3. COUNT
• The simplest and most frequently performed quantitative measure in
epidemiology is a count.
• A count refers merely to the number of cases of a disease or other health
phenomenon being studied.
• Several examples of counts are the number of:
• Cases of covid-19 reported in Punjab, Pakistan, during September of a 2021
• Traffic fatalities in the Lahore during a 24-hour period
4. RATIO
• A ratio is defined as “The value obtained by dividing one quantity by another.”
• RATE, PROPORTION, and PERCENTAGE are types of ratios.
• A ratio therefore consists of a numerator and a denominator.
• A ratio may be expressed as follows: ratio = X/Y.
7. PROPORTION
• A proportion is a type of ratio in which the numerator is part of the denominator; proportions may be
expressed as percentages.
• Let us consider how a proportion can be helpful in describing health issues by reexamining a count.
• For a count to be descriptive of a group, it usually should be seen relative to the size of the group.
• Suppose there were 10 college dorm residents who had hepatitis. How large a problem did these 10
cases represent? To answer this question, one would need to know whether the dormitory housed
20 students or 500 students. If there were only 20 students, then 50% (or 0.50) were ill. Conversely,
if there were 500 students in the dormitory, then only 2% (or 0.02) were ill. Clearly, these two
scenarios paint a completely different picture of the magnitude of the problem.
8. EXAMPLE
Number of deaths among African-
American boys (A)
Number of deaths among white
boys (B)
Total
(A+B)
1150 3810 4960
Calculation of the Proportion of African-American Male Deaths Among African-
American and White Boys Aged 5 to 14 Years
Proportion = A/(A + B) × 100 = (1,150/4,960) × 100 =
23.2%
9. RATE
• A rate also is a type of ratio; however, a rate differs from a proportion because the
denominator involves a measure of time.
• The numerator consists of the frequency of a disease over a specified period of
time, and the denominator is a unit size of population.
• It is critical to remember that to calculate a rate, two periods of time are involved:
the beginning of the period and the end of the period.
11. RATES
• Rates improve one’s ability to make comparisons, although they also have limitations.
• Rates of mortality or morbidity for a specific disease reduce that standard of comparison to a
common denominator, the unit size of population.
• To illustrate, the U.S. crude death rate for diseases of the heart in 2003 was 235.6 per 100,000.
One also might calculate the heart disease death rate for geographic subdivisions of the country.
These rates could then be compared with one another and with the rate for the United States to
judge whether the rates found in each geographic area are higher or lower. For example, the
crude death rates for diseases of the heart in New York and Texas were 288.0 and 188.9 per
100,000, respectively. It would appear that the death rate is higher in New York than in Texas
based on the crude death rates. This may be a specious conclusion, however, because there
may be important differences in population composition that would affect mortality experience.
12. RATES
• Rates can be expressed in any form that is convenient (e.g., per 1,000, per 100,000, or per
1,000,000).
• Many of the rates that are published and routinely used as indicators of public health are
expressed in particular conventions.
• For example, cancer rates are typically expressed per 100,000 population, and infant mortality is
expressed per 1,000 live births.
• One of the determinants of the size of the denominator is whether the numerator is large enough
to permit the rate to be expressed as an integer or an integer plus a trailing decimal (e.g., 4 or
4.2). For example, it would be preferable to describe the occurrence of disease as 4 per 100,000
(or 4.2 per 100,000) rather than 0.04 per 1,000 (or 0.042 per 1,000), even though both are
perfectly correct.
13. PREVALENCE
• The term prevalence refers to the number of existing cases of a disease or health
condition in a population at some designated time.
• Prevalence data provide an indication of the extent of a health problem and thus
may have implications for the scope of health services needed in the community.
• Prevalence can be expressed as a number, a percentage, or number of cases per
unit size of population
14. POINT PREVALENCE
• When the time period is unspecified, prevalence usually implies a
particular point in time.
