Maternal and child health data quality in health care facilities at the Cape Coast Metropolis, Ghana

BMC Health Services Research, Volume 22, No. 1, Year 2022

Interpretation

Background: The demand for quality maternal and child health (MCH) data is critical for tracking progress towards attainment of the Sustainable Development Goal 3. However, MCH cannot be adequately monitored where health data are inaccurate, incomplete, untimely, or inconsistent. Thus, this study assessed the level of MCH data quality. Method: A facility-based cross-sectional study design was adopted, including a review of MCH service records. It was a stand-alone study involving 13 healthcare facilities of different levels that provided MCH services in the Cape Coast Metropolis. Data quality was assessed using the dimensions of accuracy, timeliness, completeness, and consistency. Health facilities registers were counted, collated, and compared with data on aggregate monthly forms, and a web-based data collation and reporting system, District Health Information System (DHIS2). The aggregate monthly forms were also compared with data in the DHIS2. Eight MCH variables were selected to assess data accuracy and consistency and two monthly reports were used to assess completeness and timeliness. Percentages and verification factor were estimated in the SPSS version 22 package. Results: Data accuracy were recorded between the data sources: Registers and Forms, 102.1% (95% CI = 97.5%—106.7%); Registers and DHIS2, 102.4% (95% CI = 94.4%—110.4%); and Forms and DHIS2, 100.1% (95% CI = 96.4%—103.9%). Across the eight MCH variables, data were 93.2% (95% CI = 82.9%—103.5%) complete in Registers, 91.0% (95% CI = 79.5%—102.5%) in the Forms, and 94.9% (95% CI = 89.9%—99.9%) in DHIS2 database. On the average, 87.2% (95% CI = 80.5%—93.9%) of the facilities submitted their Monthly Midwife’s Returns reports on time, and Monthly Vaccination Report was 94% (95% CI = 89.3%—97.3%). The overall average data consistency was 93% (95% CI = 84%—102%). Conclusion: Given the WHO standard for data quality, the level of MCH data quality in the health care facilities at the Cape Coast Metropolis, available through the DHIS2 is complete, reported on timely manner, consistent, and reflect accurately what exist in facility’s source document. Although there is evidence that data quality is good, there is still room for improvement in the quality of the data. A facility-based cross-sectional study design involving review of records of MCH service data was used. The study was conducted in 13 healthcare facilities in the Cape Coast Metropolis—the only metropolis, out of the 22 districts in the Central Region of the Ghana. The Metropolis has fertility rate of 2.2 and a general fertility rate of 59.2 births per 1000 women aged 15–49 years [23]. In 2019, the metropolis had 38 health facilities of all types. The sampling was done in two stages, one was the selection of the Cape Coast Metropolis out of the 22 districts in the region. The Metropolis was purposefully selected because of its uniqueness as one of the largest districts in the region and the only one with the full cadre of health facilities, including, a Teaching Hospitals. The second stage involved the selection of the health facilities. Desk review of documents showed 38 health facilities both government and private are situated in the Metropolis. Thirteen health facilities; 4 private and 9 government/public that met the inclusion criteria of providing MCH services in the Metropolis, were selected. We relied on key variables for conducting MCH data quality assessment recommended by WHO [24]. Based on this recommendation, the MCH variables selected were, antenatal care first (ANC1) coverage, antenatal care first fourth (ANC4) coverage, first dose of intermittent preventive treatment in pregnancy (IPT1), administration of Tetanus–Diphtheria Vaccine (Td2 +) in pregnancy, deliveries attended by a skilled birth attendant/midwife in a health facility, access to early postnatal care (PNC), pentavalent vaccine first and third (Penta1 and Penta3) dose coverage in children under one year of age (Table (Table11). MCH variables with definition and data source Source: Ghana Health Service: Standard Operating Procedures for Health Information Managers, 2012 A data collation sheet was used to collect data. Three data sources were used to assess the routine data quality metrics: primary source data at health facilities (antenatal registers, delivery registers, postnatal registers, and EPI tally sheets); facility aggregate data (Midwife’s returns form and vaccination form); and facility-reported data in DHIS2. The ANC registers, PNC registers, Delivery book registers, and EPI tally book were used to collect data on accuracy of MCH variables. For each selected MCH variable, we recounted the data in the register on monthly basis and the results documented in a data collation sheet. Further, data in the monthly midwives, and vaccination report forms were documented in the data collation sheet for each of the selected variables. The same process was repeated for facility-reported data in DHIS2 for midwives returns report and vaccination report. The focus for data completeness, timeliness, and consistency was the data in DHIS2 database and not the registers or facility forms. Therefore, two main reports (the reporting rate summary and the summary reporting form) were extracted from DHIS2 database. The reporting rate summary was used to assess the completeness and timeliness of facility reporting, whereas summary reporting form assessed the completeness