Population-based Incidence Estimates of Influenza-associated Respiratory Failure Hospitalizations, 2003 to 2009

Title: Population-Based Incidence Estimates of Influenza-Associated Respiratory Failure

Hospitalizations, 2003 - 2009

Authors:

• Justin R. Ortiz1,2,3

• Kathleen M. Neuzil1,2,3

• Tessa C. Rue4

• Hong Zhou5

• David K. Shay5

• Po-Yung Cheng5

• Colin R. Cooke6

• Christopher H. Goss1

Affiliations:

1. Department of Medicine, University of Washington, Seattle, WA, USA

2. Department of Global Health, University of Washington, Seattle, WA, USA

3. Vaccine Access and Delivery Global Program, PATH, Seattle, WA, USA

4. Department of Biostatistics, University of Washington, Seattle, WA, USA

5. Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA

6. Department of Medicine, University of Michigan, Ann Arbor, MI, USA

This article has an online data supplement, which is accessible from this issue's table of

content online at www.atsjournals.org.

Running Head: Influenza-Associated Respiratory Failure

Total word count for body of manuscript: 3275

Total word count for abstract: 250 words

Corresponding Author: Justin R. Ortiz, University of Washington Medical Center,

Division of Pulmonary and Critical Care Medicine, Box 356522, 1959 NE Pacific St.,

Seattle, WA 98195-6522. Phone: (206) 543-3166. Fax: (206) 685-8673. Email:

[email protected].

Author Contributions:

JO, KN, HZ, DS, and CG contributed to the conception and design of the study. JO

acquired study data. JO, KN, HZ, DS, TR, CC, and CG contributed to the analysis and

interpretation of the data. JO drafted the article. KN, HZ, DS, TR, CC, and CG revised

the article critically for important intellectual content. All authors contributed to the final

version of the article and approve of the final version to be published.

Subject Code List for Classification of Articles Submitted: 10.5 Epidemiology

(Microbiology and Pulmonary Infections)

Page 1 of 48 AJRCCM Articles in Press. Published on 15-July-2013 as 10.1164/rccm.201212-2341OC

Funding: This study was supported with funding from the Robert Wood Johnson Harold

Amos Medical Faculty Development Program and the University of Washington

Housestaff Association John B. Coombs Research Award (Dr. Ortiz), and Robert Wood

Johnson Foundation Clinical Scholars program (Dr. Cooke). The funders had no role in

preparation of the manuscript or in the decision to submit the manuscript for publication.

No additional external funding was received for this manuscript.

This article was previously presented as an abstract at the American Thoracic Society

annual meeting in San Francisco, CA, USA, on May 18, 2012.

At a Glance Commentary:

• Scientific Knowledge on the Subject: In the United States, influenza typically is

associated with the most annual morbidity and mortality of any vaccine-

preventable disease. However, the incidence of influenza-associated acute

respiratory failure is unknown.

• What This Study Adds to the Field: This study estimates the population-based

incidence of influenza-associated acute respiratory failure. Influenza virus is

associated with an annual estimated 3.8% of all respiratory failure

hospitalizations during the influenza season, and disease severity increases

considerably with advancing age. Clinicians should maintain a high index of

suspicion for influenza infection among hospitalized patients with acute

respiratory illness when influenza is known to be circulating in a community.


Page 1

ABSTRACT

Rationale: The incidence of influenza-associated acute respiratory failure is unknown.

Objectives: We conducted this study to estimate the population-based incidence of

influenza-associated acute respiratory failure hospitalizations.

Methods: This is a cohort study from January 2003 through March 2009 using

hospitalization databases for Arizona, California, and Washington from the Healthcare

Cost and Utilization Project and influenza surveillance data for regions encompassing

these states. Acute respiratory failure requiring mechanical ventilation was defined by

ICD-9-CM code. We used negative-binomial regression modeling to estimate the

incidence of influenza-associated events.

Measurements and Main Results: The incidence of influenza-associated acute

respiratory failure was 2.7 per 100,000 person-years (95% CI 0.2, 23.5), and during the

influenza season, 3.8% of all respiratory failure hospitalizations were attributable to

influenza. Compared with adults aged 18-49 years, the incidence rate ratio (IRR) for

influenza-associated acute respiratory failure was lower among children aged 1-4 years

(0.9) and 5-17 years (0.3); however, it was higher among adults aged 50-64 years (4.8),

65-74 years (10.4), 75-84 years (19.9), and 85 years and older (33.7). Results were

similar with more sensitive and specific outcome definitions and in a sensitivity analysis

using only Arizona-specific outcome and surveillance data.

Conclusion: Our data indicate that influenza was an important contributor to respiratory

failure hospitalizations during 2003-2009. Clinicians should maintain a high index of

suspicion for influenza among hospitalized patients with acute respiratory illness when


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influenza is circulating in a community. Influenza has a greater effect on respiratory

failure in the elderly, for whom better prevention measures are needed.

Key Words: Epidemiology; Respiratory Tract Infections; Critical Care; Respiration,

Artificial; State Inpatient Database


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INTRODUCTION

During a typical year in the United States, influenza is responsible for the highest

morbidity and mortality of any vaccine-preventable disease (1, 2). However, the burden

of severe influenza disease is difficult to ascertain. Published prospective surveillance

studies of hospitalized patients with laboratory confirmation of influenza infection have

small sample sizes limiting analyses and are too costly to conduct in many sites or over

multiple influenza seasons (2). Further, because influenza symptoms are non-specific

and laboratory testing of hospitalized patients is uncommon, analyses of administrative

datasets for patients with influenza diagnoses may underestimate disease incidence (3).

For these reasons, the World Health Organization (WHO) recommends use of

regression modeling methods to estimate influenza burden of disease when

administrative hospitalization datasets and robust influenza surveillance data are

available (4).

From influenza modeling studies, US Centers for Disease Control and Prevention (CDC)

estimates that an average of 23,607 influenza-associated deaths occurred annually in

the United States between 1977 and 2007 (5). In the CDC study, mortality varied widely,

depending on the circulating influenza virus types and subtypes, with annual nationwide

death estimates ranging from 3,349 to 48,614 (5). CDC estimates of influenza-

associated hospitalizations also vary considerably from year to year. A recent modeling

study using the State Inpatient Database (SID) from the Healthcare Cost and Utilization

Project (HCUP) estimates all-age US influenza hospitalization rates ranged from 63.5 to

100.3 hospitalizations per 100,000 person-years between 1993 and 2008 (2).

While there are reports that 2009 influenza A (H1N1) pandemic stressed critical care

services worldwide (6), there are no incidence estimates of respiratory failure associated


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with seasonal or pandemic influenza. The objective of this study was to address this

knowledge gap by estimating a population-based incidence of seasonal influenza-

associated hospitalizations for respiratory failure in the western US states of Arizona,

California, and Washington. Some of the results of this study have been previously

reported in the form of an abstract (7).

METHODS

Study Design

We conducted a retrospective cohort study from January 2003 through March 2009

using hospitalization databases from the Healthcare Cost and Utilization Project (HCUP)

and regional influenza surveillance data from the CDC. We included all inpatient

discharge abstracts from community hospitals in the HCUP State Inpatient Database

(SID) from Arizona, California, and Washington during the study period. Our primary

objective was to estimate the population-based incidence of seasonal influenza-

associated hospitalizations for acute respiratory failure. Our exposures of interest were

positive surveillance tests for influenza A (H1N1), influenza A (H3N1), and influenza B

from the western United States. The primary outcome was acute respiratory failure

requiring mechanical ventilation defined by ICD-9-CM code. We linked the

hospitalization datasets with the surveillance datasets by calendar time and geographic

region. We then used negative-binomial regression models to estimate the incidence of

influenza-associated events overall and by several age groups. Sensitivity analyses

were done to assess choice of outcome and choice of predictor of interest.

Virologic Surveillance Data


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We obtained influenza surveillance data from the CDC which administers surveillance

conducted by the US WHO Collaborating Laboratories and National Respiratory and

Enteric Virus Surveillance System (NREVSS) Laboratories (8) (See Online Supplement

for extended study methods). We used surveillance data from two contiguous US

regions with similar influenza activity from July 2002 through June 2009: US Federal

Region 9 (Arizona, California, Hawaii, and Nevada) data for analyses of Arizona and

California hospitalizations and US Federal Region 10 (Alaska, Idaho, Oregon, and

Washington) data for analyses of Washington hospitalizations. Data linkages to

individual persons, clinical or epidemiological data, or tests for other respiratory viruses

are not available. Previously, we have shown that virologic surveillance data from CDC

are highly correlated with other commonly used nationwide influenza surveillance

systems (9).

Influenza viruses typically circulate during winter months and across calendar years.

