HS 370 Epidemiology Lesson 3
HS 370 Epidemiology Lesson 3 HS 370
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This 5 page Class Notes was uploaded by Cindy Cannon on Wednesday February 10, 2016. The Class Notes belongs to HS 370 at Brigham Young University - Idaho taught by Watson, Tyler A. in Fall 2016. Since its upload, it has received 39 views. For similar materials see Epidemiology in Nursing and Health Sciences at Brigham Young University - Idaho.
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Date Created: 02/10/16
Epidemiology Lesson 3 Summarizing Data Part 1 Statistic Review Organizing Data Whether you are conducting routine surveillance, investigating an outbreak, or conducting a study, you must first compile information in an organized manner One common method is to create a Line List or Line Listing o The line listing is one type of epidemiologic database, and is organized like a spreadsheet with rows and columns Typically, each row is called a record or observation and represents one person or case of disease Type of Variables The type of value influence the way in which the variables can be summarized The 4 Common Levels of Quantitative Data o A Nominal-Scale Variable: values are categories without any numerical ranking, such as county of residence (usually 2 categories I.E. dead or alive) o An Ordinal-Scale Variable: has values that can be ranked but are not necessarily evenly spaced such as stage of cancer or “good, better, best” or “Infants, Toddlers, Kids, Teenagers and Adults” o An Interval-Scale Variable: is measured on a scale of equally spaced units, but without a true 0 point, such as date of birth or seasons of the year or shoe size o A Ratio-Scale Variable: an interval variable with a true 0 point, such as height in centimeters or duration of illness. (But that doesn’t necessarily mean that it can’t into negative numerical data) Nominal and Ordinal- Scale variables are considered Qualitive or Categorical variables Interval and Ratio-Scale variables are considered Quantitative or Continuous variables Sometimes the same variable can be measured using both a nominal scale and a ratio scale Frequency Distribution A frequency distribution displays the values a variable can take and the number of persons or records with each value o Ex: data from a study of women with ovarian cancer and wish to look and the number of items each woman birth (Parity) Epidemiology Lesson 3 1 First, list all the values that the variable parity can take, from the lowest possible to the highest Then for each value, record the number of women who had that number of births Parity # of Cases 1 45 2 25 3 43 Total 113 Properties of Frequency Distributions The data in a frequency distribution can be graphed, it’s called a histogram Central Location o In this example the symmetric distribution or, bell-shaped curve is a normal distribution o The clustering is at a particular value is know as the central location or central tendency of a frequency distribution Measures of central location are commonly used in epidemiology: arithmetic mean, median and mode; midrange and geometric means Measures of central location can be in the middle or off to one side or the other Spread: the distribution out from a central value Measures of spread commonly used in epidemiology are range and standard deviation Epidemiology Lesson 3 2 o For most distributions seen in epidemiology, the spread of a frequency distribution is independent of it’s central location Shape: o Frequency distributions are some characteristics of human populations tend to be symmetrical o On the other hand, they can be asymmetrical or, commonly called, skewed A distribution that has a central location to the left and a tail off to the right is said to be positively skewed or skewed to the right A distribution that has a central location to the right and a tail off to the left is said to be negatively skewed or skewed to the left. Normal or Gaussian distribution’s mean, median and mode coincide at the central peak, and the area under the curve helps determine measures of spread such as the standard deviation and confidence interval Measures of Central Location A measure of central location provides a single value that summarizes an entire distribution of data Measures of central location include the mode, median, arithmetic mean, midrange, and geometric mean o Selecting the best measure to use for a given distribution depends largely on 2 factors The shape or skewedness of the distribution The intended used of the measure Mode: the value that occurs most often in a set of data o The mode is preferred measure of central location for addressing which value is the most popular and the most common Median: the middle value of a set of data that has been put into rank order o The median is a good descriptive measure, particularly for data that are skewed, because it is the central pint of the distribution Epidemiology Lesson 3 3 Arithmetic Mean: commonly called the mean or average o The arithmetic mean is the best descriptive measure for data that are normally distributed Range: Midrange (Midpoint of an Interval): the halfway point or the midpoint of a set of observations o (Maximum Value-Minimum Value)/2 For age data (Minimum Value +Maximum Value+1)/2 o The midrange is not commonly reported as a measure of central location but used as a intermediate step in other calculations, or for plotting graphs of data collected in intervals Standard Deviation: the measure of variance or how spread out the data are from the mean o 68.2% of the population values are within 1 standard deviation (plus or minus) from the average o 95% of the population values are within 2 standard deviations (plus or minus) from the average o And 99.7% of the population is within 3 standard deviations of the average. All the above information is for use with a normal distribution (What happens when your data are not normally distributed?) Skewness: an asymmetrical distribution of data Geometric Mean: the mean or average of a set of data measured on a logarithmic scale o The geometric mean is used when the logarithms of the observations are distributed symmetrically Epidemiology Lesson 3 4 o The geometric mean tends to dampen the effect of extreme vales and is less sensitive than the arithmetic mean to one or few extreme values Once you add the log values and divide by the total number of values then raised 10 to the number hoy have (10^x) to reach the answer Ex: log using the average you just calculated as the exponent15, 27, 58, 180, 441, 818, 1098, 3116, 6600 o First order the numbers o Second, take the log (either base log or log10) of each number o Then add all the logged numbers and take the average o Then reverse the log using the average you just calculated as the exponent (log(15)+log(27)+log(58)+log(180)+log(441)+log(81 8)+log(1098)+ log(3116)+log(6600))/n(9)=2.504121238 10^(2.504121238)=319.24 One method to decided whether or not to use geometric mean is by comparing the median and standard (arithmetic) mean o If they are close then you don’t need to use geometric means o If they are far apart then geometric means is preferred Epidemiology Lesson 3 5
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