Methodological considerations in implementing the WHO Global Survey for Monitoring Maternal and Perinatal Health

Bulletin of the World Health Organization, Volume 86, No. 2, Year 2008

Ukuhunyushwa

Objective: To set up a global system for monitoring maternal and perinatal health in 54 countries worldwide. Methods: The WHO Global Survey for Monitoring Maternal and Perinatal Health was implemented through a network of health institutions, selected using a stratified multistage cluster sampling design. Focused information on maternal and perinatal health was abstracted from hospital records and entered in a specially developed online data management system. Data were collected over a two- to three-month period in each institution. The project was coordinated by WHO and supported by WHO regional offices and country coordinators in Africa and the Americas. Findings: The initial survey was implemented between September 2004 and March 2005 in the African and American regions. A total of 125 institutions in seven African countries and 119 institutions in eight Latin American countries participated. Conclusion: This project has created a technologically simple and scientifically sound system for large-scale data management, which can facilitate programme monitoring in countries. The survey eventually will be implemented in 54 countries, four from each of the 14 WHO defined subregions. WHO subregions, classified by the levels of under-five child and adult mortality rates2 were used as a proxy for the burden of maternal and perinatal mortality. A stratified multistage cluster sampling design was used to obtain a sample of countries and health institutions worldwide. From each subregion, four countries were selected with probability proportional to population size (Table 1). When there were less than four countries in a subregion, all countries within that subregion were included. This process resulted in 12 subregions having four countries each, and two subregions having three countries each (Table 1). It was decided that no replacement would be made for a country that did not participate. AFRO, African Region; AMRO, Americas Region; EMRO, Eastern Mediterranean Region; EURO, European Region; SEARO, South-east Asian Region; WPRO, Western Pacific Region.a See reference 2 for further details on the WHO regions which are subdivided based on child and adult mortality strata: A, very low child and very low adult mortality; B,low child and low adult mortality; C, low child and high adult mortality; D, high child and high adult mortality; E, high child and very high adult mortality. In each country, the capital city was always included in the sample. In addition, two provinces were randomly selected from the other administrative areas. The third-stage sampling unit was obtained by drawing a random sample of up to seven health institutions, each of which reported at least 1000 deliveries in the year before the implementation of the survey. If there were fewer than seven eligible health institutions in the capital city or other provinces, then all available health institutions were selected. In each country, an up-to-date census of health institutions in the selected areas was obtained. In the absence of a recent census, a list of health institutions was prepared by the country coordinators, in collaboration with WHO country offices and ministries of health. All women who were delivered in the participating sites during the specified period comprised the study population. Those delivered elsewhere were not included. Data were collected over a two- or three-month period depending on the annual number of deliveries in each health institution. For those health facilities with less than 6000 deliveries, data were collected for three months; for those with over 6000 deliveries, data were collected for a two-month period. As a one-time event, an institutional level data collection form (available at: http://www.who.int/making_pregnancy_safer/health_systems/global_survey/en/index.html) was completed by institution’s medical director. Data were collected on services influencing maternal and perinatal care and outcomes such as laboratory tests, anaesthesiology resources, intrapartum care including emergency obstetric care, and human resources for maternal and perinatal health. Individual level data were abstracted directly from medical records onto a two-page data collection form (available at: http://www.who.int/making_pregnancy_safer/health_systems/global_survey/en/index.html) by trained data collectors. These included: maternal risk indicators, mode of delivery, and maternal and newborn outcomes up to hospital discharge or up to a maximum stay of seven days. These forms were completed after delivery and before hospital discharge of the woman and newborn. Incomplete data in medical records were updated in consultation with attending staff before patients’ discharge. Data were entered online (via Internet) at the health institutions and/or country level using existing computing facilities. Criteria for medical record data abstraction and definitions were described in the operational manual, available to all participating health institutions. A cross-checking mechanism was also incorporated to identify missing data. A separate manual was available for data transfer from individual forms to the online data entry system; this described data entry, cross-checking of data and mechanisms for handling