Article

Impact of the Fall 2009 Influenza A(H1N1)pdm09 Pandemic on U.S. Hospitals

*Office of the Assistant Secretary for Preparedness and Response, Department of Health and Human Services, Washington, DC †Agency for Healthcare Research and Quality, Rockville, MD ‡Fogarty International Center, National Institutes of Health, Bethesda, MD §Weill Cornell Medical Center, New York, NY ∥National Center for Immunization and Respiratory Diseases ¶National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA #Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI **Department of Emergency Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA ††Social and Scientific Systems Inc., Silver Spring, MD.
Medical care (Impact Factor: 3.23). 01/2013; 51(3). DOI: 10.1097/MLR.0b013e31827da8ea
Source: PubMed
ABSTRACT
Background:
Understanding how hospitals functioned during the 2009 influenza A(H1N1)pdm09 pandemic may improve future public health emergency response, but information about its impact on US hospitals remains largely unknown.

Research design:
We matched hospital and emergency department (ED) discharge data from the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project with community-level influenza-like illness activity during each hospital's pandemic period in fall 2009 compared with a corresponding calendar baseline period. We compared inpatient mortality for sentinel conditions at high-surge versus nonsurge hospitals.

Results:
US hospitals experienced a doubling of pneumonia and influenza ED visits during fall 2009 compared with prior years, along with an 18% increase in overall ED visits. Although no significant increase in total inpatient admissions occurred overall, approximately 10% of all study hospitals experienced high surge, associated with higher acute myocardial infarction and stroke case fatality rates. These hospitals had similar characteristics to other US hospitals except that they had higher mortality for acute cardiac illnesses before the pandemic. After adjusting for 2008 case fatality rates, the association between high-surge hospitals and increased mortality for acute myocardial infarction and stroke patients persisted.

Conclusions:
The fall 2009 pandemic period substantially impacted US hospitals, mostly through increased ED visits. For a small proportion of hospitals that experienced a high surge in inpatient admissions, increased mortality from selected clinical conditions was associated with both prepandemic outcomes and surge, highlighting the linkage between daily hospital operations and disaster preparedness.

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Impact of the Fall 2009 Influenza A(H1N1)pdm09
Pandemic on US Hospitals
Lewis Rubinson, MD, PhD,* Ryan Mutter, PhD,
w
Cecile Viboud, PhD,
z
Nathaniel Hupert, MD, MPH,
y
Timothy Uyeki, MD, MPH, MPP,8 Andreea Creanga, MD, PhD,
z
Lyn Finelli, DrPH,8
Theodore J. Iwashyna, MD, PhD,# Brendan Carr, MD, MS,** Raina Merchant, MD, MS,**
Devi Katikineni, MS,
ww
Frances Vaughn, PhD,* Carolyn Clancy, MD,
w
and Nicole Lurie, MD, MSPH*
Background: Understanding how hospitals functioned during the
2009 influenza A(H1N1)pdm09 pandemic may improve future
public health emergency response, but information about its impact
on US hospitals remains largely unknown.
Research Design: We matched hospital and emergency department
(ED) discharge data from the Agency for Healthcare Research and
Quality (AHRQ) Healthcare Cost and Utilization Project with
community-level influenza-like illness activity during each hospi-
tal’s pandemic period in fall 2009 compared with a corresponding
calendar baseline period. We compared inpatient mortality for sen-
tinel conditions at high-surge versus nonsurge hospitals.
Results: US hospitals experienced a doubling of pneumonia and
influenza ED visits during fall 2009 compared with prior years,
along with an 18% increase in overall ED visits. Although no sig-
nificant increase in total inpatient admissions occurred overall,
approximately 10% of all study hospitals experienced high surge,
associated with higher acute myocardial infarction and stroke case
fatality rates. These hospitals had similar characteristics to other US
hospitals except that they had higher mortality for acute cardiac
illnesses before the pandemic. After adjusting for 2008 case fatality
rates, the association between high-surge hospitals and increased
mortality for acute myocardial infarction and stroke patients
persisted.
