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<\/a>Population screening for disease<\/a>\u00a0 is a concept that has been around for many decades. Its main purpose is early detection and treatment of asymptomatic disease, or risk assessment and prevention of future disease, in order to improve health outcomes in individuals and populations. Examples include mammography in breast cancer screening<\/a> and colonoscopy in colorectal cancer screening<\/a>. Principles and criteria for population screening programs have been developed by many organizations and have evolved over time. These principles ensure that the benefits of screening programs outweigh potential harms such as overdiagnosis, inappropriate interventions and anxiety<\/a>.<\/p>\nA criticism often leveled against criteria for population screening is that screening guidelines typically apply to the \u201caverage\u201d person in the population<\/a> and may not be relevant to subgroups of the population with differing levels of risk. \u00a0Advances in genomics promise more targeted or personalized approaches to screening such as stratifying the population on the basis of differing levels of genetic risks. \u00a0For example, in breast cancer screening, the United States Preventive Services Task Force currently recommends biennial screening mammography for women aged 50 to 74 years. However, they do acknowledge that the decision to start regular screening mammography before the age of 50 years should be an individual one and take patient context into account, including the patient’s other risk factors, as well as values regarding specific benefits and harms. This second recommendation reflects the continued debate and uncertainty about the balance of benefits and risks of breast cancer screening under age 50.<\/p>\n Could advances in genomics improve the benefits of population screening beyond \u201caverage risk\u201d screening guidelines by stratifying the population into high risk subgroups that require earlier or more frequent screening, and lower risk subgroups that require no or less frequent screenings. Hereditary breast and ovarian cancer<\/a> due to mutations in BRCA<\/em> genes provides an example of a high risk subgroup of the population that may require more targeted interventions. Currently, the U.S. Preventive Services Task Force recommends that all women with certain family history patterns for breast and ovarian cancer be offered genetic counseling and evaluation for BRCA<\/em> testing in order to reduce morbidity and mortality from breast and ovarian cancer.<\/p>\n Although BRCA<\/em> mutations greatly increase the risk of breast and ovarian cancer in affected persons, they still account for a small fraction of cases of breast cancer in the population. However, there are many more genes with smaller disease risk that could account for much larger proportion of breast cancer. Can we construct a rational scenario for using such genetic information to guide breast screening recommendations that are largely based on age? In the online issue of Genetics in Medicine, Chowdhury et al. report<\/a> on the recommendations of multidisciplinary expert workshops convened by the Foundation for Genomics and Population Health<\/a> in the United Kingdom. Participants examined scientific, ethical and logistical aspects of personalized population screening for prostate and breast cancer based on polygenic susceptibility. The authors recognized the promise of genetic stratification in population screening. For example, instead of only using an age cutoff for screening, the combination of age and genetic risk profile can theoretically provide a more efficient screening program. However, key issues need to be addressed before genetic stratification can be implemented in practice.<\/p>\n In our accompanying commentary<\/a>, we elaborate on some of the issues that need to be answered before testing for multiple genes can be integrated into population screening. These include 1) the absence of credible epidemiological data on genetic and environmental \u00a0risk factors on disease occurrence in the population to be tested; 2) the absence of information on whether or not genes can identify individuals who will actually benefit from early detection and can benefit from earlier interventions; 3) the absence of information on benefits, harms and costs of\u00a0using different interventions at different levels of genetic risk; \u00a04) the absence of information on\u00a0acceptability of genetic stratification by the population; 5)\u00a0 the lack of readiness of the healthcare and public health systems in integrating such information in practice.<\/p>\n Population screening for rare genetic diseases with high disease risk and evidence-based\u00a0 interventions (such as newborn screening<\/a>) will continue to be a mainstay for genetic screening for a while. However, technological developments will drive the interest in using genetic testing for multiple genes to stratify risk in population screening programs and for personal genomic tests. As Dent et al<\/a> recently commented \u201cstratified screening based on genetic testing is a radically new approach to prevention. Various organizational issues would need to be considered before it could be introduced, and a number of questions require further research.\u201d \u00a0In the meantime, full engagement of the scientific community, clinical and public health practice, consumers and policy makers is needed to prepare for the evidence-based integration of genomic information into health care and disease prevention.<\/p>\n We invite our readers\u2019 comments and opinions regarding the appropriate role of genetic testing in population screening for various diseases.<\/p>\n","protected":false},"excerpt":{"rendered":" Scientific and implementation challenges Population screening for disease\u00a0 is a concept that has been around for many decades. Its main purpose is early detection and treatment of asymptomatic disease, or risk assessment and prevention of future disease, in order to improve health outcomes in individuals and populations. Examples include mammography in breast cancer screening and<\/p>\n","protected":false},"author":121,"featured_media":1977,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5236,15981],"tags":[5738,5739,170],"_links":{"self":[{"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/posts\/1963"}],"collection":[{"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/users\/121"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/comments?post=1963"}],"version-history":[{"count":38,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/posts\/1963\/revisions"}],"predecessor-version":[{"id":5487,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/posts\/1963\/revisions\/5487"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/media\/1977"}],"wp:attachment":[{"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/media?parent=1963"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/categories?post=1963"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/tags?post=1963"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}