MDS (PUBLIC HEALTH DENTISTRY)

1. Measures of
Disease Frequency,
Association and Impact
SEMINAR NO: 5
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2. Contents
1. Rationale for measurement of health and disease
2. Tools of measurement
3. Measures of disease frequency
4. Measures of association
5. Measures of impact
6. Summary
7. References
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3. Rationale
Measurement of health and disease is important due to the following reasons:
a. Need for data to guide efforts towards reducing the consequences of disease
b. To understand and assess health status of a population and its changes with time
c. Need for evidence-based decisions in healthcare
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4. How do we measure disease?
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Count Divide Compare

5. These are: Count, Rate, Ratio, Proportion
Count:
• Simplest and most basic form of measurement.
• It represents the number of individuals who meet the case definition.
• Limitation:
(i) Count do not consider the size of population at risk
(ii)Count do not specify the time of observation
Tools of measurement- Count
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6. Count
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Example:
Count:
Number of new
cases
Year Population
City A 58 1990 25,000
City B 35 1989-90 7,000
Divide:
City A (58/25,000)/1 year=0.00232
City B (35/7,000)/2 years= 0.0025
Compare:
City A 232/100,000 per year
City B 250/100,000 per year

7. Common tools of measurement used in research are – rate, ratio, proportion.
Basic formula:
• Numerator - count of an event in a population during a specific time period.
• Denominator - population pool
Tools of measurement
ꭓ 10𝑛
𝑁𝑢𝑚𝑒𝑟𝑎𝑡𝑜𝑟
𝐷𝑒𝑛𝑜𝑚𝑖𝑛𝑎𝑡𝑜𝑟
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What is in the
denominator?

8. • Measures the occurrence of an event in a defined population during a given
period of time.
• It is a statement of the risk of developing a condition and is used for
comparison between different locations, time periods or groups.
• E.g., death rate and is given by the formula:
Death rate =
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𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑒𝑎𝑡ℎ𝑠 𝑖𝑛 𝑜𝑛𝑒 𝑦𝑒𝑎𝑟
𝑀𝑖𝑑 − 𝑦𝑒𝑎𝑟 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
∗ 1000
Rate
Rate- expressed per 1000 or round figure (10,000;100,000) selected according to convenience to avoid fractions.
numerator
denominator
time
multiplier

9. • Concept of numerator in rate: Numerator is a component of denominator in calculating a rate.
• Concept of denominator in rate: Appropriate denominator should be chosen to calculate a rate.
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Denominator
Related to
Population
Mid year
population
Population at
risk
Person-time
Subgroups of
the
population
Related to
Total events

10. 10
Mid -year population
• The population size changes
daily due to births, deaths or
migration.
•Mid-point refers to the
population estimated as on
first of July of a year.
Population at risk
•Focus is on the groups at risk
of disease rather than on
individuals.
• The concept of ‘population
at risk’ is restricted solely to
those who are capable of
having or acquiring the
disease or condition in
question.
Person -time
• When persons enter the
study at different times, they
are under observation for
varying time period.
•In such cases the
denominator is a
combination of persons and
time (as in person -years,
person -months, person -
weeks or man- hours).
•Person – distance is a variant
of person – time as in
passenger -miles.
Sub-groups of the population
•Denominator may be
subgroup of a population
•e.g., age, gender, occupation
etc.
These denominators have the advantage of summarizing the experience of persons with different durations of
observation or exposure.
a) Related to the population:

11. Example:
Mortality rate of tetanus in Monduli in 1995
Tetanus deaths: 17
Population in 1995: 58 million
Mortality rate =0.029/ 100,000 per year
The number of death due to tetanus is 2900 per 100,000 total death.

12. • In some instances, the denominator may be related to total events instead of total population.
• E.g. in the case of motor vehicle accidents, the number of accidents “per 1000 vehicles” will be a more
useful denominator than the total population (as many of them may not be using a vehicle).
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b) Related to total events

13. • This tool of measurement express a relation
in size between two random quantities.
• Numerator is not a component of the
denominator.
• The numerator and denominator may
involve an interval of time or may be
instantaneous in time.
Ratio is expressed as:
X: Y or 𝑿/𝒀 13
Ratio
Example:
• # beds per doctor
850 beds/10doctors
Ratio= 85 beds for 1 doctor
• Sex-ratio
• Dentist – population ratio

