Comparative mRNA booster effectiveness against death or hospitalization with COVID-19 pneumonia across at-risk US Veteran populations.

Studies of comparative mRNA booster effectiveness among high-risk populations can inform mRNA booster-specific guidelines. The study emulated a target trial of COVID-19 vaccinated U.S. Veterans who received three doses of either mRNA-1273 or BNT162b2 vaccines. Participants were followed for up to 32 weeks between July 1, 2021 to May 30, 2022. Non-overlapping populations were average and high risk; high-risk sub-groups were age ≥65 years, high-risk co-morbid conditions, and immunocompromising conditions. Of 1,703,189 participants, 10.9 per 10,000 persons died or were hospitalized with COVID-19 pneumonia over 32 weeks (95% CI: 10.2, 11.8). Although relative risks of death or hospitalization with COVID-19 pneumonia were similar across at-risk groups, absolute risk varied when comparing three doses of BNT162b2 with mRNA-1273 (BNT162b2 minus mRNA-1273) between average-risk and high-risk populations, confirmed by the presence of additive interaction. The risk difference of death or hospitalization with COVID-19 pneumonia for high-risk populations was 2.2 (0.9, 3.6). Effects were not modified by predominant viral variant. In this work, the risk of death or hospitalization with COVID-19 pneumonia over 32 weeks was lower among high-risk populations who received three doses of mRNA-1273 vaccine instead of BNT162b2 vaccine; no difference was found among the average-risk population and age >65 sub-group.

Comparing mental and physical health of U.S. veterans by VA healthcare use: implications for generalizability of research in the VA electronic health records.

OBJECTIVE: The Department of Veterans Affairs' (VA) electronic health records (EHR) offer a rich source of big data to study medical and health care questions, but patient eligibility and preferences may limit generalizability of findings. We therefore examined the representativeness of VA veterans by comparing veterans using VA healthcare services to those who do not. METHODS: We analyzed data on 3051 veteran participants age ≥ 18 years in the 2019 National Health Interview Survey. Weighted logistic regression was used to model participant characteristics, health conditions, pain, and self-reported health by past year VA healthcare use and generate predicted marginal prevalences, which were used to calculate Cohen's d of group differences in absolute risk by past-year VA healthcare use. RESULTS: Among veterans, 30.4% had past-year VA healthcare use. Veterans with lower income and members of racial/ethnic minority groups were more likely to report past-year VA healthcare use. Health conditions overrepresented in past-year VA healthcare users included chronic medical conditions (80.6% vs. 69.4%, d = 0.36), pain (78.9% vs. 65.9%; d = 0.35), mental distress (11.6% vs. 5.9%; d = 0.47), anxiety (10.8% vs. 4.1%; d = 0.67), and fair/poor self-reported health (27.9% vs. 18.0%; d = 0.40). CONCLUSIONS: Heterogeneity in veteran sociodemographic and health characteristics was observed by past-year VA healthcare use. Researchers working with VA EHR data should consider how the patient selection process may relate to the exposures and outcomes under study. Statistical reweighting may be needed to generalize risk estimates from the VA EHR data to the overall veteran population.

Incidence of Severe COVID-19 Illness Following Vaccination and Booster With BNT162b2, mRNA-1273, and Ad26.COV2.S Vaccines.

