Abstract
Background
To evaluate the degree to which clinical comorbidities or combinations of comorbidities are associated with SARS-CoV-2 breakthrough infection.
Materials and Methods
A breakthrough infection was defined as a positive test at least 14 days after a full vaccination regimen. Logistic regression was used to calculate aORs, which were adjusted for age, sex, and race information.
Results
A total of 110,380 patients from the UC CORDS database were included. After adjustment, stage 5 CKD due to hypertension (aOR: 7.33; 95% CI: 4.86-10.69; p<.001; power=1) displayed higher odds of infection than any other comorbidity. Lung transplantation history (aOR: 4.79; 95% CI: 3.25-6.82; p<.001; power= 1), coronary atherosclerosis (aOR: 2.12; 95% CI: 1.77-2.52; p<.001; power=1), and vitamin D deficiency (aOR: 1.87; 95% CI: 1.69-2.06; p<.001; power=1) were significantly correlated to breakthrough infection. Patients with obesity in addition to essential hypertension (aOR: 1.74; 95% CI: 1.51-2.01; p<.001; power=1) and anemia (aOR: 1.80; 95% CI: 1.47-2.19; p<.001; power=1) were at additional risk of breakthrough infection compared to those with essential hypertension and anemia alone.
Conclusions
Further measures should be taken to prevent breakthrough infection for individuals with these conditions, such as acquiring additional doses of the SARS-CoV-2 vaccine to boost immunity.
Key Indexing Terms: COVID-19, Breakthroughs, Vaccination, Comorbidities
Introduction
COVID-19 vaccinations, which are effective in reducing the risk of infection, hospitalization, and death, are important preventive health measures for at-risk individuals.1, 2, 3, 4, 5, 6 Recent studies, however, have demonstrated that the effectiveness of these vaccines wane significantly over time. In a large case-control study of 950,000 patients, effectiveness of the Pfizer-BioNTech (BNT162b2) vaccine declined from 77.5% in the first month after receiving the second dose to approximately 20% six months after vaccination.7 Studies controlling for variant exposure nonetheless saw as much as a 40% drop in effectiveness after 4 months, indicating that the reduced effectiveness may be due to waning antibody protection acquired from vaccines over this period.8, 9, 10 Further studies of antibodies in the blood specific to the spike protein receptor binding domain have confirmed that vaccine-acquired immunity is in fact significantly reduced after approximately 4-6 months, thus making individuals vulnerable to infection despite being vaccinated.11
Although comorbidities associated with COVID-19 prevalence prior to vaccination are well-documented, few studies have associated the prevalence of pre-existing risk factors to the incidence of breakthrough cases, defined as infection 14 days after full vaccination.12 , 13 Several large-scale studies of SARS-CoV-2 breakthrough patients have been published.7 , 14, 15, 16 However, these studies lack detailed information on patient comorbidities and characteristics due to the difficulties of collecting accurate clinical information from COVID-19 testing sites and nationally regulated COVID-19 surveillance programs. Additionally, some large-scale investigations report the comorbidities of their patients, but do not use statistical measurements to correlate them to breakthrough outcomes, or simply associate basic demographic variables to breakthrough cases.17 , 18 While few studies have measured the association of specific comorbidities of interest with breakthrough cases, they do not focus on the broad variety of risk factors present within the same patient population.19, 20, 21 Thus, it is not well-documented which comorbidities may pose the highest risk for breakthrough infection in comparison with others. More importantly, which combinations of comorbidities pose the highest risk for breakthrough infections are not well known.
In this retrospective cohort study, we examine the association of 11 distinct comorbidity types with SARS-CoV-2 breakthrough infection in California's largest academic health system. To our knowledge, our study is the largest and most comprehensive investigation of patient comorbidities with SARS-CoV-2 breakthrough infection to date.
Methods
Design and setting
Data was collected from the University of California COVID Research Dataset (UC CORDS) on December 25, 2021. UC CORDS is an IRB-approved database containing de-identified COVID-19 data for patients from the University of California Health System, comprising of 19 health professional schools, 5 academic medical centers, and 12 hospitals. Patients in the UC CORDS dataset are integrated from daily COVID-19 test result information aggregated in the UC Health Data Warehouse. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines were followed.22
Vaccination and breakthrough definition
5 SARS-CoV-2 vaccines were included in this study: Moderna (mRNA-1273), Pfizer-BioNTech (BNT162b2), Johnson & Johnson (Ad26.COV2.S), AstraZeneca (ChAdOx1), and Novavax (NVX-CoV2373). Breakthrough cases were identified as the confirmation of SARS-CoV-2 infection from RT-PCR on oropharyngeal and nasopharyngeal swabs or antigen tests conducted ≥ 14 days after the administration of all FDA recommended doses of an COVID-19 vaccine for full vaccination. Individuals that met the following criteria are considered eligible: 1) Received full vaccination between January 1, 2021 and November 8, 2021; and 2) Tested for SARS-CoV-2 infection ≥ 14 days after full vaccination. Our inclusion criteria are detailed in Fig. 1 .
Fig. 1.
Workflow displaying patient inclusion criteria.
Demographic and comorbidities characteristics
Demographics, comorbidities, hospitalization records, and vaccine type were extracted from UC CORDS for each patient that met the inclusion criteria. A total of 64 medical comorbidities (observed from January 1, 2018 to November 8, 2021) were analyzed as covariates in our patient cohort. Comorbidities of interest were selected if they were highly prevalent within the patient sample or are known to be risk factors for COVID-19.
