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Epidemiology notes


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A brief presentation of basic epidemiology

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Epidemiology notes

  1. 1. Epidemiology lecture notes By Tilahun Nigatu September 2006 1
  2. 2. 1. Introduction to Epidemiology 2
  3. 3. DefinitionsHealth: A state of complete physical, mental and social well-being and not merely the absence of disease or infirmity (WHO,1948)Disease: A physiological or psychological dysfunctionIllness: A subjective state of not being wellSickness: A state of social dysfunction 3
  4. 4. Definitions…Public healthThe science & art of Preventing disease, prolonging life, promoting health & efficiencythrough organized community effort (Winslow,1920) 4
  5. 5. Definitions…EpidemiologyIt is the study of frequency, distribution, anddeterminants of diseases and other health-relatedconditions in a human population andthe application of this study to the prevention ofdisease and promotion of health 5
  6. 6. Components of the definition•Study: Systematic collection, analysis andinterpretation of dataEpidemiology involves collection, analysis andinterpretation of health related dataEpidemiology is a science 6
  7. 7. Components…2. Frequency: the number of times an eventoccursEpidemiology studies the number of times adisease occursIt answers the question How many?Epidemiology is a quantitative science 7
  8. 8. Components…3. Distribution: Distribution of an event by person,place and timeEpidemiology studies distribution of diseasesIt answers the question who, where and when?Epidemiology describes health events 8
  9. 9. Components…4. Determinants: Factors the presence/absence ofwhich affect the occurrence and level of an eventEpidemiology studies what determines healtheventsIt answers the question how and why?Epidemiology analyzes health events 9
  10. 10. Components…5. Diseases & other health related eventsEpidemiology is not only the study of diseasesThe focus of Epidemiology are not only patientsIt studies all health related conditionsEpidemiology is a broader science 10
  11. 11. Components…6. Human populationEpidemiology diagnoses and treats communities/populationsClinical medicine diagnoses and treats patientsEpidemiology is a basic science of public health 11
  12. 12. Components…7. ApplicationEpidemiological studies have direct and practicalapplications for prevention of diseases &promotion of healthEpidemiology is a science and practiceEpidemiology is an applied science 12
  13. 13. History of EpidemiologySeven land marks in the history of Epidemiology• Hippocrates (460BC): Environment & human behaviors affects health• John Graunt (1662): Quantified births, deaths and diseases• Lind (1747): Scurvy could be treated with fresh fruit 13
  14. 14. History…4. William Farr (1839): Established application of vital statistics for the evaluation of health problems5. John Snow (1854): tested a hypothesis on the origin of epidemic of cholera6. Alexander Louis (1872): Systematized application of numerical thinking (quantitative reasoning) 14
  15. 15. History…7. Bradford Hill (1937): Suggested criteria for establishing causationEpidemiological thought emerged in 460 BCEpidemiology flourished as a discipline in 1940s 15
  16. 16. Scope of EpidemiologyOriginally, Epidemiology was concerned withinvestigation & management of epidemics ofcommunicable diseasesLately, Epidemiology was extended to endemiccommunicable diseases and non-communicableinfectious diseasesRecently, Epidemiology can be applied to alldiseases and other health related events 16
  17. 17. Purpose/use of EpidemiologyThe ultimate purpose of Epidemiology isprevention of diseases and promotion of healthHow?1. Elucidation of natural history of diseases2.Description of health status of population3. Establishing determinants of diseases4. Evaluation of intervention effectiveness 17
  18. 18. Types of EpidemiologyTwo major categories of Epidemiology•Descriptive EpidemiologyDefines frequency and distribution of diseasesand other health related eventsAnswers the four major questions: how many,who, where, and when? 18
  19. 19. Types…2. Analytic EpidemiologyAnalyses determinants of health problemsAnswers two other major questions: how? andwhy?Generally, Epidemiology answers six majorquestions: how many, who, where, when, how andwhy? 19
  20. 20. Basic Epidemiological assumptions• Human diseases doesn’t occur at random or by chance2. Human diseases have causal and preventive factors 20
  21. 21. Basic features of Epidemiology1. Studies are conducted on human population2. It examines patterns of events in people3. Can establish cause-effect relationship without the knowledge of biological mechanism4. It covers a wide range of conditions5. It is an advancing science 21
  22. 22. 2. Communicable disease Epidemiology 22
  23. 23. Disease causationThe cause of a diseaseAn event, a condition or a characteristic thatcomes before the disease and without which thedisease wouldn’t occur 23
  24. 24. Theories of disease causalityWhat causes a disease?Ninetieth century theories3. Contagion theory4. Supernatural theory5. Personal behavior theory6. Miasma theory 24
  25. 25. Theories…Twentieth century theories2. Germ theory3. Lifestyle theory4. Environmental theory5. Multi-causal theory 25