Conclusions: The fall 2009 pandemic period substantially impacted
US hospitals, mostly through increased ED visits. For a small
proportion of hospitals that experienced a high surge in inpatient
admissions, increased mortality from selected clinical conditions
was associated with both prepandemic outcomes and surge, high-
lighting the linkage between daily hospital operations and disaster
preparedness.
Key Words: pandemic influenza, hospital surge capacity,
emergency department
(Med Care 2013;00: 000–000)
I
n the decade preceding the influenza A(H1N1)pdm09
pandemic, governments worldwide engaged in exten-
sive pandemic planning.
1,2
Between April 2009 and April
2010, an estimated 60.8 milli on Americans became ill,
274,000 were hospitalized, and 12,500 died due to pH1N1.
3
Postpandemic review confirmed that visits to specialized,
pediatric emergency departments (EDs) increased dramati-
cally.
4
Despite some media reports of overwhelmed EDs and
inpatient settings in the United States,
5
no nationwide anal-
yses of the impact of the pH1N1 pandemic on acute care
hospitals or EDs are available.
We linked the Healthcare Cost and Utilization Project
(HCUP), a nationwide, administrative data source that cap-
tures hospital admissions and ED encoun ters,
6
with the US
Centers for Disea se Control and Prevention’s (CDC) Influ-
enza-Like Illness Surveillance Program (ILI Net) data, to
assess the impact of the 2009 pH1N1 fall wave on US hos-
pitals and EDs.
3,7,8
METHODS
General Approach
To measur e pH1N1 surge and its impacts, we con-
ducted 3 types of analyses. First, we compared the volume of
ED visits and inpatient admissions during the pH1N1 fall
wave with previous years as baseline. Second, we compared
ED and inpatient admission volume between the pH1N1 fall
From the *Office of the Assistant Secretary for Preparedness and Response,
Department of Health and Human Services, Washington, DC; wAgency
for Healthcare Research and Quality, Rockville, MD; zFogarty Inter-
national Center, National Institutes of Health, Bethesda, MD; yWeill
Cornell Medical Center, New York, NY; 8National Center for Immu-
nization and Respiratory Diseases; zNational Center for Chronic Disease
Prevention and Health Promotion, Centers for Disease Control and
Prevention, Atlanta, GA; #Department of Internal Medicine, University
of Michigan Health System, Ann Arbor, MI; **Department of Emer-
gency Medicine, Perelman School of Medicine at the University of
Pennsylvania, Philadelphia, PA; and wwSocial and Scientific Systems
Inc., Silver Spring, MD.
The findings and conclusions in this report are those of the author(s) and do
not necessarily represent the views of the Department of Health and
Human Services or its components.
The authors declare no conflict of interest.
Reprints: Ryan Mutter, PhD, Agency for Healthcare Research and Quality,
Center for Delivery, Organization and Markets, 540 Gaither Road,
Rockville, MD 20850. E-mail: Ryan.Mutter@ahrq.hhs.gov.
Supplemental Digital Content is available for this article. Direct URL cita-
tions appear in the printed text and are provided in the HTML and PDF
versions of this article on the journal’s Website, www.lww-medical
care.com.
Copyright
r
2013 by Lippincott Williams & Wilkins
ISSN: 0025-7079/13/000-000
BRIEF REPORT
Medical Care
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wave and the 2003–2004 influenza season, the most severe
season in the last decade.
9
Third, we compared in-hospital
mortality for selected conditions in high-surge, medium-
surge, and nonsurge hospitals during the pH1N1 fall
wave. Table 1 provides an overview of data sources and
methods.
Data Sources
Patient-encounter data came from HCUP State In-
patient Databases (SID) and State Emergency Department
Databases (SEDD).
6
HCUP contains patient age, sex, pri-
mary expected payer, severity of illness, length of stay
(inpatient only), and discharge disposition.
10
We identified
patient comorbidities using the Elixhauser Comorbidity
Software.
11
We included data from 2387 and 1832 acute care
hospitals and EDs, respectively, in 26 SIDs and 19 SEDDs
(Supplemental Digital Content 1, http://links.lww.com/MLR/
A418 for map of included states). We analyzed ED treat-and-
release visits, all ED visits, and inpatient admissions, as well
as inpatient census:bed ratio. We considered the following
patient subcategories: pregnancy and elective admissions
with a procedure performed
12
(Supplemental Digital Content
1, http://links.lww.com/MLR/A418 for detailed definitions).
