|
 
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| ORIGINAL ARTICLE |
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| Year : 2023 | Volume
: 6
| Issue : 2 | Page : 47-55 |
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What explains the rural − Urban inequalities in maternal health services utilization in tanzania? A fairlie decomposition analysis
Magashi Joseph Ntegwa1, Evaline Gabriel Mcharo2, Joseph Faustine Mlay3
1 Graduate School of Public Policy, Nazarbayev University, Astana, Kazakhstan; Department of Geography and Economics, University of Dar es Salaam, Tanzania
2 Department of Geography and Economics, University of Dar es Salaam, Tanzania
3 Department of Social Science, Catholic University College of Mbeya, Mbeya, Tanzania
| Date of Submission | 12-Jan-2023 |
| Date of Decision | 31-Mar-2023 |
| Date of Acceptance | 03-Apr-2023 |
| Date of Web Publication | 30-May-2023 |
Correspondence Address:
Magashi Joseph Ntegwa
Graduate School of Public Policy, Nazarbayev University, Astana, Kazakhstan.
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/shb.shb_5_23
Introduction: Maternal mortality remains a public health issue in developing countries, with rural areas having higher rates as compared to the urban areas. Since effective utilization of maternal health-care services has the potential to reduce maternal mortality, this study measures the factors contributing to the existing rural − urban differences in the utilization of maternal healthcare services in Tanzania. Methods: We used Tanzania Demographic and Health Survey 2015/2016 data. Multi-stage stratified sampling was used to select the study participants and we estimated a Fairlie decomposition to understand the factors that contribute to inequality in maternal health-care services utilization between the rural and urban areas in Tanzania using Stata 17. Results: The study findings revealed a considerably wide rural-urban disparity in the utilization of maternal health-care services. As the findings indicated, household wealth status is the major factor for that disparity. Other factors are women's exposure to the media, women's working status, and distance from the homesteads to the health facilities. Conclusion: Therefore, any policy geared toward bridging the gap between the rural and urban areas in the utilization of maternal health-care services should focus on empowering women in terms of household economic status, media exposure, and employment.
Keywords: Fairlie decomposition, maternal health care, rural − urban inequalities, sustainable development goals, Tanzania
How to cite this article:
Ntegwa MJ, Mcharo EG, Mlay JF. What explains the rural − Urban inequalities in maternal health services utilization in tanzania? A fairlie decomposition analysis. Asian J Soc Health Behav 2023;6:47-55 |
How to cite this URL:
Ntegwa MJ, Mcharo EG, Mlay JF. What explains the rural − Urban inequalities in maternal health services utilization in tanzania? A fairlie decomposition analysis. Asian J Soc Health Behav [serial online] 2023 [cited 2023 Sep 23];6:47-55. Available from: http://www.healthandbehavior.com/text.asp?2023/6/2/47/377926 |
| Introduction | |  |
The present study is aimed at exploring the factors for the rural − urban disparities in maternal health-care services utilization in Tanzania by applying the Fairlie decomposition analysis. Worldwide, there has been a significant decrease in the maternal mortality rate by 38% from 342 deaths to 211 deaths per 100,000 live births between 2000 and 2017.[1] Despite this reduction, maternal mortality remains a major challenge in the public health systems in most developing countries and Tanzania since a significant proportion of maternal deaths occur in low- and middle-income countries. For instance, in 2017, the maternal mortality rate in sub-Saharan Africa (SSA) was 542 per 100,000 live births, more than double the global maternal mortality rate of 211 per 100,000 live births.[2] Despite efforts to meet the global Sustainable Development Goal of only 70 maternal deaths per 100,000 live births by 2030, the persistently high rates of maternal mortality in SSA suggest that achieving this goal is unlikely.[3]
The utilization of maternal health-care services among pregnant women is effective in preventing maternal mortality and morbidity.[4],[5] The utilization of such services would lessen maternal morbidity and mortality that are pregnancy related. For instance, the utilization of maternal health-care services reduces the complications associated with pregnancy and prevents or reduces the deaths of pregnant women. Furthermore, it is important to note that the utilization of maternal health-care services among pregnant women is in accordance with the World Health Organization (WHO) guidelines and recommendations for a great transformation and improvement of maternal healthcare in general.
