MPH Ist Year
BASIC EPIDEMIOLOGY
Prabesh Ghimire
BASIC EPIDEMIOLOGY
MPH 1st Year
Table of Contents UNIT 1: INTRODUCTION TO EPIDEMIOLOGY; DEFINITION SCOPE AND USES............................................... 4 Concepts of Epidemiology ........................................................................................................................ 4 Historical Development of Epidemiology ................................................................................................. 4 Uses and Scope of Epidemiology .............................................................................................................. 5 Determinants of Health ............................................................................................................................ 6 Basic Measurement in Epidemiology ........................................................................................................ 7 Counts, Rate Ratio and Proportion ....................................................................................................... 7 Incidence and Prevalence Rates ........................................................................................................... 9 Measures of Risk ..................................................................................................................................... 12 UNIT 2: EPIDEMIOLOGICAL MEASUREMENTS ............................................................................................ 17 Frequently Used Measures of Mortality ................................................................................................. 17 Standardized Mortality Rate: .................................................................................................................. 18 Frequently used measure of fertility ...................................................................................................... 18 Quality Adjusted Life Years (QALY) ......................................................................................................... 19 Disability Adjusted Life Years (DALY) ...................................................................................................... 19 UNIT 3: EPIDEMIOLOGICAL STUDY DESIGN ................................................................................................ 20 Case Report, Case Series and Ecological Studies .................................................................................... 20 Cross-sectional Study .............................................................................................................................. 21 Case-Control Studies ............................................................................................................................... 23 Nested Case Control Study ..................................................................................................................... 25 Case –Cohort Study ................................................................................................................................. 26 Cohort Study ........................................................................................................................................... 27 Experimental Study ................................................................................................................................. 29 Epidemiological Bias and Error ............................................................................................................... 33 Different Sources of Bias ......................................................................................................................... 39 Prevention of Biases ............................................................................................................................... 40 Causal Inference...................................................................................................................................... 41 Unit 4: CLINICAL EPIDEMIOLOGY ................................................................................................................ 44 Concept, Scope and Approach ................................................................................................................ 44 Validity and Reliability............................................................................................................................. 45
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Sensitivity, Specificity, Likelihood Ratio and Predictive Value ................................................................ 47 Normality vs Abnormality ....................................................................................................................... 48 Receiver Operating Characteristics (ROC) Curve .................................................................................... 49 UNIT 5: FIELD EPIDEMIOLOGY .................................................................................................................... 50 Control of Outbreak/Epidemics .............................................................................................................. 52 Disease Surveillance ................................................................................................................................ 55 Screening................................................................................................................................................. 59 Establishment of Causal Relationship ..................................................................................................... 60 Ethical Issues in Epidemiological Studies ................................................................................................ 63
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UNIT 1: INTRODUCTION TO EPIDEMIOLOGY; DEFINITION SCOPE AND USES Concepts of Epidemiology Epidemiology as defined by John M. Last is “the study of the distribution and determinants of healthrelated states or events in specified populations, and the application of this study to the prevention and control of health problems”
Historical Development of Epidemiology The underpinnings of epidemiology and its relationship to health promotion and disease prevention go back as far as ancient Greek civilization. The table below provides an abbreviated summary of key milestones in the evolution of epidemiology. Year 400 B.C. 1600s 1662 1747 1839 1849-1854 1920 1949 1950 1954
Epidemiological Milestones Hippocrates suggested that the development of human disease might be related to lifestyle factors and the external environment Bacon and others developed principles of inductive logic, forming a philosophical basis for epidemiology Graunt analyzed births and deaths in London and quantified disease in a population Lind conducted a study of treatments for scurvy- one of the first experimental trials William Farr set up a system for routine summaries of causes of death John Snow formed and tested a hypothesis on the origins of cholera in London- one of the first studies in analytical epidemiology Goldberg published a descriptive field study showing the dietary origins of pellagra The Framingham Heart Study was begum- among the first cohort studies Doll and Hill, Levin et al., Schreck et al., and Wynder and Graham published the first case-control studies of cigarette smoking and lung cancer Field trial of the Salk polio vaccine was conducted – the largest formal human experiment
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1960 1970s 1980s present
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MacMohan published the first epidemiology text with a systematic focus on study design New multivariate statistical methods developed, such as log-linear and logistics analysis 1980s, epidemiology was extended to the studies of injuries and violence Development and application of techniques in molecular biology to large populations. Emergence and re-emergence of infectious diseases (Ebola virus, HIV/AIDS, SARS drug-resistant Mycobacterium tuberculosis, Avian influenza) have been challenging epidemiologists.
Uses and Scope of Epidemiology i.
Understanding the disease process a. Study of the natural history of disease - Epidemiology is concerned with the course and outcome of diseases in individuals and groups. - Natural history of disease is best identified by cohort studies. b. Searching for the cause and risk factors of diseases - Much epidemiologic research is devoted to searching for causal factors that influence one’s risk of disease. Ideally, the goal is to identify a cause so that appropriate public health action might be taken. c. -
History study of rise and fall of diseases Small pox rose to its peak, killed millions and was finally eradicated; plague almost vanished after killing huge proportions of humanity and then again reappeared. Epidemiological studies of such rise and fall of diseases are essential to understand the various factors which can be effectively utilized in preventing the occurrence or re-emergence of other diseases.
d. To identify syndromes - The idea of syndrome is that two or more different medical phenomena (constellation of signs/symptoms) occur more frequently together. - It is only after obtaining data on hundreds of patients from various countries about signs and symptoms of a related nature, through epidemiological methods, that we are able to put the pieces together and identify syndromes and their etiological factors. ii.
Uses in public health practice a. Investigation of epidemics and other field investigations - While epidemiology today is involved in practically all of medicine and health case, the fact remains that it originally emerged as a science dealing with investigations of epidemics and even today, this remains one of the most important duties of the epidemiologists. b. Surveillance of diseases - Surveillance essentially monitors trends in the occurrence of selected diseases, thereby giving early warning about increase in their occurrence so that early control measures can be instituted.
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Today we have huge national and international surveillance systems which all essentially involve epidemiological principles.
c. -
Assessing the programmes for mass screening for diseases Based on the epidemiological principle of diagnostic test assessment, the mass screening programs are planned and subsequently evaluated for their effectiveness in large population groups.
iii. Uses in Health Care Management a. Making a community diagnosis: - In health care of large community, the health provider must make a community diagnosis by epidemiological methods to obtain information on the important health problems and their associated socio-demographic characteristics, quantifying and summarizing them. - Once a community diagnosis has been made, we can decide as to which programmes would be best for improving the health status of the community. b. Planning and evaluation of health services - Any planning process will need accurate information about the socio-demographic profile, the disease, the health care facilities, etc. - Similarly, while evaluating a health programme, we will again need current information about various diseases and compare it with the baseline state. - This quantifies and summarized information is available only through epidemiological steps. Determinants of Health i. ii. iii. iv. v. vi. vii. viii. ix. x.
Income and social status: Higher income and social status are linked to better health. The greater the gap between the richest and poorest people, the greater the differences in health. Education: Low education levels are linked with poor health, more stress and lower self-confidence. Physical environment: Safe water and clean air, healthy workplaces, safe houses, communities and roads all contribute to good health. Employment and working conditions: People in employment are healthier, particularly those who have more control over their working conditions Social support networks: Greater support from families, friends and communities is linked to better health. Culture: Customs and traditions, and the beliefs of the family and community all affect health. Genetics: Inheritance plays a part in determining lifespan, healthiness and the likelihood of developing certain illnesses. Personal behaviour and coping skills: Balanced eating, keeping active, smoking, drinking, and how we deal with life’s stresses and challenges all affect health. Health services: Access and use of services that prevent and treat disease influences health Gender: Men and women suffer from different types of diseases at different ages.
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Basic Measurement in Epidemiology Counts, Rate Ratio and Proportion Population at Risk: An important factor in calculating measure of disease frequency is the correct estimate of the numbers of people under study. Ideally these numbers should only include people who are potentially susceptible to the disease being studied. Those people who are susceptible to a given disease are called the population at risk, and can be identified by demographic, geographic or environmental factors. Example: men should not be included when calculating the frequency of cervical cancer. For instance, occupational injuries occur only among working people, so the population at risk id the workforce. In some countries, brucellosis occurs only among people handling infected animals, so the population at risk consists of those working on farms and slaughterhouse.
Population at risk in a study of carcinoma of the cervix
Rates Rate measures the occurrence of some particular event (development of disease or the occurrence of death) in a population during a given time period. In epidemiology, a rate is a measure of the frequency with which an event (development of disease or the occurrence of death) occurs in a defined population over a specified period of time. Because rates put disease frequency in the perspective of the size of the population, rates are particularly useful for comparing disease frequency in different locations, at different times, or among different groups of persons with potentially different sized populations; that is, a rate is a measure of risk. The various categories of rates are: i. Crude Rate: These are the actual observed rates such as crude birth and death rates. ii. Specific rates: These are the actual observed rates due to specific causes (e.g. tuberculosis); or occurring in specific groups (e.g. age group) or during specific time period (e.g. annual, monthly or weekly rates). Eg: case fatality rate, age specific fertility rate, cause specific mortality rate, etc iii. Standardized rates: These are obtained by direct or indirect method of standardization or adjustment e.g. age and sex standardized rates.
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Ratio A ratio is the relative magnitude of two quantities or a comparison of any two values. It is calculated by dividing one interval- or ratio-scale variable by the other. The numerator is not a component of the denominator. Broadly, a ratio is the result of dividing one quantity by another. It is expressed in the form of X/Y or X:Y. It is important to note that in certain ratios, the numerator and denominator are different categories of the same variable, such as males and females (e.g. sex-ratio). In other ratios, the numerator and denominator are completely different variables such as number of doctors and the size of populations (e.g. doctor population ratio) Uses of ratio in epidemiology As a descriptive measure, ratios can describe the male to female ratio of participants in the study, or the ratio of controls to cases (e.g two controls per case) As an analytical tool, ratios can be calculated for occurrence of illness, injury, or death between two groups. These ration measures including risk ratio, rate ratio and odds ratio are important measures in epidemiology. Proportion A proportion is the comparison of a part to the whole. It is a type of ratio in which the numerator is included in the denominator. We might use a proportion to describe what fraction of clinic patients tested for HIV, or what percentage of population is younger than 5 years of age. A proportion may be expressed as decimal, a fraction or a percentage 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 =
𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑜𝑜𝑜𝑜 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑤𝑤𝑤𝑤𝑤𝑤ℎ 𝑎𝑎 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑐𝑐ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 × 10𝑛𝑛 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑜𝑜𝑜𝑜 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑜𝑜𝑜𝑜 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑜𝑜𝑜𝑜 𝑤𝑤ℎ𝑖𝑖𝑖𝑖ℎ 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑖𝑖𝑖𝑖 𝑎𝑎 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠
Uses of proportion in epidemiology In epidemiology, proportions are used most often as descriptive measures. For example one could calculate the proportion of children in a VDC immunized against measles, or the proportion of persons who developed illness among all the persons who used a pond water. Proportions are also used to describe the amount of disease that can be attributed to a particular exposure. For example, one the basis of studies of smoking and lung cancer, public health officials have estimated that greater than 90% of the lung cancer cases that occur are attributable to cigarette smoking. Summaries of some of the common epidemiologic measures as rates, ratio or proportions Condition Rate Ratio Proportion Morbidity (Disease)
Incidence rate Attack rate Person time rate
Mortality (Death)
Crude Death Rate Case Fatality Rate Cause Specific Mortality Rate Age Specific Mortality Rate Maternal Mortality Rate Infant Mortality Rate
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Risk Ratio (Relative Risk) Rate Ratio Odds Ratio Period Prevalence Death to Case Ratio Maternal Mortality Ratio Proportionate mortality rate
Attack Rate (Cumulative Incidence) Secondary attack rate Point prevalence Attributable proportion Proportionate mortality
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Fertility
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Crude Birth Rate Age Specific Fertility Rate
Incidence and Prevalence Rates Incidence Rate Incidence refers to the rate at which new events occur in a population. Incidence takes into account the variable time periods during which individuals are disease-free and thus “at risk” of developing the disease. Two types of incidence are commonly used — cumulative incidence and person time incidence rate. i. -
Cumulative incidence Cumulative incidence is a simpler measure of the occurrence of a disease or health status. Cumulative incidence is the proportion of an initially disease-free population that develops disease, becomes injured, or dies during a specified (usually limited) period of time. Synonyms include attack rate, risk, probability of getting disease, and incidence proportion It measures the denominator only at the beginning of a study.
