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Respiratory illness of unknown etiology with onset since February 1, 2003, and the following criteria:
- Documented temperature > 100.4°F (>38.0°C)
- One or more symptoms with respiratory illness (e.g., cough, shortness of breath, difficulty breathing, or radiographic findings of pneumonia or acute respiratory distre... | {
"Header 1": "**The Epidemiologic Approach**",
"Header 2": "*Criteria in case definitions*",
"Header 3": "**Suspected case**",
"token_count": 215,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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Case definitions may also vary according to the purpose for classifying the occurrences of a disease. For example, health officials need to know as soon as possible if anyone has symptoms of plague or anthrax so that they can begin planning what actions to take. For such rare but potentially severe communicable disease... | {
"Header 1": "**The Epidemiologic Approach**",
"Header 2": "*Variation in case definitions*",
"token_count": 895,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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As noted, one of the basic tasks in public health is identifying and counting cases. These counts, usually derived from case reports submitted by health-care workers and laboratories to the health department, allow public health officials to determine the extent and patterns of disease occurrence by time, place, and pe... | {
"Header 1": "**Check your answers on page 1-82**",
"Header 3": "*Using counts and rates*",
"token_count": 636,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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As noted earlier, every novice newspaper reporter is taught that a story is incomplete if it does not describe the what, who, where, when, and why/how of a situation, whether it be a space shuttle launch or a house fire. Epidemiologists strive for similar comprehensiveness in characterizing an epidemiologic event, whet... | {
"Header 1": "**Descriptive Epidemiology**",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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The occurrence of disease changes over time. Some of these changes occur regularly, while others are unpredictable. Two diseases that occur during the same season each year include influenza (winter) and West Nile virus infection (August– September). In contrast, diseases such as hepatitis B and salmonellosis can occur... | {
"Header 1": "**Descriptive Epidemiology**",
"Header 3": "*Time*",
"token_count": 1762,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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Describing the occurrence of disease by place provides insight into the geographic extent of the problem and its geographic variation. Characterization by place refers not only to place of residence but to any geographic location relevant to disease occurrence. Such locations include place of diagnosis or report, birth... | {
"Header 1": "**Descriptive Epidemiology**",
"Header 3": "*Place*",
"token_count": 2025,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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Because personal characteristics may affect illness, organization and analysis of data by "person" may use inherent characteristics of people (for example, age, sex, race), biologic characteristics (immune status), acquired characteristics (marital status), activities (occupation, leisure activities, use of medications... | {
"Header 1": "**Descriptive Epidemiology**",
"Header 3": "*Person*",
"token_count": 1385,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
As noted earlier, descriptive epidemiology can identify patterns among cases and in populations by time, place and person. From these observations, epidemiologists develop hypotheses about the causes of these patterns and about the factors that increase risk of disease. In other words, epidemiologists can use descripti... | {
"Header 1": "**Analytic Epidemiology**",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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In an experimental study, the investigator determines through a controlled process the exposure for each individual (clinical trial) or community (community trial), and then tracks the individuals or communities over time to detect the effects of the exposure. For example, in a clinical trial of a new vaccine, the inve... | {
"Header 1": "**Analytic Epidemiology**",
"Header 3": "*Experimental studies*",
"token_count": 211,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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In an observational study, the epidemiologist simply observes the exposure and disease status of each study participant. John Snow's studies of cholera in London were observational studies. The two most common types of observational studies are cohort studies and case-control studies; a third type is cross-sectional st... | {
"Header 1": "**Analytic Epidemiology**",
"Header 3": "*Observational studies*",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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A number of models of disease causation have been proposed. Among the simplest of these is the epidemiologic triad or triangle, the traditional model for infectious disease. The triad consists of an external **agent**, a susceptible **host**, and an **environment** that brings the host and agent together. In this model... | {
"Header 1": "**Concepts of Disease Occurrence**",
"Header 2": "*Causation*",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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Because the agent-host-environment model did not work well for many non-infectious diseases, several other models that attempt to account for the multifactorial nature of causation have been proposed. One such model was proposed by Rothman in 1976, and has come to be known as the Causal Pies.42 This model is illustrate... | {
"Header 1": "**Concepts of Disease Occurrence**",
"Header 2": "*Causation*",
"Header 3": "*Component causes and causal pies*",
"token_count": 928,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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Anthrax is an acute infectious disease that usually occurs in animals such as livestock, but can also affect humans. Human anthrax comes in three forms, depending on the route of infection: cutaneous (skin) anthrax, inhalation anthrax, and intestinal anthrax. Symptoms usually occur within 7 days after exposure.
- Cut... | {
"Header 1": "**Concepts of Disease Occurrence**",
"Header 2": "*Causation*",
"Header 3": "**What is anthrax?**",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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Source: Centers for Disease Control and Prevention. Principles of epidemiology, 2nd ed. Atlanta: U.S. Department of Health and Human Services;1992.