Statistical analysis
Data was extracted from the UC CORDS database on December 25, 2021. All data analyses were performed in R version 4.0.2. Adjusted Odds Ratio (aOR) analysis was performed (with a 95% confidence interval) to examine the effect of demographic and clinical factors on the risk of SARS-CoV-2 breakthrough cases in vaccinated individuals. To account for the effect of confounding variables, we used a logistic regression model to adjust for several factors that are known to significantly impact COVID-19 outcomes. Unadjusted, age adjusted, age-sex adjusted, and age-sex-race-adjusted odds ratios were reported (Supplemental Table 1 and Table 2). To further validate the reliability of our model, the power calculation for each comorbidity was estimated. The methodology as well as implementation was supported by literature.23 , 24 A p-value < 0.05 was used to measure statistical significance. Additionally, a power calculation value > 0.8 was used as a cutoff to determine whether the patient sample size is sufficient for each comorbidity examined.
Table 1.
Demographic and clinical characteristics of patients meeting the final inclusion criteria (N = 110, 380).
| Total | Breakthrough | Non-breakthrough | |
|---|---|---|---|
| (N=110,380) | (N=2,705) | (N=107,675) | |
| Characteristics | n (% of total) | n (% of total) | n (% of total) |
| Median age (iqr) | 56 (38, 70) | 50 (35, 65) | 56 (38, 70) |
| Age Group (years) | |||
| <21 | 6,560 (5.94) | 94 (3.5) | 6,466 (6.0) |
| 21-30 | 8,911 (8.07) | 348 (12.9) | 8,563 (8.0) |
| 31-40 | 15,774 (14.29) | 526 (19.4) | 15,248 (14.2) |
| 41-50 | 14,508 (13.14) | 413 (15.3) | 14,095 (13.1) |
| 51-60 | 18,011 (16.32) | 445 (16.5) | 17,566 (16.3) |
| 61-70 | 20,974 (19.00) | 458 (16.9) | 20,516 (19.1) |
| 71-80 | 17,601 (15.95) | 293 (10.8) | 17,308 (16.1) |
| >=81 | 8,041 (7.28) | 128 (4.7) | 7,913 (7.3) |
| Sex | |||
| Men | 47,369 (42.91) | 1,264 (46.7) | 46,105 (42.8) |
| Women | 62,914 (57.00) | 1,439 (53.2) | 61,475 (57.1) |
| Other/unknown | 97 (0.09) | 2 (0.1) | 95 (0.1) |
| Race | |||
| White | 65,726 (59.55) | 1,627 (60.1) | 64,099 (59.5) |
| Black | 5,367 (4.86) | 150 (5.5) | 5,217 (4.8) |
| Asian | 14,150 (12.82) | 241 (8.9) | 13,909 (12.9) |
| American Indian or Alaska Native | 345 (0.31) | 9 (0.3) | 336 (0.3) |
| Native Hawaiian or Other Pacific Islander | 591 (0.54) | 24 (0.9) | 567 (0.5) |
| Other | 4,505 (4.08) | 161 (6.0) | 4,344 (4.0) |
| Multi Race | 2,380 (2.16) | 73 (2.7) | 2,307 (2.1) |
| Unknown | 17,316 (15.69) | 420 (15.5) | 16,896 (15.7) |
| Ethnicity | |||
| Hispanic/Latino | 15,852 (14.36) | 462 (17.1) | 15,390 (14.3) |
| Not Hispanic/Latino | 86,967 (78.79) | 2,048 (75.7) | 84,919 (78.9) |
| Missing/Unknown | 7,561 (6.85) | 195 (7.2) | 7,366 (6.8) |
| Vaccine Type | |||
| mRNA-1273 (Moderna) | 52,695 (47.74) | 1,001 (37.0) | 51,694 (48.0) |
| BNT162b2 (Pfizer) | 54,860 (49.70) | 1,594 (58.9) | 53,266 (49.5) |
| Ad26.COV2.S (J&J) | 2,722 (2.47) | 106 (3.9) | 2,616 (2.4) |
| AstraZeneca | 18 (0.02) | 2 (0.00) | 16 (0.01) |
| Novavax | 2 (0.00) | 0 (0.00) | 2 (0.00) |
| Unspecified | 83 (0.08) | 2 (0.0) | 81 (0.07) |
| Severity | |||
| Hospitalized | 56 (0.05) | 55 (2.0) | 1 (0.0) |
| Deceased | 1,857 (1.68) | 37 (1.4) | 1,820 (1.7) |
| Comorbidity | |||
| Vascular Disease/Blood Flow | |||
| Essential Hypertension | 37,815 (34.26) | 900 (33.27) | 36,915 (34.28) |
| Secondary Hypertension | 794 (0.72) | 34 (1.26) | 760 (0.71) |
| Cerebrovascular Disease | 887 (0.80) | 30 (1.11) | 857 (0.80) |
| Coronary Atherosclerosis | 3,459 (3.13) | 144 (5.32) | 3,315 (3.08) |
| Low Blood Pressure | 4,795 (4.34) | 149 (5.51) | 4,646 (4.31) |
| Blood Abnormalities | |||
| Hyperlipidemia | 33,716 (30.55) | 845 (31.24) | 32,871 (30.53) |
| Hypoxemia | 4,762 (4.31) | 180 (6.65) | 4,582 (4.26) |
| Anemia | 15,565 (14.10) | 477 (17.63) | 15,088 (14.01) |
| Pure Hypercholesterolemia | 13,241 (12.00) | 368 (13.60) | 12,873 (11.96) |
| Sepsis | 4,883 (4.42) | 189 (6.99) | 4,694 (4.36) |
| Heart Diseases | |||
| Congestive Heart Failure | 5,723 (5.18) | 145 (5.36) | 5,578 (5.18) |
