦 𝑖𝑗 = 0 𝑖𝑓 𝑗 𝑖𝑠 𝑁𝑂𝑇 𝑐ℎ𝑜𝑠𝑒𝑛
The study examines the log odds of individual i choosing delivery alternatives 2 (public hospitals) and 3 (private hospitals or clinics) compared to the reference alternative of home delivery (alternative 1), with alternative 2 serving as the base outcome It focuses solely on chooser-related characteristics due to the limitations of MICS 4 data, which does not include healthcare provider characteristics The independent variables are categorized into three groups: individual, household, and community, mirroring the analysis of prenatal care visit demand.
The mlogit command in Stata is utilized for estimating the multinomial logistic model, which analyzes the impact of independent variables categorized into three distinct groups: individual-level, household-level, and community-level characteristics Detailed explanations of these variables will be provided in the following section.
Data
The Multiple Indicator Cluster Survey (MICS) in Vietnam, conducted by the General Statistics Office in collaboration with UNICEF, aimed to provide national estimates on the status of children and women across various urban and rural areas and six geographic regions The survey utilized three questionnaires: one focused on household information and economic status, another on female household members aged 15-49, and the last on children under five and their caretakers MICS5 included a sample of 10,018 households, encompassing 9,827 women and 3,316 children, with the analysis specifically concentrating on 1,479 women who had given birth to a live child within two years prior to the survey to minimize recall errors.
Variables definition
The study evaluates maternal health care service utilization by examining antenatal care coverage and the place of delivery According to MICS, antenatal care coverage refers to the percentage of women aged 15–49 who had a live birth in the two years preceding the survey and received care from skilled health personnel at least once The World Health Organization advises that pregnant women should ideally attend a minimum of four antenatal care visits during their pregnancy Early attendance at these visits is vital for identifying and preventing health issues that could impact both the mother and the baby, emphasizing the importance of continuous antenatal care throughout the pregnancy.
“Skilled personnel” includes accredited health professionals such as midwives, physicians and nurses, but not traditional birth attendants Therefore, the antenatal care visits are integer variables
The place of delivery is categorized into three groups: home births, public hospital deliveries, and private hospital or clinic births Delivering in a health facility—whether public or private—significantly reduces health risks for both mothers and babies Access to proper medical care and hygienic conditions during childbirth can lower the likelihood of complications and infections, ultimately decreasing morbidity and mortality rates Thus, the location of delivery plays a crucial role in ensuring safer outcomes for both mothers and their newborns.
In this study, explanatory variables at both the individual-household and community levels were selected based on existing theoretical and empirical literature to assess the use and availability of maternal healthcare services Since two separate regressions will be conducted for the research objectives, both analyses will utilize the same set of independent variables A comprehensive description of the chosen variables is provided below.
The study examines various individual-level factors influencing maternal outcomes, including the mother's age at childbirth, birth order, education, marital status, exposure to mass media, and pregnancy intention Maternal age is treated as a continuous variable ranging from 15 to 49 years Education is categorized into five dummy variables: no education, primary, lower secondary, upper secondary, and tertiary Birth order is quantified as a continuous variable reflecting the total number of children a woman has given birth to Marital status is represented by dummy variables indicating whether a woman was previously married (1) or never married (0) Exposure to mass media is assessed through four dummy variables based on the frequency of access to mobile phones, newspapers, radio, and television Finally, pregnancy intention is measured as 1 if a woman did not intend to become pregnant and 0 otherwise.
Household level factors such as the ethnicity and religion of the household head, along with household wealth status, play a significant role in determining living standards Ethnicity is categorized as Kinh or Hoa (1) versus non-Kinh/Hoa (0) Despite being the sixth largest minority group in Vietnam, non-Kinh/Hoa households often experience living standards comparable to the Kinh majority The religion variable is treated as a dummy variable, with a value of 1 indicating the absence of religion and 0 otherwise Additionally, household wealth status is also represented as a dummy variable, where 1 signifies that the household belongs to the poorest or poor quintiles, and 0 indicates otherwise.
