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Applying Criterion-Referenced Standards to Epidemiology

This is an excerpt from Measurement and Evaluation in Human Performance 6th Edition With HKPropel Access by James R. Morrow, Jr.,Dale P. Mood,Weimo Zhu & Minsoo Kang.

Epidemiology is the study of the distribution and determinants of health-related states and events in populations and the applications of this study to the control of health problems (Last, 1992). This fundamental science of public health uses hypothesis testing, statistics, and research methods to develop an understanding of the frequency and distribution of mortality (death) and morbidity (disease or injury), and more importantly, the risk factors that are causally related to mortality and morbidity (Stone, Armstrong, Macrina, and Pankau, 1996). Descriptive epidemiology seeks to describe the frequency and distribution of mortality and morbidity according to time, place, and person—for instance, what was the rate of breast cancer in adult women in the United States during the 1990s? Analytical epidemiology pursues the causes and prevention of mortality and morbidity—for example, does obesity increase the risk of breast cancer in women? In women who are obese, does moving into a healthy weight range lower the risk of breast cancer? Epidemiology uses research approaches that are both prospective (tracking a study group into the future) and retrospective (looking back at a database of previously collected data). Epidemiologists use a variety of research designs, some of which are depicted in table 7.9.

Table 7.9 Research Designs in Epidemiology

In the field of human performance, modern epidemiologic research has clearly discovered the increased risk for a variety of chronic diseases related to a sedentary or physically inactive lifestyle (Ainsworth and Matthews, 2001; Caspersen, 1989; USDHHS 1996; 2008). This type of research is becoming increasingly popular in human performance measurement. It is closely related to CRT because the variables are often nominal in nature and some of the statistics used are those calculated from a 2 × 2 contingency table. The criterion measure is categorical (e.g., alive or dead; has a disease or does not have a disease). The predictor variables can be nominal (e.g., gets sufficient physical activity or does not get sufficient physical activity) or continuous (e.g., weight). It is when the predictor and criterion variables are both nominal that epidemiologic statistics are most like those of CRT (and can even be calculated with SPSS Crosstabs or Excel).

Epidemiology requires the use of advanced statistics and complicated multivariate models to understand the relationships between risk factors and mortality and morbidity while controlling for confounding factors or extraneous variables. Those types of analyses are beyond the scope of this text and are not necessary for us to know at present. However, we do need to know some basic procedures and statistics to understand how CR standards play a role in epidemiology. Two basic statistics are the calculations of incidence and prevalence.

  • Incidence. The number, proportion, rate, or percentage of new cases of mortality and morbidity. Incidence could be calculated in a randomized clinical trial or a prospective, longitudinal cohort study.
  • Prevalence. The number, proportion, rate, or percentage of total cases of mortality and morbidity. Prevalence is calculated in a cross-sectional study.

Values of incidence and prevalence are often expressed as a rate, which is the number of cases per unit of the population. An example would be 10 cases per 1000 in the population or 100 deaths per 100,000 in the population. The value of expressing incidence and prevalence as a rate is that two populations of different sizes can be compared—for example, the mortality rate in Dallas, Texas, can be compared with that in New York City.

In analytical epidemiology, we convert measures of incidence or prevalence into estimates of risk:

  • Absolute risk. The risk (proportion, percentage, rate) of mortality or morbidity in a population that is exposed or not exposed to a risk factor.
  • Relative risk. The ratio of risks between the exposed or unexposed populations. This statistic is calculated with incidence measures.
  • Odds ratio. An estimate of relative risk used in prevalence studies.
  • Attributable risk. The risk of mortality and morbidity directly related to a risk factor. This risk can be thought of as the reduction in risk related to removing a risk factor.

Let’s combine CR standards with an example of a simple analysis in epidemiology by examining the results of a theoretical epidemiologic study about the relationship of cholesterol and mortality attributable to heart attack. High cholesterol is defined by the American Heart Association and the National Heart, Lung, and Blood Institute as a value of 240 mg/dl or above (e.g., the CR standard for total cholesterol). Examine table 7.10, which is a 2 × 2 contingency table. We have labeled the cells A, B, C, and D to make all descriptive and analytical calculations quite simple. We also conduct our analyses on incidence and prevalence bases. In this study, 56 participants with high cholesterol and 44 without high cholesterol are compared. All had a genetic history of early coronary heart disease. Note that both variables are categorical in this example.

Table 7.10 Results of a Hypothetical Study Relating Cholesterol and Heart Attack Mortality

If you examine all the results in figure 7.5 you can observe the following:

  • All calculations can be made from the easy-to-follow formulas using the A, B, C, and D cell identifiers.
  • The absolute risk for heart attack death was 32% for all participants, 45% for those with high cholesterol, and 16% for those without high cholesterol.
  • If a participant had high cholesterol, the relative risk of 2.81 indicated that high cholesterol elevated the risk of heart attack mortality by a multiplier of 2.81.
  • If a participant had high cholesterol, the odds ratio indicated elevated odds of heart attack mortality by a multiplier of 4.26.
  • The attributable risk indicated that high cholesterol contributed to 64% of the heart attack mortality. Thus, heart attack mortality could be reduced by 64% if high cholesterol were no longer present in people of this population.

The example used in table 7.10 and figure 7.5 serves as a simple demonstration of some basic concepts and analyses in epidemiology. However, research studies using epidemiologic methods have demonstrated strong relationships between levels of physical activity and fitness and a variety of mortality and morbidity outcomes from chronic diseases. Chapter 10 will discuss some of those specific findings in more detail.

Figure 7.5 Statistical analysis of epidemiological data in table 7.10.
Figure 7.5 Statistical analysis of epidemiological data in table 7.10.

Mastery Item 7.4

Note that in table 7.10, a positive exposure (i.e., classified as having high cholesterol) is listed in the first row, followed by negative exposure in the second row. Likewise, the positive outcome (record showed heart attack death) is listed in the first column and the negative outcome is listed in the second column. We recommend that you construct the contingency table in this fashion. You can reorder either or both of the variables and arrive at similar conclusions, but setting it up as we suggest will generally make your interpretation more understandable.

More Excerpts From Measurement and Evaluation in Human Performance 6th Edition With HKPropel Access