Medical Decision Making最新文献

{"title":"An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions.","authors":"S Moler-Zapata, A Hutchings, R Grieve, R Hinchliffe, N Smart, S R Moonesinghe, G Bellingan, R Vohra, S Moug, S O'Neill","doi":"10.1177/0272989X241289336","DOIUrl":"10.1177/0272989X241289336","url":null,"abstract":"<p><strong>Background: </strong>Machine learning (ML) methods can identify complex patterns of treatment effect heterogeneity. However, before ML can help to personalize decision making, transparent approaches must be developed that draw on clinical judgment. We develop an approach that combines clinical judgment with ML to generate appropriate comparative effectiveness evidence for informing decision making.</p><p><strong>Methods: </strong>We motivate this approach in evaluating the effectiveness of nonemergency surgery (NES) strategies, such as antibiotic therapy, for people with acute appendicitis who have multiple long-term conditions (MLTCs) compared with emergency surgery (ES). Our 4-stage approach 1) draws on clinical judgment about which patient characteristics and morbidities modify the relative effectiveness of NES; 2) selects additional covariates from a high-dimensional covariate space (<i>P</i> > 500) by applying an ML approach, least absolute shrinkage and selection operator (LASSO), to large-scale administrative data (<i>N</i> = 24,312); 3) generates estimates of comparative effectiveness for relevant subgroups; and 4) presents evidence in a suitable form for decision making.</p><p><strong>Results: </strong>This approach provides useful evidence for clinically relevant subgroups. We found that overall NES strategies led to increases in the mean number of days alive and out-of-hospital compared with ES, but estimates differed across subgroups, ranging from 21.2 (95% confidence interval: 1.8 to 40.5) for patients with chronic heart failure and chronic kidney disease to -10.4 (-29.8 to 9.1) for patients with cancer and hypertension. Our interactive tool for visualizing ML output allows for findings to be customized according to the specific needs of the clinical decision maker.</p><p><strong>Conclusions: </strong>This principled approach of combining clinical judgment with an ML approach can improve trust, relevance, and usefulness of the evidence generated for clinical decision making.</p><p><strong>Highlights: </strong>Machine learning (ML) methods have many potential applications in medical decision making, but the lack of model interpretability and usability constitutes an important barrier for the wider adoption of ML evidence in practice.We develop a 4-stage approach for integrating clinical judgment into the way an ML approach is used to estimate and report comparative effectiveness.We illustrate the approach in undertaking an evaluation of nonemergency surgery (NES) strategies for acute appendicitis in patients with multiple long-term conditions and find that NES strategies lead to better outcomes compared with emergency surgery and that the effects differ across subgroups.We develop an interactive tool for visualizing the results of this study that allows findings to be customized according to the user's preferences.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"944-960"},"PeriodicalIF":3.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11542320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142511893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Use of Nudge Strategies in Improving Physicians' Prescribing Behavior: A Systematic Review and Meta-analysis.","authors":"Maya Fey Hallett, Trine Kjær, Line Bjørnskov Pedersen","doi":"10.1177/0272989X241270001","DOIUrl":"10.1177/0272989X241270001","url":null,"abstract":"<p><strong>Background: </strong>Nudges have been proposed as a method of influencing prescribing decisions.</p><p><strong>Purpose: </strong>The purpose of this article is to 1) investigate associations between nudges' characteristics and effectiveness, 2) assess the quality of the literature, 3) assess cost-effectiveness, and 4) create a synthesis with policy recommendations.</p><p><strong>Methods: </strong>We searched health and social science databases. We included studies that targeted prescribing decisions, included a nudge, and used prescribing behavior as the outcome. We recorded study characteristics, effect size of the primary outcomes, and information on cost-effectiveness. We performed a meta-analysis on the standardized mean difference of the studies' primary outcomes, tested for associations between effect size and key intervention characteristics, and created a funnel plot evaluating publication bias.</p><p><strong>Synthesis: </strong>We identified 21 studies containing 25 nudges. In total, 62 of 85 (73%) outcomes showed a statistically significant effect. The average effect size was -0.22 standardized mean difference. No studies included heterogeneity analyses. We found no associations between effects and selected study characteristics. Study quality varied and correlated with study design. A total of 7 of 21 (33%) studies included an evaluation of costs. These studies suggested that the interventions were cost-effective but considered only direct effects. We found evidence of publication bias.