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COVID-19 and influenza are important sources of morbidity and mortality among older adults. Understanding how outcomes differ for older adults hospitalized with either infection is important for improving care. We compared outcomes from infection with COVID-19 and influenza among hospitalized older adults.We conducted a retrospective study of 30-day mortality among veterans aged 65+ hospitalized with COVID-19 from March 1, 2020-December 31, 2020 or with influenza A/B from September 1, 2017 to August 31, 2019 in Veterans Affairs Health Care System (VAHCS). COVID-19 infection was determined by a positive PCR test and influenza by tests used in the VA system. Frailty was defined by the claims-based Veterans Affairs Frailty Index (VA-FI). Logistic regressions of mortality on frailty, age, and infection were adjusted for multiple confounders.A total of 15,474 veterans were admitted with COVID-19 and 7867 with influenza. Mean (SD) ages were 76.1 (7.8) and 75.8 (8.3) years, 97.7% and 97.4% were male, and 66.9% and 76.4% were white in the COVID-19 and influenza cohorts respectively. Crude 30-day mortality (95% CI) was 18.9% (18.3%-19.5%) for COVID-19 and 4.3% (3.8%-4.7%) for influenza. Combining cohorts, the odds ratio for 30-day mortality from COVID-19 (versus influenza) was 6.61 (5.74-7.65). There was a statistically significant interaction between infection with COVID-19 and frailty, but there was no significant interaction between COVID-19 and age. Separating cohorts, greater 30-day mortality was significantly associated with older age (p: COVID-19:0.001, Influenza:0.001) and for frail compared with robust individuals (p for trend: COVID-19:0.001, Influenza:0.001).Mortality from COVID-19 exceeded that from influenza among hospitalized older adults. However, odds of mortality were higher at every level of frailty among those admitted with influenza compared to COVID-19. Prevention will remain key to reducing mortality from viral illnesses among older adults.

Background Electronic frailty indices (eFIs) are increasingly used to identify patients at risk for morbidity and mortality. Whether eFIs capture the spectrum of frailty change, including decline, stability, and improvement, is unknown. Methods In a nationwide retrospective birth cohort of U.S. Veterans, a validated eFI, including 31 health deficits, was calculated annually using medical record and insurance claims data (2002–2012). K-means clustering was used to assign patients into frailty trajectories measured 5 years prior to death. Results There were 214 250 veterans born between 1927 and 1934 (mean [SD] age at death = 79.4 [2.8] years, 99.2% male, 90.3% White) with an annual eFI in the 5 years before death. Nine frailty trajectories were identified. Those starting at nonfrail or prefrail had 2 stable trajectories (nonfrail to prefrail, n = 29 786 and stable prefrail, n = 28 499) and 2 rapidly increasing trajectories (prefrail to moderately frail, n = 28 244 and prefrail to severely frail, n = 22 596). Those who were mildly frail at baseline included 1 gradually increasing trajectory (mildly to moderately frail, n = 33 806) and 1 rapidly increasing trajectory (mildly to severely frail, n = 15 253). Trajectories that started at moderately or severely frail included 2 gradually increasing trajectories (moderately to severely frail, n = 27 662 and progressing severely frail, n = 14 478) and 1 recovering trajectory (moderately frail to mildly frail, n = 13 926). Conclusions Nine frailty trajectories, including 1 recovering trajectory, were identified in this cohort of older U.S. Veterans. Future work is needed to understand whether prevention and treatment strategies can improve frailty trajectories and contribute to compression of morbidity toward the end of life.

Background: Frailty is associated with a higher risk for adverse outcomes after aortic valve replacement (AVR) for severe aortic valve stenosis, but whether or not frail patients derive differential benefit from transcatheter (TAVR) versus surgical (SAVR) AVR is uncertain. Methods: We linked adults ≥65 years old in the US CoreValve HiR trial (High-Risk) or SURTAVI trial (Surgical or Transcatheter Aortic-Valve Replacement in Intermediate-Risk Patients) to Medicare claims, February 2, 2011, to September 30, 2015. Two frailty measures, a deficit-based and phenotype-based frailty index (FI), were generated. The treatment effect of TAVR versus SAVR was evaluated within FI tertiles for the primary end point of death and nondeath secondary outcomes, using multivariable Cox regression. Results: Of 1442 (linkage rate =60.0%) individuals included, 741 (51.4%) individuals received TAVR and 701 (48.6%) received SAVR (mean age 81.8±6.1 years, 44.0% female). Although 1-year death rates in the highest FI tertiles (deficit-based FI 36.7% and phenotype-based FI 33.8%) were 2- to 3-fold higher than the lowest tertiles (deficit-based FI 13.4%; hazard ratio, 3.02 [95% CI, 2.26–4.02], P P P >0.05). Results remained consistent across individual trials, frailty definitions, and when considering the nonlinked trial data. Conclusions: Two different frailty indices based on Fried and Rockwood definitions identified individuals at higher risk of death and functional impairment but no differential benefit from TAVR versus SAVR.