The cumulative incidence can be calculated as follows:
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ii. -
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Number of new cases of disease or injury during specified time period Cumulative Incidence = × 10n Number of people free of disease in the population at risk at the beginning of the period
In a statistical sense, the cumulative incidence is the probability that individuals in the population get the disease during the specified period. The period can be of any length but is usually several years, or even the whole lifetime. The simplicity of cumulative incidence rates makes them useful when communicating health information to the general public. Incidence Rate or Person Time Incidence Incidence rate or person-time rate is a measure of incidence that incorporates time directly into the denominator. A person-time rate is generally calculated from a long-term cohort follow-up study, wherein enrollees are followed over time and the occurrence of new cases of disease is documented. Typically, each person is observed from an established starting time until one of four “end points” is reached: onset of disease, death, migration out of the study (“lost to follow-up”), or the end of the study. Each person in the study population contributes one person-year to the denominator for each year (or day, week, month) of observation. The numerator strictly refers only to first events of disease. The units of incidence rate must always include a unit of time (cases per 10n and per day, week, month, year, etc.). For each individual in the population, the time of observation is the period that the person remains disease-free. The denominator used for the calculation of incidence is therefore the sum of all the disease-free person-time periods during the period of observation of the population at risk. Incidence rate is calculated as follows Number of new events in a specified period I= × 10n Number of persons exposed to risk during this period
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Prevalence Rate Prevalence, sometimes referred to as prevalence rate, is the proportion of persons in a population who have a particular disease or attribute at a specified point in time or over a specified period of time. - Prevalence differs from incidence in that prevalence includes all cases, both new and preexisting, in the population at the specified time, whereas incidence is limited to new cases only. - Prevalence rather than incidence is often measured for chronic diseases such as diabetes or osteoarthritis which have long duration and dates of onset that are difficult to pinpoint. - Point prevalence refers to the prevalence measured at a particular point in time. It is the proportion of persons with a particular disease or attribute on a particular date. - Period prevalence refers to prevalence measured over an interval of time. It is the proportion of persons with a particular disease or attribute at any time during the interval. Prevalence of a disease is calculated as follows: Number of person with disease or condition during a given time P= × 10n Number of people in the populaiton at risk at the same time
Factors that determine prevalence The severity of illness (if many people who develop a disease die within a short time, its prevalence is decreased); The duration of illness (if a disease lasts a short time its prevalence is lower than if it lasts a long time); The number of new cases (if many people develop a disease, its prevalence is higher than if few people do so).
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BASIC EPIDEMIOLOGY
Comparison of Incidence and Prevalence Basis of Incidence Comparison Numerator New cases of diseases of interest occurring over the defined time Denominator All persons who were at risk of developing the disease at the start of follow up Relation to time There is always a specified time period of follow up Rate or proportion
It is truly a rate since it relates to a time period of follow up
Number of times a subject is examined
Each subject is examined at least twiceonce initially to exclude the presence of disease, so that only those who are at risk of developing disease are followed up and later to examine if these subjects have developed the disease or not Incidence gives a proof of temporal relationship since we start observations at a point of time when the disease had not occurred and the follow up till a point of time when the disease occurs; thus we are sure that the risk factor actually preceded the disease Involves high cost and efforts since a large number of subjects have to be examined, assessed on a least two occasions and followed up for a period of time. Express the risk of becoming ill The main measure of acute diseases or conditions, but also used for chronic diseases
Evidence Temporal relationship
of
Time, money and logistic efforts
Uses
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Prevalence Cases found to be present at the time of study All persons who were examined at the time of study No relation to time period of follow up; cases detected with disease are taken to be at that point of time. It is not truly a rate but only a proportion since there is no relation to time Subjects are examined only one.
Prevalence gives only the indication of association but does not give a proof of temporal relation
Results are obtained quickly since only one assessment is done and no follow up is done. For this reason, prevalence studies are cheaper, less difficult and quicker Express the probability of the population being ill Useful in the study of the burden of chronic diseases and implication for health services
Risk Factors - A risk factor refers to an aspect of personal habits or an environmental exposure that is associated with an increased probability of occurrence of a disease. - Since risk factors can usually be modified, intervening to alter them in a favourable direction can reduce the probability of occurrence of disease. - Risk factors can include tobacco and alcohol use, diet, physical inactivity, blood pressure and obesity. - Since risk factors can be used to predict future disease, their measurement at a population level is important, but also challenging.
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Measures of Risk Relative Risk Both case-control and cohort studies are designed to determine whether there is an association between exposure to a factor and development of a disease. If an association exists, we intend to measure the strength of this association. Used in Cohort study, relative risk (also called the risk ratio) is a measure of the strength of an association between an exposure or attribute and a disease. The relative risk can also be defined as the probability of an event (developing a disease) occurring in exposed people compared to the probability of the event in non-exposed people, or as the ratio of the two probabilities. 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 = 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢
Interpreting the Relative Risk There are three possibilities in interpreting the relative risk i. If the relative risk is equal to 1, the numerator equals the denominator, and the risk in exposed persons equals the risk in non-exposed persons. Therefore, no evidence exists for any increased risk in exposed individuals or for any association of the disease with the exposure in question. ii. If the relative risk is greater than 1, the numerator is greater than the denominator, and the risk in exposed persons is greater than the risk in non-exposed persons. This is evidence of a positive association, and may be causal. iii. If the relative risk is less than 1, the numerator is less than the denominator, and the risk in exposed persons is less than the risk in non-exposed persons. This is evidence of a negative association, and it may be indicative of a protective effect. Such a finding can be observed in people who are given an effective vaccine (“exposed” to the vaccine). RR Value If RR = 1 If RR>1 If RR<1
Interpretation Risk in exposed equal to risk in non-exposed (no association) Risk in exposed greater than risk in non-exposed (positive association; possibly causal) Risk in exposed less than risk in non-exposed (negative association; possibly protective)
Calculating Relative Risk in Cohort Studies In cohort study, relative risk can be calculated by using 2×2 contingency table. Disease Develops Disease Does Not Totals Develop Exposed a b a+b Not Exposed
C
d
𝑎𝑎
=Incidence in exposed,
𝑎𝑎+𝑏𝑏
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c+d 𝑐𝑐
𝑐𝑐+𝑑𝑑
= Incidence in non-exposed
Incidence Rates of Disease 𝑎𝑎 𝑎𝑎 + 𝑏𝑏 𝑐𝑐 𝑐𝑐 + 𝑑𝑑
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In this table, we see that the incidence in exposed individuals is individuals is
𝑐𝑐
𝑐𝑐+𝑑𝑑
𝑎𝑎
𝑎𝑎+𝑏𝑏
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and the incidence in non- exposed
We calculate the relative risk as follows: 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 = 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢
𝑎𝑎 𝑎𝑎 + 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 = 𝑐𝑐 𝑏𝑏 𝑐𝑐 + 𝑑𝑑
Absolute Risk Absolute Risk or simply risk is the probability of an event occurring: that is, the probability that a specified condition will affect the health status of an individual or a population.
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𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 =
Incidence is a measure of absolute risk
𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑎𝑎𝑎𝑎 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟
Attributable Risk and Attributable Risk Fraction Attributable Risk (AR) Attributable Risk or Risk Difference or Absolute Risk Difference is the portion of the incidence of a disease in the exposed that is due to the exposure. It is the incidence of a disease in the exposed that would be eliminated if exposure were eliminated. Attributable Risk = Incidence in exposed – Incidence in non-exposed Interpretation: If we have AR= 80%, we interpret as- “ there will be 80% extra cases of lung cancer in the smokers as compared to non-smokers. Attributable Risk Proportion or Fraction Attributable risk proportion is the percent of incidence of a disease in the disease in the exposed that is due to exposure. It is the proportion of the incidence of a disease in the exposed that would be eliminated if exposure were eliminated. The AR% is calculated by dividing the attributable risk (AR) by the incidence in the exposed and then multiplying the product by 100 to obtain a percentage. 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑅𝑅𝑅𝑅𝑅𝑅𝑘𝑘 % =
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑖𝑖𝑖𝑖 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 − 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑖𝑖𝑖𝑖 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 × 100% 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑖𝑖𝑖𝑖 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒
When the data on disease incidence is not available, we can use the relative risk (RR)
𝑅𝑅𝑅𝑅 − 1 × 100% 𝑅𝑅𝑅𝑅 Interpretation: If we have AR%=43% we interpret as “43% of all cases of lung cancer among smokers are attributable to smoking.” 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 % =
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BASIC EPIDEMIOLOGY
MPH 1st Year
Population Attributable Risk and Population Attributable Risk Fraction Population Attributable Risk (PAR) Population Attributable Risk is the portion of the incidence of a disease in the population (exposed and unexposed) that is due to exposure. It is the incidence of a disease in the population that would be eliminated if exposure were eliminated. The PAR is calculated by subtracting the incidence in the unexposed from the incidence in the total population (exposed and unexposed) Population Attributable Risk = Incidence in population – Incidence in unexposed Interpretation: If we have PAR= 28%, we interpret as- “there will be 28% extra cases of lung cancer in the population due to smoking. Population Attributable Proportion or Fraction Population Attributable Proportion is the percent of the incidence of a disease in the population (exposed and unexposed) that is due to exposure. It is the percent of the incidence of a disease in the population that would be eliminated if exposure were eliminated. The PAR % is calculated by dividing the population attributable risk (PAR) by the incidence in the total population and then multiplying the product by 100 to obtain a percentage. 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 % =
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑖𝑖𝑖𝑖 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 − 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑖𝑖𝑖𝑖 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 × 100% 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑖𝑖𝑖𝑖 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 % =
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑖𝑖𝑖𝑖 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 × (𝑅𝑅𝑅𝑅 − 1) × 100% 1 + 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑖𝑖𝑖𝑖 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 (𝑅𝑅𝑅𝑅 − 1)
Interpretation: If we have PAR%= 79%, we interpret as- “79% of all cases of lung cancer in the population are attributable to smoking. Odds Ratio An odds ratio (OR) is a measure of association between an exposure and an outcome. The odds ratio measure is the ratio of the odds of an exposure between cases and controls and in most cases approximates the relative risk. Odds ratios are most commonly used in case-control studies, however they can also be used in crosssectional and cohort study designs as well (with some modifications and/or assumptions). When is it used? - Odds ratios are used to compare the relative odds of the occurrence of the outcome of interest (e.g. disease or disorder), given particular exposure to the variable of interest (e.g. health characteristic, aspect of medical history). - The odds ratio can also be used to determine whether a particular exposure is a risk factor for a particular outcome, and to compare the magnitude of various risk factors for that outcome.
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BASIC EPIDEMIOLOGY
MPH 1st Year
Interpreting the Odds Ratio OR Value Interpretation If OR = 1 Exposure does not affect odds of outcome If OR>1 Exposure associated with higher odds of outcome If OR<1 Exposure associated with lower odds of outcome Calculating Odds Ratio in Case Control Study Odds Ratio can be calculated by using 2×2 frequency table. Diseased (Cases) Exposed a
Non- Diseased (controls) b
Not Exposed
d
c
a= no. of exposed cases, b= no. of exposed non-cases c= no. of unexposed cases, d= no. of unexposed non-cases We calculate the odds risk as follows: (n)exposed cases (n)unexposed cases Odds Ratio = �(n)exposed non − cases� (n)unexposed non − cases a ad Odds Ratio = c = b bc d
Odds Ratio in Cohort Study In a cohort study, to answer the question of whether there is an association between the exposure and the disease, we can either use the relative risk or we can use the odds ratio (also called the relative odds). In a cohort study, the odds ratio is defined as the ratio of the odds of development of disease in exposed persons to the odds of development of disease in non-exposed persons, and it can be calculated as follows (n)exposed cases (n)exposed non − cases Odds Ratio = �(n)unexposed cases� (n)unexposed non − cases 𝑎𝑎 𝑎𝑎𝑎𝑎 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = 𝑏𝑏𝑐𝑐 = 𝑏𝑏𝑏𝑏 𝑑𝑑
Odds Ratio as a Good Estimate of the Relative Risk The odds ratio (relative odds) obtained in a case-control study are considered a good approximation of the relative risk in the population when the following three conditions are met: i. When the cases studied are representative, with regard to history of exposure, of all people with the disease in the population from which the cases were drawn. ii. When the controls studied are representative, with regard to history of exposure, of all people without the disease in the population from which the cases were drawn. iii. When the disease being studied does not occur frequently.
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BASIC EPIDEMIOLOGY
MPH 1st Year
Numerical Example ## 3000 adult male populations were followed for 10 years. Among them 800 were smokers and 2,200 were non-smokers. Among smokers 12 developed lung cancer and 13 developed lung cancer among non-smokers. a) Construct 2×2 table b) Compute cumulative incidence of lung cancer among smokers c) Calculate risk ratio and interpret it d) Compute attributable risk of developing lung cancer among the smokers. Solution: a)Ans: 2*2 contingency table is: Lung cancer No lung cancer Total Smokers
a=12
b=800-12= 788
800
Non-smokers
c=13
d=2200-13= 2187
2200
Total
25
2975
3000
b) Ans: For cumulative incidence of lung cancer, we have New cases among smoker = 12 Population at risk (smokers) = 800 So, Cumulative Incidence = 12/800 = 0.015 = 15 per 1000 smokers c) Ans: Now for Risk Ratio: Incidence among exposed = 12/800 Incidence among unexposed = 13/ 2200 So, Risk Ratio = Incidence among exposed/ Incidence among unexposed = (12/800)/(13/2200) = 2.54 Interpretation: Since RR>1, there is a positive association between smoking and lung cancer. The risk of lung cancer is 2.54 times higher among smokers than non-smokers. d) Ans: Attributable Risk
=Incidence in exposed – Incidence in non-exposed = (12/800) – (13/2200) = 0.009091 = 0.9 per hundred There is 0.9% (approx 1%) extra cases of lung cancer among smokers as compared to non-smokers.