  26. 26. Necessary Vs SufficientNecessary: the disease will not occur without thepresence of the factorExample: Mycobacterium TB for TB Sufficient: the presence of the factor always resultin diseaseExample: Rabies virus for rabies 26
  27. 27. Etiology of a diseaseThe sum of all factors contribution to theoccurrence of a diseaseAgent factors +Host factors +Environmentalfactors = Etiology of a disease 27
  28. 28. Disease modelsHow do diseases develop?Three best known models4.Epidemiological triangleThe interaction of an agent and host in anappropriate environment results in disease 28
  29. 29. Disease models…2. Web of causation Complex interaction of factors results in disease3. Wheel modelThe hub (host) having a genetic make up as its core,surrounded by an environment schematically dividedin to biological, physical and social 29
  30. 30. Natural history of diseaseThe progression of disease process in anindividual overtime in the absence of interventionFour stages in the natural history of a disease•Stage of susceptibilityPresence of factorsNo disease 30
  31. 31. Natural history…2. Stage of sub-clinical diseasePresence of pathogenic changes (biological onset)No disease manifestations3. Stage of clinical diseasePresence of sign and symptoms (clinical onset)4. Stage of recovery, disability,or death 31
  32. 32. Levels of disease preventionThree major levels of disease prevention2. Primary preventionTargeted at healthy peopleObjectives are Promotion of health Prevention of exposure and Prevention of disease 32
  33. 33. Levels of disease…2. Secondary preventionTargeted at sick individualsObjective is to stop or slow the progression ofdisease and to prevent or limit permanent damagethrough early detection & treatment 33
  34. 34. Levels of disease…3. Tertiary preventionTargeted at people with chronic diseases &disabilities that can’t be curedObjective is to prevent further disability or deathand to limit impacts of disability throughrehabilitation 34
  35. 35. Infectious disease processThere are six components of the infectious diseaseprocess constituting chain of disease transmission3.The agent 2. Its reservoir3. Its portal of exit 4. Its mode of transmission5. Its portal of entry 6. Susceptible host 35
  36. 36. The agentPossible outcomes of exposure to an infectious agentInfection: invasion & multiplication in the hostInfectivity: the proportion of exposed who becomesinfected Infection rate= Infected/exposedDisease: A clinically apparent infection 36
  37. 37. The agent…Pathogenicity:the proportion of infected who develop clinical disease Clinical-to-Subclinical ratioVirulence: the proportion clinical cases resulting in severe clinical disease Case fatality & hospitalization rateImmunogenecity:the infection’s ability to produce specific immunity 37
  38. 38. Reservoir Vs CarrierReservoirAn organism or habitat in which an infectiousagent normally lives, transforms, develops and/ormultipliesCarrierA person who doesn’t have apparent clinicaldisease, but is a potential source of infection toother people 38
  39. 39. Types of carriers•Incubatory carriers: transmits the disease duringincubation periodExample: Measles, mumps2. Convalescent carriers: transmits the diseaseduring convalescent periodExample: Typhoid fever 39
  40. 40. Types of carriers…3. Asymptomatic carriers: transmitting the diseasewithout showing manifestationsExample: polio, Amoebiasis4. Chronic Carriers: transmitting the disease forlong time/indefinite transmissionExample: Viral hepatitis, typhoid fever 40
  41. 41. Importance of carriers• Number- carriers may outnumber cases• Difficulty in recognition- carriers don’t know that they are infected• Mobility- carriers are mobile, cases are restricted• Chronicity- carriers re-introduce infection and contribute to endemicity 41
  42. 42. Effect of carriers on disease transmission• Ice-berg effect in temperate zone• Hippopotamus effect in tropical zoneThese are the fact that carriers constitute a hidden reservoir of infection and that they may outnumber actual cases 42
  43. 43. Modes of disease transmission1.Direct transmissionDirect contact: physical contact with body part ofinfected person: Touching, kissing,biting,sex Example: HIVDirect projection: projection of saliva dropletswhile coughing, sneezing,spitting,talking,singingetc Example: Common coldTransplacental: Transmission from mother tofetus through the placenta Example: Syphilis 43
  44. 44. Modes of disease…2. Indirect transmissionVehicle-borne: transmission throughinanimate objects/non-living substances e.gHIV by needlesAir-borne: transmission by dust or dropletnuclei 44
  45. 45. Modes of disease…Vector-borne: infectious agent is conveyed by an arthropod to host Biological: there is multiplication and/or development in the vector Salivarian: Injects infected saliva e.g mosquito Stercorarian: infects by infected feaces e.g louse Mechanical: simple transfer without biological stages in the vector e.g flies 45
  46. 46. Importance of mode of transmission• A disease often has several modes of transmission• It is important to distinguish between the predominant mode of transmission and those of secondary importance• Identifying primary and secondary modes of transmission is important to identify most effective prevention and control measures 46
