1 / 67

EPIDEMIOLOGY REVISION LECTURE

EPIDEMIOLOGY REVISION LECTURE. Aisha Chaudry (AC7714). Focussed Revision Topics – SBA’s (12 SBA’s). Lectures 1,2, 4 & 5 - Global Patterns of disease (infectious & chronic diseases & their global caused and epidemiological transition )

rwallach
Télécharger la présentation

EPIDEMIOLOGY REVISION LECTURE

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. EPIDEMIOLOGY REVISION LECTURE Aisha Chaudry (AC7714)

  2. Focussed Revision Topics – SBA’s (12 SBA’s) • Lectures 1,2, 4 & 5 - Global Patterns of disease (infectious & chronic diseases & their global caused and epidemiological transition) • Tutorial 1 – Statistical measures of disease (including glossary, and incidence prevalence etc) • Lecture 6 - Observational Studies & Routine Data • Lecture 12 - Health promotion approaches

  3. Lecture 1 & 2 – Non-infectious disease: Cancer & Cardiovascular • Lectures 1,2, 4 & 5 - Global Patterns of disease (infectious & chronic diseases & their global caused and epidemiological transition) • Tutorial 1 – Statistical measures of disease (including glossary, and incidence prevalence etc) • Lecture 6 - Observational Studies & Routine Data • Lecture 12 - Health promotion approaches

  4. Explain the concept of epidemiological transition • 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 diseases, which increase the relative role of chronic non-communicable diseases (like cancers, CVS, chronic respiratory disease and diabetes) This transition is complex and dynamic • It is as a result of: demographic, socioeconomic, technological, cultural, environmental and biological changes • Some diseases disappear (small pox) and others appear(AIDS) or re-emerge (TB,Dengue) • There is a decline in stomach cancer, rise and fall of lung cancer and a shift from stroke to heart disease

  5. Identify the current burden of non-communicable diseases, with emphasis on cancers and cardiovascular diseases, and their worldwide patterns. Cancer • Lung, breast and colorectal are the MOST COMMONLY DIAGNOSED • Lung, liver and stomach are the MOST COMMON CAUSE OF CANCER DEATH -Burden is shifting to less developed countries. Incidence varies between populations. - ONE THIRD of cancers are likely to be preventable through a small number of lifestyle and environmental approaches  - SMOKING is the largest preventable cause of cancer in the world

  6. Cancer deaths attributable to 9 major risks: • Smoking • Low intake of fruit and veg • alcohol use • Unsafe sex • Overweight and obesity • physical inactivity • contaminated injections in healthcare • Urban air pollution • Indoor smoke from household solid fuel use - Cancer deaths can also be due to infectionse.g Hepatitis, H.pylori, HPV, EBV, HIV, Schistosomes • In 2008; cancer deaths attributable to infection were 8.1% of all cancer deaths in developed countries and 26.9% of all cancer deaths in developing countries Major known carcinogens: Tobacco, Alcohol, Air pollution, Occupational Agents, Fungus (Aspergillus)

  7. CVD • CHD and stroke rank 1st and 2nd respectively among cause-specific mortality worldwide • More deaths in developing world than developed • Low rates in Japan • High rates (and rising) in formerly socialist economies of Europe and in the Middle East • Rates are higher in men than women-although the gap is shrinking in some countries • Trends in CHD and stroke mortality have been declining in many countries recently • THREE MAIN RISK FACTORS:High blood pressure, Tobacco smoking& Cholesterol level

  8. List the commonest non-infectious causes of world mortality and some of the causes underlying their high incidence

  9. Lecture 4- Infectious disease Lecture 5AIDS-History and progression • Lectures 1,2, 4 & 5 - Global Patterns of disease (infectious & chronic diseases & their global caused and epidemiological transition) • Tutorial 1 – Statistical measures of disease (including glossary, and incidence prevalence etc) • Lecture 6 - Observational Studies & Routine Data • Lecture 12 - Health promotion approaches

  10. Identify the current burden of infectious diseases and their disparities worldwide 62 • Infectious diseases are the leading cause of death in sub-Saharan Africa 31 31 11 18 8 Deaths attributed to infectious diseases in each regions.

