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Nowadays, the Internet of Things (IoT) is used in every domain, especially in the healthcare system it plays a very important role in our life and made our life easier by providing various facilities in every area. On the Internet of Medical Things (IoMT), IoT devices collect real-time data from patients and send it to the cloud through the Internet. These gathered data are extorted to centralized processing and computational power. Centralization can lead to a single point of failure, data modification, snooping, security, and privacy evasion. Blockchain can resolve such issues by providing decentralized computational power and better storage capacity for IoT data. Therefore, integrating IoT and blockchain technologies can be an intelligent way to implement a decentralized IoT-based smart healthcare system. We find that integration of the blockchain and IoT is persuasive and can cause remarkable transformations across the healthcare system. We discussed blockchain and its applicability in the healthcare industry. The paper explores blockchain's dimensions, making the innovative healthcare system more secure and stable. We provide a comparative analysis of well-known recent research on IoT-based smart healthcare system security using blockchain based on different parameters such as architecture, data integrity, medical information sharing, distributed electronic health records, patient encryption key, access control, hardware implementation, etc. The paper focuses on the current security issues in IoT-based smart healthcare systems, architectures, taxonomy, and research gap and their possible solutions we point out various research challenges that should be considered before the deployment of a blockchain network in an IoT setting. This paper will be helpful to new researchers to get an overview of IoT-based healthcare system security. [ABSTRACT FROM AUTHOR]

Parkinson's disease is related to nervous system disorders in human beings. In this paper, the status of Parkinson's disease has been predicted based on the analysis of features of two Parkinson's disease symptoms: Dysphonia and Dysarthria. The Dysphonia symptom has been measured by analyzing the features extracted from the vocal sounds of a patient. The Dysarthria symptom of Parkinson's disease has been analyzed by the features extracted from the speech signals of a patient. Hence, two different prediction models have been built for deriving the status of Parkinson's disease. The derivation of these prediction systems is based on statistical and discriminant feature analysis. The statistical-based feature analysis is carried out using descriptive and inferential analysis. In contrast, the enteric, deviation, and correlation-based information between the features are utilized for descriptive analysis. The inferential analysis has been performed using hypothetical testing of the distribution of features. The descriptive and inferential feature analyses are used to derive effective features for the Parkinson's disease problem based on the Dysphonia and Dysarthria symptoms. In the discriminant feature analysis, the feature sets undergo deriving discriminant features. The best-selected features are used to build a prediction model for detecting Parkinson's disease problems in a patient. Extensive experimentation has been carried out using a Dysphonia dataset containing 195 samples. Each sample has 23 features. One dataset for Dysarthria symptoms contains 756 observations, with each observation having 754 features. The best prediction models that have been obtained correspond to these datasets, and 93.50% is obtained for Dysphonia and 87.63% for Dysarthria symptoms databases, respectively. The performance of the proposed system has been compared with some existing methods concerning each employed dataset, showing the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Healthcare quality measures are statistics that serve to evaluate healthcare providers and identify those that need to improve their care. Before using these measures in clinical practice, developers and reviewers assess measure reliability, which describes the degree to which differences in the measure values reflect actual variation in healthcare quality, as opposed to random noise. The Inter-Unit Reliability (IUR) is a popular statistic for assessing reliability, and it describes the proportion of total variation in a measure that is attributable to between-provider variation. However, Kalbfleisch et al. (Health Services and Outcomes Research Methodology, 18, 215–225, (2018)) have argued that the IUR has a severe limitation in that some of the between-provider variation may be unrelated to quality of care. In this paper, we illustrate the practical implications of this limitation through several concrete examples. We show that certain best-practices in measure development, such as careful risk adjustment and exclusion of unstable measure values, can decrease the sample IUR value. These findings uncover potential negative consequences of discarding measures with IUR values below some arbitrary threshold. [ABSTRACT FROM AUTHOR]

A method commonly used in health care research is the waiting room (WR) survey. While patients are waiting for their appointment, they are asked to complete a questionnaire measuring their attitudes, behaviors and other characteristics. In this paper we synthesize practical guidelines for WR surveys by comparing the method with two similar approaches: public intercept (PI) surveys and drop-off-pick-up (DOPU) surveys. In this comparison we use the Total Survey Error approach Groves (Survey Methodology, Wiley, New York, 2004); (Groves in Public Opinion Quarterly 74(5): 849-879, 2010) and apply it to three case examples in which one of the three surveys is used. We take into account measurement (validity, measurement- and processing error) and representation (coverage-, sampling- and nonresponse error). From our review, we conclude that waiting room surveys, though limited to patients and their caregivers, can provide useful information on patients' perspective on health care. Response rates in waiting rooms are usually high, but often not even reported. We recommend adjustment for sampling bias by taking into account the number of visits to the hospital per respondent and sample times proportionate to the number of sample members expected on a particular time. These surveys also allow for collection of para-data; i.e., relevant information in the circumstances of a request to participate in survey research, and behavior of surveyors can easily be controlled, or investigated in an experimental design. [ABSTRACT FROM AUTHOR]

This paper aims to apply a multi-criteria decision-making (MCDM) framework to evaluate the environmental performance of the medical service supply chain using a case study in India. An application of MCDM method, the fuzzy TOPSIS (Technique for order of preference by similarity to ideal solution) model, is used for Medical Support Service Provide firms (MSSPF) environmental performance evaluation. We require multiple and conflicting criteria to fix real-life decision-making challenges, which in turn led to the inception of (MCDM). However, MCDM is contracted with various contradictory requirements in which the knowledge of decision-makers is generally vague and thereby more challenging. In this situation, inclusions of fuzzy set theory in MCDM Methods would be good to solve the MCDM problem. In this study, the authors used a fuzzy TOPSIS MCDM model to solve the evaluation framework. Data were collected from the experts from the MSSPF about the more perfect criteria to assess environmental performance using a questionnaire. Case assesses the environmental performance, based on defined criteria, of three MSSPF by the experts. Fuzzy TOPSIS Method ranks three MSSPF according to environmental performance in the following order: Firm B, Firm A and Firm C. The result suggested that 'Firm B' performed superior environmental performance followed by 'Firm A' and 'Firm C'. This evaluation framework will help MSSPF to identify potential areas of further improvement in Environmental performance, and thereby creates strategies to overcome and formulate new innovations. Notably, this is one of the few studies to have used this method to gauge the environmental performance of the medical service supply chain in India. [ABSTRACT FROM AUTHOR]