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BASIC EPIDEMIOLOGY
MPH 1st Year
UNIT 2: EPIDEMIOLOGICAL MEASUREMENTS Frequently Used Measures of Mortality Measures
Numerator
Denominator
10n
Crude Death Rate
Total number of deaths during a given time period
Mid-year population
1,000 100,000
Death
Number of deaths assigned to a specific cause during a given time period
Mid-Year Population
100,000
Age Specific Mortality Rate
Number of deaths in a particular age group during a given time period
Total number of deaths in that age group in the same time period
100 or 1,000
Proportionate Mortality
Number of deaths assigned to a specific cause during a given time period
Total number of deaths from all causes during the same time period
100 or 1,000
Case Fatality Rate
Number of cause-specific deaths among the incident cases
Number of incident cases
100 or 1,000
Perinatal Mortality Rate
Number of deaths between 22 weeks gestation to 7 days of birth during a given time period Number of deaths among infants <28 days of age during a given time period
Total births (live births + still births) during the same period Number of live births during the same time period
1,000
Post-Neonatal Mortality Rate
Number of deaths among infants between 28 days and less than 1 yr of age during a given time period
Number of live births during the same time period
1,000
Infant Mortality Rate
Number of deaths among children <1 year of age during a given time period
Number of live births during the same time period
1,000
Under 5 Mortality Rate
Number of deaths among children <5 year of age during a given time period
Number of live births during the same time period
1,000
Maternal Mortality Ratio
Number of deaths assigned to pregnancy related causes during a given time period
Number of live births during the same time period
100,000
Maternal Mortality Rate
Number of deaths assigned to pregnancy related causes during a given time period
Number of women of reproductive age (15-49 years)
1,000
Cause-Specific Rate
Neonatal Mortality Rate
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or
1,000
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BASIC EPIDEMIOLOGY
MPH 1st Year
Standardized Mortality Rate: -
-
-
i. -
ii. -
-
When comparing two or more populations with respect to a health outcome, it is temptiing to compare crude rates of disease, i.e., the number of disease events divided by the size of the population. Comparisons of crude rates can be misleading because of confounding if the populations being compared have different distributions of other determinants of disease, such as age which has an important effect on many heatlh outcomes. As a result, differences in age can distort other comparisons between populations, and this distortion is called confounding. To eliminate the distortion caused by different underlying age distributions in different populations, statistical techniques are used to adjust or standardize the rates among the populations to be compared. The two closely related techniques are commonly used to compute "age-adjusted" summary rates that facilitate compartisons among population. Direct standardization (Age-adjusted rate) Direct standardization applies a standard age distribution to the populations being compared in order to compute summary rates indicating how overall rates would have compared if the populations had had the same age distibution. This method is used when age-specific rates of disease are known for the populations being compared. It yields a summary figure age-adjusted rate Indirect standardization (Standardized mortality ratio) Indirect standardization applies a standard set of age-specific rates of disease to the populations being compared in order to compute the number of cases of disease that would be expected in a given population, based on its size and age-distribution. It yields a summary figure standardized (mortality) ratio.
Frequently used measure of fertility Measures
Numerator
Denominator
10n
Crude Birth Rate
Total number of births during a given time period
Mid-year population
1,000 100,000
Number of births by women of a particular age group during a given time period
Total number of women in that age group in the same time period
100 or 1,000
Age Specific Rate
Fertility
or
Total Fertility Rate
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BASIC EPIDEMIOLOGY
MPH 1st Year
Quality Adjusted Life Years (QALY) The QALY was invented in the 1970’s and has become an internationally recognized standard tool since the mid-1990s. A QALY is the arithmetic product of life expectancy combined with a measure of the quality of life-years remaining. The calculation is relatively straightforward; the time a person is likely to spend in a particular state of health is weighted by a utility score from standard valuations. In such valuation systems, ‘1’ equates perfect health and ‘0’ equates death. Since certain health states that are characterized by severe disability and pain are regarded as worse than death, they are assigned negative values. If an intervention provided perfect health for one additional year, it would produce one QALY. Likewise, an intervention providing an extra two years of life at a health status of 0.5 would equal one QALY. This effect is related to cost; cost per QALY. For example, if a new treatment gave an additional 0.5 QALYs and the cost of the new treatment per patient is a 5,000 then cost per QALY would be a 10,000 (5,000/0.5). Limitations of QALY - QALYs can lack sensitivity and may be difficult to apply to chronic disease and preventative treatment. Disability Adjusted Life Years (DALY) The DALY is an alternative tool which emerged in the early 90s, as a means of quantifying the burden of disease. DALY is an indicator of burden of disease in a population. It takes into account not only premature mortality, but also disability caused by disease or injury. One DALY can be thought of as one lost year of "healthy" life. The sum of these DALYs across the population, or the burden of disease, can be thought of as a measurement of the gap between current health status and an ideal health situation where the entire population lives to an advanced age, free of disease and disability. The DALY combines in one measure the time lived with disability and the time lost due to premature mortality. DALYs sum years of life lost (YLL) due to premature mortality and years lived in disability/disease (YLD). DALY = YLL + YLD YLL are calculated as the number of deaths at each age multiplied by the standard life expectancy for each age. YLD represent the number of disease/disability cases in a period multiplied by the average duration of disease/disability and weighted by a disease/disability factor. As an example, a woman with a standard life expectancy of 82.5 years and dying at age 50 would suffer 32.5 YLL. If she additionally turned blind at aged 45, this would add 5 years spent in a disability state with a weight factor of 0.33, resulting in 0.33 x 5 = 1.65 YLD. In total, this would amount to 34.15 DALYs. For DALYs, the scale used to measure health state is inverted to a ‘severity scale’, whereby ‘0’ equates perfect health and ‘1’ equates death. The weight factors are age-adjusted to reflect social preference towards life years of a young adult (over an older adult or young child). Furthermore, they are discounted with time, thus favouring immediate over future health benefits.
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BASIC EPIDEMIOLOGY
MPH 1st Year
UNIT 3: EPIDEMIOLOGICAL STUDY DESIGN Epidemiological studies can be classified as either observational or experimental. i. Observational Studies - Observational studies allow nature to take its course: the investigator measures but does not intervene. - They include studies that can be called descriptive or analytical: a. Descriptive Study - A descriptive study is limited to a description of the occurrence of a disease in a population and is often the first step in an epidemiological investigation. - It includes case report, case series and some forms of cross-sectional studies. b. Analytical study - An analytical goes further by analyzing relationships between health status and other variables. - It includes cross-sectional studies, cohort studies and case-control studies. ii. -
-
Experimental Studies In an experimental study, epidemiologists evaluate the effects of an assigned intervention on an outcome; the investigators intervene in the study by influencing the exposure of the study participants. As such, experimental studies are commonly called intervention studies. Experimental study is the most useful for supporting cause-effect relationships and for evaluating the efficacy of prevention and therapeutic interventions. Major experimental study designs include the following: • Randomized controlled trials using patients as subjects (clinical trial) • Field trials in which the participants are healthy people, and • Community trials in which the participants are the communities themselves
Case Report, Case Series and Ecological Studies Case Report - A case report is the description of a single case of a disease. - The case report includes the description of the disease manifestations, clinical course, and prognosis. - Because it reports a single case, a case report does not provide enough useful information to compare cases of the disease. - But case reports are useful in describing how other people have diagnosed and treated a disease as well as the clinical signs after treatment. - Case reports document unusual medical occurrences and can represent the first clues in the identification of new or adverse effects of exposures. Case Series - A case series is a descriptive observational study that evaluates a series of cases of a disease. - The purpose of a case series is to review and describe the clinical signs, disease progression, and disease prognosis. - Case series are helpful when attempting to formulate hypotheses to be tested using analytical studies. - Case series in themselves cannot be used to test hypotheses because only people with disease are evaluated.
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BASIC EPIDEMIOLOGY
MPH 1st Year
Ecological Studies - Ecological studies examine the correlation between a potential explanatory variable and disease frequency both within and countries. - The distinction between this and other epidemiological study designs is that the units of analysis are groups of people rather than individuals. - These studies are often useful for suggesting hypotheses but cannot be used to draw conclusions. - Ecological studies provide no information as to whether the people who were exposed to the characteristic were the same people who developed the disease, whether the exposed or the onset of disease came first, or whether there are other explanations for the observed association. - Ecological fallacy is an error that could occur when an association between variables based on group (ecological) characteristics is used to make inferences about the association at an individual level when such association does not exist. Ecological Fallacy - An ecological fallacy or bias results if inappropriate conclusions are drawn on the basis of ecological data. - The bias occurs because the association observed between variables at the group level does not necessarily represent the association that exists at the individual level. - Ecological fallacy exists if it meets all of these three criteria • Results must be obtained with ecological data • Data must be inferred to individuals. • Results obtained with individual data are contradictory Cross-sectional Study -
-
Cross-sectional studies are analytical observational studies with a comparison group, in which exposure and outcome are analyzed simultaneously. These studies are also known as prevalence studies, since they enable calculation of disease frequency in a particular sample. They can determine the presence or absence of a disease (such as the percentage of people with lung cancer who are smokers) or exposure to a particular causal factor at a particular time (such as the influence of smoking on coronary disease). They are also often used to determine the diagnostic characteristics of a test, by comparing it to a gold standard and deriving the classic measures of association. The measure of association in cross-sectional studies is prevalence.
Methodological Design i. Formal Structure of Design - In a cross-sectional study all measurements are made at one time point. Its formal structure is similar to that of a cohort study, except for the time at which the measurements are made. - Unlike cohort studies, in cross-sectional studies there is no clear time relation between exposure and outcome. ii. -
Sample Selection Selection of the study groups naturally begins by selecting the relevant population. The next step after determining the population is to select a sample. i.e. those who will be the subjects of the study. In larger populations, sampling may be systematic or random.
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BASIC EPIDEMIOLOGY
MPH 1st Year
iii. Analysis of measures of association - The measure of association in cross-sectional studies is prevalence, the ratio between the diseased subjects at one point in time and all subjects at risk at the point of time. - Findings of prevalence surveys must be interpreted cautiously; the mere fact that two variables are associated does not mean that they are causally related.
Figure: Design of Cross-Sectional Study Advantages of cross-sectional studies - No need to wait for the outcome, and so there are no risks for loss to follow up; - It is the only study design that can determine the disease prevalence; - Sequential causal networks can be examined; - It can be accomplished in short duration of time as compared to other studies - Cross-sectional study can serve as the first step of a cohort study - Several outcomes can be studies at the same time Limitations - Impossible for rare predictors or outcomes - Impossible to establish a sequence of events - Cannot be used to calculate incidence or relative risk - Cannot establish causality or the natural history or prognosis of a disease
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BASIC EPIDEMIOLOGY
MPH 1st Year
Case-Control Studies -
-
Case-control studies provide a relatively simple way to investigate causes of diseases, especially rare diseases. In a case–control study, a number of cases and non-cases (controls) are identified, and the occurrence of one or more prior exposures is compared between groups to evaluate drug–outcome associations. A case–control study runs in reverse relative to a cohort study.
When to use a case-control design - To investigate risk factors for a rare disease where a prospective study would take too long to identify sufficient cases - To investigate an acute outbreak in order to identify causal factor quickly. Design of a case-control study i. Selection of cases - The first step in a case–control study is to identify the cases through application of explicitly defined inclusion and exclusion criteria. - Ideally, cases should be directly sampled from the source population in a manner that is unrelated to the drug exposures of interest; however, the source population that gave rise to the cases is often unknown and difficult to identify (except in a nested-case control study, where the source population is known). - The case-selection process and the data sources from which cases were selected should be described in detail, especially if cases are from a variety of sources, such as hospital and communitybased sources. - Selecting only hospital-based cases may lead to systematic error related to hospital admission practices (a phenomenon known as Berksonian bias). - Furthermore, only new (incident) cases should be selected, as non-incident cases usually overrepresent long-term survivors, and diagnostic practices may change over time, introducing potential bias. ii. -
-
Selection of controls The selection of controls in a case–control study is loaded with difficulty and is often the source of significant bias. Essentially, the controls should come from a population with the same exposure distribution as the cases. Common choices of control include • Patients in the same hospital but with unrelated disease or conditions • Patients one to one matched to controls for key prognostic factors such as age and sex. • A random sample of the population from which the cases come Clearly the best control group is the third option, but this is rarely possible. For this reason some case-control studies include more than one control group for robustness.
iii. Matching - To control for potential confounding, cases and controls are often matched on one or more patient characteristics, such as age or sex.
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BASIC EPIDEMIOLOGY
-
MPH 1st Year
The study investigator must be careful not to match on too many factors or on factors that are not confounders, as doing so might lead to overmatching and bias. Furthermore, matching should not involve variables that the investigator is intended in examining in association with an outcome.
iv. Analysis of measure of association - In a case-control study, the odds ratio is the usual measure of association reported. - This measure is the ratio of the odds of an exposure between cases and controls and in most cases approximates the relative risk.
Figure: Design of Case-Control Study Advantages - They are efficient for rare diseases or diseases with a long latency period between exposure and disease manifestation. - Cost-effective relative to other analytical studies such as cohort studies. - No problems of attrition (loss to follow up) - No risks to subjects. So, ethical problems are minimal Disadvantages - Particularly prone to bias; especially selection, recall and observer bias. - They are inefficient for rare exposures. - The temporal sequence between exposure and disease may be difficult to determine. - Selection of control group may be difficult
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BASIC EPIDEMIOLOGY
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Nested Case Control Study -
A nested case-control study is a type of case-control study that draws its cases and controls from a cohort population that has been followed for a period of time. A nested-case control study depends on the pre-existence of a cohort that has been followed over time. This cohort, at its inception or during the course of follow-up, has had exposure information collected that are of interest to the investigator. The investigator identifies cases of disease that occurred in the cohort during the follow-up period. The investigator also identifies disease-free individuals within the cohort to serve as controls. Using previously collected data and obtaining additional measurements of exposures from available bio-specimens, the investigator compares the exposure frequencies in cases and controls as in a non-nested case-control study
Figure: Design of Nested Case-Control Study Strengths of Nested Case-Control Study - It is more efficient than a cohort design. i.e. it can detect differences as statistically significant with a smaller sample size than that required for a cohort analysis. - Exposure histories are not subject to recall bias because they are determined before the cases are diagnosed. - This design also avoids the potential bias of not including fatal cases and may minimize the potential bias of non-participation, since exposure data is collected before diagnosis of disease. - It also minimizes selection bias introduced when cases and controls are not selected from the same populations. - Eliminates suspicion bias Limitations - Data on exposure must be collected on the entire cohort at baseline. Therefore the cost of data collection is likely to be higher than traditional case control study. - The time required longer and less suitable for very rare disease or those with long latent periods
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Case –Cohort Study -
The case-cohort study design was originally proposed by Prentice. Nested within a larger cohort, the study comprises a random “sub cohort” if individuals from the original cohort (sampled irrespective of disease status), together with all cases.
