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DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
DESCRIPTIVE EPIDEMIOLOGY
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DESCRIPTIVE EPIDEMIOLOGY

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THIS IS A LECTURE ON DESCRIPTIVE EPIDEMIOLOGY MEANT FOR MEDICAL /HEALTH/MPH STUDENTS

THIS IS A LECTURE ON DESCRIPTIVE EPIDEMIOLOGY MEANT FOR MEDICAL /HEALTH/MPH STUDENTS

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  • 1. DescriptiveDescriptive epidemiologyepidemiology Dr. KANUPRIYA CHATURVEDIDr. KANUPRIYA CHATURVEDI
  • 2. How we view the world…..How we view the world…..  Pessimist:Pessimist: The glass isThe glass is half empty.half empty.  OptimistOptimist: The glass is: The glass is half full.half full.  EpidemiologistEpidemiologist: As: As compared to what?compared to what?
  • 3. Epidemiology is...
  • 4. Epidemiology is...  "The worst taught course in Medical school."  Medical Student
  • 5. Epidemiology is...  "The science of making the obvious obscure."  Clinical Professor
  • 6. Epidemiology is...  "The science of long division.... I'=[(480)(log2)(10E6)]/[(9.1)(0.955po) +0.45n]"  Statistician
  • 7. Definition of Epidemiology* "The STUDY of the DISTRIBUTION and DETERMINANTS of HEALTH- RELATED STATES in specified POPULATIONS, and the application of this study to CONTROL of health problems." *Last, J.M. 1988. A Dictionary of Epidemiology, 2nd ed.
  • 8. Epidemiology: DefinitionEpidemiology: Definition Dynamic study of the Determinants Occurrence Distribution Control Pattern Of health and disease in a population
  • 9. EpidemiologyEpidemiology EPI DEMO LOGOS Upon,on,befall People,population,man the Study of The study of anything that happens to people “That which befalls man”
  • 10. Definition of Epidemiology  A quantitative basic science, built on a working knowledge of probability, statistics and sound research methods.  A method of causal reasoning, based on developing and testing biologically plausible hypothesis pertaining to occurrence and prevention of morbidity and mortality.  A tool for public health action to promote and protect the public's health based on science, causal reasoning, and a dose of practical common sense.
  • 11. Epidemiology is a QuantitativeEpidemiology is a Quantitative DisciplineDiscipline  Measures of frequencyMeasures of frequency  Counts and ratesCounts and rates  Measures of associationMeasures of association  Relative riskRelative risk  Odds ratioOdds ratio  Statistical inferenceStatistical inference  P-valueP-value  Confidence limitsConfidence limits
  • 12. Clinician Epidemiologist Patient’s diagnostician Investigations Diagnosis Therapy Cure  Community’s diagnostician  Investigations  Predict trend  Control  Prevention
  • 13. EpidemiologyEpidemiology  DescribesDescribes  health eventshealth events  cause and risk factors of diseasecause and risk factors of disease  clinical pattern of diseaseclinical pattern of disease  Identify syndromesIdentify syndromes  Identify control and/or preventive measuresIdentify control and/or preventive measures
  • 14. So, EpidemiologySo, Epidemiology  Is theIs the basic sciencebasic science of public healthof public health  Provides insight regarding theProvides insight regarding the naturenature,, causescauses,, andand extentextent of health and diseaseof health and disease  Provides information needed toProvides information needed to planplan andand targettarget resourcesresources appropriatelyappropriately
  • 15. Kinds of EpidemiologyKinds of Epidemiology  DescriptiveDescriptive  AnalyticAnalytic  ExperimentalExperimental Further studies to determine the validity of a hypothesis concerning the occurrence of disease. Deliberate manipulation of the cause is predictably followed by an alteration in the effect not due to chance Study of the occurrence and distribution of disease
  • 16. Overview of epidemiologic designOverview of epidemiologic design strategiesstrategies  DescriptiveDescriptive  Populations{Correlational studies}Populations{Correlational studies}  IndividualIndividual  Case reportCase report  Case seriesCase series  Cross sectional studiesCross sectional studies  Analytic studiesAnalytic studies  ObservationalObservational  Case controlCase control  CohortCohort  RetrospectiveRetrospective  ProspectiveProspective  Interventional/ExperimentalInterventional/Experimental  Randomized controlled trialRandomized controlled trial  Field trialField trial  Clinical trialClinical trial
  • 17. Descriptive vs. Analytic EpidemiologyDescriptive vs. Analytic Epidemiology DescriptiveDescriptive  Used when little isUsed when little is known about theknown about the diseasedisease  Rely on preexistingRely on preexisting datadata  Who, where, whenWho, where, when  Illustrates potentialIllustrates potential associationsassociations AnalyticAnalytic  Used when insight aboutUsed when insight about various aspects of disease isvarious aspects of disease is availableavailable  Rely on development of newRely on development of new datadata  WhyWhy  Evaluates the causality ofEvaluates the causality of associationsassociations Both are
