Tanzania's first Marburg Viral Disease outbreak response: Describing the roles of FELTP graduates and residents.

Viral Haemorrhagic Fever Outbreak presents a significant public health threat, requiring a timely, robust, and well-coordinated response. This paper aims to describe the roles of the Tanzania Field Epidemiology and Laboratory Training Program (TFELTP) graduates and residents in responding to Tanzania's first Marburg Viral Disease (MVD) outbreak. We performed a secondary data analysis using a range of documents, such as rosters of deployed responders and the TFELTP graduate and resident database, to count and describe them. Additionally, we conducted an exploratory textual analysis of field deployment reports and outbreak situational reports to delineate the roles played by the residents and graduates within each response pillar. A total of 70 TFELTP graduates and residents from different regions were involved in supporting the response efforts. TFELTP graduates and residents actively participated in several interventions, including contact tracing and follow up, sensitising clinicians on surveillance tools such as standard case definitions, alert management, supporting the National and Kagera Regional Public Health Emergency Operations Centres, active case search, risk communication, and community engagement, coordination of logistics, passenger screening at points of entry, and conducting Infection Prevention and Control (IPC) assessments and orientations in 144 Health Facilities. The successes achieved and lessons learned from the MVD response lay a foundation for sustained investment in skilled workforce development. FELTP Training is a key strategy for enhancing global health security and strengthening outbreak response capabilities in Tanzania and beyond.

Evaluation of the influenza-like illness sentinel surveillance system: A national perspective in Tanzania from January to December 2019.

BACKGROUND: The World Health Organization (WHO) recommends periodic evaluations of influenza surveillance systems to identify areas for improvement and provide evidence of data reliability for policymaking. However, data on the performance of established influenza surveillance systems are limited in Africa, including Tanzania. We aimed to assess the usefulness of the Influenza surveillance system in Tanzania and to ascertain if the system meets its objectives, including; estimating the burden of disease caused by the Influenza virus in Tanzania and identifying any circulating viral strains with pandemic potential. METHODOLOGY: From March to April 2021, we collected retrospective data through a review of the Tanzania National Influenza Surveillance System electronic forms for 2019. Furthermore, we interviewed the surveillance personnel about the system's description and operating procedures. Case definition (ILI-Influenza Like Illness and SARI-Severe Acute Respiratory Illness), results, and demographic characteristics of each patient were obtained from the Laboratory Information System (Disa*Lab) at Tanzania National Influenza Center. The United States Centers for disease control and prevention updated guidelines for evaluating public health surveillance systems were used to evaluate the system's attributes. Additionally, the system's performance indicators (including turnaround time) were obtained by evaluating Surveillance system attributes, each being scored on a scale of 1 to 5 (very poor to excellent performance). RESULTS: A total of 1731 nasopharyngeal and oropharyngeal samples were collected from each suspected influenza case in 2019 from fourteen (14/14) sentinel sites of the influenza surveillance system in Tanzania. Laboratory-confirmed cases were 21.5% (373/1731) with a predictive value positive of 21.7%. The majority of patients (76.1%) tested positive for Influenza A. Thirty-seven percent of patients' results met the required turnaround time, and 40% of case-based forms were incompletely filled. Although the accuracy of the data was good (100%), the consistency of the data was below (77%) the established target of ≥ 95%. CONCLUSION: The overall system performance was satisfactory in conforming with its objectives and generating accurate data, with an average performance of 100%. The system's complexity contributed to the reduced consistency of data from sentinel sites to the National Public Health Laboratory of Tanzania. Improvement in the use of the available data could be made to inform and promote preventive measures, especially among the most vulnerable population. Increasing sentinel sites would increase population coverage and the level of system representativeness.

The quality of malaria case management in different transmission settings in Tanzania mainland, 2017-2018.

Tanzania is undergoing an epidemiological transition for malaria transmission with some areas of the country having <10% (hypoendemic) and other areas 10% - 50% malaria prevalence (mesoendemic). It is not known whether there is a difference in the quality of malaria case management based on endemicity in Tanzania mainland. We examined the influence of endemicity on the quality of malaria case management at health facilities. We conducted a cross-sectional analysis of 1713 health facilities in Tanzania mainland, using data collected by the National Malaria Control Program through an assessment tool to evaluate quality of malaria case management. The data was gathered from September 2017 to December 2018. Using standard quality factors, mean scores from facilities in the different endemicity regions were compared by a Student's t-test. Simple and multiple linear regression analyses were performed to determine the association between facility performance (score) and endemicity (mesoendemic vs. hypoendemic). Facilities in mesoendemic regions scored higher than those in hypoendemic regions on the overall quality of services [difference in mean scores ([Formula: see text]) = 2.52; (95% Confidence Interval (CI) 1.12, 3.91)], site readiness [[Formula: see text] = 2.97; (95% CI 1.30, 4.61)], availability of malaria reference materials [[Formula: see text] = 4.91; (95% CI 2.05, 7.76)], availability of Health Management Information System tools [[Formula: see text] = 5.86; (95% CI 3.80, 7.92)] and patient satisfaction [[Formula: see text] = 6.61; (95% CI 3.75, 9.48)]. Predictors associated with lower facility scores included; being located in a hypoendemic region [ß: -2.49; (95% CI -3.83, -1.15)] and urban area [ß: -3.84; (95% CI -5.60, -2.08)]. These findings highlight the differences in quality of malaria case management based on endemicity, but there is still a need to target improvement efforts in underperforming facilities, regardless of endemicity.