Trends over time in the risk of adverse outcomes among patients with SARS-CoV-2 infection

Abstract

Objectives: We aimed to describe trends in the incidence of adverse outcomes among patients who tested positive for SARS-CoV-2 between February and September 2020 within a national healthcare system. Setting: US Veterans Affairs national healthcare system. Participants: Enrollees in the VA healthcare system who tested positive for SARS-CoV-2 between 2/28/2020 and 9/30/2020 (n=55,952). Outcomes: Death, hospitalization, intensive care unit (ICU) admission and mechanical ventilation within 30 days of testing positive. The incidence of these outcomes was examined among patients infected each month and trends were evaluated using an interrupted time-series analysis. Results: Between February and July 2020, during the first wave of the US pandemic, there were marked downward trends in the 30-day incidence of hospitalization (44.2% to 15.8%), ICU admission (20.3% to 5.3%), mechanical ventilation (12.7% to 2.2%), and death (12.5% to 4.4%), with subsequent stabilization between July and September 2020. These trends persisted after adjustment for sociodemographic characteristics, comorbid conditions, and documented symptoms and after additional adjustment for laboratory test results among hospitalized patients, including among subgroups admitted to the ICU and treated with mechanical ventilation. Among hospitalized patients, use of hydroxychloroquine (56.5% to 0%), azithromycin (48.3% to 16.6%) vasopressors (20.6% to 8.7%), and dialysis (11.6% to 3.8%) decreased while use of dexamethasone (3.4% to 53.1%), other corticosteroids (4.9% to 29.0%) and remdesivir (1.7% to 45.4%) increased from February to September. Conclusions: Among patients who tested positive for SARS-CoV-2 in a large national US healthcare system, risk for a range of adverse outcomes decreased markedly between February and July, with subsequent stabilization from July to September. These trends were not explained by changes in measured baseline patient characteristics.

Comparison of Moderna versus Pfizer-BioNTech COVID-19 vaccine outcomes: A target trial emulation study in the U.S. Veterans Affairs healthcare system

“…Entropy balancing of means in all matching characteristics was included as a refinement in the matching process. The characteristics used in the propensity score logistic regression model were selected a priori and were characteristics associated with the likelihood of getting vaccinated by BNT162b2 versus mRNA-1273 (the exposure) and the risk of developing SARS-CoV-2 infection, hospitalization or death (the outcomes) in the VA population, 17 , 18 , 20 , 22 and categorized as shown in Table 1 . These characteristics were: age, sex, self-reported race and ethnicity, urban/rural residence (based on zip codes, using data from the VA Office of Rural Health, 23 which uses the Secondary Rural-Urban Commuting Area [RUCA] for defining rurality), VISN, CCI, body mass index (BMI, calculated using measured weight and height), diabetes, congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD) and the Care Assessment Need (CAN) score.…”

“…SARS-CoV-2-related death was defined as death from any cause within 30 days of a positive test or COVID-19 diagnosis. 17 , 18 , 20 , 22 Deaths occurring both within and outside the VA are comprehensively captured in CDW from a variety of VA and non-VA sources including VA inpatient files, VA Beneficiary Identification and Records Locator System (BIRLS), Social Security Administration (SSA) death files, and the Department of Defense. 25 …”

COVID-19 mortality in Italy varies by patient age, sex and pandemic wave

“…In hospitalized patient, the excess risk of death was 1.4-fold higher in the first wave than later. The excess risk of death associated with pandemic wave was first reported in an Italian study of hospitalized patients [12], and then confirmed by studies of Massachusetts healthcare workers [13], patients of the U.S. Veterans Affairs healthcare system [25], and UK patients [26]. The reasons for this effect could include the initial lack of preparedness of national health systems for pandemic management, the lack of knowledge about the most effective therapies for COVID-19 patients with severe disease, and the possibility that frailer people were more affected at the beginning of the pandemic than the rest of the population.…”

Declining COVID-19 case-fatality in Georgia, USA, March 2020 to March 2021: a sign of real improvement or a broadening epidemic?

“…While previous studies have examined trends in COVID-19 case-fatality in the US, these studies included hospitalized cases only and did not consider changes in the proportion of cases that were LTCF residents [8] , [9] , [10] . In this analysis, we examine the risk of COVID-19 death among reported cases in the state of Georgia.…”