• Example
• The prevalence of diarrhea in a children’s camp on July 13 was 15,
• The prevalence of phenylketonuria-associated mental disabilities in institutions for the
developmentally disabled was 15%, and
• The prevalence of obesity among women aged 55–69 years was 367 per 1,000.
• These examples illustrate that the designated time can be specified (e.g., one day) or
unspecified.
16. PERIOD PREVALENCE
• A second type of prevalence measure is period prevalence, which denotes the total number of
cases of a disease that exist during a specified period of time, for instance a week, month, or
longer time interval.
• To determine the period prevalence, one must combine the number of cases at the beginning of
• the time interval) with the new cases that occur during the interval.
• Note that for period prevalence, cases are counted even if they die, migrate, or recur as
episodes during the period.
18. INCIDENCE
• Prevalence measures the frequency of existing cases of disease in a population. In
contrast, incidence is a measure of the number of new cases of a disease (or
another health outcome) that develop in a population of individuals at risk, during
a specified time period.
• There are two main measures of incidence:
19.
20. RISK
• This is also known as cumulative incidence because it refers to the occurrence of risk events,
such as disease or death, in a group studied over time.
• It is the proportion of individuals in a population initially free of disease who develop the
disease within a specified time interval.
• Incidence risk is expressed as a percentage (or, if small, as “per 1000 persons”).
•
21. RISK
• Remember that the denominator is the total number of people who were free of disease at the
start of the study period (the population at risk). The cumulative incidence assumes that the
entire population at risk at the beginning of the study period has been followed for the
specified time period for the development of the outcome under investigation. This is called
a closed population.
• However, in reality in a cohort study, for example, participants are followed up for a long period
of time and the population will change as people enter and leave. This is called
a dynamic population. Some may develop the outcome of interest, or be lost during follow-
up, for a variety of other reasons:
Refusal to continue to participate in the study
Migration
Death
22. INCIDENCE RATE
• Incidence rates also measure the frequency of new cases of disease in a population, but take
into account the sum of the time that each participant remained under observation and at risk
of developing the outcome under investigation.
• This measurement also seeks to account for varying time periods of follow up, which may
occur for the reasons outlined above.
23. INCIDENCE RATE
• An example of incidence measured as a frequency is the number of new cases of HIV infection
diagnosed in a population in a given year: A total of 164 HIV diagnoses were reported among
American Indians or Alaska natives in the United States during 2009.
25. TIME AT RISK
• In a dynamic population, individuals in the group may have been at risk for different lengths of
time, so instead of counting the total number of individuals in the population at the start of the
study, the time each individual spends in the study before developing the outcome of interest
needs to be calculated.
• The denominator in an incidence rate is the sum of each individual's time at risk (i.e. the length
of time they were followed up in the study). It is commonly expressed as person years at risk.
• The incidence rate is the rate of contracting the disease among those still at risk. When a study
subject develops the disease, dies or leaves the study, they are no longer at risk and will no
longer contribute person-time units at risk.
26. TIME AT RISK
• Figure illustrates the
calculation of person-
time units (years) at
risk of a hypothetical
population of 5
individuals in a 5 year
cohort study.
27. ODDS
Another method of measuring incidence is to calculate the odds of disease. Instead of
using the number of individuals who are disease-free at the start of the study, odds
are calculated using the number disease-free at the end of the time period.
28. THE RELATIONSHIP BETWEEN PREVALENCE AND INCIDENCE
• The proportion of the population that has a disease at a point in time (prevalence)
and the rate of occurrence of new disease during a period of time (incidence) are
closely related.
• Prevalence depends on:
1. The incidence rate
2. The duration of disease
29. THE RELATIONSHIP BETWEEN PREVALENCE AND INCIDENCE
• For example, if the incidence of a disease is low but the duration of disease (i.e. time until
recovery or death) is long, the prevalence will be high relative to the incidence. An example of
this would be diabetes.
• Conversely, if the incidence of a disease is high and the duration of the disease is short, the
prevalence will be low relative to the incidence. An example of this would be influenza.
