he U.S. census has been criticized for selective undercounting, particularly of minorities and illegal aliens living in the inner cities. Undercounting affects the level of federal contributions to welfare programs as well as the apportionment of congressional representation. In some Third World nations the problem of undercounting is more severe, especially in countries like India, with a large homeless population, or those with nomadic populations. An increasing U.S. homeless population resulted in 1990 in the first large-scale effort by the U.S. Census Bureau to count that group. Census workers visited several thousand shelters and open-air sites in cities mainly of 50,000 or more population
Gives less weight to diseases of old age than than diseases of childhood. Results depend on age defined as normal.. Used to be 65, is not 85.
The authors have contributed equally much to the presentation. In alphabetical order: Trude M. Arnesen, MD, MPH. PhD student, National Institute of Public Health, Pb 4404 Nydalen, 0403 Oslo, Norway. E-mail: email@example.com Ole Frithjof Norheim, MD, Ph.D. Associate professo, Department of Public Health and Primary Care, University of Bergen, Norway. E-mail: firstname.lastname@example.org
The DALY approach is increasingly cited as a powerful tool for decision-makers in international health (Bobadilla et al 1994, Bobadilla 1996, Murray and Lopez 1996). It’s attractiveness lies in the fact that it combines information about mortality and morbidity in a single number. DALYs allow the losses due to disability and the losses due to premature death to be expressed in the same unit. Hence, DALYs facilitate comparisons of different (in theory all) types of health states or health outcomes. In particular, this makes it easier to include the burden caused by disability and chronic diseases in cost-effectiveness studies. For instance, with such an index in place, one could say, that the number of DALYs due to the premature death of one girl aged 5, equals the number of DALYs caused by three girls of the same age suffering a below- knee amputation.
DALYs can be used in three interrelated areas: i) for epidemiological surveillance of the total disease burden (number of DALYs) ii) to measure cost- effectiveness of interventions (cost per avoided DALY) iii) to decide what should be included in a country’s ‘core services’ (the package of essential health care services). Within a fixed budget, it has been suggested that only the most cost-effective interventions should be included (cost per avoided DALY) . The concept of DALYs thus provide challenging information for policy makers concerned with international health, the health of developing countries, and national priority setting.
The Global Burden of Disease Study is a collaboration between WHO, the World Bank, and Harvard School of Public Health. (Murray, 1996 ) The aims of the study was to provide information and projections about disease burden on a global scale. The method used has been described as a “meta-synthesis” of available information. (Murray, 1996 ).
The main results from the Global Burden of Disease Study where first published for the World Bank report of 1993, and the latest complete and validated results were published in a series of four articles in 1997. (Murray, 1997) The following table shows the leading causes of world-wide lost DALYs for both sexes in 1990 and the projections for 2020.
The figure shows the projections made for the year 2020. ( Murray and Lopez, 1997 a) We note that the importance of the disorders typical for children and the poor seem to decline, and the disorders typical for older age groups incline. One reason is the expected demographic transition (life expectancy increases in many countries) that is followed by an epidemiological transition (the disease burden in total changes with increasing number of people in the various age groups).
It is essential to understand what the DALY-concept measures and how it is constructed. The following three figures visualise how the burden of disease is measured for a 'standard' individual. Burden is measured along two dimensions: time lived with disability and time lost due to premature mortality.
The x-axis shows life expectancy for the 'normal' life. The "standardised" maximum life span, 82.5 years for females and 80 years for males, is taken from the country with the highest life expectancy in the world: Japan. The y-axis shows degree of disability. The 'normal' life is quantified as the total area in the box, a combination of the number of years lived and the quality of life, or degree of disability. From this ideal state of the world it is possible to calculate the burden of disease caused by premature death or disability. If for example a girl aged 5 happens to become a victim of a mine explosion causing a below-knee amputation, and she does not die but is rehabilitated to a health state with some loss of physical functioning, her DALY loss could be depicted as the red area in the figure. Her loss is 77.5 years adjusted by a disability weight i . If this weight is, say, 0.3, her loss is 0.3 x 77,5 = 23.3.