| Cardiac Arrhythmia | 10,085 (9.14) | 344 (12.72) | 9,741 (9.05) |
| Hypertrophic Cardiomyopathy | 470 (0.43) | 12 (0.44) | 458 (0.43) |
| Congenital Heart Disease | 513 (0.46) | 15 (0.55) | 498 (0.46) |
| Chronic Lung/Respiratory Diseases | |||
| Chronic Obstructive Lung Disease | 4,777 (4.33) | 122 (4.51) | 4,655 (4.32) |
| Asthma | 4,528 (4.10) | 265 (9.80) | 4,263 (3.96) |
| Uncomplicated Asthma | 10,583 (9.59) | 292 (10.79) | 10,291 (9.56) |
| Cystic Fibrosis | 128 (0.12) | 10 (0.37) | 118 (0.11) |
| Pleural Effusion | 7,244 (6.56) | 225 (8.32) | 7,019 (6.52) |
| Pulmonary Emphysema | 1,933 (1.75) | 63 (2.33) | 1,870 (1.74) |
| Substance Abuse | |||
| Nicotine Dependence | 5,739 (5.20) | 155 (5.73) | 5,584 (5.19) |
| Tobacco Dependence Syndrome | 2,405 (2.18) | 115 (4.25) | 2,290 (2.13) |
| Alcohol Abuse | 2,132 (1.93) | 78 (2.88) | 2,054 (1.91) |
| Alcohol Dependence | 1,583 (1.43) | 46 (1.70) | 1,537 (1.43) |
| Transplant | |||
| Transplanted Heart Valve Present | 1,589 (1.44) | 26 (0.96) | 1,563 (1.45) |
| Transplanted Kidney Present | 1,093 (0.99) | 70 (2.59) | 1,023 (0.95) |
| Disorder of Transplanted Kidney | 672 (0.61) | 54 (2.00) | 618 (0.57) |
| Transplanted Liver Present | 431 (0.39) | 23 (0.85) | 408 (0.38) |
| Bone Marrow Transplant Present | 389 (0.35) | 13 (0.48) | 376 (0.35) |
| Transplanted Lung Present | 306 (0.28) | 32 (1.18) | 274 (0.25) |
| Chronic Kidney Disease | |||
| Chronic Kidney Disease | 6,159 (5.58) | 195 (7.21) | 5,964 (5.54) |
| Chronic Kidney Disease Stage 1 | 441 (0.40) | 20 (0.74) | 421 (0.39) |
| Chronic Kidney Disease Stage 2 | 1,954 (1.77) | 67 (2.48) | 1,887 (1.75) |
| Chronic Kidney Disease Stage 3 | 5,076 (4.60) | 161 (5.95) | 4,915 (4.56) |
| Chronic Kidney Disease Stage 3A | 2,106 (1.91) | 59 (2.18) | 2,047 (1.90) |
| Chronic Kidney Disease Stage 3B | 1,637 (1.48) | 42 (1.55) | 1,595 (1.48) |
| Chronic Kidney Disease Stage 4 | 1,785 (1.62) | 59 (2.18) | 1,726 (1.60) |
| Chronic Kidney Disease Stage 5 | 1,084 (0.98) | 53 (1.96) | 1,031 (0.96) |
| Chronic Kidney Disease Due to Type 2 Diabetes Mellitus | 4,087 (3.70) | 124 (4.58) | 3,963 (3.68) |
| Hypertensive Heart And Chronic Kidney Disease | 1,089 (0.99) | 35 (1.29) | 1,054 (0.98) |
| Chronic Kidney Disease Due to Hypertension | 5,357 (4.85) | 157 (5.80) | 5,200 (4.83) |
| Anemia in Chronic Kidney Disease | 3,158 (2.86) | 107 (3.96) | 3,051 (2.83) |
| Chronic Kidney Disease Stage 5 Due to Hypertension | 202 (0.18) | 30 (1.11) | 172 (0.16) |
| Liver Diseases | |||
| Cirrhosis of Liver | 2,019 (1.83) | 59 (2.18) | 1,960 (1.82) |
| Steatosis of Liver | 5,461 (4.95) | 166 (6.14) | 5,295 (4.92) |
| Disease of Liver | 4,749 (4.30) | 159 (5.88) | 4,590 (4.26) |
| Chronic Nonalcoholic Liver Disease | 3,256 (2.95) | 130 (4.81) | 3,126 (2.90) |
| Liver Function Tests Abnormal | 2,503 (2.27) | 115 (4.25) | 2,388 (2.22) |
| Hepatic Failure | 1,255 (1.14) | 43 (1.59) | 1,212 (1.13) |
| Autoimmune Disorders | |||
| Rheumatoid Arthritis | 1,457 (1.32) | 49 (1.81) | 1,408 (1.31) |
| Multiple Sclerosis | 561 (0.51) | 24 (0.89) | 537 (0.50) |
| Alopecia | 2,808 (2.54) | 128 (4.73) | 2,680 (2.49) |
| Systemic Lupus Erythematosus | 799 (0.72) | 19 (0.70) | 780 (0.72) |
| Postoperative State | 22,548 (20.43) | 576 (21.29) | 21,972 (20.41) |
| Diabetes | |||
| Prediabetes | 11,350 (10.28) | 304 (11.24) | 11,046 (10.26) |
| Type 1 Diabetes Mellitus | 716 (0.65) | 32 (1.18) | 684 (0.64) |
| Type 1 Diabetes Mellitus Without Complication | 833 (0.75) | 33 (1.22) | 800 (0.74) |
| Type 2 Diabetes Mellitus | 9,046 (8.20) | 264 (9.76) | 8,782 (8.16) |
| Type 2 Diabetes Mellitus Without Complication | 13,482 (12.21) | 390 (14.42) | 13,092 (12.16) |
| Age-related & other conditons | |||
| Obesity | 13,369 (12.11) | 488 (18.04) | 12,881 (11.96) |
| Osteoarthritis | 8,868 (8.03) | 264 (9.76) | 8,604 (7.99) |
| Vitamin D Deficiency | 13,486 (12.22) | 513 (18.96) | 12,973 (12.05) |
| Hypothyroidism | 12,524 (11.35) | 318 (11.76) | 12,206 (11.34) |
| Dementia | 1,601 (1.45) | 29 (1.07) | 1,572 (1.46) |
Table 2.
