Statistics And Epidemiology - AHIMA
Slide 1Statistics and EpidemiologyReview of Book 7SEER ProgramSelf Instructional Manual for Cancer RegistrarsCancer Information Management ProgramOutcomes, Data Quality and Data Utilization CourseStatistics, Epidemiology and Data Utilization ModuleWhen or where do you use statistics in your everyday operations? Annual reports, datarequests? How about evaluating quality or productivity, cancer conferencedocumentation, survey documentation, budgets just to name a few?If you stop and think about it, you may see that you are already doing many of thestatistical techniques discussed in Book 7. You may just not think about yourself interms of being a “statistician”.This presentation will provide a brief overview of the statistical and epidemiologicalmethods introduced in Book 7: Statistics and Epidemiology for Cancer Registries. Youmay find it helpful to follow along in your book.
Slide 2StatisticsBranch of mathematicsCollection, summarization, analysis,interpretation, and presentation ofmasses of numerical dataWe will begin with statistics.Statistics is the science of gathering and interpreting facts and figures. Statistics can befurther described as descriptive or inferential.Descriptive statistics will be discussed in more detail in the following slides.Inferential statistics refer to the procedures used to make a statement about apopulation based upon the results of a sample. Inferential statistics is related tomethods applied in hypothesis testing. Cancer registrars would usually seek assistancefrom a statistician or researcher before attempting to conduct a study using hypothesistesting.
Slide 3Statistical AnalysisSummarize the essential features andrelationships of the dataReveal the major characteristics of thepatient groupDetermine broad patterns of behavioror tendenciesStatistical Analysis is the process of:-summarizing data-identifying the major characteristics of the patient group based on the data-then using the data and those major characteristics identified to determine broadpatterns of behavior of a group of people.
Slide 4Descriptive StatisticsSEER Book 7, Section BDescriptive statistics uses numerical summaries to describe an observed frequencydistribution.Study Hint:Have you ever read a definition of a term or phrase and you had to look up the definitionof the words contained in the original definition?Try to think of the principles of statistics as stepping stones. Such as you need to knowhow to add and subtract before you can do algebra. It is important to have a clearunderstanding of the basic terminology. You will see them used again and again as weget into the more complicated side of statistics and epidemiology.For example: Once you have a clear understanding of the use of the term “mean”,those same principles are applied to any other definition or phrase including the word“mean”. A mean tumor size or mean survival time is still based on calculating averages.
Slide 5Shorthand notationX value of an observed measurement (X ) sum of the values of Xn number (count) of observations in our groupn – 1 degrees of freedom (the number ofobservations that are free to vary)X mean, average valueSD standard deviation square rootThis slide contains a reference key of symbols that will be used in the formulas on theupcoming slides. It is not necessary to memorize these symbols. It is more for yourown use during your independent study time and for understanding the shorthand usedin the workbook.The calculation of variance introduces the term “Degrees of freedom”. If you have 10observations and the sum of the observation is 100, the first 9 (10-1) can be anynumber. The last number cannot. The last number has to be the number that whenadded to the 1st nine equals 100. Therefore, 9 of the numbers are free to vary.
Slide 6How do we summarize a set of data?Characterize a set of data in terms of:1)2)Central values about which the data tendto clusterThe amount of spread or the dispersionof the observationsIf you will remember, statistics is the collection and summation of masses of numericaldata, so If measurable characteristics among individuals did not vary, describing a set of datawould be completed after the first observation.Example: If everyone’s blood pressure was 110/70, then there would be no need intaking BP at each doctor visit.We will see this concept again when we look at the normal curve.
Slide 7Measures of Central TendencyCentral values about which the datatend to cluster“Typical” valuesExample: Average tumor sizeMeasuring central tendency are important because measurable characteristics (such as ageand stage) vary from individual to individual. Therefore, we need to summarize the data in orderto analyze the results.Typical values what you see most often.The graphic at the bottom is a visual reminder. When you think of measures of centraltendency, think about how the data clusters in the middle of the observations.Slide 8Measures of Central TendencyWidely used measures of centraltendency MeanMedianModeThe tools we use to calculate the typical values are the 3 M’s mean, median, and mode. Tohelp describe mean, median, and mode, we will use an example of five tumor sizes.
