Hubbry Logo
Screening (medicine)Screening (medicine)Main
Open search
Screening (medicine)
Community hub
Screening (medicine)
logo
8 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
Screening (medicine)
Screening (medicine)
Screening (medicine)
View on Wikipedia
from Wikipedia
A coal miner completes a screening survey for coalworker's pneumoconiosis.

In medicine, screening is a strategy used to look for as-yet-unrecognised conditions or risk markers.[1][2][3] This testing can be applied to individuals or to a whole population without symptoms or signs of the disease being screened.

Screening interventions are designed to identify conditions which could at some future point turn into disease, thus enabling earlier intervention and management in the hope to reduce mortality and suffering from a disease. Although screening may lead to an earlier diagnosis, not all screening tests have been shown to benefit the person being screened; overdiagnosis, misdiagnosis, and creating a false sense of security are some potential adverse effects of screening. Additionally, some screening tests can be inappropriately overused.[4][5] For these reasons, a test used in a screening program, especially for a disease with low incidence, must have good sensitivity in addition to acceptable specificity.[6]

Several types of screening exist: universal (population-based) screening involves testing of all individuals in a certain category (for example, all children of a certain age). Case finding involves testing a smaller group of people based on the presence of risk factors (for example, because a family member has been diagnosed with a hereditary disease). When delivered to large numbers of people at the population level rather than by individual clinicians, testing asymptomatic people for disease because they have one or more risk factors is sometimes referred to as targeted or stratified screening.[7] Screening interventions are not designed to be diagnostic, and often have significant rates of both false positive and false negative results.

In the US, frequently updated recommendations for screening are provided by the independent panel of experts, the United States Preventive Services Task Force.[8] In the UK, recommendations are provided by the UK National Screening Committee.[9]

Principles

[edit]

In 1968, the World Health Organization published guidelines on the Principles and practice of screening for disease, which is often referred to as the Wilson and Jungner criteria.[10] The principles are still broadly applicable today:

  1. The condition should be an important health problem.
  2. There should be a treatment for the condition.
  3. Facilities for diagnosis and treatment should be available.
  4. There should be a latent stage of the disease.
  5. There should be a test or examination for the condition.
  6. The test should be acceptable to the population.
  7. The natural history of the disease should be adequately understood.
  8. There should be an agreed policy on whom to treat.
  9. The total cost of finding a case should be economically balanced in relation to medical expenditure as a whole.
  10. Case-finding should be a continuous process, not just a "once and for all" project.

In 2008, with the emergence of new genomic technologies, the WHO synthesised and modified these with the new understanding as follows:

Synthesis of emerging screening criteria proposed over the past 40 years

  • The screening programme should respond to a recognized need.
  • The objectives of screening should be defined at the outset.
  • There should be a defined target population.
  • There should be scientific evidence of screening programme effectiveness.
  • The programme should integrate education, testing, clinical services and programme management.
  • There should be quality assurance, with mechanisms to minimize potential risks of screening.
  • The programme should ensure informed consent, confidentiality and respect for personal, bodily autonomy.
  • The programme should promote equity and access to screening for the entire target population.
  • Programme evaluation should be planned from the outset.
  • The overall benefits of screening should outweigh the harm.

In summation, "when it comes to the allocation of scarce resources, economic considerations must be considered alongside 'notions of justice, equity, personal freedom, political feasibility, and the constraints of current law'."[11]

Types

[edit]
A mobile clinic used to screen coal miners at risk of black lung disease
A mobile clinic used to screen coal miners at risk of black lung disease
  • Mass screening (sometimes termed population-based screening): The screening of a whole population or subgroup. It is offered to all, irrespective of the risk status of the individual.
  • High risk or targeted screening or selective screening: High risk screening is conducted only among high-risk people.
  • Multiphasic screening: The application of two or more screening tests to a large population at one time, instead of carrying out separate screening tests for single diseases.
  • When done thoughtfully and based on research, identification of risk factors can be a strategy for medical screening.[12]

Examples

[edit]

Common programs

[edit]

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:[citation needed]

School-based

[edit]

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. [citation needed]

Screening for social determinants of health

[edit]

The social determinants of health are the economic and social conditions that influence individual and group differences in health status.[14] 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.[15] 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.[16][17]

Policy background in the United States

[edit]

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.[15] 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".[18]

Clinical programs

[edit]

Social determinants of health include social status, gender, ethnicity, economic status, education level, access to services, education, immigrant status, upbringing, and much, much more.[19][20] 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.[21] 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.[17][22] They can be structured in different ways, for example, online or in person, and yield different outcomes based on the patient's responses.[17] 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.[23]

Medical equipment used

[edit]

Medical equipment used in screening tests is usually different from equipment used in diagnostic tests as screening tests are used to indicate the likely presence or absence of a disease or condition in people not presenting symptoms; while diagnostic medical equipment is used to make quantitative physiological measurements to confirm and determine the progress of a suspected disease or condition. Medical screening equipment must be capable of fast processing of many cases, but may not need to be as precise as diagnostic equipment.[citation needed]

Limitations

[edit]

Screening can detect medical conditions at an early stage before symptoms present while treatment is more effective than for later detection.[24] In the best of cases lives are saved. Like any medical test, the tests used in screening are not perfect. The test result may incorrectly show positive for those without disease (false positive), or negative for people who have the condition (false negative). Limitations of screening programmes can include:

  • Screening can involve cost and use of medical resources on a majority of people who do not need treatment.
  • Adverse effects of screening procedure (e.g. stress and anxiety, discomfort, radiation exposure, chemical exposure).
  • Stress and anxiety caused by prolonging knowledge of an illness without any improvement in outcome. This problem is referred to as overdiagnosis (see also below).
  • Stress and anxiety caused by a false positive screening result.
  • Unnecessary investigation and treatment of false positive results (namely misdiagnosis with Type I error).
  • A false sense of security caused by false negatives, which may delay final diagnosis (namely misdiagnosis with Type II error).

Screening for dementia in the English NHS is controversial because it could cause undue anxiety in patients and support services would be stretched. A GP reported "The main issue really seems to be centred around what the consequences of a such a diagnosis is and what is actually available to help patients."[25]

Analysis

[edit]

To many people, screening instinctively seems like an appropriate thing to do, because catching something earlier seems better. However, no screening test is perfect. There will always be the problems with incorrect results and other issues listed above. It is an ethical requirement for balanced and accurate information to be given to participants at the point when screening is offered, in order that they can make a fully informed choice about whether or not to accept.[citation needed]

Before a screening program is implemented, it should be looked at to ensure that putting it in place would do more good than harm. The best studies for assessing whether a screening test will increase a population's health are rigorous randomized controlled trials.When studying a screening program using case-control or, more usually, cohort studies, various factors can cause the screening test to appear more successful than it really is. A number of different biases, inherent in the study method, will skew results.[citation needed]

Overdiagnosis

[edit]
Further information: Overdiagnosis

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".[26] 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.[27]

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.[citation needed]

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.[28] 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) [29]

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 it is 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.[30]

Another example of overdiagnosis happened with thyroid cancer: its incidence tripled in United States between 1975 and 2009, while mortality was constant.[31] 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.[32] The increase in incidence was associated with the introduction of ultrasonography screening.[33]

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 is not treated and dies from other causes.[34] 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."[34]

Lead time bias

[edit]
Further information: Lead time bias
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 (with screening vs without screening) patients die at the same time, just because the disease was diagnosed earlier by screening, the survival time since diagnosis is longer in screened people than in persons who was not screened. This happens even when life span has not been prolonged. As the diagnosis was made earlier without life being prolonged, the patient might be more anxious as he must live with knowledge of his diagnosis for longer.[citation needed]

If screening works, it must introduce a lead time. So statistics of survival time since diagnosis tends to 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. As survival statistics suffers from this and other biases, comparing the disease mortality (or even all-cause mortality) between screened and unscreened population gives more meaningful information.[citation needed]

Length time bias

[edit]
Further information: Length time bias
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.[35] 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.[citation needed]

Selection bias

[edit]
Further information: Selection bias

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

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.[citation needed]

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.[citation needed]

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,[29] 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.[29] 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.[36] Cardiovascular risk screening is a vital tool in reducing the global incidence of cardiovascular diseases.[37]

Study Design for the Research of Screening Programs

[edit]

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.[citation needed]

All-cause mortality vs disease-specific mortality

[edit]

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.[38] 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.[39]

See also

[edit]

References

[edit]

Further reading

[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Screening (medicine)

View on Grokipedia
from Grokipedia
Screening in entails the systematic application of tests or examinations to individuals or subgroups to detect preclinical , defects, or risk factors, with the objective of enabling early intervention to avert or mitigate adverse outcomes. The foundational principles for implementing screening programs, as articulated in the 1968 Wilson and Jungner criteria, emphasize that the targeted condition must represent a significant burden, possess a latent detectable phase, and have an available, effective treatment; moreover, the screening test itself must be reliable, cost-efficient, and broadly acceptable, while the program's overall benefits—such as reduced morbidity or mortality—must demonstrably exceed its risks and expenses. These criteria underscore first-principles evaluation of causal pathways from detection to improved survival, rather than mere identification, and have informed global decisions despite ongoing refinements to address modern complexities like genomic testing. Proven benefits of screening include mortality reductions in conditions amenable to early detection, such as through cytological testing or via tests, where randomized trials have established net gains in for screened cohorts when treatments are effective. However, reveals that not all advocated programs deliver these gains, as screening efficacy hinges on disease biology, test specificity, and follow-up protocols, with population-level implementation requiring rigorous validation to avoid resource misallocation. Central controversies arise from inherent limitations, including false-positive results prompting unnecessary invasive procedures, psychological burdens from diagnostic uncertainty, and systemic biases that inflate perceived efficacy: lead-time bias, whereby earlier diagnosis extends recorded survival without altering disease course; and length-time bias, which preferentially detects indolent, slower-progressing cases over aggressive ones, skewing outcomes toward apparent success. —treating non-progressive lesions as threats—exemplifies these issues in , as seen in and contexts where estimates of excess diagnoses range widely (up to 75% in some models), often without corresponding mortality benefits and at the cost of overtreatment harms. Rigorous appraisal, prioritizing trial data over observational correlations, is essential to discern genuine causal benefits from these artifacts.

