Title: DALYs: Estimating healthy time lost to selected diseases and injuries
1DALYs Estimating healthy time lost to selected
diseases and injuries
- John Powles
- MSt in Public Health
2DALYs
- DALYs YLL YLD
- ie healthy time
- completely lost by death (YLL)
-
- time lost to less than perfect health weighted to
time equivalent to being dead (YLD)
3DALYs are (usually) incidence-based
- ie DALYs attributable to events in a year
- the expected flow of 'years of life lost from
deaths occurring in the year (YLL) -
- the expected flows of 'disabled' life from
disease onsets beginning in the year - (equivalent life years completely lost ie to the
equivalent of death), years lived with a
disability (YLD)
4The steps in principle
- For a given condition (disease or type of injury)
and reference (calendar) period, estimate the
flows of healthy life lost - 1. Completely, by death (YLL)
- 2. Partially, by onsets of impaired health
expressed as equivalent years completely lost
(YLD). - Eg 10 years lost to a state half as bad as death
(ie with a disability weight of 0.5)
counts as equivalent to 5 years completely lost
5Analytic choices
- Reference year
- Age groups
- GBD 2000
- 0-4
- 5-14
- 15-29
- 30-44
- 45-59
- 60-69
- 70-79
- 80
6Disease and injury categories
- Mutually exclusive and exhaustive
- 4 levels of disaggregation
- total over 100
- each lowest level group includes a residual
category - Choose diseases and injuries of most importance
in population of interest - should reduce sum of residual categories to lt12
of deaths
7GBD standard list 1st level of disaggregation
- Gp I Communicable, maternal, perinatal and
nutritional - Gp II Non-communicable diseases
- Gp III Injuries
- (nb some NCDs have infective causes eg
cancers of stomach, liver and cervix, valvular
disease of the heart)
82nd level of disaggregationExample Gp I
Communicable, maternal etc
- Infectious and parasitic diseases
- Respiratory infections
- Maternal conditions
- Conditions arising during the perinatal period
- Nutritional deficiencies
93rd level of disaggregationExample A.
Infectious and parasitic diseases
- Tuberculosis
- Sexually transmitted (excl HIV)
- HIV/AIDS
- Diarrhoeal diseases
- Childhood cluster diseases
- Meningitis
- Hepatitis B and C
- Malaria
- Tropical cluster diseases
- Leprosy
- Dengue
- Japanese encephalitis
- Trachoma
- Intestinal nematode infections
- Other infectious diseases
104th level of disaggregationExample 5.
Childhood cluster diseases
- Pertussis
- Poliomyelitis
- Diphtheria
- Measles
- Tetanus
112nd and 3rd levels of disaggregation for injuries
- Unintentional injuries
- Road traffic accidents
- Poisonings
- Falls
- Fires
- Drownings
- Other unintentional injuries
- Intentional injuries
- Self-inflicted injuries
- Violence
- War
- Other intentional injuries
12Problems in summing to a plausible total
- Dealing with the residuals (other )
- eg Take YLL for deaths due to other . (ie
group total - those in the chosen list) - Assume same ratio of YLD to YLL as calculated for
the mean for chosen causes in that group - Avoiding double counting
- eg HIV TB
- -gt precedence rules
13Precedence rules
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16Basic disease model
17Elaborated model for an infectionexample
Hepatitis B
18Elaborated model for an infectionexample
Hepatitis B
19Elaborated model for an infectionexample
Hepatitis B
20Elaborated disease modelexample breast cancer
gt5cm at diagnosis
Source Australian Burden of Disease Study
21Information sources for modelling the
epidemiology of selected conditions
- Usually unhelpful
- Routine health surveys (eg Health Survey for
England) - Information on services provided (eg hospital
discharges, physician consultations)buthospital
data can be useful for eg perinatal and maternal
conditions, meningitis, stroke, MI, serious
injuries
- Potentially helpful
- Disease registers
- Disease-specific surveys with validated
instruments eg for depression - Epidemiological studies (eg cohort/natural
history) - Expert panels
22Why is self-report information from health
surveys usually unhelpful?
- Mismatch of lay and professional definitions
- eg Asthma
- Prevalence estimated from self-reports of
wheezing or doctors diagnosis - 2-3 times higher than
- that from surveys using objective test of airways
hyper-responsiveness
23Why is self-report information from health
surveys usually unhelpful?
- Subjects unaware of their condition
- eg diabetes, hypertension
- Biased perception
- eg 2cm on height, - 2 Kg on weight reduced
prevalence of obesity by 40 in Australia
24Commonly need to rely on
- Consulting disease experts
- for estimates of occurrence parameters
(incidence, remission, case-fatality, duration) - GBD1990 established 100 expert panels!
- Modelling software (DISMOD II)
- to force expert assessments into internal
consistency (experience showed them to be often
internally inconsistent by large amounts)
25Dementia example of inconsistency in
meta-analysis findings (Australian BOD study)
- Meta-analyses of the prevalence of dementia in
(mainly) EME countries (Jorm et al. 1987, Hofman
et al. 1991 and Ritchie et al. 1992) - One meta-analysis of incidence (Jorm and Jolley
1998 -far fewer studies). - Reasonable similarity in the reported prevalence
of dementia across countries with the exception
of prevalence estimates for the very old (85
years). - Not possible to create a DisMod model that was
consistent between the incidence and prevalence
figures of the meta-analyses - Decided to model incidence from the meta-analysis
prevalence figures reported by Jorm et al.
26Dismod II