Assessment of study success using conditional power (CP), the predictive power of success (PPoS) and probability of success (PoS) is becoming increasingly common for resource optimization and adaption of trials in clinical investigation. Determination of these measures is often a non-trivial mathematical task. Further, the terminologies used across the literature are not consistent, and there is no consolidated presentation on this. We have made a structured presentation on these measures for both trial success and clinical success: first, we have summarized the expressions of CP, PPoS and PoS in a general setting and subsequently, expressions for these measures are obtained for continuous, binary, and time-to-event endpoints in single-arm and two-arm trial settings. Many of these expressions are previously published; however, some of the expressions are very new including the expressions for testing median of time-to-event endpoint in a single-arm trial. We have also shown that 1 / (no. of events) consistently underestimates the variance of log(median) and alternative expression for variance was derived. Examples are given along with the comparison of CP and PPoS. Expressions presented in this paper are implemented in LongCART package in R and in R shiny app https://ppos.shinyapps.io/public/. [ABSTRACT FROM AUTHOR]

Purpose: This paper describes a step-by-step process on how to conduct a literature review using a qualitative analysis approach in conducting a literature review using NVivo to drive the analysis and explore the state of nursing innovation. Data synthesis: This manuscript makes a unique contribution highlighting the importance of a comprehensive literature review following conceptual and methodological guidance. Previous literature on the literature review process usually describes the process quickly and without a specific step by step process. This manuscript describes a six-step process to conduct a literature review using a qualitative analytical program. The researcher selects the scope and establishes a search strategy, determines and applies the criteria for the selection process, selects the qualitative software, imports the data, extracts and codes the data, and analyzes it. Conclusion: Qualitative research applications, such as NVivo, support nurses' literature review process by improving rigor and reproducibility. The tools within the applications help better organize the literature, enhance transparency to the analytical process, and provide tools to visualize the data to improve the review's overall quality. Having the steps documented and organized allows better collaboration between various researchers. [ABSTRACT FROM AUTHOR]

Interoperability plays a crucial role in facilitating seamless data sharing across system boundaries, necessitating collaborative efforts among multiple systems. To achieve this, the presence of a common reference model for data exchange and dedicated interfaces is essential. However, the current state of Health Information Systems (HIS) often falls short in providing effective design elements that enable smooth and continuous data exchange across diverse domains. To address this challenge, a comprehensive investigation of the technical, syntactic, semantic, and other important aspects of interoperability interfaces is necessary, both in theory and practice. This article aims to address the design challenges associated with interoperability interfaces in Electronic Medical Record (EMR) systems. We commence by establishing theoretical guidelines that serve as foundation for designing effective interfaces. These guidelines encompass key considerations for the usability of interoperability interfaces, such as importing and exporting data of EMRs. Building upon this theoretical framework, we evaluate the interoperability interfaces within OpenMRS, a widely used EMR system. By aligning the evaluations with the established theoretical guidelines, we propose a set of generic interoperability interfaces designed to enhance the seamless exchange of data across different domains. To assess their usability, we employ a predictive usability inspection, providing valuable insights into the practical aspects of these interfaces. Through this research, we aim to contribute to the improvement of interoperability interfaces in EMR systems, bridging the gap between theoretical guidelines and practical implementation. By addressing the design challenges, conducting evaluations, and proposing interoperability interfaces, we strive to enhance data exchange design and promote a more integrated and collaborative healthcare system. [ABSTRACT FROM AUTHOR]

Growing healthcare costs have been accompanied by increased policymakers' interest in the efficiency of healthcare systems. Network formation by hospitals as a vehicle for consolidation and achieving economies of scale has emerged as an important topic of conversation among academics and practitioners. Within networks, consolidation of particular specialties or entire campuses is expected and encouraged to take place. This paper describes the main findings of an effort to build gravity-type models to describe patient choices in inpatient and daycare hospital facilities. It analyzes the distance decay effects as a function of car travel times and great-circle distance, and it offers a method for inclusion of university hospitals. Additionally, it reviews the impact of driving and transit accessibility on hospital attraction and reviews the differences in distance decay for patient age groups and hospitalization types. In the described application, the best models achieve a Mean Absolute Percentage Error of around 10% in non-metropolitan areas, and 14.5% across different region types. Results in metropolitan areas suggest that latent factors unrelated to proximity and size have a significant role in determining hospital choices. Furthermore, the effects of relative driving and transit accessibility are found to be small or non-existent. [ABSTRACT FROM AUTHOR]

Analysis of public policy affecting dual eligibles requires accurate identification of survey respondents eligible for both Medicare and Medicaid. Doing so for Medicaid is particularly challenging given the complex eligibility rules tied to income and assets. In this paper we provide guidance on how to best identify eligible respondents in household surveys that have limited income or asset information, such as the National Health Interview Survey, American Community Survey, Current Population Survey, and Medical Expenditure Panel Survey. We show how two types of errors—false negative and false positive errors—are impacted by incorporating limited income or asset information, relative to the commonly-used approach of solely comparing total income to the income threshold. With the 2018 Health and Retirement Study, which has detailed income and asset information, we mimic the income and asset information available in those other household surveys and quantify how errors change when imposing income or asset tests with limited information. We show that incorporating all available income and asset data results in the lowest number of errors and the lowest overall error rates. We recommend that researchers adjust income and impose the asset test to the fullest extent possible when imputing Medicaid eligibility for Medicare enrollees. [ABSTRACT FROM AUTHOR]

In a medical setting, observational studies commonly involve patients who initiate a particular treatment (e.g., medication therapy) and others who do not, and the goal is to draw causal inferences about the effect of treatment on a time-to-event outcome. A difficulty with such studies is that the notion of a treatment initiation time is not well-defined for the control group. In this paper, we propose a Bayesian approach to estimate treatment effects in longitudinal observational studies where treatment is given by indication and thereby the exact timing of treatment is only observed for treated units. We present a framework for conceptualizing an underlying randomized experiment in this setting based on separating the time of indication for treatment, which we model using a latent state-space process, from the mechanism that determines assignment to treatment versus control. Next, we develop a two-step inferential approach that uses Markov Chain Monte Carlo (MCMC) posterior sampling to (1) infer the unobserved indication times for units in the control group, and (2) estimate treatment effects based on inferential conclusions from Step 1. This approach allows us to incorporate uncertainty about the unobserved indication times which induces uncertainty in both the selection of the control group and the measurement of time-to-event outcomes for these controls. We demonstrate our approach to study the effects on mortality of inappropriately prescribing phosphodiesterase type 5 inhibitors (PDE5Is), a medication contraindicated for certain types of pulmonary hypertension, using data from the Veterans Affairs (VA) health care system. [ABSTRACT FROM AUTHOR]

