ERIC at the UNC CH Department of Epidemiology Medical Center. Cross- sectional studies, in cross-sectional studies we do not follow individuals over time. Instead, we only Karin B. Yeatts, PhD, MS. Second Edition The prevalence ratio (PR) is analogous to the risk ratio (RR) of cohort relationship. These studies. introduction to various aspects of risk, attributable and relative risk Risk in Pharmacoepidemiology: • Health research involves the estimation of risk. . TIME RISK RELATIONSHIP: • The outcome of an exposure to a drug is. Clinical epidemiology and evidence-based medicine glossary: Epidemiology terminology. John Gay, DVM PhD DACVPM AAHP FDIU VCS in the at-risk group that experience the event during one time unit (per hour, day, week, month, year, ). . Association is necessary for a causal relationship to exist but association.
Other sets of risk factors than can cause the condition of interest which coexist with the set of factors of interest, that is; those things that cause "red herring" cases in outbreak investigations. Time between exposure to a specific risk factor and the initiation of the disease. Generally the longer the induction period, the more difficult is the assessment of the association between the risk factor s and the disease and thus the evaluation of causality. Time between biologic onset of disease and disease detection clinical - appearance of clinical signs or subclinical - positive diagnostic tests.
A term applied to risk factors that a veterinarian can modify or eliminate in a specific situation to prevent or correct the disease. The risk in the group exposed to a risk factor minus the risk in the group not exposed to that risk factor. The underlying or background risk without that exposure is usually assumed to be the same in both groups.
Population attributable risk The proportion of all cases of a disease that are attributable to an exposure or risk. This is the proportion of the disease in the population that would be eliminated if that exposure were eliminated or prevented. A ratio ranging from 0 to infinity that indicates the strength of the association between the risk factor and the disease outcome and is calculated by dividing the risk in the group exposed to a risk factor by the risk in the unexposed group.
A RR value statistically significantly larger than 1 indicates the exposure is associated with increased risk of disease, a RR value not statistically significantly different from 1 indicates there is no association between the exposure and the risk of disease, and a RR value statistically significantly less than 1 indicates the exposure is associated with decreased risk of disease; that is, the exposure is protective.
Exposure Odds Ratio OR: Otherwise, it over-estimates relative risk. The odds-ratio is interpreted in the same fashion as relative risk. NNT is the number of individuals a clinician would need to treat to prevent one adverse outcome in that group of similar individuals at risk of the problem. This measure establishes the benefit of an intervention compared to doing nothing against a disease in individuals at risk of that disease when adverse events would still be expected even with the intervention e.
NNT is the reciprocal of the attributable risk or the reciprocal of the difference between the proportions of treated and non-treated individuals experiencing events over some period of time. An association exists if two variables appear to be related by a mathematical relationship; that is, a change of one appears to be related to the change in the other.
Association is necessary for a causal relationship to exist but association alone does not prove that a causal relationship exists. A correlation coefficient or the risk measures often quantify associations. Negative Association Inverse Relationship: The magnitude of one variable appears to move in the opposite direction of the other associated variable.
The correlation coefficient is negative and, if the relationship is causal, higher levels of the risk factor are protective against the outcome. Positive Association Direct Relationship: The magnitudes of both variables appear to move together up or down. The correlation coefficient is positive and, if the relationship is causal, higher levels of the risk factor cause more of the outcome. The combination of necessary and sufficient factors e. The actions of risk factors acting individually, in sequence, or together that result in disease in an individual.
These pathways are often different with different sets of risk factors for individuals in different situations. Understanding these pathways and their differences is necessary to devise effective preventive or corrective measures interventions for a specific situation. These examples show that methods for measuring exposure, even for addressing the same clinical question, can vary.
Thus, the intent of this chapter is to identify important issues to consider in the determination of exposure and describe the strengths and limitations of different options that are available given the nature of the research question. Conceptual Considerations for Exposure Measurement Linking Exposure Measurement to Study Question A study's conceptual basis should serve as the foundation for developing an operational definition of exposure.
