MDM Policy and Practice最新文献

{"title":"Economic and Chronologic Optimization of Fecal Donors Screening Process.","authors":"Bar Levy, Naomi Fliss Isakov, Tomer Ziv-Baran, Moshe Leshno, Nitsan Maharshak, Lael Werner","doi":"10.1177/23814683241254809","DOIUrl":"10.1177/23814683241254809","url":null,"abstract":"<p><p><b>Background.</b> Fecal microbial transplantation (FMT) is the delivery of fecal microbiome, isolated from healthy donors, into a patient's gastrointestinal tract. FMT is a safe and efficient treatment for recurrent <i>Clostridioides difficile</i> infection. Donors undergo strict screening to avoid disease transmission. This consists of several blood and stool tests, which are performed in a multistage, costly process. We performed a cost-minimizing analysis to find the optimal order in which the tests should be performed. <b>Methods.</b> An algorithm to optimize the order of tests in terms of cost was defined. Performance analysis for disqualifying a potential healthy donor was carried out on data sets based on either the published literature or our real-life data. For both data sets, we calculated the total cost to qualify a single donor according to the optimal order of tests, suggested by the algorithm. <b>Results.</b> Applying the algorithm to the published literature revealed potential savings of 94.2% of the cost of screening a potential donor and 7.05% of the cost to qualify a single donor. In our cohort of 87 volunteers, 53 were not eligible for donation. Of 34 potential donors, 10 were disqualified due to abnormal lab tests. Applying our algorithm to optimize the order of tests, the average cost for screening a potential donor resulted in potential savings of 49.9% and a 21.3% savings in the cost to qualify a single donor. <b>Conclusions.</b> Improving the order and timing of the screening tests of potential FMT stool donors can decrease the costs by about 50% per subject.</p><p><strong>Highlights: </strong>What is known:Fecal microbial transplantation (FMT) is the transfer of microbiome from healthy donors to patients.Fecal donors undergo multiple strict screening tests to exclude any transmissible disease.Screening tests of potential fecal donors is expensive and time consuming.FMT is the most efficient treatment for recurrent <i>C difficile</i> infection.What is new here:An algorithm to optimize the order of donors' screening tests in terms of cost was defined.Optimizing the order tests can save nearly 50% in costs of screening a potential donor.</p>","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"9 1","pages":"23814683241254809"},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11171430/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinician Perceptions on Using Decision Tools to Support Prediction-Based Shared Decision Making for Lung Cancer Screening.","authors":"Sarah E Skurla, N Joseph Leishman, Angela Fagerlin, Renda Soylemez Wiener, Julie Lowery, Tanner J Caverly","doi":"10.1177/23814683241252786","DOIUrl":"10.1177/23814683241252786","url":null,"abstract":"<p><strong>Background: </strong>Considering a patient's full risk factor profile can promote personalized shared decision making (SDM). One way to accomplish this is through encounter tools that incorporate prediction models, but little is known about clinicians' perceptions of the feasibility of using these tools in practice. We examined how clinicians react to using one such encounter tool for personalizing SDM about lung cancer screening (LCS).</p><p><strong>Design: </strong>We conducted a qualitative study based on field notes from academic detailing visits during a multisite quality improvement program. The detailer engaged one-on-one with 96 primary care clinicians across multiple Veterans Affairs sites (7 medical centers and 6 outlying clinics) to get feedback on 1) the rationale for prediction-based LCS and 2) how to use the DecisionPrecision (DP) encounter tool with eligible patients to personalize LCS discussions.</p><p><strong>Results: </strong>Thematic content analysis from detailing visit data identified 6 categories of clinician willingness to use the DP tool to personalize SDM for LCS (adoption potential), varying from \"Enthusiastic Potential Adopter\" (<i>n</i> = 18) to \"Definite Non-Adopter\" (<i>n</i> = 16). Many clinicians (<i>n</i> = 52) articulated how they found the concept of prediction-based SDM highly appealing. However, to varying degrees, nearly all clinicians identified challenges to incorporating such an approach in routine practice.</p><p><strong>Limitations: </strong>The results are based on the clinician's initial reactions rather than longitudinal experience.</p><p><strong>Conclusions: </strong>While many primary care clinicians saw real value in using prediction to personalize LCS decisions, more support is needed to overcome barriers to using encounter tools in practice. Based on these findings, we propose several strategies that may facilitate the adoption of prediction-based SDM in contexts such as LCS.