Background and Objectives: Meningitis is an inflammatory disease of the meninges and spinal cord. This study aimed to investigate the epidemiological trends and clinical-laboratory patterns of meningitis in Iran.
Methods: This descriptive-analytical study used meningitis surveillance data from the Iranian Ministry of Health portal (2013–2023), analyzed with SPSS-27 using chi-square and logistic regression.
Results: of 78181 suspected cases of meningitis 3.1% were confirmed as definitive cases (positive blood/CSF culture or PCR) over the study period. The highest number of confirmed cases was in children under five years. Geographically, most cases were in Isfahan (15.6%) and Tehran (13.2%) provinces, while Semnan (0.29%) reported the fewest. Peak incidence occurred in spring, with fever, headache, vomiting, and neck stiffness as common symptoms. The annual proportion of confirmed cases ranged from 3.4% to 13.8% (highest in 2014, lowest in 2021). Etiological agents among confirmed cases included Streptococcus pneumoniae (16.4%), viral agents (13.1%), Neisseria meningitidis (9.1%), Haemophilus influenzae type b (3.7%), and other pathogens (57.7%). Outcomes were recovery (42.1%), death (6.6%), ongoing treatment (46.1%), and unknown (5.2%). Multivariable logistic regression showed that older age significantly increased the likelihood of definitive diagnosis (OR=1.158; 95%CI:1.001–1.339), as did bacterial meningitis versus viral meningitis (OR=1.671; 95%CI:1.514–1.844). Sex and place of residence were not significant.
Conclusion: Meningitis remains a major public health challenge. Strengthening surveillance systems, early diagnosis, and preventive strategies are essential.
Azam Beik Mirza, S. Zahraei, Fatemeh Azimian Zavareh et al.· Iranian Journal of Epidemiol...· 0 citations
ObjectiveThis study aimed to identify characteristics of adverse events following immunization (AEFI) reporting systems and vaccine adverse events reporting systems (VAERS) including technical platforms, user groups, data elements, functional and non-functional requirements.MethodsIn this scoping review, various databases were searched from 1st January 2015 to 31st December 2024, and all types of studies that explained AEFI reporting system/VAERS characteristics were considered. The findings were reported descriptively.ResultsDifferent technical platforms including web-based, mobile-based, or hybrid platforms were used by multiple user groups. Data elements included personal, clinical, vaccination, and adverse events data. The functional requirements included recording vaccination and adverse events data as well as generating reports. Non-functional requirements were related to system security, data privacy, etc.ConclusionThis review presented a set of characteristics that has been considered for different AEFI reporting systems and VAERS. The results can be used for designing more comprehensive AEFI reporting systems in different countries. These features along with new digital technologies and analytical tools including artificial intelligence offer more potential to enhance efficiency and effectiveness. Future research should focus on AI-driven methodologies, including natural language processing, machine learning techniques, and predictive analytics, while addressing ethical, regulatory, and practical challenges.
Hassan Asadi, H. Ayatollahi, S. Zahraei et al.· Health Informatics Journal· 0 citations
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