Can Variation in Hospital Procedure Rates Identify Candidates for Health Technology Reassessment and Disinvestment?

  • Hollingworth W
  • Busby J
  • Jones H
  • et al.
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Abstract

A470 VA L U E I N H E A LT H 1 6 (2 0 1 3) A 3 2 3 – A 6 3 6 Objectives: To define and develop risk -and more specifically market access risk– as a framework towards understanding and evaluating stability in market access systems at an individual country level. MethOds: We created a combination model of rating quantitative and qualitative variables which affect a country's ability and willingness to pay for new drugs. The criterion for selection of variables is based on relevance, availability and uniformity in our model. We included a total of 42 variables categorised under three verticals -quantitative, qualitative and measures of stability. In order to derive a non-recursive model of ratings, we fit the regression equation for quantitative and qualitative variables as: Y(1) = α i + ∑β i *X i + ε (Equation 1.1) Y(2) = α j + ∑β j *X j + ε (Equation 1.2) where Y(1) and Y(2) are the market access risk ratings for quantitative and qualitative variables, X i and X j are vectors of independ-ent quantitative and qualitative variables, and ε is the error term. The final score was derived by taking the geometric mean of the two ratings together with ratings for the measures of stability and is described as below: Total Risk Score = √Y(1)^2* Weight of Y(1) + Y(2)^2*Weight of Y(2) + Risk Rating (Measures of Stability)^2*Weight of (Measures of Stability). Results: We decided to aggregate risk scores from dif-ferent countries into defined clusters – such as BRICS (Brazil, Russia, India, China and South Africa), BRICS-MT (Brazil, Russia, India, China, South Africa, Mexico and Turkey), and Emerging Europe (Czech Republic, Hungary and Poland) -for easier comparison. Their respective risk scores were 4.17, 4.96 and 3.30. cOnclusiOns: Market Access Risk ratings could serve as a starting point for crafting tailored strategies to fully capitalise new opportunities. These ratings could also serve as a benchmark for a country to improve its overall access to pharmaceutical products and improve quality of care. Objectives: The process of disinvestment from inefficient health care involves identification and prioritisation of candidates, a health technology reassessment (HTR)of evidence, implementation and monitoring of discontinuance. We evalu-ate whether variation in procedure rates is a useful tool for identifying potential candidates for HTR and disinvestment. MethOds: We used English Hospital Episode Statistics (HES) data to identify inpatient procedures. We selected the 181 most frequent interventional procedures for analysis. For each procedure we used Poisson regression to estimate the variance in procedure rates, adjusting for age, gender and other proxies of clinical need, between Primary Care Trusts in England. We conducted multivariate regression analyses to examine factors that might be associated with high variation in procedure rates (e.g. coding uncertainty, evolving evidence). Results: The degree of inter-PCT variation in procedure rates differed vastly from procedure to procedure. Among the five procedures with the highest inter-PCT variance, the procedure rate was more than thirty times higher in the PCT at the ninetieth percentile than the PCT at the tenth percentile. The multivari-able analysis provided strong evidence that large increases in procedure use, large decreases in procedure use, the presence of a substitute procedure, and shorter length of stay were all associated with higher inter-PCT variation in procedure rates. cOnclusiOns: The widespread geographic variation in hospital procedure rates in England are not solely due to variance in clinical need and are likely to reflect clinical uncertainty about appropriate procedure use which might be reduced by HTR. The relevant HTR questions often concern the appropriate procedure setting and patient subgroups or the relative value of two alternative procedures rather than the value of a single procedure per se. In some circumstances knowledge of geographic variation might lead to NHS savings and disinvestment or discontinu-ation of inefficiently used procedures. PHP104 inferenCe on inCremental Cost effeCtiVeness tHresHolds influenCing niCe deCisions: a Bayesian analysis

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Hollingworth, W., Busby, J., Jones, H., & Sterne, J. (2013). Can Variation in Hospital Procedure Rates Identify Candidates for Health Technology Reassessment and Disinvestment? Value in Health, 16(7), A470. https://doi.org/10.1016/j.jval.2013.08.853

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