JournalTOCs

Health Services and Outcomes Research Methodology
Journal Prestige (SJR): 0.683

Citation Impact (citeScore): 1
Number of Followers: 6

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1387-3741 - ISSN (Online) 1572-9400
Published by Springer-Verlag

[2469 journals]

Ephemeral pseudonym based de-identification system to reduce impact of
inference attacks in healthcare information system
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  Abstract: Abstract 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).
  PubDate: 2022-09-01
Incorporating respondent-driven sampling into web-based discrete choice
experiments: preferences for COVID-19 mitigation measures
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  Abstract: Abstract 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.
  PubDate: 2022-09-01
The Barkin Index of Maternal Functioning: an evaluation and foundations
for a new parental functioning scale
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  Abstract: Abstract In some contexts, including those that involve community healthcare, the functional status of mothers who have infants is of particular interest. This status has been assessed with the Barkin Index of Maternal Functioning (BIMF), proposed by its developers as an improvement over preexisting scales. The present study comprises a description and evaluation of the BIMF, which is revealed to have a number of shortcomings. Solutions proposed to overcome these shortcomings result in a new scale, the Parenting-an-Infant Competence Scale (PICS). This new scale has the prospect of greater psychometric acceptability as well as wider clinical and research applicability.
  PubDate: 2022-09-01
Incorporating external trial data to improve survival extrapolations: a
pilot study of the COU-AA-301 trial
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  Abstract: Abstract Survival extrapolation plays a key role within cost effectiveness analysis and is often subject to substantial uncertainty. Use of external data to improve extrapolations has been identified as a key research priority. We present findings from a pilot study using data from the COU-AA-301 trial of abiraterone acetate for metastatic castration-resistant prostate cancer, to explore how external trial data may be incorporated into survival extrapolations. External trial data were identified via a targeted search of technology assessment reports. Four methods using external data were compared to simple parametric models (SPMs): informal reference to external data to select appropriate SPMs, piecewise models with, and without, hazard ratio adjustment, and Bayesian models fitted with a prior on the shape parameter(s). Survival and hazard plots were compared, and summary metrics (point estimate accuracy and restricted mean survival time) were calculated. Without consideration of external data, several SPMs may have been selected as the ‘best-fitting’ model. The range of survival probability estimates was generally reduced when external data were included in model estimation, and external hazard plots aided model selection. Different methods yielded varied results, even with the same data source, highlighting potential issues when integrating external trial data within model estimation. By using external trial data, the most (in)appropriate models may be more easily identified. However, benefits of using external data are contingent upon their applicability to the research question, and the choice of method can have a large impact on extrapolations.
  PubDate: 2022-09-01
Comparison of estimation methods and sample size calculation for
parameter-driven interrupted time series models with count outcomes
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  Abstract: Abstract 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.
  PubDate: 2022-09-01
Rasch analysis reveals multidimensionality in the public speaking anxiety
scale
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  Abstract: 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.
  PubDate: 2022-09-01
Development, methodology, and adaptation of the Medicare Consumer
Assessment of Healthcare Providers and Systems (CAHPS®) patient
experience survey, 2007–2019
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  Abstract: Abstract 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.
  PubDate: 2022-07-29
Health status balancing weights for estimation of health care disparities
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  Abstract: Abstract 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.
  PubDate: 2022-07-28
Extending computations for disparity testing when data sources are
uncertain
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  Abstract: Abstract The topic of this article is one-sided hypothesis testing on the means of two populations when there is uncertainty as to which population a datum is drawn. Along with each datum, a probability is given as to which of the populations the datum emanated. Such situations arise, for example, in the use of Bayesian imputation methods to assess racial and ethnic disparities with certain survey, health, and financial data. By use of a Bayesian framework and Markov Chain Monte Carlo sampling from the joint posterior distribution of the population means, the probability of a disparity hypothesis is estimated. This approach extends sample size limitations of previous methods given in the literature from a few dozen to well into the thousands. Four methods are developed and compared. Three methods are implemented in R codes and one method in WinBUGS. All the codes are provided in the appendices.
  PubDate: 2022-07-18
Methodological considerations for estimating policy effects in the context
of co-occurring policies
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  Abstract: Abstract 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.
  PubDate: 2022-07-09
Adjustment for biased sampling using NHANES derived propensity weights
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  Abstract: Abstract 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.
  PubDate: 2022-07-08
Test-specific funnel plots for healthcare provider profiling leveraging
individual- and summary-level information
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  Abstract: Abstract 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.
  PubDate: 2022-07-06
Authorization and privacy preservation in cloud-based distributed ehr
system using blockchain technology and anonymous digital ring signature
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  Abstract: Abstract 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.
  PubDate: 2022-06-27
Addressing unmeasured confounding bias with a prior knowledge guided
approach: coronary artery bypass grafting (CABG) versus percutaneous
coronary intervention (PCI) in patients with stable ischemic heart disease
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  Abstract: Abstract 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.
  PubDate: 2022-06-21
Dealing with endogeneity in non-randomized medical studies: a study of
acute kidney injury following cardiopulmonary bypass surgery
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  Abstract: Abstract 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.
  PubDate: 2022-06-10
PcBEHR: patient-controlled blockchain enabled electronic health records
for healthcare 4.0
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  Abstract: Abstract 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.
  PubDate: 2022-06-07
A two-stage super learner for healthcare expenditures
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  Abstract: Abstract 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.
  PubDate: 2022-06-06
Advanced models for improved prediction of opioid-related overdose and
suicide events among Veterans using administrative healthcare data
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  Abstract: Abstract 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.
  PubDate: 2022-06-01
  DOI: 10.1007/s10742-021-00263-7
Estimating heterogeneous policy impacts using causal machine learning: a
case study of health insurance reform in Indonesia
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  Abstract: Abstract 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.
  PubDate: 2022-06-01
  DOI: 10.1007/s10742-021-00259-3
Incidence rate and financial burden of medical errors and policy
interventions to address them: a multi-method study protocol
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  Abstract: Abstract Medical error is one of the most critical challenges facing medical services. They pose a substantial threat to patient safety, and their costs draw attention from policymakers, health care planners and researchers. We aim to make a realistic estimation of medical error incidence and related costs and identify factors influencing this incidence in Iranian hospitals. In the first phase of this multi-method study, through two reviews of systematic reviews and a meta-analysis, we will estimate the incidence of medical errors and the strategies to reduce them. We will extract available data among 41 hospitals supervised by the East Azerbaijan University in the second phase. We will also develop a model and use a Delphi method to predict medical errors incidence and calibrate our model output using the Monte Carlo simulation. We will compare this estimation with the incidence rate based on meta-analysis results from the first phase. In the third phase, we will investigate the relationship between several factors potentially influencing medical error incidence. In the fourth phase, we will estimate costs associated with medical errors by conducting a patient records review and matching those with claims related to medical errors. In the fifth phase, we will present a policy brief related to strategies for medical errors and associated costs reduction in Iran. Our findings could benefit Iranian and policymakers in other countries to reduce medical errors and associated costs.
  PubDate: 2022-06-01
  DOI: 10.1007/s10742-021-00261-9