Article

Relationship between community prevalence of obesity and associated behavioral factors and community rates of influenza-related hospitalizations in the United States

Children's Hospital Informatics Program, Children's Hospital Boston, Boston, MA, USA Division of General Pediatrics, Department of Medicine, Children's Hospital Boston, Harvard Medical School, Boston, MA, USA Surveillance Lab, McGill Clinical and Health Informatics, McGill University, Montreal, Canada Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada Agence de la santé et des services sociaux de Montréal, Direction de santé publique, Montreal, Canada Department of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA Agency for Healthcare Research and Quality, Rockville, MD, USA Division of Science and Environmental Policy, California State University, Monterey Bay, Seaside, CA, USA NASA Ames Research Center, Moffett Field, Sunnyvale, CA, USA.
Influenza and Other Respiratory Viruses (Impact Factor: 2.2). 11/2012; 7(5). DOI: 10.1111/irv.12019
Source: PubMed

ABSTRACT

Background: Findings from studies examining the association between obesity and acute respiratory infection are inconsistent. Few studies have assessed the relationship between obesity-related behavioral factors, such as diet and exercise, and risk of acute respiratory infection. Objective To determine whether community prevalence of obesity, low fruit/vegetable consumption, and physical inactivity are associated with influenza-related hospitalization rates. Methods Using data from 274 US counties, from 2002 to 2008, we regressed county influenza-related hospitalization rates on county prevalence of obesity (BMI≥30), low fruit/vegetable consumption (<5servings/day), and physical inactivity (<30minutes/month recreational exercise), while adjusting for community-level confounders such as insurance coverage and the number of primary care physicians per 100000 population. Results A 5% increase in obesity prevalence was associated with a 12% increase in influenza-related hospitalization rates [adjusted rate ratio (ARR) 1·12, 95% confidence interval (CI) 1·07, 1·17]. Similarly, a 5% increase in the prevalence of low fruit/vegetable consumption and physical inactivity was associated with an increase of 12% (ARR 1·12, 95% CI 1·08, 1·17) and 11% (ARR 1·11, 95% CI 1·07, 1·16), respectively. When all three variables were included in the same model, a 5% increase in prevalence of obesity, low fruit/vegetable consumption, and physical inactivity was associated with 6%, 8%, and 7% increases in influenza-related hospitalization rates, respectively. Conclusions Communities with a greater prevalence of obesity were more likely to have high influenza-related hospitalization rates. Similarly, less physically active populations, with lower fruit/vegetable consumption, tended to have higher influenza-related hospitalization rates, even after accounting for obesity.

Full-text

Available from: David L Buckeridge, Oct 09, 2014
Relationship between community prevalence of obesity
and associated behavioral factors and community rates
of influenza-related hospitalizations in the United
States
Katia M. Charland,
a,b,c,d
David L. Buckeridge,
c,d,e
Anne G. Hoen,
f
Jay G. Berry,
b
Anne Elixhauser,
g
Forrest Melton,
h,i
John S. Brownstein
a,b,d
a
Children’s Hospital Informatics Program, Children’s Hospital Boston, Boston, MA, USA.
b
Division of General Pediatrics, Department of
Medicine, Children’s Hospital Boston, Harvard Medical School, Boston, MA, USA.
c
Surveillance Lab, McGill Clinical and Health Informatics,
McGill University, Montreal, Canada.
d
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada.
e
Agence de la sante
´
et des services sociaux de Montre
´
al, Direction de sante
´
publique, Montreal, Canada.
f
Department of Community and Family
Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
g
Agency for Healthcare Research and Quality, Rockville, MD, USA.
h
Division of Science and Environmental Policy, California State University, Monterey Bay, Seaside, CA, USA.
i
NASA Ames Research Center,
Moffett Field, Sunnyvale, CA, USA.
Correspondence: Katia Charland, 1140 Pine Avenue West, Montreal, Quebec H3A 1A3, Canada.
E-mail: katia.charland@mcgill.ca
Accepted 5 September 2012. Published Online 08 November 2012.
Background Findings from studies examining the association
between obesity and acute respiratory infection are inconsistent.
Few studies have assessed the relationship between obesity-related
behavioral factors, such as diet and exercise, and risk of acute
respiratory infection.
Objective To determine whether community prevalence of
obesity, low fruit vegetable consumption, and physical inactivity
are associated with influenza-related hospitalization rates.
Methods Using data from 274 US counties, from 2002 to 2008,
we regressed county influenza-related hospitalization rates on
county prevalence of obesity (BMI 30), low fruit vegetable
consumption (<5 servings day), and physical inactivity
(<30 minutes month recreational exercise), while adjusting for
community-level confounders such as insurance coverage and the
number of primary care physicians per 100 000 population.
Results A 5% increase in obesity prevalence was associated with
a 12% increase in influenza-related hospitalization rates [adjusted
rate ratio (ARR) 1Æ12, 95% confidence interval (CI) 1Æ07, 1Æ17].
Similarly, a 5% increase in the prevalence of low fruit vegetable
consumption and physical inactivity was associated with an
increase of 12% (ARR 1Æ12, 95% CI 1Æ08, 1Æ17) and 11% (ARR
1Æ11, 95% CI 1Æ07, 1Æ16), respectively. When all three variables
were included in the same model, a 5% increase in prevalence of
obesity, low fruit vegetable consumption, and physical inactivity
was associated with 6%, 8%, and 7% increases in influenza-related
hospitalization rates, respectively.
Conclusions Communities with a greater prevalence of obesity
were more likely to have high influenza-related hospitalization
rates. Similarly, less physically active populations, with lower
fruit vegetable consumption, tended to have higher influenza-
related hospitalization rates, even after accounting for obesity.
Key words Diet, exercise, influenza, influenza-like illness, obesity.
Please cite this paper as: Charland et al. (2013) Relationship between community prevalence of obesity and associated behavioral factors and community rates
of influenza-related hospitalizations in the United States. Influenza and Other Respiratory Viruses 7(5), 718–728.
Introduction
In the United States, direct medical costs associated with
seasonal influenza were estimated at over $10 billion dollars
in 2003, and hospitalizations contributed heavily to the
economic burden.
1,2
Influenza is considered ambulatory
care sensitive as risk of influenza-related hospitalizations,
and severe outcomes may be mitigated through appropriate
primary care.
3
Preventative efforts in the form of vaccina-
tion and the detection and control of chronic diseases, such
as type II diabetes, can reduce rates of influenza-related
hospitalizations.
4,5
Despite these effective clinical interven-
tions, the public health and economic impact of influenza
epidemics remains high and motivates the need to identify
DOI:10.1111/irv.12019
www.influenzajournal.com
Original Article
718 ª 2012 John Wiley & Sons Ltd
Page 1
additional individual- and community-level risk factors
that may respond to public health interventions.
Though studies report conflicting findings, some studies
suggest that obesity and related chronic conditions increase
the risk of influenza and other acute respiratory infec-
tions.
6–9
Behavioral factors associated with obesity, such as
a low consumption of fruits and vegetables and physical
inactivity, may independently increase rates of infection but
there is limited empirical evidence of an association
between acute respiratory infections and diet exercise after
accounting for obesity. Any observed association between
diet exercise and risk of infection could primarily be attrib-
uted to their correlation with body mass index (BMI). The
relationship between obesity diet exercise and risk of influ-
enza-related infections may be further complicated by com-
munity-level factors such as climate, which can influence
both lifestyle and influenza virus transmission rates.
10,11
Also, barriers of access to primary care, such as limited
health insurance coverage, may also confound the relation-
ship between risk of influenza-related infections and
obesity diet exercise.
