1. Basic Analytical Techniques Used in epidemiology and public health intelligence Justine Fitzpatrick

2. Acknowledgements This material is based on that produced by South West and East Midlands Public Health Observatories.

3. Overview of Session Methods of quantifying disease in populations –Absolute counts, ratio, proportion, percentage, rate, risk, odds –Special measures – incidence and prevalence Standardisation –Rationale –Direct, Indirect Advantages and disadvantages Practical Sessions

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

5. Quiz Which London boroughs had a) the highest and b) the lowest infant mortality rates in 2005-07? Which London boroughs had a) the highest and b) the lowest crude death rates in 2007? Which London boroughs had a) the highest and b) the lowest all age SMRs in 2005-07? Which London boroughs had a) the highest and b) the lowest teenage conception rates in 2004- 06?

6. Methods of quantifying disease Absolute counts Ratio Proportion Percentage Rate Risk Odds Special measures – prevalence and incidence

7. Absolute counts of disease Number of cases of disease that occurred in a specific population 100 cases of lung cancer in Area A 50 cases of lung cancer in Area B Can not conclude that lung cancer is more frequent in Area A – Need to know the size of the population and the time period involved.

8. Ratio One number divided by another. E.g. the ratio of female to male deaths. In 2005 there were a total of 512,692 deaths from all causes across all ages. –269,368 of these were deaths occurring in females. –243,324 of these were deaths occurring in males. Thus the ratio of female to male deaths is about 1.11 (269,368 / 243,324)

9. Infant mortality rate Is in fact a ratio Number of infant deaths * 1000 Number of live births

10. Infant mortality rate 2005-07

11. Definition of infant deaths Conception 24 Weeks gestation Live birth Miscarriage Stillbirths 1 Week or < 7 Days Early neonatal 28 days or 4 Weeks Late neonatal 1 Year or 12 Months Post neonatal Infant Mortality Perinatal Deaths

12. Proportion Those who are included in the numerator are also included in the denominator. E.g. the proportion of all men with a particular disease. In 2005 there were a total of 512,692 deaths from all causes across all ages. –269,368 of these were deaths occurring in females. –243,324 of these were deaths occurring in males. The proportion of deaths in 2005 that occurred among females is 0.52 (= 269,368/512,692)

13. Percentage Those who are included in the numerator are also included in the denominator. E.g. the percentage of all men with a particular disease. In 2005 there were a total of 512,692 deaths from all causes across all ages. –269,368 of these were deaths occurring in females. –243,324 of these were deaths occurring in males. The percentage of deaths in 2005 that occurred among females is 52.5% (= 269,368/512,692*100)

14. Rate Number of observed events Total number in whom this event might occur X 1,000 The denominator of a rate consists of all those, and only those, who might appear in the numerator. That is, those AT RISK. A rate specifies the time period during which the events might occur. Usually multiplied by 1,000 or 100,000 depending on the numbers involved.

15. Rate E.g. the death rate per 100,000 population at risk. In 2005 there were a total of 512,692 deaths from all causes across all ages. –269,368 of these were deaths occurring in females. –243,324 of these were deaths occurring in males. –Total population in 2005 = 53,390,244 Population at risk usually, but not always, estimated by the mid year population estimate from ONS or population projections from GLA. The death rate in 2005 = 960.3 (= 512,692/53,390,244*100,000)

16. Crude death rate Total number of deaths Total population X 1,000 The deaths and population numbers MUST be for the same time period

17. Crude death rate – What do these data show? Mid-year population Crude death rate UK59,23710.6 London7,1878.7 South West4,90111.3

18. Crude death rate 2007

19. Crude death rates – uses Looking at trends in mortality over time in a particular country or area as long as age structure is not changing To compare with crude birth rates to see if the population is likely to be increasing or decreasing NOT to be used for comparing mortality between areas, population groups or countries

20. Age-specific death rate Total number of deaths aged e.g. 25-44 Total population aged e.g. 25-44 X 1,000 The age group in the numerator and the denominator MUST be the same. The deaths and population numbers MUST be for the same time period. Can also be applied to other categories e.g. gender, country of birth, social class etc

21. Age-specific death rates – England and Wales What do these data show?

22. Teenage conception rate In 2004-2006 there were 3,232 conceptions amongst under 18s in the South West. Expressed as a rate per 1,000 females aged 15- 17. In the South West, the under-18 conception rate for the 2004-2006 period was 33.7 per 1,000 females aged 15-17. Why is the denominator females aged 15-17?

23. Teenage conception rate 2004-06

24. Prevalence Prevalence is a measure of the individuals in a population who have the disease at a specific instant. Can be expressed as a proportion, percentage or per 1,000 population. Can be point, period or lifetime prevalence Often referred to as prevalence rate, but it is not strictly speaking a rate.

