Risk of Severe Coronavirus Disease 2019 in Patients With Inflammatory Bowel Disease in the United States: A Multicenter Research Network Study

Patients with inflammatory bowel disease (IBD), both Crohn’s disease (CD) and ulcerative colitis (UC), may be at an increased risk for severe coronavirus disease 2019 (COVID-19) owing to their immunosuppressant medications or the chronic inflammatory disease state.¹ Recently, a worldwide registry Surveillance Epidemiology of Coronavirus Under Research Exclusion (SECURE-IBD) consisting of physician-reported patients with IBD with COVID-19 reported the clinical course of COVID-19 among patients with IBD and the factors associated with severe COVID-19.² However, there are limited data regarding the comparison of clinical characteristics and outcomes among patients with IBD with COVID-19 and other patients. Moreover, the outcomes of patients with IBD with COVID-19 predominantly in the United States remain unexplored. Our study aimed to evaluate the characteristics and outcomes of patients with IBD with COVID-19 in the United States and compare them to a large cohort of patients without IBD with COVID-19.

Methods

This was a population-based retrospective cohort study conducted using TriNetX (Cambridge, MA), a federated health research network data set. We performed a real-time search and analysis of electronic health records of more than 40 million patients from multiple health care organizations (HCOs) globally to identify patients with IBD diagnosed with COVID-19 between January 20, 2020, and May 26, 2020, based on a positive laboratory test result or assignment of COVID-19–specific ICD code. During the same time period, patients diagnosed with COVID-19 and who had no history of or documentation of a diagnosis of IBD ever were included in the non-IBD control group. The outcome of interest was the risk of severe COVID-19 disease, defined as a composite outcome of hospitalization and/or 30-day mortality postdiagnosis of COVID-19. Outcomes were compared in patients with IBD with COVID-19 and patients without IBD with COVID-19 after 1:1 propensity score matching for demographics and comorbid conditions (listed in Table 1 ) using logistic regression and greedy nearest-neighbor matching algorithm with a caliper of 0.1 pooled standard deviations. Details of data source, quality checks, codes used for patient selection and medications, and statistical analysis have been described previously³ and are discussed in the Supplementary Materials.

Table 1.

Comparison of Patient Demographics, Clinical Presentation, Laboratory Findings Among Patients With IBD With COVID-19 and Patients Without IBD With COVID-19^a

Clinical Presentation, Laboratory findings and Outcomes	Demographics and comorbidities
	Before propensity score matching			After propensity score matching
	IBD (n = 232)	Non-IBD (n = 19776)	P value	IBD (n = 232)	Non-IBD (n = 232)	P value
Age, y, mean ± SD	51.2 ± 18.1	49.5 ± 19.1	.18	51.2 ± 18.1	51.2 ± 18.9	.89
Female, n (%)	147 (63.36)	10,937 (55.30)	.01	147 (63.36)	149 (64.22)	.84
Race, n (%)
White	177 (76.29)	10,110 (51.12)	<.0001	177 (76.29)	183 (78.87)	.51
Black or African American	29 (12.5)	4082 (20.64)	<.0001	29 (12.5)	30 (12.93)	.89
Unknown Race	23 (9.91)	4957 (25.06)	<.0001	23 (9.91)	17 (10.42)	.32
Body Mass Index (BMI), kg/m2, mean ± SD	29.5 ± 7.41	30.5 ± 8.02	.09	29.5 ± 7.41	30.4 ± 8.21	.32
Comorbid conditions, n (%)
Essential hypertension	121 (52.12)	5861 (29.64)	<.0001	121 (52.12)	118 (50.86)	.78
Chronic lower respiratory diseases (asthma and COPD)	91 (39.22)	3583 (18.11)	<.0001	91 (39.22)	92 (39.65)	.92
Diabetes mellitus	62 (26.72)	3113 (15.74)	<.0001	62 (26.72)	55 (23.71)	.45
Ischemic heart diseases	49 (21.12)	1892 (9.56)	<.0001	49 (21.12)	45 (19.39)	.64
Chronic kidney disease	38 (16.38)	1377 (6.96)	<.0001	38 (16.38)	35 (15.08)	.70
Heart failure	37 (15.95)	1251 (6.33)	<.0001	37 (15.95)	35 (15.08)	.80
Cerebrovascular diseases	30 (12.93)	1164 (5.88)	<.0001	30 (12.93)	27 (11.63)	.67
Nicotine dependence	35 (15.09)	1597 (8.08)	<.0001	35 (15.09)	30 (12.93)	.50
Alcohol-related disorders	11 (4.74)	618 (3.12)	.16	11 (4.74)	12 (5.17)	.83