  47. 47. Herd immunityIt is host resistance at a population levelIt is defined as the resistance of a community (group) to invasion and spread of an infectious agent, based on immunity of high proportions of individuals in the community.It has implications on vaccination programs 47
  48. 48. Conditions under which herd immunity best functions1. Single reservoir2. Direct transmission3. Total immunity4. No carrier state5. Uniform distribution of immunes6. No overcrowdingSeldom all fulfilled 48
  49. 49. Time course of an infectious diseasePre-patent period: between biological onset and first sheddingIncubation period: between biological onset and clinical onsetCommunicable period: time during which agent is being shed 49
  50. 50. Time course of…Latent period: between recovery and relapse inclinical diseaseConvalescent period: between recovery and timewhen shedding stopsGeneration period: between exposure/infection andmaximum communicability of exposed host 50
  51. 51. Application of time periods• Pre-patent period – When should we investigate?• Incubation period – When was time of exposure?• Communicable period – When should we take care of infectiousness?• Latent period – When would relapse occur?• Convalescent period – When,after recovery an individual becomes non-infectious?• Generation time – When is the maximum risk for contacts? 51
  52. 52. Factors which influence the development of disease Strain of the agent Dose of the agent Route of infection Host age, nutritional status, immune status Influence of treatment Influence of season 52
  53. 53. 3. Measures of disease occurrence 53
  54. 54. What are measures of disease occurrence? These are measurements of the frequency/magnitude/amount of disease in populations 54
  55. 55. How do we measure diseases?Four quantitative descriptors• Numbers• Ratios• Proportions• Rates 55
  56. 56. DescriptorsNumbers: Use of actual number of events e.g 100 cases of TB in community ARatios: Quantifies the magnitude of one occurrence X, in relation to another event Y as X/Y e.g Ratio of TB cases in community A to B is 1:10 56
  57. 57. DescriptorsProportions: a ratio in which the numerator is included in the denominatore.g proportion of TB cases in community A is 10%Rates: a proportion with time elementIt measure the occurrence of an event overtimee.g U5 measles cases in 2000/U5 population in 2000 57
  58. 58. Which community is more affected? Community A has 100 cases of disease X andCommunity B has 1000 cases of disease X, which community is more affected? 58
  59. 59. When we call..• When we call a measure a ratio, we mean a non- proportional ratio• When we call a measure a proportion, we mean a proportional ratio that doesn’t measure an event overtime• When we call a rate, we mean a proportional ratio that does measure an event in a population overtime 59
  60. 60. Types of rates• Crude rates: Apply to the total population in a given area• Specific rates: Apply to specific subgroups in the population (age, sex etc) or specific diseases3. Standardized rates: used to permit comparisons of rates in population which differ in structure (e.g age structure)Two methods of standardization:Direct, indirect 60
  61. 61. Morbidity rates Morbidity rates are rates that are used to quantify the magnitude/frequency of diseases Two common morbidity ratesIncidence rates(Cumulative incidence, incidence density)Prevalence (Period prevalence, point prevalence) 61
  62. 62. Incidence rateThe proportion of a population that develops a disease overtimeThe risk/probability of an individual developing a disease overtimeThe rapidity with which new cases of a disease develop overtimeThe proportion of unaffected individuals who on average will contract the disease overtime 62
  63. 63. Cumulative incidence Number of new cases of aCumulative = disease during a specified periodIncidence Population at risk in the same Period 63
  64. 64. Practical challenges in measuring incidence rate1. Identification of population at risk Population at risk constitutes all those free of the disease and susceptible to it2. Population is not static/it fluctuates/as a result of births, deaths and migration3. People are at risk only until they get the disease and then no more at risk 64
  65. 65. Practical solution to the challenges• Use the total population as a denominator This gives an estimate of the incidence rate and not the actual incidence rate2. Use person-time at risk Incidence density=number of new cases of a disease over a specified period/person-time at risk 65
  66. 66. Prevalence rate It measures the proportion of a population with a disease during a specified period or at a point in timeTwo types4. Point prevalence rate5. Period prevalence rate 66
  67. 67. Point prevalence rateMeasures the proportion of a population with a disease at a point in timePoint prevalence rate=All persons with a disease at a point in time/Total populationIt is not a rate, but a true proportion 67
  68. 68. Period prevalence rateMeasures the proportion of a population with a disease in a specified time periodPeriod prevalence rate=All persons with a disease overtime period/Average(mid- year)population in the same period 68