Hospital size, ownership, and teaching status were
derived from the American Hospital Association’s (AHA)
2008 Annual Survey.
13
Influenza cases were generally uncommon during
the fall seasons of baseline years, except 2003. To provide
a meaningful comparison between periods; therefore, we
based our main analyses on total encoun ters and pneumonia
and influenza (P&I) encounters (Supplemental Digital Con-
tent 2, http://links.lww.com/MLR/A419 for influenza-only
analysis).
Study Periods
For our primary analyses, we used ILINet to identify
hospital-specific pandemic periods
8
during fall 2009, defined
as weeks in which influenza-like illness activity in a hospi-
tal’s Core Based Statistical Ar ea
14
was >3 SDs above base-
line.
3,15–17
We restricted each hospitals’ data to its Core
Based Statis tical Area-specific pandemic time period.
Not all hospitals, especially those in rural locations,
could be included in this primary analysis since the ILINet
surveillance did not encompass all geographic regions.
Therefore, we performed a sensitivity analysis using all
hospitals based on a uniform pandemic time period—August
30 to December 12, 2009—the first and last weeks when
TABLE 1. Description of Overall Approach, Data, and Statistical Methods Used in Analyses of Hospital and ED Impacts Associated
With the pH1N1 Fall Wave in the United States
Outcomes Pandemic Period
Comparison
Period Statistical Analysis
Analysis 1: Surge in the pH1N1 pandemic period compared with prior baseline years
No. inpatient admissions* pH1N1 pandemic weeks in fall 2009, defined at the
CBSA level based on CDC’s ILINet surveillance.
Sensitivity analysis using a fixed pandemic period
(August 30–December 12, 2009)
Corresponding
weeks in
2003–2008
% change in number of encounters in 2009
vs. baseline, t test
No. ED encounters (all and treat-
and-release only)*
pH1N1 pandemic weeks in fall 2009, defined at the
CBSA level based on CDC’s ILINet surveillance.
Sensitivity analysis using a fixed pandemic period
(August 30–December 12, 2009)
Corresponding
weeks in
2005–2008
% change in number of encounters in 2009
vs. baseline, t test
Census:bed ratio* pH1N1 pandemic weeks in fall 2009, defined at the
CBSA level based on CDC’s ILINet surveillance
Corresponding
weeks in
2003–2008
% hospitals in 2009 with a >20% increase in
census:bed ratio over baseline period
Analysis 2. Surge in the pH1N1 pandemic period compared with severe 2003–04 influenza season
No. inpatient admissions
w
August 30–December 12, 2009 November 2,
2003–
January 10,
2004
% change in number of encounters and
encounters per day in 2009 vs. 2003–04
No. ED encounters (all and treat-
and-release only)
w
August 30–December 12, 2009 November 2,
2003–
January 10,
2004
% change in number of encounters and
encounters per day in 2009 vs. 2003–04
Analysis 3: Association of in-hospital mortality with high surge
In-hospital mortality for AMI, CHF,
stroke, injury (adults), and injury,
and chronic conditions (pediatrics)
pH1N1 pandemic weeks in fall 2009, defined at the
CBSA level based on CDC’s ILINet surveillance.
Sensitivity analysis using (1) ED surge; (2)
additional hospital and local area control variables;
and (3) a fixed pandemic period (August 30–
December 12, 2009)
Logistic regression with controls for patient
and hospital characteristics, including
2008 in-hospital mortality rate for
condition
*If a facility did not report SID or SEDD data during a baseline year, we imputed the missing data using average values from the available baseline years. We required the
baseline comparison periods to have no more than approximately 10% of imputed data.
w
Restricted to hospitals providing SID and SEDD data in 2003, 2004 and 2009.
AMI indicates acute myocardial infarction, CBSA, Core Based Statistical Area; CDC, Centers for Disease Control and Prevention; CHF, congestive heart failure; ED,
emergency department; SEDD, State Emergency Depart ment Databases; SID, State Inpatient Databases.
Rubinson et al Medical Care
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national influenza-like illness prevalence was >3 SDs above
baseline.