</p><p><strong>Limitations: </strong>Heterogeneity and few studies limit the possibilities of statistical inference about effectiveness.</p><p><strong>Conclusions: </strong>Nudges may be effective at directing prescribing decisions, but effects are small and health effects and cost-effectiveness are unclear. Future nudge studies should contain a rationale for the chosen nudge, prioritize the use of high-quality study designs, and include evaluations of heterogeneity, cost-effectiveness, and health outcomes to inform decision makers. Moreover, preregistration of the protocol is warranted to limit publication bias.</p><p><strong>Highlights: </strong>Nudging as a method to improve prescribing decisions has gained popularity during the past decade.We find that nudging can improve prescribing decisions, but effect sizes are mostly small, and the size of derived health outcomes is unclear.Most studies use feedback and error-stopping nudges to target excessive opioid or antibiotic prescribing, making heterogeneity analyses across nudge types difficult.Further research on the cost-effectiveness of nudges and generalizability is needed to guide decision makers considering nudging as a tool to guide prescribing decisions.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"986-1011"},"PeriodicalIF":3.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reframing SDM Using Implementation Science: SDM Is the Intervention.","authors":"Marla L Clayman, A Rani Elwy, Jason L Vassy","doi":"10.1177/0272989X241285418","DOIUrl":"10.1177/0272989X241285418","url":null,"abstract":"","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"859-861"},"PeriodicalIF":3.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Calculation to Communication: Using Risk Score Calculators to Inform Clinical Decision Making and Facilitate Patient Engagement.","authors":"Hoda Fakhari, Courtney L Scherr, Sydney Moe, Christin Hoell, Maureen E Smith, Laura J Rasmussen-Torvik, Rex L Chisholm, Elizabeth M McNally","doi":"10.1177/0272989X241285036","DOIUrl":"10.1177/0272989X241285036","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Risk score calculators are a widely developed tool to support clinicians in identifying and managing risk for certain diseases. However, little is known about physicians' applied experiences with risk score calculators and the role of risk score estimates in clinical decision making and patient communication.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Physicians providing care in outpatient community-based clinical settings (&lt;i&gt;N&lt;/i&gt; = 20) were recruited to participate in semi-structured individual interviews to assess their use of risk score calculators in practice. Two study team members conducted an inductive thematic analysis using a consensus-based coding approach.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Participants referenced at least 20 risk score calculators, the most common being the Atherosclerotic Cardiovascular Disease Risk Calculator. Ecological factors related to the clinical system (e.g., time), patient (e.g., receptivity), and physician (e.g., experience) influenced conditions and patterns of risk score calculator use. For example, compared with attending physicians, residents tended to use a greater variety of risk score calculators and with higher frequency. Risk score estimates were generally used in clinical decision making to improve or validate clinical judgment and in patient communication to serve as a motivational tool.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The degree to which risk score estimates influenced physician decision making and whether and how these scores were communicated to patients varied, reflecting a nuanced role of risk score calculator use in clinical practice. The theory of planned behavior can help explain how attitudes, beliefs, and norms shape the use of risk score estimates in clinical decision making and patient communication. Additional research is needed to evaluate best practices in the use of risk score calculators and risk score estimates.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;The risk score calculators and estimates that participants referenced in this study represented a range of conditions (e.g., heart disease, anxiety), levels of model complexity (e.g., probability calculations, scales of severity), and output formats (e.g., point estimates, risk intervals).Risk score calculators that are easily accessed, have simple inputs, and are trusted by physicians appear more likely to be used.Risk score estimates were generally used in clinical decision making to improve or validate clinical judgment and in patient communication to serve as a motivational tool.Risk score estimates helped participants manage the uncertainty and complexity of various clinical situations, yet consideration of the limitations of these estimates was relatively minimal.Developers of risk score calculators should consider the patient- (e.g., response to risk score estimates) and physician- (e.g., training status) related characteristics that influence risk score calculator use in addition to that of the clinical ","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"900-913"},"PeriodicalIF":3.