of indicator data and consistency of data (consistency over time, consistency between related data, and outliers in the referenced year). The reporting periods for data accuracy, timeliness, and completeness assessment were January 2020 to December, 2020, and that of consistency was January 2017 to December 2020. Consequently, a yearly report for the three years, (January 2017 to December 2019), was downloaded from the DHIS2 database to serve as comparison for assessing the consistency of data overtime. A two-day training (with pre-test) was given to two research assistants (RAs with bachelor’s degree in information studies) who subsequently reviewed the documents. The first author did daily supervision to ensure that all collected data were complete and consistent among the two RA. There was largely agreement between the two RAs recounted data, except in one facility where variations were observed once in their figures for two variables (deliveries, and PNC). Subsequently, new collation sheets were given to the RAs to recount the data for the two variables, where the figures tallied. Data analysis was carried out using the Statistical Package for the Social Sciences (SPSS, version 22) for Windows. Frequencies and percentages and verification factors (VF) were calculated to characterise data quality by accuracy, completeness, timeliness and consistency. MCH data accuracy was determined through data accuracy checks, which involved verification of the numerical consistency of the recoded data in the (1) RHIS registers kept at the facility, (2) monthly aggregated form generated from the registers, and (3) data found in DHIS2 database, for the eight selected MCH variables, using VF. Verification factor is a summary indicator that measures the ratio of the number of recounted events from source documents to the number of reported events over the same period. Thus, VF is equal to the number of recounted data in the source document divided by the number of reported data in the forms or DHIS2 multiplied by 100. The mean and associated 95% confidence interval (95% CI) of each variable was calculated. When the value of the re-count data and variable data reported are equal, VF is equal to 1 and the report is said to be ideal. Any deviation from VF of 1 is indicative of either under (VF greater than 1) or over reporting (VF less than 1). The difference of an ideal reported VF and observed VF (1-VF) demonstrates either under-reporting or over-reporting. A report was considered accurate if the VF was within ± 10 precision (between 0.9 and 1.10), and inaccurate if the ratio of recount data to the reported data was less than 0.9 or greater than 1.10. Three types of VFs were calculated for data accuracy across the three data sources (registers, aggregated forms, and DHIS2 database). Verification factor 1 (VF1) measures the error in data transfer from the registers to the aggregate data forms; VF2 measures the error in data transfer from the registers to the DHIS2 database; and VF3 measures the error in transferring data from the aggregate form to the DHIS2 platform, as shown in Fig. 1 below. Verification factor from facility register to DHIS2 database Data completeness was assessed in two strands: completeness of the reports, and completeness of variable data reported in DHIS2. Two reports (Monthly Midwife’s Returns for maternal health variables, and Monthly Vaccination Report for child health variables) were considered for completeness of the reports. Facilities which submitted these two reports for the 12 months of 2020 into the DHIS2 platform were assessed. The ratio of total reports available/received to the total reports expected were calculated to show the level of completeness of the reports. Completeness of indicator data reported in DHIS2 was assessed by finding the ratio of number of reports that are complete to the total reports available/received. Timeliness of facility reporting data into DHIS2 was assessed by finding the percentage of facility’s expected monthly reports against the actual reports submitted into the DHIS2 on or before a Ghana Health Service (GHS) scheduled date (5th of the ensuing month) for Monthly Midwife’s returns report, and Monthly Vaccination Report. Consistency of the data was assessed under three groupings: consistency over time, consistency between related variables, and consistency of event reporting. Consistency over time was analysed by finding the mean ratio of an indicator for reference year (2020) to the mean of the same indicator for the three preceding years (2017, 2018, 2019) combined. Data was considered consistent over time if the reported value for the reference year is within ± 33% of the mean value for the preceding three years, taking into consideration any expected changes in the patterns of service delivery [24]. Consistency over time was also assessed to ascertain how individual facility’s values were consistent or different from the district values for the eight MCH data reported into DHIS2 database. Consistency of related variables was analysed by calculating the facility’s ratio for values of indicator-pairs that have a predictable relationship. The indicator pairs considered includes: Penta1 and ANC1; Penta1 and Penta3; and ANC1 and ANC4. Outlier analysis was used to assess consistency of event reporting. Two types of outliers (moderate and extreme) were calculated. Values that were at least two standard deviations from the average value for the MCH variable at a specified time were considered moderate and three standard deviations were considered extreme outliers.