  11. List the six commonest infectious causes of world mortality and some of the causes underlying their high incidence Quoted in iBook page 37 (stats from 2002): 1. Lower respiratory infections- 3.9M 2. HIV/AIDS-2.8M 3. Diarrheal diseases -1.8M 4. Tuberculosis- 1.6M 5. Malaria- 1.2M 6. Measles-0.6M • Changes in Mortality and Incidence reflect changes in – Treatment, Exposure, Diagnosis and Screening i.e. We diagnose more (e.g. through screenings) and treat early therefore incidence increases and Mortality falls

  12. Define and distinguish incidence, prevalence, and mortality • CASE: Person who has the disease, health disorder or suffers the event of interest • INCIDENCE: Number of new cases of a disease within a specified time interval • PREVALENCE: Frequency of a disease in a population at a point in time (point prevalence) • MORTALITY: Number of deaths attributed to a specific condition in a given timeperiod • MORBIDITY: Number of cases of ill health, complications, side effects attributed to a specific condition over a particular time period. No. new cases in population in given time period Deaths from disease in given time period No. cases in a population No. disease free persons at beginning of time period No. of people in population Population at start of time period

  13. List the drivers of an AIDS epidemic, success and challenges of the response AIDS epidemic controlled by: • Broad access to Anti-Retroviral Therapy • Effective HIV prevention methods- Safer sex, safer injection practices, condom use and male circumcision • Decline in HIV prevalence in pregnant women • Interventions have only been able to drive HIV infection rates down to a certain level and in order to further reduce incidence, biomedical tools will be needed (an effective vaccine)  

  14. Understand the impact of prevalence incidence and how these data can be used to guide and monitor policy and interventions Prevalence/ Incidence is increasing Mortality is decreasing

  15. Understand the impact of prevalence incidence and how these data can be used to guide and monitor policy and interventions HIV infection in the UK In the UK The numbers of new cases of HIV being diagnosed each year (incidence) is rising. The numbers of deaths from AIDS has declined, due to improved treatment (HAART). Therefore the duration of disease is increasing. The consequence is a steep increase in the prevalence of HIV (the number of people living with HIV). Universal HIV testing and treatment for all ?

  16. Questions • State the most common cause of infectious disease worldwide • State where infectious diseases are most notably the leading cause of death • In 2050 there will be about 100 new cases of lung cancer in Doncaster. About 80 people will die of this disease. The population of Doncaster in 2050 is 1000. Calculate incidence and mortality. Incidence: 100 / 1000 = 1 X 10-1 = 1 in 10. Mortality: 80 / 1000 = 8 x 10-2 = 8 in 100. No. new cases in population in given time period Deaths from disease in given time period No. disease free persons at beginning of time period Population at start of time period

  17. EXAM QUESTION Q: Explain the relationship between prevalence and mortality? Answer: Define prevalence and mortality. • PREVALENCE: Frequency of a disease in a population at a point in time (point prevalence) • MORTALITY: Number of deaths attributed to a specific condition in a given timeperiod For diseases where treatment confers survival benefit (eg ART for people living with HIV) the mortality falls as people start ART. Hence these people survive and overall population level prevalence increases(fewer people dying an exiting the pool). For diseases that are rapidly fatal (e.g. Ebola) the mortality is rapid and high and so large number of cases but overall prevalence is low.