Importance: Evidence describing the incidence of severe COVID-19 illness following vaccination and booster with BNT162b2, mRNA-1273, and Ad26.COV2.S vaccines is needed, particularly for high-risk populations. Objective: To describe the incidence of severe COVID-19 illness among a cohort that received vaccination plus a booster vaccine dose. Design, Setting, and Participants: Retrospective cohort study of adults receiving care at Veterans Health Administration facilities across the US who received a vaccination series plus 1 booster against SARS-CoV-2, conducted from July 1, 2021, to May 30, 2022. Patients were eligible if they had received a primary care visit in the prior 2 years and had documented receipt of all US Food and Drug Administration-authorized doses of the initial mRNA vaccine or viral vector vaccination series after December 11, 2020, and a subsequent documented booster dose between July 1, 2021, and April 29, 2022. The analytic cohort consisted of 1 610 719 participants. Exposures: Receipt of any combination of mRNA-1273 (Moderna), BNT162b2 (Pfizer-BioNTech), and Ad26.COV2.S (Janssen/Johnson & Johnson) primary vaccination series and a booster dose. Main Outcomes and Measures: Outcomes were breakthrough COVID-19 (symptomatic infection), hospitalization with COVID-19 pneumonia and/or death, and hospitalization with severe COVID-19 pneumonia and/or death. A subgroup analysis of nonoverlapping populations included those aged 65 years or older, those with high-risk comorbid conditions, and those with immunocompromising conditions. Results: Of 1 610 719 participants, 1 100 280 (68.4%) were aged 65 years or older and 132 243 (8.2%) were female; 1 133 785 (70.4%) had high-risk comorbid conditions, 155 995 (9.6%) had immunocompromising conditions, and 1 467 879 (91.1%) received the same type of mRNA vaccine (initial series and booster). Over 24 weeks, 125.0 (95% CI, 123.3-126.8) per 10 000 persons had breakthrough COVID-19, 8.9 (95% CI, 8.5-9.4) per 10 000 persons were hospitalized with COVID-19 pneumonia or died, and 3.4 (95% CI, 3.1-3.7) per 10 000 persons were hospitalized with severe pneumonia or died. For high-risk populations, incidence of hospitalization with COVID-19 pneumonia or death was as follows: aged 65 years or older, 1.9 (95% CI, 1.4-2.6) per 10 000 persons; high-risk comorbid conditions, 6.7 (95% CI, 6.2-7.2) per 10 000 persons; and immunocompromising conditions, 39.6 (95% CI, 36.6-42.9) per 10 000 persons. Subgroup analyses of patients hospitalized with COVID-19 pneumonia or death by time after booster demonstrated similar incidence estimates among those aged 65 years or older and with high-risk comorbid conditions but not among those with immunocompromising conditions. Conclusions and Relevance: In a US cohort of patients receiving care at Veterans Health Administration facilities during a period of Delta and Omicron variant predominance, there was a low incidence of hospitalization with COVID-19 pneumonia or death following vaccination and booster with any of BNT162b2, mRNA-1273, or Ad26.COV2.S vaccines.

Substance use disorders and COVID-19: An analysis of nation-wide Veterans Health Administration electronic health records.

BACKGROUND: Substance use disorders (SUD) elevate the risk for COVID-19 hospitalization, but studies are inconsistent on the relationship of SUD to COVID-19 mortality. METHODS: Veterans Health Administration (VHA) patients treated in 2019 and evaluated in 2020 for COVID-19 (n=5,556,315), of whom 62,303 (1.1%) tested positive for COVID-19 (COVID-19+). Outcomes were COVID-19+ by 11/01/20, hospitalization, ICU admission, or death within 60 days of a positive test. Main predictors were any ICD-10-CM SUDs, with substance-specific SUDs (cannabis, cocaine, opioid, stimulant, sedative) explored individually. Logistic regression produced unadjusted and covariate-adjusted odds ratios (OR; aOR). RESULTS: Among COVID-19+ patients, 19.25% were hospitalized, 7.71% admitted to ICU, and 5.84% died. In unadjusted models, any SUD and all substance-specific SUDs except cannabis use disorder were associated with COVID-19+(ORs=1.06-1.85); adjusted models produced similar results. Any SUD and all substance-specific SUDs were associated with hospitalization (aORs: 1.24-1.91). Any SUD, cocaine and opioid disorder were associated with ICU admission in unadjusted but not adjusted models. Any SUD, cannabis, cocaine, and stimulant disorders were inversely associated with mortality in unadjusted models (OR=0.27-0.46). After adjustment, associations with mortality were no longer significant. In ad hoc analyses, adjusted odds of mortality were lower among the 49.9% of COVID-19+ patients with SUD who had SUD treatment in 2019, but not among those without such treatment. CONCLUSIONS: In VHA patients, SUDs are associated with COVID-19 hospitalization but not COVID-19 mortality. SUD treatment may provide closer monitoring of care, ensuring that these patients received needed medical attention, enabling them to ultimately survive serious illness.

Self-reported Changes in Cannabis Use Due to the COVID-19 Pandemic among US Adults.

