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1. Measure of health and
Health Related Events
By Abera B(MPH, Assistant professor)
1

2. Measure of health and Health
Related Events
Learning outcomes : At the end of this session ,
participants should be able to:
• Explain how to measure health and health related events
in a given population
• Discuss the importance of different mortality rates and
ratio for assessing the health status of the given
community.
• Measure the impact of mortality in a given population.
2

3. Content
Measures of Health and Disease states in
Population
• The concept of measurements
• Absolute number, ratio, proportion and
rates
• Measure of Morbidity/disease frequency
• Measure of Mortality
• Measure of Association
3

4. Concepts of Measurements
• Epidemiology is mainly a quantitative
discipline, so we should quantify health and
health related events.
• Types of Measurements:
Absolute numbers
Ratio, Proportion, Rate
4

5. Ratios, Proportion and Rates,
Ratio: the result of dividing one quantity by
another.
More specifically, the numerator and
denominator are two separate and distinct
quantities, and may be measured in the same
or different units.
Examples:
 [Ratio = A/B].
 Sex ratio = (No. of males) / (No. of females)

6. Rates
• Rate, is a special form of proportion, with an added dimension (time).
• it measures the occurrence of an event in a population over time. The
basic formula for a rate is as follows:
Notice three important aspects of this formula.
1. The persons in the denominator must reflect the population from
which the cases in the numerator arose.
2. The counts in the numerator and denominator should cover the
same time period.
3. In theory, the persons in the denominator must be “at risk” for the
event, that is, it should have been possible for them to experience
the event.
6

7. Differences between ratio, proportion and rates
– When we call a measure a ratio, we usually mean
a non-proportional ratio;
– when we call a measure a proportion, we usually
mean a proportional ratio that doesn’t measure
an event over time, and
– when we use the term rate, we frequently refer to
a proportional ratio that does measure an event
in a population over time.
7

8. Table1.1 Neonatal sepsis, Hospital A, Ethiopia, 2003
8

9. Exercise 1
The line listing in the Table 1.1 presents some of the
information collected on infants born at Hospital A
with neonatal sepsis.
1. What is the ratio of males to females?
2. What proportion of infants lived?
3. What proportion of infants were delivered in a
delivery room?
4. What is the ratio of operating room deliveries to
delivery room deliveries? 9

10. Measures of Morbidity
Incidence rate
Cumulative Incidence
Incidence Density
Prevalence rate
Point Prevalence
Period Prevalence
10

11. Cumulative incidence (incidence
proportion)
• Cumulative Incidence (CI): is the
proportion of people who become
diseased during a specified period of time
and is calculated as:
CI= Number of new cases during a specified period of
time
Total Population at risk in the specified period of
time
11

12. Incidence Density/person-time rate (incidence
rate)
• Measures the rate at which new cases of disease occur in
the population at risk during a defined period
• The population at risk is dynamic and each person in the
population contributes the amount of time that they remained
under observation and free from disease (person-time)
ID = number of new cases of disease in specified period x10n
Total person-time of observation
• The numerator is still the number of new cases, but
the denominator is the sum of the time each person
is observed, totalled for all persons.
12

13. Incidence Density/person-time
rate
What is person-time for?
1.100 people followed for 1 year each
2.10 people followed for 10 years each
3.50 people followed for 1 year each plus 25
people followed for 2 years each
4.Time unit=month, year, day; person-
time=person-year, person-month,
13

14. Incidence density/person-time rate continued
…
• Person-time rates are often used in cohort (follow-up) studies
of diseases with long incubation or latency periods, such as
occupationally related diseases, AIDS, and chronic diseases.
• Total person-time for the denominator is computed by either…
– Summing the amount of person-time contributed by each person in the
population during the study period, or
– Multiplying the average size of the population at the mid-point of the
study period times the number of years representing the total study
period
14

15. Continued…
Example
• Investigators enrolled 2,100 men in a study and followed them over 4
years to determine the rate of heart disease.
• We assume that persons diagnosed with disease and those lost to follow-
up were disease-free for half of the year, and thus contribute ½ year to the
denominator.
• Initial enrolment: 2,100 men free of disease
– After 1 year: 2,000 disease-free, 0 with disease, 100 lost to follow-up
– After 2 years: 1,900 disease-free, 1 with disease, 99 lost to follow-up
– After 3 years: 1,100 disease-free, 7 with disease, 793 lost to follow-up
– After 4 years: 700 disease-free, 8 with disease, 392 lost to follow-up
1. Identify x: x = cases diagnosed = 1 + 7 + 8 = 16
2. Calculate y, the person-years of observation:
15