In addition to individual and household factors, community-level influences are considered to assess their impact Residence is categorized into urban (coded as 1) and rural (coded as 0) Furthermore, six regional dummies are utilized to represent various socioeconomic regions, including the Red River Delta, Northern Midlands, and Mountain areas.
The study focuses on various regions, including the Central area, Central Coastal area, Central Highlands, South East, and Mekong River Delta It assesses the illiteracy rate by measuring the percentage of illiterate women within these communities Additionally, the poverty rate is determined by the proportion of women residing in the lowest wealth quintile of each commune Lastly, the analysis includes the percentage of women who give birth in hospitals, highlighting access to healthcare services in these areas.
ANC Number of antenatal care visits visits
Where the women give birth 1- At home
2- Government hospital or commune health centre 3- Private hospital or clinic categories
AGE Age in years years
NOEDU Dummy variable indicating the individual has not finished primary school Yes = 1, No = 0
PRIMARY Dummy variable indicating the individual finished primary school Yes = 1, No = 0
The educational attainment of an individual can be measured using three dummy variables: LOWSECOND, UPSECOND, and TERTIARY LOWSECOND is a dummy variable that indicates whether an individual has completed lower secondary school, with a value of 1 representing completion and 0 representing non-completion Similarly, UPSECOND is a dummy variable that signifies the completion of upper secondary school, also coded as 1 for completion and 0 for non-completion Lastly, TERTIARY is a dummy variable that denotes the completion of college or higher education, with 1 indicating completion and 0 indicating non-completion.
MARITAL Whether the women is separated or never married Yes = 1, No = 0
CEB Number of children ever born children
WORKING Whether the woman has been working in the last two years Yes = 1, No = 0
MOBIPHONE Whether the woman reads or writes SMS messages everyday Yes = 1, No = 0
NEWSPAPER Whether the woman reads Newspaper or Magazine everyday Yes = 1, No = 0
RADIO Whether the woman listens to radio everyday Yes = 1, No = 0
TV Whether the woman watches TV everyday Yes = 1, No = 0
UNWANTED Whether the woman wanted the last child No = 1, Yes=0
POOR Whether women belong to the poorest and poor quintiles group Yes = 1, No = 0
HHSIZE Number of household members persons
ETHNIC Whether the household head belong to the ethnic minority group Yes = 1, No = 0
NORELI Whether the household head has no religion Yes = 1, No = 0
RURAL Whether the women live in rural area Yes = 1, No = 0
RRD Red River Delta Yes = 1, No = 0
MN Northern Midlands and Mountainous Area Yes = 1, No = 0
NC North Central and Central Coastal Area Yes = 1, No = 0
CH Central Highlands Yes = 1, No = 0
SE South East Yes = 1, No = 0
MD Mekong River Delta Yes = 1, No = 0
In the commune, the percentage of women living in poverty, specifically those in the poorest and second quintiles, is a critical indicator of socio-economic challenges Additionally, the illiteracy rate among women, represented by the percentage of those without education certificates, highlights the need for improved educational opportunities Furthermore, the hospital delivery ratio, which indicates the percentage of women who gave birth to their last child in hospitals, underscores the importance of accessible healthcare services for maternal and child health.
CHAPTER IV RESULTS AND DISCUSSIONS
This chapter outlines the findings of a study examining the relationships between determinants influencing prenatal care visits and the selection of delivery care providers Initially, it presents descriptive statistics for both dependent and independent variables The analysis then explores bivariate associations between each dependent variable and the independent variables Finally, it includes a regression analysis of the demand for prenatal care visits and the choice of delivery care, comparing these results with those from previous studies.
Descriptive Results
The summary statistics reveal that among 1,479 women who gave birth in the past two years, the average number of prenatal care visits is six, exceeding the World Health Organization's recommendation of at least four visits for a safe pregnancy and fetal development The average age of these women is 28, with ages ranging from 15 to 47, and they have an average of two children, aligning with Vietnam's two-child policy Notably, some women, particularly in rural areas, have had as many as 11 children Alarmingly, 100% of women in the poorest household wealth quintiles reside in communities where basic needs remain unmet, and over half of the women in certain areas are illiterate, lacking even a primary education.
