Erpho: a whistle-stop tour of public health intelligence james.harrison@erpho.uk

1. Erpho: a whistle-stop tour of public health intelligencejames.harrison@erpho.org.uk

2. A short talk about populations

3. Why are we interested in populations?

4. Why are we interested in populations? Any ideas?

5. Why are we interested in populations? • Populations show us the make-up of an area. Without understanding an area we cannot know what to commission (buy) for that area.

6. Why are we interested in populations? • Populations show us the make-up of an area. Without understanding an area we cannot know what to commission (buy) for that area. • Without information on the population we cannot understand some of the issues within that area.

7. Why are we interested in populations? • Populations show us the make-up of an area. Without understanding an area we cannot know what to commission (buy) for that area. • Without information on the population we cannot understand some of the issues within that area. • And we will not know how to effectively tackle these issues, and plan for the future.

8. Know your population - how would you describe these populations?

9. Know your population - how would you describe these populations?

10. Know your population - how would you describe these populations?

11. Questions • How would the different age structures in the three areas have an impact on Midwifery? • What is missing? • Why might it have an impact on midwifery?

12. Comparison of the ethnic diversity

13. Other aspects to consider • Deprivation

14. Other aspects to consider • Deprivation • Rurality

15. Other aspects to consider • Deprivation • Rurality • Access

16. Other aspects to consider • Deprivation • Rurality • Access • Local resources

17. Other aspects to consider • Deprivation • Rurality • Access • Local resources • Qualitative information

18. Data.....or......

19. Data.....or......more simply ......some of the terms and phrases you may wish to know about and keep in mind

20. Key terms We shall now look at the following: • Numerator • Denominator • Proportion (as a percentage) • Rate • Prevalence • Incidence These cover the majority of terms you will come across and are intended to ease you in to looking at and understanding just what the data are showing.

21. Numerator • The numerator is a count of something e.g. number of obese people in the UK (goes above the line).

22. Numerator 30 Which one is the Numerator? A B 0

23. Denominator • The denominator is the number we divide the numerator by, e.g. population of the UK (goes below the line).

24. Denominator 30 Which one is the Denominator? A B 0

25. Proportion • Proportion: number in a subgroup of the population (numerator) divided by the whole population (denominator)

26. Proportion • Proportion: number in a subgroup of the population (numerator) divided by the whole population (denominator) • It is often expressed as a percentage

27. Proportion • Proportion: number in a subgroup of the population (numerator) divided by the whole population (denominator) • It is often expressed as a percentage • E.g. Proportion obese children in Bedfordshire in 2008 is the number of obese children in Bedfordshire in 2008 divided by the 2008 child population

28. 98 children in a school of 650 get the winter vomiting bug in one month – what proportion of the school succumbs? 30 • 10% • 12% • 15% • 20% 0

29. 98 children in a school of 650 get the winter vomiting bug in one month – which figure is the numerator? 30 • 98 sick children • 650 school population 0

30. The East of England has an Asian population of 253,000. To measure the proportion of Asians in the East of England what Denominator do we need? 30 • The UK population figure • The East of England population figure • The world population • The population of Cambridge 0

31. We want to know how many Obese people there are in Cambridgeshire We have the total population for Cambridgeshire What will the numerator be? 30 • The total amount of obese people in the UK • The total amount of obese children in the East of England • The total amount of obese people in Cambridgeshire • The City of Cambridge population 0

32. Rate • Is a number per population per unit time

33. Rate • Is a number per population per unit time • Often expressed per 1,000, per 10,000 or per 100,000

34. Rate • Is a number per population per unit time • Often expressed per 1,000, per 10,000 or per 100,000 • E.g. The rate of injury admissions for under 17s in Cambridgeshire is the total number of injury admissions for children 0-17 in Cambridgeshire divided by the total child population (0-17) of Cambridgeshire multiplied by the ‘per’ multiplier.

35. Rate ExampleCalculate the crude rate of Injury Admissions to hospital for all children (0-17) in 2011 in the PCTs below:

36. Answer

37. Prevalence • Prevalence is the number of individuals in a population who have the disease at a specific point in time

38. Prevalence • Prevalence is the number of individuals in a population who have the disease at a specific point in time • Usually expressed as a proportion of the population at risk Prevalence = Total number of cases at a given time Total population at that time

39. Incidence • Incidence is the number of new cases of disease that develop in a population of individuals at risk during a specified time period

40. Incidence • Incidence is the number of new cases of disease that develop in a population of individuals at risk during a specified time period • Incidence measures the rate at which new events occur Incidence = Number of new cases in period of time Population at risk

41. Example High incidence and low prevalence e.g. influenza 1st February January February March Cases of flu in class 4J. Class size: 20

42. Example High prevalence and low incidence e.g. asthma 1st February January February March Cases of asthma in class 4J. Class size: 20

43. Incidence and prevalence model Cured Incidence Got better Deaths Prevalence

44. Working with imperfect data: Understanding the limits of routine data and how to deal with them

45. We live in a world of imperfect data.....

46. In a perfect world.... √ • The right data • At the right geographical or organisational level • At the right time • Of sufficient reliability • With the appropriate comparisons √ √ √ √

47. In the real world... (√) • The right data • At the right geographical or organisational level • At the right time • Of sufficient reliability • With the appropriate comparisons X (√) ? (√)

48. What should you do? 30 • Nothing because we can’t take action if the data is not perfect • Use lots of resources to make the data perfect • Use some resources to try to improve the data • Make the best decisions you can based on the imperfect data • It will depend on the circumstances 0

49. Can we make the data perfect? • Often relatively minor changes can be made to improve the quality of the data • However, to achieve perfection may require a lot of resources • Can the additional expenditure be justified? Will it make a difference to your decisions? • There is likely need to balance the desire for perfect data quality against the resources needed to achieve it. • Can you think of some examples where it would be important to allocate resources to ensure accurate, reliable data and where sometimes allocating lots of resources may be not be justified?

50. Working with data in the real world • Data will never be perfect • Often have to make decisions based on imperfect data BUT: • Should try to minimise imperfections and improve data quality as far as is possible & practical • Be aware of the imperfections and recognise the implications for decision making