</p><p><strong>Highlights: </strong>Encounter tools that incorporate prediction models promote personalized shared decision making (SDM), but little is known about clinicians' perceptions of the feasibility of using these tools in practice.We examined how clinicians react to using one such encounter tool for personalizing SDM about lung cancer screening (LCS).While many clinicians found the concept of prediction-based SDM highly appealing, nearly all clinicians identified challenges to incorporating such an approach in routine practice.We propose several strategies to overcome adoption barriers and facilitate the use of prediction-based SDM in contexts such as LCS.</p>","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"9 1","pages":"23814683241252786"},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11110512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141082604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Longer Life or a Quality Death? A Discrete Choice Experiment to Estimate the Relative Importance of Different Aspects of End-of-Life Care in the United Kingdom.","authors":"Chris Skedgel, David John Mott, Saif Elayan, Angela Cramb","doi":"10.1177/23814683241252425","DOIUrl":"10.1177/23814683241252425","url":null,"abstract":"<p><p><b>Background.</b> Advocates argue that end-of-life (EOL) care is systematically disadvantaged by the quality-adjusted life-year (QALY) framework. By definition, EOL care is short duration and not primarily intended to extend survival; therefore, it may be inappropriate to value a time element. The QALY also neglects nonhealth dimensions such as dignity, control, and family relations, which may be more important at EOL. Together, these suggest the QALY may be a flawed measure of the value of EOL care. To test these arguments, we administered a stated preference survey in a UK-representative public sample. <b>Methods.</b> We designed a discrete choice experiment (DCE) to understand public preferences over different EOL scenarios, focusing on the relative importance of survival, conventional health dimensions (especially physical symptoms and anxiety), and nonhealth dimensions such as family relations, dignity, and sense of control. We used latent class analysis to understand preference heterogeneity. <b>Results.</b> A 4-class latent class multinomial logit model had the best fit and illustrated important heterogeneity. A small class of respondents strongly prioritized survival, whereas most respondents gave relatively little weight to survival and, generally speaking, prioritized nonhealth aspects. <b>Conclusions.</b> This DCE illustrates important heterogeneity in preferences within UK respondents. Despite some preferences for core elements of the QALY, we suggest that most respondents favored what has been called \"a good death\" over maximizing survival and find that respondents tended to prioritize nonhealth over conventional health aspects of quality. Together, this appears to support arguments that the QALY is a poor measure of the value of EOL care. We recommend moving away from health-related quality of life and toward a more holistic perspective on well-being in assessing EOL and other interventions.</p><p><strong>Highlights: </strong>Advocates argue that some interventions, including but not limited to end-of-life (EOL) care, are valued by patients and the public but are systematically disadvantaged by the quality-adjusted life-year (QALY) framework, leading to an unfair and inefficient allocation of health care resources.Using a discrete choice experiment, we find some support for this argument. Only a small proportion of public respondents prioritized survival in EOL scenarios, and most prioritized nonhealth aspects such as dignity and family relations.Together, these results suggest that the QALY may be a poor measure of the value of EOL care, as it neglects nonhealth aspects of quality and well-being that appear to be important to people in hypothetical EOL scenarios.</p>","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"9 1","pages":"23814683241252425"},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11100281/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141065916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Practical Realities of Local-Level Economic Evaluations: Toward Informed Decision Making in Health Care.","authors":"Todd H Wagner, Alayna Carrandi","doi":"10.1177/23814683241247151","DOIUrl":"https://doi.org/10.1177/23814683241247151","url":null,"abstract":"","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"9 1","pages":"23814683241247151"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11025424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140858494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Scoping Review of Personalized, Interactive, Web-Based Clinical Decision Tools Available for Breast Cancer Prevention and Screening in the United States.","authors":"Dalya Kamil, Kaitlyn M Wojcik, Laney Smith, Julia Zhang, Oliver W A Wilson, Gisela Butera, Jinani Jayasekera","doi":"10.1177/23814683241236511","DOIUrl":"10.1177/23814683241236511","url":null,"abstract":"<p><p><b>Introduction.