MDM Policy and Practice最新文献

{"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}

{"title":"Informed Random Forest to Model Associations of Epidemiological Priors, Government Policies, and Public Mobility.","authors":"Tsaone Swaabow Thapelo, Dimane Mpoeleng, Gregory Hillhouse","doi":"10.1177/23814683231218716","DOIUrl":"10.1177/23814683231218716","url":null,"abstract":"<p><p><b>Background.</b> Infectious diseases constitute a significant concern worldwide due to their increasing prevalence, associated health risks, and the socioeconomic costs. Machine learning (ML) models and epidemic models formulated using deterministic differential equations are the most dominant tools for analyzing and modeling the transmission of infectious diseases. However, ML models can be inconsistent in extracting the dynamics of a disease in the presence of data drifts. Likewise, the capability of epidemic models is constrained to parameter dimensions and estimation. We aimed at creating a framework of informed ML that integrates a random forest (RF) with an adapted susceptible infectious recovered (SIR) model to account for accuracy and consistency in stochasticity within the dynamics of coronavirus disease 2019 (COVID-19). <b>Methods.</b> An adapted SIR model was used to inform a default RF on predicting new COVID-19 cases (NCCs) at given intervals. We validated the performance of the informed RF (IRF) using real data. We used Botswana's pharmaceutical interventions (PIs) and non-PIs (NPIs) adopted between February 2020 and August 2022. The discrepancy between predictions and observations is modeled using loss functions, which are minimized, interpreted, and used to assess the IRF. <b>Results.</b> The findings on the real data have revealed the effectiveness of the default RF in modeling and predicting NCCs. The use of the effective reproductive rate to inform the RF yielded an excellent predictive power (84%) compared with 75% by the default RF. <b>Conclusion.</b> This research has potential to inform policy and decision makers in developing systems to evaluate interventions for infectious diseases.</p><p><strong>Highlights: </strong>This framework is initiated by incorporating model outputs from an epidemic model to a machine learning model.An informed random forest (RF) is instantiated to model government and public responses to the COVID-19 pandemic.This framework does not require data transformations, and the epidemic model is shown to boost the RF's performance.This is a baseline knowledge-informed learning framework for assessing public health interventions in Botswana.</p>","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"8 2","pages":"23814683231218716"},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10752195/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139049473","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":"Estimating the Health Care Expenditure to Manage and Care for Type 2 Diabetes in Nepal: A Patient Perspective.","authors":"Padam Kanta Dahal, Lal Rawal, Zanfina Ademi, Rashidul Alam Mahumud, Grish Paudel, Corneel Vandelanotte","doi":"10.1177/23814683231216938","DOIUrl":"https://doi.org/10.1177/23814683231216938","url":null,"abstract":"<p><p><b>Background.</b> This study aimed to estimate the health care expenditure for managing type 2 diabetes (T2D) in the community setting of Nepal. <b>Methods.</b> This is a baseline cross-sectional study of a heath behavior intervention that was conducted between September 2021 and February 2022 among patients with T2D (<i>N</i> = 481) in the Kavrepalanchok and Nuwakot districts of Nepal. Bottom-up and micro-costing approaches were used to estimate the health care costs and were stratified according to residential status and the presence of comorbid conditions. A generalized linear model with a log-link and gamma distribution was applied for modeling the continuous right-skewed costs, and 95% confidence intervals were obtained from 10,000 bootstrapping resampling techniques. <b>Results.</b> Over 6 months the mean health care resource cost to manage T2D was US $22.87 per patient: 61% included the direct medical cost (US $14.01), 15% included the direct nonmedical cost (US $3.43), and 24% was associated with productivity losses (US $5.44). The mean health care resource cost per patient living in an urban community (US $24.65) was about US $4.95 higher than patients living in the rural community (US $19.69). The health care costs per patient with comorbid conditions was US $22.93 and was US $22.81 for those without comorbidities. Patients living in rural areas had 16% lower health care expenses compared with their urban counterparts. <b>Conclusion.</b> T2D imposes a substantial financial burden on both the health care system and individuals. There is a need to establish high-value care treatment strategies for the management of T2D to reduce the high health care expenses.