Abstract
Background
The relationship between common patient characteristics, such as sex and metabolic comorbidities, and mortality from coronavirus disease 2019 (COVID-19) remains incompletely understood. Emerging evidence suggests that metabolic risk factors may also vary by age. This study aimed to determine the association between common patient characteristics and mortality across age-groups among COVID-19 inpatients.
Methods
We performed a retrospective cohort study of patients discharged from hospitals in the Premier Healthcare Database between April–June 2020. Inpatients were identified using COVID-19 ICD-10-CM diagnosis codes. A priori-defined exposures were sex and present-on-admission hypertension, diabetes, obesity, and interactions between age and these comorbidities. Controlling for additional confounders, we evaluated relationships between these variables and in-hospital mortality in a log-binomial model.
Results
Among 66 646 (6.5%) admissions with a COVID-19 diagnosis, across 613 U.S. hospitals, 12 388 (18.6%) died in-hospital. In multivariable analysis, male sex was independently associated with 30% higher mortality risk (aRR, 1.30, 95% CI: 1.26–1.34). Diabetes without chronic complications was not a risk factor at any age (aRR 1.01, 95% CI: 0.96–1.06), and hypertension without chronic complications was a risk factor only in 20–39 year-olds (aRR, 1.68, 95% CI: 1.17–2.40). Diabetes with chronic complications, hypertension with chronic complications, and obesity were risk factors in most age-groups, with highest relative risks among 20–39 year-olds (respective aRRs 1.79, 2.33, 1.92; P-values ≤ .002).
Conclusions
Hospitalized men with COVID-19 are at increased risk of death across all ages. Hypertension, diabetes with chronic complications, and obesity demonstrated age-dependent effects, with the highest relative risks among adults aged 20–39.
Keywords: COVID-19, metabolic comorbidities, hypertension, sex, claims data
We evaluated the association between common patient characteristics and mortality among 66 646 COVID-19 inpatients across 613 U.S. hospitals. In multivariable analysis, men had 30% greater mortality risk. Hypertension and obesity demonstrated age-dependent effects, with highest relative risks in 20–39 year-olds.
Despite increased clinical experience with severe acute respiratory syndrome coronavirus -2 (SARS-CoV-2) and its resulting disease, coronavirus disease-2019 (COVID-19), and a rapidly expanding body of literature, the relationship between common risk factors and mortality from COVID-19 remains incompletely understood. Prior studies have identified male sex, older age, and metabolic comorbidities such as hypertension, obesity, and diabetes as risk factors for poor outcomes and mortality in COVID-19 patients [1–7]. However, these comorbidities often co-occur and are more common among men and older age groups [8–10]. Emerging data also suggest that the effect of metabolic comorbidities on risk of severe COVID-19 outcomes may be age-dependent [11–14]. Without large sample sizes, isolating the independent effects of these clustered characteristics and examining effects by age is challenging. Many studies have also combined comorbidity severities in order to maintain adequate strata sizes, thereby obscuring potentially meaningful clinical granularity. Given that half of American adults are men and more than half have hypertension or diabetes [15, 16], evaluating the precise relationship between these common characteristics and mortality in U.S. patients is important. The availability of large claims databases makes studying these questions possible.
To address these knowledge gaps, the objective of the current study was to determine the effect of common patient characteristics on mortality risk across age groups in a large sample of more than 60 000 COVID-19 inpatients across more than 600 geographically diverse U.S. hospitals.
METHODS
Study Design and Data Source
We conducted a retrospective observational cohort study of patients who were discharged from hospitals in the Premier Healthcare Database (“Premier Database”), an all-payer repository of claims and clinical data from more than 120 million U.S. inpatient admissions [17]. Premier Database hospitals cover highly geographically diverse areas across the U.S. and capture approximately 1 of every 4 U.S. hospital discharges (see Supplementary Material for further database detail). Premier internally validates all data before its release into the Premier Database. This study did not include personally identifiable information and was exempt from institutional review board review.
Study Population and COVID-19 Case Definition
All inpatient admissions with discharge dates in the second quarter of 2020 (April, May, and June) and present in the Premier Database as of the data extract date of July 20, 2020 were included in the study. Inpatients were designated as COVID-19 positive if their admission included an ICD-10-CM diagnosis code of “COVID-19” (U07.1). The U07.1 diagnosis code became formally effective for use on April 1, 2020 and is assigned for diagnoses of COVID-19 based upon: (1) documented confirmed or presumptive-positive test results; or (2) a clinical provider’s statement that a patient has COVID-19 (neither documentation of the type of test nor a copy of the test results in the record are required) [18–20]. In order to ensure national uniformity and accurate code usage, substantial governmental and professional organization guidance accompanied release of the U07.1 diagnosis code [18, 19]. We stratified admissions by (a) any COVID-19 diagnosis (principal, secondary, and/or admitting), and (b) admitting and/or principal COVID-19 diagnoses only.
Outcome
The primary study outcome was mortality during the current hospitalization. Because select hospitals may transfer severely ill patients for higher-acuity care, and our analysis includes patients who transferred into Premier Database hospitals, to avoid double-counting some patients we performed sensitivity analyses excluding patients who were discharged to another hospital for acute care.
Collected Data and Primary Patient Characteristics of Interest
For each admission, we extracted data on hospital characteristics, including teaching status, urban versus rural location, and U.S. census geographic region and division. To approximate resource utilization intensity, we calculated each hospital’s monthly percentage of COVID-19 patients and of mechanically ventilated patients. We also extracted the following patient-level data for each admission: (a) admission and discharge characteristics (eg, pre-admission location, discharge location or death, month of admission and discharge); (b) sociodemographics (eg, age, sex, race, and ethnicity); (c) all ICD-10-PCS procedure codes; and (d) all ICD-10-CM diagnosis codes, including whether a diagnosis was present-on-admission. We mapped present-on-admission diagnosis codes to Elixhauser comorbidities using standardized Agency for Healthcare Research and Quality (AHRQ) methodology and software [21].
Our primary, a priori-defined patient characteristics of interest were sex and the following Elixhauser comorbidities present-on-admission: hypertension with no other hypertension end-organ complications (herein referred to as “uncomplicated hypertension,” as defined by Elixhauser et al.), hypertension with other end-organ complications (“complicated hypertension,”) uncomplicated and complicated diabetes using the same criteria for end-organ complications, and obesity. As defined in the Elixhauser Comorbidity Index, end-organ complications primarily include heart disease, renal disease, and/or vascular complications [22]. We hypothesized based upon emerging evidence from prior, smaller studies that the effect of the preceding comorbidities on mortality risk may also vary by age [11–14] and tested for age-based interaction effects during model-building (see Statistical Methods).
Statistical Methods
Descriptive statistics for patient and hospital characteristics were calculated using mean (standard deviation [SD]), median (range or interquartile range [IQR]), or frequency count (percentage). We included all patients with COVID-19 diagnoses in the primary study cohort. To calculate age-stratified rates of study outcomes, we categorized age by decade of life.
