Adjusting the Human Development Index for Health and Longevity
By: Sari Fink
Bachelor of Science in Economics, University of Victoria An extended essay submitted in partial fulfillment of the
requirement for the degree of MASTER OF ARTS in the Department of Economics
We accept this extended essay as conforming to the required standard
Dr. Merwan Engineer, Supervisor (Department of Economics)
Dr. Nilanjana Roy, Department Member (Department of Economics) © Sari Fink, 2006
University of Victoria
All rights reserved. This extended essay may not be reproduced in whole or in part, by photocopy or other means, without the permission of the author.
Abstract:
The Human Development Index (HDI) has become a standard for measuring human development. Its component indices measure life expectancy, literacy/education, and per capita Gross Domestic Product (GDP). The life expectancy component in the index is used to proxy ‘a long and healthy life’. This paper argues that life expectancy alone does not describe quality of life with respect to health and examines alternative proxies for a long and healthy life. In particular, a new measure called the ‘health adjusted life expectancy’ (HALE) is examined, as it is based on updated life tables as well as including a measure of ‘years lost to disability’. Though HALE is a superior proxy of health and longevity, adjusting the HDI to include this measure does not substantially change the HDI ranking of nations.
List of Tables and Figures:
Table 1: Spearman’s HDI Results --- 36
Table 2: Spearman’s LE Results --- 37
Table 3: HDIHALE Calculation Results --- 47
Table 4: HDIYLD Calculation Results --- 51
Table 5: DALYs per 100,000 Population for Selected States for 2002 --- 55
Figure 1: YLD versus HDI Rank --- 38
Abstract --- ii
List of Tables and Figures --- iii
Table of Contents --- iv
1. Introduction --- 1
2. The Human Development Index --- 3
2.1 History and Origins --- 3
2.1.1 Birth of the Human Development Index --- 3
2.1.2 The First Human Development Report, 1990 --- 5
2.2 Constructing the Human Development Index --- 7
2.3 Critiques of the Human Development Index --- 9
2.4 Human Development Report, 2004 --- 11
3. Life Expectancy --- 11
3.1 Calculating Life Expectancy --- 11
3.2 Critiques of Life Expectancy Calculation Methods --- 12
4. Alternative Health and Longevity Measures --- 15
4.1 Summary Measures of Population Health --- 15
4.2 Disability Adjusted Life Years (DALY) --- 17
4.2.1 Birth of the DALY --- 17
4.2.2 Calculating DALY --- 19
4.2.3 Critiques of DALY --- 22
4.3 Health Adjusted Life Expectancy (HALE) --- 27
5. Incorporating Alternative Health and Longevity Indices into the HDI --- 30
5.1 Rationale --- 30
5.2 Incorporating HALE into the HDI --- 30
5.2.1 Directly Replacing LE with HALE in the HDI (HDIHALE) --- 30
5.2.2 An Adjusted HALE Index for the HDI (HDIAdHALE) --- 32
5.3 Examining Variation from the LE and Morbidity Components of HALE --- 33
5.3.1 Using LE from WHO in the HDI (HDIWHO) --- 33
5.3.2 Using the Morbidity YLD Measure with UN LE in the HDI (HDIYLD) - 34 6. Data Correlation Calculations --- 39
6.1 The Spearman’s Rank Correlation Coefficient --- 35
6.2 Examining YLD --- 38
7. Conclusion --- 40
8. References --- 41
9. Appendices --- 46
9.1 Appendix A: HDI Calculation Results --- 46
9.2 Appendix B: Spearman’s Coefficient Calculations --- 54
9.3 Appendix C: DALYs for Selected States --- 58
9.4 Appendix D: Glossary --- 62
1
Introduction
The last century was on the whole, one of unprecedented worldwide growth in human development, as measured by the standard indicators of life expectancy, education and per capita income. The standard measure of development was GDP, based on the assumption that economic growth and human development are closely linked. By the 1980’s it was becoming evident that there was no automatic connection between
economic growth and human development (Haq, 2005b; Sen, 2005). In many parts of the developing world human development was arguably stalled or moving backwards, even in the face of expanding production and income; while in other parts of the world, several countries had achieved impressive gains in human well-being despite having low
economic growth (Haq, 2005b; Sen2005). In some countries the structural adjustments based on traditional economic growth theory undertaken by the International Monetary Fund had been particularly costly in human terms(Haq, 2005b).1
Dissatisfaction with the hegemony of the all mighty gross domestic product (GDP) set in motion efforts to create a better proxy for measuring human development. Much of the impetus for this change came from influential writers such as Amartya Sen and Mahbub ul Haq2, who were instrumental in giving rise to the Human Development Index (HDI), as developed by the United Nations (UN). The HDI has become a standard for measuring human development. Its component indices measure per capita GDP, life expectancy and literacy/education. A lot of attention has been paid to the use of the GDP component and many writers have tackled the issues surrounding it. This paper focuses on the life
The HDI puts forth the notion that life expectancy is an adequate proxy of both ‘health’ and ‘longevity’ for the index. This paper argues that life expectancy alone does not describe quality of life with respect to health. New models are being developed to capture the health component of life, by institutions such as the World Bank and the World Health Organization (WHO). These new models may serve as a useful means to add more information to the ‘health and longevity’ component of the HDI.
This paper proceeds as follows. Section 2 outlines the development of the HDI and how it is constructed and highlights some of the main critiques. Section 3 explains how standard life expectancy is calculated and points out some of the issues and shortcomings in this measure. Section 4 discusses some alternative ways of calculating life expectancy measures that include deductions for premature mortality and ill-health. This paper focuses on one particular measure called the disability adjusted life year (DALY), developed by the World Health Organization and the World Bank sponsored Global Burden of Disease Project. The WHO’s main publication, World Health Report, introduced the health adjusted life expectancy (HALE) in 2000. HALE is derived from DALY morbidity data. Section 5 of this paper conducts a series of exercises
reconstructing the HDI using different variations of adjusted life expectancy measures in place of standard UN life expectancy. It then examines the resulting changes in HDI country rankings. In section 6 correlations are calculated on the various outcomes using Spearman’s rank order correlation coefficient. Section 7 concludes.
