Wealth inequalities in reproductive and child health preventive care in Mozambique: a decomposition analysis

Global Health Action, Volume 15, No. 1, Year 2022

Interpretation

Background: Assessing the gap between rich and poor is important to monitor inequalities in health. Identifying the contribution to that gap can help policymakers to develop interventions towards decreasing that difference. Objective: To quantify the wealth inequalities in health preventive measures (bed net use, vaccination, and contraceptive use) to determine the demographic and socioeconomic contribution factors to that inequality using a decomposition analysis. Methods: Data from the 2015 Immunisation, Malaria and AIDs Indicators Survey were used. The total sample included 6946 women aged 15–49 years. Outcomes were use of insecticide-treated nets (ITN), child vaccination, and modern contraception use. Wealth Index was the exposure variable and age, marital status, place of residence, region, education, occupation, and household wealth index were the explanatory variables. Wealth inequalities were assessed using concentration indexes (Cindex). Wagstaff-decomposition analysis was conducted to assess the determinants of the wealth inequality. Results: The Cindex was −0.081 for non-ITN, −0.189 for lack of vaccination coverage and −0.284 for non-contraceptive use, indicating a pro-poor inequality. The results revealed that 88.41% of wealth gap for ITN was explained by socioeconomic factors, with education and wealth playing the largest roles. Lack of full vaccination, socioeconomic factors made the largest contribution, through the wealth variable, whereas geographic factors came next. Finally, the lack of contraceptive use, socioeconomic factors were the main explanatory factors, but to a lesser degree than the other two outcomes, with wealth and education contributing most to explaining the gap. Conclusion: There was a pro-poor inequality in reproductive and child preventive measures in Mozambique. The greater part of this inequality could be attributed to wealth, education, and residence in rural areas. Resources should be channeled into poor and non-educated rural communities to tackle these persistent inequities in preventive care. Mozambique, located in south-eastern Africa, has an estimated population of around 29 million inhabitants, the majority living in rural areas [26]. The proportion of people living under the poverty line has worsened from 52.8% in 2003 to 60% in 2021, whereas the unemployment rate for 2020 was 3.4% [27]. However, nearly 80% of the poor live in areas distant from basic public services, and the unemployment rate was 20.7% in 2015 [28,29]. The National Health Service is structured in four nested levels, from specialised hospitals in the four main cities to health centres and health posts at the community level. Health preventive services such as provision of insecticide-treated bed nets, child vaccinations and contraceptives are provided free of charge at level II (district hospitals) and level I (health posts and health centres) facilities [30]. The AIDS and Malaria Indicators Survey (IMASIDA) is a countrywide household survey of men and women aged 15–59 years. The Demographic and Health Survey Program (DHS) conducted this survey in Mozambique from June to September 2015. A three-stage multistage cluster sampling design was used to provide representative national and province-level estimates, with stratification for rural and urban areas within provinces [17]. This process resulted in a selection of 7,368 households, out of which 7,169 participated in the study. From these households, 6,946 women of reproductive age (15–49 years) were interviewed (95% response rate) all of whom were included in the analytical sample for contraceptive use. In the analyses for two of three outcomes of our study, namely insecticide-treated nets and vaccination, the sample size was reduced to 4,709 and 2,694, respectively, due to the exclusion criteria implied in the definition of outcomes described below. Detailed methodological procedures of the survey have been previously described. The data are publicly available and were downloaded with permission from the Demographic and Health Survey at www.dhsprogram.com/data/available-datasets.cfm. The IMASIDA data were collected during face-to-face interviews using three questionnaires: the household, the women’s, and the men’s questionnaire. For the purpose of this study, only the women’s questionnaire was used. This questionnaire collected data on age, place of residence, marriage, occupation, education, wealth, vaccination of children, family bed net use, antenatal care, reproductive history, use of contraceptive methods, recent sexual activity, and fertility preferences. Portuguese was the language used in the interviews, and all the survey instruments were pre-tested in urban and rural areas. Three different outcomes were used in this study capturing the lack of access to preventive health measures: Use of insecticide treated bed nets (ITN) for children, full child vaccination, and modern contraceptive use. These specific outcomes were selected because they are key monitoring indicators of the sustainable development goal 3 in the country. Lack of ITN use was categorized as either ‘yes’ if at least one child under five had not slept under an ITN the day before the survey, or as ‘no’ if all children had slept under a bed net. For vaccination, we only included the youngest child of each woman, aged 12–59 months. The child was considered fully immunised if it had received all the recommended doses and vaccines according to the national immunisation schedule [16,17]: Bacille Calmette-Guérin (BCG) (birth dose), three doses of DPT, three doses of polio vaccine and one dose of measles vaccine. The child was classified as ‘not fully immunised’ if any of the recommended doses could not be verified by a card or reported by the mother. Lack of modern contraceptive use was captured by asking the respondent if she had used any contraceptive methods at the last intercourse. If the answer was yes, then the woman was asked which methods she had used. Lack of modern contraceptive use was categorized as