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Imagine that you work in a county or state health department. The department must prepare an annual summary of the individual surveillance reports and other public health data from the year that just ended. This summary needs to display trends and patterns in a concise and understandable manner. You have been selected ... | {
"Header 1": "**DISPLAYING PUBLIC HEALTH DATA**",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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Data analysis is an important component of public health practice. In examining data, one must first determine the data type in order to select the appropriate display format. The data to be displayed will be in one of the following categories:
- Nominal
- Ordinal
- Discrete
- Continuous
Nominal measurements have n... | {
"Header 1": "**Introduction to Tables and Graphs**",
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- Use a clear and concise title that describes person, place and time what, where, and when of the data in the table. Precede the title with a table number.
- Label each row and each column and include the units of measurement for the data (for example, years, mm Hg, mg/dl, rate per 100,000).
- Show totals for rows and... | {
"Header 1": "**Introduction to Tables and Graphs**",
"Header 2": "**Tables**",
"Header 3": "**More About Constructing Tables**",
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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.<sup>2</sup> (Frequency distributions are discussed in Lesson 2.) In this type of frequency distribu... | {
"Header 1": "**Introduction to Tables and Graphs**",
"Header 2": "**Tables**",
"Header 3": "*One-variable tables*",
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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.
| NUMBER OF CASES | | | | |... | {
"Header 1": "**Introduction to Tables and Graphs**",
"Header 2": "**Tables**",
"Header 3": "*Two- and three-variable tables*",
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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... | {
"Header 1": "**Introduction to Tables and Graphs**",
"Header 2": "**Tables**",
"Header 3": "*Composite tables*",
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Although you cannot analyze data before you have collected them, epidemiologists anticipate and design their analyses in advance to delineate what the study is going to convey, and to expedite the analysis once the data are collected. In fact, most protocols, which are written before a study can be conducted, require a... | {
"Header 1": "**Introduction to Tables and Graphs**",
"Header 2": "**Tables**",
"Header 3": "*Table shells*",
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If the epidemiologic hypothesis for the investigation involves variables such as "gender" or "exposure to a risk factor (yes/no)," the construction of tables as described thus far in this chapter should be straightforward. Often, however, the presumed risk factor may not be so conveniently packaged. We may need to inve... | {
"Header 1": "**Introduction to Tables and Graphs**",
"Header 2": "*Creating class intervals*",
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With this strategy, you can create three, four, or six class intervals. First, calculate the mean and standard deviation of the distribution of data. (Lesson 2 covers the calculation of these measures.) Then use the mean plus or minus different multiples of the standard deviation to establish the upper limits for the i... | {
"Header 1": "**Introduction to Tables and Graphs**",
"Header 2": "*Creating class intervals*",
"Header 3": "*Strategy 2: Base intervals on mean and standard deviation*",
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This method is the simplest and most commonly used, and is most readily adapted to graphs. The selection of groups or categories is often arbitrary, but must be consistent (for example, age groups by 5 or 10 years throughout the data set). To use equal class intervals, do the following:
Find the range of the values i... | {
"Header 1": "**Introduction to Tables and Graphs**",
"Header 2": "*Creating class intervals*",
"Header 3": "*Strategy 3: Divide the range into equal class intervals*",
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(Note: If the states in Table 4.13 had been listed alphabetically rather than in rank order, the first step would have been to sort the data into rank order by rate. Fortunately, this has already been done.)
1. Divide the list into four equal sized groups of places:
50 states / 4 = 12.5 states per group. Because st... | {
"Header 1": "**Introduction to Tables and Graphs**",
"Header 2": "*Creating class intervals*",
"Header 3": "**Strategy 1: Divide the data into groups of similar size**",
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| a. | Oklahoma through Kentucky | 89.4–116.1 |
|----|-------------------------------|------------|
| b. | Pennsylvania through Missouri | 76.5–88.5 |
| c. | Connecticut through Florida | 66.4–75.3 |
| d. | Utah through New York | 39.7–66.2 |
| EXAMPLE: Creating Class Interval Categories (Continued) ... | {
"Header 1": "**Introduction to Tables and Graphs**",
"Header 2": "*Creating class intervals*",
"Header 3": "2. Identify the rate for the first and last state in each group:",
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A graph (used here interchangeably with chart) displays numeric data in visual form. It can display patterns, trends, aberrations, similarities, and differences in the data that may not be evident in tables. As such, a graph can be an essential tool for analyzing and trying to make sense of data. In addition, a graph i... | {
"Header 1": "**Graphs**",
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An arithmetic-scale line graph (such as Figure 4.1) shows patterns or trends over some variable, often time. In epidemiology, this type of graph is used to show long series of data and to compare several series. It is the method of choice for plotting rates over time.
