Publication

Article

The American Journal of Managed Care
February 2023
Volume 29
Issue 2

Outcomes of Antiviral Treatment for Influenza in Type 2 Diabetes

Antiviral treatment was associated with lower health care resource utilization and costs in patients with type 2 diabetes and a diagnosis of influenza.

ABSTRACT

Objectives: To evaluate the long-term effects of antiviral treatment on influenza-related health care resource utilization (HCRU) and costs in patients with type 2 diabetes (T2D) and a diagnosis of influenza.

Study Design: Retrospective cohort study.

Methods: Claims data from the IBM MarketScan Commercial Claims Database were used to identify patients with T2D and a diagnosis of influenza between October 1, 2016, and April 30, 2017. Patients who received antiviral treatment within 2 days of influenza diagnosis were identified and propensity score–matched 1:1 with a comparator cohort of untreated patients. HCRU (number of outpatient visits, emergency department visits, hospitalizations, and duration of hospitalization) and costs were assessed over 1 full year and over each quarter after influenza diagnosis.

Results: Treated and untreated matched cohorts consisted of 2459 patients each. In the treated cohort vs the untreated cohort, emergency department visits were reduced 24.6% over 1 year after influenza diagnosis (mean [SD], 0.94 [1.76] vs 1.24 [2.47] visits; P < .0001) and were also reduced significantly during each quarter; the duration of hospitalization decreased 35.6% in the treated cohort vs the untreated cohort over 1 full year (mean [SD], 0.71 [3.36] vs 1.11 [5.60] days; P < .0023). Mean (SD) total health care costs were 17.68% lower in the treated cohort ($20,212 [$58,627]) than in the untreated cohort ($24,552 [$71,830]) over a whole year following the index influenza visit (P = .0203).

Conclusions: Antiviral treatment in patients with T2D and influenza was associated with significantly lower HCRU and costs over at least 1 year after infection.

Am J Manag Care. 2023;29(2):e43-e50. https://doi.org/10.37765/ajmc.2023.89320

_____

Takeaway Points

We used insurance claims data to evaluate the long-term effects of antiviral treatment on health care resource utilization and costs in patients with type 2 diabetes and a diagnosis of influenza.

  • After adjusting for differences between treated and untreated groups in baseline clinical and demographic characteristics, antiviral treatment was associated with a reduced number of emergency department visits, shorter duration of hospitalization, and lower total health care costs both over a full year and in each quarter.
  • Prompt antiviral treatment may reduce influenza-related health care resource utilization and costs in patients with type 2 diabetes.

_____

Type 2 diabetes (T2D) is a chronic metabolic disease characterized by increased blood glucose levels arising from inadequate insulin secretion and impaired response to insulin. The prevalence of T2D in the United States is estimated at more than 30 million (~10.5% of the population), and an additional 88 million adults in the United States (34.5%) have prediabetes.1 Influenza is a contagious viral infection of the respiratory tract that can lead to complications resulting in hospitalization or death. Since 2010, influenza has resulted in up to 45 million illnesses, 810,000 hospitalizations, and 61,000 deaths annually.2 Although infection with an influenza virus typically causes a self-limited illness, the consequences of influenza are more serious in individuals with T2D. Approximately 30% of adults recently reported to be hospitalized for influenza have diabetes.3 Influenza infection can act as a trigger for acute complications in patients with cardiovascular or cerebrovascular disease4-6 and enhance the already-increased risk of heart attack and stroke in patients with diabetes.7 Glucose metabolism may also be dysregulated, resulting in poor glucose control that can lead to metabolic complications such as diabetic ketoacidosis.8,9 In turn, diabetes increases the severity of influenza infection, and patients with diabetes have worse outcomes after influenza infection than patients without diabetes, including higher rates of hospitalization, intensive care unit admission, and death.10,11 Many patients with diabetes also have comorbidities, such as obesity, that can influence the severity of the infection.12-14

