1. Junior resident
Dr Priyamadhaba Behera Mortality and Morbidity ind

2. Outline
• Rate,ratio,proportion
• Measures of mortality
• Measures of morbidity
• Aggregates measures of mortality
• Aggregates measures of morbidity
and mortality

23. 12
Properties of Proportions
p takes on values between 0 and 1
(p is a fraction)
p has no units
p may be multiplied by a
constant kWhere k is a number
such as 100, 1,000, or 100,000

26. 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 trends
Your Assignment:
familiarize yourself
with the definitions
of these terms

27. Three Levels of Rates
• Crude rates
• Specific rates/ratios
• Adjusted rates

29. Crude Death Rates
• Does NOT account for differences of
age, sex, etc. in any aspect of death
• Info needed:
 total deaths
 total population
 a given period of time

30. Crude Death Rates (cont’d)

31. Cause Specific Mortality Rate

33. Cause Specific Mortality Rate

34. Case Fatality Rate

35. Specific Death Rates
• For example: Early life mortality measures

36. Specific Death Rates
• For example: infant mortality

37. Specific Death Rates
• Neonatal mortality rate
(cont’d)
• Postneonatal mortality rate

38. Specific Death Rates
• Perinatal mortality rates
(cont’d)

39. Specific Death Rates
• Fetal death rate
(cont’d)

41. More Convenient:
Summarize an entire situation with a single number calculated
for each subpopulation, a number that adjusts for difference
in composition
Two Ways:
1. Direct method of standardization
2. Indirect method of standardization

42. 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 were
to have same standard age structure
Step 3:
Compute the adjusted rate as total expected events in the
group divided by the total standard population

43. Adjusted Death Rates
Age Adjusted Rates
• Direct Method
What data available for you for direct method?

44. Adjusted Death Rates
Age Adjusted Rates
• Direct Method
(cont’d)

45. Adjusted Death Rates
Age Adjusted Rates
• Direct Method
(cont’d)

46. Indirect Method of Standardization:
Step 1: use a set of standard age-specific rates along with the
actual age composition of each population
Step 2: compute the number of events that would have occurred
in the two groups if each took on the age specific rates of the
standard population while retaining its own age distribution
Step 3: compute standardized event ratio as observed/expected
events for each group
The indirect method often concludes with this ratio.
Step 4: actual age adjusted rates for each group= event rate in
the standard population x standardized event ratio of the group

47. Adjusted Death Rates
Age Adjusted Rates
• Indirect Method
 Standard Mortality Ratio
(cont’d)

48. Standardized Mortality Ratio
(SMR)

49. Standardized Mortality Ratio
(SMR)

50. • 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 the
populations being compared, standardization is inappropriate
• Also, when direct standardization is applied, subgroup specific rates
should have same general trends in all the groups being compared as
well as in the standard population
•Direct method of standardization is used more frequently than
indirect method
• Direct method requires subgroup specific rates for all popns.
• Application of either method should lead to same conclusion

58. Life Expectancy
• Summary of all age-specific mortality
rates
• Estimates hypothetical length of life of
a cohort born in a particular year
This assumes that current mortality rates
will continue

63. Measures of Morbidity

64. Sources of Morbidity Statistics
• Clinical and hospital
• Managed care
• Registries
• Vital statistics
• Surveys
• Disease reporting
• Insurance and pre-paid med. care
plans
• Absenteeism records

65. Terms Related to Morbidity
• Morbidity
The extent of illness, injury or disability in a
defined population
• Incidence of a disease (Incidence rate)
The number of new cases of a disease that
occur during a specified time period
(numerator) in a population at risk for
developing the disease (denominator)
• Prevalence of a disease (Prevalence rate)
The number of total cases of disease present
at a particular time (numerator) in a specific
population (denominator)
• Risk
The likelihood that an individual will contract a
disease

66. Characteristics
RISK PREVALENCE INCIDENCE
RATE
Probability of
disease
% of pop. with
the disease
Rapidity of
disease
occurrence
No units No units Cases per
person-time
Newly
diagnosed
Existing Newly
diagnosed
“Cumulative
incidence”
“Incidence
density”

67. 1. “Do you currently have asthma?”
Point prevalence
2. “Have you had asthma during the
last 1 years?” Period prevalence
3. “Have you ever had asthma?”
Cumulative incidence
67

68. Other Measures of morbidity
1. Notification rate
2. Attendance rates at OPD, health
centers
3. Admission, readmission, and
discharge rates
4. Duration of stay in the hospital
5. Spells of sickness or absence from
work or school
68

69. Problems with Numerators
• Who has the disease?
• Who to include in numerator?
• Interview errors

70. Problems with Denominator
• Selective undercounting
• Everyone in denominator must have
potential to enter numerator group

