A Geospatial Method for Estimating Patient and Family Costs and CO2 Emissions for Accessing Healthcare in British Columbia, Canada (Preprint)
Bibliographic record
Abstract
BACKGROUND Patients inevitably incur some cost for accessing healthcare, even in single-payer systems such as Canada. The COVID-19 pandemic dramatically shifted healthcare delivery from in-person to virtual services, also shifting the proportion of costs offset by patients and their families by reducing the need to travel to in-person appointments. OBJECTIVE To develop a method for estimating the costs patients and their families incur and CO2 emissions attributed to attending the emergency department (ED), hospitalizations, and physician visits. METHODS We developed a method to evaluate the costs associated with in-person and virtual care appointments, from the perspective of patients, their families and the environment. We used ED locations, road distances, and duration of appointment to account for costs paid by patients (lost productivity, informal caregiving, and out-of-pocket expenses) attributed to receipt of medical care. Costs to the environment were evaluated by calculating the amount of CO2 emitted per medical visit. Using our costs calculated per-visit, we apply our method to calculate total patient costs for a simulated population over one year. RESULTS Our method estimates that patients pay up to $300 on average to attend an in-person ED visit, depending on where they live; $166 may be attributed to lost productivity, $83 to informal caregiving, and $50 to out-of-pocket expenses. These estimates more accurately reflect true costs compared to conventional, lower estimates. In addition, providing in-person care can emit up to 13 kg of CO2 per visit, depending on distance and frequency of travel to appointments. This translates to up to $0.70 in carbon costs per visit, and results in $44,120 over one year in BC, which is conventionally not included in patient cost estimates. CONCLUSIONS We present a novel method for robustly estimating patient-incurred costs and CO2 emissions from accessing healthcare, which can be applied to future investigations. We are able to apply our method in a simple simulation to estimate that patients in BC may be paying millions of dollars to access healthcare services. Our method provides a more comprehensive calculation of patient costs that will allow for more informed decision-making regarding healthcare services.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".