Population Health Impact of Disease in Canada (PHI)

Canadian summary measures

• Background
• Calculating health-adjusted life years (HALYs)
• Building on international methods
• Addressing criticisms of existing methods
• Moving forward using microsimulation

Background

The PHI is estimating the health-adjusted life years (HALYs) lost to disease and injury in 2001. These incidence-based estimates are used as a gap measure that combines the years lost to premature mortality in 2001 and the lifetime impact of disease and injury diagnosed in 2001. The contribution of several risk factors will also be estimated for each disease.

The morbidity component of the HALY uses weighting to account for the severity of limitations in health-related quality of life. Diseases are broken down into health states, generally a specific stage of disease progression or treatment. The consequences of living with the health state are documented using the Classification and Measurement System of Functional Health (CLAMES). Preference scores based on CLAMES are used to weight the years lived in each health state.

Epidemiologic data including incidence, duration, remission rates and case fatality are incorporated for the various stages in the progression of diseases and their treatment. Data are available from disease registries, population surveys, epidemiologic studies, health administrative data and vital registration (cause of death).

A growing capacity for health surveillance and the development of several large population-based health datasets over the past few years is increasing the evidence base available for policy decisions. The PHI is building a comprehensive framework to integrate these data so that they will be more accessible and useful for policy decisions.

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Calculating health-adjusted life years (HALYs)

The HALY represents the number of year-equivalents of life lost by Canadians to disease or injury. Specifically, it is the sum of two estimates: years of life lost (YLL) through premature mortality and year-equivalents lost to reduced functioning (YERFs).

YLLs estimate how many lost years of life could have been averted in the absence of a disease. For each age group, the number of deaths at that age is multiplied by the remaining life expectancy at that age (i.e., the remaining years that could have been lived). These can be added together for all ages. In simple terms,

YLL for each age group

=

number of deaths X hypothetical years of life remaining

YERFs estimate time lost to reduced functioning, weighted for severity. For each age group, the incidence for a disease is multiplied by the average duration and by a weight derived from a preference score (which quantifies the impact on functional capacity). These can be added together for all age groups. In simple terms,

YERF for each age group

=

number of new cases X duration X weight for severity

HALYs, YLLs, and YERFs for diseases and risk factors will be provided in spreadsheet workbooks¹ that document specific data sources, calculations, and assumptions for each disease. In addition to measuring the population health impact of disease in terms of YLLs, YERFs, and HALYs, the impact of the major risk factors for each disease is calculated using standard formulae for population attributable fraction (PAF). The PAF is calculated using the population prevalence of the risk factors and relative risks for the disease.² The PHI will also use microsimulation models to incorporate more complex interactions between diseases.

References

1. Flanagan W, Boswell-Purdy J, Le Petit C, Berthelot J-M. Estimating summary measures of health: a structured workbook approach. Population Health Metrics 2005; 3(1):5.
2. Jekel JF, Katz DL, Elmore JG. Epidemiology, Biostatistics, and Preventive Medicine, second edition. Philadelphia (PA): WB Saunders Company; 2001.

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Building on international methods

The PHI extends the approach of burden of disease studies to date,^1,2 adapting their methods to address diseases and injuries most relevant to Canadians, applying them to Canadian data, and measuring them within a Canadian societal context.

Disability-adjusted life years (DALYs), developed and promoted by the WHO, World Bank, and Harvard School of Public Health in the Global Burden of Disease, used an incidence approach for morbidity to provide more sensitivity to current epidemiologic trends than prevalence-based methods. DALYs were groundbreaking in that they could examine the health gap between current health outcomes and potential health targets for specific diseases, risk factors or population groups.

The PHI extends the focus on measuring morbidity, developing a classification and measurement system of functional health (CLAMES) that covers the consequences of diseases in day-to-day life in terms of health-related functioning - physical, mental, and social. Functional health provides a realistic, attainable and meaningful goal for individuals. Functional health is also attractive to policy analysts as a health endpoint or outcome for targeted interventions; maintaining functional health is a key challenge for society, particularly the aging population.

