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SGUL ArabSoc Semester 2 PPD Revision Lecture
- 2. Epidemiology • “The study of the distribution and determination of diseases in human populations” • Aim to understand – Precursors of a disease – Identify its causes – With an aim of finding a solution (prevention)
- 3. Sampling • Rarely examine entire population • Takes too long, not necessary and not enough resources • We use samples to provide estimates of population parameters.
- 4. Bias • During sampling consider Bias – Sampling bias is when the sample is selected in such a way that individuals chosen are not representative of the whole study population. – How do we avoid bias?
- 5. Rate of disease • Incidence – The rate of measuring the occurrence of new cases of disease • Prevalence – The rate of measuring existing cases of disease
- 6. Question • Is this Incidence or Prevalence? – Between 2009 and 2010, 300 individuals were diagnosed with Pneumonia – On the 24th of January 2010, it was estimated that 300 people have Pneumonia. • What is the difference between Clinical epidemiology and Public Health epidemiology?
- 7. The clinical iceberg • It relates to conditions such as diabetes, hypertension and coronary heart disease. • Why? – Only a select few with the condition are known to clinical services • Unaware • No access to local GP • Time-consuming • Think it might ‘go away’
- 8. Problem with the Clinical Iceberg • Infectious disease – Subclinical infection circulation of communicable disease – Highlights importance of herd immunity • Disease morbidity – Small risk distributed throughout the whole population is a clinical risk. – Contribute substantially to burden of CV morbidity.
- 9. Communicable disease • Endemic – They persist for long periods within a community – Example? • Epidemic – The occurrence of disease episodes increases substantially for a limited period of time. – Example?
- 10. Limitations • Positive finding – Chance – Confounding – Bias • Negative finding – Insufficient power – Negative confounding – Imprecise measurements
- 11. Risk factor vs Cause • Risk factor – It is a characteristic that identifies a group at increased or reduced risk of a disease. • Cause – It is defined as something that increases the risk of a disease. Risk factors are correlational and not necessarily causal.
- 12. Question • A 74 year old Afro-Carribean female suffered a stroke. • She has high blood pressure and high cholesterol levels. • She has a family history of Stroke. • What are the modifiable and non-modifiable risk factors above?
- 13. Prevention • Depends upon: – Identification of modifiable risk factors – Causally related to the disease – And whose effects are reversible following intervention.
- 14. Primary, Secondary and Tertiary Prevention • Primary – Reduction of incidence of disease among healthy individuals e.g. vaccination. • Secondary – Early detection of preclinical disease and treatment to prevent progression e.g. screening. • Tertiary – Treatment of established disease to prevent complication.
- 15. Concept of disease • Follows the bio-psycho-social model of disease – Disease • Presence of a biological or functional abnormality – Disability • The perception of ill health – Handicap • The social consequence of disease
- 16. Population at Risk • The population at risk is made up of those person who, if were to develop the disease, would be included in the cases • Person time at risk is more useful when assessing disease incidence • Person time at risk = Sum of periods of time at risk contributed by each person in a population
- 17. Example
- 18. Disease Frequency • Prevalence – What is it? – It is simply the state of having the disease – Which type of disease would have a low prevalence? – Therefore Prevalence = Incidence x Duration • Incidence – What is it? – It refers to the event of transition between health and disease.
- 19. Measurement of Incidence • Cumulative Incidence – Number of person diseased during follow-up period / Number of persons in the population at start of follow-up period • Incidence rate – Number of persons diseased during defined period / Total person time at risk during follow up period
- 20. Measurement of Prevalence • Point prevalence – Number of diseased person in a defined population at one point in time / number of persons in population at same point in time • Period prevalence – Number of persons with an episode of illness over a defined period of time / number of persons in the population over the same period.
- 21. Mortality and Disease Outcome • Mortality Rate – Incidence rate x Case Fatality • Case Fatality – Number of deaths among person with disease over defined period of follow-up/ Number of newly incident cases of disease at start of follow up period.