Introduction: Heart failure (HF) increases morbidity, mortality, and costs, with complex pathogenic mechanisms. Interleukin-6 (IL-6) is a key cytokine and immune regulator with broad impact on inflammation – a critical component of HF pathophysiology. Circulating levels of IL-6 receptor (IL-6R) determines the activity of the IL-6 signaling pathway. We aim to estimate the causal effect of the IL-6R on HF and two clinical subtypes: HF with reduced (HFrEF) and preserved ejection fraction (HFpEF) in a Mendelian Randomization (MR) framework including mediators. Hypothesis: Plausible causal associations between IL-6R and HF, HFrEF and HFpEF are mediated by body mass index (BMI) or coronary artery disease (CAD). Methods: Two-sample MR analyses were conducted using genetic association results for composite HF, HFrEF and HFpEF among >300,000 MVP participants of European ancestry. Thirty-four independent genetic variants within 250 kb from the IL6R gene associated with plasma IL-6R levels were used as genetic instrumental variables. We estimated the direct effect of IL-6R levels on HF and subtypes, and the indirect effect mediated through BMI and CAD. Results: Causal effects of IL-6R on composite HF, HFrEF and HFpEF were estimated as odds ratios (OR): 0.97 (CI 0.96-0.99, p=0.02), 0.96 (CI 0.93-0.98, p=0.003) and 1.00 (CI 0.97-1.03, p=0.89), respectively. Mediation analyses for composite HF and HFrEF showed significant mediation effects by CAD, with ORs 0.98 (CI 0.97-0.99) and 0.98 (CI 0.96-1.00), respectively. No mediation effect of BMI was identified. Conclusions: These findings support a causal role of IL-6 signaling in composite HF and HFrEF, but not HFpEF. Although the IL-6 signaling pathway plays a role in both BMI and CAD, the effect of IL-6 signaling on composite HF and HFrEF may be mediated through by CAD. Our findings demonstrate the role of IL-6 signaling in HF and its difference between HF subtypes, which may lead to more specific therapeutic targets for HF.

Background In aortic valve disease, the relationship between claims‐based frailty indices (CFIs) and validated measures of frailty constructed from in‐person assessments is unclear but may be relevant for retrospective ascertainment of frailty status when otherwise unmeasured. Methods and Results We linked adults aged ≥65 years in the US CoreValve Studies (linkage rate, 67%; mean age, 82.7±6.2 years, 43.1% women), to Medicare inpatient claims, 2011 to 2015. The Johns Hopkins CFI, validated on the basis of the Fried index, was generated for each study participant, and the association between CFI tertile and trial outcomes was evaluated as part of the EXTEND‐FRAILTY substudy. Among 2357 participants (64.9% frail), higher CFI tertile was associated with greater impairments in nutrition, disability, cognition, and self‐rated health. The primary outcome of all‐cause mortality at 1 year occurred in 19.3%, 23.1%, and 31.3% of those in tertiles 1 to 3, respectively (tertile 2 versus 1: hazard ratio, 1.22; 95% CI, 0.98–1.51; P =0.07; tertile 3 versus 1: hazard ratio, 1.73; 95% CI, 1.41–2.12; P Conclusions In linked Medicare and CoreValve study data, a CFI based on the Fried index consistently identified individuals with worse impairments in frailty, disability, cognitive dysfunction, and nutrition and a higher risk of death, hospitalization, bleeding, and major adverse cardiovascular and cerebrovascular events, independent of age and risk category. While not a surrogate for validated metrics of frailty using in‐person assessments, use of this CFI to ascertain frailty status among patients with aortic valve disease may be valid and prognostically relevant information when otherwise not measured.

This chapter examines the experience of aspiring teachers in a course on the foundations of learning taught inside a juvenile hall. The author describes what happens when he brings together incarcerated youth and college students across profound social boundaries to study educational inequalities and develop proposals to promote educational justice. Exploring the challenges of teaching toward more justice and more equity, while working with students inside the juvenile justice system, the author describes the potential of the Inside-Out Prison Exchange program to transform preservice teachers as they learn to put ideas about critical pedagogy into practice. The chapter pays particular attention to how the experience of taking a course inside a juvenile hall challenges aspiring teachers to reimagine the connections between communities and schools.