  • 18. Descriptive StudiesDescriptive Studies  Relatively inexpensive and less time-consumingRelatively inexpensive and less time-consuming than analytic studies, they describe,than analytic studies, they describe,  Patterns of disease occurrence, in terms of,Patterns of disease occurrence, in terms of,  Who gets sick and/or who does notWho gets sick and/or who does not  Where rates are highest and lowestWhere rates are highest and lowest  Temporal patterns of diseaseTemporal patterns of disease  Data provided are useful for,Data provided are useful for,  Public health administrators (for allocation of resources)Public health administrators (for allocation of resources)  Epidemiologists (first step in risk factor determination)Epidemiologists (first step in risk factor determination)
  • 19. Descriptive EpidemiologyDescriptive Epidemiology  Correlational studiesCorrelational studies  Case reportsCase reports  Case seriesCase series  Cross sectional studiesCross sectional studies
  • 20. Correlational Studies (Ecological Studies)Correlational Studies (Ecological Studies)  Uses measures that represent characteristics ofUses measures that represent characteristics of entire populationsentire populations  It describes outcomes in relation to age, time,It describes outcomes in relation to age, time, utilization of services, or exposuresutilization of services, or exposures  ADVANTAGESADVANTAGES  We can generate hypotheses for case-control studies andWe can generate hypotheses for case-control studies and environmental studiesenvironmental studies  We can target high-risk populations, time-periods, orWe can target high-risk populations, time-periods, or geographic regions for future studiesgeographic regions for future studies
  • 21. Correlational StudiesCorrelational Studies  LIMITATIONSLIMITATIONS  Because data are for groups, we cannot link disease andBecause data are for groups, we cannot link disease and exposure in individualexposure in individual  We cannot control for potential confoundersWe cannot control for potential confounders  Data represent average exposures rather than individualData represent average exposures rather than individual exposures, so we cannot determine a dose-responseexposures, so we cannot determine a dose-response relationshiprelationship  Caution must be taken to avoid drawing inappropriateCaution must be taken to avoid drawing inappropriate conclusions, orconclusions, or ecological fallacyecological fallacy
  • 22. Patterns of disease Occurrence :Patterns of disease Occurrence : CorrelationCorrelation ofof PopulationPopulation statisticsstatistics  EcologicEcologic (( correlationcorrelation ) studies) studies  Used as first step in determining associationUsed as first step in determining association plotplot :: disease (population) burden [ Y axis ]disease (population) burden [ Y axis ] vs.vs. prevalence of “risk factor” [ X axis ]prevalence of “risk factor” [ X axis ] e.g. smoking vs. lung cancere.g. smoking vs. lung cancer  -- correlation coefficient : r ; + 1 to -1-- correlation coefficient : r ; + 1 to -1  Quantifies linear relationship between exposure & diseaseQuantifies linear relationship between exposure & disease
  • 23. Case Reports (case series)Case Reports (case series)  Report of a single individual or a group ofReport of a single individual or a group of individuals with the same diagnosisindividuals with the same diagnosis  AdvantagesAdvantages  We can aggregate cases from disparate sources to generateWe can aggregate cases from disparate sources to generate hypotheses and describe new syndromeshypotheses and describe new syndromes Example: hepatitis, AIDSExample: hepatitis, AIDS  LimitationsLimitations  We cannot test for statistical association because there is noWe cannot test for statistical association because there is no relevant comparison grouprelevant comparison group  Based on individual exposure {may simply be coincidental}Based on individual exposure {may simply be coincidental}
  • 24. Case report/Case series(contd.)Case report/Case series(contd.)  ImportantImportant interfaceinterface between clinical medicine &between clinical medicine & epidemiologyepidemiology  Most common type of studies published inMost common type of studies published in medical journals{1/3medical journals{1/3rdrd of all}of all}  e.g. Frisbee finger , break dancing necke.g. Frisbee finger , break dancing neck  AIDS ~ b/w oct1980-may81, 5 cases of P.cariniiAIDS ~ b/w oct1980-may81, 5 cases of P.carinii pneumonia were diagnosed among previously healthypneumonia were diagnosed among previously healthy young homosexual males in L.A.young homosexual males in L.A.
