1. Public Health Phase 3A Abby Aitken - alaitken1@shef.ac.uk
Jonathan Cunliffe -
Jamie Scuffell -
The Peer Teaching Society is not liable for false or misleading information.

2. Aims
To ensure that you pick up 10% extra marks on your Phase 3A exams this year.

3. What we will cover PPD Incidence and prevalence
Sensitivity, specificity, predictive values
Screening criteria
Relative risks
Interpreting associations
Bradford-Hill criteria
Study designs
Health needs assessment
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4. Health needs assessment
Health needs assessment is a systematic method for reviewing the health issues facing a population, leading to agreed priorities and resource allocation that will improve health and reduce inequalities.
Need / Supply / Demand

5. Bradshaw - Sociological approach
Felt need - individual perception
eg. having a headache
Expressed need - demand
eg. going to GP about headache
Normative need - professional defines
eg. GP decides you need a CT scan
Comparative need - looking at similar populations
eg. Doncaster needs a CT scanner as STH has one

6. Public Health approach
Epidemiological
define problem, size, services available, look at evidence base
Comparative
compare services received with other groups
Corporate
ask people - commissioners, providers, patients, politicians

7. Stages of change

8. Errors - types Sloth Fixation / loss of perspective
Communication breakdown
Playing the odds
Bravado
Ignorance
Miss-triage
Lack of skill
System error

9. Errors - why? Swiss cheese model System failure Human factors
Judgement failure
Neglect
Poor performance
Misconduct

10. Negligence Was there a duty of care? Was there a breach in that duty?
Was the patient harmed?
Was the harm due to the breach in care?
Bolam - Would a group of reasonable doctors do the same?
Bolitho - Would that be reasonable?
Assess cost - loss of income, extra care, pain and suffering

11. Never events
Serious, largely preventable patient safety incidents that should not occur if the available preventative measures have been implemented
Intolerable and inexcusable
Eg. operating on the wrong knee / leaving a scalpel inside someone

12. Incidence
Number of new cases in a population during a specific time period.
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13. Prevalence
Number of existing cases in a population during a specific point in time.
The Peer Teaching Society is not liable for false or misleading information.

14. Headache, cold
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15. Incidence of lung cancer
Over a ten year period, there were 50 cases of lung cancer in Crookes (a population of 1,000 people).
What is the incidence (risk) of lung cancer over those 10 years? 50
1000
Risk =
= 5%
= 0.5% per year

16. Comparing incidence 5 45 = 15% = 0.7% 700 300 15 = 21.4 0.7
In Crookes (a population of 1000 people), 300 of them smoke. Of those who do smoke, 45 of them developed lung cancer. 5 of the non-smokers developed lung cancer.
What is the relative risk of lung cancer in smokers?
Risk of lung cancer in smokers (‘exposed’)
Risk of lung cancer in non-smokers (‘unexposed’) 45
300
= 15% 5
700
= 0.7% 15
0.7
= 21.4
Relative risk =
(risk ratio)

17. Comparing incidence 5 45 = 15% = 0.7% 700 300 15 - 0.7 = 14.3 100 100
In Crookes (a population of 1000 people), 300 of them smoke. Of those who do smoke, 45 of them developed lung cancer. 5 of the non-smokers developed lung cancer.
What is the risk of lung cancer attributable to smoking?
Risk of lung cancer in smokers (‘exposed’)
Risk of lung cancer in non-smokers (‘unexposed’) 45
300
= 15% 5
700
= 0.7% =
14.3
Attributable risk =
Absolute risk reduction
Risk difference
100
100
100

