Equity in Maternal Health in South Africa: Analysis of Health Service Access and Health Status in a National Household Survey

PLoS ONE, Volume 8, No. 9, Year 2013

Ukuhunyushwa

Background:South Africa is increasingly focused on reducing maternal mortality. Documenting variation in access to maternal health services across one of the most inequitable nations could assist in re-direction of resources.Methods:Analysis draws on a population-based household survey that used multistage-stratified sampling. Women, who in the past two years were pregnant (1113) or had a child (1304), completed questionnaires and HIV testing. Distribution of access to maternal health services and health status across socio-economic, education and other population groups was assessed using weighted data.Findings:Poorest women had near universal antenatal care coverage (ANC), but only 39.6% attended before 20 weeks gestation; this figure was 2.7-fold higher in the wealthiest quartile (95%CI adjusted odds ratio = 1.2-6.1). Women in rural-formal areas had lowest ANC coverage (89.7%), percentage completing four ANC visits (79.7%) and only 84.0% were offered HIV testing. Testing levels were highest among the poorest quartile (90.1% in past two years), but 10% of women above 40 or with low education had never tested. Skilled birth attendant coverage (overall 95.3%) was lowest in the poorest quartile (91.4%) and rural formal areas (85.6%). Around two thirds of the wealthiest quartile, of white and of formally-employed women had a doctor at childbirth, 11-fold higher than the poorest quartile. Overall, only 44.4% of pregnancies were planned, 31.7% of HIV-infected women and 68.1% of the wealthiest quartile. Self-reported health status also declined considerably with each drop in quartile, education level or age group.Conclusions:Aside from early ANC attendance and deficiencies in care in rural-formal areas, inequalities in utilisation of services were mostly small, with some measures even highest among the poorest. Considerably larger differences were noted in maternal health status across population groups. This may reflect differences between these groups in quality of care received, HIV infection and in social determinants of health. © 2013 Wabiri et al. This paper is a sub-analysis of the third South African National HIV Prevalence, Incidence, Behaviour and Communication Survey [6]; data available from http://curation.hsrc.ac.za/Datasets-PFAJLA.phtml. This cross-sectional population-based household survey was conducted from May 2008 to March 2009, using multistage stratified sampling by: province; locality (urban formal, urban informal, rural formal including commercial farms, and rural informal or tribal areas); and predominant racial groups. Sampling frames were based on enumerator areas (EA) used in the national census, updated to reflect changes in the socio-demographic profile of the country since 2001. A total of 1000 EAs were selected from a database of 86,000 EAs as the primary sampling units; 15 households within each EA constituted the secondary sampling unit (15,000 households) and four eligible individuals selected within households formed the final sampling unit. Only one person in each age group (0–1, 2–11, 12–14, 15–24, 25 or more years) was selected in each household. If a household contained two or more persons in an age category, such a two children under the age of two years, a Kish table was used for selecting one person in each age group per household [7]. Any person who slept in the household on the night preceding the survey (including visitors) was considered a household member. All household members in the selected households were eligible to participate, including those living in hostels, but people staying in educational institutions, old-age homes, hospitals and uniformed-service barracks, as well as homeless people, were excluded from the survey. Study activities were approved by the Human Science’s Research Council’s Research Ethics Committee and Human Subjects Review from the Centre for Disease Control and Prevention’s Global AIDS Programme. Dried blood spot (DBS) specimens were used for HIV antibody testing. An algorithm of three HIV enzyme immunoassays was used to test for HIV antibodies [6]. Full details of the survey methodology, including sample weighting, fieldwork procedures and quality control measures are described elsewhere [6], [8]. Based on the multistage stratified sampling described above, this study draws on data collected from two groups of women aged 15–55: those who had been pregnant in the past two years and those interviewed as the parent or guardian of a child under 2 years. Data are drawn from four face-to-face questionnaires, specifically: a household-level questionnaire; a children below 2 years (reported by mother or guardian) questionnaire; a female youth aged 15–24 years questionnaire; and a women aged 25 to 55 years questionnaire. Socio-economic quartiles (SEQ) were derived from measures of household-living standards, such as infrastructure and housing characteristics (source of drinking water, access to electricity, main source of energy for cooking, and type of toilet used) and household ownership of durable assets (presence of a working refrigerator, radio, television, cell phone and landline phone) captured in the household questionnaire. Quartiles were generated using multiple correspondence analysis [9], [10]. Socio-economic quartile groups were used instead of the more widely used quintiles because women overwhelmingly predominated in the poorer households, with few in the richer groups. For example, households in the 5th quintile contained only 61 (0.8%) of the total 8859 women aged 15 years and above, too low a frequency for meaningful analysis. Also, the socio-economic differentials between groups in rural communities are very narrow, given similar income-generation activities in these areas [11]. Hence, we deemed it most appropriate to use four socio-economic groups to differentiate households. Study outcomes are drawn from two different study instruments: a health questionnaire completed by women aged 15–55 years who had been pregnant in the past two years (N = 1113), and women interviewed as the parent of a child born in the past two years (N = 1304). Only 632 women fell into both groups (only one respondent was selected