AI Digest

Study Justification:

The study aimed to assess the quality of maternal and child health (MCH) data in healthcare facilities in the Cape Coast Metropolis, Ghana. This is important because accurate and reliable data is crucial for monitoring progress towards achieving Sustainable Development Goal 3, which focuses on good health and well-being. Without high-quality data, it is difficult to effectively monitor and improve MCH services.

Highlights:

– The study used a facility-based cross-sectional design and reviewed MCH service records in 13 healthcare facilities in the Cape Coast Metropolis.
– Data quality was assessed based on the dimensions of accuracy, timeliness, completeness, and consistency.
– The study found that data accuracy between different data sources (registers, forms, and the District Health Information System) was high, ranging from 100.1% to 102.4%.
– Data completeness ranged from 91.0% to 94.9% across different MCH variables.
– On average, 87.2% of facilities submitted their Monthly Midwife’s Returns reports on time, and the Monthly Vaccination Report had a submission rate of 94%.
– The overall average data consistency was 93%.

Recommendations:

– Although the study found good data quality overall, there is still room for improvement. It is recommended to continue efforts to enhance the quality of MCH data in healthcare facilities.
– Strengthening training and capacity-building programs for healthcare workers on data collection, recording, and reporting can help improve data accuracy and completeness.
– Regular monitoring and supervision of data collection processes should be conducted to ensure timely and accurate reporting.
– The use of digital health information systems, such as the District Health Information System, should be further promoted and supported to enhance data quality and reporting efficiency.

Key Role Players:

– Ministry of Health: Responsible for providing policy guidance and support for improving MCH data quality.
– Ghana Health Service: Responsible for implementing and overseeing MCH programs and data collection processes.
– Healthcare facility managers: Responsible for ensuring proper data collection, recording, and reporting within their facilities.
– Health information managers: Responsible for managing and analyzing MCH data and providing support to healthcare workers.

Cost Items for Planning Recommendations:

– Training and capacity-building programs for healthcare workers: Includes costs for organizing training sessions, materials, and trainers.
– Monitoring and supervision activities: Includes costs for conducting regular visits to healthcare facilities, transportation, and personnel.
– Digital health information system implementation and maintenance: Includes costs for software development, hardware procurement, and technical support.
Please note that the provided cost items are general categories and the actual costs would depend on the specific context and requirements of the implementation.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study design is clearly described, and the data collection methods are explained in detail. The results are presented with percentages and confidence intervals, which adds to the credibility of the findings. However, the abstract could be improved by providing more information on the sample size and the characteristics of the study population. Additionally, it would be helpful to include information on the limitations of the study and suggestions for future research. To improve the evidence, the researchers could consider increasing the sample size to enhance the generalizability of the findings. They could also conduct a follow-up study to assess the impact of interventions aimed at improving data quality in MCH services.