either ‘yes‘ if the woman had not used a modern contraceptive, or as ‘no’ if she had used a modern contraceptive, based on the WHO definition of modern contraceptive methods [31]. Modern contraceptives included female and male sterilisation, implants (Norplant), contraceptive pills, injectables (Depo-Provera), intrauterine contraceptive device and condoms. We classified as non-modern methods the following: periodic abstinence (rhythm, calendar method), withdrawal (coitus interruptus) and folk methods. If a respondent reported using both a modern and a non-modern method, this was counted as modern method use. In this study, modern contraceptives will be referred to as contraceptives here and after. The variable used to depict socioeconomic status was the wealth index, obtained by principal component analysis. This was calculated based on the following assets in the participant’s household: television and car; dwelling characteristics such as flooring material; type of drinking water source; toilet facilities. The variable was used as continuous for calculating the concentration index [32]. Three groups of variables were considered: sociodemographic (age, marital status), geographic (region and place of residence) and socioeconomic (education, occupation and wealth), based on the availability of data and their health relevance according to the literature [33]. Regarding the sociodemographic factors, age of the mother was categorised in three groups (15–24, 25–39, 40–49 years old) and marital status was divided into single/never in union, married (married/living with partner), and others (widowed, divorced, no longer living together). Geographical factors included place of residence (dichotomised into rural or urban residence), and the three administrative regions: Northern, Central and Southern. Maternal education was classified in three categories: no education, completed primary school, and completed secondary school or above. Nine categories of maternal occupation were captured in the IMASIDA survey, but due to the low sample size of some categories, four groups were created: (a) non-manual (managerial, clerical, sales, and services), (b) farmers, (c) manual (household and domestic, skilled, and unskilled manual) and (d) not working. The wealth index was categorised for the decomposition analysis into quintiles, the richest being the reference group [34]. Descriptive statistics were calculated for all explanatory variables and the three outcomes. The wealth inequality in the health preventive measures was quantified by the concentration index (Cindex), calculated based on the cumulative percentage of health preventive care and the population ranked from the poorest to the richest. The Cindex is defined as twice the area between the concentration curve and the line of equality (45-degree line) and its value can vary between −1 to +1. Concentration curves (CC) were created to illustrate the inequality for each outcome. The Cindex can be computed as twice the covariance of the health variable and a person’s relative rank in terms of the socioeconomic status, divided by the variable mean according to the equation. where Cindex is concentration index; Yi the health preventive care utilisation measure; Ri the fractional rank of individual i in the distribution of wealth positions; μ is the mean of the health preventive care variable of the sample and cov denotes the covariance. A negative value of the concentration index implies that a variable (here: the preventive health measure) is concentrated among the poor (pro-poor inequality), while the opposite is the case for a positive value (pro-rich inequality). The value of Cindex measures the severity of the wealth inequality in the outcome, the larger the absolute value of Cindex, the greater the inequality. When there is no inequality, the CI will be zero [35]. The CC plots the cumulative percentage of the health outcome (y-axis) against the cumulative percentage of the population, ranked by the wealth index (x-axis). If the health outcome takes a higher (lower) value among poorer people, the concentration curve will lie above (below) the line of equality. To determine the contribution of each sociodemographic, geographic, and socioeconomic determinant to the observed wealth inequality in each health preventive measure, a Wagstaff decomposition analysis of the Cindex was conducted. The total Cindex can be decomposed into the contributions of k social determinants, in which each contribution is obtained by multiplying the sensitivity of the health outcome variable with respect to the determinant and the degree of wealth-related inequality in that factor. Equation (2) shows that the overall wealth inequality in health preventive measures has two components, a deterministic or ‘explained’ component and an ‘unexplained’ component. In the first component, βk is the coefficient from regressing the health outcome on determinant k. When the coefficients are weighted by the frequency of the determinant using the mean of the determinant k (xk) and the mean of the outcome (μ), the elasticity is calculated; hence, a category that has a high (low) coefficient might have a relatively low (high) elasticity if the category has a low (high) frequency. Ck is the concentration index for each determinant k and interpreted in the same way as the Cindex of the outcome. The elasticity indicates how much change in the health dependant variable is associated with one unit of change in the explanatory k variable. In the second component, GCε is the generalised Cindex for the error term, representing the amount of inequality not explained by the selected factors. Since the reproductive and child health preventive measures in this study were binary, probit regression models were applied to analyse the effect of determinants on the probability of the outcomes [35,36]. In order to adjust our results to the IMASIDA sampling strategy, weighting procedures were also applied. All analyses were performed with Stata version 15. From the DHS website [http://www.measuredhs.com], IMASIDA data were obtained for this study. These data are all anonymous and publicly available and no ethical approval was required.