Design of case-cohort study - In a case-cohort study, cases are defined as those participants of the cohort who developed the disease of interest, but controls are identified before the cases develop. - This means that controls are randomly chosen from all cohort participants regardless of whether they have the disease of interest or not, and baseline data are collected early in the study. - Case-cohort studies are very similar to nested case-control studies. The main difference between nested case control study and a case-cohort study is the way in which controls are chosen.
Figure: Pictorial representation of an unstratified case cohort design Advantages - The main advantage of case-cohort study is that the same control group can be used for comparison with different case groups in a case-cohort study. - Sub- cohort can be used to calculate person time risk Limitations - The main disadvantage of the case-cohort design is that it requires a more complicated statistical analysis (Cox proportional hazards regression model) and it can be less efficient than a nested casecontrol study under some circumstances.
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Cohort Study Cohort study is a type of analytical (observational) study which is usually undertaken to obtain additional evidence to refute or support the existence of an association between suspected cause and disease. The distinguishing features of cohort study are The cohorts are identified prior to the appearance of the disease under investigation The study groups are observed over a period of time to determine the frequency of disease among them. The study proceeds forward from cause to effect. Types of cohort studies i. Prospective cohort studies - Prospective cohort study is one in which the outcome has not yet occurred at the time the investigation begins. - The study subjects are classified on the basis of presence or absence of exposure and followed up to find the development of the outcome of interest. ii. -
iii. -
Retrospective (historical) cohort study The subjects are classified on the basis of presence or absence of exposure but in this type the exposure and the outcome of interest have already occurred at the beginning of the study. A historical cohort study depends upon the availability of good data or records that allow the reconstruction of the exposure of cohorts to a suspected risk factor. Combination of retrospective and prospective cohort studies In this type of study, both the retrospective and prospective elements are combined. The cohort is identified from past records, and is assessed of date for the outcome. The same cohort is followed up prospectively into future for further assessment of outcome.
Design of cohort study i. Selection of study subjects - The subjects of a cohort study are usually assembled either from general population or select groups of the population that can be readily studies(e.g. persons with different degrees of exposures to the suspected causal factor). - The cohorts must be free from the disease under study. ii. -
Obtaining data on exposure Exposure information should be collected in such a manner that the study group can be classified according to the degree of exposure. Information about the exposure may be obtained from number of sources • From cohort members through interview or mailed questionnaire • Review of records • Medical examination or special tests • Environmental surveys
iii. Selection of comparison group - There are many ways of assembling comparison groups:
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a. Internal comparison: - A single general cohort is entered in the study then its members are classified into different exposure groups on the basis of information obtained before the development of disease. b. External comparison: - When information on degree of exposure is not available, it is necessary to put up an external control, to evaluate the experience of the exposed group. - E.g., a cohort of radiologist can be compared with cohort of ophthalmologist to investigate the effect of radiation on development of malignancy. c. -
Comparison with general population rates If none is available, the morbidity/mortality experience of the exposed group is compared with the experience of the general population in the same geographic area as the exposed people. E.g., comparison of frequency of cancer among asbestos workers with the rate in general population in the same geographic area.
iv. Follow up - At the beginning of the study, method should be developed to obtain data for assessing the outcome. - The entire study participant should be followed up from point of exposure. v. -
Analysis of measure of association For cohort studies, the exposure outcome association is usually expressed as a relative risk or incidence rates of outcome among exposed and non-exposed.
Figure: Design of Cohort Study Strengths - The temporal sequence- exposure preceding outcome is explicit in the study design - Cohort studies are relatively efficient for studying rare exposures. - Multiple outcomes may be assessed for a single exposure. - The incidence of a particular outcome among persons exposed can be directly calculated.
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Weakness - Long observation periods may be more susceptible to losses to follow-up (attrition) and to inaccurate measurement of exposures and outcomes. - They are unsuitable for investigating rare diseases or diseases with low incidence. - Since the study take place for longer periods, it is difficult to keep large number of people under surveillance indefinitely. - Cohort studies are expensive. Experimental Study -
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In an experimental study, epidemiologists evaluate the effects of an assigned intervention on an outcome; the investigators intervene in the study by influencing the exposure of the study participants. As such, experimental studies are commonly called intervention studies. Experimental study is the most useful for supporting cause-effect relationships and for evaluating the efficacy of prevention and therapeutic interventions. Major experimental study designs include the following: • Randomized controlled trials using patients as subjects (clinical trial) • Field trials in which the participants are healthy people, and • Community trials in which the participants are the communities themselves
Randomized Controlled Trial (RCT) - A randomized controlled trial is an epidemiological experiment designed to study the effects of a particular intervention, usually a treatment for a specific disease (clinical trial). - Subjects in the study population are randomly allocated to intervention and control groups, and the results are assessed by comparing outcomes. - To ensure that the groups being compared are equivalent, patients are allocated to them randomly, i.e. by chance. If the initial selection and randomization is done properly, the control and treatment groups will be comparable at the start of the investigation; any differences between groups are chance occurrences unaffected by the conscious or unconscious biases of the investigators. Design of RCT The basic steps in conducting a RCT include the following i. Writing the protocol - One of the essential features of a randomized controlled trial is that the study is conducted under a strict protocol. - The protocol specifies the aims and objectives of the study, questions to be answered, criteria for the selection of study and control groups, size of the sample, procedures for allocation of subjects and controls, treatment to be applied, etc. - The protocol aims at preventing bias and to reduce the sources of error in the study. ii.
Selecting a reference and experimental population a. Reference population - It is the population to which the findings of the trial are expected to be applied. - Reference population depends upon the nature of the study and may be geographically limited or limited to persons in specific age, sex or social groups.
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b. -
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Experimental or study population The study population is derived from the reference population. It is the actual population that participates in the experimental study. Ideally, it should be randomly chosen from the reference population, so that it has the same characteristics as the reference population.
iii. Randomization - Randomization is the heart of the randomized controlled trial. - It is the statistical procedure by which the participants are allocated into groups usually called study and control groups, to receive or not to receive an intervention. - Randomization is an attempt to eliminate bias and allow for comparability by matching both known and unknown confounders. - By randomization, every individual gets an equal chance of being allocated to either groups. iv. Follow up - This implies examination of the experimental and control group subjects at pre set conditions till the final assessment of the outcome. - The duration of follow up usually depends upon the study undertaken. - Efforts should be made to minimize the losses to follow-up. v. -
Assessment The final step is the assessment of the outcome of the trial in terms of positive and negative results. The incidence of positive/negative results is rigorously compared in both the groups and the differences, if any are tested for statistical significance.
Strengths - Excellent internal validity - Provides precise measures of efficacy and acute toxicity of new therapies under ideal conditions. - Because of randomization, measurement of effect size is less prone to bias. - Removes validity threats Weakness - Limited external validity - Patients with co-morbidity are under-represented in RCTs. - Have limited ability to detect rare and chronic toxicities, especially those that occur in patients after the completion of the trial. - Chances of experimental mortality (attrition) cannot be ruled out.
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Figure: Design of Randomized Controlled Trial (RCT) Comparison between case-control and cohort study Case-Control Study Proceeds from effect to cause Starts with the disease Tests whether the suspected cause occurs more frequently in those with the disease than among those without the disease Usually the first approach to the testing of a hypothesis, but also useful for exploratory studies Involves fewer number of subjects Yields relatively quick results Suitable for the study of rare diseases Generally yields only estimate of RR (odds ratio) Cannot yield information about diseases other than that selected for study Relatively inexpensive
Cohort Study Proceeds from cause to effect Starts with people exposed to risk factor or suspected cause Tests whether disease occurs more frequently in those exposed, than in those not exposed. Reserved for testing of precisely formulated hypothesis Involves larger number of subjects Long follow up period often needed, involving delayed results Inappropriate when the disease under investigation is rare Yields incidence rates, RR as well as AR Can yield information about more than one disease outcome Expensive
Field trials - Field trials, in contrast to clinical trials, involve people who are healthy but presumed to be at risk; data collection takes place “in the field,” usually among non-institutionalized people in the general population. - Since the subjects are disease-free and the purpose is to prevent diseases that may occur with relatively low frequency, field trials are often logistically complicated and expensive endeavours.
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One of the largest field trials was that testing the Salk vaccine for the prevention of poliomyelitis, which involved over one million children. Field trials can be used to evaluate interventions aimed at reducing exposure without necessarily measuring the occurrence of health effects. For instance, different protective methods for pesticide exposure have been tested in this way and measurement of blood lead levels in children has shown the protection provided by elimination of lead paint in the home environment. Such intervention studies can be done on a smaller scale, and at lower cost, as they do not involve lengthy follow-up or measurement of disease outcomes.
Figure: Design of field trial Community Trials - In this form of experiment, the treatment groups are communities rather than individuals. This is particularly appropriate for diseases that are influenced by social conditions, and for which prevention efforts target group behaviour. - Cardiovascular disease is a good example of a condition appropriate for community trials although unanticipated methodological issues can arise in large community intervention trials. - A limitation of such studies is that only a small number of communities can be included and random allocation of communities is usually not practicable; other methods are required to ensure that any differences found at the end of the study can be attributed to the intervention rather than to inherent differences between communities.
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Epidemiological Bias and Error Random error (chance) - Chance is a random error appearing to cause an association between an exposure and an outcome. - A principal assumption in epidemiology is that we can draw an inference about the entire population based on the evaluation of a sample of the population. - However a problem with drawing such an inference is that the play of chance may affect the results of an epidemiological study because of the effects of random variation from sample to sample. - The effect of random error may produce an estimate that is different from the true underlying value. This may result in either an underestimation or overestimation of the true value. - Because of chance, different samples will produce different results and therefore it must be taken into account when using a sample to make inferences about a population. This difference is referred to as the sampling error and its variability is measured by the standard error. - Sampling error may result in • Type I error - Rejecting the null hypothesis when it is true • Type II error - Accepting the null hypothesis when it is false There are usually two types of errors a researcher can make, Type I error and Type II error. Type I error - A type I error is characterized by the rejection of the null hypothesis when it is true and is referred by alpha (α) level. - Alpha level or the level of the significance of a test is the probability researchers are willing to take in the making of a type I error. - In public health research, alpha level is usually set at a level of 0.05 or 0.01. - Type I error can be minimized by increasing the sample size. Type II error - Type II error is the failure to reject false null hypothesis. - The probability of making a type II error is called beta (ß), and the probability of avoiding type II error is called power (1- ß). It is important to point out that both Type I and Type II errors are always going to be there in the decision making process. The situation is further complicated by the fact that a reduction in probability of committing a type I error increases the risk of committing a type II error and vice versa. Thus, researchers have to find a balanced type I and type II errors they are willing to allow for. H 0 True
H 0 false
Reject H 0
Type I error
Correct Decision
Do not reject H0
Correct decision
Type II error
Minimizing chance (sampling) error - Sampling error cannot be eliminated but with an appropriate study design can be reduced to an acceptable level. - In general, sampling error decreases as the sample size increases.
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Bias Bias may be defined as any systematic error in an epidemiological study that results in an incorrect estimate of the true effect of an exposure on the outcome of interest. - Bias results from systematic errors in the research methodology. - The effect of bias will be either above or below the true value, depending on the type of systematic error. - Limited scope exists for the adjustment of most forms of bias at the analysis stage. As a result careful consideration and control of the ways in which bias may be introduced during the design and conduct of the study is essential in order to limit the effects on the validity of the study results. Bias in epidemiological studies can broadly be grouped into three categories • Selection Bias • Information Bias • Confounding Selection Bias Selection bias is a method of participant selection that distorts the exposure-outcome relationship from that present in the target population. Selection bias occurs when the two groups being compared differ systematically. That is, there are differences in the characteristics between those who are selected for a study and those who are not selected, and where those characteristics are related to either the exposure or outcome under investigation. Common types of selection bias i. Attrition Bias (Loss to follow up) - A differential loss to follow up is problematic because it can reduce the power of the study to detect associations that are truly present and it can bias the study. - Bias will occur if loss to follow-up results in the differential risk for disease in the exposed and/or unexposed groups in the final sample than in the original cohort that was enrolled. - Bias will also occur if those who adhere to the study have a different disease risk than those who drop out or do not adhere (‘compliance bias’). ii. -
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Berksonian Bias (Hospital patient selection bias) In a case-control study, where both cases and control are selected from the hospital, the controls tend to be more likely to be exposed to the exposure under consideration than the general population from which the cases came from. This tends to cause the odds ratio to be underestimated.
iii. Healthy Worker Effect - The healthy worker effect is another form of selection bias that occurs in cohort study of occupational exposures. - The healthy worker effect occurs in these studies when the general population which consists of both healthy and ill people is selected for comparison to a relatively healthy working population. - As a result, the comparisons of outcomes will be biased.
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iv. Non response Bias: - Non-response bias occurs when a representative sample is chosen for a survey but some of the selected individuals did not participate, either because they could not be contacted or because they did not respond. - If these participants had different responses, it would not be represented and sample results would be biased. v. -
Sampling Bias: Sampling bias is systematic error due to a non-random sample of a population, causing some members of the population to be less likely to be included than others, resulting in a biased sample.