Clinical presentation
	IBD (n = 232), n (%)	Non-IBD (n = 19,776), n (%)	P value
Cough	56 (24.14)	4716 (23.84)	.91
Fever	38 (16.37)	3395 (17.16)	.75
Dyspnea	30 (12.93)	2827 (14.29)	.55
Nausea and vomiting	25 (10.77)	813 (4.11)	<.0001
Malaise and fatigue	20 (8.62)	1167 (5.90)	.08
Diarrhea	19 (8.19)	1018 (5.14)	.03
Abdominal pain	18 (7.75)	535 (2.70)	<.0001
Sore throat	14 (6.03)	1040 (5.25)	.59
Hypoxemia	12 (5.17)	1444 (7.30)	.21

Laboratory findings after COVID-19 diagnosis
	IBD (n = 232), mean ± SD (n)	Non-IBD (n = 19,776), mean ± SD (n)	P value
Leukocytes, 1000/μL	7.53 ± 3.60 (67)	7.54 ± 5.66 (5572)	.98
Lymphocytes, 1000/μL	1.35 ± 0.87 (86)	1.45 ±5.08 (6437)	.84
Creatinine, mg/dL	1.15 ± 1.14 (94)	1.12 ± 1.29 (7418)	.79
Alanine aminotransferase, U/L	28.55 ± 21.48 (85)	45.01 ± 116.13 (6276)	.19
Aspartate aminotransferase, U/L	32.27 ± 25.23 (85)	54.16 ± 288.14 (6304)	.48
Alkaline phosphatase, U/L	95.95 ± 101.84 (85)	89.40 ± 65.26 (6275)	.36
Gamma glutamyl transferase, U/L	174.6 ± 138.50 (10b)	186.24 ± 310.69 (159)	.93
Total bilirubin, mg/dL	0.43 ± 0.23 (84)	0.61 ± 1.06 (6239)	.13
Albumin, g/dL	3.54 ± 0.71 (83)	3.4 ± 0.70 (6265)	.05
Prothrombin time, s	14.74 ± 5.53 (56)	14.28 ± 5.80 (3689)	.55
Activated partial thromboplastin time, s	31.16 ± 5.93 (49)	32.88 ± 14.69 (3020)	.41
Ferritin, ng/mL	682.52 ± 804.27 (52)	882.19 ± 2015.49 (4401)	.47
C-reactive protein, mg/L	46.49 ± 74.79 (66)	50.00 ± 69.21 (4870)	.68
Erythrocyte sedimentation rate, mm/h	33.42 ± 19.03 (19)	41.8 ± 27.42 (1407)	.11
Lactate dehydrogenase, mmol/L	296.45 ± 210.79 (53)	374.57 ± 350.33 (4438)	.11
Interleukin 6, pg/mL	53.36 ± 45.52 (12)	314.63 ± 1839.22 (1152)	.62
Procalcitonin, ng/mL	0.32 ± 0.58 (15)	1.75 ± 7.8 (1313)	.48

Outcomes
	Before propensity matching			After propensity matching
Outcomes	Overall risk n/total (%)	Risk ratio (95% CI)	P value	Overall risk n/total (%)	Risk ratio (95% CI)	P value
Severe COVID-19	IBD 56/232 (24.14)	1.15 (0.92–1.45)	.23	IBD 56/232 (24.14)	0.93 (0.68–1.27)	.66
	Non-IBD 4139/19,776 (20.92)			Non-IBD 60/232 (25.86)
Hospitalizations	IBD 56/232 (24.14)	1.20 (0.96–1.51)	.11	IBD 56/232 (24.14)	1.10 (0.74–1.40)	.91
	Non-IBD 3960/19,776 (20.02)			Non-IBD 55/232 (23.70)

COPD, chronic obstructive pulmonary disease; SD, standard deviation.

^aDemographics and comorbidities are compared before and after propensity matching of cohorts.

^bNumbers rounded off to 10 to protect Protected Health Information (PHI).