14. • A proportion indicates the relation in
magnitude of a part of the whole
(comparison of a part to the whole).
• Numerator is always included in the
denominator.
• Proportion is usually expressed as a
percentage.
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Number of persons or events with a particular
characteristic (x)
Total number of persons or events, of which the
numerator is a subset(x+y)
∗ 100
𝑃𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛 =
Proportion
Example:
Population- 3500 women, 6500 men
• Proportion of men=
6500/(3500+6500)
= 0.65 or 65 %
• M:F ratio = 6500/3500= 1.86
• F:M ratio =3500/6500=0.54

15. 15
Measures of Disease Frequency
Population at risk
• Part of a population which is susceptible to a disease is called the population at risk.
• Risk factor is a characteristic which is more frequent in a group of subjects who develop a
certain disease than in subjects who do not develop the disease .
Risk is the probability of becoming ill, or the proportion of people who become ill (new cases)
during a specified time interval.
• The risk is therefore a proportion, its minimum value is 0 and maximum value is 1
• Population at risk can be defined on the basis of demographic or environmental
factors.
Risk = Number of new cases during a period of time
Population at risk at the beginning of period

16. • It is the number of new cases occurring in a
defined population during a specified period of
time.
Uses of incidence data
• Describe trends in diseases
• Evaluate impact of primary prevention
programmes
• Research into etiology, pathogenesis and
distribution of diseases
• helps in taking action to control disease.
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Incidence Risk of disease

17. 17
Incidence
Risk or cumulative incidence- is related to the population at risk at the beginning of the study period
Incidence risk=
Number of new cases of disease in a specified period of
time
Number of disease- free persons at the beginning of the
time period
Incidence rate=
Number of new cases of disease in a given time period
Total person-time at risk * during the study period
For e.g, some participants may : develop the outome under investigation, refuse to continue to participate
in the study, migrate, die, enter the study some time after it starts
Incidence rate/ incidence density-Is related to a more precise measure of the population at risk during the
study period and is measured in person-time units.
Difference between
incidence risk and
incidence rate and why
incidence rate is better

18. 18
Refers to all current (old and new) cases existing at a given point in time or over a period of time in a given
population.
Point prevalence: Point prevalence of a disease is defined as the number of all current cases (old and new) of a
disease at one point of time, in relation to a defined population.
The “point” in the point prevalence, may for all practical purposes consist of a day, several days or even a few weeks
depending on the time it takes to examine the population sample.
Point prevalence =
Number of all current cases of a specific disease at a
given point of time
Estimated population at the same point in time
𝑥100
Prevalence Burden of disease

19. 19
Point prevalence =
Number of all current cases of a specific disease at a
given point of time
Estimated population at the same point in time
𝑥100
Prevalence
Example:
Scenario:
• 150 children in a school
• Screening for refractory errors at time ‘t’
• 15 children require glasses
Prevalence of refractory errors = 15/150=10%

20. 20
Period prevalence: Measures the frequency of all current (old and new) cases over a period of time (e.g. annual
prevalence) in a defined population.
Period prevalence =
Number of all current cases of a specific disease during a
given period of time interval
Estimated mid −interval population at risk
𝑥100
Example:
Scenario:
• Population of 150 persons
• Follow up for one year
• 25 had a disease of interest at the beginning
• Another 15 new cases developed during the year
Period prevalence=(25+15)/150=0.27 (27%)

21. Uses of prevalence data:
• Assessing health care needs
• Planning health services
• Measure occurrence of conditions with gradual onset
• Study chronic diseases
Causes of increase and decrease of prevalence:
Increase Decrease
• Long duration
• Low cure rate
• Low case fatality
• Increase in new cases
• Immigration of patients
• Improved detection
• Emigration of healthy people
• Shorter duration
• High cure rate
• High case fatality
• Decrease in new cases
• Emigration of patients
• Improved cure rate
• Immigration of healthy people
Changes in prevalence may have many causes and are difficult to interpret

22. Factors influencing prevalence:
• Number of new cases
• Duration of the illness
• If the disease is short, the prevalence is reduced
• The prevalence of sudden infant death = 0
• If the disease is long, the prevalence is increased
• Rare lifelong disease can accumulate to build up a large prevalence

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Relationship between Prevalence (P) and Incidence (I)
P = I x D, where D is duration of illness
Change in prevalence from one time period to another may be the result of changes in
incidence rates, changes in the duration of disease, or both.
Patterns of incidence and prevalence
• High prevalence and low incidence
e.g., Diabetes Mellitus
• Low prevalence and high incidence
e.g., Common cold