for each questionnaire among all eligible household respondents). Women who had been pregnant in the past two years provided information on their general health status, whether their pregnancy had been planned, HIV testing in the past two years and their parity. Those who had a child under two gave data on their utilization of antenatal clinic services and delivery with a skilled birth attendant. Survey instruments had not been specifically designed to measure maternal health status, thus available proxy indicators had to be used as measures of maternal health access and maternal health outcomes. Measures of access to health services were utilisation of antenatal clinics, HIV testing and having a skilled attendant at birth. In the absence of better indicators, having a doctor present at birth was included as a measure of health service access, even though interpretation of this indicator, like caesarean section rate, is not straightforward. The outcome HIV infection is included as a health status outcome, but we also examined whether there were systematic differences in access to services between those with and without HIV infection. Women responding with fair or poor to the question “In general, would you say that your health is excellent, good, fair or poor?”, were categorised as having a lower self-assessed health status and compared with those reporting good or excellent health. We included planned pregnancy and multiparity (five or more children) as measures of overall maternal health status, given their well-recognised links with health outcomes in pregnancy [12]. Distribution of access to services and of self-assessed health status was assessed across the following PROGRESS-Plus equity analysis groups: Place of Residence (province; locality as urban formal and informal, and rural formal and informal), Race, Occupation, Education, Socioeconomic Status (employment of the mother and being the household head), and age and HIV status representing the Plus category [13]. Maps were developed to show the distribution of antenatal and skilled birth coverage across districts of the country, using ArcGIS Desktop Version 10.0. Data were analysed using Stata version 11.0 (College Station, Texas, United States), taking into account the complex multilevel sampling design and participant non-response. Weighting of the sample by age, race group and province was applied to ensure the study estimates are representative of the general population. Summary indices for descriptive analysis are weighted percentages, and unweighted counts are provided. Clustering was not accounted for given that the large number of primary sampling units (1000) in the study is comparable to respondent number, diminishing such effects. Additionally, clustering at the household level was rare. Only 40 (3.6%) of the 1113 women, who had been pregnant in the past two years, were from the same household (one selected from women in the household 15–24 years and another from women 25–55 years). In univariable analysis, the distribution of maternal health outcomes across population groups were compared using the Rao-Scott F statistic to determine P values [14]. Multivariate logistic regression analysis, using backward fitting, was used to identify factors associated with utilisation of ANC before 20 weeks, SBA and having a doctor present at birth. These indicators of access to services were selected for further analysis as they have critical implications for outcomes of pregnancy and childbirth in this setting. An absolute indicator of inequality (difference between QIV and QI) was calculated to measure inequalities in health access and status. Also, we used the slope index of inequality (SII) the relative index of inequality (RII) and the concentration index [15], [16]. These have the following desirable characteristics, they reflect: the socio-economic dimensions of health inequalities; the experience of the entire population rather than only that of Q1 and QIV; and changes in the distribution of the population across socio-economic groups [16]. SII is a measure of absolute effect, while the RII measures relative effects. Both measures are interpreted as the effect on health access or status of moving from the lowest to the highest socio-economic group (QI to QIV). We followed standard methods for the calculation of equity indicators [15], [16]. Briefly, to calculate SII and RII, quartile groups were ordered from lowest to highest. The population of each quartile group is given a rank score based on the midpoint of its range in the cumulative distribution in the population. For example, biological mothers with four or more children in QI constituted 35.8% of the population, followed by 32.4% in the next highest quartile. QI was assigned a rank score of [0+ (0.358–0)/2] = 0.178, and next highest quartile a score of [0.358+ (0.680–0.358)/2) = 0.518 and so on. SII is then calculated as a weighted regression [16], of the health outcome and the rank of SEQ distribution, with weights as the number of individuals in the socio-economic quartile group. By weighting the quartile groups by their population share, the SII incorporates changes in the distribution of social groups’ that affect the population health burden of health disparities. The SII is the regression coefficient of the weighted regression model in Equation (1). Where is the population size of QI, is the estimated health status of a hypothetical person at the bottom quartile and , represents the SII, and is the absolute difference in health status between the bottom and top of the quartile, and is the rank score. A unit change in relative rank is equivalent to moving from the bottom to the top of the quartile distribution. RII is calculated using Equation (2), with the population average of the specific health outcome. The concentration curve plots the cumulative proportion of health outcome against the cumulative proportion of the population, ranked by SEQ [17]. If health access is equally distributed across SEQ, concentration curves coincide with the diagonal line of equality. Concentration index- twice the area between the concentration curve and line of equality- ranges from –1 to 1. Zero represents perfect equality, while positive values indicate richer individuals have greater coverage (or worse health outcomes) than poorer individuals [17].