That is, if the objective of the study is to examine the impact of chronic use of a new medication on patient outcomes, then the measurement of exposure should match this goal.
Specifically, the definition of exposure should capture the long-term use of the medication and not simply focus on a single-use event. The exposure measurement could include alternative measures that capture single-use events; however, the exposure measurement should be able to distinguish short-term use from long-term use so that the primary study question can be adequately addressed.
The biological mechanism of action, whether known or hypothesized, should guide the development of the exposure definition. If the primary exposure of interest in the analysis is a medication, it may be relevant to briefly describe how the pharmacology, the pharmacodynamics the effects of medication on the bodyand the pharmacokinetics the process of drug absorption, distribution, metabolism, and excretion from the body informed the exposure definition.
For example, in a comparison of bisphosphonates for the prevention of osteoporotic fractures, the exposure definition would need to be tailored to the specific bisphosphonate due to differences in the pharmacokinetics of the various medications. The definition of exposure for ibandronate, which is a bisphosphonate indicated for osteoporosis administered once per month and has a very long half-life, would likely need to be different than the definition of exposure for alendronate, a treatment alternative that is administered orally daily or weekly.
When operationalizing exposure to these two medications, it would be insufficient to examine medication use in the last week for identifying current use of ibandronate, but sufficient for current use of alendronate.
Analogous scenarios can be envisioned for nonpharmacological interventions. For example, in a study examining a multivisit educational intervention for weight loss, the effect of the intervention would not be expected until individuals participated in at least one or some of the sessions.
Therefore, it would not be appropriate to create an exposure definition based on registration in the program unless subject participation could be verified.
Several examples of exposure and event relationships are displayed in Figure 4. These panels show how an exposure might be associated with an increased likelihood of a benefit or harm. The first column A—C shows multiple exposures over time where the timing of the exposure is not consistent and stops midway through the observation period.
Clinical Epidemiology & EBM Glossary: Epidemiology Terminology
In defining exposure under this scenario, it would be important to define the cumulative amount of exposure. For example, if evaluating the comparative effectiveness of antibiotics for the treatment of acute infection, there may be a threshold of exposure above which the medication is considered effective treatment. In this case, the exposure measurement should measure the cumulative exposure to the medication over the observation timeframe and define individuals as exposed when the threshold is surpassed if the exposure variable is dichotomized.
This situation contrasts with that in Panel B, in which the association between the exposure and the effect decreases rapidly after the exposure is removed.
This type of association could be encountered when evaluating the comparative effectiveness of antihypertensive medications for blood pressure control.
In this case, there may be a some minimum amount of exposure necessary for the medication to begin to have an effect and b an association between the frequency of administration and effectiveness. When the exposure is removed, however, blood pressure may no longer be controlled and effectiveness decreases rapidly. In operationalizing this exposure-event association it would be necessary to measure the amount of exposure, the frequency with which it occurred, and when exposure ended.
In panel C, there is an increase in the likelihood of the outcome with each exposure that diminishes after the exposure is removed. This may represent an educational weight loss intervention.
In this example, continued exposure improves the effectiveness of the intervention, but when the intervention is removed, there is a slow regain of weight. Similarly to Panel B, it is important to consider both the timing and the amount of exposure for the weight loss intervention.
Because the effectiveness diminishes slowly only after the exposure is removed, it is important to consider a longer exposure window than when effectiveness diminishes rapidly. The second column shows scenarios where the exposure of interest occurs at a single point in time, such as a surgical procedure or vaccination.
The relationship in panel D shows an immediate and sustained effect following exposure. This could represent a surgical procedure and is a situation in which the measurement of exposure is straightforward as long as the event can be accurately identified, as exposure status would not vary across the observation period.
Measurement of exposure in panels E and F is more complex. In panel E, the exposure is a single event in time with an immediate effect that diminishes over time. An example of this could be a percutaneous coronary intervention PCI where the time scale on the x-axis is measured in years.