  69. 69. Incidence Vs prevalenceIncidence rate considers only new cases of a diseasePrevalence rate considers all (new + old) cases of a diseaseIncidence rate considers population at risk as a denominatorPrevalence rate considers total population as a denominatorIncidence & period prevalence rates require follow up studiesPoint prevalence rate requires cross- sectional study 69
  70. 70. Relationship between prevalence & incidence ratesPrevalence α incidencePrevalence α Average durationPrevalence α Incidence X Average durationAn increase in prevalence rate may not necessarily be due to an increase in incidence rate, it could due to an increase in average duration of a disease due to decrease in death and/or recovery rates 70
  71. 71. Mortality ratesThese rates measures magnitude of deaths in acommunitySome are crude like the crude death rateOthers are cause-specific mortality rateSome others are adjusted like standardizedmortality ration 71
  72. 72. Common Mortality rates• Crude death rate • Perinatal mortality rate• Age-specific mortality rate • Neonatal mortality rate• Sex-specific mortality rate • Infant mortality rate• Cause-specific mortality rate • Child mortality rate• Proportionate mortality ratio • Under-five mortality• Case fatality rate rate• Fetal death rate • Maternal mortality ratio 72
  73. 73. 4. Measures of association 73
  74. 74. 2X2 table Disease Yes (+) No (+) TotalExposure Yes (+) a b a+b No (+) c d c+d Total a+c b+d a+b+c+d 74
  75. 75. CellsA= Exposed, and diseasedB= Exposed, Not diseasedC= Not exposed, diseasedD= Not exposed, Not diseased 75
  76. 76. TotalsMarginal totals a+b= Exposed c+d= Non-exposed a+c= Diseased b+d= Non-diseasedGrand total n = a+b+c+d 76
  77. 77. Chi-square statistics Chi-square tests whether there is an association between two categorical variablesHo: There is no association between row & column variablesHa: There is an association between row and column variablesChi-square statistic has a degree of freedom (r-1) (c-1), where r is number of rows & c number of columns 77
  78. 78. Chi-Square… Χ 2= Σ (O - E)2 EO: Observed cellsE: Expected cellsExpected value = (Row total)X(Column total) Grand totalFor a 2X2, table Χ2= (/ad-bc/-n/2)2n (a+b)(a+c)(c+d)(b+d) 78
  79. 79. Importance of Chi-squareIf the calculated chi-square value is greater thanthe critical or P<0.05 we say that there isassociationChi-square statistics tells only whether there isassociation. It doesn’t tell us how much strong anassociation is. 79
  80. 80. Relative risk (RR)Expresses risk of developing a diseases in exposedgroup (a + b) as compared to non-exposed group (c + d) RR= Incidence (risk) among exposed Incidence (risk) among non-exposed RR= a/(a+b) c/(c+d) 80
  81. 81. Interpretation of relative risk What does a RR of 2 mean? Risk in exposed =RRX Risk in non-exposed RR of 2 means Risk in exposed=2X Risk in non-exposedThus a relative risk of 2 means the exposed groupis two times at a higher risk when compared tonon-exposed 81
  82. 82. Strength of associationIn general strength of association can be considered as:High if RR>3Moderate if RR is between 1.5 & 2.9Weak if RR is between 1.2 & 1.4 82
  83. 83. Odds ratio (OR)Odds ratio is the ratio of odds of exposure amongdiseased to odds of exposure among non-diseasedOdds of an event E is the ratio of probability of theevent to its complementOdds=P(E)/P(E’)=P(E)/(1-P(E)) 83
  84. 84. Odds ratio…Odds of exposure among exposed=a/cOdds of exposure among non-diseased=b/dOR = Odds of exposure among diseased Odds of exposure among non-diseasedOR= (a/c)/(b/d)OR= ad/bc (it is also called cross-product ratio)Interpretation of OR is the same as that of RR 84
  85. 85. Odds ratio… RR can be best estimated by OR if the following conditions are fulfilled3. Controls are representative of general population4. Selected cases are representative of all cases5. The disease is rare 85
  86. 86. Attributable Risk (AR)AR indicates how much of the risk is due to/attributable/ to the exposureQuantifies the excess risk in the exposed that can beattributable to the exposure by removing the risk of thedisease occurred due to other causesAR= Risk (incidence) in exposed- Risk (incidence) in non-exposedAR= {a/(a+b)} / {c/(c+d)))}Attributable risk is also called risk difference 86
  87. 87. Interpreting ARWhat does attributable risk of 10 mean?10 of the exposed cases are attributable to theexposureBy removing the exposure one can prevent 10cases from getting the disease 87
  88. 88. Attributable risk percent (AR%) Estimates the proportion of disease among the exposed that is attributable to the exposure The proportion of the disease in the exposed that can be eliminated by eliminating the exposureAR%= (Risk in exposed – Risk in non- exposed)X100% Risk in non-exposed 88
  89. 89. Interpretation of AR%What does AR% of 10% mean?10% of the disease can be attributed to the exposure10% of the disease can be eliminated if we avoid the exposure 89
  90. 90. Population Attributable Risk (PAR)Estimates the rate of disease in total populationthat is attributable to the exposurePAR = Risk in population – Risk in unexposedPAR = ARX prevalence rate of exposure 90
  91. 91. Population attributable risk percent (PAR %) Estimates the proportion of disease in the study population that is attributable to exposure and thus could be eliminated if the exposure were eliminatedPAR%= Risk in population – Risk in unexposed Risk in population 91
  92. 92. Possible outcomes in studying therelationship between exposure & disease 1. No association RR=1 AR=0 2. Positive association RR>1 AR>0 3. Negative association RR<1 (fraction) AR<0 (Negative) 92
  93. 93. Risk Vs Preventive factorsA risk factor is any factor positively associatedwith a disease (RR>1)It is associated with an increased occurrence of adiseaseA preventive factor is any factor negativelyassociated with a disease (RR<1)It is associated with a decreased occurrence of adiseaseRisk and preventive factors may (not) amenable tochange (e.g. Smoking, age) 93
  94. 94. 5. Evaluation of Evidence (Judgment of causality) 94
  95. 95. Association Vs Causation The existence of an association doesn’t itselfconstitute a proof of causation An observed association could be a fact or anartifact Hence, an association is a necessary but not asufficient condition for causation 95
  96. 96. Possible explanations for observed association 1. Chance 2. Bias 3. Confounding 4. Reverse causation 5. Reciprocal causation 6. Cause-effect relationship 96
  97. 97. Accuracy of measurementAccuracy = Validity + PrecisionValidity is the extent to which a measuredvalue actually reflects truthThere are two types of validity – Internal validity – External validity 97
  98. 98. Types of validityInternal validity: Is the degree to which a measured value is true within the sampleExternal validity: Is the extent to which a measured value apply beyond the sample This is related to generalizability 98
  99. 99. PrecisionPrecision is the extent to which random error altersthe measurement of effectsThreats to validity of study: Random error (chance): is sampling error Systematic error (bias): is error in the conductof the study 99
  100. 100. Judgment of causalityJudgment of causality has two steps3. Check whether the observed association between exposure and disease is Valid (Rule out chance, bias and confounding)2. Check whether the observed association is causal (Does the totality of evidence supports the findings) 100
  101. 101. Role of chanceThe role of chance as an alternative explanationfor an association emerges from samplingvariabilityEvaluation of the role of chance is mainly thedomain of statistics and involves 1. Test of statistical significance 2. Estimation of confidence interval 101
  102. 102. 1. Test of statistical significanceP-value quantifies the degree to which chance accounts for observed associationP-value is the probability of obtaining a result at least as extreme as the observed by chance aloneP<0.05 indicates statistical significance for medical research 102
  103. 103. Test of statistical…A very small difference may be significant if you have large sampleA large difference may not achieve statistical significance is you have small sampleOne can’t make a definite decision based on p-value only 103
  104. 104. 2. Estimation of confidence intervalConfidence interval represents the range withinwhich true magnitude of effect lies within acertain degree of assuranceIt is more informative than p-value because it reflects on both the size of the sample and the magnitude of effect 104
  105. 105. Role of biasBias is any systematic error in the design, conduct or analysis of an epidemiologic study that results in an incorrect estimate of association between exposure and diseaseUnlike chance bias can’t be statistically evaluatedThere are two major types of bias 1. Selection bias 2. Information bias 105
  106. 106. Selection biasAny systematic error that arises in the process ofidentifying the study populationIt affects the representativeness of the studyIt occurs when there is a difference between sampleand population with respect to a variableExamples of selection bias: 1. Diagnostic bias 2. Volunteer bias 3. Non-response bias 4. Loss to follow-up bias 106
  107. 107. Information/observation/biasAny systematic error in the measurement ofinformation on exposure or diseaseExamples of information bias:1. Interviewer bias/observer bias2. Recall bias / Response bias3. Social desirability bias4. Placebo effect 107
  108. 108. Ways to minimize bias1. Choose study design carefully2. Choose objective rather than subjective outcomes3. Blind interviewers whenever possible4. Use close ended questions whenever possible5. Collect data on variables you don’t expect to differ between two groups 108
  109. 109. Role of confoundingConfounding refers to the mixing of the effect ofan extraneous variable with the effect of theexposure and disease of interestCharacteristics of a confounding variable 1. Associated with disease in absence of exposure 2. Associated with exposure but not as a consequence of exposure 3. The frequency of the confounding variable vary between the groups that are compared Example: In association between smoking and lung cancer 109 alcohol drinking suspected as a confounding
  110. 110. Effect of confoundingTotally or partially account for the apparent effectMask an underlying true associationReverse the actual direction of association 110
  111. 111. Control of confounding variables During designing stage: – Randomization – Restriction – Matching During analysis stage – Standardization – Stratification/pooling – Multivariate analysis 111
  112. 112. Criteria to asses the strength of evidence for cause and effect relationship Observational studies have many biases andconfounding. Experimental studies if properlydone can show cause-effect relationship. But theyare not usually feasible due to ethical issues In the absence of an experimental trail, thefollowing criteria (Bradford Hill criteria) are usedto asses the strength of evidence for a cause andeffect relationship 112
  113. 113. Criteria to asses the strength…– Strength of association: The stronger the association the more likely it is causal2. Consistency of association: The more consistent, the more likely it is causal 3. Specificity of association: If single exposure linked to single disease more likely4. Temporal relationship: The exposure must comebefore the 113
  114. 114. Criteria to asses the strength…5. Dose-response relationship: risk of disease increase with increasing exposure with factor6. Biological plausibility: Knowledge of association coherent with biology and descriptive epidemiology of the disease7. Reversibility: Eliminating the exposure should be followed by a decrease in the incidence rate of the disease 114
  115. 115. 6. Epidemiologic Study Designs 115
  116. 116. Study design Study design is the arrangement of conditions forthe collection and analysis of data to provide themost accurate answer to a question in the mosteconomical way. 116
  117. 117. Types of Epidemiologic study designsI. Based on objective/focus/research question3. Descriptive studies – Describe: who, when, where & how many2. Analytic studies – Analyse: How and why 117
  118. 118. Types…II. Based on the role of the investigator3. Observational studies – The investigator observes nature – No intervention5. Intervention/Experimental studies – Investigator intervenes – He has a control over the situation 118
  119. 119. Types…III. Based on timing1. One-time (one-spot) studies – Conducted at a point in time – An individual is observed at once2. Longitudinal (Follow-up) studies – Conducted in a period of time – Individuals are followed over a period of time 119
  120. 120. Types…IV. Based on direction of follow-up/data collection3. Prospective – Conducted forward in time2. Retrospective – Conducted backward in time 120
  121. 121. Types…V. Based on type of data they generate3. Qualitative studies – Generate contextual data – Also called exploratory studies2. Quantitative studies – Generate numerical data – Also called explanatory studies 121
  122. 122. Types…VI. Based on study setting3. Community-based studies – Conducted in communities2. Institution-based studies – Conducted in communities3. Laboratory-based studies – Conducted in major laboratories 122
  123. 123. Types…VII. Standard classification3. Cross-sectional studies4. Case-control studies5. Cohort studies6. Experimental studies 123
  124. 124. Cross-sectional studiesIn this study design information about the status of anindividual with respect to presence/absence ofexposure and diseased is assessed at a point in time. Cross-sectional studies are useful to generate ahypothesis rather that to test it For factors that remain unaltered overtime (e.g. sex,race, blood group) it can produce a valid association 124
  125. 125. Cross-sectional…• Comparison groups are formed after data collection• The object of comparison are prevalence of exposure or disease• Groups are compared either by exposure or disease status• Cross-sectional studies are also called prevalence studies• Cross-sectional studies are characterized by concurrent classification of groups 125
  126. 126. Cross-sectional…Advantages of cross-sectional studies• Less time consuming• Less expensive• Provides more information• Describes well• Generates hypothesis 126
  127. 127. Cross-sectional…Limitations of cross-sectional studies• Antecedent-consequence uncertainty “Chicken or egg dilemma”• Data dredging leading to inappropriate comparison• More vulnerable to bias 127
  128. 128. Cross-sectional…Types of cross-sectional studies1. Single cross-sectional studies – Determine single proportion/mean in a single population at a single point in time2. Comparative cross-sectional studies – Determine two proportions/means in two populations at a single point in time4. Time-series cross-sectional studies – Determine a single proportion/mean in a single population at multiple points in time 128
  129. 129. Cross-sectional…In general, cross-sectional studies are – Simplest to conduct – Commonest to find – Least useful to establish causation 129
  130. 130. Case-control studies• Subjects are selected with respect to the presence (cases) or absence (controls) of disease, and then inquiries are made about past exposure• We compare diseased (cases) and non-diseased (controls) to find out the level of exposure• Exposure status is traced backward in time 130
  131. 131. Case-control…Steps in conducting case-control studiesI. Define who is a case – Establish strict diagnostic criteria – All who fulfil the criteria will be “case population – Those who don’t fulfil will be “control population”II. Select a sample of cases from case population – This sample must be representative of the case population 131
  132. 132. Case-control…Sources of cases2. Hospitals (Health institution) – Cost-less – Bias-more2. Population (Community) – Cost-more – Bias-less 132