Despite the existence of empirical evidence showing that effective utilization of maternal health-care services reduces maternal mortality and morbidity,[6],[7],[8],[9] the utilization of the said services remains low in SSA countries including Tanzania. This could be attributed to the presence of barriers in the channel for demand and supply of maternal health-care services. The demand-related barriers include the costs of seeking and receiving maternal health-care services; socioeconomic status; knowledge about danger signs; decision-making power; objections from husbands and in-laws, which limit pregnant women's access to maternal healthcare services.[10],[11],[12],[13] On the other hand, the supply-side barriers are related to the quality of the services offered, and the presence of only a few choices that women could make: These also limit pregnant women's access to maternal healthcare services.[10],[14] In general, the low uptake of maternal health-care services is reported to be associated with numerous and multifaceted factors which include behavioral, cultural, economic, and socioeconomic factors at the individual level, while they include remoteness of health facilities, insufficient infrastructures, insufficient skilled personnel for health care at the community level.[15]
The available literature has identified several factors associated with the uptake of maternal health-care services. Socioeconomic factors are among the determinants of disparities in the utilization of maternal health-care services. For instance, the literature shows that women who belong to the highest wealth quintile are more likely to utilize maternal health-care services such as antenatal care (ANC) and delivery at health facilities.[16],[17],[18] Other determinants of disparities in the utilization of maternal health-care services include maternal education,[5],[19] exposure to the mass media,[19] place of residence,[15],[17],[20] and the women's working status.[17]
Furthermore, inequality in the utilization of maternal health-care services is related to other inequalities between the rural and urban areas. Women in rural areas are more likely to be affected by distance from their homesteads to the nearby health facilities than their urban counterparts.[21],[22] Differences in the accessibility and availability of maternal healthcare services, wealth index, exposure to the mass media, and the educational level of women also explain the rural-urban differences in the utilization of maternal health-care services.[9],[20]
In Tanzania, utilization of maternal health-care services is reported to be limited among rural pregnant women. For instance, in 2016, the proportion of women receiving ANC from skilled personnel was 18% in the urban areas but only 9% in the rural areas.[23] Skilled assistance during delivery was 87% in the urban areas but it was only 55% in the rural areas. The timing of the first ANC was late, and the number of ANC visits was low among rural women compared to their urban counterparts.[23] Moreover, delivery at health facilities was 86% in the urban areas while it was 54% in the rural areas, and timely postnatal care was 48% in urban areas while it was 29% in rural areas.
It is not clear to what extent the different factors identified in the literature contribute to the existing disparity between urban and rural areas in the utilization of maternal healthcare services in Tanzania. Understanding the disparity and the factors that contribute to the existing gap in the utilization of maternal health-care services between rural and urban areas in Tanzania can inform appropriate policy interventions intended to close the existing gap. This study analyzes what explains the existing gap in the utilization of maternal health-care services between urban and rural areas by using the data from the 2016 Tanzania Demographic and Health Survey (TDHS), which is based on a comparable and nationally representative sample of reproductive women aged 15–49 in Tanzania. The study's findings will aid in the development of policies and programs intended to address the maternal health-care disparity between rural and urban areas.