I-AI Digest

Study Justification:

– South Africa is focused on reducing maternal mortality
– Documenting variation in access to maternal health services could assist in re-directing resources

Highlights:

– Poorest women had near universal antenatal care coverage, but attendance before 20 weeks gestation was low
– Women in rural-formal areas had lowest antenatal care coverage and skilled birth attendant coverage
– Disparities in access to services were small, with some measures even highest among the poorest
– Larger differences were noted in maternal health status across population groups

Recommendations:

– Improve early attendance for antenatal care, especially in rural-formal areas
– Increase access to skilled birth attendants, particularly in the poorest quartile and rural formal areas
– Address disparities in access to HIV testing, especially among women above 40 or with low education
– Enhance quality of care received, particularly for women in rural-formal areas
– Address social determinants of health that contribute to differences in maternal health status

Key Role Players:

– Ministry of Health
– Health service providers
– Community health workers
– Non-governmental organizations
– Researchers and academics

Cost Items:

– Training and capacity building for health service providers
– Infrastructure improvement in rural areas
– Outreach programs and community health worker support
– Health education and awareness campaigns
– Research and data collection

Amandla Obufakazi

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong as it is based on a population-based household survey using multistage-stratified sampling. The study assesses the distribution of access to maternal health services and health status across socio-economic, education, and other population groups. The findings provide valuable insights into the inequities in maternal health in South Africa. To improve the evidence, the abstract could include more specific details about the sample size, response rate, and statistical analysis methods used.

