Magnitude and trends in socio-economic and geographic inequality in access to birth by cesarean section in Tanzania: evidence from five rounds of Tanzania demographic and health surveys (1996-2015)

Archives of Public Health, Volume 78, No. 1, Year 2020

Interpretação

Background: Majority of maternal deaths are avoidable through quality obstetric care such as Cesarean Section (CS). However, in low-and middle-income countries, many women are still dying due to lack of obstetric services. Tanzania is one of the African countries where maternal mortality is high. However, there is paucity of evidence related to the magnitude and trends of disparities in CS utilization in the country. This study examined both the magnitude and trends in socio-economic and geographic inequalities in access to birth by CS. Methods: Data were extracted from the Tanzania Demographic and Health Surveys (TDHSs) (1996-2015) and analyzed using the World Health Organization’s (WHO) Health Equity Assessment Toolkit (HEAT) software. First, access to birth by CS was disaggregated by four equity stratifiers: wealth index, education, residence and region. Second, we measured the inequality through summary measures, namely Difference (D), Ratio (R), Slope Index of Inequality (SII) and Relative Index of Inequality (RII). A 95% confidence interval was constructed for point estimates to measure statistical significance. Results: The results showed variations in access to birth by CS across socioeconomic, urban-rural and regional subgroups in Tanzania from 1996 to 2015. Among the poorest subgroups, there was a 1.38 percentage points increase in CS coverage between 1996 and 2015 whereas approximately 11 percentage points increase was found among the richest subgroups within same period of time. The coverage of CS increased by nearly 1 percentage point, 3 percentage points and 9 percentage points among non-educated, those who had primary education and secondary or higher education, respectively over the last 19 years. The increase in coverage among rural residents was 2 percentage points and nearly 8 percentage points among urban residents over the last 19 years. Substantial disparity in CS coverage was recorded in all the studied surveys. For instance, in the most recent survey, pro-rich (RII = 15.55, 95% UI; 10.44, 20.66, SII = 15.8, 95% UI; 13.70, 17.91), pro-educated (RII = 13.71, 95% UI; 9.04, 18.38, SII = 16.04, 95% UI; 13.58, 18.49), pro-urban (R = 3.18, 95% UI; 2.36, 3.99), and subnational (D = 16.25, 95% UI; 10.02, 22.48) absolute and relative inequalities were observed. Conclusion: The findings showed that over the last 19 years, women who were uneducated, poorest/poor, living in rural settings and from regions such as Zanzibar South, appeared to utilize CS services less in Tanzania. Therefore, such subpopulations need to be the central focus of policies and programmes implemmentation to improve CS services coverage and enhance equity-based CS services utilization. Tanzania is a country that has a a population of about 55 million as of 2016 and it is situated in the Eastern part of Africa [22]. The country has several climatic and topographic condition, which range from the hot and humid coastal lowlands of the Indian Ocean shoreline to the high inland mountain and lake region of the northern border, making it the home to different flora and fauna life [23]. Over the last decade, Tanzania has recorded relatively high economic growth, averaging 6–7% annually. There is evidence of an increase in real gross domestic product (GDP) growth from 6.8% in 2017 to 7% in 2018. Although the country managed to reduce its rate of poverty, the same success has not been repeated with respect to reducing the absolute number of poor people in the country due to high rate of population growth. Efforts by the government to boost coverage of social services like education, health, and water have been hampered by their declining quality as the size of the population does not correspond to the supply of the services [22]. Tanzania has a Human Development Index (HDI) value of 0.528 and ranks 159 out of the 189 countries and territories in 2018. The human development report positioned the country in the low human development category, an indication of poor performance in the three important dimensions of the human development, namely life expectancy, decent standard of living and accessibility to knowledge and learning [24]. In Tanzania, the probability of children under five dying before celebrating their fifth birthday is 53 deaths per 1000 live births. While neonatal mortality remained unchanged, Tanzania had seen a fall in the burden of post neonatal mortality rates, child mortality rates, infant mortality and under-five mortality rates [25]. Individuals aged 15 to 60 have a probability of dying of 299 and 222 deaths per 1000 population respectively. Expenditure on health has a share of 5.6% of the total Gross Domestic Product (GDP) [26]. Tanzania’s health system follows a pyramidal structure, from village dispensaries and community-based activities at the base (under the responsibility of local government authorities), to ward, district, and regional level hospitals and finally referral and national hospitals at the summit. The government runs four health insurance schemes alongside multiple private options, but the vast majority of the population remains uninsured, leading to significant inequities in access to care. Tanzania’s 4th Health Sector Strategic Plan (2015–2020) provides for a new health financing strategy aimed at helping the country achieve universal health coverage, by addressing this complex and fractured health insurance market [27]. Data from the 1996 to 2015 TDHS, which are publicly available via Measure DHS were used in this study. TDHS are nationally representative household surveys with a strong focus on maternal and child health issues such as fertility levels and preferences, marriage, sexual activity, awareness and use of family planning methods, breastfeeding practices and use of maternal healthcare services [17]. DHSs serve as important sources of data for monitoring population health indicators and vital statistics in low- and middle-income countries and known by their design, which are highly comparable among different settings and over time. The sample design, selection and methodology of survey approach in each round were similar and has been available elsewhere [17]. Inequality in CS delivery 5 years preceding the surveys was measured for four equity stratifiers (economic status, education, place of residence and region). In this study, we refer to CS as primary variable and we do not use the word ‘outcome’ as we did not run any regression-based model. CS was measured as proportion of births that occurred via CS in the 5 years prior to the surveys. The World Health Organization (WHO) has defined equity stratifiers, also known as dimensions of inequality, as subpopulations that are used to disaggregate health indicators [9]. According to the WHO, a health inequality should be analyzed and interpreted using all dimensions of inequality as far as the available dimensions are relevant for the health indicator of interest, as well as data is available for each category of the subpopulations. In health disparity literature, big attention has been given to health inequality by economic status. However, the WHO recommends other dimensions as well such as place of residence, race or ethnicity, occupation, gender, religion, education, and social capital or resources. In the present study, we employed four dimensions of inequality to analyze CS inequality: economic status, education status, residence and subnational regions. Our selection of the equity stratifiers was based on the fact that they are relevant to CS and data on CS were also available for each of them. Economic status was approximated through a wealth index in the DHS computed using easy-to-collect data on household assets and ownerships such as televisions and bicycles; materials used for housing construction; and types of water access and sanitation facilities, following the methodology explained elsewhere [28] and was categorized into poorest, poorer, middle, richer and richest. Wealth index was computed for each of the four surveys conducted in Tanzania using principal component analysis (PCA) [29]. The wealth index variable used here is comparable across the survey years. In large household surveys like DHS where data on income cannot be collected, wealth index has been used as a proxy for household income and or expenditure measures [30]. The wealth index is a summary measure that reflects a household’s total economic well-being and allows for the identification of problems particular to the poor, such as unequal access to health care, as well as those particular to the wealthy, such as elevated risk of contracting HIV infection [28]. Maternal educational status was classified as no-education, primary education, and secondary education, place of residence as rural vs. urban and sub-national regions categorized into 30 regions. The latest version of the WHO’s HEAT software was adopted for the analysis [31]. In the software, CS delivery were analyzed and disaggregated by the four equity stratifiers-economic status, education, place of residence and region and were presented through the four of the 15 commonly used summary measures of health inequality [29]. In addition to disaggregation, we computed summary measures of inequality. Out of the 15 summary measures available in the software, we chose to use four, namely Difference (D), Ratio (R), Slope Index of Inequality (SII) and Relative Index of Inequality (RII) due to their wider application in health care inequality studies [9, 32]. Both simple and complex summary measures were calculated for each equity stratifier to better understand inequality involved in the utilization of CS delivery [9]. For the economic status and education dimensions of inequality, Difference, Ratio, SII and RII were used. For place of residence, Difference and Ratio were calculated. Difference and Ratio are simple measures of health inequality, whereas the SII, and RII are complex measures [29]). While simple measures of health inequality are suitable for pairwise comparison of a health indicator of interest, they do not account for the subpopulations in the middle when applied to an equity stratifier with more than two categories, such as wealth index. This issue is avoided by the adoption of complex measures, whereby estimates are based on the sizes of all categories of a particular dimension of inequality [9, 29]. As step-by-step procedure for the calculation of each summary measure included in the health equity database are discussed in detail in the HEAT software technical notes [29] and the WHO handbook on the health inequality monitoring [9]. With economic status and education dimensions of inequality, Difference was calculated as CS delivery in the richest group minus in the poorest group, and CS delivery utilization among the group that has acquired at least secondary education minus the uneducated group. Similarly, for place of residence, Difference pertains to what exists between urban and rural populations. Finally, with the sub-national regions, Difference relates to the Difference between regions with the highest and the lowest CS coverage. R is calculated as the ratio of two subgroups: R = Yhigh / Ylow. For place of residence, Yhigh and Ylow are urban and rural residents respectively. Whereas in educational status, Yhigh and Ylow refers to respectively the most advantaged subgroups which are secondary schools and above and the most disadvantaged are subgroups with no education groups. In case of economic status, Yhigh and Ylow refers to the most advantaged subgroups which are the richest quintile and the most disadvantaged subgroups which are the poorest quintile respectively. Finally, SII and RII were calculated through a generalized linear model with a logit link. Their computation was restricted to ordered dimensions (education and economic status) and requires ranking of a weighted sample in order from the most disadvantaged (rank 0) to the most advantaged (rank 1) subgroups. The poorest and uneducated individuals were considered the most disadvantaged, but those that have completed secondary education and the richest subgroups were deemed most advantaged. Then, CS delivery was predicted for those at the two extremes and the difference in the predicted value between rank 1 and rank 0 produces SII. The RII was computed by dividing the predicted cesarean section delivery coverage for rank 1 by that of rank 0. Owing to the complex sampling structure of the DHS datasets, our analysis took this complexity into account in order to generate findings that are not biased as well as are representative. That is, the survey specifications were considered during analysis to redress problems introduced because of the sampling process and to generate reliable findings. As a measure of statistical significance, 95% Confidence Intervals (CI) were computed around point estimates. While interpreting inequality existence, Difference and SII lower and upper bounds of CI shall not entail zero. R and RII inequality exist if CIs do not involve one. In the case of inequality trend interpretation, CIs of the summary measures for different survey years shall not overlap to conclude a change in inequality over time. We did the analyses using publicly available DHS dataset. Because the ethical clearance was approved by the institution that commissioned, funded and managed the overall DHS program, further ethical clearance was not required. Informed consent from the participants prior to survey was obtained in the course of the survey. ICF international and the ethical review Board (IRB) of Tanzania also ensured that the protocols are in compliance with the U.S. Department of Health and Human Services regulations for the protection of human subjects.

