What is the difference between prevalence measures and incidence measures

If the 18 households included 86 persons, calculate the secondary attack rate. Incidence rate or person-time rate is a measure of incidence that incorporates time directly into the denominator.

A person-time rate is generally calculated from a long-term cohort follow-up study, wherein enrollees are followed over time and the occurrence of new cases of disease is documented. Similar to the incidence proportion, the numerator of the incidence rate is the number of new cases identified during the period of observation. However, the denominator differs. The denominator is the sum of the time each person was observed, totaled for all persons.

This denominator represents the total time the population was at risk of and being watched for disease. Thus, the incidence rate is the ratio of the number of cases to the total time the population is at risk of disease. In a long-term follow-up study of morbidity, each study participant may be followed or observed for several years. One person followed for 5 years without developing disease is said to contribute 5 person-years of follow-up.

What about a person followed for one year before being lost to follow-up at year 2? Therefore, the person followed for one year before being lost to follow-up contributes 1. The same assumption is made for participants diagnosed with the disease at the year 2 examination — some may have developed illness in month 1, and others in months 2 through So, on average, they developed illness halfway through the year.

The denominator of the person-time rate is the sum of all of the person-years for each study participant. So, someone lost to follow-up in year 3, and someone diagnosed with the disease in year 3, each contributes 2.

Example A: Investigators enrolled 2, women in a study and followed them annually for four years to determine the incidence rate of heart disease. After one year, none had a new diagnosis of heart disease, but had been lost to follow-up. After two years, one had a new diagnosis of heart disease, and another 99 had been lost to follow-up. After three years, another seven had new diagnoses of heart disease, and had been lost to follow-up.

After four years, another 8 had new diagnoses with heart disease, and more had been lost to follow-up. The study results could also be described as follows: No heart disease was diagnosed at the first year. Heart disease was diagnosed in one woman at the second year, in seven women at the third year, and in eight women at the fourth year of follow-up. One hundred women were lost to follow-up by the first year, another 99 were lost to follow-up after two years, another were lost to follow-up after three years, and another women were lost to follow-up after 4 years, leaving women who were followed for four years and remained disease free.

Calculate the incidence rate of heart disease among this cohort. The incidence proportion underestimates the true rate because it ignores persons lost to follow-up, and assumes that they remained disease-free for all four years. Example B: The diabetes follow-up study included diabetic women and 3, nondiabetic women. By the end of the study, 72 of the diabetic women and of the nondiabetic women had died. The diabetic women were observed for a total of 1, person years; the nondiabetic women were observed for a total of 36, person years.

Calculate the incidence rates of death for the diabetic and non-diabetic women. Prevalence, sometimes referred to as prevalence rate , is the proportion of persons in a population who have a particular disease or attribute at a specified point in time or over a specified period of time. Prevalence differs from incidence in that prevalence includes all cases, both new and preexisting, in the population at the specified time, whereas incidence is limited to new cases only.

Point prevalence refers to the prevalence measured at a particular point in time. It is the proportion of persons with a particular disease or attribute on a particular date. Period prevalence refers to prevalence measured over an interval of time. It is the proportion of persons with a particular disease or attribute at any time during the interval.

The value of 10 n is usually 1 or for common attributes. The value of 10 n might be 1,, ,, or even 1,, for rare attributes and for most diseases. In a survey of 1, women who gave birth in Maine in , a total of reported taking a multivitamin at least 4 times a week during the month before becoming pregnant. We would then need to do additional blood tests to determine how many new cases developed during the span of time.

Because some of the residents might die or be transferred to other facilities during the year, we ideally would like to take blood tests frequently, but for financial and logistical reasons, we might simply conduct a second series of blood tests after one year. Note that we are describing the time span, i. When incidence is determined in this way, that is, by evaluating the presence of disease at the beginning and then dividing the number of known new cases by the number of people "at risk" at the beginning, it is referred to as a cumulative incidence and can also be thought of as the incidence proportion.

While people commonly refer to this as a 'rate,' this is really a proportion. It is the proportion of the "at risk" group that developed disease over a stated block of time. Cumulative incidence is easy to measure and is commonly used in a wide variety of circumstances. For example, if we wanted to determine the incidence of AIDS in Massachusetts during calendar year , it isn't feasible for us to check every citizen at the beginning and end of the year.

