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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.

IntroductionStatistical literacy is essential in clinical and translational science (CTS). Statistical competencies have been published to guide coursework design and selection for graduate students in CTS. Here, we describe common elements of graduate curricula for CTS and identify gaps in the statistical competencies.MethodsWe surveyed statistics educators using e-mail solicitation sent through four professional organizations. Respondents rated the degree to which 24 educational statistical competencies were included in required and elective coursework in doctoral-level and master's-level programs for CTS learners. We report competency results from institutions with Clinical and Translational Science Awards (CTSAs), reflecting institutions that have invested in CTS training.ResultsThere were 24 CTSA-funded respondents representing 13 doctoral-level programs and 23 master's-level programs. For doctoral-level programs, competencies covered extensively in required coursework for all doctoral-level programs were basic principles of probability and hypothesis testing, understanding the implications of selecting appropriate statistical methods, and computing appropriate descriptive statistics. The only competency extensively covered in required coursework for all master's-level programs was understanding the implications of selecting appropriate statistical methods. The least covered competencies included understanding the purpose of meta-analysis and the uses of early stopping rules in clinical trials. Competencies considered to be less fundamental and more specialized tended to be covered less frequently in graduate courses.ConclusionWhile graduate courses in CTS tend to cover many statistical fundamentals, learning gaps exist, particularly for more specialized competencies. Educational material to fill these gaps is necessary for learners pursuing these activities.

Abstract Background Assessing the quality of primary care is becoming a priority in national healthcare agendas. Audit and feedback on healthcare quality performance indicators can help improve the quality of care provided. In some instances, fewer numbers of more comprehensive indicators may be preferable. This paper describes the use of the Summary Quality Index (SQUID) in tracking quality of care among patients and primary care practices that use an electronic medical record (EMR). All practices are part of the Practice Partner Research Network, representing over 100 ambulatory care practices throughout the United States. Methods The SQUID is comprised of 36 process and outcome measures, all of which are obtained from the EMR. This paper describes algorithms for the SQUID calculations, various statistical properties, and use of the SQUID within the context of a multi-practice quality improvement (QI) project. Results At any given time point, the patient-level SQUID reflects the proportion of recommended care received, while the practice-level SQUID reflects the average proportion of recommended care received by that practice's patients. Using quarterly reports, practice- and patient-level SQUIDs are provided routinely to practices within the network. The SQUID is responsive, exhibiting highly significant (p < 0.0001) increases during a major QI initiative, and its internal consistency is excellent (Cronbach's alpha = 0.93). Feedback from physicians has been extremely positive, providing a high degree of face validity. Conclusion The SQUID algorithm is feasible and straightforward, and provides a useful QI tool. Its statistical properties and clear interpretation make it appealing to providers, health plans, and researchers.

Abstract Background In the United States, life expectancy is significantly lower among blacks than whites. We examined whether socioeconomic status (SES) and cardiovascular disease (CVD) risk factors may help explain this disparity. Methods Forty years (1961 through 2000) of all-cause mortality data were obtained on a population-based cohort of 2,283 subjects in the Charleston Heart Study (CHS). We examined the influence of SES and CVD risk factors on all-cause mortality. Results Complete data were available on 98% of the original sample (647 white men, 728 white women, 423 black men, and 443 black women). After adjusting for SES and CVD risk factors, the hazard ratios (HRs) for white ethnicity were 1.14 (0.98 to 1.32) among men and 0.90 (0.75 to 1.08) among women, indicating that the mortality risk was 14% greater for white men and 10% lower for white women compared to their black counterparts. However the differences were not statistically significant. Conclusion While there are marked contrasts in mortality among blacks and whites in the CHS, the differences can be largely explained by SES and CVD risk factors. Continued focus on improving and controlling cardiovascular disease risk factors may reduce ethnic disparities in survival.