15
Analyses
ED and Inpatient Volume
To measure the extent of inpatient surge during the
pH1N1 fall wave, we calculated the difference between ad-
missions during the pandemic period and mean admissions
during corresponding weeks in prior years separately for
each hospital. We then divided the difference by the SD of
the baseline number of admissions (Z-score). We categorized
hospitals based on the Z-score as follows: “high-surge hos-
pitals” (Z-scoreZ 2), “medium-surge hospitals” (0 < Z-
score < 2), and “no-surge hospitals” (Z-scorer0). We re-
peated these analyses with uniform pandemic periods and
used an analogous approach with ED data.
Next, we calculated the hospital-specific weekly cen-
sus:bed ratio to account for patients admitted to the facility
and patients who were still in the facility during pandemic
weeks (ie, the number of patients who were present in the
hospital during an outbreak week divided by the number of
“set up and sta ffed” hospi tal beds). We compared the 2009
pandemic census:bed ratio to the hospital-specific baseline
average for corresponding weeks in previous years.
We also compared ED visits and inpatient admissions
during the pH1N1 fall wave with those during the severe
2003–2004 influenza season. Given the different length of
influenza activity periods in this analysis, we compared
average daily volumes and cumulative volumes.
Mortality Risk Analyses
To assess whether increased patient volume during the
pH1N1 fall wave impacted hospitals’ capabilities to deliver
quality health care, we anal yzed the mortality risk for pa-
tients wi th conditions commonly used to assess hospitals’
processes of care: adults with acute myocardial infarction
(AMI), congestive heart failure (CHF), stroke, traumatic
injury, and pediatric patients with traumatic injury or chronic
comorbidities. For each condition, we assessed the associa-
tion of in-hospital mortality with surge (high, medium,
and no surge) during the pH1N1 fall wave. We analyzed
encounter-level data with multivar iable logistic regression
models controlling for patient sex, age, comorbidities, P&I
diagnosis, severity of illness, hospital bed size, teaching
status, and ownership. To control for baseline quality of care,
we included the 2008 hospital-specific mortality rate for the
studied clinical condition as a covariate. We used 2008 rather
than the whole range of 2003–2008 to establish the baseline
mortality for the regression analyses to avoid confounding
with secular trends in improved care for these conditions
from 2003 to 2007. We used logistic regression with
SEs adjusted for the clustering of admissions in hospitals
(Supplemental Digital Content 1, http://links.lww.com/MLR/
A418 for variable definitions and Supplemental Digital
Content 2 for sensitivity analyses).
RESULTS
Encounter Volume in Pandemic and
Comparison Periods
We obtained hospital-specific pandemic period in-
formation for 1047 (43.9%) SID hospitals and 760 (41.5%)
SEDD hospitals. Pandemic activity lasted a median of
8 weeks for each hospital.
Using the hospital-specific pandemic period, EDs had
4,468,880 total visits and 3,756,251 treat-and-release en-
counters during 2009 pandemic weeks, an approximately
18% increase over baseline (P < 0.05, Table 2 and Supple-
mental Digital Content 2, http://links.lww.com/MLR/A419).
More than 88% of EDs experienced an increase in visits.
Sensitivity analyses using the uniform pandemic period
demonstrated smaller percentage increases (Table 2).
In comparison with 2003–2004 seasonal influenza,
total ED encounters and treat-and-release ED encounters
per day were 31% and 34% higher, respectively, during
the pH1N1 fall wave. Much of the increase was due to
visits with diagnostic codes for conditions other than P&I
(Table 3).
