Abstract
Background:
While influenza A(H3N2)-predominant seasons tend to have increased rates of influenza-associated hospitalizations and deaths, little is known about differences in clinical presentation and hospitalization outcomes by influenza type and subtype.
Methods:
Data from hospitalized adults aged ≥18 years in four U.S. states from 2017–2020 were used to evaluate the association between influenza type/subtype and severe influenza presentation and outcomes. Log binomial regression and modified Poisson regression with robust error variance were used to estimate adjusted risk ratios (aRR) for clinical indicators within 24 hours of hospital admission and severity outcomes. Multivariable Cox proportional hazard models were used to estimate adjusted hazard ratios for length of hospital stay and intensive care unit (ICU) stay. All models were adjusted for underlying conditions, age group, influenza vaccination, and site.
Results:
Patients with influenza A(H1N1) were more likely to be admitted to an ICU (aRR=1.42) than patients with A(H3N2). Patients with influenza A(H1N1) had higher risk of hypoxemia (SpO2 <90%) than patients with A(H3N2) (aRR=1.24) and B (aRR=1.43). Patients with influenza A(H1N1) compared with A(H3N2) also had higher risk of hyponatremia (sodium <135 mmol/L, aRR=1.19) and compared with B had higher risk of fever (>38°C, aRR=1.56).
Conclusions:
Adult patients hospitalized with influenza A(H1N1) had a higher risk of multiple severity indicators. Better understanding of influenza severity related to both host and virus type are important for reducing the burden of severe influenza in adults.
Keywords: influenza, severity, hospital outcomes, clinical indicators, acute respiratory illness
Summary of article’s main points:
Adults hospitalized with influenza A(H1N1) virus infection had a higher risk of ICU admission compared to those with influenza A(H3N2) and a higher risk several clinical indicators related to respiratory distress compared to those with influenza A(H3N2) or B infection.
Introduction
Influenza virus infection caused an estimated 120,000–710,000 hospitalizations and 6,300–52,000 deaths annually in the United States from 2010–2024, varying by season and circulating influenza types/subtypes.1 Understanding the clinical features and outcomes of severe influenza, including by type (e.g., influenza A vs B) and subtype (e.g., H1N1 vs H3N2), is essential to anticipating both patient risks and the overall healthcare-associated burden during each influenza season. While influenza–associated deaths and hospitalizations tend to be highest when A(H3N2) viruses predominate, it is unclear whether this seasonal effect results from the clinical severity of A(H3N2) illness itself or from factors associated with patients who have the illness (e.g., age, underlying conditions).2,3 Although influenza vaccination and treatment with antiviral medications such as oseltamivir can attenuate disease severity,4–6 contextualizing the effectiveness of disease attenuation also requires phenotyping the clinical spectrum of influenza disease. Previous studies have characterized hospitalized patients with influenza at the point of admission but lack sample size or testing to compare their clinical picture across virus subtypes.7,8
To better understand variations in the severity of outcomes and related clinical indicators arising from circulating influenza A(H1N1)pdm09 (referred to as A[H1N1] in this paper), A(H3N2), and B viruses in hospitalized adult patients, we leveraged data from the Hospitalized Adult Influenza Vaccine Effectiveness Network (HAIVEN), a consortium of hospitals in four U.S. states that operated during five influenza seasons from 2015–2020. Previous HAIVEN studies have identified predictors of severe acute respiratory infection and the impact of influenza vaccination on severe influenza and influenza-associated pneumonia.9–13 In this analysis, we characterize the clinical manifestations and severe outcomes of hospitalized adult influenza illness across influenza types/subtypes during the 2017–2018 through 2019–2020 influenza seasons.14–16
Methods
Patients and Settings
HAIVEN consisted of academic and community hospitals in Texas, Tennessee, Pennsylvania, and Michigan (comprising 10 hospitals during 2017–2018, 12 during 2018–2019, and 14 during 2019–2020). HAIVEN’s data collection methods and inclusion/exclusion criteria have been described elsewhere.11,12 Briefly, hospitalized adults (≥18 years) meeting criteria for acute respiratory illness (ARI), defined as new or worsening cough and/or change in sputum production with onset during the previous ≤10 days, were recruited for enrollment. Following informed consent by patients or their proxies, enrolled patients had an upper respiratory specimen collected within 10 days of illness onset and 72 hours of hospital admission. Influenza testing was performed using a reverse transcription polymerase chain reaction (RT-PCR) assay developed by the Centers for Disease Control and Prevention (CDC)17 or a respiratory virus panel molecular assay ordered in a clinical setting, with determination of influenza A subtype where possible.
Trained study personnel completed a structured interview with patients or their proxies to collect information on demographic characteristics, vaccination status, and general health status. HAIVEN sites extracted data from medical charts on underlying medical conditions, presentation of illness, clinical course, and death within 30 days of hospitalization. Influenza vaccination status was verified through review of medical records, external immunization administration/claims records, state immunization registries, and self- or surrogate-report. A participant was considered vaccinated if they received the current season’s influenza vaccine ≥14 days prior to illness onset based on verified records or plausible self-report with date and location were provided. A participant was considered unvaccinated if they did not receive the vaccine prior to illness onset. Participants vaccinated ≤13 days prior to illness and participants with unknown vaccination status were excluded from analysis. Underlying medical conditions were derived from the medical record and included specific International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes recorded ≤1 year prior to enrollment as well as body mass index (BMI) scores ≥30 and self-/surrogate-reported organ transplant, chemotherapy/radiation, and immunosuppressive medications (see Supplementary Table 1).
Illness Severity Variables
Clinical and laboratory indicators during the first 24 hours of hospitalization were collected from the medical record. Abnormal clinical indicators included altered mental status, tachypnea (respiratory rate >20 breaths/min), hypotension (systolic blood pressure <90 mmHg), fever (>38oC), tachycardia (heart rate >100 beats/min), hyponatremia (sodium levels <135 mmol/L), anemia (hematocrit <42% for males and <37% for females), abnormal white blood cell count (<4 cells×109/L or >12 cells×109/L), hypoxemia (peripheral oxygen saturation <90%), and high creatinine levels (>1.3 mg/dL for males and >1.1 mg/dL for females) based on the most extreme value recorded within 24 hours of hospital admission. Severe clinical outcomes included supplemental oxygen use (non-invasive and invasive mechanical ventilation), intensive care unit (ICU) admission, length of hospital and ICU stay, and death during hospitalization or within 30 days of discharge. Additionally, we created a composite variable for intensive organ support, which included the use of invasive mechanical ventilation, vasopressors, or extracorporeal membrane oxygenation (ECMO) during hospitalization.
