Optimization Model and Algorithm Help to Screen and Treat Sexually Transmitted Diseases
K. Zhao, Guantao Chen, T. Gift, G. Tao
{"title":"Optimization Model and Algorithm Help to Screen and Treat Sexually Transmitted Diseases","authors":"K. Zhao, Guantao Chen, T. Gift, G. Tao","doi":"10.4018/jcmam.2010100101","DOIUrl":null,"url":null,"abstract":"Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (GC) are two common sexually transmitted diseases among women in the United States. Publicly funded programs usually do not have enough money to screen and treat all patients. Therefore, the authors propose a new resource allocation model to assist clinical managers to make decisions on identifying at-risk population groups, as well as selecting a screening and treatment strategy for CT and GC patients under a fixed budget. At the same time, the authors also develop a two-step branch-and-bound algorithm tailor-made for our model. Running on real-life data, the algorithm calculates the optimal solution within a very short time. The new algorithm also improves the accuracy of an approximate solution obtained by Excel Solver. This study has shown that a resource allocation model and algorithm might have a significant impact on real clinical issues. monly reported sexually transmitted diseases (STDs) in the United States. Most infections are asymptomatic and would not be detected without asymptomatic screening, especially for women. In 2008, 1,210,523 cases of chlamydia were reported to the Centers for Disease Control and Prevention (CDC) in the United States. This case count corresponds to a rate of 401.3 cases per 100,000 population, an increase of 9.2% compared with the rate in 2007 DOI: 10.4018/jcmam.2010100101 2 International Journal of Computational Models and Algorithms in Medicine, 1(4), 1-18, October-December 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. (Centers for Disease Control and Prevention, 2010b). In 2008, 336,742 cases of gonorrhea were reported to CDC in the United States, corresponding to a rate of 111.6 per 100,000 population (Centers for Disease Control and Prevention, 2010b). Many CT and GC infections are detected through screening and treatment in public clinics. In reality, these clinics may not have sufficient budgets to screen all eligible women with the most effective CT/GC tests and to offer patients more expensive, single-dose treatments that improve adherence. To use limited resources effectively, CT and GC control programs usually provide selective screening based on defined guidelines. For example, CDC and the U.S. Preventive Services Task Force (USPSTF) recommend annual screening for CT for all sexually active females 25 years and younger (Centers for Disease Control and Prevention, 2010a). In addition, USPSTF also recommends screening all sexually active women younger than 25 years, including those who are pregnant, for GC if they are at increased risk for infections (U.S. Preventive Services Task Force, 2005). Identifying which subpopulations to screen for CT and GC is just one part of the real-life problem. The availability of several testing assays with various performance parameters and costs presents a challenge for screening strategies: newer diagnostic tests that are less invasive and more sensitive offer increased opportunities for screening, but at a greater cost. In other words, the problem is whether more infections can be diagnosed and treated using a more sensitive and expensive test to screen fewer patients, or to use a relatively cheaper and less sensitive test to screen a greater number of patients. To further complicate the issue, test manufacturers market combination tests or bundled tests at prices that are less expensive than the price of separate single-pathogen tests. This situation encourages the testing for GC even when its prevalence in the population is extremely low. Overview of Creating Resource Allocation Models for STDs There are not a lot of resource allocation (optimization) models regarding the control of CT and GC infections. But many efforts have been made to develop models to investigate and evaluate HIV prevention and control programs (Brandeau & Zaric, 2009; Kaplan & Pollack, 1998; Lasry, Zaric, & Carter, 2007; Sendi & Al, 2003). To correlate with the practical relevance to CT infections, researchers initially developed a resource allocation model to determine the optimal strategy for curing CT infections among asymptomatic