Medical stats Flashcards
levels of prevention
Primordial- health promotion using legislation .g. banning alcohol
- prevent development of risk factors
Primary- vaccination, exercise
- prevent onset of disease
Secondary- screening for diseases to catch early and treat
- catch it early
Tertiary- treatment- stopping the progress of established disease
- reduce mortality and morbidity
health improvement examples
- Smoking cessation
- Public mental health
- Sexual health services
- Substance misuse services
- NHS health checks
- Weight management
five criteria for screening
- The condition
- The test
- The intervention
- The screening programme
- Implementation
the condition
important health problem
the test
- sage, precise and validated screening tool
- acceptable to target population
- diagnostic test available for those who test tissue
the intervention
effective treatment if found to have condition
- treating early should give better prognosis
screening programme
- Proven effectiveness in reducing mortality or morbidity
- beenfit gaine dby individual should outwieght nay harm
sensitivity
is the proportion of cases which the test correctly detects
specificity
is the proportion of non-cases which the test correctly detects
positive predictive value
is the proportion of positive tests who are cases
negative predictive value
proportion pf negative cases who are not cases
screening is diff to evaluate because
- Lead to time bias
- Length time bias
- Selection bias
lead time bias
- Early diagnosis falsely appears to prolong survival
- Screened patients appear to survive longer, but only because they were diagnosed earlier
length time bias
- Screening programmes better at picking up slowly growing, unthreatening cases than aggressive, fast growing ones
- Diseases that are detectable through screening are more likely to have a favourable prognosis, may indeed never have caused a problem
selection bias
Studies of screening are often skewed by healthy volunteer effect
calculate
the positive predictive value is 132/1,115 = 0.118, or 11.8%.
null value for odds ratio
1.0
null value for risk ratio
0
answer
Answer: b
An odds ratio of less than the null value of 1.0 indicates that being a gym member might be a protective factor against depression (but note that the confidence interval spans the null value of 1).
Types of study design
- Studies can be qualitative or quantitative
1) Experimental- intervention of the researcher, observation of what happens
-
Randomised controlled trials (RCT)
- Reduce confounding/ bias
- Compare two treatments
- Compare new treatment against placebo/ usual care
-
Non-randomised controlled trials
- Comparing results of two treatment pathways one used in one hospital, the other in a different hospital
2) Observational- subjects are observed, no action from researcher
- Cohort studies
-
Case- control studies
- May be vital in identifying new emerging diseases
- May be useful in suggesting aetiological associations
-
Cross-sectional
- Often studies of prevalence
- May explore the link between disease and possible exposure
- Issue with confounding
- Often used to address questions of “time, place, person”
- Ecological
Reviews
Systematic reviews combine study results together
- RCT
- Observational studies
- Qualitative studies
forest plots
forest plots
a graph that compares several clinical or scientific studies studying the same thing → i.e. for representation of a meta-analysis
what does the size of the squares represent
- proportion of weight given to each study i.e. effect
- e.g. size of study
what do the horizontal lines represent
95% confidence intervals
- if it crosses 1- no significant difference in outcomes
- OR
what does the vertical line represent
- the null hypothesis OR
- 1= no difference
what does the diamond represent
- Meta-analysis estimates
- width of the diamond represents the confidence intervals
confidence intervals above >1 or <1
- significant results
- 0.05%
analyse this forest plot
Example interpretation
- 6 out of the 7 RCTs had an OR > 1.00 indicating greater odds for survival amongst patients taking aspirin after MI
- Only 1 RCT (the largest) had a statistically significant result, but its OR was less than the other RCTs with an OR > 1.00
- Pooled estimate OR = 1.11 (95% CI: 1.04 to 1.19) leads to the conclusion that aspirin increases the chance of surviving after a MI (p<0.05)