Browsing by Author "Boenecke, Juliane"

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    Evaluation of the acceptability of ESIDA app, a smartphone-based clinical decision support application to improve infectious disease outbreak detection in Tanzania: clinician perspectives
    (Mustafa et al. BMC Public Health, 2025) Mustafa, Ummul; Elingarami, Sauli; Kreppel, Katharina; Boenecke, Juliane; Brinkel, Johanna
    Epidemic-prone infectious diseases (EPIDs) such as dengue fever, coronavirus disease 2019 (COVID-19), leptospirosis, Marburg virus disease, measles and cholera, to name a few, place a significant burden on the Tanzanian population and health system. Clinicians working in primary health facilities (PHFs), such as dispensaries, health centres, and basic hospitals, play a vital role in detecting cases and outbreaks. However, they often face challenges, including insufficient knowledge of these diseases and a lack of supporting resources, including surveillance guidelines, standard case definitions, and, most importantly, access to confirmatory diagnostic tests. Although Tanzania reports outbreaks of infectious diseases almost every year, a significant number of cases go undetected and contribute to delayed response and recurrent outbreaks. Smartphone-based clinical diagnostic decision support systems (CDSS) have been proven to help bridge case detection gaps. The Epidemiological Surveillance for Infectious Diseases in Sub-Saharan Africa (ESIDA) project proposed developing the ESIDA app, a smartphone-based CDSS, to aid clinicians in the detection of EPIDs. Before developing the app, the ESIDA project evaluated its acceptability among clinicians, the primary target users. The aim was to gather insights to inform the app development, ensuring its design and features are relevant and applicable in Tanzania context.In-depth interviews were conducted with 21 clinicians, including medical doctors and clinical officers from public and private facilities in the Dar es Salaam region, which has reported frequent outbreaks of dengue and cholera. Data were collected and analyzed using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Clinicians were positive about the proposed ESIDA app and intended to use it once it was available. The facilitators and barriers to acceptability focused on performance expectancy, effort expectancy, facilitating conditions and social influence. Expected benefits, ease of use, and government involvement emerged as facilitators of acceptability, while high internet costs, workload, time constraints, infrastructure gaps, and patient resistance were identified as potential barriers. The development of the ESIDA app should prioritize maximizing system performance and benefits. They must also be user-friendly and in line with social norms. The necessary infrastructure must be in place for effective implementation.
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    Integrated rapid risk assessment for dengue fever in settings with limited diagnostic capacity and uncertain exposure: Development of a methodological framework for Tanzania
    (Public Library of Science, 2025-03-28) Belau, Matthias; Boenecke, Juliane; Ströbele, Jonathan; Himmel, Mirko; Dretvić, Daria; Mustafa, Ummul-Khair; Kreppel, Katharina; Sauli, Elingarami; Brinkel, Johanna; Clemen, Ulfia; Clemen, Thomas; Streit, Wolfgang; May, Jürgen; Ahmad, Amena; Reintjes, Ralf; Becher, Heiko
    Background Dengue fever is one of the world’s most important re-emerging but neglected infectious diseases. We aimed to develop and evaluate an integrated risk assessment framework to enhance early detection and risk assessment of potential dengue outbreaks in settings with limited routine surveillance and diagnostic capacity. Methods Our risk assessment framework utilizes the combination of various methodological components: We first focused on (I) identifying relevant clinical signals based on a case definition for suspected dengue, (II) refining the signal for potential dengue diagnosis using contextual data, and (III) determining the public health risk associated with a verified dengue signal across various hazard, exposure, and contextual indicators. We then evaluated our framework using (i) historical clinical signals with syndromic and laboratory-confirmed disease information derived from WHO’s Epidemic Intelligence from Open Sources (EIOS) technology using decision tree analyses, and (ii) historical dengue outbreak data from Tanzania at the regional level from 2019 (6,795 confirmed cases) using negative binomial regression analyses adjusted for month and region. Finally, we evaluated a test signal across all steps of our integrated framework to demonstrate the implementation of our multi-method approach. Results The result of the suspected case refinement algorithm for clinically defined syndromic cases was consistent with the laboratory-confirmed diagnosis (dengue yes or no). Regression between confirmed dengue fever cases in 2019 as the dependent variable and a site-specific public health risk score as the independent variable showed strong evidence of an increase in dengue fever cases with higher site-specific risk (rate ratio = 2.51 (95% CI = [1.76, 3.58])). Conclusions The framework can be used to rapidly determine the public health risk of dengue outbreaks, which is useful for planning and prioritizing interventions or for epidemic preparedness. It further allows for flexibility in its adaptation to target diseases and geographical contexts.