Age-, sex-, and race- adjusted Odds Ratios (aOR) for comorbidities of interest. Lower and Upper bounds refer to the lower and upper bounds of the calculated 95% confidence interval.
|
Age-, Sex-, and Rac-Adjusted |
|||||
|---|---|---|---|---|---|
| Comorbidity | aOR | aOR Lower Bound | aOR Upper bound | P-value | Power Calculation |
| Vascular Disease/Blood Flow | |||||
| Essential Hypertension | 1.18 | 1.07 | 1.29 | < 0.001 | 0.97 |
| Secondary Hypertension | 1.88 | 1.30 | 2.62 | < 0.001 | 0.86 |
| Cerebrovascular Disease | 1.69 | 1.15 | 2.40 | 0.005 | 0.73 |
| Coronary Atherosclerosis | 2.12 | 1.77 | 2.52 | < 0.001 | 1.00 |
| Low Blood Pressure | 1.45 | 1.21 | 1.71 | < 0.001 | 0.99 |
| Blood Abnormalities | |||||
| Hyperlipidemia | 1.30 | 1.18 | 1.42 | < 0.001 | 1.00 |
| Hypoxemia | 1.81 | 1.54 | 2.11 | < 0.001 | 1.00 |
| Anemia | 1.46 | 1.31 | 1.61 | < 0.001 | 1.00 |
| Pure Hypercholesterolemia | 1.35 | 1.20 | 1.52 | < 0.001 | 1.00 |
| Sepsis | 1.79 | 1.53 | 2.08 | < 0.001 | 1.00 |
| Heart Diseases | |||||
| Congestive Heart Failure | 1.23 | 1.03 | 1.45 | 0.02 | 0.64 |
| Cardiac Arrhythmia | 1.62 | 1.44 | 1.82 | < 0.001 | 1.00 |
| Hypertrophic Cardiomyopathy | 1.12 | 0.59 | 1.89 | 0.71 | 0.05 |
| Congenital Heart Disease | 1.08 | 0.61 | 1.73 | 0.78 | 0.04 |
| Chronic Lung/Respiratory Diseases | |||||
| Chronic Obstructive Lung Disease | 1.23 | 1.01 | 1.48 | 0.03 | 0.58 |
| Asthma | 2.55 | 2.23 | 2.90 | < 0.001 | 1.00 |
| Uncomplicated Asthma | 1.14 | 1.00 | 1.29 | 0.04 | 0.47 |
| Cystic Fibrosis | 2.82 | 1.38 | 5.12 | 0.002 | 0.46 |
| Pleural Effusion | 1.49 | 1.29 | 1.71 | < 0.001 | 1.00 |
| Pulmonary Emphysema | 1.58 | 1.21 | 2.02 | < 0.001 | 0.92 |
| Substance Abuse | |||||
| Nicotine Dependence | 1.09 | 0.92 | 1.28 | 0.31 | 0.15 |
| Tobacco Dependence Syndrome | 2.04 | 1.67 | 2.46 | < 0.001 | 1.00 |
| Alcohol Abuse | 1.45 | 1.14 | 1.82 | 0.002 | 0.79 |
| Alcohol Dependence | 1.15 | 0.85 | 1.53 | 0.34 | 0.12 |
| Transplant | |||||
| Transplanted Heart Valve Present | 0.73 | 0.48 | 1.06 | 0.12 | 0.47 |
| Transplanted Kidney Present | 2.72 | 2.11 | 3.46 | < 0.001 | 1.00 |
| Disorder of Transplanted Kidney | 3.47 | 2.59 | 4.56 | < 0.001 | 1.00 |
| Transplanted Liver Present | 2.27 | 1.44 | 3.38 | < 0.001 | 0.86 |
| Bone Marrow Transplant Present | 1.35 | 0.74 | 2.26 | 0.29 | 0.12 |
| Transplanted Lung Present | 4.79 | 3.25 | 6.82 | < 0.001 | 1.00 |
| Chronic Kidney Disease | |||||
| Chronic Kidney Disease | 1.52 | 1.30 | 1.77 | < 0.001 | 1.00 |
| Chronic Kidney Disease Stage 1 | 2.04 | 1.26 | 3.12 | 0.002 | 0.71 |
| Chronic Kidney Disease Stage 2 | 1.60 | 1.23 | 2.03 | < 0.001 | 0.93 |
| Chronic Kidney Disease Stage 3 | 1.61 | 1.36 | 1.90 | < 0.001 | 1.00 |
| Chronic Kidney Disease Stage 3A | 1.34 | 1.02 | 1.73 | 0.03 | 0.55 |
| Chronic Kidney Disease Stage 3B | 1.26 | 0.91 | 1.70 | 0.14 | 0.29 |
| Chronic Kidney Disease Stage 4 | 1.58 | 1.20 | 2.04 | < 0.001 | 0.89 |
| Chronic Kidney Disease Stage 5 | 2.19 | 1.63 | 2.86 | < 0.001 | 1.00 |
| Chronic Kidney Disease Due to Type 2 Diabetes Mellitus | 1.47 | 1.21 | 1.77 | < 0.001 | 0.98 |
| Hypertensive Heart And Chronic Kidney Disease | 1.48 | 1.04 | 2.06 | 0.02 | 0.52 |
| Chronic Kidney Disease Due to Hypertension | 1.44 | 1.21 | 1.70 | < 0.001 | 0.99 |
| Anemia in Chronic Kidney Disease | 1.56 | 1.27 | 1.89 | < 0.001 | 0.99 |
| Chronic Kidney Disease Stage 5 Due to Hypertension | 7.33 | 4.86 | 10.69 | < 0.001 | 1.00 |
| Liver Diseases | |||||
| Cirrhosis of Liver | 1.29 | 0.98 | 1.66 | 0.06 | 0.40 |