Statistical Analysis
Our analysis included patients testing positive for influenza A(H1N1), A(H3N2), or B by molecular assay. We excluded patients coinfected with ≥2 influenza types/subtypes. We described demographic and clinical characteristics of patients by influenza type/subtype using counts and percentages or medians and interquartile ranges (IQR). We compared type/subtype groups using the Pearson Chi-square test for categorical variables or Wilcoxon rank-sum testing for continuous variables. Post-hoc pairwise comparisons were made using the Bonferroni correction for type I error when the overall Chi-square test was significant.
We used log binomial regression to calculate adjusted risk ratios (aRR) for severe clinical outcomes and abnormal clinical indicators among patients with influenza A(H1N1), influenza A(H3N2), and influenza B. Modified Poisson regression with robust error variance was used when log binomial regression models did not converge. After satisfying the proportional hazards assumption using the cox.zph() function in R, we used multivariable Cox proportional hazards models to determine the association of influenza type/subtype with length of hospital and ICU stay. Patients were censored at their time of death if they died during hospitalization or ICU stay. Resulting adjusted hazard ratios (aHR) >1 indicated an increased risk of earlier discharge. We adjusted the log binomial regression and hazards models for a priori confounders, including categorical variables for underlying conditions (0, 1–2, 3–4, ≥5 categories), age group (18–49, 50–64, 65–74, ≥75 years), current season influenza vaccination status (vaccinated, unvaccinated), and site (Michigan, Texas, Pennsylvania, Tennessee). Due to high numbers of underlying conditions among those who died, we redefined this confounder as a binary variable based on this population’s median number of underlying conditions (≤5 and >5 categories) for the death outcome model. Patients missing data on an outcome or covariate in a regression model were excluded from the corresponding analysis. P-values of <0.05 or 95% confidence intervals excluding the null value were considered significant for all analyses.
Analyses were conducted using SAS version 9.4 (Cary, North Carolina, USA) and R version 4.4.1 (R Core Team, Vienna, Austria). This study was reviewed and approved by the CDC and all participating institutions’ institutional review boards (See 45 C.F.R. part 46.114; 21 C.F.R. part 56.114).
Results
Patient characteristics
Overall, 11,872 patients hospitalized with ARI, including 4,107 in 2017–2018, 3,975 in 2018–2019, and 3,790 in 2019–2020, were enrolled (Figure 1). Among patients with ARI, 2,079 (18%) had laboratory-confirmed influenza, including 767 (37%) with A(H1N1), 847 (41%) with A(H3N2), 379 (18%) with B, 85 (4%) with influenza A with no subtype result, and 1 (0%) with an influenza A and B coinfection. Our final analytic sample was comprised of 1,993 patients infected with influenza A(H1N1), A(H3N2), or B. The median age of influenza-positive patients was 63 years (IQR 53–74 years), and the majority (96%) had one or more underlying conditions (Table 1). A total of 197 (10%) influenza-positive patients were admitted to the ICU and 43 (2%) died during hospitalization or within 30 days of hospital discharge (Table 2). The median hospital stay was 3 days (IQR: 2–5 days), and the median ICU stay was 2 days (IQR: 1–4 days).
Figure 1.
Exclusion flowchart, HAIVEN, 2017–2020. Abbreviation: HAIVEN, Hospitalized Adult Influenza Vaccine Effectiveness Network.
Table 1.
Demographic and clinical characteristics of hospitalized adults with influenza by type/subtype, HAIVEN, 2017–2020.
| Characteristics | Overall (n = 1,993) | Influenza Type/Subtype |
p-value | ||
|---|---|---|---|---|---|
| A(H1N1) (n = 767) |
A(H3N2) (n = 847) |
B (n = 379) |
|||
|
| |||||
| Demographics | |||||
|
| |||||
| Season | <0.01 | ||||
| 2017–2018 | 913 (46) | 114 (15) | 558 (66) | 241 (64) | |
| 2018–2019 | 528 (26) | 240 (31) | 273 (32) | 15 (4) | |
| 2019–2020 | 552 (28) | 413 (54) | 16 (2) | 123 (32) | |
| Study site | <0.01 | ||||
| Michigan | 531 (27) | 206 (27) | 217 (26) | 108 (28) | |
| Texas | 513 (26) | 148 (19) | 244 (29) | 121 (32) | |
| Pennsylvania | 549 (28) | 239 (31) | 233 (28) | 77 (20) | |
| Tennessee | 400 (20) | 174 (23) | 153 (18) | 73 (19) | |
| Female | 1130 (57) | 427 (56) | 474 (56) | 229 (60) | 0.26 |
| Age, years (Median [IQR]) | 63 [53–74] | 61 [52–70] | 66 [56–77] | 63 [51–74] | <0.01 |
| Age group, years | <0.01 | ||||
| 18–49 | 410 (21) | 165 (22) | 155 (18) | 90 (24) | |
| 50–64 | 642 (32) | 291 (38) | 237 (28) | 114 (30) | |
| 65–74 | 475 (24) | 189 (25) | 203 (24) | 83 (22) | |
| ≥75 | 466 (23) | 122 (16) | 252 (30) | 92 (24) | |
| Race/Ethnicity | 0.05 | ||||
| White, non-Hispanic | 1289 (65) | 504 (66) | 554 (65) | 231 (61) | |
| Black, non-Hispanic | 531 (27) | 194 (25) | 235 (28) | 102 (27) | |