Information Bias (Measurement Bias) Information bias results from systematic differences in the way data on exposure or outcome are obtained from the various study groups. This may mean that individuals are assigned to the wrong outcome category, leading to an incorrect estimate of the association between exposure and outcome. Errors in measurement may be introduced by the observer (observer bias), the study participant (responder bias) or the instruments being used to make the measurement, such as weighing scales (instrument bias). Errors in measurement are also known as misclassifications, and the magnitude of the effect of bias depends on the type of misclassification that has occurred. There are two types of misclassification – differential and non-differential. • Differential misclassification occurs when the level of misclassification differs between the two groups • Non-differential misclassification occurs when the level of misclassification does not differ between the two groups i. -
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ii. -
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Observer Bias Observer bias occurs when there are systematic differences in the way observer collects the information for the groups being studied. This may be a result of the investigator’s prior knowledge of the hypothesis under investigation or knowledge of an individual's exposure or disease status. Such information may influence the way information is collected, measured or interpretation by the investigator for each of the study groups. For example, in a trial of a new medication to treat hypertension, if the investigator is aware which treatment arm participants were allocated to, this may influence their blood pressure measurements. Observers may underestimate the blood pressure in those who have been treated, and overestimate it in those in the control group. Recall bias In a case-control study data on exposure is collected retrospectively. The quality of the data therefore, is determined to a large extent on the patient's ability to accurately recall past exposures. Recall bias may occur when the information provided on exposure differs between the cases and controls. For example an individual with the outcome under investigation (case) may report their exposure experience differently than an individual without the outcome (control) under investigation. That is, cases may tend to have a better recall on past exposures than controls. Recall bias may result in either an underestimate or overestimate of the association between exposure and outcome.
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Confounding - In a study of the association between exposure to a cause (or risk factor) and the occurrence of disease, confounding can occur when another exposure exists in the study population and is associated both with the disease and the exposure being studied. - A problem arises if this extraneous factor is itself a determinant or risk factor for the health outcome and is unequally distributed between the exposure subgroups. - Confounding occurs when the effects of two exposures (risk factors) have not been separated and the analysis concludes that the effect is due to one variable rather than the other. - Confounding arises because non-random distribution of risk factors in the source population also occurs in the study population thus providing misleading estimates of effect. In this sense, it might appear to be a bias, but in fact it does not result from systematic error in research design. - Age and social class are often confounders in epidemiological studies. - In a study of whether Factor A is a risk factor for Disease B, X is a confounder if: • It is a risk factor for Disease B • It is associated with Factor A (but is not a result of exposure to factor A)
Example to explain confounding
While attempting to explain the relationship demonstrated between coffee drinking and the risk of coronary heart disease, tobacco use can appear as a confounding. It is known that coffee consumption is associated with tobacco use: people who drink coffee are more likely to smoke than people who do not drink coffee. Measures to control and eliminate confounding The methods commonly used to control confounding in the design of an epidemiological study are: • randomization • restriction • matching At the analysis stage, confounding can be controlled by: • stratification • statistical modeling.
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i. -
ii. -
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Randomization In experimental studies, randomization is the ideal method for ensuring that potential confounding variables are equally distributed among the groups being compared. The sample sizes have to be sufficiently large to avoid random mal-distribution of such variables. Randomization avoids the association between potentially confounding variables and the exposure that is being considered. Restriction One way to control confounding is to limit the study to people who have particular characteristics. For example, in a study on the effects of coffee on coronary heart disease, participation in the study could be restricted to nonsmokers, thus removing any potential effect of confounding by cigarette smoking.
iii. Matching - Matching is used to control confounding by selecting study participants so as to ensure that potential confounding variables are evenly distributed in the two groups being compared. - For example, in a case-control study of exercise and coronary heart disease, each patient with heart disease can be matched with a control of the same age group and sex to ensure that confounding by age and sex does not occur. - Matching has been used extensively in case-control studies. iv. Stratification - In large studies it is usually preferable to control for confounding in the analytical phase rather than in the design phase. - Confounding can then be controlled by stratification, which involves the measurement of the strength of associations in well defined and homogeneous categories (strata) of the confounding variable. - If age is a confounder, the association may be measured in, say, 10-year age groups. v. -
Statistical modeling Although stratification is conceptually simple and relatively easy to carry out, it is often limited by the size of the study and it cannot help to control many factors simultaneously, as is often necessary. In this situation, multivariate statistical modeling is required to estimate the strength of the associations while controlling for several confounding variables simultaneously A range of statistical techniques is available for these analyses.
SHORT NOTES: Stratification - Stratification allows the association between exposure and outcome to be examined within different strata of the confounding variable. For example by age, sex or alcohol consumption. - If we were conducting a study to examine the association between lung cancer and urban atmospheric pollution, controlling for smoking, the population could be stratified according to smoking status. The association between air pollution and cancer can then be assessed separately within each stratum. - Stratification allows for the assessment of modifying effects as well as controlling for confounding factors; e.g. stratification makes it possible to examine the effect of smoking on the association between atmospheric pollution and lung cancer.
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The problem with simply creating strata is that in general, strata with more individuals will tend to have a more precise estimate of the association (with a smaller standard error) than strata with fewer individuals. Therefore we calculate a weighted average where greater weight is given to the strata with more data. The most common weighting scheme used is the Mantel-Haenszel method. When using the Mantel-Haenszel method, an overall estimate of the relative risk adjusted for the potential confounder can be calculated by pooling the stratum specific rates. This gives us an overall summary measure of effect. Mantel-Haenszel estimates can be calculated using all of the usual statistical packages.
Randomization Randomization is a method of experimental control that has been extensively used in human clinical trials and other biological experiments. -
It prevents the selection bias and insures against the accidental bias. Randomization ensures that each patient has an equal chance of receiving any of the treatment under study, generate comparable intervention groups, which are alike in all the important aspects for theintervention each group receives.
Benefits of randomization - It eliminates the selection bias - It balances the groups with respect to many known and unknown confounding - It forms the basis for statistical tests Types of randomization Common randomization techniques include simple randomization, block randomization, stratified randomization and covariate adaptive randomization. i. ii. -
Simple randomization Radomization based on a single sequence of random assignments is known as simple randomization. This technique maintains complete randomness of the assignment of a subject to a particular group. Random number table can be used for simple randomization of subjects. Block randomization Block randomization method is designed to randomize subjects into groups that result in equal sample sizes. This method is used to ensure a balance in sample size across groups over time. Blocks are small and balanced with predetermined group assignments, which keeps the numbers of subjects in each group similar at all times.
iii. Stratified randomization - The stratified randomization addresses the need to control and balance the influence of covariates. - Stratified randomization can balance the control and treatment groups for age or other identified covariates.
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iv. Covariate adaptive randomization - One potential problem with small to moderate size clinical research is that simple randomization (with or without stratification) may result in imbalance of important covariates among treatment groups. - Covariate adaptive randomization has been recommended by many researchers as a valid alternative randomization for clinical research. - In covariate adaptive randomization, a new participant is sequentially assigned to a particular treatment group by taking into account the specific covariates and previous assignments of participants. Different Sources of Bias 1. Sources of Selection Bias i. Sources of selection bias in randomized controlled trials - During randomization: subversion of randomization due to inadequate concealment of allocation - During follow up: Attrition (withdrawal, loss to follow up, competing risks) ii. -
Sources of selection bias in cohort studies Bias due to non-representative “unexposed” group Bias due to non-response Bias due to attrition (loss to follow up, compliance bias)
iii. -
Sources of selection bias in case-control studies Berksonian bias Bias in selection of cases (e.g. referral bias) Bias in selection of controls
iv. Sources of bias in cross-sectional studies - Bias due to sampling • Healthy worker effect • Selection of survivors or prevalent cases • Non-random sampling schemens • Volunteer bias • Membership bias - Bias due to non-participation • Non-response bias 2. Sources of Information bias i. Sources of information bias in randomized controlled trials - Lack of blinding can cause detection bias (knowledge of intervention can influence assessment or reporting of outcomes) • Subjects (participant expectation bias) • Observer bias • Data analysts ii. -
Sources of information bias in cohort studies Misclassification of exposures at baseline Changes in exposure status over time
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Ascertainment of outcomes during follow up (which can be influenced by knowledge of exposure status: detection bias or outcome identification bias or diagnostic suspicion bias)
iii. Sources of information bias in case-control studies - Poor recall of past exposures (recall bias) - Differential recall between cases and controls (recall bias or exposure identification bias or exposure suspicion bias) - Differential ascertainment of exposure (interviewer or observer bias) Prevention of Biases i.
1 2 3
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ii.
1
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Prevention of Selection bias Types of selection Prevention measures bias Berksonian bias - Aviod selecting subjects from hospitals (in cross-sectional design) - Use population based cases and controls (in case-control design) Prevalence/ - Include non-surviving subjects in the study through proxy interviews incidence bias Detection bias - Cases and controls should be restricted to patients who have under gone identical detection manoeuveres (in case control design) - Exposed and unexposed subjects should be under identical disease detection (in cohort design) Healthy worker effect Loss-to follow up
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Use working cohorts for comparison Use multiple comparison cohorts Maintain a high follow-up rate Select motivated participants
Prevention of Information Bias Types of Prevention measures information bias Observer bias - Use of trained and experienced interviewer - Training interviewers according to standard protocols - Use of same interviewer for study and comparison groups - Blind observers to the hypothesis under investigation. - Blind investigators and participants to treatment and control group (double blind randomized controlled trial). - Use of standardized questionnaires or calibrated instruments Recall bias
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Collecting exposure data from biomarkers or from pre-existing medical records Blinding participants to the study hypothesis.
3
Diagnostic suspicion
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Both exposed and non-exposed should be observed using comparable methods
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Exposure suspicion
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Both cases and controls should be observed using comparable methods
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Causal Inference Miasmic Theory - The miasma theory (also called the miasmatic theory) held that diseases such as cholera, chlamydia, or the Black Death were caused by a miasma, a noxious form of "bad air", also known as night air. - The theory held that the origin of epidemics was due to a miasma, emanating from rotting organic matter. - The method of prevention of infectious diseases was to establish sanitary measures to clean the streets of garbage, sewage, animal carcasses and wastes. - This provided the basis for the Sanitary Movement, with great benefit to improving health conditions. - Though miasma theory is typically associated with the spread of disease, some academics in the early nineteenth century suggested that the theory extended to other conditions as well, e.g. one could become obese by inhaling the odor of food. - The miasma theory was accepted from ancient times in Europe, India, and China. The theory was eventually given up by scientists and physicians after germ theory of disease came into existence. Germ Theory of Disease Louis Pasteur and others introduced the germ theory in 1878. In 1890 Robert Koch proposed specific criteria that should be met before concluding that a disease was caused by a particular bacterium. These became known as Koch's Postulates, which are as follows: i. The bacteria must be present in each and every case of the disease (i.e., the bacteria is necessary) ii. The bacteria can occur in no other disease as a chance or non-pathogenic parasite (i.e., the bacteria is specific) iii. The agent must be isolated from the body of an infected host in pure culture and must be capable of repeatedly causing the disease in a susceptible host. (i.e. the agent is sufficient). Epidemiological Triad Model The traditional model of infectious disease causation is the epidemiological triad. - It has three components: an external agent, a susceptible host, and an environment that brings agent and host together. - In this model, the environment influences the agent, the host, and the route of transmission of the agent from a source to the host. i. -
ii. -
Agent Factors Agent originally referred to an infectious microorganism- a bacterium, virus, parasite, or other microbe. Generally, these agents must be present for disease to occur i.e. they are necessary but not always sufficient to cause disease. As epidemiology has been applied to non-infectious conditions, the concept of agent in this model has been broadened to include chemical and physical causes of disease. Host Factors Host factors are intrinsic factors that influence an individual’s exposure, susceptibility, or response to a causative agent. Age, race, sex, socioeconomic status and behaviours (smoking, lifestyle, eating habits) are some of the important host factors.
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iii. Environment - Environmental factors are extrinsic factors that affect the agent and the opportunity for exposure. - Generally, environmental factors include physical factors such as climate, physical surroundings; biological factors such as insects that transmit the agent; and socio-economic factors such as crowding, sanitation, and the availability of health services. Agent, host and environmental factors interrelate in a variety of complex ways to produce disease in humans. When we search for causal relationships, we must look at all three components and analyze their interactions. Multifactorial Causation - Pettenkofer of Munich was an early proponent of this concept. - Multifactorial causation means that the disease or trait is determined by the interaction of influences and a polygenic (many gene) predeposition.
Figure: Multi-factorial causation
Wheel Model - The wheel model is similar to the epidemiologic triad and web of causation with respect to involving multiple causality. - The wheel of causation model has a characteristic hub, which is the host. - The aspects of the model that surround the hub show the biological, social and physical determinants of disease. - This model has the ability to represent the multiple causes that are common with many diseases.
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Figure: Wheel of Causation Model Web of Causation Model - The causal web is a metaphor that emphasizes the interconnectedness of casual components in a population. - We speak of direct causes and indirect causes comprising causal webs. - Direct causes are proximal to pathogenic events. Indirect causes are distal ("upstream") from pathological events. - For example, occlusion of a coronary artery via a blood clot is a direct cause of a myocardial infarction, whereas social and environmental factors that lead to hyperlipidemia, obesity, a sedentary lifestyle, arteriosclerosis, and coronary stenosis are indirect causes. - In considering a given disease, indirect and direct causes form a hierarchical causal web, often with reciprocal relations among factors. - Levels of cause in a causal web may be classified as: • Macrolevel (including social, economic, and cultural determinants) • Individual-level (including personal, behavioral, and physiological determinants) • Microlevel (including organ system, tissue, cellular, and molecular determinants) Rothman’s Causal Pie Model - Rothman's (1976) causal pie (sufficient/component cause) model helps clarify how necessary and nonnecessary component causes contribute to disease occurrence on a population level. - The figure below shows a causal pie model representing a particular disease. - Two causal mechanism are possible, each involving multiple causal components. Factor A in this example is a necessary component cause, since it always precedes the disease. - Factors B, C, and D are non necessary component causes. - A causal complement of a factor is the factor or set of factors that completes a given causal mechanism. - In the pie, the causal complements of A are (B + C) and D; the causal complement of factor B is (A + C); the causal complement of factor C is (A + C); the causal complement of factor D is A. - Factors that work together in a given causal mechanism are said to be interdependent or interact causally. - For example, when a person develops an infectious disease, the infectious agent interacts with lack of immunity to cause disease. Smoking interacts with genetic susceptibility and other environmental carcinogens to cause lung cancer. Thus, causal interdependencies (interactions) have direct health relevance.