AI Digest

Study Justification:

This study aims to assess wealth inequalities in reproductive and child health preventive care in Mozambique. Understanding these inequalities is crucial for monitoring health disparities and developing interventions to reduce them. By quantifying the wealth gap and identifying the factors contributing to it, policymakers can make informed decisions to address these inequities.

Highlights:

1. The study found a pro-poor inequality in reproductive and child preventive measures in Mozambique.
2. Wealth, education, and residence in rural areas were the main factors contributing to this inequality.
3. Socioeconomic factors explained a significant portion of the wealth gap for insecticide-treated net use, lack of vaccination coverage, and non-contraceptive use.
4. Geographic factors also played a role, particularly for lack of vaccination coverage.
5. Resources should be directed towards poor and non-educated rural communities to address these persistent inequities in preventive care.

Recommendations:

1. Develop targeted interventions to improve access to reproductive and child health preventive measures for disadvantaged populations.
2. Increase investment in education and wealth creation programs to address the socioeconomic factors contributing to wealth inequalities.
3. Improve healthcare infrastructure and services in rural areas to reduce geographic disparities in preventive care.
4. Strengthen health promotion and education campaigns to raise awareness about the importance of preventive measures and increase their uptake.

Key Role Players:

1. Ministry of Health: Responsible for implementing and coordinating interventions to address health inequalities.
2. Non-governmental organizations (NGOs): Collaborate with the government to provide support and resources for targeted interventions.
3. Community health workers: Play a crucial role in delivering preventive care services and educating communities about their importance.
4. Local leaders and community organizations: Engage and mobilize communities to participate in preventive care programs.

Cost Items for Planning Recommendations:

1. Education programs: Budget for initiatives aimed at improving access to education, such as scholarships, school infrastructure development, and teacher training.
2. Healthcare infrastructure: Allocate funds for the construction and renovation of health facilities, especially in rural areas.
3. Training and capacity building: Provide resources for training healthcare workers and community health workers to deliver quality preventive care services.
4. Health promotion campaigns: Set aside a budget for developing and implementing awareness campaigns to promote the importance of preventive measures.
5. Monitoring and evaluation: Allocate funds for monitoring and evaluating the effectiveness of interventions and adjusting strategies as needed.
Please note that the cost items provided are general categories and may vary based on the specific context and priorities of Mozambique.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it presents a clear research objective, describes the methods used, and provides specific results. However, to improve the evidence, the abstract could include more information about the sample size, statistical analysis methods, and limitations of the study.