Canadian summary measures will reflect the Canadian societal context, which gives a different perspective than previous studies. To date, burden of disease studies have elicited preference scores primarily from medical panels. Canada is the first to use preference measurements among the general population for a burden of disease study. Health preferences may be affected by individual factors such as health status or sociodemographics.^3,4,5 Our panels included individuals of all ages and those with a wide variety of health conditions. Focus testing indicated that Canadians strongly believe that lay Canadians should be included in the process of guiding policy decisions.

1. Harvard School of Public Health on behalf of the World Health Organization and the World Bank. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries and risk factors in 1990 and projected to 2020. Murray CJL, Lopez AD, editors. Harvard University Press; 1996.
2. Mathers C, Vos T, Stevenson C. The burden of disease and injury in Australia. AIHW cat. no. PHE 17. Canberra: Australian Institute of Health and Welfare; 1999.
3. Nord E, Richardson J, Macarounas-Kichmann K. Social evaluation of health care versus personal evaluation of health states. Int. J Tech Assess in Health Care 1993;9(4):463-78.
4. Williams A. EuroQol - A new facility for the measurement of health-related quality of life. Health Policy 1990;16:190-208.
5. Baron J, Asch DA, Fagerlin A, et al. Effect of assessment method on the discrepancy between judgments of health disorders people have and do not have: a web study. Med Decis Making 2003;23(5):422-34.

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Addressing criticisms of existing methods

The PHI also addresses many criticisms of burden of disease methods by the research and policy community. Disability weights to represent disease severity in the original burden of disease estimates were developed using person trade-off techniques. Canadian focus testing of these techniques reinforced criticism in the literature that these techniques "value" the lives of healthy individuals more than those of individuals with functional limitations. The PHI used standard gamble as the main tool to measure health state preferences because it is grounded in utility theory; participants in focus groups preferred it to other techniques (e.g., time trade-off, person trade-off).

The burden of disease methods were also criticized for their approach to age weighting. Age weighting places a greater emphasis on morbidity and mortality in young adulthood. Some would argue that these "best years of life" should be valued more, but age weighting has been criticized as valuing those of working age more than children and seniors.^1-4 The methodologic validity of age-weighting has also been challenged.⁴ For these reasons, current burden of disease studies, including the PHI, are not using age-weighting.

The use of discounting in burden of disease studies has also been challenged. Discounting, which assigns lower values to health effects in the future than those in the present, is widely used in health economics, particularly in cost-effectiveness studies.^1,3  Canadian estimates will apply a discount rate of 3% and provide tools that allow discount rates to be changed.

1. Anand S, Hanson K. Disability-adjusted life years: a critical review. J Health Econ 1997;16:685-702.
2. Barker C, Green A. Opening the debate on DALYs (disability-adjusted life years). Health Policy Plan 1996;11:179-83.
3. Arnesen T, Nord E. The value of DALY life: problems with ethics and validity of disability adjusted life years. BMJ 1999;319:1423-5.
4. Barendregt JJ. Disability-adjusted life years (DALYs) and disability-adjusted life expectancy (DALE). In: Determining life expectancies. Robine JM, Jagger C, Mathers D et al., eds. Chichester (UK): Wiley, 2003. p 247-261.

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Moving forward using microsimulation

The PHI is developing microsimulation models that integrate data for many diseases simultaneously and model the interplay between diseases. While some estimates have been made for diseases that commonly occur together (comorbid conditions), most burden of disease estimates are for a single disease.

When health interventions target one specific disease, they can be identified, justified, and evaluated based on disease-specific benefits. In reality, however, individuals may have more than one disease (comorbidity) and more complex modeling is needed to examine the potential impacts of interventions. With broader goals of healthy living, integrated strategies need to address multiple diseases through their common determinants and/or risk factors.

At the same time, policy analysts are increasingly concerned that the benefit achieved by reduction of one disease could be lost due to an increase in other diseases. When mortality is reduced for one disease, individuals will die from another cause. The critical questions become: How many years of life are added? What other health conditions will be experienced during these added years of life? When an intervention shifts the impact of disease from one cause to another, it increases years of life lived, but does the gain in years provide extended life in good health, or an extended period of life with reduced functioning?

Microsimulation models 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.

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