  133. 133. Case-control…III. Select controls from a control population – Should be representative of control population – Should be similar to cases except outcome – Should be selected by the same method as casesSources of controls3. Hospital (Health institution) controls – Readily available – Low recall bias 133
  134. 134. Case-control…However, hospital controls are – Less representative – More confounding2. Population (community) controls – More representative – Less confounding – Costly and time consuming – More recall bias – Less cooperative 134
  135. 135. Case-control…IV. Measure the level of exposure in cases & controls – Review or interview for exposure status – Use same method for case and controlsV. Compare the exposure between cases & controls – Prepare 2X2 table – Calculate OR – Perform statistical tests 135
  136. 136. Case-control…Types of case-control studiesII. Based on case identification4. Retrospective case-control – Uses prevalent cases – Increased sample size – Difficult to establish temporal sequence – Useful for rare outcomes 136
  137. 137. Case-control…2. Prospective case-control – Uses incident cases – Establish temporal sequence – Recall is not a serious problem – Records are easily obtainable 137
  138. 138. Case-control…II. Based on matching Matching: Relating cases and controls with respect to certain variable4. Matched case-control studies5. Unmatched case-control studies 138
  139. 139. Case-control…Advantages of case-control studies• Optimal for evaluation of rare diseases• Examines multiple factors of a single disease• Quick and inexpensive• Relatively simple to carry out• Guarantee the number of people with disease 139
  140. 140. Case-control…Limitations of case-control studies• Inefficient for evaluation of rare exposure• Can’t directly compute risk• Difficult to establish temporal sequence• Determining exposure will often rely on memory 140
  141. 141. Cohort studies• Subjects are selected by exposure and followed to see development of disease• Two types of cohort studies5. Prospective (classical) – Outcome hasn’t occurred at the beginning of the study – It is the commonest and more reliable 141
  142. 142. Cohort…2. Retrospective (Historical)• Both exposure and disease has occurred before the beginning of the study• Faster and more economical• Data usually incomplete and in accurate 142
  143. 143. Cohort…Steps in conducting cohort studies3. Define exposure4. Select exposed group5. Select non-exposed group6. Follow and collect data on outcome7. Compare outcome b/n exposed & non-exposed 143
  144. 144. Cohort…Advantages of cohort studies• Valuable when exposure is rare• Examines multiple effects of a single exposures• Temporal relationship is known• Allow direct measurement of risk• Minimize bias in ascertainment of exosure 144
  145. 145. Cohort…Limitations of cohort studies• Inefficient for evaluation of rare disease• Expensive• Time-consuming• Loss to follow-up creates a problem 145
  146. 146. Experimental studies• Individuals are allocated in to treatment and control groups by the investigator• If properly done, experimental studies can produce high quality data• They are the gold standard study design 146
  147. 147. Experimental…Experimental studies can be2. Therapeutic trials – Conducted on patients – To determine the effect of treatment on disease2. Preventive trials – Conducted on healthy people – To determine the effect of prevention on risk 147
  148. 148. Experimental…Three different ways of classifying intervention studiesII. Based on population studies – Clinical trial: on patients in clinical settings – Field trial: on healthy people in the field – Community trial: on the community as a wholeII. Based on design – Uncontrolled trial: no control (self-control) – Non-randomized controlled: allocation not random – Randomized control: Allocation random 148
  149. 149. Experimental…I. Based on objective – Phase I: to determine toxic effect – Phase II: to determine toxic effect – Phase III: to determine applicability Challenges in intervention studies• Ethical issues – Harmful treatment shouldn’t be given – Useful treatment shouldn’t be denied 149
  150. 150. Experimental…• Feasibility issues – Getting adequate subjects – Achieving satisfactory compliance• Cost issues – Experimental studies are expensive 150
  151. 151. Experimental…• The quality of “Gold standard” in experimental studies can be achieved through – Randomization – Blinding – Placebo 151
  152. 152. Experimental…Randomization: random allocation of study subjects in to treatment & control groups Advantage: Avoids bias & confounding Increases confidence on resultsBlinding: Denying information on treatment/control status 152
  153. 153. Experimental… Single blinding: study subjects don’t know to which group they belong Double blinding: Care givers also don’t know to which group study subjects belong Triple blinding: data collectors also don’t know allocation statusAdvantage: Avoids observation bias 153
  154. 154. Experimental…Placebo: an inert material indistinguishable from active treatmentPlacebo effect: tendency to report favourable response regardless of physiological efficacyPlacebo is used as blinding procedure 154