missing data. Data abstraction instruments were pre-tested on a convenience sample of records and at the hospital level in 4 countries. A pilot test was performed after a two-week training period to check the skills acquired by data collectors and to identify further problems with individual forms. Revisions were made based on these pre-tests. Country coordinators were trained during two coordinators’ meetings at WHO Headquarters. Hospital coordinators and data collectors were trained by country coordinators and the WHO coordinating unit, with the support of regional staff. One person, usually a labour ward midwife, was responsible for daily data collection in each health institution, while the hospital coordinator (midwife or obstetrician) was responsible for supervision and data quality monitoring before forwarding to the provincial or country coordinator. Data were entered online at hospital, provincial and/or national level depending on available resources. The numbers of completed forms were checked against the number of deliveries recorded in the logbook in the health institution. Completed data forms were sent to the provincial or country coordinator. When data entry was not possible in the health institution, it was done by the national coordinating unit. Random checks were performed periodically by the country coordinator using the online data entry system to check for completeness and accuracy of data. Online data were also checked for quality by the overall project coordinator. Problems identified were addressed immediately by the country coordinator; technical questions were resolved in consultation with the project coordinator. Survey data were managed in collaboration with the WHO coordinating unit by an online systems provider (MedSciNet AB, Stockholm, Sweden), which developed and provided the application and stores the data on its server. The system enables data collection and storage in a user-friendly format that allows for reporting and downloading data for analysis. It also allows for use of different languages and for data to be entered online using Microsoft Explorer and a dial-up connection. The system was pilot tested in Africa and Latin America and modified wherever required. Online screens corresponded to the sections of the individual data collection form. The system prompted for the next field to be filled in; nonapplicable fields were automatically skipped. During data entry, fields were validated on screen according to pre-specified validation rules. A cross-checking validation was performed to ensure that only forms without errors were saved. Data were transmitted after encryption using 128-bit key security. The system provided the facility to search, sort and update patient information, and to generate descriptive analysis reports; system description, manuals, and data entry tutorials; the facility to share information by uploading and downloading other documents; and, at project coordinating unit level, the facility to create and modify user information. The application permitted different types of access to the site and data at global, national, sub-national and health institutional levels. Each data entry operator could access only the data that they had entered. Administrators had access to information at their level and below, but not to information at higher level. The project coordinator had administrative rights to access all data. Preparatory work commenced in mid-2003. This included discussions with WHO regional offices, the selection of countries and provinces, and the preparation of a sampling framework obtained from the participating countries. Following the first meeting, with investigators from Africa and the Americas, to explore the feasibility of the study, all health institutions randomly selected were informed about the nature of the project. Institutional consent was obtained from the responsible authorities. Plans for data collection were tested, from September to November 2003, in both regions in selected health facilities. The second global preparatory meeting, in November 2003, concentrated on finalization of individual and institutional data forms, training plans for the health institution staff, as well as data monitoring and management. At the third global meeting, in June 2004, final decisions on the implementation of the project in both regions were made. The country coordinator was responsible for project supervision at the national level, while the overall project was coordinated by WHO Headquarters in Geneva, supported by the WHO regional offices and country coordinators in Africa and the Americas. Each institution submitted the ethical clearance approval before commencing the project. Ethical clearance was provided by the institutional committees of the participating facilities, where available, or by the national review committees (available at: http://www.who.int/making_pregnancy_safer/health_systems/global_survey/en/index.html). In addition, ethical clearance was obtained from WHO’s Scientific and Ethical Review Group and Ethics Review Committee. Individual informed consent was not obtained as this was a cluster-level study, where data were extracted from medical records without any subject identification. However, key subject information (name, study number, birth date and delivery date) was recorded in the logbook at the institution level by the data collector to assist with follow-up if required.