This study also contributes useful insights to the existing body of knowledge on rural-urban disparities in maternal health-care utilization in the context of Tanzania.[15] The context is important since there have been very few studies on the topic in Tanzania, using different methodologies and outcome variables. Unlike the previous studies, this one examines the rural-urban disparity using five variables of maternal health services utilization: Skilled ANC provider, the timing of the first ANC visit, number of ANC visits, delivery at a health facility, and being assisted by a skilled birth attendant during delivery. Previous studies on the rural-urban disparity in maternal health-care services utilization used different and fewer outcome variables depending on their research interests.[9],[15] Therefore, results from the current study are informed by and comparable to previous studies' findings.
| Methods | | |
Data source
This study used cross-sectional data from the TDHS of 2015/2016. Data from the Demographic and Health Surveys (DHS) cover demographic, health, and nutrition information about households, women, and children.[23] TDHS uses a stratified two-stage cluster design based on the 2012 population and housing census, something that makes it representative both at the national level and at the residential (urban-rural) level. The first stage involved choosing “clusters” from enumeration areas. The second stage involved the systematic selection of households from each cluster. Only 7050 women aged 15–49 who had given birth in the last 5 years were considered for this study. Due to the missing information on some of the independent variables, the final estimation of the logistic model and decomposition analysis was done using 5559 observations of the timing of the first antenatal visit while 5673 observations were used in the estimation of the other outcome variables.
Before the surveys are implemented, the ICF Institutional Review Board analyzes and approves all processes and questionnaires.[23] Therefore, before conducting the survey, all potential respondents gave informed verbal consent to be involved in the study, which is a typical DHS practice.[23]
Variables
Dependent variables
The dependent variables in this study include skilled ANC providers, the timing of the first ANC visit, the number of ANC visits, delivery at a health facility, and being attended by a skilled birth attendant during delivery. These dependent variables are based on previous studies on maternal health-care services utilization in developing countries.[5],[7],[15]
ANC provider as a variable is about whether women received ANC from a skilled provider such as a doctor, an assistant medical officer, a clinical officer, or an assistant clinical officer during their most recent deliveries. As a result, the variable “ANC provider” takes a value of “1” for a woman who received ANC from a skilled provider and a value of '0' for a woman who did not receive ANC from skilled providers. Furthermore, the timing of the first antenatal visit is grouped into categories of no antenatal visit, <=3 months, 4–5 months, 6–7 months, and 8+ months. The WHO recommends that pregnant women should make their first antenatal visit during their first trimester.[24] Under this, the timing of the first ANC visit is accorded a value of “1” if it occurs within the first trimester of pregnancy and “0” if it does not.
On the other hand, the place of delivery is categorized by considering where women are delivered, whether at a public or private health facility, at home, or in other places. Therefore, women who delivered at health facilities (public or private) are coded as “1” while those who delivered at home or other places are coded as “0.” Moreover, a skilled birth attendant during delivery is categorized as a doctor, nurse/midwife, auxiliary nurse/midwife, or other health workers. This variable is coded as “1” if a woman was assisted by a skilled birth attendant and “0” if she was assisted by a nonskilled birth attendant. Furthermore, given WHO's recommendation that a pregnant mother should make at least four ANC visits during her pregnancy (Please note that in 2016, WHO's new ANC model raised the minimum number of visits a pregnant woman has made to health providers from four to eight. The current study considers the minimum of four ANC visits, which was the standard used during the data collection phase for the 2015 Tanzania Demographic Health Survey[25]), we coded a woman as “1” if she had attended ANC services at least four times during her pregnancy and “0” if she attended less than that number of times.
Grouping variable
Area of residence (urban or rural) was the variable used to group the study participants during the analysis.
Independent variables
Based on the previous literature, the independent variables used in this study include household wealth index (the household wealth index is calculated using principal component analysis, which includes household assets and other wealth facilities such as asset ownership and dwelling characteristics, source of drinking water, and sanitation facilities.[26],[27],[28] Finally, the predicted wealth index was used to create five wealth quintiles) (poorest, poorer, middle, richer, and richest), women's education (continuous variable), a woman's age (continuous variable), women's current marital status (never married, married, and divorced/widowed/separated), women's working status (yes and no), distance to the health facility as a problem (yes and no), and women's exposure to the mass media (yes and no). The latter refers to the extent to which the child's mother was exposed to radio, television, newspapers, or magazines in the last week, and acts as a proxy for the potential exposure to information on maternal healthcare services utilization.