Vaccination remains the principal strategy to control influenza and its complications in patients with chronic conditions.3,15 Patients with T2D derive substantial benefits from seasonal vaccination, but coverage rates remain suboptimal.16,17 To provide a second line of defense after vaccination by shortening the duration of illness and reducing the risk of complications, guidelines recommend early treatment (within 48 hours of symptom onset) with antivirals, particularly among high-risk patients such as those with diabetes.18,19 Currently recommended antivirals include the neuraminidase inhibitors oseltamivir (Tamiflu), zanamivir (Relenza), and peramivir (Rapivab) and the cap-dependent endonuclease inhibitor baloxavir marboxil (Xofluza).18 In the general population, early treatment with antivirals has been shown to reduce the severity and duration of illness, as well as influenza-related complications and hospitalization.20-22 In patients with preexisting cardiovascular disease, antiviral agents may protect against recurrent cardiac events.23 However, rates of antiviral prescribing are low both in the general population24-26 and among high-risk patients with respiratory illness.27,28

We recently showed that influenza infection has a significant long-term impact on morbidity in patients with T2D, including worsening of diabetes.29 However, scant data are available on antiviral treatment in patients with diabetes,30 and there is an unmet need to quantify the long-term treatment effect of antivirals in patients with T2D. In this study, we used real-world US claims data from the 2016-2017 influenza season to investigate the long-term effect of antiviral treatment on influenza-related health care resource utilization (HCRU) and costs in patients with T2D over a 1-year period following infection.

METHODS

Data Source

Data for this study were extracted from the IBM MarketScan Commercial Claims Database. These data included health insurance claims across the continuum of care (eg, inpatient, outpatient, outpatient pharmacy, carve-out behavioral health care) and enrollment data from large employers and health plans across the United States that provide private health care coverage for more than 203 million employees, their spouses, and dependents.31 The study used deidentified data and was exempt from institutional review board review. The research was compliant with the Health Insurance Portability and Accountability Act.

Study Design

This was a retrospective cohort study (Figure 1). Patients 18 years and older with T2D were identified using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes. A previously validated algorithm was used to define patients with a diagnosis of T2D as those with at least 2 outpatient claims that included a diagnosis of T2D within 2 years or 1 inpatient claim with a diagnosis of T2D, together with at least 1 prescribed antihyperglycemic drug during the look-back period (October 1, 2015-October 1, 2016).32 Patients with a diagnosis of type 1 diabetes (defined as those with 3 outpatient or 2 inpatient claims) were excluded.

The study enrolled patients with T2D who received a diagnosis of influenza between October 1, 2016, and April 30, 2017. Patients were included if they had a claim associated with a diagnosis suggesting influenza (ICD-9-CM codes 487.xx or 488.xx; ICD-10-CM codes J09.xx, J10.xx, or J11.xx). Patients with an inpatient diagnosis of influenza were excluded because less than 3% of the study population received this diagnosis in an inpatient setting; additionally, patients with an inpatient influenza diagnosis are not comparable with those with a diagnosis outside an inpatient setting. Patients also needed to be continuously enrolled in their health plan from October 1, 2015, through April 30, 2018. The study included a look-back period (October 1, 2015-October 1, 2016). The index date was the date of first influenza diagnosis between October 1, 2016, and April 30, 2017. The follow-up period was the 364 days following the index date.

The treated cohort consisted of patients who had a claim for an antiviral agent (oseltamivir, zanamivir, rimantadine, or peramivir) within 2 days of the index date. Treated patients were propensity score–matched 1:1 with untreated control patients using caliper matching (caliper equal to 0.2 of the SD of the logit of the propensity score). Patients were matched on October 1, 2016, with a 1-year look-back period to create the propensity scores. The propensity score model used several covariates that predicted treatment with antivirals. These included age; sex; region; health plan type; receipt of influenza vaccine; conditions considered to increase risk of severe influenza complications (neurologic disorders, blood disorders, liver disease, pulmonary disorders, heart disease, kidney disease, and obesity)33; Diabetes Complications Severity Index (DCSI), a 14-level metric validated for use in claims analyses to quantify the severity of diabetes complications (cardiovascular, cerebrovascular, or metabolic complications; retinopathy; nephropathy; neuropathy; and peripheral vascular disease) and updated using ICD-10-CM codes34,35; diabetes drug classification (a proxy for the severity of diabetes based on the American Diabetes Association diabetes pharmacologic step therapy guidelines36 as described in earlier work29); all-cause hospitalization in the preceding 12 months; and an emergency department (ED) visit in the preceding 12 months. A comorbidity index was not used because of overlap with the DCSI and the number of conditions considered to increase risk of severe influenza complications.