AI Digest

Study Justification:

This study aimed to examine the magnitude and trends in socio-economic and geographic inequalities in access to birth by cesarean section (CS) in Tanzania. The justification for this study is that maternal deaths are avoidable through quality obstetric care, including CS. However, in low- and middle-income countries like Tanzania, many women still die due to lack of obstetric services. This study fills a gap in the existing evidence by providing insights into the disparities in CS utilization in the country.

Highlights:

– The study analyzed data from the Tanzania Demographic and Health Surveys (TDHS) conducted between 1996 and 2015.
– Access to birth by CS was disaggregated by four equity stratifiers: wealth index, education, residence, and region.
– The study measured inequality using summary measures such as Difference (D), Ratio (R), Slope Index of Inequality (SII), and Relative Index of Inequality (RII).
– The results showed variations in access to birth by CS across socioeconomic, urban-rural, and regional subgroups in Tanzania over the 19-year period.
– Disparities in CS coverage were observed among different subgroups, with the poorest, uneducated, rural residents, and certain regions having lower utilization of CS services.
– The study highlights the need for policies and programs that focus on improving CS services coverage and enhancing equity-based utilization, particularly among disadvantaged subpopulations.

Recommendations:

Based on the findings, the study recommends the following:

1. Policies and programs should prioritize improving access to CS services for women who are uneducated, poorest/poor, living in rural settings, and from regions with low CS coverage.
2. Efforts should be made to reduce the disparities in CS utilization by addressing the underlying socio-economic and geographic factors that contribute to the inequalities.
3. Health systems should ensure equitable distribution of CS services, taking into account the specific needs of disadvantaged subpopulations.
4. Monitoring and evaluation systems should be in place to track progress in reducing inequalities in CS utilization over time.

Key Role Players:

To address the recommendations, key role players may include:

1. Ministry of Health: Responsible for developing and implementing policies and programs related to maternal health and CS services.
2. Health professionals: Obstetricians, gynecologists, and other healthcare providers involved in delivering CS services.
3. Community health workers: Engaged in community outreach and education to promote awareness and utilization of CS services.
4. Non-governmental organizations (NGOs): Involved in supporting maternal health programs and advocating for improved access to CS services.
5. International organizations: Provide technical assistance, funding, and expertise to support the implementation of maternal health programs.

