MDM Policy and Practice最新文献

{"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}

{"title":"Using Expert Elicitation to Adjust Published Intervention Effects to Reflect the Local Context.","authors":"Jodi Gray, Tilenka R Thynne, Vaughn Eaton, Rebecca Larcombe, Mahsa Tantiongco, Jonathan Karnon","doi":"10.1177/23814683231226335","DOIUrl":"10.1177/23814683231226335","url":null,"abstract":"<p><p><b>Background.</b> Local health services make limited use of economic evaluation to inform decisions to fund new health service interventions. One barrier is the relevance of published intervention effects to the local setting, given these effects can strongly reflect the original evaluation context. Expert elicitation methods provide a structured approach to explicitly and transparently adjust published effect estimates, which can then be used in local-level economic evaluations to increase their local relevance. Expert elicitation was used to adjust published effect estimates for 2 interventions targeting the prevention of inpatient hypoglycemia. <b>Methods.</b> Elicitation was undertaken with 6 clinical experts. They were systematically presented with information regarding potential differences in patient characteristics and quality of care between the published study and local contexts, and regarding the design and application of the published study. The experts then assessed the intervention effects and provided estimates of the most realistic, most pessimistic, and most optimistic intervention effect sizes in the local context. <b>Results.</b> The experts estimated both interventions would be less effective in the local setting compared with the published effect estimates. For one intervention, the experts expected the lower complexity of admitted patients in the local setting would reduce the intervention's effectiveness. For the other intervention, the reduced effect was largely driven by differences in the scope of implementation (hospital-wide in the local setting compared with targeted implementation in the evaluation). <b>Conclusions.</b> The pragmatic elicitation methods reported in this article provide a feasible and acceptable approach to assess and adjust published intervention effects to better reflect expected effects in the local context. Further development and application of these methods is proposed to facilitate the use of local-level economic evaluation.</p><p><strong>Highlights: </strong>Local health services make limited use of economic evaluation to inform their decisions on the funding of new health service interventions. One barrier to use is the relevance of published intervention evaluations to the local setting.Expert elicitation methods provide a structured way to consider differences between the evaluation and local settings and to explicitly and transparently adjust published effect estimates for use in local economic evaluations.The pragmatic elicitation methods reported in this article offer a feasible and acceptable approach to adjusting published intervention effects to better reflect the effects expected in the local context. This increases the relevance of economic evaluations for local decision makers.</p>","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"9 1","pages":"23814683231226335"},"PeriodicalIF":1.9,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10812103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139571880","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":"Application of Time-Series Analysis and Expert Judgment in Modeling and Forecasting Blood Donation Trends in Zimbabwe.","authors":"Coster Chideme, Delson Chikobvu","doi":"10.1177/23814683231222483","DOIUrl":"10.1177/23814683231222483","url":null,"abstract":"&lt;p&gt;&lt;p&gt;&lt;b&gt;Background.&lt;/b&gt; Blood cannot be artificially manufactured, and there is currently no substitute for human blood. The supply of blood in transfusion facilities requires constant and timely collection of blood from donors. Modeling and forecasting trends in blood collections are critical for determining both the current and future capacity requirements and appropriate models of adequate blood provision. &lt;b&gt;Objectives.&lt;/b&gt; The objective of this study is to determine blood collection or donation patterns and develop time-series models that can be updated and refined in predicting future blood donations in Zimbabwe when given the historical data. &lt;b&gt;Materials and Methods.&lt;/b&gt; Monthly blood donation data for the period 2009 to 2019 were collected retrospectively from the National Blood Service Zimbabwe database. Time-series models (i.e., the Seasonal Autoregressive Integrated Moving Average [SARIMA] and Error, Trend and Seasonal [ETS]) models were applied and compared. The models were chosen because of their ability to handle the seasonality and other time-series components evident in the blood donation data. Expert opinions and experience were used in selecting the models and in making inferences in the analysis. &lt;b&gt;Results.&lt;/b&gt; Time-series plots of blood donations showed seasonal patterns, with significant drops in blood donations in months associated with Zimbabwe's school holidays (April, August, and December) and public holidays. During these holidays, there is a reduced number of school donors, while at about the same time, there is increasing blood demand as a result of road accidents. Model identification procedures established the &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;SARIMA&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;12&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt; model as the appropriate model for forecasting total blood donation in Zimbabwe. The results and forecasts show an upward trend in blood donations. According to the accuracy measures used, the SARIMA model outperforms the ETS model. &lt;b&gt;Conclusions.&lt;/b&gt; Expert knowledge in the blood donation process, coupled with statistical models, can help explain trends exhibited in blood donation data in Zimbabwe. These findings help the blood authorities plan for blood donor campaign drives. The findings are key indicators of where to allocate more resources toward blood donation and when to collect more blood units. The increasing blood donation projections ensure a stable blood bank inventory in the near future.