</b> Personalized web-based clinical decision tools for breast cancer prevention and screening could address knowledge gaps, enhance patient autonomy in shared decision-making, and promote equitable care. The purpose of this review was to present evidence on the availability, usability, feasibility, acceptability, quality, and uptake of breast cancer prevention and screening tools to support their integration into clinical care. <b>Methods.</b> We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews Checklist to conduct this review. We searched 6 databases to identify literature on the development, validation, usability, feasibility, acceptability testing, and uptake of the tools into practice settings. Quality assessment for each tool was conducted using the International Patient Decision Aid Standard instrument, with quality scores ranging from 0 to 63 (lowest-highest). <b>Results.</b> We identified 10 tools for breast cancer prevention and 9 tools for screening. The tools included individual (e.g., age), clinical (e.g., genomic risk factors), and health behavior (e.g., alcohol use) characteristics. Fourteen tools included race/ethnicity, but no tool incorporated contextual factors (e.g., insurance, access) associated with breast cancer. All tools were internally or externally validated. Six tools had undergone usability testing in samples including White (median, 71%; range, 9%-96%), insured (99%; 97%-100%) women, with college education or higher (60%; 27%-100%). All of the tools were developed and tested in academic settings. Seven (37%) tools showed potential evidence of uptake in clinical practice. The tools had an average quality assessment score of 21 (range, 9-39). <b>Conclusions.</b> There is limited evidence on testing and uptake of breast cancer prevention and screening tools in diverse clinical settings. The development, testing, and integration of tools in academic and nonacademic settings could potentially improve uptake and equitable access to these tools.</p><p><strong>Highlights: </strong>There were 19 personalized, interactive, Web-based decision tools for breast cancer prevention and screening.Breast cancer outcomes were personalized based on individual clinical characteristics (e.g., age, medical history), genomic risk factors (e.g., BRCA1/2), race and ethnicity, and health behaviors (e.g., smoking). The tools did not include contextual factors (e.g., insurance status, access to screening facilities) that could potentially contribute to breast cancer outcomes.Validation, usability, acceptability, and feasibility testing were conducted mostly among White and/or insured patients with some college education (or higher) in academic settings. There was limited evidence on testing and uptake of the tools in nonacademic clinical settings.</p>","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"9 1","pages":"23814683241236511"},"PeriodicalIF":1.9,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10946080/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140159173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the Impact of Different Modes of Information Provision on Preferences for a Newborn Bloodspot Screening Program in the United Kingdom.","authors":"Stuart J Wright, Caroline M Vass, Fiona Ulph, Katherine Payne","doi":"10.1177/23814683241232935","DOIUrl":"10.1177/23814683241232935","url":null,"abstract":"<p><p><b>Introduction.</b> This study aimed to understand the impact of alternative modes of information provision on the stated preferences of a sample of the public for attributes of newborn bloodspot screening (NBS) in the United Kingdom. <b>Methods.</b> An online discrete choice experiment survey was designed using 4 attributes to describe NBS (effect of treatment on the condition, time to receive results, whether the bloodspot is stored, false-positive rate). Survey respondents were randomized to 1 of 2 survey versions presenting the background training materials using text from a leaflet (leaflet version) or an animation (animation version). Heteroskedastic conditional logistic regression was used to estimate the effect of mode of information provision on error variance. <b>Results.</b> The survey was completed by 1,000 respondents (leaflet = 525; animation = 475). Preferences for the attributes in the DCE were the same in both groups, but the group receiving the animation version had 9% less error variance in their responses. Respondents completing the animation version gave higher ratings compared with the leaflet version in terms of ease of perceived understanding. Subgroup analysis suggested that the animation was particularly effective at reducing error variance for women (20%), people with previous children (16.5%), and people between the ages of 35 and 45 y (11.8%). <b>Limitations.</b> This study used simple DCE with 4 attributes, and the results may vary for more complex choice questions. <b>Conclusion.</b> This study provides evidence that that supplementing the information package offered to parents choosing to take part in NBS with an animation may aid them their decision making. Further research would be needed to test the animation in the health system. <b>Implications.