2  Introduction to Health Metrics

“The concept of health also has highly subjective connotations, precisely because it is conditioned by the personal view of happiness.” René Dubos1

Health metrics are key variables to understanding the state of health of a population. In this first section of the book we’ll explore about the history of these metrics, how to calculate them, and how to account for their variation due to causes and risks. We will examine the components of these metrics and the challenges in providing standardized values for global comparison. Additionally, in the following sections, we will learn how to build a model, evaluate the effect of infectious disease spread on health metrics, and how it impacts the overall health status of a population.

2.1 The History of Health Metrics

The history of health metrics is a fascinating journey that spans centuries, evolving from rudimentary observations to sophisticated statistical methods, as illustrated by Figure 2.1.

Figure 2.1: Health Metrics Development Timeline

One of the earliest forms of health metrics can be traced back to the work of John Graunt, an Englishman, in the 17th century.2 In 1662, Graunt published a landmark work titled “Natural and Political Observations Made upon the Bills of Mortality.” Graunt defined as the father of human demography was largely self-educated, and his interest in mortality data was driven by the Bills of Mortality, which were weekly and annual records of deaths in London, and provided information on the number of births and deaths in the city. Graunt meticulously analyzed this mortality data and produced tables that presented various patterns and trends. His work laid the foundation for modern demography and statistical analysis. Among his notable contributions were the concepts of life expectancy and mortality rates. One of Graunt’s key observations was the consistent pattern of higher mortality among infants and young children. He noted the difference in life expectancy between males and females and provided insights into factors influencing mortality, such as epidemics and seasons.

Over time, advancements in mathematics, medicine, and statistics led to the development of more sophisticated health metrics. The 19th century saw the emergence of life tables, which provided a systematic way to analyze mortality and life expectancy. More specifically, Mary Dempsey in 1940s wrote an article3 for the National Tuberculosis Association about the concept of Years of Life Lost (YLL), assessing time-lost as a metric for health for the first time. She was then followed by other more advanced improvements spanning all throughout of the rest of the century.

“The quality of life depends also on the state of”public health”-namely on factors that affect the biological welfare of the community as a whole.”4

With the growth of epidemiology the 20th century was a key century for the expansion of the health metrics, the use of life table and modification of expected survival time to include weights metrics.

The term Summary Measures of Population Health (SMPH)5 was established in the field, originating from the need to encapsulate both fatal and non-fatal health outcomes into a single, comprehensive measure, representing a significant evolution in health metrics. Used for various purposes, the SMPH compare the health of populations and include the contributions of different diseases, injuries and risk factors to the total disease burden in a population. SMPH, also referred to as a composite indicator6, is divided into two broad families: health expectancies and health gaps.

Health expectancies are summary measures that combine information on mortality and morbidity to represent the average number of years that an individual can expect to live in good health. Such as healthy life expectancy (HALE) and the quality adjusted life expectancy (QALE) using health-related quality of life.

Health gaps, on the other hand, represent the difference between the current health status of a population and an ideal health situation, providing a measure of the potential for improvement in health outcomes. Divided into the disability adjusted life year (DALY)7 in the global burden of disease (GBD) study and the quality adjusted life year (QALY) used as the outcome index of the cost-utility analysis.

In the contemporary era, the integration of computing power and big data has further transformed health metrics. Today, we have complex models, machine learning algorithms, and global health databases that allow for real-time tracking of diseases and health outcomes.

John Graunt’s work may have been a humble beginning, but it set in motion a scientific approach to understanding population health. The journey from Graunt’s Bills of Mortality8 to today’s sophisticated health metrics reflects the continuous quest to quantify and comprehend the complexities of human health and disease.

2.1.1 Quality-Adjusted Life Years (QALYs)

The concept of Quality-Adjusted Life Years (QALYs) emerged in the late 1970s, originating from the idea of combining both the quantity and quality of life by Joseph S. Pliskin, then it became a widely used metric for evaluating the cost-effectiveness of healthcare interventions.9 One QALY corresponds to one year in perfect health, it ranges from 1 (perfect health) to 0 (dead). However, this metric, used in various sectors such as health insurance, is somewhat criticized due to its lack of addressing disparities among individuals. It might result in discrimination against individuals with specific medical conditions or disabilities.

A controversial result followed 25 years of research and studies due to the concerning validity of the metric to be inclusive. Started in the early 1989s, to step in the 2010 with the European Commission10 which started the largest-ever study specifically dedicated to testing the assumptions of the QALY,11 ending in 2013 with the recommended “not using QALYs in healthcare decision making”, arguing that patients with disabilities are valued less under a QALY-based system then individuals with no disabilities. This result has been further criticized and remains a topic of debate.

