Surveillance for influenza-associated hospitalizations
We used data gathered from October 1, 2012 to April 30, 2013, from the Centers for Disease Control and Prevention’s (CDC) Influenza Hospitalization Surveillance Network (FluSurv-NET), a population-based, laboratory-based surveillance system. FluSurv-NET includes 81 counties in 15 states: California, Colorado, Connecticut, Georgia, Iowa, Maryland, Michigan, Minnesota, New Mexico, New York, Rhode Island, Ohio, Oregon, Tennessee, and Utah. Information on demographic characteristics, medical history, influenza vaccination status, clinical course during hospitalization, and treatment with influenza antiviral medications was collected through review of medical charts using a standardized abstraction form. Data were entered locally and uploaded into a CDC database. Data on influenza vaccination were verified using a vaccine registry or patients’ primary care provider. FluSurv-NET has been determined to be public health practice not requiring institutional review board approval for human research protection.
Case definition
We defined a case as laboratory-confirmed influenza in a person who resided in the surveillance area and was admitted to one of the surveillance hospitals ≤14 days after or ≤3 days before the positive influenza test. Laboratory-confirmed influenza was determined by a positive test result by real time reverse transcription polymerase chain reaction, viral culture, direct or indirect fluorescent antibody staining, or a rapid influenza diagnostic test. Influenza testing was ordered at the discretion of the treating clinicians.
Body mass index
We determined body mass index (BMI) values using weight and height measurements collected from medical records (kg/m2) as follows: underweight = BMI <18.5, normal = BMI 18.5 to <25, overweight = BMI 25 to <30, obese = BMI 30 to <35, and severely obese = BMI ≥ 35. We excluded pregnant women and individuals aged <20 due to differences in methods for categorizing BMI. If height or weight values were missing, or for cases with extreme values of study-calculated BMI (e.g., BMI ≥ 100 or BMI ≤ 10), a BMI that had been calculated and recorded in the medical chart was used.
Main outcome measures
We explored the relationship between BMI category and three influenza severity outcomes: artificial ventilation, ICU admission, and diagnosis of community-acquired, influenza-associated pneumonia. We defined artificial ventilation as either invasive mechanical ventilation or extracorporeal membrane oxygenation (ECMO). ICU admission excluded admission to a step-down unit or transitional care unit. Community-acquired, influenza-associated pneumonia was defined by new abnormal radiographic findings (including consolidation/opacity or pleural effusion) diagnosed within the first 3 days of hospital admission.
Covariates
Potential confounders were defined a priori based on biological plausibility and previous findings in the literature. We included in the multivariate analysis medical comorbidities recognized by the Advisory Committee on Immunization Practices (ACIP) as risk factors for influenza associated complications as well as lifestyle, demographic characteristics, and patient care factors that could be associated with BMI or selected influenza outcomes.
Demographic characteristics
The selected demographic characteristics we controlled for were age, sex, and race/ethnicity. Age was classified into five categories: 20 to <50, 50 to <65, 65 to <75, 75 to <85, and ≥85 years. Race/ethnicity was abstracted from medical records and classified as White non-Hispanic, Black non-Hispanic, Hispanic, and other/unknown to ensure a sufficient number of subjects in each category to perform statistical analyses.
Comorbidities
We controlled for the following comorbidities: cardiovascular disease (CVD), chronic lung disease (CLD), chronic metabolic disease (CMD), neuromuscular disorder, neurologic disorder, immunocompromised condition, renal disease, and asthma. CVD included conditions such as coronary heart disease, cardiac valve disorders, and congestive heart failure (excluding isolated hypertension). CLD included conditions such as chronic obstructive pulmonary disease and interstitial lung disease. CMD included conditions such as diabetes mellitus and thyroid dysfunction. Neuromuscular disorder included conditions such as multiple sclerosis and muscular dystrophy. Neurologic disorder included conditions such as seizures, cerebral palsy, and cognitive dysfunction. Immunocompromised condition included immunoglobulin deficiency, leukemia, HIV/AIDS, organ transplantation, and individuals taking immunosuppressive medications. Renal diseases included acute or chronic renal failure and nephrotic syndrome.
Lifestyle factors
The selected lifestyle factors we controlled for were alcohol abuse and smoking status. Alcohol abuse was defined as follows: “current” if the patient had indication of current (past 12 months) alcohol abuse or if no time frame was specified, “former” if the patient quit abusing alcohol more than 12 months ago, or “non-abuser” if there was no indication of alcohol abuse, dependency, or alcoholism on the patient’s medical record. Smoking was defined as follows: “current” if the patient indicated that s/he was currently (past 12 months) smoking or if no time frame was specified, “former” if the patient quit smoking more than 12 months ago, or “non-smoker” if there was no indication of smoking on the patient’s medical record.
Patient care factors
We classified influenza vaccination into the following: (1) patients who received influenza vaccine at least 14 days prior to hospitalization, or (2) patients who did not receive influenza vaccine for the 2012–2013 season or patients who received influenza vaccine during hospitalization.
Antiviral administration was classified into three categories: “prompt” for patients who received antivirals on day of hospital admission, “late” for patients who received antivirals from day 2 to day 5 (inclusive) in the hospital, “none” for patients who received antivirals after day five in hospital or no antivirals at all.
Statistical analyses
We performed a bivariate analysis using the Chi square test of homogeneity to determine whether, for a given covariate, the frequency of cases was distributed equally across the five BMI categories. To evaluate the association between obesity or severe obesity and severe influenza-related outcomes, we used logistic regression models with logit link function to calculate the odds of having a severe outcome for each of the five BMI categories. For each outcome—artificial ventilation, ICU admission, and community-acquired pneumonia—we ran unadjusted, sex- and age-adjusted, and fully-adjusted (for all covariates of interest) models. The normal weight BMI category was the reference group for all models. We produced odds ratio (OR) point estimates, 95 % Wald confidence limits, and associated p values for each BMI categories for our 3 outcome variables. SAS Version 9.3 was used for all statistical analyses.