Adult onset T1D is often misdiagnosed as T2D, leading to poor metabolic control and increased risk of microvascular complications and cardiovascular morbidity and mortality. We examined characteristics associated with a T1D genetic risk score in Veterans, a population where new diagnoses are generally presumed to be T2D, since persons with typical juvenile-onset T1D are excluded from military service. A 30 SNP T1D genetic risk score (GRS), previously validated with the Wellcome Trust Case Control Consortium (WTCCC), was applied to 111,663 Veterans with diabetes in the MVP. Patients were stratified by T1D GRS centile groups (≤5th, >5th-≤36th, >36th-≤50th, and >50th) which corresponded to T1D specificity in the WTCCC of 29%, 53%, 90%, and 95%, respectively. Typical T1D clinical characteristics were significantly more common (p Conclusions: The 10% of US Veterans with diabetes in MVP in the two highest T1D GRS centile groups have characteristics suggestive of T1D, although only 5% were tested for GAD antibodies, and most had no use of T1D diagnosis codes. Since adult-onset T1D may be misdiagnosed as T2D in settings where T2D is more common, genetic analyses and/or clinical characteristics may warrant definitive testing and appropriate management. Disclosure P. K. Yang: None. M. K. Rhee: None. C. Mercado: None. R. A. Oram: Consultant; Self; Janssen Research & Development, LLC. M. Vujkovic: None. P. Reaven: Research Support; Self; AstraZeneca, Dexcom, Inc. K. Cho: None. L. S. Phillips: Other Relationship; Self; Diasyst Inc., Research Support; Self; AbbVie Inc., Eli Lilly and Company, GlaxoSmithKline plc., Janssen Pharmaceuticals, Inc., Kowa Pharmaceuticals America, Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk Pharma Ltd., Pfizer Inc., Sanofi-Aventis. A. Leong: None. S. Jackson: None. B. R. Charest: None. M. N. Weedon: None. Y. J. Cheng: None. Y. V. Sun: None. S. Raghavan: None. E. M. Litkowski: None. B. T. Legvold: None. Funding U.S. Department of Veterans Affairs (CSP2008, I01CX001899, I01CX001737); National Institutes of Health (R18DK066204, R21AI156161, U01DK091958, U01DK098246, UL1TR002378)

Electronic frailty indices based on data from administrative claims and electronic health records can be used to estimate frailty in large populations of older adults with cancer where direct frailty measures are lacking. The objective of this study was to use the electronic Veterans Affairs Frailty Index (VA-FI-10)—developed and validated to measure frailty in the national United States (US) VA Healthcare System—to estimate the prevalence and impact of frailty in older US veterans newly treated for multiple myeloma (MM) with contemporary therapies. We designed a retrospective cohort study of 4924 transplant-ineligible veterans aged ≥ 65 years initiating MM therapy within VA from 2004 to 2017. Initial MM therapy was measured using inpatient and outpatient treatment codes from pharmacy data in the VA Corporate Data Warehouse. In total, 3477 veterans (70.6%) were classified as frail (VA-FI-10 &gt, 0.2), with 1510 (30.7%) mildly frail (VA-FI-10 &gt, 0.2–0.3), 1105 (22.4%) moderately frail (VA-FI-10 &gt, 0.3–0.4), and 862 (17.5%) severely frail (VA-FI-10 &gt, 0.4). Survival and time to hospitalization decreased with increasing VA-FI-10 severity (log-rank p-value &lt, 0.001), the VA-FI-10 predicted mortality and hospitalizations independently of age, sociodemographic variables, and measures of disease risk. Varying data sources and assessment periods reclassified frailty severity for a substantial portion of veterans but did not substantially affect VA-FI-10’s association with mortality. Our study supports use of the VA-FI-10 in future research involving older veterans with MM and provides insights into its potential use in identifying frailty in clinical practice.