Definition and Principles

Definition of Medical Screening

Medical screening refers to the systematic application of tests, examinations, or procedures to individuals or populations to detect unrecognized diseases, defects, or risk factors at an early stage, prior to the onset of symptoms that would prompt clinical evaluation. This approach targets apparently healthy people to identify those at higher risk, enabling interventions that may prevent disease progression, reduce morbidity, or improve rates. Unlike routine health checks prompted by symptoms, screening is proactive and population-based, often focusing on prevalent conditions where early detection confers net benefits. The primary objective of medical screening is to shift disease detection from symptomatic stages to preclinical phases, potentially enhancing treatment efficacy and in systems. For instance, effective programs have demonstrated reductions in mortality for conditions like through cytological screening, where precancerous lesions are identified and treated before occurs. However, screening tests are typically not diagnostic; a positive result necessitates confirmatory procedures to establish the presence of , as initial tests prioritize sensitivity over specificity to minimize missed cases. Key characteristics of medical screening include its application to defined target groups based on age, , or exposure risks, rather than universal use, to optimize yield and cost-effectiveness. Tests must balance detection accuracy against potential harms, such as or unnecessary follow-up, which can lead to patient anxiety or iatrogenic without altering outcomes. Screening is distinct from , which monitors known cases, and from , which evaluates probabilities without immediate testing.

Fundamental Principles

The fundamental principles of medical screening emphasize the identification of asymptomatic individuals at risk for specific diseases to enable early intervention, provided that such detection demonstrably improves health outcomes. Central to these principles are the criteria outlined by Wilson and Jungner in their 1968 World Health Organization report, which provide a framework for evaluating the suitability of screening programs. These include: the condition must represent a significant health problem; an effective treatment must exist for detectable cases; diagnostic and treatment facilities must be accessible; a latent or early symptomatic phase must be identifiable; a reliable test must be available; the test must be acceptable to the population; the disease's natural history must be well understood; treatment policies must be clear; costs must be balanced against overall healthcare expenditures; and screening must be ongoing rather than episodic. These principles underscore that screening should not merely increase detection rates but must reduce morbidity, mortality, or both, as evidenced by randomized controlled trials comparing screened versus unscreened populations. A screening test's validity hinges on its sensitivity—the proportion of true positives correctly identified—and specificity—the proportion of true negatives correctly identified—relative to a gold standard diagnostic method. High sensitivity minimizes false negatives, ensuring few cases are missed, while high specificity reduces false positives, avoiding unnecessary follow-up burdens. Tests must also demonstrate reliability through reproducible results across settings and operators. However, apparent benefits can be confounded by biases: lead-time bias occurs when earlier detection artificially extends observed survival without altering disease course, as the patient's lifespan from diagnosis to death increases solely due to the timing of identification. Length-time bias arises because screening preferentially detects slower-progressing conditions that are more likely to be and present during testing intervals, overrepresenting less aggressive cases and inflating perceived efficacy. , the detection of non-progressive or indolent diseases that would never cause symptoms, further complicates interpretation, potentially leading to harms from overtreatment without mortality reduction. Ethical considerations demand that benefits outweigh risks, including psychological distress, procedural complications, and resource diversion, with programs justified only when net gains are empirically supported.

Historical Development

Origins and Early Concepts

The concept of medical screening emerged in the late 19th and early 20th centuries, initially through efforts to inspect children for contagious diseases and physical defects, marking a shift from reactive to proactive detection in populations. By the 1890s, U.S. cities implemented medical examinations in schools to identify and exclude children with infectious conditions, evolving by 1900 to broader assessments of impairments such as vision and hearing; surveys in 1905 and 1906 revealed that 20-33% of children had vision defects and up to 50% required medical attention. State mandates followed, with requiring vision checks in 1899, mandating eye, ear, and throat examinations in 1904, and making general medical inspections compulsory in public schools by 1906. Early advocates promoted periodic health examinations for adults as a preventive measure, conceptualizing screening as routine akin to vehicle inspections. In 1900, physician George M. Gould proposed exams every 1-5 years to detect preclinical conditions. The Life Extension Institute, founded in 1913-1914 and backed by life insurance interests, conducted over 250,000 examinations by 1924, emphasizing early detection through physical checks and basic tests; the endorsed annual exams in 1922. Industrial programs began around 1910, such as tuberculosis exams in factories, with 10% of the largest U.S. corporations offering pre-employment screening by 1917. Disease-specific screening tools advanced these concepts, particularly for infectious conditions amenable to early intervention. The Wassermann serological test for , introduced in 1906, enabled population-based detection and contributed to incidence declines, while chest X-rays for , promoted by the National Association for the Study and Prevention of Tuberculosis (formed 1904), gained traction by 1912. One of the earliest organized programs was psychiatric screening in the U.S. Army during , using psychological assessments to identify recruits at risk of mental breakdown before deployment. These efforts laid groundwork for viewing screening as a strategy, though uptake remained limited—e.g., only 2.5% of Metropolitan policyholders accepted free exams offered starting in 1914.

Post-War Expansion and Standardization

Following , medical screening expanded rapidly in civilian populations, building on wartime experiences with mass examinations, such as the assessment of approximately 20 million U.S. military inductees between 1940 and 1946, which identified one-third as unfit due to various health issues. Advances in diagnostic technologies, including portable units and serological tests, combined with the availability of effective treatments like penicillin for and for , shifted focus from military to applications. This era saw increased government and organizational investment in preventive medicine, driven by epidemiological data showing early detection could reduce , particularly in industrialized nations facing postwar and . Key programs emphasized targeted screenings for prevalent conditions. The Papanicolaou (Pap) test for cervical cancer, validated in 1941, achieved widespread use starting in the 1940s, with U.S. programs reducing cervical cancer incidence and mortality by at least 50% through routine cytological examination of asymptomatic women. Diabetes screening via urine glucose tests emerged as a mass effort in the 1940s, enabling detection in apparently healthy individuals and prompting dietary or insulin interventions. Tuberculosis control expanded with mass miniature radiography (MMR) in Britain from the late 1940s, screening millions via mobile units, while U.S. efforts built on wartime chest X-rays of 10 million personnel to implement community-wide tuberculin skin testing and follow-up radiography. Standardization advanced through systematic approaches to multiple conditions. In 1951, initiated automated multiphasic health screening in response to physician shortages, processing thousands annually with standardized batteries of tests for blood pressure, vision, hearing, and biochemistry, as detailed in early publications from 1955. Concurrently, J.G. Wilson outlined criteria for effective screening in a 1951 U.S. Service report, emphasizing importance, test simplicity, treatment availability, and cost-effectiveness, which influenced program design and later formalized in the 1968 World Health Organization memorandum with G. Jungner. These frameworks promoted uniform protocols, reducing variability and enhancing yield, though implementation varied by resource availability.

Classification of Screening

By Target Population and Approach

Screening programs in are classified by target population into mass screening, which encompasses entire populations or large demographic subgroups without regard to individual risk factors, and selective screening, which targets subgroups with heightened risk based on criteria such as age, occupation, , or exposure history. Mass screening, also termed population-based or universal screening, applies standardized tests to broad groups to detect preclinical disease, as in neonatal testing for (PKU) where all newborns undergo heel-prick blood analysis within days of birth to identify metabolic disorders treatable by early dietary intervention. This approach maximizes reach but can yield low positivity rates in low-prevalence settings, with yields as low as 8 cases per 1,000 screened in some historical surveys. Selective screening concentrates efforts on high-risk populations to improve diagnostic yield and cost-effectiveness, such as mobile chest radiography units deployed for coal miners susceptible to or due to occupational dust exposure. For instance, programs targeting men over 35 with static 100-mm camera units for lung abnormalities have shown higher detection rates than unselected mass efforts. Case-finding, often distinguished from population-level screening, involves targeted detection within clinical encounters for individuals with potential symptoms or risks, such as routine hemoglobinometry in to uncover , yielding about 1.4% positives in large cohorts; however, it lacks the systematic coverage of true screening and may overlap with diagnostic evaluation. By approach, screenings divide into organized programs, which feature centralized planning, active invitations to eligible individuals, , and follow-up protocols to achieve equitable coverage, and opportunistic screening, conducted during routine consultations without predefined population targeting. Organized approaches, like systematic invitations in national initiatives, outperform opportunistic methods in mortality reduction—evidenced by programs where organized fecal immunochemical testing decreased incidence more effectively than sporadic clinic-based efforts.35100-X/fulltext) 35100-X/fulltext) Opportunistic screening, while flexible, often results in uneven participation and lower overall uptake, as seen in testing where lack of coordination limits population impact compared to structured protocols.01261-5/fulltext) These distinctions guide program design to balance yield, equity, and resource use, with organized selective strategies frequently favored for resource-constrained settings.

By Disease or Condition

Screening in medicine is classified by the targeted or condition, with programs designed to detect preclinical stages of specific pathologies in asymptomatic individuals to enable early intervention. This approach tailors tests to the , , and available diagnostics for each disorder, such as biochemical assays for metabolic conditions or for malignancies. Examples span neoplastic, cardiovascular, infectious, genetic, and endocrine diseases, where screening aims to identify cases before symptoms manifest, though efficacy depends on disease prevalence and test performance. Cancer screening targets common malignancies with established tests recommended by bodies like the U.S. Preventive Services Task Force (USPSTF). Breast cancer screening uses mammography for women aged 40-74 at average risk, detecting microcalcifications or masses indicative of ductal carcinoma in situ or invasive tumors. Cervical cancer screening employs Pap smears or HPV testing every 3-5 years for women aged 21-65, identifying squamous intraepithelial lesions from human papillomavirus infection. Colorectal cancer screening includes fecal immunochemical tests (FIT) annually or colonoscopy every 10 years starting at age 45, aiming to find adenomas or early adenocarcinomas. Lung cancer screening with low-dose CT scans is advised for high-risk smokers aged 50-80 with at least 20 pack-years history, reducing mortality by identifying small nodules. Prostate cancer screening via prostate-specific antigen (PSA) blood tests is debated for men over 50 due to risks of overdiagnosis, but offered with shared decision-making. Newborn screening focuses on rare but treatable genetic and metabolic disorders, typically via heel-prick blood tests within 24-48 hours of birth, mandated in all U.S. states for a core panel of conditions. is detected by elevated levels, allowing dietary intervention to prevent . screening identifies abnormal hemoglobin variants, enabling prophylactic antibiotics and vaccinations to avert infections and vaso-occlusive crises. is screened through immunoreactive trypsinogen and genetic confirmation, facilitating early enzyme replacement and nutritional support. Additional conditions include via measurement, preventing developmental delays with , and via excision circle analysis, allowing . By 2024, over 30 core conditions are screened nationwide, with state variations adding for oxidation disorders. Cardiovascular disease screening addresses and related risks through routine measurements in adults. screening involves checks starting at age 18, with targets below 130/80 mmHg for most, identifying risks for and via ambulatory or office monitoring. screening uses panels for levels above 160 mg/dL in low-risk adults over 20, every 4-6 years, prompting statins or changes to prevent coronary events. screening with ultrasound is recommended once for men aged 65-75 who smoked, detecting dilatations over 3 cm to avert rupture. Infectious disease screening targets transmissible pathogens in at-risk populations. screening uses fourth-generation antigen- tests for individuals aged 15-65 or with risk factors, detecting acute within 18-45 days to enable antiretroviral and reduce transmission. screening employs interferon-gamma release assays or skin tests in high-prevalence groups like immigrants or healthcare workers, identifying latent for isoniazid preventive . C screening via HCV tests is advised for adults born 1945-1965 or with injection drug history, confirming with assays for direct-acting antiviral treatment. Endocrine and metabolic screening extends beyond newborns to adults, such as screening with fasting plasma glucose or HbA1c tests every 3 years for overweight individuals over 45 or with risk factors like family history, detecting (HbA1c 5.7-6.4%) for lifestyle or metformin intervention to delay onset. screening uses (DEXA) for bone mineral density in postmenopausal women over 65 or younger with fractures, identifying T-scores below -2.5 for therapy. Occupational screenings, like chest X-rays for coal workers' (black lung), target silica-exposed miners to detect progressive early.

