Download Population Perspective

PP
…it’s really interesting
Things to try and cover
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Vital statistics
Confidence Intervals
Rates and Ratios
OR vs RR
Specificity, sensitivity, PPV, NPV
Extracting info out of flow charts to make calculations
Apply to some relevant questions
Other things worth having a look at
Vital statistics
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P value: probability of event occurring by chance
Null hypothesis – no significant difference
Chi-sq: Hypothesis test for categorical data (O/E)
Type I error ~ False positive ~ alpha value (p value)
– ↑p value will ↑false positive
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Type II error ~ False negative ~ beta value
Statistical power = 1- Beta
Sample size is key
Power detecting what you want to detect
– ↑sample size = ↑Power
Confidence intervals
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Confidence Interval: a range which is likely to contain the popn mean
Usually 95% CI quoted i.e. 95% of time the mean will be in this range
Does not denote significance unlike a p value
Related to sample size: ↑sample size equates to narrow CI
Options:
a) 95% confidence interval
b) statistical measure of dispersion of continuous data
c) hypothesis test for categorical data
d) statistical power- ability of study to find what you want to find
e) type I error
f)
type II error
g) statistical significance level
This is:
1. the failure to detect a true difference f)
2. the Chi-squared test c)
3. the statistical assessment of a statement formulated in the negative, such as “there is
no association between osteoarthritis and nutrition” c)
4. illustrated by looking at the difference between the 5th and the 95th centile of a set
of blood pressure measurements in millimetres of mercury b)
5. a measure of the precision of an estimated proportion of patients on an orthopaedic
waiting list who are likely to be over 65 years old a)
Rates and raw numbers
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Learn these- one of the favoured questions
Most on the hand out
Fertility and death rates
Will always be over a specified time period
Often specified per 1000 population
Rates will be over time / mention proportion
Raw numbers if asking for things to make a rate i.e. if ask
for the numerator / denominator
• Almost certainly ask what represents numerator or
denominator
– Remember: Numerator
Denominator
Options:
For a given geographic area:
a) number of women at a specified time
b) number of livebirths in specified time period
c) number of stillbirths in specified time period
d) number of deaths in the first month of life in specified time period
e) average number of women aged 15-44 years in specified time period (mid-year estimate)
f)
number of deaths in the first year of life in specified time period
g) total number of births (live and still) in specified time period
This is the:
1. denominator for general fertility rate
Things you know:
• It’s something to do with birth i.e. not death! Exclude d) f)
• It’s general (all births regardless of outcome). Exclude b) c)
• It’s the denominator so should be lower portion of equation
• It’s regarding fertility (always women aged 15-44 years). Exclude a)
• What’s left? E) & G)
• Does G fit the definition? No as it’s the numerator
• Does E fit the definition? Yes
• General fertility rate = no. live and still births in a time period
number of women 15-44yrs in spec time
Options:
For a given geographic area:
a) number of women at a specified time
b) number of livebirths in specified time period
c) number of stillbirths in specified time period
d) number of deaths in the first month of life in specified time period
e) average number of women aged 15-44 years in specified time period (mid-year estimate)
f) number of deaths in the first year of life in specified time period
g) total number of births (live and still) in specified time period
This is the:
2. denominator for infant mortality rate
Things you know:
• It’s to do with infants. Exclude all options with women i.e. a) e)
• Infant deaths: first year of life. Exclude c) d)
• It’s a denominator so can’t be what’s defined in the question i.e. number of
infant deaths. Exclude F)
• What’s left? B) & G)
• Does G fit the definition? No