We analyzed the relationship between in-hospital mortality and patient, hospital, and temporal characteristics among adult patients using log-binomial models to estimate relative risk (RR) and corresponding 95% confidence intervals (CIs). Based on our a priori hypotheses that the relationship between metabolic comorbidities and mortality may vary by age, we evaluated age-specific interaction terms for complicated and uncomplicated diabetes, complicated and uncomplicated hypertension, and obesity. If these interactions were not statistically significant based upon a global Wald Chi-square test (ie, if the effect of a comorbidity did not differ significantly between age groups), we did not include the interaction between age and the comorbidity in our final model. Our final model therefore included (1) our primary characteristics of interest, which were parameterized separately for each age strata if there was significant interaction between the characteristic and age; and (2) confounding variables (hospital-level variables, temporal variables, and other patient-level variables such as chronic pulmonary disease or liver disease) that were selected a priori through literature review and expert clinical consensus (A. H., E. P., A. L., and J. B.). Because two selected confounders, cardiovascular failure and renal failure, may be complications of hypertension or diabetes, we evaluated these variables for collinearity before including them in the final model. We also performed a sensitivity analysis excluding them. All tests were 2-tailed, and P values ≤ .05 were used for statistical significance testing. Analyses were performed using SAS version 9.4 (SAS Institute Inc.) and STATA 15.0 (Stata Corp.). Log-binomial models were fit using the modified Poisson regression approach described by Zou (2004) [23–25].
RESULTS
During the study period, which included discharges in the second quarter of 2020, we identified 1 028 032 unique admissions across 763 hospitals. 66 646 (6.5%) admissions from 613 hospitals had a COVID-19 diagnosis. Of the COVID-19 admissions, 42 102 (63.1%) had a principal or admitting diagnosis of COVID-19. COVID-19 patients were identified from every U.S. census geographic division. However, the majority (33 148; 49.7% of COVID-19 admissions) were hospitalized in the Middle Atlantic (New York, New Jersey, and Pennsylvania) (see Supplementary Material for geographic distribution data).
The mean (SD) age of COVID-19 patients was 62.8 (17.9) years, and 35 246 (52.9%) were male (Table 1). A total of 14 758 (22.1%) COVID-19 patients received ICU-level care, and 10 908 (16.4%) received mechanical ventilation (Table 2). On average, COVID-19 patients had 3 Elixhauser comorbidities (SD, 2.1) present-on-admission. Compared to non-COVID-19 patients, COVID-19 patients were more likely to be Black (22.9% vs. 14.3%) and Hispanic (19.8% vs. 9.9%) (Table 1).
Table 1.
Patient and Hospital Characteristics of COVID-19 and Non-COVID-19 Inpatients Among Discharges in the Second Quarter of 2020
| Non-COVID-19 Admissions | COVID-19 Admissions | |
|---|---|---|
| Characteristic | n = 961 386 (n, %) |
n = 66 646 (n, %) |
| Admission Characteristics | ||
| Transfer from another acute care hospital | 68 038 (7.1%) | 4806 (7.2%) |
| Admitted from skilled nursing or intermediate care facility | 9570 (1.0%) | 3721 (5.6%) |
| Admission month | ||
| Pre-March | 2705 (0.3%) | 57 (0.1%) |
| March | 58 026 (6.0%) | 10 033 (15.1%) |
| April | 385 997 (40.2%) | 37 493 (56.3%) |
| May | 351 607 (36.6%) | 14 754 (22.1%) |
| June | 163 051 (17.0%) | 4309 (6.5%) |
| Hospital length of stay, days (mean, SD) | 4.46 (7.80) | 8.46 (10.4) |
| Demographic Characteristics | ||
| Age, mean (SD) | 46.80 (27.96) | 62.83 (17.89) |
| Age decade of life, y | ||
| Less than 10 | 151 289 (15.7%) | 222 (0.3%) |
| 10–19 | 22 758 (2.4%) | 398 (0.6%) |
| 20–29 | 102 170 (10.6%) | 2682 (4.0%) |
| 30–39 | 114 495 (11.9%) | 4689 (7.0%) |
| 40–49 | 71 416 (7.4%) | 6847 (10.3%) |
| 50–59 | 106 308 (11.1%) | 11 138 (16.7%) |
| 60–69 | 140 497 (14.6%) | 14 343 (21.5%) |
| 70–79 | 134 510 (14.0%) | 12 855 (19.3%) |
| 80 and Older | 117 943 (12.3%) | 13 472 (20.2%) |
| Male sex | 421 328 (43.8%) | 35 246 (52.9%) |
| Race | ||
| White | 679 239 (70.7%) | 29 085 (43.6%) |
| Black | 137 275 (14.3%) | 15 270 (22.9%) |
| Unknown | 46 649 (4.9%) | 5612 (8.4%) |
| Other | 98 223 (10.2%) | 16 679 (25.0%) |
| Hispanic ethnicity | 94 865 (9.9%) | 13 178 (19.8%) |
| Elixhauser Comorbidities, present-on-admission a | ||
| Alcohol abuse | 52 841 (5.5%) | 1404 (2.1%) |
| Any hypertension | 422 326 (43.9%) | 42 813 (64.2%) |
| Blood loss anemia | 21 256 (2.2%) | 605 (0.9%) |
| Chronic peptic ulcer disease | 8143 (0.9%) | 358 (0.5%) |
| Chronic pulmonary disease | 170 623 (17.8%) | 13 606 (20.4%) |
| Coagulopathy | 47 284 (4.9%) | 5722 (8.6%) |
| Congestive heart failure | 147 780 (15.4%) | 9893 (14.8%) |
| Deficiency anemias | 156 262 (16.3%) | 13 583 (20.4%) |
| Depression | 109 163 (11.4%) | 6827 (10.2%) |
| Diabetes, complicated | 147 724 (15.4%) | 16 186 (24.3%) |
| Diabetes, uncomplicated | 67 176 (7.0%) | 9425 (14.1%) |
| Fluid and electrolyte disorders | 232 638 (24.2%) | 31 237 (46.9%) |
| HIV and AIDS | 1993 (0.2%) | 216 (0.3%) |
| Hypertension, complicated | 194 661 (20.3%) | 17 978 (27.0%) |
| Hypertension, uncomplicated | 227 665 (23.7%) | 24 835 (37.3%) |
