Lesson 4: Displaying Public Health Data

Section 2: Tables

A table is a set of data arranged in rows and columns. Almost any quantitative information can be organized into a table. Tables are useful for demonstrating patterns, exceptions, differences, and other relationships. In addition, tables usually serve as the basis for preparing additional visual displays of data, such as graphs and charts, in which some of the details may be lost.

Tables designed to present data to others should be as simple as possible.(1) Two or three small tables, each focusing on a different aspect of the data, are easier to understand than a single large table that contains many details or variables.

A table in a printed publication should be self-explanatory. If a table is taken out of its original context, it should still convey all the information necessary for the reader to understand the data. To create a table that is self-explanatory, follow the guidelines below.

One-variable tables

In descriptive epidemiology, the most basic table is a simple frequency distribution with only one variable, such as Table 4.1a, which displays number of reported syphilis cases in the United States in 2002 by age group.(2) (Frequency distributions are discussed in Lesson 2.) In this type of frequency distribution table, the first column shows the values or categories of the variable represented by the data, such as age or sex. The second column shows the number of persons or events that fall into each category. In constructing any table, the choice of columns results from the interpretation to be made. In Table 4.1a, the point the analyst wishes to make is the role of age as a risk factor of syphilis. Thus, age group is chosen as column 1 and case count as column 2.

To create a frequency distribution from a data set in Analysis Module:

Select frequencies, then choose variable under Frequencies of.

(Since Epi Info 3 is the recommended version, only commands for this version are provided in the text; corresponding commands for Epi Info 6 are offered at the end of the lesson.)

Often, an additional column lists the percentage of persons or events in each category (see Table 4.1b). The percentages shown in Table 4.1b actually add up to 99.9% rather than 100.0% due to rounding to one decimal place. Rounding that results in totals of 99.9% or 100.1% is common in tables that show percentages. Nonetheless, the total percentage should be displayed as 100.0%, and a footnote explaining that the difference is due to rounding should be included.

The addition of percent to a table shows the relative burden of illness; for example, in Table 4.1b, we see that the largest contribution to illness for any single age category is from 35–39 year olds. The subsequent addition of cumulative percent (e.g., Table 4.1c) allows the public health analyst to illustrate the impact of a targeted intervention. Here, any intervention effective at preventing syphilis among young people and young adults (under age 35) would prevent almost half of the cases in this population.

The one-variable table can be further modified to show cumulative frequency and/or cumulative percentage, as in Table 4.1c. From this table, you can see at a glance that 46.7% of the primary and secondary syphilis cases occurred in persons younger than age 35 years, meaning that over half of the syphilis cases occurred in persons age 35 years or older. Note that the choice of age-groupings will affect the interpretation of your data.(3)

Two- and three-variable tables

Tables 4.1a, 4.1b, and 4.1c show case counts (frequency) by a single variable, e.g., age. Data can also be cross-tabulated to show counts by an additional variable. Table 4.2 shows the number of syphilis cases cross-classified by both age group and sex of the patient.

To create a two-variable tablefrom a data set in Analysis Module:

Select frequencies, then choose variable under Frequencies of. Output shows table with row and column percentages, plus chi-square and p-value. For a two-by-two table, output also provides odds ratio, risk ratio, risk difference and confidence intervals. Note that for a cohort study, the row percentage in cells of ill patients is the attack proportion, sometimes called the attack rate.

A two-variable table with data categorized jointly by those two variables is known as a contingency table. Table 4.3 is an example of a special type of contingency table, in which each of the two variables has two categories. This type of table is called a two-by-two table and is a favorite among epidemiologists. Two-by-two tables are convenient for comparing persons with and without the exposure and those with and without the disease. From these data, epidemiologists can assess the relationship, if any, between the exposure and the disease. Table 4.3 is a two-by-two table that shows one of the key findings from an investigation of carbon monoxide poisoning following an ice storm and prolonged power failure in Maine.(4) In the table, the exposure variable, location of power generator, has two categories — inside or outside the home. Similarly the outcome variable, carbon monoxide poisoning, has two categories — cases (number of persons who became ill) and controls (number of persons who did not become ill).

Table 4.4 illustrates a generic format and standard notation for a two-by-two table. Disease status (e.g., ill versus well, sometimes denoted cases vs. controls if a case-control study) is usually designated along the top of the table, and exposure status (e.g., exposed versus not exposed) is designated along the side. The letters a, b, c, and d within the 4 cells of the two-by-two table refer to the number of persons with the disease status indicated above and the exposure status indicated to its left. For example, in Table 4.4, “c” represents the number of persons in the study who are ill but who did not have the exposure being studied. Note that the “H_i” represents horizontal totals; H₁ and H₀ represent the total number of exposed and unexposed persons, respectively. The “V_i” represents vertical totals; V₁ and V₀ represent the total number of ill and well persons (or cases and controls), respectively. The total number of subjects included in the two-by-two table is represented by the letter T (or N).

When producing a table to display either in print or projection, it is best, generally, to limit the number of variables to one or two. One exception to this rule occurs when a third variable modifies the effect (technically, produces an interaction) of the first two. Table 4.5 is intended to convey the way in which race/ethnicity may modify the effect of age and sex on incidence of syphilis. Because three-way tables are often hard to understand, they should be used only when ample explanation and discussion is possible.

Table 4.6 Line Listing for Exercise 4.1

Data Source: Kalluri P, Crowe C, Reller M, Gaul L, Hayslett J, Barth S, Eliasberg S, Ferreira J, Holt K, Bengston S, Hendricks K, Sobel J. An outbreak of foodborne botulism associated with food sold at a salvage store in Texas. Clin Infect Dis 2003;37:1490–5.

Tables of statistical measures other than frequency

Tables 4.1–4.5 show case counts (frequency). The cells of a table could also display averages, rates, relative risks, or other epidemiological measures. As with any table, the title and/or headings must clearly identify what data are presented. For example, the title of Table 4.7 indicates that the data for reported cases of primary and secondary syphilis are rates rather than numbers.

Table 4.7 Rate per 100,000 Population for Reported Cases of Primary and Secondary Syphilis, by Age and Race — United States, 2002

Data Source: Centers for Disease Control and Prevention. Sexually Transmitted Disease Surveillance 2002. Atlanta: U.S. Department of Health and Human Services; 2003.

Composite tables

To conserve space in a report or manuscript, several tables are sometimes combined into one. For example, epidemiologists often create simple frequency distributions by age, sex, and other demographic variables as separate tables, but editors may combine them into one large composite table for publication. Table 4.8 is an example of a composite table from the investigation of carbon monoxide poisoning following the power failure in Maine.(4)

It is important to realize that this type of table should not be interpreted as for a three-way table. The data in Table 4.8 have not been arrayed to indicate the interrelationship of sex, age, smoking, and disposition from medical care. Merely, several one variable tables (independently assessing the number of cases by each of these variables) have been concatenated for space conservation. So this table would not help in assessing the modification that smoking has on the risk of illness by age, for example. This difference also explains why portraying total values would be inappropriate and meaningless for Table 4.8.