24. 24
Other commonly used measures of disease frequency in epidemiology
Mortality rates and ratios:
Crude death rate
= Number of deaths during the year
Mid-year population
𝑥1000
Specific death rate due to tuberculosis
=
Number of deaths from TB during a calendar year
Mid-year population
𝑥1000
=
Total number of deaths due to a particular disease
Total number of cases due to the same disease
𝑥100
Case fatality rate (ratio)

25. 25
Proportional mortality rate (ratio) from a specific disease
= Number of deaths from the specific disease in a year
Total deaths from all causes in that year
𝑥100
Survival rate
=
Total number of patients alive after 5 years
Total number of patient diagnosed or treated
𝑥100
Morbidity:
Attack rate
Number of new cases of a specified disease
during a specified time interval
Total population at risk during the same
interval
𝑥100
=
Other commonly used measures of disease frequency in epidemiology
Primary attack rate-
primary source
Secondary attack rate-
secondary source
Eg: coronavirus

26. • quantifies the relationship between exposure and outcome.
• compare measures of disease occurrence among the exposed and unexposed groups.
Measures of association are –
• Relative Risk (RR)
• Odds Ratio (OR)
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Measures of Association

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Measures of Association- 2X2 Table
Outcome
Total
Yes No
Exposure
Yes a b a+b
No c d c+d
Total a+c b+d N

28. • Also known as Risk Ratio
• Defined as the ratio between the incidence of disease among exposed persons and incidence among non-
exposed.
• Relative Risk can be exactly determined only from a cohort study.
Relative Risk (RR)
RR =
𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑎𝑚𝑜𝑛𝑔 𝑒𝑥𝑝𝑜𝑠𝑒𝑑
𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑎𝑚𝑜𝑛𝑔 𝑛𝑜𝑛 𝑒𝑥𝑝𝑜𝑠𝑒𝑑
Null value is 1 (divide)

29. Relative Risk (RR)
Interpretation of RR:
• RR =1; identical risk among 2 groups (no association between exposure & outcome)
• RR > 1; increased risk for the exposed group (positive association) (risk factor)
• RR < 1; decreased risk for the exposed group (negative association; exposure protects against disease occurrence)
Lung cancer
Lung cancer
absent
Smokers
(exposed)
15 (a) 45 (b)
Non-smokers
(unexposed)
6(c ) 24 (d)
Example:
RR = 𝑎/𝑎+𝑏÷𝑐/𝑐+𝑑
= (15/60)/(6/30)
= 1.25
RR =
𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑎𝑚𝑜𝑛𝑔 𝑒𝑥𝑝𝑜𝑠𝑒𝑑
𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑎𝑚𝑜𝑛𝑔 𝑛𝑜𝑛 𝑒𝑥𝑝𝑜𝑠𝑒𝑑
Interpretation: The risk of smokers developing lung cancer
is 1.25 times higher than non smokers.

30. 30
• Also known as Cross Product Ratio
• Measure of the strength of association between risk factor and outcome.
• Odds ratio is the key parameter in the analysis of case control studies.
• It is based on three assumptions:
a) the disease being investigated must be relatively rare
b) the cases must be representative of those with the disease
c)the controls must be representative of those without the disease
Odds Ratio (OR)

31. 31
Odds Ratio (OR)
Interpretation of OR:
• OR =1; No association between exposure and outcome
• OR > 1; positive association(risk factor)
• OR < 1; negative association (protective factor)
Lung cancer
Lung cancer
absent
Smokers
(exposed)
17 (a) 83 (b)
Non-smokers
(unexposed)
1(c ) 99 (d)
•Odds in exposed group (a/b) = (smokers with
lung cancer) / (smokers without lung cancer) =
17/83 = 0.205
•Odds in not exposed group (c/d) = (non-
smokers with lung cancer) / (non-smokers
without lung cancer) = 1/99 = 0.01
•Odds ratio (ad/bc)= (odds in exposed group) /
(odds in not exposed group) = 0.205 / 0.01 =
20.5
Example:
Interpretation: Smokers showed a
risk of having lung cancer 20.5
times that of non -smokers