| Hypothyroidism | 103 638 (10.8%) | 8248 (12.4%) |
| Liver disease | 51 189 (5.3%) | 2698 (4.1%) |
| Lymphoma | 6607 (0.7%) | 486 (0.7%) |
| Metastatic cancer | 27 104 (2.8%) | 740 (1.1%) |
| Obesity | 150 659 (15.7%) | 14 044 (21.1%) |
| Other neurological disorders | 87 275 (9.1%) | 8329 (12.5%) |
| Paralysis | 23 526 (2.5%) | 1836 (2.8%) |
| Peripheral vascular disease | 40 991 (4.3%) | 2159 (3.2%) |
| Psychoses | 44 583 (4.6%) | 3786 (5.7%) |
| Pulmonary circulation disease | 8428 (0.9%) | 1250 (1.9%) |
| Renal failure | 144 732 (15.1%) | 13 770 (20.7%) |
| Rheumatoid arthritis/ collagen vascular diseases | 23 889 (2.5%) | 1736 (2.6%) |
| Solid tumor w/out metastasis | 22 172 (2.3%) | 1057 (1.6%) |
| Substance use disorder | 58 762 (6.1%) | 1123 (1.7%) |
| Valvular disease | 46 290 (4.8%) | 2198 (3.3%) |
| Weight loss | 49 784 (5.2%) | 4664 (7.0%) |
| Total Elixhauser score, present-on-admission (mean, SD) | 2.57 (2.40) | 3.30 (2.10) |
| Hospital Characteristics | ||
| Bed size | ||
| 000–099 | 61 851 (6.4%) | 2032 (3.1%) |
| 100–199 | 123 812 (12.9%) | 6295 (9.5%) |
| 200–299 | 163 154 (17.0%) | 12 214 (18.3%) |
| 300–399 | 163 593 (17.0%) | 14 116 (21.2%) |
| 400–499 | 117 386 (12.2%) | 5947 (8.9%) |
| 500+ | 331 590 (34.5%) | 26 042 (39.1%) |
| Urbanb | 833 394 (86.7%) | 61 708 (92.6%) |
| Teaching | 474 327 (49.3%) | 42 951 (64.5%) |
| US Census Divisionc | ||
| East North Central | 163 758 (17.0%) | 7452 (11.2%) |
| East South Central | 63 411 (6.6%) | 1305 (2.0%) |
| Middle Atlantic | 129 720 (13.5%) | 33 148 (49.7%) |
| Mountain | 39 160 (4.1%) | 1583 (2.4%) |
| New England | 20 005 (2.1%) | 2015 (3.0%) |
| Pacific | 87 274 (9.1%) | 1799 (2.7%) |
| South Atlantic | 260 328 (27.1%) | 11 983 (17.9%) |
| West North Central | 70 500 (7.3%) | 2856 (4.3%) |
| West South Central | 127 230 (13.2%) | 4505 (6.8%) |
Abbreviations: COVID-19, coronavirus disease 2019; HIV, human immunodeficiency virus; SD, standard deviation.
a Elixhauser comorbidity categories were modified to include principal diagnoses, in addition to secondary diagnoses, that were present-on-admission. Elixhauser scores represent unweighted Elixhauser comorbidity sums (1 point per comorbidity present-on-admission).
b Designation provided by Premier, based upon American Hospital Association Annual Survey response.
c U.S. census divisions comprise four U.S. census regions: NORTHEAST (Middle Atlantic, New England), SOUTH (South Atlantic, East South Central, West South Central), MIDWEST (East North Central, West North Central), WEST (Mountain, Pacific). States in each U.S. census division are the following: (New England Division): Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; (Middle Atlantic Division): New Jersey, New York, Pennsylvania; (East North Central Division): Illinois, Indiana, Michigan, Ohio, Wisconsin; (West North Central Division): Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota; (South Atlantic Division): Delaware, District of Columbia, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia; (East South Central Division): Alabama, Kentucky, Mississippi, Tennessee; (West South Central Division): Arkansas, Louisiana, Oklahoma, Texas; (Mountain Division): Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Wyoming; (Pacific Division): Alaska, California, Hawaii, Oregon, Washington.
Table 2.
In-Hospital Mortality Status by Patient and Hospital Characteristics of COVID-19 Inpatients
| Characteristics | All COVID-19 Patients | Died in-Hospital |
|---|---|---|
| Receipt of ICU-Level Care | ||
| No | 51 888 | 6698 (12.9%) |
| Yes | 14 758 | 5690 (38.6%) |
| Time-to-ICU, days (mean, SD)a | 2.8 (3.9) | 3.4 (5.0) |
| Receipt of Mechanical Ventilation | ||
| No | 55 738 | 6152 (11.0%) |
| Yes | 10 908 | 6236 (57.2%) |
| Time-to-ventilation, days (mean, SD)a | 4.07(13.9) | 4.70 (18.1) |
| Admission Characteristics (%) | ||
| Admitted from skilled nursing facility | ||
| No | 62 925 | 11 106 (17.6%) |
| Yes | 3721 | 1282 (34.5%) |
| Transferred from another acute care hospital | ||
| No | 61 840 | 11 220 (18.1%) |
| Yes | 4806 | 1168 (24.3%) |
| Admission month | ||
| Pre-March | 57 | 15 (26.3%) |
| March | 10 033 | 2452 (24.4%) |
| April | 37 493 | 7684 (20.5%) |
| May | 14 754 | 1935 (13.1%) |
| June | 4309 | 302 (7.0%) |
| Length of stay (time-to-death for patients who died in-hospital), days (mean, SD) | 8.5 (10.4) | 10.0 (15.5) |
| Demographic Characteristics (%) | ||
| Age, y (mean, SD) | 62.8 (17.9) | 73.3 (12.8) |
| Age, decade of life | ||
| Less than 10 | 222 | 2 (0.9%) |
| 10–19 | 398 | 2 (0.5%) |
| 20–29 | 2682 | 42 (1.6%) |
| 30–39 | 4689 | 142 (3.0%) |
| 40–49 | 6847 | 398 (5.8%) |
| 50–59 | 11 138 | 1183 (10.6%) |
| 60–69 | 14 343 | 2575 (18.0%) |
| 70–79 | 12 855 | 3412 (26.5%) |
| 80 and Older | 13 472 | 4632 (34.4%) |
| Male sex | ||
| No | 31 400 | 5169 (16.5%) |
| Yes | 35 246 | 7219 (20.5%) |
| Race | ||
| White | 29 085 | 5935 (20.4%) |
| Black | 15 270 | 2602 (17.0%) |
| Other | 16 679 | 2872 (17.2%) |
| Unknown | 5612 | 979 (17.4%) |
| Hispanic ethnicity | ||
| No | 53 468 | 10 680 (20.0%) |
| Yes | 13 178 | 1708 (13.0%) |
| Elixhauser comorbidities, present-on-admission (%) b | ||
| Alcohol abuse | ||
| No | 65 242 | 12 175 (18.7%) |
| Yes | 1404 | 213 (15.2%) |
| Hypertension (any) | ||
| No | 23 833 | 2960 (12.4%) |
| Yes | 42 813 | 9428 (22.0%) |
| Blood loss anemia | ||
| No | 66 041 | 12 330 (18.7%) |