The process begins with the appropriate exposure to or accumulation of factors sufficient for the disease process to begin in a susceptible host. For an infectious diseas... | {
"Header 1": "**Natural History and Spectrum of Disease**",
"Header 3": "**Figure 1.18 Natural History of Disease Timeline**",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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Although disease is not apparent during the incubation period, some pathologic changes may be detectable with laboratory, radiographic, or other screening methods. Most screening programs attempt to identify the disease process during this phase of its natural history, since intervention at this early stage is likely t... | {
"Header 1": "**Natural History and Spectrum of Disease**",
"Header 3": "**Table 1.7 Incubation Periods of Selected Exposures and Diseases**",
"token_count": 418,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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The reservoir of an infectious agent is the habitat in which the agent normally lives, grows, and multiplies. Reservoirs include humans, animals, and the environment*.* The reservoir may or may not be the source from which an agent is transferred to a host. For example, the reservoir of *Clostridium botulinum* is soil,... | {
"Header 1": "**Chain of Infection**",
"Header 2": "*Reservoir*",
"token_count": 759,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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An infectious agent may be transmitted from its natural reservoir to a susceptible host in different ways. There are different classifications for modes of transmission. Here is one classification:
- Direct Direct contact Droplet spread
- Indirect Airborne Vehicleborne Vectorborne (mechanical or biologic)
In **dire... | {
"Header 1": "**Chain of Infection**",
"Header 2": "*Portal of exit*",
"Header 3": "*Modes of transmission*",
"token_count": 652,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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The portal of entry refers to the manner in which a pathogen enters a susceptible host. The portal of entry must provide access to tissues in which the pathogen can multiply or a toxin can act. Often, infectious agents use the same portal to enter a new host that they used to exit the source host. For example, influenz... | {
"Header 1": "**Chain of Infection**",
"Header 2": "*Portal of entry*",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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The final link in the chain of infection is a susceptible host. Susceptibility of a host depends on genetic or constitutional factors, specific immunity, and nonspecific factors that affect an individual's ability to resist infection or to limit pathogenicity. An individual's genetic makeup may either increase or decre... | {
"Header 1": "**Chain of Infection**",
"Header 2": "*Portal of entry*",
"Header 3": "*Host*",
"token_count": 224,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Knowledge of the portals of exit and entry and modes of transmission provides a basis for determining appropriate control measures. In general, control measures are usually directed against the segment in the infection chain that is most susceptible to intervention, unless practical issues dictate otherwise.
For some... | {
"Header 1": "**Chain of Infection**",
"Header 2": "*Portal of entry*",
"Header 3": "*Implications for public health*",
"token_count": 788,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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Dengue is an acute infectious disease that comes in two forms: dengue and dengue hemorrhagic fever. The principal symptoms of dengue are high fever, severe headache, backache, joint pains, nausea and vomiting, eye pain, and rash. Generally, younger children have a milder illness than older children and adults.
Dengue... | {
"Header 1": "**Chain of Infection**",
"Header 2": "*Portal of entry*",
"Header 3": "**What is dengue?**",
"token_count": 239,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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There is no vaccine for preventing dengue. The best preventive measure for residents living in areas infested with Aedes aegypti is to eliminate the places where the mosquito lays her eggs, primarily artificial containers that hold water.
Items that collect rainwater or are used to store water (for example, plastic c... | {
"Header 1": "**Chain of Infection**",
"Header 2": "*Portal of entry*",
"Header 3": "**What can be done to reduce the risk of acquiring dengue?**",
"token_count": 233,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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The amount of a particular disease that is usually present in a community is referred to as the baseline or **endemic** level of the disease. This level is not necessarily the desired level, which may in fact be zero, but rather is the observed level. In the absence of intervention and assuming that the level is not hi... | {
"Header 1": "**Epidemic Disease Occurrence**",
"Header 2": "*Level of disease*",
"token_count": 807,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Epidemics can be classified according to their manner of spread through a population:
- Common-source
- Point
- Continuous
- Intermittent
- Propagated
- Mixed
- Other
A **common-source outbreak** is one in which a group of persons are all exposed to an infectious agent or a toxin from the same source.
If the grou... | {
"Header 1": "**Epidemic Disease Occurrence**",
"Header 2": "*Epidemic Patterns*",
"token_count": 1500,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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As the basic science of public health, epidemiology includes the study of the frequency, patterns, and causes of health-related states or events in populations, and the application of that study to address public health issues. Epidemiologists use a systematic approach to assess the What, Who, Where, When, and Why/How ... | {
"Header 1": "**Summary**",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
| | Female Male | | Total |
|-----------------|-------------|------|-------|
| Persons at risk | 462 | 851 | 1,313 |
| Survivors | 308 | 142 | 450 |
| Deaths | 154 | 709 | 863 |
| Death rate (%) | 33.3 | 83.3 | 65.7 |
| | Chil... | {
"Header 1": "**Summary**",
"Header 2": "**Table A. Deaths and Death Rates for an Unusual Event, By Sex and Socioeconomic Status**",
"Header 3": "**Table B. Deaths and Death Rates for an Unusual Event, By Sex**",
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1.
- a. Agent *Bacillus anthracis*, a bacterium that can survive for years in spore form, is a necessary cause.
- b. Host People are generally susceptible to anthrax. However, infection can be prevented by vaccination. Cuts or abrasions of the skin may permit entry of the bacteria.
- c. Environment Persons at risk fo... | {
"Header 1": "**Summary**",
"Header 2": "*Exercise 1.8*",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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- 1. A, B, C. In the definition of epidemiology, "distribution" refers to descriptive epidemiology, while "determinants" refers to analytic epidemiology. So "distribution" covers time (when), place (where), and person (who), whereas "determinants" covers causes, risk factors, modes of transmission (why and how).
- 2. A... | {
"Header 1": "**Answers to Self-Assessment Quiz**",
"token_count": 1362,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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Imagine that you work in a county health department and are faced with two challenges. First, a case of hepatitis B is reported to the health department. The patient, a 40-year-old man, denies having either of the two common risk factors for the disease: he has never used injection drugs and has been in a monogamous re... | {
"Header 1": "**SUMMARIZING DATA**",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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A **variable** can be any characteristic that differs from person to person, such as height, sex, smallpox vaccination status, or physical activity pattern. The **value** of a variable is the number or descriptor that applies to a particular person, such as 5'6" (168 cm), female, and
never vaccinated.