Cost Items for Planning Recommendations:

While the actual cost will depend on the specific interventions and strategies implemented, some potential cost items to consider in planning the recommendations include:

1. Training and capacity building for healthcare providers on CS procedures and best practices.
2. Infrastructure development and improvement of healthcare facilities to ensure safe and adequate CS services.
3. Outreach and awareness campaigns to educate communities about the importance of CS and address cultural barriers.
4. Development and implementation of monitoring and evaluation systems to track progress and identify areas for improvement.
5. Health system strengthening to ensure equitable access to CS services, including supply chain management, equipment, and staffing.
Please note that the above cost items are general considerations and a detailed budget would require a comprehensive assessment of the specific context and needs of Tanzania’s healthcare system.

Força da prova

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on data from five rounds of Tanzania Demographic and Health Surveys (TDHSs) conducted between 1996 and 2015. The study used the World Health Organization’s Health Equity Assessment Toolkit (HEAT) software to analyze the data and measure inequality in access to birth by cesarean section (CS) across socioeconomic, urban-rural, and regional subgroups. The study provides specific findings on the magnitude and trends of disparities in CS utilization in Tanzania, highlighting variations in access to CS across different subgroups. The study also presents summary measures of inequality, such as Difference (D), Ratio (R), Slope Index of Inequality (SII), and Relative Index of Inequality (RII), to quantify the extent of inequality. The findings suggest that women who are uneducated, poorest/poor, living in rural settings, and from certain regions have lower utilization of CS services. The conclusion emphasizes the need for policies and programs to improve CS services coverage and enhance equity-based utilization among these subpopulations. To improve the evidence, future studies could consider conducting qualitative research to explore the underlying factors contributing to the observed disparities and to gather perspectives from women and healthcare providers. Additionally, it would be beneficial to include more recent data to assess if there have been any changes in CS utilization and inequality since 2015.