Results

Of 196,403 patients with IBD from 31 HCOs, 1901 patients underwent testing for COVID-19, and a total of 232 patients with IBD (CD, 101; UC, 93; indeterminate, 38) were diagnosed with COVID-19. During the same time period, 19,776 patients without IBD were also diagnosed with COVID-19 from the same HCOs. The mean age was similar between the groups, and there were more female patients and more prevalent comorbidities in the IBD group (Table 1). A higher proportion of patients in the IBD group presented with nausea and vomiting (10.77% vs 4.31%, P < .01), diarrhea (8.19% vs 5.14%, P < .01), and abdominal pain (7.75% vs 2.70%, P < .01) (Table 1). In a crude, unadjusted analysis, there was no difference in the risk of severe COVID-19 between the IBD and non-IBD groups (risk ratio [RR], 1.15; 95% confidence interval [CI], 0.92–1.45; P = .23). After propensity score matching, both groups were well balanced, and the risk of severe COVID-19 was similar (RR, 0.93; 95% CI, 0.68–1.27; P = .66) (Table 1). Overall, patients with IBD with severe COVID-19 were older and had a higher proportion of multiple comorbidities (Supplementary Table 1).

Medication data were collected up to 1 year preceding the diagnosis of COVID-19 and were available for 166 patients in the IBD group. Sixty-two patients were on immune-mediated therapy (biologics, 37 and/or immunomodulators, 34), 32 patients were on aminosalicylate therapy, and 111 patients had received corticosteroids. Subgroup analysis based on the use of immune-mediated therapy in the preceding 1 year was not associated with a higher risk of severe COVID-19 compared to patients with IBD not on immune-mediated therapy (RR, 1.01; 95% CI, 0.62–1.65; P = .97). The risk of severe COVID-19 was higher in an unadjusted analysis of 71 patients with IBD who received corticosteroids up to 3 months before the diagnosis of COVID-19 (30.98%) compared to patients who did not receive corticosteroids (19.25%) (RR, 1.60; 95% CI, 1.01–2.57; P = .04) (Supplementary Table 2).

Discussion

The composite outcome of hospitalization or mortality after COVID-19 in patients with IBD is similar to patients without IBD. In addition, patients with IBD with COVID-19 on long-term biologics or nonsteroid immunomodulatory therapies did not have a higher risk of poor COVID-19 outcomes. However, recent corticosteroid use that may as well imply poor disease control may be related to worse outcomes. The risk for severe COVID-19 in patients with IBD is also similar to the widely recognized risk factors for COVID-19 outcomes, such as advanced age and comorbidities,⁴ and such patients should be closely monitored.

There are concerns that patients with IBD may be at increased risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)–induced infection and poor outcomes. SARS-CoV-2 has been detected in stool samples of patients with COVID-19,⁵ and high concentrations of angiotensin-converting enzyme 2 (ACE2), the binding site for SARS-CoV-2, are found in the terminal ileum and colon⁶ and can increase in the inflamed gut of patients with IBD.⁷ However, there is no evidence that these factors can influence the course, infectivity, or severity of COVID-19. Another concern in patients with IBD with COVID-19 relates to the use of immune-mediated therapies. Generally, these therapies can be associated with an increased risk of infections. However, these medications are key in inducing and maintaining remission of IBD with subsequent prevention of disease flare-up that may require hospitalizations and corticosteroids, which can increase the risk of severe COVID-19. Furthermore, the use of these therapies could be advantageous in suppressing the inflammatory response or cytokine storm described in patients with severe COVID-19.⁸

Our study is limited by the inherent limitations of an electronic health records based database. A composite primary outcome of hospitalization or death was chosen because the number of individual events was small to evaluate separate endpoints. Despite limitations, this is the first attempt to compare characteristics and estimate the risk of severe COVID-19 in patients with IBD compared to other patient populations while adjusting for confounding variables. IBD patients in remission and on immunomodulators and biologics should stay on their medications and should exercise social distancing principles like the general population. Patients with IBD with advanced age, multiple comorbidities, or with a poorly controlled disease requiring corticosteroids who develop COVID-19 infection should be aggressively managed, given the increased risk of worse outcomes.