Abstract

This paper is a sub-analysis of the third South African National HIV Prevalence, Incidence, Behaviour and Communication Survey [6]; data available from http://curation.hsrc.ac.za/Datasets-PFAJLA.phtml. This cross-sectional population-based household survey was conducted from May 2008 to March 2009, using multistage stratified sampling by: province; locality (urban formal, urban informal, rural formal including commercial farms, and rural informal or tribal areas); and predominant racial groups. Sampling frames were based on enumerator areas (EA) used in the national census, updated to reflect changes in the socio-demographic profile of the country since 2001. A total of 1000 EAs were selected from a database of 86,000 EAs as the primary sampling units; 15 households within each EA constituted the secondary sampling unit (15,000 households) and four eligible individuals selected within households formed the final sampling unit. Only one person in each age group (0–1, 2–11, 12–14, 15–24, 25 or more years) was selected in each household. If a household contained two or more persons in an age category, such a two children under the age of two years, a Kish table was used for selecting one person in each age group per household [7]. Any person who slept in the household on the night preceding the survey (including visitors) was considered a household member. All household members in the selected households were eligible to participate, including those living in hostels, but people staying in educational institutions, old-age homes, hospitals and uniformed-service barracks, as well as homeless people, were excluded from the survey. Study activities were approved by the Human Science’s Research Council’s Research Ethics Committee and Human Subjects Review from the Centre for Disease Control and Prevention’s Global AIDS Programme. Dried blood spot (DBS) specimens were used for HIV antibody testing. An algorithm of three HIV enzyme immunoassays was used to test for HIV antibodies [6]. Full details of the survey methodology, including sample weighting, fieldwork procedures and quality control measures are described elsewhere [6], [8]. Based on the multistage stratified sampling described above, this study draws on data collected from two groups of women aged 15–55: those who had been pregnant in the past two years and those interviewed as the parent or guardian of a child under 2 years. Data are drawn from four face-to-face questionnaires, specifically: a household-level questionnaire; a children below 2 years (reported by mother or guardian) questionnaire; a female youth aged 15–24 years questionnaire; and a women aged 25 to 55 years questionnaire. Socio-economic quartiles (SEQ) were derived from measures of household-living standards, such as infrastructure and housing characteristics (source of drinking water, access to electricity, main source of energy for cooking, and type of toilet used) and household ownership of durable assets (presence of a working refrigerator, radio, television, cell phone and landline phone) captured in the household questionnaire. Quartiles were generated using multiple correspondence analysis [9], [10]. Socio-economic quartile groups were used instead of the more widely used quintiles because women overwhelmingly predominated in the poorer households, with few in the richer groups. For example, households in the 5th quintile contained only 61 (0.8%) of the total 8859 women aged 15 years and above, too low a frequency for meaningful analysis. Also, the socio-economic differentials between groups in rural communities are very narrow, given similar income-generation activities in these areas [11]. Hence, we deemed it most appropriate to use four socio-economic groups to differentiate households. Study outcomes are drawn from two different study instruments: a health questionnaire completed by women aged 15–55 years who had been pregnant in the past two years (N = 1113), and women interviewed as the parent of a child born in the past two years (N = 1304). Only 632 women fell into both groups (only one respondent was selected for each questionnaire among all eligible household respondents). Women who had been pregnant in the past two years provided information on their general health status, whether their pregnancy had been planned, HIV testing in the past two years and their parity. Those who had a child under two gave data on their utilization of antenatal clinic services and delivery with a skilled birth attendant. Survey instruments had not been specifically designed to measure maternal health status, thus available proxy indicators had to be used as measures of maternal health access and maternal health outcomes. Measures of access to health services were utilisation of antenatal clinics, HIV testing and having a skilled attendant at birth. In the absence of better indicators, having a doctor present at birth was included as a measure of health service access, even though interpretation of this indicator, like caesarean section rate, is not straightforward. The outcome HIV infection is included as a health status outcome, but we also examined whether there were systematic differences in access to services between those with and without HIV infection. Women responding with fair or poor to the question “In general, would you say that your health is excellent, good, fair or poor?”, were categorised as having a lower self-assessed health status and compared with those reporting good or excellent health. We included planned pregnancy and multiparity (five or more children) as measures of overall maternal health status, given their well-recognised links with health outcomes in pregnancy [12]. Distribution of access to services and of self-assessed health status was assessed across the following PROGRESS-Plus equity analysis groups: Place of Residence (province; locality as urban formal and informal, and rural formal and informal), Race, Occupation, Education, Socioeconomic Status (employment of the mother and being the household head), and age and HIV status representing the Plus category [13]. Maps were developed to show the distribution of antenatal and skilled birth coverage across districts of the country, using ArcGIS Desktop Version 10.0. Data were analysed using Stata version 11.0 (College Station, Texas, United States), taking into account the complex multilevel sampling design and participant non-response. Weighting of the sample by age, race group and province was applied to ensure the study estimates are representative of the general population. Summary indices for descriptive analysis are weighted percentages, and unweighted counts are provided. Clustering was not accounted for given that the large number of primary sampling units (1000) in the study is comparable to respondent number, diminishing such effects. Additionally, clustering at the household level was rare. Only 40 (3.6%) of the 1113 women, who had been pregnant in the past two years, were from the same household (one selected from women in the household 15–24 years and another from women 25–55 years). In univariable analysis, the distribution of maternal health outcomes across population groups were compared using the Rao-Scott F statistic to determine P values [14]. Multivariate logistic regression analysis, using backward fitting, was used to identify factors associated with utilisation of ANC before 20 weeks, SBA and having a doctor present at birth. These indicators of access to services were selected for further analysis as they have critical implications for outcomes of pregnancy and childbirth in this setting. An absolute indicator of inequality (difference between QIV and QI) was calculated to measure inequalities in health access and status. Also, we used the slope index of inequality (SII) the relative index of inequality (RII) and the concentration index [15], [16]. These have the following desirable characteristics, they reflect: the socio-economic dimensions of health inequalities; the experience of the entire population rather than only that of Q1 and QIV; and changes in the distribution of the population across socio-economic groups [16]. SII is a measure of absolute effect, while the RII measures relative effects. Both measures are interpreted as the effect on health access or status of moving from the lowest to the highest socio-economic group (QI to QIV). We followed standard methods for the calculation of equity indicators [15], [16]. Briefly, to calculate SII and RII, quartile groups were ordered from lowest to highest. The population of each quartile group is given a rank score based on the midpoint of its range in the cumulative distribution in the population. For example, biological mothers with four or more children in QI constituted 35.8% of the population, followed by 32.4% in the next highest quartile. QI was assigned a rank score of [0+ (0.358–0)/2] = 0.178, and next highest quartile a score of [0.358+ (0.680–0.358)/2) = 0.518 and so on. SII is then calculated as a weighted regression [16], of the health outcome and the rank of SEQ distribution, with weights as the number of individuals in the socio-economic quartile group. By weighting the quartile groups by their population share, the SII incorporates changes in the distribution of social groups’ that affect the population health burden of health disparities. The SII is the regression coefficient of the weighted regression model in Equation (1). Where is the population size of QI, is the estimated health status of a hypothetical person at the bottom quartile and , represents the SII, and is the absolute difference in health status between the bottom and top of the quartile, and is the rank score. A unit change in relative rank is equivalent to moving from the bottom to the top of the quartile distribution. RII is calculated using Equation (2), with the population average of the specific health outcome. The concentration curve plots the cumulative proportion of health outcome against the cumulative proportion of the population, ranked by SEQ [17]. If health access is equally distributed across SEQ, concentration curves coincide with the diagonal line of equality. Concentration index- twice the area between the concentration curve and line of equality- ranges from –1 to 1. Zero represents perfect equality, while positive values indicate richer individuals have greater coverage (or worse health outcomes) than poorer individuals [17].