I-AI Digest

Study Justification:

The objective of this study was to establish a global system for monitoring maternal and perinatal health in 54 countries worldwide. The study aimed to create a technologically simple and scientifically sound system for large-scale data management, which can facilitate program monitoring in countries.

Highlights:

– The study implemented the WHO Global Survey for Monitoring Maternal and Perinatal Health.
– A network of health institutions in 54 countries participated in the survey.
– Data on maternal and perinatal health were collected from hospital records and entered into an online data management system.
– The survey was conducted over a two- to three-month period in each institution.
– The project was coordinated by WHO and supported by WHO regional offices and country coordinators in Africa and the Americas.

Recommendations:

– Implement the WHO Global Survey for Monitoring Maternal and Perinatal Health in the remaining 40 countries.
– Strengthen the capacity of health institutions to collect and manage data on maternal and perinatal health.
– Regularly monitor and evaluate the data management system to ensure its effectiveness and efficiency.
– Use the collected data to inform and guide maternal and perinatal health programs and policies.

Key Role Players:

– WHO Headquarters in Geneva: Responsible for overall project coordination.
– WHO regional offices: Provide support and coordination at the regional level.
– Country coordinators: Responsible for project supervision and coordination at the national level.
– Health institutions: Participate in data collection and management.

Cost Items for Planning Recommendations:

– Training: Budget for training country coordinators, hospital coordinators, and data collectors.
– Data management system: Allocate funds for the development, maintenance, and hosting of the online data management system.
– Communication and coordination: Include costs for communication and coordination between WHO Headquarters, regional offices, country coordinators, and health institutions.
– Monitoring and evaluation: Set aside funds for monitoring and evaluating the data management system and the implementation of the survey.
– Capacity building: Allocate resources for strengthening the capacity of health institutions to collect and manage data on maternal and perinatal health.
Please note that the provided cost items are for planning purposes and do not reflect actual costs.

Amandla Obufakazi

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study describes a global system for monitoring maternal and perinatal health implemented in multiple countries. The methods used a stratified multistage cluster sampling design and collected data from hospital records. The findings indicate that the initial survey was implemented in African and American regions. The conclusion states that the project has created a technologically simple and scientifically sound system for large-scale data management. However, the abstract lacks specific details on the sample size, data collection process, and the statistical analysis performed. To improve the evidence, the abstract could include more information on the sample size, data collection methods, and statistical analysis techniques used.