Cannabis use may confer high COVID-19 risk. This study examined self-reported changes in cannabis use that US adults attributed to the pandemic and factors associated with any changes. We conducted a national, cross-sectional survey among US adults in August 2020. The analytic sample included 957 past-year cannabis users (Mage = 43 years old; 51% male). Weighted multinomial regression examined associations between forms and reasons of cannabis used, perceived addictiveness and safety, co-use of cannabis with tobacco/alcohol, state legalization, and the outcome (self-reported increase/decrease in cannabis use vs. no change). Overall, 14.8% reported decreasing cannabis use due to the pandemic, 16.1% reported increasing, and 65.4% reported not changing. Factors associated with increased cannabis use included past-year use of vaporized (AOR = 1.7, 95% CI = 1.0, 3.0) or edible cannabis (AOR = 2.4, CI = 1.3, 4.3), and simultaneous use of cannabis and tobacco (AOR = 2.6; CI = 1.4, 5.2). Young adults (18-29 years old) had higher odds of self-reporting both increased (AOR = 4.8; CI = 1.8, 13.1) and decreased use (AOR = 3.3; CI = 1.5, 7.5). The pandemic has had a mixed impact on cannabis use, with participants reporting both increased and decreased use. Efforts may target users of vaporized and edible cannabis, co-users of cannabis and tobacco, and young adults to prevent increased cannabis use during the pandemic.

Digital Health Skillsets and Digital Preparedness: Comparison of Veterans Health Administration Users and Other Veterans Nationally.

BACKGROUND: As health care systems shift to greater use of telemedicine and digital tools, an individual's digital health literacy has become an important skillset. The Veterans Health Administration (VA) has invested resources in providing digital health care; however, to date, no study has compared the digital health skills and preparedness of veterans receiving care in the VA to veterans receiving care outside the VA. OBJECTIVE: The goal of the research was to describe digital health skills and preparedness among veterans who receive care within and outside the VA health care system and examine whether receiving care in the VA is associated with digital preparedness (reporting more than 2 digital health skills) after accounting for demographic and social risk factors. METHODS: We used cross-sectional data from the 2016-2018 National Health Interview Survey to identify veterans (aged over 18 years) who obtain health care either within or outside the VA health care system. We used multivariable logistic regression models to examine the association of sociodemographic (age, sex, race, ethnicity), social risk factors (economic instability, disadvantaged neighborhood, low educational attainment, and social isolation), and health care delivery location (VA and non-VA) with digital preparedness. RESULTS: Those who received health care within the VA health care system (n=3188) were younger (age 18-49 years: 33.3% [95% CI 30.7-36.0] vs 24.2% [95% CI 21.9-26.5], P<.01), were more often female (34.7% [95% CI 32.0-37.3] vs 6.6% [95% CI 5.5-7.6], P<.01) and identified as Black (13.1% [95% CI 11.2-15.0] vs 10.2% [95% CI 8.7-11.8], P<.01), and reported greater economic instability (8.3% [95% CI 6.9-9.8] vs 5.5% [95% CI 4.6-6.5], P<.01) and social isolation (42.6% [95% CI 40.3-44.9] vs 35.4% [95% CI 33.4-37.5], P<.01) compared to veterans who received care outside the VA (n=3393). Veterans who obtained care within the VA reported more digital health skills than those who obtained care outside the VA, endorsing greater rates of looking up health information on the internet (51.8% [95% CI 49.2-54.4] vs 45.0% [95% CI 42.6-47.3], P<.01), filling a prescription using the internet (16.2% [95% CI 14.5-18.0] vs 11.3% [95% CI 9.6-13.0], P<.01), scheduling a health care appointment on the internet (14.1% [95% CI 12.4-15.8] vs 11.6% [95% CI 10.1-13.1], P=.02), and communicating with a health care provider by email (18.0% [95% CI 16.1-19.8] vs 13.3% [95% CI 11.6-14.9], P<.01). Following adjustment for sociodemographic and social risk factors, receiving health care from the VA was the only characteristic associated with higher odds (adjusted odds ratio [aOR] 1.36, 95% CI 1.12-1.65) of being digitally prepared. CONCLUSIONS: Despite these demographic disadvantages to digital uptake, veterans who receive care in the VA reported more digital health skills and appear more digitally prepared than veterans who do not receive care within the VA, suggesting a positive, system-level influence on this cohort.

Factors Associated With Hospitalization and Death After COVID-19 Diagnosis Among Patients With Rheumatic Disease: An Analysis of Veterans Affairs Data.