| Other, non-Hispanic | 75 (4) | 34 (4) | 24 (3) | 17 (4) | |
| Hispanic | 98 (5) | 35 (5) | 34 (4) | 29 (8) | |
|
| |||||
| Patient Characteristics | |||||
|
| |||||
| Received current season vaccinationa | 1185/1927 (61) | 407/740 (55) | 569/821 (69) | 209/366 (57) | <0.01 |
| Housed in assisted living or nursing home | 64 (3) | 20 (3) | 32 (4) | 12 (3) | 0.41 |
| Home oxygen use | 55 (3) | 30 (4) | 17 (2) | 8 (2) | 0.05 |
| Receiving in-home medical care | 336 (17) | 131 (17) | 139 (16) | 66 (17) | 0.89 |
| Charlson Comorbidity Index (Median [IQR]) | 3 [1–5] | 2 [1–5] | 3 [1–5] | 3 [1–5] | 0.06 |
| Charlson Comorbidity Index | 0.10 | ||||
| 0 | 281 (14) | 108 (14) | 116 (14) | 57 (15) | |
| 1–2 | 686 (34) | 291 (38) | 269 (32) | 126 (33) | |
| 3–4 | 440 (22) | 168 (22) | 187 (22) | 85 (22) | |
| ≥5 | 586 (29) | 200 (26) | 275 (32) | 111 (29) | |
| Coinfected with RSV | 24/1881 (1) | 7/742 (1) | 9/790 (1) | 8/349 (2) | 0.16 |
|
| |||||
| Underlying Conditions | |||||
|
| |||||
| Total number of underlying conditions (Median [IQR]) | 4 [2–5] | 4 [2–5] | 4 [2–5] | 4 [2–5] | 0.39 |
| Patients with any underlying condition | 1909 (96) | 738 (96) | 809 (96) | 362 (96) | 0.75 |
| Number of underlying condition categories | 0.56 | ||||
| 0 | 84 (4) | 29 (4) | 38 (4) | 17 (4) | |
| 1–2 | 496 (25) | 203 (26) | 198 (23) | 95 (25) | |
| 3–4 | 768 (39) | 305 (40) | 321 (38) | 142 (37) | |
| ≥5 | 645 (32) | 230 (30) | 290 (34) | 125 (33) | |
| Underlying condition categories | |||||
| Metabolic/endocrine disease | 1579 (79) | 603 (79) | 665 (79) | 311 (82) | 0.32 |
| Cardiovascular disease | 1138 (57) | 417 (54) | 497 (59) | 224 (59) | 0.15 |
| Pulmonary disease | 1349 (68) | 548 (71) | 557 (66) | 244 (64) | 0.02 |
| Immunosuppression | 820 (41) | 315 (41) | 349 (41) | 156 (41) | 1.00 |
| Chronic renal disease | 804 (40) | 293 (38) | 357 (42) | 154 (41) | 0.27 |
| Neurological/musculoskeletal disease | 686 (34) | 232 (30) | 331 (39) | 123 (32) | <0.01 |
| Malignancy | 518 (26) | 201 (26) | 230 (27) | 87 (23) | 0.30 |
| Liver disease | 247 (12) | 92 (12) | 96 (11) | 59 (16) | 0.11 |
Abbreviations: HAIVEN, Hospitalized Adult Influenza Vaccine Effectiveness Network; IQR, interquartile range; RSV, respiratory syncytial virus.
A participant was considered vaccinated if the current season’s influenza vaccine was received ≥14 days prior to illness onset based on verified records or plausible self-report, if date and location were provided.
Table 2.
Clinical presentation (within 24 hours of admission) and outcomes of hospitalized adults with influenza by type/subtype, HAIVEN, 2017–2020
| Characteristicsa | Overall (n = 1,993) |
Influenza Type/Subtype |
p-value | ||
|---|---|---|---|---|---|
| A(H1N1) (n = 767) |
A(H3N2) (n = 847) |
B (n = 379) |
|||
|
| |||||
| Clinical Presentation | |||||
|
| |||||
| Altered mental status detectedb | 80/1869 (4) | 27/684 (4) | 37/842 (4) | 16/343 (5) | 0.85 |
| Respiratory rate, breaths / minute | |||||
| First recording (Median [IQR]) | 20 [18–22] | 20 [18–22] | 20 [18–22] | 20 [18–24] | 0.39 |
| Tachypnea (>20) | 1286/1982 (65) | 517/760 (68) | 540/847 (64) | 229/375 (61) | 0.05 |
| Systolic blood pressure, mmHg | |||||
| First recording (Median [IQR]) | 136 [119–152] | 135 [119–151] | 136 [119–153] | 137 [120–153] | 0.36 |
| Hypotension (<90) | 193/1980 (10) | 81/760 (11) | 79/846 (10) | 33/374 (9) | 0.54 |
| Temperature (°C) | |||||
| First recording (Median [IQR]) | 37.2 [36.8–37.9] | 37.3 [36.8–37.9] | 37.2 [36.8–37.9] | 37.1 [36.7–37.5] | <0.01 |
| Fever (>38) | 687/1984 (35) | 289/762 (38) | 304/847 (36) | 94/375 (25) | <0.01 |
| Pulse rate, beats / minute | |||||
| First recording (Median [IQR]) | 98 [84–112] | 99 [85–113] | 99 [84–113] | 93 [81–108] | <0.01 |
| Tachycardia (>100) | 1241/1984 (63) | 494/762 (65) | 536/847 (63) | 211/375 (56) | 0.02 |
| Sodium levels, mmol/L | |||||
| First recording (Median [IQR]) | 136 [134–139] | 136 [134–139] | 137 [134–139] | 136 [134–139] | 0.11 |
| Hyponatremia (<135) | 665/1974 (34) | 281/762 (37) | 262/837 (31) | 122/375 (33) | 0.05 |
| Hematocrit levels (%) | |||||
| First recording (Median [IQR]) | 38.9 [35.0–42.5] | 39.4 [35.0–43.1] | 38.7 [34.6–42.0] | 38.9 [35.3–42.1] | 0.08 |
| Anemia (<42 [males], <37 [females]) | 1264/1915 (66) | 467/747 (63) | 566/820 (69) | 231/348 (66) | 0.02 |
| White blood cell count [(cells×109)/L] | |||||
| First recording (Median [IQR]) | 7.5 [5.5–10.4] | 7.2 [5.3–10.4] | 7.7 [5.9–10.6] | 7.4 [5.3–10.2] | <0.01 |
| Abnormal (<4 or >12) | 747/1931 (39) | 284/751 (38) | 317/824 (38) | 146/356 (41) | 0.59 |
| O2 saturation (%) | |||||
| First recording (Median [IQR]) | 95 [93–97] | 95 [92–97] | 96 [93–97] | 96 [94–98] | <0.01 |
| Hypoxemia (<90) | 489/1962 (25) | 213/751 (28) | 199/834 (24) | 77/377 (20) | 0.01 |
| Creatinine, mg/dL | |||||
| First recording (Median [IQR]) | 1.0 [0.8–1.4] | 1.0 [0.8–1.3] | 1.0 [0.8–1.4] | 1.1 [0.8–1.4] | 0.75 |