</b> Researchers designing DCE should carefully consider the design of their training materials to improve the quality of data collected.</p><p><strong>Highlights: </strong>Prior to completing a discrete choice experiment about newborn bloodspot screening, respondents were shown information using either a leaflet-based or animated format.Respondents receiving information using an animation version reported that the information was slightly easier to understand and exhibited 9% less error variance in expressing their preferences for a newborn screening program.Using the animation version to present information appeared to have a larger impact in reducing the error variance of responses for specific respondents including women, individuals with children, individuals between the ages of 35 and 45 y, and individuals educated to degree level.</p>","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"9 1","pages":"23814683241232935"},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10913504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140040565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What Affects Perceived Trustworthiness of Online Medical Information and Subsequent Treatment Decision Making? Randomized Trials on the Role of Uncertainty and Institutional Cues.","authors":"Gabriel Recchia, Karin S Moser, Alexandra L J Freeman","doi":"10.1177/23814683241226660","DOIUrl":"10.1177/23814683241226660","url":null,"abstract":"&lt;p&gt;&lt;p&gt;&lt;b&gt;Background.&lt;/b&gt; Online, algorithmically driven prognostic tools are increasingly important in medical decision making. Institutions developing such tools need to be able to communicate the precision and accuracy of the information in a trustworthy manner, and so many attempt to communicate uncertainties but also use institutional logos to underscore their trustworthiness. Bringing together theories on trust, uncertainty, and psychological distance in a novel way, we tested whether and how the communication of uncertainty and the presence of institutional logos together affected trust in medical information, the prognostic tool itself, and treatment decisions. &lt;b&gt;Methods.&lt;/b&gt; A pilot and 2 online experiments in which UK (experiment 1) and worldwide (experiment 2) participants (N&lt;sub&gt;total&lt;/sub&gt; = 4,724) were randomized to 1 of 12 arms in a 3 (uncertainty cue) × 4 (institutional cue) between-subjects design. The stimulus was based on an existing medical prognostic tool. &lt;b&gt;Results.&lt;/b&gt; Institutional trust was consistently associated with trust in the prognostic tool itself, while uncertainty information had no consistent effect. Institutional trust predicted the amount of weight participants reported placing on institutional endorsements in their decision making and the likelihood of switching from passive to active treatment in a hypothetical scenario. There was also a significant effect of psychological distance to (perceived hypotheticality of) the scenario. &lt;b&gt;Conclusions/Implications.&lt;/b&gt; These results underline the importance of institutions demonstrating trustworthiness and building trust with their users. They also suggest that users tend to be insensitive to communications of uncertainty and that communicators may need to be highly explicit when attempting to warn of low precision or quality of evidence. The effect of the perceived hypotheticality of the scenario underscores the importance of realistic decision-making scenarios for studies and the role of familiarity with the decision dilemma generally.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;In a world where information for medical decision making is increasingly going to be provided through digital, online tools, institutions providing such tools need guidance on how best to communicate about their trustworthiness and precision.We find that people are fairly insensitive to cues designed to communicate uncertainty around the outputs of such tools. Even putting \"ATTENTION\" in bold font or explicitly pointing out the weaknesses in the data did not appear to affect people's decision making using the tool's outputs. Institutions should take note, and further work is required to determine how best to communicate uncertainty in a way that elicits appropriate caution in lay users.People were much more sensitive to institutional logos associated with the outputs. Generalized institutional trust (rather than trust in the specific institution whose logo was shown) was associated with how trustw","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"9 1","pages":"23814683241226660"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10870812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139900595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Patient Involvement Interventions within Clinical Practice: A Mixed-Methods Study of Health Care Professional Reasoning.","authors":"Anna Holm, Lotte Ørneborg Rodkjær, Hilary Louise Bekker","doi":"10.1177/23814683241229987","DOIUrl":"10.1177/23814683241229987","url":null,"abstract":"<p><p><b>Background.