In an arithmetic-scale line graph, a set distance... | {
"Header 1": "**Graphs**",
"Header 3": "*Arithmetic-scale line graphs*",
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When you create an arithmetic-scale line graph, you need to select a scale for the x- and y-axes. The scale should reflect both the data and the point of the graph. For example, if you use the data in Table 4.14 to graph the number of cases of measles cases by year from 1990 to 2002, then the scale of the x-axis will m... | {
"Header 1": "**Graphs**",
"Header 3": "**More About the X-axis and the Y-axis**",
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In some cases, the range of data observed may be so large that proper construction of an arithmetic-scale graph is problematic. For example, in the United States, vaccination policies have greatly reduced the incidence of mumps; however, outbreaks can still occur in unvaccinated populations. To portray these competing ... | {
"Header 1": "**Graphs**",
"Header 3": "*Semilogarithmic-scale line graphs*",
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To create a semilogarithmic graph from a data set in Analysis Module:
To calculate data for plotting, you must define a new variable. For example, if you want a semilog plot for annual measles surveillance data in a variable called MEASLES, under the VARIA... | {
"Header 1": "**Graphs**",
"Header 3": "**Figure 4.6 Comparison of Arithmetic-scale Line Graph and Semilogarithmic-scale Line Graph for Hypothetical Country A (Constant Increase in Number of People) and Country B (Constant Increase in Rate of Growth)**",
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"source_pdf": "datasets/websources/M... |
A histogram is a graph of the frequency distribution of a continuous variable, based on class intervals. It uses adjoining columns to represent the number of observations for each class interval in the distribution. The area of each column is proportional to the number of observations in that interval. Figures 4.7a and... | {
"Header 1": "**Graphs**",
"Header 2": "*Histograms*",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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A population pyramid displays the count or percentage of a population by age and sex. It does so by using two histograms most often one for females and one for males, each by age group — turned sideways so the bars are horizontal, and placed base to base (Figures 4.10 and 4.11). Notice the overall pyramidal shape of th... | {
"Header 1": "**Graphs**",
"Header 2": "*Population pyramid*",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
A frequency polygon, like a histogram, is the graph of a frequency distribution. In a frequency polygon, the number of observations within an interval is marked with a single point placed at the midpoint of the interval. Each point is then connected to the next with a straight line. Figure 4.13 shows an example of a fr... | {
"Header 1": "**Graphs**",
"Header 2": "*Population pyramid*",
"Header 3": "*Frequency polygons*",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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As its name implies, a cumulative frequency curve plots the cumulative frequency rather than the actual frequency distribution of a variable. This type of graph is useful for identifying medians, quartiles, and other percentiles. The x-axis records the class intervals, while the y-axis shows the cumulative frequency ei... | {
"Header 1": "**Graphs**",
"Header 2": "*Cumulative frequency and survival curves*",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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A scatter diagram (or "scattergram") is a graph that portrays the relationship between two continuous variables, with the x-axis representing one variable and the y-axis representing the other.15 To create a scatter diagram you must have a pair of values (one for each variable) for each person, group, country, or other... | {
"Header 1": "**Other Data Displays**",
"Header 3": "*Scatter diagrams*",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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A bar chart uses bars of equal width to display comparative data. Comparison of categories is based on the fact that the length of the bar is proportional to the frequency of the event in that category. Therefore, breaks in the scale could cause the data to be misinterpreted and should not be used in bar charts. Bars f... | {
"Header 1": "**Other Data Displays**",
"Header 3": "*Bar charts*",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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A grouped bar chart is used to illustrate data from two-variable or three-variable tables. A grouped bar chart is particularly useful when you want to compare the subgroups within a group. Bars within a group are adjoining. The bars should be illustrated distinctively and described in a legend. For example, consider th... | {
"Header 1": "**Other Data Displays**",
"Header 3": "*Grouped bar charts*",
"token_count": 606,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
A stacked bar chart is used to show the same data as a grouped bar chart but stacks the subgroups of the second variable into a single bar of the first variable. It deviates from the grouped bar chart in that the different groups are differentiated not with separate bars, but with different segments within a single bar... | {
"Header 1": "**Other Data Displays**",
"Header 2": "*Stacked bar charts*",
"token_count": 350,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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A 100% component bar chart is a variant of a stacked bar chart, in which all of the bars are pulled to the same height (100%) and show the components as percentages of the total rather than as actual values. This type of chart is useful for comparing the contribution of different subgroups within the categories of the ... | {
"Header 1": "**Other Data Displays**",
"Header 2": "*Stacked bar charts*",
"Header 3": "*100% component bar charts*",
"token_count": 342,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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While many bar charts show only positive values, a deviation bar chart displays both positive and negative changes from a baseline. (Imagine profit/loss data at different times.) Figure 4.25 shows such a deviation bar chart of selected reportable diseases in the United States. A similar chart appears in each issue of C... | {
"Header 1": "**Other Data Displays**",
"Header 2": "*Stacked bar charts*",
"Header 3": "*Deviation bar charts*",
"token_count": 891,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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- Conventionally, pie charts begin at 12 o'clock.