Supplementary Methods

Data Source

TriNetX (Cambridge, MA) uses electronic health record data collected from member HCOs. A typical HCO is a large academic health center with data coming from the majority of its affiliates. A single HCO frequently has more than 1 facility, including main and satellite hospitals and outpatient clinics. In the majority of cases, the data originate from the primary electronic health record system. A typical organization has a complex enterprise architecture where the data flow through several different databases, such as a data warehouse and a research data repository, on its way to TriNetX. In addition to electronic health record data, which are usually available in a structured fashion (eg, demographics, diagnoses, procedures, medications, laboratory test results, and vital signs), TriNetX has also the ability to extract facts of interest from the narrative text of clinical documents using natural language processing. TriNetX maps the data to a standard and controlled set of clinical terminologies. The data are then transformed into a proprietary data schema. This transformation process includes an extensive data quality assessment that includes data cleaning, which rejects records that do not meet the TriNetX quality standards.

Data Quality Checks

The TriNetX software checks the basic formatting to ensure, for example, that dates are properly represented. It enforces a list of fields that are required (eg, patient identifier) and rejects those records where the required information is missing. Referential integrity checking is done to ensure that data spanning multiple database tables can be successfully joined together. As the data are refreshed, the software monitors changes in volumes of data over time to ensure data validity. TriNetX requires at least 1 nondemographic fact for a patient to be counted in a given data set. Patient records with only demographic information are not included in data sets.

Coding System

Clinical fact	Coding system
Demographics	Health Level Seven (HL7), version 3 (administrative standards)
Diagnoses	The International Classification of Diseases, Ninth and 10th Revisions, Clinical Modification (ICD-9-CM and ICD-10-CM)a AND Chronic Condition Indicator
Procedures	The International Classification of Diseases, Procedural Classification System, Ninth and 10th Revision, OR Healthcare Common Procedure Coding System
Medications	RxNorm
Laboratory test results, vital signs, and findings	Logical observation identifiers names and codes (LOINC)b

^aIf an HCO provides data in ICD-9-CM, a 9–to–10-CM mapping based on general equivalence mappings (GEM) plus custom algorithms and curation to transform data from ICD-9-CM to ICD-10-CM.

^bTo ease finding and using common laboratory test values, LOINC codes are combined up to the clinically significant level for most frequent laboratory values and coded as TNX: LAB.