| Yes | 605 | 58 (9.6%) |
| Chronic peptic ulcer disease | ||
| No | 66 288 | 12 323 (18.6%) |
| Yes | 358 | 65 (18.2%) |
| Chronic pulmonary disease | ||
| No | 53 040 | 9494 (17.9%) |
| Yes | 13 606 | 2894 (21.3%) |
| Coagulopathy | ||
| No | 60 924 | 10 794 (17.7%) |
| Yes | 5722 | 1594 (27.9%) |
| Congestive heart failure | ||
| No | 56 753 | 9356 (16.5%) |
| Yes | 9893 | 3032 (30.6%) |
| Deficiency anemias | ||
| No | 53 063 | 9141 (17.2%) |
| Yes | 13 583 | 3247 (23.9%) |
| Depression | ||
| No | 59 819 | 11 044 (18.5%) |
| Yes | 6827 | 1344 (19.7%) |
| Diabetes, complicated | ||
| No | 50 460 | 8307 (16.5%) |
| Yes | 16 186 | 4081 (25.2%) |
| Diabetes, uncomplicated | ||
| No | 57 221 | 10 806 (18.9%) |
| Yes | 9425 | 1582 (16.8%) |
| Fluid and electrolyte disorders | ||
| No | 35 409 | 4795 (13.5%) |
| Yes | 31 237 | 7593 (24.3%) |
| HIV and AIDS | ||
| No | 66 430 | 12 352 (18.6%) |
| Yes | 216 | 36 (16.7%) |
| Hypertension, complicated | ||
| No | 48 668 | 7201 (14.8%) |
| Yes | 17 978 | 5187 (28.9%) |
| Hypertension, uncomplicated | ||
| No | 41 811 | 8147 (19.5%) |
| Yes | 24 835 | 4241 (17.1%) |
| Hypothyroidism | ||
| No | 58 398 | 10 440 (17.9%) |
| Yes | 8248 | 1948 (23.6%) |
| Liver disease | ||
| No | 63 948 | 11 872 (18.6%) |
| Yes | 2698 | 516 (19.1%) |
| Lymphoma | ||
| No | 66 160 | 12 224 (18.5%) |
| Yes | 486 | 164 (33.7%) |
| Metastatic cancer | ||
| No | 65 906 | 12 141 (18.4%) |
| Yes | 740 | 247 (33.4%) |
| Obesity | ||
| No | 52 602 | 10 077 (19.2%) |
| Yes | 14 044 | 2311 (16.5%) |
| Other neurological disorders | ||
| No | 58 317 | 10 202 (17.5%) |
| Yes | 8329 | 2186 (26.2%) |
| Paralysis | ||
| No | 64 810 | 11 823 (18.2%) |
| Yes | 1836 | 565 (30.8%) |
| Peripheral vascular disease | ||
| No | 64 487 | 11 798 (18.3%) |
| Yes | 2159 | 590 (27.3%) |
| Psychoses | ||
| No | 62 860 | 11 592 (18.4%) |
| Yes | 3786 | 796 (21.0%) |
| Pulmonary circulation disease | ||
| No | 65 396 | 12 148 (18.6%) |
| Yes | 1250 | 240 (19.2%) |
| Renal failure | ||
| No | 52 876 | 8230 (15.6%) |
| Yes | 13 770 | 4158 (30.2%) |
| Rheumatoid arthritis/collagen vascular diseases | ||
| No | 64 910 | 12 015 (18.5%) |
| Yes | 1736 | 373 (21.5%) |
| Solid tumor without metastasis | ||
| No | 65 589 | 12 089 (18.4%) |
| Yes | 1057 | 299 (28.3%) |
| Substance use disorder | ||
| No | 65 523 | 12 276 (18.7%) |
| Yes | 1123 | 112 (10.0%) |
| Valvular disease | ||
| No | 64 448 | 11 760 (18.2%) |
| Yes | 2198 | 628 (28.6%) |
| Weight loss | ||
| No | 61 982 | 11 042 (17.8%) |
| Yes | 4664 | 1346 (28.9%) |
| Total Elixhauser score (mean, SD) | 3.3 (2.1) | 4.2 (2.1) |
| Hospital Characteristics (%) | ||
| Bed size | ||
| 000–099 | 2032 | 228 (11.2%) |
| 100–199 | 6295 | 911 (14.5%) |
| 200–299 | 12 214 | 2551 (20.9%) |
| 300–399 | 14 116 | 2786 (19.7%) |
| 400–499 | 5947 | 1187 (20.0%) |
| 500+ | 26 042 | 4725 (18.1%) |
| Urban hospitalc | ||
| No | 4938 | 846 (17.1%) |
| Yes | 61 708 | 11 542 (18.7%) |
| Academic hospital | ||
| No | 23 695 | 4041 (17.1%) |
| Yes | 42 951 | 8347 (19.4%) |
Abbreviations: COVID-19, coronavirus disease 2019; ICU, intensive care unit; SD, standard deviation.
a Calculated only among patients who experienced the outcome (receipt of ICU-level care or mechanical ventilation).
b Elixhauser comorbidity categories were modified to include principal diagnoses, in addition to secondary diagnoses, that were present-on-admission. Elixhauser scores represent unweighted Elixhauser comorbidity sums (1 point per comorbidity present-on-admission).
c Designation provided by Premier, based upon American Hospital Association Annual Survey response.
Mortality Rates and Risk Factors in COVID-19 Inpatients
Overall, 12 388 (18.6%) of COVID-19 patients died in the hospital (Table 2). Figure 1 reflects crude mortality rates by patient age. In-hospital mortality was lowest among pediatric patients. Among adults, mortality increased with each decade of life (1.6%, 20–29 years — 34.4%, 80+ years) (Fig. 1). Across every adult age-strata, men had a higher rate of death compared to women (Supplementary Fig. 1). In the sub-sample of patients with a principal or admitting diagnosis of COVID-19, 6288 (14.9%) died in-hospital (age-stratified rates in Supplementary Fig. 2). When excluding patients transferred on discharge to other acute care hospitals (n = 2171), the percentage of patients with in-hospital mortality in the total cohort rose to 19.2% (12 388/64 475).
Figure 1.
In-hospital mortality by decade of life among inpatients with coronavirus disease 2019 (COVID-19) diagnoses (n = 66 646).
In a multivariable model controlling for patient, hospital, and temporal characteristics based upon month of admission, male sex was independently associated with a 30% higher risk of death during the admission (adjusted RR, 1.30, 95% CI: 1.26–1.34) (Table 3). The adjusted risk of in-hospital death increased with every decade of life; relative to patients aged 50–59, patients aged 60–69, 70–79, and 80+ had 1.7, 2.7, and 4.3 times the risk of death, respectively (adjusted RRs and 95% CIs: 1.72, 1.53–1.94; 2.70, 2.40–3.03; 4.26, 3.82–4.75). Relative to White race, Black race was associated with lower risk of in-hospital death (adjusted RR 0.90, 95% CI: 0.87–0.94). Admission to an academic hospital was associated with a 7% lower risk of in-hospital death (adjusted RR 0.93, 95% CI: 0.90–0.97) (Table 3).
Table 3.