Slide 9MeanAverage (X)Influenced more by extreme values8 5 3 6 3 25Sum of all values XX -------------------- ---- ---------------- --- # of valuesn555Mean the statistical verbiage for average.Q: What is the mean in our example of tumor sizes?A: Add all of the values together (25) and divide by the number of values (5).The mean is 5.Q: What would happen if we added an extreme value (20) to our set of tumor sizes?A: Add 20 to the list of tumor sizes. Mean 45 / 6 7.5.As you can see, the mean is more influenced by extreme values than is median andmode.Note: It is not necessary to memorize the formulas in this presentation. They areprovided only to help with demonstrating and understanding the use of the terms anddefinitions.
Slide 10MedianMiddle valueSort the observations in order fromsmallest to largestStable measure835335638Median is the 50th percentile – ½ of the values are smaller and ½ are largerQ: What is the median in our example of tumor sizes?A: First we have to sort them in order from smallest to largest (click), then take themiddle value (click). Median is 5 (click).Q: What would happen to our median if we added an extreme value. Let’s use 20again?A: Add 20 to the list of tumor sizes - 3,3,5,6,8,20. The middle falls between 5 and 6. Ifthe middle falls between two values, then average the two middle values. Median isnow 5.5.Stable measure – adding extreme values to a series of observations tends to cause onlya limited change in the value of the median
Slide 11ModeMost frequently seen value33568There may be no modal value (3, 5, 6, 8)There may be more than one modal value (3, 3, 5, 6, 6, 8)The mode is the value that occurs most frequently.A distribution with two most-common values is called a bimodal distribution.Slide 12Measures of VariationAmount of spread or dispersion of theobservationsExample: Fluctuation of tumor sizesAgain, the graphic at the bottom is a visual reminder.When you think of measures of variation, think about how the data spreads out alongthe observations.
Slide 13Measures of VariationWidely used measures of variation RangeStandard Deviation (SD)The tools we use to measure variation are range and standard deviation.The standard deviation is a companion to the mean.Q: What type of measure is the mean?A: Central Tendency (typical values, clustering in the middle)Q: And, what type of measure is the standard deviation?A: Variation (spread, dispersion)So, the standard deviation expresses the spread of data about the mean.Make a mental note of this. You will see this concept again when we talk about thenormal curve.
Slide 14RangeDifference between the highestand lowest valuesEasiest measure of variationGreatly influenced by extreme valuesHighest # - Lowest # 8 – 3 5Easiest measure it’s just simple subtraction.Q: What is the range in our example of tumor sizes?A: The highest number was 8. The lowest number was 3. 8-3 5.Q: What would happen to the range if we added an extreme value. Let’s use 20 again?A: 20 – 3 17As you can see, range is greatly influenced by extreme values.Study Hint:An exam question may not be worded exactly as they are seen on the slides. It may beasked with a slight twist, so-to-speak. We’ve talked mostly in terms of measures thatare most influenced by extreme values. A good exam question that comes to mind is:Q: Given a set of values, which is least likely to be influenced by an extreme value?Mean, Median, Mode, RangeA: Median
Slide 15Standard DeviationHow far the observations tend tovary from the meanSquare root of varianceSD (X – X)2--------------- (n-1)1818(5-1)4 ------ ---- 4.5 2.12Standard Deviation .sounds and looks complicated, but the calculation is based on fairly simplemathematics.Q: First of all, how did we get 18 in the example above?A: Remember, we are still using our example of 5 tumor sizes (8,5,3,6,3) and the symbols used here arefrom our reference key. Take each value from our list of 5 tumor sizes (x), subtract the mean (which is 5,refer to slide 9 if needed), square it, and add them together.22222(8-5) (5-5) (3-5) (6-5) (3-5)22222 (3) (0) (-2) (1) (-2) 