Resumo

Tanzania is a country that has a a population of about 55 million as of 2016 and it is situated in the Eastern part of Africa [22]. The country has several climatic and topographic condition, which range from the hot and humid coastal lowlands of the Indian Ocean shoreline to the high inland mountain and lake region of the northern border, making it the home to different flora and fauna life [23]. Over the last decade, Tanzania has recorded relatively high economic growth, averaging 6–7% annually. There is evidence of an increase in real gross domestic product (GDP) growth from 6.8% in 2017 to 7% in 2018. Although the country managed to reduce its rate of poverty, the same success has not been repeated with respect to reducing the absolute number of poor people in the country due to high rate of population growth. Efforts by the government to boost coverage of social services like education, health, and water have been hampered by their declining quality as the size of the population does not correspond to the supply of the services [22]. Tanzania has a Human Development Index (HDI) value of 0.528 and ranks 159 out of the 189 countries and territories in 2018. The human development report positioned the country in the low human development category, an indication of poor performance in the three important dimensions of the human development, namely life expectancy, decent standard of living and accessibility to knowledge and learning [24]. In Tanzania, the probability of children under five dying before celebrating their fifth birthday is 53 deaths per 1000 live births. While neonatal mortality remained unchanged, Tanzania had seen a fall in the burden of post neonatal mortality rates, child mortality rates, infant mortality and under-five mortality rates [25]. Individuals aged 15 to 60 have a probability of dying of 299 and 222 deaths per 1000 population respectively. Expenditure on health has a share of 5.6% of the total Gross Domestic Product (GDP) [26]. Tanzania’s health system follows a pyramidal structure, from village dispensaries and community-based activities at the base (under the responsibility of local government authorities), to ward, district, and regional level hospitals and finally referral and national hospitals at the summit. The government runs four health insurance schemes alongside multiple private options, but the vast majority of the population remains uninsured, leading to significant inequities in access to care. Tanzania’s 4th Health Sector Strategic Plan (2015–2020) provides for a new health financing strategy aimed at helping the country achieve universal health coverage, by addressing this complex and fractured health insurance market [27]. Data from the 1996 to 2015 TDHS, which are publicly available via Measure DHS were used in this study. TDHS are nationally representative household surveys with a strong focus on maternal and child health issues such as fertility levels and preferences, marriage, sexual activity, awareness and use of family planning methods, breastfeeding practices and use of maternal healthcare services [17]. DHSs serve as important sources of data for monitoring population health indicators and vital statistics in low- and middle-income countries and known by their design, which are highly comparable among different settings and over time. The sample design, selection and methodology of survey approach in each round were similar and has been available elsewhere [17]. Inequality in CS delivery 5 years preceding the surveys was measured for four equity stratifiers (economic status, education, place of residence and region). In this study, we refer to CS as primary variable and we do not use the word ‘outcome’ as we did not run any regression-based model. CS was measured as proportion of births that occurred via CS in the 5 years prior to the surveys. The World Health Organization (WHO) has defined equity stratifiers, also known as dimensions of inequality, as subpopulations that are used to disaggregate health indicators [9]. According to the WHO, a health inequality should be analyzed and interpreted using all dimensions of inequality as far as the available dimensions are relevant for the health indicator of interest, as well as data is available for each category of the subpopulations. In health disparity literature, big attention has been given to health inequality by economic status. However, the WHO recommends other dimensions as well such as place of residence, race or ethnicity, occupation, gender, religion, education, and social capital or resources. In the present study, we employed four dimensions of inequality to analyze CS inequality: economic status, education status, residence and subnational regions. Our selection of the equity stratifiers was based on the fact that they are relevant to CS and data on CS were also available for each of them. Economic status was approximated through a wealth index in the DHS computed using easy-to-collect data on household assets and ownerships such as televisions and bicycles; materials used for housing construction; and types of water access and sanitation facilities, following the methodology explained elsewhere [28] and was categorized into poorest, poorer, middle, richer and richest. Wealth index was computed for each of the four surveys conducted in Tanzania using principal component analysis (PCA) [29]. The wealth index variable used here is comparable across the survey years. In large household surveys like DHS where data on income cannot be collected, wealth index has been used as a proxy for household income and or expenditure measures [30]. The wealth index is a summary measure that reflects a household’s total economic well-being and allows for the identification of problems particular to the poor, such as unequal access to health care, as well as those particular to the wealthy, such as elevated risk of contracting HIV infection [28]. Maternal educational status was classified as no-education, primary education, and secondary education, place of residence as rural vs. urban and sub-national regions categorized into 30 regions. The latest version of the WHO’s HEAT software was adopted for the analysis [31]. In the software, CS delivery were analyzed and disaggregated by the four equity stratifiers-economic status, education, place of residence and region and were presented through the four of the 15 commonly used summary measures of health inequality [29]. In addition to disaggregation, we computed summary measures of inequality. Out of the 15 summary measures available in the software, we chose to use four, namely Difference (D), Ratio (R), Slope Index of Inequality (SII) and Relative Index of Inequality (RII) due to their wider application in health care inequality studies [9, 32]. Both simple and complex summary measures were calculated for each equity stratifier to better understand inequality involved in the utilization of CS delivery [9]. For the economic status and education dimensions of inequality, Difference, Ratio, SII and RII were used. For place of residence, Difference and Ratio were calculated. Difference and Ratio are simple measures of health inequality, whereas the SII, and RII are complex measures [29]). While simple measures of health inequality are suitable for pairwise comparison of a health indicator of interest, they do not account for the subpopulations in the middle when applied to an equity stratifier with more than two categories, such as wealth index. This issue is avoided by the adoption of complex measures, whereby estimates are based on the sizes of all categories of a particular dimension of inequality [9, 29]. As step-by-step procedure for the calculation of each summary measure included in the health equity database are discussed in detail in the HEAT software technical notes [29] and the WHO handbook on the health inequality monitoring [9]. With economic status and education dimensions of inequality, Difference was calculated as CS delivery in the richest group minus in the poorest group, and CS delivery utilization among the group that has acquired at least secondary education minus the uneducated group. Similarly, for place of residence, Difference pertains to what exists between urban and rural populations. Finally, with the sub-national regions, Difference relates to the Difference between regions with the highest and the lowest CS coverage. R is calculated as the ratio of two subgroups: R = Yhigh / Ylow. For place of residence, Yhigh and Ylow are urban and rural residents respectively. Whereas in educational status, Yhigh and Ylow refers to respectively the most advantaged subgroups which are secondary schools and above and the most disadvantaged are subgroups with no education groups. In case of economic status, Yhigh and Ylow refers to the most advantaged subgroups which are the richest quintile and the most disadvantaged subgroups which are the poorest quintile respectively. Finally, SII and RII were calculated through a generalized linear model with a logit link. Their computation was restricted to ordered dimensions (education and economic status) and requires ranking of a weighted sample in order from the most disadvantaged (rank 0) to the most advantaged (rank 1) subgroups. The poorest and uneducated individuals were considered the most disadvantaged, but those that have completed secondary education and the richest subgroups were deemed most advantaged. Then, CS delivery was predicted for those at the two extremes and the difference in the predicted value between rank 1 and rank 0 produces SII. The RII was computed by dividing the predicted cesarean section delivery coverage for rank 1 by that of rank 0. Owing to the complex sampling structure of the DHS datasets, our analysis took this complexity into account in order to generate findings that are not biased as well as are representative. That is, the survey specifications were considered during analysis to redress problems introduced because of the sampling process and to generate reliable findings. As a measure of statistical significance, 95% Confidence Intervals (CI) were computed around point estimates. While interpreting inequality existence, Difference and SII lower and upper bounds of CI shall not entail zero. R and RII inequality exist if CIs do not involve one. In the case of inequality trend interpretation, CIs of the summary measures for different survey years shall not overlap to conclude a change in inequality over time. We did the analyses using publicly available DHS dataset. Because the ethical clearance was approved by the institution that commissioned, funded and managed the overall DHS program, further ethical clearance was not required. Informed consent from the participants prior to survey was obtained in the course of the survey. ICF international and the ethical review Board (IRB) of Tanzania also ensured that the protocols are in compliance with the U.S. Department of Health and Human Services regulations for the protection of human subjects.

