A simulation model of building intervention impacts on
indoor environmental quality, pediatric asthma, and costs
Maria Patricia Fabian, ScD,a,bGary Adamkiewicz, PhD,bNatasha Kay Stout, PhD,cMegan Sandel, MD,dand
Jonathan Ian Levy, ScDa,b
Background: Although indoor environmental conditions can
affect pediatric asthmatic patients, few studies have
characterized the effect of building interventions on asthma-
related outcomes. Simulation models can evaluate such complex
systems but have not been applied in this context.
Objective: We sought to evaluate the impact of building
interventions on indoor environmental quality and pediatric
asthma health care use, and to conduct cost comparisons
between intervention and health care costs and energy savings.
Methods: We applied our previously developed discrete event
simulation model (DEM) to simulate the effect of environmental
factors, medication compliance, seasonality, and medical history
on (1) pollutant concentrations indoors and (2) asthma
outcomes in low-income multifamily housing. We estimated
health care use and costs at baseline and subsequent to
interventions, and then compared health care costs with energy
savings and intervention costs.
Results: Interventions, such as integrated pest management
and repairing kitchen exhaust fans, led to 7% to 12%
reductions in serious asthma events with 1- to 3-year payback
periods. Weatherization efforts targeted solely toward
tightening a building envelope led to 20% more serious asthma
events, but bundling with repairing kitchen exhaust fans and
eliminating indoor sources (eg, gas stoves or smokers)
mitigated this effect.
Conclusion: Our pediatric asthma model provides a tool to
prioritize individual and bundled building interventions
based on their effects on health and costs, highlighting the
tradeoffs between weatherization, indoor air quality, and
health. Our work bridges the gap between clinical and
environmental health sciences by increasing physicians’
understanding of the effect that home environmental changes
can have on their patients’ asthma. (J Allergy Clin Immunol
Key words: Air pollution, allergen, asthma, discrete event simula-
tion, energy savings, green building, housing, intervention, indoor
air, lung function, NO2, PM2.5
Asthma is a classic example of a dynamic and nonlinear
disease, withnumerousfactors influencing diseasecourse.1In the
of childhood across all socioeconomic classes and is the most
frequent cause of hospitalization among children after birth.2
A number of studies have documented relationships between
asthma exacerbation and exposure to indoor environmental
stressors found in residential settings, such as allergens (eg, dust
mites and cockroach), air pollutants (eg, ozone, nitrogen dioxide
[NO2], and fine particulate matter), and environmental tobacco
smoke.3,4Other factors influencing asthma exacerbations include
infections, such as rhinovirus.5,6
A recent systematic review by the Centers for Disease Control
and Prevention found that multitrigger, multicomponent, home-
based environmental interventions for children were effective at
improving asthma quality of life and productivity. However, the
interventions.7Many studies have demonstrated that indoor
environmental interventions can lead to significant reductions in
contaminants known to influence pediatric asthma. For example,
integrated pest management (IPM) can reduce cockroach allergen
levels,8replacement mattresses and hypoallergenic pillow covers
can reduce dust mite concentrations,9intensive cleaning can
reduce levels of multiple allergens and fungi,10and air cleaners
uled clinic visits,11-13but few were able to document significant
changes in hospital admissions, emergency department (ED)
nents of asthma-related direct costs.14Without evidence of
changes in health care use, it is challenging to develop generaliz-
able insights for policy analysis or requisite inputs to compare
the benefits and costs of candidate interventions.
Intensive field investigations of indoor environmental inter-
ventions face logistic challenges because of small study popula-
tions (power constraints), the rareness of many serious asthma
outcomes, and potential confounders, particularly in complex
populations, such as low-income multifamily residents. These
studies are further challenged by the emphasis in manystudies on
bundled interventions. Even the studies with adequate power to
capture some changes in health care use11-13could not separate
out the influence of various individual components, an important
with demonstrated effectiveness. This takes on added importance
when some interventions (eg, tightening the building envelope)
Fromathe Department of Environmental Health, Boston University School of Public
Health;bthe Department of Environmental Health, Harvard School of Public Health;
cthe Department of Population Medicine, Harvard Medical School and Harvard
Pilgrim Health Care Institute; anddthe Department of General Pediatrics, Boston
Medical University School of Medicine.