Izinto zokwakha

https://efashare.b-cdn.net/materials/8676a36d8c7ca8f2ef2f279e7bcf3ff8695de7a8bfd68a5e4868bf2e0c4c0902/pone.0073864.s001.docx

Emisha

Based on the information provided, here are some potential innovations that could improve access to maternal health:

1. Mobile clinics: Implementing mobile clinics that can travel to rural and remote areas to provide maternal health services, including antenatal care, HIV testing, and skilled birth attendance.

2. Telemedicine: Utilizing telemedicine technology to connect pregnant women in underserved areas with healthcare professionals who can provide remote consultations, monitoring, and guidance throughout their pregnancy.

3. Community health workers: Training and deploying community health workers who can provide basic maternal health services, education, and support in their local communities, particularly in areas with limited access to healthcare facilities.

4. Health education programs: Developing and implementing comprehensive health education programs that focus on maternal health, including prenatal care, nutrition, family planning, and the importance of early antenatal care attendance.

5. Improving transportation infrastructure: Investing in transportation infrastructure, such as roads and transportation networks, to improve access to healthcare facilities for pregnant women in remote areas.

6. Strengthening referral systems: Establishing and strengthening referral systems between primary healthcare facilities and higher-level healthcare facilities to ensure timely access to emergency obstetric care for high-risk pregnancies.

7. Financial incentives: Introducing financial incentives, such as cash transfers or subsidies, to encourage pregnant women to seek early and regular antenatal care and to deliver with a skilled birth attendant.

8. Quality improvement initiatives: Implementing quality improvement initiatives in healthcare facilities to ensure that maternal health services are provided in a safe, respectful, and culturally sensitive manner.

9. Public-private partnerships: Collaborating with private healthcare providers and organizations to expand access to maternal health services, particularly in underserved areas.

10. Data-driven decision-making: Using data from national household surveys and other sources to identify gaps in access to maternal health services and to inform evidence-based decision-making and resource allocation.

These are just a few potential innovations that could be considered to improve access to maternal health. It is important to note that the specific context and needs of South Africa should be taken into account when designing and implementing these innovations.

AI Innovations Description

The recommendation that can be developed into an innovation to improve access to maternal health based on the information provided is to focus on addressing the disparities in access to maternal health services across different population groups in South Africa. This can be achieved through targeted interventions and resource allocation to ensure that all women, regardless of their socio-economic status, education level, or geographic location, have equal access to quality maternal health services.

