Economic Burden of Asthma-Chronic Obstructive Pulmonary Disease Overlap among Older Adults in the United States.
Mona NiliNilanjana DwibediMegan AdelmanTraci LeMastersS Suresh MadhavanUsha SambamoorthiPublished in: COPD (2021)
The objective of this study is to estimate the excess economic burden of Asthma-COPD Overlap (ACO) among older adults in the United States. We used a cross-sectional study design with data from a nationally representative survey of Medicare beneficiaries (Medicare Current Beneficiary Survey) linked to Medicare fee-for-service claims. Older adults with ACO had higher average total healthcare expenditures ($45,532 vs. $12,743) and higher out-of-pocket spending burden (19% vs. 8.5%) compared to those with no-asthma no-COPD (NANC). Individuals with ACO also had almost two, and 1.5 times higher expenditures compared to individuals with asthma only and COPD only, respectively. Multivariable regression models indicated that the adjusted associations of ACO to economic burden remained positive and statistically significant. In comparison with NANC, nearly three-quarters of the excess total healthcare expenditures and 83% of the out-of-pocket spending burden of older adults with ACO were explained by differences in predisposing, enabling, need, personal healthcare practices, and external factors among the two groups. The higher number of unique medications and the increased incidence of fragmented care were the leading contributors to the excess economic burden among older adults with ACO comparing to NANC individuals. Interventions that reduce the number of medications and fragmented care have the potential to reduce the excess economic burden among older adults with ACO.
Keyphrases
- chronic obstructive pulmonary disease
- healthcare
- lung function
- affordable care act
- health insurance
- physical activity
- cystic fibrosis
- air pollution
- risk factors
- palliative care
- mental health
- primary care
- middle aged
- cross sectional
- quality improvement
- big data
- community dwelling
- allergic rhinitis
- machine learning
- artificial intelligence
- chronic pain
- data analysis
- human health