Premature death from a myocardial infarction, say at age 50, would produce the DALY-loss as depicted by the read area in the figure. This patient’s loss is 33.5 years. No adjustment is made for disability because the patient dies.
This schematic illustration shows a woman who lives with a disability, for instance deafness from the age of 5 and dies prematurely at the age of 50. The calculation of her DALY score would be as follows on the next slide:
This example is, for didactic reasons, a simplified way of calculating DALY loss, omitting age-weighitng and discounting.
DALYs may be called a modification of QALYs Both approaches multiply number of years lived by the ”quality” of those years. This process is called ”Quality adjustment” in QALYs and ”Disability adjustment” in DALYs. The complementarity between the two concepts can be illustrated schematically:
Scematic illustration of the complementarity between QALYs and DALYs. QALYs are years of healthy life lived - DALYs are years of healthy life lost. Whereas DALYs represent a loss and should be minimised, QALYs represent a gain and should be maximised. In the DALY approach, the years are disability weighted on a scale from zero, which indicates perfect health (no disability), to one, which indicates death. In the QALY approach, the scale goes the opposite way: A quality weighting (sometimes called “utility”) of 1 indicates perfect health, whereas 0 indicates no quality of life, and is synonymous to death. ( Age weighting and discounting factors are not included in this illustration).
If, for instance, we want to find the burden of bach ache, we must first define what we mean by bach ache. Do we mean an incapacitating condition or do we mean a slight discomfort? Only when the condition is defined and described will it be meaningful to ask how much this condition reduces the quality of life of the one carrying it. How much should the value of a life year be adjusted for back ache? The disabilty weighting is the most difficult and controversial part of the DALY approach. We will come back to this. Here we show how it is currently done.
The disability weights are derived from presumably representative answers to questions of the type: "how many outcomes of one kind (e.g. saving girls from premature death) do you consider equivalent in social value to y outcomes of another kind (below-knee amputation for girls who have suffered mine explosions)"? The method used to assess these social preferences from a representative sample of persons is a deliberative person-trade-off technique. [Murray, 1996 ; Nord, 1994; Nord, 1995 ]
In the Global Burden of Disease approach, future burdens are discounted at a rate of 3% per year, and the value of the lifetime is weighted so that years of life in childhood and old age are counted less. These choices are explained and discussed in ( Murray, 1996 ). Discountingmeans that future gains and losses are counted less than if they had occured today. This is common practice when it comes to valuing material goods. For instance, a bank may require 500 dollars in 10 years time to compensate for a loan of 100 dollars today. However, it is controversial whether if it is correct to apply discounting on human values. It has for instance been asked why future generations should be counted as less valuable.
Age weighting means that life years in young and old age are counted less. This figure shows the relative value assigned to each year of life in the calculation of disease burden. Source:World Bank, 1993 . The relative value of a life year is below one for children under 10, and for persons more than about 55 years of age. This implies that in the calculations, a life year lost for children is given less weight than a life year lost for adults below 55. The adjustments made, introduces (explicitly), a bias both against children and the elderly. In a defence of age-weights, Christopher Murray argues that there is a widespread preference for age weighting in most cultures (Murray, 1996), and, that on average, these preferences can be expressed as in the function given.
The result of combining the use of age weights and discounting future health benefits is shown in this figure. As we see, the effects of age-weighting and discounting are additive. In fact, if a 5 years old child dies, the resulting DALY score is lower than if a child of 10 year dies! Similarly, if a person dies when he is 24 or if he dies when he is new born, yields the same number of DALYs.
Consider a five-year-old girl with a below-knee amputation after an accident with landmines. DALYs measure life years lost multiplied with a disability weight, multiplied with an age weight a 1 , multiplied with a discounting factor d 2 (3 % for each year). The estimated DALY loss would be 77.5 (82.5 - 5) years multiplied with 0.3 and adjusted with age weights and the discounting factor which give an estimated 10.5 disability adjusted life years lost.