Association between Patient, Hospital, and Temporal Characteristics and Mortality During the Hospital Admission Among COVID-19 Inpatients in a Multivariable Modela
| Characteristic | Relative Risk (aRR) and 95% CI for In-Hospital Death (n = 66 026b) |
P Value |
|---|---|---|
| Transferred from another acute care hospital | 1.31 (1.24–1.37) | <.001 |
| Male sex | 1.30 (1.26–1.34) | <.001 |
| Age, yc | ||
| 20–39 | 0.21 (0.17–0.27) | <.001 |
| 40–49 | 0.47 (0.39–0.57) | <.001 |
| 50–59 | REF | REF |
| 60–69 | 1.72 (1.53–1.94) | <.001 |
| 70–79 | 2.70 (2.40–3.03) | <.001 |
| 80+ | 4.26 (3.82–4.75) | <.001 |
| Race | ||
| White | REF | REF |
| Black | 0.90 (0.87–0.94) | <.001 |
| Unknown | 1.16 (1.09–1.23) | <.001 |
| Other | 1.02 (0.98–1.06) | .37 |
| Hispanic ethnicity | 0.95 (0.90–0.99) | .02 |
| Elixhauser Comorbidities, present-on-admissiond | ||
| Congestive heart failure | 1.16 (1.11–1.21) | <.001 |
| Pulmonary circulation disorders | 1.04 (0.93–1.16) | .48 |
| Chronic pulmonary disease | 1.02 (0.99–1.06) | .21 |
| Liver Disease | 1.09 (1.01–1.18) | .03 |
| Renal failure | 1.12 (1.07–1.17) | <.001 |
| Malignancye | 1.30 (1.22–1.38) | <.001 |
| Uncomplicated diabetes | 1.01 (0.96–1.06) | .63 |
| Comorbidity score of all other Elixhauser comorbidities not included in the model as binary variables (above), Present-on-Admissionf | 1.12 (1.11–1.14) | <.001 |
| Uncomplicated hypertension by ageg | ||
| 20–39 | 1.68 (1.17–2.40) | .01 |
| 40–49 | 1.08 (0.86–1.35) | .52 |
| 50–59 | 0.91 (0.80–1.04) | .17 |
| 60–69 | 0.85 (0.78–0.93) | .001 |
| 70–79 | 0.87 (0.80–0.95) | .001 |
| 80+ | 0.81 (0.76–0.87) | <.001 |
| Complicated hypertension by ageg | ||
| 20–39 | 2.33 (1.50–3.60) | <.001 |
| 40–49 | 1.88 (1.46–2.42) | <.001 |
| 50–59 | 1.34 (1.15–1.56) | <.001 |
| 60–69 | 1.03 (0.93–1.14) | .62 |
| 70–79 | 0.98 (0.89–1.07) | .58 |
| 80+ | 0.79 (0.74–0.85) | <.001 |
| Diabetes with chronic complications by ageg | ||
| 20–39 | 1.79 (1.23–2.61) | .002 |
| 40–49 | 1.44 (1.15–1.80) | .001 |
| 50–59 | 1.23 (1.09–1.39) | .001 |
| 60–69 | 1.32 (1.22–1.43) | <.001 |
| 70–79 | 1.13 (1.06–1.20) | <.001 |
| 80+ | 1.05 (0.99–1.11) | .09 |
| Obesity by ageg | ||
| 20–39 | 1.92 (1.43–2.57) | <.001 |
| 40–49 | 1.57 (1.30–1.90) | <.001 |
| 50–59 | 1.33 (1.19–1.49) | <.001 |
| 60–69 | 1.26 (1.16–1.36) | <.001 |
| 70–79 | 1.16 (1.08–1.25) | <.001 |
| 80+ | 1.11 (1.02–1.22) | .02 |
| Academic hospital | 0.93 (0.90–0.97) | <.001 |
| Urban hospital | 1.06 (1.00–1.13) | .05 |
| Admission month | ||
| Pre-March | N/Ah | -- |
| March | N/Ah | -- |
| April | REF | REF |
| May | 0.81 (0.77–0.85) | <.001 |
| June | 0.53 (0.48–0.59) | <.001 |
| Hospital’s percentage of COVID-19 patients (monthly)i | 1.004 (1.003–1.004) | <.001 |
| Hospital’s percentage of mechanically ventilated patients (monthly)i | 1.03 (1.02–1.03) | <.001 |
Abbreviations: aRR, adjusted relative risk; CI, confidence interval; COVID-19, coronavirus disease 2019; N/A, not applicable; REF, reference; RR, relative risk.
aAssocations were evaluated using a multivariable modified poisson regression model with robust variance estimation.
bPatients aged <20 years (n = 620) were excluded from adjusted analyses.
c aRR estimates for each age strata reflect the adjusted effect of age, relative to the reference category of 50–59 years, among those without complicated or uncomplicated hypertension, diabetes with chronic complications, or obesity. For all variables (eg, sex, race) not estimated separately by age group, effect estimates reflect the independent effect of that variable, holding age constant.
dElixhauser comorbidity categories were modified to include principal diagnoses, in addition to secondary diagnoses, that were present-on-admission.
eA combined category of the following Elixhauser comorbidities: lymphoma, metastatic cancer, and solid tumor without metastasis.
fElixhauser scores represent unweighted Elixhauser comorbidity sums (1 point per comorbidity present-on-admission) counting only among Elixhauser comorbidities that were not already included in the model as binary variables. The aRR of 1.12 reflects a 12% increase in mortality risk for each one-unit increase in the Elixhauser comorbidity score, ie, for each additional comorbidity that a patient had present-on-admission, such as fluid and electrolyte disorders or iron-deficiency anemia.
gComorbidity effects were estimated separately for each age group in the final multivariable model if there was evidence of statistically significant effect modification by age based upon a global Wald Chi-square test. Each adjusted RR estimate reflects the adjusted effect of a given comorbidity in patients of this age, holding other factors constant. The reference categories for complicated hypertension and complicated diabetes are patients with no hypertension or no diabetes, respectively.
hPatients were not eligible for cohort inclusion unless they were discharged on or after April 1, 2020. Therefore, adjusted mortality estimates for admission dates prior to April are not validly interpretable, because only patients who survived long enough to be discharged in April are present in the cohort. These patients may not be representative of the broader patient population if mortality is associated with length of stay.
iCalculated to approximate resource-utilization intensity. Variables were calculated by admission month for each hospital as the number of COVID-19 admissions or the number of admissions with mechanical ventilation divided by the total number of the hospital’s admissions during the same month.
