The Social, Demographic, and Clinical Predictors of COVID-19 Severity: a Model-based Analysis of United States Veterans.

PURPOSE: This study aims to identify the contributions of individual and community social determinants of health (SDOH), demographic, and clinical factors in COVID-19 disease severity through a model-based analysis. METHODS: This national cross-sectional study focused on hospitalization among those tested for COVID-19 and use of intensive care, analyzing data on 220,848 Veterans tested between February 20, 2020 and October 20, 2021. Multiple logistic regression models were constructed using backwards elimination. The predictive value of each model was assessed with a c-statistic. RESULTS: Those hospitalized were older, more likely to be male, of Black or Asian race, have an income less than $39,999, live in an urban residence, and have medical comorbidities. The strongest predictors for hospitalization included Gini inequality index, race, income, heart failure, chronic kidney disease (CKD), and chronic obstructive pulmonary disease (COPD). For intensive care, Asian race, rural residence, COPD, and CKD were the strongest predictors. C-statistics were c = 0.749 for hospitalization and c = 0.582 for ICU admission. CONCLUSIONS: A combination of clinical, demographic, individual and community SDOH factors predict COVID-19 hospitalization with good predictive ability and can inform risk stratification, discharge planning, and public health interventions. Racial disparities were not explained by social or clinical factors. Intensive care models had low discriminative power and may be better explained by other characteristics.

Older Patients' Perspectives on Managing Complexity in CKD Self-Management.

BACKGROUND AND OBJECTIVES: Patients with CKD are asked to perform self-management tasks including dietary changes, adhering to medications, avoiding nephrotoxic drugs, and self-monitoring hypertension and diabetes. Given the effect of aging on functional capacity, self-management may be especially challenging for older patients. However, little is known about the specific challenges older adults face maintaining CKD self-management regimens. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We conducted an exploratory qualitative study designed to understand the relationship among factors facilitating or impeding CKD self-management in older adults. Six focus groups (n=30) were held in August and September of 2014 with veterans≥70 years old with moderate-to-severe CKD receiving nephrology care at the Atlanta Veterans Affairs Medical Center. Grounded theory with a constant comparative method was used to collect, code, and analyze data. RESULTS: Participants had a mean age (range) of 75.1 (70.1-90.7) years, 60% were black, and 96.7% were men. The central organizing concept that emerged from these data were managing complexity. Participants typically did not have just one chronic condition, CKD, but a number of commonly co-occurring conditions. Recommendations for CKD self-management therefore occurred within a complex regimen of recommendations for managing other diseases. Participants identified overtly discordant treatment recommendations across chronic conditions (e.g., arthritis and CKD). Prioritization emerged as one effective strategy for managing complexity (e.g., focusing on BP control). Some patients arrived at the conclusion that they could group concordant recommendations to simplify their regimens (e.g., protein restriction for both gout and CKD). CONCLUSIONS: Among older veterans with moderate-to-severe CKD, multimorbidity presents a major challenge for CKD self-management. Because virtually all older adults with CKD have multimorbidity, an integrated treatment approach that supports self-management across commonly occurring conditions may be necessary to meet the needs of these patients.

Increased cardiovascular disease, resource use, and costs before the clinical diagnosis of diabetes in veterans in the southeastern U.S.

IMPORTANCE: Screening for diabetes might be more widespread if adverse associations with cardiovascular disease (CVD), resource use, and costs were known to occur earlier than conventional clinical diagnosis. OBJECTIVE: The purpose of this study was to determine whether adverse effects associated with diabetes begin prior to clinical diagnosis. DESIGN: Veterans with diabetes were matched 1:2 with controls by follow-up, age, race/ethnicity, gender, and VA facility. CVD was obtained from ICD-9 codes, and resource use and costs from VA datasets. SETTING: VA facilities in SC, GA, and AL. PARTICIPANTS: Patients with and without diagnosed diabetes. MAIN OUTCOME MEASURES: Diagnosed CVD, resource use, and costs. RESULTS: In this study, the 2,062 diabetic patients and 4,124 controls were 63 years old on average, 99 % male, and 29 % black; BMI was 30.8 in diabetic patients vs. 27.8 in controls (p<0.001). CVD prevalence was higher and there were more outpatient visits in Year -4 before diagnosis through Year +4 after diagnosis among diabetic vs. control patients (all p<0.01); in Year -2, CVD prevalence was 31 % vs. 24 %, and outpatient visits were 22 vs. 19 per year, respectively. Total VA costs/year/veteran were higher in diabetic than control patients from Year -4 ($4,083 vs. $2,754) through Year +5 ($8,347 vs. $5,700) (p<0.003) for each, reflecting underlying increases in outpatient, inpatient, and pharmacy costs (p<0.05 for each). Regression analysis showed that diabetes contributed an average of $1,748/year to costs, independent of CVD (p<0.001). CONCLUSIONS AND RELEVANCE: VA costs per veteran are higher--over $1,000/year before and $2,000/year after diagnosis of diabetes--due to underlying increases in outpatient, inpatient, and pharmacy costs, greater number of outpatient visits, and increased CVD. Moreover, adverse associations with veterans' health and the VA healthcare system occur early in the natural history of the disease, several years before diabetes is diagnosed. Since adverse associations begin before diabetes is recognized, greater consideration should be given to systematic screening in order to permit earlier detection and initiation of preventive management. Keeping frequency of CVD and marginal costs in line with those of patients before diabetes is currently diagnosed has the potential to save up to $2 billion a year.