Materiais

N/A

Inovações

The study titled “Magnitude and trends in socio-economic and geographic inequality in access to birth by cesarean section in Tanzania: evidence from five rounds of Tanzania demographic and health surveys (1996-2015)” provides valuable insights into the disparities in access to cesarean section (CS) services in Tanzania. The study highlights the need for innovative recommendations to improve access to maternal health, particularly CS services.

Based on the findings of the study, here are some recommendations that can be developed into innovations to improve access to maternal health in Tanzania:

1. Targeted interventions for disadvantaged populations: Develop innovative interventions to specifically target disadvantaged populations, such as women who are uneducated, living in rural areas, and from poorer socioeconomic backgrounds. This can include implementing mobile health clinics or outreach programs to provide CS services in remote rural areas, ensuring that women in these areas have access to quality obstetric care.

2. Strengthening healthcare infrastructure: Focus on strengthening healthcare infrastructure in regions with lower CS coverage. This can include improving the availability of trained healthcare professionals, ensuring the availability of necessary medical equipment and supplies, and enhancing referral systems to ensure timely access to CS services.

3. Community-based education and awareness programs: Implement community-based education and awareness programs to educate women and their families about the benefits of CS and the importance of seeking timely obstetric care. This can be done through the use of local community leaders, health workers, and mobile technology platforms to disseminate information and promote behavior change.

4. Strengthening health insurance coverage: Expand health insurance coverage, particularly for vulnerable populations. Develop affordable and accessible health insurance schemes targeted towards women of reproductive age, ensuring that they have financial protection and access to necessary maternal health services, including CS.

5. Continuous monitoring and evaluation: Establish a robust monitoring and evaluation system to track the utilization of CS services, identify gaps, and measure the impact of interventions. Use data analytics and technology to continuously monitor and evaluate the effectiveness of the innovative approaches, and make necessary improvements to ensure sustained improvements in access to maternal health.

By implementing these innovative recommendations, Tanzania can work towards reducing disparities in access to maternal health, specifically CS services, and improve overall maternal health outcomes in the country.

AI Innovations Description

Based on the findings of the study, here is a recommendation that can be developed into an innovation to improve access to maternal health in Tanzania:

1. Targeted interventions for disadvantaged populations: The study reveals that women who are uneducated, living in rural areas, and from poorer socioeconomic backgrounds have lower utilization of CS services. To address this disparity, innovative interventions can be developed to specifically target these disadvantaged populations. For example, mobile health clinics or outreach programs can be implemented to provide CS services in remote rural areas, ensuring that women in these areas have access to quality obstetric care.

2. Strengthening healthcare infrastructure: The study identifies regional disparities in access to CS services. To address this, innovative solutions can focus on strengthening healthcare infrastructure in regions with lower CS coverage. This can include improving the availability of trained healthcare professionals, ensuring the availability of necessary medical equipment and supplies, and enhancing referral systems to ensure timely access to CS services.