OBJECTIVE: Individuals with autoimmune rheumatic disease (RD) are considered to be at increased risk for infection. However, few US population-based studies have assessed whether these patients are at increased risk of hospitalization or death due to COVID-19 compared with those without RD. METHODS: We performed a retrospective cohort study using national Veterans Affairs Health Care System data for individuals who tested positive for SARS-CoV-2. Outcomes of interest were hospitalization or death due to any cause within 30 days of COVID-19 diagnosis. Outcomes were compared among veterans with RD and those without RD by using propensity score matching (PSM) and mixed-effects multivariate logistic regression. RESULTS: Of 26,116 veterans with COVID-19, 501 (1.9%) had an underlying RD. Prior to matching, patients with RD were more likely to have poor outcomes compared with controls (37.7% vs. 28.5% hospitalized; 6.4% vs. 4.5% died). In the PSM analysis, RD was not a significant predictor for poor outcomes; however, patients with prescriptions for glucocorticoids had increased odds of poor outcomes in a dose-dependent manner (odds ratio [95% confidence interval] for hospitalization or death: 1.33 [1.20-1.48] for doses >0 and ≤10 mg/day; 1.29 [1.09-1.52] for doses >10 mg/day). CONCLUSION: Among US veterans with COVID-19, we did not find a significant association between RD and hospitalization or death. Poor outcomes appear to be mostly driven by age and other comorbidities, similar to the general veteran population. However, we observed an increased risk for poor outcomes among patients who received glucocorticoids, even at daily doses less than or equal to 10 mg.

Association of Social and Behavioral Risk Factors With Mortality Among US Veterans With COVID-19.

Importance: The US Department of Veterans Affairs (VA) offers programs that reduce barriers to care for veterans and those with housing instability, poverty, and substance use disorder. In this setting, however, the role that social and behavioral risk factors play in COVID-19 outcomes is unclear. Objective: To examine whether social and behavioral risk factors were associated with mortality among US veterans with COVID-19 and whether this association might be modified by race/ethnicity. Design, Setting, and Participants: This cohort study obtained data from the VA Corporate Data Warehouse to form a cohort of veterans who received a positive COVID-19 test result between March 2 and September 30, 2020, in a VA health care facility. All veterans who met the inclusion criteria were eligible to participate in the study, and participants were followed up for 30 days after the first SARS-CoV-2 or COVID-19 diagnosis. The final follow-up date was October 31, 2020. Exposures: Social risk factors included housing problems and financial hardship. Behavioral risk factors included current tobacco use, alcohol use, and substance use. Main Outcomes and Measures: The primary outcome was all-cause mortality in the 30-day period after the SARS-CoV-2 or COVID-19 diagnosis date. Multivariable logistic regression was used to estimate odds ratios, clustering for health care facilities and adjusting for age, sex, race, ethnicity, marital status, clinical factors, and month of COVID-19 diagnosis. Results: Among 27 640 veterans with COVID-19 who were included in the analysis, 24 496 were men (88.6%) and the mean (SD) age was 57.2 (16.6) years. A total of 3090 veterans (11.2%) had housing problems, 4450 (16.1%) had financial hardship, 5358 (19.4%) used alcohol, and 3569 (12.9%) reported substance use. Hospitalization occurred in 7663 veterans (27.7%), and 1230 veterans (4.5%) died. Housing problems (adjusted odds ratio [AOR], 0.96; 95% CI, 0.77-1.19; P = .70), financial hardship (AOR, 1.13; 95% CI, 0.97-1.31; P = .11), alcohol use (AOR, 0.82; 95% CI, 0.68-1.01; P = .06), current tobacco use (AOR, 0.85; 95% CI, 0.68-1.06; P = .14), and substance use (AOR, 0.90; 95% CI, 0.71-1.15; P = .41) were not associated with higher mortality. Interaction analyses by race/ethnicity did not find associations between mortality and social and behavioral risk factors. Conclusions and Relevance: Results of this study showed that, in an integrated health system such as the VA, social and behavioral risk factors were not associated with mortality from COVID-19. Further research is needed to substantiate the potential of an integrated health system to be a model of support services for households with COVID-19 and populations who are at risk for the disease.

Heterogeneity in COVID-19 patient volume, characteristics and outcomes across US Department of Veterans Affairs facilities: an observational cohort study.

