Dr. Hannah Jordan Lecturer in Public Health ScHARR Screening Dr. Hannah Jordan Lecturer in Public Health ScHARR

Learning objectives Define and calculate sensitivity and specificity of a screening test Define and calculate positive and negative predictive values Explain the relationship between prevalence and predictive values Discuss criteria for screening Describe specific biases in relation to screening (selection bias, lead-time bias, length-time bias)

Background: cultural norms US – annual health check Screening tests and treatments may be offered by different agencies UK – rare to be screened Identification through to treatment all in the NHS Or… May be no ‘system’, just a test Is lots of screening a “good thing”?

What is screening? A process which sorts out apparently well people who probably have a disease (or precursors or susceptibility to a disease) from those who probably do not. Why? To make a real difference to health It is not intended to be diagnostic (diagnostic tests are different). It is a process, not simply a test

The Purpose of screening Prevention, treatment and information The Purpose of screening

Prevention No disease No symptoms Clinical disease Primary prevention Secondary prevention Tertiary prevention

Main purpose of screening Secondary prevention Aim – detect early disease in order to alter the course of the disease e.g. screening by mammography for breast cancer in order to treat it early Primary prevention Aim - prevent a disease from occurring screening to identify people with risk factors and reduce risk factor levels

What do we want to achieve? Reduce the risk of developing disease Provide treatment Provide information E.g. pre-natal screening for genetic disorders

The sieve Screened population The screening test Further investigation Discharged from screening Screened population The screening test

Measuring the effectiveness of screening Sensitivity and specificity Measuring the effectiveness of screening

What screening does The truth Disease Present Disease Absent The test result Positive True positive a False positive b a+b Negative False negative c True negative d c+d a+c b+d a+b+c+d

Definitions: sensitivity Sensitivity – the proportion of people with the disease who are correctly identified by the screening test a / a+c The truth Disease Present Disease Absent The test result Positive True positive a False positive b a+b Negative False negative c True negative d c+d a+c b+d a+b+c+d

Definitions: specificity Specificity – the proportion of people without the disease who are correctly excluded by the screening test d / b+d The truth Disease Present Disease Absent The test result Positive True positive a False positive b a+b Negative False negative c True negative d c+d a+c b+d a+b+c+d

Definitions: PPV a / a+b Positive predictive value – the proportion of people with a positive test result who actually have the disease a / a+b The truth Disease Present Disease Absent The test result Positive True positive a False positive b a+b Negative False negative c True negative d c+d a+c b+d a+b+c+d

Definitions: NPV d / c+d Negative predictive value – the proportion of people with a negative test result who do not have the disease d / c+d The truth Disease Present Disease Absent The test result Positive True positive a False positive b a+b Negative False negative c True negative d c+d a+c b+d a+b+c+d

Example 1: high prevalence cancer present absent cytology positive 14 (a) 2 (b) negative 6 (c) 78 (d) Sensitivity = 14/20 = 70% Specificity = 78/80 = 97.5% Prevalence = 20/100 = 20% Prevalence of “no disease” = 80/100 = 80% Positive predictive value = 14/16 = 87.5% Negative predictive value = 78/84 = 92.9% Sensitivity: a/a+c Specificity: d/b+d Prevalence: a+c/a+b+c+d PPV: a/a+b NPV: d/c+d

Example 2: low prevalence cancer present absent cytology positive 7 (a) 250 (b) negative 3 (c) 9740 (d) Sensitivity = 7/10 = 70% Specificity = 9740/9990 = 97.5% Prevalence = 10/10000 = 0.10% Prev “no disease” = 9990/10000 = 99.9% PPV = 7/257 = 2.7 % NPV= 9740/9743 = 99.97% Sensitivity: a/a+c Specificity: d/b+d Prevalence: a+c/a+b+c+d PPV: a/a+b NPV: d/c+d

Why is the positive predictive value so different? Predictive values are dependent on underlying prevalence (Sensitivity and specificity are not)

Continuous screening variable – effect of cut-off chosen on sensitivity and specificity

Wilson and Jungner (and others) Screening criteria

Criteria for screening Most based on Wilson and Jungner criteria The condition The condition sought should be an important health problem The natural history of the condition should be well understood There should be a detectable early stage

Criteria for screening The treatment There should be an accepted treatment for patients with recognized disease. Facilities for diagnosis and treatment should be available Adequate health service provision should be made for the extra clinical workload resulting from screening

Criteria for screening The Test A suitable test should be devised for the early stage The test should be acceptable Intervals for repeating the test should be determined (not a one off)

Criteria for screening Risks and benefits There should be an agreed policy on whom to treat The costs should be balanced against the benefits Additionally The risks, both physical and psychological, should be less than the benefits

Evaluation of screening Ideally by RCT individual cluster Could use other methods but potential for bias Well recognised biases Selection bias Lead-time bias Length-time (or length) bias

Selection bias People who choose to participate in screening programmes may be different from those who do not May be at higher risk e.g. women with family history of breast cancer more likely to attend May be at lower risk e.g. women in higher socioeconomic groups (lower risk of cervical cancer) more likely to attend

Lead time bias Disease starts Patient A diagnosed after screening Death Patient B diagnosed when symptoms develop

Length-time (or length) bias

Screening examples

e.g. breast cancer screening Eligible group: women (within an age range) Test: 2 view mammography Further tests: mammography, biopsy (tissue), ultrasound, cytology (cells) Treatment: surgery, radiotherapy, chemotherapy Avoiding: overall deaths in screened women

Types of screening Population-based screening programmes (“mass” screening) Thailand, national diabetes and hypertension screening Opportunistic screening Prevention and control of substance abuse Screening for communicable diseases Heaf test? Pre-employment and occupational medicals Vision test for commercial drivers? Commercially provided screening Screening is more than a test, it is a programme

Pros and cons of screening Ethics? Mistakes? Limitations? Risk reduction or disease diagnosis?

Some summary points Healthy people are more likely to get screened than less healthy people. This bias means that outcome in screened people looks good even if screening makes no difference. Screening is more likely to pick up cases that stay symptomless for a long time and less likely to pick up rapidly progressive disease. This means that cases found by screening are a different group compared with cases that present clinically. They have better prognosis, even if screening makes no difference. This bias is called length time bias. Screening often uncovers cases that would have no clinical impact in the person’s lifetime. These cases are a different group compared with cases that present clinically. They have excellent prognosis, and screening leads to the person receiving diagnosis and treatment that they would not otherwise have. This makes outcome in a screen-detected group appear good. This bias is called overdiagnosis bias. Test performance can be expressed as sensitivity, specificity and predictive values To describe all the consequences of screening properly, the evidence about benefits and harms should be summarized as simple frequencies. This can be done using a flow diagram for a screened population. Development of comprehensive information for potential participants is a relatively new venture, but is progressing

Next Two workbooks on Minerva You have timetabled space to complete these, both this afternoon and Thursday afternoon.