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# Epidemiology

### From ANCC Review

Epidemiology is the study of the distribution and determinants of health related states or events in specified populations and the application of this study to the control of health problems
Uses of epidemiology identifying the cause of disease (ex. legionnaire's) completing the clinical picture of disease (tuskegee experiment) determining effectiveness of therapeutic & preventive measures (mammograms, clinical trial) identifying new syndromes (hep varieties)
Epidemiology can also be used to monitor the health of a community, region or nation (surveillance) identify risks in terms of probability statements (DES children) studying trends over time to make predictions for future (smoking/lung cancer)
according to the ANCC, the difference between an endemic and an epidemic is endemic has a few number of cases of the disease while as time goes on, more people have the disease and it becomes an epidemic
statistics are a branch of applied mathematics which utilizes procedures for condensing, describing, analyzing and interpreting sets of information
biostatistics are a subset of statistics used to handle health relevant information
2 types of statistics descriptive and inferential
descriptive statistics methods of producing quantitative summaries of information
descriptive statistics measures central tendency dispersion
inferential statistics are methods of making generalizations about a larger group based on information about a subset (sample) of that group
4 classifications of data qualitative or quantitative discrete or continuous
Qualitative is non-numerical or categorical (gender, ethnicity)
Quantitative is numeric
Discrete is having a fixed number of values (marital status, blood type, etc)
Continuous is having an infinite number of values (height, weight, temp)
2 types of qualitative data norminal and ordinal
nominal qualitative data data which falls into mutually exclusive categories (discrete) for which there is no natural order ex) race/ethnicity, gender, marital status, yes no
ordinal qualitative data data which falls into mutually exclusive categories (discrete) which have rank or graded order ex) grades, SES, stage of disease, low medium high
Quantitative data has this type interval
interval quantitative data is data which are measured by standard units. The scale measures not only that one data point is different than another, but by how much. Ex) # of days since onset (discrete) temp in F or C (continuous)
mean is the average
median is the middle number - if odd data, have one number if even add two middles together and divide by two
mode is the most frequently occurring number
range the difference between the lowest number and the highest number
variance is the averages of the squared differences from the mean (number - mean)squared / total numbers
standard deviation is the square root of the variance
frequency distribution how often certain data points occur
histograms a graphical representation of data, the kind of chart I was trying to create in excel but couldn't
variance and standard deviation measure the dispersion or scatter of the values about the mean
if the numbers are near the mean then variance is small
if the numbers are far from the mean variance is large
in a symmetrical distribution mean median and mode are all in the center
in a skewed distribution the mean, median and mode are spread more
descriptive statistics are used as a first step to look at health related outcomes examine numbers of cases to identify an increase (epidemic) examine patters of cases to see who gets sick (demographic variables) and where and when they get sick (space/time variables)
epidemiological study design experimental/observational
If there is a comparison group in an epidemiological study that would be case cohort or cohort
if no comparison group in an epidemiological study correlational case series case reports cross sectional migrant studies
experimental epidemioloic studies include RCTs, Community Trials, Lab Trials
Cross Sectional Design is general design - define a population and determine presence or absence of exposure and presence or absence of disease for each individual each subject can be categorized into one of four possible subgroups.
Possible subgroups in cross sectional design exposed with or without disease not exposed with or without disease
Using a cross sectional design to compare disease can compare prevalence of disease and exposure - percentage of people with disease or with exposure
To determine association compare prevalence
To measure association utilize odds ratio
The odds ratio is odds of exposure among cases/ odds of exposure among controls exposed with disease x not exposed no disease/not exposed with disease x exposed no disease ex) 4x times more likely to have disease if smoke
confounders are third variables that are associated with both the exposure and the disease
confounders can distort the true association between the disease and exposure.
Created by: jonquil

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