Insights into leprosy epidemiology from an isolated population located in the Brazilian Amazon
Bibliographic record
Abstract
Leprosy, a chronic infectious disease caused mainly by Mycobacterium leprae (M. leprae), is still an important public health problem in countries such as Brazil and India. Here, we estimate key epidemiological parameters in the Prata Village, a unique, hyper-endemic, former leprosy colony isolated in the Brazilian Amazon. This is a population-based study in which the entire Prata Village population has been enrolled. Clinical, socioeconomic, and demographic data were obtained and validated by cross-checking using three independent information sources. Validated data was used for descriptive epidemiological analysis. From a total of 2,005 inhabitants by the time of the enrollment, 1,084 (56.2%) were born in the Village and, therefore, likely under lifelong exposure to leprosy cases. We observed differences between the sub-populations born and not born in the village in the cumulative prevalence of leprosy (5.9% vs. 22.9%, respectively) and the median age at diagnosis (15 years vs. 28 years, respectively). In contrast, there was no difference in the distribution of cases between males and females. Although extrapolating our findings to more open populations must be done carefully, we believe we used a unique population as a model to provide additional insights into the epidemiology of leprosy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".