</b> Patient involvement interventions are complex interventions that improve patient involvement in treatment and care in health care systems. Studies report several benefits of patient involvement interventions and that health care professionals are positive about using them. However, they have not been explored as a collected group of interventions throughout the continuum of care and treatment. In addition, the relationship between patient involvement interventions and the clinical reasoning process of health care professionals has not been thoroughly studied. <b>Design.</b> This mixed-methods study was conducted at Aarhus University Hospital in Denmark between April and November 2022 using interview data from 12 health care professionals and survey data from 420 health care professionals. Informants were medical doctors, nurses, midwives, dietitians, physiotherapists, and occupational therapists who had direct contact with patients during their daily care and treatment. Quantitative data were analyzed using descriptive statistics; qualitative data were analyzed via inductive and deductive content analysis. <b>Results.</b> Communication and interaction were seen as overarching aspects of patient involvement, with patient involvement interventions being defined as concrete tools and methods to enhance health care professionals' explicit clinical reasoning process. <b>Limitations.</b> It is unclear if results are representative of all health care professionals at the hospital or only those with a positive view of patient involvement interventions. <b>Conclusions.</b> Patient involvement interventions are viewed as beneficial for patients and fit with the clinical reasoning of health care professionals. Clinical reasoning may be an active ingredient in the development and implementation of patient involvement interventions. <b>Implications.</b> In practice, health care professionals need training in person-centered communication and the ability to articulate their clinical reasoning explicitly. In research, a more in-depth understanding of the interrelations between patient involvement interventions and clinical reasoning is needed.</p><p><strong>Highlights: </strong>Communication and interaction are the fundamental goals of patient involvement in practice, regardless of which patient involvement intervention is being used.Clinical reasoning is often an unconscious process using tacit knowledge, but the use of patient involvement interventions may be a way for health care professionals (at both individual and group levels) to become more explicit about and aware of their reflections.Clinical reasoning can be viewed as a mechanism of change in the development and implementation of patient involvement interventions.</p>","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"9 1","pages":"23814683241229987"},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10868494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139742203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting COVID-19 Outbreaks in Correctional Facilities Using Machine Learning.","authors":"Giovanni S P Malloy, Lisa B Puglisi, Kristofer B Bucklen, Tyler D Harvey, Emily A Wang, Margaret L Brandeau","doi":"10.1177/23814683231222469","DOIUrl":"10.1177/23814683231222469","url":null,"abstract":"&lt;p&gt;&lt;p&gt;&lt;b&gt;Introduction.&lt;/b&gt; The risk of infectious disease transmission, including COVID-19, is disproportionately high in correctional facilities due to close living conditions, relatively low levels of vaccination, and reduced access to testing and treatment. While much progress has been made on describing and mitigating COVID-19 and other infectious disease risk in jails and prisons, there are open questions about which data can best predict future outbreaks. &lt;b&gt;Methods.&lt;/b&gt; We used facility data and demographic and health data collected from 24 prison facilities in the Pennsylvania Department of Corrections from March 2020 to May 2021 to determine which sources of data best predict a coming COVID-19 outbreak in a prison facility. We used machine learning methods to cluster the prisons into groups based on similar facility-level characteristics, including size, rurality, and demographics of incarcerated people. We developed logistic regression classification models to predict for each cluster, before and after vaccine availability, whether there would be no cases, an outbreak defined as 2 or more cases, or a large outbreak, defined as 10 or more cases in the next 1, 2, and 3 d. We compared these predictions to data on outbreaks that occurred. &lt;b&gt;Results.