Abstract Background Health disparities are a growing concern. Recently, we conducted a practice-based trial to help primary care physicians improve adherence with 21 quality indicators relevant to the primary and secondary prevention of cardiovascular disease and stroke. Although the primary concern in that study was whether patients in intervention practices outperformed those in control practices, we were also interested in determining whether minority patients were more, less, or just as likely to benefit from the intervention as non-minorities. Methods Baseline (fourth quarter 2000) and follow-up (fourth quarter 2002) data were obtained from 3 intervention practices believed to have at least 10% minority representation. Two practices had a black (non-Hispanic) population sufficient for analysis, while the other had a sufficient Hispanic population. Within each practice, changes in the 21 indicators were compared between the minority patient population and the entire patient population. The proportion of measures in which minority patients exhibited greater improvement was calculated for each practice and for all 3 practices combined, and comparisons were made using non-parametric methods. Results For all black patients, the observed improvement in 50% of 22 eligible study indicators was better than that observed among all white patients in the same practices. The average changes in the study indicators observed among the black and white patients were not significantly different (p = 0.300) from one another. Likewise for all minority patients in all 3 practices combined, the observed improvement in 14 of 29 (43.3%) eligible study indicators was better than that observed among all white patients. The average changes in the study indicators among all minority patients were not significantly different from the changes observed among the white patients (p = 0.272). Conclusions Among 3 intervention practices involved in a quality improvement project, there did not appear to be any significant disparity between minority and non-minority patients in the improvement in study indicators.

IntroductionStatistical literacy is essential in clinical and translational science (CTS). Statistical competencies have been published to guide coursework design and selection for graduate students in CTS. Here, we describe common elements of graduate curricula for CTS and identify gaps in the statistical competencies.MethodsWe surveyed statistics educators using e-mail solicitation sent through four professional organizations. Respondents rated the degree to which 24 educational statistical competencies were included in required and elective coursework in doctoral-level and master's-level programs for CTS learners. We report competency results from institutions with Clinical and Translational Science Awards (CTSAs), reflecting institutions that have invested in CTS training.ResultsThere were 24 CTSA-funded respondents representing 13 doctoral-level programs and 23 master's-level programs. For doctoral-level programs, competencies covered extensively in required coursework for all doctoral-level programs were basic principles of probability and hypothesis testing, understanding the implications of selecting appropriate statistical methods, and computing appropriate descriptive statistics. The only competency extensively covered in required coursework for all master's-level programs was understanding the implications of selecting appropriate statistical methods. The least covered competencies included understanding the purpose of meta-analysis and the uses of early stopping rules in clinical trials. Competencies considered to be less fundamental and more specialized tended to be covered less frequently in graduate courses.ConclusionWhile graduate courses in CTS tend to cover many statistical fundamentals, learning gaps exist, particularly for more specialized competencies. Educational material to fill these gaps is necessary for learners pursuing these activities.

PurposeTo learn the size, composition, and scholarly output of biostatistics, epidemiology, and research design (BERD) units in U.S. academic health centers (AHCs).MethodEach year for four years, the authors surveyed all BERD units in U.S. AHCs that were members of the Clinical and Translational Science Award (CTSA) Consortium. In 2010, 46 BERD units were surveyed; in 2011, 55; in 2012, 60; and in 2013, 61.ResultsResponse rates to the 2010, 2011, 2012, and 2013 surveys were 93.5%, 98.2%, 98.3%, and 86.9%, respectively. Overall, the size of BERD units ranged from 3 to 86 individuals. The median FTE in BERD units remained similar and ranged from 3.0 to 3.5 FTEs over the years. BERD units reported more availability of doctoral-level biostatisticians than doctoral-level epidemiologists. In 2011, 2012, and 2013, more than a third of BERD units provided consulting support on 101 to 200 projects. A majority of BERD units reported that between 25% and 75% (in 2011) and 31% to 70% (in 2012) of their consulting was to junior investigators. More than two-thirds of BERD units reported their contributions to the submission of 20 or more non-BERD grant or contract applications annually. Nearly half of BERD units reported 1 to 10 manuscripts submitted annually with a BERD practitioner as the first or corresponding author.ConclusionsThe findings regarding BERD units provide a benchmark against which to compare BERD resources and may be particularly useful for institutions planning to develop new units to support programs such as the CTSA.