Researchers are often faced with evaluating the effect of a policy or program that was simultaneously initiated across an entire population of units at a single point in time, and its effects over the targeted population can manifest at any time period afterwards. In the presence of data measured over time, Bayesian time series models have been used to impute what would have happened after the policy was initiated, had the policy not taken place, in order to estimate causal effects. However, the considerations regarding the definition of the target estimands, the underlying assumptions, the plausibility of such assumptions, and the choice of an appropriate model have not been thoroughly investigated. In this paper, we establish useful estimands for the evaluation of large-scale policies. We discuss that imputation of missing potential outcomes relies on an assumption which, even though untestable, can be partially evaluated using observed data. We illustrate an approach to evaluate this key causal assumption and facilitate model elicitation based on data from the time interval before policy initiation and using classic statistical techniques. As an illustration, we study the Hospital Readmissions Reduction Program (HRRP), a US federal intervention aiming to improve health outcomes for patients with pneumonia, acute myocardial infraction, or congestive failure admitted to a hospital. We evaluate the effect of the HRRP on population mortality among the elderly across the US and in four geographic subregions, and at different time windows. We find that the HRRP increased mortality from pneumonia and acute myocardial infraction across at least one geographical region and time horizon, and is likely to have had a detrimental effect on public health. [ABSTRACT FROM AUTHOR]

The supply chain is a complex network in healthcare that crosses organizational and geographical borders. The inherent complexity of such structures can introduce impurities inclusive of erroneous facts, lack of transparency, and restricted records provenance. In the healthcare business, counterfeit pills are one of the major reasons for the harmful impact on human health and also for financial losses. Thus, pharmaceutical supply chains and end-to-end tracking systems are the recent research in healthcare. In this paper, we propose blockchain based traceability of counterfeited drugs (BBTCD) that implements tracking of counterfeited drugs using smart contracts on the Ethereum blockchain. We propose a solution to fully decentralize the tracking in healthcare by storing BBTCD on IPFS (Inter Planetary File System) to provide transparency, cost-effectiveness. [ABSTRACT FROM AUTHOR]

Exploitation of the ever-increasing volume of observational data has become a major challenge for real-world evidence (RWE). Causal inference can be viewed as emulations of clinical trials (ECTs) from real-world data (RWD). This requires in-depth knowledge of data management and modern statistical models, which evolve considerably. We have developed Plug-Stat® to facilitate these analyses. Plug-Stat® is a user-friendly graphical interface of R software implemented with JavaScript and VB.net. The implementation of Plug-Stat® for a specific database consists of two main tasks. The first concerns the automatic importation of data to link the software with the data and to avoid the repetition of data management activities for each study. The second is to define the outcomes and the associated possible confounders. To perform an ECT, we proposed the use of inverse probability weighting, flexible parametric estimation of the propensity score model with B-splines, and robust estimation of the variance. The statistical modeling steps are automatized to direct the user toward the correct methodology without data management. We illustrated the usefulness of Plug-Stat® with several recently published ECTs. In this paper, we presented the outcome comparison of preemptive deceased-donor kidney transplantations versus transplantations after dialysis. The interactive interfaces assist the user in conducting causal research based on ECT, and tutorials are available, offering self-training opportunities. Plug-Stat® reduces the time and, consequently, the cost necessary for different studies from the same cohort. [ABSTRACT FROM AUTHOR]

In health intervention research, epidemiologists and economists are more and more interested in estimating causal effects based on observational data. However, collaboration and interaction between both disciplines are regularly clouded by differences in the terminology used. Amongst others, this is reflected in differences in labeling, handling, and interpreting the sources of bias in parameter estimates. For example, both epidemiologists and economists use the term selection bias. However, what economists label as selection bias is labeled as confounding by epidemiologists. This paper aims to shed light on this and other subtle differences between both fields and illustrate them with hypothetical examples. We expect that clarification of these differences will improve the multidisciplinary collaboration between epidemiologists and economists. Furthermore, we hope to empower researchers to select the most suitable analytical technique from either field for the research problem at hand. [ABSTRACT FROM AUTHOR]

Electronics-Health Care is one of the new ideas in the healthcare industry. For analyzing the patient's records, doctors need a centralized record of the patients, which gives useful and enormous information. There will be a threat of leaking of confidential e-health data, which will seriously endanger by modifying the health care data of medical and its individual entities. Protection of the confidential data of patients will be a primary constraint in the health care industry. There is a lot of progressiveness in the human services area as well as there is a lot of usage of the information gathered from various sensors and this information is stored in the cloud. As per the existing security algorithms, there is still some security breach and also it won't get satisfied with integrity and consistency while transferring the data from one vendor to others in the health care system. It also causes high spoofing of IP and side channel hackers. With the increase in the use of the internet, there is an increase in hacking also, especially in the health-care sector, e-commerce, military, and Education. Dealing with the huge volume of information in the electronics-healthcare area requires the processing of patient's information day by day and its memory allocation. Cloud is the best option to store these health records in a distributed manner rather than on a single server. So, whatever the information gathered from it will be encoded on block chain technology and an anonymous digital ring signature will be used to authorize the intended user and maintain the privacy of patient cloud-based data distributed Electronics health record system before sending the information to the cloud storage. In this paper, the protection of Personal data of the patient health data is enhanced with block chain and anonymous ring signatures in the e-healthcare cloud; thus making it suitable for real-time applications. [ABSTRACT FROM AUTHOR]

The Medicare Consumer Assessment of Healthcare Providers and Systems (CAHPS®) surveys collect standardized information about patient experiences of care from nationally representative samples of people with Medicare to support consumers' enrollment choices and enable the Centers for Medicare & Medicaid Services to monitor care quality and incentivize high quality patient-centered care. Since 2007, protocols for data collection, analysis, and reporting have evolved to address expanded Medicare coverage options and a shift from a single survey vendor to a model in which health plans hire approved vendors to administer the survey. During that time, response rates for all types of surveys have declined; increasing effort has gone toward increasing survey participation, especially among people whose preferred language is not English. In this paper, we describe the history, goals, and current use of the Medicare CAHPS surveys. We also summarize key methodological issues, such as sample design, field implementation and data cleaning, adjustment, scoring, and report production. Additionally, we discuss issues that may arise more generally in managing a large, annual national survey that has direct impact on policy, and consider how a long-running survey of this nature may need to evolve to reflect changes in health care delivery and promote standardization in survey administration while maintaining survey content. [ABSTRACT FROM AUTHOR]