| Steatosis of Liver | 1.30 | 1.11 | 1.53 | 0.001 | 0.86 |
| Disease of Liver | 1.55 | 1.31 | 1.82 | < 0.001 | 1.00 |
| Chronic Nonalcoholic Liver Disease | 1.76 | 1.47 | 2.10 | < 0.001 | 1.00 |
| Liver Function Tests Abnormal | 2.00 | 1.64 | 2.41 | < 0.001 | 1.00 |
| Hepatic Failure | 1.49 | 1.08 | 2.00 | 0.01 | 0.61 |
| Autoimmune Disorders | |||||
| Rheumatoid Arthritis | 1.63 | 1.20 | 2.15 | < 0.001 | 0.87 |
| Multiple Sclerosis | 1.82 | 1.17 | 2.68 | 0.004 | 0.63 |
| Alopecia | 1.98 | 1.64 | 2.37 | < 0.001 | 1.00 |
| Systemic Lupus Erythematosus | 1.02 | 0.62 | 1.56 | 0.95 | 0.03 |
| Postoperative State | 1.13 | 1.02 | 1.24 | 0.01 | 0.64 |
| Diabetes | |||||
| Prediabetes | 1.26 | 1.11 | 1.42 | < 0.001 | 0.95 |
| Type 1 Diabetes Mellitus | 1.74 | 1.20 | 2.45 | 0.002 | 0.68 |
| Type 1 Diabetes Mellitus Without Complication | 1.57 | 1.08 | 2.19 | 0.01 | 0.53 |
| Type 2 Diabetes Mellitus | 1.40 | 1.22 | 1.59 | < 0.001 | 1.00 |
| Type 2 Diabetes Mellitus Without Complication | 1.42 | 1.27 | 1.60 | < 0.001 | 1.00 |
| Age-related & other conditons | |||||
| Obesity | 1.62 | 1.46 | 1.79 | < 0.001 | 1.00 |
| Osteoarthritis | 1.53 | 1.34 | 1.75 | < 0.001 | 1.00 |
| Vitamin D Deficiency | 1.87 | 1.69 | 2.06 | < 0.001 | 1.00 |
| Hypothyroidism | 1.22 | 1.08 | 1.37 | 0.002 | 0.89 |
| Dementia | 0.98 | 0.66 | 1.40 | 0.93 | 0.03 |
Results
Study population
Of the 687,239 patients in the UC CORDS database, 336,500 (48.9%) received a full regimen of a SARS-CoV-2 vaccine as of November 8, 2021, and 110,380 (16.1%) met the final inclusion criteria (Fig. 1). The final study population had a median (IQR) age of 56 (38-70) and included 62,914 (57.0%) females (Table 1 ). Of the 110,380 individuals tested ≥14 days after full vaccination, 65,726 (59.5) identified as white, 5,367 (4.9%) Black, and 14,150 (12.8%) as Asian (Table 1). 107,858 (97.72%) of included patients have at least one recorded comorbidity. 2,705 (2.5%) positive and 107,675 (97.5%) negative patients tested ≥14 days after full vaccination were identified as meeting our full inclusion criterion for the study (Table 1). The vast majority of breakthrough cases in this study (95.6%) were detected on or after May 1, 2021 (Fig. 2 ), which corresponds to the primary Delta variant period in California.25
Fig. 2.
Graph of the proportion of individuals fully vaccinated, infected, hospitalized (after infection), and deceased (after infection) in the studied population over time.
Patient demographics associated with higher odds of breakthrough infection
SARS-CoV-2 breakthrough infection was primarily associated with patients aged 20-29 (OR: 1.49; 95% CI: 1.29 - 1.73; p<.001; power=1) with the median age range (50-59) as the reference group. After adjusting for age, fully vaccinated males have significantly higher adjusted odds of infection (aOR: 1.25; 95% CI: 1.15 - 1.35; p<.001; power=1) compared to females (Supplemental Table 2). In age- and sex-adjusted analysis, Asians displayed significantly lower adjusted odds of breakthrough infection (aOR: 0.66; 95% CI: 0.57 - 0.75; p<.001; power=1) whereas patients identifying as Native American or Pacific Islander were associated with increased adjusted odds of infection (aOR: 1.58; 95% CI: 1.03 - 2.33; p=.03; power=0.39), compared to white individuals (Supplemental Table 1). Hispanic ethnicity was associated with slightly higher adjusted odds of infection in age- and sex-adjusted analyses (aOR: 1.16; 95% CI: 1.03 - 2.33; p=.003; power=0.54) (Supplemental Table 2).