Health Sciences. The content is solely the responsibility of the authors and does not
necessarily represent the official views of the National Institute of Environmental
Health Sciences or the National Institutes of Health.
Disclosure of potential conflict ofinterest: All of the authors received grant support from
the National Institute of Environmental Health Sciences (NIEHS) for this study.
Received for publication November 20, 2012; revised May 29, 2013; accepted for pub-
lication June 3, 2013.
Available online July 31, 2013.
Corresponding author: Maria Patricia Fabian, ScD, 715 Albany St, Talbot 4W, Boston,
MA 02118. E-mail: firstname.lastname@example.org.
? 2013 American Academy of Allergy, Asthma & Immunology
DEM: Discrete event simulation model
ED: Emergency department
FEV1%: Percent predicted forced expiratory volume in 1 second
IPM: Integrated pest management
MEPS: Medical Expenditure Panel Survey
NO2: Nitrogen dioxide
PM2.5: Particulate matter less than 2.5 mm in diameter
might yield energy savings and reduce outdoor air infiltration but
increase the influence of indoor sources, whereas others might
influence different indoor contaminants to varying degrees.
Simulation models have been used previously to evaluate
complex systems for application to cost-benefit analysis. In this
context simulation modeling refers to a systems science approach
involving modeling of a complex system that evolves over time
given changes in state variables that occur at defined points in
time.15We previously developed and evaluated a discrete event
simulation model (DEM) of pediatric asthma16to simulate the
sonality, and medical history on asthma outcomes (symptom days,
tifamily housing. The model allows for evaluation of changes in
asthma health outcomes and pollutant concentrations caused by
changes in building characteristics, as would occur with energy-
saving measures and other interventions that alter the indoor resi-
dential environment. In this study we apply our model to quantify
the effect of multiple building interventions in low-income multi-
family dwellings, focusing on health care use in comparison with
estimated costs of implementing interventions. For building con-
struction changes meant to be implemented as energy-saving mea-
sures, we also consider the economic benefits of the interventions.
Results from these analyses can help decision makers prioritize
among candidate environmental interventions.
We used a DEM of pediatric asthma to simulate health outcomes over a
rangeof buildinginterventions (Fig 1).Themodel beginswitha baselinepop-
ulation of high-risk children characterized by demographic, residential, and
behavioral factors. We modeled indoor environmental concentrations of 4
contaminants that can potentially affect a child’s lung function and asthma
status: 2 combustion pollutants(NO2and particulate matter <2.5 mm in diam-
eter [PM2.5]), cockroach allergen (Bla g 1 and Bla g 2), and dampness, which
serves as a proxy for mold. Other common pollutants associated with asthma
included because we either lacked a critical mass of literature linking the
exposure with percent predicted forced expiratory volume in 1 second
(FEV1%) or an ability to readily model indoor concentrations. NO2and
PM2.5were simulated by using CONTAM (NIST, Gaithersburg, Md; http://
www.bfrl.nist.gov/IAQanalysis/).17Cockroach allergen was probabilistically
estimated from prior field studies. Dampness or mold growth was a function
of sustained relativehumidityovertime and was affected by showering, occu-
Repository at www.jacionline.org and described elsewhere.16
Other time-varying characteristics included age, outdoor temperature,
indoor and outdoor relative humidity, daily random variation in baseline
FEV1%, and changes in FEV1% caused by all risk factors. As previously
described16and summarized in Supplement 2 (Table E5) in this article’s
Online Repository at www.jacionline.org, contaminant concentrations are
for asthma medication.18-20The daily value of FEV1% determined the proba-
bility of asthma exacerbations and health care use. Asthma outcomes were
computed daily for each child, derived from a prior model of the association
between FEV1% and asthma symptoms or serious asthma events (ED visits,
hospitalizations, and clinic visits with prescribed oral steroid bursts).21The
model was then used to evaluate changesin exposures and responses resulting
from multiple interventions, as described below.