2 The Human Development Index
2.1 History and Origins
2.1.1 Birth of the Human Development Index
Prior to the 1990’s no good comprehensive alternative to GDP as a measure of socio-economic progress existed. The events of the 1980’s3 prompted Mahbub ul Haq to present an idea for a new type of development report to the United Nations Development Program (UNDP). The report would be based on the concept of ‘human centered’
economic development and would explore new ways of measuring and quantifying progress. The UNDP administrators liked the idea and in 1989 the UNDP launched the Human Development Report (HDR) initiative (Haq, 2005b). The first HDR was
published the following year and introduced the now well-known Human Development Index (HDI).
Several overarching principles guided the development of the HDI. These principles are discussed in detail by Mahbub ul Haq (2005a) in ‘The Birth of the Human Development Index’ and summarized below:
1) The new index would reflect the basic concept that development is a means to enlarge people’s choices. The choices covered a wide range of quality of life issues: to live long, to acquire knowledge, to have a comfortable standard of living, to be gainfully employed, to breathe clean air, to be free and to live in a community. It was recognized that many of these factors would be difficult if not
impossible to quantify but some measures reflecting these values needed to be added.
2) The index would consist of only a small number of variables in order to keep it simple, manageable and understandable. The desire was to be able to present the index as a policy enabling measure of the societies in which people live.
3) Instead of creating a multitude of separate indices, a single composite would be constructed.
4) The new index would include both economic and social choices as per the development goals expressed in number one. It was recognized that a synergy existed between economic growth and social progress. Each fed on and was dependent on the other. Including indicators of both would merge the two concepts.
5) An important directive was to keep the coverage and methodology of the new index flexible, in order for it to be able to evolve as critiques emerged. A worthwhile socioeconomic indicator would require patient long-term investigation and research.
6) The new index would not be held back due to a lack of reliable data. Estimates would be employed where necessary and would serve to highlight the need for policy-makers to invest in creating more and better reliable data.
In the years since its inception the HDI has exceeded all expectations of its developers and has become a highly influential measure of socioeconomic progress.
2.1.2 The First Human Development Report, 1990
The first HDR was subtitled Concept and Measurement of Human Development and
opened with the following statement: “People are the real wealth of a nation. The basic objective of development is to create an enabling environment for people to enjoy long, healthy, and creative lives” (United Nations Development Program (UNDP), 1990). It explored the link between economic growth and human development. The report emphasized the need for real growth in national production but stated that the important thing to study was how or why growth sometimes contributed to human development and other times did not. The report strove to remain practical and examined the economic development experiences of several countries in order to draw real insights into the process and hence advise policy-makers (UNDP, 1990).
At the time, this HDR presented a unique and revolutionary view of development. It was prepared under the guidance of Mahbub ul Haq, former Finance and Planning Minister of Pakistan and in consultation with Amartya Sen. The report put forth a new definition of human development:
Box 1.1 Human Development defined.
Human development is a process of enlarging people’s choices. In principle, these choices can be infinite and change over time. But at all levels of development, the three essential ones are for people to lead a long and healthy life, to acquire knowledge and to have access to resources needed for a decent standard of living. If these essential choices are not available, many other opportunities remain inaccessible.
But human development does not end there. Additional choices, highly valued by many people, range from political, economic and social freedom to opportunities for being creative and productive, and enjoying personal self-respect and guaranteed human rights.
Human development has two sides: the formation of human capabilities – such as improved health, knowledge and skills – and the use people make of their
social and political affairs. If the scales of human development do not finely balance the two sides, considerable human frustration may result.
According to this concept of human development, income is clearly only one portion that people would like to have, albeit an important one. But it is not the sum total of their lives. Development must, therefore, be more than just the expansion of income and wealth. Its focus must be people. (UNDP, 1990, p. 10)
HDR 1990 presented the first Human Development Index and outlined how it had been conceived and developed. The creators expressed a desire to keep the index simple and understandable while still capturing several key facets of human development. They considered many elements of human life and settled on three key components –
longevity, knowledge and decent living standards (UNDP, 1990). After examining many different indicators the following proxy measures were selected:
1) Life expectancy – this indicator was meant to capture the value of a long life, implying also that since life expectancy is increased through indirect benefits such as adequate nutrition and good health care, it is therefore a proxy for both ‘health and longevity’.
2) Adult literacy – this was chosen to proxy knowledge. “Years of schooling” was added later in response to critiques that literacy was inadequate. This then became the education index.
3) Per capita GDP – chosen as a proxy for income. (UNDP, 1990) The authors acknowledged that human development has many more facets than what these three indicators can measure, but for the sake of simplicity they chose to focus on what they term three main ‘deprivations’.4
With respect to the choice of life expectancy as a primary measure the authors justify it with the following:
Box 1.2 What price human life?
The use of life expectancy as one of the principal indicators of human
development rests on three considerations: the intrinsic value of longevity, its value in helping people pursue various goals and its association with other characteristics, such as good health and nutrition.
The importance of life expectancy relates primarily to the value people attach to living long and well. That value is easy for theorists to underestimate in countries where longevity is already high. Indeed, when life expectancy is very high the challenge of making the lives of the old and infirm happy and worthwhile may be regarded by some as an exacting task. For the less fortunate people of the world, however, life is battered by distress, deprivation and the fear of premature death. They certainly attach a higher value to longer life expectancy.
Longevity also helps in the pursuit of some of life’s other most valued goals. Living long may not be people’s only objective, but their other plans and ambitions clearly depend on having a reasonable life span to develop their abilities, use their talents and carry out their plans.
A long life correlates closely with adequate nutrition, good health and education and other valued achievements. Life expectancy is thus a proxy measure for several other important variables in human development.
(UNDP, 1990, p. 11)
2.2 Constructing the Human Development Index
As stated previously the HDI is a composite index made up of three individual indices. It has evolved over the years, incorporating some of the more constructive and feasible critiques. Two major changes were the addition of a ‘years of schooling’5 measure to the education component and the adoption of fixed minimum and maximum values.6 The following is a description of the calculation from the HDR 2004 statistical annex used to calculate the 2002 HDI.7
Three individual indices are created first:
1. Life expectancy: minimum and maximum values are twenty-five and eighty-five years respectively. These goal posts were chosen as fixed values just outside the range of observed values. The LE index:
LEindex = (x – 25)/(85-25) ,
where x is the particular life expectancy value for a certain
country, and
LEindex is Life Expectancy Index.
