Explain the concept of epidemiological transition? Lectures 5 and 6 Global patterns of diseases 2 Noninfectious diseases Cancer and Cardiovascular disease
This is the changes in levels and causes of mortality, which is commonly summarised as a decline in total mortality, and a significant reduction in infectious and deficiency diseases, which increase the relative role of chronic noncommunicable disease like cancer, cardiovascular diseases and diabetes. It accompanies sociodemographic and health systems changes among poorer countries but continues in more industrialised nations. The epidemiologic transition is that process by which the pattern of mortality and disease is transformed from one of high mortality among infants and children and episodic famine and epidemic affecting all age groups to one of degenerative and manmade diseases (such as those attributed to smoking) affecting principally the elderly. In demography and medical geography, epidemiological transition is a phase of development witnessed by a sudden and stark increase in population growth rates brought by medical innovation in disease or sickness therapy and treatment, followed by a releveling of population growth from subsequent declines in fertility rates. "Epidemiological transition" accounts for the replacement of infectious diseases by chronic diseases over time due to expanded public health and sanitation. During the epidemiologic transition, a longterm shift occurs in mortality and disease patterns whereby pandemics of infection are replaced by degenerative and manmade diseases…. The epidemiologic transition is that process by which the pattern of mortality and disease is transformed from one of high mortality among infants and children and episodic famine and epidemic affecting all age groups to one of degenerative and manmade diseases (such as those attributed to smoking) affecting principally the elderly.
Why does Evidence based medicine matter to clinicians?
 BETTER SERVICE FOR PATIENTS (most important reason)
 Patient Care and Safety
 Medical Knowledge Part of professional practice.
 Revalidation. Constantly have to demonstrate you are up to date and applying evidence in practice through revalidation (every 5 years for consultants).
 Professionalism
 PracticeBased Learning and Improvement
 Interpersonal and Communication skills
What is the role of EBM in clinical medicine?
The Role of Evidence Based Practice in clinical medicine

Clinical findings
 how to properly gather and interpret findings from the history and physical examination.

Aetiology
 how to identify causes for disease (including its iatrogenic forms).

Clinical manifestations of disease
 knowing how often and when a disease causes its clinical manifestations.

Differential diagnosis
 when considering the possible causes of a patient’s clinical problem, how to select those that are likely, serious and responsive to treatment.

Diagnostic tests
 how to select and interpret diagnostic tests, to confirm or exclude a diagnosis, based on considering their precision, accuracy, acceptability, expense, safety, etc.

Prognosis
 how to estimate a patient’s likely clinical course over time and anticipate likely complications of the disorder.

Therapy
 how to select treatments to offer a patient that do more good than harm and that are worth the efforts and costs of using them.

Prevention
 how to reduce the chance of disease by identifying and modifying risk factors and how to diagnose disease early by screening.
List and define the hierachy of evidence in study design?
 Systematic reviews and metaanalyses
 A way of getting around the problems of expense and needing a large sample size for Randomised Controlled Trials.
 Can use a series of smaller studies in systematic review to select trials based on quality and then do an analysis.
 Results of studies pooled to essentially give you results for a larger study.
 Randomised Controlled Trials
 Selection for people you want to experiment on.

GOLD STANDARD for clinical trials and surgical interventions.
 HOWEVER with some treatment effect might be weak so trials may need to be very large to demonstrate an effect.
 Expensive!

Cohort studies (useful for common exposures)
 Involves use of a group of people before they develop a condition. Then look at exposures and risk factors.
 They are then followed up over time to see which succumb to disease of interest. Better for common conditions.
 Less prone to bias.
 Casecontrol studies
 Cases of people with condition compared with people without the condition(controls).
 More useful for rare conditions than Cohort studies.

Ecological studies
 Type of descriptive study.
 Uses correlations between different populations, using different exposures.
 E.g. Alcohol consumption by country per capita vs liver cirrhosis rates.

Descriptive/crosssectional studies – (surveys or analysis on routinely taken data)
 Based on routinely collected data. Difficult to show causal relationship. E.g. surveys, census.
 Case report/series
 Description of single case/ series of cases.

NOT evidence used to support practice BUT sometimes useful in picking out new syndromes or conditions.
What is a systematic review and metaanalyses
 Systematic reviews and metaanalyses
 A way of getting around the problems of expense and needing a large sample size for Randomised Controlled Trials.
 Can use a series of smaller studies in systematic review to select trials based on quality and then do an analysis.
 Results of studies pooled to essentially give you results for a larger study.
A systematic review answers a defined research question by collecting and summarising all empirical evidence that fits prespecified eligibility criteria. Ametaanalysis is the use of statistical methods to summarise the results of these studies.
What is a randomised control trial?
 Selection for people you want to experiment on.