Outcomes

Outcomes were assessed for the full year and for each of the 4 quarters (each 91 days) following the diagnosis of influenza. Metrics included mean number of outpatient visits, ED visits, and hospitalizations; mean duration of hospitalization; and total medical costs (defined as total costs of all outpatient, inpatient, and ED visit claims, adjusted to 2018 US$).

Statistical Analysis

Following propensity score matching, the cohort balance was compared using the standardized difference, with a standardized difference of less than 0.10 considered to indicate negligible correlation.37 To assess differences between the 2 cohorts, prematch and postmatch variables were compared using the t test for continuous variables and the χ2 test for categorical variables.

Outcomes for each time period (quarters 1-4 [Q1-Q4] and the full year) were compared between treated and matched untreated cohorts using the t test. Analyses were conducted with SAS version 9.4 (SAS Institute Inc), with statistical significance set at 0.05.

RESULTS

Study Population

Of 7776 patients with T2D who received a diagnosis of influenza in the 2016-2017 influenza season, 5115 patients were treated with an antiviral agent and 2463 were untreated; 198 patients whose influenza was diagnosed in the inpatient setting were excluded (eAppendix Figure [available at ajmc.com]). Following propensity score matching, treated and untreated cohorts of 2459 patients each were identified. Other than 1 patient who received zanamivir and 1 patient who received rimantadine, all patients in the treated cohort had received oseltamivir.

Several characteristics differed between the cohorts before propensity score matching, including the number of high-risk conditions for severe influenza complications, DCSI score, sex, age group, receipt of influenza vaccine, and ED visit in preceding 12 months (Table 1). Following propensity score matching, the cohorts were well balanced, and the standardized difference did not exceed the threshold of 0.1 for any characteristic (Table 2). The mean age of patients was 52 years (with more than two-thirds of patients in the group aged 50-64 years), with a mean of 1.5 high-risk conditions and a mean DCSI score of 2.3. Most patients (61%) were taking insulin or an antihyperglycemic drug other than or in addition to metformin. Less than one-third of patients had received the influenza vaccine. Approximately 8% had been hospitalized in the preceding year, and approximately 27% had visited the ED in the preceding year.

HCRU

Antiviral treatment was associated with a significant reduction in the mean number of ED visits over 1 year following influenza infection (mean [SD], 0.94 [1.76] vs 1.24 [2.47]; P < .0001), resulting in a reduction in ED visits of approximately 25% compared with the untreated cohort (Table 3 and Figure 2). A significant reduction in ED visits was also observed during each quarter and ranged from 29.2% in Q1 to 21.3% in Q4. No significant differences were observed between treated and untreated cohorts in the number of outpatient visits over the full year or during each quarter, with the exception of Q3, in which treated patients experienced a significantly higher number of visits.

For all time periods examined, the number of hospitalizations among the treated cohort was lower compared with the untreated cohort; however, the differences were not significant. The duration of hospitalization, however, was significantly reduced in the treated cohort compared with the untreated cohort over the full year (mean [SD], 0.71 [3.36] days vs 1.11 [5.60] days; P < .0023) and in Q1 (mean [SD], 0.29 [1.75] vs 0.48 [3.27] days; P = .0075], representing a reduction in hospitalization days of 35.6% and 40.5%, respectively (Table 3 and Figure 2). Hospitalization duration was also lower in the treated cohort during Q2, Q3, and Q4, but the difference was not significant.