71. Problems with Hospital Data
• Selective (many reasons)
• Data may be unavailable, etc

72. Incidence
The two forms of incidence are:
• Cumulative incidence
• "risk of disease“
• measures the proportion of
persons who develop a disease in a
known span of time
• Incidence rate
• "rate of disease“
• measures the rate of new disease
occurrence over time

73. Cumulative incidence
• Cumulative Incidence =
Number of people who get a disease
during a specified period * 1000
Number of people free of the
disease in the population at risk at
the beginning of a study period
73

74. Incidence Rate
• Measures the rapidity with which newly
diagnosed cases of the disease of interest
develop
 observe a population
 count # of new cases
 measure net time
• individuals in population at risk of developing disease
• person-time
 person-years
 patient-days

75. Incidence rate per 1,000
• Number of new cases of a disease
occurring in the population during a
specified period of time * 1000
Number of persons who are at risk
of developing the disease during that
period of time
75

76. Incidence density
• if people at risk are observed for
different periods of time
• The denominator consists of the sum
of the units of time that each individual
was at risk and was observed.
• This is called person-time and is often
expressed in terms of person-months
or person-years of observation.
76

77. Person time
• 1 person at risk who is observed for
one year = 1 person-year.
• 1 person at risk observed for 5 years
= 5 person-years.
• 5 people at risk, each of whom is
observed for only 1 year = 5 person-
years.
77

78. Incidence Rate (Attack Rate) (cont.)
• Can be used for specific exposures
• Also used for multiple exposures
• Other terms:
primary case
secondary attack
• secondary cases

80. Attack rates

81. Incidence and Attack Rates
• Primary Attack rates

82. Incidence and Attack rates
(cont’d)
• Secondary Attack rates

83. Prevalence
• Measure of the number (or proportion) of
cases in a given population
• What is the difference between prevalence
and incidence?
Prevalence → a slice thru a population at a
given point in time that determines who has
the disease and who does not, while Incidence
only looks at new cases
• In steady state situation (no change in rate
or net population)
Prevalence = Incidence X Duration of disease

84. Prevalence
• Point prevalence- point in time
• Period prevalence- during a defined
range of time

85. 85

86. Prevalence Rates

87. Prevalence Rates
• Point
• Period
(cont’d)

88. 88

89. 2. Aggregate Measures:
Mortality-Based
Indicators
Life expectancy
Expected years of life lost
Potential years of life lost

90. Expectancies and Gaps
• From a typical survival
curve, we can either
consider the life
expectancy (“E”), or the
gap (“G”) between
current life expectancy
and some ideal.
• Expectancies are
generic; gaps can be
disease-specific (e.g.,
life yrs lost due to
cancer)
G
0%
20%
40%
60%
80%
100%
0 10 20 30 40 50 60 70 80 90 100
E

91. Classifying Health Gaps
• Gaps: Compare population health to
some target. = Difference between time
lived in health states less than ideal
health, and the specified target
• The implied norm or target can be
arbitrary, but must be explicit and the
same for all populations being compared.
The precise value does not matter

92. Gaps: Expected Years of Life
Lost
• Uses population life expectancy at the
individual’s age of death
Problems: different countries may have different
life expectancies. It’s overall mortality, so cannot
identify 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

93. Potential Years of Life Lost
(PYLL)
• PYLL =  ( “normal age at death” – actual
age at death). Doesn’t much matter what
age is chosen as reference; typically 75
• Attempts to represent impact of a disease
on the population: death at a young age is
a greater loss than death of an elderly
person
• Focuses attention on conditions that kill
younger people (accidents; cancers)
• All-causes or cause-specific

94. 3. Aggregate Measures that
Combine Mortality &
Morbidity
Health expectancies
Health gaps

95. Composite Measures
• Aim to represent overall health of a population
• Composite measures combine morbidity and
mortality into a health index. (An index is a
numerical summary of several indicators of
health)
• Mortality data typically derived from life
tables; morbidity indicators from health
surveys, e.g.
• Self-rated health
• Disability or activity limitations
• A generic health index

96. Different Types of Morbidity Scales
for Use in Composite Measures
• Generic instruments cover a wide range of
health topics, e.g. reflecting the WHO
definition. These can be health profiles (e.g.,
Sickness Impact Profile, SF-36) or “health
indexes” (e.g., Health Utilities Index,
EuroQol)
• Specific instruments
Disease-specific (e.g., Arthritis Impact
Measurement Scale)
Age-specific (e.g., Child Behavior Checklist)
Gender-specific (e.g., Women’s Health
Questionnaire)

