The role of socio-demographic factors in premature cervical cancer mortality in Colombia
Bibliographic record
Abstract
BACKGROUND: While cervical cancer (CC) is an important cause of premature mortality in Colombia, the impact of socio-demographic factors on CC mortality in young women is not well understood. The primary objective of this study was to identify differences in CC mortality among Colombian women aged 20-49 years associated with education, type of health insurance, urban or rural and region of residence, and to determine whether differences in mortality associated with education or insurance varied by age. METHODS: Cervical cancer deaths for 2005-2013 and risk factors were obtained from the National Administrative Department of Statistics. Populations at risk were calculated from age-stratified population projections and the 2010 National and Demographic Health Survey. Negative binomial regression models, stratified by age, were used to examine associations between socio-demographic factors and mortality rates and whether the effects of education and health insurance varied by age. Multiple imputation was used to examine the importance of missing data. RESULTS: Differences of CC mortality were identified among women with limited to no education compared to highly educated women, with the largest disparity in the youngest age group (IRR 26.8, 95 % CI 6.65-108). Differences in mortality associated with health insurance also varied based on age group. Women with contributory and special health insurance had lower mortality rates than women with subsidised or no health insurance, except in the youngest age group. No differences were observed between women with subsidised and those with no insurance in any age group. Mortality rates were high among women who resided in urban areas and in the Atlantic, Central, Pacific, and Amazon-Orinoquía regions of Colombia. Missing values in the mortality database did not impact the findings from this study. CONCLUSIONS: Limited education was most strongly associated with premature CC mortality in the youngest women. Subsidised insurance did not appear to provide significant protection against CC mortality when compared to not having insurance, suggesting the need to examine diagnostic and treatment services available under the subsidised insurance plan. Our results could be used to target interventions to optimise the impact of resources to prevent premature mortality due to CC in Colombia.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".