3
In this study, using county-level data from 19 states of
the United States, we assessed the association between com-
munity prevalence of obesity and community rates of influ-
enza-related hospitalizations, after adjusting for several
county-level confounders. We determined whether influ-
enza-related hospitalization rates were also associated with
community prevalence of low consumption of fruits and
vegetables and prevalence of physical inactivity after
accounting for community prevalence of obesity. As a sec-
ondary objective, with respect to their effect on influenza-
related hospitalization rates, we examined the interaction
between the obesity, diet and exercise variables, and barri-
ers to primary care access, using county measures of
insurance coverage as a marker of access.
Materials and methods
Data
The number of counties states for which hospitalization
and survey data were available increased throughout the
years. Consequently, limiting the study period to the most
recent years would improve geographic representation.
However, given the substantial year-to-year variability in
vaccine match, vaccine uptake, and influenza epidemic
intensity, a longer period of study would provide more
generalizable results. In an attempt to balance geographic
representation with length of study period, we obtained
survey, census, and hospitalization data for 274 counties in
19 states from 2002 to 2008. The 19 states that were
included in the study are listed in Table A1.
For each county in our study, we compiled the total
number of influenza hospitalizations over the study period
and summaries of several county-level variables. From a
non-exhaustive review of the literature, we identified
potential confounders: insurance coverage,
12,13
number of
primary care physicians per 100 000 population,
14,15
envi-
ronmental humidity,
11,16
chronic disease and pregnancy
rates,
17–20
percentage of the county population living
below the poverty level,
12,21,22
vaccination uptake,
23–25
racial composition,
26–29
population density,
30,31
and preva-
lence of smoking.
32–34
A concise description of data
sources and variable definitions for all covariates is
provided in Table 1.
Hospitalizations
Hospitalization records were compiled from the State Inpa-
tient Databases (SID) of the Healthcare Cost and Utiliza-
tion Project (HCUP).
35
Records were aggregated from 2002
to 2008 by county, age group, diagnoses, and sex. Age stan-
dardization of rates was carried out using age groups: 0–4,
5–9, 10–18, 19–39, 40–64, 65–79, 80+ years. To best cap-
ture influenza hospitalizations, we only included hospital-
izations with admission dates between the last week of
October and the third week of May. This date range was
informed by the Centers for Disease Control and Preven-
tion (CDC)’s epidemic curves of lab-confirmed influenza
(http://www.cdc.gov/flu/weekly/weeklyarchives2007-2008/07
-08summary.htm).
Accurately capturing influenza-related hospitalizations
can be challenging as the influenza case definition based
only on influenza diagnostic codes (i.e., International Clas-
sification of Diseases (ICD-9) codes starting with 487) is
known to be highly specific but not sensitive.
36
For this
reason, as a sensitivity analysis, we compared findings from
the analyses of data based on two definitions of an influ-
enza-related hospitalization: (i) a hospitalization with a pri-
mary or secondary diagnosis of influenza or pneumonia
(i.e., Influenza ICD-9 code 487 or Pneumonia, organism
unspecified code 486) and (ii) a hospitalization with an
ICD-9 code belonging to the set of ICD-9 codes listed in
the Centers for Disease Control and Prevention (CDC)’s
Influenza-like Illness definition.
36
Primary independent variables
County prevalence of obesity (BMI 30), low fruit
vegetable consumption (<5 servings of fruits vegetables per
day), and physical inactivity (<30 minutes recreational
exercise per month) were obtained from the United States
Department of Health and Human Services Community
Health Status Indicators (CHSI) (http://www.community
health.hhs.gov/homepage.aspx?j=1). These data were com-
piled from the CDC’s Behavioral Risk Factor Surveillance
System surveys (http://www.cdc.gov/brfss). Though only
adults were surveyed, obesity and obesity-related factors
tend to cluster within families and communities so
Obesity, diet, exercise, and influenza
ª 2012 John Wiley & Sons Ltd 719
Page 2
prevalence of adult obesity should correlate strongly with
prevalence of obesity in children.
29,37
Survey and census data
Data on the prevalence of smoking in adults (18 years),
percent of the population without health insurance
(<65 years) (‘% uninsured’), vaccination uptake (65
years), and the number of primary care physicians per
100 000 (‘PCP rate’) were compiled from the CHSI. The
percentage of the county population living below poverty
level (‘poverty’), the percentage of the county population
identifying themselves as Caucasian or Asian, and popula-
tion sizes by age–sex strata were obtained from the United
States Census 2000 (http://www.census.gov/main/www/
cen2000.html). We did not have data on county vaccine
uptake in the general population, but had vaccination data
for the population 65 years. Though not necessarily repre-
sentative of vaccine coverage in the whole population, the
greatest proportion of influenza hospitalizations is in indi-
viduals 65 years.
38
Nevertheless, we conducted additional
analyses to assess the sensitivity of the results to vaccine
coverage in the general population.
Chronic conditions
Using the State Inpatient Databases (HCUP), we identified
the incidence of hospitalizations with ICD-9 codes corre-
sponding to several chronic diseases and predisposing con-
ditions (i.e., pregnancy). These conditions (Table A2),
hereafter referred to as ‘chronic conditions’, are risk factors
for influenza-related hospitalizations.
19
For all ages com-
bined and separately for the pediatric population
(£18 years), we obtained the total number of diagnoses of
each condition in hospitalized patients for each county,
from 2002 to 2008.
Table 1. Description of data sources
Variable Data source Description
Median (1st quartile,
3rd quartile)
Influenza-related
hospitalizations
HCUP State Inpatient
Databases (SID)
Inpatient stays with ICD-9 487 and or 486 from 2002 to 2008* 1046 (331, 2984)
Obesity CHSI** BRFSS***
Aggregation of surveys
from 2000 to 2006
Percentage adults in the population with BMI 30Æ022Æ0 (19Æ3, 24Æ6)
Low
fruit vegetable
consumption
CHSI BRFSS
Aggregation of surveys
from 2000, 2002, 2003, and 2005
Percentage adults reporting consumption
of fewer than 5 servings of fruits and vegetables
75Æ6 (72Æ7, 78Æ3)
Physical inactivity CHSI BRFSS
Aggregation of surveys
from 2000, 2001, 2003, and 2005
Percentage adults reporting less than 30-minute
recreational exercise in past month
23Æ1 (19Æ6, 26Æ0)
Chronic condition
rate
HCUP 2002–2008 Number of diagnoses of specific conditions (Table A1)
in hospitalized patients divided by county person-years.
1Æ87 (1Æ56, 2 Æ 31)
Smokers CHSI BRFSS
Aggregation of surveys
from 2000 to 2006
Percentage adult population that reported that
they smoke at the time of the survey
20Æ6 (18Æ2, 23Æ2)
Vaccine uptake CHSI BRFSS
Aggregation of surveys
from 2001–2003 and 2005–2006
Percentage population aged 65 years and older,
vaccinated against influenza within the previous year
70Æ0 (65Æ4, 74Æ4)
Primary Care Physician
(PCP) rate
CHSI Health Resources and
Services Administration, AMA
Rate active, non-federal physicians per 100 000
population in 2007
85Æ1 (58Æ8, 110Æ8)
Uninsured CHSI US Census Bureau Percentage population aged 64 years or younger that were
uninsured in 2006
16Æ7 (12Æ6, 21Æ1)
Poverty US Census Bureau Percentage population living below the poverty level in 2000 12Æ0(9Æ2, 14 Æ 3)
Environmental
Humidity (VPD)
Terrestrial Observation
and Prediction System
VPD averaged from November 1 to March 31
(over 2002 to 2008) (Pascal)
355Æ7 (252Æ6, 493Æ2)
Population density US Census Bureau Estimated population size divided by land area
(square miles) in 2000
281Æ0 (55Æ5, 745Æ0)
Caucasian or Asian US Census Bureau Percentage population that are Caucasian or Asian 92Æ1 (85Æ5, 95Æ7)
AMA, American Medical Association; VPD, vapor pressure deficit.