women at clinics (Tao, Gift, Walsh, Irwin, & Kassler, 2002). Two years later, researchers proposed a mixed-integer program to model re-screening women who test positive for CT infections (Tao, Abban, Gift, Chen, & Irwin, 2004). These two optimization models are able to offer simple guidelines for clinics on the selection of test and treatment for certain populations. However, these models are not able to manage two or more infections (e.g. CT and GC) at the same time at given clinics. Overview of Algorithms for Solving STDs Resource Allocation Models Many health care researchers rely on existing resource allocation model software to solve their proposed models because some software applications are easy to use (Gift, Walsh, Haddix, & Irwin, 2002; Lasry, Carter, & Zaric, 2008; Tao et al., 2002, 2004). However, these applications sometimes may not provide the best outcomes due to the complexity of proposed models and the limitations of algorithms used in the software. For example, the resource allocation models used in the previous STD studies were nonlinear programming models and the optimal outcomes generated by the algorithm were never verified. With respect to the nature of the resource allocation models that are typical nonlinear models, the algorithms for these models in 16 more pages are available in the full version of this document, which may be purchased using the \"Add to Cart\" button on the product's webpage: www.igi-global.com/article/optimization-model-algorithmhelp-screen/51667?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Medicine, Healthcare, and Life Science. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. 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引用次数: 9
Abstract
Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (GC) are two common sexually transmitted diseases among women in the United States. Publicly funded programs usually do not have enough money to screen and treat all patients. Therefore, the authors propose a new resource allocation model to assist clinical managers to make decisions on identifying at-risk population groups, as well as selecting a screening and treatment strategy for CT and GC patients under a fixed budget. At the same time, the authors also develop a two-step branch-and-bound algorithm tailor-made for our model. Running on real-life data, the algorithm calculates the optimal solution within a very short time. The new algorithm also improves the accuracy of an approximate solution obtained by Excel Solver. This study has shown that a resource allocation model and algorithm might have a significant impact on real clinical issues. monly reported sexually transmitted diseases (STDs) in the United States. Most infections are asymptomatic and would not be detected without asymptomatic screening, especially for women. In 2008, 1,210,523 cases of chlamydia were reported to the Centers for Disease Control and Prevention (CDC) in the United States. This case count corresponds to a rate of 401.3 cases per 100,000 population, an increase of 9.2% compared with the rate in 2007 DOI: 10.4018/jcmam.2010100101 2 International Journal of Computational Models and Algorithms in Medicine, 1(4), 1-18, October-December 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. (Centers for Disease Control and Prevention, 2010b). In 2008, 336,742 cases of gonorrhea were reported to CDC in the United States, corresponding to a rate of 111.6 per 100,000 population (Centers for Disease Control and Prevention, 2010b). Many CT and GC infections are detected through screening and treatment in public clinics. In reality, these clinics may not have sufficient budgets to screen all eligible women with the most effective CT/GC tests and to offer patients more expensive, single-dose treatments that improve adherence. To use limited resources effectively, CT and GC control programs usually provide selective screening based on defined guidelines. For example, CDC and the U.S. Preventive Services Task Force (USPSTF) recommend annual screening for CT for all sexually active females 25 years and younger (Centers for Disease Control and Prevention, 2010a). In addition, USPSTF also recommends screening all sexually active women younger than 25 years, including those who are pregnant, for GC if they are at increased risk for infections (U.S. Preventive Services Task Force, 2005). Identifying which subpopulations to screen for CT and GC is just one part of the real-life