The association between uncomplicated diabetes (diabetes without chronic complications) and mortality did not significantly vary by age. Uncomplicated diabetes was not a risk factor for in-hospital death (adjusted RR 1.01, 95% CI: 0.96–1.06) (Table 3). The association between other metabolic comorbidities and mortality varied by age (P values ≤ .007), and we estimated the effect of these comorbidities separately for each age group. Uncomplicated hypertension was only a risk factor for in-hospital death among patients aged 20–39 years and was not a risk factor in any other age groups (adjusted RR in 20–39 year-olds, 1.68, 95% CI: 1.17–2.40; P value for interaction = .007). Complicated hypertension, complicated diabetes, and obesity were independent risk factors in most age groups and the relative risks differed significantly by age (P values ≤ .001). The relative risk for each comorbidity was highest among 20–39 year-olds and generally decreased with each decade of life (Table 3 and Supplementary Table 1; P value for trends ≤ .002). Apart from complicated diabetes, which did not maintain an inverse trend with age, all other multivariable model estimates were similar when restricting to patients with principal/admitting COVID-19 diagnoses (Supplementary Table 2).
Temporal Trends
For every adult age group, mortality was lower among patients admitted in May than among patients admitted in April, and equal or lower again among patients admitted in June (Fig. 2). After controlling for patient and hospital characteristics, patients admitted in May averaged a 19% lower risk of death (RR, 0.81, 95% CI: 0.77–0.85) compared to patients admitted in April (Table 3). This estimate accounted for a hospital’s percentage of COVID-19 patients and mechanically ventilated patients during the same time period, which we calculated to approximate a hospital’s resource utilization intensity. As each of these percentages increased, a patient’s mortality risk also independently increased (Table 3).
Figure 2.
In-hospital mortality by decade of life and month of admission among inpatients with coronavirus disease 2019 (COVID-19) diagnoses (n = 66 646).
DISCUSSION
To our knowledge, this study of 613 U.S. hospitals and more than 66 000 COVID-19 hospitalizations is the largest peer-reviewed and published U.S. study to-date analyzing risk factors and outcomes among patients hospitalized with COVID-19. As expected, mortality rates increased with each decade of life. Notably, mortality rates were higher among adult men compared to women in every decade of life, and male sex was independently associated with a 30% greater risk of mortality during hospitalization. The data also demonstrated that among adult hospitalized patients, patients with uncomplicated diabetes were not at increased risk of death, and patients with uncomplicated hypertension were not at increased risk of death unless they were in the youngest age demographic. Adjusting for many hospital and patient characteristics, patients admitted in May had improved survival compared to patients admitted in April.
Our finding that male sex is a risk factor for increased mortality is supported by prior studies [1, 5–7, 26, 27]. However, our study controlled for numerous other comorbid conditions that are more common among men and demonstrated that men have higher mortality than women in every decade of adult life. Thus, we are confident that male sex is a strong, independent risk factor for mortality among hospitalized COVID-19 patients. This finding also comports with hypotheses that immune function differences may explain higher COVID-19 mortality rates in men [28], including recent data demonstrating sex-based differences in COVID-19 immune responses [29].
Previous studies have examined whether hypertension and diabetes are risk factors for mortality in hospitalized COVID-19 patients but have reached conflicting results. These studies generally did not stratify by comorbidity severity or patient age [1–3, 30]. Our large sample permitted a more detailed evaluation of hypertension and diabetes as risk factors. Unexpectedly, uncomplicated diabetes was not associated with in-hospital mortality in any age group, and uncomplicated hypertension was only associated with in-hospital mortality among adults in their 20s and 30s after adjustment for other model variables. Understanding that most hypertensive or diabetic patients are not at increased risk of death compared to other comparable hospitalized patients, provided they do not have additional complications such as heart disease or renal failure, may guide resource allocation and intervention efforts among hospitalized patients.
In contrast, complicated diabetes, complicated hypertension, and obesity were strong, independent risk factors for death in most age groups. As with uncomplicated hypertension, these comorbidities demonstrated pronounced age-specific trends that persisted in full multivariable models. The relative risk for each of these comorbidities was highest among 20–39 year-olds, after which it generally decreased with each subsequent decade of life (P value tests for trend all ≤ .002). After age 59, complicated hypertension was not associated with increased mortality risk, and once patients reached the oldest age group (80+ years), complicated diabetes was also no longer associated with increased mortality risk. Obesity was an independent risk factor for all age groups, consistent with a recent meta-analysis [31]. However, the magnitude of its effect decreased with age. This finding extends observations from 3 previous, smaller studies in U.S. hospitalized patients which found that the effect of obesity on risk of severe COVID-19 outcomes was greater in younger patients [12–14].
Our findings suggest that among hospitalized patients, young adults are particularly vulnerable from metabolic comorbidities. Better understanding whether differences in metabolic phenotypes or biological pathways, such as inflammatory processes [32], in younger patients may underlie our observed associations would be an important area of future study. Moreover, the role of common antihypertensive and diabetes medications, including angiotensin-converting enzyme (ACE) inhibitors and metformin, on COVID-19 clinical outcomes has become an active research area, with emerging research suggesting possibly protective effects [33–37]. National cohorts have documented age-based differences in antihypertensive and diabetes medication prescribing patterns and disease control among U.S. adults [38–40]. Evaluating whether differences in metabolic disease control and medication usage between younger and older adults mediate COVID-19 mortality risk would be an important area for future research.
Yet even without a full understanding of why young hospitalized adults with these comorbidities face higher mortality risks, these findings may still inform current clinical decision-making, such as earlier or more aggressive therapeutic intervention in younger patients with these comorbidities. They may also suggest, although they cannot definitively establish, that lifestyle modifications to reduce body mass index and blood pressure might reduce COVID-19 mortality risk in younger adults [11, 12, 41, 42]. Ultimately, although the absolute mortality among young patients with comorbidities may still be less than older patients with no comorbidities, the years of potential life lost among young patients is greater, making these findings important.
Our study consists of hospitalized patients and should not be generalized to the wider population of all individuals who acquire COVID-19 infection. For example, Black race and/or Hispanic ethnicity were associated with lower mortality risk in adjusted models. This finding is consistent with some prior studies [43, 44]. However, these and other studies have also observed that Black and/or Hispanic patients are hospitalized for COVID-19 at higher rates than White patients [43–45], and we observed that Black and/or Hispanic patients were over-represented among COVID-19 admissions in our cohort. Our study was not designed to determine whether specific characteristics or comorbidities are risk factors among the broader population of infected patients.