Table 3 presents descriptive statistics for dummy variables related to childbirth preferences among women The majority opted for delivery in government hospitals or community health centers to ensure safe childbirth, though 136 women still chose to give birth at home Most participants had completed lower secondary education, comprising 35% of the sample, followed by upper secondary, tertiary, and primary education, with only 6% having no formal education Additionally, 3% of the women reported being previously married or never married Notably, nearly 20% experienced unintended pregnancies, and many were unemployed Regarding media exposure, women predominantly engaged with television and SMS messages over newspapers and radio, reflecting the widespread use of television and mobile phones in Vietnam for accessing current affairs and communication.
Over 40% of women reside in the poorest household wealth quintiles, with around 24% belonging to ethnic minority groups Most of these women live in households that do not practice any religion, although ancestor worship is prevalent in Vietnamese culture Among those who do follow a religion, Buddhism is the most common, followed by Catholicism A significant factor influencing maternal health care utilization is residential location, with many interviewed women living in rural areas that lack adequate health care facilities and have less developed economies compared to urban settings Notably, there is no significant difference in the number of women across six socio-economic regions.
Table 2:Descriptive Results – Numeric Variables
Variable Obs Mean Std Dev Min Max
Table 3 :Descriptive Results - Dummy Variables
Home 136 9.2 Not using every day 1,299 87.83
Public Hospital 1,280 86.54 Using every day 180 12.17
Education level Not using every day 245 16.57
No certificate 89 6.02 Using every day 1,234 83.43
Martial status Kinh/ Hoa Group 1,129 76.34
Analysis of Demand for prenatal care
Table 4 highlights the bivariate analysis of the relationship between the number of prenatal care visits and various social determinants Notably, there is a significant disparity in prenatal care visits among women with no educational qualifications compared to those with higher education, with the former attending three fewer visits Additionally, women from rural areas, low-income households, and certain ethnic groups face considerable challenges in accessing prenatal care This trend is particularly evident in developed regions like the Red River Delta, where disadvantaged women are less likely to receive adequate prenatal services.
In the Mekong River Delta and Southeast regions, the likelihood of utilizing prenatal care is higher compared to less developed areas like the Central Highlands and Northern Midlands However, demand for maternal health care shows minimal differences between working women and religious women As illustrated in Figure 9, the probability of accessing maternal health care declines with the age of women, the number of children they have, and the community's illiteracy rate.
Table 4: Bivariate analysis in the demand of prenatal care visits
ANC Observation Mean Std Dev Min Max
T ime s re ce ive d a n te n a ta l ca re
T ime s re ce ive d a n te n a ta l ca re
Figure 7: The association between the demand of maternal care visits and numerical independent variables
4.2.2 Analysis of Negative Binomial Model
The regression analysis begins by testing the likelihood ratio to determine if the dispersion parameter alpha is equal to zero A test statistic with a p-value close to zero indicates that the response variable exhibits over-dispersion, making the Negative Binomial model more suitable than the Poisson model, as detailed in Appendix 6 To address heteroscedasticity, the regression is conducted using robust methods, and the findings from the Negative Binomial model are presented in Table 5.
The study reveals a statistically significant coefficient of 0.016 for the variable AGE, indicating that an increase of one year in a woman's age is associated with an increase of 0.08 in prenatal care visits This suggests that older women tend to seek maternal care more frequently However, these findings contradict the results of previous studies by Arthur (2012) and Tsawe & Susuman (2014) This discrepancy may be attributed to older pregnant women being more informed about the potential risks associated with pregnancy.
Research indicates that educational attainment, particularly the lack of education and completion of primary and lower secondary certificates, significantly impacts outcomes, highlighting clear disparities among individuals with lower educational qualifications (Arthur 2012, Bbaale 2011, Navaneetham & Dharmalingam 2002).