Technologies and Tools

Conventional Screening Methods

Conventional screening methods in primarily consist of established, non-invasive or minimally invasive procedures validated through longitudinal studies for detecting in at-risk populations. These approaches rely on physical assessments, biochemical analyses, and radiographic imaging, emphasizing accessibility, cost-effectiveness, and proven reductions in when applied selectively. Unlike such as biopsies or AI-driven analytics, conventional methods prioritize simplicity and broad implementation, though they often face limitations in sensitivity for early-stage detection or require follow-up diagnostics. Cardiovascular risk screening exemplifies conventional techniques, with using sphygmomanometers identifying in adults, where readings exceeding 130/80 mmHg prompt intervention; the established its prognostic value since 1948, linking early detection to a 20-25% reduction in mortality. profiling via blood tests measures total , LDL, HDL, and triglycerides, with levels above 200 mg/dL for total cholesterol signaling elevated atherosclerotic ; meta-analyses confirm that such screening in adults over 40 prevents coronary events by enabling therapy initiation. In cancer detection, employs low-dose X-rays to visualize tissue abnormalities, recommended biennially for women aged 50-74 by U.S. Preventive Services guidelines based on randomized trials showing 20-40% mortality reductions in screened cohorts. screening uses Papanicolaou (Pap) smears or HPV DNA testing on cervical cells, with U.S. incidence dropping 75% since 1955 due to widespread adoption starting in the 1960s; co-testing every 5 years for ages 30-65 detects precancerous lesions effectively. Colorectal screening via tests (FOBT) or examines stool for or visualizes colonic polyps, with evidence from the Minnesota Colon Cancer Control Study (1971-1986) demonstrating 33% mortality reduction through annual FOBT. For metabolic disorders, fasting plasma glucose or hemoglobin A1c tests screen for , with thresholds of 126 mg/dL or 6.5% indicating or disease; the Prevention Program (2002) showed screening-guided lifestyle interventions delaying onset by 34% in high-risk adults. Occupational health screening, such as chest X-rays and for miners, detects via radiographic patterns and lung function decline; U.S. federal programs since 1970 have identified black lung in over 10% of screened workers, facilitating compensation and exposure mitigation.
ConditionMethodKey Metric/ThresholdEvidence of Efficacy
Blood pressure cuff≥130/80 mmHg20-25% reduction via early treatment
Serum lipid panelTotal >200 mg/dLPrevents coronary events with statins
BI-RADS categorization20-40% mortality drop in ages 50-74
FOBT/Positive or polyps33% mortality reduction
HbA1c/Glucose test≥6.5% or 126 mg/dL34% onset delay with interventions
These methods' efficacy hinges on population selection and adherence, with over-screening risks like false positives leading to unnecessary procedures; for instance, PSA testing for , a blood for antigen levels >4 ng/mL, has shown inconsistent mortality benefits in trials like the European Randomized Study (1993-2003), prompting selective use.

Emerging Technologies

Artificial intelligence (AI) and (ML) algorithms are increasingly integrated into screening protocols to enhance diagnostic accuracy in imaging-based tests, such as for and low-dose CT scans for . These systems analyze datasets to detect subtle patterns indicative of disease, often outperforming human radiologists in sensitivity for early-stage lesions; for instance, ML models have achieved up to 94% accuracy in identifying lung nodules on CT images in validation studies. However, real-world implementation requires prospective trials to confirm reductions in false positives and , as retrospective data may inflate performance due to . Liquid biopsies, which detect (ctDNA) or cells in blood samples, represent a non-invasive alternative to tissue biopsies for , enabling earlier detection without procedural risks. Multi-cancer early detection (MCED) tests, such as GRAIL's Galleri and Exact Sciences' Cancerguard launched in September 2025, aim to identify signals from over 50 cancer types with reported sensitivities of 38-83% for stage I-III cancers in developmental cohorts. Despite these advances, as of 2025, no completed randomized controlled trials demonstrate mortality benefits from MCED screening, with systematic reviews citing insufficient evidence for routine clinical use due to risks of false positives leading to unnecessary interventions. Wearable biosensors and devices are emerging for continuous screening of chronic conditions, such as via ECG-enabled smartwatches or glucose fluctuations in using non-invasive sweat analysis. FDA-authorized devices like continuous glucose monitors have shown utility in identifying at-risk individuals through longitudinal data, potentially enabling pre-symptomatic interventions. Integration with AI further refines risk stratification, though long-term validation is needed to establish causal links to improved outcomes beyond . These technologies prioritize for population-level screening but face challenges in data and equity, as adoption disparities may exacerbate health inequalities without targeted implementation.

Notable Screening Programs

Infectious Disease Screening

Infectious disease screening targets carriers or early infections to curb transmission, enable timely treatment, and avert severe outcomes such as congenital transmission or progression to active . Unlike symptomatic , screening employs serological, molecular, or radiographic tests in defined populations, with programs tailored to prevalence, test sensitivity (typically 90-99% for enzyme immunoassays), and impact. Evidence from controlled trials and guidelines supports targeted or universal approaches for high-burden infections like , (TB), , and , where early detection reduces incidence by 20-50% in modeled scenarios, though effectiveness hinges on linkage to care and adherence. HIV screening programs exemplify broad implementation, with U.S. Preventive Services Task Force (USPSTF) recommendations for one-time screening of all adolescents and adults aged 15-65 years, plus high-risk groups, based on serologic accuracy exceeding 99% and randomized trials demonstrating superior yield from nontargeted strategies over risk-based alone. A 2021 multicenter trial of 76,561 visits found nontargeted screening detected new diagnoses at 0.2% , identifying 1.5 times more cases than targeted methods, with cost-effectiveness at approximately $41,700 per quality-adjusted life-year gained when excluding transmission benefits. Universal offers in antenatal and substance use settings further boost uptake, with meta-analyses of trials showing 1.5-2-fold increases in testing rates and linkage, though real-world reductions in transmission require sustained antiretroviral therapy access. Tuberculosis screening prioritizes latent TB infection (LTBI) detection via interferon-gamma release assays or skin tests in migrants from high- areas and close contacts, with European Centre for Disease Prevention and Control reviews indicating cost-effectiveness in endemic settings where LTBI exceeds 20%. Contact investigation meta-analyses report pooled active TB yield of 1-2% among household contacts, yielding 20-30% of LTBI treatable with regimens like 3-month rifapentine-isoniazid, reducing progression risk by 60-90% in trials. Community-wide active case-finding via chest or in high-burden populations shows variable efficacy, with a 2024 of randomized trials finding modest reductions (10-15%) but high costs per case detected ($500-2000), limited by low yield in low- subgroups. Syphilis screening, particularly universal prenatal testing at first visit, 28 weeks, and delivery, prevents congenital syphilis, where untreated maternal infection causes adverse fetal outcomes in 70-80% of cases, including stillbirth (40%) and neonatal death. CDC and WHO guidelines, supported by cohort data, show penicillin treatment post-screening averts 98% of congenital cases when diagnosed early, with rapid point-of-care tests improving outcomes in resource-limited settings by enabling same-day therapy and reducing third-trimester incidence by 50-70% in implementation studies. Hepatitis B virus (HBV) screening has shifted to universal triple-panel testing (HBsAg, anti-HBs, anti-HBc) for all U.S. adults aged ≥18 years, per 2023 CDC guidance, informed by modeling that predicts prevention of 3.3 decompensated cases and 1.5 hepatocellular carcinomas per 100,000 screened, at net savings over lifetimes. Prenatal HBsAg screening, with antiviral prophylaxis for high viral loads (>200,000 IU/mL), cuts mother-to-child transmission from 30-50% to <5%, as evidenced by longitudinal cohorts; cost-effectiveness exceeds $50,000 per quality-adjusted life-year in high-prevalence immigrant groups.

