Noncommunicable Disease Emergencies During Arbaeenia Mass Gathering at Public Hospitals in Karbala, Najaf, and Babel Governorates, Iraq, 2014: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Arbaeenia is the largest religious mass gathering (MG) in Iraq where millions of people from Iraq and many other countries visit Karbala city, south Iraq. MGs are associated with high rates of morbidity and mortality from different noncommunicable diseases (NCDs) such as cardiovascular diseases, diabetes mellitus, and asthma. There is a scarcity of publications that address MGs in Iraq. OBJECTIVE: This study aimed to explore the NCD emergencies in public hospitals in Karbala, Najaf, and Babel governorates in Iraq, during the Arbaeenia MG and assess predisposing factors for NCD emergencies. METHODS: The study was conducted from November 27 to December 16, 2014. Data were collected in the pre-event and during MG event from 7 selected hospitals. The pre-event data were collected from emergency room (ER) registers and logbooks, and the data on the MG event were collected daily through direct interview with patients and treating physicians using a structured questionnaire. RESULTS: In total, 4425 NCD emergencies were recorded. Of these, 80.13% (3546/4425) were collected during the MG event. The NCD emergencies attended at ER hospitals during MG were severe hypertension (HT), diabetes (hyperglycemia), ischemic heart disease (IHD), asthma, and pulmonary edema. The load of NCD emergencies and the daily average emergencies increased 4-fold and 2-fold during the MG event, respectively. Most of the NCD emergencies were treated at ER departments, and a few were hospitalized. Intense physical activities and poor adherence to diet and medication were risk factors for IHD, severe HT, and hyperglycemic diabetes emergencies. Exposure to noxious gases or fumes and recent respiratory infections were risk factors for asthma emergencies. CONCLUSIONS: As the pilgrims approached Karbala city during the Arbaeenia MG, the hospitals on the roads leading to the city experienced an increased load of patients because of different NCD emergencies. Although hospitals should be equipped with the necessary supplies, health education for pilgrims is mandatory, particularly on the factors that can exacerbate their diseases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".