Infections and Chronic Non-Communicable Diseases Among People Experiencing Homelessness: The Need for a Targeted Community-Based Intervention
Bibliographic record
Abstract
Objetivo: Analizar los datos de infecciones y enfermedades crónicas de personas en situación de sinhogarismo y compararlos con población general de un Centro de Atención Primaria de referencia de Girona (Cataluña). Método: Estudio ecológico comparativo a partir de un muestro no probabilístico en el que participaron 3.854 personas en situación de sinhogarismo y 24.321 personas correspondiente a la población general atendida en el Centro de Atención Primaria de referencia de Girona (Cataluña). Resultados: Las personas en situación de sinhogarismo presentaron mayor prevalencia de: a) enfermedades infecciosas: hepatitis C (9.5% frente a 0.5%), tuberculosis (2.9% frente a 0.1%) y VIH (3.8% frente a 0.1%); y b) enfermedades no transmisibles: diabetes mellitus-2 (7.1% frente a 5.5%) y EPOC (5.1% frente a 1.4%) mientras que la población general presentó mayor prevalencia de hipertensión arterial (12.2% frente a 15.7%) y obesidad (8.3% frente a 13.6%). Conclusiones: Las personas sin hogar presentan una mayor dificultad para adherirse al tratamiento de enfermedades no transmisibles como obesidad o diabetes mellitus, por lo que a la hora de llevar a cabo intervenciones sanitarias se deberían tener en cuenta las barreras y factores que presentan. Es necesario realizar actividades de vigilancia y coordinación entre los diferentes servicios que asisten a estas personas. Goal: To analyze the data on infections and chronic diseases of homeless people and compare them with the general population of Catalonia. Design: Multi-group ecological. Study Settings: Primary Care of Girona (Catalonia). Method: Comparative ecological study based on a non-probabilistic sample in which 3,854 homeless people and 24,321 people corresponding to the general population attended in the l of a reference Primary Care Center in Girona (Catalonia) participated. Results: Homeless people have a higher prevalence of infections: hepatitis C (9.5% vs. 0.5%), tuberculosis (2.9% vs. 0.1%) and HIV (3.8% vs. 0 .1%); and non-communicable diseases: diabetes mellitus-2 (7.1% vs. 5.5%) and COPD (5.1% vs. 1.4%) than the general population, which has a higher prevalence of arterial hypertension (12.2% vs. 15.7%) and obesity (8.3% vs. 13.6%). Conclusions: Homeless people present greater difficulties in adhering to treatment for non-communicable diseases such as obesity or diabetes mellitus, so when carrying out health interventions, they should consider the barriers and factors they present. It is necessary to carry out surveillance and coordination activities between the different services that assist these people.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".