Diagnosis Codes Used to Identify Patient Cohorts

Coding System	Code	Description
ICD-10	B34.2	Coronavirus infection unspecified
ICD-10	B97.29	Other coronavirus as the cause of diseases classified elsewhere
ICD-10	J12.81	Pneumonia due to SARS-associated coronavirus
ICD-10	U07.1	2019-nCoV acute respiratory disease (WHO)
ICD-10	K50	Crohn’s disease [regional enteritis]
ICD-10	K51	Ulcerative colitis
COVID-19–related diagnostic tests
CPT	87635	Infectious agent detection by nucleic acid (DNA or RNA); severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (coronavirus disease [COVID-19]), amplified probe technique
HCPCS	U0001	2019 novel coronavirus real-time RT-PCR diagnostic test panel–CDC
HCPCS	U0002	2019 novel coronavirus real-time RT-PCR diagnostic test panel–non-CDC
LOINC	94307-6	SARS coronavirus 2 N gene [presence] in unspecified specimen by nucleic acid amplification using CDC primer-probe set N1
LOINC	94307-6	SARS coronavirus 2 N gene [presence] in unspecified specimen by nucleic acid amplification using CDC primer-probe set N2
LOINC	94309-2	SARS coronavirus 2 RNA [presence] in unspecified specimen by NAA with probe detection
LOINC	94310-0	SARS-like coronavirus N gene [presence] in unspecified specimen by NAA with probe detection
LOINC	94314-2	SARS coronavirus 2 RdRp gene [presence] in unspecified specimen by NAA with probe detection
LOINC	94315-9	SARS coronavirus 2 E gene [presence] in unspecified specimen by NAA with probe detection
LOINC	94316-7	SARS coronavirus 2 N gene [presence] in unspecified specimen by NAA with probe detection
LOINC	94500-6	SARS coronavirus 2 RNA [Presence] in respiratory specimen by NAA with probe detection
LOINC	94501-4	Middle East respiratory syndrome coronavirus (MERS-CoV) RNA [presence] in respiratory specimen by NAA with probe detection
LOINC	94502-2	SARS-related coronavirus RNA [presence] in respiratory specimen by NAA with probe detection
LOINC	94532-9	SARS-related coronavirus + MERS coronavirus RNA [presence] in respiratory specimen by NAA with probe detection
LOINC	94533-7	SARS coronavirus 2 N gene [presence] in respiratory specimen by NAA with probe detection
LOINC	94534-5	SARS coronavirus 2 RdRp gene [presence] in respiratory specimen by NAA with probe detection
LOINC	94559-2	SARS coronavirus 2 ORF1ab region [presence] in respiratory specimen by NAA with probe detection
LOINC	94565-9	SARS coronavirus 2 RNA [presence] in nasopharynx by NAA with nonprobe detection
LOINC	94639-2	SARS coronavirus 2 ORF1ab region [presence] in unspecified specimen by NAA with probe detection
LOINC	94640-0	SARS coronavirus 2 S gene [presence] in respiratory specimen by NAA with probe detection
LOINC	94641-8	SARS coronavirus 2 S gene [presence] in unspecified specimen by NAA with probe detection
LOINC	94647-5	SARS-related coronavirus RNA [presence] in unspecified specimen by NAA with probe detection
LOINC	94660-8	SARS coronavirus 2 RNA [presence] in serum or plasma by NAA with probe detection
LOINC	94756-4	SARS coronavirus 2 N gene [presence] in respiratory specimen by nucleic acid amplification using CDC primer-probe set N1
LOINC	94757-2	SARS coronavirus 2 N gene [presence] in respiratory specimen by nucleic acid amplification using CDC primer-probe set N2
LOINC	94758-0	SARS coronavirus 2 E gene [presence] in respiratory specimen by NAA with probe detection
LOINC	94759-8	SARS coronavirus 2 RNA [presence] in nasopharynx by NAA with probe detection
LOINC	94765-5	SARS coronavirus 2 E gene [presence] in serum or plasma by NAA with probe detection
LOINC	94766-3	SARS coronavirus 2 N gene [presence] in serum or plasma by NAA with probe detection
LOINC	94767-1	SARS coronavirus 2 S gene [presence] in serum or plasma by NAA with probe detection

CDC, Centers for Disease Control and Prevention; NAA, nucleic acid amplification; nCoV, novel coronavirus; ORF, open reading frame; RT-PCR, reverse-transcription polymerase chain reaction; WHO, World Health Organization.

Codes Used toIdentify Medications

Coding System	Code	Description	Classification
RXNORM	327361	Adalimumab	Biological therapy
RXNORM	819300	Golimumab	Biological therapy
RXNORM	709271	Certolizumab	Biological therapy
RXNORM	191831	Infliximab	Biological therapy
RXNORM	1538097	Vedolizumab	Biological therapy
RXNORM	354770	Natalizumab	Biological therapy
RXNORM	847083	Ustekinumab	Biological therapy
RXNORM	6851	Methotrexate	Immunomodulators
RXNORM	1256	Azathioprine	Immunomodulators
RXNORM	52582	Mesalamine	Amino salicylates
RXNORM	32385	Olsalazine	Amino salicylates
RXNORM	9524	Sulfasalazine	Amino salicylates
RXNORM	8640	Prednisone	Corticosteroids
RXNORM	19831	Budesonide	Corticosteroids

Statistical Analysis

All statistical analyses were performed in real time using TriNetX. The TriNetX uses a custom-built platform developed from Java 1.8.0_171, R 3.4.4 (R Core Team, Vienna, Austria), and Python 3.6.5 with their software language packages to ensure the accuracy and validity of results. The means, standard deviations, and proportions were used to describe and compare patient characteristics. Categorical variables were compared by using the Pearson chi-square test and continuous variables by using an independent-samples t test. Logistic regression on our input covariates was used to obtain propensity scores for each patient in both cohorts. Logistic regression was performed in Python using standard libraries numpy and sklearn. The same analyses were also performed in R software to ensure that the outputs match. After the calculation of propensity scores, matching was performed using a greedy nearest-neighbor matching algorithm with a caliper of 0.1 pooled standard deviations. The order of the rows in the covariate matrix can affect the nearest neighbor matching; therefore, the order of the rows in the matrix was randomized to eliminate this bias. For each outcome, the risk ratio (RR) with a 95% CI was calculated to compare the association of obesity with the outcome. An a priori defined 2-sided alpha of less than .05 was used for statistical significance. TriNetX obfuscates patient counts to safeguard protected health information by rounding patient counts in analyses up to the nearest 10.

