Improving disease surveillance data analysis, interpretation, and use at the district level in Tanzania

Datos Bibliográficos

ID19530968
AutoresIrene R Mremi (0000-0003-2578-7500, Southern African Centre for Infectious Disease Surveillance, autor de correspondencia), Calvin Sindato (0000-0003-3040-165X, Southern African Centre for Infectious Disease Surveillance), Coleman Kishamawe (0000-0002-0670-9841, National Institute for Medical Research), Susan F Rumisha (0000-0001-7058-488X, The Kids Research Institute Australia), Sharadhuli I Kimera (0000-0002-2295-0643, Sokoine University of Agriculture), Leonard E G Mboera (0000-0001-5746-3776, Southern African Centre for Infectious Disease Surveillance)
Año2022
Volumen15
Número1
Páginas2090100-2090100
Fecha de publicación2022-12-31
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaGlobal Health Action (JOURNAL)
Identificadores de la revistaISSN: 1654-9716 • E-ISSN: 1654-9880
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/16549716.2022.2090100
PMID35916840
OpenAlexW4289522456
IdiomaEN
Referencias citadas35

An effective disease surveillance system is critical for early detection and response to disease epidemics. This study aimed to assess the capacity to manage and utilize disease surveillance data and implement an intervention to improve data analysis and use at the district level in Tanzania. Mapping, in-depth interview and desk review were employed for data collection in Ilala and Kinondoni districts in Tanzania. Interviews were conducted with members of the council health management teams (CHMT) to assess attitudes, motivation and practices related to surveillance data analysis and use. Based on identified gaps, an intervention package was developed on basic data analysis, interpretation and use. The effectiveness of the intervention package was assessed using pre-and post-intervention tests. Individual interviews involved 21 CHMT members (females = 10; males = 11) with an overall median age of 44.5 years (IQR = 37, 53). Over half of the participants regarded their data analytical capacities and skills as excellent. Analytical capacity was higher in Kinondoni (61%) than Ilala (52%). Agreement on the availability of the opportunities to enhance capacity and skills was reported by 68% and 91% of the participants from Ilala and Kinondoni, respectively. Reported challenges in disease surveillance included data incompleteness and difficulties in storage and accessibility. Training related to enhancement of data management was reported to be infrequently done. In terms of data interpretation and use, despite reporting of incidence of viral haemorrhagic fevers for five years, no actions were taken to either investigate or mitigate, indicating poor use of surveillance data in monitoring disease occurrence. The overall percentage increase on surveillance knowledge between pre-and post-training was 37.6% for Ilala and 20.4% for Kinondoni indicating a positive impact on of the training. Most of CHMT members had limited skills and practices on data analysis, interpretation and use. The training in data analysis and interpretation significantly improved skills of the participants

Data collection · Disease · Disease surveillance · Environmental health · Environmental planning · Family medicine · Geography · Public health · Statistics · Tanzania · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Medicine · Nursing · Viral Infections and Outbreaks Research