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Unit 4: CLINICAL EPIDEMIOLOGY Concept, Scope and Approach Clinical epidemiology is the application of epidemiological principles and methods to the practice of clinical medicine. - It usually involves a study conducted in a clinical setting, mostly often by clinicians, with patients as the subjects of study. - The discipline refines methods developed in epidemiology and integrates them with the science of clinical medicine.
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Basic Characteristics The main objective of clinical epidemiology is to develop and apply methods of clinical observation that will lead to the making of correct decisions about the care of patients.
Scope of clinical epidemiology - Definition of normality and abnormality - Accuracy of diagnostic tests (sensitivity, specificity) - Natural history and prognosis of disease - Effectiveness of treatment and - Prevention in clinical practice
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Validity and Reliability Validity Validity is an expression of the degree to which a test is capable of measuring what it is intended to measure. A study is valid if its results correspond to the truth; there should be no systematic error and the random error should be as small as possible. Broadly, there are two types of validity: i. Internal Validity ii. External Validity i. -
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Internal Validity Internal Validity is the degree to which the results of a study are correct for the sample of patients being studied. It is internal because it applies to the conditions of a particular group of persons being studied and not necessarily to others outside the study population. The internal validity of a health research is determined by how well the design, data collection, and analyses are carried out, and it is threatened by all the biases and random variation. Example of internal validity, measurements of blood haemoglobin must distinguish accurately participants with anaemia as defined in the study. Analysis of the blood in a different laboratory may produce different results because of systematic error, but the evaluation of associations with anaemia, as measured by one laboratory, may still be internally valid. There are four main types of study instrument validity. These are a) face validity; b) content validity; c) criterion validity; d) construct validity a. Face validity - Face validity is concerned with reading over a study instrument and judging whether the items (questions) on it appear to measure what they are supposed to measure. - This is the weakest form of validity. - A common strategy used by researcher in determining face validity is requesting experts to view their study instruments and obtaining a rating on a scale 1-5. An instrument receiving a rate of ≥4 would be deemed face valid. b. Content Validity - Content validity is an assessment of how well the instrument represents all the components of the variable to be measured. - The research instrument is presented to a group of experts for evaluation of the content validity of instrument. - The experts evaluate each item on the instrument with regard to the degree to which the variable to be tested is represented, as well as instrument’s overall suitability for use. c. -
Criterion validity There are two types of criterion validity: concurrent validity and predictive validity Concurrent validity is used to determine the accuracy of a data collection instrument by comparing it with another data collection instrument. To use the new instrument if it was not deemed to be concurrently valid would undermine the validity of the result of the assessment. Predictive validity indicates how accurately a measurement instrument or test will predict outcomes at a future time.
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d. Construct validity - Construct validity is the degree to which a measure correlates with other measures it is theoretically expected to correlate with. - Construct validity tests the theoretical framework within ehich the instrument is expected to perform. - An instrument that has construct validity will possess both convergent and discriminant validity. - Convergent validity requires that the measurement instrument correlate with related variables. - Discriminant validity requires that the measurement instrument not correlate with dissimilar variables. ii. -
External Validity External validity refers to the extent to which the findings of a study can be generalized to other subjects, settings and treatments. It is the degree to which the results of a study hold true in other settings. Generalizability expresses the validity of assuming that participants in a study are similar to other population. External validity requires external quality control of the measurement and judgements about the degree to which the results of a study can be extrapolated.
Reliability - Reliability is the extent to which a measurement instrument will produce the same or nearly same result each time it is used. - Reliability estimates are used to evaluate • The stability of measures administered at different times to same individuals or using same standard (test-retest reliability) • The equivalence of sets of items from the same test (internal consistency) or of different observers scoring a behavior or event using the same instrument (inter-rater reliability). - Reliability is measured by reliability coefficient which range from 0 to 1, with higher coefficient indicating higher levels of reliability. Measure of establishing reliability i. Stability (Test-retest reliability) - Stability of a measurement is determined by administering a test at two different points in time to the same individuals and determining the correlation or strength of association of the two sets of score. - The same process may be used when calibrating a medical instrument such as a scale. - The timing of second administration is critical when tests are administered repeatedly. - Ideally, the interval between administrations should be long enough that values obtained from the second administration will not be affected by the previous measurement (e.g. memory effect) ii. -
Internal consistency Internal consistency gives an estimate of the equivalence of sets of items from the same test (e.g. a set of questions aimed at assessing disease severity). The coefficient of internal consistency provides an estimate of the reliability of measurement and is based on the assumption that items measuring the same construct should correlate. Perhaps the most widely used method for estimating internal consistency is Cronbach’s alpha.
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iii. Inter- rater reliability - Interrater reliability established the equivalence of ratings obtained with an instrument when used by different observers. - If a measurement process involves judgements or ratings by observers, a reliable measurement will require consistency between different raters. - Interrater reliability requires completely independent ratings of the same event by more than one rater. - Reliability is determined the correlation of the scores from two or more independent raters. For categorical variables, Cohen’s Kappa is commonly used to determine the coefficient of agreement. Sensitivity, Specificity, Likelihood Ratio and Predictive Value Sensitivity Sensitivity is the ability of a test to correctly identify the persons who have the disease – the percentage of those who have the disease and are proven to have the disease as demonstrated by a test. Sensitivity shows the proportion of truly diseases person (true positive) in a population who underwent screening and who are correctly identified as being diseased by the screening test. 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 =
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 × 100 (𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑎𝑎𝑎𝑎 %) 𝐴𝐴𝐴𝐴𝐴𝐴 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑤𝑤𝑤𝑤𝑤𝑤ℎ 𝑡𝑡ℎ 𝑒𝑒 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
Specificity Specificity is the ability of a test to correctly identify the persons who do not have the disease- the percentage of those who do not have the disease and are proved to not have the disease as demonstrated by a test. Specificity shows the proportion of truly non-diseased persons (true negatives) in a population who underwent screening and who are correctly identified as not being diseased by the screening test. 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 =
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 × 100 (𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑎𝑎𝑎𝑎 %) 𝐴𝐴𝐴𝐴𝐴𝐴 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑤𝑤𝑤𝑤𝑤𝑤ℎ 𝑜𝑜𝑜𝑜𝑜𝑜 𝑡𝑡ℎ 𝑒𝑒 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 Diseased
Not Diseased
Test Positive
True Positive (A)
False Positive (B)
Test Negative
False Negative (C)
True Negative (D)
Total test negative (C+D)
Totals
Total diseased (A+C)
Total non-diseased (B+D)
A+B+C+D
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Totals Total test positive (A+B)
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Predictive Value of a Test - The predictive value of a screening test is the most important aspect of a test. - The ability of a test to predict the presence or absence of a disease is a determinant of test’s worth. - The predictive value of a positive test is the percentage of true positives among those individuals with positive results from the test. - The predictive value of a negative test is the percentage of non-diseased persons among those having negative test results. - The formula for the predictive values are
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝑜𝑜𝑜𝑜 𝑎𝑎 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 =
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝐷𝐷 = 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 + 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝐶𝐶 + 𝐷𝐷
Likelihood Ratio Likelihood ratios are an alternative way of describing the performance of a diagnostic test. They summarize the same kind of information as sensitivity and specificity and can be used to calculate the probability of disease after a positive or negative test. The likelihood ratio for a particular value of diagnostic test is defined as the probability of that test result in people with the disease divided by the probability of the result in the people without disease. Positive Likelihood ratio (LR+) of a test is a ratio of the proportion of diseased people with a positive test result (sensitivity) to the proportion of non-disease people with a positive result (1- specificity). 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 Likelihood 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 (𝐿𝐿𝐿𝐿 +) = 1 − 𝑆𝑆𝑆𝑆𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒
A negative Likelihood Ratio (LR-) of a test is calculated when the result is negative. In this case, it is the ratio of the proportion of diseased people with a negative test result (1- sensitivity) to the proportion of non-diseased people with a negative test result (specificity).
𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 Likelihood 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 (𝐿𝐿𝐿𝐿 −) = Normality vs Abnormality -
1 − 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆
The first priority in any clinical consultation is to develop whether the patient’s symptoms, signs or diagnostic test results are normal or abnormal. This is necessary before further action are taken. It would be easy if there were always a clear distinction between the frequency distributions of observations on normal and abnormal people. However, this is very rare, except in genetic disorders, determined by a singlr dominant gene. Three types of criteria have been used to help clinicians make practical decisions:
a. Normal as common - This definition classifies values that occur frequently as normal and those that occur infrequently as abnormal. - We assume that an arbitrary cut-off point on the frequency distribution (often two standard deviations above or below the mean) is the limit of normality and consider all values beyond this point abnormal. This is called an operational definition of abnormality.
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If the distribution is in fact Gaussian (normal in the statistical sense), we would identify 2.5% of the population as abnormal by using this cut-off. An alternative approach, which does not assume a statistically normal distribution, is to use percentiles: we can consider that the 95th percentile point is the dividing line between normal and abnormally high values, thus classifying 5% of the population as abnormal. However, there is no biological basis for using an arbitrary cut-off point as a definition of abnormality for most variables.
b. Abnormality associated with disease - The distinction between normal and abnormal can be based on the distribution of the measurements for both healthy and diseased people, and we can attempt to define a cut-off point that clearly separates the two groups. - A comparison of two frequency distributions often shows considerable overlap – as illustrated by serum cholesterol distributions for people with and without coronary heart disease. - Choosing a cut-off point that neatly separates cases from non-cases is clearly impossible. There are always some healthy people on the abnormal side of the cut-off point, and some true cases on the normal side. - These two types of classification error can be expressed quantitatively in terms of the sensitivity and specificity of a test. c. -
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Abnormal as treatable For some conditions, particularly those that are not troublesome in their own right (asymptomatic), it is better to consider a measurement abnormal only if treatment at that levels leads to a better outcome This is because not every condition that is associated with an increased risk can be successfully treated; the removal of condition may not remove risk, either because the condition itself is not a cause but is only related to a cause or because irreversible damage has already occurred. Also, to label people abnormal can cause worry and a sense of vulnerability that are not justified if treatment cannot improve the outlook.
Receiver Operating Characteristics (ROC) Curve -
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In clinical tests used to measure continuous variables such as serum calcium, blood glucose or blood pressure, the choice of the best cutoff point is often difficult. To help determine an appropriate cutoff point for a positive test, the relationship between sensitivity and specificity can be clarified by plotting a test’s true positive (sensitivity) against it false positive rate (100- specificity) for different cutoff points. Such a plot is a receiver operating characteristic (ROC) curve. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular cutoff point or decision threshold. A test that discriminates perfectly between the presence and absence of disease would have an ROC curve that passes through the upper left corner (100% sensitivity, 100% specificity) – so the closer the curve is to the upper left corner, the higher the overall accuracy of the test. A completely random test would give an ROC curve that is actually the dashed line. The shape of the curve therefore reflects the quality of the test; the better the result, the more the curve moves to the upper left. This can be quantified in terms of the area under the curve (AUC). The worst case is 0.5 (the dashed line), and the best is 1.00 (upper left-hand corner).
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A good test is one with a high rate of true positives and a low rate of false positives over a reasonable range of cutoff values. In other words, the test is good if it has a high AUC as the curve moves towards the upper left corner.
Example to explain ROC curve The figure below shows ROC curve with the thee different cutoff points for the diagnosis of diabetes (point A represents a cutoff of 160 mg/100ml, point B 126 mg/100ml and point C 100 mg/100ml). This curve has an excellent AUC of above 0.9. An alternative test for diabetes, such as test for urine glucose, would likely have a much lower rate of true positives for a given rate of false positives, as shown in the figure by dotted line, with a lower AUC. As a rule of thumb, an AUC of 0.5 to 0.6 is almost useless, 0.6 to 0.7 is poor, 0.7 to 0.8 is fair and 0.8-0.9 is very good.
UNIT 5: FIELD EPIDEMIOLOGY An outbreak is an "epidemic limited to a localized increase in the incidence of a disease" (Last 1995) and often involves infectious disease. Common types of disease outbreak in Nepal - Outbreak of vaccine preventable diseases • Measles outbreak • Diphtheria outbreak -
Vector borne disease outbreak • Outbreak of dengue • Japanese encephalitis • Scrub typhus
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Outbreak of water-borne diseases • Diarrhoeal disease outbreak • Cholera outbreak • Enteric fever
Steps of outbreak investigation i. Confirm the existence of an epidemic or an outbreak - Once it has been alerted about the unexpected increase in the number of cases of a particular disease, the first step is to make sure that the information is correct and that there truly is an outbreak to investigate. - The general rule is to compare the current rate of occurrence of the disease to what “normally” occurs so as to determine if there is a rise in cases beyond what is normally experienced. - However, for diseases not often seen in a given area, two or more cases are usually the general rule for declaring an outbreak. ii. -
Verify the diagnosis Verifying the diagnosis is done by obtaining appropriate clinical histories and proper specimens, patient and/or environmental, for laboratory study. A diagnosis might already been established as in the case when someone notices an increase in positive lab results for a certain disease. However, if the diagnosis is not clearly established, then the first step is to obtain clinical histories on the patients.
iii. Define a case - After establishing that an outbreak is occurring and attempting to verify the correct diagnosis, a crucial step is to define what constitute a case in this investigation. This is called case definition. - The case definition is then used to identify and count cases. - The common elements of a case definition include information on symptoms, laboratory results, and the essential elements of person, place and time. - Case definition are often broken into sub-categories: suspected, probable and confirmed iv. Identify and count cases a. Developing a line listing - A line listing is a simple list of case patients used to keep track of pertinent basic data for cases and potential cases as they are identified. - Cases are listed in a table with pertinent information such as age, sex, onset time and symptoms. - This permits a simple means for comparison of many characteristics at one time, giving a quick way to look for possible patterns, similarities or associations. b. Developing the questionnaire/survey - A common method of finding cases and simultaneously gathering, organizing and analyzing initial risk factor data is to conduct a questionnaire or survey among the population believed to be at risk. - This assists in developing better hypotheses about the etiologic agent’ v. -
Describing the data in terms of person, place and time The information obtained from line listing and survey is plotted on a graph to get an epidemic curve. Time is plotted on the horizontal axis and the number of cases is plotted on the vertical axis.