  18. Lecture 6 – Observational studies & Routine Data • Lectures 1,2, 4 & 5 - Global Patterns of disease (infectious & chronic diseases & their global caused and epidemiological transition) • Tutorial 1 – Statistical measures of disease (including glossary, and incidence prevalence etc) • Lecture 6 - Observational Studies & Routine Data • Lecture 12 - Health promotion approaches

  19. To recall the major sources of routine data on health and illness in the UK • Routine data - Data that are routinely collected and recorded in an ongoing systematic way, often for administrative or statutory purposes and without any specific research question in mind at the time of collection • E.g. Deaths, hospital admissions, screening, immunisation uptakes, census counts, GP consultation data etc. • Major sources or routine data in the UK: • 2001 Census • Health Survey for England • NHS Inpatient Survey on patient experience

  20. To critique routine health data by identifying their strengths and weaknesses Advantages • Relatively cheap • Already collected and available • Standardised collection procedures • Relatively comprehensive – population coverage, large numbers • Wide range of recorded items • Available for past years Disadvantages • May not answer the question (no information or not enough detail) • Incomplete ascertainment (not every case captured) • Variable quality (e.g. variable diagnosis fields) • Validity may be variable (i.e. do they measure what you think they measure?) • Disease labelling may vary over time or by area • Need careful interpretation

  21. To define standardised mortality ratios and demonstrate with examples their use in comparing health in populations • Standardized Mortality Ratio (SMR) is a ratio between the observed number of deaths in an study population and the number of deaths would be expected (accounting for age and often sex) • SMR = • Age Standardised Death Rates: Measuring how many people die each year and why they have died is one of the most important means of assessing the effectiveness of a country's health system.   No. of observed death No. of expected death if experienced the same age specific rates as standard population

  22. Question Calculate SMR • Observed deaths = 61 • Expected deaths = 47.7 • O/E • 61/47.7 = 1.28 • An SMR greater than 1.0 indicates that there were "excess deaths" compared to what was expected.

  23. To explain standardised mortality ratios (SMRs) and how they can be used in comparing health in populations

  24. Lecture 12 – Health Promotion Approaches • Lectures 1,2, 4 & 5 - Global Patterns of disease (infectious & chronic diseases & their global caused and epidemiological transition) • Tutorial 1 – Statistical measures of disease (including glossary, and incidence prevalence etc) • Lecture 6 - Observational Studies & Routine Data • Lecture 12 - Health promotion approaches

  25. List and define the main approaches of intervention to improve health and demonstrate with examples • Clinical intervention Biomedical (classically thought of under the category Prevention-but others can be prevention too!) • Health education Traditional type of health promotion (knowledge- attitudes-behaviour-practice). • Healthy public policy Legal, fiscal and regulatory (HIA, European directive). • Community development Individuals setting up their own initiatives

  26. List and distinguish the different levels of disease prevention and demonstrate with examples • Primordial Prevention Prevention of factors promoting the emergence of lifestyles, behaviours, exposure patterns which contribute to increased risk of disease. (E.g Smoking/Alcohol is injurious to health in movies) • Primary Prevention Actions to prevent the onset of disease. To limit exposure to risk factors by individual behaviour change and by actions in the community. Includes health promotion (e.g. health education, prescriptive diets) and specific protection (e.g. vaccination) • Secondary Prevention To halt progression once the illness is already established. Early detection followed by prompt, effective treatment. Special consideration of asymptomatic individuals. • Tertiary Prevention Tertiary: rehabilitation of people with established disease to minimise residual disability and complications. Quality of life action even if disease cannot be cured. (E.g smoking cessation)

  27. Tutorial 1 – Tools of the trade • Lectures 1,2, 4 & 5 - Global Patterns of disease (infectious & chronic diseases & their global caused and epidemiological transition) • Tutorial 1 – Statistical measures of disease (including glossary, and incidence prevalence etc) • Lecture 6 - Observational Studies & Routine Data • Lecture 12 - Health promotion approaches

  28. Define sampling and sampling variation • A sample is a group of people, objects, or items that are taken from a larger population for measurement.  • Variation is variation of observations (the data points) in a single sample. • From a sample, estimates of the true underlying risk in a population can be calculated

  29. - Define and interpret a P value and a confidence interval- Explain the role of statistical hypothesis testing and confidence intervals when dealing with chance • P-value is the probability that the null hypothesis is true • Null hypothesis is hypothesis that there is no significance • P-value– If p<0.05 statistically significant (reject null hypothesis), if p>0.05 no significant difference.