*Influenza-like illness definition
36
was used in the sensitivity analysis.
**Community Health Status Indicators.
***The Behavioral Risk Factor Surveillance System.
Charland
et al.
720 ª 2012 John Wiley & Sons Ltd
Page 3
Environmental humidity
Experimental studies have demonstrated that influenza
transmission rates depend on absolute humidity.
11
Vapor
pressure deficit (VPD) is a measure of humidity with high
VPD indicating low humidity. We obtained daily estimates
of average saturation vapor pressure deficit (VPD) from
spatially continuous surfaces for the US from the Terres-
trial Observation and Prediction System (TOPS).
39
These
daily meteorological surfaces were averaged from Novem-
ber to March (over 2002–2008) to have a representative
humidity value for each county during the time period pre-
ceding and during weeks with elevated influenza activity
(http://www.cdc.gov/flu/about/season/flu-season.htm).
Statistical analyses
Assuming the number of hospitalizations in a county is
Poisson distributed, we regressed the log-transformed age–
sex standardized hospitalization rates on each of the pri-
mary independent variables and potential confounders sep-
arately (‘univariable’ analyses) and then each primary
independent variable while adjusting for covariates (‘multi-
variable’ analyses). Covariates were percentage uninsured,
poverty, VPD, PCP rate, percentage smokers, vaccine
uptake, percentage Caucasian or Asian, population density,
and chronic condition rate. The covariates included in the
multivariable analyses of the pediatric population were the
same as those included in the analyses of the general popu-
lation with the exception of vaccine uptake, which was
measured in the population aged 65 years or older. We
used a quasi-Poisson regression model as we expected the
variation in the hospitalization rates to exceed that
assumed by the Poisson model. Also, influenza-related hos-
pitalization rates from counties belonging to the same state
could be correlated; so, all univariable and multivariable
regression models included indicator variables to represent
the state to which the county belongs.
Studies that have sufficient statistical power to assess
main effects may have insufficient power for interactions.
40
Therefore, we limited the covariates to those variables that
were not highly correlated (0Æ5)
41
with percentage unin-
sured. We expected to gain little by including highly corre-
lated variables because they would reduce the precision of
the regression coefficient estimates and would make the
interpretation of the results more challenging. To assess the
interaction between each independent variable and percent-
age uninsured, we added the interaction term to the model
with the independent variable and the covariates. We did
not assess interactions in the general population because in
the United States, nearly all persons aged 65 years and
older have access to health insurance through Medicare
(http://www.medicare.gov).
In addition to a sensitivity analysis of the influenza-related
hospitalization definition, we conducted a sensitivity analysis
to estimate the impact of vaccine coverage (in the popula-
tion <65 years) on the association between the three primary
independent variables and influenza-related hospitalization
rates. This was accomplished by restricting the data to years
in which there was a poor vaccine match (2003–2004 and
2007–2008). If associations from the restricted analysis were
weaker than those from the full analysis, then vaccine cover-
age in the population <65 years may be driving the associa-
tions between obesity, low fruit vegetable consumption,
physical inactivity, and influenza-related hospitalizations. In
this case, failing to account for vaccine coverage in the
population <65 years could bias the results.
Results
From 2002 to 2008, from a combined population of 116
146 020 in 274 counties, there were 3 076 699 hospitaliza-
tions using the case definition based on ICD-9 codes 486
and 487, and 4 254 939 hospitalizations using the CDC’s
influenza-like illness definition. Since the two influenza case
definitions produced similar findings (Tables A3 and A4)
(the influenza-like illness definition resulted in weaker asso-
ciations for some variables), we present only the results
based on the first case definition, that is, using ICD-9 codes
486 and 487. There was also little difference in the adjusted
rate ratios from the analysis of hospitalizations from 2002
to 2008 and the analysis restricted to data from 2003 to
2004 and 2007 to 2008 (Table A4).
General population
In univariable analyses, a 5% increase in prevalence of obes-
ity, low fruit vegetable consumption, and physical inactivity
was associated with an increase in influenza-related hospital-
ization rates of 17% [rate ratio (RR) 1Æ17, 95% confidence
interval (CI) 1Æ 13, 1Æ21], 16% (RR 1Æ16, 95% CI 1Æ11, 1Æ22),
and 15% (RR 1Æ15, 95% CI 1Æ13, 1Æ18) (Table 2). After
adjusting for potential confounders, there remained a 12%
[adjusted rate ratio (ARR) 1Æ12, 95% CI 1Æ07, 1Æ17], 12%
(ARR 1Æ12, 95% CI 1Æ 08, 1Æ17), and 11% (ARR 1Æ11, 95% CI
1Æ07, 1Æ16) increase in hospitalization rates associated with
prevalence of obesity, low fruit vegetable consumption, and
physical inactivity, respectively (Table 3, Models 1–3).
Including all three independent variables in a single model
while adjusting for confounders, the increase in rates was
lower at 6% (ARR 1Æ06, 95% CI 1Æ01, 1Æ11), 8% (ARR 1Æ08,
95% CI 1Æ04, 1Æ13), and 7% (ARR 1Æ07, 95% CI 1Æ03, 1Æ11)
for obesity, low fruit vegetable consumption, and physical
inactivity, respectively (Table 3, Model 4).
Pediatric population
In univariable analyses of the pediatric population
(£18 years), a 5% increase in obesity, low fruit vegetable
consumption, and physical inactivity was associated with a
Obesity, diet, exercise, and influenza
ª 2012 John Wiley & Sons Ltd 721
Page 4
25% (RR 1Æ25, 95% CI 1Æ18, 1Æ32), 16% (RR 1Æ16, 95% CI
1Æ07, 1Æ26), and 26% (RR 1Æ26, 95% CI 1Æ21, 1Æ32) increase in
influenza-related hospitalization rates, respectively. We
found that the relative increase in hospitalization rates asso-
ciated with obesity, low fruit vegetable consumption, and
physical inactivity was 21% (ARR 1Æ21, 95% CI 1Æ12, 1Æ31),
14% (ARR 1Æ14, 95% CI 1Æ06, 1Æ23), and 19% (RR 1Æ19, 95%
CI 1Æ12, 1Æ26), respectively (Table 3). When all three inde-
pendent variables were included in the same model, the
increase in hospitalization rates associated with a 5%
increase in obesity, low fruit vegetable consumption, and
physical inactivity was 13%, 6%, and 13% (ARR 1Æ 13, 95%
CI 1Æ05, 1Æ23; ARR 1Æ06, 95% CI 0Æ98, 1Æ14; ARR 1Æ13, 95%
1Æ06, 1Æ 21), respectively.
Interaction of insurance coverage and
obesity-related variables in children
Only the correlation between VPD and percentage unin-
sured was high at 0Æ73, exceeding our threshold of 0Æ5, so
in the model used to assess interactions we adjusted for the
same covariates as in the multivariable analysis of the pedi-
atric population with the exception of VPD. We found an
interaction between percentage uninsured and both obesity
(regression coefficient: 0Æ0016, 95% CI 0Æ00025, 0Æ0029)
and physical inactivity (regression coefficient: 0Æ 0012, 95%
CI 0Æ000051, 0Æ0023). Evidence of an interaction with low
fruit vegetable consumption was inconclusive (regression
coefficient: 0Æ0010, 95% CI )0Æ00083, 0Æ0027). This suggests
that low insurance coverage (i.e., high percentage unin-
sured) is associated with increased rates of influenza-related
hospitalizations, and the size of the increase in hospitaliza-
tion rates depends on the county’s prevalence of obesity
and physical inactivity. For example, increasing percentage
uninsured from 15% to 25%, the estimated increase in
influenza-related hospitalization rates is 13% in counties
with physical inactivity prevalence equal to 25%, but the
increase in influenza-related hospitalization rates is only
7% for counties with physical inactivity prevalence equal to
20%.