Some critical articles on the DALY approach have questioned both the validity of the results (Cooper, 1998) as well as the underlying value-judgements (Anand, 1997, Arnesen and Nord 1999). In the Journal of Health Economics Anand and Hanson argues that: "the conceptual and technical basis for disability-adjusted life years is flawed, and that the assumptions and value judgements underlying it are open to serious question.” (Anand, 1997). In particular, the implications for resource allocation and the just distribution of health benefits needs to be scrutinised.
The most difficult part of any approach combining data on quality of life and length of life, is how to measure quality of life. How should one value health states numerically on a scale of zero to one? Many philosophical questions as well as questions regarding the limits of natural sciences are aroused. The first requirement of a valid measurement is that one know what one is measuring. The concept of quality of life is, however, vaguely defined, and different people as well as different cultures may have very different opinions of the main elements of a good life.
The Person Trade-off questions are difficult to understand, even for trained researchers in the field. Through the imposition of consistency between substantially different questions, people participating in evaluation panels are forced to adopt discriminatory positions on the value of life of disabled people. In as much as the disability weightings do not correspond to a clear preference but are the results of forced compromise, they must be seen basically as artefacts.(Arnesen and Nord 1999) A general question regards who should be asked to perform the valuations, whose values should count? Lately, the WHO has signalized that they will change the approach of disability weighting in later versions of the Global Burden of Disase studies.
Cooper et al have argued that the data used for e.g Sub-Saharan Africa are of so poor quality that the estimates made for this regions are seriously in doubt (Cooper, 1998 ). Lack of uniform diagnostic criteria, example: -do different countries agree on whom to call and count as “depressed”? -do different panel members mean the same when evaluating the burden of being “depressed”?
The approach has been criticized for violating the principle of treating people as equals. In the following these concerns are examined.
The burden of disease (and effect of interventions) for young people, is given less importance by the combined effect of age weighing and discounting. The principle of equal worth of people would require the same age weight for all. It remains an open questions whether the reasons given for departing from equal age weights for all are acceptable. Considering the consequences (children might be given less priority when calculating burden or effect of interventions), it could be seen as an unreasonable (and unnecessary) value judgment.
Age weights implies, for example, that living with a disability, e.g. blindness, for a person aged 80 is considered less bad than living with blindness for a 25 year old person. It is unclear why the DALY measure needs to discriminate between the value of a life years at different ages. (Harris1985, Williams 1997)
On the issue of discounting researchers disagree, and there are good reasons for adopting both views. For a defense of discounting, see ( Murray, 1996) . For a rejection of discounting, see ( Anand, 1997).
The DALY approach which is the basis of the Global Burden of Disease currently in use has been much critisized because the method presupposes that life years of disabled people are worth less than life years of people without disabilities. The method assumes that disabled people are less entitled to scarce health resources for interventions that would extend their lives. The line of thought from the first question to the application in cost effectiveness analyses seems to be that the healthier the person, the more valuable their life is to themself and to society and the greater their claim on restricted healthcare resources to have their life extended. This makes sense only if the value of life is not seen as a dimension distinct from health, but rather as a direct positive function of health. At worst, this line of thinking could lead to the following table:
The PTO1 questions regards which number of disabled people is required for a life extension of 1 year for this group to be equally valuable as for a group of healthy people. The higher the number of disabled people “on the balance”, the higher the disability weight of this diagnostic group. From the current, published, disability weights, we can calculate the mean answer of the panel participants to this question as shown above. A valuation of human beings according to their functional capacity is in contrast to the humanistic values laid down in the Declaration of Human Rights: "recognition of the inherent dignity and of the equal and inalienable rights of all members of the human family is the foundation."19 The WHO department responsible for the global burden of disease project aims at "strengthening the scientific and ethical foundations of health policies.... The aim of the work is to promote equity, quality, and efficiency.”(ref) The current DALY protocol does not seem to accord with this. (Arnesen and Nord, 1999)
Gender gap is adjusted to correspond to ‘biological differences in survival potential’ according to (Murray, 1994) Critique: If maximum standard expectation of life at birth is, say 78 years for men, but is adjusted to 80 years in the Burden of Disease study, the estimated burden for men thus becomes greater relative to women. DALYs lost underestimate burden of disease for females relative to males(Anand, 1997, Sundby 1999).