  30. - Define and interpret a P value and a confidence interval- Explain the role of statistical hypothesis testing and confidence intervals when dealing with chance • P-value– If p<0.05 statistically significant (reject null hypothesis), if p>0.05 no significant difference. • (95%) Confidence interval – If value falls between the range can say “95% sure the result is not due to chance” • Incidence – No. of new cases in a population in a given time period / No. of disease-free persons at the beginning of that time period • Prevalence - No. of cases in a population / No. of people in the population You can use either P values or confidence intervals to determine whether your results are statistically significant

  31. Questions • The P value (0.031) is less than the significance level (0.05), which indicates that our results are statistically significant

  32. Differentiate probability and odds and interpret measures of association (relative risk, attributable risk, odds ratio) from simple examples • Attributable Risk - the differencein rate of a condition between an exposed population and an unexposed population Relative Risk - the ratio of the probability of an event occurring (for example, developing a disease, being injured) in an exposed group to the probability of the event occurring in a comparison, non-exposed group. • Attributable quantifies the risk (fact / actual risk) • Relative estimates the magnitude of association.

  33. Differentiate probability and odds and interpret measures of association (relative risk, attributable risk, odds ratio) from simple examples • Attributable Risk = Incidence in the exposed – Incidence in the unexposed/100 • Relative Risk = Incidence in the exposed/ Incidence in the unexposed Cases Non- Cases • Incidence in Exposed = A/(A+B) • Incidence in Unexposed = C/(C+D) Exposed Non- Exposed

  34. Attributable risk is a subtraction of exposed and unexposed • Relative risk is a division of exposed and unexposed

  35. Worked Example 20 out of 100 smokers got lung cancer over a given period of time compared with 5 out 100 non smokers. • What is the Attributable Risk? 20/100 – 5/100 = 15/100 15 per 100 • What is the Relative Risk? 20/100 x 100/5 4 Cases Non- Cases Exposed Non-Exposed

  36. MORE DEFITINITIONS (see iBook p92-98) • Odds Ratio - Likelihood of having the exposure if you have the disease relative to the likelihood of having the exposure if you don’t have the disease

  37. Question A study looking at breast cancer in women compared cases with non cases, and found that 75/100 cases did not use calcium supplements compared with 25/100 of the non-cases. • Develop a table to display the data. • Calculate the odds of exposure in cases and non-cases. • case group: a/c = 75/25 = 3 • control group: b/d = 25/75 = 1/3 • Calculate the odds ratio using the cross-product ratio. 3 / 1/3 = 9 • What does your result show? no calcium increased risk of breast cancer. Cases Controls No calcium supplement Calcium supplement

  38. Define confounding and identify the problems associated with it. List some methods for dealing with confounding (including stratification, standardisation and regression). • Mixing of effects between exposure, the disease and a third factor. Can be dealt with at the design stage by: • Randomisation(in a randomised controlled trial), • Restrictionor matching (in a case-control study). Can be controlled for at the analysisstageby: • Stratification(splitting the analysis e.g. by age group) • Standardisation • Regression(building a statistical model)

  39. Detailed revision topics – ARQs (10 ARQ’s) • Lectures 8 &9 – Evidence-based Medicine (hierarchy of evidence, association vs causation, confounding) • Lecture 7– Clinical Trials • Lecture 14 & Tutorial 2 - Critical appraisal of medical evidence • Lecture 11- Systematic reviews & meta-analysis • Lectures 15 - Screening(definition of concepts e.g. sensitivity, PPV etc, examples of UK screening programmes

  40. Lecture 8– Why Evidence-Based Medicine & Lecture 9– Association and Causation • Lectures 8 & 9 – Evidence-based Medicine (hierarchy of evidence, association vs causation, confounding) • Lecture 7 – Clinical Trials • Lecture 14 & Tutorial 2 - Critical appraisal of medical evidence • Lecture 11- Systematic reviews & meta-analysis • Lectures 15 - Screening(definition of concepts e.g. sensitivity, PPV etc, examples of UK screening programmes

