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Record W4414416604 · doi:10.1007/s12325-025-03373-7

The IMPACT Survey: The Economic Impact of Caring for an Individual with Osteogenesis Imperfecta

2025· article· en· W4414416604 on OpenAlexaff
Frank Rauch, Taco van Welzenis, Lena Lande Wekre, Cathleen Raggio, Oliver Semler, Ingunn Westerheim, Heather Mulhall, Melanie Anderson, Samantha J. Prince, Tracy Hart

Bibliographic record

VenueAdvances in Therapy · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsMcGill University
FundersMereo BioPharma
KeywordsEconomic impact analysisOsteogenesis imperfectaRheumatologyProductivitySocial impactHealth careImpact assessmentHealth economics

Abstract

fetched live from OpenAlex

The IMPACT Survey (“IMPACT”) investigated the economic, clinical, and humanistic challenges of osteogenesis imperfecta (OI) on affected individuals, caregivers, and the broader community. Prior publications detail the methodology, initial findings, healthcare expenditures, and quality of life (QoL) impact on adults with OI. Here, data is presented on the productivity and finances of caregivers and any predictors of impact. We hypothesise that caring for an individual with OI will impact the productivity and finances of caregivers. IMPACT, fielded July through September 2021 in eight languages, targeted adults and adolescents with OI, caregivers (with or without OI), and close relatives. Survey items covered demographics, socioeconomic factors, clinical characteristics, treatment patterns, QoL, and health economics. We performed descriptive analyses of caregivers’ productivity and finances and exploratory regression analyses to identify independent associations between care recipient and caregiver characteristics (“predictors”), and their economic impact on caregivers. Of 528 caregivers (without OI) with one care recipient, 64% were in paid employment. Of these, 50% reported missing workdays in the preceding 4 weeks (mean 1.9 days). Caregivers reported impacted finances, spending a mean total of €209 out of pocket (OOP) with the most spent on travel to medical appointments (mean €83) and medicine (mean €46) in the preceding 4 weeks. Caregiver spending varied across regions. Caregivers in the USA spent more in 4 weeks (mean €334) than caregivers in EU4 (France, Germany, Italy, and Spain) and UK (mean €163) or Nordic countries (mean €33). Predictors of productivity and OOP spending included caregiver age, sex and employment status, care recipient age, and various signs, symptoms, and events. Our results suggest that caring for an individual with OI may impact caregivers’ productivity and finances. The degree of impact may be predicted by caregiver and care recipient age, fracture frequency, and dental problems. The IMPACT Survey looked at how caring for someone with osteogenesis imperfecta affects caregivers’ work and finances. It aimed to understand the challenges caregivers face and what factors might make the experience of the caregivers worse. The survey was done in 2021 and was open to caregivers (both with and without osteogenesis imperfecta), as well as people with osteogenesis imperfecta. It asked about health, daily life, treatments, and the costs of care. The survey also looked at how often caregivers missed work and how much they spent on healthcare costs like travel and medication. Of 528 caregivers, 64% were employed. Half of them missed work in the last month, averaging 1.9 days off in 4 weeks. Caregivers reported spending an average of €209 on healthcare in the previous 4 weeks, with the most spent on travel to medical appointments (€83) and medicine (€46). Caregiver spending varied by country, with those in the USA spending more (€334) compared with Europe and Nordic countries (€33–163). The survey found that factors like the caregiver’s age, employment status, and the person they care for (e.g. their age and how often they fracture) impacted how much their work and finances were affected. The results show that caregiving for someone with osteogenesis imperfecta can impact both a caregiver’s job and finances.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.394
Teacher spread0.372 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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