- 22. Risk and Odds • Odds of disease – Number of cases / Number of non-cases • Risk of disease – Number of cases / Catchment population
- 23. Risk, Cause and Prevention • Relative Risk – This is used in longitudinal studies – It is a ratio measure including Odds ratio, Risk ratio and Rate ratio. • Excess Risk – Rate in exposed group – Rate in unexposed group • Population attributable Risk – Rate difference x Proportion exposed.
- 24. Confusion between Relative Risk and Odds Ratio • Take the following example:
- 25. Association and Causation • Two variables are associated if changing the level of one alters the level of the other. • Association does not necessarily indicate a causal link • Causality can only be inferred when chance, confounding and bias have been eliminated.
- 26. Chance • A single set of observations, even in absence of sampling bias, may misrepresent the truth because of error arising from random variation.
- 27. Confounding • To be a confounding variable a factor must: – Be associated with disease – Be associated with, but not a consequence of the exposure • Providing that potential confounding variables are identified in advance, their effect can be controlled: – Design • Matching for confounding variables • Randomisation – Data Analysis
- 28. Bias • Selection Bias – Case-control studies – lack of comparability in the method of selection of subjects into two groups to be compared – Cohort studies – differential loss to follow-up in different exposure groups • Information Bias – lack of comparability in the information obtained for two comparison groups. This is particularly relevant in which studies?
- 29. Standardisation • This is a method for allowing for differences in characteristics such as age and sex composition of populations. • This is significant because they are significant risk factors in disease and therefore influence incidence / prevalence of most diseases. • There are two methods, but Indirect standardisation is the only one discussed in your book.
- 30. Example
- 31. Example Cont’d
- 32. Study Designs • Observational – Three types • Cross sectional • Cohort • Case-control – Describing disease in a population • Interventional – Clinical Trial
- 33. Cross Sectional Study • This focuses on all persons in a defined population (or sample) and aims to determine their disease and/or exposure status at that point in time. • Vitally important to distinguish between Cohort and Case-control • It is either descriptive or analytical. – Descriptive – assessing the burden of disease in a population using – Point Prevalence – How? – Analytical – explain the observed pattern of disease by looking at possible aetiological factors using risk ratios.
- 34. Design Issues • Study Group – Study sample – Study population – Target population • Sample Size • Non respondents
- 35. Cross Sectional Study • Advantages – Multiple consequences of a given exposure can be studied – Disease prevalence is measured which is important in assessing health service burden • Disadvantages – Not suitable for rare diseases or diseases with short duration
- 36. Cohort Studies • This aims to answer the question: “Do persons with a characteristic develop the disease more frequently than those who do not have the characteristic”? • It is observational. • It can either be prospective or retrospective – Difference?
- 37. Measure of Risk from Cohort Study • Incidence Risk – Number of new cases of disease in a specified period of time / number at risk of acquiring disease at the beginning of the period • Incidence Rate • Number of new cases of disease in a specified period of time / Number of person years at risk during period. • Relative Risk – Incidence rate in exposed / incidence rate in non-exposed • Excess Risk – Rate in exposed minus rate in non-exposed
- 38. Example • A cohort study looked at shisha smoking as a risk factor for development of COPD. • Looking at the table, how would you calculate the Rate Ratio; Total Cases Non-cases Rate Rate Ratio Exposed 10000 100 9900 0.01 Unexposed 10000 10 9990 0.001
- 39. Cohort Studies • Advantages – Exposure is measured before disease onset, reducing potential for bias accuracy of causality – Multiple diseases can be studied for any one exposure – Incidence can be measured in both groups • Disadvantages – Slow, expensive and administratively difficult because needs large numbers over long time. – Losses to follow up may introduce selection bias
- 40. Cohort Design Issues • Selecting controls – Virtually identical • Measurement of exposure • Measurement of confounding • Measurement of disease outcome • Methods of follow-up • Study Size
- 41. Case Control Studies • This starts with identification of a group of cases (with a particular illness) and a group of controls (without the illness). • The prevalence of a particular exposure is then measured in the two groups and compared. • If the exposure is more common among cases than controls, it may be a risk factor, and if it is less common it may be a protective factor.