Discussion
Increasing county prevalence of obesity was associated with
increasing county rates of influenza-related hospitalizations.
Prevalence of low consumption of fruits and vegetables and
physical inactivity was also associated with influenza
Table 2. Rate ratios and confidence intervals for influenza-related hospitalizations from univariable analyses
Covariate
All ages
RR*,**
All ages
95% CI
Children
RR
Children
95% CI
Obesity (%) 1Æ17 1Æ13, 1Æ21 1Æ25 1Æ18, 1Æ32
Low fruit vegetable consumption (%) 1Æ16 1Æ11, 1Æ22 1Æ16 1Æ07, 1Æ26
Physical inactivity (%) 1Æ15 1Æ13, 1Æ18 1Æ26 1Æ21, 1Æ32
PCP rate 0Æ93 0Æ90, 0Æ97 0Æ91 0Æ85, 0Æ97
Uninsured (%) 1Æ14 1Æ10, 1,18 1Æ52 1Æ35, 1Æ71
Poverty (%) 1Æ10 1Æ07, 1Æ14 1Æ21 1Æ16, 1Æ26
Population density 1Æ001 0Æ998, 1Æ003 1Æ003 1Æ000, 1Æ
007
Caucasian or Asian 0Æ94 0Æ91, 0Æ98 0Æ98 0Æ92, 1Æ05
Respiratory 1Æ20 1Æ15, 1Æ26
Cardiac 1Æ10 1Æ06, 1Æ16
Neurologic-Central 1Æ19 1Æ13, 1Æ26
Neurologic-peripheral 1Æ17 1Æ12, 1Æ21
Endocrine 1Æ28 1Æ23, 1Æ34
Diabetes 1Æ12 1Æ08, 1Æ16
Renal 1Æ14 1Æ10, 1Æ18
Immune 1Æ05 1Æ03, 1Æ08
Hematologic 1Æ20 1Æ13, 1Æ28
Pregnancy 1Æ18 1Æ13, 1Æ24
chronic condition rate 1Æ21 1Æ16, 1Æ26 1Æ19 1Æ10, 1Æ30
Smokers (%) 1Æ14 1Æ
09, 1Æ18 1Æ07 0Æ99, 1Æ15
Vaccine uptake (%) 0Æ87 0Æ84, 0Æ91
VPD 1Æ05 1Æ00, 1Æ10 1Æ14 1Æ06, 1Æ23
PCP, Primary Care Physician; VPD, vapor pressure deficit.
*Rate ratio for 5% change in obesity, low fruit vegetable consumption, and physical inactivity; for all other variables, rate ratio is for change cor-
responding to inter-quartile range.
**Adjusting for the state to which a county belongs.
Charland
et al.
722 ª 2012 John Wiley & Sons Ltd
Page 5
hospitalization rates, even after adjusting for prevalence of
obesity and other county-level covariates. In addition to
these associations, we found that low insurance coverage
was strongly associated with higher rates of influenza-
related hospitalizations in the pediatric population. Fur-
thermore, the interaction between insurance coverage and
obesity physical activity suggests that the increase in hospi-
talization rates associated with low insurance coverage was
greater when there was also a high prevalence of
obesity physical inactivity.
Table 3. Adjusted rate ratios and confidence intervals
Covariates
All ages
RR
*
All ages
95% CI
Children
RR
Children
95% CI
Model 1 Obesity (%) 1Æ12 1Æ07, 1Æ17 1Æ21 1Æ12 1Æ31
Uninsured (%) 1Æ05 1Æ02, 1Æ09 1Æ40 1Æ23, 1Æ59
Chronic condition rate 1Æ09 1Æ04, 1Æ14 1Æ13 1Æ05, 1Æ22
Vaccine uptake (%) 0Æ93 0Æ90, 0Æ97
PCP rate 1Æ03 0Æ99, 1Æ08 1Æ00 0Æ94, 1Æ07
Poverty (%) 0Æ95 0Æ91, 0Æ99 1Æ08 1Æ03, 1Æ14
Smokers (%) 1Æ04 0Æ99, 1Æ08 0Æ94 0Æ87, 1Æ01
VPD 0Æ98 0Æ94, 1Æ02 0Æ95 0Æ89, 1
Æ02
Population density 0Æ997 0Æ998, 1Æ002 1Æ002 0Æ999, 1Æ006
Caucasian or Asian 0Æ97 0Æ94, 1Æ01 1Æ03 0Æ98, 1Æ09
Model 2 Low fruit vegetable
consumption (%)
1Æ12 1Æ08, 1Æ17 1Æ14 1Æ06, 1Æ23
Uninsured (%) 1Æ06 1Æ02, 1Æ09 1Æ32 1Æ17, 1Æ51
Chronic condition rate 1Æ12 1Æ07, 1Æ17 1Æ15 1Æ06, 1Æ24
Vaccine uptake (%) 0Æ95 0Æ92, 0Æ99
PCP rate 1Æ03 0Æ99, 1Æ07 097 0Æ91, 1Æ03
Poverty (%) 0Æ97 0Æ93, 1Æ01 1Æ14 1Æ09, 1Æ
19
Smokers (%) 1Æ04 1Æ00, 1Æ09 0Æ97 0Æ90, 1Æ04
VPD 1Æ00 0Æ96, 1Æ04 0Æ99 0Æ93, 1Æ06
Population density 0Æ998 0Æ996, 1Æ00 1Æ00 0Æ997, 1Æ004
Caucasian or Asian 0Æ95 0Æ93, 0Æ98 1Æ00 0Æ95, 1Æ06
Model 3 Physical inactivity (%) 1Æ11 1Æ07, 1Æ16 1Æ19 1Æ12, 1Æ26
Uninsured (%) 1Æ02 0Æ99, 1Æ06 1Æ20 1Æ05, 1Æ36
Chronic condition rate 1Æ09 1Æ04, 1Æ14 1Æ10 1Æ02, 1Æ19
Vaccine uptake (%) 0Æ97 0Æ93, 1Æ01
PCP rate 1Æ
04 1Æ00, 1Æ08 1Æ01 0Æ95, 1Æ07
Poverty (%) 0Æ97 0Æ93, 1Æ01 1Æ07 1Æ01, 1Æ12
Smokers (%) 1Æ06 1Æ02, 1Æ10 0Æ96 0Æ89, 1Æ03
VPD 1Æ00 0Æ96, 1Æ04 1Æ02 0Æ95, 1Æ09
Population density 1Æ000 0Æ997, 1Æ001 1Æ003 1Æ000, 1Æ007
Caucasian or Asian 0Æ97 0Æ94, 1Æ00 0Æ98 0Æ92, 1Æ05
Model 4 Obesity (%) 1Æ06 1Æ01, 1Æ11 1Æ13 1Æ05, 1Æ23
Low fruit vegetable
consumption (%)
1Æ08 1Æ04, 1Æ13 1Æ
06 0Æ98, 1Æ14
Physical inactivity (%) 1Æ07 1Æ03, 1Æ11 1Æ13 1Æ06, 1Æ21
Uninsured (%) 1Æ04 1Æ01, 1Æ08 1Æ29 1Æ13, 1Æ47
Chronic condition rate 1Æ10 1Æ05, 1Æ15 1Æ14 1Æ05, 1Æ23
Vaccine uptake (%) 0Æ97 0Æ93, 1Æ01
PCP rate 1Æ06 1Æ02, 1Æ10 1Æ04 0Æ97, 1Æ11
Poverty (%) 0Æ94 0Æ90, 0Æ98 1Æ04 0Æ98, 1Æ10
Smokers (%) 1Æ03 0Æ99, 1Æ08 0Æ93 0Æ86, 1Æ00
VPD 1Æ00 0Æ96, 1Æ03 0Æ99 0Æ92, 1
Æ06
Population density 1Æ000 0Æ997, 1Æ001 1Æ003 1Æ000, 1Æ007
Caucasian or Asian 0Æ97 0Æ94, 1Æ00 0Æ98 0Æ92, 1Æ05
PCP, Primary Care Physician; VPD, vapor pressure deficit.
