What makes a good screening programme

There may be psychological consequences such as anxiety for both the patient and their family. For example, a woman with a false positive mammogram undergoing surgical investigation e. A person undergoing a colonoscopy as a result of a false positive faecal occult blood test faces the possibility of a bowel perforation during the procedure. This risk might be as high as one in [4]. There are concerns that false negative results can give rise to legal action by people whose cancers appear to have been missed.

This must be communicated effectively to the potential participants in a screening program to allow informed consideration of their involvement before any test is done.

Communications must address differences in literacy and language competency to ensure that individuals are properly informed. Implementation of possible screening programs will be influenced by consideration of the equal distribution of limited resources across the whole community for maximum benefit. Resources allocated to a screening program will lower resources available for other health needs.

Determining the costs of screening involves the costs of the test and subsequent diagnostic tests and the costs associated with any hazard of the test as well as the costs of over-treatment. Bowel cancer. Cervical cancer. Breast cancer. Prostate cancer. Liver cancer. Position statement - Testicular cancer. Position statement - Early detection of skin cancer. National Cancer Control Policy.

Principles of screening Related chapters Bowel cancer Cervical cancer Breast cancer Prostate cancer Liver cancer Related position statements Testicular cancer Skin cancer.

Jump to: navigation , search. Fundamental issues in screening for cancer. In: D Schottenfeld, J Fraumeni. Cancer epidemiology and prevention. Second edition. New York: Oxford University Press; Principles and practices of screening for disease. If a test is reliable, it gives consistent results with repeated tests. Variability in the measurement can be the result of physiologic variation or the result of variables related to the method of testing.

For example, if one were using a sphygmomanometer to measure blood pressure repeatedly over time in a single individual, the results might vary depending on:. Test validity is the ability of a screening test to accurately identify diseased and non-disease individuals.

An ideal screening test is exquisitely sensitive high probability of detecting disease and extremely specific high probability that those without the disease will screen negative. However, there is rarely a clean distinction between "normal" and "abnormal. The validity of a screening test is based on its accuracy in identifying diseased and non-diseased persons, and this can only be determined if the accuracy of the screening test can be compared to some "gold standard" that establishes the true disease status.

The gold standard might be a very accurate, but more expensive diagnostic test. Alternatively, it might be the final diagnosis based on a series of diagnostic tests. If there were no definitive tests that were feasible or if the gold standard diagnosis was invasive, such as a surgical excision, the true disease status might only be determined by following the subjects for a period of time to determine which patients ultimately developed the disease.

For example, the accuracy of mammography for breast cancer would have to be determined by following the subjects for several years to see whether a cancer was actually present. A 2 x 2 table, or contingency table, is also used when testing the validity of a screening test, but note that this is a different contingency table than the ones used for summarizing cohort studies, randomized clinical trials, and case-control studies.

The 2 x 2 table below shows the results of the evaluation of a screening test for breast cancer among 64, subjects. The contingency table for evaluating a screening test lists the true disease status in the columns, and the observed screening test results are listed in the rows. The table shown above shows the results for a screening test for breast cancer. There were women who were ultimately found to have had breast cancer, and 64, women remained free of breast cancer during the study.

Among the women with breast cancer, had a positive screening test true positives , but 45 had negative tests false negatives. Among the 64, women without breast cancer, 63, appropriately had negative screening tests true negatives , but incorrectly had positive screening tests false positives. If we focus on the rows, we find that 1, subjects had a positive screening disease, i.

However, only of these were found to actually have disease, based on the gold standard test. Also note that 63, people had a negative screening test, suggesting that they did not have the disease, BUT, in fact 45 of these people were actually diseased. One measure of test validity is sensitivity , i. When thinking about sensitivity, focus on the individuals who, in fact, really were diseased - in this case, the left hand column. Table - Illustration of the Sensitivity of a Screening Test.

What was the probability that the screening test would correctly indicate disease in this subset? The probability is simply the percentage of diseased people who had a positive screening test, i. I could interpret this by saying, "The probability of the screening test correctly identifying diseased subjects was Specificity focuses on the accuracy of the screening test in correctly classifying truly non-diseased people.

