The IMPACT Survey: the economic impact of osteogenesis imperfecta in adults
Bibliographic record
Abstract
Abstract Background The IMPACT survey aimed to elucidate the humanistic, clinical and economic burden of osteogenesis imperfecta (OI) on individuals with OI, their families, caregivers and wider society. Research methodology, demographics and initial insights from the survey have been previously reported. The cost of illness (healthcare resource use, productivity loss, out-of-pocket spending) and drivers of the economic impact of OI are reported here. Methods IMPACT was an international mixed-methods online survey in eight languages (fielded July–September 2021) targeting adults (aged ≥ 18 years) or adolescents (aged ≥ 12–17 years) with OI, caregivers with or without OI and other close relatives. Survey domains included demographics, socioeconomic factors, clinical characteristics, treatment patterns, quality of life and health economics. The health economic domain for adults, which included questions on healthcare resource use, productivity loss and out-of-pocket spending, was summarised. Regression and pairwise analyses were conducted to identify independent drivers and associations with respondent characteristics. Results Overall, 1,440 adults with OI responded to the survey. Respondents were mostly female (70%) and from Europe (63%) with a median age of 43 years. Within a 12-month period, adults with OI reported visiting a wide range of healthcare professionals. Two-thirds (66%) of adults visited a hospital, and one-third (33%) visited the emergency department. The mean total number of diagnostic tests undergone by adults within these 12 months was 8.0. Adults had undergone a mean total of 11.8 surgeries up to the time point of the survey. The proportions of adults using queried consumables or services over 12 months ranged from 18–82%, depending on the type of consumable or service. Most adults (58%) were in paid employment, of which nearly one-third (29%) reported missing a workday. Of the queried expenses, the mean total out-of-pocket spending in 4 weeks was €191. Respondent characteristics such as female sex, more severe self-reported OI and the experience of fractures were often associated with increased economic burden. Conclusion IMPACT provides novel insights into the substantial cost of illness associated with OI on individuals, healthcare systems and society at large. Future analyses will provide insights into country-specific economic impact, humanistic impact and the healthcare journey of individuals with OI.
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".