Abstract

Mozambique, located in south-eastern Africa, has an estimated population of around 29 million inhabitants, the majority living in rural areas [26]. The proportion of people living under the poverty line has worsened from 52.8% in 2003 to 60% in 2021, whereas the unemployment rate for 2020 was 3.4% [27]. However, nearly 80% of the poor live in areas distant from basic public services, and the unemployment rate was 20.7% in 2015 [28,29]. The National Health Service is structured in four nested levels, from specialised hospitals in the four main cities to health centres and health posts at the community level. Health preventive services such as provision of insecticide-treated bed nets, child vaccinations and contraceptives are provided free of charge at level II (district hospitals) and level I (health posts and health centres) facilities [30]. The AIDS and Malaria Indicators Survey (IMASIDA) is a countrywide household survey of men and women aged 15–59 years. The Demographic and Health Survey Program (DHS) conducted this survey in Mozambique from June to September 2015. A three-stage multistage cluster sampling design was used to provide representative national and province-level estimates, with stratification for rural and urban areas within provinces [17]. This process resulted in a selection of 7,368 households, out of which 7,169 participated in the study. From these households, 6,946 women of reproductive age (15–49 years) were interviewed (95% response rate) all of whom were included in the analytical sample for contraceptive use. In the analyses for two of three outcomes of our study, namely insecticide-treated nets and vaccination, the sample size was reduced to 4,709 and 2,694, respectively, due to the exclusion criteria implied in the definition of outcomes described below. Detailed methodological procedures of the survey have been previously described. The data are publicly available and were downloaded with permission from the Demographic and Health Survey at www.dhsprogram.com/data/available-datasets.cfm. The IMASIDA data were collected during face-to-face interviews using three questionnaires: the household, the women’s, and the men’s questionnaire. For the purpose of this study, only the women’s questionnaire was used. This questionnaire collected data on age, place of residence, marriage, occupation, education, wealth, vaccination of children, family bed net use, antenatal care, reproductive history, use of contraceptive methods, recent sexual activity, and fertility preferences. Portuguese was the language used in the interviews, and all the survey instruments were pre-tested in urban and rural areas. Three different outcomes were used in this study capturing the lack of access to preventive health measures: Use of insecticide treated bed nets (ITN) for children, full child vaccination, and modern contraceptive use. These specific outcomes were selected because they are key monitoring indicators of the sustainable development goal 3 in the country. Lack of ITN use was categorized as either ‘yes’ if at least one child under five had not slept under an ITN the day before the survey, or as ‘no’ if all children had slept under a bed net. For vaccination, we only included the youngest child of each woman, aged 12–59 months. The child was considered fully immunised if it had received all the recommended doses and vaccines according to the national immunisation schedule [16,17]: Bacille Calmette-Guérin (BCG) (birth dose), three doses of DPT, three doses of polio vaccine and one dose of measles vaccine. The child was classified as ‘not fully immunised’ if any of the recommended doses could not be verified by a card or reported by the mother. Lack of modern contraceptive use was captured by asking the respondent if she had used any contraceptive methods at the last intercourse. If the answer was yes, then the woman was asked which methods she had used. Lack of modern contraceptive use was categorized as either ‘yes‘ if the woman had not used a modern contraceptive, or as ‘no’ if she had used a modern contraceptive, based on the WHO definition of modern contraceptive methods [31]. Modern contraceptives included female and male sterilisation, implants (Norplant), contraceptive pills, injectables (Depo-Provera), intrauterine contraceptive device and condoms. We classified as non-modern methods the following: periodic abstinence (rhythm, calendar method), withdrawal (coitus interruptus) and folk methods. If a respondent reported using both a modern and a non-modern method, this was counted as modern method use. In this study, modern contraceptives will be referred to as contraceptives here and after. The variable used to depict socioeconomic status was the wealth index, obtained by principal component analysis. This was calculated based on the following assets in the participant’s household: television and car; dwelling characteristics such as flooring material; type of drinking water source; toilet facilities. The variable was used as continuous for calculating the concentration index [32]. Three groups of