16. Alternative methods
• A second way to calculate the person-years of observation is to turn the
data around to reflect how many people were followed for how many
years, as follows:
• 700 men x 4.0 years = 2,800 person-years
• 8 + 392 = 400 menx3.5 years = 1,400 person-years
• 7 + 793 = 800 menx2.5 years = 2,000 person-years
• 1 + 99 = 100 menx1.5 years = 150 person-years
• 0 + 100 = 100 menx0.5 years = 50 person-years
• Total = 6,400 person-years of observation
• This is exactly equal to the average population at risk (1,600) times
duration of follow-up (4 years).
16

17. Prevalence rate
• Prevalence, sometimes referred to as prevalence rate, is the
proportion of persons in a population who have a particular disease
or attribute at a specified point in time or over a specified period of
time.
– Numerator is number of existing cases
– Denominator is total population of interest
The formula for presence of disease is:
17

18. Point, period and Life time prevalence
Point prevalence
• Point prevalence is the amount of disease present in a population at a single point in
time.
Point prevalence =all the cases of factor of interest at a given time x 10n
total population
Period prevalence.
The numerator in period prevalence is the number of persons who
had a particular disease or attribute at any time during a particular
interval (week, month, year, decade, or any other specified time period).
Period prevalence = all cases (old and new) of the factor of interest during the time period x 10n
average population during the given period of time
Lifetime prevalence (cumulative lifetime frequency): proportion of the population that
has a history of a given disorder at some point in time
• The Numerator in lifetime prevalence is the proportion of the population who ever had
a history of disease or attribute at some point in time.
Life time prevalence= number who ever had the factor of interest during lifetime x 1on
Population at risk (at the beginning of the time period)
18

19. Example
Two surveys were done in the same community 12 months apart. Of 5,000
people surveyed the first time, 25 had antibodies to histoplasmosis.
Twelve months later, 35 had antibodies, including the original 25.
Calculate the prevalence at the second survey, and compare the
prevalence with the 1-year incidence.
1. Prevalence at the second survey:
x = antibody positive at second survey = 35
y = population = 5,000
x/y X10n = 35/5,000 x 1,000 = 7 per 1,000
2. Incidence during the 12-month period:
x = number of new positives during the 12-month period = 35 - 25 = 10
y = population at risk = 5,000 - 25 = 4,975
x/y x10n = 10/4,975 x1,000 = 2 per 1,000
19

20. Characteristics of Prevalence
• Cause and effect measured simultaneously
– Impossible to infer causation
• Useful for planning (e.g. beds, clinics, workforce
needs)
• High prevalence  high risk
– could reflect increased survival(improved care, behavior
change - long duration)
• Low prevalence  could reflect rapid fatal or cure
process - short duration)
• Easy to obtain need only 1 measurement
20

21. Figure 1.2 Relationship between prevalence and
incidence
21

22. Relationship between prevalence and
incidence
• Prevalence is based on both incidence (risk) and
duration of disease. High prevalence of a disease within
a population may reflect high risk, or it may reflect
prolonged survival without cure.
• Conversely, low prevalence may indicate low incidence, a
rapidly fatal process, or rapid recovery
• Thus, prevalence rate is directly proportional to both
incidence rate and to the average duration of the disease
and thus expressed as
p ~ IR x D
22

23. Attack Rate
• An attack rate is a variant of an incidence rate, applied to a
narrowly defined population observed for a limited time, such as
during an epidemic.
• The attack rate is usually expressed as a percent.
Example
• Of 75 persons who attended a church picnic, 46 subsequently
developed gastroenteritis. Calculate the attack rate of
gastroenteritis.
The attack rate is …….?
Answer=61.3%
23

24. Secondary Attack Rate
• A secondary attack rate is a measure of the frequency of new
cases of a disease among the contacts of known cases.
• To calculate the total number of household contacts, we usually subtract the number
of n casesprimary cases from the total number of people residing in those
households.
Example:
Seve of hepatitis A occurred among 70 children attending a child care center. Each
infected child came from a different family. The total number of persons in the 7
affected families was 32. One incubation period later, 5 family members of the 7
infected children also developed hepatitis A. We will calculate the attack rate in the
child care center and the secondary attack rate among family contacts of those
cases.
1. Attack rate in child care center:
x = cases of hepatitis A among children in child care center = 7
y = number of children enrolled in the child care center = 70
7/70*100=10%
24

25. Continued…
25

26. Mortality Measures
Mortality Rates
• A mortality rate is a measure of the frequency of occurrence
of death in a defined population during a specified interval. For
a defined population, over a specified period of time,
• The following are frequently used mortality measures.
26

27. Case-fatality rate
• The case-fatality rate is the proportion of persons
with a particular condition (cases) who die from that
condition.
Maternal mortality ratio
• The maternal mortality ratio is used to measure mortality
associated with pregnancy.
MMR= # of death of women from pregnancy related causes in a year
# of live births in same year
27