Whether you a... | {
"Header 1": "**Organizing Data**",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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Look again at the variables (columns) and values (individual entries in each column) in Table 2.1. If you were asked to summarize these data, how would you do it?
First, notice that for certain variables, the values are **numeric**; for others, the values are **descriptive**. The type of values influence the way in w... | {
"Header 1": "**Organizing Data**",
"Header 2": "**Types of Variables**",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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Look again at the data in Table 2.1. How many of the cases (or case-patients) are male?
When a database contains only a limited number of records, you can easily pick out the information you need directly from the raw data. By scanning the 5th column, you can see that 12 of the 20 case-patients are male.
With large... | {
"Header 1": "**Frequency Distributions**",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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Note that the data in Figure 2.1 seem to cluster around a central value, with progressively fewer persons on either side of this central value. This type of symmetric distribution, as illustrated in Figure 2.2, is the classic bell-shaped curve — also known as a normal distribution. The clustering at a particular value ... | {
"Header 1": "**Properties of Frequency Distributions**",
"Header 2": "*Central location*",
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"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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A third property of a frequency distribution is its **shape**. The graphs of the three theoretical frequency distributions in Figure 2.4 were completely **symmetrical**. Frequency distributions of some characteristics of human populations tend to be symmetrical. On the other hand, the data on parity in Figure 2.5 are *... | {
"Header 1": "**Properties of Frequency Distributions**",
"Header 2": "*Shape*",
"token_count": 536,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
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A measure of central location provides a single value that summarizes an entire distribution of data. Suppose you had data from an outbreak of gastroenteritis affecting 41 persons who had recently attended a wedding. If your supervisor asked you to describe the ages of the affected persons, you could simply list the ag... | {
"Header 1": "**Measures of Central Location**",
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- The mode is the easiest measure of central location to understand and explain. It is also the easiest to identify, and requires no calculations.
- The mode is the preferred measure of central location for addressing which value is the most popular or the most common. For example, the mode is used to describe which da... | {
"Header 1": "**EXAMPLES: Identifying the Mode Example A:** Table 2.8 (on page 2-17) provides data from 30 patients who were hospitalized and received antibiotics. For the variable \"length of stay\" (LOS) in the hospital, identify the mode. **Step 1.** Arrange the data in a frequency distribution. LOS Frequency LOS... |
- **Step 1.** Arrange the observations into increasing or decreasing order.
- **Step 2.** Find the middle position of the distribution by using the following formula:
Middle position = (n + 1) / 2
- a. If the number of observations (n) is **odd**, the middle position falls on a single observation.
- b. If the numbe... | {
"Header 1": "**EXAMPLES: Identifying the Mode Example A:** Table 2.8 (on page 2-17) provides data from 30 patients who were hospitalized and received antibiotics. For the variable \"length of stay\" (LOS) in the hospital, identify the mode. **Step 1.** Arrange the data in a frequency distribution. LOS Frequency LOS... |
- The median is a good descriptive measure, particularly for data that are skewed, because it is the central point of the distribution.
- The median is relatively easy to identify. It is equal to either a single observed value (if odd number of observations) or the average of two observed values (if even number of obse... | {
"Header 1": "**EXAMPLES: Identifying the Mode Example A:** Table 2.8 (on page 2-17) provides data from 30 patients who were hospitalized and received antibiotics. For the variable \"length of stay\" (LOS) in the hospital, identify the mode. **Step 1.** Arrange the data in a frequency distribution. LOS Frequency LOS... |
Find the mean of the following incubation periods for hepatitis A: 27, 31, 15, 30, and 22 days.
**Step 1**. Add all of the observed values in the distribution.
27 + 31 + 15 + 30 + 22 = 125
**Step 2.** Divide the sum by the number of observations.
125 / 5 = 25.0
Therefore, the mean incubation period is 25.0 da... | {
"Header 1": "**EXAMPLES: Identifying the Mode Example A:** Table 2.8 (on page 2-17) provides data from 30 patients who were hospitalized and received antibiotics. For the variable \"length of stay\" (LOS) in the hospital, identify the mode. **Step 1.** Arrange the data in a frequency distribution. LOS Frequency LOS... |
• The mean has excellent statistical properties and is commonly used in additional statistical manipulations and analyses. One such property is called the **centering property of the mean***.* When the mean is subtracted from each observation in the data set, the sum of these differences is zero (i.e., the negative su... | {
"Header 1": "**EXAMPLES: Identifying the Mode Example A:** Table 2.8 (on page 2-17) provides data from 30 patients who were hospitalized and received antibiotics. For the variable \"length of stay\" (LOS) in the hospital, identify the mode. **Step 1.** Arrange the data in a frequency distribution. LOS Frequency LOS... |
- **Step 1.** Identify the smallest (minimum) observation and the largest (maximum) observation
- **Step 2.** Add the minimum plus the maximum, then divide by two.
*Exception: Age differs from most other variables because age does not follow the usual rules for rounding to the nearest integer.* Someone who is 17 yea... | {
"Header 1": "*Epi Info Demonstration: Finding the Mean* **Question**: In the data set named SMOKE, what is the mean weight of the participants? **Answer**: In Epi Info: Select Analyze Data. Select Read (Import). The default data set should be Sample.mdb. Under Views, scroll down to view SMOKE, and double click, or ... |
**Example A:** Find the midrange of the following incubation periods for hepatitis A: 27, 31, 15, 30, and 22 days.
**Step 1.** Identify the minimum and maximum values.
Minimum = 15, maximum = 31
**Step 2.** Add the minimum plus the maximum, then divide by two.
Midrange = 15 + 31 / 2 = 46 / 2 = 23 days
**Examp... | {
"Header 1": "*Epi Info Demonstration: Finding the Mean* **Question**: In the data set named SMOKE, what is the mean weight of the participants? **Answer**: In Epi Info: Select Analyze Data. Select Read (Import). The default data set should be Sample.mdb. Under Views, scroll down to view SMOKE, and double click, or ... |
A logarithm is the power to which a base is raised.