3. Community-based education and awareness programs: Lack of education and awareness about the importance of CS services may contribute to lower utilization rates. Innovative approaches can involve community-based education and awareness programs to educate women and their families about the benefits of CS and the importance of seeking timely obstetric care. This can be done through the use of local community leaders, health workers, and mobile technology platforms to disseminate information and promote behavior change.

4. Strengthening health insurance coverage: The study highlights the issue of significant inequities in access to care due to lack of health insurance coverage. Innovative solutions can focus on expanding health insurance coverage, particularly for vulnerable populations. This can involve the development of affordable and accessible health insurance schemes targeted towards women of reproductive age, ensuring that they have financial protection and access to necessary maternal health services, including CS.

5. Continuous monitoring and evaluation: To ensure the effectiveness of these innovative interventions, it is crucial to establish a robust monitoring and evaluation system. This can involve the use of data analytics and technology to track the utilization of CS services, identify gaps, and measure the impact of interventions. Continuous monitoring and evaluation will help in identifying areas for improvement and refining the innovative approaches to ensure sustained improvements in access to maternal health.

By implementing these innovative recommendations, Tanzania can work towards reducing disparities in access to maternal health, specifically CS services, and improve overall maternal health outcomes in the country.

AI Innovations Methodology

To simulate the impact of the main recommendations on improving access to maternal health in Tanzania, the following methodology can be employed:

1. Targeted interventions for disadvantaged populations: To assess the impact of targeted interventions, a pilot program can be implemented in a specific region or district in Tanzania. The program can involve the establishment of mobile health clinics or outreach programs to provide CS services in remote rural areas. Data can be collected on the number of women accessing CS services before and after the intervention, as well as their demographic characteristics such as education level, socioeconomic status, and place of residence. This data can be compared to a control group in a different region or district without the intervention. Statistical analysis can be conducted to determine the impact of the intervention on improving access to CS services among disadvantaged populations.

2. Strengthening healthcare infrastructure: To evaluate the impact of strengthening healthcare infrastructure, a before-and-after study design can be employed. A region or district with lower CS coverage can be selected for infrastructure improvement, such as the availability of trained healthcare professionals, medical equipment, and referral systems. Data can be collected on CS utilization rates before and after the infrastructure improvement, as well as the availability of healthcare resources. This data can be compared to a control group in a different region or district without infrastructure improvement. Statistical analysis can be conducted to determine the impact of the intervention on improving access to CS services in the targeted region.

3. Community-based education and awareness programs: To assess the impact of community-based education and awareness programs, a randomized controlled trial (RCT) can be conducted. Women of reproductive age from different communities can be randomly assigned to either an intervention group or a control group. The intervention group can receive education and awareness programs on the benefits of CS and the importance of seeking timely obstetric care, while the control group receives standard care. Data can be collected on CS utilization rates and knowledge about CS before and after the intervention. Statistical analysis can be conducted to determine the impact of the intervention on improving access to CS services and increasing knowledge among women in the intervention group compared to the control group.

4. Strengthening health insurance coverage: To evaluate the impact of strengthening health insurance coverage, a longitudinal study design can be employed. A group of women of reproductive age can be enrolled in a health insurance scheme specifically targeted towards maternal health services, including CS. Data can be collected on CS utilization rates and health insurance coverage before and after enrollment in the scheme. This data can be compared to a control group of women without health insurance coverage. Statistical analysis can be conducted to determine the impact of health insurance coverage on improving access to CS services among women enrolled in the scheme.

5. Continuous monitoring and evaluation: To monitor and evaluate the impact of the innovative interventions, a monitoring and evaluation system can be established. Data on CS utilization rates, demographic characteristics, and the implementation of the interventions can be collected regularly. Data analytics and technology can be used to analyze the data and track the progress of the interventions over time. Regular reports can be generated to identify areas for improvement and inform decision-making.

By employing these methodologies, the impact of the main recommendations on improving access to maternal health in Tanzania can be assessed, allowing for evidence-based decision-making and the refinement of interventions to ensure sustained improvements in access to CS services.

Autor

Dima Health

Statistics:

Citations: 2

Identifiers:

DOI: 10.1186/s13690-020-00466-3

ISSN: 07787367

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:

Tanzania

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female

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