1. Junior residentDr Priyamadhaba Behera Mortality and Morbidity ind
2. Outline• Rate,ratio,proportion• Measures of mortality• Measures of morbidity• Aggregates measures of mortality• Aggregates measures of morbidityand mortality
3. 12Properties of Proportionsp takes on values between 0 and 1(p is a fraction)p has no unitsp may be multiplied by aconstant kWhere k is a numbersuch as 100, 1,000, or 100,000
4. Types of Mortality Rates/Ratio• annual death rate• crude death rates• infant mortality rates (ratio)• neonatal mortality rates• postneonatal mortality rates• perinatal mortality rates• fetal death rates• fetal death ratios• abortion rates• maternal mortality rates• adjusted mortality rates• standardized mortality ratio• specific death rates• proportionate mortality rate- case fatality rate• mortality crossover – mortality time trendsYour Assignment:familiarize yourselfwith the definitionsof these terms
5. Three Levels of Rates• Crude rates• Specific rates/ratios• Adjusted rates
6. Crude Death Rates• Does NOT account for differences ofage, sex, etc. in any aspect of death• Info needed: total deaths total population a given period of time
7. Crude Death Rates (cont’d)
8. Cause Specific Mortality Rate
9. Cause Specific Mortality Rate
10. Case Fatality Rate
11. Specific Death Rates• For example: Early life mortality measures
12. Specific Death Rates• For example: infant mortality
13. Specific Death Rates• Neonatal mortality rate(cont’d)• Postneonatal mortality rate
14. Specific Death Rates• Perinatal mortality rates(cont’d)
15. Specific Death Rates• Fetal death rate(cont’d)
16. More Convenient:Summarize an entire situation with a single number calculatedfor each subpopulation, a number that adjusts for differencein compositionTwo Ways:1. Direct method of standardization2. Indirect method of standardization
17. Direct Method of Standardization:Step 1:Select the standard population.Step 2:compute the expected events that would result if,instead of having different age distributions, all populations wereto have same standard age structureStep 3:Compute the adjusted rate as total expected events in thegroup divided by the total standard population