*Rate ratio for 5% change in obesity, low fruit vegetable consumption, and physical inactivity; for all other variables, rate ratio is for change cor-
responding to inter-quartile range.
Obesity, diet, exercise, and influenza
ª 2012 John Wiley & Sons Ltd 723
Page 6
Findings from studies of the association between body
mass index and acute respiratory infection are inconsis-
tent,
6
but in general, studies powered to assess the effect of
obesity on hospitalizations and or mortality found
increased risk associated with obesity
8,9,42,43
while studies
examining the effect of obesity on healthcare service utiliza-
tion for influenza-like illness found no association or
increased utilization associated with low BMI.
4,44–46
Thus,
obesity may not be related to increased utilization of out-
patient healthcare services for influenza-like illness, but
there is some evidence that obesity is related to increased
rates of influenza-related hospitalizations.
Several biological mechanisms have been proposed to
explain the relationship between obesity and severe influ-
enza infection. Studies have shown that obesity leads to
impaired immune and lung function.
47–49
Obesity is also a
risk factor for conditions that, in turn, increase risk of
severe respiratory infection or severe outcomes, for exam-
ple, hyperglycemia, obstructive sleep apnea, and aspiration
associated with gastroesophageal reflux disease.
50–52
An
association between obesity and influenza-related hospital-
izations may also be attributed to the increased risk of car-
diovascular events following influenza infection.
53
Furthermore, given that obesity is a risk factor for a num-
ber of chronic conditions, such as cardiovascular disease
and obstructive sleep apnea, physicians may be more
inclined to admit an obese patient than a non-obese
patient with similar influenza symptom severity.
Coleman et al.,
54
in their study of the effect of obesity
on risk of influenza, pointed to the need to examine the
effects of diet and exercise on risk of influenza infection.
Previous research, mainly focusing on populations in devel-
oping nations, linked malnutrition and micronutrient defi-
ciency with respiratory infection.
55–58
Several studies also
demonstrated benefits of chronic moderate exercise in
stimulating immune function and increasing serum con-
centrations of vitamin D (25 (OH) D).
59–61
Due to limita-
tions in data availability, we only considered specific
definitions of poor diet and physical inactivity but other
forms of malnutrition and physical inactivity may also play
a role. For example, protein-energy malnutrition has been
associated with decreases in immune function,
62
and non-
recreational forms of physical activity may also be protec-
tive. Though the observed associations appeared robust to
adjustment for a number of potential confounders, we
could not account for all aspects of self-care and material
deprivation; thus, it is conceivable that other factors that
are correlated with fruit and vegetable consumption and
physical activity underlie the observed associations with
influenza-related hospitalizations.
A limitation of our study was our reliance on survey
data for county-level prevalence estimates of obesity, low
fruit vegetable consumption, and physical inactivity. How-
ever, the BRFSS survey measures from which our preva-
lence estimates are derived, that is, height, weight,
fruit vegetable intake, and leisure-time physical activity,
have moderate to high reliability and validity.
63
In addi-
tion, though only adults were surveyed, Agras et al.
64
reported that having obese parents was the strongest inde-
pendent predictor of childhood obesity. For this reason, we
saw value in assessing the effect of prevalence of obesity,
low fruit vegetable consumption, and physical inactivity in
adults on rates of influenza-related hospitalizations in chil-
dren. Another limitation of our study is that our findings,
which are observed at the county level, do not necessarily
imply causation at the individual level. However, the diver-
sity of the county environments and populations in our
study permitted us to demonstrate generalizability of the
association between the obesity variables and influenza-
related hospitalizations. For example, we had counties rep-
resenting each of the climate zones, states at the extremes
with respect to insurance coverage rates (http://www.
census.gov/hhes/www/hlthins/), obesity prevalence (http://
www.cdc.gov/obesity/data/adult.html/), and primary care
physician rates (https://www.aamc.org/download/55436/
data/statephysdec2007.pdf). In addition, we were able to
assess and adjust for the impact of a variety of community-
level factors.
Our study findings suggest that county prevalence of
obesity, low consumption of fruits vegetables, and physical
inactivity is each associated with county rates of influenza-
related hospitalizations, even after accounting for neighbor-
hood and environmental confounders. In addition to these
associations, we found that low insurance coverage was
associated with higher rates of hospitalizations in children
and the increase in hospitalization rates was more pro-
nounced in counties that also had a high prevalence of
physical inactivity and obesity. Though we can only extrap-
olate these findings to the individual with caution, we have
preliminary evidence that regardless of body mass index, a
low dietary intake of fruits and vegetables and insufficient
recreational exercise are associated with increased risk of
severe influenza.
Acknowledgements
The authors are grateful to Rick Jordan for technical help
and Chris Delaney for fruitful discussions. Hospitalization
data were contributed by the following HCUP State Inpa-
tient Database: California Office of Statewide Health Plan-
ning and Development, Colorado Hospital Association,
Florida Agency for Health Care Administration, Illinois
Department of Public Health, Kansas Hospital Association,
Maryland Health Services Cost Review Commission, Mas-
sachusetts Division of Health Care Finance and Policy,
Minnesota Hospital Association, New Jersey Department of
Charland
et al.
724 ª 2012 John Wiley & Sons Ltd
Page 7
Health, New York State Department of Health, Ohio Hos-
pital Association, Oregon Health Policy and Research, Ore-
gon Association of Hospitals and Health Systems, South
Carolina State Budget & Control Board, South Dakota
Association of Healthcare Organizations, Texas Department
of State Health Services, Utah Department of Health, Ver-
mont Association of Hospitals and Health Systems, Wash-
ington State Department of Health, and Wisconsin
Department of Health Services. This work was supported
by the National Aeronautics and Space Administration
grant number Feasibility-08-0024.
References
1 Molinari NA, Ortega-Sanchez IR, Messonnier ML et al. The annual
impact of seasonal influenza in the US: measuring disease burden
and costs. Vaccine 2007; 25:5086–5096.
2 Nichol KL, Nordin J, Mullooly J et al. Influenza vaccination and
reduction in hospitalizations for cardiac disease and stroke among
the elderly. N Engl J Med 2003; 348:1322–1332.
3 Bindman AB, Grumbach K, Osmond D et al. Preventable hospital-
izations and access to health care. JAMA 1995; 274:305–311.
4 Almirall J, Bolibar I, Serra-Prat M et al. New evidence of risk factors
for community-acquired pneumonia: a population-based study. Eur
Respir J 2008; 31:1274–1284.
5 Kornum JB, Thomsen RW, Riis A, Lervang HH, Schonheyder HC,
Sorensen HT. Type 2 diabetes and pneumonia outcomes: a popula-
tion-based cohort study. Diabetes Care 2007; 30:2251–2257.
6 Kornum JB, Norgaard M, Dethlefs en C et al. Obesity and risk of
subsequent hospitalisation with pneumonia. Eur Respir J 2010;
36:1330–1336.
