Never mix incident with prevalent cases in epidemiologic analyses. This count of incident cases over time in a population is called incidence. For incident cases, specify the period during which the cases occurred. Always check whether data sources are providing incident (new events among the population) or prevalent (an existing event at a specific point in time) cases. Use alternating light shading of rows to assist readers in following data across a table.Ī first and simple step in determining how much is to count the cases in the population of interest.Avoid using dividing lines, grids, and other embellishments within the data space.Place numbers close together, which might require using abbreviations in column headings.Align columns of numbers on the decimal point (or ones column).Use the table layout to guide the eye.When the row or column headings are numeric (e.g., age groups), they should govern the order of the data.Organizing data columns and rows by the magnitude of the marginal summary statistics is often helpful.Use the most important epidemiologic features on which to sort the data.Organize data by magnitude (sort) across rows and down columns.Numbers are more easily compared down a column than across a row.Use columns for most crucial data comparisons.Provide marginal averages, rates, totals, or other summary statistics for rows and columns whenever possible.Effective figures refers to numbers that contain additional, leading non-zero digits that do not vary (e.g., 123, 145, 168, or 177) or vary slightly (see BMI columns in Table 6.3) within a column or row.More precision is usually not needed for epidemiologic purposes.Using three or more significant figures interferes with comparison and comprehension.Round data to two statistically significant or effective numbers.For example, initial respiratory symptoms might indicate exposure through the upper airways, as in Table 6.2. If the disease cause is unknown, this arrangement can assist the epidemiologist in developing hypotheses regarding possible exposures. Commonly in descriptive epidemiology, you organize cases by frequency of clinical findings ( Table 6.2) (3). The line-listing in Table 6.1 has been sorted by days between vaccination and onset to reveal the pattern of this important time–event association. This arrangement facilitates sorting to reorganize cases by relevant characteristics. In arranging analytical tables, you should begin with the arrangement of the data space by following a simple set of guidelines ( Box 6.3) ( 1).Ĭases are customarily organized in a table called a line-listing ( Table 6.1) ( 2). A well-structured analytical table that is organized to focus on comparisons will help you understand the data and explain the data to others. In addition to the previously mentioned elements in common to all data displays ( Box 6.2), tables have column and row headings that identify the data type and any units of measurement that apply to all data in that column or row. Tables are commonly used for characterizing disease cases or other health events and are ideal for displaying numeric values. Through this process of organization, inspection, and interpretation of data, descriptive epidemiology serves multiple purposes ( Box 6.1). After the data are organized and displayed, descriptive epidemiology then involves interpreting these patterns, often through comparison with expected (e.g., historical counts, increased surveillance, or output from prevention and control programs) patterns or norms.
The last three questions are assessed as patterns of these data in terms of time, place, and person. “How much?” is expressed as counts or rates.
The first question is answered with a description of the disease or health condition. This task, called descriptive epidemiology, answers the following questions about disease, injury, or environmental hazard occurrence: As a field epidemiologist, you will collect and assess data from field investigations, surveillance systems, vital statistics, or other sources.
0 kommentar(er)