2. Education: this has two components – adult literacy rate and combined primary, secondary and tertiary gross enrolment rate. Individual indices are calculated first then combined with a two-third to one-third weighting respectively. The
maximum and minimum values for both rates are one hundred percent and zero percent. The education index is:
Litindex = y/100 , Enindex = z/100 ,
Edindex = 2/3(Litindex) + 1/3(Enindex) ,
where y is the literacy rate for a particular country, z is the enrollment rate for a particular country, Litindex is the Literacy Index,
Enindex is the Enrollment Index, and Edindex is the Education Index.
3. Income: this is calculated using purchasing power parity (US$) adjusted per capita GDP. The maximum and minimum values are $40,000 US and $100 US.
The maximum value is thought to reflect the basic income requirement to lead a decent life. Following further on the principle that unlimited income is not necessary; the income figures are further adjusted using logarithms. The GDP index is:
GDPindex = (log(i) – log(100)) / (log(40000) – log(100)) ,
where i is the per capita GDP in a particular country, and GDPindex is the GDP Index.
The HDI is an average of the three individual indices with equal weighting to each:
HDI = 1/3(LEindex) + 1/3(Edindex) + 1/3(GDPindex)
2.3 Critiques of the Human Development Index
The HDI has gained a great deal of popularity amongst policy circles in the years since its inception. Naturally this attention has resulted in increased scrutiny and critical
assessments by various researchers. The creators of the HDI fully expected critiques of their index, which is why they left the framework open and flexible. They state that critiques are reviewed seriously with an eye towards continuous improvement and some have even led to changes in the methodology over the years (Raworth and Stewart, 2005).
As mentioned above, one of the most notable changes early on was the addition of the years of schooling to the education component. This was in response to the criticism that literacy data for many developed countries was incomplete; thus literacy was assumed to be one hundred percent, which led to bunching at the top end of the spectrum (Raworth
and maximum values in response to criticisms about the lack of time series comparability (Raworth and Stewart, 2005; Morse, 2003). Even so, one author has suggested that this is not an optimal solution as the goal posts for the world are themselves changing over time (Mazumdar, 2003). Other technical critiques include: the use of heavy discounting for incomes above the maximum value (Raworth and Stewart, 2005); the use of equal weighting on the three components when they have different means and variances
(Noorbakhsh, 1998c); and a high degree of correlation between the individual indices, the HDI and GDP, which implies that no real new information is being added (Cahill, 2005).8
The HDI has also been criticized for what it leaves out: political freedom, human rights (Ranis, Stewart & Samman, 2005; Fukuda-Parr, 2005), distributional issues (Hicks, 1997), and environmental sustainability (Sagar and Najam, 1998; Neumayer, 2001). The HDR team has acknowledged that HDI is silent on several important issues to do with freedoms, rights and distribution. But the original purpose of the HDI was to keep it as a simple and understandable snapshot of human development. The team has instead developed and incorporated new indicators into the HDR reports to address this deficiency. These include the Human Poverty Index for developing countries (HPI-1), Human Poverty Index for selected OECD (Organisation for Economic Co-operation and Development) countries (HPI-2), Gender-related Development Index (GDI), and the Gender Empowerment Measure (GEM).9
2.4 Human Development Report 2004
The HDR 2004 contains the HDI for the year 2002. The 2004 report was subtitled
Cultural Liberty in Today’s Diverse World and tackled the question of how culturally
diverse people could exist in harmony (UNDP, 2004). The report explored many topics on multiculturalism and how to mitigate conflicts based on ethnic differences. The report stated: “If the world is to reach the Millennium Development Goals10 and ultimately eradicate poverty, it must first successfully confront the challenge of how to build inclusive and culturally diverse societies” (UNDP, 2004, p. v).
3 Life
Expectancy
3.1 Calculating Life Expectancy
The Human Development Index team chose life expectancy (LE) as a standard measure to proxy human ‘well-being’ or ‘quality of life’. Using LE as an indicator for quality of life has long been an accepted practice. The rationale is that if people live longer on average in a given society then they must be living at a higher standard, living better lives. This seems like a reasonable practice when looking at human history and how life spans have changed over time. Seldom questioned however is how those LE figures are derived.
The traditional method for calculating LE is the period life table system first developed by demographers in the early 20th century. A period life table is a collection of mortality
Nations Secretariat (UNS), 2002). The standard period is one year, with deaths recorded per a five-year age grouping.
The first set of life tables was constructed by the United Nations Population Division (UNPoP) in the 1950’s and the UNPoP still uses essentially the same methods for constructing these tables (UNS, 2002). The LE at birth statistic is calculated from these same period life tables. It is fundamentally a synthetic construct that shows the average life span of an individual if they were to experience the mortality profile as presented by the life table (UNS, 2002). Bongaarts and Feeney (2002) put forth the following
definition: “Period life expectancy at birth is defined as the average age at death that would be observed for a group of persons who experience, over the course of their lives, the age-specific death rates observed during the time period” (p. 14).
3.2 Critiques of Life Expectancy Calculation Methods
There are several issues with regard to the construction of LE, especially pertaining to the mortality data, which are open to criticism. Very little research in this regard however has been done in the last few decades. As one author states: “Methods for the measurement of mortality are regarded by many demographers as an all but closed subject” (Bongaarts & Feeney, 2002, p. 13). Ofkey significance is the lack of adequate data and how mortality figures are estimated in the absence of such data.
use various indirect methods for extrapolating a mortality profile for a country. These techniques involve choosing a representative sample from a single country, estimating mortality rates from the sample, and then applying that rate profile to other countries with a similar socio-economic makeup. These indirect estimation techniques themselves have changed very little over the last several decades and are based on certain essential
assumptions. These assumptions are: the underlying age pattern of mortality does not change significantly and has been converging over time, a stable population, and linear trends (UNS, 2002; Murray, Ahmad, Lopez & Salomon 2000; Bongaarts & Feeney, 2003).
In recent years a few researchers have begun to question these long standing assumptions. Ram (2006) has shown that recent empirical evidence refutes the assumption of cross-country convergence in life expectancy. Ram used data from 163 countries and computed least-squares and quantile-regression analysis of convergence models. His study showed that convergence did occur during the 1960’s and 1970’s but this pattern does not hold true for the 1980’s and beyond. Ram (2006) concluded the following: “The numbers indicate that convergence is likely to have ended during the late 1980s or the early 1990s and divergence seems to have begun” (p.521). He speculates that HIV/AIDS may be the cause but after removing high AIDS countries from his sample there is still significant divergence observed (Ram, 2006).
