Nutrition and Scarlet Fever Mortality during the Epidemics of 1860-90: The Sundsvall Region
Bibliographic record
Abstract
This article examines the social and economic contexts in which three scarlet fever epidemics appeared in rural parishes surrounding the Swedish town of Sundsvall during the later nineteenth century. This preliminary investigation challenges studies that have discounted a possible relationship between nutrition and scarlet fever. It suggests that poor nutrition during pregnancy may have caused women to give birth to children who were particularly susceptible to scarlet fever. It is also possible that years of food shortages may have made it difficult for some mothers to nurse their infants and that this increased the likelihood that they would fall victim to the disease when the next epidemic occurred. Food shortages, brought about by a rapidly increasing population, below average harvests, the extraordinary development of the sawmill industry, and severe winters, often hampered the most determined efforts of local inhabitants to find sufficient amounts of nutritional food. Frequently, this compromised the immuno‐competence of young children. This study uses patterns in crop yields, annual wages, and various demographic indicators to identify those years in which it would have been most difficult to maintain adequate levels of nutrition. District physicians’ reports and computerized parish records provide the mortality data used to identify patterns of scarlet fever mortality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".