Cancer Screening Initiatives

Cancer screening initiatives encompass organized, population-based programs aimed at early detection of malignancies, primarily targeting , , , and where randomized controlled trials have demonstrated mortality reductions. These efforts, often led by national health authorities or international bodies, prioritize high-risk groups and evidence-based modalities such as , cytology or HPV testing, , and low-dose computed tomography (LDCT). For instance, from 1975 to 2020, prevention and screening for these four cancer types averted an estimated 4.75 million deaths in the United States alone, with colorectal screening contributing to a 30-50% mortality reduction in trial settings. The World Health Organization's Global Breast Cancer Initiative (GBCI), launched in 2021, seeks to reduce breast cancer mortality by 2.5% annually through strategies emphasizing early detection via clinical breast examination and mammography in resource-limited settings, alongside timely diagnosis and management. In Europe, national programs vary but commonly invite women aged 50-69 for biennial mammography, achieving coverage rates of 50-80% in countries like the United Kingdom and Sweden, where meta-analyses of randomized trials indicate a 20-30% reduction in breast cancer mortality for screened cohorts, though with acknowledged risks of overdiagnosis affecting up to 20% of detected cases. The UK's National Health Service Breast Screening Programme, operational since 1988, has screened over 30 million women, correlating with stage shifts toward earlier detection, yet independent reviews highlight persistent debates over net benefits due to lead-time and length biases inflating survival statistics without necessarily extending life. Cervical cancer screening initiatives, leveraging Pap smears or primary HPV testing, have proven highly effective in preventing progression from precancerous lesions, with programs like those recommended by the U.S. Centers for Disease Control and Prevention (CDC) targeting women aged 21-65 and contributing to a 50-70% incidence decline in vaccinated and screened populations. In Europe, organized programs in 25 countries as of 2024 cover primarily ages 30-60 with three-yearly cytology or five-yearly HPV testing, reducing mortality by over 80% in adherent groups per long-term cohort data. Colorectal initiatives, such as the UK's Bowel Cancer Screening Programme using biennial FIT for ages 60-74 since 2006, have increased early-stage detections by 10-15% and lowered mortality by 15-20% in intention-to-screen analyses from European trials. Lung cancer screening with annual LDCT for high-risk smokers aged 50-80, endorsed by the U.S. Preventive Services Task Force following the 2011 National Lung Screening Trial (NLST) which showed a 20% relative mortality reduction, has been implemented in the U.S. through Medicare coverage since 2015, screening over 1 million individuals by 2023. European efforts, coordinated via networks like the International Cancer Screening Network, face implementation hurdles but report similar benefits in pilot programs, though false-positive rates exceeding 20% necessitate rigorous risk stratification to mitigate harms. Globally, the International Agency for Research on Cancer's CanScreen5 project monitors over 100 programs, revealing coverage disparities—high in high-income nations (60-90%) but low elsewhere (<20%)—and underscoring the need for context-specific adaptations to avoid unevidenced expansion, as seen in prostate-specific antigen (PSA) screening where large trials like ERSPC showed modest benefits outweighed by overdiagnosis in low-risk men.

Newborn and Genetic Screening

Newborn screening involves testing infants shortly after birth, typically via a heel prick blood sample collected between 24 and 48 hours of life, to detect rare but treatable genetic, metabolic, endocrine, hemoglobin, and other disorders that can cause serious health issues if untreated. This public health initiative originated in the United States in 1963 with the development of a bacterial inhibition assay by Robert Guthrie for , a metabolic disorder leading to intellectual disability without early dietary intervention; by 1966, all U.S. states mandated PKU screening. Advancements like in the 1990s enabled multiplex screening for dozens of conditions simultaneously, expanding programs globally. In the U.S., the Recommended Uniform Screening Panel (RUSP), maintained by the Health Resources and Services Administration (HRSA), guides state programs and currently includes 38 core conditions for which screening is recommended due to evidence of net benefit from early detection and intervention, plus 26 secondary conditions often detected incidentally. Core conditions encompass metabolic disorders like PKU (incidence ~1 in 10,000-15,000 births), treatable with phenylalanine-restricted diets that prevent cognitive impairment when started neonatally, as demonstrated by longitudinal studies showing IQ preservation in screened cohorts versus historical unscreened cases with mean IQ below 50. Other examples include cystic fibrosis (early antibiotic and nutritional therapy reduces mortality by up to 90% in first decade), sickle cell disease (penicillin prophylaxis cuts mortality risk by 84% before age 5), and congenital hypothyroidism (levothyroxine replacement averts developmental delays in over 95% of cases).
CategoryExamples of Core RUSP Conditions
MetabolicPhenylketonuria (PKU), Maple Syrup Urine Disease, Medium-Chain Acyl-CoA Dehydrogenase Deficiency (MCAD)
EndocrineCongenital Adrenal Hyperplasia, Congenital Hypothyroidism
HemoglobinSickle Cell Disease, Other Hemoglobinopathies
OtherCystic Fibrosis, Severe Combined Immunodeficiency (SCID), Spinal Muscular Atrophy
Genetic screening within newborn programs primarily targets heritable disorders via biochemical markers of genetic mutations, with whole-genome sequencing pilots emerging for broader detection but not yet standard due to cost, interpretation challenges, and lack of proven interventions for many variants. Efficacy relies on high specificity and sensitivity; for , false-negative rates drop below 0.3% with timely sampling, enabling causal prevention of downstream neurological damage through enzyme deficiency management. Population-level data from U.S. programs show NBS has identified over 1 million cases since inception, averting disabilities and saving billions in lifetime care costs, though benefits vary by condition—strongest for high-morbidity, treatable ones like and MCAD deficiency, where early intervention halves mortality risk. Internationally, programs differ in scope; the endorses universal NBS for conditions meeting Wilson-Jungner criteria, including feasibility and net benefit, with over 100 countries screening for at least as of 2024. The International Society for Neonatal Screening (ISNS) provides guidelines emphasizing pilot studies for additions, with Europe often screening 20-50 conditions via consortia like the European Newborn Screening Network. In low-resource settings, WHO prioritizes high-burden disorders like in malaria-endemic areas, where screening reduces under-5 mortality by enabling hydroxyurea and transfusions. Challenges include false-positive rates, which rise with panel expansion—up to 50 per true positive for some assays—prompting parental anxiety and follow-up testing, though empirical studies find no lasting psychological harm in most families and underscore the value of confirmatory diagnostics. Ethical concerns arise over mandatory screening without opt-out options and residual sample storage for research, but evidence supports public health gains outweighing these for core panel disorders, with pilot data required for expansions to avoid screening low-benefit conditions.

Assessing Efficacy

Statistical Measures of Performance

Sensitivity, or the true positive rate, quantifies a screening test's ability to correctly identify individuals with the target condition among those who truly have it, calculated as the number of true positives divided by the sum of true positives and false negatives: Sensitivity = TP / (TP + FN). This measure is particularly critical in screening programs, where missing cases (false negatives) can delay intervention and worsen outcomes, prompting prioritization of high sensitivity to minimize such errors, even at the cost of more false positives requiring follow-up. Specificity, the true negative rate, measures the proportion of individuals without the condition who correctly test negative: Specificity = TN / (TN + FP), where TN denotes true negatives and FP false positives. High specificity reduces unnecessary diagnostic procedures and patient anxiety from false alarms, which is vital in population-based screening with low disease prevalence, as false positives can strain resources and lead to overtesting. However, an exclusive focus on specificity risks overlooking true cases, necessitating a balance informed by the screening context. Positive predictive value (PPV) represents the probability that a positive test result indicates the presence of the condition: PPV = TP / (TP + FP). Unlike sensitivity and specificity, which are intrinsic to the test, PPV varies with disease prevalence; in low-prevalence settings typical of screening, PPV declines sharply even for tests with strong sensitivity and specificity, often resulting in many false positives. For instance, a test with 90% sensitivity and 95% specificity in a population with 1% prevalence yields a PPV of approximately 15%, highlighting the need for confirmatory diagnostics post-screening. Negative predictive value (NPV) is the probability that a negative test result rules out the condition: NPV = TN / (TN + FN). NPV rises with decreasing prevalence, making it a reassuring metric in screening for reassuring those testing negative, though it assumes accurate test execution and low false negative rates. Likelihood ratios provide a prevalence-independent summary of test performance. The positive likelihood ratio (LR+) equals sensitivity / (1 - specificity) and indicates how much a positive result elevates the odds of disease; values above 10 suggest strong diagnostic utility. The negative likelihood ratio (LR-) is (1 - sensitivity) / specificity, with values below 0.1 indicating reliable exclusion of disease. These ratios facilitate Bayesian updating of pretest probabilities to posttest odds in clinical decision-making. The receiver operating characteristic (ROC) curve plots sensitivity against 1 - specificity across varying thresholds, visualizing trade-offs in test performance. The area under the ROC curve (AUC) quantifies overall discriminatory ability, ranging from 0.5 (no discrimination) to 1.0 (perfect); AUC values of 0.7-0.8 indicate acceptable performance for screening applications. In screening evaluations, ROC analysis aids in threshold selection to optimize for high sensitivity while maintaining reasonable specificity.
MetricFormulaInterpretation in Screening
SensitivityTP / (TP + FN)Proportion of diseased correctly identified; prioritize high to avoid missed cases.
SpecificityTN / (TN + FP)Proportion of non-diseased correctly identified; high values curb false positives.
PPVTP / (TP + FP)Probability disease given positive test; prevalence-dependent, often low in screening.
NPVTN / (TN + FN)Probability no disease given negative test; high in low-prevalence settings.
LR+Sensitivity / (1 - Specificity)Increases odds of disease post-positive; >10 desirable.
LR-(1 - Sensitivity) / SpecificityDecreases odds post-negative; <0.1 desirable.
These metrics, derived from the confusion matrix of true/false positives and negatives, underpin rigorous assessment but must be interpreted alongside prevalence and downstream validation to avoid overreliance on isolated values.

Criteria for Program Implementation

The implementation of medical screening programs requires rigorous evaluation to ensure benefits outweigh harms and costs, guided primarily by the 10 principles outlined by Wilson and Jungner in their 1968 World Health Organization report. These criteria emphasize empirical evidence of disease burden, effective interventions, and feasible detection methods, serving as a foundational framework adopted by public health authorities worldwide. Modern assessments, such as those from the U.S. Preventive Services Task Force (USPSTF), build on these by incorporating randomized controlled trial data and cost-effectiveness analyses, rejecting programs lacking demonstrated mortality reductions. Key criteria include the disease's importance as a public health problem, defined by prevalence, severity, and impact on ; for instance, screening via has been debated due to modest 1-2% absolute mortality reductions in women aged 50-69 from trials like the Health Insurance Plan study (1963-1966), which tracked 62,000 participants. An effective, timely treatment must exist, as seen in screening with Pap smears leading to precursor lesion removal, reducing incidence by up to 80% in screened populations per UK data from 1988-2019. The disease must have a detectable preclinical phase where intervention alters outcomes, excluding conditions like advanced with rapid progression undetectable by current tests. Screening tests must exhibit high sensitivity (>90% ideally) and specificity (>95%) to minimize false positives, which can lead to unnecessary procedures; fecal immunochemical testing (FIT) meets this with 74-92% sensitivity for advanced adenomas in meta-analyses of over 100,000 participants. Population acceptance is crucial, influenced by test invasiveness—e.g., low uptake of (PSA) screening in some cohorts due to risks—and cultural factors, as evidenced by 40-60% participation rates in U.S. programs despite . Costs must be balanced against benefits, with economic modeling required; the UK's NHS Breast Screening Programme costs £100 million annually for marginal gains, prompting periodic reviews. Ongoing evaluation through continuous monitoring and randomized trials is essential, as initial enthusiasm for programs like PSA screening waned after trials (e.g., ERSPC, 2009-2018) showed no overall mortality benefit and increased by 50%. Programs should target high-risk groups via agreed policies, avoiding universal application without stratification, as in for (PKU), implemented since 1963 in the U.S. after proving 99% prevention of via early diet intervention in cohort studies. Failure to meet these—such as inadequate natural history understanding—has led to discontinuations, like routine ovarian cancer screening deemed ineffective by USPSTF in 2012 based on PLCO trial data showing no survival gains.