variables were considered: sociodemographic (age, marital status), geographic (region and place of residence) and socioeconomic (education, occupation and wealth), based on the availability of data and their health relevance according to the literature [33]. Regarding the sociodemographic factors, age of the mother was categorised in three groups (15–24, 25–39, 40–49 years old) and marital status was divided into single/never in union, married (married/living with partner), and others (widowed, divorced, no longer living together). Geographical factors included place of residence (dichotomised into rural or urban residence), and the three administrative regions: Northern, Central and Southern. Maternal education was classified in three categories: no education, completed primary school, and completed secondary school or above. Nine categories of maternal occupation were captured in the IMASIDA survey, but due to the low sample size of some categories, four groups were created: (a) non-manual (managerial, clerical, sales, and services), (b) farmers, (c) manual (household and domestic, skilled, and unskilled manual) and (d) not working. The wealth index was categorised for the decomposition analysis into quintiles, the richest being the reference group [34]. Descriptive statistics were calculated for all explanatory variables and the three outcomes. The wealth inequality in the health preventive measures was quantified by the concentration index (Cindex), calculated based on the cumulative percentage of health preventive care and the population ranked from the poorest to the richest. The Cindex is defined as twice the area between the concentration curve and the line of equality (45-degree line) and its value can vary between −1 to +1. Concentration curves (CC) were created to illustrate the inequality for each outcome. The Cindex can be computed as twice the covariance of the health variable and a person’s relative rank in terms of the socioeconomic status, divided by the variable mean according to the equation. where Cindex is concentration index; Yi the health preventive care utilisation measure; Ri the fractional rank of individual i in the distribution of wealth positions; μ is the mean of the health preventive care variable of the sample and cov denotes the covariance. A negative value of the concentration index implies that a variable (here: the preventive health measure) is concentrated among the poor (pro-poor inequality), while the opposite is the case for a positive value (pro-rich inequality). The value of Cindex measures the severity of the wealth inequality in the outcome, the larger the absolute value of Cindex, the greater the inequality. When there is no inequality, the CI will be zero [35]. The CC plots the cumulative percentage of the health outcome (y-axis) against the cumulative percentage of the population, ranked by the wealth index (x-axis). If the health outcome takes a higher (lower) value among poorer people, the concentration curve will lie above (below) the line of equality. To determine the contribution of each sociodemographic, geographic, and socioeconomic determinant to the observed wealth inequality in each health preventive measure, a Wagstaff decomposition analysis of the Cindex was conducted. The total Cindex can be decomposed into the contributions of k social determinants, in which each contribution is obtained by multiplying the sensitivity of the health outcome variable with respect to the determinant and the degree of wealth-related inequality in that factor. Equation (2) shows that the overall wealth inequality in health preventive measures has two components, a deterministic or ‘explained’ component and an ‘unexplained’ component. In the first component, βk is the coefficient from regressing the health outcome on determinant k. When the coefficients are weighted by the frequency of the determinant using the mean of the determinant k (xk) and the mean of the outcome (μ), the elasticity is calculated; hence, a category that has a high (low) coefficient might have a relatively low (high) elasticity if the category has a low (high) frequency. Ck is the concentration index for each determinant k and interpreted in the same way as the Cindex of the outcome. The elasticity indicates how much change in the health dependant variable is associated with one unit of change in the explanatory k variable. In the second component, GCε is the generalised Cindex for the error term, representing the amount of inequality not explained by the selected factors. Since the reproductive and child health preventive measures in this study were binary, probit regression models were applied to analyse the effect of determinants on the probability of the outcomes [35,36]. In order to adjust our results to the IMASIDA sampling strategy, weighting procedures were also applied. All analyses were performed with Stata version 15. From the DHS website [http://www.measuredhs.com], IMASIDA data were obtained for this study. These data are all anonymous and publicly available and no ethical approval was required.

