Background Mortality statistics are traditionally used to quantify the burden of disease and to determine the relative importance of the various causes of death. Some of the most frequently used indices to quantify the burden of disease are the years of potential life lost (YPLL) and years of potential productive life lost (YPPLL). These two measures reflect the mortality trends in younger age groups and they provide a more accurate picture of premature mortality. This study was carried out to determine YPLL, YPPLL and cost of productivity lost (CPL) due to premature mortality caused by selected causes of deaths in Tanzania. Methods and findings Malaria, respiratory diseases, HIV/AIDS, tuberculosis, cancers and injuries were selected for this analysis. The number of deaths by sex and age groups were obtained from hospital death registers and ICD-10 reporting forms in 39 public hospitals in Tanzania, covering a period of 2006-2015. The life expectancy method and human capital approach were used to estimate the YPLL, YPPLL and CPL due to premature mortality. During 2006-2015, malaria, HIV/AIDS, tuberculosis, respiratory diseases, HIV+tuberculosis, cancer and injury were responsible for a total of 96,834 hospital deaths, of which 46.4% (n = 57,508) were among individuals in the productive age groups (15-64 years). The reported deaths contributed to 2,850,928 YPLL (female = 1,326,724; male = 1,524,205) with an average of 29 years per death. The average YPLL among females (32) was higher than among males (28). Malaria (YPLL = 38 per death) accounted for over one-third (35%) of the total YPLL. There was a significant increase in YPLL due to the selected underlying causes of death over the 10-year period. Deaths from the selected causes resulted into 1,207,499 YPPLL (average = 21 per death). Overall, HIV/AIDS contributed to the highest YPPLL (323,704), followed by malaria (243,490) and injuries (196,505). While there was a general decrease in YPPLL due to malaria, there was an increase of YPPLL due to HIV/AIDS, respiratory diseases, cancer and injuries during the 10-year period. The total CPL due to the six diseases was US$ 148,430,009 for 10 years. The overall CPL was higher among males than females
Introduction This systematic review aimed to analyse the performance of the Integrated Disease Surveillance and Response (IDSR) strategy in Sub-Saharan Africa (SSA) and how its implementation has embraced advancement in information technology, big data analytics techniques and wealth of data sources. Methods HINARI, PubMed, and advanced Google Scholar databases were searched for eligible articles. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols. Results A total of 1,809 articles were identified and screened at two stages. Forty-five studies met the inclusion criteria, of which 35 were country-specific, seven covered the SSA region, and three covered 3–4 countries. Twenty-six studies assessed the IDSR core functions, 43 the support functions, while 24 addressed both functions. Most of the studies involved Tanzania (9), Ghana (6) and Uganda (5). The routine Health Management Information System (HMIS), which collects data from health care facilities, has remained the primary source of IDSR data. However, the system is characterised by inadequate data completeness, timeliness, quality, analysis and utilisation, and lack of integration of data from other sources. Under-use of advanced and big data analytical technologies in performing disease surveillance and relating multiple indicators minimises the optimisation of clinical and practice evidence-based decision-making. Conclusions This review indicates that most countries in SSA rely mainly on traditional indicator-based disease surveillance utilising data from healthcare facilities with limited use of data from other sources. It is high time that SSA countries consider and adopt multi-sectoral, multi-disease and multi-indicator platforms that integrate other sources of health information to provide support to effective detection and prompt response to public health threats.
Background Health surveillance is an important element of disease prevention, control, and management. During the past two decades, there have been several initiatives to integrate health surveillance systems using various mechanisms ranging from the integration of data sources to changing organizational structures and responses. The need for integration is caused by an increasing demand for joint data collection, use and preparedness for emerging infectious diseases. Objective To review the integration mechanisms in human and animal health surveillance systems and identify their contributions in strengthening surveillance systems attributes. Method The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) 2015 checklist. Peer-reviewed articles were searched from PubMed, HINARI, Web of Science, Science Direct and advanced Google search engines. The review included articles published in English from 1900 to 2018. The study selection considered all articles that used quantitative, qualitative or mixed research methods. Eligible articles were assessed independently for quality by two authors using the QualSyst Tool and relevant information including year of publication, field, continent, addressed attributes and integration mechanism were extracted. Results A total of 102 publications were identified and categorized into four pre-set integration mechanisms: interoperability (35), convergent integration (27), semantic consistency (21) and interconnectivity (19). Most integration mechanisms focused on sensitivity (44.1%), timeliness (41.2%), data quality (23.5%) and acceptability (17.6%) of the surveillance systems. Generally, the majority of the surveillance system integrations were centered on addressing infectious diseases and all hazards. The sensitivity of the integrated systems reported in these studies ranged from 63.9 to 100% (median = 79.6%, n = 16) and the rate of data quality improvement ranged from 73 to 95.4% (median = 87%, n = 4). The integrated systems were also shown improve timeliness where the recorded changes were reported to be ranging from 10 to 91% (median = 67.3%, n = 8). Conclusion Interoperability and semantic consistency are the common integration mechanisms in human and animal health surveillance systems. Surveillance system integration is a relatively new concept but has already been shown to enhance surveillance performance. More studies are needed to gain information on further surveillance attributes.
Background Effective animal health surveillance systems require reliable, high-quality, and timely data for decision making. In Tanzania, the animal health surveillance system has been relying on a few data sources, which suffer from delays in reporting, underreporting, and high cost of data collection and transmission. The integration of data from multiple sources can enhance early detection and response to animal diseases and facilitate the early control of outbreaks. This study aimed to identify and assess existing and potential data sources for the animal health surveillance system in Tanzania and how they can be better used for early warning surveillance. The study used a mixed-method design to identify and assess data sources. Data were collected through document reviews, internet search, cross-sectional survey, key informant interviews, site visits, and non-participant observation. The assessment was done using pre-defined criteria. Results A total of 13 data sources were identified and assessed. Most surveillance data came from livestock farmers, slaughter facilities, and livestock markets; while animal dip sites were the least used sources. Commercial farms and veterinary shops, electronic surveillance tools like AfyaData and Event Mobile Application (EMA-i) and information systems such as the Tanzania National Livestock Identification and Traceability System (TANLITS) and Agricultural Routine Data System (ARDS) show potential to generate relevant data for the national animal health surveillance system. The common variables found across most sources were: the name of the place (12/13), animal type/species (12/13), syndromes (10/13) and number of affected animals (8/13). The majority of the sources had good surveillance data contents and were accessible with medium to maximum spatial coverage. However, there was significant variation in terms of data frequency, accuracy and cost. There were limited integration and coordination of data flow from the identified sources with minimum to non-existing automated data entry and transmission. Conclusion The study demonstrated how the available data sources have great potential for early warning surveillance in Tanzania. Both existing and potential data sources had complementary strengths and weaknesses; a multi-source surveillance system would be best placed to harness these different strengths.
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