Screening (medicine)

In many countries there are population-based screening programmes. In some countries, such as the UK, policy is made nationally and programmes are delivered nationwide to uniform quality standards. Common screening programmes include:

• Cancer screening
  • Pap smear or liquid-based cytology to detect potentially precancerous lesions and prevent cervical cancer
  • Mammography to detect breast cancer
  • Colonoscopy and fecal occult blood test to detect colorectal cancer
  • Dermatological check to detect melanoma
  • PSA to detect prostate cancer
• PPD test to screen for exposure to tuberculosis
• Beck Depression Inventory to screen for depression
• SPAI-B, the Liebowitz Social Anxiety Scale and Social Phobia Inventory to screen for social anxiety disorder
• Alpha-fetoprotein, blood tests and ultrasound scans for pregnant women to detect fetal abnormalities
• Bitewing radiographs to screen for interproximal dental caries
• Ophthalmoscopy or digital photography and image grading for diabetic retinopathy
• Ultrasound scan for abdominal aortic aneurysm
• Screening of potential sperm bank donors
• Screening for metabolic syndrome
• Screening for potential hearing loss in newborns

Most public school systems in the United States screen students periodically for hearing and vision deficiencies and dental problems. Screening for spinal and posture issues such as scoliosis is sometimes carried out, but is controversial as scoliosis (unlike vision or dental issues) is found in only a very small segment of the general population and because students must remove their shirts for screening. Many states no longer mandate scoliosis screenings, or allow them to be waived with parental notification. There are currently bills being introduced in various U.S. states to mandate mental health screenings for students attending public schools in hopes to prevent self-harm as well as the harming of peers. Those proposing these bills hope to diagnose and treat mental illnesses such as depression and anxiety.

The social determinants of health are the economic and social conditions that influence individual and group differences in health status.[11] Those conditions may have adverse effects on their health and well-being. To mitigate those adverse effects, certain health policies like the United States Affordable Care Act (2010) gave increased traction to preventive programs, such as those that routinely screen for social determinants of health.[12] Screening is believed to a valuable tool in identifying patients' basic needs in a social determinants of health framework so that they can be better served.[13][14]

When established in the United States, the Affordable Care Act was able to bridge the gap between community-based health and healthcare as a medical treatment, leading to programs that screened for social determinants of health.[12] The Affordable Care Act established several services with an eye for social determinants or an openness to more diverse clientele, such as Community Transformation Grants, which were delegated to the community in order to establish "preventive community health activities" and "address health disparities".[15]

Social determinants of health include social status, gender, ethnicity, economic status, education level, access to services, education, immigrant status, upbringing, and much, much more.[16][17] Several clinics across the United States have employed a system in which they screen patients for certain risk factors related to social determinants of health.[18] In such cases, it is done as a preventive measure in order to mitigate any detrimental effects of prolonged exposure to certain risk factors, or to simply begin remedying the adverse effects already faced by certain individuals.[14][19] They can be structured in different ways, for example, online or in person, and yield different outcomes based on the patient's responses.[14] Some programs, like the FIND Desk at UCSF Benioff Children's Hospital, employ screening for social determinants of health in order to connect their patients with social services and community resources that may provide patients greater autonomy and mobility.[20]

Screening may identify abnormalities that would never cause a problem in a person's lifetime. An example of this is prostate cancer screening; it has been said that "more men die with prostate cancer than of it".[23] Autopsy studies have shown that between 14 and 77% of elderly men who have died of other causes are found to have had prostate cancer.[24]

Aside from issues with unnecessary treatment (prostate cancer treatment is by no means without risk), overdiagnosis makes a study look good at picking up abnormalities, even though they are sometimes harmless.

Overdiagnosis occurs when all of these people with harmless abnormalities are counted as "lives saved" by the screening, rather than as "healthy people needlessly harmed by overdiagnosis". So it might lead to an endless cycle: the greater the overdiagnosis, the more people will think screening is more effective than it is, which can reinforce people to do more screening tests, leading to even more overdiagnosis.[25] Raffle Mackie and Gray call this the popularity paradox of screening: "The greater the harm through overdiagnosis and overtreatment from screening, the more people there are who believe they owe their health, or even their life, to the programme"(p56 Box 3.4) [26]

The screening for neuroblastoma, the most common malignant solid tumor in children, in Japan is a very good example of why a screening program must be evaluated rigorously before its implemented. In 1981, Japan started a program of screening for neuroblastoma by measuring homovanillic acid and vanilmandelic acid in urine samples of six-month-old infants. In 2003, a special committee was organized to evaluate the motivation for the neuroblastoma screening program. In the same year, the committee concluded that there was sufficient evidence that screening method used in the time led to overdiagnosis, but there was no enough evidence that the program reduced neuroblastoma deaths. As such, the committee recommended against screening and the Ministry of Health, Labor and Welfare decided to stop the screening program.[27]