Abstract

The survey eventually will be implemented in 54 countries, four from each of the 14 WHO defined subregions. WHO subregions, classified by the levels of under-five child and adult mortality rates2 were used as a proxy for the burden of maternal and perinatal mortality. A stratified multistage cluster sampling design was used to obtain a sample of countries and health institutions worldwide. From each subregion, four countries were selected with probability proportional to population size (Table 1). When there were less than four countries in a subregion, all countries within that subregion were included. This process resulted in 12 subregions having four countries each, and two subregions having three countries each (Table 1). It was decided that no replacement would be made for a country that did not participate. AFRO, African Region; AMRO, Americas Region; EMRO, Eastern Mediterranean Region; EURO, European Region; SEARO, South-east Asian Region; WPRO, Western Pacific Region.a See reference 2 for further details on the WHO regions which are subdivided based on child and adult mortality strata: A, very low child and very low adult mortality; B,low child and low adult mortality; C, low child and high adult mortality; D, high child and high adult mortality; E, high child and very high adult mortality. In each country, the capital city was always included in the sample. In addition, two provinces were randomly selected from the other administrative areas. The third-stage sampling unit was obtained by drawing a random sample of up to seven health institutions, each of which reported at least 1000 deliveries in the year before the implementation of the survey. If there were fewer than seven eligible health institutions in the capital city or other provinces, then all available health institutions were selected. In each country, an up-to-date census of health institutions in the selected areas was obtained. In the absence of a recent census, a list of health institutions was prepared by the country coordinators, in collaboration with WHO country offices and ministries of health. All women who were delivered in the participating sites during the specified period comprised the study population. Those delivered elsewhere were not included. Data were collected over a two- or three-month period depending on the annual number of deliveries in each health institution. For those health facilities with less than 6000 deliveries, data were collected for three months; for those with over 6000 deliveries, data were collected for a two-month period. As a one-time event, an institutional level data collection form (available at: http://www.who.int/making_pregnancy_safer/health_systems/global_survey/en/index.html) was completed by institution’s medical director. Data were collected on services influencing maternal and perinatal care and outcomes such as laboratory tests, anaesthesiology resources, intrapartum care including emergency obstetric care, and human resources for maternal and perinatal health. Individual level data were abstracted directly from medical records onto a two-page data collection form (available at: http://www.who.int/making_pregnancy_safer/health_systems/global_survey/en/index.html) by trained data collectors. These included: maternal risk indicators, mode of delivery, and maternal and newborn outcomes up to hospital discharge or up to a maximum stay of seven days. These forms were completed after delivery and before hospital discharge of the woman and newborn. Incomplete data in medical records were updated in consultation with attending staff before patients’ discharge. Data were entered online (via Internet) at the health institutions and/or country level using existing computing facilities. Criteria for medical record data abstraction and definitions were described in the operational manual, available to all participating health institutions. A cross-checking mechanism was also incorporated to identify missing data. A separate manual was available for data transfer from individual forms to the online data entry system; this described data entry, cross-checking of data and mechanisms for handling missing data. Data abstraction instruments were pre-tested on a convenience sample of records and at the hospital level in 4 countries. A pilot test was performed after a two-week training period to check the skills acquired by data collectors and to identify further problems with individual forms. Revisions were made based on these pre-tests. Country coordinators were trained during two coordinators’ meetings at WHO Headquarters. Hospital coordinators and data collectors were trained by country coordinators and the WHO coordinating unit, with the support of regional staff. One person, usually a labour ward midwife, was responsible for daily data collection in each health institution, while the hospital coordinator (midwife or obstetrician) was responsible for supervision and data quality monitoring before forwarding to the provincial or country coordinator. Data were entered online at hospital, provincial and/or national level depending on available resources. The numbers of completed forms were checked against the number of deliveries recorded in the logbook in the health institution. Completed data forms were sent to the provincial or country coordinator. When data entry was not possible in the health institution, it was done by the national coordinating unit. Random checks were performed periodically by the country coordinator using the online data entry system to check for completeness and accuracy of data. Online data were also checked for quality by the overall project coordinator. Problems identified were addressed immediately by the country coordinator; technical questions were resolved in consultation with the project coordinator. Survey data were managed in collaboration with the WHO coordinating unit by an online systems provider (MedSciNet AB, Stockholm, Sweden), which developed and provided the application and stores the data on its server. The system enables data collection and storage in a user-friendly format that allows for reporting and downloading data for analysis. It also allows for use of different languages and for data to be entered online using Microsoft Explorer and a dial-up connection. The system was pilot tested in Africa and Latin America and modified wherever required. Online screens corresponded to the sections of the individual data collection form. The system prompted for the next field to be filled in; nonapplicable fields were automatically skipped. During data entry, fields were validated on screen according to pre-specified validation rules. A cross-checking validation was performed to ensure that only forms without errors were saved. Data were transmitted after encryption using 128-bit key security. The system provided the facility to search, sort and update patient information, and to generate descriptive analysis reports; system description, manuals, and data entry tutorials; the facility to share information by uploading and downloading other documents; and, at project coordinating unit level, the facility to create and modify user information. The application permitted different types of access to the site and data at global, national, sub-national and health institutional levels. Each data entry operator could access only the data that they had entered. Administrators had access to information at their level and below, but not to information at higher level. The project coordinator had administrative rights to access all data. Preparatory work commenced in mid-2003. This included discussions with WHO regional offices, the selection of countries and provinces, and the preparation of a sampling framework obtained from the participating countries. Following the first meeting, with investigators from Africa and the Americas, to explore the feasibility of the study, all health institutions randomly selected were informed about the nature of the project. Institutional consent was obtained from the responsible authorities. Plans for data collection were tested, from September to November 2003, in both regions in selected health facilities. The second global preparatory meeting, in November 2003, concentrated on finalization of individual and institutional data forms, training plans for the health institution staff, as well as data monitoring and management. At the third global meeting, in June 2004, final decisions on the implementation of the project in both regions were made. The country coordinator was responsible for project supervision at the national level, while the overall project was coordinated by WHO Headquarters in Geneva, supported by the WHO regional offices and country coordinators in Africa and the Americas. Each institution submitted the ethical clearance approval before commencing the project. Ethical clearance was provided by the institutional committees of the participating facilities, where available, or by the national review committees (available at: http://www.who.int/making_pregnancy_safer/health_systems/global_survey/en/index.html). In addition, ethical clearance was obtained from WHO’s Scientific and Ethical Review Group and Ethics Review Committee. Individual informed consent was not obtained as this was a cluster-level study, where data were extracted from medical records without any subject identification. However, key subject information (name, study number, birth date and delivery date) was recorded in the logbook at the institution level by the data collector to assist with follow-up if required.

Izinto zokwakha

N/A

Emisha

Some potential innovations to improve access to maternal health based on the information provided include:

1. Online data management system: The development of a technologically simple and scientifically sound online data management system can facilitate large-scale data collection and analysis, making it easier to monitor maternal and perinatal health in countries.

2. Stratified multistage cluster sampling design: The use of a stratified multistage cluster sampling design allows for the selection of a representative sample of countries and health institutions worldwide, ensuring that data collected is diverse and comprehensive.