9 0 4 1 4 18Q: How did we get the number 4 in the equation?A: This is the degrees of freedom (refer to slide 5 if necessary). The total number of values in our list oftumor sizes was 5. Therefore, n 5. n-1 or 5-1 4.Variance 18 divided by 4 4.5. Standard deviation is the square root of the variance.Q: So, what does a standard deviation of 2.12 tell us?A: This tells us that using our example of tumor sizes, our observations will tend to fall within plus orminus 2.12 of the mean. Our mean is 5. So in other words, our observations will tend to be between2.88 (5 - 2.12) and 7.12 (5 2.12).The numbers in our example ranged from 3 to 8 which corresponds with the standard deviation.Significance of the standard deviation will be discussed later with the normal curve.Remember our measures of central tendency (mean, median, mode) focus on the center of ourobservations. So the standard deviation describes how the data spreads out on either side of the mean.The more widely the values are spread out, the larger the standard deviation.Q: Why do we use the square root of the variance?A: The variance is a squared number (a number that has been squared). The mean that we calculated isnot a squared number. When making comparisons, you want your units to be the same. Therefore, wetake the square root of the variance so it will be the same units as the mean – a number that is notsquared.
Slide 16Kinds of alOrdinalIntervalRatioQualitative (Categories, Classes): Serves to classify data into related groups. It is not ameasurement value.There are two types of qualitative data: Nominal and Ordinal.Nominal (Classification) – Assigning a name. Has no numerical meaning.Example: male 1, female 2, True/False, Yes/NoYou can’t add all of the 2’s together to tell you how many female cases you have.Ordinal (Ranked) – placed into order or ranking.Example: Stage of disease 0 – IV, GradeThe higher the number represents a different degree or severity, etc.Quantitative (Measurements): Actual values or measuresThere are also two types of quantitative data: Interval and Ratio.Interval (Arbitrary zero) – no set starting point.Example: SurvivalStarting point depends on when you were diagnosed.Ratio (Absolute zero) - zero none, starts at zero.Example: Tumor size, Age, Amount of radiationDiscrete: Variable can have only a particular set of values.Example: A person can own 1 car, but not 1.5 cars.Continuous: Variable can have more precise values with further refinements of the measuringscale.Example: Approx 30yo can be refined to 30y 4mo which can be refined to 30y 4mo 2 days, andso on.
Slide 17Preparation of ReportsFirst step is to define the problemDefine the objective and scopeSelect, Assemble, Present, andAnalyze the dataSo, we now know the options for summarizing the data, but how do we go aboutpreparing reports about the data? Begin by asking the following questions:What information does the user want?What information is available in the registry?Are the data routinely collected by the registry or will it require a collection of additionaldata?The objective and scope is what the purpose of the final data will be (what it will be usedfor).
Slide 18Selection of CasesDetermine the casesto be includedDetermine the dataitemsSampleBiasPopulationSampleRandom SamplePopulationSelection methods apply to both statistics and epidemiology techniques.When selecting data, if all cases are not to be used then bias should be avoided.Clearly define the population to be studied. A sample should be a random sample.Bias: Tendency of a statistical estimate to deviate in one direction from the true value.The selection does not fairly represent the population being studied.Population: Any set of individuals having some common observable characteristic.Sample: A subset of the population under study that represents the entire population.Random Sample: Each individual has an equal and independent chance of beingchosen. To select a random sample, a random numbers table or a systematic method(every 5th case) can be used.Using a random sample will help avoid bias.