Therefore, we defined July through June of the following year as a “surveillance year” so

that an entire influenza season was studied. To compare events during periods of

differing influenza activity, we defined three time periods: the “influenza season” is all

months in which ≥10% of regional surveillance tests were positive for influenza. All other

months were “non-influenza winter months” (from October through April) or “summer

months” (from May through September). To reduce possible bias associated with

differences in specimen sampling and laboratory methods over time, we standardized

monthly numbers of specimens that tested positive for influenza A (H1N1), influenza A

(H3N2), and influenza B by dividing by the total number of specimens tested that

surveillance year. For a sensitivity analysis, we used Arizona-specific virologic

surveillance data obtained from Arizona Department of Health Services. We calculated

the Pearson’s correlation coefficient describing the relationship of Arizona-specific and


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Region 9 data, and we compared Arizona outcome estimates derived from the two

surveillance datasets.

Hospitalization Data

We obtained the complete State Inpatient Database (SID) for Arizona, California, and

Washington from the Healthcare Cost and Utilization Project (HCUP), Agency for

Healthcare Research and Quality for 2003 through 2009 (10). The SID contains all

inpatient discharge abstracts from state hospitals, excepting some specialty hospitals

unlikely to have many acute respiratory failure events (10). The outcome of interest for

this study was acute respiratory failure. We defined hospitalizations with acute

respiratory failure as any hospitalization that had a code for acute respiratory distress or

failure (ICD-9-CM 518.5, 518.81, or 518.82) and a procedure code for continuous

mechanical ventilation (ICD-9-CM 96.7), as had been done previously (11). Because the

primary outcome definition may miss acute respiratory failure hospitalizations in which

acute respiratory distress or failure had not been coded, we performed sensitivity

analyses with more sensitive and specific definitions to represent upper- and lower-

range incidence estimates (Table 1).

We used seven age categories: 1-4 years, 5-17 years, 18-49 years, 50-64 years, 65-74

years, 75-84 years, and 85 years and older. Children <1 year of age were excluded from

this analysis to address confounding due to respiratory syncytial virus (RSV)

hospitalizations.

Statistical Analysis

We modified negative binomial regression models developed to estimate US influenza-

associated hospitalizations (2, 5). Negative binomial regression was used given the


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distribution of the data and concern that assumptions implicit in Poisson regression

would not apply to these data. A recent study has validated this approach to estimating

influenza-associated hospitalizations against prospective surveillance for laboratory-

confirmed influenza in children (12).

Age-specific negative binomial regression models were fit to monthly acute respiratory

failure hospitalizations. Covariates for the standardized proportion of specimens testing

positive each month for influenza categorized as H1N1, H3N2, and B were included in

the models. Additional covariates accounted for seasonal trend, secular trend, and state.

To estimate excess respiratory failure hospitalizations associated with influenza, we

subtracted expected baseline events from a full model incorporating all viral terms,

where the baseline represented a model in which a viral covariate was set to zero. This

method defines the difference between predicted and baseline events as influenza-

associated events. The baseline accounts for seasonal variability in hospitalizations not

associated with influenza. However, risk factors for acute respiratory failure that are

collinear with influenza activity could still confound the analysis. Previous studies have

found collinearity between RSV and influenza virus circulation when the surveillance

data were analyzed at the month level; this situation resolves when the influenza and

RSV data were analyzed at the week level (13). Unfortunately, week-level hospitalization

data are not publicly available from SID. To minimize the impact of not having RSV in

our model, we excluded children <1 year of age. The rationale for this was based on a

systematic review and meta-analysis of RSV in childhood which concluded that RSV

deaths among children >1 year are negligible (14); a modeling study similar to ours

which estimated >75% of RSV hospitalizations occur among infants aged <1 year (2);

and, finally, two systematic global burden of disease studies which concluded that RSV

disease was not an important contributor to adult morbidity in developed country settings


Page 8

(15, 16). While other respiratory viruses also circulate in the wintertime, such as

adenovirus, human metapneumovirus, and the human parainfluenza viruses, they

typically are not the most common respiratory pathogens in any age group and they had

different temporal activity than influenza during the study period (2, 17). The 95%

confidence intervals (CIs) were estimated with the model variance for the predicted

values from regression models as had been done in previous studies (2, 5, 13). The

model was fit to data from Jan 2003 through March 2009 to exclude the 2009 influenza

A (H1N1) pandemic from this analysis.

The modeled monthly outcomes were summed for each viral term across the study

period and states by influenza surveillance year for each age category. Annual state

population estimates were acquired from the US census and were used to calculate

population-based monthly incidence rates of outcomes (18). Sensitivity analyses were

done to assess choice of outcome and choice of predictor of interest.

This study received exempt review status from the Human Subjects Division at the

University of Washington and was performed with SAS statistical software (version 9.3;

Cary, NC).

RESULTS

Description of Primary Outcome

From January 2003 through March 2009, there were 568,772 hospitalizations for acute

respiratory failure in Arizona (14.9%), California (74.8%), and Washington (10.3%). The

calculated population-based incidence rate of acute respiratory failure hospitalizations

was 191.0 per 100,000 person-years (Table 2). Incidence of acute respiratory failure

differed greatly by age group. Children aged 1-4 years had an acute respiratory failure


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incidence rate of 33.3 per 100,000 person-years, which declined in the 5-17 years age

group to 18.8 per 100,000 person-years, and increased with increasing age thereafter.

Description of Influenza Surveillance Database

From January 2003 through March 2009, there were 132,905 influenza surveillance

laboratory tests performed in US Federal Region 9, and of these, 14,530 (11%) were

positive for influenza virus. Over this same period, there were 52,192 influenza

surveillance laboratory tests performed in US Federal Region 10, with 6,773 (13%)

testing positive for influenza virus. The majority of Federal Region 9 and 10 laboratory

tests were from the states in this analysis. Surveillance tests were positive for influenza

virus during every month, and different influenza virus types and subtypes circulated

during the study period (Figure E1 in online supplement). Among all positive influenza

surveillance tests, 50% were for influenza A (H3N2), 25% were for influenza A (H1N1),

and 26% were for influenza B.

Influenza-Associated Acute Respiratory Failure

There was a temporal relationship between acute respiratory failure hospitalization

incidence and influenza activity (Figure 1). Concomitant increases in acute respiratory

failure hospitalizations and influenza positive surveillance tests typically occurred during

January to March of each year, except in 2003-04 and 2005-06 in which both increased

earlier in the surveillance year.

Using multivariate regression methods, we estimated an influenza-associated acute

respiratory failure incidence rate of 2.7 per 100,000 person-years (95% CI: 0.2, 23.5)

(Table 2). The percentage of total acute respiratory failure hospitalizations attributable to

influenza was 1.4%. The incidence of influenza-associated acute respiratory failure

increased with age. Compared to the 18-49 years age group, the incidence rate ratio of


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influenza-associated acute respiratory failure was similar in persons 1-4 years (0.9) and

decreased in persons 5-17 years (0.3); however, the incidence rate ratio increased in

50-64 years (4.8), 65-74 years (10.4), 75-84 years (19.9), and 85 years and older (33.7).

During the influenza season, the incidence of influenza-associated respiratory failure

events was 7.9 per 100,000 person-years. 3.8% of all acute respiratory failure

hospitalizations are attributable to influenza during these periods. The percentage of

influenza-associated outcomes were proportional to the percentage of positive influenza

tests during the influenza season (85% and 78%), non-influenza winter months (11%

and 16%), and summer months (4% and 6%). The proportion of acute respiratory failure

associated with influenza types and subtypes was similar to the proportion of circulating

viruses identified (Table E1 in online supplement), except influenza B was associated

with a higher incidence of acute respiratory failure among the elderly, and influenza A

(H3N2) was associated with all the influenza-associated events in young children.

Influenza-associated acute respiratory failure incidence varied by surveillance year (see

Table E2 in online supplement). In 2003-04, 3.9% of all respiratory failure events were

attributable to influenza in the 1-4 years age group. This proportion is > 1.5 times the

population-attributable risk for the same age group over the entire study period (2.3%).

Notably, during 2003-04, there was no change in event rates among adult populations.

Our primary analysis incidence rate estimate was similar to secondary analyses that

used outcome definitions that we considered to be more sensitive and more specific for

acute respiratory failure. Influenza-associated respiratory and circulatory diagnoses with

mechanical ventilation or non-invasive ventilation occurred at a rate of 3.5 per 100,000

person-years (95% CI: 0.3, 32.6); and the primary outcome excluding all hospitalizations

with a major therapeutic surgery occurred at a rate of 2.1 per 100,000 person-years


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(95% CI: 0.2, 17.2) (see Table E3 in online supplement). Incidence rate estimates within

age groups were also similar.