The model, which was built in R (R 2.12.1, R Foundation of Statistical
Computing), generates an ensemble or cohort of children and their associated
households. We simulated one million children to ensure an ability to detect
changes in relatively infrequent asthma outcomes associated with changes in
each environmental risk factor.
Study population and housing characteristics
The simulated cohort was comprised of children living in low-income
multifamily housing consistent with public housing residents, a population
known to have increased asthma prevalence and severity.22-24Inputs describ-
ing demographic and housing characteristics were drawn from studies in
Boston public housing or other publications related to low-income urban
populations. In this population 89% had a gas stove,2538% used the oven
and 13% had a functioning kitchen exhaust fan.25Although we lacked suffi-
cientsymptomand severitydata to applythe NationalHeart,Lung,and Blood
Institute’s classification guidelines for managing asthma,6we used the FEV1
% cutoffs that correspond with severity classification for patients with persis-
tent asthma (>80% for mild, 60% to 80% for moderate, and <60% for severe
asthma)and used these to determine the prescribedmedications.Patients with
intermittent asthma were excluded from the simulation because of the limited
environmental literature on this severity class.
Asthma medication use and cost data
are shown in Table I stratified by FEV1% categorization. Although patients
with persistent asthma should be prescribed controller medications,6studies
have found gaps for a number of reasons.26Therefore we simulated the prob-
ability of using a controller medication as a function of FEV1% (see
Supplement 2 in this article’s Online Repository).
Children were evaluated at the end of each simulated year to determine
changes in their asthma medication prescriptions, approximating adjustments
that would happen during a yearly physical examination. At every year
anniversary, we compared each child’s average FEV1% value during the past
365 days with his or her FEV1% value at the beginning of the year and reclas-
sified the child’s asthma severity category, if appropriate. Although severity
classification and changes in medication are based on many components be-
yond FEV1%, we simplified this step by using the standard ranges of
FEV1% associated with each severity classification.16In addition to long-
term control and quick-relief asthma medication, children were prescribed
an oral steroid burst if they visited the ED or the clinic. The medication cost
assigned to the oral steroid burst was $10 (2010$) per visit based on predni-
Health care use costs
Costs for asthma-related clinic visits ($156, Current Procedural Terminol-
ogy code 99244), ED visits ($638), and hospitalizations ($10,167) were
derived from the 2007/2008 Massachusetts Medicaid Reimbursement Sur-
for Healthcare Research and Quality Healthcare cost and utilization project,30
respectively. For the simulation all costs were adjusted to 2009 dollars based
on the Medical Care Consumer Price Index.31Although numerous indirect
tions,14such as lost work days or missed school, we focused on direct health
care costs to be aligned with prior cost-benefit analyses of asthma interven-
tions.12In addition, we focused on low-income multifamily housing, where
J ALLERGY CLIN IMMUNOL
78 FABIAN ET AL
health coverage might be through partially or totally government-subsidized
health care (eg, Medicaid and Medicare). For this subpopulation, the
government (broadly defined) might both be responsible for building re-
pairs/improvements and paying health care costs, and therefore it is
valuable to determine the benefit-cost comparisons from a governmental
We evaluated a number of candidate interventions for improving indoor
environmental conditions and also considered an intervention aimed at
reducing energy costs that could influence the indoor environment. The
interventions included (1) fix and/or operate kitchen and bathroom exhaust
fans; (2) replace gas stoves with electric stoves; (3) eliminate use of the stove
for heating by fixing the heating system; (4) institute a smoke-free housing
policy; (5) use high-efficiency particulate air filters; (6) IPM; and (7)
weatherization. Supplement 3 in this article’s Online Repository at www.
jacionline.org presents the intervention list, along with the rationale for
each intervention and the changes implemented in the model to simulate
each intervention. We also tested bundles of interventions that couple weath-
erization with interventions that can potentiallyoffset the indoor environmen-
tal effects (interventions 8-10, listed at the end of Table E6). Interventions
were added in order of ascending cost (fans being the cheapest intervention
and a no-smoking policy being more expensive).