Data analysis
The fairlie decomposition technique was used to estimate and decompose the rural-urban disparity in the utilization of maternal health-care services in Tanzania. The technique is an extension of the Blinder-Oaxaca for the nonlinear models such as logit and probit.[29] It can be implemented using STATA as developed by Jann in 2018.[30] It can be mathematically expressed as in equation 1 below.
Where:  and  represents Maternal Health-care Services Utilization (MHSU) in the rural and urban areas, respectively; NR and NU represent the number of rural and urban respondents, respectively; XiR = distribution of explanatory variable i in the rural areas;  = the coefficient for that explanatory variable in rural areas; XiU = distribution of explanatory variable i in the urban areas;  = the coefficient for that explanatory variable in the urban areas.
In equation 1, the difference in MHSU is represented by  , is decomposed into rural-urban differences in the distribution of the measurable exposure variables (endowments) represented here by the first term, and the rural-urban differences in the processes (effects/coefficients) determining the level of the difference represented by the second term. The second term also captures the rural-urban difference in MHSU that is due to unmeasurable endowments. Similar to previous studies that applied the decomposition technique, our study did not focus on the unmeasurable portion of the gap because of the difficulty in interpreting the results.[30]
The fairlie decomposition technique tests the extent to which the difference between rural and urban areas in terms of maternal health-care services utilization can be explained by differences in the variables included in the analysis. Furthermore, the contribution of each variable to the explained difference in maternal health-care services utilization between the rural and urban areas is estimated.
To estimate the effects of each variable on maternal health-care services utilization, a logit regression model was fitted. The odds ratio, standard errors, and level of significance were all reported. Following that, decomposition was used to estimate the rural-urban disparity in the utilization of maternal health-care services, the explained difference, and the contribution of various variables to the explained difference. All statistical analyses were conducted using Stata 17. Clustering weighting was used, and its results are reported at a 1%, 5%, and 10% significance level.
A negative coefficient would result in a positive contribution to the rural-urban inequality in the utilization of maternal healthcare services in the Fairlie decomposition technique and would be interpreted as increasing the rural-urban inequality in maternal health-care services utilization if the inequality is negative, which is the case here. Similarly, a positive coefficient would have a negative contribution to the rural-urban inequality in maternal health-care services utilization, hence reducing the inequality if the inequality is negative, as is the case in this study.
| Results | | |
Descriptive statistics
Descriptive statistics are presented in [Table 1]. The table shows that the average age of women was 29 years, both in rural and urban areas. Furthermore, the average number of years spent in school among rural and urban women was almost 5 years. Further, while 93% of women in urban areas had access to the mass media, only 78% of rural women had access to the mass media. Furthermore, the majority of women both in rural areas and in urban areas were married; 84% were in the rural areas and 75% were in the urban areas, respectively. The results also indicated that distance from the homesteads to the health facilities was a problem for 47% of rural women and 31% of urban women. Moreover, more women were from poor households in rural areas compared to women in urban areas where most of them were from rich households. | Table 1: Descriptive statistics of respondent's characteristics and socioeconomic status
Click here to view |
Maternal healthcare services utilization
The descriptive statistics for the outcome variables in relation to the socioeconomic status of rural and urban women are shown in [Table 2]. The findings revealed that most of the MHSUs favor rich women, both in rural areas and urban areas. This implies that women from the rich quintile had better outcomes in all the outcome variables. However, the rural-urban discrepancy in terms of utilization of maternal health-care services prevailed irrespective of socioeconomic status, with urban women having better outcomes compared to their rural counterparts. For instance, while access to skilled ANC providers was only 11.37% among the richest in rural areas, it was 20.75% among the richest in the urban areas. Again, while delivery in health facilities was 85% in rural areas, it was 94% in urban areas. These results indicate a rural-urban inequality in the utilization of maternal healthcare services based on socioeconomic status and place of residence. In most of the outcome variables, rich and urban women exhibited better outcomes compared to the poor and rural women. | Table 2: Percentage utilization of maternal health-care services by household wealth and place of residence
