Neuropsychiatric disorders among Syrian and Iraqi refugees in Jordan, 2012-2013: A retrospective, cohort study (P2.324)
Bibliographic record
Abstract
OBJECTIVE: To characterize neuropsychiatric disorders requiring exceptional, United Nations (UN)-funded care among refugees in Jordan, predominantly from Syria and Iraq from 2012-2013. BACKGROUND: Ongoing conflicts in the Syrian Arab Republic and Iraq have resulted in a large population of refugees facing long-term displacement, creating unprecedented strain on host countries’ health systems. There are limited reported data on neurological and psychiatric disorders among Syrian and Iraqi refugees. DESIGN/METHODS: The UN High Commissioner for Refugees, through exceptional care committees (ECCs), addresses expensive and emergency tertiary care for refugees in countries of first asylum. Neuropsychiatric diagnoses were identified through a review of applications to the Jordanian ECC and characterized by prevalence, cost, and demographic trends by country of refugee origin. RESULTS: There were 264 applications among 223 refugees (40[percnt] female; median age 35 years, range: newborn-87 years; 57[percnt] Syrian, 36[percnt] Iraqi, 7[percnt] other; 67[percnt] for emergency care) representing 11[percnt] of all ECC applications reviewed. The total amount requested for neuropsychiatric disorders was 925,674 USD. The most expensive care per person was due to brain tumor (7,905USD), multiple sclerosis (7,502USD), and nervous system trauma (6,466USD). Stroke was the most frequent diagnosis. Schizophrenia was the most costly and frequent diagnosis among the psychiatric disorders (2,269 USD per person, 27,226 USD total). Eighty percent of applications were approved for the full amounts. Iraqi applicants with neuropsychiatric disorders were older than Syrians (mean age 44 vs. 33 years, p<0.001) and applied for more money per application (5,462 versus 3,550 USD, p=0.02); however, approved amounts were not significantly different between Iraqis and Syrians (p>0.05). CONCLUSIONS:There is a need for long-term planning and financing for neuropsychiatric disorders traditionally considered outside of refugee health. Possible interventions may include stroke risk factor reduction and targeted medication donations for multiple sclerosis, epilepsy, and schizophrenia. Study Supported by: No funding recieved.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".