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142394776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical Maximizing Orientation and the Desire for Low-Value Screening: An Examination of Mediating Mechanisms.","authors":"Soela Kim","doi":"10.1177/0272989X241285009","DOIUrl":"10.1177/0272989X241285009","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Medical maximizing orientation is a stable, traitlike inclination to actively use health care, often associated with pursuing low-value care. Despite attempts to reduce the overuse of low-value care by targeting this orientation directly, such interventions have not always been effective. To design effective interventions to reduce the overuse of low-value care, it is critical to understand the underlying mechanisms that govern the impact of medical maximizing orientation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To examine whether risk perception (deliberative, affective, and experiential) and knowledge of the benefits and harms of low-value screening mediate the potential impact of medical maximizing orientation on attitudes toward screening uptake and screening decisions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A secondary analysis was conducted on data from a Web-based experiment examining various communication tactics in an information booklet regarding low-value thyroid ultrasonography among South Korean women (&lt;i&gt;N&lt;/i&gt; = 492). Multiple linear, zero-inflated negative binomial and multinomial logistic regressions were used to examine the relationships between medical maximizing orientation and other study variables. A mediation analysis was performed to test mediating mechanisms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Medical maximizing orientation was associated with an increased positive attitude toward screening uptake and a lower likelihood of deciding not to get screened or being uncertain regarding screening decisions (relative to deciding to get screened). Knowledge and affective risk perception partially mediated the relationship between medical maximizing orientation and positive attitudes. Knowledge, deliberative, and affective risk perceptions partially mediated the relationship between medical maximizing orientation and the screening decision.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Interventions should prioritize targeting more amenable factors arising from medical maximizing orientation, such as inflated risk perceptions, particularly affective risk perception, and limited comprehension or acceptance of information about the benefits and risks associated with low-value care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;This study demonstrated that people's medical maximizing orientation can increase their positive attitudes toward the uptake of low-value screening and make them more likely to undergo it. This can happen both directly and indirectly by decreasing their understanding of the benefits and risks of screening and increasing their perception of disease risk.The study suggests that to effectively mitigate the excessive utilization of low-value care through patient-centered interventions, it is crucial to tackle 2 key issues associated with a medical maximizing mindset: inflated risk perceptions (specifically affective risk perception) and limited comprehension or acceptance of information about the benefits and risks of lo","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"927-943"},"PeriodicalIF":3.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142394777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shared Decision Making \"Ought\" to Be Done, but Definitions Need Simplicity: Response to \"Reframing SDM Using Implementation Science: SDM Is the Intervention\".","authors":"Dan D Matlock, Laura Scherer","doi":"10.1177/0272989X241286880","DOIUrl":"10.1177/0272989X241286880","url":null,"abstract":"","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"865-866"},"PeriodicalIF":3.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Decisional Needs for Adult Tracheostomy and Prolonged Mechanical Ventilation Decision Making to Inform Shared Decision-Making Interventions.","authors":"Anuj B Mehta, Steven Lockhart, Allison V Lange, Daniel D Matlock, Ivor S Douglas, Megan A Morris","doi":"10.1177/0272989X241266246","DOIUrl":"10.1177/0272989X241266246","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Decision making for adult tracheostomy and prolonged mechanical ventilation is emotionally complex. Expectations of surrogate decision makers and physicians rarely align. Little is known about what surrogates need to make goal-concordant decisions. Currently, little is known about the decisional needs of surrogates and providers, impeding efforts to improve the decision-making process.