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Regression analysis
The multivariable regression controlling all variables in [Table 3] indicates that the household wealth quintile is a significant determinant of maternal health-care services utilization. This is true for the middle, richer, and the richest as opposed to the poorest. On the other hand, distance to health facilities is a problem that makes it less likely for women to deliver at health facilities and receive ANC from skilled personnel. Further, the results indicated that an increase in a year of a woman's education is associated with increased timing of the first ANC visit. | Table 3: Multivariable logistic regression odds ratio of the determinants of maternal health-care services utilization
Click here to view |
Decomposition analysis
[Table 4] presents the estimates of nonlinear decomposition techniques for the rural-urban disparity in maternal health-care services utilization using five different outcome variables. The contributions of individual variables including rural-urban differences in women's age, women's education, women's access to media, women's working status, the distance problem, women's marital status, and wealth quintile are reported. | Table 4: Decomposition of the disparity in maternal health-care services utilization between rural and urban residences
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The difference between the rural and urban women's timing of the first ANC visit was −0.10524. As expected, the largest factor for this difference is the household wealth quintile to which one belongs, which accounts for −0.00918 and 0.06888 (or 65% and 9%) for the richer and richest respectively in explaining the rural-urban gap in the probability of utilizing ANC services within the recommended time. Also, distance to the health facilities was found to be a problem contributing to the existing gap between rural and urban areas in terms of the number of ANC services by 3% and place of delivery by 4% and was statistically significant.
Moreover, women's working status contributes to the increasing gap in having access to skilled ANC providers by 5%, while women's media access contributes to the existing gap between rural and urban women's delivery at health facilities by 3%. However, it was found that women's age, education, and marital status have little to do with the existing gap in the utilization of maternal healthcare services. The decomposition results revealed that the existing gap between rural and urban areas in terms of utilization of maternal healthcare services is largely explained by the household's wealth quintile. As the results indicated, the rich and richest wealth quintiles contribute to an increasing gap in maternal healthcare utilization between urban and rural areas. On the other hand, the middle and poorer wealth quintile contribute to narrowing the gap in the utilization of maternal healthcare services in Tanzania.
| Discussion | | |
The analysis of data in this study has come up with findings that are important in improving the utilization of maternal healthcare services in Tanzania, in both rural and urban areas. First, the descriptive statistics indicated that utilization of maternal healthcare services is high among women from better-off households compared to those who are from poor households, both in rural areas and in urban areas. Second, multivariate logistic regression revealed that an increase in a woman's education by a year is associated with an increase in that woman's likelihood to have early timing of her first ANC visit. In addition, compared to the poorest wealthy quintiles, all the other quintiles are associated with a high likelihood of early timing of the first ANC visit. Moreover, compared to the poorest wealth quintile, women from better-off households are more likely to make 4 or more ANC visits.
The increase in a woman's age and the distance from the homestead to the health facility are associated with less likelihood of that woman delivering at a health facility. At the same time, distance to health facilities is also associated with less likelihood to receive ANC from skilled personnel. Moreover, while women from better-off households are more likely to deliver at health facilities and receive ANC from skilled personnel, women from poor households are less likely to deliver at health facilities and receive ANC from skilled health personnel. Accordingly, women from higher wealth quintiles are more likely to be assisted by skilled birth attendants compared to the women from the poorest wealth quintile.