Background The Veterans Affairs Frailty Index (VA-FI) is an electronic frailty index developed to measure frailty using administrative claims and electronic health records data in Veterans. An update to ICD-10 coding is needed to enable contemporary measurement of frailty. Method International Classification of Diseases, ninth revision (ICD-9) codes from the original VA-FI were mapped to ICD-10 first using the Centers for Medicaid and Medicare Services (CMS) General Equivalence Mappings. The resulting ICD-10 codes were reviewed by 2 geriatricians. Using a national cohort of Veterans aged 65 years and older, the prevalence of deficits contributing to the VA-FI and associations between the VA-FI and mortality over years 2012–2018 were examined. Results The updated VA-FI-10 includes 6422 codes representing 31 health deficits. Annual cohorts defined on October 1 of each year included 2 266 191 to 2 428 115 Veterans, for which the mean age was 76 years, 97%–98% were male, 78%–79% were White, and the mean VA-FI was 0.20–0.22. The VA-FI-10 deficits showed stability before and after the transition to ICD-10 in 2015, and maintained strong associations with mortality. Patients classified as frail (VA-FI > 0.2) consistently had a hazard of death more than 2 times higher than nonfrail patients (VA-FI ≤ 0.1). Distributions of frailty and associations with mortality varied with and without linkage to CMS data and with different assessment periods for capturing deficits. Conclusions The updated VA-FI-10 maintains content validity, stability, and predictive validity for mortality in a contemporary cohort of Veterans aged 65 years and older, and may be applied to ICD-9 and ICD-10 claims data to measure frailty.

Hypoglycemia is a preventable adverse effect of treatment in diabetes patients, but actionable pharmacogenetic evidence to minimize hypoglycemia risk is lacking. We used the MVP biospecimen and clinical biobank to perform a case/control GWAS of hypoglycemia in 96,406 diabetes patients receiving care in the U.S. Veterans Health Administration. Cases had an outpatient random plasma glucose (RPG) 1%. GWAS were conducted in 72,244 participants (22,045 cases) of European ancestry [EUR] and 24,162 (10,441 cases) of African ancestry [AFR]), adjusted for age at MVP enrollment, sex, and 10 genetic principal components. We identified a single locus associated with hypoglycemia in EUR individuals (rs1064173, HLA-DQB1 locus, odds ratio [OR] 1.15, p=5.6E-19, MAF 0.20) and in AFR individuals (rs12712928, SIX3 locus, OR 0.87, p=2.7E-8, MAF 0.17). In a phenomewide association study (PheWAS) in MVP EUR diabetes patients, rs1064173 was associated with type 1 diabetes (T1D, OR 1.41, p=7.8E-25) and diabetes complications (retinopathy, OR 1.24, p=9E-24; neuropathy, OR 1.16, p=1.3E-5). To address the known association of the HLA locus with T1D, we performed a sensitivity analysis excluding patients with any use of T1D diagnosis codes. In this sample enriched for type 2 diabetes, rs1064173 remained nominally associated with hypoglycemia (OR 1.23, p=0.02) and retinopathy (OR 1.10, p=0.003). Rs12712928 - previously associated with glucose and hemoglobin A1c in an East Asian sample - was not associated with any diagnoses in a PheWAS of MVP AFR diabetes patients. In conclusion, a GWAS of hypoglycemia in a clinical biobank found ancestry-specific genetic associations that may have treatment implications. Disclosure S. Raghavan: None. Z. Wang: None. A.M. Hung: None. B.R. Charest: None. L. Costa: None. E.M. Litkowski: None. A. Leong: None. J.B. Meigs: Consultant; Self; Quest Diagnostics. P.W. Wilson: None. P. Reaven: Consultant; Self; Boston Heart Diagnostics, Intercept Pharmaceuticals, Inc. Research Support; Self; Bristol-Myers Squibb, Lysulin. M.K. Rhee: Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc. Y.V. Sun: None. L.S. Phillips: Research Support; Self; AbbVie Inc., Diasome Pharmaceuticals, Inc., Eli Lilly and Company, GlaxoSmithKline plc., Novartis Pharmaceuticals Corporation, Novo Nordisk Inc., Pfizer Inc., Sanofi-Aventis. Other Relationship; Self; Diasyst, Janssen Pharmaceuticals, Inc. Funding U.S. Department of Veterans Affairs (I01-CX001737, IK2-CX001907-01)

This chapter discusses the notions of equity, hope and sense of belonging and their vital roles in enhancing student engagement. The main focus is to present how hope, sense of belonging and engagement can be nurtured by putting equity in the centre of what we do in facilitating student learning. In paying careful attention to these concepts, especially in implementing equity as a transformative solution to various educational challenges, educators are warned to be prepared to recognize student diversity and redistribute resources and services to all students who need them, experience anxiety and discomfort as they engage in difficult conversations, value and honour the unique backgrounds of each student, expect cultural mismatches in working with diverse students, use data consistently to inform and differentiate instruction and expect that the work in promoting equity is an ongoing and long-lasting advocacy.

