Table 1.
Availability of variables within the Integrated Dataset for the evaluation of influenza vaccine effectiveness compared with WHO benchmarks [9].
| Identified by the WHO [9] | Integrated Dataset | |
|---|---|---|
| Key Variables | Rationale | |
| Influenza vaccines | Needed to identify vaccinated individuals | Available |
| Age | An important stratification factor for VE estimates, as VE may differ in different age groups Both vaccination coverage and risk of influenza virus infection vary by age |
Available Limitation: maximum age in variable is 89 years and artificially constrained in patients >89 years of age to remain compliant with HIPAA (i.e., to decrease the risk of potential re-identification of individuals) |
| Sex | May be a strong variable related to healthcare utilization and vaccination in non-high-resource settings | Available |
| Race, ethnicity | Correlated with healthcare utilization in many parts of the world | Available |
| Date of symptom onset | Important variable for characterizing the influenza epidemic in the population: needed in cohort studies to calculate person-time at risk, and needed in case–control studies to sample controls (if using incidence-density sampling) | Available |
| Calendar time | Key variable in test-negative studies, because non-cases that are enrolled outside of an influenza season must be excluded from analyses to avoid bias Calendar time is also correlated with vaccine uptake and incidence of influenza, creating potential confounding by calendar time, although this confounding may not be meaningful in some settings |
Available The healthcare interactions captured within the primary care EMRs, pharmacy claims, and medical claims are tied to calendar time |
| Time from symptom onset to specimen collection | May be associated with the sensitivity or specificity of influenza testing | May be available within the EMR if testing for influenza was conducted within primary care |
| Use of antivirals | Patients who have used antiviral medicines, either for treatment or for prophylaxis, are more likely to have false-negative test results; this can be used to exclude subjects from study enrollment | Available Prescription for influenza antiviral medication available, although adherence is not |
| Non-critical Variables | ||
| Receipt of other vaccines (such as pneumococcal vaccines) | May be a marker for care-seeking behavior and/or propensity to seek influenza vaccination | Available |
| Prior history of influenza vaccination | Receipt of the prior year’s influenza vaccine may affect the effectiveness of the current season’s vaccine | Available |
| Presence and severity of cardiac or pulmonary comorbidities | Persons with chronic cardiac or pulmonary disease are at increased risk of influenza-associated complications if they are infected, and are therefore more likely to become cases in a hospital-based study In high-resource settings, underlying disease is also correlated with receipt of influenza vaccine, although in a non-linear fashion |
Available Information on hospitalization and reason for hospitalization (indicator of disease severity) |
| Measure of outcome severity | Measures such as duration, subsequent hospitalization (particularly for outpatient outcomes), or death may be useful for assessing whether influenza vaccine reduces severity of outcomes in the vaccinated population (although this is complicated to estimate) | Available Diagnosis information available for inpatients (potential marker of disease severity) |
| Immunocompromising conditions | Generally, have been uncommon among subjects included in VE studies in high-resource settings and so have not been important confounders. However, in settings in which the prevalence of HIV/AIDS is high, HIV/AIDS may be an important confounder to measure | Available |
| Functional and cognitive limitations | Shown to be important confounders in VE studies among elderly adults in high-resource settings and particularly in relation to serious outcomes (i.e., hospitalization) | Specific information on functional and cognitive limitations is not available; however, the construct of frailty may be generated using summary scores that leverage available information within the Integrated Dataset |
| Access to medical care | Access to medical care will be population-dependent In some settings, availability and use of health insurance may affect patients’ ability to seek care at certain facilities |
Integrated Dataset represents individuals with health insurance; therefore, all individuals within the Integrated Dataset theoretically have access to medical care |
| Socioeconomic status | Likely to be highly correlated with vaccination and with healthcare-seeking behavior | Specific information on patient socioeconomic status not available within the Integrated Dataset |
| Distance to study hospital/clinic | May be correlated both with access to vaccination and access to medical care | Specific information not available within the Integrated Dataset as data evaluated retrospectively (no study hospital/clinic) and granular information on subject location of residence not available as per HIPAA requirements |
AIDS: acquired immune deficiency syndrome. EMR: electronic medical record. HIPAA: Health Insurance Portability and Accountability Act. HIV: human immunodeficiency virus. VE: vaccine effectiveness. WHO: World Health Organization.