97. Survivorship Functions for Health
States
G
0%
20%
40%
60%
80%
100%
0 10 20 30 40 50 60 70 80 90 100
H
Survivors
Age
This diagram extends the earlier
one by recognizing that not all
survivors are perfectly healthy.
The lower area ‘H’ shows the
proportion of people in good health
(however defined); it shows healthy
life expectancy. The top curve
shows deaths; intermediate area
represents levels of disability.
Area ‘G’ again represents the
health gap. The question arises
whether the people with a disability
ought to be counted with H or with
G.
Deaths

98. Health expectancies
• Generic term: any expectation of life in
various states of health. Includes
other, more specific terms, such as
Disability Free Life Expectancy
• Two main classes:
Dichotomous rating: two health states
Health state valuations for a range of
levels

99. I. Dichotomous
expectancies
• Here full health is rated 1, and any state of
poor health (mild, moderate, severe
disability) is rated 0.
• This leads to Disability-free life expectancy
(DFLE): weight of 1 for “no disability” and 0
for all other states.
• = Expectation of life with no disability, or
Healthy Life Expectancy (HLE)
• Very sensitive to threshold of disability
chosen
Sullivan(1971)

100. II. Polytomous states and valuations
(Wilkins and Adams-1983)
• These incorporate many levels of disability into
life expectancy estimates and count time spent
with each level of disability.
• Polytomous model (three or more health states
defined: weights assigned to each; generally 0 to
1.0. These may be added together and compared
across diseases)
• = Health-adjusted life expectancy (HALE)
• First calculated for Canada by Wilkins. Four
levels of severity & arbitrary weights.
• Recent work uses utility weights. E.g. from
Health Utilities Index, Quality of Well-Being Scale,
EUROQoL, etc.

101. Polytomous Curves Showing
Quality of Survival
G
0%
20%
40%
60%
80%
100%
0 10 20 30 40 50 60 70 80 90 100
H
Survivors
Age(years)
This diagram illustrates
several classes of disability,
each having a separate
severity weighting.
The area ‘H’ again includes
healthy people, but the
definition may have changed.
The top curve shows deaths;
intermediate curves represent
various levels of disability.
Deaths

102. Relationship between Life Expectancy,
Health Expectancy and Health-Adjusted
Life Expectancy
Health-Adjusted
Life Expectancy
Life
Expectancy
Healthy
Life
Expectancy
By down-weighting the
various levels of
disability,the HALE falls
between LE and HLE

103. Gap Measures: QALYs &
DALYs
• Gap measures can also use a weighting for
intermediate health states. This is necessary
to combine time lost due to ill health with
time 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 and
quality of that life
Typically use utility scale running from 0 to 1
• DALYS (disability-adjusted life years) lost

104. Complementarity of Health
Expectancies and Health Gaps
SLE
LE
HALE
HLE
LE SEYLL
SURVIVAL
HALE HALY
POLYTOMOUS
HLE ?
DICHOTOMOUS
Birth
LE = Life Expectancy; SLE = Standard LE; HALE = Health-Adjusted LE;
HLE = Healthy LE; SEYLL = Standard Expected Years of Life Lost
HALY = Health-Adjusted Life Years Lost
Gaps
Expectancies
Age

105. Disability Adjusted Life
Years
Possibilities and Problems

106. What are DALYs?
• DALYs = Disability Adjusted Life
Years
• A common measurement unit for
morbidity and mortality
• Facilitates comparisons of all
types of health outcomes

107. Possible use of DALYs
• Quantitative analysis of the burden
of disease
• Analysis of cost-effectiveness of
alternative interventions
• Selection of a package or list of
interventions deliverable within the
available budget
JL Bobadilla, WHO: 1996

108. What is the Global Burden of
Disease study?
• Backed by the WHO and the World
Bank
• A quantitative overview of the burden
of disease world-wide
• Combines information about loss of
quality of life with traditional
epidemiological information on
mortality
• All health outcomes are expressed in
DALYs

109. Possible use of the Global
Burden of Disease Study
• Epidemiological surveillance of
trends across borders and over time
• Projections for future burden of
disease
• Basis of information for decision-
making on priorities in health
research and health policy

110. CLICK TO ENLARGE

111. How are DALYs
constructed?
• A DALY is a health outcome
measure with two main
components
Quality of life reduced due to a
disability
Lifetime lost due to premature
mortality.

112. DALYs due to living with disability
(Red area measures DALYs. Red + white is a “normal”
life)
82,5 YEARS
NO
DISABILITY

113. DALYs due to early death
(Red area measures DALYs. Red + white is a standard
life)
NO
DISABILITY
82,5 YEARS

114. DALYs due to disability and premature
death combined.
NO
DISABILITY
82,5 YEARS

115. Calculation of DALYs
(age-weighting and discounting are
omitted for didactic reasons)
• The calculation of DALYs of a woman who has
been deaf since she was 5 and dies when she
is 50: ( Disability weight of deafness is set at
0.33) :
• Number of healthy life years × the disability
weight of full health (0) + life years with
disability (50) × disabilty weight for deafness
(0,33) + life years lost (30) × the weighting of
death (1)
• 5 × 0+ 45 × 0,33 + 30 × 1 = 47.35 DALYs

116. DALYs and QALYs
• DALY is a modification of QALY
(Quality Adjusted Life Years).
• Both concepts combine information
about length of life and quality of life.
• A DALY is a negative QALY.