Bongaarts and Feeney (2002) argue that the assumption of stable populations and linear trends is questionable. They have shown that LE calculations are prone to a ‘tempo
effect’ that leads to distortions whenever a population is changing. They conclude: “…minor fluctuations in the mean age of death lead to substantial fluctuations in the conventional life expectancy …” (Bongaarts & Feeney, 2002, p.22). This holds true not only for developing countries experiencing migration from famines and wars, high mortality rate changes from AIDS, but as Bongaarts & Feeney show, it is also true for developing countries due to the ‘greying of the population’ phenomenon (Bongaarts & Feeney, 2002).
The World Health Organization (WHO) has been attempting to address these shortfalls. To increase the availability of more accurate data the WHO is involved with an effort to create a system of Demographic and Health Surveillance11 (DHS) sites. Over 30 DHS sites now exist; compiling data on health and mortality while providing health service research (Byass et al., 2002; Baiden, Hodgson & Binka, 2006; Tollman & Zwi, 2000). The WHO has also created and adopted a new life table system that addresses some of the shortfalls with the more traditional models and creates a more accurate picture of mortality based on the current available data (Murray et al., 2000). The World Health Report 2003, incorporated the new model and DHS site information as much as possible to create a set of life tables for calculating life expectancy and health adjusted life expectancy.
4 Alternative Health and Longevity Measures
4.1 Summary Measures of Population Health
How do you measure the health of a population? This is a question researchers have been asking for decades. In a world of limited resources, the need to find an equitable and efficient way to allocate those resources is of prime importance. Since the 1960’s, there has been research into creating a summary measure of population health, one that combines both morbidity and mortality data in a single number (Murray, 1994; Murray, Salomon & Mathers, 2000). Murray (1994) states: “Decision makers who allocate resources to competing health programmes must choose between the relative importance of different health outcomes such as mortality reduction or disability prevention” (p.429). Year-to year decisions were mostly made based on past years expenditure patterns due to a lack of systematic information (Hollinghurst, Bevan & Bowie, 2000). Summary
measures of population health also have uses that go beyond resource allocation. Murray et al. (2000, p. 982) list the following potential applications:
1) Comparing the health of one population to the health of another population. 2) Comparing the health of the same population at different points in time. 3) Identifying and quantifying overall health inequalities within populations. 4) Providing appropriate and balanced attention to the effects of non-fatal health
outcomes on overall population health.
5) Informing debates on priorities for health service delivery and planning.
6) Informing debates on priorities for research and development in the health sector. 7) Improving professional training curricula in public health.
8) Analyzing the benefits of health interventions for use in cost-effectiveness
analyses.
The use of these measures has far-reaching consequences that can mean life or death to many people and therefore should be constructed with utmost care.
Life expectancy has long been used as a standard proxy for the health and quality of life experienced by a population. But as argued earlier in this paper the simple LE measure has its problems and leaves much information out of the equation. Researchers in the last four decades have proposed many different types of LE measures adjusted for time spent in ill-health and measures of health expectancy, for example: active life expectancy, disability-free life expectancy, disability adjusted life expectancy, years of healthy life, quality-adjusted life expectancy, dementia-free life expectancy (Murray et al., 2000). Many of these measures were linked to specific causes (such as dementia) or to a single definition of health (Murray et al., 2000).
One of the concepts most familiar to economist is the Quality Adjusted Life Year (QALY). A QALY is a form of cost-utility analysis used by health economists to calculate the incremental gains from specific health care interventions (Nord, 1999). QALYs use standard gamble and time trade-off methods to assess the utility of certain health states, which then inform decision makers about funding priorities. One of the difficulties in creating a summary measure is how to compare outcomes that are different in kind. QALYs overcome this difficulty by ranking health states, assigning a utility value between zero and one to each different illness and disability (Nord, 1999). A health
outcome, or gain, is then a product of the increase in the utility of a person’s health state (as measured by its rank) and the number of years of improvement (Nord, 1999). Nord (1999) states, “The measurement of outcomes in terms of QALYs in theory allows for comparisons of cost-effectiveness ratios across all kinds of conditions and interventions and also for the calculation of the total societal value of health plans” (p. xix). A
fundamental assumption inherent in QALYs is that the value of society wide health benefits is simply an un-weighted sum of individual health benefits, regardless of how those benefits are distributed (Nord, 1999). Nord further argues that this assumption of
distributive neutrality, inherent in the QALY approach, does not lead to outcomes that
society considers equitable in terms of health care resource allocation. Instead, this indicates that any measure of population health inevitably leads to making value choices.
This is due to the fact that ill-health is relative and must always be measured against some ideal health state and lifespan.
4.2 Disability Adjusted Life Years (DALY)
4.2.1 Birth of the DALY
Recognizing the need for more comprehensive health data, in 1993 the World Bank, World Health Organization and the Harvard School of Public Health launched a new initiative titled The Global Burden of Disease Project (GBD). This ambitious undertaking set out to create a comprehensive catalogue of disease and disability state estimates for the whole world for the year 1990, and has subsequently continued the report yearly, starting in 2000. The GBD team had “three explicit aims:
1. to incorporate non-fatal conditions into assessments of health status;
2. to disentangle epidemiology from advocacy12 in order to produce objective, independent and demographically plausible assessments of the burdens of particular conditions and diseases; and
3. to measure disease and injury burden in a currency that can also be used to assess the cost-effectiveness of interventions, in terms of the cost per unit of disease burden averted” (Burden of Disease Unit, 2006a, p.6).
The GBD Project wanted to be able to create data that linked diseases and injuries to the underlying major risk factors from which they arose and then disaggregate the
information by age, sex and region; this change would allow decision makers to focus debates on more then just mortality (Murray & Acharya, 1996).
Under the direction of C.J.L. Murray of the Harvard School of Public Health, the GBD team developed a new metric, which they called the Disability Adjusted Life Year (DALY), designed to combine mortality and morbidity data in a single number. The GBD team designed the DALY in order to perform two tasks:
1. “as a unit for measuring the magnitude of premature death and non-fatal health outcomes attributable to proximal biological causes, including diseases and injuries or attributable to more distal causes such as poor water supply, tobacco use or socio-economic inequality” (Murray & Acharya, 1996, p.2).