18. Comparing incidence* 5 45 = 15% 700 = 0.7% 300 = 6.99 15 - 0.7 = 14.3
Risk of lung cancer in smokers (‘exposed’)
Risk of lung cancer in non-smokers (‘unexposed’) 45
300
= 15% 5
700
= 0.7% =
14.3
Attributable risk =
Absolute risk reduction
Risk difference
100
100
100
In a simplified world where smoking causes lung cancer, and there is no lag time, previous damage to lungs etc. then, if 7 people gave up smoking, you would prevent one case of lung cancer in this population.
Equally, if 7 fewer people in this population smoked, then one case of lung cancer would be prevented.
The number needed to treat (NNT) is really useful clinically, and better interpreted in randomised controlled trials.
The best NNT is 1 - ie. for every 1 patient you treat, you reduce the disease occurrence by 1 (ie. everyone is perfectly treated). Antibiotics come quite close to this (triple therapy for H. pylori has an NNT of 1.1).
How many people would have to give up smoking to prevent one death of lung cancer? (ignoring previous damage to lungs, lag time etc.) 1 1
Number needed to treat =
= 6.99
attributable risk =
14.3
100

19. Person-time at risk of developing disease
Other measures of disease frequency*
Risk
Number of cases
Total population size
per year
Odds
Number of non-cases
Rate
Person-time at risk of developing disease
per person-year
There are three measures of disease frequency.
Risk is what has been calculated above. It’s a proportion: the proportion of people developing a disease in a given time period.
Odds describes the number of cases of disease there are per non-case. These are harder to interpret, and only calculated as odds ratios.
Rates allow for the fact that, once a person has developed a disease, they are no longer at risk of developing the disease. It also accounts for diseases that people recover from and get repeatedly, for example, acute respiratory infections. The person-time at risk in the population is the amount of time that each person is at risk of developing the disease. More details are at
Risks, rates, and odds are similar when the disease/outcome is rare.
Relative risk reduction is the percentage reduction in relative risk with the intervention in randomised controlled trials.
For example, take a randomised controlled trial where half of those who were vaccinated against rotavirus, and half received a placebo.
The relative risk of rotavirus infection might be 0.20: those in the intervention group had 5 times fewer infections than those who didn’t receive the vaccine.
The relative risk reduction (and vaccine efficacy) of the rotavirus vaccine will therefore be = 0.8 = 80%
(Relative risk reduction = 1 - relative risk)
Randomised controlled trial of rotavirus vaccination: relative risk 0.05
Relative risk reduction = = 0.95 = 95%

20. Interpreting association
Bias
Chance
Confounding
Reverse causality
or….
It’s actually a true association!

21. Bias - a form of error Think:
was the study the same for cases and controls?
was the study the same for exposed and unexposed?
Selection bias - sample chosen is not representative of the population you want to generalise to.
Information bias - bias from measurement of either the exposure or outcome.
differential misclassification
non-differential misclassification
(diagnostic bias, recall bias, etc etc etc)
There’s no need to learn this, just keep it in your mind.

22. Confounding
A factor associated with exposure and the outcome, which is not on the causal pathway between them.
Drinking coffee
Lung cancer
There’s no need to learn this, just keep it in your mind.
Smoking

23. Causality or association?
Things that provide evidence for causality:
Bradford-Hill Criteria
Temporality
Dose-response
Strength
Reversibility
Consistency
...plus another four that are probably overkill
Strength - very high relative risk (Relative Risk of 21)
Temporality - most important - exposure occurs before outcome (people smoke before developing lung cancer
Dose-response - more risk of outcome with more exposure (the more you smoke the higher the risk of lung cancer)
Reversibility - if you take away the exposure then the risk of disease decreases or is eliminated (stop smoking and you have a decreased risk of lung cancer after 10 years or so)
Consistency - the association is seen in different geographical areas, using different study designs, in different subjects (smoking is associated with lung cancer in dogs, mice and people, all over the world)

24. Study designs
Cross-sectional - Outcome and exposure are measured simultaneously, looking at prevalence. Eg. a survey of a population to see if they have both previous asbestos exposure and asbestosis.
Cohort - take a group of people, and follow them up until they reach outcome or the study ends. Eg. Occupational cohort of people exposed to asbestos, followed up until they get asbestosis.
Case-control - Take a group with the outcome, and determine exposure. Eg. Take asbestosis patients, ask if they were previously exposed to asbestos.