  • 25. Cross-Sectional Studies (prevalence studies)Cross-Sectional Studies (prevalence studies)  Measures disease and exposure simultaneously in aMeasures disease and exposure simultaneously in a well-defined populationwell-defined population  AdvantagesAdvantages  They cut across the general population, not simply thoseThey cut across the general population, not simply those seeking medical careseeking medical care  Good for identifying prevalence of common outcomes, suchGood for identifying prevalence of common outcomes, such as arthritis, blood pressure or allergiesas arthritis, blood pressure or allergies  LimitationsLimitations  Cannot determine whether exposure preceded diseaseCannot determine whether exposure preceded disease  It considers prevalent rather than incident cases, resultsIt considers prevalent rather than incident cases, results will be influenced by survival factorswill be influenced by survival factors  Remember: P = I x DRemember: P = I x D
  • 26. Cross-Sectional StudiesCross-Sectional Studies  Can be used as a type of analytic study for testingCan be used as a type of analytic study for testing hypothesis, when;hypothesis, when;  Current values of exposure variables are unalterable overCurrent values of exposure variables are unalterable over timetime  Represents value present at initiation of diseaseRepresents value present at initiation of disease  E.g. eye colour or blood groupE.g. eye colour or blood group  If risk factor is subject to alterations by disease, onlyIf risk factor is subject to alterations by disease, only hypothesis formulation can be donehypothesis formulation can be done
  • 27. The epidemiologic approach:The epidemiologic approach: Steps to public health actionSteps to public health action MEASURES  Counts  Times  Rates  Risks/Odds  Prevalence METHODS  Design  Conduct  Analysis  Interpretation ALTERNATIVE EXPLANATION S  Chance  Bias  Confounding INFERENCES  Epidemiologic  Causal ACTION  Behavioural  Clinical  Community  Environmental DESCRIPTIVE  What (case definition)  Who (person)  Where (place)  When (time)  How many (measures) ANALYTIC  Why (Causes)  How (Causes)
  • 28. Key questionsKey questions  Why now?Why now?  Why here?Why here?  Why in this group?Why in this group?
  • 29. Descriptive EpidemiologyDescriptive Epidemiology  Study of the occurrence and distribution of disease  Terms:  Time  Place  Person
  • 30. What are the three categories ofWhat are the three categories of descriptive epidemiologic clues?descriptive epidemiologic clues?  □□ Person:Person: WhoWho is getting sick?is getting sick?  □□ Place:Place: WhereWhere is the sickness occurring?is the sickness occurring?  □□ Time:Time: WhenWhen is the sickness occurring?is the sickness occurring?  PPT = person, place, timePPT = person, place, time
  • 31. TimeTime  Secular  Periodic  Seasonal  Epidemic
  • 32. Secular TrendSecular Trend The long-time trend of disease occurrence
  • 33. Tetanus – by year, USA, 1955-2000Tetanus – by year, USA, 1955-2000 During 2000, a total of 35 cases of tetanus were reported. The percentage of cases among persons aged 25-59 years Has increased in the last decade. Note: A tetanus vaccine was first available in 1933. 0 100 200 300 400 500 600 700 800 900 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 Year
  • 34. Possible Reasons for Changes inPossible Reasons for Changes in TrendsTrends  ArtifactualArtifactual  Errors in numerator due toErrors in numerator due to Changes in the recognition of diseaseChanges in the recognition of disease Changes in the rules and procedures forChanges in the rules and procedures for classification of causes of deathclassification of causes of death Changes in the classification code of causes ofChanges in the classification code of causes of deathdeath Changes in accuracy of reporting age at deathChanges in accuracy of reporting age at death Errors in the denominator due to error in theErrors in the denominator due to error in the enumeration of the populationenumeration of the population
  • 35. Possible Reasons for Changes inPossible Reasons for Changes in Trends (cont.)Trends (cont.)  RealReal  Changes in age distribution of the populationChanges in age distribution of the population  Changes in survivorshipChanges in survivorship  Changes in incidence of disease resultingChanges in incidence of disease resulting fromfrom  Genetic factorsGenetic factors  Environmental factorsEnvironmental factors
  • 36. Other phrasesOther phrases  Cyclic trends ~ recurrent alterations in occurrence , interval or frequency of disease Secular cyclicity Levels of immunizations Build up of susceptibles  e.g. Hep A-7 yr cycle,Measles-2yr cycle Short term cyclicity  Chickenpox,salmonella(yearly basis)
  • 37. Periodic TrendPeriodic Trend Temporal interruption of the general trend of secular variation
  • 38. Whooping Cough - Four-monthlyWhooping Cough - Four-monthly admissions, 1954-1973admissions, 1954-1973
  • 39. SeasonalSeasonal  A cyclic variation in disease frequencyA cyclic variation in disease frequency by time of year & seasonby time of year & season.. Seasonal fluctuations in,Seasonal fluctuations in, Environmental factorsEnvironmental factors Occupational activitiesOccupational activities Recreational activitiesRecreational activities
  • 40. Seasonal TrendSeasonal Trend Pneumonia-Influenza Deaths – By year,Pneumonia-Influenza Deaths – By year, 1934-19801934-1980
  • 41. EpidemicEpidemic An increase in incidence above the expected in a defined geographic area within a defined time period
  • 42. Endemic, Epidemic and Pandemic  Endemic - The habitual presence (or usual occurrence) of a disease within a given geographic area  Epidemic - The occurrence of an infectious disease clearly in excess of normal expectancy, and generated from a common or propagated source  Pandemic - A worldwide epidemic affecting an exceptionally high proportion of the global population Number of Cases of Disease Time
  • 43. Time clusteringTime clustering Time Place Cluster/disease clusterTime Place Cluster/disease cluster  A group of cases occur close togetherA group of cases occur close together & have a well aligned distribution& have a well aligned distribution patternpattern {{in terms ofin terms of time and placetime and place}}  Cluster analysis-used for rare or special diseaseCluster analysis-used for rare or special disease events.events.
  • 44. Time/Place clustering analysis using theTime/Place clustering analysis using the Poisson modelPoisson model {Poisson spatial/nearest neighbor distribution}{Poisson spatial/nearest neighbor distribution}  Poisson probability distribution is an inferential statistics probabilityPoisson probability distribution is an inferential statistics probability measure.measure.  Describes objects/events as they are distributed geographically.Describes objects/events as they are distributed geographically.  Geographical area divided into a series of equal square areas.Geographical area divided into a series of equal square areas.  Randomization i.e. each case has equal probability of falling into eachRandomization i.e. each case has equal probability of falling into each square.square.  If clustering occurs, probability of cause-effect relationship goes up &If clustering occurs, probability of cause-effect relationship goes up & vice versa.vice versa.
  • 45. PlacePlace  Diagnosis is Made  Contact occurred between agent and host  Source became infected Geographic Area Example Action Level Home – Patient ill Restaurant – Food Eaten Farm – Eggs Infected Investigation Control Prevention
  • 46. PersonPerson Age Hobbies Sex Pets Occupation Travel Immunization status Personal Habits Underlying disease Stress Medication Family unit Nutritional status School Socioeconomic factors Genetics Crowding Religion
  • 47. Descriptive epidemiologyDescriptive epidemiology :: Patterns of Disease OccurrencePatterns of Disease Occurrence  distributiondistribution of disease in populationsof disease in populations numerator ( “event” count ) / denominator ( group “atnumerator ( “event” count ) / denominator ( group “at risk” )risk” )  by “by “personperson” : age , race / ethnicity , gender ,” : age , race / ethnicity , gender , occupation , education , marital status , geneticoccupation , education , marital status , genetic marker , sexual preferencemarker , sexual preference  by “by “placeplace” : residence (urban vs. rural) , worksite ,” : residence (urban vs. rural) , worksite , social eventsocial event  by “by “timetime” : week , month , year ; sporadic , seasonal” : week , month , year ; sporadic , seasonal , trends, trends --- incubation period ; latency--- incubation period ; latency
  • 48. Sources of informationSources of information  Census dataCensus data  Vital statistical recordsVital statistical records  Employment health examinationsEmployment health examinations  Clinical records from hospitalsClinical records from hospitals  National figures on food consumption ,National figures on food consumption , medications, health events etcmedications, health events etc
  • 49. Epidemiologic (Epidemiologic ( scientificscientific ) Approach) Approach  1. Identify a PROBLEM1. Identify a PROBLEM :: clinical suspicion ; case series ; review of medical literatureclinical suspicion ; case series ; review of medical literature  2. Formulate a HYPOTHESIS2. Formulate a HYPOTHESIS ( asking the right question ) ;( asking the right question ) ; good hypotheses are: Specific, Measurable, and Plausiblegood hypotheses are: Specific, Measurable, and Plausible  3. TEST that HYPOTHESIS3. TEST that HYPOTHESIS ( assumptions vs. type of data )( assumptions vs. type of data )  4. always Question the VALIDITY of the result(s)4. always Question the VALIDITY of the result(s) :: Chance ; Bias ; and CausalityChance ; Bias ; and Causality
  • 50. Epidemiologic Study: threats to ValidityEpidemiologic Study: threats to Validity  ChanceChance : role of: role of randomrandom error in outcome measure(s)error in outcome measure(s) ( p - value ; power of the study and the confidence interval )( p - value ; power of the study and the confidence interval ) --- largely determined by sample size--- largely determined by sample size  BiasBias : role of: role of systematicsystematic error in outcome measure(s)error in outcome measure(s)  SelectionSelection bias - subjects not representativebias - subjects not representative  InformationInformation bias - error(s) in subject data / classificationbias - error(s) in subject data / classification  ConfoundingConfounding - 3rd variable (causal) assoc. w/ both X and Y- 3rd variable (causal) assoc. w/ both X and Y
  • 51. What is a hypothesis?What is a hypothesis?  An educated guessAn educated guess  an unproven ideaan unproven idea  based on observation or reasoning, that can bebased on observation or reasoning, that can be proven or disproven through investigation.proven or disproven through investigation.
  • 52. What goes into a hypothesis?What goes into a hypothesis?  Characteristics of the diseaseCharacteristics of the disease  The illnessThe illness  Established modes of transmissionEstablished modes of transmission  DistributionDistribution  In timeIn time  By placeBy place  By personBy person
  • 53. Hypothesis formulationHypothesis formulation  4 methods {derived from4 methods {derived from 5 canons of inductive5 canons of inductive reasoningreasoning by John Stuart Mill}by John Stuart Mill}  Method of differenceMethod of difference  Method of agreementMethod of agreement  Method of concomitant variationMethod of concomitant variation  Method of analogyMethod of analogy
  • 54. MeasuresMeasures  Morbidity: Refers to the presence of disease in aMorbidity: Refers to the presence of disease in a populationpopulation  Mortality: Refers to the occurrence of death in aMortality: Refers to the occurrence of death in a populationpopulation
  • 55. Methods for MeasuringMethods for Measuring How do we determine disease frequency for aHow do we determine disease frequency for a population?population?  Rate = Frequency of defined events in specifiedRate = Frequency of defined events in specified population for given time periodpopulation for given time period  Rates allow comparisons between two or moreRates allow comparisons between two or more populations of different sizes or of a populationpopulations of different sizes or of a population over timeover time
  • 56. Compute Disease RateCompute Disease Rate Number of persons at risk = 5,595,211Number of persons at risk = 5,595,211 Number of persons with disease = 17,382Number of persons with disease = 17,382 Rate =Rate = 17,382 persons with heart disease17,382 persons with heart disease 5,595,211 persons5,595,211 persons == ..003107 heart disease / resident / year003107 heart disease / resident / year
  • 57. RatesRates Rates are usually expressed as integers andRates are usually expressed as integers and decimals for populations at risk during specifieddecimals for populations at risk during specified periods to make comparisons easier.periods to make comparisons easier. .003107 heart disease / resident / year.003107 heart disease / resident / year x 100,000x 100,000 == 310.7310.7 heart disease /heart disease / 100,000100,000 residents / yearresidents / year
  • 58. PrevalencePrevalence vsvs. Incidence. Incidence  Prevalence is the number ofPrevalence is the number of existingexisting cases ofcases of disease in the population during a defineddisease in the population during a defined period.period.  Incidence is the number ofIncidence is the number of newnew cases ofcases of disease that develop in the population during adisease that develop in the population during a defined period.defined period.
  • 59. IncidenceIncidence  Incidence rate is a measure of theIncidence rate is a measure of the probability of the event among persons atprobability of the event among persons at risk.risk.
  • 60. Incidence RatesIncidence Rates  Population denominator:Population denominator: IR =IR = # new cases during time period X K# new cases during time period X K specified populationspecified population at riskat risk
  • 61. Example (Incidence Rate)Example (Incidence Rate) During a six-month time period, a total of 53 nosocomialDuring a six-month time period, a total of 53 nosocomial infections were recorded by an infection control nurseinfections were recorded by an infection control nurse at a community hospital. During this time, there wereat a community hospital. During this time, there were 832 patients with a total of 1,290 patient days. What is832 patients with a total of 1,290 patient days. What is the rate of nosocomial infections per 100 patient days?the rate of nosocomial infections per 100 patient days? I R = 53 X 100 1,290 pt. days = 4.1 infections per 100 pt. days
  • 62. Mortality RatesMortality Rates  A special type of incidence rateA special type of incidence rate  Number of deaths occurring in a specifiedNumber of deaths occurring in a specified population in a given time periodpopulation in a given time period
  • 63. Use of Mortality ratesUse of Mortality rates  Mortality rates are used to estimate diseaseMortality rates are used to estimate disease frequency when…frequency when…  incidence data are not available,  case-fatality rates are high,  goal is to reduce mortality among screened or targeted populations
  • 64. Mortality Rates: ExamplesMortality Rates: Examples  Crude mortalityCrude mortality: death rate in an entire: death rate in an entire populationpopulation  Rates can also be calculated for sub-groups withinRates can also be calculated for sub-groups within the populationthe population  Cause-specific mortalityCause-specific mortality: rate at which deaths: rate at which deaths occur for a specific causeoccur for a specific cause
  • 65. Mortality Rates: ExamplesMortality Rates: Examples  Case-fatalityCase-fatality: Rate at which deaths occur from a: Rate at which deaths occur from a disease among those with the diseasedisease among those with the disease  Maternal mortalityMaternal mortality: Ratio of death from: Ratio of death from childbearing for a given time period per numberchildbearing for a given time period per number of live births during same time periodof live births during same time period
  • 66. Mortality Rates: ExamplesMortality Rates: Examples  Infant mortalityInfant mortality: Rate of death for children less: Rate of death for children less than 1 year per number of live birthsthan 1 year per number of live births  Neonatal mortalityNeonatal mortality: Rate of death for children: Rate of death for children less than 28 days of age per number of liveless than 28 days of age per number of live birthsbirths
  • 67. PrevalencePrevalence  Prevalence: Existing cases in a specifiedPrevalence: Existing cases in a specified population during a specified time period (bothpopulation during a specified time period (both new and ongoing cases)new and ongoing cases)  Prevalence is a measure of burden of disease orPrevalence is a measure of burden of disease or health problem in a populationhealth problem in a population
  • 68. PrevalencePrevalence Prevalence: The number of existing cases in thePrevalence: The number of existing cases in the population during a given time period.population during a given time period. PRPR == # existing cases during time period# existing cases during time period population at same point in timepopulation at same point in time Prevalence rates are often expressed as a percentage.Prevalence rates are often expressed as a percentage.