Genome-wide association studies (GWAS) of Alzheimer's disease (AD) have identified a large number of susceptibility genes, but most of AD heritability remains unexplained, implying the existence of additional genes. Furthermore, the majority of the GWAS have been conducted in people of European descent, and the genes important for AD susceptibility in people of African descent have been underexplored. In this hypothesis-generating prospective cohort study, we genotyped 191 African Americans (AAs) from three longitudinal cohorts on aging for the IgG3 allotype GM6, which is expressed exclusively in people of African descent, and assessed its interaction with IGHG, FCGRIIB, and HLA-DRB1 genes. Cox proportional hazards modeling showed that GM6 by itself was not significantly associated with AD development. However, there was evidence of epistatic interaction: The risk of developing AD associated with GM6 positivity was significantly different (p = 0.0098) in non-GM17/GM17 participants compared with GM 17/GM17 participants. Specifically, in non-GM17/GM17 participants, the risk of AD was over fourfold higher in GM6-positive participants compared with GM 6-negative participants (HR = 4.63). Similarly, risk of developing AD associated with GM6 positivity was marginally different in non-FCGRIIB TT participants compared with FCGRIIB TT participants. In non-FCGRIIB TT participants, the risk of developing AD was over twofold higher in GM6-positive participants compared with GM6-negative participants (HR = 2.44). This is the first report suggesting that immunoglobulin GM allotypes might play a role in AD etiology among AAs; however, since this was largely a hypothesis-generating study, replication in larger cohorts would be required to confirm this finding. [ABSTRACT FROM AUTHOR]

Background: Positive associations between suicidal behaviors and asthma have been established in previous adolescent studies. Few studies consider social risk factors, such as bullying. This study involved an analysis of suicidal behaviors and asthma, but also includes an assessment of whether these relationships were modified by the co-occurrence of bullying. Methods: Data included 13,154 participants from the 2013 Youth Risk Behavior Survey (YRBS), collected by the US Centers for Disease Control and Prevention. Logistic regression models were constructed and summarized using odds ratios (ORs) and 95% confidence intervals (95% CIs). Results: When comparing adolescents with asthma who were bullied at school to those who were not bullied at school, the odds of contemplating suicide were increased by nearly 2-fold (OR = 1.8, 95% CI = 1.5-2.3), and the odds of creating a suicide plan were 2.3 times higher (OR = 2.3, 95% CI = 1.7-3.1). The odds of a suicide attempt and incurring an injury from a suicide attempt were also substantially increased. Similarly, increased odds of suicidal behaviors were observed for adolescents with asthma who were bullied electronically. Conclusion: Having asthma and being bullied are both associated with increased odds of suicidal behaviors.

Background: Diffractive microscopy creates contrast within samples that are otherwise uniform under bright light. This technique can highlight subtle differences in refractive indices within birefringent samples containing varying amounts of mature collagen. Dermatofibroma (DF) and dermatofibrosarcoma protuberans (DFSP) possess differences in their mature collagen content and, therefore, may be distinguishable using diffractive microscopy. Methods: Two hundred forty‐two DF and 85 DFSP hematoxylin–eosin (H&E)‐stained specimens were analyzed using diffractive microscopy. Data regarding the distribution pattern and strength of refractility was recorded. Results: DFSP was more frequently found to be focally, weakly, or non‐refractile (82.9%; n = 68) under diffractive microscopy, while DF more often showed diffusely bright refractility (52.9%; n = 128). DFSP samples with diffuse refractility in portions of the lesion (17.1%; n = 14) also exhibited a unique checkerboard pattern distinct from that which was seen in DF samples. Conclusions: The absence of diffuse refractility was more closely associated with DFSP, as was the presence of a unique checkerboard diffraction pattern. Despite high sensitivity (Sn = 82.9%), absent refractility was not a specific test (Sp = 52.9%), with 47.1% (n = 114) of DF samples sharing this feature. The distinction between DF and DFSP is often diagnosed using H&E alone. In difficult cases, examination of collagen under diffractive microscopy may be useful in distinguishing DFSP from DF and provide an alternative cost‐effective tool to immunohistochemical staining. [ABSTRACT FROM AUTHOR]

