E-dengue System Insights: Exploring the Factors Influencing Dengue-related Deaths in an Urbanized State in a Low-Middle Income Country (LMIC).

Autor: Abdul Rahman FK; Department of Public Health Medicine, Faculty of Medicine, National University of Malaysia, Kuala Lumpur, Malaysia. farahkhalida27@gmail.com., Binti Wan Puteh SE; Department of Public Health Medicine, Faculty of Medicine, National University of Malaysia, Kuala Lumpur, Malaysia., Bin Zainuddin MA; Vector Borne Disease Control Unit, Penang State Health Department, Penang, Malaysia.
Jazyk: angličtina
Zdroj: BMC public health [BMC Public Health] 2024 Nov 05; Vol. 24 (1), pp. 3055. Date of Electronic Publication: 2024 Nov 05.
DOI: 10.1186/s12889-024-20545-2
Abstrakt: Background: Dengue has emerged as a rapidly escalating health issue in low- and middle-income countries, with its burden and geographic spread increasing over the years. Malaysia, in particular, has witnessed a significant rise in dengue cases, accompanied by a spike in mortality rates. Several studies have identified various factors, primarily focusing on the 27 clinical aspects of severe dengue infection and the development of dengue-related fatalities. Expanding on this focus, this study aims to identify the demographic, clinical, and environmental factors contributing to dengue mortality, providing a more comprehensive understanding of the variables influencing dengue-related fatalities.
Methods: This study utilized a 1:2 case-control design, analyzing data from the E-dengue system database and medical records from January 2015 to December 2022, involving 219 participants (73 dengue fatalities as cases and 146 recovered patients as controls). Dengue deaths were confirmed by the Penang State Mortality Review Committee, and controls were randomly selected from laboratory-confirmed dengue cases. Statistical analyses were performed using SPSS software, including descriptive statistics, chi-square tests, and multivariable logistic regression to identify predictors of dengue mortality, with variables included in the multivariable model if p < 0.05.
Results: Several significant predictors of dengue-related mortality, including clinical and environmental factors were identified. Key predictors were a platelet count below 50,000/µL (OR 15.70; 95% CI: 5.65-43.53), presence of one comorbid condition (OR 2.90; 95% CI: 1.22-6.90), more than two comorbid conditions (OR 10.15; 95% CI: 3.53-29.23), bronchial asthma (OR 12.00; 95% CI: 1.08-132.13), and outbreak locality status (OR 2.3; 95% CI: 1.11-4.79). An interaction was also found between locality status and platelet levels.
Conclusion: The study emphasizes the need for developing risk profiles for dengue patients by integrating factors such as platelet levels, comorbidities, and locality status to improve clinical care. Nuanced protocols are needed to address the specific challenges of single-case and outbreak areas. In single-case localities, patients with low platelet counts (below 100,000/µL) should be prioritized for rapid intervention to mitigate severe outcomes. In outbreak areas, healthcare systems should bolster resources and apply comprehensive triage approaches considering platelet levels and other risk factors. Implementing predictive models that account for geographical factors can enhance resource allocation and preparedness for dengue outbreaks. These recommendations aim to empower public health personnel, healthcare providers, and communities to collectively reduce dengue-related mortality rates.
(© 2024. The Author(s).)
Databáze: MEDLINE
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