Health Care Costs

Mean (SD) total health care costs were significantly lower in the treated cohort ($20,212 [$58,627]) than in the untreated cohort ($24,552 [$71,830]) over a whole year following the index influenza visit (P = .0203), representing a cost reduction of 17.7% (Table 3 and Figure 2). A significant reduction in costs of 21.7% was also realized in Q1 ($5042 [$19,272] vs $6443 [$26,382]; P = .0335). Costs in Q2, Q3, and Q4 were also lower in the treated cohort but did not reach statistical significance.

DISCUSSION

Patients with T2D are at increased risk for severe medical outcomes and increased HCRU following influenza compared with the general population.38,39 Interventions that can limit the impact of influenza may provide clinical and economic benefits. Although vaccination remains the most effective way to prevent influenza, vaccine effectiveness varies from season to season depending on the match between vaccine components and the dominant circulating strains,40 and vaccination rates among high-risk patients remain suboptimal.41 Antiviral treatment is also recommended for high-risk groups, including individuals with diabetes. The impact of antiviral treatment for influenza on outcomes in patients with T2D, however, remains largely undocumented. In this study we used real-world claims data from the 2016-2017 influenza season to assess short-term and long-term HCRU and costs of antiviral treatment in patients with diabetes. This study showed that patients with diabetes who received an antiviral within 2 days of an influenza diagnosis had significantly fewer ED visits, significantly reduced duration of hospitalization, and significantly lower medical costs compared with untreated patients. Importantly, the beneficial effect of antiviral treatment was long-lasting and was observed not only in the acute postinfluenza period but also over a full year after the index influenza episode.

To ensure the comparability of the 2 cohorts in this study, patients were propensity score–matched for variables that could influence outcomes. We used both the DCSI, a metric that encompasses a variety of diabetic complications, and diabetes drug classification to control for the severity of diabetes. Additionally, to ensure that baseline morbidity was comparable between the groups, we matched for health conditions associated with a higher risk of severe influenza complications and for hospitalization and ED use in the preceding year.

The results show that both HCRU and costs were lower in the treated cohort than in the untreated cohort over multiple time periods. Importantly, the beneficial impact of antiviral treatment was sustained for a full year. The reduction in number of ED visits during each quarter over a full year ranged from 21.3% to 29.2%. Although the number of hospitalizations was not significantly reduced in the treated cohort, the duration of hospitalization was reduced significantly both in Q1 and over the full ensuing year, with a nonsignificant reduction in duration also observed for Q2, Q3, and Q4. Although the proportion of patients who needed to be hospitalized was not significantly different between the cohorts, the duration of hospitalization—as a surrogate for severity of condition—was shortened significantly in the treated cohort. Costs over a full year were reduced significantly in the treated group compared with the untreated group, with lower costs reported in all quarters for the treated group.

Some notable trends are apparent in the study results. Although reductions in all categories of postinfluenza HCRU were not statistically significant in all quarters, the treated cohort had consistently lower numerical HCRU for each quarter. The exception to this trend was the number of outpatient visits, which increased in the treated cohort by 7.1% and 4.2% in Q3 and Q4, respectively. However, the consistent trend in reduction of HCRU for the treated cohort in each quarter following influenza infection is compelling. Future studies should evaluate whether these results are generalizable to other high-risk patient populations. Also, the current study evaluated outcomes up to 1 year following influenza infection, but based on the results, benefits of antiviral treatment may extend beyond 1 year. The duration of the benefit of antiviral treatment beyond 1 year would therefore also be worth examining. In addition, future investigations could examine differences in the long-term effect of antivirals in the treatment of influenza among a variety of patients with diabetes, such as those who are receiving oral antihyperglycemics vs insulin.