Because we used aggregated regional influenza surveillance data to make inferences

about disease activity in only three US states, we repeated the analysis with the Arizona

hospitalization dataset and two different sources of Arizona influenza surveillance data

(Federal Region 9 and state-specific data) to determine whether use of these different

surveillance data would lead to similar results. The overall incidence rate of influenza-

associated acute respiratory failure was 3.7 per 100,000 person-years (95% CI: 0.3,

47.7) using Arizona-specific data and 4.1 per 100,000 person-years (95% CI: 0.1, 43.4)

using Federal Region 9 data (see Table E4 in online supplement). Incidence rate

estimates within age groups were also similar. The Pearson correlation coefficients

describing the relationship of standardized monthly frequencies of positive influenza

virus tests for US Federal Region 9 and Arizona state surveillance from June 2003

through March 2009 were very high for influenza A (H3N2) (0.91), influenza A (H1N1)

(0.92), and influenza B (0.95).

DISCUSSION

This study provides the first population-based incidence of influenza-associated acute

respiratory failure. In a cohort that includes over 50 million people from a defined

geographic region over 5 years, we estimate the incidence of influenza-associated acute

respiratory failure to be 2.7 events per 100,000 person-years. During the influenza

season, 3.8% of all respiratory failure hospitalizations are attributable to influenza. If the

risk of influenza-associated acute respiratory failure is similar to the risk of influenza and

pneumonia mortality, we would anticipate that our results are likely to be generalizable to

the rest of the United States. Pneumonia and influenza mortality from 2009 National Vital


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Statistics Reports were similar in Arizona (15.1 deaths/100,000 population), California

(17.9 deaths/100,000 population), Washington (10.7 deaths/100,000 population), and

the United States overall (16.2 deaths/100,000 population) (19). If data from this study

were extrapolated to the entire US population, we estimate that there were 8,506 cases

annually of influenza-associated acute respiratory failure between January 2003 and

March 2009.

The finding of disproportionate burden of severe influenza disease in the elderly is

consistent with clinical research identifying this group as at risk for severe influenza

disease and death (5). While young children had low incidence of acute respiratory

failure, many of these events were also associated with influenza. We found that the

population-attributable risk for influenza-associated acute respiratory failure was

substantially increased during the 2003-04 influenza season among children aged 1- 4

years. This finding is consistent with the early, intense activity and increased pediatric

influenza deaths seen during that season (20). Despite the large increase in influenza-

associated acute respiratory failure incidence in young children during 2003-04, the

absolute risk increase was small. Older age groups were spared increases in very

severe disease during the 2003-04 season, which was noted at the time and has been

attributed to pre-existing immunity (21).

We may have underestimated the actual US incidence of influenza-associated

respiratory failure for several reasons. Our study was conducted during a period of lower

influenza-associated morbidity than prior studies, as we excluded the 1990s which were

characterized by circulation of more pathogenic influenza A (H3N2) viruses (2). We

excluded children < 1 year of age to address confounding by RSV disease, however this

age group has high influenza morbidity and mortality (2, 5, 14). Our primary outcome

definition required mechanical ventilation while many patients with influenza-associated


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respiratory failure may be treated by non-invasive means. Further, while our estimates of

influenza-associated outcomes were consistent, even when we used more sensitive and

specific outcome measures, our reliance on month level hospitalization events likely

decreased our incidence estimates and their precision.

Our analysis is subject to limitations. There is no consensus ICD-9-CM code definition

for acute respiratory failure; however, our incidence estimates are similar to studies

using different methodologies (11, 22, 23). Some exposure data came from states in

which no outcomes were measured; however, we chose the largest western US states

for this study which accounted for most of the surveillance testing in their census

regions, and our sensitivity analysis using Arizona-specific data demonstrated influenza

activity was highly correlated to data from its region. Despite our use of multiple years of

data from large states, our estimates have large 95% confidence intervals. The

frequency of specimen collection and physician testing practice vary throughout the

seasons and thus could influence our estimates (2). Finally, our model did not account

for RSV. Small, single center surveillance studies conducted over few seasons have

found high rates of adult RSV hospitalizations (24, 25). If similar rates occurred in our

study population, they could lead to over-estimates of the rates of influenza-associated

events

The best way to prevent influenza disease is by immunization. In the United States, all

persons 6 months and older are recommended to receive influenza vaccine annually

(26). Despite these universal recommendations, vaccine coverage remains below goals

(27). The highest burden of severe influenza disease occurs in elderly populations (26).

Vaccine efficacy estimates are lower in the elderly than in younger adults (28-30). While

observational influenza vaccine studies in the elderly should be interpreted with caution

given the potential for bias and confounding (31), recent observational studies have


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found influenza vaccine can be effective at reducing hospitalizations and all-cause

mortality in the elderly (32-34). Our study highlights that there is a desperate need to

improve influenza prevention in the elderly. While representing only 12% of the study

population, they had 60% of the severe influenza-associated disease. Several efforts are

ongoing to improve vaccine performance in the elderly, including increased antigen dose

and the use of adjuvants in recently licensed vaccines (26, 35), and the active

investigation by manufacturers of inactivated quadrivalent influenza vaccines that

include both circulating influenza B lineages (36).

Severe influenza-associated disease in the ICU may appear similar to severe illnesses

due to many other etiologies. We found that the risk of influenza-associated acute

respiratory failure is proportional to positive surveillance tests for influenza within a state

or region. Clinicians should maintain a high index of suspicion for influenza infection

among hospitalized patients with acute respiratory illness when influenza is known to be

circulating in their communities. WHO and CDC both advocate empiric oseltamivir

therapy for patients with severe acute respiratory illness during periods of influenza

activity while specific testing is being performed (37, 38). To aid clinicians, CDC and

many state and local health departments have webpages with relevant influenza

surveillance data for most communities (39). There are also novel monitoring tools which

track influenza disease at the community level (40).

Prospective studies with laboratory-confirmed endpoints to measure influenza incidence

are required to better assess the contribution of influenza to severe morbidity, however

such studies may never be done due to the high costs and sample sizes that are

required. Our study suggests that influenza disease is an important contributor to acute

respiratory failure hospitalizations, it has a high impact in the elderly, and it will likely

remain important for years to come.


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ACKNOWLEDGEMENTS

The authors would like to thank Billy Kreuter for valuable programming help and the

following individuals for their assistance understanding influenza surveillance in their

states: Laura M. Erhart, Arizona Department of Health Services; Meileen Acosta,

California Department of Public Health; and Kathy Lofy, Washington State Department

of Health. We would also like to acknowledge the organizations that contributed to the

State Inpatient Databases (SID) of the Healthcare Cost and Utilization Project (HCUP)

used in this study: Arizona Department of Health Services, California Office of Statewide

Health Planning and Development, and Washington State Department of Health.

DISCLAIMER

The opinions expressed by authors contributing to this journal do not necessarily reflect

the opinions of the Centers for Disease Control and Prevention or the institutions with

which the authors are affiliated.


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REFERENCES

1. Roush S, Murphy T, Group V-PDTW. Historical comparisons of morbidity and

mortality for vaccine-preventable diseases in the united states. JAMA: The Journal of the

American Medical Association 2007;298:2155-2163.

2. Zhou H, Thompson WW, Viboud CG, Ringholz CM, Cheng P-Y, Steiner C, Abedi

GR, Anderson LJ, Brammer L, Shay DK. Hospitalizations associated with influenza and

respiratory syncytial virus in the united states, 1993–2008. Clinical Infectious Diseases

2012;54:1427-1436.

3. Dawood FS, Fiore A, Kamimoto L, Bramley A, Reingold A, Gershman K, Meek J,

Hadler J, Arnold KE, Ryan P, Lynfield R, Morin C, Mueller M, Baumbach J, Zansky S,

Bennett NM, Thomas A, Schaffner W, Kirschke D, Finelli L. Burden of seasonal

influenza hospitalization in children, united states, 2003 to 2008. The Journal of

pediatrics 2010;157:808-814.

4. World Health Organization. A practical guide for designing and conducting

influenza disease burden studies. 2008 March 26, 2013]. Available from:

http://www.wpro.who.int/emerging_diseases/documents/docs/GuideforDesigningandCon

ductingInfluenzaStudies.pdf.

5. Centers for Disease Control and Prevention. Estimates of deaths associated with

seasonal influenza --- united states, 1976-2007. MMWR Morbidity and mortality weekly

report 2010;59:1057-1062.

6. World Health Organization. Clinical management of influenza and other acute

respiratory illness in resource-limited settings: Learning from the influenza

pandemic(h1n1)2009. 2012 March 26, 2013]. Available from:

http://www.who.int/influenza/patient_care/clinical/858-

WHOGIPReport_A4_WEB_FA.pdf.


Page 17

7. Ortiz JR, Rue TC, Neuzil KM, Goss CH. Population-based incidence of influenza-

associated respiratory failure hospitalizations in the us west coast, 2003 to 2008.

