Agreement between Self‐reported and Routinely Collected Health‐care Utilization Data among Seniors
Bibliographic record
Abstract
OBJECTIVE: To examine the agreement between self-reported and routinely collected administrative health-care utilization data, and the factors associated with agreement between these two data sources. DATA SOURCES/STUDY SETTING: A representative sample of seniors living in an Ontario county within Canada was identified using the Ontario Ministry of Health's Registered Persons Data Base in 1992. Health professional billing information and hospitalization data were obtained from the Ontario Ministry of Health and Long-Term Care (OMH) and the Ontario Health Insurance Plan (OHIP). STUDY DESIGN: A cross-sectional survey was carried out to assess any contact and frequency of contacts with health professionals and hospital admissions. Similar information was obtained from routinely collected administrative data. The level of agreement was assessed using the proportion of absolute agreement, Cohen's kappa statistic (kappa), and the intraclass correlation coefficient (ICC). Logistic and linear regressions were used to identify factors that were associated with the magnitude and direction of disagreement respectively. DATA COLLECTION/EXTRACTION METHODS: Telephone interviews were conducted on 1,054 seniors, and complete data were available for 1,038 seniors. Each respondent's personal health number was used to electronically link survey data with health professional billing and hospitalization databases. PRINCIPAL FINDINGS: Substantial to almost perfect agreement was found for the contact utilization measures, while agreement on volume utilization measures varied from poor to almost perfect. In surveys, seniors overreported contact with general practitioners and physiotherapists or chiropractors, and underreported contact with other medical specialists. Seniors also underreported the number of contacts with general practitioners and other medical specialists. The odds of agreement decreased if respondents were male, aged 75 years and older, had incomes of less than $25,000, had poor/fair/good self-assessed health status, or had two or more chronic conditions. CONCLUSION: The findings of this study indicate that there are substantial discrepancies between self-reported and administrative data among older adults. Researchers seeking to examine health-care use among older adults need to consider these discrepancies in the interpretation of their results. Failure to recognize these discrepancies between survey and administrative data among older adults may lead to the establishment of inappropriate health-care policies.
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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 | 0.026 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".