Day 1 - Session 2 How do we measure the health of the public? An introduction to epidemiology - PowerPoint PPT Presentation

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Day 1 - Session 2 How do we measure the health of the public? An introduction to epidemiology
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Day 1 - Session 2 How do we measure the health of the public? An introduction to epidemiology

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  1. Day 1 - Session 2 How do we measure the health of the public?An introduction toepidemiology Dr Alison Hill Director, South East Public Health Observatory

  2. What is epidemiology and what are its uses? • Descriptive epidemiology • Incidence and prevalence • Qualitative information • Analytical epidemiology • Types of studies • Association and causation

  3. What is Epidemiology? “The study of the frequency, distribution and determinants of health problems and disease in human populations” The unit of interest is the population

  4. Purpose of epidemiology “To obtain, interpret and use health information to promote health and reduce disease”

  5. In the news… No evidence to support the role of antioxidant supplements in increasing the lifespan of healthy people or patients with various diseases Diagnostic heart tests are underused in older people, women, south Asians, and people from deprived areas Uptake of HPV vaccine by adolescent schoolgirls in Manchester is good overall, but lower in areas with a higher proportion of ethnic minority girls

  6. How does Epidemiology help? (1) It allows the distribution of health and illness in a population to be described in terms of: • WHERE and WHEN does this health problem occur in the population? • WHAT is the problem and its frequency? • WHO is affected? • WHY does it occur in this particular population?

  7. How does Epidemiology help? (2) Epidemiological information is used to: • Prevent illness and promote health • To help treat people with existing disease • Evaluate existing health services

  8. Descriptive Analytic Observational Experimental Epidemiological Studies

  9. What is descriptive epidemiology? Frequency (of disease) (incidence & prevalence) + Distribution (of disease) = “descriptive epidemiology”

  10. Descriptive epidemiology • Usually makes use of routinely collected data, (e.g. death certification data, hospital episode statistics, infectious disease notifications) • May require special surveys (usually cross sectional) • Can’t answer ‘why?’ but can raise a causal hypothesis • Can often provide sufficient information for public health action to be taken

  11. TIME, PLACE, PERSON • Time: Trends, seasonal variations, cohort effects… • Place: Variations between geographical areas – local, national, international… • Person: Variations in health by age, sex, ethnicity, occupation, leisure interests... • All help us examine variations (inequalities) in health

  12. Example 1: Pneumococcal meningitis incidence rate per 100,000 population by age group, England and Wales, 1996-2005 (Source HPA surveillance data)

  13. Public Health Action On 4th September 2006 Pneumococcal vaccination introduced into childhood immunisation schedule!

  14. Cumulative weekly number of reports of Invasive Pneumococcal Disease due to any of the seven serotypes in Prevenar™ : Children aged < 2 Years in England and Wales by Epidemiological Year: July-June (2003- To Date)

  15. Example 2 Why might death rates in the UK be high?

  16. Descriptive epidemiology • By studying frequency and distribution we can describe patterns of disease • This can raise further questions and help us to generate hypotheses for the causes of disease • It also helps us to respond to public health problems

  17. Measures of disease frequency There are 2 major types of measure of disease frequency: Incidence Prevalence

  18. What is incidence? The incidence is the number of NEW CASES of disease that develop, in a population of individuals at risk, during a specified time period Usually expressed as the number of new cases, per 100,000 population per year

  19. Example 3: Measuring incidence Incidence of cervical cancer in a PCT during 2002 • Number of new cases during 2002 = 18 • Number of disease-free persons (‘population at risk’) at the beginning of 2002 = 200,000 Incidence is (18/200,000) x 100,000 • 9 cases of cervical cancer per 100,000 in 2002 N.B. The denominator might be taken as the population at risk at the beginning, or the mid-point of the year, or the total person-time at risk

  20. What is prevalence? Prevalence is the total number of EXISTING CASES of disease in a population at one point in time. It is expressed as a proportion of the total number of persons in that population. Also referred to as “point prevalence” Period prevalence is a variation which represents the number of persons who were a case at any time during a specified period, as a proportion of the total number of persons in that population

  21. Prevalence • Prevalence is expressed as a proportion (0-100%) …or as a rate (e.g. X cases per 100,000 population) • It does not take into account WHEN people became infected / diseased

  22. Example 4: Labouring the point!Incidence and prevalence Cases of cold infections in class 4J. Class size: 20 January February March What is the period prevalence during February? What is the point prevalence on the 28th February? What is the incidence in February? 6/20 = 30% 1/20 = 5% 4/?

  23. Incidence and prevalence Incidence (new cases) Healthy population Sick population (Prevalence) recover die (mortality)

  24. Descriptive Analytic Observational Experimental Epidemiological Studies

  25. Analytic epidemiology Descriptive epidemiology (who, what, when) + Analysis of cause and effect (why, how) = “analytic epidemiology”

  26. Example 5: John Snow • John Snow, physician (1813-1858) • Outbreaks of Cholera were common in London during the 19th century • But what was causing the cholera? The popular theory at the time was that bad gases caused it (‘miasma’ theory)

  27. What did he do? Analysis by place: he mapped the cases – most were near Broad Street (…miasma would predict even spread) Anecdote: People had complained that the water smelt bad. Cases from further afield had water delivered by cart from Broad Street.

