The Economic Impact of Migraine: An Analysis of Direct and Indirect Costs
Bibliographic record
Abstract
OBJECTIVE: This study examined whether individuals with migraine incurred greater direct and indirect costs than a matched group free of migraines. METHODS: Using population-based survey data, we matched individuals with migraine (n = 1087) and a migraine-free control group one-to-one for age, sex, employment status, and number of comorbidities. We assessed the prior six months' direct medical care in terms of self-reported hospital days and emergency department and physician visits. Costs were computed by multiplying utilization by unit costs and summing across categories. Indirect costs were calculated based on the number of days missed from employment or household activities. RESULTS: The sample was 80% female and had an average of 39 years and 0.4 comorbid conditions. Two-thirds were employed. Migraineurs had higher direct medical costs over the prior six months (522 dollars versus 415 dollars, P =.039), primarily due to a greater frequency of physician and emergency department visits. The cost of lost productivity for the migraine group was also higher, by more than 200 dollars (P =.014). The combined total for direct and indirect costs was 1,242 dollars for migraineurs and 929 dollars for the comparison group (P =.006). Additional analyses comparing those with moderate versus severe migraine demonstrated that more severe migraineurs had higher costs for lost productivity (1,021 dollars versus 251 dollars, P<.001) and higher costs when direct and indirect costs were combined (1,656 dollars versus 685 dollars, P<.001). CONCLUSION: Migraine is an expensive illness and two-thirds of the financial burden is linked to indirect costs. Consequently, individuals with migraine, employers, and insurance companies all have an economic stake in reducing the migraine burden.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".