To what power would you need to raise a base of 10 to get a value of 100? Because 10 times 10 or 102 equals 100, the log of 100 at base 10 equals 2. Similarly, the log of 16 at base 2 equals 4, because 24 = 2 x 2 x 2 x 2 = 16.
20 = 1 (anything raised to the 0 powe... | {
"Header 1": "*Epi Info Demonstration: Finding the Mean* **Question**: In the data set named SMOKE, what is the mean weight of the participants? **Answer**: In Epi Info: Select Analyze Data. Select Read (Import). The default data set should be Sample.mdb. Under Views, scroll down to view SMOKE, and double click, or ... |
**Example A: Using Method A**
Calculate the geometric mean from the following set of data.
10, 10, 100, 100, 100, 100, 10,000, 100,000, 100,000, 1,000,000
Because these values are all multiples of 10, it makes sense to use logs of base 10.
**Step 1.** Take the log (in this case, to base 10) of each value.
log... | {
"Header 1": "*Epi Info Demonstration: Finding the Mean* **Question**: In the data set named SMOKE, what is the mean weight of the participants? **Answer**: In Epi Info: Select Analyze Data. Select Read (Import). The default data set should be Sample.mdb. Under Views, scroll down to view SMOKE, and double click, or ... |
On most scientific calculators, the sequence for calculating a geometric mean is:
- Enter a data point.
- Press either the <Log> or <Ln> function key.
- Record the result or store it in memory.
- Repeat for all values.
- Calculate the mean or average of these log values.
- Calculate the antilog value of this mean (<1... | {
"Header 1": "*Epi Info Demonstration: Finding the Mean* **Question**: In the data set named SMOKE, what is the mean weight of the participants? **Answer**: In Epi Info: Select Analyze Data. Select Read (Import). The default data set should be Sample.mdb. Under Views, scroll down to view SMOKE, and double click, or ... |
Measures of central location are single values that summarize the observed values of a distribution. The mode provides the most common value, the median provides the central value, the arithmetic mean provides the average value, the midrange provides the midpoint value, and the geometric mean provides the logarithmic a... | {
"Header 1": "*Epi Info Demonstration: Finding the Mean* **Question**: In the data set named SMOKE, what is the mean weight of the participants? **Answer**: In Epi Info: Select Analyze Data. Select Read (Import). The default data set should be Sample.mdb. Under Views, scroll down to view SMOKE, and double click, or ... |
**Step 1.** Arrange the observations in increasing order.
**Step 2.** Find the position of the 1<sup>st</sup> and 3<sup>rd</sup> quartiles with the following formulas. Divide the sum by the number of observations.
Position of 1<sup>st</sup> quartile $(Q_1) = 25^{th}$ percentile = (n + 1) / 4Position of 3<sup>rd</... | {
"Header 1": "*Epi Info Demonstration: Finding the Mean* **Question**: In the data set named SMOKE, what is the mean weight of the participants? **Answer**: In Epi Info: Select Analyze Data. Select Read (Import). The default data set should be Sample.mdb. Under Views, scroll down to view SMOKE, and double click, or ... |
- The interquartile range is generally used in conjunction with the median. Together, they are useful for characterizing the central location and spread of any frequency distribution, but particularly those that are skewed.
- For a more complete characterization of a frequency distribution, the 1st and 3rd quartiles ar... | {
"Header 1": "**EXAMPLE: Finding the Interquartile Range** Find the interquartile range for the length of stay data in Table 2.8 on page 2-17. **Step 1.** Arrange the observations in increasing order. 0, 2, 3, 4, 5, 5, 6, 7, 8, 9, 9, 9, 10, 10, 10, 10, 10, 11, 12, 12, 12, 13, 14, 16, 18, 18, 19, 22, 27, 49 **Step 2.... |
- The numeric value of the standard deviation does not have an easy, non-statistical interpretation, but similar to other measures of spread, the standard deviation conveys how widely or tightly the observations are distributed from the center. From the previous example, the mean incubation period was 25 days, with a s... | {
"Header 1": "**EXAMPLE: Finding the Interquartile Range** Find the interquartile range for the length of stay data in Table 2.8 on page 2-17. **Step 1.** Arrange the observations in increasing order. 0, 2, 3, 4, 5, 5, 6, 7, 8, 9, 9, 9, 10, 10, 10, 10, 10, 11, 12, 12, 12, 13, 14, 16, 18, 18, 19, 22, 27, 49 **Step 2.... |
Find the mean of the following incubation periods for hepatitis A: 27, 31, 15, 30, and 22 days.
**Step 1.** Calculate the arithmetic mean.
Mean = (27 + 31 + 15 + 30 +22) / 5 = 125 / 5 = 25.0
**Step 2.** Subtract the mean from each observation. Square the difference.
| | | Value Minus Mean | Difference | Di... | {
"Header 1": "**EXAMPLE: Finding the Interquartile Range** Find the interquartile range for the length of stay data in Table 2.8 on page 2-17. **Step 1.** Arrange the observations in increasing order. 0, 2, 3, 4, 5, 5, 6, 7, 8, 9, 9, 9, 10, 10, 10, 10, 10, 11, 12, 12, 12, 13, 14, 16, 18, 18, 19, 22, 27, 49 **Step 2.... |
Often, epidemiologists conduct studies not only to measure characteristics in the subjects studied, but also to make generalizations about the larger population from which these subjects came. This process is called inference. For example, political pollsters use samples of perhaps 1,000 or so people from across the co... | {
"Header 1": "**EXAMPLE: Finding the Interquartile Range** Find the interquartile range for the length of stay data in Table 2.8 on page 2-17. **Step 1.** Arrange the observations in increasing order. 0, 2, 3, 4, 5, 5, 6, 7, 8, 9, 9, 9, 10, 10, 10, 10, 10, 11, 12, 12, 12, 13, 14, 16, 18, 18, 19, 22, 27, 49 **Step 2.... |
Find the 95% confidence interval for a mean total cholesterol level of 206, standard error of the mean of 3.