Interrupted time series (ITS)—a quasi-experimental design—is often used to evaluate the effectiveness of a health policy intervention. When the outcome of interest is rare, for example, for certain hospital-acquired infections, a common practice is to focus on aggregated count outcomes. However, analyzing ITS with count outcomes is challenging due to the needs to consider possible overdispersion and to account for serial correlation. In this paper, we compare the performance of three estimation methods, the generalized estimating equation (GEE) method, the generalized linear model (GLM) method, and the composite likelihood (CL) method, to fit parameter-driven time series models with count outcomes, and develop a simulation-based approach to calculate the sample size and power for designing such studies. [ABSTRACT FROM AUTHOR]

As healthcare data is extremely sensitive, it poses a risk of invading individuals' privacy if stored or exported without proper security measures. De-identification entails pseudonymization or anonymization of data, which are methods for temporarily or permanently removing an individual's identity. These methods are most suitable to keep user healthcare data private. Inference attacks are a commonly overlooked weakness of de-identification techniques. In this paper, I discuss a method for de-identifying Electronic Healthcare Records (EHR) using chained hashing to generate short-lived pseudonyms to reduce the impact of inference attacks, as well as a mechanism for re-identification based on information self-determination. It also removes the weaknesses of existing de-identification algorithms and resolve them by using appropriate real-time de-identification algorithm, Ephemeral Pseudonym Generation Algorithm (EPGA). [ABSTRACT FROM AUTHOR]

Policymakers seeking to target health policies efficiently towards specific population groups need to know which individuals stand to benefit the most from each of these policies. While traditional approaches for subgroup analyses are constrained to only consider a small number of pre-defined subgroups, recently proposed causal machine learning (CML) approaches help explore treatment-effect heterogeneity in a more flexible yet principled way. Causal forests use a generalisation of the random forest algorithm to estimate heterogenous treatment effects both at the individual and the subgroup level. Our paper aims to explore this approach in the setting of health policy evaluation with strong observed confounding, applied specifically to the context of mothers' health insurance enrolment in Indonesia. Comparing two health insurance schemes (subsidised and contributory) against no insurance, we find beneficial average impacts of enrolment in contributory health insurance on maternal health care utilisation and infant mortality, but no impact of subsidised health insurance. The causal forest algorithm identified significant heterogeneity in the impacts of contributory insurance, not just along socioeconomic variables that we pre-specified (indicating higher benefits for poorer, less educated, and rural women), but also according to some other characteristics not foreseen prior to the analysis, suggesting in particular important geographical impact heterogeneity. Our study demonstrates the power of CML approaches to uncover unexpected heterogeneity in policy impacts. The findings from our evaluation of past health insurance expansions can potentially guide the re-design of the eligibility criteria for subsidised health insurance in Indonesia. [ABSTRACT FROM AUTHOR]

Emergency general surgery (EGS) is a critical component of emergency care in the United States. Due to the time sensitiveness of EGS conditions, ensuring adequate spatial access to EGS services is paramount for reducing patient morbidity and mortality. Past studies have used travel time to measure spatial access to EGS services, which has its limitations. The major purpose of this paper is to evaluate the utility of a gravity-based spatial access model in measuring spatial access to EGS services in California. Our data sources include the American Hospital Association 2015 Annual Survey, the American Community Survey 2013–2017 five-year average dataset, and background geospatial datasets. We implemented both the gravity-based model and the shortest travel time method and compared them in measuring spatial access to EGS-capable hospitals in California at the census block group level. We analyzed each metric's ability to identify disparities in spatial access for the population as a whole, and subsequently to identify socio-demographic disparities. Overall, we found that both methods identified similar geographic and socio-demographic patterns of the spatial access. Native Americans and rural residents experienced the greatest disadvantage in spatial access to both general EGS services and advanced EGS services. However, the gravity-based model revealed more disparities in spatial access to EGS services than the travel time model, suggesting that using travel cost alone to measure spatial access to EGS services may underestimate the magnitude of disparities. These findings call for the use of gravity-based models that incorporate measures of population demand and hospital capacity when assessing spatial access to surgical services, and have implications for reallocating surgery resources to address disparities in spatial access. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Propensity score methods have been widely adopted in observational studies, however research on propensity score-based weighting (PSW) methods in complex survey data settings is lacking, particularly for binary outcomes. We conducted a simulation study to compare eight propensity score weighting approaches for estimating treatment effects using survey weighted data. Each of the eight methods is applied to estimation of two measures of the population-level treatment effect: the population average treatment effect (PATE), and the population average treatment effect on the treated (PATT). The methods are compared in terms of mean relative bias and coverage probability under different scenarios by varying the treatment effect, degrees of model misspecification, and levels of overlap in the propensity score. The results demonstrate that the two-stage methods with predicted outcomes weighted by survey weights consistently outperform the other methods for estimating the PATT; for estimating the PATE, the best performing PSW method depends on the degree of model misspecification and propensity score overlap. When the outcome model is correctly specified, four two-stage methods produce better estimates depending on the propensity score overlap. The methods are applied to the 2015 National Health Interview Survey data to estimate the effect of provider-patient discussion about smoking on smoking cessation. [ABSTRACT FROM AUTHOR]

Adoption and use of real-world data (RWD) for decision-making has been complicated by concerns regarding whether RWD was fit-for-purpose or was of sufficient validity to support the creation of credible RWE. This has greater urgency as regulatory agencies begin to use real world evidence (RWE) to inform decisions about treatment effectiveness. Researchers need an efficient and systematic method to screen the quality of RWD sources considered for use in studies of effectiveness and safety. Based on a literature review we developed a listing of screening criteria that have been previously proposed to assess the quality of RWD sources. We also developed an additional criterion based on Modern Validity Theory. While there has occurred some convergence of conceptual frameworks to assess data quality (DQ) and there is much agreement on specific assessment criteria, consensus has yet to emerge on how to assess whether a specific RWD source is reliable and fit-for-purpose. To create a user-friendly tool to assess whether RWD sources may have sufficient quality to support a well-designed RWE study for submission to a regulatory authority, we grouped the quality criteria with a view to harmonize published frameworks and to be consistent with how researchers generally evaluate existing RWD sources for research that they intend to submit to regulatory agencies. Screening data quality criteria were grouped into five dimensions after a comprehensive literature review via PubMed: authenticity, transparency, relevance, accuracy, and track record. The resultant tool was tested for its response burden using a hypothetical administrative claims data source. Providing responses to the screening criteria required only few hours effort by an experienced data source manager. Thus, the tool should not be an onerous burden on data source providers if asked by prospective researchers to provide the required information. Assessing whether a particular data source is fit-for-purpose will be facilitated by the use of this tool, but it will not be sufficient by itself. Fit-for-purpose judgements will still require further careful consideration based on the context and the specific scientific question of interest. Unlike prior DQ frameworks (DQF), the track record dimension of the tool adds the consideration of experience with RWD sources consistent with Modern Validity Theory. However, the tool does not address issues of study design and analysis that are critical to regulatory agencies in evaluating the robustness and credibility of the real-world evidence generated. [ABSTRACT FROM AUTHOR]