28. Proportionate mortality Ratio
• Proportionate mortality describes the proportion of deaths
in a specified population over a period of time attributable to
different causes. Each cause is expressed as a percentage
of all deaths, and the sum of the causes must add to 100%.
28

29. Measures of Association
• Risk is a proportion
The chances of something happening
The chances of all things happening
• Odds is a ratio
The chances of something happening
The chances that it is not happening
29

30. A. Relative Risk (RR)
• RR shows the magnitude of association between
exposure & disease
• Indicates the likelihood of developing the
disease in exposed group relative to non
exposed
• RR can also be used to compare risks of death,
injury, and other possible outcomes of the
exposure
• RR = Incidence among exposed (Ie)
Incidence among nonexposed (Io)
RR = a/a+b = CIe/CIo
c/c+d
30

31. Cont….
Rate ratio
• Incidence density ratio
– Incidence density in exposed
Incidence density in non exposed
IDe/IDo = a/PY1
c/PY0
31

32. Example 1
• Among 2390 women aged 16 to 49 years
who were free from bacteriuria, 482 were
OC users at the initial survey in 1973,
while 1908 were not. At a second survey in
1976, 27 of the OC users had developed
bacteriuria, as had 77 of the non users.
Calculate the measure of association and
interpret it.
32

33. Example:
O
C
U
S
E
Bacteruria
Yes No Total
Yes 27 455 482
No 77 1831 1908
Total 104 2286 2390
33

34. • Calculate RR
RR = Ie = 27
lo 482 = 1.4
77
1908
Interpretation: women who used oral
contraceptive had 1.4 times higher risk of
developing bacteruria when compared to
non-users.
34

35. Example 2
• A study on postmenopausal hormone use
and coronary heart disease among
postmenopausal female nurses showed
that after a total of 54,308.7 person-years
of follow-up, 30 women who reported that
they had used hormones developed CHD.
For the “never users”, 60 developed CHD
among 51,477.5 person-years of follow-
up. Draw a two-by-two table and calculate
the measure of association.
35

36. Measures of Association
Hormone
use
CHD Person-Years
Yes No
Yes 30 - 54,308.7
No 60 - 51,477.5
Total 90 105,786.2
36

37. • Ie/Io = IDe/IDo = a/PY1
c/PYo
= 30/54,308.7
60/51,477.5
= 0.5
37

38. B. Odds Ratio (OR)
• In case control studies, RR can be
estimated by calculating the ratio of the
odds of exposure among the cases to that
among the controls i.e
RR= OR = a/c = ad
b/d bc
38

39. Example 3
• Of 156 women with Myocardial Infarction
(MI), 23 were current OC users at the time
of their hospital admission. Of the 3120
control women without MI, 304 were
current OC users. Calculate the measure
of association and interpret it.
39

40. Example:
Current
OC use
Myocardial infarction
yes No Total
Yes 23 304 327
No 133 2816 2949
total 156 3120 3276
40

41. Calculate OR
OR = ad = (23) (2816) = 1.6
bc (304) (133)
Interpretation: Women who were current OC users
had 1.6 times higher risk of developing myocardial
infarction when compared to non-users of OC
RR can be estimated by OR if the following conditions
are fulfilled:
• The controls are representative of the general
population
• The selected cases are representative of all cases
• The disease is rare
41

42. Impact Measures
• Attributable Risk (AR)
• Attributable Risk Percent (ARP)
• Population Attributable Risk (PAR)
• Population Attributable Risk Percent
(PARP)
42

43. Attributable Risk (AR) / Risk
Difference (RD)
• AR provides information about the
absolute effect of the exposure or the
excess risk of disease in those exposed.
• AR = Incidence among exposed (Ie) _
Incidence among non-exposed (Io)
• AR quantifies the risk of disease in the
exposed group that is attributable to the
exposure by removing the risk of disease
that occurs due to other causes.
43

44. Alternative Naming of AR
• Risk difference
• Rate difference
• Cumulative incidence difference
• Incidence density difference
• Same by attributable risk
44

45. Example: Refer to example 1 and calculate
AR
AR = 27 _ 77
482 1908
=0.0156=1566 per 100,000 OC users
Interpretation: The excess occurrence of
bacteruria among OC users attributable to
their OC use is 1566 per 100,000 OC
users
45

46. Attributable Risk Percent
• Estimates the proportion of the disease
among the exposed that is attributable to
the exposure, or
• the proportion of the disease in the
exposed group that could be prevented by
eliminating the exposure
AR % = (Ie - Io) X 100
Ie
46