Slide 19Selection of CasesBiasScreeningRandom tivitySpecificityPositive Predicted ValueEqualIndependentBias results from:Misclassification – individuals are assigned to the wrong group.Selection - Individuals do not have the same opportunity to be included.Confounding - Mixing up the effects from several risk factors. Cases share certaincharacteristics or prognostic factors (age, stage of disease)Screening: A method of searching for occult or early disease.Q: Does screening have to be a formal screening program such as a skin screening that takesplace at the mall?A: No, it could be a test that is performed during a routine physical, such as a PSA.Sensitivity: ability of a test to find diseased people (screening test is positive when person issick). This is also known as the true positive rate.Specificity: ability of a test to find well people (screening test is negative when person is notsick). This is also known as true negative rate.Positive Predicted Value: percent of people who are said to have disease that actually dohave disease (the probability that a person has the disease given a positive test result).And of course, there are opposites to these:False negative: a person who tests as negative but who is actually positiveFalse positive: a person who tests as positive but who is actually negativeEqual: each name has an equal chance of being picked.Independent: selecting a certain name is not affected by the names previously selected.Examples: drawing names out of hat and putting that name back in the hat before the next draw(so the next draw has an equal chance of being selected). A random numbers table may alsobe used.
Slide 20Assemble the DataReview for obvious errors or highlyunusual casesSummarize the dataMutually exclusive categoriesFrequency Distributions CountsRelative FrequenciesWe will take a look at assembling the data using mutually exclusive categories andfrequency distributions. But remember, first you should review the report results forobvious errors or highly unusual cases and make any corrections to the data beforecontinuing.
Slide 21Mutually Exclusive CategoriesMutually exclusive: Case falls into one and only one category. Let’s look at theexamples in A, B, C, and D.Q: What’s wrong (or right) with each of these categories?A: AmbiguousQ: Where should 2cm be counted?B: Not clear what the limits of each class are (0-1, 0-2, or 1-2)C: Appropriate for discrete data only (whole numbers).Q: Where is 1.8 counted? Round up to 2?D: Most suitable for continuous data when some values could include a decimal valueQ: But what about 10 and above?A: For this to be complete there should be a 10 in the last class. This categoryassumes there is not going to be a tumor size greater that 9.9.
Slide 22Frequency DistributionsAbsolute FrequencyCOUNT of the number of casesInformation on magnitudeExample: 250 male patients for the year 2000A frequency distribution is created by setting up categories (may also be called classes,groups, or bins)An absolute frequency is the count of the number of cases that fall into each category.When we say “magnitude”, we mean “how many”.Q: What type of absolute frequency distributions might you provide from your registrydata?A: In your annual report, you probably provide a distribution of the major primary sites.
Slide 23Frequency DistributionsRelative FrequencyPERCENTAGEProportion of the part to the wholeFacilitates comparisonsExample: 250 male patients out of 500 totalpatients 50% males# in each group------------------------------ X 100Total # in the populationRelative frequency converts the count in the absolute frequency to a percentage.Example: In your annual report, you may also provide a distribution of the major primarysites comparing them to state and national data.Q: If your facility saw 240 new lung cases and there were 5000 new lung casesreported in the state and 170,000 new lung cases in the US, would you create a graphthat shows these actual numbers (240, 5000, and 170,000)?A: No, that would be a mighty big graph because the number would range from 240 to170,000. And, what would it tell you about your data? Not much. By converting eachnumber to a percentage (using the formula on the slide), you can compare whether ornot the incidence at your facility is high or low – compared to state and national data.Relative frequency can be applied to any type of measure – primary site, gender, race,etc.
Slide 24Analyzing the DataReviewing the data for completeness,errors, and inconsistenciesDeciding on appropriate format forgraphs and tablesDetermining how the data will begroupedAnytime you generate data, you want to analyze it for accuracy and completenessbefore you release that information to someone else. Build other’s confidence in yourdata by having it be as accurate as possible.Slide 25Presentation of DataTABLES1009080706050403020100GRAPHSABCDSo, now we’ve gathered all of this information, we know how to summarize it usingfrequencies, central tendencies, variations, etc. Now, how do we share this data withothers so that it can be understood accurately?First of all, it depends on the purpose of the data. Tables would be used for complex,detailed information. Graphs would be used to show relationships in data.