Social processes and social context are increasingly recognized as key factors shaping health-related behaviors and outcomes. One social process that may be acting within social networks is social influence, in which an individual's characteristic (e.g., specific health behavior) is potentially impacted by the corresponding characteristic of connected individuals in the network. In the health services context, healthcare providers who work together and share patients may influence each other through the knowledge transmission or development of clinical practice norms. Although many statistical techniques assume independence of data points, when analyzing data that may reflect social processes acting across a social network, it is imperative to account for the interdependencies (i.e., non-independence) across individuals. In practice, studies account for nonindependence in the context of estimating bivariate relations (correlations or linear regression) using a variety of analytic methods (some of which have previously been shown to yield biased results). To date, it is unclear which methods yield acceptable false positive rates, unbiased coefficient estimates, and acceptable statistical power, because there have been no systematic simulation studies comparing methods for addressing network nonindependence arising from social influence. To address this gap, we compared eight commonly used methods that purport to account for nonindependence using simulated network data. While results indicated that none of the techniques reduced false positive rates to the predicted (nominal) 0.05 level, random sampling of network nodes was the method that yielded the smallest false positive rates, yet came at a price of reduced statistical power. Further methodological development is needed. [ABSTRACT FROM AUTHOR]

The purpose of this study is to illustrate how matching approaches based on different balancing scores lead to variations in the treatment effect estimators. We introduced a case study evaluating the effect of anti-thrombotic agents on the severe progression of patients with intracerebral hemorrhage. We extracted subpopulations based on propensity and prognostic scores and then estimated the relative risk between the treatment groups. Furthermore, to illustrate the situation where the treatment effect estimates varied depending on employed balancing scores, we conducted a simulation experiment. In the case study, the matching using different balancing scores extracted subpopulations with different characteristics. Then, the estimated relative risk (95% confidence interval) was 1.27 (0.98–1.94) among the propensity score matched cohort, whereas it was 0.91 (0.76–1.08) among the prognostic score matched cohort. In the simulation experiments, the results indicated that the matching schemes based on different balancing scores created distinct matched cohorts, leading to varying estimates under treatment effect heterogeneity. Moreover, the variability of the estimated effect becomes substantial when there are covariates strongly related to the dependent variable of the nuisance functions. The difference in the selected subpopulation via matching based on different balancing scores is a thoughtful factor that can result in different estimates when there is effect heterogeneity. In practice, we recommend assessing the characteristics of the matched subpopulation and employing the balancing score that can estimate the treatment effect among the target population of interest in each study. Plain Language Summary: This study aimed to understand why different methods of matching patients in a study can lead to different estimates of how well a treatment works. We used a real example of a study where the treatment's effectiveness varied for different patients and compared how different methods of matching patients affected the results. We also did a series of experiments to see how the different methods affected estimates of treatment effect when the effectiveness varied among patients. Our study found that the different matching methods extracted the different patients. The difference in the matched patients led to very different results, especially when the treatment's effectiveness was not the same for all patients. Therefore, it is important to check the characteristics of patients after matching and consider whether the treatment's effectiveness is not the same for all patients. [ABSTRACT FROM AUTHOR]

Reliability refers to how measurements can produce consistent results and are crucial for any scientific research measurement. Intraclass correlation coefficient (ICC) is the most widely used method to determine the reproducibility of measurements of various statistical techniques. Calculated ICC and its confidence interval that reveal the underlying sampling distribution may help detect an experimental method's ability to identify systematic differences between research participants in a test. This study aimed to introduce a new SAS macro, ICC6, for calculating different ICC forms and their confidence intervals. A SAS macro that employs the PROC GLM procedure in SAS was created to generate two-way random effects (ANOVA) estimates. A simulated dataset was used to input the macro to calculate the point estimates for different ICCs. The ICC forms' upper and lower confidence interval limits were calculated using the F statistics distribution. Our SAS macro provides a complete set of various ICC forms and their confidence intervals. A validation analysis using commercial software packages STATA and SPSS delivered identical results. A development of SAS methodology using publicly available statistical approaches in estimating six distinct forms of ICC and their confidence intervals has been reported in this article. This work is an extension of general methodology supported by a few other statistical software packages to SAS. [ABSTRACT FROM AUTHOR]

This article clarifies how the biostatistical literature on time-varying treatments (TVT) can provide tools for dealing with time-varying confounding in difference-in-differences (DiD) studies. I use a simulation study to compare the bias and standard error of inverse probability weighting estimators from the TVT framework, a DiD framework, and hybrid approaches that combine ideas from both frameworks. I simulated longitudinal data with treatment effect heterogeneity over multiple time points using linear and logistic models. Simulation settings looked at both time-invariant confounders and time-varying confounders affected by prior treatment. Estimators that combined ideas from both frameworks had lower bias than standard TVT and DiD estimators when assumptions were unmet. The TVT framework provides estimation tools that can complement DiD tools in a wide range of applied settings. It also provides alternate estimands for consideration in policy settings. [ABSTRACT FROM AUTHOR]

Disease-specific quality indicators are used to compare institutions and health care providers in terms of processes or outcomes relevant to treatment of a particular condition. In the context of surgical cancer treatments, the performance variations can be due to hospital and/or surgeon level differences, creating a hierarchical clustering. We consider how the observed variation in care received at patient level can be decomposed into that causally explained by the hospital performance, surgeon performance within hospital, patient case-mix, and unexplained (residual) variation. For this purpose, we derive a four-way variance decomposition, with particular attention to the causal interpretation of the components. For estimation, we use inputs from a mixed-effect model with nested random hospital/surgeon-specific effects, and a multinomial logistic model for the hospital/surgeon-specific patient populations. We investigate the performance of our methods in a simulation study and demonstrate them through analysis of administrative data on kidney cancer care in Ontario. [ABSTRACT FROM AUTHOR]