Although our simulation model was constructed and evaluated with
reference to the published literature, because of the probabilistic nature of
our simulation, someimplausiblyhighvalues for exposures oroutcomeswere
possible. We established exclusion criteria for outcomes and air pollution
field measurement data,32-35consulting with the study pediatrician, and per-
forming a standard review for outliers. Exclusion values were 225 per year
for asthma symptom days, 24 per year for serious asthma events, 3 per year
for hospitalizations, 7 per year for ED visits, 24 per year for clinic visits,
200 ppb for NO2, and 200 mg/m3for PM2.5.
The cohort of one million children was simulated for the
baseline scenario and for each intervention over a 10-year
children were excluded. In the absence of interventions, baseline
average yearly health outcomes stratified by asthma severity
classification arepresented in Table II.Asexpected,childrenwith
lower FEV1% values had a higher incidence of asthma symptom
days and serious asthma events. We evaluated the stability of the
mean estimates by dividing the group of 1 million simulated chil-
dren intosubgroups of 100,000 and calculatingSEs for the means
of all pollutant and asthma health outcomes. The ratio of SE to
mean varied between 0.0002 and 0.003, evidence of the stability
of our calculated means across subpopulations.
Fig2 presents the changes in NO2and PM2.5concentrations at-
tributable to each intervention compared with baseline values.
Concentrations of Bla g 1 and Bla g 2 are not shown in Fig 2
because they were only affected by IPM, which resulted in
concentration decreases of 75% and 89%, respectively. In the
baseline scenario 19% of homes were damp at the end of the
10-year simulation. Installation of fans decreased this to 17%,
whereas weatherization led to dramatic increases in damp homes
(ie, to 67%). The combined intervention of weatherization plus
operating kitchen and bathroom exhaust fans increased the per-
centage of damp homes to 66%.
FIG 1. Schematic of the pediatric asthma discrete event simulation model.
TABLE I. Asthma medication prescriptions, use, and cost stratified by asthma severity classification
Cost of medicine per day
Daily, 2 puffs/d
Daily, 6 puffs/d
Low-dose ICS $4.05?
60% to 80%SABA
Medium-dose ICS 1 LABA
ICS, Inhaled corticosteroids; LABA, long-acting b2-agonists; SABA, short-acting b2-agonists.
*Based on values reported in the 2010 Red Book27adjusted to 2009 dollars based on the Medical Care Consumer Price Index.31
?Assumes a cost of $25 per albuterol canister, with 200 doses per canister.
?Based on cost of Flovent HFA (GlaxoSmithKline, Research Triangle Park, NC), $125 per month.
§Based on cost of Advair HFA (GlaxoSmithKline), $200 per month.
kBased on cost of Flovent HFA plus Singulair, $256 per month.
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FABIAN ET AL 79
Health outcomes decrease under most intervention scenarios
but increase with the weatherization efforts, even when coupled
with some of the source-reduction measures in the bundled
interventions (Fig 3). Interventions such as IPM and repairing
kitchen exhaust fans led to 7% to 12% reductions in serious
asthma events. Although repairing exhaust fans reduces indoor
concentrations of combustion pollutants, leading to significant
outcome improvements for intervention 8 (weatherize plus fix
TABLE II. Baseline health care outcomes from a simulation of asthmatic children over 10 years
Days with asthma
symptoms per year (SD)*
events per year (SD)
per year (SD)
per year (SD)
Clinic visits with
prescribed oral steroid
bursts per year (SD)
60% to 80%
Across all categories
*SD across the million children.
FIG 2. Percentage change in pollutant concentrations for each intervention scenario compared with the
baseline scenario: A, NO2; B, PM2.5. HEPA, High-efficiency particulate air.
J ALLERGY CLIN IMMUNOL
80 FABIAN ET AL
exhaust fans) relativetoweatherization alone, there remains a net
increase in symptoms given the limited influence of localized
kitchen exhaust fans on reducing apartment humidity. These
each intervention (Fig 4). The highest savings in health care use
were associated with IPM, fixing exhaust fans, and replacing
the gas stove, and the intervention with the highest cost was
weatherization without any other intervention.