GOLD STANDARD for clinical trials and surgical interventions.
 HOWEVER with some treatment effect might be weak so trials may need to be very large to demonstrate an effect.
 Expensive!
A study in which a number of similar people are randomly assigned to 2 (or more) groups to test a specific drug, treatment or other intervention. One group (the experimental group) has the intervention being tested, the other (the comparison or control group) has an alternative intervention, a dummy intervention (placebo) or no intervention at all. The groups are followed up to see how effective the experimental intervention was. Outcomes are measured at specific times and any difference in response between the groups is assessed statistically. This method is also used to reduce bias.
What is a cohort study?

Cohort studies (useful for common exposures)
 Involves use of a group of people before they develop a condition. Then look at exposures and risk factors.
 They are then followed up over time to see which succumb to disease of interest. Better for common conditions.
 Less prone to bias.
 They are then followed up over time to see which succumb to disease of interest. Better for common conditions.
What is a case control study?
 Casecontrol studies
 Cases of people with condition compared with people without the condition(controls).
 More useful for rare conditions than Cohort studies.
What is an ecological study?

Ecological studies
 Type of descriptive study.
 Uses correlations between different populations, using different exposures.
 E.g. Alcohol consumption by country per capita vs liver cirrhosis rates.
List all the observational and experimental types of studies?
What is a decriptive crosssectional study?

Descriptive/crosssectional studies – (surveys or analysis on routinely taken data)
 Based on routinely collected data. Difficult to show causal relationship. E.g. surveys, census.
In a crosssectional study, data are collected on the whole study population at a single point in time to examine the relationship between disease (or other health related state) and other variables of interest.
What is a case report?
 Case report/series
 Description of single case/ series of cases.