Abstract

A facility-based cross-sectional study design involving review of records of MCH service data was used. The study was conducted in 13 healthcare facilities in the Cape Coast Metropolis—the only metropolis, out of the 22 districts in the Central Region of the Ghana. The Metropolis has fertility rate of 2.2 and a general fertility rate of 59.2 births per 1000 women aged 15–49 years [23]. In 2019, the metropolis had 38 health facilities of all types. The sampling was done in two stages, one was the selection of the Cape Coast Metropolis out of the 22 districts in the region. The Metropolis was purposefully selected because of its uniqueness as one of the largest districts in the region and the only one with the full cadre of health facilities, including, a Teaching Hospitals. The second stage involved the selection of the health facilities. Desk review of documents showed 38 health facilities both government and private are situated in the Metropolis. Thirteen health facilities; 4 private and 9 government/public that met the inclusion criteria of providing MCH services in the Metropolis, were selected. We relied on key variables for conducting MCH data quality assessment recommended by WHO [24]. Based on this recommendation, the MCH variables selected were, antenatal care first (ANC1) coverage, antenatal care first fourth (ANC4) coverage, first dose of intermittent preventive treatment in pregnancy (IPT1), administration of Tetanus–Diphtheria Vaccine (Td2 +) in pregnancy, deliveries attended by a skilled birth attendant/midwife in a health facility, access to early postnatal care (PNC), pentavalent vaccine first and third (Penta1 and Penta3) dose coverage in children under one year of age (Table (Table11). MCH variables with definition and data source Source: Ghana Health Service: Standard Operating Procedures for Health Information Managers, 2012 A data collation sheet was used to collect data. Three data sources were used to assess the routine data quality metrics: primary source data at health facilities (antenatal registers, delivery registers, postnatal registers, and EPI tally sheets); facility aggregate data (Midwife’s returns form and vaccination form); and facility-reported data in DHIS2. The ANC registers, PNC registers, Delivery book registers, and EPI tally book were used to collect data on accuracy of MCH variables. For each selected MCH variable, we recounted the data in the register on monthly basis and the results documented in a data collation sheet. Further, data in the monthly midwives, and vaccination report forms were documented in the data collation sheet for each of the selected variables. The same process was repeated for facility-reported data in DHIS2 for midwives returns report and vaccination report. The focus for data completeness, timeliness, and consistency was the data in DHIS2 database and not the registers or facility forms. Therefore, two main reports (the reporting rate summary and the summary reporting form) were extracted from DHIS2 database. The reporting rate summary was used to assess the completeness and timeliness of facility reporting, whereas summary reporting form assessed the completeness of indicator data and consistency of data (consistency over time, consistency between related data, and outliers in the referenced year). The reporting periods for data accuracy, timeliness, and completeness assessment were January 2020 to December, 2020, and that of consistency was January 2017 to December 2020. Consequently, a yearly report for the three years, (January 2017 to December 2019), was downloaded from the DHIS2 database to serve as comparison for assessing the consistency of data overtime. A two-day training (with pre-test) was given to two research assistants (RAs with bachelor’s degree in information studies) who subsequently reviewed the documents. The first author did daily supervision to ensure that all collected data were complete and consistent among the two RA. There was largely agreement between the two RAs recounted data, except in one facility where variations were observed once in their figures for two variables (deliveries, and PNC). Subsequently, new collation sheets were given to the RAs to recount the data for the two variables, where the figures tallied. Data analysis was carried out using the Statistical Package for the Social Sciences (SPSS, version 22) for Windows. Frequencies and percentages and verification factors (VF) were calculated to characterise data quality by accuracy, completeness, timeliness and consistency. MCH data accuracy was determined through data accuracy checks, which involved verification of the numerical consistency of the recoded data in the (1) RHIS registers kept at the facility, (2) monthly aggregated form generated from the registers, and (3) data found in DHIS2 database, for the eight selected MCH variables, using VF. Verification factor is a summary indicator that measures the ratio of the number of recounted events from source documents to the number of reported events over the same period. Thus, VF is equal to the number of recounted data in the source document divided by the number of reported data in the forms or DHIS2 multiplied by 100. The mean and associated 95% confidence interval (95% CI) of each variable was calculated. When the value of the re-count data and variable data reported are equal, VF is equal to 1 and the report is said to be ideal. Any deviation from VF of 1 is indicative of either under (VF greater than 1) or over reporting (VF less than 1). The difference of an ideal reported VF and observed VF (1-VF) demonstrates either under-reporting or over-reporting. A report was considered accurate if the VF was within ± 10 precision (between 0.9 and 1.10), and inaccurate if the ratio of recount data to the reported data was less than 0.9 or greater than 1.10. Three types of VFs were calculated for data accuracy across the three data sources (registers, aggregated forms, and DHIS2 database). Verification factor 1 (VF1) measures the error in data transfer from the registers to the aggregate data forms; VF2 measures the error in data transfer from the registers to the DHIS2 database; and VF3 measures the error in transferring data from the aggregate form to the DHIS2 platform, as shown in Fig. 1 below. Verification factor from facility register to DHIS2 database Data completeness was assessed in two strands: completeness of the reports, and completeness of variable data reported in DHIS2. Two reports (Monthly Midwife’s Returns for maternal health variables, and Monthly Vaccination Report for child health variables) were considered for completeness of the reports. Facilities which submitted these two reports for the 12 months of 2020 into the DHIS2 platform were assessed. The ratio of total reports available/received to the total reports expected were calculated to show the level of completeness of the reports. Completeness of indicator data reported in DHIS2 was assessed by finding the ratio of number of reports that are complete to the total reports available/received. Timeliness of facility reporting data into DHIS2 was assessed by finding the percentage of facility’s expected monthly reports against the actual reports submitted into the DHIS2 on or before a Ghana Health Service (GHS) scheduled date (5th of the ensuing month) for Monthly Midwife’s returns report, and Monthly Vaccination Report. Consistency of the data was assessed under three groupings: consistency over time, consistency between related variables, and consistency of event reporting. Consistency over time was analysed by finding the mean ratio of an indicator for reference year (2020) to the mean of the same indicator for the three preceding years (2017, 2018, 2019) combined. Data was considered consistent over time if the reported value for the reference year is within ± 33% of the mean value for the preceding three years, taking into consideration any expected changes in the patterns of service delivery [24]. Consistency over time was also assessed to ascertain how individual facility’s values were consistent or different from the district values for the eight MCH data reported into DHIS2 database. Consistency of related variables was analysed by calculating the facility’s ratio for values of indicator-pairs that have a predictable relationship. The indicator pairs considered includes: Penta1 and ANC1; Penta1 and Penta3; and ANC1 and ANC4. Outlier analysis was used to assess consistency of event reporting. Two types of outliers (moderate and extreme) were calculated. Values that were at least two standard deviations from the average value for the MCH variable at a specified time were considered moderate and three standard deviations were considered extreme outliers.