American journal of respiratory and critical care medicine 2012;185:A1795.

8. Centers for Disease Control and Prevention. Past weekly surveillance reports.

2012 March 26, 2013]. Available from: http://www.cdc.gov/flu/weekly/pastreports.htm.

9. Ortiz JR, Zhou H, Shay DK, Neuzil KM, Fowlkes AL, Goss CH. Monitoring

influenza activity in the united states: A comparison of traditional surveillance systems

with google flu trends. PloS one 2011;6:e18687.

10. Healthcare Cost and Utilization Project (HCUP). Hcup state inpatient databases

(sid). Rockville, MD: Agency for Healthcare Research and Quality; 2003-2009.

11. Behrendt CE. Acute respiratory failure in the united states: Incidence and 31-day

survival. Chest 2000;118:1100-1105.

12. Yang L, Chiu SS, Chan KP, Chan KH, Wong WH, Peiris JS, Wong CM.

Validation of statistical models for estimating hospitalization associated with influenza

and other respiratory viruses. PloS one 2011;6:e17882.

13. Thompson WW, Shay DK, Weintraub E, Brammer L, Bridges CB, Cox NJ,

Fukuda K. Influenza-associated hospitalizations in the united states. JAMA : the journal

of the American Medical Association 2004;292:1333-1340.

14. Nair H, Nokes DJ, Gessner BD, Dherani M, Madhi SA, Singleton RJ, O'Brien KL,

Roca A, Wright PF, Bruce N, Chandran A, Theodoratou E, Sutanto A, Sedyaningsih ER,

Ngama M, Munywoki PK, Kartasasmita C, Simoes EA, Rudan I, Weber MW, Campbell

H. Global burden of acute lower respiratory infections due to respiratory syncytial virus in

young children: A systematic review and meta-analysis. Lancet 2010;375:1545-1555.

15. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, Abraham J,

Adair T, Aggarwal R, Ahn SY, Alvarado M, Anderson HR, Anderson LM, Andrews KG,

Atkinson C, Baddour LM, Barker-Collo S, Bartels DH, Bell ML, Benjamin EJ, Bennett D,


Page 18

Bhalla K, Bikbov B, Bin Abdulhak A, Birbeck G, Blyth F, Bolliger I, Boufous S, Bucello C,

Burch M, Burney P, Carapetis J, Chen H, Chou D, Chugh SS, Coffeng LE, Colan SD,

Colquhoun S, Colson KE, Condon J, Connor MD, Cooper LT, Corriere M, Cortinovis M,

de Vaccaro KC, Couser W, Cowie BC, Criqui MH, Cross M, Dabhadkar KC, Dahodwala

N, De Leo D, Degenhardt L, Delossantos A, Denenberg J, Des Jarlais DC, Dharmaratne

SD, Dorsey ER, Driscoll T, Duber H, Ebel B, Erwin PJ, Espindola P, Ezzati M, Feigin V,

Flaxman AD, Forouzanfar MH, Fowkes FG, Franklin R, Fransen M, Freeman MK,

Gabriel SE, Gakidou E, Gaspari F, Gillum RF, Gonzalez-Medina D, Halasa YA, Haring

D, Harrison JE, Havmoeller R, Hay RJ, Hoen B, Hotez PJ, Hoy D, Jacobsen KH, James

SL, Jasrasaria R, Jayaraman S, Johns N, Karthikeyan G, Kassebaum N, Keren A, Khoo

JP, Knowlton LM, Kobusingye O, Koranteng A, Krishnamurthi R, Lipnick M, Lipshultz

SE, Ohno SL, Mabweijano J, MacIntyre MF, Mallinger L, March L, Marks GB, Marks R,

Matsumori A, Matzopoulos R, Mayosi BM, McAnulty JH, McDermott MM, McGrath J,

Mensah GA, Merriman TR, Michaud C, Miller M, Miller TR, Mock C, Mocumbi AO,

Mokdad AA, Moran A, Mulholland K, Nair MN, Naldi L, Narayan KM, Nasseri K, Norman

P, O'Donnell M, Omer SB, Ortblad K, Osborne R, Ozgediz D, Pahari B, Pandian JD,

Rivero AP, Padilla RP, Perez-Ruiz F, Perico N, Phillips D, Pierce K, Pope CA, 3rd,

Porrini E, Pourmalek F, Raju M, Ranganathan D, Rehm JT, Rein DB, Remuzzi G, Rivara

FP, Roberts T, De Leon FR, Rosenfeld LC, Rushton L, Sacco RL, Salomon JA,

Sampson U, Sanman E, Schwebel DC, Segui-Gomez M, Shepard DS, Singh D,

Singleton J, Sliwa K, Smith E, Steer A, Taylor JA, Thomas B, Tleyjeh IM, Towbin JA,

Truelsen T, Undurraga EA, Venketasubramanian N, Vijayakumar L, Vos T, Wagner GR,

Wang M, Wang W, Watt K, Weinstock MA, Weintraub R, Wilkinson JD, Woolf AD, Wulf

S, Yeh PH, Yip P, Zabetian A, Zheng ZJ, Lopez AD, Murray CJ, AlMazroa MA, Memish

ZA. Global and regional mortality from 235 causes of death for 20 age groups in 1990


Page 19

and 2010: A systematic analysis for the global burden of disease study 2010. Lancet

2012;380:2095-2128.

16. World Health Organization. Global burden of disease 2004 update. 2004 May 10,

2013]. Available from:

http://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_full.pdf.

17. Centers for Disease Control and Prevention. The national respiratory and enteric

virus surveillance system (nrevss). 2013 March 26, 2013]. Available from:

http://www.cdc.gov/surveillance/nrevss/.

18. United States Census Bureau. U.S. Population projections. 2010 17 November

2010]. Available from: http://www.census.gov/population/www/projections/.

19. Kochanek KD, Xu J, Murphy SL, Minino AM, Kung HK. Deaths: Final data for

2009. National vital statistics reports : from the Centers for Disease Control and

Prevention, National Center for Health Statistics, National Vital Statistics System

2011;60:1-115.

20. Bhat N, Wright JG, Broder KR, Murray EL, Greenberg ME, Glover MJ, Likos AM,

Posey DL, Klimov A, Lindstrom SE, Balish A, Medina MJ, Wallis TR, Guarner J,

Paddock CD, Shieh WJ, Zaki SR, Sejvar JJ, Shay DK, Harper SA, Cox NJ, Fukuda K,

Uyeki TM. Influenza-associated deaths among children in the united states, 2003-2004.

New England Journal of Medicine 2005;353:2559-2567.

21. Johnson BF, Wilson LE, Ellis J, Elliot AJ, Barclay WS, Pebody RG, McMenamin

J, Fleming DM, Zambon MC. Fatal cases of influenza a in childhood. PloS one

2009;4:e7671.

22. Lewandowski K. Contributions to the epidemiology of acute respiratory failure.

Critical care (London, England) 2003;7:288-290.

23. Luhr OR, Antonsen K, Karlsson M, Aardal S, Thorsteinsson A, Frostell CG,

Bonde J. Incidence and mortality after acute respiratory failure and acute respiratory


Page 20

distress syndrome in sweden, denmark, and iceland. The arf study group. American

journal of respiratory and critical care medicine 1999;159:1849-1861.

24. Falsey AR, Hennessey PA, Formica MA, Cox C, Walsh EE. Respiratory syncytial

virus infection in elderly and high-risk adults. The New England journal of medicine

2005;352:1749-1759.

25. Widmer K, Zhu Y, Williams JV, Griffin MR, Edwards KM, Talbot HK. Rates of

hospitalizations for respiratory syncytial virus, human metapneumovirus, and influenza

virus in older adults. The Journal of infectious diseases 2012;206:56-62.

26. Centers for Disease Control and Prevention. Prevention and control of influenza

with vaccines: Recommendations of the advisory committee on immunization practices

(acip), 2011. MMWR Morbidity and mortality weekly report 2011;60:1128-1132.

27. Centers for Disease Control and Prevention. March flu vaccination. Coverage

united states, 2011-12 influenza season. 2012 16 July 2012]. Available from:

http://www.cdc.gov/flu/pdf/professionals/vaccination/national-flu-survey-mar2012.pdf.

28. Centers for Disease Control and Prevention. Interim adjusted estimates of

seasonal influenza vaccine effectiveness - united states, february 2013. MMWR

Morbidity and mortality weekly report 2013;62:119-123.

29. McMenamin J, Andrews N, Robertson C, Fleming D, Durnall H, von Wissmann

B, Ellis J, Lackenby A, Cottrell S, Smyth B, Zambon M, Moore C, Watson J, Pebody R.