3. Collaboration with WHO regional offices and country coordinators: Working in collaboration with WHO regional offices and country coordinators in Africa and the Americas can help ensure the successful implementation of the project and provide support at the local level.

4. Training programs for health institution staff: Implementing training programs for health institution staff, such as hospital coordinators and data collectors, can ensure that they have the necessary skills to collect and manage data accurately and efficiently.

5. Use of existing computing facilities: Utilizing existing computing facilities at health institutions and/or country level for data entry can streamline the process and make it more accessible.

6. Encryption and security measures: Implementing encryption and security measures when transmitting data can help protect patient information and ensure data privacy.

7. User-friendly data entry system: Developing a user-friendly data entry system that prompts for the next field to be filled in and validates fields on screen can help minimize errors and improve data accuracy.

8. Different levels of access to data: Implementing a system that allows different types of access to the data at global, national, sub-national, and health institutional levels can ensure that relevant stakeholders have access to the information they need.

9. Ethical clearance and consent procedures: Obtaining ethical clearance from institutional and national review committees, as well as obtaining clearance from WHO’s Scientific and Ethical Review Group and Ethics Review Committee, ensures that the project is conducted ethically and with proper consent procedures in place.

AI Innovations Description

The recommendation that can be developed into an innovation to improve access to maternal health is the implementation of the WHO Global Survey for Monitoring Maternal and Perinatal Health in more countries. This survey is a technologically simple and scientifically sound system for large-scale data management, which can facilitate program monitoring in countries. By expanding the implementation of this survey to more countries, it will provide valuable data on maternal and perinatal health, allowing for better monitoring and evaluation of maternal health programs. This data can then be used to identify areas of improvement and develop targeted interventions to improve access to maternal health services. Additionally, the online data management system used in the survey can be further developed to include real-time data reporting and analysis, enabling timely decision-making and resource allocation for maternal health programs.

AI Innovations Methodology

In order to improve access to maternal health, here are some potential recommendations:

1. Telemedicine: Implementing telemedicine programs can provide remote access to healthcare professionals for prenatal and postnatal care. This can be especially beneficial for women in rural or remote areas who may have limited access to healthcare facilities.

2. Mobile health (mHealth) interventions: Developing mobile applications or text messaging services that provide information and reminders about prenatal care, nutrition, and postnatal care can help improve access to maternal health information and support.

3. Community health workers: Training and deploying community health workers who can provide basic maternal health services, education, and referrals in underserved areas can help improve access to care.

4. Transportation support: Providing transportation services or vouchers for pregnant women to attend prenatal appointments and deliver at healthcare facilities can help overcome geographical barriers to accessing maternal health services.

5. Financial incentives: Offering financial incentives or subsidies for pregnant women to seek prenatal care and deliver at healthcare facilities can help reduce financial barriers to accessing maternal health services.

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

1. Define the target population: Identify the specific population that would benefit from the recommendations, such as pregnant women in rural areas or low-income communities.

2. Collect baseline data: Gather data on the current access to maternal health services in the target population, including the percentage of women receiving prenatal care, delivering at healthcare facilities, and experiencing complications or adverse outcomes.

3. Design the intervention: Develop a simulation model that incorporates the recommended interventions, taking into account factors such as the number of community health workers deployed, the coverage of telemedicine services, or the availability of transportation support.

4. Input data and assumptions: Input relevant data and assumptions into the simulation model, such as the population size, the effectiveness of the interventions, and the cost of implementation.

5. Run simulations: Run multiple simulations using the model to estimate the potential impact of the interventions on improving access to maternal health services. This could include measuring changes in the percentage of women receiving prenatal care, delivering at healthcare facilities, and experiencing positive maternal and neonatal outcomes.

6. Analyze results: Analyze the simulation results to determine the potential benefits and challenges of implementing the recommendations. This could include assessing the cost-effectiveness of the interventions, identifying potential barriers to implementation, and evaluating the scalability of the interventions.

7. Refine and iterate: Based on the simulation results, refine the interventions and the simulation model as needed. Iterate the simulation process to further explore different scenarios and refine the recommendations.

By following this methodology, policymakers and healthcare professionals can gain insights into the potential impact of different interventions on improving access to maternal health and make informed decisions on implementing the most effective strategies.

Umbhali

Dima Health

Statistics:

Citations: 79

Identifiers:

DOI: 10.2471/BLT.06.039842

ISSN: 00429686

Research Areas:

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

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female

Yabelana ngalokhu: