Dr. LakshmiAssistant Professor (Epidemiology)School of Public HealthPGIMER, ChandigarhStudy designs in Epidemiology

Initial QuestionsAppropriate study designSample sizeSampling Data collectionData analysis

First Step: Objective of the studyBurden of the disease / Hypothesis generationEg: What is the health seeking behaviour of chest symptomatics in a community?AssociationEg: What are the risk factors of death after TB treatment?

Burden of Disease or Hypothesis Generation: Cross-sectional studiesProvide snap shot of population at a single point of timeExposure and disease assessment at the same pointOutcome Measurement: PrevalenceMeasure of association: Prevalence ratio or odds ratio

Example:Research Question: To find out the prevalence of chest symptomatics in urban and rural areasConduct the survey in urban and rural areasEstimate the chest symptomatics in each areaIf chest symptomatics are more in rural area, we can generate hypothesis that residing in rural area causes prolonged cough

Cross sectional Surveys: Chest Symptomatics, 15 years and above

Limitations: Cross-sectional studiesCannot distinguish temporal sequence of exposure and disease

Affected by factors influencing prevalence

Association: Analytical StudiesCase controlCohortInterventional studies

Case-Control StudiesControlsNo ExposureExposure

Case Control Study: ExampleResearch Question: To find out the risk factors for death after TB treatmentCases: Deaths after TB treatmentControls: Those survived after TB treatmentExposure: For Example, Smoking Compare odds of smoking among cases and controls by calculating Odds Ratio

Odds of Smoking among Deaths: 5/5=1Odds of Smoking among Survivors: 2/8 =0.25Odds Ratio: 1/0.25=4DEATHSSURVIVORSHypothetical Case Study: Among ten deaths of TB following treatment, five were smokers. Among 10 people who survived after TB treatment 2 were smokers.

Case control studiesMeasure of association: Odds ratioOdds Ratio = (a/c) /(c/d) = 1/0.25=4

Case control studiesAdvantagesWhen aetiology is unknownLess time and moneyRare diseases and diseases with long latent periodsDynamic populationDisadvantagesDifficult to establish temporal sequenceRare exposuresGreater chance of bias

Cohort studiesAssessSelect

Cohort studiesProspectiveExposed and Unexposed populations followed into future for the development of outcomeRetrospectiveExposure and outcome have already occurredBasic study design is always from exposure to disease

ExampleResearch Question: To know the risk factors for death after treatment for TBCohort: All newly diagnosed cases of TB who were kept on DOTS and completed the treatment and followed to see how many died after treatmentExposure: For example, SmokingOutcome: Death after treatment with TBDivide the cohort into smokers and non smokersCalculate risk of dying in each group and compare by using Relative risk

Risk of dying among smokers: 5/10=0.5Risk of dying among Nonsmokers: 2/10 =0.2Relative risk: 0.5/0.2=2.5SMOKERSNON SMOKERSHypothetical Case Study: Among ten smokers who had treatment for TB 5 died after treatment with TB whereas among 10 non smokers only 2 died

Cohort studiesMeasure of association: Relative riskRisk of dying among smokers: 5/10 (a/a+b) = 0.5Risk of dying among Non smokers: 2/10 (c/c+d) = 0.2Relative risk = 0.5 /0.2 = 2.5

Cohort studiesAdvantagesRare exposuresDirect estimation of riskLess chance of Bias (Prospective)Multiple effects of a single diseaseWell defined temporal sequenceDisadvantagesTime consuming costly (Prospective)Rare outcomes / long latent periods (Prospective)Bias (Retrospective)Poor information on exposures and outcomes (Retrospective)

Intervention studiesInvestigator assigns exposure to the participants

Study design is from exposure to outcome

Ethical problems for hazardous exposuresStudied by attempts to eliminate the exposures

Intervention studies

AdvantagesMost reliable evidence in epidemiological research because the exposure is randomly assignedRandomisation controls the effects of the risk factors not considered at the time of the study.

Sampling

Sampling MethodsSimple Random SamplingSystematic Random SamplingStratified Random SamplingMulti Stage samplingCluster sampling

Simple Random SamplingEqual probability of selectionSampling frame requiredRandom number table/Calculator/ Computer

03 47 43 73 68 97 74 24 67 62 16 76 62 27 66 12 56 85 99 26 55 59 56 35 64

Systematic Random SamplingNo sampling frame requiredPrecision similar to SRSTotal population divide by sample size to get the sampling interval (300/30=10)Random sample of a sampling unit between the first unit and the sampling interval (1-10)Subsequent units selected by adding the sampling interval in the first random selected unit, and then in the second unit and so on.

Stratified SamplingSRS represents strata in the same proportion as in the populationSeparate estimate may be requires for each stratum (rural, urban)Divide the sample frame into strataUse SRS or systematic random sample to draw sample in each strataWeighted average of the estimate stratum estimates gives population estimates

Multi stage SamplingTwo stage first stage of administrative units (PSUs)Second stage of the sampling units in the PSUThree or multi stage

SRS or Systematic Random Sampling method used for various stage sample units

Cluster SamplingSelf weighted/ Equal Probability SamplingEach unit has equal chance of being in samplePopulation Proportion to Size (PPS)Avoids selection of more clusters with smaller size Clusters or groups (villages, towns, PHCs) are chosen by systematic random sampling after arranging these in a list with the populationEqual number of units chosen from each cluster

Cluster SampleSampling Interval = 3000/2= 1500Random Number=300

Villages (Clusters)PopulationCumulative PopA500500B10001500C15003000Total3000

Selection of units in the clusterSampling frame: SRSNo sampling frame: Center of village/clusterChoose random directionCount number of household up to end Random selection of household from this listSubsequent household, next nearest, every 5th etc. so as to have a wide coverage of unit.

No. of Clusters & Size of ClustersInter cluster variationIntra cluster variationIf inter cluster variation is more then more clustersIf intra cluster variation is more then more size in the clustersWHO EPI 30 Cluster is not a standardEvery population and variable has different variability

Sample sizeMeasurement of interestPrevalence, Incidence, Mean, Proportion, OR, RR errorRejecting Null Hypothesis when it is trueUsually 5% 0r 1% errorFailing to reject Null Hypothesis when it is falsePower= 1- , usually 80% 0r 90%

Statistical AnalysisBinarySingle ProportionTwo ProportionsQuantitativeSingle MeanTwo MeansDistributionNormalNot Normal

Test of SignificanceMeanSingle: Z / t testTwo: Z / t testPaired: Paired t testMore than two: ANOVAProportion Single: z testTwo: Chi square / z testMore than two: Chi square(Assumption: Normal Distribution)Non parametric tests if distribution is not normal