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The shape of the epidemic curve may suggest what kind of outbreak is occurring (whether it is a point source or is spread person to person).
vi. Develop hypotheses - Using the information gathered so far, the next step is to consider which specific exposure(s) may have caused the disease and develop a hypothesis. - To test or prove the hypothesis, analytical techniques such as statistical testing need to be applied using the data collected. vii. Evaluate hypothesis (Analyze and interpret data) - In order to evaluate a hypothesis, one must compare the hypothesis with established facts. - There are many ways to do this, including lab testing and environmental investigation, which may confirm or deny the plausibility of a given hypothesis. - The primary tools that epidemiologists use in food and waterborne outbreaks are specific study designs (case-control and cohort studies). viii. Implement control and prevention measures - Once outbreak is identified, control measures are important for interrupting disease transmission and/or limiting exposure to the source of infection. - If a pathogen or other suspected source of the outbreak is identified, control measures should target specific agents, sources or reservoirs of infection. - Control measures should begin at the first step of investigation and should occur concurrently with other investigation steps. - Non-specific control measure can be put into place regardless of the type of disease or source. ix. Communicate findings - After analysis of epidemiologic and environmental data, conclusions should be summarized in a report and sent to higher authority. - This is one of the most important steps in outbreak investigation. - Not only does the report detail the investigator’s efforts, but identifies potential source of the outbreak and suggests control measures to prevent future occurrence. Control of Outbreak/Epidemics Some of the measures for control of outbreak/epidemics include i. Controlling the source of pathogen - Removing the source of contamination - Removing persons from exposure - Inactivating or neutralizing the pathogen ii. -
Interrupting the transmission Sterilizing or interrupting environmental sources of spread (e.g., water, milk, air) Controlling mosquito or insect transmission Improving personal sanitation (e.g. hand washing)
iii. Controlling or modifying the host response to exposure - Immunizing the susceptible - Using prophylactic chemotherapy
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Roles of EDCD i. Rumor verification and situation update ii. Outbreak Investigation and response • Response at local level: o Awareness programs o Case management o Community mobilization iii. Preparedness for outbreak • Pre‐positioning of emergency health kits and medicines • Provision of buffer stock of medicines at center, region, district & community level • Coordination meeting with stakeholders Management of Malaria Outbreak In case of epidemics, following principles should be followed for proper case mamagement of malaria cases.(according to WHO guidelines) i. -
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Management of uncomplicated falciparum malaria Most malaria patients in epidemic are non-immune, partially immune or otherwise vulnerable to severe disease. An active search should be made for febrile patients to ensure that as many patients as possible receive adequate treatment, rather than relying on patients to come to a fixed clinic. The antimalarials to be used for treatment must be highly efficacious (>95% cure), safe and well tolerated so that adherence to treatment is high. •
Mass fever treatment
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Mass fever treatment is the treatment of suspected cases on clinical grounds without laboratory confirmation for each patient. This may be a temporary operational necessity in epidemic when medical staff are dealing with overwhelming malaria case-loads during a confirmed malaria epidemic When this strategy is adopted, a full treatment course should always be given.
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Management of severe falciparum malaria Managemetn of severe P. falciparum malaria in epidemic situation will often take place in situations in which intensive case monitoring is difficult. Drug treatment should therefore be as simple and safe as possible, with simple dosing and minimal need for monitoring the treatment. Intramuscular artemether with its simple one-a day regimen and ease of administration is an attractive treatment option in overburdened epidemic situations.
iii. Areas prone to mixed falciparum/vivax malaria epidemics - During mixed falciparum/vivax malaria epidemics, ACTs (except artesunate plus sulfadoxinepyrimethamine) should be used for treatment as they are highly effective against all malaria species. iv. Areas prone to vivax malaria epidemics - In areas with pure P.vivax epidemics, and where drug resistance has never been reported, chloroquine is the most appropriate medicine.
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Control and Management of Food Poisoning Outbreak i. Controlling source a. Closing food premises - If site inspections reveal a situation that poses a continuing health risks to consumers it is important to close the premises until the problems has been solved. - This should be in agreement with the existing food rules and regulations. b. Removing implicated foods from the market - The objective of food recall and food seizure is to remove implicated foods as rapidly and completely as possible from the market. - A food recall may be undertaken by the manufacturer, wholesaler or retailer under the request of authority. - Food seizure may be undertaken by removing a food product from the market. ii.
Control of transmission a. Public awareness - If a contaminated food cannot be controlled at its source, source, appropriate public awareness may be issues during a period of outbreak, for example: • Boiling of contaminated water or avoidance of chemically contaminated water. • Advice on proper preparation of foods • Emphasizing personal hygiene measures b. Exclusion of infected persons from work or school - In general, following groups with diarrhoea or vomiting should stay away from work or school until they are no longer infectious: • Food handlers • People who have direct contact with highly susceptible patients or persons in whom gastrointestinal problem would have serious consequences (elderly, immune compromised) • Children under 5 years
Emergency preparedness and post-disaster management of earthquake victims - Epidemiologists help to identify disaster-related outcomes, consider risk factors for disaster-related outcomes, and determine relevant risk factors for affected population groups. - Epidemiologists have a role to play during all phases of the disaster cycle. But the epidemiologist’s primary role is during the preparedness and response phases Emergency Preparedness - During the preparedness phase, an epidemiologist’s role is to conduct activities such as hazard mapping, translating data into policy, vulnerability analysis, educating the local community, and providing guidelines for community needs assessment and disaster-related morbidity and mortality surveillance. - In addition, epidemiologists play a vital role in providing training and building partnerships among potential disaster response agencies, health departments, national or international governmental and nongovernmental organizations, and academic institutions. - Throughout the disaster cycle, identifying the key partners in disaster response and including them in response plan development helps to smooth relationships among agencies.
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Post-disaster response - During a post earthquake situation, the assistance of an epidemiologist is usually requested. The request is typically to support immediate response efforts. - During this phase, the epidemiologist employs scientific data collection and analysis methods to conduct a rapid assessment of health and medical needs through surveys and investigations. Using information obtained through needs assessments and surveillance, the epidemiologist can make recommendations for the distribution of health resources and other resources to affected populations. i. . ii. -
Rapid needs assessment (RNA) An epidemiologist might conduct a rapid need assessment during the response phase of a disaster. Rapid assessments quickly identify a community’s basic and health needs. The assessments help determine the magnitude of a community’s needs and aid in planning and implementing relief efforts. Surveillance During a disaster response, an epidemiologist might conduct surveillance of the health problems faced by the affected populations. Morbidity surveillance detects disease outbreaks and tracks disease trends. Early detection and response can mitigate the likelihood of outbreaks. Conducting health surveillance allows for informed decisions about allocating resources, targeting interventions to meet specific needs, and planning for future disasters.
Disease Surveillance Health surveillance is the ongoing systematic collection, analysis and interpretation of health data essential for planning, implementing and evaluating public health activities. By this process, public health professionals can determine actions that are needed to prevent outbreaks and reduce the burden of diseases of communicable diseases. Disease surveillance is the responsibility of Epidemiology and Disease Control Division (EDCD) Types of disease surveillance in Nepal i. Active surveillance - Active surveillance of infectious disease is done by EDCD if an outbreak has occurred or is suspected to keep close track to the number of cases. - Designated officer regularly looks for diseases of interest using standard case definition for notifiable diseases. - One of the best examples of active surveillance is the surveillance of immunization preventable diseases. As of 2014, there were 96 active surveillance sites for surveillance of acute flaccid paralysis (AFP) surveillance and other immunization preventable diseases. ii. -
Routine Surveillance In Nepal, health staffs collect information about the number of cases of reportable, communicable disease that occur in their catchment area. On a monthly basis, they send this information to HMIS. In routine HMIS, case definitions are not strictly followed; so the quality of the data is questionable.
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iii. Sentinel Surveillance - Sentinel surveillance is the collection and analysis of data by designated institutions selected for their geographic location, medical specialty, and ability to accurately diagnose and report high quality data. - Sentinel reports can serve as a useful early warning system. - Example: National Influenza Surveillance Network (NISN) has ten sentinel sites to collect samples for influenza like illness (ILI) and Sever Acute Respiratory Infection (SARI). - Early Warning Reporting System has 60 operational hospital based sentinel sites for surveillance of six priority diseases. - There are 631 AFP surveillance sites (weekly reporting). Surveillance of Immunization Preventable Diseases in Nepal Nepal’s surveillance of immunization preventable diseases includes surveillance of acute flaccid paralysis (AFP for polio), measles, rubella, neonatal tetanus (NT) and acute enecephalitis syndrome (AES). Methods and approaches Surveillance of immunization preventable diseases is conducted by three reporting systems i. Active Surveillance by Immunization Preventable Disease Unit (WHO-IPD) - The IPD surveillance network consists of Surveillance Medical Officers (SMOs) that cover the entire country. - Each SMO covers three to seven districts and conducts case investigations, monitors and supervises AFP, NNT, measles, and AES/JE surveillance. - SMOs include surveillance for measles-like illness in their active surveillance visits and measles is also reported through the zero reporting system. - Active surveillance for neonatal tetanus is also carried out by SMOs. - There are now 96 active surveillance sites for surveillance of AFP and other immunization preventable diseases. ii. -
Routine Surveillance The Health Management Information System (HMIS) collects surveillance data from all 75 districts on a monthly basis. The system provides data for assessing disease distribution, morbidity, and mortality. Peripheral health facilities record reportable immunization preventable disease cases in their area and send the information to the District (Public) Health Offices on a monthly basis. This approach of surveillance is not timely or sensitive enough to detect sporadic cases or outbreaks for programmes with elimination (such as measles) and eradication goals (such as polio).
iii. Sentinel Surveillance (Early Warning and Reporting System) - Early Warning and Reporting System (EWARS) is a hospital based sentinel reporting system. - It monitors six priority diseases/syndromes- malaria, kala-azar, dengue, acute gastroenteritis (AGE). Cholera and sever acute respiratory infection (SARI) and epidemic prone disease like enteric fever. Additionally three vaccine preventable disease (AFP, measles and neonatal tetanus) are monitored. - The sentinel sites send weekly reports to VBDRTC where reports are consolidated and forwarded to EDCD.
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Advancement s in VPD Surveillance Surveillance for Vaccine Preventable Diseases (VPDs) started in 1996 through the Early Warning Reporting System (EWARS) under the Ministry of Health and Population (MoHP). In 1998 through the collaboration between the MoHP and WHO, Polio Eradication Nepal PEN) was established to increase the sensitivity of the existing AFP surveillance system. In 2003, measles and neonatal tetanus was integrated into AFP surveillance system. Similarly, surveillance for Japanese encephalitis (JE) was integrated in 2004. In 2005, PEN changed its name to the Programme for Immunization Preventable Diseases (IPD) to reflect its expanded activities. Approaches of Surveillance of specific immunization preventable diseases i. Measles Surveillance - Case-based measles surveillance has been integrated into the AFP surveillance activities; and SMOs include surveillance for measles-like illness in their active surveillance visits - Measles is also reported through the zero reporting system. - Usually, rash and fever cases are reported and investigated by district or hospital staff, and the SMO assists with outbreak investigation. ii. -
Neonatal tetanus surveillance Active surveillance for neonatal tetanus is also carried out by SMOs. The country has a successful MNT elimination programme, which was validated in 2005. Since 2009, all suspected cases are reported through the HMIS and clinically confirmed or discarded by the SMOs
iii. AFP Surveillance - An Early Warning and Reporting System for AFP surveillance was established in Nepal with the support of WHO in 1996. - WHO-IPD has a network of surveillance medical officer to carry out surveillance of AFP and other IPDs. - WHO has established eleven indicaors for AFP surveillance. These indicators fall into five categoris as follows: • Throughness of AFP reporting • Timeliness of reporting • Timely and adequate follow up on AFP cases • Outbreak response immunization • Timeliness of laboratory results - There are 631 weekly zero reporting sites for AFP surveillance around the country. These reporting units submit a weekly “zero” or negative case report to the concerned SMOs. - In addition to weekly zero reporting sites, there are now 89 active surveillance sites. - These are major hospitals which would more likely see AFP cases. - Surveillance monitoring officers visit these sites weekly, or biweekly or monthly depending on priority. - SMOs carry out active case search, surveillance and epidemiological investigations for AFP. Surveillance of Influenza - Influenza Surveillance started since 2004 from Jhapa, eastern part of Nepal with the aim to identify the influenza viruses from suspected cases of influenza like illness (ILI) and immediate response to minimize the circulation of viruses during outbreak. - Initially, specimens collected from suspected cases of ILI were performed by Rapid Diagnostic Test (RDT) for identification of influenza viruses.
