Abstract:ObjectivesTo evaluate the long-term cost-effectiveness of germline BRCA1 and BRCA2 (collectively termed “BRCA”) testing in women with epithelial ovarian cancer, and testing for the relevant mutation in first- and second-degree relatives of BRCA mutation–positive individuals, compared with no testing. Female BRCA mutation–positive relatives of patients with ovarian cancer could undergo risk-reducing mastectomy and/or bilateral salpingo-oophorectomy.MethodsA cost-effectiveness model was developed that included t… Show more
“…Clinical cancer genetics is a subspecialty in oncology which aims to identify individuals at increased risk of developing cancer, enabling important informed cancer risk management decisions to be made (Robson et al 2010). These interventions have been demonstrated to have a significant impact on cancer prevention, early detection and outcome (Desmond et al 2015;Finch et al 2014;Wang et al 2012), which is cost-effective and has been found to decrease long-term healthcare costs overall (Barrow et al 2015;Eccleston et al 2017).…”
The increase in demand for clinical cancer genetics services has impacted the ability to provide services timeously. Given limited resources, this often results in extended appointment waiting times. Over the last 3 years, the Cancer Genetics Service at the National Cancer Centre Singapore has continued to experience a steady increase in demand for its service. Nevertheless, significant no-show rates have been reported. This study sought to determine whether an association exists between appointment waiting times and attendance rates. Data was gathered for all participants meeting inclusion criteria. Attendance rates and appointment waiting times were calculated. The relationship between mean waiting times for those who did and did not attend their scheduled appointments was evaluated using Welch's t test and linear regression model. The results showed a significant difference in mean appointment waiting times between patients who did and did not attend (32.66 versus 43.50 days respectively; p < 0.0001). Furthermore, patients who waited for longer than 37 days were significantly less likely to attend. No-show rates increased as the waiting time increased, at a rate of 19.60% per 20 days and 21.40% per 30 days. In conclusion, appointment waiting time is a significant predictor for patient attendance. Strategies to ensure patients receive an appointment within the necessary timeframe at the desired setting are important to ensure that individuals at increased cancer risk attend their appointments in order to manage their cancer risks effectively.
“…Clinical cancer genetics is a subspecialty in oncology which aims to identify individuals at increased risk of developing cancer, enabling important informed cancer risk management decisions to be made (Robson et al 2010). These interventions have been demonstrated to have a significant impact on cancer prevention, early detection and outcome (Desmond et al 2015;Finch et al 2014;Wang et al 2012), which is cost-effective and has been found to decrease long-term healthcare costs overall (Barrow et al 2015;Eccleston et al 2017).…”
The increase in demand for clinical cancer genetics services has impacted the ability to provide services timeously. Given limited resources, this often results in extended appointment waiting times. Over the last 3 years, the Cancer Genetics Service at the National Cancer Centre Singapore has continued to experience a steady increase in demand for its service. Nevertheless, significant no-show rates have been reported. This study sought to determine whether an association exists between appointment waiting times and attendance rates. Data was gathered for all participants meeting inclusion criteria. Attendance rates and appointment waiting times were calculated. The relationship between mean waiting times for those who did and did not attend their scheduled appointments was evaluated using Welch's t test and linear regression model. The results showed a significant difference in mean appointment waiting times between patients who did and did not attend (32.66 versus 43.50 days respectively; p < 0.0001). Furthermore, patients who waited for longer than 37 days were significantly less likely to attend. No-show rates increased as the waiting time increased, at a rate of 19.60% per 20 days and 21.40% per 30 days. In conclusion, appointment waiting time is a significant predictor for patient attendance. Strategies to ensure patients receive an appointment within the necessary timeframe at the desired setting are important to ensure that individuals at increased cancer risk attend their appointments in order to manage their cancer risks effectively.
“…Different mainstream models of integrating BRCA testing into the breast cancer pathway have been implemented internationally with great success and high levels of patient and clinician satisfaction . In the United Kingdom, the Mainstreaming Cancer Genetics programme ran from 2013 to 2018 and included testing at diagnosis for young onset (≤45), triple negative, two primary breast cancers ≤60, male breast cancer, or breast cancer plus a first degree relative with breast cancer .…”
Section: How Does Genomic Testing Inform Treatment Pathways For Commomentioning
confidence: 99%
“…Different mainstream models of integrating BRCA testing into the breast cancer pathway have been implemented internationally with great success and high levels of patient and clinician satisfaction. [14][15][16][17][18] In the United Kingdom, the Mainstreaming Cancer Genetics programme ran from 2013 to 2018 and included testing at diagnosis for young onset (≤45), triple negative, two primary breast cancers ≤60, male breast cancer, or breast cancer plus a first degree relative with breast cancer. 16 It is predicted that if the United Kingdom adopted the MCG breast cancer mainstream testing guidelines, this would result in 2500 BRCA mutations identified per year and find all BRCA mutations from testing only a third of breast cancer patients most likely to carry a mutation.…”
“…Авторы рассчитали модель, включающую риски и эффекты при обращении к превентивным мерам (операция), а также стоимость и влияние на снижение уровня смертности от онкологических заболеваний. В итоге ученые сформулировали основной вывод: направление всех пациенток с раком яичников на прохождение генетического тестирования будет эффективным в долгосрочной перспективе и позволит сократить смертность [15]. Кроме того, в фокусе внимания представителей данного направления оказывается анализ жизненных шансов индивидов в зависимости от таких структурных переменных, как класс, возраст, гендер, раса, коллективность, условия жизни.…”
Section: основные направления социологического анализа практики генетunclassified
“…Парадокс 2: Генетическое тестирование одновременно производит ощущение контроля над ситуацией и риск. Это проявляется на нескольких уровнях: а) когда человек узнает, что в семье были случаи рака молочной железы; б) когда человек переходит на новую модель повседневной жизни и постоянно проходит скрининги [20]; в) когда человек решается пройти тест на наличие генетических мутаций и узнает о положительном результате; г) когда человек осознает свою ответственность перед семьей за то, чтобы они тоже прошли этот тест (наиболее остро этот вопрос стоит в отношении детей, в частности ситуации планирования детей); д) когда человек решается на оперативное удаление груди или медикаментозное лечение [15]. Все эти ситуации объединяет то, что, с одной стороны, обретая информацию о предрасположенности к развитию онкологического заболевания, человек получает больший контроль над своей жизнью.…”
Section: парадоксы создаваемые генетическим тестированием на наличиеunclassified
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