Evidence of Benefits

Proven Mortality and Morbidity Reductions

Screening programs for , primarily using cytology-based Pap smears supplemented by human papillomavirus (HPV) testing, have achieved substantial mortality reductions in populations with organized implementation. , incidence and mortality rates declined by more than 50% over the four decades following widespread adoption of Pap smear screening starting in the . The International Agency for Research on Cancer has determined sufficient evidence that regular reduces mortality by 80% or more among adherent women, based on longitudinal cohort and case-control studies adjusting for participation rates. Observational data from organized programs further indicate 41% to 92% mortality reductions among women who attend screening, with invitation to screening yielding 17% to 79% reductions. For colorectal cancer, randomized controlled trials of guaiac-based fecal occult blood testing (gFOBT) and fecal immunochemical testing (FIT) have confirmed reductions in disease-specific mortality. Long-term follow-up from trials such as the Minnesota Colon Cancer Control Study and Nordic programs demonstrated persistent colorectal cancer mortality reductions of 15% to 33% with biennial or annual screening over 13 to 30 years, without influencing all-cause mortality. Meta-analyses of flexible sigmoidoscopy trials report 20% to 30% reductions in colorectal cancer mortality, alongside incidence decreases through polyp removal, with compliance-adjusted estimates reaching 41% for specific cohorts. Colonoscopy-based screening, evaluated in population-based case-control and cohort studies, is associated with 40% to 60% mortality reductions for cancers within reach of the instrument. In high-risk populations, low-dose computed (LDCT) screening for has shown mortality benefits in randomized trials. The National Lung Screening Trial (NLST), involving over 53,000 current and former heavy smokers, found a 20% relative reduction in mortality with three annual LDCT screens compared to chest . Extended screening in the Multicentric Italian Lung Detection (MILD) trial reported a 39% reduction in mortality with low-dose CT over 10 years versus no screening. Newborn screening for (PKU), an inborn error of , exemplifies morbidity reduction through early intervention. Prior to mandatory screening programs initiated in the 1960s, untreated PKU led to profound and neurological impairment in nearly all cases; post-screening has normalized cognitive outcomes and prevented severe morbidity in screened and treated infants, yielding substantial quality-adjusted life years gained. Similar principles apply to other metabolic disorders in expanded newborn panels, where timely detection averts irreversible damage and reduces long-term healthcare burdens. These examples highlight causal links between screening, early detection, and improved outcomes, though benefits accrue primarily to participants with high adherence and depend on effective follow-up treatment.

Supporting Empirical Data from Trials

Randomized controlled trials (RCTs) have demonstrated mortality reductions from screening in select cancers, particularly through early detection enabling timely intervention. The National Lung Screening Trial (NLST), conducted from 2002 to 2004 with follow-up through 2009, involved 53,454 high-risk individuals aged 55-74 who smoked or had quit within 15 years; low-dose computed (LDCT) screening yielded a 20% relative reduction in mortality compared to chest (247 vs. 309 deaths per 100,000 person-years). The Dutch-Belgian NELSON trial, initiated in 2003 with over 15,000 participants, reported a 24-26% mortality reduction with volume CT screening versus no screening after extended follow-up. For , guaiac-based testing (gFOBT) in the Minnesota Colon Cancer Control Study (1975-1982, n=46,551 aged 50-80) achieved a 33% reduction in cumulative mortality after 13 years, sustained at 30 years with biennial screening (rate ratio 0.67, 95% CI 0.56-0.80). Meta-analyses of FOBT trials, including , , and studies, confirm 16-22% mortality reductions (RR 0.78-0.84), with flexible adding further benefit (RR 0.72 for combined approaches). Mammography RCTs, such as the Swedish Two-County Trial (1977-1984, n≈134,000 women aged 40-74), showed a 25-31% mortality reduction in invited women after 10-20 years' follow-up, with regular attendance yielding up to 47% lower risk. The Age Trial (1994-2004, n=160,921 aged 39-41) demonstrated 25% mortality reduction from screening starting at age 40.
TrialScreening ModalityPopulationMortality ReductionFollow-up DurationCitation
NLST ()Low-dose CT vs. radiographyHigh-risk smokers, n=53,45420% Median 6.5 years
CCSSBiennial gFOBTAges 50-80, n=46,55133% 30 years
Swedish Two-CountyAges 40-74, n≈134,00025-31% 20 years
Cervical screening RCTs are fewer and often cluster-randomized; a Finnish trial (1960s-1990s) linked organized Pap smear programs to 80% cervical cancer mortality reductions in screened cohorts, though attribution relies on historical controls. Overall, these trials substantiate causal benefits via intention-to-screen analyses, adjusting for compliance, but effects vary by adherence and cancer biology.

Potential Harms and Limitations

Diagnostic Errors and Their Consequences

Diagnostic errors in medical screening encompass false-positive results, which indicate disease absence as present, and false-negative results, which fail to detect existing disease, alongside delays or misses in interpretation. These errors arise from test limitations, such as imperfect , human factors in reading results, or biological variability in disease presentation. In programs, false-positive rates vary by modality; for instance, yields a cumulative false-positive of approximately 49% after 10 screening rounds in women aged 40-74, often necessitating additional or biopsies. False-negative errors, while less frequent in aggregate, can exceed 10-30% in low-prevalence settings for cytological screenings like Pap tests, influenced by specimen quality and interpreter experience. Consequences of false-positive results include psychological distress, with affected individuals reporting heightened anxiety and altered perceptions of cancer , potentially deterring adherence to future screenings. Physical harms stem from follow-up invasive procedures; in low-dose CT lung screening, false positives prompt biopsies or surgeries in about 1% of cases, with major complications like occurring at rates of 4.1 per 10,000 participants. Economic burdens arise from escalated healthcare costs, including unnecessary diagnostics estimated to contribute significantly to the $20-50 billion annual U.S. expenditure on diagnostic errors broadly. In ovarian screening trials, false-positive rates ranged from 0.1% to 23.3%, leading to surgical interventions in up to 54.9% of high-risk cases, with complications such as bowel injury reported in select studies. False-negative errors delay disease detection, permitting progression to advanced stages with poorer prognoses; in cervical cancer screening, such misses heighten risks of invasive disease due to undetected high-grade lesions. These errors erode public trust in screening programs and invite litigation, as patients attribute worsened outcomes to overlooked abnormalities. Systemically, false negatives in population-based programs like newborn screening for metabolic disorders can result in irreversible harm if conditions progress undetected, underscoring the need for robust quality controls despite inherent test imperfections. Overall, diagnostic errors in screening contribute to an estimated 40,000-800,000 annual U.S. cases of serious harm or death from misdiagnosis across settings, with screening-specific incidents amplifying debates on net benefit versus risk.

Overdiagnosis and Overtreatment

Overdiagnosis refers to the detection of conditions, particularly indolent cancers, that would not progress to cause morbidity or mortality during a patient's lifetime, resulting in unnecessary labeling as diseased. This phenomenon arises in screening programs due to lead-time and length-time biases, where slow-growing lesions are preferentially identified, but it fundamentally stems from the biological heterogeneity of diseases, including non-progressive or regressive tumors. from randomized controlled trials and incidence-mortality analyses demonstrates overdiagnosis rates varying by cancer type and screening modality, often comprising 10-50% of detected cases. For instance, in mammography-based , estimates indicate that 15-31% of screen-detected invasive cancers may represent , with one U.S. analysis attributing approximately one in seven diagnosed cases to this issue. In via (PSA) testing, affects about 29% of white men and 44% of black men, contributing to 1.5-1.9 million excess U.S. diagnoses from 1988-2017 without corresponding mortality reductions. Overtreatment follows overdiagnosis when detected indolent lesions prompt interventions like , , or , exposing patients to avoidable harms without benefits. In , overtreatment rates reached 0.9-1.5 million cases in the U.S. over three decades of PSA screening, leading to complications such as (up to 16% post-prostatectomy), (up to 80%), and bowel issues from . screening exemplifies extreme overtreatment, with incidence surging 15-fold in since 1993 due to and , yet mortality remaining stable, implicating 60-90% of cases as overdiagnosed and subjecting patients to thyroidectomy risks including (up to 50% temporary), damage (1-2%), and lifelong dependency. overtreatment from involves unnecessary mastectomies or lumpectomies, with associated psychological distress, (15-20% post-surgery), and toxicities, despite that accounts for up to 48% of screen-detected cases in some European cohorts. In low-dose CT lung screening trials like the National Lung Screening Trial, estimates of 18.5% translate to surgical resections for non-lethal nodules, incurring risks and reduced . Quantifying net harms requires balancing these against true positives, but modeling and trial data consistently show overtreatment burdens, particularly in older populations where competing mortality risks amplify —e.g., exceeding 50% in women over 85 undergoing . Strategies to mitigate include risk-stratified screening, active surveillance for low-risk detections (e.g., ), and refined diagnostic thresholds, as implemented in guidelines to curb biopsies of subcentimeter nodules. Peer-reviewed analyses emphasize that while screening reduces mortality in select high-risk groups, unaddressed erodes cost-effectiveness and patient autonomy, with economic costs in billions annually from superfluous treatments.

Methodological Biases

Temporal and Detection Biases

Lead-time bias arises in screening evaluations when earlier detection advances the diagnosed time of disease onset without altering its progression or ultimate outcome, thereby inflating apparent duration from . This bias occurs because screening identifies preclinical cases, creating an artificial extension of observed that does not reflect prolonged life. For instance, in screening programs in high-risk Chinese populations, lead-time bias was estimated to overestimate 5-year cause-specific rates by more than 10%, with lead times averaging around 1.5 years based on tumor growth models. To mitigate this, assessments should prioritize disease-specific mortality rates over from , as the latter confounds temporal shifts with true therapeutic gains. Length-time bias, a form of detection , preferentially identifies indolent, slower-progressing diseases during intermittent screening intervals because these cases spend more time in the detectable preclinical phase compared to aggressive, rapidly evolving ones. Consequently, screen-detected cases often exhibit better prognoses not due to early intervention but inherent tumor biology, skewing efficacy estimates upward. In breast cancer screening models, length bias has been quantified to account for up to 50% of observed advantages in initial screens, with faster-growing tumors underrepresented in detected cohorts. Empirical corrections involve modeling preclinical sojourn times and adjusting for differential growth rates, often revealing that apparent benefits diminish when accounting for this selection effect. These temporal and detection biases compound in observational studies lacking , underscoring the need for randomized controlled trials that compare overall and disease-specific mortality between screened and unscreened groups to isolate causal effects from artifactual improvements. In a theoretical framework for screening analysis, including lead-time and length-time can be decomposed alongside , with simulations showing they collectively inflate survival metrics by 20-40% in scenarios with modest true benefits. High-quality sources, such as peer-reviewed epidemiological models, emphasize that uncorrected have historically led to overstated endorsements of programs like screening, where length favors low-grade cancers unlikely to cause harm.