2. “as an outcome measure for cost-effectiveness analyses of interventions that could reduce the burden of either the proximal biological causes or the more distal risk factors and socio-economic determinants” (Murray & Acharya, 1996, p.2).
“The development of this measure, DALYs, was intended to make the ethical dimensions of quantifying health more transparent” (Murray & Acharya, 1996, p.2). A DALY is essentially a variant of the more familiar QALY in that itemploys weights to differentiate states of ill-health or disability. The difference is that QALYs measure health gains whereas DALYs measure health gaps.13 DALYs are a combination of Years of Life Lost (YLL) to premature mortality from specific causes and Years of Life Lived with a Disability (YLD) attributed to specific conditions of ill health. DALYs can therefore be thought of as a sort of reverse QALYs. Like QALYs, there are inherent value judgments built into the construction of DALYs; an unavoidable circumstance embedded within any type of health measure.
DALYs are governed by four broad principles:
1. “To the extent possible, any health outcome that represents a loss of welfare should be included in an indicator of health status” (Murray, 1994, p.430).
2. “The characteristics of the individual affected by a health outcome that should be considered in calculating the associated burden of disease should be restricted to
age and sex” (Murray, 1994, p.430).
3. “Treating like health outcomes as like” (Murray, 1994, p.431).
4. “Time is the unit of measure for the burden of disease” (Murray, 1994, p.431).
4.2.2 Calculating DALY
span. As mentioned above DALYs are a sum of years of life lost (YLL) and years of life lived with a disability (YLD), calculated for each age group and sex. The following is a simplified version of the calculation, from WHO’s website:
YLL = N * LE ,
where N is the number of deaths in a given population, and LE is life expectancy in years at the age of those deaths.
YLL’s are broken down by cause, age and sex (WHO, 2006b). For example N could be
the number of deaths among females aged 30-44 from heart disease in a given year and
LE would then be the LE of females in that cohort. In an effort to maintain equity
between regions, life expectancy used for calculating YLLs was standardized at the highest observed national level (Japans) of 82.5 for females and 80 for males.
Deriving YLDs is as follows:
YLD = I * DW * L ,
where I is the number of cases of a particular cause in a particular time
period (generally a year),
DW is the weight for that cause (disease/injury), and
L is the average duration of the disease in years (WHO, 2006b).
For the above example, I would be the number of cases of heart disease for females for
that cohort in a year, DW would be the weight assigned to heart disease, and L would be
the average number of years a person in that cohort would live with heart disease. The DALY for females aged 30-44 for heart disease would be:
where YLLHF3 is the YLL by that cohort to heart disease, and YLDHF3 is the YLD for that cohort due to heart disease.
DALYs can be aggregated in many ways, for example: single cause, class of causes, sex, cohort, etc. An age weighting and discount rate is also applied. All the DALYs for that particular cohort would be age-weighted according to the weight assigned for that cohort. DALYs are also subject to a continuous discount rate of 3%.14
The first piece of information necessary for the calculation is detailed incidence data. The WHO has spent an enormous amount of effort to gather data on the incidence and
prevalence of disease and injury in its member states. The GBD has created 135 specific disease and injury cause categories15 (Mathers et.al., 2003). Each cause category was assigned a certain weighting between 0 and 1 signifying its severity. Weightings were derived using a person trade-off methodology. The WHO convened a series of expert panels consisting of professionals from numerous different occupations in the health care field. These panels used the person trade-off method to assign relative weights to all of the various incident causes. There was a surprising amount of consensus on what the weights should be from all the different groups’ assembled (Mathers et al., 2003). YLD tables were then constructed using the prevalence data for each member state weighted according to the derived severity weights.
The GBD Project divided the globe into six regions which were each then divided into a further 14 sub-regions differentiated by mortality data. Population was divided into eight age groups: 0-4, 5-14, 15-29, 30-44, 45-59, 60-69, 70-79, 80+ (Mathers et al, 2003).
DALYs were then calculated for each age group and differentiated by gender. The values were also weighted differently for each age group, with lower weights being assigned to years lived in infancy and old age. To get the total DALYs for a country for a particular year the individual DALYs were aggregated (simple summation). The aggregate DALYs were further subjected to a time weighting. This was attributable to the assumption that a year of good health today is worth more than a year in the future. DALYs were
discounted using a standard 3% rate. See Appendix C for a list of DALYs by cause for selected WHO member states.
4.2.3 Critiques of DALY
The DALY has gained much currency in the years since its introduction and come under much criticism. Critiques include:
1) DALYs (the YLL portion) are calculated against an ideal optimal life span of 82.5 years for women and 80 years for men, something that is highly unrealistic
for many areas of the world where life expectancy is nowhere near this level
(Yazbeck, 2001; Williams, 1999). DALY proponents argue that the entire premise of the DALY is that of egalitarianism, that only age and sex should be considered when differentiating the burden disease places on an individual and so a death at a certain age will contribute equally to the total no matter what area of the planet the death occurred (Murray, 1994; Burden, 2006a). “For example, if a 35 year-old woman dies in childbirth in an African country where she might have expected to live anther 30 years, her years of life lost would be deemed unfairly
where she might otherwise have expected to live another 48 years” (Burden of Disease Unit, 2006a, p.8). The previous statement illustrates how the use of a standard life expectancy for all areas is valid. This works because DALYs are a measure of health ‘gaps’ rather then health gains. A gap in a life is of the same magnitude no matter how long that life may have ended up being.
2) DALYs use an age dependent weighting system, where disease in children and the elderly are weighted lower. This leads to discrimination based on age and
could result in less funding to the most needy (Bastian, 2000; Anand & Hanson,
1997; Arnesen & Kapiriri, 2004; Lyttkens, 2003; Yazbeck, 2001). This is one of the more vociferous criticisms that have been heaped upon DALYs. The idea of valuing a life differently because of age seems in contravention to anything resembling fairness. The authors above argue that it leads to an inequitable allocation of resources. Some feel that the DALY approach is good overall but that it should be calculated without the addition of age weighting (Arnesen & Kapiriri, 2004; Yazbeck, 2001). Yazbeck (2001) contends that: “The age weights do not reflect common preferences among health specialists, economists, and the general population” (p. 4). This intuitively sounds true, but as unappealing as it may seem there is most likely a great deal of truth to the notion that adult lives are of greater value. Adults play a central role in the life of a community and in society where children and elders are dependent on those same adults for their well-being. Age-weighting is justified on the grounds that several studies do indeed show a preference among numerous societies for giving greater value to
preventing death or disability in young adults (Murray, 1994; Murray & Acharya, 1996; Burden of Disease Unit, 2006a). Even one of the critics of DALY has reluctantly admitted, “Unpleasant though these implications are, they are probably true” (Williams, 1999, p.3).