This study is a retrospective analysis of administrative claims data and is therefore subject to several limitations. First, claims data may misclassify some patient characteristics, and the U07.1 COVID-19 code has not been externally validated. However, many strong COVID-19 risk factors (eg, age, sex) are robustly captured in claims data; we identified COVID-19 patients and Elixhauser comorbidities using the same standardized ICD-10-CM diagnosis code sets that are used for national COVID-19 surveillance; and clinician statements that a patient has COVID-19 will support use of the U07.1 code even where testing may have been unavailable [18, 21, 46, 47]. Moreover, isolated elevated blood pressure or glucose readings are insufficient to trigger hypertension or diabetes codes [48]. Rather, there are separate codes for elevated readings without formal diagnoses, thus reducing the risk that features of patients’ COVID-19 clinical course at admission would be miscategorized as pre-existing comorbidities [49, 50]. Second, although routine COVID-19 screening was not widespread during the cohort period, it is possible that some cohort patients were identified incidentally through hospital-based screening. We therefore performed sensitivity analyses restricted to only the subset of patients with principal or admitting COVID-19 diagnoses, and results were substantially similar to primary analyses. Third, although our database included a large and diverse number of U.S. hospitals, regions where COVID-19 was most prevalent in the second quarter of 2020 were over-represented, and state-level data were not available. We also did not have outpatient mortality or medication data. Investigating whether age-dependent differences in the effect of metabolic comorbidities on mortality risk persist after accounting for use of antihypertensive and diabetes medications would be an important area of future study. Finally, due to our data extract date, some longer June and possibly late May admissions may not be in our cohort. If longer admissions have different mortality rates, this could have introduced some bias, although our findings comport with large international studies and a study in one U.S. health system that have also identified improving survival in hospitalized patients [51–53]. More research is necessary to investigate reasons for declining mortality, such as clinical practice changes and new therapeutics.
This study of more than 66 000 COVID-19 hospitalizations across 613 U.S. hospitals found that even after controlling for many co-occurring characteristics, men face a 30% greater risk of in-hospital mortality compared to women. Given that half of U.S. adults are men, population-based interventions and COVID-19 prevention efforts that target men could have a significant public health impact. Moreover, we found that 20–39 year-olds are especially vulnerable from metabolic comorbidities, including uncomplicated hypertension and obesity, compared to older age groups. Although the absolute mortality risk remains lower among 20–39 year-olds, the years of life lost in these patients are significant. Our findings suggest this is a higher-risk subgroup whom prevention efforts should not neglect.
Supplementary Data
Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.
Notes
Acknowledgments. The authors thank Taura Major for her guidance on ICD-10-CM coding and billing practices.
Financial support. Access to the Premier Healthcare Database was purchased through Departmental funds provided by the Department of Epidemiology and Public Health in the University of Maryland School of Medicine. The authors otherwise received no financial support for the research, authorship, and/or publication of this article. Premier, Inc. had no participation in the design, analysis, or drafting of the manuscript.
Potenial conflicts of interest. A. D. H. reports serving as infection control section editor for UpToDate outside the submitted work. All other authors report no potential conflicts.
All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
References
- 1. Gupta S, Hayek SS, Wang W, et al. Factors associated with death in critically ill patients with coronavirus disease 2019 in the US. JAMA Intern Med 2020; 180:1436–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Banerjee A, Pasea L, Harris S, et al. Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study. Lancet 2020; 395:1715–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Grasselli G, Zangrillo A, Zanella A, et al. ; COVID-19 Lombardy ICU Network . Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region, Italy. JAMA 2020; 323:1574–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Miller LE, Bhattacharyya R, Miller AL. Diabetes mellitus increases the risk of hospital mortality in patients with Covid-19: Systematic review with meta-analysis. Medicine (Baltimore) 2020; 99:e22439. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Jie L, Daniel QH, Biyao Z, et al. Epidemiology of COVID‐19: A systematic review and meta‐analysis of clinical characteristics, risk factors, and outcomes. J Med Virol Available at: https://onlinelibrary.wiley.com/doi/10.1002/jmv.26424. Accessed 3 November 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Albitar O, Ballouze R, Ooi JP, Sheikh Ghadzi SM. Risk factors for mortality among COVID-19 patients. Diabetes Res Clin Pract 2020; 166:108293. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Palaiodimos L, Kokkinidis DG, Li W, et al. Severe obesity, increasing age and male sex are independently associated with worse in-hospital outcomes, and higher in-hospital mortality, in a cohort of patients with COVID-19 in the Bronx, New York. Metabolism 2020; 108:154262. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Thompson P, Logan I, Tomson C, Sheerin N, Ellam T. Obesity, sex, race, and early onset hypertension: implications for a refined investigation strategy. Hypertension 2020; 76:859–65. [DOI] [PubMed] [Google Scholar]
- 9. Peters SAE, Muntner P, Woodward M. Sex differences in the prevalence of, and trends in, cardiovascular risk factors, treatment, and control in the United States, 2001 to 2016. Circulation 2019; 139:1025–35. [DOI] [PubMed] [Google Scholar]
- 10. Gosmanov AR, Mendez CE, Umpierrez GE. Challenges and strategies for inpatient diabetes management in older adults. Diabetes Spectr 2020; 33:227–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Kass DA. COVID-19 and severe obesity: a big problem? Ann Intern Med 2020; 173:840–1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Tartof SY, Qian L, Hong V, et al. Obesity and mortality among patients diagnosed with COVID-19: results from an integrated health care organization. Ann Intern Med 2020; 173:773–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Goyal P, Ringel JB, Rajan M, et al. Obesity and COVID-19 in New York City: a retrospective cohort study. Ann Intern Med 2020; 173:855–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Anderson MR, Geleris J, Anderson DR, et al. Body mass index and risk for intubation or death in SARS-CoV-2 infection: a retrospective cohort study. Ann Intern Med 2020; 173:782–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. U.S. Centers for Disease Control and Prevention (CDC). Hypertension prevalence in the U.S. | Million Hearts®. 2020. Available at: https://millionhearts.hhs.gov/data-reports/hypertension-prevalence.html. Accessed 26 August 2020.
- 16. National Diabetes Statistics Report 2020. Estimates of diabetes and its burden in the United States. 2020;:32. [Google Scholar]
- 17. PremierHealthcareDatabaseWhitepaper.pdf. Available at: https://products.premierinc.com/downloads/PremierHealthcareDatabaseWhitepaper.pdf. Accessed 26 August 2020.
- 18. Centers for Disease Control and Prevention. ICD-10-CM official coding and reporting guidelines April 1, 2020 through September 30, 2020. Available at: https://www.cdc.gov/nchs/data/icd/COVID-19-guidelines-final.pdf. Accessed 26 August 2020.
- 19. Frequently Asked Questions Regarding ICD-10-CM Coding for COVID-19 | AHA. Available at: https://www.aha.org/fact-sheets/2020-03-30-frequently-asked-questions-regarding-icd-10-cm-coding-covid-19. Accessed 26 August 2020.
- 20. ICD-10 Version:2019. Available at: https://icd.who.int/browse10/2019/en#/U07.1. Accessed 26 August 2020.
- 21. Elixhauser Comorbidity Software for ICD-10-CM Version 2020.1 (beta version). 2019. Available at: https://www.hcup-us.ahrq.gov/toolssoftware/comorbidityicd10/comorbidity_icd10.jsp#download. Accessed 26 August 2020.
- 22. Agency for Healthcare Research and Quality Hospital-Acquired Infections Collaborative. Creation of format library for comorbidity groups ICD-10-CM elixhauser comorbidity software, version 2020.1. Available at: https://www.hcup-us.ahrq.gov/toolssoftware/comorbidityicd10/comformat_icd10cm_2020_1.txt. Accessed 26 August 2020.
