key: cord-0919880-ijdk1nnc
authors: Dang, Alexander; Thakker, Ravi; Li, Shuang; Hommel, Erin; Mehta, Hemalkumar B.; Goodwin, James S.
title: Hospitalizations and Mortality From Non–SARS-CoV-2 Causes Among Medicare Beneficiaries at US Hospitals During the SARS-CoV-2 Pandemic
date: 2022-03-09
journal: JAMA Netw Open
DOI: 10.1001/jamanetworkopen.2022.1754
sha: ff0d0f112aa01295620e4e60e674869f8f102c44
doc_id: 919880
cord_uid: ijdk1nnc

IMPORTANCE: The increased hospital mortality rates from non–SARS-CoV-2 causes during the SARS-CoV-2 pandemic are incompletely characterized. OBJECTIVE: To describe changes in mortality rates after hospitalization for non–SARS-CoV-2 conditions during the COVID-19 pandemic and how mortality varies by characteristics of the admission and hospital. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study from January 2019 through September 2021 using 100% of national Medicare claims, including 4626 US hospitals. Participants included 8 448 758 individuals with non–COVID-19 medical admissions with fee-for-service Medicare insurance. MAIN OUTCOMES AND MEASURES: Outcome was mortality in the 30 days after admission with adjusted odds generated from a 3-level (admission, hospital, and county) logistic regression model that included diagnosis, demographic variables, comorbidities, hospital characteristics, and hospital prevalence of SARS-CoV-2. RESULTS: There were 8 448 758 non–SARS-CoV-2 medical admissions in 2019 and from April 2020 to September 2021 (mean [SD] age, 73.66 [12.88] years; 52.82% women; 821 569 [11.87%] Black, 438 453 [6.34%] Hispanic, 5 351 956 [77.35%] White, and 307 218 [4.44%] categorized as other). Mortality in the 30 days after admission increased from 9.43% in 2019 to 11.48% from April 1, 2020, to March 31, 2021 (odds ratio [OR], 1.20; 95% CI, 1.19-1.21) in multilevel logistic regression analyses including admission and hospital characteristics. The increase in mortality was maintained throughout the first 18 months of the pandemic and varied by race and ethnicity (OR, 1.27; 95% CI, 1.23-1.30 for Black enrollees; OR, 1.25; 95% CI, 1.23-1.27 for Hispanic enrollees; and OR, 1.18; 95% CI, 1.17-1.19 for White enrollees); Medicaid eligibility (OR, 1.25; 95% CI, 1.24-1.27 for Medicaid eligible vs OR, 1.18; 95% CI, 1.16-1.18 for noneligible); and hospital quality score, measured on a scale of 1 to 5 stars with 1 being the worst and 5 being the best (OR, 1.27; 95% CI, 1.22-1.31 for 1 star vs OR, 1.11; 95% CI, 1.08-1.15 for 5 stars). Greater hospital prevalence of SARS-CoV-2 was associated with greater increases in odds of death from the prepandemic period to the pandemic period; for example, comparing mortality in October through December 2020 with October through December 2019, the OR was 1.44 (95% CI, 1.39-1.49) for hospitals in the top quartile of SARS-CoV-2 admissions vs an OR of 1.19 (95% CI, 1.16-1.22) for admissions to hospitals in the lowest quartile. This association was mostly limited to admissions with high-severity diagnoses. CONCLUSIONS AND RELEVANCE: The prolonged elevation in mortality rates after hospital admission in 2020 and 2021 for non–SARS-CoV-2 diagnoses contrasts with reports of improvement in hospital mortality during 2020 for SARS-CoV-2. The results of this cohort study suggest that, with the continued impact of SARS-CoV-2, it is important to implement interventions to improve access to high-quality hospital care for those with non–SARS-CoV-2 diseases.

Both methods have similar estimation and fit statistics. However, RSPL takes about 1 hour per model, while LAPLACE requires approximately 16 hours per model. We chose RSPL for the analysis.

We performed the same main three-level logistic regression analyses for different cohorts, including the cohort in 2019 and April 2020 to March 2021 in Table 1 , and the 6 quarterly cohorts in Table 2 . All models included the same fixed effects and the same random effects.

We further included interaction terms between time period and patient/hospital characteristics into the models. For Table 3 , we added an interaction term for each variable into the model for Table 1 . We checked the F statistics for the type 3 test of fixed effects for the interaction term.