T ime s re ce ive d a n te n a ta l ca re
T ime s re ce ive d a n te n a ta l ca re
T ime s re ce ive d a n te n a ta l ca re
T ime s re ce ive d a n te n a ta l ca re
The analysis reveals significant disparities in prenatal care visits based on education levels Individuals without any certification are projected to have 1.67 times fewer prenatal care visits compared to those with a tertiary certificate, while controlling for other variables Furthermore, those with primary and lower secondary education are expected to have 0.76 and 4.98 times fewer visits, respectively, than tertiary certificate holders Interestingly, there is minimal difference in the number of prenatal care visits between individuals holding upper secondary certificates and those with tertiary qualifications.
The study indicates that exposure to mass media significantly influences prenatal care visits, with a notable finding that individuals who read newspapers and magazines daily have 0.39 more prenatal care visits compared to those who read less frequently or not at all In contrast, the frequency of reading SMS messages, listening to the radio, and watching TV did not show statistically significant effects on prenatal care visits.
On the contrary to Wado et al (2013), the coefficient of unwanted pregnancy is not statistically significant Similarly, the coefficient of working status also is not statistically significant
The coefficient of non-union in marital status shows a significant negative parameter, supporting the findings of Sepehri et al (2008) and Chen et al.
In 2003, research indicated that women who are unmarried or living apart from their husbands are 1.16 times less likely to seek prenatal care compared to those who are married This suggests that single expectant mothers may experience stigma when accessing maternal healthcare services, as childbirth is often perceived as a consequence of marriage.
The analysis reveals a highly significant negative correlation between birth order and the frequency of prenatal visits, with a p-value close to zero Specifically, each additional child is associated with a decrease of 0.52 visits, likely due to the increased responsibilities that women face in caring for multiple children This finding aligns with previous research conducted by Navaneetham & Dharmalingam (2002), Sepehri et al (2008), Arthur (2012), and Tsawe & Susuman (2014).
The study reveals that living in a poor household, belonging to an ethnic minority group, and not adhering to any religion significantly impact women's access to healthcare, while household size does not show a significant effect Notably, women from the poorest quintiles experience 1.1 fewer healthcare visits compared to those in higher quintiles Financial constraints hinder these women from affording prenatal care and transportation, supporting the findings of previous studies by Bbaale (2011) and Tsawe.
& Susuman 2014) that the household wealth income negatively affects the demand of prenatal check-ups
Similar to these studies of Celik & Hotchkiss (2000), Navaneetham & Dharmalingam
Research indicates that women from ethnic minority households have 0.6 fewer prenatal care visits compared to those from Kinh or Hoa groups, likely due to language barriers and cultural differences Additionally, women whose household heads do not adhere to any religion show a positive influence on the demand for prenatal care, with a marginal effect of 0.3 This suggests that religious norms may hinder access to prenatal care for women in religious households These findings align with previous studies by Bbaale (2011) and Tsawe & Susuman (2014).
The study findings indicate no significant difference in prenatal care demand between rural areas and communities with higher poverty rates, challenging the conclusions of Sepehri et al (2008) However, two community-level factors—the proportion of women with no education and the proportion of women delivering in hospitals—are highly significant Consistent with Gage (2007), expectant mothers in areas with higher illiteracy rates have 0.05 more prenatal care visits compared to those in lower illiteracy areas Conversely, a higher hospital delivery ratio positively influences antenatal care demand, with women in communities with a greater hospital delivery ratio having 0.03 more visits This suggests that community practices significantly affect women's attitudes and healthcare-seeking behaviors, supporting the findings of Ononokpono et al (2013).
There is significant regional variation in the utilization of prenatal care visits, consistent with findings from Celik & Hotchkiss (2000) and Sepheri et al (2008) Residents in underdeveloped regions, such as the Central Coast, Central Highlands, and Northern Midlands and Mountainous Areas, are 1.05, 0.76, and 0.71 times less likely to access prenatal care frequently compared to those in the Mekong Delta However, no statistically significant difference is observed between residents of the Southeast and those in the Mekong River Delta.