NOT evidence used to support practice BUT sometimes useful in picking out new syndromes or conditions.
List possible causes for an observed association ?
( chance, bias, confounding and causation
 An association refers to the statistical dependence between 2 variables, that is the degree to which the rate of disease in persons with a specific exposure is either higher or lower than the rate of disease without that exposure
 A link, relationship or correlation.
 Association and causation chance, bias, confounding or a causal relationship (always consider the first 3 before assuming a causal relationship)
What is chance?
Chance
 Chance is a random error appearing to cause an association between an exposure and an outcome
 Most studies based on an estimate from samples rather than whole populations
 The role of chance can be assessed by performing appropriate statistical significance tests by calculating confidence intervals (p value the probability that a result could simply be due to chance, threshold is usually <0.05à ie if p<0.05 we can be sure that the result of the study is not due to chance)
 Confidence intervals; the range within which the ‘true’ value (e.g. the strength of an association) is expected to lie with a given degree of certainty (e.g. 95 % or 99%)
 If independent samples are taken repeatedly from the same population, and a confidence interval calculated for each sample, then a certain percentage (e.g. 95%0 of the intervals will include the true underlying population parameter
What is Bias?
Bias
Bias may be defined as any systematic error in an epidemiological study that results in an incorrect estimate of the association between exposure and risk of disease.
 Bias is a systematic error leading to an incorrect estimate of the effect of an exposure on the development of a disease or outcome of interest. The observed effect will be either above or below the true value, depending on the nature of the systematic error
 Bias is a consequence of defects in a design or execution of an epidemiological study
 Bias cannot be controlled in the analysis of a study, and it cannot be eliminated by increasing the sample size
 Two broad types;
 Selection 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.
N0nrepsonse bias
Healthy entrant effect e.g. healthy worker
Loss to followup (attrition bias)
 Measurement (or information) occurs when measurements or classifications of disease or exposure are inaccurate
 Recall bias
What is a confounding factor?
Confounding:
 A potential confounder is any factor which is believed to have a real effect on the risk of the disease under investigation and is also related to the risk factor under investigation
 This includes;
 Factors that have a direct causal link with the disease (e.g. smoking and lung cancer)
 Factors that re good proxy measure of more direct unknown causes (e.g. age and social class)
 Common confounders; age, sex, socioeconomic status (poorer people have rate of almost all disease), Geography (disease prevalence varies greatly by place)
What is causation?
Causation:
 Judgment based on a chain of logic that addressee 2 main areas;
 Observed association between an exposure and a disease is valid
 Totality of evidence taken from several sources supports a judgement of causality
What is measurement bias?
Measurement bias results from poorly measuring the outcome you are measuring. For example:
The survey interviewers asking about deaths were poorly trained and included deaths which occurred before the time period of interest.
This would lead to an overestimate of the mortality rate because deaths which should not be included are included.
One survey team's portable machine to measure haemoglobin malfunctioned and was not checked, as should be done every day. It measured everyone's haemoglobin as 0.3 g/L too high.
This would lead to an underestimate of the prevalence of anaemia because the readings would overestimate the haemoglobin for everyone measured by that team.
List the BradfordHill criteria for establishing causation?
Bradford Hill 1965à Factors to consider;
1)Strength
 Strength of association measured by magnitude of relative risk.
 Strong association more likely causal than weak association(likely result of confounding or bias)
 BUT weak does not mean noncausal i.e. lung cancer and smoking.
2)Consistency
 More likely to be causal if similar results in different populations using different study designs unlikely studies subject to same type of errors.
 A lack of consistency does not exclude a causal association since different exposure levels and other conditions may reduce the impact of the causal factor.
3)Specificity
 If a particular exposure increases the risk of a certain disease but not the risk of other diseases then this is strong evidence in favour of a causeeffect relationship e.g. Mesothelioma an aspestos.
 BUT onetoone relationships between exposure and disease are rare and lack of specificity should not be used to refute a causal relationship e.g. cigarette smoking causes many diseases.
4)Temporal Relationship(ONLY CRITERIA WHICH IS ABSOLUTELY NECESSARY)
 Essential criterion.
 For a putative risk factor to be the cause of a disease it has to precede the disease.
 Generally easier to establish from cohort studies but rather difficult to establish from crosssectional or casecontrol studies when measurements of the possible cause and the effect are made at the same time.
 HOWEVER, it does not follow that a reverse time order is evidence against the hypothesis.
5)Doseresponse relationship
 Further evidence of a causal relationship is provided if increasing levels of exposure lead to increasing risks of disease.
 Some causal associations, however, show a single jump (threshold) rather than a monotonic trend.
6)Plausibility
 Association is more likely to be causal if consistent with other knowledge (e.g. animal experiments, biological mechanisms, etc.).
 However, this criterion should not be taken too seriously because lack of plausibility may simply reflect lack of scientific knowledge.
 The idea of microscopic animals or animalcules as cause of disease was distinctly implausible before Van Leeuwenhoek’s microscope
7)Experimental evidence
 Experimental evidence on humans or animals.
 Evidence from human experiments is seldom available and animal research relates to different species and different levels of exposure.
8)Coherence*
 Implies that a cause and effect interpretation does not conflict with what is known of the natural history.
 BUT absence of coherent information as distinguished from the presence of conflicting information, should not be taken as evidence against an association being causal.
9)Analogy*
 At best analogy provides a source of more elaborate hypotheses about the association in question.
 Absence of such analogies only reflects lack of imagination or experience, not falsity of the hypothesis (Bradford Hill 1965).
*vague and not very important in assessing causation.
Another factor not considered by Bradford hill is reversibility (if cause is removed is the consequence affected)
Differentiate odds and probability?
The probability that an event will occur is the fraction of times you expect to see that event in many trials. Probabilities always range between 0 and The odds are defined as the probability that the event will occur divided by the probability that the event will not occur
Epidemiology – Odds Ratio (OR) The Odds Ratio is a measure of association which compares the odds of disease of those exposed to the odds of disease those unexposed.
What does an odds ratio of 1.5 mean?
It means that the odds of a case having had exposure #1 are 1.5 times the odds of its having the baseline exposure. This is not the same as being 1.5times as probable: odds are not the same as probability (odds of 2:1 againstmeans a probability of 1 ).
An odds ratio (OR) is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure.
Without going into too much detail, probability is a number between 0 and 1 that tells you the fractional likelihood that something will happen. So a probability of 0 means there’s literally no chance of that thing happening, a probability of 0.5 means there’s a 50% chance, and a probability of 1 means that it’s certain to happen. As you can see, the idea of probability is relatively simple. But the idea of odds, on the other hand, is a bit more complicated…mostly because there’s more than one way to write them. The most common way is what’s called “bookmakers odds.” For example, 31 (pronounced “three to one”) odds of a horse winning a race mean that for every four races (the total of the two numbers in 31), the horse will lose three times (the first number in 31) and win once (the second number in 31).
 The odds is another way to express probability, e.g. the odds of exposure is the number of people who have been exposed divided by the number of people who have not been exposed.
 The mathematical relationship between odds and probability is:
Odds = probability / (1 – probability)
 The relative risk is used as a measure of association between an exposure and disease.
 It is the ratio of the incidence rate in the exposed group and the incidence rate in the nonexposed group.
What is relative risk?
Calculate the relative risk from below
What does your value for realtive risk mean?
What is the relative risk?
 The relative risk is the incidence in the exposed group (noncircumcised) divided by the incidence in the nonexposed group (circumcised)
 So, 22.8/2.8 = 8.14
What is incidence?
 Overall incidence = new cases in cohort/total number in cohort (24/293) = 8.2 per 100
What is the relative risk?
 The relative risk is the incidence in the exposed group (noncircumcised) divided by the incidence in the nonexposed group (circumcised)
 So, 22.8/2.8 = 8.14
(e) What does this mean?
 The risk of HIV infection in the noncircumcised men is more than eight times higher than in circumcised men
What is incidence?
Calculate it from the table below
What is the incidence in the noncircumcised men?
What is the incidence in the circumcised men?
(a) What is incidence?
 Overall incidence = new cases in cohort/total number in cohort (24/293) = 8.2 per 100
(b) What is the incidence in the noncircumcised men?
 18/79 = 22.8 per 100
(c) What is the incidence in the circumcised men?
 6/214 = 2.8 per 100
What is prevalence?
Number of cases of a disease within a defined population measured at a specific point in time. Prevalent cases include both new (incident) and existing cases.
What is attributable risk?
 The attributable risk is a measure of exposure effect that indicates, on an absolute scale, how much greater the frequency of disease in the exposed group is compared with the unexposed, assuming the relationship between exposure and disease is causal (an important assumption).
It is the difference between the incidence rate in the exposed and non exposed groups, i.e. it represents the risk attributable to the exposure of interest
In epidemiology, attributable risk or excess risk is the difference in rate of a condition between an exposed population and an unexposed population.
if 20 out of 100 smokers got lung cancer (in a given period of time) compared with 5 out of 100 nonsmokers,
Calculate the relative risk and attibutable risk?
 if 20 out of 100 smokers got lung cancer (in a given period of time) compared with 5 out of 100 nonsmokers, the relative risk (see below) would be 20/5 = 4, but the attributable risk would be (20  5)/100 = 15 per 100.
 This may also be expressed as an excess fraction; 15 per 100/20 per 100 = 75%. Of the 20 cases of lung cancer in the smoking population, 15 of them (75%) could be attributed to smoking.
 The attributable risk is especially useful in evaluating the impact of introduction or removal of risk factors.
 Its value indicates the number of cases of the disease among the exposed group that could be prevented if the exposure were completely eliminated.
What is odds ratio?