- 42. Cont’d • It is best represented in a 2x2 table; Exposed Unexposed Case A B Control C D
- 43. Advantages and Disadvantages • Advantages – Cheap, quick and efficient – Effective in looking at rare disease – Can investigate a number of exposures for rare diseases • Disadvantages – Prone to bias – No prevalence measure – Unsuitable for rare exposure.
- 44. Design Issues • Case Definition and Selection – Disease definition – Incidence v Prevalence – Exclusion criteria – Identification • Control Definition and Selection – Selection bias
- 45. Matching • Control for confounding factors. • Can either be individual or stratum • Improves statistical power, especially when variables that are matched for are strong confounders. • However, there are practical limitations on the number of variables that can be matched and a danger of over-matching.
- 46. So now… • A prospective, randomised, double-blind study was performed to compare the effects of propranolol and placebo on sudden cardiac death in a high risk group of patients who survived a myocardial infarction.”
- 47. Placebo • In intervention studies we want to be sure that any health benefits from an active treatment are truly from the active treatment and not because of the placebo effect. • The placebo effect is an alleviation of symptoms following an intervention which the patient believes to be effective against the disease, but which, in fact, is completely neutral e.g. sugar pill. • It is based on the person’s own self-healing capacity triggered by belief they are receiving medication.
- 48. Interventional Studies • This is a clinical trial designed to evaluate the effects of various treatments. • Individuals are randomly allocated to two groups. • Investigators are blinded to the allocation of individuals to minimise bias. • One group is assigned the preventative measure under investigation while the other group does not receive this intervention.
- 49. Randomisation and Blinding • Randomisation ensures that neither the observors nor the individual participating in the study can influence which is allocated to receive which intervention. • Blinding avoids prior expectations of trial participants or observors influencing the results.
- 50. Blinding and Bias • The effects of blinding of observor avoids: – Selection bias – decision to enter patient into trial. – Assessor bias – response to treatment is recorded – Bias in withdrawal of patient from a trial. • The effects of blinding of patient avoids: – Selection bias – whether to agree to participate – Response bias – way in which health outcomes are recorded by trial participants. – Bias in withdrawal from trial – When should the blinding code be revealed?
- 51. Cross Over Trial
- 52. Factorial Trial • Two or more interventions are carried out simultaneously. • Best explained with an example.
- 53. Example • Participants are randomised to receive either aspirin or placebo. • Participants are also randomised to receive behavioural intervention or standard care. • This is known as a 2x2 factorial trial. • The intention is to achieve two trials for the price of one. • The assumption is that the effects of the different active interventions are independent – no synergy.
- 54. Example Cont’d
- 55. Intention to Treat • This is an analysis in which all the participants in a trial are analysed according to the intervention to which they were allocated whether they received it or not. • Rationale: – Estimate effects of allocation of intervention in practice • Compare this to per-protocol analysis.
- 56. Per protocol analysis • An alternative is PPA aka OTA (on treament analysis) • This means limiting the analysis of the data to patients that comply with treatment. • The major problem with this is that those that comply with treatment (Hmm..) are often different in nature to those who do not. • Therefore it is difficult to decipher whether treatment effect is due to intervention or difference in make up of individuals
- 57. Number Needed to Treat • Assesses effectiveness of a health care intervention • It is the average number of patients who need to be treated to prevent one additional bad outcome. • It is described as the inverse of the absolute risk reduction. • The ideal NNT is 1 i.e. everyone improves with treatment and no-one improves with control. • They are time-specific
- 58. This is what you need to know • Absolute Risk Reduction (ARR) – Control Event Rate – Experimental Event Rate • NNT = 1 / ARR • NNT values are time specific so if a study ran for five years and the NTT value was 100 during the 5 year period, the NTT would have to be multiplied by 5 to assume the right NNT for a one year period.