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;A SARIMA model can be used to predict the flow of blood donations in Zimbabwe.The seasonal blood donation pattern peaks in the months of March, June/July, and September.The donations troughs are in the months of April, August, December, and January. These are the months coinciding with school holidays in Zimbabwe.Both t","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"9 1","pages":"23814683231222483"},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10798106/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139514116","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 Treatment Preferences for Patients with Tricuspid Regurgitation.","authors":"Vijay Iyer, Nadeen N Faza, Michael Pfeiffer, Mark Kozak, Brandon Peterson, Mortiz Wyler von Ballmoos, Sarah Mollenkopf, Melissa Mancilla, Diandra Latibeaudiere-Gardner, Michael J Reardon","doi":"10.1177/23814683231225667","DOIUrl":"10.1177/23814683231225667","url":null,"abstract":"<p><p><b>Background.</b> Tricuspid regurgitation (TR) is a high-prevalence disease associated with poor quality of life and mortality. This quantitative patient preference study aims to identify TR patients' perspectives on risk-benefit tradeoffs. <b>Methods.</b> A discrete-choice experiment was developed to explore TR treatment risk-benefit tradeoffs. Attributes (levels) tested were treatment (procedure, medical management), reintervention risk (0%, 1%, 5%, 10%), medications over 2 y (none, reduce, same, increase), shortness of breath (none/mild, moderate, severe), and swelling (never, 3× per week, daily). A mixed logit regression model estimated preferences and calculated predicted probabilities. Relative attribute importance was calculated. Subgroup analyses were performed. <b>Results.</b> An online survey was completed by 150 TR patients. Shortness of breath was the most important attribute and accounted for 65.8% of treatment decision making. The average patients' predicted probability of preferring a \"procedure-like\" profile over a \"medical management-like\" profile was 99.7%. This decreased to 78.9% for a level change from severe to moderate in shortness of breath in the \"medical management-like\" profile. Subgroup analysis confirmed that patients older than 64 y had a stronger preference to avoid severe shortness of breath compared with younger patients (<i>P</i> < 0.02), as did severe or worse TR patients relative to moderate. New York Heart Association class I/II patients more strongly preferred to avoid procedural reintervention risk relative to class III/IV patients (<i>P</i> < 0.03). <b>Conclusion.</b> TR patients are willing to accept higher procedural reintervention risk if shortness of breath is alleviated. This risk tolerance is higher for older and more symptomatic patients. These results emphasize the appropriateness of developing TR therapies and the importance of addressing symptom burden.</p><p><strong>Highlights: </strong>This study provides quantitative patient preference data from clinically confirmed tricuspid regurgitation (TR) patients to understand their treatment preferences.Using a targeted literature search and patient, physician, and Food and Drug Administration feedback, a cross-sectional survey with a discrete-choice experiment that focused on 5 of the most important attributes to TR patients was developed and administered online.TR patients are willing to accept higher procedural reintervention risk if shortness of breath is alleviated, and this risk tolerance is higher for older and more symptomatic patients.</p>","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"9 1","pages":"23814683231225667"},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10798093/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139514122","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 and Bridging Gaps in the Use of Evidence from Modeling for Evidence-Based Policy Making in Nigeria's Health System.","authors":"Chinyere Mbachu, Prince Agwu, Felix Obi, Obinna Onwujekwe","doi":"10.1177/23814683231225658","DOIUrl":"10.1177/23814683231225658","url":null,"abstract":"<p><p><b>Background.</b> Modeled evidence is a proven useful tool for decision makers in making evidence-based policies and plans that will ensure the best possible health system outcomes. Thus, we sought to understand constraints to the use of models in making decisions in Nigeria's health system and how such constraints can be addressed. <b>Method.</b> We adopted a mixed-methods study for the research and relied on the evidence to policy and Knowledge-to-Action (KTA) frameworks to guide the conceptualization of the study. An online survey was administered to 34 key individuals in health organizations that recognize modeling, which was followed by in-depth interviews with 24 of the 34 key informants. Analysis was done using descriptive analytic methods and thematic arrangements of narratives. <b>Results.</b> Overall, the data revealed poor use of modeled evidence in decision making within the health sector, despite reporting that modeled evidence and modelers are available in Nigeria. However, the disease control agency in Nigeria was reported to be an exception. The complexity of models was a top concern. Thus, suggestions were made to improve communication of models in ways that are easily comprehensible and to improve overall research culture within Nigeria's health sector. <b>Conclusion.</b> Modeled evidence plays a crucial role in evidence-based health decisions. Therefore, it is imperative to strengthen and sustain in-country capacity to value, produce, interpret, and use modeled evidence for decision making in health. To overcome limitations in the usage of modeled evidence, decision makers, modelers/researchers, and knowledge brokers should forge viable relationships that regard and promote evidence translation.</p><p><strong>Highlights: </strong>Despite the use of modeling by Nigeria's disease control agency in containing the COVID-19 pandemic, modeling remains poorly used in the country's overall health sector.Although policy makers recognize the importance of evidence in making decisions, there are still pertinent concerns about the poor research culture of policy-making institutions and communication gaps that exist between researchers/modelers and policy makers.Nigeria's health system can be strengthened by improving the value and usage of scientific evidence generation through conscious efforts to institutionalize research culture in the health sector and bridge gaps between researchers/modelers and decision makers.</p>","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"9 1","pages":"23814683231225658"},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10798080/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139514118","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}