7 Schreter I, Kristian P, Tkacova R. Obesity and risk of pneumonia in
patients with influenza. Eur Respir J 2011; 37:1298. author reply
9–300.
8 Kwong JC, Campitelli MA, Rosella LC. Obesity and respiratory hos-
pitalizations during influenza seasons in Ontario, Canada: a cohort
study. Clin Infect Dis 2011; 53:413–421.
9 Gardner EM, Beli E, Clinthorne JF, Duriancik DM. Energy intake and
response to infection with influenza. Annu Rev Nutr 2011; 31:353–
367.
10 Lowen AC, Mubareka S, Steel J, Palese P. Influenza virus transmis-
sion is dependent on relative humidity and temperature. PLoS
Pathog 2007; 3:1470–1476.
11 Shaman J, Kohn M. Absolute humidity modulates influenza survival,
transmission, and seasonality. Proc Nat Acad Sci USA 2009;
106:3243–3248.
12 Levine JA. Poverty and obesity in the U.S. Diabetes 2011; 60:2667–
2668.
13 Miller RR III, Markewitz BA, Rolfs RT et al. Clinical findings and
demographic factors associated with ICU admission in Utah due to
novel 2009 influenza A(H1N1) infection. Chest 2010; 137:752–758.
14 Allen NB, Diez-Roux A, Liu K, Bertoni AG, Szklo M, Daviglus M.
Association of health professional shortage areas and cardiovascular
risk factor prevalence, awareness, and control in the Multi-Ethnic
Study of Atheros clerosis (MESA). Circ Cardiovasc Qual Outcomes
2011; 4:565–572.
15 Laditka JN, Laditka SB, Probst JC. More may be better: evidence of
a negative relationship between physician supply and hospitalization
for ambulatory care sensitive conditions. Health Serv Res 2005;
40:1148–1166.
16 Merrill RM, Shields EC, White GL Jr, Druce D. Climate conditions
and physical activity in the United States. Am J Health Behav 2005;
29:371–381.
17 Andreyeva T, Michaud PC, van Soest A. Obesity and health in Euro-
peans aged 50 years and older. Public Health 2007; 121:497–509.
18 Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH. The
disease burden associated with overweight and obesity. JAMA
1999; 282:1523–1529.
19 Ward KA, Spokes PJ, McAnulty JM. Case-control study of risk fac-
tors for hospitalization caused by pandemic (H1N1) 2009. Emerg
Infect Dis 2011; 17:1409–1416.
20 SAGE Working Group. Background paper on influenza vaccines and
immunization. 2011.
21 Charland KM, Brownstein JS, Verma A, Brien S, Buckeridge DL.
Socio-economic disparities in the burden of seasonal influenza: the
effect of social and material deprivation on rates of influenza infec-
tion. PLoS ONE 2011; 6:e17207.
22 Hawker JI, Olowokure B, Sufi F, Weinberg J, Gill N, Wilson RC.
Social deprivation and hospital admission for respiratory infection:
an ecological study. Respir Med 2003; 97:1219–1224.
23 Gross PA, Hermogenes AW, Sacks HS, Lau J, Levandowski RA. The
efficacy of influenza vaccine in elderly persons. A meta-analysis and
review of the literature. Ann Intern Med 1995; 123:518–527.
24 Heinonen S, Silvennoinen H, Lehtinen P, Vainionpaa R, Ziegler T,
Heikkinen T. Effectiveness of inactivated influenza vaccine in chil-
dren aged 9 months to 3 years: an observational cohort study. Lan-
cet Infect Dis 2011; 11:23–29.
25
Ostbye T, Taylor DH Jr, Yancy WS Jr, Krause KM. Associations
between obesity and receipt of screening mammography, Papanico-
laou tests, and influenza vaccination: results from the Health and
Retirement Study (HRS) and the Asset and Health Dynamics Among
the Oldest Old (AHEAD) Study. Am J Public Health 2005; 95:1623–
1630.
26 Centers for Disease Control and Prevention (CDC). Differences in
prevalence of obesity among black, white, and Hispanic adults -
United States, 2006-2008. MMWR Morb Mortal Wkly Rep 2009;
58:740–744.
27 Chowell G, Ayala A, Berisha V, Viboud C, Schumacher M. Risk fac-
tors for mortality among 2009 A H1N1 influenza hospitalizations in
Maricopa County, Arizona, April 2009 to March 2010. Comput
Math Methods Med 2012; 2012:914196.
28 Christensen KL, Holman RC, Steiner CA, Sejvar JJ, Stoll BJ, Schon-
berger LB. Infectious disease hospitalizations in the United States.
Clin Infect Dis 2009; 49:1025–1035.
29 Kirby JB, Liang L, Chen HJ, Wang Y. Race, place, and obesity: the
complex relationships among community racial ethnic composition,
individual race ethnicity, and obesity in the United States. Am J
Public Health 2012; 102:1572–1578.
30 Cardoso MR, Cousens SN, de Goes Siqueira LF, Alves FM, D’Angelo
LA. Crowding: risk factor or protective factor for lower respiratory
disease in young children? BMC Public Health 2004; 4:19.
31 Murray EL, Klein M, Brondi L et al. Rainfall, household crowding,
and acute respiratory infections in the tropics. Epidemiol Infect
2012; 140:78–86.
32 Chiolero A, Faeh D, Paccaud F, Cornuz J. Consequences of smoking
for body weight, body fat distribution, and insulin resistance. Am J
Clin Nutr 2008; 87:801–809.
33 Arcavi L, Benowitz NL. Cigarette smoking and infection. Arch Intern
Med 2004; 164:2206–2216.
34 Wong CM, Yang L, Chan KP et al. Cigarette smoking as a risk fac-
tor for influenza-associated mortality: evidence from an elderly
cohort. Influenza Other Respir Viruses 2012; doi: 10.1111/j.1750-
2659.2012.00411.x.
Obesity, diet, exercise, and influenza
ª 2012 John Wiley & Sons Ltd 725
Page 8
35 HCUP State Inpatient Databases (SID). Healthcare Cost and Utiliza-
tion Project (HCUP). Rockville, MD, Agency for Healthcare Research
and Quality, 2002–2008. http://www.hcup-us.ahrq.gov/sidover-
view.jsp.
36 Marsden-Haug N, Foster VB, Gould PL, Elbert E, Wang H, Pavlin JA.
Code-based syndromic surveillance for influenza like illness by Inter-
national Classification of Diseases, Ninth Revision. Emerg Infect Dis
2007; 13:207–216.
37 Koplan JP, Liverman CT, Kraak VI. Preventing childhood obesity:
health in the balance: executive summary. J Am Diet Assoc 2005;
105:131–138.
38 Thompson WW, Shay DK, Weintraub E et al. Influenza-associated
hospitalizations in the United States. JAMA 2004; 292:1333–
1340.
39 Nemani R, Hashimoto H, Votava P et al. Monitoring and forecasting
ecosystem dynamics using the Terrestrial Observation and Prediction
System (TOPS). Remote Sens Environ 2009; 113:1497–1509.
40 Greenland S. Basic problems in interaction assessment. Environ
Health Perspect 1993; 101(Suppl 4):59–66.
41 Zuur AF. Mixed Effects Models and Extensions in Ecology With R.
New York, NY: Springer, 2009. Xxii, 574 pp.
42 Kornum JB, Norgaard M, Dethlefsen C et al. Obesity and risk of
subsequent hospitalisation with pneumonia. Eur Respir J 2010;
36:1330–1336.
43 Morgan OW, Bramley A, Fowlkes A et al. Morbid obesity as a risk fac-
tor for hospitalization and death due to 2009 pandemic influenza
A(H1N1) disease. PLoS ONE 2010; 5:e9694.