  • 69. Factors Influencing PrevalenceFactors Influencing Prevalence Increased by:  Longer duration of the disease  Prolongation of life of patients without cure  Increase in new cases  (increase in incidence)  In-migration of cases  Out-migration of healthy people  In-migration of susceptible people  Improved diagnostic facilities  (better reporting) Decreased by: Shorter duration of disease High case-fatality rate from disease Decrease in new cases (decrease in incidence) In-migration of healthy people Out-migration of cases  Improved cure rate of cases
  • 70. Basic Measures of AssociationBasic Measures of Association  Relative risk& odds ratioRelative risk& odds ratio  We often need to know the relationship betweenWe often need to know the relationship between an outcome and certain factors (e.g., age, sex,an outcome and certain factors (e.g., age, sex, race, smoking status, etc.)race, smoking status, etc.)  Used to guide planning and interventionUsed to guide planning and intervention strategiesstrategies
  • 71. 2 x 2 contingency table for Calculation of2 x 2 contingency table for Calculation of Measures of AssociationMeasures of Association    Outcome Outcome  ExposureExposure PresentPresent AbsentAbsent TOTALTOTAL PresentPresent aa bb a+ba+b AbsentAbsent cc dd c+dc+d TOTALTOTAL a+ca+c b+db+d a+b+c+da+b+c+d Note: “Exposure” is a broad term that represents any factor that may be related to an outcome.
  • 72. Relative RiskRelative Risk  Ratio of the incidence rates between two groupsRatio of the incidence rates between two groups  Can only be calculated from prospective studiesCan only be calculated from prospective studies (cohort studies)(cohort studies)  InterpretationInterpretation  RR > 1: Increased risk of outcome among “exposed”RR > 1: Increased risk of outcome among “exposed” groupgroup  RR < 1: Decreased risk, or protective effects, amongRR < 1: Decreased risk, or protective effects, among “exposed” group“exposed” group  RR = 1: No association between exposure andRR = 1: No association between exposure and outcomeoutcome
  • 73. Calculation of Relative RiskCalculation of Relative Risk incidence rate among exposedincidence rate among exposed RR =RR = incidence rate among non-exposedincidence rate among non-exposed
  • 74. Calculation of Relative RiskCalculation of Relative Risk    OutcomeOutcome   ExposureExposure PresentPresent AbsentAbsent TOTALTOTAL PresentPresent aa bb a+ba+b AbsentAbsent cc dd c+dc+d TOTALTOTAL a+ca+c b+db+d a+b+c+da+b+c+d Relative Risk = a a b c c d +       +      
  • 75. Relative Risk Case StudyRelative Risk Case Study    Birth WeightBirth Weight Smoking statusSmoking status <2500 g<2500 g >>2500 g2500 g TOTALTOTAL SmokerSmoker 120120 240240 360360 Non-smokerNon-smoker 6060 580580 640640 TOTALTOTAL 180180 820820 10001000 Smoking and low birth weight
  • 76. Answers to Relative Risk Case StudyAnswers to Relative Risk Case Study  1. Incidence of LBW among1. Incidence of LBW among smokerssmokers  2. Incidence of LBW among2. Incidence of LBW among non-smokersnon-smokers  3.3. Relative riskRelative risk for having afor having a LBW baby among smokersLBW baby among smokers versus non-smokersversus non-smokers = = 1 2 0 3 6 0 1 0 0 0 3 3 3 3x , . = = 6 0 6 4 0 1 0 0 0 9 3 8x , . = ≈ 3 3 3 3 9 3 8 3 6 . . .
  • 77. Understanding Probability and OddsUnderstanding Probability and Odds  Probability:Probability: Chance or risk of an event occurring (aChance or risk of an event occurring (a proportion)proportion)  Probability=Probability= no. of times an event occursno. of times an event occurs no. of times an event can occurno. of times an event can occur  Odds:Odds: ratio of the probability of an event occurring toratio of the probability of an event occurring to the probability of an event not occurringthe probability of an event not occurring  Odds = P/(1-P)Odds = P/(1-P)
  • 78. Calculation of Odds RatioCalculation of Odds Ratio    OutcomeOutcome   ExposureExposure PresentPresent AbsentAbsent TOTALTOTAL PresentPresent aa bb a+ba+b AbsentAbsent cc dd c+dc+d TOTALTOTAL a+ca+c b+db+d a+b+c+da+b+c+d Odds Ratio = a d b c
  • 79. Odds RatioOdds Ratio  The odds ratio (OR) is a ratio of two odds.The odds ratio (OR) is a ratio of two odds.  The OR can be calculated for all three studyThe OR can be calculated for all three study designsdesigns  Cross-sectionalCross-sectional  Case-controlCase-control  Cohort.Cohort.