25. Prevalence - Example In 24 practices in Scotland with a total male population of size 60,577 there were 577 male patients with epilepsy. Thus the prevalence of epilepsy in this population is

26. Prevalence – Example 2 In 2003-2005 –21.5% of adults in the South West smoke –15.3% of adults in the South West binge drink –25.9% of adults in the South West eat healthily –12.6% of adults in the South West were physically active –23.2% of adults in the South West were obese Source: Health Profiles (using Health Survey for England)

27. Incidence Incidence quantifies the number of new cases of disease that develop in a population of individuals at risk during a specified time period. Usually expressed as a rate Incidence = number new cases x 1,000 population at risk Can also be expressed as a risk or odds The denominator “population at risk” should consist of the entire population in which new cases can occur.

28. Incidence – things to remember Those having the disease or those who cannot develop the disease because of age or immunisation should not be included in the denominator. When persons not at risk are included in the denominator, the resulting rate underestimates the true incidence. If the proportion not at risk is relatively small then including these persons in the denominator won ’ t significantly influence the incidence rate.

29. Example In 24 practices in Scotland with a total male population of size 60,577 there were 165 new patients in one year with epilepsy.

30. Labouring the point! Incidence and prevalence – Questions? Cases of cold infections in class 4J. Class size: 20 JanuaryFebruaryMarch

31. How are incidence and prevalence related? Diseases with a low incidence rate but a long duration e.g. diabetes or asthma, the prevalence will be high relative to the incidence. If the incidence of a disease is high, but it has a short duration, the prevalence will be low relative to the incidence. Prevalence = incidence * average duration

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

33. However… Changes in prevalence over time can be due to changes in incidence rates and/or changes in the duration of the disease. –Decreasing incidence rates due to new preventive measures might result in a low incidence in a population where the prevalence is higher. –Preventive measures may also increase the chance of survival for those already with the disease, thus affecting prevalence

34. When to use incidence or prevalence Prevalence descriptive studies can describe extent of a particular disease in a community can predict the health care requirements Incidence studying aetiology can establish the sequence of events not susceptible to bias by survival

35. Exercise 1 Calculate the prevalence (%) of diabetes, coronary heart disease and hypertension using the QOF data for each practice and for each PCT

36. Exercise 2 Calculate the age specific diabetes prevalence for practices in Somerset

37. Age standardisation Occurrence of disease in one area may appear to be higher than in another because: – population structures are different –one area is older than another Standardisation used to adjust for the effects of age on mortality rates or other rates Direct or Indirect Involves the calculation of numbers of expected events which are then compared with numbers of observed events.

38. Example of London and Camden LondonCamden AgePopDeathASDR per 1,000 PopDeathsASDR per 1,000 0-4500,0005,00010.08,00011013.8 5-141,500,0005,0003.327,0001053.9 15-443,000,00010,0003.3115,0004003.5 45-642,000,00015,0007.540,0003508.8 65+1,000,00025,00025.010,00033533.5 Total8,000,00060,0007.5200,0001,3006.5

39. Percentage of the population by age group

40. The problem… The crude rates are not comparable because the age structure of the populations are different What would the expected number of deaths be in London and Camden if the age structures were the identical? This is DIRECT STANDARDISATION called DIRECT STANDARDISED RATES What would the expected number of deaths be in London and Camden if the age specific rates were identical? This is INDIRECT STANDARDISATION called STANDARDISED MORTALITY RATIO

41. Direct standardisation – method 1.Decide which standard population to use – EUROPEAN STANDARD POPULATION 2.Calculate expected deaths if both London and Camden had the same population structure as the European standard. 3.Express as a rate per 1,000 or 100,000 population

42. European population structure

43. Direct standardisation Example of London and Camden LondonCamden Age ASDR per 1,000 A Euro pop B Expected deaths A*B/1000 ASDR per 1,000 A Euro pop B Expected deaths A*B/1000 0-410.010,000100.013.810,000138.0 5-143.310,00033.03.910,00039.0 15-443.335,000115.53.535,000122.5 45-647.525,000187.58.825,000220.0 65+25.020,000500.033.520,000670.0 Total7.5100,000  936 6.5100,000  1189.5 DSR936/100,000 * 1,000 = 9.41189.5/100,000 * 1,000 = 11.9

44. Indirect standardisation – method 1.Decide which standard rates to use – AGE SPECIFIC MORTALITY RATES FOR ENGLAND 2.Calculate expected deaths if both London and Camden had the same age specific rates as England. 3.Express as a ratio of observed to expected deaths multiplied by 100 – standardised mortality ratio SMR = O * 100 E

45. Indirect standardisation Example of London and Camden LondonCamden Age England ASDR A Pop B Expected deaths A*B/1000 England ASDR A Pop B Expected deaths A*B/1000 0-44.0500,00020004.08,00032 5-140.11,500,0001500.127,0003 15-441.13,000,00033001.1115,000127 45-647.52,000,000149007.540,000298 65+45.01,000,0004500045.010,000450 Total8,000,000  65350 200,000  909 SMR60,000/65,350 * 100 = 921,300/909 * 100 = 143 SMR for England = 100

46. Interpretation of SMRs/SIRs SMR < 100 : lower rate than expected SMR = 100 : Expected/standard rate SMR > 100 : higher rate than expected An SMR of 180 represents a mortality rate that is 80% higher than expected.