• Does B fit the definition? Yes(must know the definition here)
• Get to a 1in2 chance rather than 1in7 by using these approaches
Options:
For a given geographic area:
a) number of women at a specified time
b) number of livebirths in specified time period
c) number of stillbirths in specified time period
d) number of deaths in the first month of life in specified time period
e) average number of women aged 15-44 years in specified time period (mid-year estimate)
f)
number of deaths in the first year of life in specified time period
g) total number of births (live and still) in specified time period
This is the:
5. numerator for infant mortality rate
Use previous workings and apply to this
• Previously got to B), F) and G)
• It’s a numerator so will be what’s defined in the question i.e. number of infant
deaths. Select F)
Study design
(observation to association to causality)
• Descriptive (hypothesis generating)
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Case report (single event / single patient
Case series (collection of patients)
Cross-sectional (survey / population studies)
Correlation (aka ecological)
• Analytical (Observational looking at hypothesis)
– Case-control (Disease / no disease with exposures)
– Cohort (follow up over time)
– Meta-analysis: Combine results of many studies
• Experimental (test hypothesis)
– RCT (Gold standard vs new intervention)
Relate to Bradford-Hill criteria (causality)
• Temporal relationship – Does the cause precede the effect
• Plausibility – Is the association consistent with other
knowledge
• Consistency – Have similar results been shown in other
studies
• Strength – What is the strength of the association between
the cause and the effect (relative risk)
• Dose-response relationship – Is increased exposure to the
possible cause associated with increased effect
• Reversibility - Does the removal of a possible cause lead to
reduction of disease risk
• Study design – Is the evidence based on a strong study design
• Judging the evidence – How many lines of evidence lead to
the conclusion
Options:
a) undertaking record linkage: bringing two pieces of information together
b) population screening: opportunistic / targeting high risk pops
c)
Surveillance: regular / continuous
d) undertaking a case-control study
e) matching: e.g. making sure that two groups in case control are as similar apart form disease
f)
undertaking a service evaluation study
g) reporting a case study
This would be illustrated by:
c)
1. estimating point prevalence regularly for disease control purposes
2. publishing about an episode of adverse reaction to a new oral contraceptive
g)
3. checking regularly the weight of pregnant women c)
4. offering a test for Down’s syndrome to all older pregnant women b)
5. checking childhood immunization uptake using a continuous system c)
6. checking the blood pressure of all women of child-bearing age whenever they consult a
general practitioner b)
Approaches for minimising bias
• Confounder: factor related to both exposure and
outcome- not an approach but important to
identify so that you can adjust
• Adjustment: statistical approach to minimise
population differences
• Matching: try and make cases and controls as
similar by demographics e.g. age, sex, ethnicity
Stratification: sub-sections of populations
(homogenous groups)
• Randomisation: Used in clinical trials
• Standardisation: used commonly to account for
confounding effect of age
Standardisation
Direct Standardisation
Standard population
structure
Direct method of standardisation - calculation of the number of expected deaths
for countries A and B applied to a standard population.
Indirect Standardisation
Indirect method of standardisation is used to calculate how many deaths would be
expected in Country B if it had the same age-specific mortality rates as Country A.
Direct Vs Indirect Standardisation
• Break down age spec groups in both • Used when age spec mortality rate
pops into mortality rates
unavailable (more realistic)
• Apply rate to standard population
• Set of age spec rate from standard
numbers given
population to unkn population
• Standard mortality rate
• Get a estimated death / morbidity
rate for each age group
– Number of deaths / 1000 if were in
standardised population structure
• Add altogether to have total
• DIES
estimated deaths
– Death rate