If the p-value was <0.05, we considered that interaction term significant. For significant interaction terms, we further performed stratified analyses by stratifying the cohort into subcohorts by that stratification variable. We then re-performed the same main model and reported the odd ratios (95% CI) for each sub-cohort in Table 3 . For Table 4 , we stratified the 6 quarterly cohorts into sub-cohorts by hospital SARS-CoV-2 prevalence during that quarter.

Hospital SARS-CoV-2 prevalence was categorized by quartiles as a hospital-level variable.

However, if >25% of hospitals in a time period had prevalence, we categorized the prevalence into 3 groups. We performed the three-level logistic regression model for the sub-cohorts (high prevalence vs. low prevalence) hospitals for each quarterly cohort separately and reported odd ratios (95% CI) in Table 4 .

The SAS code for the main analysis is below (Table 1) . We initially tried the quad method (QPOINTS=X). We abandoned this method because of insufficient memory capacity. We then compared the LAPLACE method and the default RSPL method; both introduced similar estimations. However, the LAPLACE approximation takes about 16 hours computing time and the RSPL takes about 1 hour, so we choose the RSPL technique for our analysis.

For the interaction term analysis in Table 3 , we added one interaction term at a time into the main model. For example, for the interaction term of Medicaid and time in Table 3 , the SAS command is below.

F statistic was significant, we performed stratified analysis for the sub-cohorts by each characteristics.

The models for Table 2 are the same as the main model in Table 1 . The only difference was the cohort. Table 1 used the cohort of year 2019 and April 2020 to March 2021. Table 2 Table 4 used the same 6 quarterly cohorts as in Table 2 . We further stratified each quarterly cohort by the hospital SARS-CoV-2 prevalence in that pandemic quarter. We then performed the same model as that used in Table 1 for the sub-cohorts with high prevalence and low prevalence (12 models total). The odds ratios between 2020/2021 and 2019 were obtained for each sub-cohort. Since the model was the same, the SAS code is not included here again. 15%) a For analyses exploring interactions between admission characteristics and hospital prevalence of SARS-CoV-2, (Tables e5-e10), we substituted a "disease severity" measure for the 20 individual admission diagnoses. In order to generate a measure of severity for the 20 admission diagnoses, we calculated the unadjusted and adjusted mortality in the 30 days post admission using 2018 fee for service Medicare data. In the adjusted analyses we controlled for all the admission characteristics used in the analyses in Table 1 including the comorbidities listed in Table e3 . Thus, the mortality rate associated with each diagnosis is independent of other admission characteristics. CI: confidence interval; UTI: urinary tract infection; COPD: Chronic obstructive pulmonary disease; CHF: congestive heart failure; AMI: acute myocardial Infarction. Given the very large sample size, all differences in the characteristics in 4/1/20-3/3121 were statistically significant compared to those in 2019 at P < 0.05 level. Also, all differences in mortality rates between categories of characteristics were statistically significant at P<0.05. Table 1 presented in Tables e5-e10 , we first tested for interactions between admission or hospital characteristics and the percentages of SARS-CoV-2 admissions at each hospital (prevalence of SARS-CoV-2). Tables 5-10 present the stratified analyses based on those interactions. Each table presents analyses from a three-month period, because SARS-CoV-2 prevalence in hospitals changed over time. If an admission or hospital characteristic showed a significant interaction with hospital SARS-CoV-2 prevalence in any time period, then it was included in the stratified analyses for all the time periods. . OR, odds ratio; CI, confidence interval. 

2021: N=5,755,309 (100%) Step 2. Keep the patients who have part A and no HMO during hospitalization

01%) Step 5. Keep those with complete data for main analysis

Odds of mortality in the 30 days after hospital admission from July through September of 2021 by quartile of prevalence of SARS-CoV-2 in the hospital

1.10) 1.09 (1.02, 1.17) 1.27 (1.17, 1.39) 81-85

Unadjusted rates and adjusted odds of mortality after hospitalization for non-SARS-CoV-2 medical admissions in 2020 vs. 2019, using different methods to exclude SARS-CoV-2 cases

Excluding hospitalizations with a SARS-CoV-2 admission diagnosis or the first two discharge diagnoses. (Method used in our analyses) 11.44% vs. 9

Excluding hospitalizations with a SARS-CoV-2 admission diagnosis or any diagnosis of SARS-CoV-2 in any position in the discharge diagnoses

Exclusions in #2 and #3 plus any SARS-CoV-2 diagnosis in the 30 days after hospital discharge. 11.18% vs 9