- 59. Worked Example
- 60. Some things you need to cover… • Inequalities in health • The control of Communicable Disease in the UK
- 61. Tomorrow • IFP – Professionalism – Ethics – Gillick Competency – Battery / Negligence – Lots of theories! – Confidence Intervals / P values / Reference Ranges / Standard Error etc.
Notas del editor
- Cliical epidemiology is used to describe studies of disease outcome e.g. death whereas public health epidemiology is useful when referring to epidemiological studies that investigate the incidence of disease among a population of healthy individuals.
- Risk factors are correlational and not necessary causal because correlation does not prove causation.Statistical methods are frequently used to assess the strength of an association and provide causal evidence. Therefore the use of statistical analysis together with biological sciences can establish that some risk factors are causal.
- There is often in-complete overlap between these three dimensions because for example: someone may be expected to adopt a sick role without feeling ill, perhaps because screening has detected a subclinical disease which is felt to warrant treatment.
- Suppose an investigator is conducting a study on the incidence of a second MI. He follows 5 subjects from first MI for 10 weeks. The results are displayed as as shown above.The graph shows how many days each subject remained in the study as a non-case from the baseline. From the graph, the investigator can calculate person-time. Person-time is the sum of total time contrbuted by all subjects. The unit for person time in this study is person-days.Subject A, B, C, D, E = 236 person days.236 person days becomes the denominator for calculating the Incidence Rate.The numerator is the total number of subjects becoming cases = 3 = incidence measure.Therefore the incidence rate for a secondary MI is 3/236 = 0.0127 per person-day. By multiplying the numerator and denominator by a 1000, it becomes 12.7 cases per 1000 person-days. However, in order to calculate person time when an investigator is examinig patients at a specified interval e.g. yearly, the investigaotr must determine when a newly diagnosed case acquiried the disease within the last year. In order to determine the amount of person-time adequately, the investigator often assumes the onset of disease occurred at the midpoint of the time interval between being disease free and becoming a case.
- Cumulative incidence is the number of new cases within a specified time period divided by the size of the population initially at risk. For example, if a population initially contains 1000 non-diseased people and 28 develop a condition over two years, the cumulative incidence is 28 cases per 1000 people i.e. 2.8%Cumulative incidence takes no account of whether the cases arise early or late in follow up period, and is difficult to apply in a dynamic population. Therefore, the more widely sused measure is a incidence rate which can be applied to both closed cohorts or dynamic populations.
- Measure of the burden of disease in a population at a given time point…there is a clear distrinction to be made here, in that Prevalence is at a given time point whereas Incidence is a measure of change from health to disease over a given time and therefore gives an indication on the RATE of disease.Point prevalence is often readily derived from survery data provided that the disease is detectable at the time of the survey. However, certain diseases are characterised by episodicmanifestations which may not be readily decteable using Pont prevalence e.g. asthma / migraine and therefore it is more practical to measure point prevalence.
- This is survival of passengers on the Titanic, so there are 462 females of which 308 survived and 154 died. There were 851 males of which 142 survived and 709 died. Clearly the male passengers were more likely to die than a female passenger but how much more likely?Odds ratio compares the relative odds of dying in each group so for females it will be 154/308 = 0.5 and for males the odds are 709/142 = almost 5. The odds ratio is therefore 4.993/0.5 = 9.986 so there is a ten fold greater odds of death for males than females.The relative risk compares the probability of death in each group rather than the odds. For females, the probablity of death is 33% - 154/462 and for males it is 83%. The relative risk is 0.83 / 0.33 = 2.5 therefore there is a 2.5 greater probablity of death for males than females.
- Association between rise in temp and rise in drowning incident. Summer does not cause drowning. No causal link. More people swimming, more chance of drowning. Remember to take into consideration confounding variables, chance and bias.