Effectiveness of seasonal 2012/13 vaccine in preventing laboratory-confirmed influenza

infection in primary care in the united kingdom: Mid-season analysis 2012/13. Euro

surveillance : bulletin europeen sur les maladies transmissibles = European

communicable disease bulletin 2013;18.

30. Osterholm MT, Kelley NS, Sommer A, Belongia EA. Efficacy and effectiveness of

influenza vaccines: A systematic review and meta-analysis. Lancet Infect Dis

2012;12:36-44.


Page 21

31. Jackson LA, Jackson ML, Nelson JC, Neuzil KM, Weiss NS. Evidence of bias in

estimates of influenza vaccine effectiveness in seniors. International journal of

epidemiology 2006;35:337-344.

32. Puig-Barbera J, Diez-Domingo J, Arnedo-Pena A, Ruiz-Garcia M, Perez-Vilar S,

Mico-Esparza JL, Belenguer-Varea A, Carratala-Munuera C, Gil-Guillen V, Schwarz-

Chavarri H. Effectiveness of the 2010-2011 seasonal influenza vaccine in preventing

confirmed influenza hospitalizations in adults: A case-case comparison, case-control

study. Vaccine 2012;30:5714-5720.

33. Talbot HK, Griffin MR, Chen Q, Zhu Y, Williams JV, Edwards KM. Effectiveness

of seasonal vaccine in preventing confirmed influenza-associated hospitalizations in

community dwelling older adults. The Journal of infectious diseases 2011;203:500-508.

34. Fireman B, Lee J, Lewis N, Bembom O, van der Laan M, Baxter R. Influenza

vaccination and mortality: Differentiating vaccine effects from bias. American journal of

epidemiology 2009;170:650-656.

35. Banzhoff A, Pellegrini M, Del Giudice G, Fragapane E, Groth N, Podda A. Mf59-

adjuvanted vaccines for seasonal and pandemic influenza prophylaxis. Influenza and

other respiratory viruses 2008;2:243-249.

36. Ambrose CS, Levin MJ. The rationale for quadrivalent influenza vaccines.

Human vaccines & immunotherapeutics 2012;8:81-88.

37. Fiore AE, Fry A, Shay D, Gubareva L, Bresee JS, Uyeki TM. Antiviral agents for

the treatment and chemoprophylaxis of influenza --- recommendations of the advisory

committee on immunization practices (acip). MMWR Recommendations and reports :

Morbidity and mortality weekly report Recommendations and reports / Centers for

Disease Control 2011;60:1-24.

38. World Health Organization. Who guidelines for pharmacological management of

pandemic (h1n1) 2009 influenza and other influenza viruses. 2010 6 June 2012].


Page 22

Available from:

http://www.who.int/csr/resources/publications/swineflu/h1n1_use_antivirals_20090820/e

n/index.html.

39. Centers for Disease Control and Prevention. Overview of influenza surveillance

in the united states. 2012 March 26, 2013]. Available from:

http://www.cdc.gov/flu/pdf/weekly/overview.pdf.

40. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L.

Detecting influenza epidemics using search engine query data. Nature 2009;457:1012-

1014.

41. Healthcare Cost and Utilization Project (HCUP). Hcup procedure classes. 2009

March 26, 2013]. Available from: http://www.hcup-

us.ahrq.gov/toolssoftware/procedure/procedure.jsp.


Page 23

Table 1. Acute Respiratory Failure Hospitalization Definitions

Primary Outcome

Respiratory Failure (11) • Any code for acute respiratory distress or failure

(ICD-9-CM 518.5, 518.81, or 518.82)

• AND has a procedure code for continuous

mechanical ventilation (ICD-9-CM 96.7)

Secondary Outcomes

Upper Range Estimate:

Respiratory or Circulatory

Hospitalizations with either

Mechanical Ventilation or

Non-Invasive Ventilation

• Respiratory or circulatory hospitalizations (ICD-9-

CM codes 390-519)

• AND any mechanical ventilation (All codes: 96.7x)

OR any non-invasive ventilation (93.90)

Lower Range Estimate:

Respiratory Failure

Hospitalizations (excluding

all those with a major

therapeutic surgery) (11, 41)

• Any code for acute respiratory distress or failure

(ICD-9-CM 518.5, 518.81, or 518.82)

• AND has a procedure code for continuous

mechanical ventilation (ICD-9-CM 96.7)

• NOT any patient who had a major therapeutic

surgery during the same hospitalization


Page 24

Table 2. Influenza-Associated Respiratory Failure Hospitalizations by Age Group; AZ, CA, and WA, January 2003 – March

2009

Age

Group

(years)

Total

Respiratory

Failure

Hospitaliza

-tions

Person-

Years

Respiratory

Failure

Incidence

Rate per

100,000

Person-

Years

Total Influenza-

Associated

Respiratory

Failure

Hospitalizations

Influenza-

Associated

Respiratory

Failure

Incidence

Rate per

100,000

Person-

Years

95%

Confidence

Interval

% of Total

Outcomes

Attributable

to Influenza

1-4 5,897 17,728,574 33.3 136 0.8 0.1, 10.6 2.3%

5-17 10,651 56,453,066 18.8 141 0.3 0.0, 4.5 1.3%

18-49 114,367 140,843,295 81.2 1,163 0.8 0.0, 9.6 1.0%

50-64 144,398 48,957,999 294.9 1,959 4.0 0.2, 32.4 1.4%

65-74 115,264 17,372,578 663.5 1,502 8.7 0.3, 77.7 1.3%

75-84 123,619 11,864,963 1041.9 1,956 16.5 1.5, 126.2 1.6%

>84 54,576 4,505,319 1211.4 1,259 27.9 3.2, 170.4 2.3%

All Ages 568,772 297,725,793 191.0 8,116 2.7 0.2, 23.5 1.4%


Page 25

Figure 1: Respiratory Failure Hospitalization Incidence, Arizona and California, January 2003- March 2009 and US Federal Region 9 Positive Influenza Surveillance Tests

Note: US Federal Region 9 includes Arizona, California, Nevada, and Hawaii


Figure 1: Respiratory Failure Hospitalization Incidence, Arizona and California, January 2003- March 2009 and US Federal Region 9 Positive Influenza Surveillance Tests

254x147mm (72 x 72 DPI)


Supplement Page 1

ONLINE DATA SUPPLEMENT

Title: Population-Based Incidence of Influenza-Associated Respiratory Failure Hospitalizations,

2003 - 2009

Authors:

• Justin R. Ortiz1,2,3

• Kathleen M. Neuzil1,2,3

• Tessa C. Rue4

• Hong Zhou5

• David K. Shay5

• Po-Yung Cheng5

• Colin R. Cooke6

• Christopher H. Goss1

Affiliations:

1. Department of Medicine, University of Washington, Seattle, WA, USA

2. Department of Global Health, University of Washington, Seattle, WA, USA

3. Vaccine Access and Delivery Global Program, PATH, Seattle, WA, USA

4. Department of Biostatistics, University of Washington, Seattle, WA, USA

5. Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA

6. Department of Medicine, University of Michigan, Ann Arbor, MI, USA


Supplement Page 2

EXTENDED METHODS

Study Design

We conducted a retrospective cohort study from January 2003 through March 2009 using

hospitalization databases from the Healthcare Cost and Utilization Project (HCUP) and regional

influenza surveillance data from the CDC. We included all inpatient discharge abstracts from

community hospitals in the HCUP State Inpatient Database (SID) from Arizona, California, and

Washington during the study period. Our primary objective was to estimate the population-

based incidence of seasonal influenza-associated hospitalizations for acute respiratory failure.

Our exposures of interest were positive surveillance tests for influenza A (H1N1), influenza A

(H3N1), and influenza B from the western United States. The primary outcome was acute

respiratory failure requiring mechanical ventilation defined by ICD-9-CM code. We linked the

hospitalization datasets with the surveillance datasets by calendar time and geographic region.

We then used negative-binomial regression models to estimate the incidence of influenza-

associated events overall and by several age groups. Sensitivity analyses were done to assess

choice of outcome and choice of predictor of interest.

Virologic Surveillance Data

We obtained influenza surveillance data from the CDC which administers surveillance

conducted by the US World Health Organization (WHO) Collaborating Laboratories and

National Respiratory and Enteric Virus Surveillance System (NREVSS) laboratories (E1, E2).

US influenza surveillance data are publicly available as datasets aggregated by US Federal

Region. State-level data are not publicly available for our states of interest; therefore, we used

surveillance data from two contiguous US regions with similar influenza activity from July 2002

through June 2009: US Federal Region 9 (Arizona, California, Hawaii, and Nevada) data for

analyses of Arizona and California hospitalizations and US Federal Region 10 (Alaska, Idaho,


Supplement Page 3

Oregon, and Washington) data for analyses of Washington hospitalizations (E3). The majority of

Federal Region 9 and 10 laboratory tests were from the states in this analysis. From 2003-04

through 2007-08 influenza seasons, California and Arizona accounted for 43% and 5% of total

Region 9 surveillance laboratory tests, while Washington accounted for 74% of total Region 10

surveillance laboratory tests (personal communication Laura Erhart, Arizona Department of

Health Services; Meileen Acosta, California Department of Public Health; and Kathy Lofy,

Washington State Department of Health).