</p><p><strong>Highlights: </strong>More than 60% of health care expenses comprise the direct medical cost, 15% direct nonmedical cost, and 24% patient productivity losses. The costs of diagnosis, hospitalization, and recommended foods were the main drivers of health care costs for managing type 2 diabetes.Health care expenses among patients living in urban communities and patients with comorbid conditions was higher compared with those in rural communities and those with without comorbidities.The results of this study are expected to help integrate diabetes care within the existing primary health care systems, thereby reducing health care expenses and improving the quality of diabetes care in Nepal.</p>","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"8 2","pages":"23814683231216938"},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10725113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138810488","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":"Parents' Psychological and Decision-Making Outcomes following Prenatal Diagnosis with Complex Congenital Heart Defect: An Exploratory Study.","authors":"Alistair Thorpe, Rebecca K Delaney, Nelangi M Pinto, Elissa M Ozanne, Mandy L Pershing, Lisa M Hansen, Linda M Lambert, Angela Fagerlin","doi":"10.1177/23814683231204551","DOIUrl":"10.1177/23814683231204551","url":null,"abstract":"&lt;p&gt;&lt;p&gt;&lt;b&gt;Background.&lt;/b&gt; Parents with a fetus diagnosed with a complex congenital heart defect (CHD) are at high risk of negative psychological outcomes. &lt;b&gt;Purpose.&lt;/b&gt; To explore whether parents' psychological and decision-making outcomes differed based on their treatment decision and fetus/neonate survival status. &lt;b&gt;Methods.&lt;/b&gt; We prospectively enrolled parents with a fetus diagnosed with a complex, life-threatening CHD from September 2018 to December 2020. We tested whether parents' psychological and decision-making outcomes 3 months posttreatment differed by treatment choice and survival status. &lt;b&gt;Results.&lt;/b&gt; Our sample included 23 parents (average Age&lt;sub&gt;[years]&lt;/sub&gt;: 27 ± 4, range = 21-37). Most were women (&lt;i&gt;n&lt;/i&gt; = 18), non-Hispanic White (&lt;i&gt;n&lt;/i&gt; = 20), and married (&lt;i&gt;n&lt;/i&gt; = 21). Most parents chose surgery (&lt;i&gt;n&lt;/i&gt; = 16), with 11 children surviving to the time of the survey; remaining parents (&lt;i&gt;n&lt;/i&gt; = 7) chose comfort-directed care. Parents who chose comfort-directed care reported higher distress (&lt;math&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt;&lt;mo&gt;¯&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt; = 1.51, &lt;i&gt;s&lt;/i&gt; = 0.75 v. &lt;math&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt;&lt;mo&gt;¯&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt; = 0.74, &lt;i&gt;s&lt;/i&gt; = 0.55; Mdifference = 0.77, 95% confidence interval [CI], 0.05-1.48) and perinatal grief (&lt;math&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt;&lt;mo&gt;¯&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt; = 91.86, &lt;i&gt;s&lt;/i&gt; = 22.96 v. &lt;math&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt;&lt;mo&gt;¯&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt; = 63.38, &lt;i&gt;s&lt;/i&gt; = 20.15; Mdifference = 27.18, 95% CI, 6.20-48.16) than parents who chose surgery, regardless of survival status. Parents who chose comfort-directed care reported higher depression (&lt;math&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt;&lt;mo&gt;¯&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt; = 1.64, &lt;i&gt;s&lt;/i&gt; = 0.95 v. &lt;math&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt;&lt;mo&gt;¯&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt; = 0.65, &lt;i&gt;s&lt;/i&gt; = 0.49; Mdifference = 0.99, 95% CI, 0.10-1.88) than parents whose child survived following surgery. Parents choosing comfort-directed care reported higher regret (&lt;math&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt;&lt;mo&gt;¯&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt; = 26.43, &lt;i&gt;s&lt;/i&gt; = 8.02 v. &lt;math&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt;&lt;mo&gt;¯&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt; = 5.00, &lt;i&gt;s&lt;/i&gt; = 7.07; Mdifference = 21.43, 95% CI, 11.59-31.27) and decisional conflict (&lt;math&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt;&lt;mo&gt;¯&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt; = 20.98, &lt;i&gt;s&lt;/i&gt; = 10.00 v. &lt;math&gt;&lt;mrow&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt;&lt;mo&gt;¯&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt; = 3.44, &lt;i&gt;s&lt;/i&gt; = 4.74; Mdifference = 17.54, 95% CI; 7.75-27.34) than parents whose child had not survived following surgery. Parents whose child survived following surgery reported lower grief (Mdifference = -19.71; 95% CI, -39.41 to -0.01) than parents whose child had not. &lt;b&gt;Conclusions.&lt;/b&gt; The results highlight the potential for interventions and care tailored to parents' treatment decisions and outcomes to support parental coping and well-being.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;&lt;b&gt;","PeriodicalId":36567,"journal":{"name":"MDM Policy and Practice","volume":"8 2","pages":"23814683231204551"},"PeriodicalIF":1.9,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10619352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71427617","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}