**Step 1.** Calculate the mean and its error.
Mean = 206, standard error of the mean = 3 (both given)
**Step 2.** Multiply the standard error by 1.96.
3 x 1.96 = 5.88
**Step 3.** Lower limit of the 95%... | {
"Header 1": "**EXAMPLE: Finding the Interquartile Range** Find the interquartile range for the length of stay data in Table 2.8 on page 2-17. **Step 1.** Arrange the observations in increasing order. 0, 2, 3, 4, 5, 5, 6, 7, 8, 9, 9, 9, 10, 10, 10, 10, 10, 11, 12, 12, 12, 13, 14, 16, 18, 18, 19, 22, 27, 49 **Step 2.... |
• The mean is not the only measure for which a confidence interval can or should be calculated. Confidence intervals are also commonly calculated for proportions, rates, risk ratios,
> Summarizing Data Page 2-49
odds ratios, and other epidemiologic measures when the purpose is to draw inferences from a sample surve... | {
"Header 1": "**EXAMPLE: Finding the Interquartile Range** Find the interquartile range for the length of stay data in Table 2.8 on page 2-17. **Step 1.** Arrange the observations in increasing order. 0, 2, 3, 4, 5, 5, 6, 7, 8, 9, 9, 9, 10, 10, 10, 10, 10, 11, 12, 12, 12, 13, 14, 16, 18, 18, 19, 22, 27, 49 **Step 2.... |
Measures of central location and spread are useful for summarizing a distribution of data. They also facilitate the comparison of two or more sets of data. However, not every measure of central location and spread is well suited to every set of data. For example, because the normal distribution (or bell-shaped curve) i... | {
"Header 1": "**Choosing the Right Measure of Central Location and Spread**",
"token_count": 836,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Consider the smoking histories of 200 persons (Table 2.12) and summarize the data.
**Table 2.12 Self-Reported Average Number of Cigarettes Smoked Per Day, Survey of Students (n = 200)**
| | | | | | | | Number of Cigarettes Smoked Per Day | | | | |
|----|----|----|----|----|----|----... | {
"Header 1": "**Choosing the Right Measure of Central Location and Spread**",
"Header 3": "**EXAMPLE: Summarizing Data**",
"token_count": 1349,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
- 1. Summarize the blood level data with a frequency distribution.
- 2. Calculate the arithmetic mean. [Hint: Sum of known values = 2,363]
- 3. Identify the median and interquartile range.
- 4. Calculate the standard deviation. [Hint: Sum of squares = 157,743]
- 5. Calculate the geometric mean using the log lead levels... | {
"Header 1": "**Choosing the Right Measure of Central Location and Spread**",
"Header 3": "*dust.*",
"token_count": 1171,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Frequency distributions, measures of central location, and measures of spread are effective tools for summarizing numerical variables including:
- Physical characteristics such as height and diastolic blood pressure,
- Illness characteristics such as incubation period, and
- Behavioral characteristics such as number ... | {
"Header 1": "**Choosing the Right Measure of Central Location and Spread**",
"Header 2": "**Summary**",
"token_count": 500,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
- 1. Arrange the observations in increasing order. 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 5, 6, 8, 12
- 2. Find the middle position of the distribution with 19 observations. Middle position = (19 + 1) / 2 = 10
3. Identify the value at the middle position.
0, 0, 1, 1, 1, 1, 1, 2, 2, \*2\*, 2, 3, 3, 3, 4, 5, 6,... | {
"Header 1": "**Choosing the Right Measure of Central Location and Spread**",
"Header 2": "*Exercise 2.4*",
"token_count": 204,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
- 1. Arrange the observations in increasing order. 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 5, 6, 8, 12
- 2. Find the position of the 1st and 3rd quartiles. Note that the distribution has 19 observations. Position of Q1 = (n + 1) / 4 = (19 + 1) / 4 = 5 Position of Q3 = 3(n + 1) / 4 =3(19 + 1) / 4 = 15
- 3. Identify... | {
"Header 1": "**Choosing the Right Measure of Central Location and Spread**",
"Header 2": "*Exercise 2.8*",
"token_count": 1066,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Standard error of the mean = 42 divided by the square root of 4,462 = 0.629
1. Summarize the blood level data with a frequency distribution.
| Blood Lead<br>Level (g/dL) | Frequency | Blood Lead<br>Level (g/dL) | Frequency | Blood Lead<br>Level (g/dL) | Frequency |
|----------------------------|-----------|--------... | {
"Header 1": "**Choosing the Right Measure of Central Location and Spread**",
"Header 2": "*Exercise 2.10*",
"token_count": 1154,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
- 1. Line list or line listing. A line listing is a table in which each row typically represents one person or case of disease, and each column represents a variable such as ID, age, sex, etc.