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Later on, molecular diagnostic assay based influenza surveillance was started with the introduction of Real-Time PCR (RT-PCR) at National Public Health Laboratory (NPHL) from 2009 A National Influenza Surveillance Network (NISN) consists of ten sentinel sites around the country These sentinel sites collect samples from suspected patients meeting case definition of Influenza like Illness (ILI) and Severe Acute Respiratory Infection (SARI) for virus identification and isolation. Participating hospital laboratory perform rapid test and give immediate feedback to clinicians and dispatch samples collected in viral transport media (VTM) to NPHL maintaining cold chain within 48 hours of collection.
Early Warning and Reporting System - Early Warning Reporting System (EWARS) is a hospital-based sentinel surveillance system currently operational in 60 hospitals (out of 81) throughout Nepal (EWARS Weekly Bulletin, Nov 2016). - It was established in 1997 first in 8 sentinel sites and expanded to 24 sites in 1998, 28 sites in 2003 and 40 sites in 2008. - EWARS is designed to complement the country’s Health Management Information System (HMIS) by providing timely reporting for the early detection of six priority diseases/syndromes- malaria, kalaazar, dengue, acute gastroenteritis, cholera and severe acute respiratory infection (SARI) and other epidemic potential diseases like enteric fever. - The Vector-borne Disease Research and Training Center (VBDRTC) in Hetauda serves as a focal point for EWARS by receiving and analyzing all immediate and weekly reports directly from the sentinel hospitals. - VBDRTC then consolidates the reports and forwards weekly summaries to the Epidemiology and Disease Control Division (EDCD). EDCD in turn issues a weekly bulletin summarizing case totals and information on completeness and timeliness of reporting Objectives of EWARS - To monitor and describe trends of infectious diseases through a sentinel surveillance network of hospitals followed by public health action and research. - To receive early warning signals of disease under surveillance and to detect outbreaks - To instigate a concerted approach to outbreak preparedness, investigation and response. - To disseminate data/information on infectious diseases through an appropriate feedback system Reporting System in EWARS - Immediate reporting - The sentinel hospitals prepare immediate report within 24 hours of confirmation of diagnosis of all EWARS reportable diseases except Kala-azar. - EWARS focuses on immediate reporting of one confirmed case of Cholera, and severe and complicated Malaria and one suspect/clinical case of Dengue as well as 5 or more than 5 cases of AGE and SARI from the same geographical locality in a one week period. c. -
Weekly reporting The sentinel hospitals prepare weekly reports on the basis of epidemiological week calendar. The weekly reports consist of the number of cases and deaths of reportable disease in a particular week including those reported in immediate reports.
Strengths of EWARS - Ministry of Health has grown to accept the need and importance of EWARS. - EWARS has created awareness at the district level of what an early warning system is, how it functions and why it is vital.
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Weakness - The diseases included in EWARS are not all prone to epidemic outbreaks (e.g. Neonatal tetanus and kala-azar) - There is overlap in the reporting of VDPs with the Polio Eradication Nepal (PEN) surveillance system that has a much more extensive network of reporting sites. - Reporting loop through VBDRTC is unnecessary - A hospital based system cannt provide early warning. It is too late once a patient is admitted to a hospital.
Figure: Information flow in EWARS Screening Screening is the presumptive identification of unrecognized disease or defect by the application of tests, examinations or other procedures which can be applied rapidly. Screening tests sort out the apparently well persons who probably have a disease from those who probably do not. (Last and Spasoff 2000) Types of screening There are different types of screening, each with specific aims: - mass screening aims to screen the whole population (or subset); - multiple or multiphasic screening uses several screening tests at the same time; - targeted screening of groups with specific exposures, e.g. workers in lead battery factories, is often used in environmental and occupational health ; and - case-finding or opportunistic screening is aimed at patients who consult a health practitioner for some other purpose Screening as Public Health Program Disease screening is one of the most basic tools of modern public health and preventive medicine. Screening programs have a long and distinguished history in efforts to control epidemics of infectious diseases and targeting treatment for chronic diseases. Women in prenatal care routinely receive tests for
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complete blood count and blood type, diabetes, syphilis, and other conditions. Newborn children are routinely tested for errors of inborn metabolism and other problems. Although most of these outcomes are rare, a positive test result triggers interventions that benefit both mother and child, and these efforts have been responsible for substantial improvements in health and well-being. Examples of screening programs in Nepal i. Pap Smear Test - Pap Smear Test is the most widely implemented screening program in Nepal. - Pap smears are samples of cells taken from the cervix in women to look for cellular changes indicative of cervical cancer. - The Pap smear is an important screening test in sexually active women under the age of 65, to detect cancer at a stage when there are often no symptoms. - It is important to understand that a Pap smear may be referred to as "abnormal," but may not mean that a person has cervical cancer. - Some organizations also recommend HPV (human papilloma virus) screening in certain populations during the Pap smear. Establishment of Causal Relationship Concept of Association Association may be defined as the occurrence of two variables more often than would be expected by chance. Association does not necessarily imply a causal relationship. Association can be broadly grouped under three headings: i. Spurious or real association ii. Indirect association iii. Direct (causal) association a. One to one causal association b. Multi-factorial causation Concept of Causation In any research field involving the conduct of scientific investigations and the analysis of data derived from such investigations to test etiologic hypotheses, the assessment of causality is a complicated issue. Sufficient and Necessary Cause A cause is termed "necessary" when it must always precede an effect. This effect need not be the sole result of one cause. A cause is termed "sufficient" when it inevitably initiates or produces an effect. Any given cause may be necessary, sufficient, neither or both. Hence, there are four general conditions under which independent variable X may cause Y: Condition 1 2 3 4
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Condition 1: X is necessary and sufficient to cause Y Both X and Y are always present together, and nothing but X is needed to cause Y, X→Y. For example, the measles virus is necessary to cause measles in an unimmunized individual or population.
Condition 2: X is necessary but not sufficient to cause Y. X must be present when Y is present, but Y is not always present when X is. Some additional factor(s) must also be present; X and Z→Y. Mycobacterium tuberculosis is the necessary cause of tuberculosis but not often is not a sufficient cause without poverty, poor nutrition, overcrowding etc. Condition 3: X is not necessary but is sufficient to cause Y Y is present when X is, but X may not be present when Y is present, because Y has other causes and can occur without X. For example, an enlarged spleen can have many separate causes that are unconnected with each other; X→Y; Z→Y. Lung cancer can be caused by cigarette smoking, asbestos fibers, or radon gas. Condition 4: X is neither necessary nor sufficient to cause Y X may or may not be present when Y is present. Under this condition, however, if X is present with Y, some additional factor must also be present. Here X is a contributory cause of Y in some causal sequences. X and Z→Y; W and Z→Y Factors in Causation of Disease Four types of factors have been differentiated with respect to the causation of disease; although they are not mutually exclusive: i. ii. -
Predisposing factors: Examples include age, sex, marital status, family size, educational attainment, previous illness experience, dependency, working environment, attitude towards the use of health services. These factors may be necessary but are rarely sufficient to cause the phenomenon under study. Enabling factors Examples include income, health insurance, nutrition, climate, housing, personal support systems and availability of medical care. These factors may be necessary but are rarely sufficient to cause the phenomenon under study.
iii. Precipitating factors - Examples include exposure to specific disease, amount or level of an infectious organism, drug, noxious agent, physical trauma, personal interaction, occupational stimulus, or new awareness or knowledge. iv. Reinforcing factors - Examples include repeated exposure to the same noxious stimulus such as an infectious agent, work, household or interpersonal environment, presence of financial incentive or disincentive, personal satisfaction or deprivation.
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Establishing the cause of a disease The process of determining whether observed associations are likely to be causal is called causal inference. This includes using guidelines and making scientific judgments. The steps in assessing the nature of relationship between a possible cause and an outcome is shown in the figure below
Hill's Criteria for Establishing Cause i. Temporal Relationship - It is clear that if a factor is believed to be the cause of a disease, exposure to the factor must have occurred before the disease developed. - It is often easier to establish a temporal relationship in a prospective cohort study than in a crosssectional study, case-control study or a retrospective cohort study where measurements of the possible cause and effect are made at the same time. - In cases where the cause is an exposure that can be at different levels,it is essential that a high enough level be reached before the disease occurs for the correct temporal relationship to exist. ii. -
Biological Plausibility If the hypothesized effect makes sense in the context of current biological knowledge, this supports a causal interpretation. - However, it is important to note that the current state of biological knowledge may be inadequate to determine biological plausibility. iii. Consistency of findings - Consistency is determined by several studies giving the same result. - However, lack of consistency does not rule out causal association, because Inconsistencies may be due to different study design features. - Therefore, when the results of several studies are being interpreted the best designed study should be given the greatest weight.
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iv. Strength of Association - The strength of the association is measured by the relative risk (or odds ratio). The stronger the association, the more likely it is that the relation is causal. - Relative risks greater than 2 can be considered strong. For e.g. cigarette smokers have an approximately two fold increase in the risk of acute myocardial infarction compared with non-smokers. v. -
Dose-Response Relationship If the disease frequency increases with the dose or level of exposure, it provides a strong evidence for a causal relationship. However, the absence of a dose-response relationship does not necessarily rule out a causal relationship. In some cases in which a threshold may exist, no disease may develop up to a certain level of exposure (a threshold); above this level, disease may develop.
vi. Reversibility - When the removal of a possible cause results in a reduced disease risk, the likelihood of the association being causal is strengthened. - For example, the cessation of cigarette smoking is associated with a reduction in the risk of lung cancer than in people who continue to smoke. This finding strengthens the likelihood that cigarette smoking causes lung cancer. vii. Study Design - The ability of a study design to prove causation is a most important consideration in causal inference. - The best evidence comes from well-designed, completely conducted randomized controlled trails. viii. Judging the evidence - Since causal inference is a tentative and judgment process based on available evidences, there are no completely reliable criteria for determining whether an association is causal or not. In judging the different criteria for causation, the correct temporal relationship is essential. Once that has been established, the greatest weight may be given to plausibility, consistency and the doseeffect relationship. - The likelihood of a causal association is heightened when many different types of evidence lead to the same conclusion. Ethical Issues in Epidemiological Studies Ethical Principles The ethical principles which guide the health research and care are: i. Principle I: Respect for the Autonomy of the Participant - The obligation to respect the dignity of participating individuals in all activities of health and biomedical research is the cornerstone of research ethics. - This principle is based on the premise that an individual when informed of all aspects of an activity can decide for her/himself a correct course of action. - This requires specific attention to the following: • An individual’s right to decide what is best for her/him cannot be overruled by any consideration of person. • Researchers must actively safeguard the interests of the persons with impaired or diminished autonomy and ensure that the vulnerable people are afforded security against harm, abuse or exploitation • No researches should take precedence over respect for human rights, fundamental freedom and human dignity, and practices contrary to human dignity should be prohibited.
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Principle II: Beneficence and Non-Malfeasance The principle of beneficence requires that the research activity should benefit the participants directly or indirectly. If benefits are not obvious, the researcher should ensure that the participation in research does not lead any harm. All attempts to maximize benefits and minimize the risks should be taken by the researcher. The principle of non malfeasance proscribes those researches which are likely to cause deliberate harm to the participants.
iii. Principle III: Justice - Justice requires that persons in similar circumstances be treated alike and that differences between persons due to circumstances be acknowledged and addressed. - In the context of health research, justice requires that persons having similar health complaints or threats be treated equally. - Justice also requires the equitable distribution of the burdens and benefits of research. iv. Principle IV: - This fundamental principle is re-enforced by WMA Declaration of Helsinki, which stresses that special precautions must be exercised for the protection of the environment in the conduct of research. - Every researcher is responsible for a moral engagement to protect the social, cultural and natural heritage of communities and societies. - This responsibility includes commitment to the following: • To ensure the proper and safe disposal of biologically hazardous waste from laboratory, clinical and filed research. • To safeguard the c cultural, linguist and religious heritage of communities and individuals. • To treat biologic and genetic heritage of the people with respect and caution. This requires respecting the principles of informed consent and confidentiality of genetic data. Ethical guidelines by NHRC According to the National Ethical Guidelines 2011, all health research conducted in Nepal must have the approval of the Ethical Review Board (ERB) of the NHRC or a similar body authorized by NHRC. The following ethical guidelines are proposed for any research involving human subjects: i. Essential Research - Research involving human participants should have been considered essential for the understanding of a problem or disease process, or to identify a better diagnostic, therapeutic or preventive approach to a disease. ii. -
Voluntary participation The human participation in research must be ensured voluntarily The voluntary participation should be secured through process of providing information to participants and comprehension by participants regarding research aims, risks and benefits and understanding that the participation is with their consent.
iii. Children in health research - No research which could be done in adults should be carried out in children. - Research that are of relevance to children should be carried only after taking informed consent from parent or guardian of the child.
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iv. Pregnant women in health research - Research involving pregnant women and lactating mothers should not be carried out unless the study is related to pregnancy and lactation. v. -
Other vulnerable people in health research Special attention should be given while recruiting participants from vulnerable groups of people such as prisoners, students or military personnel or adults who are mentally challenged.
vi. Potential benefit - The participation in a research activity should be of potential benefit to the participant or to his or her community or the population in general. vii. Harm and risks - The participation in a research activity should not in any way harm the research participant. - If there are any risks involved in the research, it should be of minimal nature. viii. Compensation - The research participants should make provisions for compensating the research participants or community for the harms incurred in the research process. - Also, the researcher should make provisions to compensate the efforts and time of the participants for the purpose of the research. ix. Qualifications and competence for the research - Principal investigator of any research must have relevant qualifications and competence to conduct research. x. -
Confidentiality The research activity is carried out in such a way that the identity and data related to human participants are kept confidential as far as possible.
xi. Transfer of biological samples outside Nepal - If the health research involves the transfer of biological samples to other countries, the researcher should provide convincing reasons for the same.
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