We assumed Region 9 and 10 rates of positive laboratory tests for the three states of interest.

All state public health laboratories participate as WHO Collaborating Laboratories along with

some county public health laboratories and some large tertiary-care or academic medical

centers. Most NREVSS laboratories participating in influenza surveillance are hospital

laboratories. Nationwide, approximately 145 laboratories participate in the WHO Collaborating

Laboratories or NRVESS (E4). The participating laboratories report the total number of

respiratory specimens tested and the number positive for influenza types A and B each week to

CDC. Most of the WHO collaborating laboratories also report the influenza A subtype (H1N1 or

H3N2) of the viruses they have isolated. The majority of NREVSS laboratories do not report the

influenza A subtype (E1). Influenza B lineages are not distinguished in these data. Data

linkages to individual persons, clinical or epidemiological data, or tests for other respiratory

viruses are not available. Previously, we have shown that virologic surveillance data from CDC

are highly correlated with other commonly used nationwide influenza surveillance systems (E5).

Influenza viruses typically circulate during winter months and across calendar years. Therefore,

we defined July 1 through June 30 of the following year as a “surveillance year” so that an entire

influenza season was studied. To compare events during periods of differing influenza activity,

we defined three time periods: the “influenza season” is all months in which ≥10% of regional

surveillance tests were positive for influenza. All other months were “non-influenza winter


Supplement Page 4

months” (from October through April) or “summer months” (from May through September).

Surveillance data include the weekly frequency of total respiratory specimens tested for

influenza and the frequency of positive influenza tests by virus type and subtype. The isolates of

unknown influenza A viruses were assumed to be H1N1 and H3N2 according to their proportion

over the entire influenza season. The weekly influenza surveillance data were aggregated to

month and were standardized to reduce possible bias associated with differences in specimen

sampling and laboratory methods over time. We did this by dividing the monthly frequency of

specimens testing positive by the sum of specimens collected per surveillance year. The

monthly standardized counts of specimens that tested positive for influenza virus by type and

subtype (A/H1N1, A/H3N2, and B) were used in estimating the effect of influenza circulation on

monthly hospitalizations. For a sensitivity analysis, we used similar, Arizona-specific virologic

surveillance data obtained from Arizona Department of Health Services which were prepared in

the same fashion as the US Federal Region data. We calculated the Pearson’s correlation

coefficient describing the relationship of Arizona-specific and Region 9 data, and we compared

Arizona outcome estimates derived from the two surveillance datasets.

Hospitalization Data

We obtained the complete State Inpatient Database (SID) for Arizona, California, and

Washington from the Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare

Research and Quality for 2003 through 2009 (E6). The SID contains all inpatient discharge

abstracts from state hospitals, excepting some specialty hospitals unlikely to have many acute

respiratory failure events (E6). The SID datasets contain the universe of the inpatient discharge

abstracts from community hospitals in participating states, translated into a uniform format to

facilitate multi-state comparisons and analyses (E6). The anonymous SID includes numerous

data elements for each hospital stay, including primary and secondary diagnoses, primary and

secondary procedures, admission and discharge status, patient demographics, and other


Supplement Page 5

information. Non-community hospitals excluded from the database include federal hospitals,

long-term hospitals, psychiatric hospitals, alcohol/chemical dependency treatment facilities and

hospitals units within institutions such as prisons.

The outcome of interest for this study was acute respiratory failure. There is no consensus ICD-

9-CM definition of respiratory failure. For our primary outcome, we used the definition set forth in

a previous estimate of US respiratory failure hospitalizations using the 1994 HCUP Nationwide

Inpatient Sample (E6). As had been done previously, we defined hospitalizations with acute

respiratory failure as any hospitalization that had a code for acute respiratory distress or failure

(ICD-9-CM 518.5, 518.81, or 518.82) and a procedure code for continuous mechanical

ventilation (ICD-9-CM 96.7) (E7). Because the primary outcome definition may miss acute

respiratory failure hospitalizations in which acute respiratory distress or failure had not been

coded, we performed sensitivity analyses with more sensitive and specific definitions to

represent upper- and lower-range incidence estimates. The upper-range estimate of acute

respiratory failure was defined as any hospitalization that had a code for any respiratory or

circulatory diagnosis (ICD-9-CM codes 390-519) and any mechanical ventilation (All codes:

96.7x) or non-invasive ventilation (93.90). As a lower-range estimate, we identified all

hospitalizations for the primary outcome and excluded all patients diagnosed with a major

therapeutic surgery using the HCUP Procedure Classes Tool (E8). We chose this secondary

outcome to exclude patients for whom mechanical ventilation was performed for surgical

reasons or complications of surgery and not primarily for treatment of respiratory disease. This

definition likely under-estimates acute respiratory failure hospitalizations because mechanical

ventilation for acute respiratory failure and major therapeutic surgery are not mutually exclusive

diagnoses. Neither the primary outcome definition nor the secondary outcome definitions

include hospitalizations in which patients with severe respiratory disease died without receiving


Supplement Page 6

any mechanical ventilation. We therefore likely underestimate the total number of acute

respiratory failure hospitalizations.

We used seven age categories: 1-4 years, 5-17 years, 18-49 years, 50-64 years, 65-74 years,

75-84 years, and 85 years and older. Children <1 year of age were excluded from this analysis

to address confounding due to respiratory syncytial virus (RSV), the most important respiratory

pathogen for this age group. Previous studies have shown collinearity between monthly RSV

and influenza virus surveillance data which resolves when analyzed at the week level (E3, E9).

However, week-level hospitalization data are not publicly available from SID, necessitating

exclusion of RSV from our model. Since >75% of RSV hospitalizations are estimated to occur

among children <1 year of age (E10), we excluded that age group from this analysis. Another

22% of total RSV hospitalizations are estimated to occur in the 1 to 4 year age group (E10),

however, as this group contributes to few respiratory failure events, we included it in this

analysis.

Statistical Analysis

We modified negative binomial regression models developed by the CDC to estimate US

influenza-associated hospitalizations (E3, E10-E12). A key difference between our model and

the CDC model is that CDC used weekly surveillance and hospitalization data, and we were

limited to month level data from HCUP. Count variables, for example the number of

hospitalizations occurring during a specific period, are often modeled by using Poisson

regression. An assumption of the Poisson model is that the observed variance in counts is

approximately equal to their sample mean. However, in practice, the observed variance

frequently exceeds the mean, a situation often termed overdispersion. The negative binomial

model is a generalization of the Poisson model, and can be used to account for overdispersion

in count data, as can other approaches, including quasi-Poisson regression. Negative binomial


Supplement Page 7

and quasi-Poisson methods have been used to estimate the burden of influenza disease in

many countries (E13-E18). Models similar to ours produce influenza mortality estimates that are

comparable to other statistical approaches used previously (E13). A recent study has validated

this approach to estimating influenza-associated hospitalizations against prospective

surveillance for laboratory-confirmed influenza in children (E19).

Age-specific negative binomial regression models were fit to monthly acute respiratory failure

hospitalizations in the three states of interest. Covariates for the standardized proportion of

specimens testing positive each month for A/H1N1, A/H3N2, and B in the two US Federal

Regions were included in the models. To estimate excess respiratory failure hospitalizations

associated with influenza, we subtracted expected baseline events from a full model

incorporating all viral terms, where the baseline represented a model in which a viral covariate

was set to zero. The baseline accounts for seasonal variability in hospitalizations not associated

with influenza. However, risk factors for acute respiratory failure that are collinear with influenza

activity could still confound the analysis.. Previous studies have found collinearity between RSV

and influenza virus circulation when the surveillance data were analyzed at the month level; this

situation resolves when the influenza and RSV data were analyzed at the week level (E3, E9).

Unfortunately, week-level hospitalization data are not publicly available from SID. To minimize

the impact of not having RSV in our model, we excluded children <1 year of age. The rationale

for this was based on a systematic review and meta-analysis of RSV in childhood which

concluded that RSV deaths among children >1 year are negligible (E10); a modeling study

similar to ours which estimated >75% of RSV hospitalizations occur among infants aged <1 year

(E11); and, finally, two systematic global burden of disease studies which concluded that RSV

disease was not an important contributor to adult morbidity in developed country settings (E12,

E13). While other respiratory viruses also circulate in the wintertime, such as adenovirus,

human metapneumovirus, and the human parainfluenza viruses, they typically are not the most


Supplement Page 8

common respiratory pathogens in any age group and they had different temporal activity than

influenza during the study period (E14, E24). The 95% confidence intervals (CIs) were

estimated with the model variance for the predicted values from regression models as had been

done in previous studies (E3, E14-E16).