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Using a thematic analysis approach, we performed a qualitative study with semistructured interviews with surrogates of adult patients receiving mechanical ventilation (MV) being considered for tracheostomy and physicians routinely caring for patients receiving MV. Recruitment was stopped when thematic saturation was reached. We describe the decision-making process, identify core decisional needs, and map the process and needs for possible elements of a future shared decision-making tool.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Forty-three participants (23 surrogates and 20 physicians) completed interviews. Hope, Lack of Knowledge Data, and Uncertainty emerged as the 3 main themes that described the decision-making process and were interconnected with one another and, at times, opposed each other. Core decisional needs included information about patient wishes, past activity/medical history, short- and long-term outcomes, and meaningful recovery. The themes were the lens through which the decisional needs were weighed. Decision making existed as a balance between surrogate emotions and understanding and physician recommendations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Tracheostomy and prolonged MV decision making is complex. Hope and Uncertainty were conceptual themes that often battled with one another. Lack of Knowledge & Data plagued both surrogates and physicians. Multiple tangible factors were identified that affected surrogate decision making and physician recommendations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Implications: &lt;/strong&gt;Understanding this complex decision-making process has the potential to improve the information provided to surrogates and, potentially, increase the goal-concordant care and alignment of surrogate and physician expectations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;Decision making for tracheostomy and prolonged mechanical ventilation is a complex interactive process between surrogate decision makers and providers.Qualitative themes of Hope, Uncertainty, and Lack of Knowledge & Data shared by both providers and surrogates were identified and described the decision-making process.Concrete decisional needs of patient wishes, past activity/medical history, short- and long-term outcomes, and meaningful recovery affected each of the larger themes and represented key information from which surrogates and providers based decisions and recommendations.The qualitative themes and decisional needs identified provide a roadmap to design a shared decision-making intervention to improve adult tracheostomy and prolonged mec","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"867-879"},"PeriodicalIF":3.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11543511/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141856993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using QALYs as an Outcome for Assessing Global Prediction Accuracy in Diabetes Simulation Models.","authors":"Helen A Dakin, Ni Gao, José Leal, Rury R Holman, An Tran-Duy, Philip Clarke","doi":"10.1177/0272989X241285866","DOIUrl":"https://doi.org/10.1177/0272989X241285866","url":null,"abstract":"<p><strong>Objectives: </strong>(1) To demonstrate the use of quality-adjusted life-years (QALYs) as an outcome measure for comparing performance between simulation models and identifying the most accurate model for economic evaluation and health technology assessment. QALYs relate directly to decision making and combine mortality and diverse clinical events into a single measure using evidence-based weights that reflect population preferences. (2) To explore the usefulness of Q², the proportional reduction in error, as a model performance metric and compare it with other metrics: mean squared error (MSE), mean absolute error, bias (mean residual), and <i>R</i>².</p><p><strong>Methods: </strong>We simulated all EXSCEL trial participants (<i>N</i> = 14,729) using the UK Prospective Diabetes Study Outcomes Model software versions 1 (UKPDS-OM1) and 2 (UKPDS-OM2). The EXSCEL trial compared once-weekly exenatide with placebo (median 3.2-y follow-up). Default UKPDS-OM2 utilities were used to estimate undiscounted QALYs over the trial period based on the observed events and survival. These were compared with the QALYs predicted by UKPDS-OM1/2 for the same period.</p><p><strong>Results: </strong>UKPDS-OM2 predicted patients' QALYs more accurately than UKPDS-OM1 did (MSE: 0.210 v. 0.253; Q²: 0.822 v. 0.786). UKPDS-OM2 underestimated QALYs by an average of 0.127 versus 0.150 for UKPDS-OM1. UKPDS-OM2 predictions were more accurate for mortality, myocardial infarction, and stroke, whereas UKPDS-OM1 better predicted blindness and heart disease. Q² facilitated comparisons between subgroups and (unlike <i>R</i>²) was lower for biased predictors.</p><p><strong>Conclusions: </strong>Q² for QALYs was useful for comparing global prediction accuracy (across all clinical events) of diabetes models. It could be used for model registries, choosing between simulation models for economic evaluation and evaluating the impact of recalibration. Similar methods could be used in other disease areas.