Screening for diabetes and prediabetes should be cost-saving in patients at high risk.

OBJECTIVE: Although screening for diabetes and prediabetes is recommended, it is not clear how best or whom to screen. We therefore compared the economics of screening according to baseline risk. RESEARCH DESIGN AND METHODS: Five screening tests were performed in 1,573 adults without known diabetes--random plasma/capillary glucose, plasma/capillary glucose 1 h after 50-g oral glucose (any time, without previous fast, plasma glucose 1 h after a 50-g oral glucose challenge [GCTpl]/capillary glucose 1 h after a 50-g oral glucose challenge [GCTcap]), and A1C--and a definitive 75-g oral glucose tolerance test. Costs of screening included the following: costs of testing (screen plus oral glucose tolerance test, if screen is positive); costs for false-negative results; and costs of treatment of true-positive results with metformin, all over the course of 3 years. We compared costs for no screening, screening everyone for diabetes or high-risk prediabetes, and screening those with risk factors based on age, BMI, blood pressure, waist circumference, lipids, or family history of diabetes. RESULTS: Compared with no screening, cost-savings would be obtained largely from screening those at higher risk, including those with BMI >35 kg/m(2), systolic blood pressure ≥130 mmHg, or age >55 years, with differences of up to -46% of health system costs for screening for diabetes and -21% for screening for dysglycemia110, respectively (all P < 0.01). GCTpl would be the least expensive screening test for most high-risk groups for this population over the course of 3 years. CONCLUSIONS: From a health economics perspective, screening for diabetes and high-risk prediabetes should target patients at higher risk, particularly those with BMI >35 kg/m(2), systolic blood pressure ≥130 mmHg, or age >55 years, for whom screening can be most cost-saving. GCTpl is generally the least expensive test in high-risk groups and should be considered for routine use as an opportunistic screen in these groups.

Screening adults for pre-diabetes and diabetes may be cost-saving.

OBJECTIVE: The economic costs of hyperglycemia are substantial. Early detection would allow management to prevent or delay development of diabetes and diabetes-related complications. We investigated the economic justification for screening for pre-diabetes/diabetes. RESEARCH DESIGN AND METHODS: We projected health system and societal costs over 3 years for 1,259 adults, comparing costs associated with five opportunistic screening tests. All subjects had measurements taken of random plasma and capillary glucose (RPG and RCG), A1C, and plasma and capillary glucose 1 h after a 50 g oral glucose challenge test without prior fasting (GCT-pl and GCT-cap), and a subsequent diagnostic 75 g oral glucose tolerance test (OGTT). RESULTS: Assuming 70% specificity screening cutoffs, Medicare costs for testing, retail costs for generic metformin, and costs for false negatives as 10% of reported costs associated with pre-diabetes/diabetes, health system costs over 3 years for the different screening tests would be GCT-pl $180,635; GCT-cap $182,980; RPG $182,780; RCG $186,090; and A1C $192,261; all lower than costs for no screening, which would be $205,966. Under varying assumptions, projected health system costs for screening and treatment with metformin or lifestyle modification would be less than costs for no screening as long as disease prevalence is at least 70% of that of our population and false-negative costs are at least 10% of disease costs. Societal costs would equal or exceed costs of no screening depending on treatment type. CONCLUSIONS: Screening appears to be cost-saving compared to no screening from a health system perspective, and potentially cost-neutral from a societal perspective. These data suggest that strong consideration should be given to screening-with preventive management-and that use of GCTs may be cost-effective.