Specific actions that can be taken include:

1. Improving early antenatal care attendance: Despite near universal antenatal care coverage among the poorest women, only 39.6% attended before 20 weeks gestation. Efforts should be made to educate and encourage women to seek antenatal care early in their pregnancies, as early attendance has been shown to improve maternal and infant outcomes.

2. Enhancing access to HIV testing: While testing levels were highest among the poorest quartile, there were still women above 40 or with low education who had never tested. Strategies should be implemented to ensure that all pregnant women have access to HIV testing and counseling services, regardless of their socio-economic background or educational level.

3. Increasing skilled birth attendant coverage: Skilled birth attendant coverage was lowest in the poorest quartile and in rural formal areas. Steps should be taken to ensure that all women have access to skilled birth attendants during childbirth, as this has been shown to significantly reduce maternal and neonatal mortality.

4. Addressing social determinants of health: The study found considerable differences in maternal health status across population groups, which may be attributed to differences in quality of care received, HIV infection rates, and social determinants of health. Efforts should be made to address these underlying factors, such as poverty, education, and access to healthcare, in order to improve overall maternal health outcomes.

5. Monitoring and evaluation: It is important to regularly monitor and evaluate the impact of interventions aimed at improving access to maternal health services. This will help identify areas of success and areas that require further attention and refinement.

By implementing these recommendations, South Africa can work towards achieving equity in maternal health and reducing maternal mortality rates.

AI Innovations Methodology

Based on the information provided, here are some potential recommendations for improving access to maternal health:

1. Increase awareness and education: Implement comprehensive public health campaigns to raise awareness about the importance of maternal health and the available services. This can include educating women and their families about the benefits of early antenatal care, skilled birth attendance, and HIV testing.

2. Improve accessibility of services: Ensure that maternal health services are easily accessible to all women, especially those in rural and low-income areas. This can be done by establishing more health facilities in underserved areas, providing transportation options for pregnant women, and extending the operating hours of clinics and hospitals.

3. Strengthen antenatal care services: Focus on improving the quality and coverage of antenatal care services. This can involve training healthcare providers to deliver comprehensive antenatal care, promoting early attendance, and addressing barriers to accessing care such as cost and distance.

4. Enhance skilled birth attendance: Increase the availability and utilization of skilled birth attendants during childbirth. This can be achieved by training more midwives and other skilled birth attendants, improving referral systems, and ensuring that all health facilities have the necessary equipment and supplies for safe deliveries.

5. Address social determinants of health: Recognize and address the social determinants of health that contribute to disparities in maternal health outcomes. This can involve implementing policies and programs that address poverty, gender inequality, and other social factors that affect access to healthcare.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define indicators: Identify specific indicators that will be used to measure access to maternal health, such as the percentage of women attending antenatal care before 20 weeks gestation or the percentage of births attended by skilled birth attendants.

2. Collect baseline data: Gather data on the current status of access to maternal health services using surveys, interviews, or existing data sources. This will provide a baseline against which to compare the impact of the recommendations.

3. Develop a simulation model: Create a simulation model that incorporates the various factors influencing access to maternal health, such as geographic location, socioeconomic status, and availability of healthcare facilities. This model can be based on statistical techniques, such as regression analysis or agent-based modeling, to estimate the potential impact of the recommendations.

4. Implement the recommendations: Apply the recommended interventions in the simulation model and assess their impact on access to maternal health. This can involve adjusting variables such as the number of health facilities, the availability of skilled birth attendants, or the level of awareness and education.

5. Evaluate the results: Analyze the simulation results to determine the projected changes in access to maternal health services. Compare these results to the baseline data to assess the effectiveness of the recommendations in improving access.

6. Refine and iterate: Use the simulation results to refine the recommendations and iterate the simulation model. This can involve adjusting the interventions or exploring alternative strategies to further improve access to maternal health.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different interventions and make informed decisions to improve access to maternal health services.

Ababhali & Co-Ababhali

Statistics:

Citations: 23

Authors: 6

Identifiers:

DOI: 10.1371/journal.pone.0073864

Research Areas:

Community Interventions, Disparities, Environmental, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

South Africa

Study Design:

Case-Control Study, Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female

Yabelana ngalokhu: