Epidemiologic Studies PDF

 2nd Lecture Epidemiologic Studies Introduction  Aims of Epidemiologic Research 1. Describe the health status of a Descriptive population epidemiology 2. To assess the public health importance of diseases 3. To describe the natural history of...

 2nd Lecture Epidemiologic Studies Introduction  Aims of Epidemiologic Research 1. Describe the health status of a Descriptive population epidemiology 2. To assess the public health importance of diseases 3. To describe the natural history of disease, 4. Explain the etiology of disease Analytic 5. Predict the disease occurrence epidemiology 6. To evaluate the prevention and control of disease 7. Control the disease distribution Applied epidemiology  Descriptive epidemiology Describes the occurrence of disease (cross- sectional) Descriptive and Analytical Analytic epidemiology: Epidemiology Observational (cohort, case control, cross- sectional, ecologic study) – researcher observes association between exposure and disease, estimates and tests it Experimental (RCT, quasi experiment) – researcher assigns intervention (treatment), and estimates and tests its effect on health outcome  Two Broad Types of Epidemiology DESCRIPTIVE EPIDEMIOLOGY ANALYTIC EPIDEMIOLOGY Examining the distribution of a Testing a specific hypothesis disease in a population, and about the relationship of a observing the basic features of its disease to a putative cause, by distribution in terms of time, conducting an epidemiologic place, and person. study that relates the exposure of interest to the Typical study design: disease of interest. community health survey (approximate synonyms - cross- Typical study designs: cohort, sectional study, descriptive case-control study) Difference between Descriptive and Analytical Epidemiological studies Types of Epidemiology Two major categories of Epidemiology - Descriptive Epidemiology Defines frequency and distribution of diseases and other health related events Answers the four major questions: how many, who, where, and when?  2. Analytic Epidemiology Types of Analyses determinants of health Epidemiology problems Answers two other major questions: how? And why ? Generally, Epidemiology answers six major questions: how many, who, where, when how and why ? 1- Descriptive Studies  Characterize who, Person: characteristics where, or when in (age, sex, occupation) Purpose relation to what of the individuals affected by the (outcome) outcome( Place: geography Time: when events (residence, work, (diagnosis, hospital) of the reporting; testing) affected individuals occurred Types  Case series: It is a collection of different case reports, thus based on more than one patient  1.1Case Reports Detailed presentation of a single case or handful of cases Generally, report a new or unique finding e.g. previous undescribed disease e.g. unexpected link between diseases e.g. unexpected new therapeutic effect e.g. adverse events Case report: It is a report that documents unusual medical occurrences that can represent the first clue in the identification of new disease or adverse effect of exposures (based on one patient).  1.2 Case Series Experience of a group of patients with a similar diagnosis Assesses prevalent disease Cases may be identified from a single or multiple sources Generally, report on new/unique condition May be only realistic design for rare disorders Case series: It is a collection of different case reports, thus based on more than one patient  Case Series Advantages Useful for hypothesis generation Informative for very rare disease with few established risk factors Characterizes averages for disorder Disadvantages Cannot study cause and effect relationships Cannot assess disease frequency Cross-Sectional Study as a Descriptive Study Purpose: To learn about the characteristics of a population at one point in time (like a photo “snapshot”) Design: No comparison group Population: All members of a small, defined group or a sample from a large group Results: Produces estimates of the prevalence of the population characteristic of interest Cross Sectional Study Design When to Conduct a Cross- Sectional Study To estimate prevalence of a health condition or prevalence of a behavior, risk factor, or potential for disease To learn about characteristics such as knowledge, attitude and practices of individuals in a population To monitor trends over time with serial cross-sectional studies Cross-Sectional Study Measures Prevalence of a condition: = number of existing cases / size of population (or population count)  Cross-sectional Studies Often used to study conditions that are relatively frequent with long duration of expression (nonfatal, chronic conditions) It measures prevalence, not incidence of disease Example: community surveys Not suitable for studying rare or highly fatal diseases or a disease with short duration of expression  Cross-sectional studies Disadvantages Weakest observational design, (it measures prevalence, not incidence of disease). Prevalent cases are survivors The temporal sequence of exposure and effect may be difficult or impossible to determine Usually don’t know when disease occurred Rare events a problem. Quickly emerging diseases a problem  Cross-sectional studies An “observational” design that surveys exposures and disease status at a single point in time (a cross-section of the population) time Study only exists at this point in time Cross-sectional Design factor present No Disease factor absent Study population factor present Disease factor absent time Study only exists at this point in time  Case Report One case of unusual findings Multiple cases of Case Series findings Descriptive Population-based Epidemiology Study cases with denominator 2- Analytical Studies Case-Control Studies Cases: Disease Controls: No disease factor present Cases (disease) factor absent Study population factor present Controls (no disease) factor absent present past time Study begins here Try to conduct the Case – Control Study Exercise  Case-Control Study Strengths – Less expensive and time consuming – Efficient for studying rare diseases Limitations – Inappropriate when disease outcome for a specific exposure is not known at start of study – Exposure measurements taken after disease occurrence – Disease status can influence selection of subjects  Epidemiologic Study Designs Cohort Studies – an “observational” design comparing individuals with a known risk factor or exposure with others without the risk factor or exposure – looking for a difference in the risk (incidence) of a disease over time – best observational design – data usually collected prospectively (some retrospective)  disease Factor present no disease Study population free of disease disease Factor absent no disease present future time Study begins here  Timeframe of Studies Prospective Study - looks forward, looks to the future, examines future events, follows a condition, concern or disease into the future time Study begins here  Prospective Cohort study Exposed Outcome Measure exposure and confounder variables Baseline Non-exposed Outcome time Study begins here  Timeframe of Studies Retrospective Study - “to look back”, looks back in time to study events that have already occurred time Study begins here  Retrospective Cohort study Exposed Outcome Measure exposure and confounder variables Baseline Non-exposed Outcome time Study begins here Cohort Study Design What is a cohort? (longitudinal study, follow-up study) A well-defined group of individuals who share a common characteristic or experience Example: Individuals born in the same year Characteristics - Participants classified according to exposure status and followed-up over time to ascertain outcome. - Can be used to find multiple outcomes from a single exposure - Appropriate for rare exposures or defined cohorts - Ensures temporality (exposure occurs before observed outcome) Types of Cohort Studies 1- Prospective cohort studies Group participants according to past or current exposure and follow-up into the future to determine if outcome occurs 2- Retrospective cohort studies At the time that the study is conducted, potential exposure and outcomes have already occurred in the past  Cohort Study Strengths – Exposure status determined before disease detection – Subjects selected before disease detection – Can study several outcomes for each exposure Limitations – Expensive and time-consuming – Inefficient for rare diseases or diseases with long latency – Loss to follow-up  Measurement of the Associations in Cohort Study Design Relative Risk (RR): Compare the risk of developing the disease when exposed to the risk factor to that in the absence of the risk factor. It shows how many times the exposed are more likely to develop the disease compared to the non-exposed Diseased Not Diseased Total Exposed A B A+B Unexposed C D C+D The incidence of disease among exposed = No. 