Selection and Participation Biases

Selection bias in medical screening arises when the group undergoing screening differs systematically from the broader target population in ways that influence disease outcomes, such as baseline status, lifestyle factors, or socioeconomic characteristics, thereby distorting estimates of screening efficacy. Participation bias, a subset of , specifically occurs when individuals who accept screening invitations or volunteer for trials are prognostically more favorable than decliners, often due to greater , fewer comorbidities, or higher for preventive care. These biases tend to inflate apparent benefits by comparing screened groups with inherently lower-risk profiles against unscreened groups that include higher-risk individuals who . In randomized controlled trials of , participation rates below 70-80% can amplify these distortions, as invitees who enroll are frequently healthier. For example, in the Danish Lung Cancer Screening Trial (DLCST), initiated in 2009 with 4,104 high-risk smokers randomized to low-dose CT screening or control, participants were disproportionately male (58% vs. 50% in invitees), had higher education levels (e.g., 28% with vocational training vs. 22% among non-participants), were more often former smokers (81% vs. 72%), and exhibited lower comorbidity burdens (mean of 0.4 vs. 0.6). Such differences suggest that trial results may overestimate mortality reductions for population-level implementation, where non-participants represent a substantial portion of eligible individuals with poorer adherence and outcomes. Observational studies of mammography screening demonstrate pronounced self-selection effects, where attenders exhibit lower mortality risks unrelated to screening itself. A 2025 analysis of Norwegian registry data from 1996-2020 found that, compared to regular attenders, non-attenders had approximately twice the risk of death (hazard ratio 2.0, 95% CI 1.8-2.2), attributable to factors like comorbidities and rather than screening absence alone. Correcting for this in case-control designs reduced estimated reductions from 40% to as low as 10% in some models. In unorganized screening settings without mandatory invitations, self-selection is exacerbated, as women proactively seeking mammograms often have elevated perceived risk or family history, further skewing toward lower-mortality cohorts. Mitigation strategies include conducting population-based randomized trials with randomized invitations to minimize differential selection, though high refusal rates (e.g., 20-50% in many European programs) persist as a challenge. Intention-to-treat analyses in RCTs partially address compliance biases, but post-randomization dropouts among higher-risk participants can still bias per-protocol estimates toward null or exaggerated effects. Empirical adjustments, such as indexing or linkage to national registries, have been proposed to quantify and correct these distortions, emphasizing that uncorrected biases systematically favor overestimation of screening's causal impact on survival.

Major Controversies

Debates in Specific Screening Modalities

(PSA) screening for remains debated due to its limited mortality benefits relative to risks of and overtreatment. The European Randomized Study of Screening for Prostate Cancer (ERSPC) trial, involving over 162,000 men, demonstrated a 20% relative reduction in prostate cancer mortality at 13 years of follow-up with PSA screening every 2-4 years, translating to an absolute reduction of 1.28 deaths per 1,000 men screened; however, this came with a 50% rate, leading to unnecessary biopsies and treatments like radical or radiation, which carry risks of incontinence and impotence. Critics argue that the number needed to invite for screening to prevent one death exceeds 700, questioning net benefit for low-risk men, while proponents highlight reductions in metastatic disease, as evidenced by a 2020 reanalysis of ERSPC and PLCO trials showing lower death rates with guideline-concordant screening. Guidelines vary: the U.S. Preventive Services Task Force recommends shared decision-making for men aged 55-69, citing insufficient evidence for broader use, whereas European bodies endorse targeted screening based on risk factors. Mammography screening for sparks controversy over optimal age, frequency, and , particularly in younger and older women. For women aged 40-49, randomized trials like the Canadian National Breast Screening Study showed no significant mortality reduction, with harms from false positives and outweighing benefits due to denser breast tissue reducing sensitivity. In women over 70, rates rise sharply, estimated at 31% for ages 70-74 based on U.S. , , and End Results data modeling indolent cancers detected via screening that would not progress lethally; a Yale analysis found biennial screening from 75-79 cost-effective but with doubling averted deaths. Proponents cite 20-30% mortality reductions in women 50-69 from meta-analyses of eight trials, advocating annual screening, while skeptics, including a 2014 Nordic , estimate 30-50% of detected cancers as overdiagnosed, fueling calls for risk-stratified approaches over population-wide programs. Colorectal cancer screening debates center on colonoscopy versus fecal immunochemical testing (FIT), balancing detection efficacy, participation, and resource demands. Colonoscopy detects advanced adenomas with 90-95% sensitivity but requires bowel preparation and sedation, limiting uptake to 24-30% in outreach programs, whereas FIT achieves 40-67% participation with comparable cancer detection over time via annual use, as shown in a Dutch trial where FIT invitation yielded higher attendance than one-time colonoscopy. The 2025 COLONPREV trial reported no significant difference in 7-year colorectal cancer mortality between FIT (annual) and colonoscopy (10-year interval) invitations, with FIT reducing costs by $3.9 million annually per 100,000 screened in U.S. models due to fewer invasive procedures; however, colonoscopy's superior polyp removal prevents interval cancers better in high-adherers. Guidelines favor FIT for average-risk populations to boost equity, but colonoscopy persists as preferred for high-risk individuals given family history or symptoms. Low-dose computed (LDCT) screening for in heavy smokers (≥20 pack-years, aged 50-80) is supported by the National Lung Screening Trial, which found 20% mortality reduction over 6.5 years, yet debates persist on false positives (96% of nodules benign), (1.5-7.5 mSv per scan), and extension to never-smokers. For never-smokers, comprising 10-15% of cases, evidence lacks randomized trial support for mortality benefit, with modeling suggesting high overdiagnosis of indolent nodules; the International Association for the Study of Lung Cancer deems insufficient data for recommendation, citing potential harms exceeding gains without genetic risk stratification. Participant misconceptions, such as overestimating nodule malignancy, further complicate , per VA surveys of smokers. U.S. Preventive Services endorses annual LDCT for eligible smokers, emphasizing cessation integration to maximize net benefit.

Ethical, Economic, and Policy Challenges

Ethical challenges in medical screening programs center on balancing beneficence and non-maleficence, as screening often introduces harms such as and overtreatment that violate the principle of "do no harm," even when population-level benefits exist. For instance, unnecessary treatments like mastectomies or cervical excisions can lead to complications including preterm labor risks, prompting debates over whether individual consent adequately mitigates these iatrogenic effects. is frequently undermined by informational asymmetries, where patients receive unbalanced emphasis on benefits while risks like psychological distress from false positives or stigmatization are downplayed or omitted, particularly in commercial screening packages. Autonomy is further compromised by implicit pressures in screening invitations, which leverage medical authority to induce participation, potentially framing decisions as imperatives rather than personal choices informed by full disclosure of harms. Equity concerns arise in resource-limited settings, where screening may exacerbate disparities if access favors certain demographics, raising questions about in allocating limited healthcare interventions. These issues demand meta-information in processes, such as explanations of cognitive biases in , to validate autonomous decisions. Economically, screening programs impose substantial burdens through direct costs of tests and indirect expenses from false-positive evaluations, which can exceed screening expenditures significantly; for , every $100 spent on screening incurs an additional $33 in false-positive follow-up costs over 10 years. False positives in CT screening, influenced by factors like nodule size and patient characteristics, drive up utilization of invasive diagnostics and biopsies, amplifying overall program expenses. While some evaluations deem screenings cost-effective—such as the 2021 USPSTF guideline expansion at $72,564 per (QALY) gained—others argue ratios are underestimated by $10,300–$13,700 per QALY when accounting for quality-of-life decrements from anxiety, procedures, and . Colorectal cancer screening, despite generating $24.3 billion in U.S. medical spending in 2020 (11.6% of cancer costs), demonstrates potential long-term savings via early detection, though implementation in low-resource areas often fails cost-effectiveness thresholds due to low adherence and high false-positive rates. debates intensify when non-recommended tests in packages inflate costs without proportional benefits, diverting funds from evidence-based interventions. Policy challenges involve reconciling evidence-based guidelines with implementation, as seen in the 2009 USPSTF mammography recommendations against routine screening for women aged 40–49, which triggered a 4.3% immediate drop in utilization and widespread over perceived underemphasis on mortality reductions. Critics argued the changes prioritized modeled harms over trial data, eroding trust in bodies like the USPSTF and highlighting tensions between population modeling and individualized . Recent expansions, such as lowering lung screening age to 50 and pack-years to 20, improved cost-effectiveness but expanded eligibility by 85%, straining systems amid debates on . Disparities persist, with economically disadvantaged groups less likely to meet USPSTF colorectal or screening targets, complicating equity goals without addressing upstream barriers like access. Guideline inconsistencies—evident in divergences between USPSTF and major cancer centers on frequency and eligibility—fuel accusations of non-evidence-based influences, including diversity initiatives over empirical rigor, underscoring the need for transparent, trial-derived policymaking.

Recent Advances and Future Directions

Technological Innovations

Artificial intelligence () has enhanced the accuracy and efficiency of screening programs, particularly in image-based diagnostics. algorithms analyze mammograms, endoscopies, and dermatological images to detect anomalies such as or lesions with sensitivity comparable to or exceeding human radiologists in controlled studies. For instance, AI models trained on smartphone-captured images have achieved over 90% accuracy in identifying skin cancers, enabling accessible community-level screening in resource-limited settings. These tools reduce false positives and reader variability, though validation in diverse populations remains ongoing to address algorithmic biases from training data. Liquid biopsy technologies represent a non-invasive advancement for multi-cancer early detection, analyzing (ctDNA), exosomes, or patterns in blood or . Next-generation sequencing (NGS) combined with AI has improved ctDNA detection sensitivity to below 0.01% variant allele frequency, facilitating screening for cancers like colorectal and before clinical symptoms. Clinical trials, such as those evaluating multi-cancer assays, report detection rates of 50-80% for stage I-II tumors across 50+ cancer types, outperforming traditional biomarkers in specificity. Challenges include distinguishing clonal hematopoiesis from true tumor signals, prompting refinements in error-correction algorithms. Genomic screening innovations, including rapid whole-genome sequencing for newborns, have expanded beyond traditional to identify rare disorders earlier. The GUARDIAN study in 2024 demonstrated that genome sequencing detected over 100 conditions missed by standard newborn screens, with a diagnostic yield of 10-20% in symptomatic infants tested prospectively. Long-read sequencing and optical genome mapping further resolve structural variants intractable to short-read NGS, enhancing prenatal and carrier screening precision. Integration with AI for variant interpretation accelerates results from weeks to days, though ethical concerns over incidental findings necessitate refined consent protocols. Wearable biosensors and point-of-care devices enable continuous physiological monitoring for preclinical risk stratification. Devices measuring glucose, , or sweat biomarkers have been validated for screening, with AI algorithms predicting progression risk with AUC values above 0.85 in longitudinal cohorts. These technologies support opportunistic screening in , reducing reliance on episodic clinic visits.