&lt;/b&gt; Facilities were divided into 8 clusters of sizes varying from 1 to 7 facilities per cluster. We trained 60 logistic regressions; 20 had test sets with between 35% and 65% of days with outbreaks detected. Of these, 8 logistic regressions correctly predicted the occurrence of an outbreak more than 55% of the time. The most common predictive feature was incident cases among the incarcerated population from 2 to 32 d prior. Other predictive features included the number of tests administered from 1 to 33 d prior, total population, test positivity rate, and county deaths, hospitalizations, and incident cases. Cumulative cases, vaccination rates, and race, ethnicity, or age statistics for incarcerated populations were generally not predictive. &lt;b&gt;Conclusions.&lt;/b&gt; County-level measures of COVID-19, facility population, and test positivity rate appear as potential promising predictors of COVID-19 outbreaks in correctional facilities, suggesting that correctional facilities should monitor community transmission in addition to facility transmission to inform future outbreak response decisions. These efforts should not be limited to COVID-19 but should include any large-scale infectious disease outbreak that may involve institution-community transmission.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;The risk of infectious disease transmission, including COVID-19, is disproportionately high in correctional facilities.We used machine learning methods with data collected from 24 prison facilities in the Pennsylvania Department of Corrections to determine which sources of data best predict a coming COVID-19 outbreak in a prison facility.Key predictors included county-level measures of COVID-19, facility population, ","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"9 1","pages":"23814683231222469"},"PeriodicalIF":1.9,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10826393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139643082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resource Utilization and Costs Associated with Approaches to Identify Infants with Early-Onset Sepsis.","authors":"Grace Guan, Neha S Joshi, Adam Frymoyer, Grace D Achepohl, Rebecca Dang, N Kenji Taylor, Joshua A Salomon, Jeremy D Goldhaber-Fiebert, Douglas K Owens","doi":"10.1177/23814683231226129","DOIUrl":"10.1177/23814683231226129","url":null,"abstract":"<p><p><b>Objective.</b> To compare resource utilization and costs associated with 3 alternative screening approaches to identify early-onset sepsis (EOS) in infants born at ≥35 wk of gestational age, as recommended by the American Academy of Pediatrics (AAP) in 2018. <b>Study Design.</b> Decision tree-based cost analysis of the 3 AAP-recommended approaches: 1) categorical risk assessment (categorization by chorioamnionitis exposure status), 2) neonatal sepsis calculator (a multivariate prediction model based on perinatal risk factors), and 3) enhanced clinical observation (assessment based on serial clinical examinations). We evaluated resource utilization and direct costs (2022 US dollars) to the health system. <b>Results.</b> Categorical risk assessment led to the greatest neonatal intensive care unit usage (210 d per 1,000 live births) and antibiotic exposure (6.8%) compared with the neonatal sepsis calculator (112 d per 1,000 live births and 3.6%) and enhanced clinical observation (99 d per 1,000 live births and 3.1%). While the per-live birth hospital costs of the 3 approaches were similar-categorical risk assessment cost $1,360, the neonatal sepsis calculator cost $1,317, and enhanced clinical observation cost $1,310-the cost of infants receiving intervention under categorical risk assessment was approximately twice that of the other 2 strategies. Results were robust to variations in data parameters. <b>Conclusion.</b> The neonatal sepsis calculator and enhanced clinical observation approaches may be preferred to categorical risk assessment as they reduce the number of infants receiving intervention and thus antibiotic exposure and associated costs. All 3 approaches have similar costs over all live births, and prior literature has indicated similar health outcomes. Inclusion of downstream effects of antibiotic exposure in the neonatal period should be evaluated within a cost-effectiveness analysis.</p><p><strong>Highlights: </strong>Of the 3 approaches recommended by the American Academy of Pediatrics in 2018 to identify early-onset sepsis in infants born at ≥35 weeks, the categorical risk assessment approach leads to about twice as many infants receiving evaluation to rule out early-onset sepsis compared with the neonatal sepsis calculator and enhanced clinical observation approaches.While the hospital costs of the 3 approaches were similar over the entire population of live births, the neonatal sepsis calculator and enhanced clinical observation approaches reduce antibiotic exposure, neonatal intensive care unit admission, and hospital costs associated with interventions as part of the screening approach compared with the categorical risk assessment approach.</p>","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"9 1","pages":"23814683231226129"},"PeriodicalIF":1.9,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10826394/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139643083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}