47. Standardised mortality ratio 2005-07

48. Which method to use? If want to compare several population groups or several time periods use DIRECT as with INDIRECT can only compare each population group to the standard. INDIRECT is useful to determine if disease incidence is high or low in one area only. If age specific rates for the population groups are not available or unreliable use INDIRECT. If it is a rare event and therefore number of deaths in population groups is small (e.g. ward level CHD deaths) use INDIRECT.

49. Pros Able to compare different areas with each other. Can look at trends through time. (Only if ALL use the same standard population) Need local data for all age bands Rare diseases may have no events in specific age bands so age specific rates may be unavailable May need to merge events from different years or combine age bands Cons Pros and Cons of DSRs

50. Pros Can use where diseases are rare Don’t need local event information for all age groups Just need total number of observed and expected counts Cannot compare SMRs with each other unless population structures are identical Cannot look at trends through time Cons Pros and cons of SMRs

51. A final note! Standardisation can be use in many areas Although we’ve looked at mortality, the technique can be applied in other ways: – Hospital admissions – Prevalence/incidence of disease – Prescriptions –etc

52. Exercise 3 Direct Standardisation

53. Exercise 4: Indirect Standardisation

54. Some Questions…. Which type of measure would you use if 1.You are trying to monitor a trend 2.Trying to pick out which wards have a mortality rate higher than the regional average 3.Looking at infant mortality 4.Looking at the number of people with diabetes

55. Finding out more: APHO http://www.apho.org.uk/resource/item.aspx?RID=48457

56. Finding out more The East Region Public Health Observatory has also produced a useful briefing note on standardisation

57. Finding out more The NCHOD website also contains useful information on methodology… http://www.nchod.nhs.uk/

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

59. 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)

60. 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)

61. 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 Risk of disease = Number with disease Number with + without disease May be interested in the risk of disease in groups exposed to compare to the risk in a group not exposed.

62. A two by two table used in epidemiology

63. A two by two table used in cohort studies l e = Risk of disease in exposed = a/(a+b) l c = Risk of disease in unexposed = c/(c+d)

64. A two by two table used in cohort studies l e = Risk of disease in exposed = a/(a+b) l c = Risk of disease in unexposed = c/(c+d) Relative Risk = I e / I c Attributable Risk = I e - I c

65. Types of Risk Attributable Risk is the disease risk in exposed persons minus that in unexposed persons – risk attributable to exposure. Relative Risk is the ratio of the disease risk in exposed persons to that in people who are unexposed – measures how many times more likely exposed group are to have disease. Not possible to calculate the relative risk in case control studies. In this case we use the Odds- Ratio.

66. Case-control study Compares people with a condition (cases) to a similar group of people without the condition (controls) Often used to investigate the source of an outbreak of disease. Can not calculate the incidence risk as selected on the basis of having disease in the first place. The aim is to try and identify the risk factors which may have caused the cases to get the condition in the first place Calculate odds of exposure instead Relative risk of disease is estimated by the odds ratio for rare diseases

67. A two by two table used in case control studies Odds Ratio = (a/c)/ (b/d) Odds ratio = Odds of disease in exposed Odds of disease in unexposed If disease is rare odds ratio approximates the relative risk of disease

68. Calculating risk and odds – an imaginary example Lung cancer present yesno Smokingyes7030100 presentno2575100 95 105200 l e – risk of cancer in exposed = 70/100 = 0.7 l c – risk of cancer in unexposed = 25/100 = 0.25 Relative risk of disease (cohort study) = 0.7 = 2.8 0.25 Odds ratio (case control study) = 70/25 = 7 30/75

69. About the example From the data we can see that – 70 out of 100 smokers have lung cancer. – 25 out of 100 non smokers have lung cancer. The Relative Risk is the ratio of the disease risk in exposed persons to that in people who are unexposed, in this case –75/100 divided by 25/100 or 2.8 The Odds-Ratio is given by the ratio of the odds of exposure for each disease group, in this case – 70/25 divided by 30/75 = 7

70. Exercise 5 Using the hayfever prevalence data, calculate the relative risk, attributable risk and odds ratio for developing hayfever for eczema and non-eczema sufferers.

71. Finding out more Some further references of interest: –Bland, M. Introduction to Medical Statistics. Third Edition. Oxford University Press, 2000. –Hennekens CH, Buring JE. Epidemiology in Medicine, Lippincott Williams & Wilkins, 1987. –Larson, H.J. Introduction to Probability Theory and Statistical Inference. Third Edition. Wiley, 1982