There is an immediate effect from the exposure intervention of opening the coronary arteries that contributes to a reduced risk of acute myocardial infarction AMI. However, the effectiveness of the PCI decreases over time, with the risk of AMI returning to what it was prior to the intervention.
In this example, it is clearly important to identify and measure the intervals at which the risk is modified by PCI. After a sufficient amount of time has passed from the initial PCI, it may not be appropriate to consider the individual exposed. At the very least, the amount of time that has passed postexposure should be considered when creating the operational definition of exposure.
Panel F represents a scenario where the effect from a single exposure is not immediate but happens relatively rapidly and then is sustained. Such a situation could be imagined in a comparative effectiveness study of a vaccination. The benefits of the vaccination may not be realized until there has been an appropriate immunological response from the individual, and the exposure definition should be created based on the expected timing of the response, consistent with clinical pharmacological studies of the vaccine.
The final column of Figure 4. In each of these examples, it is important to consider the cumulative amount of exposure when developing the exposure definition. In panel G, the depicted relationship shows a dose-response in which the risk or benefit increases at a slower rate after a threshold of exposure is reached. An example of this could be a behavioral intervention that includes personal counseling for lifestyle modifications to improve hypertension management.
There may be a minimum number of sessions needed before the intervention has any effect and, after a threshold is reached, the incremental effectiveness of a single session exposure is diminished. In measuring exposure in this example, it would be important to determine the number of sessions that an individual participated in, especially if multiple exposure categories are being created.
This example may be best illustrated by a comparative safety evaluation of the impact of oral corticosteroids on fracture risk. Continued exposure to oral corticosteroids may continue to increase the risk of fracture associated with their use. Induction and Latent Periods In creating exposure definitions, it is also important to consider the induction and latent periods associated with the exposure and outcome of interest. During the induction period, additional exposures will not influence the likelihood of an event or outcome because all of the exposure necessary to cause the event or outcome has been completed.
For example, additional exposure to the vaccine for mumps during childhood will not increase or decrease the likelihood of getting mumps once the initial exposure to the vaccine has occurred. The latent period is the time from when the outcome starts to when the outcome is identified. In other words, it is the period between when the disease or outcome begins and when the outcome is identified or diagnosed.
Similar to the induction period, exposures during the latent period will not influence the outcome. Practically, it may be very difficult to distinguish between latent and induction periods, and it may be particularly difficult to identify the beginning of the latent period. However, both periods should be considered and ultimately not included in the measurement of exposure. In practical terms, it is sufficient to consider the induction and latent period as a single time period over which exposures will not have an effect on the outcome.
A timeline depicting multiple exposures, the induction period, the latent period, and the outcome of interest is shown in Figure 4. Principles of exposure measurement in epidemiology. Oxford University Press Inc. As an example of the incorporation of both the induction and latent periods in exposure measurement, consider the evaluation of the comparative effectiveness of a cholesterol-lowering medication for the prevention of myocardial infarction.
First, the induction period for the medication could be lengthy if the effectiveness is achieved through lowering cholesterol to prevent atherosclerosis. That is, the time from when the myocardial infarction starts to when it is identified will be relatively short. Any medication use that occurs during the induction and latent periods should not be included in the operational definition of exposure.
For this example, it would be inappropriate to consider an individual exposed to the medication of interest if they had a single dose of the medication the day prior to the event, as this would not have contributed to any risk reduction for the event.
Because of the short latent period, it would be unlikely that exposures occurred during that timeframe. Exposure should be measured during a time period when the use of lipid-lowering medications is expected to have an effect on the outcome. Therefore, the exposure definition should encompass a timeframe where the benefit of lipid-lowering medications is expected, and this should be justified based on what is known about the link between atherosclerosis and myocardial infarction and the known biological action of lipid lowering medications.
Changes in Exposure Status Another relevant consideration when developing exposure measurement relates to changes in exposure status, particularly if patients switch between active exposures when two or more are being investigated. If this is true, it would be necessary to extend the measurement of exposure beyond the point when the switch occurred. Similarly, depending upon the effects of the intervention that was started, it is important to consider its biological effects when developing the exposure definition following a switch.