We demonstrated an application of our DEM of pediatric
asthma to estimate differences in health care use costs comparing
7 home-based interventions plus 3 intervention bundles. Our
findings are broadly consistent with the literature. For example,
average health outcome rates across all asthma categories
(Table II) align closely with our baseline model inputs drawn
from the literature: 0.023 (SE, 0.005) hospitalizations per
year,36,370.1 (SE, 0.02) ED visits per year,38and 0.78 serious
in pollutant concentrations, with the magnitude driven by the
relatively small apartment size (700 sq ft), the presence of multi-
ple indoor combustion sources, and other assumed building
conditions and occupant behaviors. Indoor concentrations were
previously shown to agree with the observational literature16
once the assumed setting was taken into account, and the changes
in concentrations in Fig 2 are consistent with these previous com-
parisons. The weatherization intervention led to a significant
increase in the prevalence of damp homes (19% to 67%).
FIG 3. Percentage change in health outcomes for each intervention compared with the baseline scenario.
A, Asthma symptom days include days with any symptom, including wheeze, cough, and nighttime awak-
enings. B, Serious events include asthma hospitalizations, ED visits, and clinic visits. Asthma outcomes
reflect changes in exposure to NO2, PM2.5, cockroach allergen, and damp homes. HEPA, High-efficiency
J ALLERGY CLIN IMMUNOL
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FABIAN ET AL 81
Although this prevalence estimate might appear high, we were
simulating small apartments, and previous studies in a Boston
public housing development showed 42% of units to have
moisture and 43% to have mold,39which is generally consistent
with our findings.
Our estimated costs of health care use are also consistent with
the literature. In the simulated baseline population scenario, the
mean yearly asthma-related health care expenditure was $1306
(2009$), whereas the 2008 MEPS for children reported total
health care costs of $2503 and $1762 (2008$) for children with
and without asthma treatment, respectively.40The MEPS study
reported an average of $838 (with asthma treatment) and $192
(without asthma treatment) spent on prescription medication40
compared with $848 (2009$) in our population. Our estimated
costs for ED visits are similar to those in MEPS, which is unsur-
prising given our use of MEPS to estimate unit costs. In contrast,
our estimated expenditures on clinic visits and hospitalizations
are less than in MEPS, which is likely related to our use of Med-
icaid reimbursement codes for clinic visits and an alternativedata
resource for hospitalizations.In total, in our simulated population
asthma medication accounted for 65% of the asthma health care
expenditures, clinic visits accounted for 10%, ED visits ac-
counted for 5%, and hospitalizations accounted for 20%.
Given reasonable concordance between our outputs and the
literature, we can use some rough approximations of the costs of
the interventions to provide insight about the most promising
interventions. If the cost of installing a kitchen fan is between
$300 and $55041and the health care use cost savings are $175/y
per asthmatic patient (Fig 4), then the payback period is approx-
program is $200/y per unit (unpublished data from Boston public
housing13) and the corresponding health care use cost savings are
$302/y per asthmatic patient (Fig 4), the intervention pays for it-
self every year. This comparison is complicated by the fact that
IPM is often implemented on a building-wide basis, with not all
units including asthmatic patients, but the study used to derive
the reductions in cockroach allergen levels associated with
interventions involved unit-specific measures. Thus these would
appear to be highly beneficial interventions, especially if the
government is paying substantial fractions of both interventions
and health care expenditures.
In a comprehensive energy retrofit scenario in which the
building is weatherized by sealing cracks, insulating roofs and
walls, and replacing windows, the per-unit cost is approximately
$6500.42This increases the cost of health care use by $322/y per
asthmatic patient (Fig 4) but would lead to reduced energy
consumption. We can approximate energy savings using the
Lawrence Berkeley National Laboratory Home Energy Saver
calculator,43assuming a multifamily building in Boston with
1955 construction, 700 sq ft apartments, and 4 persons living in
each apartment. Weatherization was simulated by specifying
insulated floor, wall insulation, weather stripping, and roof insu-
lation, as well as replacing single-pane windows with double-
pane windows. The resulting energy savings would be $605 per
year. The payback period would therefore be 11 years if only en-
ergy savings and intervention costs were considered but 23 years
erizing, kitchen exhaust fans are made operable (additional cost
$400), then the payback period is 13 years considering all costs
(including health care) and 11.6 years considering only energy
savings and weatherization costs. Weatherization can clearly in-
clude measures with widely varying intensity, but these
FIG 4. Changes in costs of health care use for each intervention compared with baseline averaged over all
asthmatic patients. HEPA, High-efficiency particulate air.