- 23. Chen W, Qian L, Shi J, Franklin M. Comparing performance between log-binomial and robust Poisson regression models for estimating risk ratios under model misspecification. BMC Med Res Methodol 2018; 18:63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Barros AJ, Hirakata VN. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol 2003; 3:21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Zou G. A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol 2004; 159:702–6. [DOI] [PubMed] [Google Scholar]
- 26. Mo P, Xing Y, Xiao Y, et al. Clinical characteristics of refractory COVID-19 pneumonia in Wuhan, China. Clin Infect Dis 2020; ciaa270. doi: 10.1093/cid/ciaa270 [DOI] [Google Scholar]
- 27. Shah P, Owens J, Franklin J, et al. Demographics, comorbidities and outcomes in hospitalized Covid-19 patients in rural southwest Georgia. Ann Med 2020; 52:354–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Penna C, Mercurio V, Tocchetti CG, Pagliaro P. Sex-related differences in COVID-19 lethality. Br J Pharmacol 2020; 177:4375–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Takahashi T, Ellingson MK, Wong P, et al. Sex differences in immune responses that underlie COVID-19 disease outcomes. Nature 2020; 588:315–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Zeng H, Zhang T, He X, et al. Impact of chronic comorbidities on progression and prognosis in patients with COVID-19: a retrospective cohort study in 1031 hospitalized cases in Wuhan, China. Rochester, NY: Social Science Research Network, 2020. Available at: https://papers.ssrn.com/abstract=3629460. Accessed 27 August 2020. [Google Scholar]
- 31. Hussain A, Mahawar K, Xia Z, Yang W, El-Hasani S. Obesity and mortality of COVID-19. Meta-analysis. Obes Res Clin Pract 2020; 14:295–300. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
- 32. Paula Burch-celentano, Tulane University. Metabolic syndrome linked to worse outcomes for COVID-19 patients. Available at: https://www.eurekalert.org/pub_releases/2020-08/tu-msl082420.php. Accessed 28 October 2020.
- 33. Solerte SB, D’Addio F, Trevisan R, et al. Sitagliptin treatment at the time of hospitalization was associated with reduced mortality in patients with type 2 diabetes and COVID-19: a multicenter, case-control, retrospective, observational study. Diabetes Care 2020; 43:2999–3006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Diabetes drug boosts survival in patients with type 2 diabetes and COVID-19 pneumonia. Available at: https://www.eurekalert.org/pub_releases/2020-09/bch-ddb092520.php. Accessed 28 October 2020.
- 35. Patients with COVID-19 should stay on ACE inhibitors, ARBs, study finds | AJMC. Available at: https://www.ajmc.com/view/patients-with-covid-19-should-stay-on-ace-inhibitors-arbs-study-finds. Accessed 28 October 2020.
- 36. Hippisley-Cox J, Young D, Coupland C, et al. Risk of severe COVID-19 disease with ACE inhibitors and angiotensin receptor blockers: cohort study including 8.3 million people. Heart 2020; 106:1503–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Alberto P, Massimo M, De Ferrari GM, et al. Antecedent administration of angiotensin converting enzyme inhibitors or angiotensin II receptor antagonists and survival after hospitalization for SARS‐CoV‐2 (COVID‐19). J Am Heart Assoc 2020; 9:e017364. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Gopalan A, Mishra P, Alexeeff SE, et al. Initial glycemic control and care among younger adults diagnosed with type 2 diabetes. Diabetes Care 2020; 43:975–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Berkowitz SA, Meigs JB, Wexler DJ. Age at type 2 diabetes onset and glycaemic control: results from the National Health and Nutrition Examination Survey (NHANES) 2005–2010. Diabetologia 2013; 56:2593–600. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Gu A, Yue Y, Argulian E. Age differences in treatment and control of hypertension in US Physician Offices, 2003–2010: a serial cross-sectional study. Am J Med 2016; 129:50–58.e4. [DOI] [PubMed] [Google Scholar]
- 41. Lighter J, Phillips M, Hochman S, et al. Obesity in patients younger than 60 years is a risk factor for COVID-19 hospital admission. Clin Infect Dis 2020; 71:896–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Kass DA, Duggal P, Cingolani O. Obesity could shift severe COVID-19 disease to younger ages. The Lancet 2020; 395:1544–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Price-Haywood EG, Burton J, Fort D, Seoane L. Hospitalization and mortality among black patients and white patients with Covid-19. N Engl J Med 2020; 382:2534–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Gold JAW, Wong KK, Szablewski CM, et al. Characteristics and clinical outcomes of adult patients hospitalized with COVID-19 - Georgia, March 2020. MMWR Morb Mortal Wkly Rep 2020; 69:545–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Centers for Disease Control and Prevention. Age-adjusted COVID-19-associated hospitalization rates by race and ethnicity, COVID-NET, March 1–August 15, 2020. 2020. Available at: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html. Accessed 26 August 2020.
- 46. COVID-19 Provisional Counts - Weekly Updates by Select Demographic and Geographic Characteristics (Table 3). 2020. Available at: https://www.cdc.gov/nchs/nvss/vsrr/covid_weekly/index.htm. Accessed 28 October 2020.
- 47. Killerby ME, Link-Gelles R, Haight SC, et al. ; CDC COVID-19 Response Clinical Team . Characteristics associated with hospitalization among patients with COVID-19 - metropolitan Atlanta, Georgia, March-April 2020. MMWR Morb Mortal Wkly Rep 2020; 69:790–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. How to document and code for hypertensive diseases in ICD-10 -- FPM. Available at: https://www.aafp.org/fpm/2014/0300/p5.html. Accessed 28 October 2020. [PubMed]
- 49. 2021 ICD-10-CM Diagnosis Code R03.0: Elevated blood-pressure reading, without diagnosis of hypertension. Available at: https://www.icd10data.com/ICD10CM/Codes/R00-R99/R00-R09/R03-/R03.0. Accessed 28 October 2020.
- 50. 2021 ICD-10-CM diagnosis code R73: elevated blood glucose level. Available at: https://www.icd10data.com/ICD10CM/Codes/R00-R99/R70-R79/R73-/R73. Accessed 28 October 2020.
- 51. Declining death rate from COVID-19 in hospitals in England. Available at: https://www.cebm.net/covid-19/declining-death-rate-from-covid-19-in-hospitals-in-england/. Accessed 26 August 2020.
- 52. Armstrong RA, Kane AD, Cook TM. Outcomes from intensive care in patients with COVID-19: a systematic review and meta-analysis of observational studies. Anaesthesia 2020; 75:1340–9. [DOI] [PubMed] [Google Scholar]
- 53. October 23 JHMPOF, DOI: 10.12788/jhm.3552 | 10.12788/jhm.3552 2020. Trends in COVID-19 risk-adjusted mortality rates. J Hosp Med 2020; Available at: https://www.journalofhospitalmedicine.com/jhospmed/article/230561/hospital-medicine/trends-covid-19-risk-adjusted-mortality-rates. Accessed 3 November 2020. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.