| High (>1.3 [males], >1.1 [females]) | 670/1882 (36) | 245/741 (33) | 289/789 (37) | 136/352 (39) | 0.15 |
|
| |||||
| Clinical Outcomes | |||||
|
| |||||
| Hospital stay length, days (Median [IQR]) | 3 [2–5] | 3 [2–5] | 3 [2–5] | 3 [2–5] | 0.38 |
| Level of most intensive ventilator support | |||||
| Invasive mechanical ventilation | 45 (2) | 19 (2) | 17 (2) | 9 (2) | 0.81 |
| Non-invasive mechanical ventilation | 220 (11) | 80 (10) | 104 (12) | 36 (9) | 0.28 |
| Vasopressor use | 29/1948 (1) | 11/748 (1) | 14/830 (2) | 4/370 (1) | 0.73 |
| ECMO | 2/1991 (0.1) | 0/767 (0) | 1/845 (0.1) | 1/379 (0.3) | 0.41 |
| Admitted to ICU | 197/1991 (10) | 92/767 (12) | 67/845 (8) | 38/379 (10) | 0.02 |
| ICU stay length, days (Median [IQR]) | 2 [1–4] | 2 [1–4] | 2 [1–4] | 2 [1–3] | 0.30 |
| Deathc | 43/1991 (2) | 12/767 (2) | 18/845 (2) | 13/379 (3) | 0.12 |
Abbreviations: HAIVEN, Hospitalized Adult Influenza Vaccine Effectiveness Network; IQR, interquartile range; ECMO, extracorporeal membrane oxygenation; ICU, intensive care unit.
The following continuous variables had missing data: respiratory rate (N=13), systolic blood pressure (N=13), temperature (N=10), pulse rate (N=12), sodium levels (N=19), hematocrit levels (N=80), white blood cell count (N=77), O2 saturation (N=32), creatinine (N=118), and length of hospital stay (N=2).
Texas did not collect data on this variable in the 2019–2020 influenza season.
During hospitalization or within 30 days after discharge.
Clinical Epidemiology of Influenza by Type/Subtype
Influenza A(H3N2) virus infection comprised the highest proportion of influenza cases (42%) among patients and was the dominant type/subtype during the 2017–2018 (61%) and 2018–2019 (52%) seasons (Table 1). Influenza A(H1N1) predominated in 2019–2020 (75%). Overall, patients with influenza A(H3N2) were older (median: 66 years) than patients with influenza A(H1N1) (median: 61 years) and B (median: 63 years) (see Supplementary Table 2). More patients with influenza A(H3N2) virus infection were vaccinated (69%) compared with patients diagnosed with influenza B (57%) and A(H1N1) (55%). Patients with influenza A(H3N2) were more likely to have underlying neurological/musculoskeletal disease (39%) than patients with influenza A(H1N1) (30%) and B (32%). Patients with influenza A(H1N1) were diagnosed with a pulmonary condition more frequently (71%) than patients with influenza A(H3N2) (66%) and B (64%).
Severity Indicators and Outcomes by Patient Influenza Type/Subtype
In the first 24 hours of hospital admission, patients with influenza A(H1N1) had a higher risk of tachypnea and hypoxemia compared to patients with influenza A(H3N2) (aRR=1.07 [95% CI: 1.00 to 1.15] and aRR=1.24 [95% CI: 1.05 to 1.47], respectively) and B (aRR=1.12 [95% CI: 1.02 to 1.23] and aRR=1.43 [95% CI: 1.13 to 1.81], respectively) (Table 3). Several other clinical indicators were elevated in patients with influenza A(H1N1), including hyponatremia (aRR=1.19 [95% CI: 1.03 to 1.37]) when compared to patients with influenza A(H3N2), and fever (aRR=1.56 [95% CI: 1.28 to 1.91]) and tachycardia (aRR=1.15 [95% CI: 1.04 to 1.27]) when compared to patients with influenza B. The risks for fever (aRR=0.66 [95% CI: 0.54 to 0.81]) and tachycardia (aRR=0.86 [95% CI: 0.78 to 0.95]) were lower in patients with influenza B compared to patients with influenza A(H3N2).
Table 3.
Adjusteda risk ratiosb (aRR) and 95% CI of clinical indicators in hospitalized adults by influenza type/subtype, HAIVEN, 2017–2020
| Outcome | aRR (95% CI) |
||
|---|---|---|---|
| A(H1N1) vs A(H3N2) | B vs A(H3N2) | A(H1N1) vs B | |
|
| |||
| Altered mental status detected | 1.65 (0.99, 2.75) | 1.00 (0.56, 1.78) | 1.66 (0.87, 3.14) |
| Respiratory rate, breaths/minute | |||
| Tachypnea (>20) | 1.07 (1.00, 1.15) | 0.96 (0.87, 1.05) | 1.12 (1.02, 1.23) |
| Systolic blood pressure, mmHg | |||
| Hypotension (<90) | 1.18 (0.87, 1.61) | 0.95 (0.64, 1.41) | 1.25 (0.84, 1.84) |
| Temperature (°C) | |||
| Fever (>38) | 1.04 (0.91, 1.18) | 0.66 (0.54, 0.81) | 1.56 (1.28, 1.91) |
| Pulse rate, beats/minutec | |||
| Tachycardia (>100) | 0.99 (0.92, 1.06) | 0.86 (0.78, 0.95) | 1.15 (1.04, 1.27) |
| Sodium levels, mmol/L | |||
| Hyponatremia (<135) | 1.19 (1.03, 1.37) | 1.04 (0.87, 1.24) | 1.15 (0.96, 1.37) |
| Hematocrit (%)c | |||
| Anemia (<42 for males, <37 for females) | 0.94 (0.87, 1.01) | 0.96 (0.88, 1.05) | 0.97 (0.88, 1.07) |
| White blood cell count [(cells×109)/L] | |||
| Abnormal (<4 or >12) | 0.92 (0.81, 1.04) | 1.03 (0.88, 1.19) | 0.90 (0.77, 1.05) |
| Peripheral oxygen saturation (%) | |||
| Hypoxemia (<90) | 1.24 (1.05, 1.47) | 0.87 (0.68, 1.10) | 1.43 (1.13, 1.81) |
| Creatinine, mg/dLc | |||
| High (>1.3 for males, >1.1 for females) | 0.96 (0.84, 1.09) | 1.06 (0.91, 1.25) | 0.90 (0.76, 1.06) |
Abbreviations: HAIVEN, Hospitalized Adult Influenza Vaccine Effectiveness Network; CI, confidence interval.