25. Study designs (cont)
Case-control - Take a group with the outcome, and determine exposure. Eg. Take asbestosis patients, ask if they were previously exposed to asbestos.
Randomised controlled trial - Assign people randomly to two groups, expose one to something, and follow them up to an outcome. Eg. Take healthy people, randomly allocate them to two groups, expose one group to asbestos, compare the two groups for incidence of asbestosis.

26. Pros and cons Think about: Cost Practicality Ethics
What you want to measure
Speed
Confounding
Bias

27. Diagnosis and Screening

28. 2 x 2 table The Truth Disease present Disease absent Test Result
Positive
Negative

29. True positive A False positive B False negative C True negative D
The Truth
Disease present
Disease absent
Test Result
Positive
True positive A
False positive B
Negative
False negative C
True negative D
The Peer Teaching Society is not liable for false or misleading information.

30. Number of true positives
Sensitivity: the proportion of people with the disease who have a positive test result.
Number of true positives
Total with disease A
A+C
The Truth
Disease present
Disease absent
Test Result
Positive
True positive A
False positive B
Negative
False negative C
True negative D

31. Number of true negatives
Specificity: the proportion of people without the disease who have a negative test result.
Number of true negatives
Total without disease D
B+D
The Truth
Disease present
Disease absent
Test Result
Positive
True positive A
False positive B
Negative
False negative C
True negative D

32. No findings on mammogram
Example: breast cancer screening.
The Truth
Disease present
(Breast cancer)
Disease absent
(No breast cancer)
Total
Test Result
Findings on mammogram 30 60 90
No findings on mammogram 10
900
910 40
960
1000
Prevalence

33. Key Points about specificity and sensitivity
This about the test, and don’t get bulked down in it.
What matters to the individual and the clinician is the negative and positive predictive values.
In this example, the mammography screening test will pick up 75% of all breast cancers.
Mammography will pick up 75% of all breast cancers.
93% of people without breast cancer will have a negative mammogram.

34. Positive predictive value: the probability/proportion that someone has a disease, given a positive test result.
Number of true positives
Total with positive test A
A+B
The Truth
Disease present
Disease absent
Test Result
Positive
True positive A
False positive B
Negative
False negative C
True negative D
The Peer Teaching Society is not liable for false or misleading information.

35. Negative predictive value: the probability/proportion that someone does not have a disease, given a negative test result.
Number of true negatives
Total with negative test D
C+D
The Truth
Disease present
Disease absent
Test Result
Positive
True positive A
False positive B
Negative
False negative C
True negative D
BNP
D-Dimer
‘Ruling out’

36. No findings on mammogram
Example: breast cancer screening.
The Truth
Disease present
(Breast cancer)
Disease absent
(No breast cancer)
Total
Test Result
Findings on mammogram 30 60 90
No findings on mammogram 10
900
910 40
960
1000
Prevalence

37. WHO Criteria for screening
Condition - important health problem
natural history should be well understood
recognisable latent or early stage
Method - suitable method of detection
screening method must be acceptable to the population
Treatment - there should be an accepted treatment
treatment should be more effective if started early
Programme - agreed policy on who to treat
cost effective
screening should be ongoing, and not a one-off.
BNP
D-Dimer
‘Ruling out’

38. Lead time bias*
Earlier detection gives the impression of longer survival, but does not actually alter prognosis
overestimate benefit of screening

39. Length time bias*
Screening is more likely to pick up slow growing illness with a better prognosis
overestimate benefit of screening

40. Prevention
Primary prevention - preventing a disease from developing by modification of risk factors
Secondary prevention - early detection of disease to slow progression
Tertiary prevention - reducing complications or severity once the disease is established, detectable and symptomatic

41. tinyurl.com/shefphfeedback
BNP
D-Dimer
‘Ruling out’
tinyurl.com/shefphfeedback