47. Exercise:
smokers Nonsmo
kers
Ratio
(RR)
Differenc
e(RD)
Lung Ca 48.3 4.5 10.8 44
CAD 294.7 169.5 1.7 125
47

48. Exercise
• Which disease mortality is more strongly
associated with cigarette smoking? Why?
RR association
• If the number of deaths attributable to
smoking is used as an index of public
health importance which disease has more
significance?RD impact
48

49. Example:
Refer to example 1 and calculate AR%
AR % = 1566/105 X 100 =27.96 %
27/482
• Interpretation: If OC use causes
bacteruria, about 28 % of bacteruria
among women who use OC can be
attributed to their OC use and can be
eliminated if they did not use oral
contraceptives. 49

50. Population Attributable Risk
(PAR)
• PAR shows the effect of eliminating the
exposure on the population as a whole,
• PAR takes into account not only the actual
incidence rate of the outcome but also the
prevalence rate of the exposure
• PAR = AR X prevalence rate of the
exposure
50

51. Example:
• Cigarette smoking and death from lung
cancer.
• AR = 89 per 100,000 per year
• Prevalence rate of cigarette smoking = 20
%
• Calculate PAR.
• PAR = 89 per 100,000 per year X 20 %
= 17.8 per 100,000 per year
51

52. Interpretation:
• In a population of 100,000 smokers, 89
deaths from lung cancer per year could
have been avoided by preventing them
from smoking (this refers to AR)
• In a general population of 100,000 with a
prevalence rate of cigarette smoking of 20
%, about 18 deaths from lung cancer per
year would be prevented by eliminating
cigarette smoking (this refers to PAR).
52

53. • Both AR and PAR are used to estimate the
effect on disease incidence of eliminating
a given risk factor,
• AR estimates reduction in disease
incidence only in those exposed,
• PAR estimates reduction in disease
incidence in the population as a whole.
• PAR = Incidence rate in total population
minus
incidence rate in non-exposed
population
53

54. Population Attributable Risk
Percent (PAR P)
PAR % = PAR X 100
Incidence rate in total population
Example: PAR = 17.8 per 100,000 per year
Mortality rate in non-smokers = 7 per 105
Mortality rate in the total population = 24.8 per 105 per year
Calculate PAR %
PAR % = 17.8 per 105 per year X 100 =71.8%
24.8 per 105 per year
Interpretation: 72% of deaths from lung cancer occurring in the
general population could be prevented by eliminating cigarette
smoking.
54

55. POSSIBLE OUTCOMES IN STUDYING THE RELATIONSHIP
• No association between exposure and
disease : AR=0, RR=1
• Positive association between exposure
and disease (more exposure, more
disease)
AR>0, RR>1
• Negative association between exposure
and disease
(more exposure, less disease)
AR<0 (negative), RR <1(fraction)
55

56. Standardization
56

57. Definition
A method used to compare the occurrence of
disease or mortality for a country or region.
In essence, adjustment that weights an
overall figure (disease occurrence or
mortality) according to the risk-factor profile
of the country or region.

58. Types of Standardization
• Direct: depends on use of a population
whose age-structure is standardized
• Indirect: depends on standard stratum-
specific rates

59. Worked Example:
Mortality in UK vs Kenya
UK Kenya
Deaths Popn Rate Deaths Popn Rate
All
Ages
656.4 58,649 11.2 250.1 29,008 8.6

60. Deaths by age
UK Kenya
Deaths Popn Rate Deaths Popn Rate
0-29 29.0 22,287 1.3 90.0 16,825 5.4
30-59 105.9 25,219 4.2 71.0 10,443 6.8
60+ 521.6 11,143 46.8 88.2 1,740 50.7
All
Ages
656.4 58,649 11.2 250.1 29,008 8.6

61. Direct Standardization
Age Standard
Popn
UK expected Kenya expected
0-29 56,000 0.0013 x 56,000
= 72.8
0.0054 x 56,000
= 302.4
30-59 33,000 0.0042 x 33,000
= 138.6
0.0068 x 33,000
= 224.4
60+ 11,000 0.0468 x 11,000
= 514.8
0.0507 x 11,000
= 557.7
All
Ages
100,000 726.2 1084.5

62. Comparative Mortality Figure
Comparative Mortality Figure is an age-
standardized ratio:
Kenya = 1084.5 = 1.49
UK 726.5

63. Indirect Standardization
Age UK
Mortality
Kenya
Population
Kenya
expected
deaths
0-29 0.0013 16,825 21.9
30-59 0.0042 10,443 43.9
60+ 0.0468 1,740 81.4
All ages 147.2

64. Standardized Mortality Ratio (SMR)
SMR = observed deaths
expected deaths
= 250.1 x 100 = 170
147.2
ie, the number of observed deaths in Kenya is
about 70% higher than the number expected if
Kenya had the same mortality experience as
the UK.