18. Adjusted Death RatesAge Adjusted Rates• Direct MethodWhat data available for you for direct method?
19. Adjusted Death RatesAge Adjusted Rates• Direct Method(cont’d)
20. Adjusted Death RatesAge Adjusted Rates• Direct Method(cont’d)
21. Indirect Method of Standardization:Step 1: use a set of standard age-specific rates along with theactual age composition of each populationStep 2: compute the number of events that would have occurredin the two groups if each took on the age specific rates of thestandard population while retaining its own age distributionStep 3: compute standardized event ratio as observed/expectedevents for each groupThe indirect method often concludes with this ratio.Step 4: actual age adjusted rates for each group= event rate inthe standard population x standardized event ratio of the group
22. Adjusted Death RatesAge Adjusted Rates• Indirect Method Standard Mortality Ratio(cont’d)
23. Standardized Mortality Ratio(SMR)
24. Standardized Mortality Ratio(SMR)
25. • Must know when to use an adjusted rate rather than crude rate• If no confounders: the crude rate is adequate• If confounders present: subgroup specific rates are sufficient• Adjusted rates should be considered if they are meaningful• If distribution of standard population is radically different than thepopulations being compared, standardization is inappropriate• Also, when direct standardization is applied, subgroup specific ratesshould have same general trends in all the groups being compared aswell as in the standard population•Direct method of standardization is used more frequently thanindirect method• Direct method requires subgroup specific rates for all popns.• Application of either method should lead to same conclusion
26. Life Expectancy• Summary of all age-specific mortalityrates• Estimates hypothetical length of life ofa cohort born in a particular yearThis assumes that current mortality rateswill continue
27. Measures of Morbidity
28. Sources of Morbidity Statistics• Clinical and hospital• Managed care• Registries• Vital statistics• Surveys• Disease reporting• Insurance and pre-paid med. careplans• Absenteeism records
29. Terms Related to Morbidity• MorbidityThe extent of illness, injury or disability in adefined population• Incidence of a disease (Incidence rate)The number of new cases of a disease thatoccur during a specified time period(numerator) in a population at risk fordeveloping the disease (denominator)• Prevalence of a disease (Prevalence rate)The number of total cases of disease presentat a particular time (numerator) in a specificpopulation (denominator)• RiskThe likelihood that an individual will contract adisease
30. CharacteristicsRISK PREVALENCE INCIDENCERATEProbability ofdisease% of pop. withthe diseaseRapidity ofdiseaseoccurrenceNo units No units Cases perperson-timeNewlydiagnosedExisting Newlydiagnosed“Cumulativeincidence”“Incidencedensity”
31. 1. “Do you currently have asthma?”Point prevalence2. “Have you had asthma during thelast 1 years?” Period prevalence3. “Have you ever had asthma?”Cumulative incidence67
32. Other Measures of morbidity1. Notification rate2. Attendance rates at OPD, healthcenters3. Admission, readmission, anddischarge rates4. Duration of stay in the hospital5. Spells of sickness or absence fromwork or school68
33. Problems with Numerators• Who has the disease?• Who to include in numerator?• Interview errors
34. Problems with Denominator• Selective undercounting• Everyone in denominator must havepotential to enter numerator group
35. Problems with Hospital Data• Selective (many reasons)• Data may be unavailable, etc
36. IncidenceThe two forms of incidence are:• Cumulative incidence• "risk of disease“• measures the proportion ofpersons who develop a disease in aknown span of time• Incidence rate• "rate of disease“• measures the rate of new diseaseoccurrence over time
37. Cumulative incidence• Cumulative Incidence =Number of people who get a diseaseduring a specified period * 1000Number of people free of thedisease in the population at risk atthe beginning of a study period73
38. Incidence Rate• Measures the rapidity with which newlydiagnosed cases of the disease of interestdevelop observe a population count # of new cases measure net time• individuals in population at risk of developing disease• person-time person-years patient-days
39. Incidence rate per 1,000• Number of new cases of a diseaseoccurring in the population during aspecified period of time * 1000Number of persons who are at riskof developing the disease during thatperiod of time75
40. Incidence density• if people at risk are observed fordifferent periods of time• The denominator consists of the sumof the units of time that each individualwas at risk and was observed.• This is called person-time and is oftenexpressed in terms of person-monthsor person-years of observation.76
41. Person time• 1 person at risk who is observed forone year = 1 person-year.• 1 person at risk observed for 5 years= 5 person-years.• 5 people at risk, each of whom isobserved for only 1 year = 5 person-years.77
