This paper reviews methods that can be used to assess the impact …

• Abstract
• Background
• Making causal inferences
• Causal inferences from observational data
• Making causal inferences about medicine use and health outcomes
• Casual inferences from individual data on health outcomes and medicine use
• Causal inferences from aggregate data on health outcomes and medicine use
• Assessing relationships between aggregate medicine use and health outcomes
• Alternative explanations of improved population health outcomes
• Assessing the plausibility of alternative explanations
• Explaining a lack of improvement in population health outcomes
• Conclusion
• Abbreviations
• References
• Table

Biology Articles » Medicine » Assessing the impact of prescribed medicines on health outcomes

Abstract

- Assessing the impact of prescribed medicines on health outcomes

Assessing the impact of prescribed medicines on health outcomes

Wayne D Hall^1 ,2 and Jayne Lucke¹

¹School of Population Health, University of Queensland, Herston QLD, 4006, Australia
²Population Health and Uses of Medicines Unit, University of New South Wales, Sydney, Australia

This paper reviews methods that can be used to assess the impact of medicine use on population health outcomes. In the absence of a gold standard, we argue that a convergence of evidence from different types of studies using multiple methods of independent imperfection provides the best bases for attributing improvements in health outcomes to the use of medicines. The major requirements are: good evidence that a safe and effective medicine is being appropriately prescribed; covariation between medicine use and improved health outcomes; and being able to discount alternative explanations of the covariation (via covariate adjustment, propensity analyses and sensitivity analyses), so that medicine use is the most plausible explanation of the improved health outcomes. The strongest possible evidence would be provided by the coherence of the following types of evidence: (1) individual linked data showing that patients are prescribed the medicine, there are reasonable levels of patient compliance, and there is a relationship between medicine use and health improvements that is not explained by other factors; (2) ecological evidence of improvements in these health outcomes in the population in which the medicine is used. Confidence in these inferences would be increased by: the replication of these results in comparable countries and consistent trends in population vital statistics in countries that have introduced the medicine; and epidemiological modelling indicating that changes observed in population health outcomes are plausible given the epidemiology of the condition being treated.

Australia and New Zealand Health Policy 2007, 4:1. This is an Open Access article distributed under the terms of the Creative Commons Attribution License.