It implies that there are remarked differences in the implementation of health care program, the availability and accessibility of the health care services among the regions
Table 5: Negative binomial regression for the demand of prenatal care visits
MOBIPHONE (using mobile phone every day=1) 0.047 (0.030) 0.239 (0.157)
NEWSPAPER (reading newspaper every day=1) 0.075* (0.035) 0.385* (0.184)
RADIO (listening radio every day=1) -0.062 (0.040) -0.308 (0.190)
TV (watching television every day=1) -0.014 (0.046) -0.071 (0.236)
POOR (living in the poor and poorest households=1) -0.217*** (0.047) -1.081*** (0.231)
ETHNIC (being in ethnic minority group=1) -0.117* (0.052) -0.572* (0.248)
RURAL (living in rural areas =1) -0.0482 (0.033) -0.245 (0.169)
Northern Midlands and Mountainous Area -0.148* (0.059) -0.716* (0.270)
North Central and Central Coastal Area -0.227*** (0.049) -1.059*** (0.215)
*** 1% significance, ** 5% significance, * 10% significance - Robust standard errors in parenthesis
Analysis of Choice in the delivery care providers
Table 6 illustrates the relationship between delivery facility choice and various independent variables It reveals that women with a higher birth order are more inclined to opt for home childbirth compared to those with a lower birth order Furthermore, a higher average poverty and illiteracy rate is associated with women who choose to give birth at home, while those delivering at home tend to reside in communities with a lower rate of facility-based deliveries.
Table 6 : Bivariate analysis in the choice of delivery care providers - numeric independent variables
Obs Mean Std Dev Min Max
Home 136 39.73 26.78 0 77.78 Public facility 1280 4.35 10.05 0 77.78 Private Facility 63 4.36 7.53 0 28.57
RATE OF WOMEN GIVING BIRTH IN HEALTH FACILITY
Home 136 29.81 28.14 0 85.71 Public facility 1,280 96.93 11.26 7.69 100 Private Facility 63 98.12 7.29 70.00 100
Table 7 illustrates the relationship between delivery care provider choices and various independent variables, categorizing delivery locations into three types: home delivery, public hospitals, and private hospitals Home delivery, while the most cost-effective option, is often associated with lower hygiene standards and inadequate medical equipment Public hospitals, funded by the government, offer lower costs but frequently face overcrowding In contrast, private hospitals provide superior services and advanced technology at a higher price The data reveals that women opting for home births have less exposure to mass media compared to those giving birth in health facilities Additionally, the likelihood of home births increases among women in disadvantaged areas, such as the Central Highlands and North Mountainous regions, where limited healthcare access and poor infrastructure hinder access to medical facilities Interestingly, many women delivering at home had been engaged in low-paying farm work for two years prior to the interview, which may restrict their ability to afford healthcare costs Conversely, women with higher living standards and those from ethnic majority households are more likely to choose private hospitals, while rural women are less likely to access these facilities due to their urban concentration and associated costs.
Table 7: Bivariate analysis in the choice of delivery care provider – dummy independent variables
Not using every day 135 99.26 900 70.31 40 63.49 Using every day 1 0.74 380 29.69 23 36.51
Not using every day 136 100.00 1,013 79.14 45 71.43 Using every day - - 267 20.86 18 28.57
Not using every day 134 98.53 1,111 86.80 54 85.71 Using every day 2 1.47 169 13.20 9 14.29
Not using every day 74 54.41 162 12.66 9 14.29 Using every day 62 45.59 1,118 87.34 54 85.71
The middle quintiles and more 2 1.47 804 62.81 50 79.37
4.3.2 Analysis of Multinomial Logistic Model
The estimated multinomial logistic regression coefficients are detailed in Table 8, while the marginal effects are presented in Table 9 The reference group consists of individuals who opted for delivery in public hospitals The coefficients indicate the positive or negative relationships between independent variables and the likelihood of selecting home or private hospital delivery compared to public hospital delivery Additionally, the marginal effects illustrate how changes in independent variables influence the probability of choosing home or private clinic delivery, holding other variables constant It is important to note that the variable hospdeliratio has been omitted due to collinearity issues.
Childbirth at home relative to Childbirth at Public Hospital
Research indicates that living in poor households and belonging to ethnic minority groups significantly increases the likelihood of home childbirth, with a p-value close to zero This suggests that financial constraints and language or cultural barriers hinder pregnant women from opting for health facilities for delivery These findings support previous studies by Celik & Hotchkiss (2000), Stephenson et al (2006), and Wado et al (2013) Interestingly, the household head's lack of religious affiliation does not significantly impact the decision to give birth at home, contrasting with findings from Stephenson et al (2006).