42. Incidence Rate (Attack Rate) (cont.)• Can be used for specific exposures• Also used for multiple exposures• Other terms:primary casesecondary attack• secondary cases
43. Attack rates
44. Incidence and Attack Rates• Primary Attack rates
45. Incidence and Attack rates(cont’d)• Secondary Attack rates
46. Prevalence• Measure of the number (or proportion) ofcases in a given population• What is the difference between prevalenceand incidence?Prevalence → a slice thru a population at agiven point in time that determines who hasthe disease and who does not, while Incidenceonly looks at new cases• In steady state situation (no change in rateor net population)Prevalence = Incidence X Duration of disease
47. Prevalence• Point prevalence- point in time• Period prevalence- during a definedrange of time
52. 2. Aggregate Measures:Mortality-BasedIndicatorsLife expectancyExpected years of life lostPotential years of life lost
53. Expectancies and Gaps• From a typical survivalcurve, we can eitherconsider the lifeexpectancy (“E”), or thegap (“G”) betweencurrent life expectancyand some ideal.• Expectancies aregeneric; gaps can bedisease-specific (e.g.,life yrs lost due tocancer)G0%20%40%60%80%100%0 10 20 30 40 50 60 70 80 90 100E
54. Classifying Health Gaps• Gaps: Compare population health tosome target. = Difference between timelived in health states less than idealhealth, and the specified target• The implied norm or target can bearbitrary, but must be explicit and thesame for all populations being compared.The precise value does not matter
55. Gaps: Expected Years of LifeLost• Uses population life expectancy at theindividual’s age of deathProblems: different countries may have differentlife expectancies. It’s overall mortality, so cannotidentify impact of a disease.• Standard Expected Years of Life LostReference is to an “ideal” life expectancy• E.g., Japan (82 years for women)• Area between survivorship curve and the chosen norm
56. Potential Years of Life Lost(PYLL)• PYLL =  ( “normal age at death” – actualage at death). Doesn’t much matter whatage is chosen as reference; typically 75• Attempts to represent impact of a diseaseon the population: death at a young age isa greater loss than death of an elderlyperson• Focuses attention on conditions that killyounger people (accidents; cancers)• All-causes or cause-specific
58. Composite Measures• Aim to represent overall health of a population• Composite measures combine morbidity andmortality into a health index. (An index is anumerical summary of several indicators ofhealth)• Mortality data typically derived from lifetables; morbidity indicators from healthsurveys, e.g.• Self-rated health• Disability or activity limitations• A generic health index
59. Different Types of Morbidity Scalesfor Use in Composite Measures• Generic instruments cover a wide range ofhealth topics, e.g. reflecting the WHOdefinition. These can be health profiles (e.g.,Sickness Impact Profile, SF-36) or “healthindexes” (e.g., Health Utilities Index,EuroQol)• Specific instrumentsDisease-specific (e.g., Arthritis ImpactMeasurement Scale)Age-specific (e.g., Child Behavior Checklist)Gender-specific (e.g., Women’s HealthQuestionnaire)
60. Survivorship Functions for HealthStatesG0%20%40%60%80%100%0 10 20 30 40 50 60 70 80 90 100HSurvivorsAgeThis diagram extends the earlierone by recognizing that not allsurvivors are perfectly healthy.The lower area ‘H’ shows theproportion of people in good health(however defined); it shows healthylife expectancy. The top curveshows deaths; intermediate arearepresents levels of disability.Area ‘G’ again represents thehealth gap. The question ariseswhether the people with a disabilityought to be counted with H or withG.Deaths
61. Health expectancies• Generic term: any expectation of life invarious states of health. Includesother, more specific terms, such asDisability Free Life Expectancy• Two main classes:Dichotomous rating: two health statesHealth state valuations for a range oflevels
62. I. Dichotomousexpectancies• Here full health is rated 1, and any state ofpoor health (mild, moderate, severedisability) is rated 0.• This leads to Disability-free life expectancy(DFLE): weight of 1 for “no disability” and 0for all other states.• = Expectation of life with no disability, orHealthy Life Expectancy (HLE)• Very sensitive to threshold of disabilitychosenSullivan(1971)
63. II. Polytomous states and valuations(Wilkins and Adams-1983)• These incorporate many levels of disability intolife expectancy estimates and count time spentwith each level of disability.• Polytomous model (three or more health statesdefined: weights assigned to each; generally 0 to1.0. These may be added together and comparedacross diseases)• = Health-adjusted life expectancy (HALE)• First calculated for Canada by Wilkins. Fourlevels of severity & arbitrary weights.• Recent work uses utility weights. E.g. fromHealth Utilities Index, Quality of Well-Being Scale,EUROQoL, etc.
