Disease Burden on Health Facilities in Governorates South of Karbala During the Arbaeenia Mass Gathering in Iraq in 2014: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Millions of Iraqi pilgrims travel annually from the southern governorates to Karbala and pass through Thiqar, Muthana, and Diwania Governorates to join the Arbaeenia mass gathering event. During this event, participants are at high risk for diseases and death and stifle local health care resources. In addition, the mass gathering causes considerable burden on health facilities in the hosting localities. OBJECTIVE: This study aims to estimate the disease burden on health facilities caused by the pilgrims passing through Thiqar, Muthana, and Diwania Governorates en route to Karbala in Iraq. METHODS: This cross-sectional study was conducted on all health facilities in three governorates (Thiqar, Muthana, and Diwania) situated along the southern way to Karbala from Basra. The study started on December 11, 2014, and ended on December 24, 2014. The morbidity and mortality were collected from surveillance logbooks and death registers. Drug purchase data were obtained from the personnel in charge of the pharmacies. The study period was divided into three phases on the basis of the timing of the mass gathering event: pre-event, the event, and postevent. RESULTS: There were 884,834 incidents reported during the study. The majority of incidents were reported during the event phase (95%) and were attended mostly at mobile clinics (77%). The average daily incidents during the pre-event, event, and postevent phases were 4300, 56,040, and 4548 incidents, respectively. Musculoskeletal disorders were the most common illness reported (55%). The average number of daily deaths was 43, 36, and 45 during the pre-event, event, and postevent, respectively, and these values did not differ significantly. Cardiovascular diseases (43.5%), injuries (29.8%), and respiratory illnesses (12%) were the leading causes of deaths. Approximately US $1.3 million was spent on drug purchases during this mass gathering in the three governorates. CONCLUSIONS: The Arbaeenia mass gathering causes a tremendous disease and economic burden on governorates that pilgrims pass through to attend this mass gathering in Karbala. Although Iraq's Ministry of Health is aware of the high burden of this mass gathering on the health facilities in these governorates, more work is needed to ensure quality services during the event.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".