Child abuse and all-cause mortality in the Canadian population
Bibliographic record
Abstract
Abstract Background A history of child abuse is common and is associated with the later occurrence of risky health behaviors, mental disorders, and chronic conditions, strongly suggesting that child abuse may be associated with elevated mortality. However, most of the literature on child abuse has studied psychosocial/behavioral or medical outcomes and have not addressed mortality directly. Methods Data from the 2012 Canadian Community Health Survey, linked to the Canadian Vital Statistics Database, were used in the analysis. The CCHS-2012 interview (n = 19,830) retrospectively assessed childhood physical abuse, sexual abuse, and witnessing intimate partner violence before the age of 16. Each type of abuse was analyzed separately using Cox proportional hazards models for all-cause mortality. Hazard ratios (HR) and associated 95% confidence intervals (CI) were estimated with and without adjustment for covariates. Results An effect on mortality was observed among men for witnessing interpersonal violence (age-adjusted HR 2.47, 95% CI 1.48-4.12), and severe physical abuse (age-adjusted HR 2.3, 95% CI 1.21-4.36). In each case, the association was not significant for women; the age-adjusted HRs being 0.93 (95% CI: 0.51-1.70) and 0.59 (95%CI: 0.64-2.60) respectively. The association was seen only among those reporting frequent abuse ( > =10 times) and weakened (became not significant) with adjustment for covariates that may mediate the association such as smoking and chronic conditions. Conclusions As predicted by a broader literature on childhood adversity, child abuse increases the risk of mortality. The effect was significant for severe physical abuse in men, but imprecision due to a limited number of deaths may have rendered other associations non-significant. The study provides some degree of confirmation that child abuse contributes to later life mortality. Hence public health strategies that prevent child abuse and mitigate the harms of the mediators might prevent mortality. Key messages Witnessing intimate partner violence is just as harmful as actually experiencing physical abuse. Preventing children's exposure to violence in family is valuable in preventing mortality in adulthood. Enhancing child abuse prevention programs and mitigating the harmful effects of the mediators such as smoking, substance use, and chronic conditions is important in reducing mortality in adult life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".