  155. 155. 7. Screening 155
  156. 156. Screening Screening refers to the presumptive identificationof a disease/defect by application of tests,examinations or other procedures in apparentlyhealthy people.Screening is an initial examinationScreening is not intended to be diagnostic 156
  157. 157. Aims of screening program• Changing disease progression efficiently• Altering natural course of disease• Protecting society from contagious disease• Allocating resources rationally• Selection of healthy people for job• Studying the natural history of disease 157
  158. 158. Criteria for selecting diseases for screeningSeverity- The disease should be seriousTreatment- Early treatment should be more beneficialPrevalence- Pre-clinical Prevalence should be high 158
  159. 159. Criteria for establishing screening programThe problem should have public health importanceThere should be accepted treatment for positivesDiagnostic & treatment facilities should be availableRecognized latent stage in the time courseTest is acceptable, reliable & validNatural history of the disease well understoodCase-finding is economical and continuous 159
  160. 160. Screening testsThe performance of a screening test is evaluatedagainst a diagnostic test in 2X2 table Diagnostic test D+ D- Screening test T+ a b a+b T- c d c+d a+c b+d n 160
  161. 161. Definitions of cellsTrue positives (a): Diseased identified by test as diseasedFalse positives (b): Disease free falsely labelled as diseaseFalse negatives (c): Diseased falsely labelled as disease freeTrue negatives (d): Disease free identified as free 161
  162. 162. Definition of totalsD+ (a+c): total subjects with a diseaseD- (b+d): total subjects without diseaseT+ (a+b): total test positivesT- (c+d): total test negatves 162
  163. 163. Test performance Validity of a test The ability of a test to differentiate correctly those who have the disease and those who don’t• It is a function of sensitivity and specificity 163
  164. 164. Test performanceA. Sensitivity of a test• The ability of a test to correctly identify those who have the disease• The probability that a diseased individual will have a positive test result• The proportion of people with a disease who have a positive test result• True positive rate (TPR) 164
  165. 165. Test performanceSensitivity (TPR)= P(T+|D+) =TP/(TP + FN ) = a/(a+c)False negative rate (FNR)=P(T- |D+) = c/(a+c)Sensitivity= 1- FNR 165
  166. 166. Test performanceB. Specificity of a test• The ability of a test to correctly identify those who don’t have the disease• The probability that a disease-free individual will have a negative test result• The proportion of people without the disease who have a negative test result• True negative rate (TNR) 166
  167. 167. Test performanceSpecificity (TNR)= P(T-|D-) =TN/(TN + FP ) = d/(d+b)False positive rate (FPR)=P(T+ |D-) = b/(b+d)Specificity= 1- FPR 167
  168. 168. Test performance Predictive value of a testThe ability of a test to predict the presence orabsence of diseaseTwo type: Positive & negative predictive values 168
  169. 169. Test performanceA. Predictive value positive (PVP)√ The ability of a test to predict the presence of disease among who test positive√ The probability that a person with a positive test result has a disease√ The proportion of diseased individuals in a population with a positive test result√ Prior/post-test probability PVP=P(D+ | T+) = a/(a+b) 169
  170. 170. Test performanceA. Predictive value negative (PVN) The ability of a test to predict the absence of disease among who test negative The probability that a person with a negative test result is disease-free The proportion of disease-free individuals in a population with a negative test result Prior/post-test probability PVN=P(D- | T-) = d/(c+d) 170
  171. 171. Test performance Prevalence of a disease The proportion of individuals with a disease Prior/pre-test probability of a disease Prevalence = P (D+) = (a+c)/n Yield of a testProportion of cases detected by the screening program Yield = a/n 171
  172. 172. Multiple testingParallel testing: – Tests are given concurrently – At least one positive indicates disease – Results in • Greater sensitivity • Increased PVN • Decreased specificity 172
  173. 173. Multiple testingSerial testing: – Tests are administered sequentially – All positive indicates disease – Results in • Lower sensitivity • Increased specificity • Increased PVP 173
  174. 174. Reliability of a test• Ability of a test to give consistent results up on repeated measurements• Two major factors affect reliability – Method variation – Observer variation – Inter-observer variation – Intra-observer variation 174
  175. 175. Reliability of a testReliability can be classified as: – Internal reliability • Internal consistency reliability – External reliability • Alternate test reliability • Test-retest reliability 175
  176. 176. Evaluation of a screening program Evaluation of a screening program involves consideration of two issues• Feasibility: Determined by acceptability of the screening program2. Effectiveness: Determined by the outcome of the screening program 176
  177. 177. In generalIn general, a screening test should be – Reliable & valid – Sensitive & specific – Simple & acceptable – Effective & efficient 177
  178. 178. Thank you! 178