Impact of Asthma Severity and Exacerbation Frequency on Burden of Disease Related Costs in Finland
Bibliographic record
Abstract
Mariann I Lassenius,1 Juhani Aakko,1 Sara Hallberg,2 Mary Dillon,3 Kaisa Nieminen,4 Hannu Kankaanranta,5– 7 Arja Viinanen,8,9 Paula Kauppi,10 Lauri Lehtimäki5,11 1Medaffcon Oy, Espoo, Finland; 2Health Economics, AstraZeneca Nordics, Gothenburg, Sweden; 3Health Economics, AstraZeneca Nordics, Espoo, Finland; 4Medical Affairs, AstraZeneca Nordics, Espoo, Finland; 5Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; 6Department of Respiratory Medicine, Seinäjoki Central Hospital, Seinäjoki, Finland; 7Department of Internal Medicine and Clinical Nutrition, Krefting Research Centre, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden; 8Department of Pulmonary Diseases, Turku University Hospital, Turku, Finland; 9Department of Pulmonary Diseases and Clinical Allergology, University of Turku, Turku, Finland; 10Department of Pulmonary Diseases, Heart and Lung Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland; 11Allergy Centre, Tampere University Hospital, Tampere, FinlandCorrespondence: Lauri Lehtimäki, Allergy Centre, Tampere University Hospital, Tampere, 33521, Finland, Email lauri.lehtimaki@tuni.fiPurpose: Evaluating productivity loss and healthcare costs among working-aged individuals with asthma is critical for comprehending the economic burden and guiding healthcare policy decisions. An optimized care of asthma could result in substantial societal benefits by improving workforce participation and reducing healthcare resource use. Recent data on the impact of both asthma severity and exacerbation frequency on overall costs are scarce.Patients and Methods: In this retrospective study from Finland, 89,606 working aged patients with asthma were followed from national registers for four years from 2017 to 2020 with the objective to assess the impact of asthma severity and exacerbation frequency on the overall costs arising from direct healthcare resource utilization (visits and drug purchases) and productivity loss (long term sick leaves and disability pensions) with gamma regression models.Results: Severe asthma, noted in 10% of patients, increased healthcare resource utilization and productivity loss costs by 30% and frequent exacerbations, noted in 13% of patients, by 25%, independently of each other and of age, sex and Charlson comorbidity index. The annual overall cost per patient was € 14,359 for severe asthma with frequent exacerbations, followed by € 11,802 in those with non-severe asthma and frequent exacerbations. Most costs were related to productivity losses (60%) compared to direct healthcare costs (40%). The prevalence of asthma increased from 2.7% to 3.0% over the four-year period, an increase was also observed in the subgroup of those with severe asthma.Conclusion: A substantial disease burden associated with frequent exacerbations in both patients with asthma and severe asthma leads to increased productivity loss and direct healthcare costs. The estimated the total annual cost of working-aged patients with asthma was € 715 million in Finland, with 25% of additional costs associated with frequent exacerbations, indicating the potential savings that could be achievable through improved disease control.Plain Language Summary: Why was the study done?The study aimed to understand the economic burden of asthma in relation to disease severity and exacerbation frequency among working-aged individuals in Finland.What did the researchers find?The total annual cost of asthma for working-age patients in Finland was estimated at € 715 million. Patients with severe asthma had 30% higher annual healthcare and productivity loss costs, compared to those with non-severe asthma. Frequent exacerbations also increased annual costs by 25%, irrespective of asthma severity. The annual cost per patient was highest for those with severe asthma and frequent exacerbations (€ 14,359), followed by those with non-severe asthma and frequent exacerbations (€ 11,802). Most costs (60%) were related to productivity losses, with direct healthcare costs making up the remaining 40%.What do the results mean?The study highlights that the significant economic burden of asthma is driven by exacerbation frequency, suggesting that better disease control could lead to significant savings and enhance workforce participation.Keywords: asthma, burden of disease, direct costs, exacerbations, productivity loss, severe asthma
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".