• A change in the duration of a disease, for example the development of a new treatment that
prevents death but does not result in a cure, will lead to an increase in prevalence without
affecting incidence. Fatal diseases, or diseases from which a rapid recovery is common, have a
low prevalence, whereas diseases with a low incidence may have a high prevalence if they are
incurable but rarely fatal and have a long duration.
• The relationship between incidence and prevalence can be expressed as;
• P = ID
• (P = Prevalence, I = Incidence Rate, D = Average duration of the disease)
31. ATTACK RATE
• The attack rate (AR) is an alternative form
of the incidence rate that is used when the
nature of the disease or condition is such
that a population is observed for a short
time period, often as a result of specific
exposure.
• In reporting outbreaks of salmonella
infection or other foodborne types of
gastroenteritis, epidemiologists employ the
AR
As shown in this formula, the numerator
consists of people made ill as a result of
exposure to the suspected agent, and the
denominator consists of all people, whether
well or ill, who were exposed to the agent
during a time
period.
32. MEASURES OF EFFECT
• Measures of effect are used in epidemiological studies to assess the strength of an association
between a putative risk factor and the subsequent occurrence of disease.
• This is done by comparing the incidence of disease in a group of persons exposed to a
potential risk factor with the incidence in a group who have not been exposed.
• This comparison can be summarized by calculating either:
The ratio of measures of disease frequency for the two groups
The difference between the two
33. A. RELATIVE MEASURES
• Relative measures reflect the increase in frequency of disease in one population (e.g. exposed)
versus another (e.g. not exposed), which is treated as the baseline. They are often collectively
referred to as measures of relative risk.
• The relative risk is a measure of the strength of an association between an exposure and
disease, and can be used to assess whether an observed association is likely to be causal.
• The most commonly used measure of effect is the ratio of incidence rates
• Rate (or risk) in exposed
Rate (or risk) in unexposed
• There are three main measures of effect:
34.
35. INTERPRETING RELATIVE RISK (RR)
• Measures of effect such as the risk ratio provide assessments of aetiological strength, or the
strength of association between a putative risk factor and an outcome.
• A relative risk of 1 indicates that the incidence of disease in the exposed and unexposed
groups is identical and that there is no association observed between the disease and risk
factor/ exposure.
• A relative risk > 1 occurs when the risk of disease is greater among those exposed and
indicates a positive association, or an increased risk among those exposed to the risk factor
compared with those unexposed.
• A relative risk < 1 occurs when the risk of disease is lower in those exposed compared to
those unexposed and indicates a negative association.
36. ABSOLUTE MEASURES
• Relative measures help evaluate how strongly an exposure is associated with a particular disease,
but they do not give an indication of the impact of the exposure in the population. This is
important for public health prevention measures.
• Absolute measures indicate exactly what impact a disease will have on a population, in terms of
numbers or proportions affected by being exposed.
• For example, a study finds that having several CT head scans in childhood results in a three-fold
increase of your risk of developing brain cancer as an adult. This sounds like a large increase, but
because the absolute risk increase would be small (say, an increase of 0.5 cases per 10,000
children), the increased risk means one additional case of brain cancer per 20,000 children
scanned.
37. ATTRIBUTABLE RISK (RISK DIFFERENCE)
• The attributable risk (AR) is a measure of association that provides information about the
absolute effect of the exposure or excess risk of disease in those exposed compared with the
unexposed, assuming the risk is causal.
• It tells us exactly how many more people are affected in the exposed group, than in the
unexposed.
• The risk or rate difference estimates the excess risk caused by exposure in the exposed
group.
38. ATTRIBUTABLE RISK PERCENTAGE
• AR may also be expressed as the proportion of disease cases in the exposed group
attributable to the exposure (i.e. the proportion of additional cases in the exposed
group).
• This is also known as the aetiologic fraction or attributable fraction. It is calculated
as follows:
