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Abstract Background With the current focus on personalized medicine, patient/subject level inference is often of key interest in translational research. As a result, random effects models (REM) are becoming popular for patient level inference. However, for very large data sets that are characterized by large sample size, it can be difficult to fit REM using commonly available statistical software such as SAS since they require inordinate amounts of computer time and memory allocations beyond what are available preventing model convergence. For example, in a retrospective cohort study of over 800,000 Veterans with type 2 diabetes with longitudinal data over 5 years, fitting REM via generalized linear mixed modeling using currently available standard procedures in SAS (e.g. PROC GLIMMIX) was very difficult and same problems exist in Stata’s gllamm or R’s lme packages. Thus, this study proposes and assesses the performance of a meta regression approach and makes comparison with methods based on sampling of the full data. Data We use both simulated and real data from a national cohort of Veterans with type 2 diabetes (n=890,394) which was created by linking multiple patient and administrative files resulting in a cohort with longitudinal data collected over 5 years. Methods and results The outcome of interest was mean annual HbA1c measured over a 5 years period. Using this outcome, we compared parameter estimates from the proposed random effects meta regression (REMR) with estimates based on simple random sampling and VISN (Veterans Integrated Service Networks) based stratified sampling of the full data. Our results indicate that REMR provides parameter estimates that are less likely to be biased with tighter confidence intervals when the VISN level estimates are homogenous. Conclusion When the interest is to fit REM in repeated measures data with very large sample size, REMR can be used as a good alternative. It leads to reasonable inference for both Gaussian and non-Gaussian responses if parameter estimates are homogeneous across VISNs.

Abstract Background Many studies have investigated racial/ethnic disparities in medication non-adherence in patients with type 2 diabetes using common measures such as medication possession ratio (MPR) or gaps between refills. All these measures including MPR are quasi-continuous and bounded and their distribution is usually skewed. Analysis of such measures using traditional regression methods that model mean changes in the dependent variable may fail to provide a full picture about differential patterns in non-adherence between groups. Methods A retrospective cohort of 11,272 veterans with type 2 diabetes was assembled from Veterans Administration datasets from April 1996 to May 2006. The main outcome measure was MPR with quantile cutoffs Q1-Q4 taking values of 0.4, 0.6, 0.8 and 0.9. Quantile-regression (QReg) was used to model the association between MPR and race/ethnicity after adjusting for covariates. Comparison was made with commonly used ordinary-least-squares (OLS) and generalized linear mixed models (GLMM). Results Quantile-regression showed that Non-Hispanic-Black (NHB) had statistically significantly lower MPR compared to Non-Hispanic-White (NHW) holding all other variables constant across all quantiles with estimates and p-values given as -3.4% (p = 0.11), -5.4% (p = 0.01), -3.1% (p = 0.001), and -2.00% (p = 0.001) for Q1 to Q4, respectively. Other racial/ethnic groups had lower adherence than NHW only in the lowest quantile (Q1) of about -6.3% (p = 0.003). In contrast, OLS and GLMM only showed differences in mean MPR between NHB and NHW while the mean MPR difference between other racial groups and NHW was not significant. Conclusion Quantile regression is recommended for analysis of data that are heterogeneous such that the tails and the central location of the conditional distributions vary differently with the covariates. QReg provides a comprehensive view of the relationships between independent and dependent variables (i.e. not just centrally but also in the tails of the conditional distribution of the dependent variable). Indeed, without performing QReg at different quantiles, an investigator would have no way of assessing whether a difference in these relationships might exist.

Objective: To examine changes in hospitalization trends and healthcare utilization among Veterans following Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act implementation. Data Sources and Study Setting: VA Corporate Data Warehouse and Centers for Medicare and Medicaid Services datasets. Study Design: Retrospective cohort study to compare 7‐ and 30‐day rates for unplanned readmission and emergency department visits following index hospital stays based on payor type (VHA facility stay, VA‐funded stay in community facility [CC], or Medicare‐funded community stay [CMS]). Segmented regression models were used to compare payors and estimate changes in outcome levels and slopes following MISSION Act implementation. Data Collection/Extraction Methods: Veterans with active VA primary care utilization and ≥1 acute hospitalization between January 1, 2016 and December 31, 2021. Principal Findings: Monthly index stays increased for all payors until MISSION Act implementation, when VHA and CMS admissions declined while CC admissions accelerated and overtook VHA admissions. In December 2021, CC admissions accounted for 54% of index admissions, up from 25% in January 2016. From adjusted models, just prior to implementation (May 2019), Veterans with CC admissions had 47% greater risk of 7‐day readmission (risk ratio [RR]: 1.47, 95% confidence interval [CI]: 1.43, 1.51) and 20% greater risk of 30‐day readmission (RR: 1.20, 95% CI: 1.19, 1.22) compared with those with VHA admissions; both effects persisted post‐implementation. Pre‐implementation CC admissions were also associated with higher 7‐ and 30‐day ED visits, but both risks were substantially lower by study termination (RR: 0.90, 95% CI: 0.88, 0.91) and (RR: 0.89, 95% CI: 0.87, 0.90), respectively. Conclusions: MISSION Act implementation was associated with substantial shifts in treatment site and federal payor for Veteran hospitalizations. Post‐implementation readmission risk was estimated to be higher for those with CC and CMS index admissions, while post‐implementation risk of ED utilization following CC admissions was estimated to be lower compared with VHA index admissions. Reasons for this divergence require further investigation. [ABSTRACT FROM AUTHOR]

Dyslexia is a developmental disorder in reading that exhibits varied patterns of expression across children. Here we examined the degree to which different kinds of reading disabilities (defined as profiles or patterns of reading problems) contribute to brain morphology results in Jacobian determinant images that represent local brain shape and volume. A matched-pair brain morphometry approach was used to control for confounding from brain size and research site effects in this retrospective multi-site study of 134 children from eight different research sites. Parietal operculum, corona radiata, and internal capsule differences between cases and controls were consistently observed across children with evidence of classic dyslexia, specific comprehension deficit, and language learning disability. Thus, there can be common brain morphology findings across children with quite varied reading disability profiles that we hypothesize compound the developmental difficulties of children with unique reading disability profiles and reasons for their reading disability.

The economic and social costs of stroke to the society can be enormous. These costs can cause serious economic damage to both the individual and the nation. It is thus important to conduct a cost effectiveness analysis to indicate whether an intervention provides high value where its health benefits justify its costs. This study will provide evidence based on the costs of stroke with a view of improving intervention and treatments of stoke survivors in Nigeria. This study utilizes two types of economic evaluation methods - cost-effectiveness analysis and cost-benefit analysis - to determine the economic impact of Tailored Hospital-based Risk Reduction to Impede Vascular Events after Stroke (THRIVES) intervention. The study is conducted in four Nigerian hospitals where 400 patients are recruited to participate in the study. The cost-effectiveness of THRIVES post-discharge intervention is compared with the control Intervention scenario, which is the usual and customary care delivered at each health facility in terms of cost per quality adjusted life years (QALYs). It is expected that successful implementation of the project would serve as a model of cost-effective quality stroke care for implementation.