- The wedges should be labeled and arranged from largest to smallest, proceeding clockwise, although the "other" or "unknown" may be last.
- Shading may be used to distinguish between slices but is not always necessary.
- Because the eye cannot accurately gauge the area... | {
"Header 1": "**Other Data Displays**",
"Header 2": "*Pie charts*",
"Header 3": "**More About Constructing Pie Charts**",
"token_count": 565,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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A dot plot uses dots to show the relationship between a categorical variable on the x-axis and a continuous variable on the y-axis. A dot is positioned at the appropriate place for each observation. The dot plot displays not only the clustering and spread of observations for each category of the x-axis variable but als... | {
"Header 1": "**Other Data Displays**",
"Header 2": "*Dot plots and box plots*",
"token_count": 510,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
A forest plot, also called a confidence interval plot, is used to display the point estimates and confidence intervals of individual studies assembled for a meta-analysis or systematic review.19 In the forest plot, the variable on the x-axis is the primary outcome measure from each study (relative risk, treatment effec... | {
"Header 1": "**Other Data Displays**",
"Header 2": "*Dot plots and box plots*",
"Header 3": "*Forest plots*",
"token_count": 386,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
A phylogenetic tree, a type of dendrogram, is a branching chart that indicates the evolutionary lineage or genetic relatedness of organisms involved in outbreaks of illness. Distance on the tree reflects genetic differences, so organisms that are close to one another on the tree are more related than organisms that are... | {
"Header 1": "**Other Data Displays**",
"Header 2": "*Dot plots and box plots*",
"Header 3": "*Phylogenetic trees*",
"token_count": 285,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
A decision tree is a branching chart that represents the logical sequence or pathway of a clinical or public health decision.<sup>21</sup> **Decision analysis** is a systematic method for making decisions when outcomes are uncertain. The basic building blocks of a decision analysis are (1) decisions, (2) outcomes, and ... | {
"Header 1": "**Other Data Displays**",
"Header 2": "*Dot plots and box plots*",
"Header 3": "*Decision trees*",
"token_count": 556,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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• Excellent examples of the use of maps to display public health data are available in these selected publications:
- Atlas of United States Mortality, U. S. Department of Health and Human Services, Centers for Disease Control and Prevention, Hyattsville, MD, 1996 (DHHS Publication No. (PHS) 97-1015)
- Atlas of AIDS.... | {
"Header 1": "**Other Data Displays**",
"Header 2": "*Dot plots and box plots*",
"Header 3": "**More About Constructing Maps**",
"token_count": 1909,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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Source: Weisstein, Eric W. Chernoff Face. From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/ChernoffFace.html.
To convey the messages of epidemiologic findings, you must first select the best illustration method. Tables are commonly used to display numbers, rates, pro... | {
"Header 1": "**Summary**",
"Header 3": "**Figure 4.39 Example of Face Plot Faces Produced Using 10 Characteristics**",
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1. Divide the list into three equal-sized groups of places:
50 states ÷ 3 = 16.67 states per group. Because states can't be cut in thirds, two groups will contain 17 states and one group will contain 16 states.
Illinois (#17) could go into either the first or second group, but its rate (80.0) is closer to #16 Maine... | {
"Header 1": "**Summary**",
"Header 2": "*Strategy 1: Divide the data into groups of similar size*",
"token_count": 414,
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- 1. Create three categories based on the mean (77.1) and standard deviation (16.1) by finding the upper limits of three intervals:
- a. Upper limit of interval 3 = maximum value = 116.1
- b. Upper limit of interval 2 = mean + 1 standard deviation = 77.1 + 16.1= 93.2
- c. Upper limit of interval 1 = mean 1 standard dev... | {
"Header 1": "**Summary**",
"Header 2": "*Strategy 2: Base intervals on mean and standard deviation*",
"token_count": 254,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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- 1. Divide the range from zero (or the minimum value) to the maximum by 3: (116.1 – 39.7) / 3 = 76.4 / 3 = 25.467
- 2. Use multiples of 25.467 to create three categories, starting with 39.7:
- 39.7 through (39.7 + 1 x 25.467) = 39.7 through 65.2
- 65.3 through (39.7 + 2 x 25.467) = 65.3 through 90.6
- 90.7 through (39... | {
"Header 1": "**Summary**",
"Header 2": "*Strategy 3: Divide the range into equal class intervals*",
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Highest rate between 1985 and 2002 was 11.2 per 100,000 in 1990), so maximum on y-axis should be 12 per 100,000.
