Health Services & Outcomes Research Methodology

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...

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 (NHIS), American Community Survey (ACS), Current Population Survey (CPS), and Medical Expenditure Panel Survey (MEPS)...

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...

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...

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...

OBJECTIVE: 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. DATA SOURCES: Simulations, and two real-world datasets: the 2016-2017 Medical Expenditure Panel Survey (MEPS); the Back Pain Outcomes using Longitudinal Data (BOLD). STUDY DESIGN: Super learner is an ensemble machine learning approach that can combine several algorithms to improve estimation...

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...

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...

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...

Medicaid claims are an important, but underutilized source of data for neonatal health services research in the United States. However, identifying live births in Medicaid claims data is challenging due to variation in coding practices by state and year. Methods of identifying live births in Medicaid claims data have not been validated, and it is not known which methods are most appropriate for different research questions. The objective of this study is to describe and validate five approaches to identifying births using Medicaid Analytic eXtract (MAX) from 45 states (2006-2014)...

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...

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...

For patients with diabetes, annual preventive care is essential to reduce the risk of complications. Local healthcare resources affect the utilization of diabetes preventive care. Our objectives were to evaluate the relative efficiency of counties in providing diabetes preventive care and explore potential to improve efficiencies. The study setting is public and private healthcare providers in US counties with available data. County-level demographics were extracted from the Area Health Resources File using data from 2010 to 2013, and individual-level information of diabetes preventive service use was obtained from the 2010 Behavioral Risk Factor Surveillance System...

Data derived from electronic health records (EHR) are heterogeneous with availability of specific measures dependent on the type and timing of patients' healthcare interactions. This creates a challenge for research using EHR-derived exposures because gold-standard exposure data, determined by a definitive assessment, may only be available for a subset of the population. Alternative approaches to exposure ascertainment in this case include restricting the analytic sample to only those patients with gold-standard exposure data available (exclusion); using gold-standard data, when available, and using a proxy exposure measure when the gold standard is unavailable (best available); or using a proxy exposure measure for everyone (common data)...

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...

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...

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...

Marital status is recognized as an important social determinant of health, income, and social support, but is rarely available in administrative data. We assessed the feasibility of using exact address data and zip code history to identify cohabiting couples using the 2018 Medicare Vital Status file and ZIP codes in the 2011-2014 Master Beneficiary Summary Files. Medicare beneficiaries meeting our algorithm displayed characteristics consistent with assortative mating and resembled known married couples in the Health and Retirement Study linked to Medicare claims...

As the use of electronic health records (EHR) to estimate treatment effects has become widespread, concern about bias introduced by error in EHR-derived covariates has also grown. While methods exist to address measurement error in individual covariates, little prior research has investigated the implications of using propensity scores for confounder control when the propensity scores are constructed from a combination of accurate and error-prone covariates. We reviewed approaches to account for error in propensity scores and used simulation studies to compare their performance...

Medical insurance claims are becoming increasingly common data sources to answer a variety of questions in biomedical research. Although comprehensive in terms of longitudinal characterization of disease development and progression for a potentially large number of patients, population-based inference using these datasets require thoughtful modifications to sample selection and analytic strategies relative to other types of studies. Along with complex selection bias and missing data issues, claims-based studies are purely observational, which limits effective understanding and characterization of the treatment differences between groups being compared...