• Compare this to what observed
– Index population
– Exposure
– Sample populations
– Observed number of deaths
Expected number of deaths
• Standardised Mortality Ratio
• Comparative Mortality Ratio =Pop A
(SMR)
Pop B
– SMR = 100 No deviation
– SMR < 100 Less comparative deaths
• Can compare age-adjusted rates
– SMR > 100 More comparative
(but not crude rate)
deaths
• Pop A has x% mortality > Pop B
OR Vs RR
• Odds is number of times event happens vs not
• Odds ratio = Odds of exposure in cases
Odds of exposure in controls
• OR used in case control as retrospective selection based on outcome
not exposure
– In a way you can calculate the probability of exposure given outcome /
status
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Risk is probability an event will occur
Absolute risk: probability that an event will occur in spec time
Attributable risk is the excess risk due to the risk factor
Relative risk (RR) = Risk in exposed group
Risk in unexposed group
• RR used in cohort as prospective selection and outcome observed
with regards to exposures
– Can estimate probability of outcome from exposure
Odds ratio: It’s comparing outcome in exposure groups
Exposed to risk factor
(e.g. Morbid obesity)
Disease
status
BMI>40
BMI<40
Total
T2DM
A
15
B
6
A+B
21
Control
C
4
D
18
C+D
22
Total
A+C
19
B+D
24
Odds ratio: odds of T2DM if morbid obese (exposure) / odds of T2DM if not (non exposure)
Odds of T2DM if morbidly obese = A/C
Odds of T2DM if not morbidly obese = B/D
Odds ratio = (A/C)/(B/D) = AD/BC
OR=1: no difference
OR>1: T2DM more likely in exposed group
OR<1: T2DM less likely in exposed group
Confidence intervals and ORs
• If 95%CI intersects 1 then it is not significant
• If 95%CI of two groups overlap they are not
significant
Relative risk: Risk of disease given exposure
Exposed to risk factor
(e.g. Morbid obesity)
Disease
status
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BMI>40
BMI<40
Total
T2DM
A
15
B
6
A+B
21
Contro
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C
4
D
18
C+D
22
Total
A+C
19
B+D
24
RR: Risk of T2DM in BMI>40 (exposure) / Risk of T2DM in BMI<40 (non exposure)
Risk of T2DM in BMI>40 (exposure) = A/A+C
Risk of T2DM in BMI<40 (non exposure) = B/B+D
RR = (A/A+C)/(B/B+D)
RR=1: no difference
RR>1: Obesity increases T2DM risk
RR<1: Obesity reduces T2DM risk
OR != RR
• Both measure likelihood of event between groups
• Very similar in low prevalence but as it increases they are not
• A 2% mortality Vs B 1% mortality
– RR of A = 2 (what most people would deduce)
– OR = 2.02 i.e. A (2/98)/(1/99)
– Most common diseases reflect this
• A 50% mortality Vs B 25% mortality
– RR of A= 2 (what most people would deduce)
– OR = 3 because its always chance of getting versus not in each group i.e. A
(50/50)/(25/75)=1/0.333=3
• A 90% mortality Vs B 10% mortality
– RR of A = 9 (what most people would deduce)
– OR = 81 because its always chance of getting versus not in each group i.e.
A (90/10)/(10/90)
Absolute risk reduction and NNT
• Absolute risk reduction (ARR) = risk(non-exposure) –
risk(exposure)
• Number needed to treat (NNT)
– average number of patients who need to be treated to
prevent one additional bad outcome
– effectiveness of a health-care intervention
– NNT = 1 / ARR
*Remember dealing with patient numbers so round up
– NNT=1 everyone treated recovers
– ↑NNT = ↑poor intervention i.e need to treat lots of people
to gain one recovery
ARR & NNT
• ARR = 14-8 = 5 (0.05)
• Therefore NNT = 1/0.05 = 20
a)
b)
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d)
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f)
g)
sample size calculation
calculation of odds
calculation of a relative risk
surveillance
calculation of an odds ratio
minimizing bias in a cohort study
matching
This is:
1. illustrated by comparing incidence between exposed and non-exposed groups
2. illustrated by a school health service implementing an ongoing system of hearing
tests at school entry
3. illustrated by focusing on ‘loss to follow-up’
4. a way of adjusting for confounders
5. crucial for deciding whether a study has the power to detect a particular
difference
c)
d)
f)
g)
a)
Sensitivity, specificity, PPV, NPV
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True positive: Correctly identified as +ve
False positive: Identified as +ve but actually -ve