A1c accuracy is important for diabetes management but mismatches in A1c relative to underlying glucose can impede care. Since the basis for mismatches is poorly understood, we assessed the contributions of 22 A1c variants identified in GWAS that were associated with erythrocytic traits but not glycemia. 73,128 MVP patients had continuity of care and monitoring: >1 PCP visit, >1 outpatient random plasma glucose (RPG) and >1 A1c within each of 3 consecutive two-year periods. They had mean age 67 years and BMI 31 kgm-2; 71% were white and 21% were black; 51% had diabetes (ICD 9 codes and diabetes Rx). Mismatches were defined by the hemoglobin glycation index (HGI) calculated from regression residuals of mean A1c on mean RPG within each period, averaged over the 3 periods. We evaluated the associations with HGI of each A1c variant and a genetic risk score (GRS; sum of alleles weighted by their reported A1c effect sizes) by race. Mean HGI was -0.07% A1c units in whites and 0.2% in blacks. Of the 22 variants, 15 at 13 loci were associated with HGI (P Conclusions: HGI is higher in blacks than whites - a glucose-independent higher A1c. Common genetic variation explains 2-11% of the variance in HGI. Accounting for genetic variation in A1c may improve its accuracy in guiding diabetes care. Disclosure A. Leong: None. D. Posner: None. B.R. Charest: None. J.B. Meigs: None. P.W. Wilson: None. R.M. Cohen: Stock/Shareholder; Self; Bristol-Myers Squibb Company. D.R. Miller: None. Y.V. Sun: None. K. Cho: None. L.S. Phillips: Other Relationship; Self; DIASYST Inc.. Research Support; Self; Amylin Pharmaceuticals, Eli Lilly and Company, Novo Nordisk Inc., Sanofi-Aventis, PhaseBio Pharmaceuticals, Inc., Roche Diabetes Care Health and Digital Solutions, AbbVie Inc., Vascular Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc., GlaxoSmithKline plc., Pfizer Inc.. Other Relationship; Self; Novartis Pharmaceuticals Corporation, Merck & Co., Inc..

Background: While some but not all trial data have suggested a slightly elevated risk of type 2 diabetes (T2D), limited data are available on the relation of statin use with glycemia among US veterans. Objective: To test the hypothesis that treatment with a statin is associated with elevated serum glucose and a higher risk of T2D among US veterans. Methods: We studied 549,143 US veterans with electronic health records from 1998 to 2016. We used the VA Corporate Data Warehouse to obtain information on glucose (fasting and non-fasting). Statin use was captured using the pharmacy database. T2D was defined as having at least one inpatient diagnosis or at least 2 outpatient diagnoses of T2D using ICD 9 codes 250.xx, or the use of hypoglycemic agents. We addressed confounding by indication using propensity score (included 444 variables/interactions) for receiving statin and inverse probability weighting in the Cox regression. Results: The mean age was 60±12.8 y; 94.3% were men; 77.1% were white; and 15.4% were black. In this cohort, simvastatin was the most prescribed statin (84%) followed by lovastatin (7%). During a median follow-up of 4.4 years, 130,819 new cases of T2D occurred. Among 418,847 veterans receiving statin, we observed a 2 mg/dl increase in serum glucose when comparing serum glucose measured at the time of statin initiation and short-term Conclusions: Our data show a positive association between statin use and increase in both serum glucose and incidence of T2D among US veterans. If confirmed by others, this suggests closer monitoring of patients at risk of T2D when statin therapy is initiated.

In this chapter the author argues that those concerned with the “the way it's going” in public education can learn much from post-colonial theory about the relationship between education research and assessment technologies and education reform policy, curriculum development, and knowledge formation. The author argues that current neoliberal education reform in the US can best be understood through the frame of neocolonialism, where schools and communities take the shape of internal colonies, where teachers, students, and parents have little or no say about the technologies, curricula, and standardized examinations foisted upon them. Education research that supports the current policy paradigm largely benefits researchers, corporations, and policy makers, while ignoring the effects of such policies on students, teachers, and local communities. Such practices, the author suggests, are rooted in a type of colonial thinking and acting that have been rearticulated through the prevailing logic of neoliberalism.

Two new admixtures, capable of preventing water from freezing, as well as increasing the hydration rate of cement at below-freezing temperatures, were field tested at Sault Ste. Marie, Michigan. Concrete made with the admixtures was placed on a frozen subgrade during a cold winter day and was allowed to cure thermally, unprotected in the cold. Comparison to control concrete placed inside a heated shelter showed that the unprotected, admixtured concrete was equal to the control in strength and appearance. Work is continuing on the development of these admixtures for commercial use.