Medical Decision Making最新文献

PurposeSexual transmission of human papillomavirus (HPV) infection is important for capturing the indirect effects of interventions in mathematical models, but limited data create challenges for reflecting sexual behavior patterns over the lifespan of individuals and across heterogenous populations. We used nationally representative data from the United States to parameterize, calibrate, and validate a heterosexual transmission model of HPV.MethodsBased on sexual behavior data from the National Survey of Family Growth (2011-2019), we categorized respondents into 4 sexual activity categories, using their percentile of cumulative lifetime partners compared with others within their same sex and age group. We modeled probabilistic partnership acquisition and dissolution by age, sex, and sexual activity category. Partnership data were incorporated into an existing agent-based model of HPV transmission in the United States. We calibrated 1) per-partnership HPV transmission and 2) reduced risk of type-specific reinfection from natural immunity to fit age- and type-specific HPV prevalence using the National Health and Nutrition Examination Survey (NHANES 2002-2008). We validated the final model by comparing model-based projections of HPV prevalence against empirical data in the US population before and after widespread HPV vaccination.ResultsAfter calibrating to fit overall HPV prevalence, model validation exercises indicated that the distribution of prevaccine HPV prevalence across sexual activity categories closely matched NHANES estimates. Simulating vaccination rates over 10 y, the model replicated postvaccine NHANES data for prevalence of HPV16.ConclusionCapturing HPV transmission dynamics requires an understanding of sexual behavior across populations and over time. Defining sexual activity categories based on cumulative lifetime partners can capture patterns of HPV risk over a lifespan to reflect the dynamics of HPV transmission and vaccination.HighlightsUsing data from a large national survey, we developed sexual behavior inputs for an agent-based model of HPV transmission.We define 4 heterogenous risk groups using cumulative lifetime sexual behavior for males and females and find we can recreate validation data on both lifetime sexual patterns and age-specific HPV prevalence.Our calibrated model also reproduces early patterns of HPV reduction following HPV vaccine introduction.Modelers seeking to understand the long-term effects of the HPV vaccine should carefully consider the heterogeneity of sexual behavior across groups as well as changes in behavior over the lifespan.

BackgroundTo reduce variation in waiting time for elective surgery, a Dutch academic hospital introduced a classification system based on urgency scores to standardize decision making. Physicians, however, retain clinical discretion in assigning urgency scores. This facilitates the provision of personalized and efficient care but may also create variation between patients and lack of transparency. The aim of this study was to describe the prioritization of patients awaiting elective surgery, including the use of urgency scores, and to explore explanations for discrepancies between assigned scores and actual waiting times.MethodsWe conducted an ethnographic study combining interviews with physicians and observations of elective surgery planners in the academic hospital. Data were analyzed thematically, guided by 3 sensitizing concepts: professional autonomy, emotions, and traditions.ResultsThe prioritization of patients awaiting elective surgery begins with physicians' assessment of urgency and concludes with planners drafting the schedule. The assessment is guided by clinical parameters, patient- and physician-related factors, and logistical constraints. Importantly, the prioritization of patients for elective surgery is shaped by subjective and affective considerations, customary decision-making practices, as well as the considerable professional autonomy of physicians and planners.ConclusionsStandardized prioritization tools, such as urgency scores, may reduce unjustified variation in waiting times, but initial resistance to their implementation can hamper their use in decision-making practice. Moreover, such tools alone may fail to capture the complexity of clinical practice and the importance of the expertise and experience of physicians and planners therein. Rather than relying solely on stricter adherence to urgency scores, prioritization processes may be strengthened by facilitating communication and feedback exchanges to support a more integrated and context-specific approach that considers the complexity of clinical practice.HighlightsStandardized decision-making tools are implemented to standardize and support the prioritization of patients awaiting elective surgery.Prioritization decisions are made by different professionals, and nonclinical factors that include subjective perceptions and logistic constraints may guide these decisions.Standardized tools inadequately capture the complexity of clinical decision making and the professional autonomy physicians and planners.

PurposeHealth policy simulation models incorporate disease processes but often ignore social processes that influence health outcomes, potentially leading to suboptimal policy recommendations. To address this gap, we developed a novel decision-analytic modeling framework to integrate social processes.MethodsWe evaluated a simplified decision problem using two models: a standard decision-analytic model and a model incorporating our social factors framework. The standard model simulated individuals transitioning through three disease natural history states-healthy, sick, and dead-without accounting for differential health system utilization. Our social factors framework incorporated heterogeneous health insurance coverage, which influenced disease progression and health system utilization. We assessed the impact of a new treatment on a hypothetical cohort of 100,000 healthy, non-Hispanic Black and non-Hispanic white 40-y-old adults. Primary outcomes included life expectancy, cumulative incidence and duration of sickness, and health system utilization throughout a person's lifetime. Secondary outcomes included costs, quality-adjusted life years, and incremental cost-effectiveness ratios.ResultsIn the standard model, the new treatment increased life expectancy by 2.7 y for both non-Hispanic Black and non-Hispanic white adults, without affecting racial/ethnic gaps in life expectancy. However, incorporating known racial/ethnic disparities in health insurance coverage with the social factors framework led to smaller life expectancy gains for non-Hispanic Black adults (2.0 y) compared with non-Hispanic white adults (2.2 y), increasing racial/ethnic disparities in life expectancy.LimitationsThe availability of social factors data and complexity of causal pathways between factors may pose challenges in applying our social factors framework.ConclusionsExcluding social processes from health policy modeling can result in unrealistic projections and biased policy recommendations. Incorporating the social factors framework enhances simulation models' effectiveness in evaluating interventions with health equity implications.HighlightsHealth policy simulation models that ignore social processes may be biased and lead to suboptimal policy recommendations. To address this, we proposed a novel social factors framework to integrate social factors into decision-analytic models for health policy.Applying our social factors framework to a simplified example highlighted the potential bias that results from ignoring social factors. In a standard model, a hypothetical new treatment appeared to have no effect on health disparities. However, incorporating our social factors framework demonstrated that this treatment would exacerbate disparities.Incorporating a social factors framework into health policy simulation models has particular relevance for evaluating health interventions with equity implications.