Evolving Evidence and Research Priorities

Recent randomized controlled trials and meta-analyses have prompted revisions to screening guidelines for several cancers, reflecting a more nuanced understanding of net benefits versus harms such as and false positives. For instance, the U.S. Preventive Services Task Force updated its breast cancer screening recommendation in April 2024 to biennial starting at age 40 for women at , based on evidence from long-term trials like the Swedish Two-County Study and Canadian National Breast Screening Study showing a 20-30% relative reduction in mortality but persistent concerns over interval cancers and rates exceeding 20% in some cohorts. Similarly, the expanded screening eligibility in February 2024 to include adults aged 50-80 with at least 20 pack-years of history, incorporating data from the National Lung Screening Trial that demonstrated a 20% mortality reduction with low-dose CT but highlighted the need for risk-based refinements to mitigate harms in lower-risk groups. Colorectal cancer screening guidelines have also evolved, with the U.S. Multi-Society lowering the starting age to 45 in 2021, supported by modeling and observational data indicating earlier detection could avert thousands of deaths annually amid rising incidence in younger adults; post-implementation studies in 2025 confirmed increased early-stage diagnoses and projected life-years gained, though adherence remains below 70% in targeted age groups. These shifts underscore a broader trend toward evidence-driven adjustments, yet implementation lags, with screenings continuing for years after guideline changes due to inertia and patient preferences, as evidenced by a 2025 analysis showing up to 13-year delays in adopting reduced recommendations. Emerging multi-cancer early detection (MCED) tests, such as blood-based assays detecting , represent a but require rigorous , with ongoing randomized trials like PATHFINDER 2 and NHS-Galleri prioritizing endpoints of cancer-specific mortality and stage-shift over surrogate markers like sensitivity, which preliminary data suggest may exceed 80% for advanced stages but falter below 20% for early ones. Research priorities emphasize large-scale, pragmatic trials to quantify true population-level impacts, including cost-effectiveness analyses revealing potential annual U.S. expenditures exceeding $10 billion for MCED if scaled without proven net benefit. Key gaps include insufficient data on screening in diverse populations, prompting priorities for studies addressing disparities in uptake and outcomes, such as lower colorectal screening rates among racial minorities despite guideline expansions. Future efforts focus on personalized risk stratification using polygenic scores and AI-driven imaging to optimize intervals and modalities, alongside implementation science to boost participation without exacerbating overtreatment; for example, PCORI's 2025 agenda highlights funding for barrier-removal strategies in high-risk groups. Ethical considerations, including equitable access and on harms, are increasingly integrated into trial designs, reflecting causal evidence that unproven widespread adoption could inflate healthcare costs without proportional mortality gains.