Census data gives us a rough idea of how many people lived in Massachusetts during , and AIDS is a reportable disease, so we could go to the MA Department of Public Health and obtain an estimate of the number of people with AIDS at the beginning of the year, and we could subtract this number from the population size to get a denominator that represents the number of people "at risk" of developing AIDS.

This is our numerator. So, the cumulative incidence would be:. So, the cumulative incidence was about 9. Note that the denominator is just an estimate based on the last census.

In reality, people were being added to and subtracted from the population continually as a result of births, deaths, moving into the city, and moving out. We also didn't take into account exactly when they developed AIDS, although we probably don't care whether they developed it earlier or later within a one year period.

Nevertheless, this cumulative incidence is a useful number, and it is relatively easy to get the information we need to calculate it. It is important to specify the time period when reporting cumulative incidence. In the fall semester of there were students in EP at the beginning of the semester, and 55 of them reported developing a cold or other respiratory infection during the semester.

The time period of observation is expressed in words. Remember that a rate almost always contains a dimension of time. Therefore, the incidence rate is a measure of the number of new cases "incidence" per unit of time "rate". Compare this to the cumulative incidence incidence proportion , which measures the number of new cases per person in the population over a defined period of time. Because studies of incidence in epidemiology are conducted among groups of people as they move through time, the denominator is actually a combination of the number of people and the amount of time.

This is expressed as person-time. The time units can be expressed in days, months, or years, but should be tied to the length of the study and aid interpretation of the results. The most frequently encountered expression is "person-years". The characteristics of cumulative incidence and incidence rate are illustrated in the examples below. Note: While we generally refer to cumulative incidence incidence proportion and incidence rate as measures of disease frequency, they can be applied to any sort of occurrence.

For example, treatments to cure or relieve disease conditions are also measured using the incidence proportion or rate, as we will see in the example below.

The key thing to keep in mind is that either measure of incidence unlike prevalence measures a transition from one state to another: well to sick, sick to well, alive to dead, unborn to born, etc. Suppose you were asked to analyze the data from a small preliminary clinical trial with 20 subjects. All subjects had a comparable degree of knee pain from osteoarthritis, and they were being compared with respect to pain relief after receiving a standard pain medication Drug B or a new pain medication Drug A.

The 20 patients were randomly assigned to one drug or the other, and there were ten subjects in each group. After receiving the medication, the investigators checked on the subjects at hourly intervals to see if the subjects had had relief of pain.

For each subject, the time at which pain relief occurred was recorded. Results are illustrated in the graph below. Link to a text description of the results. The "X"s indicate when subjects reported pain relief. The "O"s at the end indicate subjects who did not report relief of pain.

Whenever cumulative incidence is determined, one determines the proportion of subjects who experienced the outcome of interest during a block of time, without taking into account when subjects developed the outcome. Visually, however, it is clear that if we consider when subjects experienced relief, the rate was greater in the subjects receiving the new drug. In this hypothetical study all subjects were observed for a maximum of 10 hours, and some did not achieve pain relief, while others got relief after varying periods of time.

We can calculate the average rate of pain relief in each group by adding up the duration of pain for subjects in each group and dividing by the number of subjects in each group. Note that once a subject experiences the outcome of pain relief, they are no longer considered to be under observation. So, the rate of pain relief was greater in the group receiving the new drug. What we have calculated is the incidence rate.

This is a true rate, because time is an integral part of the calculation, analogous to miles per hour a rate of speed or gallons per minute a rate of flow. Question: A participant in a prospective cohort study or a randomized clinical trial stops contributing additional "disease-free observation time" when they develop the outcome of interest or become lost to follow-up for any reason death, failure to respond to phone calls, letters and emails, etc.

Does this mean that they are no longer in the study? The study was conducted in a group of female prostitutes. The the remaining ten women were followed for six years beginning in January Each woman was contacted and retested at the beginning of January each year. The table below summarizes the findings these ten subjects. The dashed lines indicate continued follow-up. The incidence rate , however, can take these problems into account, because the denominator is the total "at risk" observation time contributed by all ten subjects.

The column at the far right indicates each subject's "at risk" observation time, and the sum for the ten subjects was 26 years. Note that person-time stopped being counted as soon as the subject was found to be HIV positive, because the subject was no longer "at risk" of developing the outcome—they already had experienced it.