**Rate (per 100,000 Population) of Reported Measles Cases by Year of Report—United States, 1985–2002**
**Number of Cases of Botulism by Date ... | {
"Header 1": "**Summary**",
"Header 2": "*PART B*",
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- 1. B, C, D. Tables and graphs are important tools for summarizing, analyzing, and presenting data. While data are occasionally collected using a table (for example, counting observations by putting tick marks into particular cells in table), this is not a common epidemiologic practice.
- 2. A, B, C, D. A table in a p... | {
"Header 1": "**Answers to Self-Assessment Quiz**",
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The term **surveillance** was used initially in public health to describe the close monitoring of persons who, because of an exposure, were at risk for developing highly contagious and virulent infectious diseases that had been controlled or eradicated in a geographic area or among a certain population (e.g., cholera, ... | {
"Header 1": "**Major Sections**",
"Header 2": "**Introduction**",
"Header 3": "**Evolution of Surveillance**",
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Public health surveillance provides and interprets data to facilitate the prevention and control of disease. To achieve this purpose, surveillance for a disease or other health problem should have clear objectives. These objectives should include a clear description of how data that are collected, consolidated, and ana... | {
"Header 1": "**Purpose and Characteristics of Public Health Surveillance**",
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Because conducting surveillance for a health problem consumes time and resources, taking care in selecting health problems for surveillance is critical. In certain countries, selection is based on criteria developed for prioritizing diseases, review of available morbidity and mortality data, knowledge of diseases and t... | {
"Header 1": "**Identifying Health Problems for Surveillance**",
"Header 3": "*Selecting a Health Problem for Surveillance*",
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Isolation of L. monocytogenes from a normally sterile site (e.g., blood or cerebrospinal fluid or, less commonly, joint, pleural, or pericardial fluid).
#### **Case classification**
Confirmed: A clinically compatible case that is laboratory-confirmed.
Source: Centers for Disease Control and Prevention. Case defin... | {
"Header 1": "**Check your answers on page 5-55**",
"Header 3": "**Laboratory criteria for diagnosis**",
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Consider the time sequence of an unsuspecting person exposed to an aerosolized agent (e.g., anthrax).
- Two days after exposure, the person experiences a prodrome of headache and fever and visits a local pharmacy to buy acetaminophen or another over-the-counter medicine.
- On day 3, he develops a cough and calls his ... | {
"Header 1": "**Check your answers on page 5-55**",
"Header 3": "**Possible Scenario for Syndromic Surveillance**",
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After the problem for surveillance has been identified and defined and the needs and scope determined, available reports and other relevant data should be located that can be used to conduct surveillance. These reports and data are gathered for different purposes from multiple sources by using selected methods. Data mi... | {
"Header 1": "**Check your answers on page 5-55**",
"Header 2": "**Identifying or Collecting Data for Surveillance**",
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- Cities and states monitor air pollutants.
- Cities and towns monitor public water supplies for bacterial and chemical contaminants.
- State and local health authorities monitor beaches, lakes, and swimming pools for increased levels of harmful bacteria and other biologic and chemical hazards.
- Health agencies monito... | {
"Header 1": "**Check your answers on page 5-55**",
"Header 2": "*Sources and Methods for Gathering Data*",
"Header 3": "**Examples of environmental monitoring**",
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A notification is the reporting of certain diseases or other healthrelated conditions by a specific group, as specified by law, regulation, or agreement. Notifications are typically made to the state or local health agency. Notifications are often used for surveillance, and they aid in the timely control of specific he... | {
"Header 1": "**Check your answers on page 5-55**",
"Header 2": "*Environmental Monitoring*",
"Header 3": "*Notification*",
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Data regarding the characteristics of diseases and injuries are critical for guiding efforts for preventing and controlling those diseases. Multiple systems exist in the United States to gather such data, as well as other health-related data, at national, state, and local levels. These systems provide the "morbidity an... | {
"Header 1": "**Check your answers on page 5-55**",
"Header 2": "*Major health data systems*",
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A notifiable disease is one for which regular, frequent, and timely information regarding individual cases is considered necessary for preventing and controlling the disease.