𝒐𝒇 𝒅𝒊𝒔𝒆𝒂𝒔𝒆 𝒄𝒂𝒔𝒆𝒔 𝒂𝒎𝒐𝒏𝒈 𝒆𝒙𝒑𝒐𝒔𝒆𝒅 𝒊𝒏𝒅𝒊𝒗𝒊𝒅𝒖𝒂𝒍𝒔 No 𝒐𝒇 𝒇𝒐𝒍𝒍𝒐𝒘𝒆𝒅 𝒖𝒑 𝒆𝒙𝒑𝒐𝒔𝒆𝒅 𝒊𝒏𝒅𝒊𝒗𝒊𝒅𝒖𝒂𝒍𝒔 𝒅𝒖𝒓𝒊𝒏𝒈 𝒂 𝒄𝒆𝒓𝒕𝒂𝒊𝒏 𝒕𝒊𝒎𝒆 𝒑𝒆𝒓𝒊𝒐𝒅 = 𝑨 𝑨+𝑩 The Incidence of disease among non-exposed = No 𝒐𝒇 𝒅𝒊𝒔𝒆𝒂𝒔𝒆 𝒄𝒂𝒔𝒆𝒔 𝒂𝒎𝒐𝒏𝒈 𝒏𝒐𝒏− 𝒆𝒙𝒑𝒐𝒔𝒆𝒅 𝑰𝒏𝒅𝒊𝒗𝒊𝒅𝒖𝒂𝒍𝒔 No 𝒐𝒇 𝒇𝒐𝒍𝒍𝒐𝒘𝒆𝒅 𝒖𝒑 𝒏𝒐𝒏−𝒆𝒙𝒑𝒐𝒔𝒆𝒅 𝒊𝒏𝒅𝒊𝒗𝒊𝒅𝒖𝒂𝒍𝒔 𝒅𝒖𝒓𝒊𝒏𝒈 𝒂 𝒄𝒆𝒓𝒕𝒂𝒊𝒏 𝒕𝒊𝒎𝒆 𝒑𝒆𝒓𝒊𝒐𝒅 = 𝑪 𝑪+𝑫 Relative Risk = 𝑰𝒏𝒄𝒊𝒅𝒆𝒏𝒄𝒆 𝒐𝒇 𝒕𝒉𝒆 𝒅𝒊𝒆𝒂𝒔𝒆 𝒂𝒎𝒐𝒏𝒈 𝒆𝒙𝒑𝒐𝒔𝒆𝒅 𝑰𝒏𝒄𝒊𝒅𝒆𝒄𝒏𝒆 𝒐𝒇 𝒕𝒉𝒆 𝒅𝒊𝒔𝒆𝒂𝒔𝒆 𝒂𝒎𝒐𝒏𝒈 𝒏𝒐𝒏−𝒆𝒙𝒑𝒐𝒔𝒆𝒅 = 𝑨 𝑪 𝑨+𝑩 𝑪+𝑫 Interpretation of Relative Risk (RR): /  Experimental Studies treatment and exposures occur in a “controlled” environment planned research designs clinical trials are the most well known experimental design. Clinical trials use randomly assigned data. Community trials use nonrandom data  Observational Studies non-experimental observational because there is no individual intervention treatment and exposures occur in a “non-controlled” environment individuals can be observed prospectively, retrospectively, or currently  Cross-sectional studies An “observational” design that surveys exposures and disease status at a single point in time (a cross-section of the population) time Study only exists at this point in time Cross-sectional Design factor present No Disease factor absent Study population factor present Disease factor absent time Study only exists at this point in time  Epidemiologic Study Designs Case-Control Studies – an “observational” design comparing exposures in disease cases vs. healthy controls from same population – exposure data collected retrospectively – most feasible design where disease outcomes are rare  Case-Control Study Strengths – Less expensive and time consuming – Efficient for studying rare diseases Limitations – Inappropriate when disease outcome for a specific exposure is not known at start of study – Exposure measurements taken after disease occurrence – Disease status can influence selection of subjects Hypothesis Testing: Case-Crossover Studies Study of “triggers” within an individual ”Case" and "control" component, but information of both components will come from the same individual ”Case component" = hazard period which is the time period right before the disease or event onset ”Control component" = control period which is a specified time interval other than the hazard period  Epidemiologic Study Designs Cohort Studies – an “observational” design comparing individuals with a known risk factor or exposure with others without the risk factor or exposure – looking for a difference in the risk (incidence) of a disease over time – best observational design – data usually collected prospectively (some retrospective)  disease Factor present no disease Study population free of disease disease Factor absent no disease present future time Study begins here  Timeframe of Studies Prospective Study - looks forward, looks to the future, examines future events, follows a condition, concern or disease into the future time Study begins here  Prospective Cohort study Exposed Outcome Measure exposure and confounder variables Baseline Non-exposed Outcome time Study begins here  Timeframe of Studies Retrospective Study - “to look back”, looks back in time to study events that have already occurred time Study begins here  Retrospective Cohort study Exposed Outcome Measure exposure and confounder variables Baseline Non-exposed Outcome time Study begins here  Cohort Study Strengths – Exposure status determined before disease detection – Subjects selected before disease detection – Can study several outcomes for each exposure Limitations – Expensive and time-consuming – Inefficient for rare diseases or diseases with long latency – Loss to follow-up Basal Cell Carcinoma (BCC)  Experimental Studies investigator can “control” the exposure akin to laboratory experiments except living populations are the subjects generally involves random assignment to groups clinical trials are the most well known experimental design the ultimate step in testing causal hypotheses  Experimental Studies In an experiment, we are interested in the consequences of some treatment on some outcome. The subjects in the study who actually receive the treatment of interest are called the treatment group. The subjects in the study who receive no treatment or a different treatment are called the comparison group.  Epidemiologic Study Designs Randomized Controlled Trials (RCTs) – a design with subjects randomly assigned to “treatment” and “comparison” groups – provides most convincing evidence of relationship between exposure and effect – not possible to use RCTs to test effects of exposures that are expected to be harmful, for ethical reasons RANDOMIZATION outcome Intervention no outcome Study population outcome Control no outcome baseline future time Study begins here (baseline point)  Epidemiologic Study Designs Randomized Controlled Trials (RCTs) – the “gold standard” of research designs – provides most convincing evidence of relationship between exposure and effect trials of hormone replacement therapy in menopausal women found no protection for heart disease, contradicting findings of prior observational studies Randomized Controlled Trials Disadvantages – Very expensive – Not appropriate to answer certain types of questions it may be unethical, for example, to assign persons to certain treatment or comparison groups  Review Questions (Developed by the Supercourse team) Describe the link between exposure and disease Describe study design sequence Describe strengths and weaknesses of each design 2. Analytic Epidemiology Analyses determinants of health problems Answers two other major questions: how? And why? Generally, Epidemiology answers six major questions: how many, who, where, when how and why ?  Types of primary studies Descriptive studies – describe occurrence of outcome Analytic studies – describe association between exposure and outcome Basic Question in Analytic Epidemiology Are exposure and disease linked? E D Exposure Disease Basic Questions in Analytic Epidemiology Look to link exposure and disease – What is the exposure? – Who are the exposed? – What are the potential health effects? – What approach will you take to study the relationship between exposure and effect? Wijngaarden Descriptive Analytic Case report Cohort study RCT Case series Case-Control study Descriptive Epidemiology Case-Crossover study Cross-sectional study Before-After study Ecologic study  Timeframe of Studies Prospective Study - looks forward, looks to the future, examines future events, follows a condition, concern or disease into the future time Study begins here  Timeframe of Studies Retrospective Study - “to look back”, looks back in time to study events that have already occurred time Study begins here  Study Design Sequence Hypothesis formation Descriptive Case reports Case series epidemiology Analytic Animal Lab epidemiology study study Clinical trials Hypothesis testing Cohort Case- Cross- control sectional  Descriptive Studies Develop hypothesis Increasing Knowledge of Disease/Exposure Investigate it’s Case-control Studies relationship to outcomes Define it’s meaning Cohort Studies with exposures Test link Clinical trials experimentally  Descriptive Studies Develop hypothesis Increasing Knowledge of Disease/Exposure Investigate it’s Case-control Studies relationship to outcomes Define it’s meaning Cohort Studies with exposures Test link Clinical trials experimentally Descriptive Studies  Case Reports Detailed presentation of a single case or handful of cases Generally report a new or unique finding e.g. previous undescribed disease e.g. unexpected link between diseases e.g. unexpected new therapeutic effect e.g. adverse events  Case Series Experience of a group of patients with a similar diagnosis Assesses prevalent disease Cases may be identified from a single or multiple sources Generally report on new/unique condition May be only realistic design for rare disorders  Case Series Advantages Useful for hypothesis generation Informative for very rare disease with few established risk factors Characterizes averages for disorder Disadvantages Cannot study cause and effect relationships Cannot assess disease frequency  Case Report One case of unusual findings Multiple cases of Case Series findings Descriptive Population-based Epidemiology Study cases with denominator Analytical Studies  Study Designs - Analytic Epidemiology Experimental Studies – Randomized controlled clinical trials – Community trials Observational Studies – Group data Ecologic – Individual data Cross-sectional Cohort Case-control Case-crossover  Experimental Studies treatment and exposures occur in a “controlled” environment planned research designs clinical trials are the most well known experimental design. Clinical trials use randomly assigned data. Community trials use nonrandom data  Observational Studies non-experimental observational because there is no individual intervention treatment and exposures occur in a “non-controlled” environment individuals can be observed prospectively, retrospectively, or currently  Cross-sectional studies An “observational” design that surveys exposures and disease status at a single point in time (a cross-section of the population) time Study only exists at this point in time Cross-sectional Design factor present No Disease factor absent Study population factor present Disease factor absent time Study only exists at this point in time  Cross-sectional Studies Often used to study conditions that are relatively frequent with long duration of expression (nonfatal, chronic conditions) It measures prevalence, not incidence of disease Example: community surveys Not suitable for studying rare or highly fatal diseases or a disease with short duration of expression  Cross-sectional studies Disadvantages Weakest observational design, (it measures prevalence, not incidence of disease). Prevalent cases are survivors The temporal sequence of exposure and effect may be difficult or impossible to determine Usually don’t know when disease occurred Rare events a problem. Quickly emerging diseases a problem  Epidemiologic Study Designs Case-Control