Slide 26Presentation of DataTables and graphs should contain enoughinformation to stand alone without textexplanationTitle should answer: Who, What, Where,WhenShould contain certain essential componentsSignificant results and relationships pointedoutSlide 27TablesMore information can be presentedExact values can be read from a tableLess work and cost required inpreparationFlexibility is maintained withoutdistortion of data
Slide 28Basic Components of a tnote:Source:Anything in a table (or graph) that cannot be understood by the reader from the title,captions and/or stub should be explained by a footnote. IE: abbreviations, types ofcases included/excluded.If you use data from outside your institution for comparison purposes, always indicatethe source of the data.
Slide 29Types of TablesSummary Table Uses grouped dataPresents specificdata for a particularuseReference Table Provides completeinformationNot intended to beread throughQ: Can you think of an example of a summary table?A: How about a list of patients with stage 3 and 4 breast cancer for year 2004 thatcontains the name, medical record number, stage, date of diagnosis, and type ofsurgery.Q: Can you think of an example of a reference table?A: How about the master patient index for the entire database. This could benotebooks worth of information for larger registries.
Slide 30GraphsAttracts attention more readilyMore easily understoodShows trends or comparisons morevividlyMore easily rememberedI’m sure you have heard the phrase: A picture is worth a 1000 words.Slide 31Basic Components of a GraphY-axis -axis (Abscissa)Footnote:Source:Study Hint: A possible CTR exam question might be: What else is the y-axis and xaxis known by?
Slide 32Types of GraphsBar GraphsHistogramsFrequency PolygonsLine GraphsPie ChartsScatter DiagramsPictographsGeographic MapsWe will discuss very briefly the major characteristics of each of these type graphs.Slide 33Bar GraphsMajor Primary Sites for 2000Number of Cases60504030Year 200020100LungColonProstateBreastMelanomaPrimary SiteFootnote: Analytic casesSource: Cancer Data ServicesQ: What else is this data called?A: Absolute frequency distribution (count of cases that falls into each category).Used to display frequencies, proportions, or percentages.Emphasizes individual amounts (discrete data).One axis is not continuous. Individual heights of each bar represent a whole.Bars of equal width.Space between bars.The value of each bar is independent of the value of other bars.
Slide 34HistogramsNumber of CasesTumor Size for Breast Cancer, 2000Size (centimeters)64 patients, Class of Case 0-2Use to present observations for one continuous variable.Sum of the heights of the bars represents all the cases. If we were to add up the valuesfor each category, we would get the total number of the patients (64).Again, bars are of equal width.BUT, there are NO spaces between the bars.A histogram is a frequency distribution in bar graph form – the total area covered by thegraph represents the whole.It is most effective when only one distribution is shown.
Slide 35Frequency PolygonsNumber of CasesTumor Size for Breast Cancer, 2000Size (centimeters)64 patients, Class of Case 0-2Histogram in line graph form. Also represents all cases.Created by joining the midpoints at the top of each interval and connecting the dots.This graph is using the same numbers as we used in the histogram in the previousslide.Allows you to plot several frequency polygons on the same graph for comparison.Q: And, what do we want to do if we want to compare values that vary widely?A: We convert them to percentages.Since the line represents the total distribution, the line always starts and ends with zero.
Slide 36Cumulative FrequencyPolygons (Ogive)Cumulative PercentCumulative Percent of Tumor Size for Breast Cancer, 2000Median (50%)Size (centimeters)Expressed in terms of percentages.The cumulative frequency for any interval on the scale is the total of the frequencies forthat interval the total for all of the lower intervals.Using the same numbers as on the frequency polygon and histogram:Group 1 27% (17 divided by 64), Group 2 63% (27% 36%), Group 3 80%(27% 36% 17%), etc.