3) DALYs are time weighted with a discount rate implying that the health of future generations is of less importance then present generations. (Williams, 1999;
Anand & Hanson, 1997; Arnesen & Nord, 1999; Arnesen & Kapiriri, 2004; Yazbeck, 2001). Discounting is an established economic principle and logically extends to the DALY concept. A year of healthy life now is worth more then a year of healthy life ten years from now (Burden, 2006a). Williams (1999) states: “At the simplest level, time preference is the economic concept that individuals prefer benefits now rather than in the future” (p.439).
4) DALYs inherently presuppose that the lives of disabled people have less value, which can lead to less funding for interventions that would increase their quality
of life (Anand & Hanson, 1998; Arnesen & Nord, 1999). This seems to be a
common misperception perhaps linked to the use of the term ‘disability’, which is often used interchangeably with ‘handicap’ even though the two terms refer to different disorders. The GBD uses 135 specific disease and injury classifications attributed to specific causes derived from the International Classification of Diseases16 (Burden, 2006b). DALYs focus on the impact that distinct illnesses or injuries have on an average individual in a certain population. It is a population
measure, rather than a judgment about any particular individual, with differentiation based only on age (age weighting) and gender.
5) The disability weights assigned to the particular causes are arbitrary and unjustified (Williams, 1999; Anand & Hanson, 1997; Bastian, 2000; Arnesen &
Kapiriri, 2004; Lyttkens, 2003; Yazbeck, 2001). Williams contends that the ‘experts’ could not possible have had enough information available to be able to make accurate value judgments. Anand and Hanson maintain that using the same weights for causes in every society is inaccurate and fails to reflect the
differences in social resource levels that can compensate for disabilities. Arnesen and Kapiriri show that changing the value choices completely alters the rankings of the disease factors and underestimates the burden attributed to communicable diseases. Yazbeck argues that the weights cannot be applied globally and country level burden studies should be conducted with country specific weights used to reflect the level of social services available. The WHO went to great lengths to develop the weights used for the DALY calculations. The authors of the GBD concede that assigning weights to health states is problematic but aver: “Yet, in order to quantify time lived with a non-fatal health outcome and assess
disabilities in a way that will help to inform health policy, disability must be defined, measured and valued in a clear framework that inevitably involves simplifying reality” (Burden of Disease Unit, 2006a, p.10). The WHO conducted many panels with health workers from all over the globe and found a surprising amount of agreement on degrees of disability presented by specific causes.
Pinto-Prades and Abellan-Perpinan, independent researchers, examined in detail the person trade-off method used by the WHO in these exercises and found the results to be robust and substantiated (Pinto-Prades & Abellan-Perpinan, 2005).
6) DALYS are calculated for each disease. Many types of health
issues/diseases/disabilities are linked in a cause/effect cycle. DALYs does not
take co-morbidities into account, which can lead to double counting (Anand &
Hanson, 1997; Gold, Stevenson & Fryback, 2002). The GBD authors
acknowledge that this is an issue. Murray discusses the difficulty of defining co-morbidities and states that to date only a few major ones have been accounted for (Murray & Acharya, 1996).17
7) Reproductive health issues do not fit neatly into disease or disability categories and therefore will be short-changed if health care funding decisions are made
utilizing DALYs (AbouZahr & Vaughan, 2000; Merrick, 2002). This is a
troubling issue. An unwanted pregnancy is not a ‘disease’ or a ‘disability’ but is often a health issue. Some of the criticism from earlier authors claiming that DALYs disadvantage women may be due to reproductive health concerns. Health funding cannot be allocated using only burden of disease calculations because funding for things like contraception and family planning education may be short changed. Ignoring reproductive health issues has a huge impact on poor women and their health.
The GBD researchers and the WHO continue to improve the DALY and to define and measure the burden of disease around the globe. This is an ambitious and ongoing project. Many individual countries have launched their own country-level studies; over 30 are at various stages in this endeavor (Mathers et al., 2003). Some of these studies have led to successful policy implementations. For example Algeria’s study led to increases in environmental protection funding and Morocco is now focusing on waterborne disease interventions due to their study highlighting this important issue (Ruta, 2005). A state level DALY study has even been conducted in the United States (Guend, 2002).
4.3 Health Adjusted Life Expectancy (HALE)
Following on the work done by GBD to quantify health ‘gaps’ through DALYs, the WHO team developed a new summary measure of overall population health called health adjusted life expectancy (HALE). Originally the GBD team created the disability
adjusted life expectancy (DALE, in keeping with the DALY terminology) designed to complement the ‘health gap’ measure with a ‘health state’ measure (Mathers et al., 2000). The measure was renamed HALE, and first reported in the World Health Report 2000. The latest HALE figures available are for the year 2002 and found in the World Health Report 2004.
The WHO team recognized that health policy development needed some kind of regular way to assess overall levels of population health because policies involve funding
(Mathers et al., 2001). Mathers et al. (2001) asserted that: “It shows: variations in levels of health across populations – where the greatest health burden lies internationally; variations in health within populations, by age and sex for example – where the greatest burden lies sub-nationally; changes in levels of health over time for populations by age and sex – whether health levels are improving; and is a crucial piece of information required to identify the major causes of poor health in populations and sub-groups – what diseases or risk factors are responsible for observed levels of poor health” (p. 2).
HALE is a measure of how long the average person in a certain population can expect to live life in a state of ‘full health’. It is essentially life expectancy minus years of life lost to disability (YLDs). In constructing HALE the WHO team begins with the WHO Life Table system for its life expectancy estimates. To estimate YLDs, the DALY calculation and weighting systems from the GBD study were utilized. The method for estimating YLDs is the same as for DALYs. The YLD for each cohort is as follows:
YLDx = IDWx * Lx ,
where IDWx is the weighted prevalence of disability/disease between
ages x and x+5, and
Lx is the average duration of the disability/disease.