The list of nationally notifiable diseases is revised periodically. For example, a disease might be added to the list as a new pathogen emerges... | {
"Header 1": "**Check your answers on page 5-55**",
"Header 2": "*Major health data systems*",
"Header 3": "**More About the National Notifiable Disease Surveillance System**",
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After morbidity, mortality, and other relevant data about a health problem have been gathered and compiled, the data should be analyzed by time, place, and person. Different types of data are used for surveillance, and different types of analyses might be needed for each. For example, data on individual cases of diseas... | {
"Header 1": "**Check your answers on page 5-55**",
"Header 2": "**Analyzing and Interpreting Data**",
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Basic analysis of surveillance data by time is usually conducted to characterize trends and detect changes in disease incidence. For notifiable diseases, the first analysis is usually a comparison of the number of case reports received for the current week with the number received in the preceding weeks. These data can... | {
"Header 1": "**Check your answers on page 5-55**",
"Header 2": "**Analyzing and Interpreting Data**",
"Header 3": "*Analyzing by time*",
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The analysis of cases by place is usually displayed in a table or a map. State and local health departments usually analyze surveillance data by neighborhood or by county. CDC routinely analyzes surveillance data by state. Rates are often calculated by adjusting for differences in the size of the population of differen... | {
"Header 1": "**Check your answers on page 5-55**",
"Header 2": "**Analyzing and Interpreting Data**",
"Header 3": "*Analyzing by place*",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Meaningful age categories for analysis depend on the disease of interest. Categories should be mutually exclusive and all-inclusive. Mutually exclusive means the end of one category cannot overlap with the beginning of the next category (e.g., 1–4 years and 5–9 years rather than 1–5 and 5–9). All-inclusive means that t... | {
"Header 1": "**Check your answers on page 5-55**",
"Header 2": "*Analyzing by time and place*",
"Header 3": "*Age*",
"token_count": 417,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
When the incidence of a disease increases or its pattern among a specific population at a particular time and place varies from its expected pattern, further investigation or increased emphasis on prevention or control measures is usually indicated. The amount of increase or variation required for action is usually det... | {
"Header 1": "**Check your answers on page 5-55**",
"Header 2": "*Interpreting results of analyses*",
"token_count": 1124,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Surveillance for a disease or other health-related problem should be evaluated periodically to ensure that it is serving a useful public health function and is meeting its objectives. Such an evaluation: (1) identifies elements of surveillance that should be enhanced to improve its attributes, (2) assesses how surveill... | {
"Header 1": "**Disseminating Data and Interpretations**",
"Header 2": "**Evaluating and Improving Surveillance**",
"token_count": 210,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
The evaluation should start with a clear statement of the purpose of surveillance, which usually facilitates prevention or control of a health-related problem. The purpose should be followed by clearly stated objectives describing how surveillance data and their interpretations are used. Considering the information nee... | {
"Header 1": "**Disseminating Data and Interpretations**",
"Header 2": "**Evaluating and Improving Surveillance**",
"Header 3": "*Purpose, objectives, and operations*",
"token_count": 207,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
The purpose of evaluating surveillance for a specific disease is to draw conclusions and make recommendations about its present state and future potential. The conclusions should state whether surveillance as it is being conducted is meeting its objectives and whether it is operating efficiently. If it is not, recommen... | {
"Header 1": "**Disseminating Data and Interpretations**",
"Header 2": "**Evaluating and Improving Surveillance**",
"Header 3": "*Recommendations*",
"token_count": 286,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Surveillance has a long history of value to the health of populations and continues to evolve as new health-related problems arise. In this lesson, we have defined public health surveillance as continued watchfulness over health-related problems through systematic collection, consolidation, and evaluation of relevant d... | {
"Header 1": "**Disseminating Data and Interpretations**",
"Header 2": "**Summary**",
"token_count": 331,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
*Acceptability* reflects the willingness of individual persons and organizations to participate in surveillance. Acceptability is influenced substantially by the time and effort required to complete and submit reports or perform other surveillance tasks.
*Flexibility* refers to the ability of the method used for surv... | {
"Header 1": "**Appendix A. Characteristics of Well-Conducted Surveillance**",
"token_count": 825,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
**What is chlamydia?** Chlamydia is a common sexually transmitted disease (STD) caused by the bacterium, *Chlamydia trachomatis*, which can damage a woman's reproductive organs. Even though symptoms of chlamydia are usually mild or absent, serious complications that can cause irreversible damage, including infertility,... | {
"Header 1": "**Appendix B. CDC Fact Sheet on Chlamydia**",
"token_count": 1658,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
The U.S. Consumer Product Safety Commission's (CPSC) National Electronic Injury Surveillance System (NEISS) is a national probability sample of hospitals in the United States and its territories (Figure 5.11). Patient information is collected from each NEISS hospital for every emergency department (ED) visit involving ... | {
"Header 1": "**Surveillance for Consumer Product-Related Injuries**",
"token_count": 480,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
CDC conducts national surveillance for asthma, a chronic disease that affects the respiratory system among both children and adults. Because of its high prevalence and substantial morbidity, asthma has been the focus of clinical and public health interventions, and surveillance has been helpful in quantifying its preva... | {
"Header 1": "**Surveillance for Asthma**",
"token_count": 576,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Reporting from states to the Centers for Disease Control and Prevention (CDC) is not limited to notifiable diseases. Surveillance for influenza is one such example. Because influenza can be widespread during the winter but its diagnosis is rarely confirmed by laboratory test, surveillance for influenza has presented ch... | {
"Header 1": "**Surveillance for Influenza**",
"token_count": 546,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Surveillance need not be perfect to be useful. However, surveillance might have limitations, particularly as a result of underreporting, lack of representativeness, and lack of timeliness, that compromise its usefulness. Fortunately, health departments can implement measures to overcome these hurdles.
