Self-reported versus administrative data records: implications for assessing healthcare resource utilization of mental disorders
Bibliographic record
Abstract
Abstract Background: Data on resourceuse are frequently required for health economic evaluation. Studies on health care utilization in individuals with mental disorders have analyzed both self-reports and administrative data, each of which with strengths and limitations. Source of data may affect the quality of cost analysis and compromise the accuracy of results. We sought to ascertain the degree of agreement between self-reports and statutory health insurance (SHI) fund claims data from patients with mental disorders to aid in the selection of data collection methods. Methods:Claims data from six German SHI and self-reported data were obtained along with a cost-effectiveness analysis performed as a part of a controlled prospective multicenter cohort study conducted in 18 psychiatric hospitals in Germany (PsychCare), including patients with pre-defined common and/or severe psychiatric disorders. Self-reported data were collected using the German adaption of the Client Sociodemographic and Service Receipt Inventory (CSSRI-D) questionnaire with a 6-month recall period. Data linkage was performed using a unique pseudonymized identifier. Healthcare utilization (HCU) was calculated for inpatient and outpatient care, day-care services, home treatment, and pharmaceuticals. Concordance was measured using Cohen’s Kappa and intraclass correlation coefficient. Regression approaches were used to investigate the effect of independent variables on the dichotomous and quantitative agreements. Results: In total 274 participants (mean age 47.8 [SD = 14.2] years; 47.08% women) were included in the analysis. Kappa values were 0.03 for outpatient contacts, 0.25 for medication use, 0.56 for inpatient days and 0.67 for day-care services. There was varied quantitative agreement between data sources, with the poorest agreement for outpatient care (ICC [95% CI] = 0.22 [0.10-0.33]) and the best for psychiatric day-care services (ICC [95% CI] = 0.72 [0.66-0.78]). Marital status and time since first treatment positively affected the chance of agreement on any use of outpatient services. Conclusions: Concordance between administrative records and patient self-reports was fair to moderate for most of the healthcare services analyzed. Health economic studies should consider using linked or at least different data sources to estimate HCU or focus the primary data-based surveys in specific utilization areas, where unbiased information can be expected.
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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 | 0.156 | 0.417 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".