Materials

N/A

Innovations

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

1. Digital Health Information Systems: Implementing a robust and user-friendly digital health information system, such as the District Health Information System (DHIS2) mentioned in the study, can improve data accuracy, timeliness, completeness, and consistency. This system allows for real-time data collection, analysis, and reporting, enabling healthcare providers to make informed decisions and track progress towards maternal health goals.

2. Mobile Health (mHealth) Applications: Developing mobile health applications that provide pregnant women and new mothers with access to important maternal health information, appointment reminders, and personalized care plans can enhance access to maternal health services. These applications can also facilitate communication between healthcare providers and patients, allowing for remote consultations and monitoring.

3. Telemedicine Services: Implementing telemedicine services, including virtual consultations and remote monitoring, can improve access to maternal health services, especially for women in rural or remote areas. This innovation enables pregnant women to receive medical advice, prenatal care, and postnatal support without the need for physical travel to healthcare facilities.

4. Community Health Workers: Training and deploying community health workers who can provide basic maternal health services, education, and support within their communities can improve access to care, particularly in underserved areas. These workers can conduct antenatal visits, assist with deliveries, provide postnatal care, and refer women to higher-level healthcare facilities when necessary.

5. Maternal Health Vouchers: Implementing voucher programs that provide pregnant women with financial assistance to access maternal health services can help overcome financial barriers. These vouchers can cover the cost of antenatal care, delivery services, postnatal care, and emergency obstetric care, ensuring that women can access the care they need without facing financial hardship.

6. Transportation Support: Providing transportation support, such as ambulances or transportation vouchers, to pregnant women in remote areas can improve access to timely and emergency obstetric care. This innovation addresses the challenge of long distances and lack of transportation infrastructure, ensuring that women can reach healthcare facilities quickly and safely.

7. Maternal Health Education Programs: Developing and implementing comprehensive maternal health education programs that target women, families, and communities can improve awareness, knowledge, and understanding of maternal health issues. These programs can cover topics such as prenatal care, nutrition, breastfeeding, family planning, and birth preparedness, empowering women to make informed decisions about their health and seek appropriate care.

8. Public-Private Partnerships: Collaborating with private sector organizations, such as telecommunications companies or pharmaceutical companies, can leverage their resources, expertise, and networks to improve access to maternal health services. These partnerships can support initiatives such as mobile health campaigns, supply chain management, and capacity building for healthcare providers.

It is important to note that the specific context, resources, and needs of the target population should be considered when implementing these innovations to ensure their effectiveness and sustainability.