Patient comorbidities associated with breakthrough infection
Clinical data on comorbidities was obtained for 118,093 patients (97.79% of included patients). aORs and statistical power calculations were calculated to measure the association between comorbidities of interest and breakthrough infection following full vaccination, adjusted for age, sex, and race (Table 2 ).
Of the studied vascular/blood flow diseases, coronary atherosclerosis (aOR: 2.12; 95% CI: 1.77 - 2.52; p<.001; power=1), secondary hypertension (aOR: 1.88; 95% CI: 1.30 - 2.62; p<.001; power=0.86), and low blood pressure (aOR: 1.44; 95% CI: 1.21 - 1.71; p<.001; power=0.99) exhibited the highest adjusted odds of breakthrough infection within this group. Interestingly, patients with secondary hypertension exhibited higher adjusted odds of infection compared to those with essential hypertension (aOR: 1.18; 95% CI: 1.07 - 1.29; p<.001; power = 0.97). Of the studied blood abnormalities, patients with hypoxemia (aOR: 1.81; 95% CI: 1.54 - 2.11; p<.001; power=.99) exhibited the highest adjusted odds of breakthrough infection. Of the studied heart diseases, only cardiac arrhythmia (aOR: 1.62; 95% CI: 1.44 - 1.82; p<.001; power=1) exhibited a significantly higher adjusted odds of breakthrough infection and had a large enough sample size.
Patients with transplanted organs exhibited the highest adjusted odds of breakthrough infection amongst all studied patient groups, notably those with previous lung (aOR: 4.79; 95% CI: 3.25 - 6.82; p<.001; power=1), kidney (aOR:2.72; 95% CI: 2.11 - 3.46; p<.001; power=1), and liver transplants (aOR: 2.27; 95% CI: 1.44 - 3.38; p<.001; power=0.86). Additionally, those who exhibited adverse effects or rejection of kidney transplants displayed even higher odds (aOR: 3.47; 95% CI: 2.59 - 4.56; p<.001; power= 1) of breakthrough than those who did not experience transplanted kidney abnormalities.
While CKD has previously been linked to severe COVID-19 outcomes25 , 26, we believe we are the first to study the association of detailed CKD staging and etiology-based disease on patient breakthrough odds. We found that individuals with stage 5 (aOR: 2.12; 95% CI: 1.63 - 2.86; p<.001; power=.99) and stage 3 (aOR: 1.61; 95% CI: 1.36 - 1.90; p<.001; power=.99) CKD exhibited the highest adjusted odds of breakthrough infection amongst statistically powerful CKD stages. Notably, patients with stage 5 CKD due to hypertension faced the highest adjusted odds of breakthrough infection out of any patient group in this study (aOR: 7.33; 95% CI: 4.86 - 10.69; p<.001; power=1).
Finally, among age-related and other conditions, we found that vitamin D deficiency (aOR: 1.87; 95% CI: 1.69 - 2.06; p<.001; power=1), obesity (aOR: 1.62; 95% CI: 1.46 - 1.79; p<.001; power=1), osteoarthritis (aOR: 1.53; 95% CI: 1.33 - 1.75; p<.001; power=1), and hypothyroidism (aOR: 1.22; 95% CI: 1.08 - 1.37; p=.001; power=.88) were significantly associated with higher adjusted odds of infection.
Combination of comorbidities associated with breakthrough infection
We also evaluated the additional added odds of breakthrough infection when a second comorbidity was present. In our analysis, we examined age-, sex-, and race- adjusted odds ratios for comorbidities in combination with others that were present in greater than 11,800 (10% of the total patient sample) patients (Table 3 ). Several comorbidities in combination with essential hypertension, anemia, and type 2 diabetes exhibited notably higher added odds of breakthrough infection. We found that patients with chronic liver disease (aOR: 1.86; 95% CI: 1.47 - 2.33; p<.001, power=.99), obesity (aOR: 1.74; 95% CI: 1.51 - 2.01; p<.001, power=1), or CKD (aOR: 1.60; 95% CI: 1.34 - 1.88; p<.001, power=1) in addition to essential hypertension put patients at additional risk of breakthrough infection compared to those with only essential hypertension. Compared to patients with only anemia, those who were also obese (aOR: 1.80; 95% CI: 1.47 - 2.19; p<.001, power=1), had CKD (aOR: 1.43; 95% CI: 1.15 - 1.77; p=.001, power=.99), and were vitamin D deficient (aOR: 1.42; 95% CI: 1.16 - 1.73; p<.001, power=.99) were at additionally higher odds of infection following vaccination. Finally, those with cardiac arrhythmia (aOR: 1.89; 95% CI: 1.51 - 2.38; p<.001, power=1) and CKD (aOR: 1.65; 95% CI: 1.29 - 2.08; p<.001, power=1) in addition to type 2 diabetes were at significantly higher odds of a breakthrough event.
Table 3.
