Cost-Effectiveness of Aspirin Adherence for Secondary Prevention of Cardiovascular Events.

BACKGROUND: Suboptimal adherence to aspirin therapy for secondary prevention of cardiovascular (CV) events is an important public health problem. Prior studies have demonstrated non-adherent patients are at higher risk of experiencing CV events. OBJECTIVES: This study aimed to estimate the clinical and economic outcomes of aspirin non-adherence in patients with a prior primary CV event. METHODS: We developed a Markov model to estimate the cost-effectiveness of aspirin adherence from a generic US managed care payer perspective over a 5-year time horizon. Costs, utilities and rates of aspirin adherence, CV events and adverse events were gathered from published literature to populate the model. Outcomes were quality-adjusted life years (QALYs), costs (US$) and incremental cost-effectiveness ratios (ICERs). We applied the model separately to a population without type II diabetes as a comorbidity (non-diabetic model) and a population with type II diabetes (type II diabetes model). A one-way sensitivity analysis was performed to assess the model uncertainty. RESULTS: The base case showed adherent patients lived 0.25 and 0.36 QALYs longer than non-adherent patients in the non-diabetic model and type II diabetes model, respectively. Adherence to aspirin had an ICER of US$25/QALY in the non-diabetic population, while it saved US$297 per patient over a 5-year period in the type II diabetes population. One-way sensitivity analysis showed the models were most sensitive to rates of non-fatal events in non-adherent patients. CONCLUSION: This study suggests aspirin adherence may improve QALYs for patients with a prior primary CV event. Further, it may decrease costs in patients with type II diabetes. While additional research is needed to validate these results, payers may wish to increase strategies to promote adherence in order to improve population health. TRIAL REGISTRATION: Not applicable.

Measuring the Cost of the Patient-Centered Medical Home: A Cost-Accounting Approach.

To explore the cost for individual practices to become more patient-centered, we inventoried and calculated the cost of costly activities involved in implementing the Patient-Centered Medical Home (PCMH) as defined by the National Committee for Quality Assurance. There were 3 key findings. The cost of each PCMH-related clinical activity can be classified in 1 of 3 major categories. Cost offsets can be used to defray part of the cost recognition. The cost of PCMH transformation varied by practice with no clear level or pattern of costs. Our study suggests that small- and medium-sized practices may experience difficulty with the financial burden of PCMH recognition.

Exploring Variation in Transformation of Primary Care Practices to Patient-Centered Medical Homes: A Mixed Methods Approach.

The objective was to quantify the activities required for patient-centered medical home (PCMH) transformation in a sample of small to medium-sized National Committee for Quality Assurance (NCQA) recognized practices, and explore barriers and facilitators to transformation. Eleven small to medium-sized PCMH practices in Southeastern Pennsylvania completed a survey, which was adapted from the 2011 NCQA standards. Semistructured follow-up interviews were conducted, descriptive statistics were computed for the quantitative analysis, and a process of thematic coding was deployed for the qualitative analysis. Practices had considerable quantitative variation in their workforce composition and the PCMH-related activities they implemented. Most practices improved access and continuity through staff training and team-based care as well as expanded data collection for population management. The barriers to PCMH recognition were least burdensome for the largest practices. The heterogeneity of the small PCMH practices within the study sample underscore the need to understand the key transformation issues as efforts to disseminate the PCMH model continue.

Analyzing the health care cost curve: a case study.

This case study uses data from a self-insured employer plan to perform an analysis into the properties of the health care cost curve. The analysis shows that one statistical property of the health care cost curve is that costs rise continuously, not on an annual or monthly basis. Graphical analysis indicates that managed care techniques used to restrain costs can also smooth utilization, producing the continuously growing cost curve observed. The analysis further illustrates that there is no one "cost curve"-analysis must be segmented by population. Finally, the power of predictive models to fit the cost curve varies by population. To the extent that these results generalize to other health plans, this analysis should be used to inform the implementation of strategies to bend the cost curve. Population health management programs and health policy should be based on continuous analysis and adaption rather than implemented as one-off changes.

Economics of genomic testing for women with breast cancer.