problem. The availability of several testing assays with various performance parameters and costs presents a challenge for screening strategies: newer diagnostic tests that are less invasive and more sensitive offer increased opportunities for screening, but at a greater cost. In other words, the problem is whether more infections can be diagnosed and treated using a more sensitive and expensive test to screen fewer patients, or to use a relatively cheaper and less sensitive test to screen a greater number of patients. To further complicate the issue, test manufacturers market combination tests or bundled tests at prices that are less expensive than the price of separate single-pathogen tests. This situation encourages the testing for GC even when its prevalence in the population is extremely low. Overview of Creating Resource Allocation Models for STDs There are not a lot of resource allocation (optimization) models regarding the control of CT and GC infections. But many efforts have been made to develop models to investigate and evaluate HIV prevention and control programs (Brandeau & Zaric, 2009; Kaplan & Pollack, 1998; Lasry, Zaric, & Carter, 2007; Sendi & Al, 2003). To correlate with the practical relevance to CT infections, researchers initially developed a resource allocation model to determine the optimal strategy for curing CT infections among asymptomatic women at clinics (Tao, Gift, Walsh, Irwin, & Kassler, 2002). Two years later, researchers proposed a mixed-integer program to model re-screening women who test positive for CT infections (Tao, Abban, Gift, Chen, & Irwin, 2004). These two optimization models are able to offer simple guidelines for clinics on the selection of test and treatment for certain populations. However, these models are not able to manage two or more infections (e.g. CT and GC) at the same time at given clinics. Overview of Algorithms for Solving STDs Resource Allocation Models Many health care researchers rely on existing resource allocation model software to solve their proposed models because some software applications are easy to use (Gift, Walsh, Haddix, & Irwin, 2002; Lasry, Carter, & Zaric, 2008; Tao et al., 2002, 2004). However, these applications sometimes may not provide the best outcomes due to the complexity of proposed models and the limitations of algorithms used in the software. For example, the resource allocation models used in the previous STD studies were nonlinear programming models and the optimal outcomes generated by the algorithm were never verified. With respect to the nature of the resource allocation models that are typical nonlinear models, the algorithms for these models in 16 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/optimization-model-algorithmhelp-screen/51667?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Medicine, Healthcare, and Life Science. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2
优化模型和算法有助于性传播疾病的筛查和治疗
沙眼衣原体(CT)和淋病奈瑟菌(GC)是美国女性中两种常见的性传播疾病。公共资助的项目通常没有足够的资金来筛查和治疗所有的病人。因此,作者提出了一种新的资源分配模型,以帮助临床管理者在固定预算下对CT和GC患者进行筛查和治疗策略的选择,以确定高危人群。同时,作者还针对我们的模型开发了一种两步分支定界算法。该算法运行在真实数据上,在很短的时间内计算出最优解。该算法还提高了用Excel求解器求得的近似解的精度。本研究表明,资源分配模型和算法可能对实际临床问题产生重大影响。性传播疾病(STDs)在美国是最常见的。大多数感染是无症状的,如果不进行无症状筛查就不会被发现,特别是对妇女。2008年,美国疾病控制和预防中心(CDC)报告了1,210,523例衣原体病例。这一病例数相当于每10万人中有401.3例病例,与2007年的比率相比增加了9.2%。2国际医学计算模型与算法杂志,1(4),1- 18,2010年10 - 12月版权所有©2010,IGI Global。未经IGI Global书面许可,禁止以印刷或电子形式复制或分发。(疾病控制和预防中心,2010b)。2008年,美国向疾病预防控制中心报告了336,742例淋病病例,相当于每10万人中有111.6例(疾病控制和预防中心,2010年b)。许多CT和GC感染是通过在公立诊所的筛查和治疗发现的。实际上,这些诊所可能没有足够的预算来用最有效的CT/GC检查筛查所有符合条件的妇女,并为患者提供更昂贵的单剂量治疗,以提高依从性。为了有效地利用有限的资源,CT和GC控制程序通常根据明确的指导方针提供选择性筛选。例如,疾病预防控制中心和美国预防服务工作组(USPSTF)建议所有25岁及以下的性活跃女性每年进行CT筛查(疾病控制和预防中心,2010a)。此外,USPSTF还建议筛查所有年龄小于25岁的性活跃女性,包括孕妇,如果她们感染风险增加,则进行GC筛查(美国预防服务工作组,2005)。确定哪些亚群需要筛查CT和GC只是现实问题的一部分。几种具有不同性能参数和成本的检测方法的可用性对筛查策略提出了挑战:侵入性更小、更敏感的新诊断测试提供了更多的筛查机会,但成本更高。换句话说,问题在于是否可以使用更敏感、更昂贵的检测方法来筛查更少的患者,从而诊断和治疗更多的感染,还是使用相对便宜、更不敏感的检测方法来筛查更多的患者。使问题进一步复杂化的是,检测制造商以比单独的单一病原体检测更便宜的价格销售组合检测或捆绑检测。这种情况鼓励对胃癌进行检测,即使它在人群中的患病率极低。关于控制CT和GC感染的资源分配(优化)模型并不多。但是,已经做出了许多努力来开发模型来调查和评估艾滋病毒预防和控制项目(Brandeau & Zaric, 2009;Kaplan & Pollack, 1998;Lasry, Zaric, & Carter, 2007;Sendi & Al, 2003)。为了与CT感染的实际相关性相关联,研究人员最初开发了一个资源分配模型,以确定在诊所治疗无症状妇女CT感染的最佳策略(Tao, Gift, Walsh, Irwin, & Kassler, 2002)。两年后,研究人员提出了一个混合整数程序,模拟对CT感染检测呈阳性的妇女进行重新筛查(Tao, Abban, Gift, Chen, & Irwin, 2004)。这两种优化模型能够为诊所选择特定人群的检测和治疗提供简单的指导。然而,这些模型不能在给定的诊所同时处理两种或两种以上的感染(例如CT和GC)。
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