Age-, sex-, and race- adjusted Odds Ratios (aOR) for comorbidities with N > 11,800 (10% of the total patient sample) in combination with others.
| Comorbidity | aOR | Lower Bound | Upper Bound | P-value | Power Calculation |
|---|---|---|---|---|---|
| Obesity | |||||
| Essential Hypertension | 1.01 | 0.81 | 1.25 | 0.96 | 0.03 |
| Hyperlipidemia | 1.26 | 1.03 | 1.55 | 0.02 | 1.00 |
| Type 2 Diabetes Mellitus without Complication | 1.44 | 1.18 | 1.74 | < 0.001 | 1.00 |
| Type 2 Diabetes Mellitus | 1.31 | 1.06 | 1.61 | 0.01 | 0.97 |
| Prediabetes | 1.06 | 0.85 | 1.32 | 0.57 | 0.14 |
| Osteoarthritis | 1.35 | 1.06 | 1.71 | 0.01 | 0.99 |
| Essential Hypertension | |||||
| Obesity | 1.74 | 1.51 | 2.01 | < 0.001 | 1.00 |
| Chronic Kidney Disease | 1.60 | 1.34 | 1.89 | < 0.001 | 1.00 |
| Steatosis Of Liver | 1.32 | 1.07 | 1.61 | 0.01 | 0.89 |
| Hypothyroidism | 1.13 | 0.95 | 1.35 | 0.17 | 0.49 |
| Chronic Nonalcoholic Liver Disease | 1.86 | 1.47 | 2.33 | < 0.001 | 1.00 |
| Anemia | |||||
| Obesity | 1.80 | 1.47 | 2.19 | < 0.001 | 1.00 |
| Vitamin D Deficiency | 1.42 | 1.16 | 1.73 | < 0.001 | 1.00 |
| Hypothyroidism | 1.03 | 0.81 | 1.30 | 0.81 | 0.06 |
| Chronic Kidney Disease | 1.43 | 1.15 | 1.77 | 0.001 | 1.00 |
| Essential Hypertension | 1.20 | 0.97 | 1.49 | 0.09 | 0.99 |
| Congestive Heart Failure | 1.12 | 0.86 | 1.43 | 0.39 | 0.22 |
| Vitamin D Deficiency | |||||
| Uncomplicated Asthma | 1.19 | 0.93 | 1.50 | 0.15 | 0.74 |
| Essential Hypertension | 1.25 | 1.02 | 1.53 | 0.03 | 1.00 |
| Type 2 Diabetes Mellitus | 1.20 | 0.94 | 1.50 | 0.13 | 0.70 |
| Type 2 Diabetes Mellitus without Complication | |||||
| Congestive Heart Failure | 1.23 | 0.94 | 1.60 | 0.12 | 0.67 |
| Cardiac Arrhythmia | 1.90 | 1.51 | 2.38 | < 0.001 | 1.00 |
| Chronic Kidney Disease | 1.65 | 1.30 | 2.09 | < 0.001 | 1.00 |
Discussion
In this retrospective cohort study, we evaluated the extent to which patients with certain clinical comorbidities are at high risk for SARS-CoV-2 breakthrough infection in comparison to other comorbidities, primarily during the Delta variant (B.1.617) period. Additionally, we found that patients with certain additional comorbidities are at higher odds of breakthrough infection compared to those with only one. While existing studies have identified and confirmed some of these comorbidities as high-risk factors for COVID-19, current literature lacks comprehensive studies comparing a broad spectrum of comorbidities within the same patient population, as well as analysis of the added risk of multiple comorbidities in combination.
After adjusting for age, sex, and race, we found that patients with CKD history were at substantial risk of SARS-CoV-2 breakthrough infection, especially when in conjunction with other comorbidities. Previous studies have identified late-stage CKD patients as high-risk individuals for severe COVID-19 outcomes, especially due to the age and existing comorbidities of these patients.27, 28, 29 In this study, we observed a much higher odds of infection in patients with stage 5 CKD, compared to those with intermediate stages of CKD. Those with non-staging specific CKD due to type 2 diabetes, hypertension, or anemia did not exhibit much higher odds of infection. However, those with CKD stage 5 due to hypertension experienced more than 3 times higher odds of infection compared to those with non-etiology specific stage 5 CKD, the highest adjusted odds ratio in this study. In our comorbidity combination risk analysis, patients with non-etiology and non-staging specific CKD in addition to essential hypertension and anemia exhibited even higher odds of breakthrough infection. Our study further emphasizes the high risk of breakthrough events for patients with CKD and is one of the first to study CKD comprehensively according to staging and etiology in association with SARS-CoV-2 breakthrough events. Thus, our findings indicate that late-stage and etiology-specific CKD may contribute to very high risk of infection, especially in conjunction with one another and with other comorbidities.
We also found that patients with former transplant history were strongly associated with SARS-CoV-2 breakthrough infection. While the risk for transplant recipients is well-documented due to the suppression of the immune system, the substantially higher risk of breakthrough infection for these patients compared to those with other known risk factors of COVID-19 (such as heart and cardiovascular diseases) was not previously known.30 Our findings, that those with lung transplants are at highest risk for breakthrough infection amongst other solid organ transplant recipients are substantiated by the characteristics of COVID-19 as a respiratory disease and the important role of Antigen-Converting Enzyme 2 (ACE2) receptor in inducing viral entry in the lung epithelium, leading to infection and disease severity.31 , 32
Fully vaccinated patients younger than 50 years of age within the UC Health system were at the highest risk of infection. However, previous studies have found older age to be one of the most significant risk factors of SARS-CoV-2 breakthrough infections,33 , 34 due to the increased prevalence of influential comorbidities in older populations and the reduced ability of the innate and adaptive immune system to respond to new pathogens with older age.35 , 36 Our patient pool does not appear to be significantly skewed by age group (Supplemental Fig. 1). Thus, it is not likely that our findings are a result of significant sample survey bias. Instead, our findings may be due to the fact that young individuals are more likely to be in positions of high exposure and to violate social distancing rules.37 , 38 Additionally, studies from previous influenza and respiratory pandemics have found more advanced age to correlate with more compliance with social distancing measures.39 , 40 Social distancing and preventive measures are also associated with education level and higher socio-economic status, both of which are generally obtained with older age.41 , 42 Regardless, our findings concerning the age of patients with breakthrough infections may need further validation.
