Tobacco use and the relationship with HIV risk behaviors in Puerto Rico residents of 18 years and over – A cross-sectional study
Bibliographic record
Abstract
Background: Currently there is no country, social class, or group of people worldwide that has not been affected by HIV/AIDS. The HIV epidemic has been surrounded by great secrecy and ignorance that has fostered the spread of the virus. HIV/AIDS and tobacco use represent the only two major causes of death worldwide. Likewise, tobacco use is a leading cause of morbidity and mortality in people without HIV and is highly prevalent in HIV positive population. The main objective of this research is to provide an epidemiological profile of people who reported having an HIV test in their lifetime and smoke (PHHT-S) in Puerto Rico. Our secondary objectives are: 1) identify the existence of statistically significant differences between PHHT-S vs. people who have had an HIV test in their lifetime and do not smoke (PHHT-NS), and 2) determine the risk of having been in a risk situation for HIV infection among PHHT-S vs. PHHT-NS. Methods: Through a cross-sectional study methodology the analysis of 2010 database of the Puerto Rico Behavioral Risk Factor Surveillance System (PRBRFSS) was performed with the Statistical Package for the Social Sciences (SPSS). In the first part, a univariate analysis was performed using frequency distributions and percentages for categorical variables and means and standard deviation calculation for continuous variables. In the second part through a bivariate analysis, smokers and non-smokers who have had an HIV test in their lifetime were compared using chi-square tests, Odds Ratio and 95% confidence intervals (CI). Results: Among the 43.2% (1,009,587) of the Puerto Ricans of 18 years and over who reported having an HIV test in their lifetime, 16.6% (167,242) reported being smokers. This study provides statistically significant data to support the existence of differences between PHHT-S and PHHT-NS, especially in the area of risk behaviors related to HIV. PHHT-S are 2.24 times more likely to engage in some risky behavior that can lead them become infected with HIV in comparison with the PHHT-NS. Conclusions: This study provides strong evidence that confirm the need for collaboration between the Tobacco Control Programs and HIV programs to implement new strategies to promote a greater number of people who smoke get tested for HIV and to influence public policy and systems change.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".