A Framework for Timely and More Informative Epidemic Diseases Surveillance: The Case of Tanzania

This research article published by the Journal of Health Informatics in Developing Countries, 2018Background: A number of health facilities in the United Republic of Tanzania use different Hospital Management Information Systems (HoMISs) for capturing and managing clinical and administrative information for report generation. Despite the potentials of the data in the systems for use in epidemic diseases surveillance, timely extraction of the data for integrated data mining and analysis to produce more informative reports is still a challenge. This paper identifies the candidate data attributes for epidemic diseases surveillance to be extracted and analyzed from the Government of Tanzania Hospital Management Information System (GoT-HoMIS). It also examines the current reporting setup for epidemic diseases surveillance in Tanzania from the health facilities to the district, regional, and national levels. Methods: The study was conducted at the Ministry of Health, Community Development, Gender, Elderly, and Children (MoHCDGEC), Tumbi Designated Regional Referral Hospital (TDRRH), Muhimbili University of Health and Allied Sciences (MUHAS), and Mzumbe Health Centre, all in the United Republic of Tanzania. A total of 10 key informants (medical doctors, epidemiologists, and focal persons for various health information systems in Tanzania) were interviewed to obtain primary data. Data entry process in the GoT-HoMIS was also observed. Documents were reviewed to broaden understanding on several aspects. Results: All the respondents (100%) suggested patients’ gender, age, and residence as suitable attributes for epidemic diseases surveillance. Other suggested attributes were occupation (85.71%), diagnosis (57.14%), catchment area population (57.14%), vital status (57.14%), date of onset (57.14%), tribe (42.86%), marital status (42.86%), and religion (14.29%). Timeliness, insufficient immediate particulars on an epidemic-prone case(s), aggregated data limiting extensive analytics, missing community data and ways to analyze rumors, and poor data quality were also identified as challenges in the current reporting setup. Conclusion: A framework is proposed to guide researchers in integrating data from health facilities with those from social media and other sources for enhanced epidemic disease surveillance. Data entrants in the systems should also be informed on the essence and applications of data they feed, as quality data are the roots of quality reports