Adjusted for underlying conditions (0, 1–2, 3–4, and ≥5 categories), age group (18–49, 50–64, 65–74, and ≥75 years), current season vaccination ≥14 days before illness onset, and site.
Multivariable log binomial regression models unless otherwise noted.
Modified Poisson regression with robust error variance.
Patients with influenza A(H1N1) were 42% more likely (aRR=1.42 [95% CI: 1.05 to 1.93]) to be admitted to the ICU than patients with influenza A(H3N2) (Table 4). There was no significant difference in the risk of supplemental oxygen use, intensive organ support, or death during hospitalization or within 30 days of discharge between patients with influenza A(H1N1) and A(H3N2). Additionally, there was no significant difference in the risk of any severe outcome for patients with influenza B compared to patients with influenza A(H3N2) or influenza A(H1N1). There was no difference in the length of hospital or ICU stay among patients with influenza A(H1N1), A(H3N2), or B (Table 5).
Table 4.
Adjusted risk ratios (aRR) and 95% CI for severe in-hospital outcomes in hospitalized adults by influenza type/subtype, HAIVEN, 2017–2020
| Outcome | aRR (95% CI) |
||
|---|---|---|---|
| A(H1N1) vs A(H3N2) | B vs A(H3N2) | A(H1N1) vs B | |
|
| |||
| Supplemental oxygen usea,b | 0.96 (0.76, 1.23) | 0.74 (0.54, 1.01) | 1.31 (0.94, 1.86) |
| ICU admissiona | 1.42 (1.05, 1.93) | 1.16 (0.79, 1.70) | 1.23 (0.86, 1.77) |
| Intensive organ supporta,c | 0.93 (0.52, 1.66) | 0.88 (0.44, 1.74) | 1.06 (0.52, 2.15) |
| Death during hospitalization or within 30 days of discharged | 0.84 (0.38, 1.84) | 1.63 (0.75, 3.51) | 0.52 (0.22, 1.20) |
Abbreviations: HAIVEN, Hospitalized Adult Influenza Vaccine Effectiveness Network; CI, confidence interval; ICU, intensive care unit.
Multivariable log binomial regression models adjusted for underlying conditions (0, 1–2, 3–4, and ≥5 categories), age group (18–49, 50–64, 65–74, and ≥75 years), current season vaccination ≥14 days before illness onset, and site.
Supplemental oxygen includes non-invasive and invasive ventilation.
Intensive organ support includes invasive ventilation, vasopressor use, or extracorporeal membrane oxygenation.
Modified Poisson regression with robust error variance adjusted for underlying conditions (0–5 and ≥6 categories), age group (18–49, 50–64, 65–74, and ≥75 years), current season vaccination ≥14 days before illness onset, and site.
Table 5.
Adjusted hazard ratiosa (aHR) and 95% CI for time to hospital and ICU discharge in hospitalized adults by influenza type/subtype, HAIVEN, 2017–2020
| Outcome | aHR (95% CI) |
||
|---|---|---|---|
| A(H1N1) vs A(H3N2) | B vs A(H3N2) | A(H1N1) vs B | |
|
| |||
| Time to hospital discharge | 0.92 (0.83, 1.02) | 1.04 (0.91, 1.18) | 0.89 (0.78, 1.01) |
| Time to ICU discharge | 1.03 (0.71, 1.48) | 1.21 (0.77, 1.91) | 0.85 (0.54, 1.34) |
Abbreviations: HAIVEN, Hospitalized Adult Influenza Vaccine Effectiveness Network; CI, confidence interval; ICU, intensive care unit.
Multivariable Cox proportional hazards models adjusted for underlying conditions (0, 1–2, 3–4, and ≥5 categories), age group (18–49, 50–64, 65–74, and ≥75 years), current season vaccination ≥14 days before illness onset, and site.
Discussion
Our findings suggest some evidence of differential severity in the clinical presentation of U.S. hospitalized adults by influenza type/subtype. Low oxygen saturation within the first 24 hours of admission was more common among patients with influenza A(H1N1) compared with both influenza A(H3N2) and B. Low sodium levels were also more common among patients with A(H1N1) compared with A(H3N2). Patients with influenza B were less likely to have an elevated temperature or pulse rate compared with both A(H1N1) and A(H3N2). Lastly, the risk of ICU admission was higher among patients with A(H1N1) compared to patients with A(H3N2), although we did not detect a difference between type/subtype for the most severe outcomes, including death and intensive organ support.