Data Sources Exposure Measurement Using Existing Electronic Data The ability to measure exposures based on available data is also an important consideration when creating an operational definition of exposure. Is there a consistent and accurate way to identify the exposure in the dataset? If the exposure of interest is a surgical procedure, for example, is there a single code that is used to identify that procedure or is it necessary to expand the identification beyond a single code?
If using more than one code, do the codes only identify the procedure of interest or is there variability in the procedures identified? For medications, the data likely reflect prescriptions or medication orders EHR or pharmacy dispensings PBM or health insurer administrative claims but not actual use. Is it necessary to know whether a given medication was taken by the patient on a particular day or time of day?
To illustrate these issues, consider the case in which the primary intervention of interest is colonoscopy. Depending on the source of the data, colonoscopies may be identified with a CPT code e. To accurately identify this procedure, it is necessary to consider more than one type of procedure code when classifying exposure.
All of these may reliably identify exposure to the procedure, but use of only one may be insufficient to identify the event. This may be influenced by the source of the data and the purpose of the data. For example, one set of codes from the list may be useful if using hospital billing data, while another may be useful for physician claims data. When making this decision, it is important for the investigators to balance the selection of the codes and the accurate identification of the exposure or intervention; creating a code list that is too broad will introduce exposure misclassification.
Overall, it will be important to provide evidence on the most accurate and valid mechanism for the identification of the exposure or intervention across the datasets being used in the analysis. Researchers should therefore cite any previous validation studies or perhaps conduct a small validation study on the algorithm proposed for the exposure measurement to justify decisions regarding exposure identification. Issues in selection of a data source are covered in detail in chapter 8 Data Sources.
Exposure Measurement via Prospective Data Collection In addition to using existing data sources, it may be feasible or necessary to prospectively collect exposure information, in some circumstances from patients or physicians, for use in an observational comparative effectiveness study.
Abstraction of paper medical records is a type of prospective data collection that draws on existing medical records that have not been compiled in a research-ready format. The validity and accuracy of self-reported exposure information may depend on the type of exposure information being collected i.
The characteristics of the exposure and the patient population are likely to influence the validity of the information that is collected. The recall of information on a surgical procedure may be much more accurate than the recall of the use of medications. For example, women may be able to accurately recall having had a hysterectomy or tubal sterilization, 7 while their ability to recall prior use of NSAIDs may be quite inaccurate.
In the medication example, factors associated with better recall were more recent use of a medication and repeated use of a medication. Similar to the use of other sources of data for exposure measurement, use of this type of data should be supported by evidence of its validity. Creating an Exposure Definition Time Window A key component in defining exposure is the time period during which exposure is defined, often referred to as the time window of exposure.
The exposure time window should reflect the period during which the exposure is having its effects relevant to the outcome of interest.
As noted in the statin example above, the exposure time window to evaluate the effectiveness of statins for preventing AMIs should be over the time period that statins can have their impact on cardiovascular events, which would be over the preceding several years rather than, for instance, over the 2 weeks immediately preceding an event.
At the same time, practical limitations of the study data should be acknowledged when defining the exposure time window. For example, lifetime exposure to a medication may be the ideal definition for an exposure in some circumstances but most existing datasets will not contain this information. It then becomes necessary to justify a more pragmatic approach to defining exposure given the length of followup on individuals available in the dataset. A variety of approaches to defining exposure time windows have been used in both cohort and case-control studies.
As highlighted in the introductory section of this chapter, investigators have selected different exposure time windows even when examining the same clinical question. In most of these examples, the choice of the exposure time window is not clearly justified. Ideally, this choice should be related back to the conceptual framework and biological plausibility of the question being addressed.
However, as noted above, there are pragmatic limitations to the ability to measure exposure, and in the case where selection of the exposure time window is arbitrary or limited by data, sensitivity analyses should be performed in order to evaluate the robustness of the results to the time window.