True negatives: Correctly identified as -ve
False negatives: Identified as -ve but actually are +ve
Sensitivity: correctly identifying true positives
Specificity: correctly identifying true negatives
Relevant for population screening etc
– Don’t want a test that either does not pick up disease or
gives people reason to worry for no reason
Sensitivity, specificity, PPV, NPV
Disease / Outcome
Test
DVT
No DVT
Total
+ve
True Positive
A
False Positive
B
PPV
A/A+B
-ve
False Negative
C
True Negative
D
NPV
D/C+D
Total
Sensitivity A/A+C Specificity B/B+D
Sensitivity
Disease / Outcome
DVT
• aka True positive rate
• Measures those correctly
identified as positive
test
+ve
True
positive
A
-ve
False
Negative
C
No DVT
Total
– e.g., the percentage of sick people
who are correctly identified as having the condition
Total
Sensitivity
A/A+C
• Quantifies avoiding false negatives
• Sensitivity 100%: all DVTs identified as DVTs
• A negative result in a test with high sensitivity is
useful for ruling out disease (SnOUT)
• A positive result in a test with high sensitivity is not
useful for ruling in disease
– Overdiagnosis
Disease / Outcome
Specificity
• aka True negative rate
• Measures those correctly
as negative
DVT
Test
No DVT
+ve
False
Positive
B
-ve
True
Negative
D
Total
– e.g., the percentage of healthy
who are correctly identified as having the condition
Total
identified
Specificity
B/B+D
people
• Quantifies avoiding false positives
• Specificity 100% - all non-DVTs are identified
• A positive result in a test with high specificity is useful for
ruling in disease (SpIN)
• A negative result in a test with high specificity is not useful for
ruling out disease
– Underdiagnosis
• Does not take into account false negatives
PPV and NPV
• PPV & NPV dependent on prevalence
– describe performance of diagnostic test
• Sensitivity and specificity are not
• PPV = TP / (TP + FP)
– will increase proportionally with prevalence
– If we test in a high prevalence setting, it is more likely
positive test truly have disease vs low prevalence
population
• NPV = TN / (FN + TN)
– will decrease with increased prevalence
• ↑prevalence ~ ↑PPV
• ↑prevalence ~ ↓NPV
Disease / Outcome
DVT
Test
+ve
-ve
67
(52+25)
No DVT
• True +ve: VTE predicted by tests and VTE
found= 67
• 25 (D-dimer +ve) + 52 (Wells score >4)
Disease / Outcome
DVT
Test
+ve
67
(52+25)
-ve
4
No DVT
• False –ve: VTE not predicted by test but VTE
found = 4
• 4 subjects with VTE but had Wells score
≤4 & D-dimer –ve
Disease / Outcome
DVT
+ve
67
(52+25)
-ve
4
Test
No DVT
268
(272-4)
• True –ve: VTE not predicted and not found =
268
• 272 (Wells score ≤4 & D-dimer –ve ) – 4
(VTE found)
Disease / Outcome
DVT
+ve
Test
-ve
No DVT
67
249
(52+25) (125+124)
4
268
(272-4)
• False +ve: VTE predicted but VTE not found =
249
• 124 (Wells score >4 but no VTE) = 176
(Wells score >4) – 52 (VTE found)
• 125 (D-dimer +ve but no VTE) = 150 (Ddimer +ve) – 25 (VTE found)
• Total = 124 + 125
Disease / Outcome
Test
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DVT
No DVT
Total
+ve
67 (TP)
A
249 (FP)
B
PPV
A/A+B
-ve
4 (FN)
C
268 (TN)
D
NPV
D/C+D
Total
Sensitivity
A/A+C
Specificity
B/B+D
Sensitivity = TP / All with DVT (TP+FN) = 67/71 (94%)
Specificity = TN / All without DVT (TN+FP)= 268/517 (52%)
Positive predictive value = TP / All that were positive (TP+FP) = 67/316
Negative predictive value = TN / All that were positive (TN+FN) = 268/272
Other things to cover / consider
• Look up options of answers on vital – very similar questions often used
• Definitions / explanations on health knowledge website
• Watch out for double negative questions (especially in null hypothesis
based questions)
• Have a look at immunisation schedule (make sure updated version)
• Normal distribution, mean, median mode, range
• Intention-to-treat (include in analysis of clinical trial)
• 3Es 3As
• Look up different types of bias
– Favourites are lead-time / length-time
• Primary, secondary and tertiary prevention
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Screening is secondary (as they already have it – picking up disease already there)
Screening diabetics for retinopathy is tertiary (they already have DM and minimising
impact of complications)
• Health economics (on hand out)
Thanks
• [email protected]
• http://www.healthknowledge.org.uk/publichealth-textbook