PurposeThis research investigates how individuals' perceived motivations for receiving the COVID-19 vaccine-specifically, feeling pressured versus vaccinating voluntarily-relate to future health-protective behaviors and perceived risk of the vaccine and the virus.MethodsIn 2 studies, with a total of N = 1,252 respondents, participants self-reported their past vaccination motivation and completed measures assessing willingness to receive future vaccines, engage in general health-protective behaviors, and perceived risks associated with the virus and the vaccine.ResultsFindings consistently show that individuals who felt pressured to vaccinate are positioned between unvaccinated individuals and those who vaccinated voluntarily in their perceptions and intentions. Compared with voluntary vaccinators, they reported lower willingness to receive future vaccines and engage in protective behaviors and greater perceived vaccine risk. However, their willingness to engage in these behaviors was still greater than that of unvaccinated individuals.LimitationsThe studies are mainly cross-sectional and do not track the same individuals over time.ConclusionsPerceived motivation for past vaccination significantly predicts vaccinated individuals' attitudes and future intentions related to health behaviors, even unrelated to COVID-19.ImplicationsTreating all vaccinated individuals as a uniform group can be overly simplistic. Public health messaging and interventions may be more effective when considering individuals' vaccination motivation.HighlightsTreating all vaccinated individuals the same can be simplistic.The perception of the vaccine and virus risks differ depending on whether vaccination felt voluntary or coerced.Different motivations behind vaccination can shape future medical decisions beyond the pandemic.

BackgroundShared decision making (SDM) is a cornerstone of patient-centered care; however, little information is available on how SDM is practiced in routine care. We aimed to assess the level of SDM perceived by patients with chronic conditions for the most important health decision in the past 12 mo.MethodsThis was a cross-sectional online survey among ComPaRe, a nationwide e-cohort of patients with chronic conditions in France. The survey asked participants about their perception of SDM using the 9-item Shared Decision-Making Questionnaire (SDM-Q-9) regarding their most important health decision in the past 12 mo. We weighted the sample to represent French patients with chronic conditions and conducted regression models to identify factors associated with higher SDM levels, adjusting for sociodemographic and clinical characteristics.ResultsIn total, 2,087 patients were analyzed (participation rate: 34.9%). In the weighted sample, 53.0% were women, the mean (SD) age was 51.0 (15) y, and the most frequent conditions were endometriosis (27.3%), inflammatory rheumatic diseases (20.7%), and high blood pressure (19.3%). The most important health decisions in the past 12 mo were mainly about drug treatments (36.5%) or surgery (20.5%). The mean (SD) SDM-Q-9 score was 63 (27)/100 (moderate level of SDM). The highest scores were observed for cancer (70 [26]) and depression (69 [26]), whereas the lowest scores were for long COVID (54 [28]) and endometriosis (58 [25]). Decisions about surgery (71 [25]) and with specialists (64 [27]) were associated with higher scores compared with medication decisions (60 [28]) or with general practitioners (62 [27]). Multivariate analysis confirmed that a higher SDM level was associated with being a man; having higher health literacy; making decisions relating to cancer, surgery, or medical devices; and specialist involvement.ConclusionsPatients with chronic conditions in France report moderate levels of SDM, with substantial variations by condition, decision type, and patient characteristics. Findings highlight the need for tailored strategies to foster SDM in chronic care.HighlightsShared decision making (SDM) is considered a key component of the chronic disease management model.This study provides the first nationwide assessment of perceived SDM levels among patients with chronic conditions in France.Patients have a moderate overall SDM score, but significant disparities exist. Patients with less recognized conditions such as long COVID or endometriosis, low health literacy, and high treatment burden reported significantly lower SDM scores as compared with others in their care decisions.These findings underscore the need for targeted interventions to improve SDM implementation.