Materials

N/A

Innovations

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

1. Mobile Health (mHealth) Solutions: Implementing mobile health technologies, such as SMS reminders for antenatal care appointments, vaccination schedules, and contraceptive use, can help improve access to maternal health services and ensure timely and appropriate care.

2. Community Health Workers: Training and deploying community health workers who can provide basic maternal health services, education, and support in rural areas can help bridge the gap in access to care, especially for those living in remote areas.

3. Telemedicine: Establishing telemedicine services that allow pregnant women to consult with healthcare providers remotely can improve access to specialized care, especially for those who are unable to travel long distances to reach healthcare facilities.

4. Financial Incentives: Introducing financial incentives, such as conditional cash transfers or vouchers, for pregnant women to seek and utilize maternal health services can help overcome financial barriers and increase access to care.

5. Public-Private Partnerships: Collaborating with private sector organizations to expand the reach of maternal health services, such as through mobile clinics or partnerships with private healthcare providers, can help increase access to quality care, particularly in underserved areas.

6. Health Education and Awareness Campaigns: Conducting targeted health education and awareness campaigns to promote the importance of maternal health services, including antenatal care, vaccinations, and contraceptive use, can help increase demand and utilization of these services.

7. Infrastructure Development: Investing in the development of healthcare infrastructure, including the construction and upgrading of healthcare facilities, can improve access to maternal health services, particularly in rural areas where facilities may be limited.

8. Supply Chain Management: Strengthening the supply chain management system for maternal health commodities, such as bed nets, vaccines, and contraceptives, can ensure a consistent and reliable availability of these essential resources, improving access for women in need.

These innovations, when implemented effectively and in combination with each other, have the potential to improve access to maternal health services and reduce the wealth inequalities observed in Mozambique.

AI Innovations Description

Based on the provided description, the following recommendation can be developed into an innovation to improve access to maternal health in Mozambique:

1. Targeted interventions for poor and non-educated rural communities: The recommendation suggests channeling resources into poor and non-educated rural communities to address the persistent inequities in preventive care. This can be achieved through the development of innovative programs and initiatives that specifically target these communities. For example, mobile health clinics or community health workers can be deployed to provide maternal health services, including access to insecticide-treated bed nets, vaccinations, and contraceptives, directly to these communities. These interventions can help bridge the gap in access to maternal health services and improve health outcomes for women and children in rural areas.

By implementing this recommendation, Mozambique can work towards reducing wealth inequalities in reproductive and child health preventive care and ensure that all women, regardless of their socioeconomic status, have equal access to essential maternal health services.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Invest in improving and expanding healthcare facilities, particularly in rural areas where the majority of the population resides. This includes increasing the number of health centers and hospitals, ensuring they are well-equipped with necessary medical supplies and equipment, and training healthcare professionals to provide quality maternal health services.

2. Enhancing education and awareness: Implement comprehensive education and awareness programs to educate women and communities about the importance of maternal health, including antenatal care, safe delivery practices, and postnatal care. This can be done through community health workers, local leaders, and mass media campaigns.

3. Increasing availability and affordability of contraceptives: Ensure that a wide range of modern contraceptive methods are readily available and accessible to women, particularly in rural areas. This includes providing contraceptives free of charge or at affordable prices, as well as training healthcare providers to offer counseling and support for contraceptive use.

4. Improving transportation and logistics: Address transportation barriers by improving road infrastructure and transportation systems, especially in remote areas. This can facilitate timely access to healthcare facilities for pregnant women in need of emergency obstetric care.

5. Empowering women and promoting gender equality: Promote women’s empowerment and gender equality through initiatives that address social and cultural norms that hinder women’s access to maternal health services. This includes promoting women’s education, economic empowerment, and decision-making power within households.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed using a combination of quantitative and qualitative data. Here is a brief outline of a possible methodology:

1. Data collection: Collect data on various indicators related to maternal health, such as maternal mortality rates, antenatal care coverage, skilled birth attendance, and contraceptive prevalence rates. This data can be obtained from national surveys, health facility records, and other relevant sources.

2. Baseline assessment: Analyze the current status of access to maternal health services in Mozambique, including identifying existing gaps and disparities. This can be done by calculating relevant indicators and conducting a situational analysis.

3. Modeling and simulation: Develop a simulation model that incorporates the recommended interventions and their potential impact on improving access to maternal health. This can involve using statistical modeling techniques, such as regression analysis or mathematical modeling, to estimate the potential changes in maternal health indicators based on the interventions.

4. Sensitivity analysis: Conduct sensitivity analysis to assess the robustness of the simulation model and explore the potential variations in outcomes under different scenarios or assumptions. This can help identify key factors or variables that have the greatest influence on the outcomes.

5. Policy recommendations: Based on the simulation results, provide evidence-based policy recommendations for decision-makers and stakeholders. These recommendations should prioritize interventions that have the greatest potential to improve access to maternal health and reduce inequalities.

6. Monitoring and evaluation: Implement a monitoring and evaluation framework to track the progress of the recommended interventions and assess their impact over time. This can involve regular data collection, analysis, and reporting to ensure that the desired improvements in access to maternal health are being achieved.

It is important to note that the methodology outlined above is a general framework and can be further refined and tailored to the specific context of Mozambique.

Authors & Co-Authors

Statistics:

Citations: 1

Authors: 4

Identifiers:

DOI: 10.1080/16549716.2022.2040150

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:

Mozambique

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female