References

  1. https://www.ncbi.nlm.nih.gov/books/NBK436014/
  2. https://pubmed.ncbi.nlm.nih.gov/28613785/
  3. https://www.who.int/europe/teams/ncd-management/screening
  4. https://pmc.ncbi.nlm.nih.gov/articles/PMC5893317/
  5. https://www.cmaj.ca/content/190/14/E414
  6. https://wellcomeopenresearch.org/articles/5-158
  7. https://pmc.ncbi.nlm.nih.gov/articles/PMC1070874/
  8. https://smw.ch/index.php/smw/article/view/1187/1259
  9. https://www.nejm.org/doi/full/10.1056/NEJMra013182
  10. https://pmc.ncbi.nlm.nih.gov/articles/PMC3790152/
  11. https://breast-cancer-research.biomedcentral.com/articles/10.1186/bcr3427
  12. https://ajronline.org/doi/10.2214/AJR.13.12052
  13. https://pmc.ncbi.nlm.nih.gov/articles/PMC9784987/
  14. https://www.aafp.org/pubs/afp/issues/2014/1101/p625.pdf
  15. https://www.bmj.com/content/350/bmj.g7773
  16. https://www.hopkinsmedicine.org/health/treatment-tests-and-therapies/screening-tests-for-common-diseases
  17. https://www.health.ny.gov/diseases/chronic/discreen.htm
  18. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/screening
  19. https://www.osha.gov/medical-surveillance/screening
  20. https://www.who.int/europe/publications/i/item/9789289054782
  21. https://pmc.ncbi.nlm.nih.gov/articles/PMC2647421/
  22. https://phprimer.afmc.ca/en/part-iii/chapter-9/
  23. https://www.ncbi.nlm.nih.gov/books/NBK557491/
  24. https://journalofethics.ama-assn.org/article/what-makes-screening-exam-good/2006-01
  25. https://catalogofbias.org/biases/lead-time-bias/
  26. https://www.cancer.gov/about-cancer/screening/research/what-screening-statistics-mean
  27. https://www.ncbi.nlm.nih.gov/books/NBK550219/
  28. https://bmcmedethics.biomedcentral.com/articles/10.1186/s12910-022-00798-5
  29. https://www.milbank.org/wp-content/uploads/mq/volume-56/issue-04/56-4-The-Emergence-of-the-Concept-of-Screening-for-Disease.pdf
  30. https://www.ncbi.nlm.nih.gov/books/NBK232693/
  31. https://ehe.health/wp-content/uploads/2022/11/EHEHealth_ALifeSavingLegacy.pdf
  32. https://pmc.ncbi.nlm.nih.gov/articles/PMC1743082/
  33. https://pmc.ncbi.nlm.nih.gov/articles/PMC5590109/
  34. https://www.sciencemuseum.org.uk/objects-and-stories/tuberculosis-part-two-treatments-and-cures
  35. https://pubmed.ncbi.nlm.nih.gov/1603400/
  36. https://pmc.ncbi.nlm.nih.gov/articles/PMC264442/
  37. https://www.bedfordshirehospitals.nhs.uk/wp-content/uploads/2018/04/criteria_for_screening.pdf
  38. https://pmc.ncbi.nlm.nih.gov/articles/PMC12286092/
  39. https://pmc.ncbi.nlm.nih.gov/articles/PMC5427344/
  40. https://niercheck.nl/wp-content/uploads/2019/06/Wilson-Jungner-1968.pdf
  41. https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-022-02291-7
  42. https://www.cdc.gov/cancer/prevention/screening.html
  43. https://www.cdc.gov/newborn-screening/about/index.html
  44. https://www.nichd.nih.gov/health/topics/newborn/conditioninfo/disorders
  45. https://www.medicare.gov/coverage/preventive-screening-services
  46. https://pmc.ncbi.nlm.nih.gov/articles/PMC4389712/
  47. https://my.clevelandclinic.org/health/diagnostics/24118-cancer-screening
  48. https://www.energy.gov/ehss/conventional-medical-screening-program
  49. https://www.nature.com/articles/s41467-025-58527-6
  50. https://eurjmedres.biomedcentral.com/articles/10.1186/s40001-025-02680-7
  51. https://investor.exactsciences.com/investor-relations/press-releases/press-release-details/2025/Exact-Sciences-Launches-Cancerguard-First-of-Its-Kind-Multi-Cancer-Early-Detection-Blood-Test/default.aspx
  52. https://www.nature.com/articles/s41698-025-01105-2
  53. https://ascopost.com/news/september-2025/study-finds-insufficient-evidence-demonstrating-clinical-benefit-of-multicancer-detection-tests/
  54. https://pubmed.ncbi.nlm.nih.gov/40953446/
  55. https://www.fda.gov/medical-devices/digital-health-center-excellence/medical-devices-incorporate-sensor-based-digital-health-technology
  56. https://pmc.ncbi.nlm.nih.gov/articles/PMC12109135/
  57. https://www.ama-assn.org/practice-management/digital-health/health-care-technology-trends-2025-ai-benefits-wearable-use-0
  58. https://www.cdc.gov/mmwr/volumes/72/rr/rr7201a1.htm
  59. https://pmc.ncbi.nlm.nih.gov/articles/PMC2921232/
  60. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2782338
  61. https://www.uspreventiveservicestaskforce.org/home/getfilebytoken/3qpQqeeozW8nV3a4_BWntp
  62. https://www.nejm.org/doi/full/10.1056/NEJMsa042657
  63. https://pmc.ncbi.nlm.nih.gov/articles/PMC8915955/
  64. https://pmc.ncbi.nlm.nih.gov/articles/PMC5894253/
  65. https://publications.ersnet.org/content/erj/58/6/2100266
  66. https://pmc.ncbi.nlm.nih.gov/articles/PMC11731502/
  67. https://www.publichealthontario.ca/-/media/Documents/U/2023/universal-prenatal-screening-congenital-syphilis.pdf?rev=dd1dc37fd58441b0b882bc320f10d9a5&sc_lang=en
  68. https://www.ncbi.nlm.nih.gov/books/NBK537087/
  69. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247649
  70. https://www.idsociety.org/science-speaks-blog/2023/screening-all-adults-for-hepatitis-b-likely-to-improve-health-outcomes-and-save-costs/
  71. https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/hepatitis-b-virus-infection-screening
  72. https://www.nih.gov/news-events/news-releases/five-cancer-types-prevention-screening-have-been-major-contributors-saving-lives
  73. https://www.who.int/initiatives/global-breast-cancer-initiative
  74. https://pmc.ncbi.nlm.nih.gov/articles/PMC4840047/
  75. https://www.cancer.gov/about-nci/organization/cgh/partnerships-dissemination/icsn-international-cancer-screening-network
  76. https://www.iarc.who.int/featured-news/the-cancer-screening-in-five-continents-canscreen5/
  77. https://www.ncbi.nlm.nih.gov/books/NBK558983/
  78. https://pmc.ncbi.nlm.nih.gov/articles/PMC9326622/
  79. https://www.hrsa.gov/advisory-committees/heritable-disorders/rusp
  80. https://newbornscreening.hrsa.gov/about-newborn-screening/recommended-uniform-screening-panel
  81. https://pmc.ncbi.nlm.nih.gov/articles/PMC11943362/
  82. https://www.cdc.gov/Mmwr/preview/mmwrhtml/rr5313a1.htm
  83. https://pmc.ncbi.nlm.nih.gov/articles/PMC5381153/
  84. https://www.cdc.gov/mmwr/preview/mmwrhtml/00000243.htm
  85. https://www.hrsa.gov/sites/default/files/hrsa/advisory-committees/heritable-disorders/newborn-uniform-screening-panel.pdf
  86. https://www.who.int/publications/i/item/9789290211389
  87. https://pmc.ncbi.nlm.nih.gov/articles/PMC11203842/
  88. https://pmc.ncbi.nlm.nih.gov/articles/PMC12192812/
  89. https://www.thelancet.com/journals/langlo/article/PIIS2214-109X%2824%2900335-8/fulltext
  90. https://pmc.ncbi.nlm.nih.gov/articles/PMC2773165/
  91. https://www.nature.com/articles/gim20115
  92. https://pmc.ncbi.nlm.nih.gov/articles/PMC9590071/
  93. https://www.hrsa.gov/sites/default/files/hrsa/advisory-committees/heritable-disorders/achdnc-pilot-studies-report.pdf
  94. https://pmc.ncbi.nlm.nih.gov/articles/PMC4975285/
  95. https://pmc.ncbi.nlm.nih.gov/articles/PMC8156826/
  96. https://pmc.ncbi.nlm.nih.gov/articles/PMC5286748/
  97. https://pmc.ncbi.nlm.nih.gov/articles/PMC2556590/
  98. https://www.sciencedirect.com/science/article/pii/S1556086415306043
  99. https://www.fightcancer.org/sites/default/files/FINAL%2520-%2520Cervical%2520Cancer%2520General%2520Factsheet%252001.08.20.pdf
  100. https://www.who.int/data/gho/indicator-metadata-registry/imr-details/3240
  101. https://www.sciencedirect.com/science/article/pii/S0959804919308688
  102. https://www.nejm.org/doi/full/10.1056/NEJMoa1300720
  103. https://pmc.ncbi.nlm.nih.gov/articles/PMC10093633/
  104. https://www.jcancer.org/v11p5953.htm
  105. https://www.nejm.org/doi/full/10.1056/NEJMoa1102873
  106. https://www.annalsofoncology.org/article/S0923-7534%2819%2931229-3/fulltext
  107. https://pmc.ncbi.nlm.nih.gov/articles/PMC8151371/
  108. https://pubmed.ncbi.nlm.nih.gov/36495999/
  109. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6121a2.htm
  110. https://www.sciencedirect.com/science/article/pii/S2666776221001563
  111. https://journals.lww.com/ebp/fulltext/2020/02000/is_annual_fecal_occult_blood_testing_as_effective.21.aspx
  112. https://www.sciencedirect.com/science/article/pii/S0959804919308706
  113. https://pubmed.ncbi.nlm.nih.gov/33141657/
  114. https://www.acr.org/News-and-Publications/Media-Center/2024/ACR-Statement-on-Annals-of-Internal-Medicine-Harris-Opinion-Editorial
  115. https://www.thelancet.com/article/S1470-2045%2820%2930398-3/fulltext
  116. https://pubmed.ncbi.nlm.nih.gov/38797981/
  117. https://www.nejm.org/doi/full/10.1056/NEJM199804163381601
  118. https://pmc.ncbi.nlm.nih.gov/articles/PMC3966132/
  119. https://www.cancer.gov/news-events/cancer-currents-blog/2024/mammogram-false-positives-affect-future-screening
  120. https://ccts.amegroups.org/article/view/51427/html
  121. https://qualitysafety.bmj.com/content/33/2/109
  122. https://bmjoncology.bmj.com/content/3/1/e000404
  123. https://pmc.ncbi.nlm.nih.gov/articles/PMC9222017/
  124. https://pmc.ncbi.nlm.nih.gov/articles/PMC9931091
  125. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2024.1479750/full
  126. https://ebm.bmj.com/content/23/1/1
  127. https://pmc.ncbi.nlm.nih.gov/articles/PMC5365181/
  128. https://corporate.dukehealth.org/news/study-estimates-one-seven-us-breast-cancers-may-be-over-diagnosed
  129. https://www.acpjournals.org/doi/10.7326/M23-0133
  130. https://pmc.ncbi.nlm.nih.gov/articles/PMC1123626/
  131. https://evidence.nejm.org/doi/full/10.1056/EVIDoa2200031
  132. https://publichealthreviews.biomedcentral.com/articles/10.1186/s40985-015-0012-1
  133. https://www.nejm.org/doi/abs/10.1056/NEJMp1409841
  134. https://pmc.ncbi.nlm.nih.gov/articles/PMC7232665/
  135. https://tcr.amegroups.org/article/view/24419/html
  136. https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2696728
  137. https://www.bmj.com/content/382/bmj.p1823
  138. https://www.cedars-sinai.org/newsroom/study-thyroid-cancer-still-overdiagnosed/
  139. https://pmc.ncbi.nlm.nih.gov/articles/PMC9786860/
  140. https://ajronline.org/doi/10.2214/AJR.17.18629
  141. https://pmc.ncbi.nlm.nih.gov/articles/PMC7491548/
  142. https://gut.bmj.com/content/65/3/538
  143. https://pubmed.ncbi.nlm.nih.gov/7397650/
  144. https://pubmed.ncbi.nlm.nih.gov/30854935/
  145. https://pubmed.ncbi.nlm.nih.gov/37278228/
  146. https://pubmed.ncbi.nlm.nih.gov/4021829/
  147. https://catalogofbias.org/biases/selection-bias/
  148. https://www.sciencedirect.com/science/article/pii/S0169500211000122
  149. https://pmc.ncbi.nlm.nih.gov/articles/PMC12053770/
  150. https://pubmed.ncbi.nlm.nih.gov/21324544/
  151. https://pmc.ncbi.nlm.nih.gov/articles/PMC10526220/
  152. https://www.sciencedirect.com/science/article/pii/S0960977613002117
  153. https://pmc.ncbi.nlm.nih.gov/articles/PMC130026/
  154. https://pubmed.ncbi.nlm.nih.gov/22045823/
  155. https://www.sciencedirect.com/science/article/abs/pii/S1078143924005349
  156. https://pmc.ncbi.nlm.nih.gov/articles/PMC9875204/
  157. https://www.health.harvard.edu/blog/time-to-rethink-the-psa-to-test-or-not-to-test-debate-2017092912463
  158. https://pmc.ncbi.nlm.nih.gov/articles/PMC3926784/
  159. https://mammography.ucsf.edu/news/screening-mammography-women-beyond-age-75-years-remains-controversial
  160. https://www.nejm.org/doi/full/10.1056/NEJMoa1600249
  161. https://smw.ch/index.php/smw/article/view/1861/2604
  162. https://www.sciencedirect.com/science/article/abs/pii/S0016508524001641
  163. https://pmc.ncbi.nlm.nih.gov/articles/PMC5228201/
  164. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736%2825%2900145-X/abstract
  165. https://www.facs.org/media-center/press-releases/2022/study-finds-less-expensive-noninvasive-test-is-an-effective-alternative-to-a-more-costly-test-for-colorectal-cancer-screening/
  166. https://gi.org/journals-publications/ebgi/yen_aug2025/
  167. https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/lung-cancer-screening
  168. https://www.iaslc.org/iaslc-news/press-release/insufficient-evidence-recommend-low-dose-ct-screening-never-smokers-lung
  169. https://www.jto.org/article/S1556-0864%2824%2900618-X/fulltext
  170. https://www.atsjournals.org/doi/full/10.1513/AnnalsATS.201804-259RL
  171. https://pmc.ncbi.nlm.nih.gov/articles/PMC12415432/
  172. https://jme.bmj.com/content/47/7/510
  173. https://www.sciencedirect.com/science/article/abs/pii/S1076633220304876
  174. https://jamanetwork.com/journals/jamaoncology/fullarticle/2784987
  175. https://pubmed.ncbi.nlm.nih.gov/31753260/
  176. https://www.cdc.gov/nccdphp/priorities/colorectal-cancer.html
  177. https://www.sciencedirect.com/science/article/abs/pii/S1546144016309346
  178. https://ajronline.org/doi/10.2214/AJR.10.5609
  179. https://www.cdc.gov/pcd/issues/2021/20_0315.htm
  180. https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370%2825%2900101-4/fulltext
  181. https://www.foxnews.com/politics/doctors-key-us-health-task-force-accused-prioritizing-dei-over-evidence-based-medicine
  182. https://pmc.ncbi.nlm.nih.gov/articles/PMC12369792/
  183. https://pmc.ncbi.nlm.nih.gov/articles/PMC9144349/
  184. https://www.nature.com/articles/s41598-024-70073-7
  185. https://pubs.rsna.org/doi/10.1148/radiol.241030
  186. https://www.nature.com/articles/s41392-024-02021-w
  187. https://pmc.ncbi.nlm.nih.gov/articles/PMC10466971/
  188. https://www.cuimc.columbia.edu/news/pioneering-study-gene-technology-outperforms-standard-newborn-screening-tests
  189. https://www.mdpi.com/2075-4418/15/20/2579
  190. https://pmc.ncbi.nlm.nih.gov/articles/PMC10376292/
  191. https://nchstats.com/breakthroughs-in-medical-test-equipment/
  192. https://www.aha.org/aha-center-health-innovation-market-scan/2025-02-11-early-disease-detection-3-tech-trends-watch
  193. https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/breast-cancer-screening
  194. https://acsjournals.onlinelibrary.wiley.com/doi/full/10.1002/cncr.35223
  195. https://www.statnews.com/2025/08/04/colon-cancer-screening-guidelines-age-45-new-studies/
  196. https://news.ufl.edu/2025/06/cancer-screening-guidelines/
  197. https://ascopubs.org/doi/pdf/10.1200/EDBK-25-473834
  198. https://schaeffer.usc.edu/research/cancer-screening/
  199. https://www.cdc.gov/pcd/issues/2025/25_0139.htm
  200. https://www.pcori.org/funding-opportunities/what-who-we-fund/research-project-agenda-topic-themes-inform-focused-funding-opportunities/2025-research-project-agenda-topic-themes
  201. https://evidence.nejm.org/doi/full/10.1056/EVIDra2100035
Add your contribution
Related Hubs
User Avatar
No comments yet.