Introduction: EQ-5D is an internationally acknowledged tool for assessing health-related quality of life. Our aim was to examine how pragmatic dynamics may influence answers to the EQ-5D-5 L in items where the structure of answer options is disjunctive. Methods: We performed a 3-step linguistic analysis building on the seminal work of Grice, including (1) examination of the lexical meanings of the answer options, (2) considerations of how conversational maxims might affect the respondent's interpretation of compatible answer options under a single item, and (3) analysis of how the questionnaire's context might counteract the problem of omitted answer options by shifting the meaning of context-sensitive expressions. Results: All items with disjunctive answer options exhibit both compatibilities and omissions. In combination with the disjunctive form of answer options these features of the EQ-5D-5 L complicates the communicative task for respondents relying on conversational norms to identify the most suitable answers to the instrument's questions. Discussion: In items where answer options have a disjunctive structure, respondents relying on Gricean conversational maxims will have to depend on their individual understanding of fine-grained details concerning the questionnaire's purpose and may have to weigh how conflicting norms should be balanced. While such dynamics are likely to go undetected in cognitive interviews, linguistic analysis may help to identify them. [ABSTRACT FROM AUTHOR]

Simulation studies were performed to investigate for which conditions of sample size of patients (n) and number of repeated measurements (k) (e.g., raters) the optimal (i.e., balance between precise and efficient) estimations of intraclass correlation coefficients (ICCs) and standard error of measurements (SEMs) can be achieved. Subsequently, we developed an online application that shows the implications for decisions about sample sizes in reliability studies. We simulated scores for repeated measurements of patients, based on different conditions of n, k, the correlation between scores on repeated measurements (r), the variance between patients' test scores (v), and the presence of systematic differences within k. The performance of the reliability parameters (based on one-way and two-way effects models) was determined by the calculation of bias, mean squared error (MSE), and coverage and width of the confidence intervals (CI). We showed that the gain in precision (i.e., largest change in MSE) of the ICC and SEM parameters diminishes at larger values of n or k. Next, we showed that the correlation and the presence of systematic differences have most influence on the MSE values, the coverage and the CI width. This influence differed between the models. As measurements can be expensive and burdensome for patients and professionals, we recommend to use an efficient design, in terms of the sample size and number of repeated measurements to come to precise ICC and SEM estimates. Utilizing the results, a user-friendly online application is developed to decide upon the optimal design, as 'one size fits all' doesn't hold. [ABSTRACT FROM AUTHOR]

Selective mortality and nonresponse in longitudinal analyses would lead to biased estimates. In this study, we draw data from the 1992–2016 Health and Retirement Study and used a multinomial logit model to examine the impacts of participants' demographics, health conditions, and socioeconomic status on both follow-up status in 2016 (Always-in; Died; Other Attritors, non-death sample attrition; Ever-out, skipped some intermediate surveys) and between-wave dropout. We then applied an inverse probability weighting approach to compensate for attrition in the analysis of education and hospitalizations. We found that many demographics (e.g., sex, age, race, ethnicity, marital status), socioeconomic factors (e.g., education, house ownership, labor force participation) and health conditions (e.g., self-reported health, and chronic conditions) had large and statistically significant associations with loss of follow-up. Our results show that loss of follow-up leads to substantial underestimation of the education-hospitalization association. After correcting attrition bias in a pooled cross-sectional analysis, the association of having a high school degree, some college, and college or above with any two-year hospitalizations increased by 59.7%, 72.9%, and 42.6%, respectively. Differences in estimates before and after correcting attrition bias were only statistically significant for college graduates, but who make up 24.7% of the "always-in" sample. In the longitudinal analysis of the association between education and hospitalization, correcting attrition bias also increases estimates of education by up to 58.9%, although not statistically significant. It suggests that empirical analyses that inform health policy decisions using the Health and Retirement Study should assess attrition bias from selective mortality and nonresponse. [ABSTRACT FROM AUTHOR]

Few rigorous studies provide a clear description of the methodological approach of developing an evidence-based implementation intervention, prior to implementation at scale. This study describes the development, mapping, rating, and review of the implementation strategies for the Care to Quit smoking cessation trial, prior to application in nine cancer services across Australia. Key stakeholders were engaged in the process from conception through to rating, reviewing and refinement of strategies and principles. An initial scoping review identified 21 barriers to provision of evidence-based smoking cessation care to patients with cancer, which were mapped to the Theoretical Domains Framework and Behaviour Change Wheel (BCW) to identify relevant intervention functions. The mapping identified 26 relevant behaviour change techniques, summarised into 11 implementation strategies. The implementation strategies were rated and reviewed against the BCW Affordability, Practicality, Effectiveness and cost-effectiveness, Acceptability, Side-effects/safety, and Equity criteria by key stakeholders during two interactive workshops to facilitate a focus on feasible interventions likely to resonate with clinical staff. The implementation strategies and associated intervention tools were then collated by form and function to provide a practical guide for implementing the intervention. This study illustrates the rigorous use of theories and frameworks to arrive at a practical intervention guide, with potential to inform future replication and scalability of evidence-based implementation across a range of health service settings. [ABSTRACT FROM AUTHOR]

Understanding how best to estimate state-level policy effects is important, and several unanswered questions remain, particularly about the ability of statistical models to disentangle the effects of concurrently enacted policies. In practice, many policy evaluation studies do not attempt to control for effects of co-occurring policies, and this issue has not received extensive attention in the methodological literature to date. In this study, we utilized Monte Carlo simulations to assess the impact of co-occurring policies on the performance of commonly-used statistical models in state policy evaluations. Simulation conditions varied effect sizes of the co-occurring policies and length of time between policy enactment dates, among other factors. Outcome data (annual state-specific opioid mortality rate per 100,000) were obtained from 1999 to 2016 National Vital Statistics System (NVSS) Multiple Cause of Death mortality files, thus yielding longitudinal annual state-level data over 18 years from 50 states. When co-occurring policies are ignored (i.e., omitted from the analytic model), our results demonstrated that high relative bias (> 82%) arises, particularly when policies are enacted in rapid succession. Moreover, as expected, controlling for all co-occurring policies will effectively mitigate the threat of confounding bias; however, effect estimates may be relatively imprecise (i.e., larger variance) when policies are enacted in near succession. Our findings highlight several key methodological issues regarding co-occurring policies in the context of opioid-policy research yet also generalize more broadly to evaluation of other state-level policies, such as policies related to firearms or COVID-19, showcasing the need to think critically about co-occurring policies that are likely to influence the outcome when specifying analytic models. [ABSTRACT FROM AUTHOR]