Glucose-independent, black-white differences in hemoglobin A1c levels: a cross-sectional analysis of 2 studies.

BACKGROUND: A previous study of participants with prediabetes found that hemoglobin A(1c) (HbA(1c)) levels differed between black and white participants with no differences in glucose concentration. OBJECTIVE: To determine whether black-white differences in HbA(1c) level are present in other populations and across the full spectrum of glycemia. DESIGN: Cross-sectional, retrospective. SETTING: Outpatient. PARTICIPANTS: 1581 non-Hispanic black and white participants between 18 and 87 years of age without known diabetes in the SIGT (Screening for Impaired Glucose Tolerance) study and 1967 non-Hispanic black and white participants older than 40 years without known diabetes in the NHANES III (Third National Health and Nutrition Examination Survey). MEASUREMENTS: HbA(1c) levels, anthropometry, and plasma glucose levels during oral glucose tolerance testing. RESULTS: Hemoglobin A(1c) levels were higher in black than in white participants with normal glucose tolerance (0.13 percentage point [P < 0.001] in the SIGT sample and 0.21 percentage point [P < 0.001] in the NHANES III sample), prediabetes (0.26 percentage point [P < 0.001] and 0.30 percentage point [P < 0.001], respectively), or diabetes (0.47 percentage point [P < 0.020] and 0.47 percentage point [P < 0.013], respectively) after adjustment for plasma glucose levels and other characteristics known to correlate with HbA(1c) levels. LIMITATION: The mechanism for the differences is unknown. CONCLUSION: Black persons have higher HbA(1c) levels than white persons across the full spectrum of glycemia, and the differences increase as glucose intolerance worsens. These findings could limit the use of HbA(1c) to screen for glucose intolerance, indicate the risk for complications, measure quality of care, and evaluate disparities in health.

Limited health care access impairs glycemic control in low income urban African Americans with type 2 diabetes.

Limited access to health care is associated with adverse outcomes, but few studies have examined its effect on glycemic control in minority populations. Our observational cross-sectional study examined whether differences in health care access affected hemoglobin A1c (HbA1c) levels in 605 patients with diabetes (56% women; 89% African American; average age, 50 years; 95% with type 2 diabetes) initially treated at a municipal diabetes clinic. Patients who had difficulty obtaining care had higher A1c levels (9.4% vs. 8.7%; p=0.001), as did patients who used acute care facilities (9.5%; p<0.001) or who had no usual source of care (10.3%; p<0.001) compared with those who sought care at doctors' offices or clinics (8.6%). In adjusted analyses, HbA1c was higher in persons who gave a history of trouble obtaining medical care (0.57%; p=0.04), among persons who primarily used an acute care facility to receive their health care (0.49%; p=0.047), and in patients who reported not having a usual source of care (1.08%; p=0.009). Policy decisions for improving diabetes outcomes should target barriers to health care access and focus on developing programs to help high-risk populations maintain a regular place of health care.

Use of thiazolidinediones and risk of heart failure in people with type 2 diabetes: a retrospective cohort study.

OBJECTIVE: To compare the incidence of heart failure in individuals with type 2 diabetes receiving thiazolidinediones (TZDs) versus other oral antihyperglycemic agents. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study using a health insurance claims database. The study sample included patients with type 2 diabetes who received an oral antihyperglycemic agent between January 1995 and March 2001. Those with any claims for TZDs were designated "exposed," and each was compared with five randomly selected unexposed patients. Those with diagnoses of heart failure or who received digoxin or a diuretic in the year before their index date were excluded. The primary measure of interest was incidence of heart failure, which was defined as a hospitalization or outpatient visit with a diagnosis of heart failure. RESULTS: TZD patients (n = 5441) were younger than control subjects (n = 28103) but more likely to have coronary artery disease or diabetes complications, receive ACE inhibitors, beta-blockers, metformin, or insulin, and have undergone HbA(1c) tests or eye exams; they also had more comorbidities and higher costs (all P < 0.05). However, TZD use was predictive of heart failure even after controlling for these variables (hazard ratio = 1.7, P < 0.001). Adjusted incidence of heart failure at 40 months was 8.2% for TZD patients and 5.3% for control subjects. CONCLUSIONS: The results of this observational study suggest that TZDs may increase the risk of heart failure. Physicians should use TZDs with caution in patients with heart failure, remain vigilant for manifestations of heart failure in those receiving these drugs (especially patients with cardiovasculopathy), and consider alternate therapies for patients who develop symptoms of heart failure, such as shortness of breath.