Previous clinical evidence on the severity of influenza types/subtypes is mixed. More severe outcomes among A(H3N2) patients have been attributed to higher attack rates in patients born before 19682 due to lack of imprinting or to antigenic drift of the circulating clade that throughout the season modifies it further from the seasonal vaccine.18 Some studies have found a higher likelihood of inflammation and infection indicators in patients with A(H3N2) compared to A(H1N1), including thrombocytosis19 and c-reactive proteins20, as well as a higher likelihood of fever compared with A(H1N1) or B.20 One study found higher excess all-cause and respiratory-related death in patients with A(H3N2) compared to A(H1N1)21, while another found limited differences in all complications between A(H1N1) and A(H3N2) virus infections.22 These findings, compared with the current study, should be interpreted in the context of geographical locations outside North America19–22, younger patient samples19–21, small sample sizes19–21, or study period preceding the 2009 pandemic A(H1N1) strain.20 Our findings are supported by evidence that A(H1N1) may be associated with greater morbidity and traditional markers of ARIs, such as fever, headache, and myalgia.23,24 Compared to patients with A(H3N2), patients with influenza A(H1N1) have more frequently experienced ICU admission,25–29 invasive mechanical ventilation,19,25,27 death,25,27–29 and longer hospital stays.19,26 Pneumonia,26,28,29 acute respiratory distress syndrome,28 and any oxygen support19 are also more common among patients with influenza A(H1N1) compared with A(H3N2). Comparative severity studies in adults that include influenza B viruses have mixed results as well; some have found that those with influenza B have a lower likelihood of severe outcomes than patients with A(H1N1)28 but slightly higher likelihood of some severe outcomes than patients with A(H3N2),27 while others have found limited differences between influenza A and B infections.22
In our study, the higher risk of ICU admission might be related to the higher likelihood of respiratory distress indicators (e.g., low oxygen saturation) and low sodium among patients with A(H1N1). There could be several explanations for these findings, including if the cause is the virus itself, the host, or biases from admission practices during the influenza season. First, these severity differences could be related to virological differences between subtypes and clades. For example, A(H1N1)pdm09 virus shows tropism for binding molecules that are present in both the upper airway and alveolar epithelium receptors, as opposed to just one site as is common for most influenza viruses.30 Though antigenic drifting is more typical of A(H3N2),30 A(H1N1) viruses were genetically drifted and had lower VE estimates during the 2019–2020 season31, which comprised most of our A(H1N1) sample and potentially played a role in our findings. However, in our analysis A(H1N1) infection did not appear to be associated with more objective severity outcomes (e.g., receipt of organ support), which may be attributable to host factors. In our analysis, patients with influenza A(H1N1) were relatively younger and less vaccinated compared to those with A(H3N2), which is mirrored in previous studies.19,28 Therefore, the observed higher risk of respiratory distress and ICU admission could be related to more robust immune responses in younger and healthier patients, which may be why these indicators did not necessarily manifest as the most severe outcomes (e.g., invasive mechanical ventilation, death). Severe presentations of A(H1N1) might also be related to lack of early-in-life immune imprinting against A(H1N1) among patients born between 1957 and 1977, when A(H1N1) viruses did not circulate. Following its 1977 reappearance and the 2009 A(H1N1) pandemic, hospitalized illness was less frequent among age groups with early exposure to A(H1N1).30 Lastly, our finding of elevated risk of ICU admission is subject to physician opinion and ICU availability. During influenza A(H3N2) seasons, influenza-associated hospitalizations and deaths are typically greater, and influenza vaccine effectiveness is typically lower due to antigenic drift. Thus, it is possible that a higher threshold for ICU admission exists during influenza A(H3N2)-predominant seasons due to lack of capacity. One study showed that during the A(H3N2)-predominant 2017–2018 season, there was an increase in the inability to accept a patient in the ICU.32
This study had several strengths. First, our models adjusted for several confounders, including age, influenza vaccination status, site, and underlying conditions, which previous comparative studies have been unable to do.29 Second, the detailed clinical and laboratory data available for our patients allowed for a reasonable analytical sample size without imputation.27 Third, we assessed severity across the hospital course, including clinical indicators of severity during the first 24 hours of hospitalization as well as severe outcomes. Fourth, we used risk ratios to avoid overestimation of difference between influenza types/subtypes. The result of these strengths is a reliable characterization of severity in presentation associated with each type/subtype. Finally, conducting severity comparisons across all three influenza types/subtypes shed light on the severity of influenza A versus B viruses. In our analysis, several at-presentation indicators of severity were less likely for influenza B compared with A(H1N1) and for a smaller set of severity indicators when compared with A(H3N2). More accurate identification of clinical presentation and outcomes associated with influenza types/subtypes can foreground public health research into the mechanisms behind observed differences, which could lead to the improvement or better targeting of influenza vaccines and antiviral treatments in affected patient groups.
The results must also be considered in the context of several limitations. While sample size was moderate in this study, the complexity of the analysis yielded wide confidence intervals. Type/subtype severity differences could possibly have been discerned with a larger sample size, especially for the most severe outcomes (e.g., individual organ support, ECMO) for which we could not calculate the risk in isolation. We lacked power to conduct individual season analyses and to differentiate influenza B viruses into its two lineages. However, the clinical presentation and severity of Victoria and Yamagata lineages have not been observed to differ significantly, and B/Yamagata has not circulated since 2020 and is no longer included in influenza vaccines.33,34 These results are generalizable only to hospitalized adults with influenza, and different gradients of severity (e.g., risk of hospital admission) might be discernible in outpatient settings. Further, residual biases may persist. The exclusion of observations with improbable values or data that were unable to be collected from the medical record led to missingness for some outcome variables. The use of ICD codes alone to identify underlying conditions can lead to misclassification due to low specificity and sensitivity when compared to validated algorithms.35 Finally, unassessed covariates, like antiviral use, can attenuate disease severity and may modify the effect of influenza type/subtype on severe outcomes.
Conclusion
We found that hospitalized adult patients with influenza A(H1N1) are at increased risk for ICU admission and elevated markers of respiratory distress compared to other influenza types/ subtypes. These findings may be explained by differences in biological manifestations of each type/subtype or by factors associated with patient populations most affected by each type/subtype in healthcare environments during specific influenza seasons (e.g., reduced ICU capacity during A[H3N2] seasons). Additional studies with larger sample sizes of severe influenza illness, including studies occurring post-COVID-19 pandemic, should continue to investigate the influence of both host and viral factors on influenza severity. Data from these studies might assist in the development of better influenza vaccines and antiviral medications and inform clinical guidance to reduce the burden of severe influenza in adults.
Supplementary Material
Acknowledgments.
