The emergence of a suburban penalty during the 1918/19 influenza pandemic in Malta: The role of a marketplace, railway, and measles
Bibliographic record
Abstract
The Malta 1918/19 influenza experience adds to our understanding of the pandemic by illustrating the importance of suburban populations, their vulnerabilities, and elevated mortality rates. Studies on the socio-geographical variation in the 1918/19 influenza mortality has largely overlooked the suburban experience, and thus the often-hidden heterogeneity of the disease experience is missing. A comparison of mortality rates across the three settlement types (urban, suburban, and rural) for the second wave of the pandemic revealed that there were significant differences across the settlement types (x2 = 22.67, 2df, p <0.0001). There was a statistically significant divide between suburban settlement type versus urban and rural communities. Further, the geographical division of the central suburban region had the highest mortality rate at 4.28 per 1000 living of all suburban regions. A closer examination of the central suburban communities revealed that the town of Birchicara was the driving force behind the elevated influenza mortality, with a rate of 5.28 per 1000 living. The exceedingly high rate of influenza mortality in Birchicara was significantly different from the other suburban communities (Z = 2.915, p = 0.004). Birchicara was notable as both a transmission and burden hotspot for influenza infection because of a unique conflation of factors not observed elsewhere on the island. Foremost, was the pitkali market, which was a produce wholesale distributing centre; second, was the fact that the train station was a central hub especially for Maltese labourers; third, was that the measles epidemic in 1916/17 contributed to elevated childhood influenza deaths because the presence of military personnel and their families. We argue that the interaction of the three factors, and in particular, the measles epidemic with childhood influenza, amounted to a syndemic. Factors associated with urbanization and high rates of infectious diseases, such as overcrowding and infant mortality, did not play a primary role in the syndemic.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".