title: Beyond case fatality rate: using potential impact fraction to estimate the effect of increasing treatment uptake on mortality.
abstract: BACKGROUND
  IMPACT is an epidemiological model that has been used to estimate how increased treatment uptakes affect mortality and related outcomes. The model calculations require the use of case fatality rate estimates under no treatment. Due to the lack of data, rates where treatment is partially present are often used instead, introducing bias. A method that does not rely on no-treatment case fatality rate estimates is needed.
  
  
  METHODS
  Potential Impact Fraction (PIF) measures the proportional reduction in the disease or mortality risk, when the distribution of a risk factor changes. Here, we first describe a probabilistic framework for interpreting quantities used in the IMPACT model, and then we show how this is connected with PIF, facilitating its use for the estimation of the relative reduction of mortality caused by treatment uptake increase. We compare the proposed and standard methods to estimate the reduction of cardiovascular disease deaths in Ontario, if utilization of coronary heart disease interventions was increased to the level of 90%.
  
  
  RESULTS
  Using the proposed method, we estimated that increasing treatment to benchmark levels uptake results in a reduction of 22.5% in cardiovascular mortality. The standard method gives a reduction of 20.8%.
  
  
  CONCLUSIONS
  Here we present an alternative method for the estimation of the effect of treatment uptake change on mortality. Our example suggests that the bias associated with the standard method may be substantial. This approach offers a useful tool for epidemiological and health care research and policy.
authors.affiliation: Toronto Health Economics and Technology Assessment (THETA) Collaborative, Toronto, Canada. n.mitsakakis@theta.utoronto.ca.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Mitsakakis
authors.forename: Harindra C
authors.initials: HC
authors.lastname: Wijeysundera
authors.forename: Murray
authors.initials: M
authors.lastname: Krahn
mesh_terms: D000465:Algorithms; D002318:Cardiovascular Diseases / Q000401:mortality* / Q000628:therapy; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D009864:Ontario / Q000453:epidemiology; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data