Although the in... | {
"Header 1": "Appendix E. Limitations of Notifiable Disease Surveillance and Recommendations for Improvement",
"token_count": 572,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
**Underreporting.** For the majority of notifiable diseases, data for surveillance are based on passive reporting by physicians and other health-care providers. Studies have demonstrated that in the majority of jurisdictions, only a fraction of cases of the notifiable diseases overall are ever reported.<sup>37-39</sup>... | {
"Header 1": "Limitations",
"token_count": 762,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
The preceding limitations of reporting systems demonstrate multiple steps that can be taken by a local or state health department to improve reporting.
**Improving awareness of practitioners**. Most important, all persons who have a responsibility to report must be aware of this responsibility. The health department ... | {
"Header 1": "Limitations",
"Header 2": "**Recommendations for Improving Notifiable Disease Surveillance**",
"token_count": 697,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
- Clinicians might ignore the requirement to report chlamydia, even if it is added to the list of notifiable diseases, if they believe the list is already too long. They might believe they should only be required to report communicable diseases with statistically significant morbidity or mortality that can lead to imme... | {
"Header 1": "Limitations",
"Header 2": "**Disadvantages**",
"token_count": 375,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Factors that influence the choice of one source of data or one dataset over another include severity of illness (e.g., hospitalization and mortality); need for laboratory confirmation of diagnosis; rarity of the condition; specialization, if any, of the health-care providers who commonly examine patients with the condi... | {
"Header 1": "Limitations",
"Header 2": "*Exercise 5.3*",
"token_count": 354,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Possible explanations for the sudden increase include those listed in the following. Each possibility should be investigated before deciding that the increase is a true increase in incidence.
> Public Health Surveillance Page 5-58
- 1. Change in surveillance system or policy of reporting.
- 2. Change in case defini... | {
"Header 1": "Limitations",
"Header 2": "*Exercise 5.4*",
"token_count": 828,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
- 1. A, B, C, D. The term *public health surveillance* includes data collection, analysis, interpretation, and dissemination to help guide health officials and programs in directing and conducting disease control and prevention activities. However, surveillance does not include control or prevention activities themselv... | {
"Header 1": "**Answers to Self-Assessment Quiz**",
"token_count": 2026,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Analysis by place can include analysis of both numbers and rates. Routine analysis by person includes age and sex, but a three-variable table of age by race and sex is probably too much stratification for routine analysis.
- 19. A, B, C, D, E. An increase in case reports during a single week might represent a true incr... | {
"Header 1": "**Answers to Self-Assessment Quiz**",
"token_count": 881,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Outbreaks of disease — the occurrence of more cases than expected — occur frequently. Each day, health departments learn about cases or outbreaks that require investigation. While CDC recorded over 500 outbreaks of foodborne illness alone each year during the 1990s,1 recognized outbreaks of respiratory and other diseas... | {
"Header 1": "**Introduction to Investigating an Outbreak**",
"Header 3": "*Uncovering outbreaks*",
"token_count": 665,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Different health departments respond to these reports in different ways. The decisions regarding whether and how extensively to investigate a potential outbreak depend on a variety of factors. These usually include some factors related to the health problem, some related to the health department, and some related to ex... | {
"Header 1": "**Introduction to Investigating an Outbreak**",
"Header 3": "Deciding whether to investigate a possible outbreak",
"token_count": 502,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
The most important public health reasons for investigating an outbreak are to help guide disease prevention and control strategies. These disease control efforts depend on several factors, including knowledge of the agent, the natural course of the outbreak, the usual transmission mechanism of the disease, and availabl... | {
"Header 1": "**Introduction to Investigating an Outbreak**",
"Header 3": "*Control and prevention*",
"token_count": 328,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Another important objective of many outbreak investigations is to advance research. For most public health problems, health officials cannot conduct randomized trials. We cannot randomize who eats the undercooked hamburger or sits near the ice resurfacing machine that emits carbon monoxide, nor should we randomize who ... | {
"Header 1": "Table 6.1 Relative Priority of Investigative and Control Efforts During an Outbreak, Based on Knowledge of the Source, Mode of Transmission, and Causative Agent",
"Header 3": "**Opportunity to learn (research opportunity)**",
"token_count": 404,
"source_pdf": "datasets/websources/Med_v1/med_textb... |
Public, political, or legal concerns can be the driving force behind the decision to conduct an investigation. A cluster of cancer cases in a neighborhood may prompt concerned residents to advocate for an investigation. Sometimes the public is concerned that the disease cluster is the result of an environmental exposur... | {
"Header 1": "Table 6.1 Relative Priority of Investigative and Control Efforts During an Outbreak, Based on Knowledge of the Source, Mode of Transmission, and Causative Agent",
"Header 3": "*Public, political, or legal concerns*",
"token_count": 229,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6... |