This is the first study to show the long-term benefit of timely antiviral treatment of an acute episode of influenza in patients with T2D. A previous subgroup analysis of a retrospective claims-based study in patients with diabetes showed a reduction in short-term outcomes (within 14 days of influenza diagnosis) in patients treated with oseltamivir following influenza diagnosis; treated patients had a significant 17% reduction in risk of respiratory illnesses and a 30% reduction in risk of hospitalization for any reason compared with an unmatched group of untreated patients.30 Another study documented a reduction in hospitalization following antiviral treatment of a cohort of patients with high-risk conditions that included diabetes.42 The long-term impact of influenza in patients with T2D has recently been documented in a claims-based study that showed significantly higher HCRU (outpatient visits, ED visits, and hospitalizations) and medical expenditures for up to at least 1 year after a diagnosis of influenza compared with no influenza diagnosis.29 A diagnosis of influenza was also associated with a higher prevalence of new or worsening diabetic complications, particularly cardiovascular and cerebrovascular complications, over a year of follow-up compared with those without influenza.

Our study shows that timely antiviral treatment of an index influenza episode in patients with diabetes may improve health care outcomes and reduce costs for a full year after infection and mitigate the long-term deleterious impact of influenza. The data support current recommendations, which advocate antiviral treatment for patients in high-risk groups. However, as shown in this study, a substantial proportion of patients in this high-risk group remained untreated; only two-thirds of patients with T2D who sought care for influenza received an antiviral agent, supporting previous reports of low antiviral prescribing rates, even among high-risk individuals.27,28 The low rate of antiviral prescribing coupled with inadequate influenza prophylaxis (more than one-third of US adults with diabetes are estimated to be unvaccinated)16,17 is reflective of suboptimal adherence to treatment guidelines following influenza infection and overall poor standard of care in this high-risk group.

Limitations

This study has a number of limitations. Because this was a claims-based study, the possibility of residual bias exists even though propensity score matching was used to reduce selection bias. The study used data from US commercial insurance plans, and results may not be generalizable to other populations such as the Medicare or Medicaid populations. Patients who received a diagnosis of influenza in the inpatient setting were excluded. Because claims are collected for billing purposes, data may be incomplete or inaccurate and diagnoses may also be miscoded. Documentation of influenza vaccination may be unreliable in claims analysis; however, this covariate was still selected as a variable for the propensity score model because its absence may cause bias. The study design required patients to be continuously enrolled in the health plan, so those who died or who were lost to follow-up were not included, leading to selection bias. Cases of influenza were identified using claims data, and the diagnosis was not verified by medical record review or confirmed with a laboratory diagnosis, resulting in potential inclusion of patients with influenza-like illness in the analysis. If misclassification of influenza was nondifferential between treated and untreated cohorts, there would likely be minimal impact on this study’s results, but if misclassification was significantly different between cohorts, results could be biased. Because the index date was the date of first diagnosis of influenza associated with a medical service and not with symptom onset, any delay in seeking care could have resulted in antiviral therapy being initiated after the 48-hour window for optimal drug efficacy and therefore an underestimation of antiviral efficacy. Further, the study evaluated all-cause HCRU, which includes conditions that may not be affected by influenza infection in the past year.

CONCLUSIONS

Claims data for the 2016-2017 influenza season showed that antiviral treatment of patients with T2D and a diagnosis of influenza was associated with reduced HCRU. In particular, the number of ED visits and the duration of hospitalization were significantly lower in treated patients with T2D compared with untreated patients over the full year after the influenza diagnosis and during Q1. Costs in treated patients were significantly lower over the first full year and numerically lower during each quarter. These findings suggest that prompt antiviral treatment of patients with T2D confers immediate and long-term benefits after a diagnosis of influenza. 

Acknowledgments

Meher M. Dustoor, PhD, and Esther Tazartes, MS, of Global Outcomes Group provided editorial assistance; these services were funded by Genentech, Inc.

Author Affiliations: Genentech, Inc (BDL, CW, TMT, HM, PD, ST), South San Francisco, CA; University of Houston (BDL), Houston, TX; Roche (SWK), Basel, Switzerland.

Source of Funding: Genentech, Inc.

Author Disclosures: Drs Lewing and Masters were employed by Genentech and did not own Genentech stock when work on this manuscript began. Drs Wallick, To, Dayal, and Tam are employed by Genentech and own Genentech stock. Dr Korom is employed by Roche and owns Roche stock.