Studies – an “observational” design comparing exposures in disease cases vs. healthy controls from same population – exposure data collected retrospectively – most feasible design where disease outcomes are rare Case-Control Studies Cases: Disease Controls: No disease factor present Cases (disease) factor absent Study population factor present Controls (no disease) factor absent present past time Study begins here  Case-Control Study Strengths – Less expensive and time consuming – Efficient for studying rare diseases Limitations – Inappropriate when disease outcome for a specific exposure is not known at start of study – Exposure measurements taken after disease occurrence – Disease status can influence selection of subjects Hypothesis Testing: Case-Crossover Studies Study of “triggers” within an individual ”Case" and "control" component, but information of both components will come from the same individual ”Case component" = hazard period which is the time period right before the disease or event onset ”Control component" = control period which is a specified time interval other than the hazard period  Epidemiologic Study Designs Cohort Studies – an “observational” design comparing individuals with a known risk factor or exposure with others without the risk factor or exposure – looking for a difference in the risk (incidence) of a disease over time – best observational design – data usually collected prospectively (some retrospective)  disease Factor present no disease Study population free of disease disease Factor absent no disease present future time Study begins here  Timeframe of Studies Prospective Study - looks forward, looks to the future, examines future events, follows a condition, concern or disease into the future time Study begins here  Prospective Cohort study Exposed Outcome Measure exposure and confounder variables Baseline Non-exposed Outcome time Study begins here  Timeframe of Studies Retrospective Study - “to look back”, looks back in time to study events that have already occurred time Study begins here  Retrospective Cohort study Exposed Outcome Measure exposure and confounder variables Baseline Non-exposed Outcome time Study begins here  Cohort Study Strengths – Exposure status determined before disease detection – Subjects selected before disease detection – Can study several outcomes for each exposure Limitations – Expensive and time-consuming – Inefficient for rare diseases or diseases with long latency – Loss to follow-up  Experimental Studies investigator can “control” the exposure akin to laboratory experiments except living populations are the subjects generally involves random assignment to groups clinical trials are the most well known experimental design the ultimate step in testing causal hypotheses  Experimental Studies In an experiment, we are interested in the consequences of some treatment on some outcome. The subjects in the study who actually receive the treatment of interest are called the treatment group. The subjects in the study who receive no treatment or a different treatment are called the comparison group.  Epidemiologic Study Designs Randomized Controlled Trials (RCTs) – a design with subjects randomly assigned to “treatment” and “comparison” groups – provides most convincing evidence of relationship between exposure and effect – not possible to use RCTs to test effects of exposures that are expected to be harmful, for ethical reasons RANDOMIZATION outcome Intervention no outcome Study population outcome Control no outcome baseline future time Study begins here (baseline point)  Epidemiologic Study Designs Randomized Controlled Trials (RCTs) – the “gold standard” of research designs – provides most convincing evidence of relationship between exposure and effect trials of hormone replacement therapy in menopausal women found no protection for heart disease, contradicting findings of prior observational studies Randomized Controlled Trials Disadvantages – Very expensive – Not appropriate to answer certain types of questions it may be unethical, for example, to assign persons to certain treatment or comparison groups  Review Questions (Developed by the Supercourse team) Describe the link between exposure and disease Describe study design sequence Describe strengths and weaknesses of each design Epidemiologic Study Designs  Study Design and Its Strength of Evidence 1. Systematic review, meta-analysis: Strongest evidence secondary data analysis 2. Randomized Controlled Trials (RCT) 3. Cohort: prospective or retrospective Quasi experiment 4. Case control: prospective or retrospective 5. Cross sectional Weakest 6. Case Reports / Case Series evidence  Which Disease if More Important to Public Health? Measure of Disease Occurence Hypothetical Data Measles Chickenpox Rubella Children exposed 251 238 218 Children ill 201 172 82 Attack rate 0.80 0.72 0.38 Attack rate = Number of Ill persons (new cases) Population at risk exposed Attack rate is a Cumulative Incidence; it shows the risk (probability) of disease to occur in a population In regard to risk, measles is the most important disease to public health while rubella being the least Description of Disease Distribution in the Population Disease affects Disease affects Disease reaches its mostly people under people living peak in frequency in five years of age alongside the river Week 6  Measure of Disease Frequency 1. Cumulative Incidence (Incidence, Risk, I, R)= Number of new case over a time period Population at risk at the outset - Indicates the risk for the disease to occur in population at risk over a time period. Value from 0 to 1. 2. Incidence Density (Incidence Rate, ID, IR)= Number of new case over a time period Person time at risk Indicates the velocity (speed) of the disease to occur in population over a time period. Value from 0 to infinity 3. Prevalence (Point Prevalence): Number of new and old cases at a point of time Population Indicates burden of disease. Value from 0 to 1.  Endemic vs. Epidemic Number of Cases of a Disease Endemic Epidemic Time Patterns (Levels) of DiseaseOccurence Sporadic level : occasional cases occurring at irregular intervals Endemic level : persistent occurrence with a low to moderate level Hyperendemic level: persistently high level of occurrence Epidemic or outbreak: occurrence clearly in excess of the expected level for a given time period Pandemic: epidemic spread over several countries or continents, affecting a large number of people  Factors Influencing Disease Transmission Agent Environment Infectivity Weather Pathogenicity Housing Virulence Geography Immunogenicity Occupational setting Antigenic stability Air quality Survival Food Host Age Sex Genotype Behaviour Nutritional status Health status Measures of Infectivity, Pathogenecity, Mortality Infectivity (ability to infect) (number infected / number susceptible) x 100 Pathogenicity (ability to cause disease) (number with clinical disease / number infected) x 100 Virulence (ability to cause death) (number of deaths / number with disease) x 100 All are dependent on host factors Preventable Causes of Disease “BEINGS” Biological factors and Behavioral Factors Environmental factors Immunologic factors Nutritional factors Genetic factors Services, Social factors, and Spiritual factors [JF Jekel, Epidemiology, Biostatistics, and Preventive Medicine, 1996] Types of Cause: Necessary cause: Mycobacterium tuberculosis Sufficient cause: HIV Contributory cause: Sufficient-Component Cause  Causal Model of Risk Factors for CVD Morbidity and Mortality Disease (Stroke, MI) Biological Risk Factors Proximate cause (Hypertension, Blood Lipids, Homocysteine) Genetic Risk Factors Behavioral Risk Factors Intermedi (Family History) (Cigarette, Diet, Exercise) ate cause Environmental Factors Distal cause (Socioeconomic Status, Work Environment)  Validity of Estimated Association and Causation Smoking Lung Cancer OR = 7.3 True association Bias? causal Confounding? non-causal Chance? 187  The Role of Bias, Confounding, and Chance in The Estimated Association absent Association ? present present Selection Bias and Information Bias? False absent association likely Confounding ? unlikely likely Chance ? unlikely 188 True association  BIAS Systematic errors in selection of study subjects, collecting or interpreting data such that there is deviation of results or inferences from the truth. Selection bias: noncomparable procedure used to select study subjects leading to noncamparable study groups in their distribution of risk factors. Example: Healthy worker bias Information bias: bias resulting from measurement error/ error in data collection (e.g. faulty instrument, differential or non- differential misclassification of disease and/ or exposure status. Example: interviewer bias, recall bias)  Confounding Observed (but spurious) association, presumed causation Birth Order Down’s syndrome Unobserved True association association Maternal age  Confounding 1. A mixing of effects between the exposure, the disease, and a third factor associated with both the exposure and the disease such that the effect of exposure on the disease is distorted by the association between the exposure and the third factor 2. This third factor is so called confounding factor  Hill’s Criteria for Causation 1. Strength of association 2. Specificity 3. Temporal sequence 4. Biologic gradient (dose-response relationship) 5. Biologic plausibility 6. Consistency 7. Coherence 8. Experimental study 9. Analogy