- 2. Sex A, D, F
Age B, G, H
Lymphocyte count B, G, H
*Sex* is a nominal variable, meaning that its categories have names... | {
"Header 1": "**Answers to Self-Assessment Quiz**",
"token_count": 1239,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
A measure of central location provides a single value that summarizes an entire distribution of data. In contrast, a frequency measure characterizes only part of the distribution. Frequency measures compare one part of the distribution to another part of the distribution, or to the entire distribution. Common frequency... | {
"Header 1": "**Major Sections**",
"Header 2": "**Frequency Measures**",
"token_count": 260,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
- Ratios are common descriptive measures, used in all fields. In epidemiology, ratios are used as both descriptive measures and as analytic tools. As a descriptive measure, ratios can describe the male-to-female ratio of participants in a study, or the ratio of controls to cases (e.g., two controls per case). As an ana... | {
"Header 1": "**Major Sections**",
"Header 2": "**Frequency Measures**",
"Header 3": "*Properties and uses of ratios*",
"token_count": 263,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Example A: A city of 4,000,000 persons has 500 clinics. Calculate the ratio of clinics per person.
500 / 4,000,000 x $10^{n} = 0.000125$ clinics per person
To get a more easily understood result, you could set $10^n = 10^4 = 10,000$ . Then the ratio becomes:
0.000125 x 10,000 = 1.25 clinics per 10,000 persons ... | {
"Header 1": "**Major Sections**",
"Header 2": "**Frequency Measures**",
"Header 3": "**EXAMPLES: Calculating Ratios for Different Variables**",
"token_count": 304,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Death-to-case ratio is the number of deaths attributed to a particular disease during a specified period divided by the number of new cases of that disease identified during the same period. It is used as a measure of the severity of illness: the death-to-case ratio for rabies is close to 1 (that is, almost everyone wh... | {
"Header 1": "**Major Sections**",
"Header 2": "**Frequency Measures**",
"Header 3": "A commonly used epidemiologic ratio: death-to-case ratio",
"token_count": 297,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
*Number of persons or events with a particular characteristic Total number of persons or events, of which the numerator is a subset x 10n*
For a proportion, 10n is usually 100 (or n=2) and is often expressed as a percentage.
| EXAMPLE: Calculating a Proportion ... | {
"Header 1": "**Major Sections**",
"Header 2": "**Frequency Measures**",
"Header 3": "*Method for calculating a proportion*",
"token_count": 976,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Proportionate mortalit*y* is the proportion of deaths in a specified population during a period of time that are attributable to different causes. Each cause is expressed as a percentage of all deaths, and the sum of the causes adds up to 100%. These proportions are not rates because the denominator is all deaths, not ... | {
"Header 1": "**Major Sections**",
"Header 2": "*A specific type of epidemiologic proportion: proportionate mortality*",
"token_count": 1150,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
In epidemiology, a rate is a measure of the frequency with which an event occurs in a defined population over a specified period of time. Because rates put disease frequency in the perspective of the size of the population, rates are particularly useful for comparing disease frequency in different locations, at differe... | {
"Header 1": "**Major Sections**",
"Header 2": "*A specific type of epidemiologic proportion: proportionate mortality*",
"Header 3": "*Definition of rate*",
"token_count": 989,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Morbidity has been defined as any departure, subjective or objective, from a state of physiological or psychological wellbeing. In practice, morbidity encompasses disease, injury, and disability. In addition, although for this lesson the term refers to the number of persons who are ill, it can also be used to describe ... | {
"Header 1": "**Morbidity Frequency Measures**",
"token_count": 422,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
**Example A:** In the study of diabetics, 100 of the 189 diabetic men died during the 13-year follow-up period. Calculate the risk of death for these men.
Numerator = 100 deaths among the diabetic men Denominator = 189 diabetic men 10<sup>n</sup> = 102 = 100
Risk = (100 / 189) x 100 = 52.9%
**Example B:** In an o... | {
"Header 1": "**Morbidity Frequency Measures**",
"Header 3": "**EXAMPLES: Calculating Incidence Proportion (Risk)**",
"token_count": 248,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
The denominator of an incidence proportion is the number of persons at the start of the observation period. The denominator should be limited to the "population at risk" for developing disease, i.e., persons who have the potential to get the disease and be included in the numerator. For example, if the numerator repres... | {
"Header 1": "**Morbidity Frequency Measures**",
"Header 3": "**More About Denominators**",
"token_count": 385,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
*Number of new cases of disease or injury during specified period Time each person was observed, totaled for all persons*
In a long-term follow-up study of morbidity, each study participant may be followed or observed for several years. One person followed for 5 years without developing disease is said to contribute ... | {
"Header 1": "**Morbidity Frequency Measures**",
"Header 3": "*Method for calculating incidence rate*",
"token_count": 278,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
- An incidence rate describes how quickly disease occurs in a population. It is based on person-time, so it has some advantages over an incidence proportion. Because person-time is calculated for each subject, it can accommodate persons coming into and leaving the study. As noted in the previous example, the denominato... | {
"Header 1": "**Morbidity Frequency Measures**",
"Header 3": "*Properties and uses of incidence rates*",
"token_count": 393,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
**Example A:** Investigators enrolled 2,100 women in a study and followed them annually for four years to determine the incidence rate of heart disease. After one year, none had a new diagnosis of heart disease, but 100 had been lost to follow-up. After two years, one had a new diagnosis of heart disease, and another 9... | {
"Header 1": "**Morbidity Frequency Measures**",