It is the probability that non-diseased subjects will be classified as normal by the screening test. Table - Illustration of the Specificity of a Screening Test.

I could interpret this by saying, "The probability of the screening test correctly identifying non-diseased subjects was Question: In the above example, what was the prevalence of disease among the 64, people in the study population? Compute the answer on your own before looking at the answer. One problem is that a decision must be made about what test value will be used to distinguish normal versus abnormal results.

Unfortunately, when we compare the distributions of screening measurements in subjects with and without disease, we find that there is almost always some overlap, as shown in the figure to the right. Deciding the criterion for "normal " versus abnormal can be difficult.

There may be a very low range of test results e. However, where the distributions overlap, there is a "gray zone" in which there is much less certainly about the results. If we move the cut-off to the left, we can increase the sensitivity, but the specificity will be worse. If we move the cut-off to the right, the specificity will improve, but the sensitivity will be worse. Altering the criterion for a positive test "abnormality" will always influence both the sensitivity and specificity of the test.

ROC curves provide a means of defining the criterion of positivity that maximizes test accuracy when the test values in diseased and non-diseased subjects overlap. As the previous figure demonstrates, one could select several different criteria of positivity and compute the sensitivity and specificity that would result from each cut point.

In the example above, suppose I computed the sensitivity and specificity that would result if I used cut points of 2, 4, or 6. If I were to do this for the example above, by table would look something like this:. I could then plot the true positive rate the sensitivity as a function of the false positive rate 1-specificity , and the plot would look like the figure below. Note that the true positive and false positive rates obtained with the three different cut points criteria are are shown by the three blue points representing true positive and false positive rates using the three different criteria of positivity.

This is a receiver-operator characteristic curve that assesses test accuracy by looking at how true positive and false positive rates change when different criteria of positivity are used. If the diseased people had test values that were always greater than the test values in non-diseased people, i. The closer the ROC curve hugs the left axis and the top border, the more accurate the test, i.

The diagonal blue line illustrates the ROC curve for a useless test for which the true positive rate and the false positive rate are equal regardless of the criterion of positivity that is used - in other words the distribution of test values for disease and non-diseased people overlap entirely. So, the closer the ROC curve is to the blue star, the better it is, and the closer it is to the diagonally blue line, the worse it is.

This provides a standard way of assessing test accuracy, but perhaps another approach might be to consider the seriousness of the consequences of a false negative test. For example, failing to identify diabetes right away from a dip stick test of urine would not necessarily have any serious consequences in the long run, but failing to identify a condition that was more rapidly fatal or had serious disabling consequences would be much worse. As a nutritionist it was an eye opener for keys place where we least expect people living with diabetes can progress to such a horrible stage without awareness.

I have recommended this course to my colleagues and I believe they might have started or yet to. I appreciate this opportunity a lot. The content was good for our learning and improvements in our diabetic eye care services. Learning about the DR ManagementTeatment Options on Protocol; guidelines s for treatment options are based on the presenting diabetic eye disease and need to involve the patient to accept the long term care and outcomes. For low resource settings we need to develop relevant models of care that can work efficiently in our setting to prevent blindness caused by DR.

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Learn more about this course. Identifying a good screening test: sensitivity, specificity and coverage Characteristics of a good screening test for diabetic retinopathy - high sensitivity, specificity, positive predictive value and high coverage. View transcript. A detailed examination of the retina, at the back of the eye, is the key procedure when screening for diabetic retinopathy.

Various examination methods are available, from ophthalmoscopy to digital retinal imaging - with or without pupil dilation. Whichever examination method is selected: Screeners must be able to find all cases of retinopathy correctly without causing unnecessary anxiety to the person being screened. And, the health system must be able to: - Pay for, and manage the purchasing of, screening equipment and; - Train personnel to use and maintain it. We can use a 2 by 2 table to plot the presence of diabetic retinopathy against the ability of the test to detect the condition correctly.

A good screening test for DR must have the following 4 key characteristics. The test must also have a high positive predictive value ensuring a high probability that each person with a positive screening test truly has retinopathy. Lets look at a hypothetical example to understand why these characteristics are important. In our population of people with diabetes, 10 have retinopathy these are known as cases. If all people are screened, we have achieved high coverage.