“Thus, medicine cannot by itself determine the quality of life. It can only help people to achieve the state of health that enables them to cultivate the art of life-but in their own way. This implies the ability to enjoy the fundamental satisfactions of the biological joie de vivre. It implies also the ability for each person to do what he wants to do and become what he wants to become, according to human values that transcend medical judgment.” R.Dubos12

2.1.2 Disability-Adjusted Life Years (DALYs)

In the 1990s, the World Bank financed a study at Harvard University to develop a sustainable index that considered not only the health status but also the identification of the level of disabilities concerned by disparities. This lead to a new way of comparing the overall health and life expectancy of different countries, released along with the Global Burden of Disease Study in 1990;13 a project that involved Christopher J. L. Murray14 and Alan Lopez,15 in collaboration with the World Health Organization (WHO). This effort resulted in the development of the Disability-Adjusted Life Years (DALYs),16 a metric that combines years of life lost due to premature death and years lived with disability.

DALYs are connected with QALYs in that both metrics seek to quantify the impact of health on quality and length of life. However, while QALYs focus on the benefits with and without medical intervention, DALYs measure the overall burden of diseases.

The DALY metric is calculated by the summing the years of life lost (YLLs) due to premature death and the years lived with disabilities (YLDs). This metric provides a comprehensive measure of a population’s health status by highlighting both mortality and disability. For example, consider a population with a life expectancy of 80 years. Individuals who die before reaching this age contribute to the YLLs, while those living with disabilities add to the YLDs. The sum of these values gives the total DALYs for the population, which can then be compared globally to assess health status and identify areas needing improvement in facilities, research, or investment.

2.1.3 Healthy-Adjusted Life Years (HALY)

Healthy Adjusted Life Years (HALY) is a metric that combine aspects of both DALYs and QALYs to provide a comprehensive measure of health outcomes in a population. HALYs quantify the burden of disease and the overall quality of life experienced by individuals, measuring the number of years of healthy life lived by a population while accounting for both mortality and morbidity. The information about the prevalence of diseases and disabilities, as well as the impact of these conditions on individuals’ quality of life is incorporated in the calculation of HALYs.

HALYs adjust life expectancy levels based on the prevalence of health conditions and their associated disability weights. Disability weights reflect the severity of different health conditions and their impact on daily functioning. By applying these weights to the prevalence of each condition, researchers can estimate the overall burden of disease in terms of years of healthy life lost, similar to the calculation of DALYs. Unlike DALYs, which focus primarily on morbidity and mortality, HALYs incorporate measures of individuals’ quality of life, allowing for a more comprehensive assessment of health outcomes.

2.1.4 Healthy-Adjusted Life Expectancy (HALE)

Healthy Life Expectancy (HALE) is a measure of overall health and well-being that accounts for both the quantity and quality of life. HALE combines years of life expectancy with the prevalence and severity of disability in a population, providing a more nuanced view of health outcomes than traditional measures of life expectancy.

HALE provides a more comprehensive view of health outcomes than other traditional measures of life expectancy, which only consider the quantity of life. The calculation of HALE typically involves estimating the number of years that an individual can expect to live in good health, taking into account the impact of diseases and injuries on quality of life. This information is then used to estimate the overall health status of a population. It is a useful tool for public health practitioners and policy makers, as it provides a more nuanced view of the health outcomes of a population. This information can help inform public health interventions and prioritize resources, as well as help track changes in health outcomes over time. Additionally, HALE can be used to compare the health outcomes of different populations and identify disparities in health outcomes, which can help inform targeted public health interventions.

Overall, the HALE metric provides a valuable perspective on the overall health and well-being of a population, combining information about both quantity and quality of life to provide a comprehensive view of health outcomes. It adjusts overall life expectancy by the amount of time lived in less than perfect health. This is calculated by subtracting from the life expectancy a figure which is the number of years lived with disability multiplied by a weighting to represent the effect of the disability1718.

2.1.5 Healthy Life Years (HLY)

The Healthy Life Years (HLY) indicator, also known as disability-free life expectancy (DFLE) or Sullivan’s Index,19 assesses the number of years an individual can expect to live in good health, free from significant health problems or disabilities. It is a measure of the quality of life-adjusted life expectancy, focusing on the years lived in full health rather than simply the total number of years lived. It specifically focuses on the number of years a person can expect to live without disability, providing valuable insights into overall health and well-being beyond just life expectancy. It takes into account both mortality and morbidity, providing a comprehensive picture of overall health and well-being.

HLY is typically calculated by subtracting the number of years lived with disability from life expectancy at birth. The resulting figure represents the number of years an individual can expect to live in good health, free from significant health-related limitations. Unlike life expectancy, which focuses solely on the length of life, HLY incorporates measures of the quality of life by considering the impact of health conditions on individuals’ ability to engage in daily activities and maintain independence.

In terms of policy implications, HLY is used to inform health policy decisions by identifying areas where interventions are needed to promote healthy aging and improve overall population health. By focusing on the number of years lived in good health, policymakers can target interventions to prevent and manage chronic diseases and disabilities effectively.

2.2 How the Metrics are Used in Global Health

These metrics are used alone or in combination with other health metrics in global health assessments conducted by organizations such as the World Health Organization (WHO), the European Commission, and the Institute for Health Metrics and Evaluation (IHME). They provide a global perspective and insights into the health status of populations, helping to track progresses towards long-term health objectives over time. The metrics also inform public health policies, healthcare interventions, and resource allocation decisions, helping to prioritize health needs and target interventions effectively.

