Background
Despite the availability of vaccines, the burden of seasonal influenza remains high in the United States (US), contributing to excess morbidity, mortality, and healthcare resource utilization (HRU). The Centers for Disease Control and Prevention (CDC) estimates that influenza accounted for 4.3–21 million medical visits, 140,000–810,000 hospitalizations, and 12,000–61,000 deaths annually in the US during the 2010–11 through 2019–20 influenza seasons [
1]. In turn, the estimated total economic burden of influenza is substantial at $11.2 billion (ranging from $6.3–$25.3 billion) [
2] and as high as $87.1 billion (95% confidence interval [CI], $47.2–$149.5) [
3]. Direct medical costs have been estimated at $3.2 billion annually, of which 70% ($2.3 billion) is due to hospitalizations [
2], despite hospitalization in only 1–2% of medically-attended influenza cases [
1].
Although influenza is generally self-limiting with mild symptoms in healthy individuals [
4], certain vulnerable populations are at elevated risk for serious influenza-related medical complications. For example, while the elderly population ≥ 65 years of age has the lowest median incidence of influenza (3.9%) compared to children 0–17 years (9.3%) or adults 18–64 years (8.8%) [
5], they account for 50–70% of influenza-related hospitalizations, 70–85% of deaths [
6], and 42.7% of direct medical costs [
2]. Chronic medical conditions, including pulmonary, cardiovascular, renal, hepatic, and metabolic disorders, have also been identified as predictors of influenza-related complications [
7‐
12]. Vaccination is recognized as the most effective prevention strategy for seasonal influenza, but in the 2019–2020 season, only 51.8% of persons 6 months or older were vaccinated [
13].
As both vaccination against influenza infections and treatment for complications evolve, there is limited up-to-date research quantifying risk factors associated with hospitalization or the added healthcare resource burden attributable to seasonal influenza among patients vulnerable to complications using real-world data. To that end, this study aimed to identify risk factors for influenza-related hospitalization (Stage 1) and to evaluate the burden of influenza in at-risk elderly populations (Stage 2).
Discussion
The findings of this real-world study demonstrate that the elderly population and those with specific comorbidities are at elevated risk of influenza-related complications that may result in hospitalizations, a primary driver of the substantial healthcare resource burden of the disease. While the severity of influenza can vary with every season, the susceptibility of the elderly to disease-related complications has been consistently reported in epidemiological studies across geographies and has been attributed to alterations in immune defenses with age [
28]. In our study, we identified more than 1.6 million influenza patients. Of these patients, only 2.5% were elderly and yet they accounted for 20.5% of influenza-related hospitalizations. Apart from age-related vulnerability, our study also identified that certain pre-existing comorbidities have a direct impact on outcomes, increasing the odds of hospitalization by 2 to 3-fold. Similar findings were reported in a meta-analysis of populations at risk for severe or complicated seasonal influenza, which found that the elderly had higher risk of hospitalization and death compared to the non-elderly (OR, 3.0; 95% CI, 1.5–5.7). Additionally, comorbidities were strongly associated with death (OR, 2.0; 95% CI, 1.7–2.4) and conditions like chronic lung disease, cardiovascular disease, COPD, and diabetes increased the probability of hospitalization and, in some cases, ventilator support [
10].
Seasonal influenza-related complications and hospitalizations are not only life-threatening to populations at risk, but also a substantial economic burden for the healthcare system [
2] and persistent efforts are needed to alleviate the burden in the high-risk populations highlighted in this study through vaccinations, early treatment, and existing prophylactic medications [
23] or novel targeted therapies, such as monoclonal antibodies [
29]. Currently, vaccination is the most promising method to prevent and control influenza by reducing the risk of ailment by 40–60% and reducing disease severity among patients with post-vaccination infections [
30]. Although the US vaccination target of 70% [
31] was not achieved in the 2019–20 influenza season, with only 51.8% of the population aged ≥6 months receiving a vaccination [
32], the impact of vaccination on morbidity and mortality was sizeable. Using a model accounting for vaccination coverage, vaccine efficacy, and disease occurrence, the CDC estimated that vaccinations prevented 7.5 million influenza illnesses, 3.7 million influenza-associated medical visits, 105,000 influenza-associated hospitalizations, and 6300 influenza-associated deaths in the 2019–20 season [
33,
34].