32. • Also known as risk difference or absolute risk reduction
This measure indicates the
• extent to which the disease under study can be attributed to the exposure.
• It is the difference in incidence rates of disease between an exposed group and non-exposed
group.
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Measures of Impact-
Attributable risk (AR)
AR = 𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 𝑎𝑚𝑜𝑛𝑔 𝑒𝑥𝑝𝑜𝑠𝑒𝑑 −𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 𝑎𝑚𝑜𝑛𝑔 𝑛𝑜𝑛 𝑒𝑥𝑝𝑜𝑠𝑒𝑑
Null value is 0 (differnce)

33. Cigarette
smoking
Lung cancer
Lung cancer
absent
Smokers
(exposed)
800 200
Non-smokers
(unexposed)
40 1960
Example:
AR%= 800-40/ 800 x 100
= 95%
AR% =
𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 𝑎𝑚𝑜𝑛𝑔 𝑒𝑥𝑝𝑜𝑠𝑒𝑑 −𝐼𝑛𝑐𝑖𝑑𝑒𝑛
𝑐𝑒 𝑟𝑎𝑡𝑒 𝑎𝑚𝑜𝑛𝑔 𝑛𝑜𝑛 𝑒𝑥𝑝𝑜𝑠𝑒𝑑
𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 𝑎𝑚𝑜𝑛𝑔 𝑒𝑥𝑝𝑜𝑠𝑒𝑑
𝑥100
AR % =
𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 𝑎𝑚𝑜𝑛𝑔 𝑒𝑥𝑝𝑜𝑠𝑒𝑑 −𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 𝑎𝑚𝑜
𝑛𝑔 𝑛𝑜𝑛 𝑒𝑥𝑝𝑜𝑠𝑒𝑑
𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 𝑎𝑚𝑜𝑛𝑔 𝑒𝑥𝑝𝑜𝑠𝑒𝑑
𝑥100
Attributable Risk percent :
Interpretation: 95% of lung cancer among
smokers is due to smoking

34. Population Attributable Risk:
Incidence in the general population- incidence in the unexposed population
Amount of risk that would be eliminated from the general population if the exposure were eliminated
Population Attributable Risk percent (PAR%):
Incidence in the general population- incidence in the unexposed population
Incidence in the general population
General population =
62
100,000
Non-smokers =
7
100,000
PAR =62-7= 55 deaths per 100,000
PAR% = 62-7/62=0.89X100=89%
Example:

35. • Excess risk is the difference between the incidence rate among exposed and non-exposed
groups.
• Base line risk is the incidence of disease among non-exposed group.
• Number Needed to Harm (NNH) or Number Needed to Treat(NNT):
• Measure of the number of people who need to be exposed to a risk factor (or a
treatment) for one person to have a particular adverse effect (or to prevent an additional
bad outcome).
• NNH (or NNT) is the reciprocal of attributable risk- NNT= 1/AR
• Lower the NNH- more the risk of harm
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36. Summary

37. Summary

38. Absolute Risk
The absolute risk of an event is a likelihood of occurrence of that event in the population at risk.
i.e the absolute risk is the probability of an event in a sample or population of interest.
It is expressed a s percentage , also in terms of person-years of exposure to the risk factor.
Absolute risk of an event=
Number of persons who experience the event
Total number of persons exposed to the risk of that
event
𝑥1000

39. Summary
• Risk: the probability of an outcome
• Relative risk is a measure of the strength of association and possibility of a causal relationship
• Attributable risk indicates the potential for prevention if the exposure could be eliminated.
• The comparisons like observed amount of disease in a population with the expected amount of
disease, can be quantified by using such measures of association as risk ratios, rate ratios, and
odds ratios. These measures provide evidence regarding causal relationships between exposures
and disease.
• RR is the risk of an event in an experimental group relative to that of control group
• OR is the odds of an event in an experimental group relative to that of control group

40. Reference
1. Park, Park’s Textbook of Preventive &Social Medicine, 25th Edition, Jabalpur: Banarsidas
Bhanot,2019.
2. Dicker RC, Coronado F, Koo D, Parrish RG. Principles of epidemiology in public health practice;
an introduction to applied epidemiology and biostatistics.
3. Noordzij M, Dekker FW, Zoccali C, Jager KJ. Measures of disease frequency: prevalence and
incidence. Nephron Clinical Practice. 2010;115(1):c17-20.
4. Hennekens CH, Buring JE. Epidemiology in Medicine, Lippincott Williams & Wilkins, 1987.
5. Rothman KJ. Epidemiology: an introduction. Oxford university press; 2012 May 4.
6. Mehendale S, Murhekar MV, Ramakrishnan R. NOC: Health Research Fundamentals.