We acknowledge contributions from Dayna Wyatt, Zhouwen Liu, Rendie McHenry, Natasha Halasa, Sandra Alvarez Calvillo, Stephanie Longmire, and Laura Stewart at Vanderbilt University Medical Center; Ryan Malosh, Joshua Petrie, Adam Lauring, Caroline Cheng, Hannah Segaloff, E J McSpadden, Emileigh Johnson, and Rachel Truscon at the University of Michigan; Lois Lamerato, Susan Davis, and Marcus Zervos at Henry Ford Health System; Meredith Tanner, Victor Escobedo, Kelsey Bounds, Lydia Clipper, Anne Robertson, Teresa O’Quinn, Wencong Chen, Tresa McNeal, Shekhar Ghamande, Kevin Chang, Justin Paradeza, Arundhati Rao, Manohar Mutnal, Kimberly Walker, Marcus Volz, Martha Zayed, Natalie Settele, Jennifer Thomas, Jaime Walkowiak, Muralidhar Jatla, Madhava Beeram, and Alejandro Arroliga at Baylor Scott and White Health; Kailey Hughes Kramer, Donald B Middleton, M Patricia Nowalk, Sean Saul, Michael Susick, and Mohamed Yassin at the University of Pittsburgh.
Funding sources.
This work was supported by the CDC through the following cooperative agreement: HAIVEN (CDC-RFA-IP-15-002). This work was additionally supported by the National Institutes of Health at the University of Pittsburgh under Grant (UL1 TR001857).
Footnotes
Potential conflicts of interest. Y. Z. reports serving as a member on a Vanderbilt University Medical Center Data Safety Monitoring Board. E. T. M. reports receiving research funding from Merck. A. S. M. reports receiving research funding from the National Institution for Allergy and Infectious Diseases. R. K. Z. reports receiving research funding from the National Institutes of Health (UL1 TR001857) and Sanofi Pasteur and donating Sanofi Pasteur and Astra Zeneca vaccines for a Centers for Disease Control and Prevention study. S. M. O. reports receiving travel support from Gates Foundation. S. C., N. M. L., H. K. T., M. G., K. M., F. P. S., M. W. T., M. M. P., and S. E. report no conflicts of interest.
Disclaimer. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention (CDC).
References
- 1.Centers for Disease Control and Prevention. Estimated US flu disease burden. Flu burden. November 26, 2024. Accessed April 2, 2025. https://www.cdc.gov/flu-burden/php/data-vis/index.html [Google Scholar]
- 2.Belongia EA, McLean HQ. Influenza Vaccine Effectiveness: Defining the H3N2 Problem. Clin Infect Dis. 2019;69(10):1817–1823. doi: 10.1093/cid/ciz411 [DOI] [PubMed] [Google Scholar]
- 3.Jester BJ, Uyeki TM, Jernigan DB. Fifty Years of Influenza A(H3N2) Following the Pandemic of 1968. Am J Public Health. 2020;110(5):669–676. doi: 10.2105/AJPH.2019.305557 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Lewis NM, Zhu Y, Peltan ID, et al. Vaccine Effectiveness Against Influenza A–Associated Hospitalization, Organ Failure, and Death: United States, 2022–2023. Clin Infect Dis. 2024;78(4):1056–1064. doi: 10.1093/cid/ciad677 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Lewis NM, Harker EJ, Grant LB, et al. Benefit of Early Oseltamivir Therapy for Adults Hospitalized With Influenza A: An Observational Study. Clin Infect Dis. Published online November 28, 2024:ciae584. doi: 10.1093/cid/ciae584 [DOI] [PubMed] [Google Scholar]
- 6.Frutos AM. Interim Estimates of 2024–2025 Seasonal Influenza Vaccine Effectiveness — Four Vaccine Effectiveness Networks, United States, October 2024–February 2025. MMWR Morb Mortal Wkly Rep. 2025;74. doi: 10.15585/mmwr.mm7406a2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Babcock HM, Merz LR, Fraser VJ. Is Influenza an Influenza-Like Illness? Clinical Presentation of Influenza in Hospitalized Patients. Infect Control Hosp Epidemiol. 2006;27(3):266–270. doi: 10.1086/501539 [DOI] [PubMed] [Google Scholar]
- 8.Arriola C, Garg S, Anderson EJ, et al. Influenza Vaccination Modifies Disease Severity Among Community-dwelling Adults Hospitalized With Influenza. Clin Infect Dis. 2017;65(8):1289–1297. doi: 10.1093/cid/cix468 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Doyle JD, Beacham L, Martin ET, et al. Relative and Absolute Effectiveness of High-Dose and Standard-Dose Influenza Vaccine Against Influenza-Related Hospitalization Among Older Adults-United States, 2015–2017. Clin Infect Dis Off Publ Infect Dis Soc Am. 2021;72(6):995–1003. doi: 10.1093/cid/ciaa160 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Tenforde MW, Chung J, Smith ER, et al. Influenza Vaccine Effectiveness in Inpatient and Outpatient Settings in the United States, 2015–2018. Clin Infect Dis. 2021;73(3):386–392. doi: 10.1093/cid/ciaa407 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ferdinands JM, Gaglani M, Martin ET, et al. Prevention of Influenza Hospitalization Among Adults in the United States, 2015–2016: Results From the US Hospitalized Adult Influenza Vaccine Effectiveness Network (HAIVEN). J Infect Dis. 2019;220(8):1265–1275. doi: 10.1093/infdis/jiy723 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Hughes K, Middleton DB, Nowalk MP, et al. Effectiveness of Influenza Vaccine for Preventing Laboratory-Confirmed Influenza Hospitalizations in Immunocompromised Adults. Clin Infect Dis Off Publ Infect Dis Soc Am. 2021;73(11):e4353–e4360. doi: 10.1093/cid/ciaa1927 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Ghamande S, Shaver C, Murthy K, et al. Vaccine Effectiveness Against Acute Respiratory Illness Hospitalizations for Influenza-Associated Pneumonia During the 2015–2016 to 2017–2018 Seasons: US Hospitalized Adult Influenza Vaccine Effectiveness Network (HAIVEN). Clin Infect Dis Off Publ Infect Dis Soc Am. 2021;74(8):1329–1337. doi: 10.1093/cid/ciab654 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Garten R, Blanton L, Elal AIA, et al. Update: Influenza Activity in the United States During the 2017–18 Season and Composition of the 2018–19 Influenza Vaccine. MMWR Morb Mortal Wkly Rep. 2018;67(22):634–642. doi: 10.15585/mmwr.mm6722a4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Xu X, Blanton L, Elal AIA, et al. Update: Influenza Activity in the United States During the 2018–19 Season and Composition of the 2019–20 Influenza Vaccine. MMWR Morb Mortal Wkly Rep. 2019;68(24):544–551. doi: 10.15585/mmwr.mm6824a3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Tenforde MW, Talbot HK, Trabue CH, et al. Influenza Vaccine Effectiveness Against Hospitalization in the United States, 2019–2020. J Infect Dis. 2021;224(5):813–820. doi: 10.1093/infdis/jiaa800 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Centers for Disease Control and Prevention. CDC 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel. Published online March 7, 2023. Accessed December 2, 2024. https://www.fda.gov/media/134922/download