Total hospitals admission volume was similar during
pandemic and baseline weeks (statistically insignificant 0.4%
increase during pandemic weeks, Table 2). Although fewer
than half of hospitals had any increase in admissions, about
TABLE 2. Change in Hospital Volume During pH1N1 Fall Wave Compared With Baseline Years,* by Disease Category and Type of
Encounter
Hospital-specific Pandemic Period Uniform Pandemic Period
Disease Category
w
2005–2008 Annual Mean 2009 % Change in 2009 (P) 2005–2008 Annual Mean 2009 % Change in 2009 (P)
Emergency departments, all encounters
All encounters 3,795,393 4,468,880 17.7 % (P = 0.001) 9,154,938 10,517,302 14.9% (P < 0.001)
Pneumonia and influenza 90,090 270,272 200.0% ( P < 0.001) 223,782 530,982 137.3% (P < 0.001)
Emergency departments, treat-and-release encounters
All encounters 3,162,509 3,756,251 18.8% (P < 0.001) 7,719,656 8,837,940 14.5% (P < 0.001)
Pneumonia and influenza 30,023 184,482 514.5% ( P < 0.001) 75,039 330,051 339.8% (P < 0.001)
Inpatient admissions
All encounters 1,994,227 2,002,614 0.4% (P = 0.44) 4,533,741 4,549,623 0.4% (P = 0.54)
Pneumonia and influenza 104,309 147,616 41.5% (P < 0.001) 251,498 338,062 34.4% (P < 0.001)
*Baseline years for the inpatient analysis are 2003–2008. For the emergency department analysis, baseline years are 2005–2008.
w
Sensitivity analysis based on influenza-specific codes presented in Supplemental Digital Content.
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8% had more than a 20% increase in admissions over
baseline; 10% of hospitals had greater than a 20% increase in
census:bed ratio.
Our sample included 106 high-surge hospitals (10%),
386 moderate-surge hospitals (37%), and 555 hospitals
(53%) that had no surge in admission s. High-surge hospitals
were of similar size, ownership, urban/rural location, and
teaching status as the other hospitals (Table 4).
In contrast to all-cause admissions, there was wide-
spread surge in P&I admissions associated with the pH1N1
fall wave, with >87% of hospitals experiencing an increase
over baseline. Yet when the pH1N1 fall wave was compared
with the 2003–2004 influenza season, daily P&I admissions
were decreased by 22% (Table 2).
In fall 2009, hospital admissions for births and other
delivery-related conditions declined by 5.5% compared with
previous years, mak ing it the clinical category responsible
for the largest reduction of admissions. We did not find
a consistent pattern of decreases in elective admissions for
procedures (data not shown).
In-hospital Mortality and Surge
Patients with stroke, CHF, or AMI at a high-surge
hospital had a significant increase in mortality risk compared
with patients with those conditions at no-surge hospitals
(Fig. 1). There was no association between hospital surge
level and mortality risk for adult or pediatric trauma or for
pediatric patients with chronic conditions.
CHF and AMI patients admitted to hospitals in 2008
that experienced high surge in 2009 had statistically sig-
nificant higher mortality risk (data not shown). Yet after
adjusting for 2008 mortality rates, an elevated mortality risk
remained at hospitals experiencing high surge in 2009 pan-
demic weeks for patients with AMI and stroke (Fig. 1).
Sensitivity analyses using the uniform pandemic period gave
similar results.
We found no significant association between ED surge
and in-hospital mortality, and these findings were not sen-
sitive to alternative definitions of ED surge based on dif-
ferent Z-score cutpoints.
TABLE 3. Hospital Volume During pH1N1 Fall Wave Compared With 2003–2004 Seasonal Influenza Epidemic, by Disease Category and Type of Encounter
2003–2004 Fall 2009
Disease Category* No. Encounters Encounters Per Day No. Encounters % Change From 2003 to 2004 Encounters Per Day % change From 2003 to 2004
Emergency departments, all encounters
w
All encounters 1,867,861 26,684 3,668,021 96.38% 34,934 30.92%
Pneumonia and influenza 125,279 1790 199,216 59.02% 1897 5.98%
Emergency departments, treat-and-release encounters
w
All encounters 1,537,290 21,961 3,098,494 101.56% 29,510 34.37%
Pneumonia and influenza 74,385 1063 130,824 75.87% 1246 17.22%
Inpatient admissions
z
All encounters 3,099,931 44,285 4,546,920 46.68% 43,304 2.22%
Pneumonia and influenza 288,956 4128 337,791 16.90% 3217 22.07%
*Sensitivity analysis based on influenza-specific codes presented in Supplemental Digital Content.
w
The emergency department analysis was performed using data from the following states, which were available in both time periods: Georgia, Hawaii, Indiana, Maryland, Missouri, Minnesota, Nebraska, South Carolina,
Tennessee, and Vermont.
z
The inpatient analysis was performed using data from the following states, which were available in both time periods: Arizona, California, Colorado, Georgia, Hawaii, Illinois, Indiana, Iowa, Kentucky, Maryland, Missouri,
Minnesota, Nebraska, Nevada, New Jersey, Ohio, Oregon, Rhode Island, South Carolina, South Dakota, Tennessee, Virginia, Vermont, Washington, and Wisconsin.