Several magnetic resonance imaging (MRI) measures for quantifying endogenous nonheme brain iron have been proposed. These correspond to distinct physical properties with varying sensitivities and specificities to iron. Moreover, they may depend not only on tissue iron concentration, but also on the intravoxel spatial pattern of iron deposition, which is complex in many brain regions. Here, the three MRI brain iron measures of R2*, magnetic field correlation (MFC), and magnetic susceptibility are compared in several deep gray matter regions for both healthy participants (HPs) and individuals with cocaine use disorder (CUD). Their concordance is assessed from their correlations with each other and their relative dependencies on age. In addition, associations between the iron measures and microstructure in adjacent white matter regions are investigated by calculating their correlations with diffusion MRI measures from the internal capsule, and associations with cognition are determined by using results from a battery of standardized tests relevant to CUD. It is found that all three iron measures are strongly correlated with each other for the considered gray matter regions, but with correlation coefficients substantially less than one indicating important differences. The age dependencies of all three measures are qualitatively similar in most regions, except for the red nucleus, where the susceptibility has a significantly stronger correlation with age than R2*. Weak to moderate correlations are seen for the iron measures with several of the diffusion and cognitive measures, with the strongest correlations being obtained for R2*. The iron measures differ little between the HP and CUD groups, although susceptibility is significantly lower in the red nucleus for the CUD group. For the comparisons made, the iron measures behave similarly in most respects, but with notable quantitative differences. It is suggested that these differences may be, in part, attributable to a higher sensitivity to the spatial pattern of iron deposition for R2* and MFC than for susceptibility. This is supported most strongly by a sharp contrast between the values of the iron measures in the globus pallidus relative to those in the red nucleus. The observed correlations of the iron measures with diffusion and cognitive scores point to possible connections between gray matter iron, white matter microstructure, and cognition. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) stands as a leading cause of death worldwide, often arising from specific genetic mutations, progressing from pre-cancerous adenomas to adenocarcinomas. Early detection through regular screening can result in a 90% 5-year survival rate for patients. However, unfortunately, only a fraction of CRC cases are identified at pre-invasive stages, allowing progression to occur silently over 10-15 years. The intricate interplay between the immune system and tumor cells within the tumor microenvironment plays a pivotal role in the progression of CRC. Immune cell clusters can either inhibit or facilitate tumor initiation, growth, and metastasis. To gain a better understanding of this relationship, we conducted N-glycomic profiling using matrix-assisted laser desorption-ionization mass spectrometry imaging (MALDI-MSI). We detected nearly 100 N-glycan species across all samples, revealing a shift in N-glycome profiles from normal to cancerous tissues, marked by a decrease in high mannose N-glycans. Further analysis of precancerous to invasive carcinomas showed an increase in paucimannose biantennary, and tetraantennary N-glycans with disease progression. Moreover, a distinct stratification in the N-glycome profile was observed between non-mucinous andmucinousCRC tissues, driven by pauci-mannose, highmannose, and bisecting N-glycans. Notably, we identified immune clusters of CD20+ B cells and CD3/CD44+ T cells distinctive and predictive with signature profiles of bisecting and branched N-glycans. These spatial N-glycan profiles offer potential biomarkers and therapeutic targets throughout the progression of CRC. [ABSTRACT FROM AUTHOR]

There is little information on the differential diagnosis and prognosis of hospitalized patients with hyperbilirubinemia. Here, we hypothesized that hyperbilirubinemia in hospitalized patients is associated with specific diseases and outcomes. This retrospective cohort analysis included patients admitted to the Medical University of South Carolina with a total bilirubin >3 mg/dL from January 9, 2015 to August 25, 2017. Collected clinical data included demographics, primary diagnosis, Charlson Comorbidity Index (CCI), laboratory data, and clinical outcomes. We separated and analyzed the cohort into seven primary diagnostic groups. We identified 1693 patients with a bilirubin level >3 mg/dL. The cohort was 42% female, had an average age of 54, average CCI of 4.8, and average length of stay of 13 days. The causes of hyperbilirubinemia included the following: primary liver disease (868/1693; 51%) with cirrhosis being most common (385/1693; 23%), benign biliary obstruction (252/1693; 15%), hemolytic anemia (149/1693; 9%), malignant biliary obstruction (121/1693; 7%), unknown etiology (108/1693; 6%), primary liver cancer (74/1693; 4%), and metastatic cancer to the liver (57/1693; 3%). Overall, the mortality/discharge to hospice rate in patients with a bilirubin >3 mg/dL was 30%, and was proportional to the severity of hyperbilirubinemia, including when controlling for the underlying severity of illness. Mortality was highest in patients with primary liver disease and malignancy and was lowest in patients with non-cancerous obstruction or hemolytic jaundice. Hyperbilirubinemia in hospitalized patients is most often due to primary liver disease, and identifies patients with a poor prognosis, particularly when caused by primary liver disease or cancer. [ABSTRACT FROM AUTHOR]