BACKGROUND: Creating the value proposition for innovations in personalized genomic medicine requires generation of evidence-based demonstrations of clinical utility and cost-effectiveness. OBJECTIVES: To assess economic studies of genomic testing for women with breast cancer and to understand the value of genomic testing for multiple stakeholders. STUDY DESIGN: Literature review. METHODS: A structured review of the literature was conducted to identify and synthesize available evidence regarding economic analyses of genomic testing for breast cancer. A search was conducted using PubMed and Google Scholar for articles published between January 1, 2005, and December 31, 2010. The search was then expanded to include articles as far back as 1981. In addition, snowball methodology was used to identify and include additional articles based on frequency of author publication and frequency of citation in the literature. RESULTS: Of the articles reviewed, a subset of 9 articles describing specific economic analysis studies were included in a more in-depth, side-by-side comparison. This review of the literature on the economics of genomic testing for women with breast cancer found that most of the economic evidence relied on modeling rather than clinical trial data. CONCLUSIONS: Facilitating the diffusion of new technology will require more data to satisfy the payer, provider, and societal perspectives. Conversely, willingness by payers and clinicians to consider economic modeling data as part of their evaluation of new technologies can help facilitate the diffusion of newly developed genomic tests.

An economic model to value companion diagnostics in non-small-cell lung cancer.

AIM: An economic model was used to evaluate the potential economic impact and cost-effectiveness of companion diagnostic testing for patients with non-small-cell lung cancer (NSCLC). MATERIALS & METHODS: A decision analysis model examined alternative patient management strategies for patients with advanced NSCLC who were not amenable to surgical treatment. A review of the literature provided the variables used to develop a timely base case and sensitivity analysis. A potential future scenario was also modeled. The model includes three options: conventional treatment (CT), new treatment (NT) and companion diagnostic (CD) strategy. RESULTS: In the base case analysis based upon current data, the cost per life-year saved for CT, NT option and CD was US$43,367, US$47,394 and US$47,779, respectively. The cost per life-year saved for CT, NT option and CD in a potential future scenario with more expensive, effective targeted therapy was US$47,748, US$69,255 and US$66,369, respectively. CONCLUSION: In the future scenario, CDs have an incremental cost-effectiveness of US$56,829 per life-year saved when compared with NT as a first-line treatment. This is one demonstration of how CDs may be a cost-effective option for the treatment of patients with advanced NSCLC when the NT is extremely expensive but the outcome is significantly improved.

Hospital quality: a PRIDIT approach.

BACKGROUND: Access to high quality medical care is an important determinant of health outcomes, but the quality of care is difficult to determine. OBJECTIVE: To apply the PRIDIT methodology to determine an aggregate relative measure of hospital quality using individual process measures. DESIGN: Retrospective analysis of Medicare hospital data using the PRIDIT methodology. SUBJECTS: Four-thousand-two-hundred-seventeen acute care and critical access hospitals that report data to CMS' Hospital Compare database. MEASURES: Twenty quality measures reported in four categories: heart attack care, heart failure care, pneumonia care, and surgical infection prevention and five structural measures of hospital type. RESULTS: Relative hospital quality is tightly distributed, with outliers of both very high and very low quality. The best indicators of hospital quality are patients given assessment of left ventricular function for heart failure and patients given beta-blocker at arrival and patients given beta-blocker at discharge for heart attack. Additionally, teaching status is an important indicator of higher quality of care. CONCLUSIONS: PRIDIT allows us to rank hospitals with respect to quality of care using process measures and demographic attributes of the hospitals. This method is an alternative to the use of clinical outcome measures in measuring hospital quality. Hospital quality measures should take into account the differential value of different quality indicators, including hospital "demographic" variables.

How risky is individual health insurance?

This paper describes the relationship between type of insurance coverage in one period and the likelihood of becoming uninsured in the next. We find that for people at the median health status, becoming uninsured is most likely for those with individual insurance, less likely for those with small-group insurance, and least likely for those with large-group insurance. However, for people in poor or fair health, the chances of losing coverage are much greater for people who had small-group insurance than for those who had individual insurance. We attribute these results to the offsetting effects of high loadings and guaranteed renewability in the individual market.