Informal Caregiving: Health System Cost Implications
Bibliographic record
Abstract
BACKGROUND: Informal caregiving is seen as a low-cost substitute for care provided by health care professionals. However, caregiving is known to negatively impact caregivers' health and, subsequently, their health care use and costs. This could potentially offset the caregivers' contributions to the health care system. OBJECTIVE: We examined the impact of caregiving on costs associated with caregivers' use of publicly funded health care services in Ontario, Canada, in comparison with non-caregivers. METHODS: We included Ontarians who participated in the Canadian Community Health Survey-Healthy Aging Supplement Survey of 2008/09 and linked responses to health care administrative databases. A difference-in-differences design was used to capture differences in caregivers' and non-caregivers' total health care costs 1 and 2 years before and after caregiving start date. Generalized Linear Models were used to model the total health care costs. RESULTS: The sample size was 4275 with 1265 caregivers and 3010 non-caregivers. We found that while health care utilization increased over time, it increased by a lesser amount for caregivers than non-caregivers. Adjusted total health care costs for caregivers were 11.32% (SE = 0.05, ρ = 0.02) lower than non-caregivers 2 years into caregiving. CONCLUSIONS: Our study reveals a critical gap in policy, practice, and research driven by a lack of routine data collection and caregiver identification. It also highlights the need for additional longitudinal research focusing on caregivers' objective health.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".