The online version of this article (doi:10.1186/1471-2288-13-109) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interests.
NM was the main contributor to the conception and design of the study, was responsible for the mathematical derivations and for the analysis and interpretation of the data, and wrote the main part of the manuscript. HW and MK were involved in conception and design and they revised the manuscript critically for important intellectual content. HW also wrote part of the manuscript. All authors read and approved the final manuscript.
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.
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%.
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%.
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.
Additional file 1: A proof that under the assumption of independence of treatment uptakes the expression of Equation8in the manuscript holds.(DOC 144 KB)12874_2012_989_MOESM1_ESM.doc
Additional file 2: An expression for the probability generating function for the multivariate Bernoulli distribution and the marginal risk under a combination of treatment, which can be used for the calculation of PIF between a baseline and a target scenario.(DOC 45 KB)12874_2012_989_MOESM2_ESM.doc
Additional file 3: A proof that under the assumption of treatment independence Equation13in the manuscript holds.(DOC 45 KB)12874_2012_989_MOESM3_ESM.doc
Wijeysundera HC, Machado M, Farahati F, Wang X, Witteman W, van der Velde G, Tu JV, Lee DS, Goodman SG, Petrella R, O’Flaherty M, Krahn M, Capewell S: Association of temporal trends in risk factors and treatment uptake with coronary heart disease mortality, 1994–2005. JAMA. 2010, 303 (18): 1841-1847. 10.1001/jama.2010.580. CrossRefPubMed
Wijeysundera HC, Mitsakakis N, Witteman W, Paulden M, van der Velde G, Tu JV, Lee DS, Goodman SG, Petrella R, O’Flaherty M, Capewell S, Krahn M: Achieving Quality Indicator Benchmarks and Potential Impact on Coronary Heart Disease Mortality. Can J Cardiol. 2011, 27 (6): 756-762. 10.1016/j.cjca.2011.06.005. CrossRefPubMed
Tuegels JL: Some Representations of the Multivariate Bernoulli and Binomial Distributions. J Multivar Anal. 1990, 32: 256-268. 10.1016/0047-259X(90)90084-U. CrossRef
Levin ML: The occurrence of lung cancer in man. Acta Unio Internationalis Contra Cancrum. 1953, 9: 531-541. PubMed
Bruzzi P, Green SB, Byar DP, Brinton LA, Schairer C: Estimating the population attributable risk for multiple risk factors using case–control data. Am J Epidemiol. 1985, 122 (5): 904-914. PubMed
Haby MM, Vos T, Carter R, Moodie M, Markwick A, Magnus A, Tay-Teo KS, Swinburn B: A new approach to assessing the health benefit from obesity interventions in children and adolescents: the assessing cost-effectiveness in obesity project. Int J Obes (Lond). 2006, 30: 1463-1475. 10.1038/sj.ijo.0803469. CrossRef
Knight K: Mathematical Statistics. 1999, Chapman & Hall/CRC CrossRef
Mason CA, Tu S: Partitioning the population attributable fraction for a sequential chain of effects. Epidemiologic Perspectives & Innovations. 2008, 5: 5-10.1186/1742-5573-5-5. CrossRef
- Beyond case fatality rate: using potential impact fraction to estimate the effect of increasing treatment uptake on mortality
Harindra C Wijeysundera
- BioMed Central
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