Authorship Information: Concept and design (BDL, CW, TMT, HM, SWK, ST); acquisition of data (BDL); analysis and interpretation of data (BDL, CW, TMT, HM, PD, SWK); drafting of the manuscript (BDL, HM, PD, SWK, ST); critical revision of the manuscript for important intellectual content (BDL, CW, TMT, HM, PD, SWK, ST); statistical analysis (BDL); administrative, technical, or logistic support (BDL); and supervision (CW, SWK).

Address Correspondence to: Benjamin D. Lewing, PhD, University of Houston, 4849 Calhoun, HB2, R4058, Houston, TX 77204-5047. Email: BLewing2@central.uh.edu.

REFERENCES

1. National Diabetes Statistics Report: estimates of diabetes and its burden in the United States. CDC. Updated June 29, 2022. Accessed April 8, 2021. https://www.cdc.gov/diabetes/data/statistics-report/index.html

2. Disease burden of flu. CDC. Updated October 4, 2022. Accessed April 8, 2021. https://www.cdc.gov/flu/about/burden/index.html

3. Flu & people with diabetes. CDC. Updated September 12, 2022. Accessed April 8, 2021. https://www.cdc.gov/flu/highrisk/diabetes.htm

4. Siriwardena AN. Increasing evidence that influenza is a trigger for cardiovascular disease. J Infect Dis. 2012;206(11):1636-1638. doi:10.1093/infdis/jis598

5. Kwong JC, Schwartz KL, Campitelli MA, et al. Acute myocardial infarction after laboratory-confirmed influenza infection. N Engl J Med.2018;378(4):345-353. doi:10.1056/NEJMoa1702090

6. Warren-Gash C, Blackburn R, Whitaker H, McMenamin J, Hayward AC. Laboratory-confirmed respiratory infections as triggers for acute myocardial infarction and stroke: a self-controlled case series analysis of national linked datasets from Scotland. Eur Respir J. 2018;51(3):1701794. doi:10.1183/13993003.01794-2017

7. Sarwar N, Gao P, Sashasai SRK, et al; Emerging Risk Factors Collaboration. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215-2222. doi:10.1016/s0140-6736(10)60484-9

8. Moghadami M, Honarvar B, Sabaeian B, et al. H1N1 influenza infection complicated with diabetic ketoacidosis. Arch Iran Med. 2012;15(1):55-58.

9. Hulme KD, Gallo LA, Short KR. Influenza virus and glycemic variability in diabetes: a killer combination? Front Microbiol. 2017;8:861. doi:10.3389/fmicb.2017.00861

10. Allard R, Leclerc P, Tremblay C, Tannenbaum TN. Diabetes and the severity of pandemic influenza A (H1N1) infection. Diabetes Care. 2010;33(7):1491-1493. doi:10.2337/dc09-2215

11. Wilking H, Buda S, von der Lippe E, et al. Mortality of 2009 pandemic influenza A(H1N1) in Germany. Euro Surveill. 2010;15(49):19741. doi:10.2807/ese.15.49.19741-en

12. Morgan OW, Bramley A, Fowlkes A, et al. Morbid obesity as a risk factor for hospitalization and death due to 2009 pandemic influenza A(H1N1) disease. PloS One. 2010;5(3):e9694. doi:10.1371/journal.pone.0009694

13. Louie JK, Acosta M, Samuel MC, et al; California Pandemic (H1N1) Working Group. A novel risk factor for a novel virus: obesity and 2009 pandemic influenza A (H1N1). Clin Infect Dis. 2011;52(3):301-312. doi:10.1093/cid/ciq152

14. Neidich SD, Green WD, Rebeles J, et al. Increased risk of influenza among vaccinated adults who are obese. Int J Obes (Lond). 2017;41(9):1324-1330. doi:10.1038/ijo.2017.131