Living in poverty and belonging to an ethnic minority group are associated with a 0.08% and 0.07% increase, respectively, in the likelihood of giving birth at home; however, these findings are statistically insignificant.
Individual characteristics significantly influence the choice of delivery location, with media exposure playing a key role Regularly listening to the radio or reading newspapers is linked to a decreased likelihood of home childbirth, as these activities enhance awareness of maternal health services Specifically, each additional prenatal care visit reduces the probability of home delivery by 0.02 percentage points This finding is consistent with previous research by Stephenson et al (2006), Sepehri et al (2008), and Wado et al (2013) Conversely, having more children increases the likelihood of home delivery by 0.01 percentage points, suggesting that women with higher birth orders may underestimate the necessity of facility-based care Interestingly, age and educational attainment do not show a significant association with the choice of delivery location.
Living in rural areas significantly increases the likelihood of home births, with a marginal effect of 0.0003 translating to a 0.03 percentage point rise in the probability of delivering at home Additionally, a higher concentration of women without educational certificates is positively correlated with the preference for home childbirth over public hospital delivery, aligning with findings from previous studies.
Childbirth at private hospital relative to Childbirth at Public Hospital
The analysis indicates that working status, rural residence, and being part of a poor household are significant factors influencing the choice between public and private hospitals, with p-values close to zero Specifically, individuals from poor households have a 0.14 percentage point lower probability of delivering at a private hospital, aligning with findings by Thind et al (2008) that suggest lower living standards correlate with a preference for public hospitals Additionally, residing in rural areas decreases the likelihood of choosing private hospitals by 0.12 percentage points Regional disparities also affect delivery choices; compared to the Mekong River Delta, living in the North Central and South East regions reduces the probability of opting for private hospitals by 0.12 and 0.1 percentage points, respectively This trend highlights that private hospitals are predominantly located in urban and developed areas, where the cost of delivery care is generally higher than that of public hospitals.
Table 8: Multinomial Logistic Regression for the choice of delivery care provider
At home vs At Public Hospital
At Private Hospital vs Public Hospital
TERTIARY (college above =1) Reference Reference
MOBIPHONE (using mobile phone every day =1) -1.358 (1.102) 0.197 (0.326)
RADIO (listening radio every day =1) -1.219* (0.689) 0.0936 (0.383)
TV (watching television every day =1) -0.442 (0.315) -0.325 (0.413)
POOR (living in the poor and poorest households=1) 1.953* (0.885) -0.917* (0.479)
ETHNIC (being in ethnic minority group=1) 1.357** (0.521) -0.121 (0.462)
RURAL (living in rural area =1) 0.873* (0.485) -0.640* (0.298)
Northern Midlands and Mountainous Area 1.827* (0.976) -17.78*** (0.346)
North Central and Central Coastal Area 1.868* (1.076) -0.968* (0.425)
Mekong River Delta Reference Reference
*** 1% significance, ** 5% significance, * 10% significance - Robust standard errors in parenthesis
Table 9: Marginal effects for the choice of delivery care provider
At Public Hospital At Private Hospital
Marginal effect Marginal effect Marginal effect
TERTIARY (college above =1) Reference Reference Reference
MOBIPHONE (using mobile phone every day =1) -0.0003
(0.0006) NEWSPAPER (reading newspaper every day =1) -0.0033
(0.0006) RADIO (listening radio every day =1) -.00030
TV (watching television every day =1) -0.0002
HHSIZE (number of members) - 0.0000 (0.0000) 0.0001 (0.0001) -0.0001 (0.0001) POOR (living in the poor and poorest households) 0.0008
(0.0008) ETHNIC (being in ethnic minority group=1) 0.0007
RURAL (living in rural area =1) 0.0003
Northern Midlands and Mountainous Area 0.0012
North Central and Central Coastal Area 0.0013
Mekong River Delta Reference Reference Reference
*** 1% significance, ** 5% significance, * 10% significance - Robust standard errors in parenthesis