Frequency and Pattern of Crimean Congo Haemorrhagic Fever in Pakistan-Analysis of Tertiary Care Hospital Data of Karachi Pakistan 2013-2022
Bibliographic record
Abstract
Background: Crimean Congo Hemorrhagic Fever (CCHF) is a tick-borne viral infection that is endemic in multiple countries in Africa, Europe, and Asia. Lack of public awareness, inadequate infrastructure, limited facilities, unhygienic practices, failure of early diagnosis, climate changes, and worst socioeconomic conditions have all contributed to rising disease incidence in underdeveloped countries like Pakistan. Methods: Concerning the dire situation, the current study aims to evaluate the relationship between multiple factors including patient demographics (age, sex, and residential area), timeframe, and climate changes to rising disease load and also the survival status. For this purpose, a descriptive study has been performed. Real-time data has been collected from tertiary care hospitals in Karachi, Pakistan from January 2013 to August 2022 that has not been reported yet. The cases were taken from the hospital records using as standard case definition for the duration of 2013 to 2022. Results: Analysis revealed that a total of 164 CCHF cases were reported of which 57% (n=98 discharged), 48% (n=68 died), and 3% (n=5 LAMA). Of all cases reported 85% (n=144) were male, of which 59% (n=85 discharged) and 41% (n=59 died). Among 20 females 65% (n=13 discharged) and 35% (n=7 died) with p -value<0.05. The highest number of cases 25% (n=41) were reported in 2018 with the greatest disease burden from July to September. The overall case fatality of CCHF was 41% (n=68). The mean age of male patients was 33.76 while female was 42.6. Mostly the cases reported were from Karachi 60% (n=99) followed by Balochistan 21% (n=35). Within Karachi, the highest no of cases was reported from Korangi district 23% (n=23). Interestingly, greater cases and mortality were recorded from highly populated, non-marginalized, less affluent areas due to poor hygiene, less awareness, illiteracy, and fewer facilities. Conclusions: Crimean Congo Haemorrhagic fever is prevailing in the country. The mortality rate associated with the disease is higher. The incidence of the disease is found to be high in the male population which is due to the occupation of animal handling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".