15. Grohskopf LA, Alyanak E, Broder KR, Walter EB, Fry AM, Jernigan DB. Prevention and control of seasonal influenza with vaccines: recommendations of the Advisory Committee on Immunization Practices — United States, 2019-20 influenza season. MMWR Recomm Rep. 2019;68(3):1-21. doi:10.15585/mmwr.rr6803a1

16. Villarroel MA, Vahratian A. Vaccination coverage among adults with diagnosed diabetes: United States, 2015. National Center for Health Statistics. December 2016. Accessed April 8, 2021. https://www.cdc.gov/nchs/data/databriefs/db265.pdf

17. Hung MC, Lu PJ, Srivastav A, Cheng YJ, Williams WW. Influenza vaccination coverage among adults with diabetes, United States, 2007-08 through 2017-18 seasons. Vaccine. 2020;38(42):6545-6552. doi:10.1016/j.vaccine.2020.08.008

18. Influenza antiviral medications: summary for clinicians: people at higher risk for influenza complications recommended for antiviral treatment. CDC. Updated September 9, 2022. Accessed April 8, 2021. https://www.cdc.gov/flu/professionals/antivirals/summary-clinicians.htm#highrisk

19. Uyeki TM, Bernstein HH, Bradley JS, et al. Clinical practice guidelines by the Infectious Diseases Society of America: 2018 update on diagnosis, treatment, chemoprophylaxis, and institutional outbreak management of seasonal influenza. Clin Infect Dis. 2019;68(6):e1-e47. doi:10.1093/cid/ciy866

20. Heneghan CJ, Onakpoya I, Jones MA, et al. Neuraminidase inhibitors for influenza: a systematic review and meta-analysis of regulatory and mortality data. Health Technol Assess. 2016;20(42):1-242. doi:10.3310/hta20420

21. Spagnuolo PJ, Zhang M, Xu Y, et al. Effects of antiviral treatment on influenza-related complications over four influenza seasons: 2006-2010. Curr Med Res Opin. 2016;32(8):1399-1407. doi:10.1080/03007995.2016.1176016

22. Doll MK, Winters N, Boikos C, Kraicer-Melamed H, Gore G, Quach C. Safety and effectiveness of neuraminidase inhibitors for influenza treatment, prophylaxis, and outbreak control: a systematic review of systematic reviews and/or meta-analyses. J Antimicrob Chemother. 2017;72(11):2990-3007. doi:10.1093/jac/dkx271

23. Casscells SW, Granger E, Kress AM, Linton A, Madjid M, Cottrell L. Use of oseltamivir after influenza infection is associated with reduced incidence of recurrent adverse cardiovascular outcomes among military health system beneficiaries with prior cardiovascular diseases. Circ Cardiovasc Qual Outcomes. 2009;2(2):108-115. doi:10.1161/circoutcomes.108.820357

24. Wallick C, Wu N, To TM, Keebler D, Moawad D. Antiviral use is associated with a decrease in the rate of influenza-related complications, health care resource utilization, and costs. J Med Econ. 2021;24(1):386-393. doi:10.1080/13696998.2021.1889572

25. Fowlkes AL, Steffens A, Reed C, Temte JL, Campbell AP; Influenza Incidence Surveillance Project Working Group. Influenza antiviral prescribing practices and the influence of rapid testing among primary care providers in the US, 2009-2016. Open Forum Infect Dis. 2019;6(6):ofz192. doi:10.1093/ofid/ofz192

26. Linder JA, Nieva HR, Blumentals WA. Antiviral and antibiotic prescribing for influenza in primary care. J Gen Intern Med. 2009;24(4):504-510. doi:10.1007/s11606-009-0933-9

27. Mulpuru S, Li L, Ye L, et al; Serious Outcomes Surveillance (SOS) Network of the Canadian Immunization Research Network (CIRN). Effectiveness of influenza vaccination on hospitalizations and risk factors for severe outcomes in hospitalized patients with COPD. Chest. 2019;155(1):69-78. doi:10.1016/j.chest.2018.10.044

28. Havers F, Flannery B, Clippard JR, et al. Use of influenza antiviral medications among outpatients at high risk for influenza-associated complications during the 2013-2014 influenza season. Clin Infect Dis. 2015;60(11):1677-1680. doi:10.1093/cid/civ146

29. Lewing BD, Wallick C, To TM, et al. Short and long-term impact of influenza infection on individuals with type 2 diabetes: effect on healthcare utilization and diabetes complications. Diabetes Manag. 2021;11(2):171-179.