Supplementary Table 1.

Characteristics of Patients With IBD With and Without the Composite Outcome of Hospitalization or 30-Day Mortality

Characteristics	Patients with composite outcomes	Patients without composite outcomes	P value
Number of patients	56	176
Age, y, mean ± SD	62.6 ± 18.6	47.6 ± 16.3	<.0001
Female, n (%)	32 (57.14)	115 (65.34)	.17
Male, n (%)	24 (42.85)	61 (34.65)	.17
Race, n (%)
White	41 (73.21)	41 (73.21)	.46
Black or African American	10a (17.85)	21 (11.93)	—
Unknown race	10a (17.85)	16 (9.09)	—
Body Mass Index (BMI) kg/m2	28.3 ± 6.85	30 ± 7.59	.09
Comorbid conditions, n (%)
Essential hypertension	42 (75)	79 (44.88)	<.0001
Diabetes mellitus	22 (39.28)	44 (22.72)	.01
Chronic lower respiratory diseases	26 (46.43)	65 (36.38)	.2
Ischemic heart diseases	24 (42.85)	25 (14.20)	<.0001
Heart failure	23 (41.07)	14 (7.95)	<.0001
Cerebrovascular diseases	17 (30.36)	13 (7.38)	<.0001
Chronic kidney disease	17 (30.36)	21 (11.93)	<.0001

^aNumbers rounded off to 10 to protect HPI.

Supplementary Table 2.

Characteristics and Outcomes of Patients With IBD Who Received Corticosteroid Therapy 3 Months Preceding the Diagnosis of COVID-19 Compared to Patients With IBD Who Did Not

Demographics	Before propensity score matching			After propensity score matching
	Steroids	Nonsteroids	P value	Steroids	Nonsteroids	P value
Number of patients	71	161		62	62
Age, y, mean ± SD	51.3 ± 14.1	51.1 ± 19.6	.93	50.2 ± 14.1	46.9 ± 20.7	.31
Female, n (%)	44 (61.97)	10,937 (55.30)	.77	40 (64.51)	42 (67.74)	.71
Male, n (%)	27 (38.02)	58 (36.02)	.77	22 (35.48)	20 (32.25)	.71
Race, n (%)
White	51 (71.83)	126 (78.26)	.28	45 (72.58)	46 (74.19)	.83
Black or African American	10a (14.08)	21 (13.04)	—	10a (16.12)	10a (16.12)	—
Unknown race	11 (15.49)	12 (7.45)	.05	10a (16.12)	10a (16.12)	—
Body Mass Index (BMI) kg/m2	30.7 ± 7.81	29 ± 7.2	.09	30.3 ± 8.05	29.6 ± 7.58	.63
Comorbid conditions, n (%)
Essential hypertension	43 (60.56)	78 (48.44)	.08	34 (54.84)	31 (50)	.58
Chronic lower respiratory diseases (asthma and COPD)	42 (59.15)	49 (30.43)	<.0001	34 (54.83)	36 (58.06)	.71
Diabetes mellitus	24 (33.80)	38 (23.60)	.11	18 (29.03)	16 (25.80)	.68
Heart failure	20 (28.17)	17 (10.55)	<.0001	14 (22.58)	11 (17.74)	.5
Ischemic heart diseases	20 (28.17)	29 (18.01)	.08	14 (22.58)	13 (20.96)	.83
Chronic kidney disease	19 (26.76)	20 (12.42)	<.0001	12 (19.35)	10 (16.13)	.63

Outcomes	Before propensity matching			After propensity matching
	Overall risk, n (%)	Risk ratio (95% confidence interval)	P value	Overall risk, n (%)	Risk ratio (95% confidence interval)	P value
Severe COVID-19	Steroids: 22 (30.98)	1.60 (1.01–2.57)	0.04	Steroids: 18 (29.03)	b	—
	Nonsteroids: 31 (19.25)			Nonsteroids ≤10a (≤16.13)

^aNumbers rounded off to 10 to protect HPI.

^bRisk ratio cannot be estimated because of outcomes of ≤10 in the nonsteroid group.