  • 80. Various approaches to Odds ratioVarious approaches to Odds ratio  Cross product/odds ratioCross product/odds ratio  2 x 2 contingency table (ad/bc)2 x 2 contingency table (ad/bc)  Prevalence odds ratioPrevalence odds ratio  cross sectional studiescross sectional studies  Exposure odds ratioExposure odds ratio(( odds of exposure in diseased vs. nondiseased)odds of exposure in diseased vs. nondiseased)  In rare cases or exotic diseasesIn rare cases or exotic diseases  Disease odds/Rate odds ratioDisease odds/Rate odds ratio(o(odds of getting a disease if exposeddds of getting a disease if exposed or unexposed)or unexposed)  Cohort & cross sectionalCohort & cross sectional  Risk odds ratioRisk odds ratio  Cross sectional ,cohort & case controlCross sectional ,cohort & case control
  • 81. Odds RatioOdds Ratio  For cohort & cross sectional studiesFor cohort & cross sectional studies: OR is a: OR is a ratio of the odds of the outcome in exposedratio of the odds of the outcome in exposed persons to the odds of the outcome in non-persons to the odds of the outcome in non- exposed persons.exposed persons.  For case-control studiesFor case-control studies: OR is a ratio of the: OR is a ratio of the odds of exposure in cases to the odds ofodds of exposure in cases to the odds of exposure in controls.exposure in controls.  Provides an estimate of the relative risk whenProvides an estimate of the relative risk when the outcome is rarethe outcome is rare
  • 82. Interpretation of Odds RatioInterpretation of Odds Ratio  OR > 1: Increased odds of exposure among thoseOR > 1: Increased odds of exposure among those with outcomewith outcome  OR < 1: Decreased odds, or protective effects,OR < 1: Decreased odds, or protective effects, among those with outcomeamong those with outcome  OR = 1: No association between exposure andOR = 1: No association between exposure and outcomeoutcome
  • 83. Keeping the Terms StraightKeeping the Terms Straight  ““Risk ratio” = “relative risk”Risk ratio” = “relative risk”  ““Relative odds” = “odds ratio”Relative odds” = “odds ratio”  Remember – the key is recognizing the termsRemember – the key is recognizing the terms “risk” and “odds”“risk” and “odds”
  • 84. Appropriateness of MeasuresAppropriateness of Measures  Remember that the relative risk can only beRemember that the relative risk can only be calculated in prospective studiescalculated in prospective studies  Odds ratio can be calculated for any designOdds ratio can be calculated for any design  Cohort / prospectiveCohort / prospective  Case-controlCase-control  Cross-sectionalCross-sectional
  • 85. InferenceInference  The relative risk and odds ratio provide theThe relative risk and odds ratio provide the magnitude of difference between some factormagnitude of difference between some factor and an outcomeand an outcome  How do we know if the magnitude isHow do we know if the magnitude is statisticallystatistically significant?significant?
  • 86. Confidence IntervalsConfidence Intervals  A confidence interval is a range of values that isA confidence interval is a range of values that is likely (e.g., 95%) to contain the true value in thelikely (e.g., 95%) to contain the true value in the underlying populationunderlying population
  • 87. The 10 Steps of Outbreak InvestigationThe 10 Steps of Outbreak Investigation  Prepare for field workPrepare for field work  Establish the existence of an outbreakEstablish the existence of an outbreak  Verify the diagnosisVerify the diagnosis  Define & identify casesDefine & identify cases  PerformPerform descriptive epidemiologydescriptive epidemiology  Develop hypothesesDevelop hypotheses  PerformPerform analytic epidemiologyanalytic epidemiology  Refine hypotheses & conduct additional studiesRefine hypotheses & conduct additional studies  Implement control & prevention measuresImplement control & prevention measures  Communicate findingsCommunicate findings
  • 88. Objectives of Descriptive EpidemiologyObjectives of Descriptive Epidemiology  To evaluate trends in health and disease and allowTo evaluate trends in health and disease and allow comparisons among countries and subgroups withincomparisons among countries and subgroups within countriescountries  To provide a basis for planning, provision andTo provide a basis for planning, provision and evaluation of servicesevaluation of services  To identify problems to be studied by analytic methodsTo identify problems to be studied by analytic methods and to test hypotheses related to those problemsand to test hypotheses related to those problems

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