</p><p><strong>Highlights: </strong>Diabetes simulation models are currently validated by examining their ability to predict the incidence of individual events (e.g., myocardial infarction, stroke, amputation) or composite events (e.g., first major adverse cardiovascular event).We introduce Q², the proportional reduction in error, as a measure that may be useful for evaluating and comparing the prediction accuracy of econometric or simulation models.We propose using the Q² or mean squared error for QALYs as global measures of model prediction accuracy when comparing diabetes models' performance for health technology assessment; these can be used to select the most accurate simulation model for economic evaluation and to evaluate the impact of model recalibration in diabetes or other conditions.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X241285866"},"PeriodicalIF":3.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calculating the Expected Net Benefit of Sampling for Survival Data: A Tutorial and Case Study.","authors":"Mathyn Vervaart","doi":"10.1177/0272989X241279459","DOIUrl":"10.1177/0272989X241279459","url":null,"abstract":"<p><strong>Highlights: </strong>The net value of reducing decision uncertainty by collecting additional data is quantified by the expected net benefit of sampling (ENBS). This tutorial presents a general-purpose algorithm for computing the ENBS for collecting survival data along with a step-by-step implementation in R.The algorithm is based on recently published methods for simulating survival data and computing expected value of sample information that do not rely on the survival data to follow any particular parametric distribution and that can take into account any arbitrary censoring process.We demonstrate in a case study based on a previous cancer technology appraisal that ENBS calculations are useful not only for designing new studies but also for optimizing reimbursement decisions for new health technologies based on immature evidence from ongoing trials.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"719-741"},"PeriodicalIF":3.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11490075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating Social Determinants of Health in Infectious Disease Models: A Systematic Review of Guidelines.","authors":"Shehzad Ali, Zhe Li, Nasheed Moqueet, Seyed M Moghadas, Alison P Galvani, Lisa A Cooper, Saverio Stranges, Margaret Haworth-Brockman, Andrew D Pinto, Miqdad Asaria, David Champredon, Darren Hamilton, Marc Moulin, Ava A John-Baptiste","doi":"10.1177/0272989X241280611","DOIUrl":"10.1177/0272989X241280611","url":null,"abstract":"<p><strong>Background: </strong>Infectious disease (ID) models have been the backbone of policy decisions during the COVID-19 pandemic. However, models often overlook variation in disease risk, health burden, and policy impact across social groups. Nonetheless, social determinants are becoming increasingly recognized as fundamental to the success of control strategies overall and to the mitigation of disparities.</p><p><strong>Methods: </strong>To underscore the importance of considering social heterogeneity in epidemiological modeling, we systematically reviewed ID modeling guidelines to identify reasons and recommendations for incorporating social determinants of health into models in relation to the conceptualization, implementation, and interpretations of models.</p><p><strong>Results: </strong>After identifying 1,372 citations, we found 19 guidelines, of which 14 directly referenced at least 1 social determinant. Age (<i>n</i> = 11), sex and gender (<i>n</i> = 5), and socioeconomic status (<i>n</i> = 5) were the most commonly discussed social determinants. Specific recommendations were identified to consider social determinants to 1) improve the predictive accuracy of models, 2) understand heterogeneity of disease burden and policy impact, 3) contextualize decision making, 4) address inequalities, and 5) assess implementation challenges.</p><p><strong>Conclusion: </strong>This study can support modelers and policy makers in taking into account social heterogeneity, to consider the distributional impact of infectious disease outbreaks across social groups as well as to tailor approaches to improve equitable access to prevention, diagnostics, and therapeutics.</p><p><strong>Highlights: </strong>Infectious disease (ID) models often overlook the role of social determinants of health (SDH) in understanding variation in disease risk, health burden, and policy impact across social groups.In this study, we systematically review ID guidelines and identify key areas to consider SDH in relation to the conceptualization, implementation, and interpretations of models.We identify specific recommendations to consider SDH to improve model accuracy, understand heterogeneity, estimate policy impact, address inequalities, and assess implementation challenges.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"742-755"},"PeriodicalIF":3.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11491037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}