Then: YWDx = LEx * (1-IDWx) ,
where YWD is the equivalent years of healthy life lived between ages
x and x+5, and
LEx is the total years of life lived by the life table population between
Then HALE at age x then is: HALEx = / w i x i x YWD I = ⎞ ⎛ ⎟ ⎜ ⎝
∑
⎠ ,where w is the last open-ended life table interval, and
Ix is the survivors at age x (Mathers et al., 2001).
Accurate incidence and duration data are of primary importance. The WHO started with the previous DALY information on incidence and prevalence of the 135 disease and injury categories. In an effort to have as accurate morbidity data as possible, the WHO also incorporated disease and injury prevalence information gathered through the WHO Multi-country Household Survey Study on Health and Responsiveness (MCSS). This survey was a two-year effort involving general population samples from over 61
countries using a standardized health status survey instrument (WHO, 2004). Information was collected on six distinct domains: mobility, self-care, usual activities, pain and discomfort, affect, and cognition (Mathers et al., 2001). The survey results and DALY information were reconciled to calculate the incidences of ill-health and the number of years lived in them, leading to an estimate for national YLDs. These combined with WHO life expectancy figures leads to an estimate for health adjusted life expectancy (WHO, 2004). This method has been criticized as incorporating a large degree of
uncertainty. The WHO team contends that all population level measures are faced with a certain amount of uncertainty and that they are attempting to create a global standard for evaluating health states using all relevant available data and that this work is still ongoing
5 Incorporating Alternative Health and Longevity
Indices into the HDI
5.1 Rationale
Standard life expectancy (LE) is used to proxy quality of life (i.e. health and longevity) in the Human Development Index (HDI). As argued earlier in this paper standard LE
measures have some serious shortfalls. The original intent of the HDI was to include a ‘human element’ into an economic development indicator. The desire was to include some alternative to reflect quality of life, health and longevity. Several different proxies were examined, such as infant mortality, but they were found to be highly correlated with life expectancy and so added no useful information to the index (Haq, 2005a). Since infant mortality is one of the components used to calculate life expectancy, it is not surprising that there is a large degree of correlation. On the other hand, the health adjusted life expectancy (HALE) does add new information as it includes data on morbidity along with mortality. In theory, using HALE in the HDI in place of standard LE will capture more of the things that make up quality of life and highlight the need for more attention to be directed at this portion of the index.
5.2 Incorporating HALE into the HDI
5.2.1 Directly Replacing LE with HALE in the HDI (HDIHALE)
was then recalculated using HALE figures from the WHR 2004 for the year 2002. Then the HDI was recalculated with the new numbers:
HDIHALE = (1/3)HALEindex + (1/3)GDPindex + (1/3)Edindex.
To derive the HALE index the LE index methodology was used:
HALEindex = (HALE – 25)/(85-25)
This formulation (unlike the one in the section below) utilizes the same maximum and minimum values as the LE index.
The table in Appendix A lists the values for the HDIHALE for 2002 and the ranking of
countries. As expected, all of the values decreased slightly, as the index is now
decreasing in morbidity (measured by YLD). Appendix A also shows how much each countries rank order changed from the UNDPs HDI ranking. The top 5 ranked countries did not change ranking, but in the top tier United States and United Kingdom both dropped several ranks. A lack of adequate medical care for many US residents is a probable reason for its decline in rank. China moves up substantially, by 14 ranks. Other large movers were: Bahamas (+11), Suriname (-10), Lebanon (-15), Grenada (+18) and Dominican Republic (+12).
An examination of the DALY figures in Appendix C leads to possible reasons for some of these other movements. The UK shows higher incidences of respiratory disorders then in similar areas (WHO, 2006a), which could point to air quality issues. The DALYs for China show very low rates of heart disease, obesity, diabetes and
nutritional deficiencies, heart disease, respiratory disease, and drug disorders; Suriname, impact of AIDS/HIV and violence; Lebanon, high rates of violent deaths; Granada and Dominican Republic, low rates of nutritional deficiencies, heart disease, and drug disorders (WHO, 2006a).
5.2.2 An Adjusted HALE Index for the HDI (HDIAdHALE)
Above HALE was inserted into the HDI directly; i.e. the same maximum (85 years) and minimum (25 years) values as the LE index. However, this raises the natural question: should different minimum and maximum ‘goal posts’ be adopted when using a different measure of ‘health and longevity’? One variation that readily presented itself is to use the sample minimum and maximum values the same way the HDI used to be calculated before adopting the fixed goal posts approach. An examination of the HALE values shows the minimum to be 28.56 and the maximum to be 74.99. Rounding these numbers yields values of 28 and 75 as reasonable min/max sample values for the index
calculation. The adjusted HALE index then becomes:
HALEindexadj= (HALE – 28)/(75-28)
The HDI was recalculated using the adjusted HALE index. The results show that changing the goal posts creates slightly more variation in the level of the index and slightly larger variations in the rankings of the countries. For example, China now moves up 19 instead of 14 ranks. This methodology once again runs into the comparability issue mentioned above. To truly compare this HDI to the original HDI, the original HDI should be reconstructed using an LE index derived from the sample maximum and minimum
5.3 Examining Variation from the LE and Morbidity Components
of HALE
Using HALE instead of LE in the HDI can change the index for two main reasons. First, HALE uses a different LE measure; second HALE includes a morbidity measure. Both components are examined below.
5.3.1 Using LE from WHO in the HDI (HDIWHO)
There are some significant differences in the model life tables used by the UN and the WHO. The UN model life tables have been constructed in essentially the same manner for several decades.18 The UN uses self-reported mortality data from countries that contain vital registration systems. The complete UN life tables are then extrapolated from this, for countries that do not report mortality data. The UN life table system has some shortcomings, as the underlying single parameter demographic techniques utilized do not adequately reflect the present circumstances that exist in the world today. These
circumstances include the impact of AIDS in the developing world and the ‘greying’ of the population in the developed world. The WHO has gone to great lengths to develop a more accurate method for calculating model life tables. They have created a multi-parameter equation system with region-specific standards (Murray et al., 2000). These life tables incorporate as much accurate mortality data as possible. This data has been collected from the ongoing survey systems developed by the WHO.19
The HDIhas been recalculated using the WHO standard life expectancy figures:
HDIWHO = 1/3(LEindexWHO) + (1/3)GDPindex + (1/3)Edindex ,
where LEindexWHO = (LEWHO –25)/(85-25), and LEWHO is the WHO’s life expectancy measure.