117. Relation between QALYs and DALYs
DALYs = healthy years lost
QALYs = healthy years gained
NO
DISABILITY
82,5 YEARS

118. How are disability adjustments
made?
The methods used to assign a disability
weightings to life years is a critical part of
the DALY approach.
– Diagnostic groups must be chosen and
defined.
– Descriptions of those diagnostic groups are
developed.
– The health states are assigned a disability
weight to indicate the relative severity of
each health state.

119. Current method used for
weighting disability
• Disability weights are obtained by
posing two different Person Trade-
Off (PTO) questions to expert panels
• PTO1 compares life extensions for
disabled and healthy people
• PTO2 compares cures for illness
with extension of life

120. Other choices behind DALY
• In addition to adjusting the value of
life years with disability weights, and
chosing a particular life expectancy,
the value of a life year is modified by
• Discounting
– the value of a life year now is set higher
than the value of future life years
• Age weighting
– life years of children and old people are
counted less

121. Age-weights

122. The effect of age-weighs and
discounting

123. Calculating DALY score,
with age weighting and discounting.
• Girl, 5 years old, with below-knee
amputation who lives until she is
82,5:
• 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

124. PROBLEMS of the DALY
approach
• Is it true?
Questions of the validity of the
results
• Is it just?
Questions of the distribution
between groups

125. General problems of validity
• What is “Quality of Life” or “Disability
weighting of life years”?
• Can quality of life be measured in a single
and precise number?
• Does the same health problem have equal
impact on different persons or groups?
• Is there a general agreement to underlying
value choices: discounting, age weighting
and choice of life expectancy

126. Validity problems of the
current PTO protocol
• Lack of simplicity, difficult to
understand
• Forced consistency between two
questions that are essentially
different
• Impossible to answer that all
individuals are equally valuable
• The expert panel may not represent
the values of other people

127. Validity problems of
epidemiological estimates
• Epidemiological data for Africa, Latin
America and Asia are crude estimates.
• The uncertainty of the figures of
prevalence, may be hidden in the
seemingly mathematical rigor of the
results.
• Lack of uniform diagnostic criteria. I.e.
what do we mean by “depression”?

128. Justice
• The DALY approach has been
criticised for discriminating
– the young
– the elderly
– future generations (future health
benefits)
– the disabled
– women

129. The young
• The 5-year-old girl in the example
above yielded 10,5 DALYs.
• However, the DALY score without
age-weight and discounting would
be
• 77.5 × 0.3 = 23,3 DALYs
• This result is twice as high, and
would give her a higher priority.

130. The elderly
• In the literature on justice in health
care, many agree that given a choice,
it is more important to save young
adults than the very old.
• This view is captured by the DALY
(as a time based measure) itself.

131. Future generations
• The practice of discounting future
benefits is also controversial.
• From society’s viewpoint, why should a
life year now be of more value than a
life year twenty years ahead?
• The implications for preventive
services versus curative services are
significant. Preventive interventions are
given less weight.

132. The disabled
• The DALY approach opens for including
chronic 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 of
disabled people have less value and
• implies that disabled people are less entitled
to health resources to extend their lives

133. Example of results
• In the protocol behind the present Global Burden of
Disease, a life year for 1000 healthy people has been
set as equally valuable as one life year for
– 9524 people with quadriplegia
– 2660 blind people
– 1686 people withDown's syndrome without
cardiac malformation
– 1499 deaf people
– 1236 infertile people
• WHO has announced a change in approach.

134. Women
• Underlying value choice: Standard
expectation of life at birth is 82.5 years
for women, 80 years for men
• The ‘true’ gender gap is greater
• Gender gap is adjusted to correspond
to ‘biological differences in survival
potential’
• Critique: Might underestimate burden
of disease for females relative to males

135. Anand S, Hanson K. Disability-adjusted life years: a critical
review. Journal of Health Economics 1997;16:658-702.
Arnesen T, Nord E. The value of DALY life: problems with
ethics and validity of disability adjusted life years. BMJ
1999; 319:1423-1425.
Bobadilla J-L, Cowley P, Musgrove P, Saxenian. Design,
content and financing of an essential national package of
health services. Bulletin of the World Health Organization
1994;72:653-662.
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