Epidemiology term used for the ‘Pattern of disease’ or ‘occurrence of the disease’. There is two types of variable. One is continuous and other is discrete variable. In continuous variable include height and weight.

By Nahal Alam, Dr. Sohail Sajid

 While in the discrete variable include having a disease or not. There is four different measures include

  • Cumulative incidence
  • Prevalence
  • Odds Ratio
  • Incidence rate

Let’s discuss one by one,

Cumulative incidence: Different terms used for cumulative incidence such as incidence proportion, risk. These terms are used interchangeably. It is defined as number of new cases in start of period over number disease free individuals at the time of interest. We will exclude those individuals who has already disease from numerator and also exclude those individuals who has not diseases at the start of our time period.

Prevalence: That means prevailing of disease in a time duration. E.g. Coronavirus was firstly occurred in 2019. When we want to find the prevalence in a specific population in a given period of time. Like in a classroom 60 students was affected from COVID-19 out of 150 at that day. Formula will be

Odds Ratio: That means the probability of an event to complements. E.g. The ratio in between the number of the people who has the COVID-19 to the number of peoples who do not have the disease. Another example students who are awakening in class divided by student who are sleeping during lecture. So, out of 120, 70 students actively taking lecture. While the 50 students falling asleep. In other words individuals have disease over individuals who has not disease.

Most of the time, Epidemiologist used this term to find the prevalence of an outbreak. Here is not using the units. Because these are dimensionless. Value from 0 to 1 or 1 to 0. If value will be one, that means number of students affected while the value 0 means no prevalence found. These both term help to identify the health status and allocate the health services.

Incidence rate: Individuals who is at risk in specific period of time like here is person-time measures of time which is spent by the participants.

Let’s take an example, 10 students took a class they was awaken at 9 o’clock but at 12 o’clock two students fall asleep, the duration is 3 hours. That means a person develop a disease for 3 hours. This one is the contribution of our individuals. One of students fall asleep at 11 o’clock that duration will be 2 hours. Total duration is 8 hours spent by our 3 study participants. But here is our leftover participants will not be included because they are not fall asleep. Here you can see 3 participants, means 3 new cases and total duration is the person time.3 over 8 would be 0.3. It would be helpful for your understanding.

Association: The term chi-square mostly used in statistical analysis to see the association among different population/variables. Compare the two populations with each other in regard to exposure and outcomes. Exposure could be the environmental factors, genetic factors, treatments, occupations. While the development of disease is called outcomes. However, we called independent variable to ‘exposure’ while the ‘outcomes’ would be our dependent variable.

For example: we give the medication (dose level) to decrease the effect of corona. So, here is medication would be our independent variable/exposure (researcher has control, which can be increased or decreased). While the coronavirus would be our dependent variable. Means to say, to check the effect of medication in case of coronavirus.

Another example is smoking causes lung cancer. Smoking would be ‘exposure’ while the lung cancer would be outcomes.

2*2 Table:

This term also used to see the association among exposure and outcomes. E.g. total participants are 500, from which 200 were taken high fat food and 300 were not taken high fat diet. While the 50 were developed the heart disease who were taking high fat food. However, 60 were developed the disease in case not taking fat food.


Chronic Heart disease







High fat food

Exposed (Yes)




Un-exposed (No)









In such a way we develop the table 2*2.

Measure of association:

This is divided into two groups

  • Relative measures
  • Absolute measures

Relative measures: Relative means includes odd ratio (OR), risk ratio (RR), cumulative incidence ratio (CIR). It is defined as ratio between exposed group and unexposed group in period of time.

E.g. In the epidemiology class, ‘group A’ of 10 student were exposed to the boring lecture, from which 4 fall asleep so it would be 4/10= 0.4. While the group b were not exposed with boring lecture from which 2 student fall asleep where the unexposed group would be 2/10= 0.2. Now put the value in above equation and get the answer 2.

  • If the RR is 1 means risk ratio is equally divided among expose and unexposed group
  • If the RR is >1 means risk ratio among the exposed group greater than the unexposed group
  • If the RR is <1 means risk ratio among the exposed group smaller than the unexposed group

Here is the risk ratio 2 means the student of exposed group had 2 times higher risk of sleeping as compared with unexposed group in one hours period.

Incidence risk ratio: That is similar as the risk ratio that defined as the incidence rate of exposed group over the unexposed group

Odd ratio: Defined as the exposed group of having disease over the unexposed group having disease.

Risk differences: we will take above example, risk of exposed group over the risk of unexposed group where we get 0.2. But in case of difference we will subtracts the exposed to unexposed group.

  • If the value of risk difference would be zero that means there is no difference among the exposed and unexposed group
  • If the value is more than zero, that means risk of disease among the exposed group would be higher than the unexposed group
  • If the risk differences would be negative in value, that means the risk in exposed group smaller than the risk of unexposed group

Incidence difference:

It can be calculated by the subtracting the risk of unexposed group from the risk of exposed group called incidence difference.

These all terms were used in epidemiology to identify disease according to requirement.

Author name: Nahal Alam, Dr. Sohail Sajid

Affiliation: Department of epidemiology and public health, university of agriculture Faisalabad