TABLE 4. Comparison of High Surge to All Other Hospitals
Characteristics
High-surge
Hospitals
All Other Hospitals
(n = 106)
P*
(n = 941)
Teaching status (%)
Teaching 37.74% 33.91% 0.43
Nonteaching 62.26% 66.09%
Ownership (%)
Public 11.32% 11.77% 0.30
Nonprofit ownership 77.26% 71.17%
For-profit ownership 11.32% 17.06%
Location (%)
Urban 98.11% 93.74% 0.11
Rural 1.89% 6.26%
Average No. set up and
staffed beds
280.3 249.1 0.10
*w
2
or t test.
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DISCUSSION
During the pH1N1 fall wave, we found large increases
in ED visits over baseline, but only a subset of hospitals
experienced high inpatient surge. We found evidence that
hospitals with poorer prepandemic outcomes may have even
poorer outcomes during times of surge.
The combination of unchanged total hospital admis-
sions and increased P&I admissions over baseline during the
pH1N1 fall wave suggests an offsetting decrease in admis-
sions for other conditions. Although pregnant woman made
up a disproportionate share of patients admitted for influenza
complications
18,19
during the pandemic, hospitals experi-
enced a relatively sharp decline in the total number of labor-
related and delivery-rela ted admissions. The reduction in
births was a likely consequence of the concurrent US eco-
nomic slowdown
20
and may have offset one third of the
increase in admissions due to P&I.
In contrast to inpatient volumes, high surge in EDs
was not associated with increased mortality risk for in-
patients with the conditions considered. A number of factors,
including changes in staffing and operations, likely con-
tributed to the ability of EDs to surge. These efforts
merit additional investigation as they could provide valuable
lessons for the future.
The SID and SEDD used in this study cover a subset of
states. Nevertheless, the states in our analyses still represent
54% and 41% of the US population, respective ly. HCUP
provides the most comprehensi ve data available on hospital
and ED use at the encounter level. When combined with
ILINet, HCUP’s broad geographic representation and in-
clusivity of all ages and payers allowed a more detailed
analysis of health care utilization than would have been
possible using administrative data from Medicare or in-
dividual health plans.
Our study is subject to important limitations. The as-
sociations among influenza activity, cardiovascular events,
and mortality have been frequently described.
21
Also, res-
piratory infections have been associated with stroke in-
cidence and stroke severity.
22
Our mortality risk analysis
could have been confounded if high-surge hospitals were
FIGURE 1. Adjusted odds ratios for mortality by condition, high-surge hospitals versus nonsurge hospitals during the pH1N1 fall
wave. Covariates (not shown on figures) include age, sex, All Patient Refined Diagnosis Related Group (APR-DRG) severity,
presence of 29 Comorbidity Software variables, hospital size, hospital teaching status, hospital ownership/control, as well as
pneumonia and influenza diagnosis.
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associated with higher influenza activity than nonsurge
hospitals and if mortality risk was higher in AMI patients
with influenza than in AMI patients without influenza.
However, we did not find evidence for such an inter-
action after adjustment for age, comorbidities, and severity
of illness.
A further limitation may have been our definition
of epidemic activity, although we considered both local
and nationally representative pandemic periods. Alternative
measures may have identified less extreme but more tem-
porally durable excursion over the normal range. In addition,
outbreaks in a given community may not immediately be
reflected in the geographically proximate hospitals.
The finding that hospital-specific pandemic surge and
mortality risk for selected conditions were associated with
prepandemic quality of care provides an intriguing insight
regarding the relationship between some specific measures of
hospital quality and emergency-specific hospital prepared-
ness. We cannot determine whether this increase in baseline
mortality is due to patient mix, hospital care processes, or
even residual confounding due to imbalanced effects of in-
fluenza on certain hospitals.