Propensity score (PS) weighting is widely used to make causal or unconfounded comparisons between groups. Particularly, the balancing weights approach unifies existing PS weighting methods and allows one to identify target populations clearly. Few studies have applied PS weighting to generate estimates that are concordant with the Institute of Medicine (IOM) definition of racial disparities. This PS weighting approach aims at estimating racial disparity conditioned on balancing the health status distributions between groups for a target population. Despite these attempts, however, no study has extensively examined the balancing weights in implementing the IOM definition. This article presents the balancing weights based on the health status PS applied to the IOM definition of racial disparity. Particularly, we propose using the absolute standardized difference to assess the degree to which specific balancing weights satisfy the IOM definition. We consider hybrid health status balancing weights, which are equivalent to the inverse probability weights and overlap weights as special cases. We propose a data-adaptive selection of the tuning parameter for the hybrid weights to minimize the bias of disparity estimates due to the alterations of the distributions of socioeconomic status variables by weighting. In our simulation study, the hybrid weights were shown to perform well in implementing the IOM definition. The practical utility of the proposed methods is illustrated in a study of bladder cancer treatment disparity. [ABSTRACT FROM AUTHOR]

Many medical studies have used non-randomized sampling, which tends to be the case in research that involves a surgical procedure. Intra-operative procedures and actions conducted by the attending surgical team may be based on pre-operative conditions of the patient, which can introduce endogeneity. Using acute kidney injury (AKI) following cardiopulmonary bypass surgery as a research setting, the present study uses a control function approach to explain why two perfusionist-directed principle components of delivered oxygen (DO2) while the patient is on pump—hemoglobin and cardiac index—need to be treated as endogenous variables rather than exogenous ones. We further show conditions in which the exogenous model understates and overstates the predicted probability of AKI. [ABSTRACT FROM AUTHOR]

Randomized controlled trials are the gold standard for measuring causal effects. However, they are often not always feasible, and causal treatment effects must be estimated from observational data. Observational studies do not allow robust conclusions about causal relationships unless statistical techniques account for the imbalance of pretreatment confounders across groups and key assumptions hold. Propensity score and balance weighting (PSBW) are useful techniques that aim to reduce the observed imbalances between treatment groups by weighting the groups to look alike on the observed confounders. Notably, there are many methods available to estimate PSBW. However, it is unclear a priori which will achieve the best trade-off between covariate balance and effective sample size for a given application. Moreover, it is critical to assess the validity of key assumptions required for robust estimation of the needed treatment effects, including the overlap and no unmeasured confounding assumptions. We present a step-by-step guide to the use of PSBW for estimation of causal treatment effects that includes steps on how to evaluate overlap before the analysis, obtain estimates of PSBW using multiple methods and select the optimal one, check for covariate balance on multiple metrics, and assess sensitivity of findings (both the estimated treatment effect and statistical significance) to unobserved confounding. We illustrate the key steps using a case study examining the relative effectiveness of substance use treatment programs and provide a user-friendly Shiny application that can implement the proposed steps for any application with binary treatments. [ABSTRACT FROM AUTHOR]

The Consent-to-Contact (C2C) registry at the University of California, Irvine collects data from community participants to aid in the recruitment to clinical research studies. Self-selection into the C2C likely leads to bias due in part to enrollees having more years of education relative to the US general population. Salazar et al. (Alzheimer's Dementia Transl Res Clin Interv 6(1):e120023, 2020, https://doi.org/10.1002/trc2.12023) recently used the C2C to examine associations of race/ethnicity with participant willingness to be contacted about research studies. To obtain representative estimates from C2C we use weighted estimation of associations of interest where the weights are related to the probability of self-selection into the convenience sample. The selection probabilities are estimated using data from the National Health and Nutrition Examination Survey (NHANES). We create a combined dataset of C2C and NHANES subjects and evaluate the trade-offs of different approaches (logistic regression, covariate balancing propensity score, entropy balancing, and random forest) for estimating the probability of membership in C2C relative to NHANES. We further propose methods to estimate the variance of parameter estimates that account for uncertainty that arises from estimating propensity weights. Simulation studies explore the impact of propensity weight estimation on uncertainty. We demonstrate the approach by repeating the analysis by Salazar et al. (Alzheimer's Dementia Transl Res Clin Interv 6(1):e120023, 2020, https://doi.org/10.1002/trc2.12023) with the deduced propensity weights for the C2C subjects and contrast the results of the two analyses. This method can be implemented using our estweight package in R available on GitHub. [ABSTRACT FROM AUTHOR]

Unmeasured confounding undermines the validity of observational studies. Although randomized clinical trials (RCTs) are considered the "gold standard" of study types, we often observe divergent findings between RCTs and empirical settings. We present the "L-table", a simulation-based, prior knowledge (e.g., RCTs) guided approach that estimates the true effect adjusting for the potential influence of unmeasured confounders when using observational data. Using electronic health record data from Kaiser Permanente Southern California, we compare the effectiveness of coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) on endpoints at 1, 3, 5, and 10 years for patients with stable ischemic heart disease. We applied the L-table approach to the propensity score adjusted cohort to derive the omitted-confounder-adjusted estimated effects. After the L-table adjustment, CABG patients are 57.6% less likely to encounter major adverse cardiac and cerebrovascular event (MACCE) at 1 year (OR [95% CI] 0.424 [0.396, 0.517]), 56.4% less likely at 3 years (OR [95% CI] 0.436 [0.369, 0.527]), and 48.9% less likely at 5 years (OR [95% CI] 0.511 [0.451, 0.538]). CABG patients are also 49.5% less likely to die by the end of 10 years than PCI patients (OR [95% CI] 0.505 [0.446, 0.582]). We found the estimated true effects all shifted towards CABG as a more effective procedure that led to better health outcomes compared to PCI. Unlike existing sensitivity tools, the L-table approach explicitly lays out probable values and can therefore better support clinical decision-making. We recommend using L-table as a supplement to available techniques of sensitivity analysis. [ABSTRACT FROM AUTHOR]

In addition to applications in meta-analysis, funnel plots have emerged as an effective graphical tool for visualizing the detection of health care providers with unusual performance. Although there already exist a variety of approaches to producing funnel plots in the literature of provider profiling, limited attention has been paid to elucidating the critical relationship between funnel plots and hypothesis testing. Within the framework of generalized linear models, here we establish methodological guidelines for creating funnel plots specific to the statistical tests of interest. Moreover, we show that the test-specific funnel plots can be created merely leveraging summary statistics instead of individual-level information. This appealing feature inhibits the leak of protected health information and reduces the cost of inter-institutional data transmission. Two data examples, one for surgical patients from Michigan hospitals and the other for Medicare-certified dialysis facilities, demonstrate the applicability to different types of providers and outcomes with either individual- or summary-level information. [ABSTRACT FROM AUTHOR]

