Improving quality, timeliness and efficacy of data collection and management in population-based surveillance of vital events

Electronic data collection (EDC), has become familiar in recent years, and has been quickly adopted in many research fields. It has become commonplace to assume that systems that entail entering data in mobile devices, connected through secure networks to central servers are of higher standard than old paper based data collection systems (PDC). Although the notion that EDC performs better than PDC seems reasonable and is widely accepted, few studies have tried to formally evaluate whether it can improve data quality, and none of these to our knowledge, are in the context of population-based longitudinal surveillance.
This thesis project aims to assess the strength of OpenHDS, a system based on EDC, used in the population-based surveillance of vital events via Health and Demographic surveillance systems (HDSS). HDSS are both sources of vital event data and have the potential to support health intervention studies in the areas where they operate. Setting up and running an HDSS is operationally challenging, and a reliable and efficient platform for data collection and management is a basic part of it. There are often major shortcomings in the data collection and management processes in running HDSS, though these have not been extensively documented.
Recent technological advances, specifically the use of mobile devices for data collection, and the adoption of OpenHDS software for data management, which makes use of best practices for data management, appear to have the potential to resolve many of these issues. The INDEPTH Network and others have invested substantial resources in the roll-out and support of OpenHDS, and there is anecdotal evidence that this has resulted in improvements, but there is considerable demand for compelling evidence.
The Swiss Tropical and Public Health Institute (Swiss TPH) has supported some INDEPTH sites to fully migrate to OpenHDS (Ifakara and Rufiji in Tanzania, Nanoro in Burkina Faso, Manhiça in Mozambique and Cross river in Nigeria) and some are in the migration process (7 sites in Ethiopia: Arba Minch, Butajira, Dabat, Gilgel Gibe, Kersa and Kilite Awlaelo). Some other sites are at different stages of evaluating the possibility of adopting OpenHDS (Navrongo in Ghana, Niakhar in Senegal, Iganga/Mayuge in Uganda, Nouna in Burkina Faso, Birbhum in India etc.) and there is a demand from all of them for evidence of the benefits of adopting this system. Demonstration of the appropriate functioning of the OpenHDS is also highly relevant in the light of recently proposed approaches for comprehensive health and epidemiological surveillance systems. Such systems will need to satisfy requirements in terms of data availability and integration which are considerable higher than in a classical HDSS.
This project assesses the benefits of OpenHDS in terms of and how the advances in data collection and management translate into improved data quality and timeliness. It asks whether the system architecture of the novel data management system can be further exploited to enable data integration approaches for near time quality control and near time response triggers. It also considers what are the main challenges in implementing such technologies in a new or an existing HDSS.
This entails:
• A description of the new system and of a set of conjectured data management best practices. For each of these best practices there is a literature review to assess if there is evidence to support it and if OpenHDS follow these practices, giving evidence of how this can be feasible and implemented in the field in two different real-life scenarios: the setting up of a new HDSS (Rusinga Island, Western Kenya and Majete Malaria Project, southern Malawi); and the migration of existing HDSSs (Ifakara, Tanzania and Nanoro, Burkina Faso) to OpenHDS. (Chapter 1)
• Describing a novel approach for data collection and management in health and demographic surveillance designed to address the shortcomings of the traditional approach (OpenHDS) and documenting the usage of this system the establishment of a new HDSS (Rusinga) in Chapter 2 and 3.
• Evaluating innovative approaches for quality control measures that are made possible by the novel data system architecture (in particular, use of satellite imagery to assess completeness of populations, using Majete HDSS as an example) in Chapter 4.
• Studying the potential benefits of electronic data collection (compared with paper) in terms of quality, timeliness, and costs by comparing both in a contemporaneous comparison of different systems in 8 villages in Nanoro, Burkina Faso and using historical comparisons of data quality (as assessed by iSHARE2) before and after migration to OpenHDS for a range of INDEPTH sites in Chapter 5.
A series of analyses were carried out to demonstrate that the OpenHDS data system for HDSSs can be implemented in both existing or newly established sites in low- and middle-income countries, and to test the hypothesis that the system is superior to previous approaches with regard of quality and timeliness of data and running costs of the system. This involved describing the novel approach to data collection and management enabled by OpenHDS, evaluating benefits in terms of quality and timeliness of the data using the OpenHDS mobile electronic data system, and the cost of electronic data collection (OpenHDS) vs. paper. It also involved evaluating the impact on the quality of the data of near-time availability and the potential of the OpenHDS system architecture for data integration for next-generation quality control and surveillance-response applications.
This work demonstrates that OpenHDS is a system that manages data in a standard reference format, using rigorous checks on demographic events, adding the flexibility to introduce entire questionnaires, variables that a longitudinal study could require, and that OpenHDS can take over old demographic surveillance systems with this new real-time low-cost paperless technology opportunity to abandon old fashion research systems, that remain in use in developing countries.