For example, Subject 1 contributed one person-year even though she was followed for all six years. Incidence rates are often computed in prospective cohort studies e. It is more accurate than cumulative incidence, but it requires repeated follow-up observations on each subject, and studies like this can be very expensive and time consuming. Also consider that subjects are sometimes recruited into studies at different times. Each subject's disease-free observation time or "at risk" time can be calculated as the time from their entry into the study until a they get the disease, b they become lost to follow-up, or c the study ends.

For example, consider a hypothetical clinical trial that was conducted to determine whether taking low-dose aspirin reduced the frequency of heart attacks in middle-aged and elderly men. The time line below summarizes events 12 subjects labeled , all of whom were allocated to the placebo-treated group.

The first 5 subjects were enrolled in , and the next 7 subjects were enrolled one year later. All subjects began taking aspirin upon enrollment.

Therefore their "exposure" to aspirin began upon enrollment as indicated by the solid black dots. The red "X"s indicate when subjects had a heart attack; their exposure time at risk ends there, since having a first heart attack means that they were no longer at risk of having a first heart attack; they had the outcome of interest at that point. Subject 2 had a heart attack in ; subject 5 had one in ; subject 11 had one in The open circles indicated six subjects who were lost to follow-up.

They stopped responding to all requests for follow up after that point. We know that they had not had a heart attack up to that point, but we don't know what happened to them after that, so they stop contributed observed exposure time at risk. Subject 1 was lost to follow up in ; 6 was lost in ; 7 was lost in ; 8 was lost in ; 9 was lost in ;.

All of this information can be taken into account in order to compute the average rate at which heart attacks occur in this group of 12 men being treated with low-dose aspirin.

We can do this in a way that is analogous to example 2 above. There were 3 heart attacks, and we divide this by the total amount of time that the men were exposed and at risk of developing a heart attack. For each man the exposure time at risk is the time from their entry into the study until one of three endpoints: a the disease occurs, b the subject is lost to follow-up, or c the study concludes.

The exposure time at risk for each man is shown in the column at the far right of the figure, and if we add these, the total exposure time for the group was years.

Data collected from the Nurses' Health Study, a prospective cohort study, was used to compare rates of coronary artery disease in post-menopausal women using hormone replacement therapy HRT and post-menopausal women who had not used HRT.

The data was summarized in the table below. Women on postmenopausal hormones had an incidence rate of 30 events during 54, Women in the untreated group had 60 events during 51, In this study, incidence rates of MI myocardial infarction were compared among five groups of women based on their body mass index BMI.

There were certainly different numbers of women in the five groups, but for each group they computed the incidence rate by counting the number who developed MI and dividing by the group's total "at risk" time of observation. The result was then converted to the number per , person-years to facilitate comparison among the five groups. By convention, all three measures of disease frequency prevalence, cumulative incidence, and incidence rate are expressed as some multiple of 10 in order to facilitate comparisons.

Consider these three examples:. An example of this might be HIV if and when an efficacious vaccine becomes widely available. Incidence values can be converted into incidence rate values, also sometimes called person-time rate , if the incidence is measured over a known time period in a population of known size.

The equation below can then be used to calculate incidence rate values. This is then often expressed as a rate per number of the population which makes the value more relatable, giving the equation.

For example, 44, new cases numerator of acquired immunodeficiency syndrome AIDS were reported in the U. For , the U. This information can be used to monitor the effectiveness of vaccination schemes or changes in the preventative measures being used to see if they cause a knock-on reduction in disease incidence rate.

Equally, if incidence rates rise, it can act as a warning that existing preventative measures are failing and therefore guide remedial action. As incidence rate values are calculated using the duration of observation of each individual in the study population, it can be a useful measure for monitoring over long time periods as it accounts for individuals joining and leaving the study population.

One drawback of this measure, however, is that it therefore assumes the risk of disease for one person over 10 years is the same as 10 people over the course of one year, which is frequently not the case. For simplicity, however, if for example the incidence rate of influenza is being calculated for a given year, typically the numerator would be the number of reported cases that year in the study population e.

UK population size on July 1 as generally speaking in a situation like this, it provides a good estimate. It is however important to consider factors like this on a case by case basis as estimates like this are not suitable for all models.

In addition to incidence rate, a number of key measures of disease frequency can be calculated. These metrics and the data required for this are summarized in the table below. Point prevalence. Number of cases new and preexisting at a specific point in time. Population at that specific point in time. Period prevalence. Number of cases new and preexisting over a specified period of time.