The investigation into finding updated metrics is a sustained commitment, aiming to develop new measures tailored to the evolving landscape of the health field. Recently, a new Summary Measure of Population Health (SMPH) was proposed, known as Well-being-Adjusted Health Expectancy (WAHE). This measure provides an estimate of the number of life years equivalent to full health20. WAHE incorporates both the traditional health status indicators and well-being factors, thus offering a more comprehensive assessment of the population’s health that aligns with contemporary health priorities. This approach reflects a growing recognition that health metrics should account for physical and clinical indicators and consider psychological and overall well-being.

2.3 Summary

Health metrics play a crucial role in understanding and improving population health. From John Graunt’s early work in the 17th century to today’s sophisticated metrics like DALYs, QALYs, HALYs, HALE, and HLY, these measures have evolved to provide a comprehensive view of health outcomes. They guide public health policies, healthcare interventions, and global health assessments, highlighting areas for improvement and helping to allocate resources effectively. As health metrics continue to evolve, incorporating both traditional and well-being indicators, they will remain essential tools in the quest to quantify and enhance the complexities of human health and disease.

  1. R Dubos, “The State of Health and the Quality of Life.” Western Journal of Medicine 125, no. 1 (July 1976): 8–9, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1237171/.↩︎

  2. “John Graunt,” January 24, 2024, https://en.wikipedia.org/w/index.php?title=John_Graunt&oldid=1198718407.↩︎

  3. Mary Dempsey, “Decline in Tuberculosis,” American Review of Tuberculosis, April 23, 2019, https://www.atsjournals.org/doi/epdf/10.1164/art.1947.56.2.157?role=tab.↩︎

  4. R Dubos, “The State of Health and the Quality of Life.” Western Journal of Medicine 125, no. 1 (July 1976): 8–9, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1237171/.↩︎

  5. Christopher J. L. Murray et al., eds., Summary Measures of Population Health: Concepts, Ethics, Measurement and Applications (World Health Organization, 2002).↩︎

  6. Adnan A. Hyder, Prasanthi Puvanachandra, and Richard H. Morrow, “Measuring the Health of Populations: Explaining Composite Indicators,” Journal of Public Health Research 1, no. 3 (December 2012): 222–28, doi:10.4081/jphr.2012.e35.↩︎

  7. C. J. Murray, “Quantifying the Burden of Disease: The Technical Basis for Disability-Adjusted Life Years.” Bulletin of the World Health Organization 72, no. 3 (1994): 429–45, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2486718/.↩︎

  8. “John Graunt,” January 24, 2024, https://en.wikipedia.org/w/index.php?title=John_Graunt&oldid=1198718407.↩︎

  9. “Quality-Adjusted Life Year,” December 27, 2023, https://en.wikipedia.org/w/index.php?title=Quality-adjusted_life_year&oldid=1192021016.↩︎

  10. “Echoutcome,” October 8, 2016, https://web.archive.org/web/20161008010513/http://echoutcome.eu/.↩︎

  11. David Holmes, “Report Triggers Quibbles over QALYs, a Staple of Health Metrics,” Nature Medicine 19, no. 3 (March 1, 2013): 248–48, doi:10.1038/nm0313-248.↩︎

  12. Dubos, “The State of Health and the Quality of Life.” July 1976.↩︎

  13. C. J. Murray, A. D. Lopez, and D. T. Jamison, “The Global Burden of Disease in 1990: Summary Results, Sensitivity Analysis and Future Directions.” Bulletin of the World Health Organization 72, no. 3 (1994): 495–509, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2486716/.↩︎

  14. “Christopher J. L. Murray,” January 1, 2024, https://en.wikipedia.org/w/index.php?title=Christopher_J._L._Murray&oldid=1192936044.↩︎

  15. “Alan Lopez,” December 11, 2023, https://en.wikipedia.org/w/index.php?title=Alan_Lopez&oldid=1189335406.↩︎

  16. “Disability-Adjusted Life Year,” December 8, 2023, https://en.wikipedia.org/w/index.php?title=Disability-adjusted_life_year&oldid=1188922629.↩︎

  17. www.healthknowledge.org.uk↩︎

  18. “Healthy Life Expectancy (HALE),” n.d., https://www.who.int/data/gho/data/themes/topics/indicator-groups/indicator-group-details/GHO/healthy-life-expectancy-(hale).↩︎

  19. “Healthy Life Years,” January 26, 2024, https://en.wikipedia.org/w/index.php?title=Healthy_Life_Years&oldid=1199224227.↩︎

  20. Magdalena Muszyńska-Spielauer and Marc Luy, “Well-Being Adjusted Health Expectancy: A New Summary Measure of Population Health,” European Journal of Population 38, no. 5 (December 2022): 1009–31, doi:10.1007/s10680-022-09628-1.↩︎