OP173 Estimating The Marginal Productivity Of Health Technology Adoption
Bibliographic record
Abstract
Introduction Decisions to adopt health technologies rely, in part, on judgements about cost effectiveness. Cost effectiveness is commonly assessed against a willingness-to-pay threshold for health gains. Building an evidence base on the marginal productivity of health spending to inform the value of the threshold is increasingly of interest for resource allocation decision-making and technology implementation. We report on an in-progress analysis to inform a threshold for policy purposes in British Columbia, Canada. Methods We developed a ten-year panel-data model with instrumental variables, which lessens the degree of time-invariant confounding and addresses biased causal inferences caused by unobserved factors, to provide estimates of the marginal cost per health unit measured using quality-adjusted life-years (QALYs). We use the Johns Hopkins Adjusted Clinical Group (ACG) system and a British Columbia Health System Matrix to classify patients into six resource use bands (RUBs) ranging from ‘healthy’ to ‘very high morbidity’. Patients are also classified by chronic conditions and types of services. Place of residence and geographical region of health authorities are considered. Variables included age, gender, mortality and comorbidity rates, costs of hospitalizations, emergency department and physician visits, residential and home care, laboratory services, diagnosis and medications, and quality of life. Instrumental variables included sociodemographic characteristics as reported in the Canadian census. Results The largest RUB was ‘moderate’ morbidity (39.3%), while the smallest was ‘healthy’ (1.5%). The youngest was the ‘low’ morbidity (mean 31, standard deviation [SD] 21) and the oldest was ‘very high’ (mean 69, SD 17). The healthy group had the smallest mean costs (CND563, SD CND4,121; equivalent to USD421, SD USD3,083). In contrast, the ‘very high’ group had the largest (CND20,398, SD CND36,188; equivalent to USD15,258, SD USD27,069). Age and gender standardized comorbidity index scores ranged from 0.05 to 6.41 (median 0.98). Additional analyses (e.g., costs per QALY) are ongoing and the results will be reported at the conference. Conclusions Our empirical approach is robust and flexible, allowing estimates of marginal productivity according to factors such as disease, geographical region, service type, and care sector. This work has applications at the provincial and national levels and adds to methodological literature in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".