|Category Reference for Presentations|
|AHE||Applied Health Economics||DEC||Decision Psychology and Shared Decision Making|
|HSP||Health Services, and Policy Research||MET||Quantitative Methods and Theoretical Developments|
* Candidate for the Lee B. Lusted Student Prize Competition
Purpose: In the United States, about 87% of stroke is ischemic stroke. Atrial fibrillation (AF) is a major risk factor for and associated with more severe, ischemic stroke. While stroke mortality and incidence rates as well as the impact of AF on stroke are well documented, the influence of AF on the cost of stroke at national level is unknown. We estimated the AF-associated costs among elderly population (age ≥ 65 years) with ischemic stroke.
Methods: Using 2010 Medicare Provider Analysis and Review data, we identified all hospitalizations with a primary diagnosis of ischemic stroke by International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) codes of 433, 434, and 436. After excluding the hospitalizations with no racial information and those with 30 or more days of hospital stays, we estimated the cost associated with AF (ICD-9-CM code 427.31, 427.32) using multivariate regression models for three population groups: National, stroke belt (an 8-state region of southeastern US), and non-stroke belt. From a societal perspective, we used total charge as the total cost for the hospital stays. We explored the association between the cost and age, sex, race, initial admission status, respectively.
Results: Among 257,595 hospitalizations with ischemic stroke, 23.5% of them had AF. The mean cost was $34,901, of which $4979 (95% confidence interval [CI], $4649-$5309) was AF-associated. Compared to the rest of the country, the cost of hospitalizations in stroke belt was $7892 lower (95% CI, $7611-$8173). After controlling for potential confounders, AF associated cost was $3436 (95% CI, $2861-$4010) in stroke belt and $5169 (95% CI, $4788-$5550) in non-stroke belt. Both mean cost and AF-associated costs decreased with age. Hospitalizations of male patients had higher mean cost, but lower AF-association cost than those of female patients. Hospitalizations of African American patients had both higher mean cost and AF-associated costs than those of whites.
Conclusions: The hospitalization cost for patients with ischemic stroke was high, and AF further increased the costs. While there has been a persistent high stroke mortality rate in the stroke-belt, both the mean and AF-associated costs of the hospitalizations in non-stroke belt were substantially higher than those in stroke-belt. Age, gender, and race are major determinants of the cost.
Method: Retrospective data analysis was conducted using the 2010 Medical Expenditure Panel Survey (MEPS), a nationally representative survey of the civilian non-institutionalized population of the U.S. Individuals with anxiety disorders were identified as those who reported having been diagnosed with anxiety disorders, or received any treatment for their condition (i.e. Clinical Classification Code of ‘651’ in the Medical Conditions file or event-level files). Total healthcare costs incurred by these individuals were estimated as the weighted-sum of their overall expenditure. The incremental costs associated with anxiety disorders were estimated using multivariate regression analysis. A Generalized Linear Model (GLM), with Poisson variance function, was used to account for distributional issues of the healthcare cost data. The model was adjusted for covariates: age, gender, race/ethnicity, marital status, education, poverty category, perceived health status, geographic region, metropolitan statistical area (MSA), insurance coverage, and comorbidities. All analyses were conducted for individuals 18 years and older.
Result: In 2010, 8.71% of individuals 18 years and older ($20.36 million persons) reported having been diagnosed with anxiety disorders. The annual total healthcare expenditure incurred by this group of individuals reached $191.08 billion in 2013 U.S. dollars. The annual adjusted per-capita and total healthcare expenditures attributable to anxiety disorders were $1,519.55 (SE: $350.76; p <0.0001), and $30.94 billion, respectively. After adjusting for all covariates, adults with anxiety disorders had 30% higher total healthcare expenditures than those without anxiety disorders, (parameter estimate: 1.30; p<0.0001).
Conclusion: Given the prevalence of self-reported anxiety disorders, the annual direct medical expenditures associated with this category of mental illness is estimated at approximately $30.94 billion in 2013 U.S. dollars, for the U.S. adult population. This accounts for more than 16% of the annual total healthcare expenditures incurred by sufferers of anxiety disorders. Even as a conservative estimate, this result shows that anxiety disorders absorb a significant portion of the U.S. healthcare resources.
Methods: All patients discharged alive after an acute care hospitalization in Ontario,
Canada in fiscal year 2006 were identified. Patients treated at HF clinics were selected based on the presence of a claim by a HF clinic physician in the 1-year after the index hospitalization. The service components at all HF clinics were scored using a 10-item validated instrument. The primary outcome was the cumulative 1-year health care costs post-discharge. Costs included all ambulatory, acute care hospitalizations, emergency room visits, same-day surgeries and HF medication costs. A hierarchical generalized linear model with a logarithmic link and gamma distribution was developed to identify patient and clinic level predictors of cost. The impact of patient and clinic level factors on the variation in costs between clinics was assessed by the proportional change in the variance of the clinic-level random effect.