The model was fit to data from Jan 2003 through March 2009 to exclude the 2009 influenza A

(H1N1) pandemic from this analysis. The model is as follows:

Y = α *exp{ β0 + β1[t] + β2[t2] + β3[t

3] + β4[t4] + β5[sin (2tπ/12)] + β6[cos (2tπ/12)] + β7[WA]

+ β8[CA] + β9[A(H1N1)] + β10[A(H3N2)] + β11[B]}

Where Yi represents the number of outcomes in a particular state during a particular month (t), α

is equal to the population size, β1 through β4 account for secular trend, β5 through β6 account for

seasonal trend, and β9 through β11 represent standardized specimens testing positive for

influenza in a given month.

The modeled monthly outcomes were summed for each viral term across the study period and

states by influenza surveillance year for each age category. Annual state population estimates

were acquired from the US census and were used to calculate population-based monthly

incidence rates of outcomes (E25).

Sensitivity analyses were done to assess choice of outcome and choice of predictor of interest.

We conducted secondary analyses using additional outcome definitions that we considered a

priori to be more sensitive and more specific than the primary outcome. Next, because exposure

to influenza in the primary analysis included some laboratory tests conducted outside the study

states, we repeated the primary analysis with only Arizona-specific hospitalization and

surveillance data. We also calculated Pearson’s correlation coefficients to compare monthly

standardized counts of all specimens that tested positive for influenza viruses in Arizona


Supplement Page 9

surveillance dataset with the aggregated Federal Region 9 surveillance dataset. If Arizona

specific analysis yielded similar incidence rates to the primary analysis, and if there was a high

correlation between the two surveillance datasets, we assumed that use of regional virologic

data was adequate for our primary analysis.

This study received exempt review status from the Human Subjects Division at the University of

Washington. Analyses were performed with SAS statistical software (version 9.3).


Supplement Page 10

Table E1. Influenza Type- and Subtype-Associated Respiratory Failure

Hospitalizations by Age Group; AZ, CA, and WA, January 2003 – March

2009

Age

Group

Seasonal

Influenza A

(H1N1)-

Associated

Events

n (%)

Influenza A

(H3N2)-

Associated

Events

n (%)

Influenza B-

Associated

Events n (%)

Total Influenza-

Associated

Events

1-4 0 (0%) 136 (100%) 0 (0%) 136

5-17 73 (51.8%) 52 (36.9%) 16 (11.3%) 141

18-49 552 (47.5%) 540 (46.4%) 71 (6.1%) 1,163

50-64 0 (0%) 995 (50.8%) 964 (49.2%) 1,959

65-74 0 (0%) 888 (59.1%) 614 (40.9%) 1,502

75-84 0 (0%) 1338 (68.4%) 618 (31.6%) 1,956

>84 0 (0%) 800 (63.5%) 459 (36.5%) 1,259

All Age 625 (7.7%) 4749 (58.5%) 2742 (33.8%) 8,116

Note: Among the 21,303 positive surveillance tests from the three states, 25.0% were for

influenza A (H1N1), 49.8% were for influenza A (H3N2), and 25.5% were for influenza B.


Supplement Page 11

Table E2. Influenza-Associated Acute Respiratory Failure Hospitalizations by Surveillance Year and Age Group; AZ, CA, and

WA, July 2003 through March 2009

Year Age Group Total Events Person-

Years

Respiratory

Failure

Incidence

Rate per

100,000

Person-

Years

Total

Influenza-

associated

Respiratory

Failure

Hospitalizati

ons (all types

and

subtypes)

Influenza-

Associated

Respiratory

Failure

Incidence

Rate per

100,000

Person-

Years

95%

Confidence

Interval

2003-04 1-4 905 2,747,298 32.9 35 1.3 0.3, 12.2

5-17 1,587 9,038,733 17.6 14 0.2 0.0, 4.3

18-49 16,104 22,306,637 72.2 126 0.6 0.0, 8.6

50-64 19,534 7,238,662 269.9 225 3.1 0.6, 31.4

65-74 17,418 2,661,007 654.6 218 8.2 1.4, 84.2

75-84 19,138 1,874,859 1020.8 338 18.0 5.5, 141.7

>84 7,853 653,079 1202.5 187 28.6 10.6, 192.1

All 82,539 46,520,274 177.4 1,143 2.5 0.6, 23.8

2004-05 1-4 884 2,803,036 31.5 19 0.7 0.0, 9.6

5-17 1,657 9,035,528 18.3 12 0.1 0.0, 4.0

18-49 17,103 22,416,841 76.3 94 0.4 0.0, 8.1


Supplement Page 12

50-64 21,206 7,522,402 281.9 393 5.2 0.1, 31.4

65-74 17,918 2,697,984 664.1 301 11.2 0.0, 80.1

75-84 19,569 1,894,498 1032.9 377 19.9 0.1, 131.4

>84 7,908 679,481 1163.8 244 35.9 0.9, 181.6

All 86,245 47,049,769 183.3 1,440 3.06 0.0, 22.7

2005-06 1-4 904 2,837,206 31.9 33 1.2 0.2, 10.1

5-17 1,710 9,038,609 18.9 16 0.2 0.0, 4.0

18-49 18,296 22,522,484 81.2 155 0.7 0.0, 8.4

50-64 22,368 7,804,299 286.6 362 4.6 0.2, 30.1

65-74 17,877 2,739,642 652.5 292 10.7 0.4, 75.8

75-84 19,522 1,905,984 1024.3 414 21.7 3.6, 126.3

>84 8,620 712,120 1210.5 252 35.4 8.3, 171.1

All 89,297 47,560,342 187.8 1,524 3.2 0.3, 22.3

2006-07 1-4 941 2,864,527 32.9 18 0.6 0.0, 9.0

5-17 1,809 9,037,652 20.0 25 0.3 0.0, 4.2

18-49 19,081 22,617,817 84.4 216 1.0 0.0, 9.2

50-64 24,179 8,080,811 299.2 214 2.7 0.0, 27.7

65-74 18,331 2,801,034 654.4 161 5.8 0.0, 62.6

75-84 19,634 1,907,530 1029.3 209 11.0 0.1, 100.0

>84 8,830 746,672 1182.6 140 18.8 0.7, 135.7

All 92,805 48,056,042 193.1 983 2.1 0.0, 20.0

2007-08 1-4 999 2,907,860 34.4 19 0.7 0.0, 10.3


Supplement Page 13

5-17 1,783 9,022,386 19.8 29 0.3 0.0, 4.7

18-49 20,623 22,725,428 90.8 232 1.0 0.0, 10.7

50-64 26,946 8,345,219 322.9 447 5.4 0.0, 35.7

65-74 19,692 2,901,778 678.6 299 10.3 0.0, 78.7

75-84 20,586 1,912,264 1076.5 356 18.6 0.0, 125.7

>84 10,131 783,076 1293.7 265 33.8 0.5, 174.3

All 100,760 48,598,009 207.3 1,647 3.4 0.0, 25.1

2008-09* 1-4 789 2,210,493 35.7 6 0.3 0.0, 11.4

5-17 1,373 6,761,208 20.3 27 0.4 0.0, 5.5

18-49 15,700 17,132,037 91.6 218 1.3 0.0, 13.0

50-64 20,913 6,417,235 325.9 193 3.0 0.1, 38.3

65-74 15,514 2,249,153 689.8 128 5.7 0.0, 82.6

75-84 15,714 1,436,591 1093.8 131 9.1 0.0, 127.1

>84 7,425 609,378 1218.5 93 15.3 0.1, 161.1

All 77,428 36,816,094 210.3 796 2.2 0.0, 27.1

*Analysis for 2008-09 influenza season was shortened to exclude pandemic influenza A (H1N1) that emerged in April 2009


Supplement Page 14

Table E3. Sensitivity Analysis -- Influenza-Associated Respiratory Failure

Incidence Rates per 100,000 Person Years by Age Group Using Three Different

Respiratory Failure Outcome Definitions; AZ, CA, and WA, January 2003 – March

2009

Age

Group

(years)

Primary Outcome: Sensitivity Analysis

Outcome #2: Lower

Estimate of Acute

Respiratory Failure

Sensitivity Analysis

Outcome #1: Upper

Estimate of Acute

Respiratory Failure

Influenza-Associated

Respiratory Failure

Incidence Rate per

100,000 Person-Years

(95% Confidence

Interval)


Respiratory Failure

Without Major

Therapeutic Surgery

Incidence Rate per


(95% Confidence

Interval)