  41. Recognise the role of evidence based practice in clinical medicine • “Methods to critically appraise clinical information and classify it according to the strength of evidence” • Concepts emerging from the literature on “critical appraisal” promoted what has become known as evidence based medicine (EBM), suggesting that clinicians should use critically appraised information in clinical practice for optimal care of their patients • Concept has been evolving over last 30 years Why EBM matters to clinicians • Patient • Medical Knowledge • Practice-Based Learning and Improvement • Interpersonal and Communication skills • Professionalism

  42. List and define possible explanations for observed associations (chance, bias, confounding, causation), and cite examples of each Association refers to the statistical dependence between two variables. Consider chance, bias, confounding, cause (CBCC) • Chance – Inference from samples rather than whole populations • Bias – A systematic error (selection / measurement bias) • Confounding - Mixing between the exposure, disease and a third factor • Casual Effect -  A simple way to remember the meaning of causal effect is: B happened because of A, and the outcome of B is strong or weak depending how much of or how well A worked.

  43. Question • Difference between single and double blinding? Blinding means that the patient does not know whether they are getting the new treatment or not. In a double blind trial neither the patient nor the doctor knows which treatment they are getting. This is to prevent bias in reporting or measurement of the outcome, measurement bias. People who are getting a new treatment (or treatment compared with no treatment) often report improvement in subjective symptoms because they are enthusiastic and hopeful. Similarly if a doctor knows that a patient is on the new or active drug they may look for more improvements.

  44. List and define the hierarchy of evidence in study design 1. Systematic reviews and Meta analyses 2. Randomised control trials 3. Cohort studies (no control group) 4. Case-control studies 5. Ecological studies 6. Descriptive/cross-sectional studies 7. Case report/series

  45. List the Bradford-Hill criteria for establishing causation and demonstrate these with specific examples – iBook page 68 • Strength- Strong association is more likely to be causal , weak association more likely to be bias/confounding. • Consistency- Similar results found in different studies since it is unlikely that they were all subject to the same type of errors. • Specificity- A particular exposure increasing the risk of a certain disease but not the risk of other diseases • Temporal relationship- For a putative risk factor to be the cause of a disease, it has to precede the disease. • Dose-response relationship- Increasing levels of exposure lead to increasing risks of disease shows evidence of a causal effect. • Plausibility- Association is more likely if consistent with other knowledge • Coherence- Cause and effect interpretation does not conflict with what is known of the natural history. • Experimental evidence – On humans (rarely available) or animals • Analogy- Provides a source of more elaborate hypotheses about the association in question. Absence only reflects lack of imagination or experience.

  46. Lecture 7 – Clinical Trials • Lectures 8 & 9 – Evidence-based Medicine (hierarchy of evidence, association vs causation, confounding) • Lecture 7 – Clinical Trials • Lecture 14 & Tutorial 2 - Critical appraisal of medical evidence • Lecture 11- Systematic reviews & meta-analysis • Lectures 15 - Screening(definition of concepts e.g. sensitivity, PPV etc, examples of UK screening programmes

  47. Recognise the unique significance of, and key components in, the clinical trial design • Phase I • Test the safety of a new treatment  • Small number of, usually healthy, volunteers   • Phase II(Biological effect) • Test to see if the treatment is efficacious - at least in the short term  • Continue to look at safety  • A few hundred people usually with the condition  • Phase III(Overall effectiveness) • Compare the new treatment with the current or placebo  • Look at how well the new treatment works (effectiveness)  • Continue to monitor side effects  • Several thousand patients • Phase IV • After the drug has been marketed   • Measure effect in various populations  • Look out for rare side effects

  48. Identify the potential biases and limitations in clinical trials • Allocation Bias - Randomisation • Measurement Bias  Blinding - single, double.  • Reporting bias - Selective reporting positive trials most likely to be published negative and neutral remain unpublished -Need access to all trial data • CONSORT ( Consolidated standards of reporting trials) ensures papers about trials include all relevant info. • Sample Size

More Related