Respiratory health in Syria: an analysis of primary data from the Syrian American Medical Society
Bibliographic record
Abstract
Abstract Despite a decade of conflict, there has been little exploration of respiratory health in Syria. We explore the burden and trends of respiratory consultations in Syrian American Medical Society (SAMS) facilities in northwest Syria using mixed methods. We performed i) a scoping review of available academic and grey literature on respiratory health in Syria between 2011 and 2020 (17 databases) and ii) a retrospective review of routinely collected data relating to respiratory presentations in SAMS' facilities between March 2017 and June 2020. We identified 23 papers (19 peer-reviewed); 7 analysed primary data. Key themes included the impact of conflict on asthma diagnosis and management, the burden of respiratory tract infections (RTIs), the impact of chemical weapons and those relating to the destruction of the health system. In our quantitative analysis, data were available for 5,058,864 consultations, of which 1,228,722 (24%) were respiratory presentations. 45% of respiratory presentations were from hospitals, 44% from primary healthcare clinics and 9% from mobile clinics. The median monthly number of respiratory cases was 30,279 (25,792-33,732) out of a median 128,923 total monthly consultations (112,917-140,189). 73% of respiratory consultations were for children. Key findings include: respiratory presentations accounted for up to 38% of consultations each month with seasonal variation. RTIs accounted for 91% of all respiratory presentations. A steep decrease in consultations occurred between the end of 2019 (160,000) and the first quarter of 2020 (90,000), correlating with an escalation of violence in Idlib governorate. This study presents the largest quantitative analysis of respiratory data collected during the Syrian conflict. Our findings support the need for improved measures to aid the prevention, diagnosis and management of respiratory conditions during conflict. Further work exploring such interventions is needed. Key messages We present the largest study of the burden of respiratory presentations during the Syrian conflict accounting for 1,228,722 (24%) of all consultations; of these 72% were for children. Despite the burden of respiratory disease in the Syrian conflict, optimisation of respiratory health is neglected. Public health measures which address the causes and consequences are needed.
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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 | 0.036 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".