"Header 3": "**EXAMPLES: Calculating Incidence Rates**",
"token_count": 1177,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
• Prevalence and incidence are frequently confused. Prevalence refers to proportion of persons who *have* a condition at or during a particular time period, whereas incidence refers to the proportion or rate of persons who *develop* a condition during a particular time period. So prevalence and incidence are similar, b... | {
"Header 1": "**Morbidity Frequency Measures**",
"Header 3": "*Properties and uses of prevalence*",
"token_count": 700,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
A mortality rate is a measure of the frequency of occurrence of death in a defined population during a specified interval. Morbidity and mortality measures are often the same mathematically; it's just a matter of what you choose to measure, illness or death. The formula for the mortality of a defined population, over a... | {
"Header 1": "**Mortality Frequency Measures**",
"Header 3": "*Mortality rate*",
"token_count": 627,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
The infant mortality rate is perhaps the most commonly used measure for comparing health status among nations. It is calculated as follows:
*Number of deaths among children < 1 year of age reported during a given time period Number of live births reported during the same time period x 1,000*
The infant mortality ra... | {
"Header 1": "**Mortality Frequency Measures**",
"Header 2": "*Infant mortality rate*",
"token_count": 478,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Table 3.5 provides the number of deaths from all causes and from accidents (unintentional injuries) by age group in the United States in 2002. Review the following rates. Determine what to call each one, then calculate it using the data provided in Table 3.5. a. Unintentional-injury-specific mortality rate for the enti... | {
"Header 1": "**Mortality Frequency Measures**",
"Header 2": "*Combinations of specific mortality rates*",
"Header 3": "**EXAMPLE: Calculating Mortality Rates**",
"token_count": 1500,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Mortality rates can be used to compare the rates in one area with the rates in another area, or to compare rates over time. However, because mortality rates obviously increase with age, a higher mortality rate among one population than among another might simply reflect the fact that the first population is older than ... | {
"Header 1": "**Mortality Frequency Measures**",
"Header 2": "*Combinations of specific mortality rates*",
"Header 3": "*Age-adjusted mortality rates*",
"token_count": 329,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
The death-to-case ratio is the number of deaths attributed to a particular disease during a specified time period divided by the number of new cases of that disease identified during the same time period. The death-to-case ratio is a ratio but not necessarily a proportion, because some of the deaths that are counted in... | {
"Header 1": "**Mortality Frequency Measures**",
"Header 2": "*Definition of death-to-case ratio*",
"token_count": 1120,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Between 1940 and 1949, a total of 143,497 incident cases of diphtheria were reported. During the same decade, 11,228 deaths were attributed to diphtheria. Calculate the death-to-case ratio.
Death-to-case ratio = 11,228 / 143,497 x 1 = 0.0783
or
= 11,228 / 143,497 x 100 = 7.83 per 100
![](_page_208_Picture_0.jpe... | {
"Header 1": "**Mortality Frequency Measures**",
"Header 2": "*Definition of death-to-case ratio*",
"Header 3": "**EXAMPLE: Calculating Death-to-Case Ratios**",
"token_count": 204,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
In an epidemic of hepatitis A traced to green onions from a restaurant, 555 cases were identified. Three of the casepatients died as a result of their infections. Calculate the case-fatality rate.
Case-fatality rate = (3 / 555) x 100 = 0.5%
The case-fatality rate is a proportion, not a true rate. As a result, some ... | {
"Header 1": "**Mortality Frequency Measures**",
"Header 2": "*Definition of death-to-case ratio*",
"Header 3": "**EXAMPLE: Calculating Case-Fatality Rates**",
"token_count": 228,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
For a specified population over a specified period,
*Deaths caused by a particular cause Deaths from all causes x 100*
The distribution of primary causes of death in the United States in 2003 for the entire population (all ages) and for persons ages 25– 44 years are provided in Table 3.1. As illustrated in that tab... | {
"Header 1": "**Mortality Frequency Measures**",
"Header 2": "*Proportionate mortality*",
"Header 3": "*Method for calculating proportionate mortality*",
"token_count": 400,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Years of potential life lost (YPLL) is one measure of the impact of premature mortality on a population. Additional measures incorporate disability and other measures of quality of life. YPLL is calculated as the sum of the differences between a predetermined end point and the ages of death for those who died before th... | {
"Header 1": "**Mortality Frequency Measures**",
"Header 2": "*Definition of years of potential life lost*",
"token_count": 323,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
- **Step 1.** Ensure that age groups break at the identified end point (e.g., 65 years). Eliminate all age groups older than the endpoint.
- **Step 2.** For each age group younger than the end point, identify the midpoint of the age group, where midpoint =
age group's youngest age in years + oldest age + 1 2
- **St... | {
"Header 1": "**Mortality Frequency Measures**",
"Header 2": "*Method for calculating YPLL from a frequency*",
"token_count": 253,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Use the data in Tables 3.9 and 3.10 to calculate the leukemia-related mortality rate for all ages, mortality rate for persons under age 65 years, YPLL, and YPLL rate.
1. Leukemia-related mortality rate, all ages
= (21,498 / 288,357,000) x 100,000 = 7.5 leukemia deaths per 100,000 population
2. Leukemia-related mo... | {
"Header 1": "**Mortality Frequency Measures**",
"Header 2": "*Method for calculating YPLL from a frequency*",
"Header 3": "**EXAMPLE: Calculating YPLL and YPLL Rates**",
"token_count": 1471,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Natality measures are population-based measures of birth. These measures are used primarily by persons working in the field of maternal and child health. Table 3.11 includes some of the commonly used measures of natality.