- 18.Park JE, Ryu Y. Transmissibility and severity of influenza virus by subtype. Infect Genet Evol. 2018;65:288–292. doi: 10.1016/j.meegid.2018.08.007 [DOI] [PubMed] [Google Scholar]
- 19.Kusznierz G, Carolina C, Manuel RJ, et al. Impact of influenza in the post-pandemic phase: Clinical features in hospitalized patients with influenza A (H1N1) pdm09 and H3N2 viruses, during 2013 in Santa Fe, Argentina. J Med Virol. 2017;89(7):1186–1191. doi: 10.1002/jmv.24758 [DOI] [PubMed] [Google Scholar]
- 20.Kaji M, Watanabe A, Aizawa H. Differences in clinical features between influenza A H1N1, A H3N2, and B in adult patients. Respirology. 2003;8(2):231–233. doi: 10.1046/j.1440-1843.2003.00457.x [DOI] [PubMed] [Google Scholar]
- 21.Kwok KO, Riley S, Perera RAPM, et al. Relative incidence and individual-level severity of seasonal influenza A H3N2 compared with 2009 pandemic H1N1. BMC Infect Dis. 2017;17(1):337. doi: 10.1186/s12879-017-2432-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Sharma Y, Horwood C, Hakendorf P, Thompson C. Clinical characteristics and outcomes of influenza A and B virus infection in adult Australian hospitalised patients. BMC Infect Dis. 2020;20(1):913. doi: 10.1186/s12879-020-05670-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Petridis D, Zarogoulidis P, Kioumis I, et al. Clinical differences between H3N2 and H1N1 influenza 2012 and lower respiratory tract infection found using a statistical classification approach. Ther Clin Risk Manag. Published online February 2014:77. doi: 10.2147/TCRM.S57429 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Guarner J, Falcón-Escobedo R. Comparison of the Pathology Caused by H1N1, H5N1, and H3N2 Influenza Viruses. Arch Med Res. 2009;40(8):655–661. doi: 10.1016/j.arcmed.2009.10.001 [DOI] [PubMed] [Google Scholar]
- 25.Cohen LE, Hansen CL, Andrew MK, et al. Predictors of Severity of Influenza-Related Hospitalizations: Results From the Global Influenza Hospital Surveillance Network (GIHSN). J Infect Dis. 2024;229(4):999–1009. doi: 10.1093/infdis/jiad303 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Minney-Smith CA, Selvey LA, Levy A, Smith DW. Post-pandemic influenza A/H1N1pdm09 is associated with more severe outcomes than A/H3N2 and other respiratory viruses in adult hospitalisations. Epidemiol Infect. 2019;147:e310. doi: 10.1017/S095026881900195X [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Sumner KM, Masalovich S, O’Halloran A, et al. Severity of influenza-associated hospitalisations by influenza virus type and subtype in the USA, 2010–19: a repeated cross-sectional study. Lancet Microbe. 2023;4(11):e903–e912. doi: 10.1016/S2666-5247(23)00187-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Delgado-Sanz C, Mazagatos-Ateca C, Oliva J, Gherasim A, Larrauri A. Illness Severity in Hospitalized Influenza Patients by Virus Type and Subtype, Spain, 2010–2017. Emerg Infect Dis. 2020;26(2):220–228. doi: 10.3201/eid2602.181732 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Caini S, Kroneman M, Wiegers T, El Guerche-Séblain C, Paget J. Clinical characteristics and severity of influenza infections by virus type, subtype, and lineage: A systematic literature review. Influenza Other Respir Viruses. 2018;12(6):780–792. doi: 10.1111/irv.12575 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Patel MM, York IA, Monto AS, Thompson MG, Fry AM. Immune-mediated attenuation of influenza illness after infection: opportunities and challenges. Lancet Microbe. 2021;2(12):e715–e725. doi: 10.1016/S2666-5247(21)00180-4 [DOI] [PubMed] [Google Scholar]
- 31.Tenforde MW, Kondor RJG, Chung JR, et al. Effect of Antigenic Drift on Influenza Vaccine Effectiveness in the United States—2019–2020. Clin Infect Dis. 2021;73(11):e4244–e4250. doi: 10.1093/cid/ciaa1884 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Lane CJ, Bhatnagar M, Lutrick K, et al. ICU Resource Limitations During Peak Seasonal Influenza: Results of a 2018 National Feasibility Study. Crit Care Explor. 2022;4(1):e0606. doi: 10.1097/CCE.0000000000000606 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Costa JC da, Siqueira MM, Brown D, et al. Vaccine Mismatches, Viral Circulation, and Clinical Severity Patterns of Influenza B Victoria and Yamagata Infections in Brazil over the Decade 2010–2020: A Statistical and Phylogeny-Trait Analyses. Viruses. 2022;14(7):1477. doi: 10.3390/v14071477 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.U.S. Food & Drug Administration. Use of Trivalent Influenza Vaccines for the 2024–2025 U.S. Influenza Season. Vaccines, Blood, & Biologics. March 5, 2024. Accessed July 2, 2025. https://www.fda.gov/vaccines-blood-biologics/lot-release/use-trivalent-influenza-vaccines-2024-2025-us-influenza-season [Google Scholar]
- 35.Quan H, Li B, Duncan Saunders L, et al. Assessing Validity of ICD-9-CM and ICD-10 Administrative Data in Recording Clinical Conditions in a Unique Dually Coded Database. Health Serv Res. 2008;43(4):1424–1441. doi: 10.1111/j.1475-6773.2007.00822.x [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.