- For example in a drug trial, treatment A may appear better than treatmet B. However, if we carried out the trial again would we obtain a similar result? Chance cannot be eliminated, but its influence can be reduced by a proper design. Given that one wishes to detect an effect of a certain magnituude one design studies to be large enough to detect any effect of interest. Having carried out a study, the degree of error associated with a particular finding can also be estimated by the appropriate use of statistics to calculate confidence limits. It is crucial to recognise the different information cnveyed by a p value (weight of evidence) and a confidence limit (magnitude of effect).However, by no means do all statistically significant associations are causal, many may be due to bias or confounding.
- In this example you can see that the crude mortality rate for Country A is higher than Country B, even though the mortality rate is lower in Country A in all three age groups. Therefore, before concluding that life in Country A is more risky than in Country B, we need to compare these rates by allowing for differences in age structure.Indirect standardisation is used when age-specific rates are unavailable. For example, say we did not have age specific mortality rates for Country B. We will calculate the number of expected deaths in Country B, if it had the same age specific-mortality rate as Country A.
- The standardised Mortality Ratio = Observed number of deaths / Expected number of deaths x 100= 160 / 100 * 100.This means that the number of observed deaths in Country B is 60% higher than the number we would expect if Country B had the same mortality experience as country A.
- Studies can be divided into two main categories; observational and interventional.Observational studies can be descriptive or analytical, with the aim of either describing a disease in a population or studying association between occurrence of disease and possible cause.Intervention studies are typically clinical trials to study the effect of a new drug or treatment.When we underatke an observational study, we aim to answer one of three questions:Do persons with the characteristic have the disease more frequently than those who do not have the characteristic?Do persons with the characteristic develop the disease more frequently than those who do not have the characteristic?Do persons with the disease have a characteristic more frequently than those without the disease.
- In order to do this; a group of invididuals are classified on the basis of their exposure to a suspected risk factor for a disease. At the time exposure status is measured, all potential subjects should be free from disease under investigation, and eligible participants are followed over a period of time to assess the occurrence of that out come. Prospective – cohort identified and exposure measured in the present and subjects are followed up whereas Retrospective – historical records are used to identify a cohort and identify occurrence of subsequent events.
- Loss to follow up bias – this occurs if the loss of follow up is associated with both exposure and outcome e.g. associated with exposed cases. This is because the people who are lost to follow up may be more likely to have developed the outcome of interest.
- The aim is to choose exposed and unexposed groups which are virtually identical with the exception of the exposure e.g. something as simple as differences in employment can have an effect on health – healthy worker effect (people who are in employment are generally healther than those who are not)An exposure is any factor being investigated as a possible prevention or cause of disease e.g. smoking.Exposures are not all or nothing and it is usually sensible to sub divide into levels of exposure e,g, assess the number of cigarettes currently smoked or in other situations such as salt intake and hypertension, the exposure of interst is continuous measure and there is no such thing as a non exposed person.Since in observational studies individuals have not been randomly assigned to the exposure of interest there are likely to be differences in the distrbution of confounding variables in exposed and non exposed subjects. Any difference in incidence rate may be due to this imbalance in counfoundingvaraibles rather than to the effect of exposure e.g. when investigating role of coffee consumption on CHD it is necssary to be sure that the exposed and non exposed groups have a similar smoking history. If not, statistical adjustment for differences in smoking will have to be made – therefore data on confounding variables are collected at the same time as exposure of interest.A major advantage of cohort studies is that a range of outcomes can be examined rather than just one as in a case control study. In developed countries – most studies use some form of routeinely collected data such as cancer registrations or cause of death from death certificates. IF the outcome of interest is not routinely recorded then it is necessary to set up a method of surveillance costlyKeeping track of large numbers of cohort members over a period of many years is often the major challenge in a highly mobile population such as the UK. A major portion of the time and cost of study staff are devoted to follow up. During the course of follow up people migrate, die, or withdraw. All of these factors may be influenced by exposure so incomplete follow up may introduce bias. In a cohort study the power of the study is dependant on the number of disease occurences in the exposed and in the non exposed populations and not on the number of subjects studied per se. Therefore study size needs to be large enough to detect any differences.