We found that age-related comorbidities were significantly associated with higher odds of breakthrough infection. Lack of Vitamin D metabolism has been linked to a variety of health problems and chronic diseases, including cancer, diabetes, anxiety, cardiovascular disease, and much more.43 Furthermore, individuals above 65 years of age are known to disproportionately suffer from Vitamin D deficiency, due to increasing adiposity and decreased production of important aging enzymes.44, 45, 46 Current studies have found evidence that Vitamin D deficiency is protective against COVID-19.47 , 48 We found that Vitamin D-deficient patients are likely at significantly higher odds of breakthrough infection than previously suspected; these patients appear to have higher odds of breakthrough infection than several cardiovascular-related conditions and obesity, including cerebrovascular disease, congestive heart disease, and chronic obstructive lung diseases (COPD). Importantly, lockdown measures pose an increased risk for individuals to develop Vitamin D deficiency, which is typically maintained through exposure to sunlight.49 We are the first study to date to report Vitamin D as a potentially major risk factor for breakthrough infection. Patients with hypothyroidism and osteoarthritis, two other age-related diseases, also exhibited significant adjusted odds of breakthrough infections, highlighting the heightened risk faced by older individuals.
Our analysis of multiple comorbidities in combination revealed that obesity is likely the most significantly contributing factor to COVID-19 susceptibility when multiple comorbidities are present. When analyzing additional comorbidities, the odds of breakthrough infection did not substantially increase compared to patients with obesity alone. Furthermore, patients with obesity in addition to essential hypertension and anemia compared to those who only had essential hypertension were at the highest odds of breakthrough infection compared to other added comorbidities.
To our knowledge, our analysis is the most comprehensive study of COVID-19 comorbidities with breakthrough status, as well as the first to study the added risk of multiple comorbidities with breakthrough infection in a statistically significant sample size. Collectively, our systematic analysis of patient comorbidities within a large academic health system demonstrated that patients with chronic kidney disease, transplant history, aging-related diseases (particularly vitamin D deficiency), and lung and vascular diseases are at substantially high risk for COVID-19 despite vaccination compared to known comorbidities. Analysis of monoclonal antibodies (such as tixagevimab/cilgavimab) was not included, as authorization of these treatments was not approved till after the study period. Public health officials should target these individuals, and their providers, so that they understand their increased risk and ways to mitigate it through additional vaccine doses and other behavioral interventions.
Limitations
Our study has several limitations. First, certain errors pertaining to data entry might be impossible to detect. Second, we are limited by the constraints of clinical reporting in an EMR system, including a lack of longitudinal follow-ups. Third, samples were only collected from UC Health and affiliated care sites, which may not be a comprehensive representation of the state of California. Thus, information for patients’ vaccine doses at non-UC affiliated sites may not be present. Fourth, since no further sequencing for variant identification was undertaken for this study, we cannot directly associate these infections with the Delta (B.1.617) SARS-CoV-2 variant. Lastly, no information pertaining to the reason for SARS-CoV-2 testing was included in this dataset (such as symptomatic or asymptomatic infection detection). Therefore, further studies are needed to confirm our findings for these comorbidities.
Conclusions
In this retrospective cohort study of a large academic health system, we found that patients with chronic kidney disease, transplant history, vitamin D deficiency, lung and vascular diseases are at highest odds of breakthrough infection when compared to other condition types. Patients with stage 5 CKD due to hypertension (aOR: 7.33; 95% CI: 4.86 - 10.69; p<.001; power=1) displayed higher odds of infection than any other comorbidity. Lung transplantation history (aOR: 4.79; 95% CI: 3.25 - 6.82; p<.001; power= 1), coronary atherosclerosis (aOR: 2.12; 95% CI: 1.77 - 2.52; p<.001; power=), and vitamin D deficiency (aOR: 1.87; 95% CI: 1.69 - 2.06; p<.00; power=1) were significantly correlated to breakthrough infection. Additionally, patients with obesity in addition to essential hypertension (aOR: 1.74; 95% CI: 1.51 - 2.01; p<.001; power=1) and anemia (aOR: 1.80; 95% CI: 1.47 - 2.19; p<.001; power=1) were at additional risk of breakthrough infection compared to those with only essential hypertension and anemia alone. Our findings are of importance for enacting public health measures to vaccinate and protect the population against the SARS-CoV-2 virus.
Role of funder/sponsor
The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Additional contributions
The authors thank the Center for Data-driven Insights and Innovation at UC Health (CDI2; https://www.ucop.edu/uc-health/functions/center-for-data-driven-insights-and-innovations-cdi2.html), for its analytical and technical support related to use of the UC Health Data Warehouse and related data assets, including the UC COVID Research Data Set (CORDS).
CRediT authorship contribution statement
Michael Hogarth: Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Daniel John: Methodology, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization. Yuxiang Li: Methodology, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization. Jessica Wang-Rodriguez: Validation, Writing – review & editing. Jaideep Chakladar: Validation, Writing – review & editing. Wei Tse Li: Methodology, Validation, Writing – review & editing. Sanjay R. Mehta: Validation, Writing – review & editing. Sharad Jain: Validation, Writing – review & editing. Weg M. Ongkeko: Conceptualization, Methodology, Validation, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.
Declaration of Competing Interest
The authors declare no conflict of interest.
Acknowledgements
Dr. Hogarth and Mr. John contributed equally and are co–first authors.
Footnotes
Supplementary material associated with this article can be found in the online version at https://doi.org/10.1016/j.amjms.2023.04.019.
Appendix. SUPPLEMENTARY MATERIALS
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