44 Almirall J, Morato I, Riera F et al. Incidence of community-acquired
pneumonia and Chlamydia pneumoniae infection: a prospective
multicentre study. Eur Respir J 1993; 6:14–18.
45 Blumentals WA, Nevitt A, Peng MM, Toovey S. Body mass index
and the incidence of influenza-associated pneumonia in a UK pri-
mary care cohort. Influenza Other Respir Viruses 2012; 6:28–36.
46 Schnoor M, Klante T, Beckmann M , et al. Risk factors for commu-
nity-acquired pneumonia in German adults: the impact of children
in the household. Epidemiol Infect. 2007;135:1389–1397.
47 Falagas ME, Kompoti M. Obesity and infection. Lancet Infect Dis
2006; 6:438–446.
48 Sheridan PA, Paich HA, Handy J et al. Obesity is associated with
impaired immune response to influenza vaccination in humans. Int J
Obes (Lond) 2012; 36(8):1072–1077.
49 Koenig SM. Pulmonary complications of obesity. Am J Biomed Sci
2001; 321:249–279.
50 Allard R, Leclerc P, Tremblay C, Tannenbaum TN. Diabetes and the
severity of pandemic influenza A (H1N1) infection. Diabetes Care
2010; 33:1491–1493.
51 Fry AM, Shay DK, Holman RC, Curns AT, Anderson LJ. Trends in
hospitalizations for pneumonia among persons aged 65 years or
older in the United States, 1988-2002. JAMA 2005; 294:2712–
2719.
52 Kornum JB, Thomsen RW, Riis A, Lervang HH, Schonheyder HC,
Sorensen HT. Diabetes, glycemic control, and risk of hospitalization
with pneumonia: a population-based case–control study. Diabetes
Care 2008; 31:1541–1545.
53 Smeeth L, Thomas SL, Hall AJ, Hubbard R, Farrington P, Vallance
P. Risk of myocardial infarction and stroke after acute infection or
vaccination. N Engl J Med 2004; 351:2611–2618.
54 Coleman LA, Waring SC, Irving SA, Vandermause M, Shay DK,
Belongia EA. Evaluation of obesity as an independent risk factor for
medically attended laboratory-confirmed influenza. Influenza Other
Respi Viruses 2012; doi: 10.1111/j.1750-2659.2012.00377.x.
55 Bahwere P, De Mol P, Donnen P et al. Improvements in nutritional
management as a determinant of reduced mortality from commu-
nity-acquired lower respiratory tract infection in hospitalized chil-
dren from rural central Africa. Pediatr Infect Dis J 2004; 23:739–
747.
56 Fawzi WW, Herrera MG, Willett WC, Nestel P, el Amin A, Moham-
ed KA. Dietary vitamin A intake and the incidence of diarrhea and
respiratory infection among Sudanese children. J Nutr 1995;
125:1211–1221.
57 Hamer DH, Sempertegui F, Estrella B et al. Micronutrient deficien-
cies are associated with impaired immune response and higher bur-
den of respiratory infections in elderly Ecuadorians. J Nutr 2009;
139:113–119.
58 Sempertegui F, Estrella B, Camaniero V et al. The beneficial effects
of weekly low-dose vitamin A supplementation on acute lower
respiratory infections and diarrhea in Ecuadorian children. Pediatrics
1999; 104:e1.
59 Konig D, Grathwohl D, Weinstock C, Northoff H, Berg A. Upper
respiratory tract infection in athletes: influence of lifestyle, type of
sport, training effort, and immunostimulant intake. Exerc Immunol
Rev 2000; 6:102–120.
60 Sim YJ, Yu S, Yoon KJ, Loiacono CM, Kohut ML. Chronic exer-
cise reduces illness severity, decreases viral load, and results in
greater anti-inflammatory effects than acute exercise during influ-
enza infection. J Infect Dis 2009; 200:1434–1442.
61 Wong CM, Lai HK, Ou CQ et al. Is exercise protective against influ-
enza-associated mortality? PLoS ONE 2008; 3:e2108.
62 Chandra RK. Nutrition and the immune system: an introduction.
Am J Clin Nutr 1997; 66:460S–463S.
63 Nelson DE, Holtzman D, Bolen J, Stanwyck CA, Mack KA. Reliability
and validity of measures from the Behavioral Risk Factor Surveil-
lance System (BRFSS). Soz Praventivmed 2001; 46(Suppl 1):S3–S42.
64 Agras WS, Hammer LD, McNicholas F, Kraemer HC. Risk factors for
childhood overweight: a prospective study from birth to 9.5 years.
J Pediatr 2004; 145:20–25.
Charland
et al.
726 ª 2012 John Wiley & Sons Ltd
Page 9
Appendix
Table A1.
States with counties that were included in the study
State
California
Colorado
Florida
Illinois
Kansas
Maryland
Massachusetts
Minnesota
New Jersey
New York
Ohio
Oregon
South Carolina
South Dakota
Texas
Utah
Vermont
Washington
Wisconsin
Table A2.
Clinical conditions potentially impacting influenza disease course and
the need for inpatient care
Condition
Coding
system
Code
numbers
Respiratory
Asthma CCS 128
Apnea Dx 3722
COPD CCS 127
Cystic fibrosis CCS 56
Neurologic central
Cerebral palsy Dx 3430, 3431, 3432,
3433, 3434, 3438, 3439
Dementia CCS 653
Epilepsy CCS 83
Stroke CCS 109
Neurologic peripheral
Muscular dystrophy Dx 3590, 3591, 3592,
3593, 3594, 3595,
3596, 3598, 35981,
35989, 3599
Multiple Sclerosis CCS 80
Paralysis CCS 82
Cardiac
Acute myocardial infarction CCS 100
Table A2.
continued
Condition
Coding
system
Code
numbers
Cardiac dysthymia CCS 106
Conduction disorders CCS 105
Congestive heart failure CCS 108
Coronary heart disease CCS 101
Other heart disease CCS 104
Pulmonary heart disease CCS 103
Hematologic
Sickle Cell Disease CCS 61
Coagulation and hemorrhage disorders CCS 62
Endocrine
Diabetes Mellitus CCS 49, 50
Renal
Acute or chronic renal failure CCS 157, 158
Immune
Chemotherapy CCS 224
HIV CCS 5
Transplant CCS 64, 105, 176
Pregnancy CCS MDC 14 and 15*
CCS, HCUP Clinical Classification Software categories (http://
www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp).
*http://www.hcup-us.ahrq.gov/reports/factsandfigures/2008/
sources_methods.jsp
Table A3.
