Health Services and Outcomes Research Methodology最新文献

To slow the spread of COVID-19, most countries implemented stay-at-home orders, social distancing, and other nonpharmaceutical mitigation strategies. To understand individual preferences for mitigation strategies, we piloted a web-based Respondent Driven Sampling (RDS) approach to recruit participants from four universities in three countries to complete a computer-based Discrete Choice Experiment (DCE). Use of these methods, in combination, can serve to increase the external validity of a study by enabling recruitment of populations underrepresented in sampling frames, thus allowing preference results to be more generalizable to targeted subpopulations. A total of 99 students or staff members were invited to complete the survey, of which 72% started the survey (n = 71). Sixty-three participants (89% of starters) completed all tasks in the DCE. A rank-ordered mixed logit model was used to estimate preferences for COVID-19 nonpharmaceutical mitigation strategies. The model estimates indicated that participants preferred mitigation strategies that resulted in lower COVID-19 risk (i.e. sheltering-in-place more days a week), financial compensation from the government, fewer health (mental and physical) problems, and fewer financial problems. The high response rate and survey engagement provide proof of concept that RDS and DCE can be implemented as web-based applications, with the potential for scale up to produce nationally-representative preference estimates.

The U.S. federal government is spending billions of dollars to test a multitude of new approaches to pay for healthcare. Unintended consequences are a major consideration in the testing of these value-based payment (VBP) models. Since participation is generally voluntary, any unintended consequences may be magnified as VBP models move beyond the early testing phase. In this paper, we propose a straightforward unsupervised outlier detection approach based on ranked percentage changes to identify participants (e.g., healthcare providers) whose behavior may represent an unintended consequence of a VBP model. The only data requirements are repeated measurements of at least one relevant variable over time. The approach is generalizable to all types of VBP models and participants and can be used to address undesired behavior early in the model and ultimately help avoid undesired behavior in scaled-up programs. We describe our approach, demonstrate how it can be applied with hypothetical data, and simulate how efficiently it detects participants who are truly bad actors. In our hypothetical case study, the approach correctly identifies a bad actor in the first period in 86% of simulations and by the second period in 96% of simulations. The trade-off is that 9% of honest participants are mistakenly identified as bad actors by the second period. We suggest several ways for researchers to mitigate the rate or consequences of these false positives. Researchers and policymakers can customize and use our approach to appropriately guard VBP models against undesired behavior, even if only by one participant.

Supplementary information: The online version contains supplementary material available at 10.1007/s10742-021-00253-9.

Veterans suffer disproportionate health impacts from the opioid epidemic, including overdose, suicide, and death. Prediction models based on electronic medical record data can be powerful tools for identifying patients at greatest risk of such outcomes. The Veterans Health Administration implemented the Stratification Tool for Opioid Risk Mitigation (STORM) in 2018. In this study we propose changes to the original STORM model and propose alternative models that improve risk prediction performance. The best of these proposed models uses a multivariate generalized linear mixed modeling (mGLMM) approach to produce separate predictions for overdose and suicide-related events (SRE) rather than a single prediction for combined outcomes. Further improvements include incorporation of additional data sources and new predictor variables in a longitudinal setting. Compared to a modified version of the STORM model with the same outcome, predictor and interaction terms, our proposed model has a significantly better prediction performance in terms of AUC (84% vs. 77%) and sensitivity (71% vs. 66%). The mGLMM performed particularly well in identifying patients at risk for SREs, where 72% of actual events were accurately predicted among patients with the 100,000 highest risk scores compared with 49.7% for the modified STORM model. The mGLMM's strong performance in identifying true cases (sensitivity) among this highest risk group was the most important improvement given the model's primary purpose for accurately identifying patients at most risk for adverse outcomes such that they are prioritized to receive risk mitigation interventions. Some predictors in the proposed model have markedly different associations with overdose and suicide risks, which will allow clinicians to better target interventions to the most relevant risks.

Supplementary information: The online version contains supplementary material available at 10.1007/s10742-021-00263-7.

Testing at a mass scale has been widely accepted as an effective way to contain the spread of the SARS-CoV-2 Virus. In the initial stages, the shortage of test kits severely restricted mass-scale testing. Pooled testing was offered as a partial solution to this problem. However, it is a relatively lesser-known fact that pooled testing can also result in significant gains, both in terms of cost savings as well as measurement accuracy, in prevalence estimation surveys. We review here the statistical theory of pooled testing for screening as well as for prevalence estimation. We study the impact of the diagnostic errors, and misspecification of the sensitivity and the specificity on the performances of the pooled as well as individual testing procedures. Our investigation clarifies some of the issues hotly debated in the context of COVID-19 and shows the potential gains for the Indian Council for Medical Research (ICMR) in using a pooled sampling for their upcoming COVID-19 prevalence surveys.

Many places within rural America lack ready access to health care facilities. Barriers to access can be both spatial and non-spatial. Measurements of spatial access, such as the Enhanced Floating 2-Step Catchment Area and other floating catchment area measures, produce similar patterns of access. However, the extent to which different measurements of socioeconomic barriers to access correspond with each other has not been examined. Using West Virginia as a case study, we compute indices based upon the literature and measure the correlations among them. We find that all indices positively correlate with each other, although the strength of the correlation varies. Also, while there is broad agreement in the general spatial trends, such as fewer barriers in urban areas, and more barriers in the impoverished southwestern portion of the state, there are regions within the state that have more disagreement among the indices. These indices are to be used to support decision-making with respect to placement of rural residency students from medical schools within West Virginia to provide students with educational experiences as well as address health care inequalities within the state. The results indicate that for decisions and policies that address statewide trends, the choice of metric is not critical. However, when the decisions involve specific locations for receiving rural residents or opening clinics, the results can become more sensitive to the selection of the index. Therefore, for fine-grained policy decision-making, it is important that the chosen index best represents the processes under consideration.