23
Despite these limitations, the
finding offers a unique opportunity to consider the broader
linkages between daily hospital operations and disaster pre-
paredness. Structural and procedural efforts to increase some
aspects of hospital quality have the potential to induce pos-
itive effects on acute and longer-term hospital emergency
response capabilities. Improving baseline quality and pro-
viding additional support during surges may improve per-
formance in hospitals with underlying quality issues. Support
may include directing patients with selected conditions away
from high-surge hospitals. However, more research is needed
to fully elucidate the association between hospita l quality
and performances in emergency situations and assess how to
best support hospitals during such events, before firm rec-
ommendations can be made.
ACKNOWLEDGMENTS
The H1N1 Impact on US Hospitals Analysis Team
(HIHAT) acknowledges the exceptional consultation and
guidance provided by Drs Richard Frank and Sherry Glied
during conceptual development of the HIHAT project. Also,
we acknowledge Alicia Livinski, MPH, MA from the National
Institutes of Health Library for her valuable assistance with
editorial review and manuscript preparation.
The authors gratefully acknowledge the data organ-
izations in participating states that contributed data to
HCUP and that we used in this study: Arizona Department of
Health Services, California Office of Statewide Health
Planning & Development, Colorado Hospital Association,
Georgia Hospital Association, Hawaii Health Information
Corporation, Illinois Department of Public Health, Indiana
Hospital Association, Iowa Hospital Association, Kentucky
Cabinet for Health and Family Services, Maryland Health
Services Cost Review Commission, Minnesota Hospital
Association, Missouri Hospital Industry Data Institute,
Nebraska Hospital Association, New Jersey Department of
Health & Senior Services, Nevada Department of Health
and Human Services, Ohio Hospital Association, Oregon
Association of Hospitals and Health Systems, Rhode Island
Department of Health, South Carolina State Budget &
Control Board, South Dakota Association of Healthcare
Organizations, Tennessee Hospital Association, Vermont
Association of Hospitals and Health Systems, Virginia
Health Information, Washington State Department of Health,
Wisconsin Department of Health Services, and Wyoming
Hospital Association.
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  • [Show abstract] [Hide abstract] ABSTRACT: Objective: Microsimulation was used to assess the financial impact on hospitals of a surge in influenza admissions in advance of the H1N1 pandemic in the fall of 2009. The goal was to estimate net income and losses (nationally, and by hospital type) of a response of filling unused hospital bed capacity proportionately and postponing elective admissions (a "passive" supply response). Methods: Epidemiologic assumptions were combined with assumptions from other literature (e.g., staff absenteeism, profitability by payer class), Census data on age groups by region, and baseline hospital utilization data. Hospital discharge records were available from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample (NIS). Hospital bed capacity and staffing were measured with the American Hospital Association's (AHA) Annual Survey. Results: Nationwide, in a scenario of relatively severe epidemiologic assumptions, we estimated aggregate net income of $119 million for about 1 million additional influenza-related admissions, and a net loss of $37 million for 52,000 postponed elective admissions. Implications: Aggregate and distributional results did not suggest that a policy of promising additional financial compensation to hospitals in anticipation of the surge in flu cases was necessary. The analysis identified needs for better information of several types to improve simulations of hospital behavior and impacts during demand surges.
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  • [Show abstract] [Hide abstract] ABSTRACT: The impact of a severe influenza pandemic could be overwhelming to hospital emergency departments, clinics, and medical offices if large numbers of ill people were to simultaneously seek care. While current planning guidance to reduce surge on hospitals and other medical facilities during a pandemic largely focuses on improving the "supply" of medical care services, attention on reducing "demand" for such services is needed by better matching patient needs with alternative types and sites of care. Based on lessons learned during the 2009 H1N1 pandemic, the Centers for Disease Control and Prevention and its partners are currently exploring the acceptability and feasibility of using a coordinated network of nurse triage telephone lines during a pandemic to assess the health status of callers, help callers determine the most appropriate site for care (eg, hospital ED, outpatient center, home), disseminate information, provide clinical advice, and provide access to antiviral medications for ill people, if appropriate. As part of this effort, the integration and coordination of poison control centers, existing nurse advice lines, 2-1-1 information lines, and other hotlines are being investigated.
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