Slide 37Line GraphsPercent SurvivingRelative Survival Rates by Year of Diagnosisfor Prostate Cancer, 1996 - 20001001 Year2 Year3 Year4 Year5 Year80604020019961997199819992000Year of DiagnosisObserved Survival by Stage, 1996 - 2000SemilogPercent SurvivingArithmetic ScaleYears After DiagnosisUsed to display trends over time and survival curves.Can display multiple sets of data on one graph (%’s should be used).Arithmetic scale – absolute numerical difference (how far).Uses equal units of measurement for the intervals on the horizontal and verticalscale.Semilog – the steeper the line, the greater the rate of change (how fast).Uses equal units of measurement for the intervals on the horizontal scale.Uses unequal units of measurement for the intervals on the vertical scale.Usually used for survival or incidence rates.
Slide 38Pie ChartsPercentage Distribution of Prostate Cancerby Stage, 200018%47%20%Stage IStage IIStage IIIStage IV15%Another way of showing the component parts of the whole.All %’s must sum to 100%.Uses a circle (360 degrees).Not as appropriate for comparing distributions.Each part is a percent of the total. 1% 3.6 degrees.Plot clockwise beginning with the largest size of the wedge – if there is NOT a logicalorder to the values (such as stage).
Slide 39Scatter m012345Means of presenting relationships between two variables.Example: Size of tumor on the X-axis and Depth of invasion on the Y-axis.Individual observations are plotted at the point of intersection of the values of the twovariables.If the points tend to form a line at an angle to the axes, there may exist either a positive(upward line) or inverse (downward) relationship. If randomly distributed, there wouldappear to be no relationship.
Slide 40Pictographs & MapsPictographs use symbols to represent numbers.Such as: 1 out of every 5 women Maps: 2 types – dot and shaded. Customarily: Dots represent general effect of density.Shading increases with rate.
Slide 41EpidemiologySlide 42EpidemiologyBranch of medical scienceConcerned with the study of thedistribution of disease in a population(descriptive epidemiology) and thesearch for the determinants of disease(analytic epidemiology)Q: What does distribution of disease mean?A: Person, Place, and Time Who, What, Where, When.
Slide 43Triad of Disease OccurrenceAgentHostEnvironmentQ: What is the basic theory behind this triad – as it relates to epidemiology?A: Disease can be prevented by eliminating any one of the three elements of the triad.Slide 44Steps in EpidemiologicReasoningObserve a statistical associationbetween an exposure and an endpointDevelop a hypothesis about therelationshipTest the hypothesis
Slide 45Descriptive EpidemiologySEER Book 7, Section CTakes into consideration the distribution of disease (cancer) in a population.Uses variables such as age, race, sex, county of residence.Identifies low and high risk subgroups of the population.
Slide 46Measurements of RiskPrimary tool for the measurement of risk iscalled a RATEThree measures of risk:}2.Incidence RatesPrevalence Rates3.Mortality Rates1.}Risk measures the strength of an association between exposure to a particular factorand risk of certain outcome.Q: So, how do we measure risk?A: By calculating morbidity and mortality rates for a population.Morbidity: Illness rateMortality: Death riskMorbidity and Mortality rates are essential for comparison of disease risk.To bring it down to the bottom line - measures the risk of getting or dying from cancer.
Incidence RateOCCURRENCERate of occurrence of NEW cases thatare diagnosed during a set time periodin a defined populationSlide 47Basic descriptive tool for epidemiologists for studying possible causes of cancer.Q: What is an example of how incidence is used in the registry?A: Annual report. For example, the number of new analytic cases diagnosed in year2004.Prevalence RatePRESENCE OF DISEASEQuantifies the TOTAL amount of activedisease present in a defined populationat a particular point in timeSlide 48May be difficult to measure since it is not always possible to determine whether aperson with a prior diagnosis of cancer still has active disease.Usually based on the TOTAL number of living cases, both new and previouslydiagnosed (historical prevalence).Higher for older registries with more historical data.True prevalence rate would be much lower for active disease.
Slide 49Mortality RateDEATHMeasures the risk of DEATH for thecause under study in a definedpopulation during a given time periodSlide 50Calculation ofMorbidity and Mortality RatesMorbidity and mortality rates arebased on TWO primary components:1)2)A count of the number of diseaseoccurrences or deaths (numerator)The size of the population – number ofpeople at risk of getting the disease(denominator)# of events------------------------------- X 100,000# in the population at riskThe next few slides will take you through calculating these rates.The base is one that is large enough to report the rate in whole numbers. In this case,100,000 was chosen. The base is usually 100,000 for adults and 1,000,000 for children.