IntroductionDuring the COVID-19 pandemic, many communities across the United States experienced surges in hospitalizations, which strained the local hospital capacity. Some risk metrics, such as the Center for Disease Control and Prevention's (CDC's) Community Levels, were developed to predict the impact of COVID-19 on the community-level health care system based on routine surveillance data. However, they had limited utility as they were not routinely updated based on accumulating data and were not directly linked to specific outcomes, such as surges in COVID-19 hospitalizations beyond local capacities.MethodsIn this article, we evaluated decision tree classifiers developed in real time to predict surges in local hospitalizations due to COVID-19 between July 2020 and November 2022. These classifiers would have provided visually intuitive and interpretable decision rules and, by being updated weekly, would have responded to changes in the epidemic. We compared the performance of these classifiers with that of logistic regression and neural network models using various metrics, including the area under the receiver-operating characteristic curve (auROC) and the area under the precision-recall curve (auPRC).ResultsDecision tree classifiers achieved an auROC of $>80\%$ for most pandemic weeks and outperformed the CDC's Community Levels in predicting high hospital occupancy. The auPRC, sensitivity, and specificity of the classifiers varied more substantially over time (between $20\%\mathrm{and}100\%$) and in sync with pandemic waves. Decision tree classifiers demonstrated similar performance compared with logistic regression and neural network models while presenting more interpretable classification rules.ConclusionsUsing routinely collected hospital surveillance data, decision tree classifiers can be adaptively updated to predict surges in local hospitalizations. However, the sensitivity and specificity of these classifiers could change markedly during different pandemic waves.HighlightsA major concern during the COVID-19 pandemic was the risk of exceeding local health care capacity due to COVID-19-related hospitalizations.To assess this risk and inform mitigating strategies, several risk assessment tools were developed during the pandemic. Many of these tools, however, did not predict local outcomes, were not updated as the pandemic progressed, and/or were not interpretable by decision makers.We propose an adaptive framework of decision tree classifiers to predict whether COVID-19-related hospital occupancy would exceed a given capacity threshold. These classifiers demonstrated reasonable and stable prediction performance over time. However, their sensitivity and specificity may change substantially over the course of pandemic waves.

Individual-level data are routinely used in trial-based economic evaluations to assess the effectiveness and costs of a given intervention. While effectiveness measures are often expressed via utility scores derived from health-related quality-of-life instruments (e.g., EQ-5D questionnaires), information on different types of health care resource use (HRU) measures (e.g., number and types of services) are collected to compute the costs. Partially complete HRU data, particularly for self-reported questionnaires, are handled via ad hoc methods that rely on some assumptions (fill in a zero) that are typically hard to justify. Although methods have been proposed to account for the uncertainty surrounding missing data, particularly in the form of multiple imputation or Bayesian methods, these have mostly been implemented at the level of costs at different times or over the entire study period, while little attention has been given to how missing values at the level of HRUs should be addressed and their implications on the final analysis. We present a general Bayesian framework for the analysis of partially observed HRUs in trial-based economic evaluations, which can accommodate the typical complexities of the data (e.g., excess zeros, skewness, missingness) and quantify the impact of missingness uncertainty on the results. We show the benefits of our approach with a motivating example and compare the results to those from more standard analyses fitted at the level of cost variables after adopting some ad hoc imputation. This article highlights the importance of adopting a comprehensive modeling approach to handle partially observed HRU data in economic evaluations and the strategic advantages of building these models within a Bayesian framework.HighlightsMissing health care service data in trial-based economic evaluations are often removed or imputed using quite restrictive assumptions (e.g., no use of service).We propose a flexible Bayesian approach to account for missing health care service uncertainty and compare the results with models fitted at more aggregated levels (e.g., total costs) using a real case study.Our results show that, depending on the (assumed) missingness assumptions and the level of data aggregation at which analyses are performed, results may be considerably changed.When feasible, analyses should be conducted at the most disaggregated level to ensure that all available information collected in the trial is used in the analysis without relying on (often) restrictive ad hoc imputation approaches.

Health state values, often in the form of value sets that list values applied to particular health states, are used in cost-effectiveness analyses of health care to calculate gains in quality-adjusted life-years. These values are subject to several sources of uncertainty, arising from the fact that values are not constants but variables and are of different types including variability, heterogeneity, statistical uncertainty, and methodological variation. Currently, these sources are not fully documented and are not fully accounted for when creating and analyzing economic evaluation models. This may provide to users of such models a false sense of the precision of quality-adjusted life-year gain estimates and therefore of cost-effectiveness. This article provides a comprehensive account of such sources of uncertainty and how they interact. It also provides a more detailed account of how uncertainty arises in studies that elicit and model value sets. Its aim is to encourage research to measure and report uncertainty around health state values so it can be better accounted for in cost-effectiveness analyses.HighlightsHealth state values (HSVs) used in cost-effectiveness analysis are subject to multiple types of uncertainty, including variability, heterogeneity, statistical uncertainty, and methodological variation.Current reporting and guidelines often fail to fully document or address all sources of uncertainty in HSVs, which can mislead users about the precision of QALY and cost-effectiveness estimates.Valuation studies should report measures of uncertainty (such as standard errors or variance/covariance matrices) for HSVs, not just point estimates.Researchers, decision modellers, and guideline developers should recognise, measure, and report HSV uncertainty more thoroughly to improve the reliability of cost-effectiveness analyses.

JEL classification: I30, J17.