- The ratio of a/b simply describes the exposure distirbution among the cases.The ratio of c/d simply describes the exposure distribution among the controls which also estimates the ratio of exposed to unexposed in the population at risk.The ratio of a/b / c/d is known as the odds ratio and is also known as the cross-product ratio and provides an estimate of the relative risk for the assocation between exposure and disease in the general population. However, since there is no follow-up in a case control study we cannot estimate incidence of the disease.Retrospective cohort studies are quite similar to Case-control studies but the key difference is that in a retrospective cohort study, subjects are selected on the basis of their exposure status and are thus efficient for studying rare exposures whereas case-control studies select subjects on the basis of disease status ad are efficient for studying rare diseases.
- Who is included in the study? High risk patients i.e. those who had survived an MIWhat does prospective mean? It means the health outcome occurred after a period of observationWhat does Randomised mean? Individuals are allocated randomly to each study group – placebo or propranolol and assignment of patient to a group is independent of the allocation of other patients i.e. each individual has an equal chance of being assigned to either placebo or intervention. This is not the same as random sample.
- These are the key features of an intervention study.The subjects are then followed up to assess the health outcome of interest.If the outcome is death, then risk or incidence rate of death is compared in the two groups.If instead, the outcome is a measurement such as BP, then we compare the level of lood pressure between the two groups.Clearly, the purpose of this sort of trial is to conclude the potential benefits of alternative treatments and assessment of their likely side effects. It is a balance of ethics (ensuring no subject suffers as a result of experimentation) and treatment evaluation (true assessment of value of treatment)
- Randomisation ensures that the intervention and control groups are as similar as possible at baseline in terms of the types of people that end in each group i.e. differences between the groups in important variables such as age, sex, severity, prognosis arise by chance alone so that any difference in health outcome between the intervention and control groups are due to intervention and not the composition of individuals in the group.This is because when individuals pariticpate in trials, they may alter other aspects of behaviour e.g. exercise, vitamins or medication that affects prognosis. This aspect of behaviour change may be different in those that know they are on the new treatment compared to those that know they are receiving the normal treatment.
- This is if the patient develpos a serious medical problem or potential side effect to the treatment during the trial, so the blinding code can be revealed, if this is deemed to be in the patients best interest.And at the end of the study to enable analysis…
- This is used when studying short term effects e.g. reduction in BP, or cholesterol lowering drugs. This trial is arranged such that each patient receives both of the treatments one after the other in which the order of treatment assignment being random for each patient. What is the purpose of the two week ‘wash out’ period?This allows for a carry over effect of first traetment to be minmised or preferably eliminated.This type of trial is only used when the effect of treatment on health outcome is reversible
- Therefore it is based on the initial treatment intent, not on the treatment eventually administered. ITT analysis is intended to avoid misleading effects that can arise in research. For example, if people who had a more serious problem tended to drop out at a higher rate, even a completely ineffective treatment may appear to be providing benefits if one merely compares the condition before and after treatment for only those who finish treatment (ignoring those who enroled originally and have since dropped out / excluded). For the purpose of ITT analysis, everyone who begins the treatment is considered to be part of the trial whether they finish it or not.This is because this sort of trial will mirror the non compliance and treatment changes that occur when intervnetion is used in practice and it also reduces the risk of withdrawal bias by observors.
- Anyone think of examples of make-up differences which may have an effect?Social background? Economic background? Ethnic backgrounds etc.Why do you think I have said ‘Hmmm?’ under compliance? This is because it is difficult to define compliance and non-compliance as compliance is on a continous scale i.e. rarely do you have someone who is 100% compliant, or totally non compliant, so there is a need to be cautious when interpreting OTA analysis.
- This is a 2 year study that investigated the effects of different drugs on reducing the effects of stroke.What is the NNR for aspirin? And How would you calculate it?Aspirin – 80Dip – 90Asp and Dip - 18