Rate ratios and confidence intervals for univariable analyses for the
pneumonia and influenza (P&I) and influenza-like illness (ILI) definitions
Covariate P&I RR*,** P&I 95% CI ILI RR ILI 95% CI
Obesity (%) 1Æ17 1Æ13, 1Æ21 1. 16 1Æ12, 1Æ19
Low fruit
vegetable
consumption (%)
1Æ16 1Æ11, 1Æ22 1Æ15 1Æ11, 1Æ20
Physical
inactivity (%)
1Æ15 1Æ
13, 1Æ18 1. 15 1Æ12, 1Æ18
PCP rate 0Æ93 0Æ90, 0Æ97 0Æ94 0Æ91, 0Æ97
Uninsured (%) 1Æ14 1Æ10, 1,18 1Æ14 1Æ10, 1,18
Poverty (%) 1Æ10 1Æ07, 1Æ14 1. 12 1Æ09, 1Æ15
VPD 1Æ05 1Æ00, 1Æ10 1Æ06 1Æ01, 1Æ11
Population density 1Æ000 0Æ998, 1Æ002 1Æ001 0Æ 999, 1Æ004
Caucasian or Asian 0Æ94 0Æ90, 0Æ97 0Æ94 0Æ91, 0Æ97
Respiratory 1Æ20 1Æ15, 1Æ26 1Æ20 1Æ16, 1Æ25
Cardiac 1Æ10 1Æ06, 1Æ16 1Æ12 1
Æ07, 1Æ16
Obesity, diet, exercise, and influenza
ª 2012 John Wiley & Sons Ltd 727
Page 10
Table A3.
continued
Covariate P&I RR*,** P&I 95% CI ILI RR ILI 95% CI
Neurologic
Central
1Æ19 1Æ13, 1Æ26 1Æ 22 1Æ16, 1Æ27
Neurologic
Peripheral
1Æ17 1Æ12, 1Æ21 1Æ 09 1Æ07, 1Æ11
Endocrine 1Æ28 1Æ23, 1Æ34 1Æ 30 1Æ26, 1Æ35
Diabetes (%) 1Æ12 1Æ08, 1Æ16 1Æ 10 1Æ07, 1Æ14
Renal 1Æ14 1Æ10, 1Æ18 1Æ16 1Æ12, 1Æ20
Immune 1Æ05 1Æ03, 1Æ08 1Æ 06 1Æ04, 1Æ08
Hematologic 1Æ20 1Æ13, 1Æ28 1Æ 25 1Æ18, 1Æ32
Chronic condition rate 1Æ21 1
Æ16, 1Æ26 1Æ23 1Æ18, 1Æ28
Smokers (%) 1Æ14 1Æ09, 1Æ18 1Æ 11 1Æ07, 1Æ15
Vaccine uptake (%) 0Æ87 0Æ84, 0Æ91 0Æ85 0Æ82, 0Æ89
PCP, Primary Care Physician; VPD, vapor pressure deficit.
*Rate ratio for 5% change in obesity, low fruit vegetable
consumption, and physical inactivity; for all other variables, rate
ratio is for change corresponding to inter-quartile range.
**With adjustment for the state to which a county belongs.
Table A4.
Adjusted rate ratios and 95% confidence intervals for the
multivariable analysis using data from all years (2002–2008), the
analysis restricted to 2003–2004 and 2007–2008, and the analysis
using the influenza-like illness (ILI) definition
All ages Variable
ARR*,**
2002–2008
ARR**,***
2003–2004
and
2007–2008
ARR*,**
ILI case
definition
Model 1 Low
fruit veg
consumption
1Æ12(1Æ08, 1Æ17) 1Æ13(1Æ09, 1Æ18) 1Æ11(1Æ07 1Æ15)
Model 2 Obesity 1Æ12(1Æ07, 1Æ17) 1Æ12(1Æ07, 1Æ17) 1Æ08(1Æ04, 1Æ13)
Model 3 Physical
inactivity
1Æ11(1Æ07, 1Æ16) 1Æ11(1Æ07, 1Æ16) 1Æ08(1Æ04, 1Æ12)
Model 4 Low
fruit veg
consumption
1Æ08(1Æ04, 1Æ13) 1Æ09(1Æ05, 1Æ14) 1Æ09(1Æ05, 1Æ13)
Obesity 1Æ06(1Æ
01, 1Æ11) 1Æ05(1Æ00, 1Æ10) 1Æ05(1Æ01, 1Æ08)
Physical
inactivity
1Æ07(1Æ03, 1Æ11) 1Æ07(1Æ03, 1Æ12) 1Æ03 (0Æ99, 1Æ07)
*ARR = adjusted rate ratio, adjusting for uninsured (%), chronic condition rate,
PCP rate, poverty (%), smokers (%), VPD, population density, Caucasian or
Asian race, and vaccine uptake (for analysis of all years).
**Rate ratio for 5% change in obesity, low fruit vegetable consumption, and
physical inactivity.
***No adjustment for county vaccination rates.
Charland
et al.
728 ª 2012 John Wiley & Sons Ltd
Page 11
  • [Show abstract] [Hide abstract] ABSTRACT: Background Obesity predisposes general surgical patients to infections such as surgical site infection and respiratory tract infection. The infection rates vary by surgical approach and the type of surgery undertaken. Bariatric surgery is increasingly used to treat obesity and obesity related co-morbidities. However, little is known about the relationship between postoperative infections and patient characteristics, such as body mass index (BMI) or diabetes status, in bariatric cohorts. The objective of this study was to examine the rates of all postoperative infection in patients after bariatric surgery in relation to known risk factors. Results A total of 815 patients were included in the final analysis. During the first month after surgery, 5.2% of patients experienced an infection-related event, and surgery-related infections were most prevalent. Between the second and twelfth month postoperatively, a further 4.7% of patients experienced an infection-related event, and nonsurgical related infections were most prevalent. Infection was associated with increased length of stay in Roux-en-Y gastric bypass (RYGB) (P<.001) and sleeve gastrectomy (SG) (P = .011) but not in laparoscopic adjustable gastric banding (LAGB) (P = .41). Diabetes status and BMI were not associated with increased infection rates during the first month after surgery. Conclusion Infection rates after bariatric surgery are relatively low and are associated with a prolonged length of hospital stay. Reassuringly, neither diabetic status nor BMI appear to increase the risk of postoperative infection after bariatric surgery.
    No preview · Article · Jan 2013 · Surgery for Obesity and Related Diseases
  • Source
    [Show abstract] [Hide abstract] ABSTRACT: Background: Obesity was first noted as a risk factor for severe illness associated with pandemic H1N1 infection in 2009, but the relationship between obesity and seasonal influenza remains unclear. Methods: We used data from a population-based cohort comprising 66 820 older (≥65 years) participants with a follow-up period from 1998 to 2012. The impact of influenza activity on respiratory mortality rates was estimated using a Cox proportional hazards model adjusted for comorbidities, meteorological factors, and other co-circulating respiratory viruses. We also tested whether the association of influenza with respiratory mortality varied with obesity and/or health status. As a control outcome, we similarly assessed the association of influenza with deaths from external causes, because these deaths should be unrelated to influenza. Results: Seasonal influenza activity was associated with higher respiratory mortality (hazard ratio [HR], 1.13 for influenza activity in the influenza season vs noninfluenza season; 95% confidence interval [CI], 1.05-1.22). The effect of seasonal influenza was 19% greater in obese individuals than normal-weight individuals (HR, 1.19; 95% CI, 1.01-1.42). The marginally significant and greater effect modification of obesity status on the association between seasonal influenza and respiratory mortality was also observed among older people in good health (HR, 1.35; 95% CI, .97-1.87). No such relations were observed for death from external causes. Conclusions: Obesity aggravates the effect of seasonal influenza on respiratory mortality. Priority for influenza vaccine should be considered for obese older people to decrease the burden of influenza.
    Preview · Article · Feb 2015 · Clinical Infectious Diseases
  • Source
    [Show abstract] [Hide abstract] ABSTRACT: We examined population-based surveillance data from the Tennessee Emerging Infections Program to determine whether neighborhood socioeconomic status was associated with influenza hospitalization rates. Hospitalization data collected during October 2007-April 2014 were geocoded (N = 1,743) and linked to neighborhood socioeconomic data. We calculated age-standardized annual incidence rates, relative index of inequality, and concentration curves for socioeconomic variables. Influenza hospitalizations increased with increased percentages of persons who lived in poverty, had female-headed households, lived in crowded households, and lived in population-dense areas. Influenza hospitalizations decreased with increased percentages of persons who were college educated, were employed, and had health insurance. Higher incidence of influenza hospitalization was also associated with lower neighborhood socioeconomic status when data were stratified by race.
    Preview · Article · Sep 2015 · Emerging Infectious Diseases
Show more