AI Innovations Description

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

1. Implement a digital health information system: To improve the accuracy, timeliness, completeness, and consistency of maternal and child health (MCH) data, it is recommended to develop and implement a digital health information system. This system should be user-friendly and accessible to healthcare facilities in the Cape Coast Metropolis, Ghana. It should allow for real-time data entry, automatic data validation, and data synchronization between different levels of healthcare facilities. By digitizing the MCH data collection and reporting process, healthcare providers can easily and accurately record and report MCH data, leading to improved data quality.

2. Provide training on data collection and reporting: To ensure that healthcare providers have the necessary skills and knowledge to accurately collect and report MCH data, it is important to provide comprehensive training on data collection and reporting. This training should cover topics such as data entry techniques, data validation procedures, and the importance of accurate and timely data reporting. By equipping healthcare providers with the necessary skills, they will be able to effectively contribute to improving the quality of MCH data.

3. Establish data quality monitoring mechanisms: To continuously monitor and improve the quality of MCH data, it is recommended to establish data quality monitoring mechanisms. This can include regular data audits, data validation checks, and feedback loops with healthcare providers. By regularly monitoring data quality, any issues or discrepancies can be identified and addressed in a timely manner, ensuring that the MCH data remains accurate and reliable.

4. Strengthen data management capacity: To support the implementation of the digital health information system and ensure effective data management, it is important to strengthen the data management capacity at healthcare facilities. This can involve providing additional resources, such as computers and internet connectivity, training staff on data management best practices, and establishing data management guidelines and protocols. By strengthening data management capacity, healthcare facilities will be better equipped to handle and utilize MCH data, ultimately improving access to maternal health services.

Overall, by implementing these recommendations, the quality of MCH data can be improved, leading to better monitoring of maternal health and ultimately improving access to maternal health services in the Cape Coast Metropolis, Ghana.

AI Innovations Methodology

Based on the provided description, here are some potential recommendations to improve access to maternal health:

1. Strengthen data collection systems: Implement electronic health records (EHR) or mobile-based data collection tools to improve the accuracy, timeliness, and completeness of maternal health data. This can help reduce errors and ensure real-time data availability.

2. Enhance training and capacity building: Provide training and capacity building programs for healthcare workers on data collection, management, and reporting. This can improve their skills and knowledge in maintaining high-quality maternal health data.

3. Implement data quality assurance mechanisms: Establish regular data quality assessments and audits to identify and address data inaccuracies, inconsistencies, and gaps. This can involve regular data validation exercises and feedback mechanisms to ensure data accuracy.

4. Strengthen data integration and interoperability: Improve the integration and interoperability of different health information systems, such as the District Health Information System (DHIS2), to ensure seamless data flow and reduce duplication of efforts. This can enhance data completeness and consistency.

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

1. Define the baseline: Collect and analyze existing data on maternal health access indicators, such as antenatal care coverage, skilled birth attendance, and postnatal care utilization. This will establish a baseline against which the impact of the recommendations can be measured.

2. Identify key variables: Select key variables that are directly influenced by the recommendations, such as data accuracy, timeliness, completeness, and consistency. These variables will serve as indicators to measure the impact of the recommendations.

3. Design intervention scenarios: Develop different intervention scenarios based on the recommendations. For example, simulate the impact of implementing electronic health records on data accuracy and timeliness, or assess the effect of training programs on data completeness.

4. Collect data: Implement the recommended interventions in a selected sample of healthcare facilities. Collect data on the selected variables before and after the interventions are implemented. This can involve surveys, interviews, or data extraction from health records.

5. Analyze and compare data: Analyze the collected data and compare the indicators before and after the interventions. Calculate the changes in data accuracy, timeliness, completeness, and consistency to determine the impact of the recommendations.

6. Interpret and report findings: Interpret the findings of the impact assessment and report the results. This can include presenting the changes in access indicators, identifying the strengths and weaknesses of the interventions, and making recommendations for further improvements.

By following this methodology, it will be possible to simulate the impact of the recommendations on improving access to maternal health and provide evidence-based insights for decision-making and policy development.

Author

Dima Health

Identifiers:

DOI: 10.1186/s12913-022-08449-6

Research Areas:

Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Workforce

Study Countries:

Ghana

Study Design:

Cross Sectional Study

Study Approach:

Mixed-methods, Quantitative, Systematic Review

Participants Gender:

Female