- 1. Prepare for field work
- 2. Establish the existence of an outbreak
- 3. Verify the diagnosis
- 4. Construct a working case definition
- 5. Find cases systematically and record information
- 6. Perform descriptive epidemiology
- 7. Develop hypotheses
- 8. Evaluate hypotheses epidemiologically
- 9. As necessary, rec... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 3": "**Table 6.2 Epidemiologic Steps of an Outbreak Investigation**",
"token_count": 296,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
As a field investigator, you must have the appropriate scientific knowledge, supplies, and equipment to carry out the investigation before departing for the field. Discuss the situation with someone knowledgeable about the disease and about field investigations, and review the applicable literature. In previous similar... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 3": "*Scientific and investigative issues*",
"token_count": 262,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
A good field investigator must be a good manager and collaborator as well as a good epidemiologist, because most investigations are conducted by a team rather than just one individual. The team members must be selected before departure and know their expected roles and responsibilities in the field. Does the team need ... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 3": "*Management and operational issues*",
"token_count": 604,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
An **outbreak** or an **epidemic** is the occurrence of more cases of disease than expected in a given area or among a specific group of people over a particular period of time. Usually, the cases are presumed to have a common cause or to be related to one another in some way. Many epidemiologists use the terms outbrea... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 3": "*Step 2: Establish the existence of an outbreak*",
"token_count": 850,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
The next step, verifying the diagnosis, is closely linked to verifying the existence of an outbreak. In fact, often these two steps are addressed at the same time. Verifying the diagnosis is important: (a) to ensure that the disease has been properly identified, since control measures are often disease-specific; and (b... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 3": "*Step 3: Verify the diagnosis*",
"token_count": 433,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
A case definition is a standard set of criteria for deciding whether an individual should be classified as having the health condition of interest. A case definition includes clinical criteria and particularly in the setting of an outbreak investigation restrictions by time, place, and person. The clinical criteria sho... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Step 4: Construct a working case definition*",
"token_count": 458,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
#### **Clinical case definition**
An illness with sudden onset of fever (>38.5°C rectal or >38.0°C axillary) and one or more of the following: neck stiffness, altered consciousness, other meningeal sign or petechial or puerperal rash.
#### **Laboratory criteria for diagnosis**
Positive cerebrospinal fluid (CSF) a... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Step 4: Construct a working case definition*",
"Header 3": "**Meningococcal Disease — PAHO Case Definition**",
"token_count": 583,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Early in an investigation, investigators may use a "loose" or sensitive case definition that includes confirmed, probable, and possible cases to characterize the extent of the problem, identify the populations affected, and develop hypotheses about possible causes. The strategy of being more inclusive early on is espec... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Step 4: Construct a working case definition*",
"Header 3": "**More About Case Definitions**",
"token_count": 768,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
\* Severe enough to affect the patient's ability to pursue usual daily activities
Eventually, public health officials agreed on the following revised case definition:<sup>26</sup>
- 1. A peripheral eosinophil count of >1,000 cells/mm<sup>3</sup> ;
- 2. Generalized myalgia at some point during the illness severe eno... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Step 4: Construct a working case definition*",
"Header 3": "**Table 6.3 Line Listing of 7 Persons with Suspected Eosinophilia-myalgia**",
"token_count": 1618,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
As noted earlier, many outbreaks are brought to the attention of health authorities by concerned healthcare providers or citizens. However, the cases that prompt the concern are often only a small and unrepresentative fraction of the total number of cases. Public health workers must therefore look for additional cases ... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Step 5: Find cases systematically and record information*",
"token_count": 1389,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Conceptually, the next step after identifying and gathering basic information on the persons with the disease is to systematically describe some of the key characteristics of those persons. This process, in which the outbreak is characterized by time, place, and person, is called **descriptive epidemiology**. It may be... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Step 6: Perform descriptive epidemiology*",
"token_count": 228,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Traditionally, a special type of histogram is used to depict the time course of an epidemic. This graph, called an **epidemic curve**, or **epi curve** for short, provides a simple visual display of the outbreak's magnitude and time trend. The classic epidemic curve, such as the one shown in Figure 6.2a from an outbrea... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Time*",
"token_count": 2038,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Start at the earliest case of the epidemic and count back the minimum incubation period, and note this date as well.