64. Polytomous Curves ShowingQuality of SurvivalG0%20%40%60%80%100%0 10 20 30 40 50 60 70 80 90 100HSurvivorsAge(years)This diagram illustratesseveral classes of disability,each having a separateseverity weighting.The area ‘H’ again includeshealthy people, but thedefinition may have changed.The top curve shows deaths;intermediate curves representvarious levels of disability.Deaths
65. Relationship between Life Expectancy,Health Expectancy and Health-AdjustedLife ExpectancyHealth-AdjustedLife ExpectancyLifeExpectancyHealthyLifeExpectancyBy down-weighting thevarious levels ofdisability,the HALE fallsbetween LE and HLE
66. Gap Measures: QALYs &DALYs• Gap measures can also use a weighting forintermediate health states. This is necessaryto combine time lost due to ill health withtime lost due to premature mortality• Quality Adjusted Life Years (QALYs) lostCommon outcome measurement in clinical trials,program evaluationRecord extra years of life provided by therapy andquality of that lifeTypically use utility scale running from 0 to 1• DALYS (disability-adjusted life years) lost
67. Complementarity of HealthExpectancies and Health GapsSLELEHALEHLELE SEYLLSURVIVALHALE HALYPOLYTOMOUSHLE ?DICHOTOMOUSBirthLE = Life Expectancy; SLE = Standard LE; HALE = Health-Adjusted LE;HLE = Healthy LE; SEYLL = Standard Expected Years of Life LostHALY = Health-Adjusted Life Years LostGapsExpectanciesAge
68. Disability Adjusted LifeYearsPossibilities and Problems
69. What are DALYs?• DALYs = Disability Adjusted LifeYears• A common measurement unit formorbidity and mortality• Facilitates comparisons of alltypes of health outcomes
70. Possible use of DALYs• Quantitative analysis of the burdenof disease• Analysis of cost-effectiveness ofalternative interventions• Selection of a package or list ofinterventions deliverable within theavailable budgetJL Bobadilla, WHO: 1996
71. What is the Global Burden ofDisease study?• Backed by the WHO and the WorldBank• A quantitative overview of the burdenof disease world-wide• Combines information about loss ofquality of life with traditionalepidemiological information onmortality• All health outcomes are expressed inDALYs
72. Possible use of the GlobalBurden of Disease Study• Epidemiological surveillance oftrends across borders and over time• Projections for future burden ofdisease• Basis of information for decision-making on priorities in healthresearch and health policy
73. CLICK TO ENLARGE
74. How are DALYsconstructed?• A DALY is a health outcomemeasure with two maincomponentsQuality of life reduced due to adisabilityLifetime lost due to prematuremortality.
75. DALYs due to living with disability(Red area measures DALYs. Red + white is a “normal”life)82,5 YEARSNODISABILITY
76. DALYs due to early death(Red area measures DALYs. Red + white is a standardlife)NODISABILITY82,5 YEARS
77. DALYs due to disability and prematuredeath combined.NODISABILITY82,5 YEARS
78. Calculation of DALYs(age-weighting and discounting areomitted for didactic reasons)• The calculation of DALYs of a woman who hasbeen deaf since she was 5 and dies when sheis 50: ( Disability weight of deafness is set at0.33) :• Number of healthy life years × the disabilityweight of full health (0) + life years withdisability (50) × disabilty weight for deafness(0,33) + life years lost (30) × the weighting ofdeath (1)• 5 × 0+ 45 × 0,33 + 30 × 1 = 47.35 DALYs
79. DALYs and QALYs• DALY is a modification of QALY(Quality Adjusted Life Years).• Both concepts combine informationabout length of life and quality of life.• A DALY is a negative QALY.