Achieving the vaccination targets for influenza would allow for even larger impacts, but complacency and concerns regarding safety and efficacy are factors responsible for lack of widespread vaccination [
35,
36]. For the elderly population at risk for complications, the vaccination target in the US is even higher at 90%, yet this target is rarely reached in the industrialized world [
37]. However, the effectiveness of the vaccine in the elderly is known to be reduced due to lower seroconversion rates from poorer immunologic response [
38] and in Stage 1 of this study, we observed higher rates of vaccination among influenza patients with hospitalization (21.4%) compared to patients without hospitalization (17.7%). Hence, despite the efforts towards widespread vaccination programs, there remains an unmet need for preventing complications of influenza in vulnerable populations, even among those who received vaccinations.
The strength of our study lies in estimating influenza-related hospitalizations in the backdrop of risk factors like pre-existing comorbidities, thus adding to the current literature related to influenza-related HRU using the most recently available data at the time of the study. The existing CDC model for reporting influenza-associated hospitalizations uses the reported number of hospitalizations to calculate hospitalization rates, which are adjusted to compensate for any under-detection. The adjusted rates are then applied to the US population by age group to estimate total number of influenza-related hospitalizations [
39]. This method is limited due to lack of inclusion of the full denominator of patient groups at-risk for influenza because of underlying comorbid conditions, which can result in inadequacies when assessing the actual healthcare resource burden.
Several limitations of this retrospective database analysis must be noted. First, clinical data were not available in this study, which may have led to misclassification of the influenza diagnoses due to the inability to verify disease status with test results. To account for this, we required evidence of an influenza lab test within 14 days of influenza diagnosis (in any position) or primary diagnosis of influenza on the claim. This study likely also underestimates the receipt of influenza vaccinations prior to the index date since we observed only about one-third of patients across the influenza and non-influenza cohorts with documented vaccination in the claims database. Only vaccinations documented by insurance would be captured in the study database; free vaccines or vaccines paid by cash are not captured. Secondly, we did not investigate outcomes like rehabilitation and nursing home care after hospitalization, which contribute to the long-term disease burden not captured in the present study and should be evaluated in future real-world studies using longer follow-up periods. Thirdly, comparisons of outcomes between the influenza and non-influenza cohorts stratified by influenza season would have been valuable in understanding the impact of influenza subtype/lineage on healthcare resource burden. Although stratified analyses were not conducted and laboratory data were not available, this study nevertheless provides valuable insights summarized over multiple influenza seasons. Fourthly, our comparisons of hospitalizations and ED visits between the influenza and non-influenza cohorts may be biased due to residual confounding from matching factors that remained slightly imbalanced after matching and unmeasured sociodemographic characteristics that may be associated with healthcare resource utilization (e.g., race/ethnicity and income). In addition, data for the end of the 2018–2019 season were not available at the time this study was conducted, which may have impacted findings in Stage 2; however, we anticipate any bias would be limited since the timing of the index date (index season and month) were considered in matching the influenza and non-influenza cohorts. Lastly, given the study population was restricted to commercially insured individuals, the patients ≥65 years of age captured in this study may represent a healthier population and our findings may not be generalizable to the elderly population insured through traditional fee-for-service Medicare. Furthermore, some patients ≥65 years with commercial insurance may be partially covered by traditional Medicare and their healthcare utilization data may be underestimated using this commercial claims database alone. These factors may be reflected in the relatively lower proportion of hospitalized influenza patients in our study ≥65 years (20.5%) compared to statistics reported by the CDC (50–70%) [
28]. Despite this limitation, we anticipate that utilization would be evenly underestimated for both the patients with and without influenza and, thus, the impact on our findings evaluating the differences in outcomes between the matched groups would be limited.
Acknowledgements
The authors would like to acknowledge the contributions of Dr. Xiao Ding of Vir Biotechnology for the statistical review of the protocol, analysis plan, and results, Hsiu-Ching Chang of IQVIA for conducting the programming and statistical analysis, and Kasturi Chatterjee for manuscript writing assistance.
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