Monthly Archives: October 2015

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Using mobile technology to support vital registration and verbal autopsy in the community: Bonsaaso Millennium Villages Project, Ghana

Prior to the inception of the Ghana Bonsaaso Millennium Villages Project in 2006,  maternal mortality was as high as 345 deaths per 100,000 live births, child mortality was 110 deaths per 1000 live births, and the institutional delivery rate was as low as 32%. The challenge of accessing healthcare was identified as the main cause of poor health indicators in the area at that time.

The Millennium Villages Project (MVP) in Ghana initiated vital registration and verbal autopsy (VRVA) in 2008 to support the improvement of maternal and child health services, and, in turn, to reduce infant, child and maternal deaths. Vital registration ensured that all community members were properly identified and included in the denominator of measures being tracked by MVP. In 2011, verbal autopsy was strengthened to ensure that any death in the village was recorded and analysed for medical and social causes so that future deaths could be prevented.

The vital registration and verbal autopsy system

At the community level, community health workers (CHWs) registered household members within their community, making sure to include pregnant women and children under five. They also collected data on the following vital statistics: birth registration (indicating date and place of birth), pregnancy outcomes, and all deaths.

Prior to 2008, a paper-based system was used to collect this data. Multiple challenges were experienced from the paper-based system, including a large volume of information being gathered making it difficult to manage and data analysis very time-consuming.

In late 2009, mobile communications were established throughout the area and, in 2010, the mobile-phone based system called ChildCount+ (CC+) was introduced by the project to address the problems of the paper-based system. The open-source system, CC+, enabled CHWs to send data via SMS text messages to a central server, collecting data in real-time. This system was later migrated to a smartphone-based system called CommCare for general home visits.

A verbal autopsy specialist assists CHWs in conducting in-depth verbal investigations into the causes of each death in the community.  This investigation gathers information from the household and the health facilities of the catchment area where the death occurred to understand the circumstances behind the death.

This more extensive data collection by the verbal autopsy specialists required the use of a complementary electronic system known as Open Data Kit (ODK), where verbal autopsy data is inputted into smartphones using mobile forms.  Specialists visit homes, record the data on the phones and later return to an area with good coverage to send data to a central electronic medical record system – OpenMRS. The OpenMRS system then automatically uses this verbal autopsy data to generate reports on the social and medical causes of each death, which were reported back to local and remote teams. The mobile phone-based system integrated with Open MRS was also eventually migrated to run on the CommCare system in 2014.

How the data is used

The data is discussed during weekly MVP health team meetings comprising selected senior doctors, nurses and heads of public health programmes in the district. During these weekly meetings, vital statistics and verbal autopsy data is reviewed and analysed in order to identify solutions to the circumstances that led to the death and/or morbidity, and to prevent future occurrences.

On a monthly basis, a meeting with a larger stakeholder group takes place where issues on all deaths and morbidity trends are discussed. During these larger stakeholder meetings the verbal autopsy specialist presents on all cases of deaths that have been investigated. Participants of this monthly meeting comprise the MVP health team and multiple representatives from where the death occurred, including from the district referral hospital, from local facilities, from communities, and CHWs.  The meeting discusses the issues presented, finds solutions, and sets timelines for their implementation.

Examples of solutions implemented as a result of the verbal autopsy data include community health information sharing sessions and staff in-service training. These would be based on recommendations from the larger, monthly stakeholder meeting.

Achievements

The transition towards using these electronic data collection systems saw a greater volume of data reporting (see tables 1 and 2), which was in real-time and more accurate. This data collection has become an important monitoring and managerial tool, providing vital information in real-time, so that resources and staff performance gaps can be quickly identified and action taken immediately.  The programme has seen improvements in health staff performance, logistics provision and management. This implies that an effective data collection system provides the edge to improve performance for better results.

Table 1 and table 2Challenges and lessons learned

MVP has been successful in using effective data systems to improve performance and health outcomes. However, this was not achieved without challenges. Key challenges and lessons learned include:

  • Although the paper-based system cost less compared to the CommCare system, it was not cost-effective due to the limitations faced in using it:  time consuming to collect, numerous errors, risk of data loss, large costs for data entry, and lack of real-time data collection limiting rapid decision-making. Key costs to consider for the CommCare system include the smartphone and data bundle.
  • While the CC+ mobile phone-based system introduced in 2010 reduced data bulkiness, there were a number of challenges in using it, including CHWs having to type a lot of information into basic-feature phones, which led to significant errors, and CC+ requiring mobile service at the point of sending SMS text messages. Transferring to the CommCare system using smart phones in 2012 helped to address these problems. CommCare enabled CHWs to enter data on the smart phone anytime, anywhere with or without a mobile network service since data can be synchronized as soon as the CHW enters a network zone.  Also, CommCare can be designed to limit the amount of typing, and therefore reducing errors, by using drop down selection boxes and multiple choice selection options.
  • Training on using the technology takes time. It takes about three days to train someone in the technology and one to two months to become skilful in using it. Allocating time for this is important.
  • There will always be the challenge of the equipment, namely the phones getting lost, broken, or faulty. To address this challenge, the programme provides supervisors with back-up phones ready to be used until a permanent replacement is found.
  • We have not experienced families being concerned about using the mobile phones to collect the verbal autopsy data. Nevertheless, it is important to be culturally sensitive on when you conduct the interview (e.g. in our case, conduct the interview one week after the death) and explain to the family how the data is to be collected before the interview and that it will not be used for any wrong motive.

Future plans and the way forward

The success of the system has meant that the Ministry of Health and the Ghana Health Service have expressed interest in scaling up. MVP is currently working with the Ghana Government to look for both domestic and foreign support to scale-up the interventions.

Acknowledgments

This case study was written by Eric Akosah and Seth Ohemeng Dapaah from Millennium Villages Project, Bonsaaso-Ghana and reviewed by Dr Andrew S. Kanter, Columbia University.

Further information

Read more about this mHealth solution in the article ‘Combining vital events registration, verbal autopsy and electronic medical records in rural Ghana for improved health services delivery’ published by Studies in health technology and informatics and written by contributors to this case study and their colleagues – S., Ohemeng-Dapaah,  P., Pronyk, Akosa, E., Nemser, B., & Kanter, A.

Photo Credit: Danielle Goldman