80. Relation between QALYs and DALYsDALYs = healthy years lostQALYs = healthy years gainedNODISABILITY82,5 YEARS
81. How are disability adjustmentsmade?The methods used to assign a disabilityweightings to life years is a critical part ofthe DALY approach.– Diagnostic groups must be chosen anddefined.– Descriptions of those diagnostic groups aredeveloped.– The health states are assigned a disabilityweight to indicate the relative severity ofeach health state.
82. Current method used forweighting disability• Disability weights are obtained byposing two different Person Trade-Off (PTO) questions to expert panels• PTO1 compares life extensions fordisabled and healthy people• PTO2 compares cures for illnesswith extension of life
83. Other choices behind DALY• In addition to adjusting the value oflife years with disability weights, andchosing a particular life expectancy,the value of a life year is modified by• Discounting– the value of a life year now is set higherthan the value of future life years• Age weighting– life years of children and old people arecounted less
85. The effect of age-weighs anddiscounting
86. Calculating DALY score,with age weighting and discounting.• Girl, 5 years old, with below-kneeamputation who lives until she is82,5:• DALYs= life years lived with disease(77,5) × disability weight (0,3) × age-weight (a1)× discounting factor (d2)• 77.5 × 0.3 × a1× d2= 10.5 DALYs
87. PROBLEMS of the DALYapproach• Is it true?Questions of the validity of theresults• Is it just?Questions of the distributionbetween groups
88. General problems of validity• What is “Quality of Life” or “Disabilityweighting of life years”?• Can quality of life be measured in a singleand precise number?• Does the same health problem have equalimpact on different persons or groups?• Is there a general agreement to underlyingvalue choices: discounting, age weightingand choice of life expectancy
89. Validity problems of thecurrent PTO protocol• Lack of simplicity, difficult tounderstand• Forced consistency between twoquestions that are essentiallydifferent• Impossible to answer that allindividuals are equally valuable• The expert panel may not representthe values of other people
90. Validity problems ofepidemiological estimates• Epidemiological data for Africa, LatinAmerica and Asia are crude estimates.• The uncertainty of the figures ofprevalence, may be hidden in theseemingly mathematical rigor of theresults.• Lack of uniform diagnostic criteria. I.e.what do we mean by “depression”?
91. Justice• The DALY approach has beencriticised for discriminating– the young– the elderly– future generations (future healthbenefits)– the disabled– women
92. The young• The 5-year-old girl in the exampleabove yielded 10,5 DALYs.• However, the DALY score withoutage-weight and discounting wouldbe• 77.5 × 0.3 = 23,3 DALYs• This result is twice as high, andwould give her a higher priority.
93. The elderly• In the literature on justice in healthcare, many agree that given a choice,it is more important to save youngadults than the very old.• This view is captured by the DALY(as a time based measure) itself.
94. Future generations• The practice of discounting futurebenefits is also controversial.• From society’s viewpoint, why should alife year now be of more value than alife year twenty years ahead?• The implications for preventiveservices versus curative services aresignificant. Preventive interventions aregiven less weight.
95. The disabled• The DALY approach opens for includingchronic illnesses and disabilities in cost-utility calculation. This is an improvement.• On the other hand, the current person trade-off protocol explicitly assumes that lives ofdisabled people have less value and• implies that disabled people are less entitledto health resources to extend their lives
96. Example of results• In the protocol behind the present Global Burden ofDisease, a life year for 1000 healthy people has beenset as equally valuable as one life year for– 9524 people with quadriplegia– 2660 blind people– 1686 people withDowns syndrome withoutcardiac malformation– 1499 deaf people– 1236 infertile people• WHO has announced a change in approach.
97. Women• Underlying value choice: Standardexpectation of life at birth is 82.5 yearsfor women, 80 years for men• The ‘true’ gender gap is greater• Gender gap is adjusted to correspondto ‘biological differences in survivalpotential’• Critique: Might underestimate burdenof disease for females relative to males
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