Tag: frequency

Defining a population

Posted on by Prav

Measuring disease and health outcomes, Part 2 – Population

As mentioned in part 1 understanding frequency alone can be misleading – which is why we need to know our population.

The fact is that health outcomes can differ for a variety of reasons. From different countries, regions, socio-economic backgrounds and even time. Therefore to study an outcome we first have to clearly identify who we want to include – this is our Target population.

Our target population is basically the individuals we want to identify or whose health we want to improve. For example being concerned about the population of Hull City. However if we were to look at the all 200,000 people in Hull City – we would probably be overwhelmed as realistically it would be too difficult to gather and quantify all that data.

To make things easier we identify a Study Population. And this would be a smaller subset of individuals with a particular disease/health outcome we would like to study. For example the study population would be the number of individuals over the age of 30 who are diabetic. If this sample is too large – lets say 50,000 individuals, then an alternative method of collecting information is needed. Which is why we use a Study Sample, which is a smaller number of the Study Population, selected at random to represent the 50,000 individuals.

Target Population = Individuals in Hull City
Study Population = Diabetics > 30 years of age living in Hull ( eg = 50,000 identified)
Study Sample = 2000 individuals chosen at random from the 50,000, to represent the diabetics over 30 years of age.

Why do we need to define our populations?

So we know who are the population at risk. These are individuals who have the risk factor for the condition being studied regardless of whether they are currently healthy or ill. This can be clear cut or vague depending on what is being studied.

For example, if the population at risk for developing lung cancer is smokers, they are clearly identifiable cause we can assess smoking histories. But in some instances such as rare diseases we cannot always identify who is at risk for a variety of reasons. In these instances we use the whole population as an estimate.

So once we identify the population, we can figure the frequency of an incident taking place. Ie if there are 500 cases of lung cancer out of a study population of 4000 smokers(population at risk), then we know the frequency is 1/8.

But let’s say we only have 2 researchers and 4000 is too large a group to manage, this is when we use a study sample. So 1000 smokers are identified at random to represent the 4000. Out of the 1000 smokers, we find 120 cases with lung cancer. The frequency in this instance would still be 1/8 or 120 per 1000 cases. Which when compared with the study population would still give you 500 cases of lung cancer out of the total population at risk of 4000 smokers.

In the next post, we will talk about how we can express frequency – incidence vs prevalence.

50,000 vs 200,000 – Which is greater?

Posted on by Prav

Measuring disease and health outcomes, Part 1 – Frequency

How do we measure how often a particular disease happens in a population? How do we identify which diseases are more far-reaching or have the worst outcomes? How do we decide at what level we should intervene?

By knowing the frequency.

Frequency guides us by telling us how often an event happens in a particular population over a specific time. It helps us understand how a disease affects a population or rather the health outcomes by measuring it.

To do this we have to clearly identify what we are looking for and this is called a case definition. This is because not all health outcomes or diseases are black and white, therefore having a clear cut-off point helps us measure how many people are affected by it. For example, if we want to see how many obese people are in a community our case definition would be to look for people with a BMI > 30. It can go beyond clinical issues such as looking at the number of people over the age of 60 who had fallen in the last month. In the latter, the case could occur once or even more than that.

Basically by getting a clear definition. We are able to identify the individuals in the population.

However, knowing the frequency of an event can be misleading as population size can affect our perception of it. For example, if I said there were 200,000 known diabetics in London and 50,000 in Hull City. You would think that Hull had fewer diabetics, right? Now, what if I told you that the population of London is 8 million people while Hull only has 200,000 people. Does that change your mind?

This is why we need to know the population and identify the population at risk.

We will talk more about that in Part 2.