OBJECTIVE: Studies describe COVID-19 patient characteristics and outcomes across populations, but reports of variation across healthcare facilities are lacking. The objectives were to examine differences in COVID-19 patient volume and mortality across facilities, and understand whether facility variation in mortality was due primarily to differences in patient versus facility characteristics. DESIGN: Observational cohort study with multilevel mixed effects logistic regression modelling. SETTING: The Veterans Health Administration (VA) is the largest healthcare system in the USA. PARTICIPANTS: Patients with COVID-19. MAIN OUTCOME: All-cause mortality within 45 days after COVID-19 testing (March-May, follow-up through 16 July 2020). RESULTS: Among 13 510 patients with COVID-19, 3942 (29.2%) were admitted (2266/3942 (57.5%) ward; 1676/3942 (42.5%) intensive care unit (ICU)) and 679/3942 (17.2%) received mechanical ventilation. Marked heterogeneity was observed across facilities in median age (range: 34.3-83.9 years; facility mean: 64.7, SD 7.2 years); patient volume (range: 1-737 at 160 facilities; facility median: 48.5, IQR 14-105.5); hospital admissions (range: 1-286 at 133 facilities; facility median: 11, IQR 1-26.5); ICU caseload (range: 1-85 at 115 facilities; facility median: 4, IQR 0-12); and mechanical ventilation (range: 1-53 at 90 facilities; facility median: 1, IQR 0-5). Heterogeneity was also observed in facility mortality for all patients with COVID-19 (range: 0%-29.7%; facility median: 8.9%, IQR 2.4%-13.7%); inpatients (range: 0%-100%; facility median: 18.0%, IQR 5.6%-28.6%); ICU patients (range: 0%-100%; facility median: 28.6%, IQR 14.3%-50.0%); and mechanical ventilator patients (range: 0%-100%; facility median: 52.7%, IQR 33.3%-80.6%). The majority of variation in facility mortality was attributable to differences in patient characteristics (eg, age). CONCLUSIONS: Marked heterogeneity in COVID-19 patient volume, characteristics and mortality were observed across VA facilities nationwide. Differences in patient characteristics accounted for the majority of explained variation in mortality across sites. Variation in unadjusted COVID-19 mortality across facilities or nations should be considered with caution.

Association of Intensive Care Unit Patient Load and Demand With Mortality Rates in US Department of Veterans Affairs Hospitals During the COVID-19 Pandemic.

Importance: Although strain on hospital capacity has been associated with increased mortality in nonpandemic settings, studies are needed to examine the association between coronavirus disease 2019 (COVID-19) critical care capacity and mortality. Objective: To examine whether COVID-19 mortality was associated with COVID-19 intensive care unit (ICU) strain. Design, Setting, and Participants: This cohort study was conducted among veterans with COVID-19, as confirmed by polymerase chain reaction or antigen testing in the laboratory from March through August 2020, cared for at any Department of Veterans Affairs (VA) hospital with 10 or more patients with COVID-19 in the ICU. The follow-up period was through November 2020. Data were analyzed from March to November 2020. Exposures: Receiving treatment for COVID-19 in the ICU during a period of increased COVID-19 ICU load, with load defined as mean number of patients with COVID-19 in the ICU during the patient's hospital stay divided by the number of ICU beds at that facility, or increased COVID-19 ICU demand, with demand defined as mean number of patients with COVID-19 in the ICU during the patient's stay divided by the maximum number of patients with COVID-19 in the ICU. Main Outcomes and Measures: All-cause mortality was recorded through 30 days after discharge from the hospital. Results: Among 8516 patients with COVID-19 admitted to 88 VA hospitals, 8014 (94.1%) were men and mean (SD) age was 67.9 (14.2) years. Mortality varied over time, with 218 of 954 patients (22.9%) dying in March, 399 of 1594 patients (25.0%) dying in April, 143 of 920 patients (15.5%) dying in May, 179 of 1314 patients (13.6%) dying in June, 297 of 2373 patients (12.5%) dying in July, and 174 of 1361 (12.8%) patients dying in August (P < .001). Patients with COVID-19 who were treated in the ICU during periods of increased COVID-19 ICU demand had increased risk of mortality compared with patients treated during periods of low COVID-19 ICU demand (ie, demand of ≤25%); the adjusted hazard ratio for all-cause mortality was 0.99 (95% CI, 0.81-1.22; P = .93) for patients treated when COVID-19 ICU demand was more than 25% to 50%, 1.19 (95% CI, 0.95-1.48; P = .13) when COVID-19 ICU demand was more than 50% to 75%, and 1.94 (95% CI, 1.46-2.59; P < .001) when COVID-19 ICU demand was more than 75% to 100%. No association between COVID-19 ICU demand and mortality was observed for patients with COVID-19 not in the ICU. The association between COVID-19 ICU load and mortality was not consistent over time (ie, early vs late in the pandemic). Conclusions and Relevance: This cohort study found that although facilities augmented ICU capacity during the pandemic, strains on critical care capacity were associated with increased COVID-19 ICU mortality. Tracking COVID-19 ICU demand may be useful to hospital administrators and health officials as they coordinate COVID-19 admissions across hospitals to optimize outcomes for patients with this illness.