Odds ratios The relative risk can be calculated from cohort studies, since the incidence of disease in the exposed and nonexposed is known.
 In casecontrol studies, however, the subjects are selected on the basis of their disease status (sample of subjects with a particular disease (cases) and sample of subjects without that disease (controls)), not on the basis of exposure.
 Therefore, it is not possible to calculate the incidence of disease in the exposed and nonexposed individuals.
 It is, however, possible to calculate the odds of exposure. The odds ratio (of exposure) is the ratio between two odds, e.g. the odds of exposure in the case s divided by the odds of exposure in the controls.
 This ratio is the measure reported in casecontrol studies instead of the relative risk. It can be mathematically shown that the odds ratio of exposure is generally a good estimate of the relative risk.
 An odds ratio of 1 tells us that exposure is no more likely in the cases than controls (which implies that exposure has no effect on case/control status); an odds ratio greater than 1 tells us that exposure is more likely in the case group (which implies that exposure might increase the risk of the disease).
 An odds ratio less than 1 tells us that exposure is less likely in the case group (which implies that exposure might have a protective effect).
What is sampling and sampling variation?
What is sampling and sampling variation?
 A sample is a relatively small number of observations (or patients) from which we try to describe the whole population from which the sample has been taken.
 Typically, we calculate the mean for the sample and use the confidence interval to describe the range within which we think the population mean lies.
 This is one of the absolutely key concepts behind all medical research (and much nonmedical research too).
 The sample is usually random.
What happens of p<0.05

If p<0.05 then null hypothesis rejected and the results are statistically significant and not due to chance.
Define p value?
 A pvalue is the probability of obtaining the study result (relative risk, odds ratio etc) if the null hypothesis is true.
 The smaller the pvalue, the easier it is for us to reject the null hypothesis and accept that the result was not just due to chance.
 A pvalue of <0.05 means that there is only a very small chance of obtaining the study result if the null hypothesis is true, and so we would usually reject the null.
 Such as result is commonly called “statistically significant”.
 A pvalue of >0.05 is usually seen as providing insufficient evidence against the null hypothesis, so we accept the null.

SUMMARY: If p<0.05 then null hypothesis rejected and the results are statistically significant and not due to chance.
What does a confidence interval of 95% mean?