The rural-urban disparity in MHSU and its contributing factors were determined using the Fairlie decomposition technique. The findings revealed a significant difference in maternal healthcare services utilization between urban and rural areas. The level of MHSU in rural areas was found to be far lower than that of urban areas. These findings are consistent with most of the previous studies.[9],[17],[19],[31] Results from the decomposition analysis showed that household wealth status explains the rural-urban disparity in MHSU. These findings are in line with previous studies in SSA and Bangladesh.[9],[32] The results imply that women in urban areas have better Maternal Healthcare Services Utilization MHSU than their counterparts in rural areas. Although inequalities in terms of wealth affect the utilization of maternal healthcare services regardless of the place of residence, rural areas tend to have a higher proportion of poor women than urban areas. This is consistent with various studies which have shown that women who are economically well off are more likely to seek and utilize maternal healthcare services than those who are poor.[16],[17],[18] Thus, the rural-urban inequalities in the uptake of maternal healthcare services are likely to widen if the socioeconomic status of most rural women will not improve.
Distance to the nearest health facility is another factor contributing to the increased rural-urban gap in the use of maternal healthcare services.[21],[22] This factor is often accompanied by a scarcity of services, poor conditions of the available health facilities, distant locations of the facility, lack of trained physicians, lack of motivation, poor public transport, and poor knowledge about the benefits of MHSU in rural areas.[9],[15],[33] With facilities located far away from most of the homesteads in rural areas, women find themselves compelled to incur additional transportation costs to access and use the health services available there.[15] Further, the lack of essential supplies related to delivery in those facilities discourages many rural women from seeking to use MHS.[34]
Women's media access and exposure in rural areas have been reported to increase MHSU uptake and thus reduce the rural-urban gap in the utilization of the services. In Ghana, Malawi, and Nepal, women's exposure to the media is reported to increase MHSU uptake.[35],[36],[37] Since exposure to the media is likely to change people's attitudes, behaviours, and knowledge of maternal issues as well as increase the likelihood of men's participation in seeking maternal health services concerted efforts are needed to make sure rural women are exposed to enough information on maternal healthcare in Tanzania.[37] Further, according to the study findings, there was high inequality in the utilization of place of delivery. Thus, women in rural areas are less likely to deliver at health facilities compared to women in urban areas. These findings are in line with the previous studies.[9],[34] Moreover, the study findings showed that woman's age and education do not significantly account for the rural-urban disparity in MHSU.
Limitations
The study's strength lies in the nonlinear decomposition technique used, which allows for the quantification of the effect of the identified factors in the urban-rural gap, as opposed to previous studies that only ended with identifying the factors responsible for the disparity. The study's limitation is the type of data used. The cross-sectional data used in the study made it difficult to deduce causal relationships from the results. Another limitation of the study is that there were no collected data on whether participants had moved from urban to rural or rural to urban. As such the potential effect of this confounding variable on the finding that women residing in urban areas had higher rates of maternal healthcare services utilization than those residing in rural areas was not analyzed. Women who migrated from urban/rural areas were still experiencing the effects of their previous urban/rural environment, which may have biased our results and should be taken into consideration when interpreting the findings of this study. Further research is needed to fully understand the complex relationship between maternal healthcare services utilization and geographic location.
| Conclusion | | |
The findings conclude that utilization of maternal healthcare services in Tanzania remains low in rural areas despite the services being free of charge. Wealth index, distance to a health facility, working, and exposure to mass media seemed to favor urban women compared to their rural counterparts The disparity in maternal healthcare services utilization between rural and urban areas calls for policy interventions aimed at closing the gap. Taking geographic and economic status into account when planning interventions is critical for improving Maternal Healthcare Services Utilization and reducing rural-urban inequalities. This should go hand in hand with increased media exposure and improving education levels both in rural areas and urban areas.
Data availability statement
The data set used is openly available upon permission from the MEASURE DHS website (https://www.dhsprogram.com/data/available-datasets.cfm).
Acknowledgment
We thank the MEASURE DHS program for availing us with the data. No funding was obtained for this study.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4]
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