PHA 5: Surveillance

1. PHA 5: Surveillance John Powles 2009

2. Objectives Will concentrate on chronic disease CD surveillance will be covered in health protection

3. Definition of surveillance ‘...the continued watchfulness over the distribution and trends of incidence through the systematic collection, consolidation and evaluation of morbidity and mortality reports and other relevant data’ together with timely and regular dissemination to those who ‘need to know’ Langmuir 1963

4. Objectives To understand the behaviour of the disease in orderto control it To assess the public health importance of the disease (or exposure)

5. Surveillance Has frequently been critical in galvanising responses to health threats Eg HIV, SIDS, road traffic injuries and deaths, cancer, birth defects And to monitoring progress towards their control

6. Examples of surveillance data sources for chronic diseases Cancer registries Diabetes registries Standardised incidence studies (MONICA) Behavioural (risk factor) surveillance (smoking, alcohol use, adiposity etc) National health examination surveys NHANES I, II, III and IV in the USA National Health Survey in the UK

8. What is a key requirement of surveillance surveys? How is it achieved?

9. Cancer registries England now national ‘Notifiable disease’ Mostly from path labs Death certificates as back up Internationally collated by IARC http://www-dep.iarc.fr/

10. Evaluation of service screening • Screening services’ in-house data can provide them with process measures • Detection rates, non-operative biopsy rates, etc. • There is also a need to estimate the effect of provision of screening on clinical outcomes • Incidence of invasive cervical carcinoma • Death from breast cancer • Late stage breast cancer? • For these endpoints, we turn to the registries

11. Diabetes registries

12. Congenital anomalies registries

13. Cardiovascular disease • Stroke registries • CHD registries • Standardised incidence studies Eg MONICA

15. Interrogating routine surveillance data • ? Secular trends • ? Geographic variation • ? ‘Outliers’ and ‘clusters’

16. Statistical assessment of surveillance data • Not straightforward

17. How would you interpret these data?

19. Problems in statistical assessment of surveillance data • Hypotheses often ‘data dependent’ • Multiple comparisons are made • Observations are not independent Eg Adjacent years Adjacent areas

20. 2005

21. Trends in death-certification rates for liver cirrhosis, 1950 -2000 Source: Leon et al Lancet, 2006, 367: 52-6

22. Group comparisons of disease incidence rates are a fundamental starting point in public health • What needs to be borne in mind in interpreting such comparisons?

23. Department of Error,The Lancet, Volume 367, Issue 9511, 25 February 2006-3 March 2006, Page 650 • After publication of our paper (Jan 7, p 52),1 we were alerted to errors in it by Fabio Levi, of the University of Lausanne. An independent review of the programs and calculations used in the study has now been carried out. Although the key conclusions of the paper remain unchanged, an inadvertent error in the program used to analyse the data has regrettably necessitated non-trivial changes to many of the numerical values quoted in the paper. We regret any confusion caused. The corrected tables and the figure are available online. We describe below the changes required in the Results section. The other sections of the paper stand as originally published.

24. What needs to be borne in mind in interpreting such comparisons? • Information error and empirical uncertainty

25. Corrected version

26. What needs to be borne in mind in interpreting such comparisons? • Information error and uncertainty Information error may or may not be consequential!

27. What needs to be borne in mind in interpreting such comparisons? • Information error and (empirical) uncertainty Information error may or may not be consequential! Comparability Are the entities comparable? Can be a major difficulty in cross-national comparisons (more on this below) • Chance (stochastic) variation

28. Potential contribution of chance to variation in event rates Assumptions: Numerator follows Poisson distribution Denominator is free of sampling error Rates Rates (λ) are like velocities with an instantaneous value (like the speedometer reading on a car) They are estimated ( ) by assuming them to be constant over an interval (eg over a calendar year – like estimating the instantaneous speed of a car by measuring its average speed between 2 mile posts)

29. Confidence intervals for rates For ‘small’ numbers of events (??<30 - 100) Exact intervals should be calculated Can use look-up tables

30. Upper 95% CI 10.24 Observed events 4 Lower 95% CI 1.09 Poisson CIs – asymmetry at low n’s From widely available look-up tables or programs Eg Altman ‘Confidence interval analysis’ (program)

31. Calculated confidence intervals for rates Where N > 30. Various approaches (see texts) One is based on Will give a symmetrical CI Where N = number of observations and Y = person-time (assumed to be free of stochastic variation)

32. Confidence intervals for age-standardised rates Does age-standardisation have any implications for the calculation of confidence intervals?

33. Confidence intervals for age-standardised rates Be aware that (direct) age-standardisation varies the weight attached to observations from each age-stratum This also re-weights the contribution of age-strata to total variance. So CIs for age-standardised rates are based on a more complex calculation (see texts or programs for details)

34. Assumptions for the use of frequentist statistics (eg CI’s) Observations are independent • Are adjacent years independent observations?

35. What methods are available for assessing the role of chance in data for successive years? Formal: time series analysis • Much used in economics Informal: ‘eyeballing’ • What does variation due to small numbers look like?

36. Lung cancer mortality ages 35-54

37. Cross-sectional (spatial) comparisons

39. Assumptions for the use of frequentist statistics (eg CI’s) Comparisons are pre-specified (‘prior hypothesis’)

40. Cf ‘Data dredging’ • Scanning a large number of comparisons some of which will, on average, be ‘significant’

41. A ‘rule of thumb’ The usual approach to the problem of multiple statistical testing and non-independence is to require a much higher apparent level of statistical significance than 5 per cent. This can be done by taking into account the number of tests being performed. For example, if 20 such tests were carried out, a significance level of (0.05/20) = 0.0025 or 0.25 per cent could be required. The difficulty with this approach for this atlas is that we do not know how many non-independent tests there would be. A simple alternative (which also avoids the need to perform hundreds of statistical tests) is to note whether the 95 per cent confidence intervals around the two rates overlap or not. If the two rates were in fact independent, then (assuming roughly equal variances) the non-overlapping of the 95 per cent confidence intervals is roughly equivalent to the rates being significantly different at a significance level of about 0.6 per cent (p=0.006). From Cancer Atlas of the United Kingdom and Ireland

42. When might you seek advice on using formal time-series analysis? • ?

43. When a new service is established that is expected to change the trend for some outcome • But data requirements are high for time series analyses • Rule of thumb: 50 data points

44. Time series data: aids to description

46. Can be thought of as method of removing ‘noise’ for descriptive purposes (cf spatial comparisons)

47. Using ‘Joinpoint’ to describe patterns of convergence by gender in national smoking epidemics

48. Group comparisons as a fundamental starting point in public health • Spatial comparisons