Population Health Impact of Disease in Canada (PHI)

Overview

The Population Health Impact of Disease in Canada (PHI) is estimating the relative impact of about 200 diseases, injuries, and risk factors relevant to Canadians.

The PHI adds to work on summary measures of population health widely promoted in the international research community over the past decades. These measures combined mortality and morbidity - in terms of impact of living with disease - into a single measure. The Global Burden of Disease methods published by the WHO and their collaborators in 1996 went one step further in developing an incidence-based health gap measure for specific diseases and risk factors: the disability-adjusted life year (DALY).

The PHI has broadened the measurement of morbidity to include all aspects of health: physical, mental and social. A new tool, the Classification and Measurement System of Functional Health (CLAMES), was developed specifically for this purpose. The consequences of living with a disease at several stages of progression and treatment were described, based on literature search and expert advice, and classified using CLAMES. Preference scores were elicited from panels of Canadians to determine the relative preference for each disease or health state compared with full health.

The PHI is estimating the health-adjusted life years (HALYs) lost to diseases, injury, and various risk factors in 2001. HALYs combine the impact of premature mortality and morbidity in terms of functional limitations. HALYs are presented in spreadsheet workbooks along with data sources and calculations for each disease or risk factor. Input data can be modified by users to suit their particular needs. Users can also create simple "what-if" scenarios, for instance, the impact on cardiovascular disease of a 10% reduction in smoking.

The PHI goes beyond current methods with microsimulation models that integrate many diseases simultaneously and model the interplay between them. These models will provide policy analysts with a broader and more realistic context that considers how diseases (and risk factors) overlap and interact. Specifically, "what-if" scenarios can examine how a change in one disease or risk factor may affect several others at the same time. This information helps assess various intervention options to identify which of them could provide optimal return on population health investments.