30. Orzeck EA, Shi N, Blumentals WA. Oseltamivir and the risk of influenza-related complications and hospitalizations in patients with diabetes. Clin Ther. 2007;29(10):2246-2255. doi:10.1016/j.clinthera.2007.10.001

31. IBM MarketScan Research Databases. Accessed May 28, 2021. https://www.ibm.com/products/marketscan-research-databases/databases

32. Chen G, Khan N, Walker R, Quan H. Validating ICD coding algorithms for diabetes mellitus from administrative data. Diabetes Res Clin Pract. 2010;89(2):189-195. doi:10.1016/j.diabres.2010.03.007

33. People at higher risk of flu complications. CDC. Updated September 6, 2022. Accessed January 24, 2023. https://www.cdc.gov/flu/highrisk/index.htm

34. Chang HY, Weiner JP, Richards TM, Bleich SN, Segal JB. Validating the adapted Diabetes Complications Severity Index in claims data. Am J Manag Care. 2012;18(11):721-726.

35. Glasheen WP, Renda A, Dong Y. Diabetes Complications Severity Index (DCSI)-update and ICD-10 translation. J Diabetes Complications. 2017;31(6):1007-1013. doi:10.1016/j.jdiacomp.2017.02.018

36. American Diabetes Association. 9. Pharmacologic approaches to glycemic treatment: Standards of Medical Care in Diabetes—2019. Diabetes Care. 2019;42(suppl 1):S90-S102. doi:10.2337/dc19-S009

37. Austin PC. Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Commun Stat Simul Comput. 2009;38(6):1228-1234. doi:10.1080/03610910902859574

38. Samson SI, Konty K, Lee WN, et al. Quantifying the impact of influenza among persons with type 2 diabetes mellitus: a new approach to determine medical and physical activity impact. J Diabetes Sci Technol. 2021;15(1):44-52. doi:10.1177/1932296819883340

39. Lau D, Eurich DT, Majumdar SR, Katz A, Johnson JA. Working-age adults with diabetes experience greater susceptibility to seasonal influenza: a population-based cohort study. Diabetologia. 2014;57(4):690-698. doi:10.1007/s00125-013-3158-8

40. Past seasons’ vaccine effectiveness estimates. CDC. Updated December 22, 2022. Accessed May 28, 2021. https://www.cdc.gov/flu/vaccines-work/past-seasons-estimates.html

41. Annunziata K, Rak A, Del Buono H, DiBonaventura M, Krishnarajah G. Vaccination rates among the general adult population and high-risk groups in the United States. PloS One. 2012;7(11):e50553. doi:10.1371/journal.pone.0050553

42. Venkatesan S, Myles PR, Leonardi-Bee J, et al. Impact of outpatient neuraminidase inhibitor treatment in patients infected with influenza A(H1N1)pdm09 at high risk of hospitalization: an individual participant data metaanalysis. Clin Infect Dis. 2017;64(10):1328-1334. doi:10.1093/cid/cix127

Related Videos
dr mohamad mohty
dr ryan jacobs
Video 2 - 3 KOLs are featured in, "Assessing Limitations, Costs, Mobility and Cognition in Progressive Multiple Sclerosis"
Video 1 - 3 KOLs are featured in, "Multiple Sclerosis: Impact, Types, Pathophysiology, and Progression"
Justin Oldham, MD, MS, an expert on IPF
Justin Oldham, MD, MS, an expert on IPF
Chesahna Kindred, MD, MBA, FAAD, board-certified dermatologist, Kindred Hair & Skin Cente
A panel of experts on Alzheimer's disease
Related Content
CH LogoCenter for Biosimilars Logo