Another example of overdiagnosis happened with thyroid cancer: its incidence tripled in United States between 1975 and 2009, while mortality was constant.[28] In South Korea, the situation was even worse with 15-fold increase in the incidence from 1993 to 2011 (the world's greatest increase of thyroid cancer incidence), while the mortality remained stable.[29] The increase in incidence was associated with the introduction of ultrasonography screening.[30]

The problem of overdiagnosis in cancer screening is that at the time of diagnosis it not possible to differentiate between a harmless lesion and lethal one, unless the patient do not treat and dies from other causes.[31] So almost all patients tend to be treated, leading to what is called overtreatment. As researchers Welch and Black put it, "Overdiagnosis—along with the subsequent unneeded treatment with its attendant risks—is arguably the most important harm associated with early cancer detection." [31]

Lead time bias leads to longer perceived survival with screening, even if the course of the disease is not altered

If screening works, it must diagnose the target disease earlier than it would be without screening (when symptoms appear).

Even if in both cases a person will die at the same time, because we diagnosed the disease earlier with screening the survival time since diagnosis is longer with screening; even in the case life span has not been prolonged, and there will be added anxiety as the patient must live with knowledge of the disease for longer.

If screening works, it must introduce a lead time. So statistics of survival time since diagnosis tends increase with screening because of the lead time introduced, even when screening offers no benefits. If we do not think about what survival time actually means in this context, we might attribute success to a screening test that does nothing but advance diagnosis; comparing statistics of mortality due to a disease in a screened and unscreened population gives more meaningful information.

Length time bias leads to better perceived survival with screening, even if the course of the disease is not altered.

Many screening tests involve the detection of cancers. Screening is more likely to detect slower-growing tumors (due to longer pre-clinical sojourn time) that are less likely to cause harm. Also, those aggressive cancers tend to produce symptoms in the gap between scheduled screening, being less likely to be detected by screening.[32] So, the cases screening often detects automatically have better prognosis than symptomatic cases. The consequence is those more slow progressive cases are now classified as cancers, which increases the incidence, and due to its better prognosis, the survival rates of screened people will be better than non-screened people even if screening makes no difference.

Not everyone will partake in a screening program. There are factors that differ between those willing to get tested and those who are not.

If people with a higher risk of a disease are more likely to be screened, for instance women with a family history of breast cancer are more likely than other women to join a mammography program, then a screening test will look worse than it really is: negative outcomes among the screened population will be higher than for a random sample.

Selection bias may also make a test look better than it really is. If a test is more available to young and healthy people (for instance if people have to travel a long distance to get checked) then fewer people in the screening population will have negative outcomes than for a random sample, and the test will seem to make a positive difference.

Studies have shown that people who attend screening tend to be healthier than those who do not. This has been called the healthy screenee effect,[26] which is a form of selection bias. The reason seems to be that people who are healthy, affluent, physically fit, non-smokers with long-lived parents are more likely to come and get screened than those on low-income, who have existing health and social problems.[26] One example of selection bias occurred in Edinbourg trial of mammography screening, which used cluster randomisation. The trial found reduced cardiovascular mortality in those who were screened for breast cancer. That happened because baseline differences regarding socio-economic status in the groups: 26% of the women in the control group and 53% in the study group belonged to the highest socioeconomic level.[33]

The best way to minimize selection bias is to use a randomized controlled trial, though observational, naturalistic, or retrospective studies can be of some value and are typically easier to conduct. Any study must be sufficiently large (include many patients) and sufficiently long (follow patients for many years) to have the statistical power to assess the true value of a screening program. For rare diseases, hundreds of thousands of patients may be needed to realize the value of screening (find enough treatable disease), and to assess the effect of the screening program on mortality a study may have to follow the cohort for decades. Such studies take a long time and are expensive, but can provide the most useful data with which to evaluate the screening program and practice evidence-based medicine.

The main outcome of cancer screening studies is usually the number of deaths caused by the disease being screened for - this is called disease-specific mortality. To give an example: in trials of mammography screening for breast cancer, the main outcome reported is often breast cancer mortality. However, disease-specific mortality might be biased in favor of screening. In the example of breast cancer screening, women overdiagnosed with breast cancer might receive radiotherapy, which increases mortality due to lung cancer and heart disease.[34] The problem is those deaths are often classified as other causes and might even be larger than the number of breast cancer deaths avoided by screening. So the non-biased outcome is all-cause mortality. The problem is that much larger trials are needed to detect a significant reduction in all-cause mortality. In 2016, researcher Vinay Prasad and colleagues published an article in BMJ titled "Why cancer screening has never been shown to save lives", as cancer screening trials did not show all-cause mortality reduction.[35]