| Measure | Numerator ... | {
"Header 1": "**Natality (Birth) Measures**",
"token_count": 241,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
The key to epidemiologic analysis is comparison. Occasionally you might observe an incidence rate among a population that seems high and wonder whether it is actually higher than what should be expected based on, say, the incidence rates in other communities. Or, you might observe that, among a group of casepatients in... | {
"Header 1": "**Natality (Birth) Measures**",
"Header 2": "**Measures of Association**",
"token_count": 263,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
The formula for risk ratio (RR) is:
*Risk of disease (incidence proportion, attack rate) in group of primary interest Risk of disease (incidence proportion, attack rate) in comparison group*
A risk ratio of 1.0 indicates identical risk among the two groups. A risk ratio greater than 1.0 indicates an increased risk ... | {
"Header 1": "**Natality (Birth) Measures**",
"Header 2": "*Method for Calculating risk ratio*",
"token_count": 756,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Public health officials were called to investigate a perceived increase in visits to ships' infirmaries for acute respiratory illness (ARI) by passengers of cruise ships in Alaska in 1998.<sup>13</sup>The officials compared passenger visits to ship infirmaries for ARI during May–August 1998 with the same period in 1997... | {
"Header 1": "**Natality (Birth) Measures**",
"Header 2": "*Rate ratio*",
"Header 3": "**EXAMPLE: Calculating Rate Ratios**",
"token_count": 712,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
An odds ratio (OR) is another measure of association that quantifies the relationship between an exposure with two categories and health outcome. Referring to the four cells in Table 3.15, the odds ratio is calculated as
Odds ratio =
$$\left(\frac{a}{b}\right)\left(\frac{c}{d}\right) = ad/bc$$
where
| a | = |... | {
"Header 1": "**Natality (Birth) Measures**",
"Header 2": "*Rate ratio*",
"Header 3": "*Odds ratio*",
"token_count": 289,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Use the data in Table 3.15 to calculate the risk and odds ratios.
1. Risk ratio
5.0 / 1.0 = 5.0
2. Odds ratio
(100 x 7,920) / (1,900 x 80) = 5.2
Notice that the odds ratio of 5.2 is close to the risk ratio of 5.0. That is one of the attractive features of the odds ratio — when the health outcome is uncommon, ... | {
"Header 1": "**Natality (Birth) Measures**",
"Header 2": "*Rate ratio*",
"Header 3": "**EXAMPLE: Calculating Odds Ratios**",
"token_count": 426,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Vaccine efficacy and vaccine effectiveness measure the proportionate reduction in cases among vaccinated persons. Vaccine efficacy is used when a study is carried out under ideal conditions, for example, during a clinical trial. Vaccine effectiveness is used when a study is carried out under typical field (that is, les... | {
"Header 1": "**Measures of Public Health Impact**",
"Header 2": "*Method for calculating attributable proportion*",
"Header 3": "*Vaccine efficacy or vaccine effectiveness*",
"token_count": 264,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
Because many of the variables encountered in field epidemiology are nominal-scale variables, frequency measures are used quite commonly in epidemiology. Frequency measures include ratios, proportions, and rates. Ratios and proportions are useful for describing the characteristics of populations. Proportions and rates a... | {
"Header 1": "**Summary**",
"token_count": 284,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
- 1. Homicide-related death rate (males)
- = (# homicide deaths among males / male population) x 100,000
- = 15,555 / 139,813,000 x 100,000
- = 11.1 homicide deaths / 100,000 population among males
Homicide-related death rate (females)
- = (# homicide deaths among females / female population) x 100,000
- = 4,753 / ... | {
"Header 1": "**Summary**",
"Header 2": "*Exercise 3.3*",
"token_count": 653,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
= (# deaths from HIV among <65 years year-olds / estimated population < 65 years, 2002) x 100,000
= (12 + 25 + 178 + 1,839 + 5,707 + 4,474 + 1,347 / 19,597 + 41,037 + 40,590 +39,928 + 44,917 + 40,084 + 26,602) x 100,000
- = 13,582 / 252,755,000 x 100,000
- = 5.4 HIV deaths per 100,000 persons under age 65 years | {
"Header 1": "**Summary**",
"Header 2": "2. HIV-related mortality rate for persons under 65 years",
"token_count": 200,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
YPLL65 rate = (291,020 / 252,755,000) x 1,000 = 1.2 YPLL per 1,000 population under age 65.
5. Compare mortality rates and YPLL for leukemia and HIV
| | Leukemia | HIV |
|-------------------------------------------------------|----------|---------|
| # cause... | {
"Header 1": "**Summary**",
"Header 2": "3. HIV-related YPLL before age 65",
"Header 3": "4. HIV-related YPLL65 rate",
"token_count": 295,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
- 1. Rate ratio comparing current smokers with nonsmokers
- = rate among current smokers / rate among non-smokers
$$= 1.30 / 0.07$$
= 18.6
2. Rate ratio comparing ex-smokers who quit at least 20 years ago with nonsmokers
= rate among ex-smokers / rate among non-smokers
= 0.19 / 0.07 = 2.7
3. The lung cancer... | {
"Header 1": "**Summary**",
"Header 2": "*Exercise 3.7*",
"token_count": 306,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
- 1. A, B, C, D. Frequency measures of health and disease include those related to birth, death, and morbidity (incidence and prevalence).
- 2. A, B. All fractions are ratios. This fraction is also a proportion, because all of the deaths from lung cancer in the numerator are included in the denominator. It is not an in... | {
"Header 1": "**Answers to Self-Assessment Quiz**",
"token_count": 722,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
- B. Generally preferred for chronic diseases without clear date of onset
- A. Used in calculation of risk ratio
- B. Affected by duration of illness
Incidence reflects new cases only; incidence proportion is a measure of risk. A risk ratio is simply the ratio of two incidence proportions. Prevalence reflects existin... | {
"Header 1": "**Answers to Self-Assessment Quiz**",
"Header 3": "10. A. Measure of risk",
"token_count": 1280,
"source_pdf": "datasets/websources/Med_v1/med_textbook/cdc_6914_DS1.pdf"
} |
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