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How to measure disease frequency?

Many different measures of health and disease are used to describe the health of populations. In this article you will be introduced to the three fundamental measures of disease occurrence: prevalence, incidence rate and cumulative incidence. These can be calculated from the results of epidemiological studies conducted specifically to measure disease frequency or from routinely collected data (e.g. surveillance systems).

Who has the most malaria cases?

According to the World Malaria Report 2009 the Democratic Republic of the Congo had 5 371 196 reported malaria cases in 2008 and Angola had 3 432 424. (1) This information may be useful for planning health services, but did the DRC really have more malaria than Angola? An alternative and more informative way to look at these data is in terms of the percentage of people in these two countries that had malaria in 2008. When you do this, the occurrence of malaria in Congo was 8.3% in the DRC in 2008 while it was 19.0% in Angola. So although the actual number of cases in the DRC was higher, the occurrence per 100 people (%) is lower than in Angola.

Many different measures of health and disease are used to describe the health of populations. In this article you will be introduced to the three fundamental measures of disease occurrence: prevalence, incidence rate and cumulative incidence. These can be calculated from the results of epidemiological studies conducted specifically to measure disease frequency or from routinely collected data (e.g. surveillance systems).

Prevalence

To measure prevalence of disease (existing cases) we need to conduct a cross-sectional study or survey in which from a group of people is determined whether the individuals had a particular condition at a particular point in time.

So, prevalence measures the amount of a disease in a population at a given point in time:

Prevalence

For example, a study on HIV Prevalence among men who have sex with men conducted in Malawi, Namibia, and Botswana found that 93 of the 536 men included in the study tested positive for HIV, giving a prevalence of 17.4% or 17.4/100 population (93 divided by 536, multiplied by 100). (2)

Prevalence has no unit (and therefore the term prevalence rate is – although often used - not correct, as it has no unit of time), but the time point at which people are counted should always be reported.

Especially when using routinely collected data, that ‘particular point in time’ for the determination of the number of people with the disease and the total number of people in the population is often broader/less clearly defined. The most recent Global Tuberculosis Control report indicates that there were an estimated 11.1 million prevalent cases of TB ‘in 2008’ globally, equivalent to 164 cases per 100 000 population. (3)

Incidence

To measure the incidence of disease (new cases) we need an epidemiological study in which we start with a group of people who are currently free of the disease of interest but who are ‘at risk’ of developing it (e.g. cohort or trial). We then follow them over time to see who actually develops the disease. And we count up the length of time the individuals were at risk of disease. An individual is at risk of developing the disease until the actual moment when they do develop it (in practice, when they are diagnosed) or until they are lost to follow-up.

The incidence rate (IR) measures how quickly people are developing a disease:

Incidence rate

For example, a randomised controlled intervention trial of male circumcision for reduction of HIV infection risk conducted in Orange Farm South Africa found a total of 69 HIV cases. (4) Participants were followed-up for 4 693 person years, giving an IR of HIV of 1.47 per 100 person years (69 divided by 4 693, multiplied by 100).

At the population level we may be dealing with millions of people and so it is clearly not feasible to calculate the person-time that each is at risk. Instead when using routinely collected data we usually work on the assumption that everyone is at risk for the whole of the year that we are interested in. The fundamental concept is the same, the essential distinction is that the ‘routine rates’ are based on population averages, whereas the ‘epidemiological rates’ are based on adding together carefully measured units of individual person-time to give a precise denominator. They may also be presented slightly differently: routine incidence rates are usually described per 100 000 people per year, whereas in epidemiological studies using individual data they are usually shown per 100 000 person years.

Therefore, when we are working with routine data, an incidence rate is calculated by dividing the total number of new cases of a specific disease in a specified period, usually one year, by the average number of people in the population during the same period. Incidence rate is a true rate because it measures the number of new infections per year.

Incidence rate

The TB report also indicated that in 2008 there were an estimated 9.4 million incident cases of TB globally, equalling 139 cases per 100 000 population. (3) Figure 1 gives an illustration of incidence rates calculated from routinely collected data.

TB incidence rates 2008

Figure 1. Estimated TB incidence rates in 2008. (3)

Cumulative incidence is a simpler measure of incidence and it measures the proportion of people who develop disease during a specified period:

Cumulative incidence

In a statistical sense the cumulative incidence is the probability or risk of individuals in the population getting the disease during the specified period. In the outbreak setting, the term attack rate is often used as a synonym for risk. In practice, cumulative incidence can only be calculated when we have a clearly defined group of people who are (almost) all followed for the whole follow-up period, and therefore it is mainly calculated in epidemiological (cohort or trial) studies.

For example, after the first case of pandemic influenza A (H1N1) virus was detected in Kenya, a group of 33 contacts of the first case were followed-to in order to assess whether they were also infected. Out of these contacts 11 were found positive, giving an attack rate of 33% (11 out of 33) during June 24-30. (5) Note that it is always important to specify the time period.

The information presented in this article will guide you to correctly use and interpret the measures of disease frequency correctly, and makes you aware that there are some differences in the use in epidemiological studies and for routinely collected data.

References:

  1. World Health Organisation. World Malaria Report 2009. Geneva: WHO; 2009. Link to report
  2. Baral S, Trapence G, Motimedi F, Umar E, Lipige S, Dausab F, Beyrer C. HIV Prevalence, risks for HIV infection, and human rights among men who have sex with men (MSM) in Malawi, Namibia, and Botswana, PloS ONE 4(3):e497. Link to article
  3. World Health Organisation. Global Tuberculosis Control – A short update to the 2009 report. Geneva: WHO; 2009. Link to report
  4. Auvert B, Taljaard D, Lagarde E, Sobngwi-Tambekou J, Sitta R, Puren A. Randomized, controlled intervention trial of male circumcision for reduction of HIV infection risk: the ANRS 1265 trial. PLoS Med 2 (11):e298. Link to article
  5. Centers for Disease Control and Prevention. Introduction and transmission of 2009 pandemic influenza A (H1N1) Virus-Kenya, June-July 2009. MMWR Morb Mortal Wkly Rep 2009, 58:1143-6. Link to article
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Format for a quantitative research article

In November 2011 I posted a format that I developed for a quantitative research proposal on my website. This has become one of my most popular posts (watched almost 7000 times in the past year) and I have received many comments from students/professionals that this has been helpful. I therefore decided to also develop a format for a quantitative research article.

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    ALLAN KAMBANDA

    good presentation of information, it will help me in my studies in community health...as a doctor in training, (MBBS)

    Reply

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