Efficient engagement: opportunities for mobile methadone maintenance in geographically underserved areas.
IF 2.8
3区 医学
Q2 HEALTH POLICY & SERVICES
引用次数: 0
Abstract
In 2021 the Drug Enforcement Agency changed regulations to reduce barriers in the registration and operation of mobile methadone clinics for treating opioid use disorder in rural and underserved areas. How these routes might best be operated and how many clients might be served by mobile units are open questions. This work identifies candidate dispensing locations from mobile units in areas that do not have geographic access to a fixed clinic. Optimal routes for mobile methadone are identified by solving a mixed integer program that maximizes the number of clients without access to care that can be served by a mobile route. Ohio is used as a case study state to explore the efficiency of routes leaving from each methadone clinic in the state. The approach is generalized nationwide, and multi-vehicle routes are analyzed. Between 48 and 68 clients can be served by a single vehicle in a day (mean of 60) under default parameter assumptions across Ohio. Similarly, nationwide routes serve a mean of 47.5 clients per day. The second and third vehicle routes from a clinic provide access to an additional 95% and 93% of the clients of single vehicle routes, respectively. The number of servable clients on any route greatly differs based on geography. This work demonstrates the potential value of mobile methadone maintenance in reaching populations without geographic access to a fixed clinic. Additional policy and funding changes are needed to incentivize the operation of mobile methadone units for serving rural and unmet demand.
有效参与：在地理上服务不足的地区提供流动美沙酮维护的机会。
2021年，缉毒局修改了法规，以减少在农村和服务不足地区治疗阿片类药物使用障碍的流动美沙酮诊所的注册和运营障碍。如何最好地运营这些路线，以及移动单位可以为多少客户提供服务，这些都是悬而未决的问题。这项工作确定候选配药地点从移动单位在没有地理上访问固定诊所的地区。通过求解一个混合整数程序来确定移动美沙酮的最佳路线，该程序使无法获得可通过移动路线提供服务的客户人数最大化。俄亥俄州被用作一个案例研究州，以探索从该州每个美沙酮诊所离开的路线的效率。该方法在全国范围内进行了推广，并对多车路线进行了分析。在俄亥俄州的默认参数假设下，一辆车每天可以为48到68个客户提供服务（平均为60个）。同样，全国航线平均每天服务47.5名客户。从诊所出发的第二和第三条车辆路线分别为单一车辆路线的额外95%和93%的客户提供了服务。任何路线上可服务的客户端数量因地理位置而有很大差异。这项工作证明了移动美沙酮维持治疗在到达没有固定诊所的人群方面的潜在价值。需要进一步改变政策和资金，以激励流动美沙酮单位的运作，以满足农村和未满足的需求。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
求助全文
约1分钟内获得全文
求助全文
来源期刊
Health Care Management Science
HEALTH POLICY & SERVICES-
CiteScore
7.20
自引率
5.60%
发文量
40
期刊介绍：
Health Care Management Science publishes papers dealing with health care delivery, health care management, and health care policy. Papers should have a decision focus and make use of quantitative methods including management science, operations research, analytics, machine learning, and other emerging areas. Articles must clearly articulate the relevance and the realized or potential impact of the work. Applied research will be considered and is of particular interest if there is evidence that it was implemented or informed a decision-making process. Papers describing routine applications of known methods are discouraged.
Authors are encouraged to disclose all data and analyses thereof, and to provide computational code when appropriate.
Editorial statements for the individual departments are provided below.
Health Care Analytics
Departmental Editors:
Margrét Bjarnadóttir, University of Maryland
Nan Kong, Purdue University
With the explosion in computing power and available data, we have seen fast changes in the analytics applied in the healthcare space. The Health Care Analytics department welcomes papers applying a broad range of analytical approaches, including those rooted in machine learning, survival analysis, and complex event analysis, that allow healthcare professionals to find opportunities for improvement in health system management, patient engagement, spending, and diagnosis. We especially encourage papers that combine predictive and prescriptive analytics to improve decision making and health care outcomes.
The contribution of papers can be across multiple dimensions including new methodology, novel modeling techniques and health care through real-world cohort studies. Papers that are methodologically focused need in addition to show practical relevance. Similarly papers that are application focused should clearly demonstrate improvements over the status quo and available approaches by applying rigorous analytics.
Health Care Operations Management
Departmental Editors:
Nilay Tanik Argon, University of North Carolina at Chapel Hill
Bob Batt, University of Wisconsin
The department invites high-quality papers on the design, control, and analysis of operations at healthcare systems. We seek papers on classical operations management issues (such as scheduling, routing, queuing, transportation, patient flow, and quality) as well as non-traditional problems driven by everchanging healthcare practice. Empirical, experimental, and analytical (model based) methodologies are all welcome. Papers may draw theory from across disciplines, and should provide insight into improving operations from the perspective of patients, service providers, organizations (municipal/government/industry), and/or society.
Health Care Management Science Practice
Departmental Editor:
Vikram Tiwari, Vanderbilt University Medical Center
The department seeks research from academicians and practitioners that highlights Management Science based solutions directly relevant to the practice of healthcare. Relevance is judged by the impact on practice, as well as the degree to which researchers engaged with practitioners in understanding the problem context and in developing the solution. Validity, that is, the extent to which the results presented do or would apply in practice is a key evaluation criterion. In addition to meeting the journal’s standards of originality and substantial contribution to knowledge creation, research that can be replicated in other organizations is encouraged. Papers describing unsuccessful applied research projects may be considered if there are generalizable learning points addressing why the project was unsuccessful.
Health Care Productivity Analysis
Departmental Editor:
Jonas Schreyögg, University of Hamburg
The department invites papers with rigorous methods and significant impact for policy and practice. Papers typically apply theory and techniques to measuring productivity in health care organizations and systems. The journal welcomes state-of-the-art parametric as well as non-parametric techniques such as data envelopment analysis, stochastic frontier analysis or partial frontier analysis. The contribution of papers can be manifold including new methodology, novel combination of existing methods or application of existing methods to new contexts. Empirical papers should produce results generalizable beyond a selected set of health care organizations. All papers should include a section on implications for management or policy to enhance productivity.
Public Health Policy and Medical Decision Making
Departmental Editors:
Ebru Bish, University of Alabama
Julie L. Higle, University of Southern California
The department invites high quality papers that use data-driven methods to address important problems that arise in public health policy and medical decision-making domains. We welcome submissions that develop and apply mathematical and computational models in support of data-driven and model-based analyses for these problems.
The Public Health Policy and Medical Decision-Making Department is particularly interested in papers that:
Study high-impact problems involving health policy, treatment planning and design, and clinical applications;
Develop original data-driven models, including those that integrate disease modeling with screening and/or treatment guidelines;
Use model-based analyses as decision making-tools to identify optimal solutions, insights, recommendations.
Articles must clearly articulate the relevance of the work to decision and/or policy makers and the potential impact on patients and/or society. Papers will include articulated contributions within the methodological domain, which may include modeling, analytical, or computational methodologies.
Emerging Topics
Departmental Editor:
Alec Morton, University of Strathclyde
Emerging Topics will handle papers which use innovative quantitative methods to shed light on frontier issues in healthcare management and policy. Such papers may deal with analytic challenges arising from novel health technologies or new organizational forms. Papers falling under this department may also deal with the analysis of new forms of data which are increasingly captured as health systems become more and more digitized.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