Results: Of the 16,300 acute care hospitalizations in 2006 for HF, 1,216 patients were seen in HF clinics. There was a 7-fold variation in mean costs by clinic ($14,670-$96,524). The between-clinic variation in costs decreased by 2.5% when patient factors were added to the null model. The variation decreased by a further 67% when clinic-level factors were added. Mean total health care costs were 14% higher for males (rate ratio (RR) 1.14; p=0.037). Chronic atherosclerosis (RR 1.23, p=0.008), valvular heart disease (RR 1.37, p=0.004), diabetes (RR 1.16, p=0.044), and peripheral vascular disease (RR 1.65, p=0.0006) were associated with higher mean costs. A history of CABG was associated with a 48% (p=0.001) reduction in mean costs. Patients seen at HF clinics which placed an emphasis on peer support had lower mean costs (RR 0.75, p=0.018). HF clinic size as reflected by total annual clinic visits was a predictor of costs, with clinics that received a moderate number of visits having a 30% reduction in costs compared to smaller clinics (p=0.037).
Conclusions: HF clinics have a substantial effect on mortality, but health outcomes, including costs vary considerably between clinics. Efforts should focus on ways to standardize care across specialty clinics to ensure effective treatment for patients, while reducing unnecessary health care spending.
Purpose: To estimate health care expenditures associated with obesity-related diseases (ORDs), including diabetes, hypertension, coronary heart disease (CHD), and stroke.
Methods: Data from the National Health Interview Survey (NHIS), 1997-2000, were linked to the Medical Expenditure Panel Survey (MEPS). Our sample was chosen from the NHIS Sample Adult Data Files, which contain data on adults aged 18 years and older. The main outcome variable was total health care expenditures. Expenditures in MEPS are defined as payments from all sources, including direct payments from individuals, private insurance, Medicare, Medicaid, and miscellaneous other sources. Our analyses were stratified by gender and adjusted for age, age squared, inverted body mass index, marital status, smoking, race, and physical activity. Total expenditures were projected based on the regression estimates. All expenditures were computed at the 2000 price level based on the Consumer Price Index for All Urban Consumers. Bootstrap was performed to resample the population 1,000 times to compute the means and standard errors. The complex sampling designs in the NHIS and MEPS were adjusted from data merging and analysis to total expenditure prediction.
Results: Our sample comprised 17,917 women and 13,928 men. The total expenditures (standard errors) for men with hypertension, diabetes, CHD, and stroke were $5,570 ($262), $7,729 ($441), $9,600 ($644), and $10,865 ($1,096) per person, respectively. The total expenditures for women were higher than those for men: $5,899 ($212) for hypertension, $9,286 ($531) for diabetes, $10,205 ($941) for stroke, and $10,480 ($867) for CHD. However, men with hypertension, diabetes, CHD, and stroke had 3.1, 4.3, 5.4, and 6.1 times more expenditures than men without any of these ORDs, while the relative expenditure ratios for women with hypertension (2.3), diabetes (3.6), CHD (4.1), and stroke (4.0) were lower as compared to women without ORDs.
Conclusions: This study provides evidence suggesting that total health care expenditures were higher for women with and without ORDs than for men. Among the total health care expenditures for women and men with ORDs, women with CHD and men with stroke had the highest expenditures. Women with or without these ORDs had higher health care expenditures than men, except for the total expenditures for people with stroke. Nonetheless, relative expenditures comparing those with ORDs to those without are higher for men than for women.
Methods: We review amendments to NICE’s methods guidelines to show how the recommended discount rates have changed three times since 2004. In particular, we consider the most recent amendment made in April 2013, which states that certain highly effective, non-preventative interventions with health effects lasting more than 30 years can be subject to lower discount rates than others. We discuss how this amendment should be interpreted. We then explore some of the possible consequences of selectively applying lower discounting using examples of cost-effectiveness analyses of alternative interventions.
Results: We show that selectively applying lower discount rates can lead to the health benefits of otherwise similar interventions being valued very differently. This can lead to large differences in cost-effectiveness ratios, which in turn can lead to marked differences in the probability of adoption. We demonstrate the paradoxical result that NICE may prefer to allocate resources to interventions that yield lower health gains than alternative treatments of the same cost. We also show the particular example in which the 30 years criterion means that individuals with shorter remaining life expectancy may not be eligible for treatment, whereas younger individuals with longer remaining life expectancy are.
Conclusions: There seems to be no valid reason to apply favourable discount rates selectively. Consequently, the differences in the valuation of health effects, cost-effectiveness ratios and the probability of adoption all appear to be unjustifiable inconsistencies. Not only is the selective application of favourable discount rates not justified on theoretical grounds, but it leads to real concerns regarding equitable resource allocation between patients, especially regarding the eligibility of older people. While there are good arguments for NICE to reduce their reference case discount rate from the current 3.5%, any reduction should be applied to all interventions and be accompanied by a review of the cost-effectiveness threshold. NICE should take to care to uphold high standards in its methods guidance when revising its discounting recommendations.