Industry 4.0 has ushered in a new era in the manufacturing industry. Healthcare delivery, like manufacturing, is on the verge of a fundamental shift into the new era of smart and connected health care, termed Health Care 4.0. Sharing healthcare data is an important step in improving the healthcare system's intelligence and service quality. Healthcare data, which is a personal asset of the patient, should be owned and managed by the patient rather than being dispersed among several healthcare systems, preventing data exchange and jeopardizing patient privacy. Electronic Health Records (EHRs) assist individuals by allowing them to combine and manage their medical data. On the other hand, today's EHR systems fall short of providing patients with traceable, trustworthy, and secure ownership over their medical data, creating serious security risks. Furthermore, the most of present EHR techniques and systems are centralized, making it difficult to share medical data and increasing the danger of a single point of failure. Blockchain technology can enable dependable, transparent, and auditable computing in the healthcare industry by utilizing a decentralized network of peers and a public ledger. In this article, we propose Patient-Controlled Blockchain Enabled Electronic Health Records as a way for patients to have safe control over their data that is decentralized, immutable, transparent, traceable, and trustworthy. Decentralized Interplanetary file storage is used in the suggested technique. Two main performance criteria are used to evaluate the suggested solutions: cost and accuracy. [ABSTRACT FROM AUTHOR]

To improve the estimation of healthcare expenditures by introducing a novel method that is well-suited to situations where data exhibit strong skewness and zero-inflation. Simulations, and two real-world datasets: the 2016–2017 Medical Expenditure Panel Survey; the Back Pain Outcomes using Longitudinal Data. Super learner is an ensemble machine learning approach that can combine several algorithms to improve estimation. We propose a two-stage super learner that is well suited for healthcare expenditure data by separately estimating the probability of any healthcare expenditure and the mean amount of healthcare expenditure conditional on having healthcare expenditures. These estimates can then be combined to yield a single estimate of expenditures for each observation. The analytical strategy can flexibly incorporate a range of individual estimation approaches for each stage of estimation, including both regression-based approaches and machine learning algorithms such as random forests. We compare the performance of the two-stage super learner with a one-stage super learner, and with multiple individual algorithms for estimation of healthcare cost under a broad range of data settings in simulated and real data. The predictive performance was compared using Mean Squared Error and R2. Our results indicate that the two-stage super learner has better performance compared with a one-stage super learner and individual algorithms, for healthcare cost estimation under a wide variety of settings in simulations and in empirical analyses. The improvement of the two-stage super learner over the one-stage super learner was particularly evident in settings when zero-inflation is high. [ABSTRACT FROM AUTHOR]

The SF-36 is a commonly used tool for measuring health status in a general population. Despite the overall moderate to high validity scores, certain communicative dynamics of the questionnaire deserve attention. Our aim was to examine how pragmatic dynamics, semantic context-sensitivity and epistemic reflection may influence answers to the SF-36. We applied a three-step Gricean analysis, including identification of the items in which pragmatic dynamics are most likely to have a significant effect, examination of how Gricean maxims might affect the answers given to the items identified, and assessment of whether the combined influence of linguistic context-sensitivity and pragmatic norms is benign. We found that the pragmatic dynamics of scalar implicatures are crucial to the interpretation of answer options but generally benign to its purpose. Regarding context, we raised concerns about the answer option 'Ved ikke' (Don't know); rather than representing a neutral midpoint, the answer is compatible with both a positive and a negative answer option. Whereas scalar implicatures are helpful to the purpose of SF-36, other contextual effects appear to be more worrisome. However, since pragmatic norms of communication, semantic context-sensitivity, and attention to epistemic error possibilities can all be expected to shape answers to the SF-36, we think that all three factors belong in a description of how the questionnaire works. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: There is a lack of well-validated self-report measures to assess public speaking anxiety. This study explored the psychometric properties of the Public Speaking Anxiety Scale (PSAS). Methods: Seventy-two adults completed the PSAS as part of the baseline screening procedure of a randomized controlled trial. Rasch analysis was used to assess the scale's response category functioning, precision, targeting, unidimensionality, and differential item functioning. Construct validity was assessed using classical test theory methods. Results: While thresholds were ordered and no systematic bias in responses for age, gender, or screen failure was found, the PSAS demonstrated evidence of multidimensionality (variance by first factor = 39.7%, eigenvalue of first contrast = 2.76). Multidimensionality was resolved after splitting the scale into two discrete subscales: Emotional and Physiological. Three misfitting items (i.e. item 5 from Emotional, items 6 and 14 from Physiological) were removed. Scale precision and targeting remained suboptimal after subscale split and removal of misfitting items (PSI = 1.41, PR = 0.67 for Emotional; PSI = 1.49, PR = 0.69 for Physiological). Conclusion: The PSAS demonstrated adequate convergent validity. Psychometric properties of the PSAS after Rasch-guided modifications were overall promising. Further studies are needed to confirm our results. [ABSTRACT FROM AUTHOR]

This study investigates individual-level misreporting among Medicaid recipients in Louisiana from 2007 to 2017. It explores whether the type of individual who misreports varies over time, including following a major policy shift (the implementation of the Affordable Care Act). Results are based on a series of biennial Medicaid Bias Studies from 2007 to 2017 in which Medicaid recipients are asked about their health care coverage, allowing us to identify individuals who misreport their status. Study participants are (1) randomly selected from state Medicaid files or (2) matched participants from a statewide health insurance survey designed to estimate uninsured rates. Survey respondents are asked a battery of questions designed to identify health insurance coverage for themselves and for each member residing within their household. Responses are then matched to Medicaid enrollment data to identify misreporting, including whether household members are misreported as uninsured, covered by an employer, or covered by some other government program. The results reveal that the level of Medicaid misreporting varies over time and that misreporting declined significantly following the Louisiana's Medicaid expansion. As a result, careful estimation of Medicaid misreporting continues to be an important source of bias in estimates of health insurance coverage. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Causal variance decompositions for a given disease-specific quality indicator can be used to quantify differences in performance between hospitals or health care providers. While variance decompositions can demonstrate variation in quality of care, causal mediation analysis can be used to study care pathways leading to the differences in performance between the institutions. This raises the question of whether the two approaches can be combined to decompose between-hospital variation in an outcome type indicator to that mediated through a given process (indirect effect) and remaining variation due to all other pathways (direct effect). For this purpose, we derive a causal mediation analysis decomposition of between-hospital variance, discuss its interpretation, and propose an estimation approach based on generalized linear mixed models for the outcome and the mediator. We study the performance of the estimators in a simulation study and demonstrate its use in administrative data on kidney cancer care in Ontario. [ABSTRACT FROM AUTHOR]