Ideally, the two dates will be similar, and represent the probable period of exposure. Since this technique is not precise, widen the probable period of exposure by, say, 20% to 50% on either side of t... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Time*",
"token_count": 200,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Consider, for example, the outbreak of hepatitis A illustrated by the epidemic curve in Figure 6.5. The incubation period for hepatitis A ranges from 15 to 50 days (roughly 2 to 7 weeks), with an average incubation period of 28–30 days (roughly one month). Because cases can occur from 15 to 50 days after exposure, all ... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Time*",
"Header 3": "**EXAMPLE: Interpreting an Epidemic Curve**",
"token_count": 895,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Assessment of an outbreak by place not only provides information on the geographic extent of a problem, but may also demonstrate clusters or patterns that provide important etiologic clues. A spot map is a simple and useful technique for illustrating where cases live, work, or may have been exposed.
Some spot maps in... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Time*",
"Header 3": "*Place*",
"token_count": 1020,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Characterization of the outbreak by person provides a description of whom the case-patients are and who is at risk. Person characteristics that are usually described include both host characteristics (age, race, sex, and medical status) and possible exposures (occupation, leisure activities, and use of medications, tob... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Time*",
"Header 3": "*Person*",
"token_count": 267,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
After characterizing an outbreak by time, place, and person, it is useful to summarize what you know. For example, during an investigation of an outbreak of Legionnaires' disease in Louisiana, members of the investigative team discussed what they knew based on the descriptive epidemiology.35 Specifically, the epidemic ... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Time*",
"Header 3": "*Summarizing by time, place, and person*",
"token_count": 212,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Although the next conceptual step in an investigation is formulating hypotheses, in reality, investigators usually begin to generate hypotheses at the time of the initial telephone call. Depending on the outbreak, the hypotheses may address the source of the agent, the mode (and vehicle or vector) of transmission, and ... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Step 7: Develop hypotheses*",
"token_count": 593,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
- 1. Single case of disease caused by an uncommon agent (e.g., glanders, smallpox, viral hemorrhagic fever, inhalational or cutaneous anthrax) without adequate epidemiologic explanation
- 2. Unusual, atypical, genetically engineered, or antiquated strain of an agent (or antibiotic-resistance pattern)
- 3. Higher morbid... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Step 7: Develop hypotheses*",
"Header 3": "**Table 6.6 Epidemiologic Clues to Bioterrorism**",
"token_count": 732,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
After a hypothesis that might explain an outbreak has been developed, the next step is to evaluate the plausibility of that hypothesis. Typically, hypotheses in a field investigation are evaluated using a combination of environmental evidence, laboratory science, and epidemiology. From an epidemiologic point of view, h... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Step 7: Develop hypotheses*",
"Header 3": "*Step 8: Evaluate hypotheses epidemiologically*",
"token_count": 458,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
A retrospective cohort study is the study of choice for an outbreak in a small, well-defined population, such as an outbreak of gastroenteritis among wedding guests for which a complete list of guests is available. In a cohort study, the investigator contacts each member of the defined population (e.g., wedding guests)... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Step 7: Develop hypotheses*",
"Header 3": "*Retrospective cohort studies*",
"token_count": 1357,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
| | | Ill | Not Ill | Total | Attack Rate (Risk) |
|-----------|-------|-----|--------------------------------|-------|--------------------|
| | Yes | 53 | 28 | 81 | 65.4% |
| Ate beef? | No | 4 | 31 ... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Step 7: Develop hypotheses*",
"Header 3": "**Table 6.8 Risk of Gastroenteritis By Consumption of Beef—Virginia, December 2003**",
"token_count": 1958,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
One formula for the chi-square
T(ad-bc)<sup>2</sup>
H1 x H0 x V1 x V<sup>2</sup>
test:
Consider the gastroenteritis and beef consumption data presented in Table 6.8. The relative risk is 5.7, which most epidemiologists would deem a "strong" association between exposure and disease. In addition, the p-value is e... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Step 7: Develop hypotheses*",
"Header 3": "**Table 6.10 Table of Chi-Squares**",
"token_count": 529,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
A cohort study is feasible only when the population is well defined and can be followed over a period of time. However, in many outbreak settings, the population is not well defined and speed of investigation is important. In such settings, the case-control study becomes the study design of choice.
In a case-control ... | {
"Header 1": "**Steps of an Outbreak Investigation**",
"Header 2": "*Step 7: Develop hypotheses*",
"Header 3": "*Case-control studies*",
"token_count": 1801,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
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