Acute Respiratory

and Circulatory

Hospitalizations with

Mechanical

Ventilation or Non-

Invasive Ventilation

Incidence Rate per


(95% Confidence

Interval)

1-4 0.8 (0.1, 10.6) 0.7 (0.1, 9.5 ) 1.6 ( 0.4, 12.8 )

5-17 0.3 (0.0, 4.5) 0.2 (0.0, 3.4 ) 0.6 ( 0.0, 6.0 )

18-49 0.8 (0.0, 9.6) 0.6 (0.0, 6.9 ) 1.5 ( 0.0, 12.0 )

50-64 4.0 (0.2, 32.4) 3.5 (0.4, 23.4 ) 4.3 ( 0.4, 48.5 )

65-74 8.7 (0.3, 77.7) 6.0 (0.6, 53.2 ) 9.6 ( 0.7, 110.8 )

75-84 16.5 (1.5, 126.2) 11.5 (1.8, 89.9 ) 19.1 ( 1.4, 178.2 )

>84 28.0 (3.2, 170.4) 23.4 (3.7, 147.7 ) 38.6 ( 7.8, 227.9 )

All 2.7 (0.2, 23.5) 2.1 (0.2, 17.2 ) 3.5 ( 0.3, 32.6 )


Supplement Page 1

Table E4. Sensitivity Analysis -- Influenza-Associated Respiratory Failure

Incidence Rate per 100,000 Person-Years by Age Group; Arizona, January 2003 –

March 2009 (Using Two Different Sources of Influenza Surveillance Data)

Age

Group

(years)

Primary Analysis:

Overall Influenza-

Associated

Respiratory Failure

Incidence Rate per


(95% Confidence

Interval)

Arizona Influenza-Associated Respiratory

Failure Rate per 100,000 Person-Years (95%

Confidence Interval)

Arizona, California,

and Washington

Modeled Using US

Federal Region 9

Surveillance Data

(per Primary

Analysis)

Modeled Using

Arizona Specific

Surveillance Data

(per Sensitivity

Analysis)

1-4 0.8 (0.1, 10.6) 0.8 (0.0, 27.3) 1.3 (0.0, 31.2)

5-17 0.3 (0.0, 4.5) 1.7 ( 0.1, 13.1) 0.6 (0.0, 13.5)

18-49 0.8 (0.0, 9.6) 1.1 ( 0.0, 18.7) 1.2 (0.0, 19.3)

50-64 4.0 (0.2, 32.4) 5.7 ( 0.2, 59.7) 4.7 (0.0, 67.8)

65-74 8.7 (0.3, 77.7) 9.8 ( 0.0, 110.4) 11.6 (1.7, 122.8)

75-84 16.5 (1.5, 126.2) 22.5 ( 0.1, 203.7) 22.5 (2.9, 225.1)

>84 28.0 (3.2, 170.4) 33.8 ( 3.0, 244.6) 24.6 (2.5, 282.9)

All 2.7 (0.2, 23.5) 4.1 ( 0.1, 43.4) 3.7 (0.3, 47.7)


Supplement Page 2

Figure E1. Number of Positive Influenza Tests by Type and Subtype, US Federal

Regions 9 and 10, January 2003- March 2009

Note: US Federal Region 9 includes Arizona, California, Nevada, and Hawaii. US

Federal Region 10 includes Alaska, Idaho, Oregon, and Washington.


Supplement Page 3

REFERENCES

E1. Centers for Disease Control and Prevention. Overview of influenza surveillance

in the united states. 2011 5 July 2012]. Available from:

http://www.cdc.gov/flu/weekly/overview.htm.

E2. Centers for Disease Control and Prevention. Past weekly surveillance reports.

2012 16 July 2012]. Available from: http://www.cdc.gov/flu/weekly/pastreports.htm.

E3. Thompson WW, Shay DK, Weintraub E, Brammer L, Bridges CB, Cox NJ,

Fukuda K. Influenza-associated hospitalizations in the united states. JAMA : the journal

of the American Medical Association 2004;292:1333-1340.

E4. Centers for Disease Control and Prevention. Overview of influenza surveillance

in the united states. 2012 2/21/13]. Available from:

http://www.cdc.gov/flu/pdf/weekly/overview.pdf.

E5. Ortiz JR, Zhou H, Shay DK, Neuzil KM, Fowlkes AL, et al. Monitoring influenza

activity in the united states: A comparison of traditional surveillance systems with google

flu trends. PloS one 2011;6:e18687.

E6. Healthcare Cost and Utilization Project (HCUP). Hcup state inpatient databases

(sid). Rockville, MD: Agency for Healthcare Research and Quality; 2003-2009.

E7. Behrendt CE. Acute respiratory failure in the united states: Incidence and 31-day

survival. Chest 2000;118:1100-1105.

E8. Healthcare Cost and Utilization Project (HCUP). Hcup procedure classes. 2009

12/24/2012]. Available from: http://www.hcup-

us.ahrq.gov/toolssoftware/procedure/procedure.jsp.

E9. Thompson WW, Moore MR, Weintraub E, Cheng PY, Jin X, et al. Estimating

influenza-associated deaths in the united states. American journal of public health

2009;99 Suppl 2:S225-230.


Supplement Page 4

E10. Nair H, Nokes DJ, Gessner BD, Dherani M, Madhi SA, et al. Global burden of

acute lower respiratory infections due to respiratory syncytial virus in young children: A

systematic review and meta-analysis. Lancet 2010;375:1545-1555.

E11. Zhou H, Thompson WW, Viboud CG, Ringholz CM, Cheng P-Y, et al.

Hospitalizations associated with influenza and respiratory syncytial virus in the united

states, 1993–2008. Clinical Infectious Diseases 2012;54:1427-1436.

E12. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, et al. Global and regional

mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic

analysis for the global burden of disease study 2010. Lancet 2012;380:2095-2128.

E13 World Health Organization. Global burden of disease 2004 update. 2004

5/10/13]. Available from:

http://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_full.pdf.

E14. Centers for Disease Control and Prevention. Estimates of deaths associated with

seasonal influenza --- united states, 1976-2007. MMWR Morbidity and mortality weekly

report 2010;59:1057-1062.

E15. Thompson WW, Comanor L, Shay DK. Epidemiology of seasonal influenza: Use

of surveillance data and statistical models to estimate the burden of disease. The

Journal of infectious diseases 2006;194 Suppl 2:S82-91.

E16. Thompson WW, Weintraub E, Dhankhar P, Cheng PY, Brammer L, et al.

Estimates of us influenza-associated deaths made using four different methods.

Influenza and other respiratory viruses 2009;3:37-49.

E17. Feng L, Shay DK, Jiang Y, Zhou H, Chen X, et al. Influenza-associated mortality

in temperate and subtropical chinese cities, 2003-2008. Bulletin of the World Health

Organization 2012;90:279-288B.


Supplement Page 5

E18. Hardelid P, Pebody R, Andrews N. Mortality caused by influenza and respiratory

syncytial virus by age group in england and wales 1999-2010. Influenza and other

respiratory viruses 2013;7:35-45.

E19. Jacks A, Ollgren J, Ziegler T, Lyytikainen O. Influenza-associated

hospitalisations in finland from 1996 to 2010: Unexpected age-specific burden during the

influenza a(h1n1)pdm09 pandemic from 2009 to 2010. Euro surveillance : bulletin

europeen sur les maladies transmissibles = European communicable disease bulletin

2012;17.

E10. Schanzer DL, Langley JM, Tam TW. Role of influenza and other respiratory

viruses in admissions of adults to canadian hospitals. Influenza and other respiratory

viruses 2008;2:1-8.

E21. Yang L, Wang XL, Chan KP, Cao PH, Lau HY, et al. Hospitalisation associated

with the 2009 h1n1 pandemic and seasonal influenza in hong kong, 2005 to 2010. Euro

surveillance : bulletin europeen sur les maladies transmissibles = European

communicable disease bulletin 2012;17.

E22. Yang L, Chiu SS, Chan KP, Chan KH, Wong WH, et al. Validation of statistical

models for estimating hospitalization associated with influenza and other respiratory

viruses. PloS one 2011;6:e17882.

E23. Centers for Disease Control and Prevention. The national respiratory and enteric

virus surveillance system (nrevss). 2013 3/1/13]. Available from:

http://www.cdc.gov/surveillance/nrevss/.

E24. United States Census Bureau. U.S. Population projections. 2010 17 November

2010]. Available from: http://www.census.gov/population/www/projections/.


Figure E1. Number of Positive Influenza Tests by Type and Subtype, US Federal Regions 9 and 10, January 2003- March 2009

254x185mm (72 x 72 DPI)


Documents

Population-based Incidence Estimates of Influenza-associated Respiratory Failure Hospitalizations, 2003 to 2009