The results display some variation just from the underlying differences in the life expectancy calculations of the UN and the WHO.
5.3.2 Using the Morbidity YLD Measure with Standard UN LE in the HDI (HDIYLD)
To isolate the impact of years of life lost, YLD, the HDI was recalculated using the standard UN life expectancy values as follows:
First the YLD was derived: YLD = LEWHO - HALE
Then a new HALE was calculated: HALEHDR = LEUN - YLD
Finally a new HDI was constructed from the new HALE:
HDIYLD = (1/3)HALEindexHDR + (1/3)GDPindex + (1/3)Edindex ,
where HALEindexHDR = (HALEHDR – 25)/(85-25)
The results are found in Appendix A. These figures show that perhaps only a portion of the variation in rank comes from the added morbidity data.
6 Data Correlation Calculations
6.1 The Spearman’s Rank Correlation Coefficient
This paper uses the Spearman’s rank correlation coefficient (ρ) to examine the correlation between the different country rankings of the indices detailed in Section 5. Rank
correlation indicates the degree of linearity between ranked variables (Mendenhall, Reinmuth, & Beaver, 1993).
Wikipedia (2006) gives the following excellent little synopsis:
“In principle, ρ is simply a special case of the Pearson product-moment coefficient in which the data are converted to ranks before calculating the coefficient. In practice, however, a simpler procedure is normally used to calculate ρ. The raw scores are converted to ranks, and the differences D between the ranks of each observation on the
two variables are calculated. ρ is then given by:
where:
D = the difference between the ranks of corresponding values of X and Y, and N = the number of pairs of values.
Spearman's rank correlation coefficient is equivalent to Pearson correlation on ranks. The formula above is a short-cut to its moment form, assuming no tie. The product-moment form can be used in both tied and untied cases.”
(http://en.wikipedia.org/wiki/Spearman's_rank_correlation_coefficient)
Rho (ρ) varies from –1 to +1, with –1 meaning the series are negatively correlated and 1 meaning the series are positively correlated. A value close to zero implies no correlation
Spearman’s were calculated for the following:
1) Original HDI (HDI) versus HDI derived from HALE data (HDIHALE).
2) Original HDI (HDI) versus the max/min adjusted HALE HDI (HDIAdHALE)
3) The original HDI (HDI) versus the HDI caculated from the new HALE figures
(HDIYLD).
4) The HDI caculated from WHO standard life expectancy (HDIWHO) versus the HDI
derived from HALE data (HDIHALE).
The Spearman’s coefficients were calculated using Free Statistics Software from Wessa (2006). The full output can be found in Appendix B.
The Spearman’s correlation results are in the following table:
Table 1: Spearman’s HDI Results
HDIHALE HDIAdHALE HDIYLD
HDI 0.995934 0.995523 0.998885
The correlation coefficients are all very close to +1 indicating a high degree of correlation between the series. The adjusted index also shows a high degree of correlation implying that the goal post issue isn’t significant.
In order to examine the differences between the life expectancy measures and HALE, Spearman’s were calculated for those series as well. The output can be found in Appendix B.
Table 2: Spearmans LE Results
HALEWHO LEWHO
LEUN
0.970467 0.974221
LEWHO
0.994761
This shows that these series are also highly correlated. It would be expected that LE and HALE are highly correlated as HALE is simply a weighted form of LE. These results perhaps point to a conclusion that small changes in the weightings of the HDI component parts do not have substantial effects on the rankings. This may imply that what little variation there is, comes from the changes in the LE component rather than the morbidity component.
6.2 Examining YLD
The value of interest here is the DALY’s years lost to disability morbidity data component and bears further investigation. Examining graphs of the values of interest may be informative. The following is a scatter plot of YLDvs HDI Rank:
Figure 1: YLD versus HDI Rank
Years Lost to Disability
0.00 2.00 4.00 6.00 8.00 10.00 12.00 0 20 40 60 80 100 120 140 160 180 200 HDI Rank YLD
The mean is 7.99 years and the standard deviation is 1.277 years, so on average the addition of YLD morbidity data is akin to adding a constant of –8 to the life expectancy index of the HDI. The graph does suggest an interesting morbidity pattern, with higher rates of YLD in medium HDI countries and lower rates in low HDI countries. Does this reflect differences in health care access and hence people in less developed countries
simply die instead of living with injuries and illnesses? This would be a good point for further investigation.
Another figure that may be of interest is a plot of YLD versus UN life expectancy.
Figure 2: YLD versus UN Life Expectancy
YLD vs UN Life Expectancy
0.00 2.00 4.00 6.00 8.00 10.00 12.00 0 10 20 30 40 50 60 70 80 90 LE YLD
This plot shows a similar curved pattern as the one before, with countries in the middle experiencing the highest YLD levels and lower life expectancy countries having lower levels of YLD. The large degree of variation in the mid-range countries may indicate differences in social policies with respect to health care access and funding. This would also be a good issue for further examination.
7 Conclusion
This paper has examined a methodology for adding useful information to the health and longevity component index of the Human Development Index (HDI). The developers of the HDI (UN Development Program) have always stated that the purpose of the life expectancy index is that it proxies ‘health and longevity’. This paper has argued that life expectancy (LE) by itself is not an adequate indicator for this task. The World Health Organization’s (WHO) new measure, health adjusted life expectancy (HALE),
incorporates both longevity and the morbidity data needed to proxy ‘health’. This paper has argued that the WHO method is more accurate and that coupled with the DALY morbidity data should be included in the HDI.
However, the results indicate no substantial changes in values or rankings occur when HALE is incorporated in the HDI. Further, much of the variation in the ranking of
countries comes from the differences in the way UN and WHO calculate life expectancy, rather than from the inclusion of a morbidity measure.
In conclusion, more realistic proxies for the ‘health and longevity’ component index only lead to slight variations in rankings of this component. In turn, slight changes in this component index do not substantially change the overall HDI rankings.
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