Slide 51Calculation of CrudeMorbidity and Mortality RatesIncidence Rate # of NEW cases diagnosedduring a given time period--------------------------------- X 100,000# in the population at riskPrevalence Rate # of TOTAL active casesat a given point in time--------------------------------- X 100,000# in the population at risk# of cancer DEATHSMortality Rate during a given time period--------------------------------- X 100,000# in the population at riskTo calculate the rates, you have to take into consideration the number of people in thepopulation at risk.Incidence the number of new cases diagnosed during a given time periodIncidence Rate the number of new cases diagnosed during a given time period out ofthe number in the population at riskRemember:-To measure risk: incidence, prevalence, and mortality are expressed in the form of aRATE.-The calculation of a rate factors in the population at risk-Crude rates are based on the entire population and includes all cancer sites.
Slide 52Calculation of SpecificMorbidity and Mortality RatesRisks for specific cancers in an entirepopulation or a subgroup of thepopulationUses the same formulasThe selection is limitedExample: An age-specific rate is specificfor persons within a given age groupTo calculate specific morbidity and mortality rates, we will use the same formulas; wejust limit the selection of cases to include.Remember crude rates take into account the entire population and includes all cancersites.For example: You may want to calculate age-specific rates. This would only includethose patients within the age group
Mean Median Mode The tools we use to calculate the typical values are the 3 M’s mean, median, and mode. To help describe mean, median, and mode, we will use an example of five tumor sizes. Slide 9 . Mean
AHIMA's Data Quality Management Model "Data Quality Management Model (2015 Update)" Journal of AHIMA 86 . AHIMA Information Governance Adoption Model for Healthcare AHIMA www .
Introduction to Epidemiology Epidemiology yIs the process to study the distribution and determinants of disease frequency yIs a discipline which approaches problems systematically and quantitatively yIs the basic science of public health The Public Health Cycle Measure/Evaluate Epidemiology Analyze Epidemiology Communicate Intervene Epidemiology
AHIMA 2008 Audio Seminar Series ii Donna Wilson, RHIA, CCS Donna Wilson is the Revenue Integrity Manager for Roper St. Frances Healthcare, Charleston, SC. Donna has served on several AHIMA and other national workgroups to define coding practices and is a distinguished member of the SCHIMA.
For more information on OP CDI please be sure to tune into our ACE audio conference in October. References: AHIMA CDI Tool Kit 2016; AHIMA; AHIMA/ACDIS 2019 Practic
The total number of ICD-10-CM/PCS continuing education units (CEUs) required, by AHIMA credential, is as follows: CHPS-1 CEU CHDA-6 CEUs RHIT-6 CEUs RHIA-6 CEUs CDIP -12 CEUs CCS-P-12 CEUs CCS -18 CEUs CCA-18 CEUs *6 CEUs 1 day oftraining (Please see the explanation under FAQs) Certificants who hold more than one AHIMA credential w ill . only
Introduction to Epidemiology and Genetic Epidemiology. Major goals in Epidemiology To obtain an unbiased & precise estimate of the true effect of an exposure or intervention on outcome in the population at risk To use this knowledge to prevent and treat disease.
Key words Epidemiology: , Familial aggregation, Genetic dominance, Genetic epidemi ology, Heritability, Popperian philosophy, Sampling, Shared environment, Twins. INTRODUCTION What is genetic epidemiology? Epidemiology has been defined as the "study of the distribution and determinants of health-related states and events in populations" [32].
Introduction to Genetic Epidemiology Different faces of genetic epidemiology K Van Steen 2 DIFFERENT FACES OF GENETIC EPIDEMIOLOGY 1 Basic epidemiology . Clayton D. Introduction to genetics (course slides Bristol 2003) Bon