  28. Public health action He removed the handle from the Broad Street pump and the number of infections fell.

  29. What did he do? Recorded deaths by water supplier The Lambeth company had started to pump water from 20 miles upstream from the Thames Conclusion: Risk of infection is highest in people using water from the Southwark and Vauxhall water company.

  30. Types of analytical study Observational studies • Cross-sectional study • (may be descriptive or analytical) • Case control study • Cohort study Intervention study (experimentation) • Randomised controlled trial (RCT)

  31. Cross-sectional study • Information on health status and other characteristics is collected from each subject at one point in time • Cross-sectional studies can be descriptive… (eg. the prevalence of cough in a population) • Or analytical… (eg. the association between cough and risk factors such as type of house lived in or whether person is a smoker)

  32. Example 6: cross sectional study A cross sectional survey of adult dental health in Cornwall, in 1996. Aims: • to provide a baseline measure of dental health status (descriptive) • to compare dental health status in deprived and affluent neighbourhoods (analytical)

  33. Sampling method deprived e.d.s randomly selected e.d.s affluent e.d.s +………..….Townsend score ……...…..…- Using deprivation data from the 1991 census, participants were selected from the most deprived and the most affluent enumeration districts, and a random selection of e.d.s in between.

  34. Survey of adult dental health in Cornwall Deprived people in both age groups were more likely than affluent people to be in poor dental health.

  35. After difficulty in finding a dentist, what was the outcome? Most people in deprived areas eventually found an NHS dentist, or gave up. People in more affluent areas were more likely to pay privately for treatment. PH action: grants for service development targeting high need areas.

  36. Case-control study • Compares people with a condition (cases) to a similar group of people without the condition (controls) • The aim is to try and identify the risk factors which may have caused the cases to get the condition in the first place • Often used to investigate the source of an outbreak of disease.

  37. Example 7: Case control study What caused an outbreak of Salmonella in south east Wales? • A case control study of people in SE Wales examined their diet and behaviour during the 3 days before illness • Those who were ill were found to have been 4.5 x more likely to have eaten sliced ham than those who were not ill • Further investigations revealed that those who were ill were 25 x more likely to have eaten ham supplied by “producer A”

  38. Exposure Outcome Exposure 1 Case (Person with outcome) Exposure 2 Exposure 3 Control (Person without the outcome Exposure 4

  39. Cohort Study • Follow up two groups of people over time and compare the occurrence of disease • One group is exposed to a possible risk factor for the disease, while the other is not (the control group) • The exposure is the starting point, the disease is the outcome of interest

  40. Example 8: Cohort study • Does being exposed to asbestos cause respiratory cancer? • Asbestos miners were followed up for 6 years. These were compared to the control group • Asbestos miners were 50% more likely to die of respiratory cancer than the control group.

  41. Outcomes Exposure Exposed Outcome Population Outcome Unexposed

  42. Cohort Study (2) • Cohorts can be retrospective too • The starting point is still the EXPOSURE • Outbreak of salmonella amongst guests at a wedding • Use wedding menu to identify potential exposures and then survey the guests • Identify most likely source of the outbreak

  43. Randomised Controlled Trial • Compares effectiveness of a new intervention against the best current alternative (or a placebo) • Can be for clinical or behaviour change interventions

  44. Randomised Controlled Trial • Select people with the same disease or characteristics (a defined target population) • Randomly allocate these people to ‘intervention’ or ‘control’ groups • Intervention group receives the new treatment, the control group receives the standard or placebo treatment • The benefits of each treatment are assessed by comparing the health gain in each group

  45. Randomised controlled trial intervention group 1 Outcome population Outcome group 2 control

  46. Example 11: RCT Didgeridoo playing as alternative treatment for obstructive sleep apnoea syndrome: randomised controlled trial. Reported in BMJ Dec 2005. • 25 adults with obstructive sleep apnoea, randomised to didgeridoo instructions and daily practice for 4 months (14), or placing on the waiting list for lessons (11). • Didgeridoo players reported less daytime sleepiness and their partners reported less night time disturbance, compared with waiting list group.

  47. Vitamin D ‘can lower cancer risk’ High doses of vitamin D can reduce the risk of developing some common cancers by as much as 50%, US scientists claim. Grapefruit 'may cut gum disease' Researchers found people with gum disease who ate two grapefruits a day for a fortnight showed significantly less bleeding from the gums. In the news…..BBC website Sibling link to heart health risk Having a brother or sister with cardiovascular disease (CVD) is bad news for your own odds of developing problems, research has found. Oily fish is a source of vitamin D Grapefruit is full of vitamin C Heart disease may run in the family

  48. Interpreting results of analytical studies • No association found • Association may be artifactual (false) • Due to chance • Due to bias in the study • Association may be real, but indirect • Apparent relationship due to a confounding factor • Association is direct (causal, true)

  49. Central dogma of epidemiology An ASSOCIATION between a risk factor (smoking) and a disease (lung cancer) DOES NOT INDICATE a CAUSAL relationship

  50. Association is not proof of causeBradford Hill’s Criteria for Causation • Strength of association • Temporal relationship • Geographical distribution • Dose-response relationship • Consistency of results • Biological plausibility (but remember John Snow) • Specificity (if a single causal agent) • Reversibility