J ALLERGY CLIN IMMUNOL
82 FABIAN ET AL
calculations illustrate some of the potential tradeoffs and mitiga-
The estimates presented in the previous paragraphs represent
payback periods for the average population we simulated. If we
restrict the population to the patients with severe asthma, then
interventions have a larger effect on health care costs, and those
interventions that might not be cost-effective when applied to all
units with asthmatic patients might be cost-effective as more
Limitations inherent in the model were discussed previously16
and include the fact that therewas only 1 suitable published study
at the time of our analysis of the relationships between lung func-
tion and asthma outcomes, limiting the generalizability of our re-
sults to other populations. Another limitation is the simplification
in classifying patients with persistent asthma for medication
assignment solely basedonFEV1%values, althoughasthma clas-
contains numerous parameters and assumptions, we could not
characterize CIs for our model outputs, given the use of common
random numbers across simulation scenarios to reduce random
noise, as well as the model complexity, computational intensity,
and lack of evidence beyond parametric uncertainty in reported
literaturevalues. One-way (single parameter) sensitivity analyses
would have been more computationally viable but would have
contributed only limited insight given the complexity of our
model, and we addressed sensitivity in part through simulation
of numerous children with varying characteristics. Broadly, we
view this modeling effort as providing an analytic infrastructure
to specific populations and buildings.
Also, because we constructed a hypothetical population and
building with a number of defined characteristics (ie, residents of
multifamily low-income housing living in Boston), the cost-
benefit comparisons might not generalize to other settings.
However, most assumptions were relevant to low-income urban
populations living in colder climates, and our model framework
characteristics but different demographics or residents living in
other types of housing. In addition, our cost-benefit comparisons
bearing the costs and benefits of each component are different.
For private housing, intervention costs would be a burden on
homeowners or landlords (and indirectly on renters), and energy
savings would affect homeowners, renters, and/or landlords,
whereas changes in health care use would be a combination of
societal and individual costs. Finally, although our analysis
included multiple environmental exposures and other stressors,
there are clearly other exposures that would be influenced by
building interventions and influence asthma, which could merit
inclusion as the quantitative evidence base evolves. As more
information becomes available on other air pollutants and their
interactions (eg, ozone, which can interact with NO245and is as-
model. In addition, a comprehensiveand decision-relevant model
cardiovascular disease. In spite of these limitations, our DEM of-
fers novel and relevant insight consistent with the small literature
database on intervention studies and asthma outcomes.11,12,47-49
Our work bridges the gap between clinical and environmental
health sciences by providing physicians with information to
increase their understanding ofthe effectthathome
environmental changes can have on their patients’ asthma.
Physicians can then educate their patients on environmental
hazards that contribute to asthma exacerbations and suggest
interventions to decrease exposures.
In conclusion, we applied a validated DEM to evaluate the
implications of home-based interventions on indoor environmen-
talqualityand pediatricasthma. Results fromthemodelhighlight
the short payback periods for key interventions, such as IPM and
repairing exhaust fans, and emphasize the importance of respon-
sibly implementing energy-saving interventions with a focus on
indoor environmental quality and health. Bundling of interven-
tions, such as installing point-source exhaust fans and removing
indoor combustion sources, can largely offset the increase in
indoor air pollution resulting from weatherization and still
maintain energy savings. Our work highlights the effect that
environmental exposures have on asthma symptoms, increases
includes both medication and environmental components, and
highlights the cost-benefits of environmental home interventions.
We thank Amelia Geggel for literature research support, Cizao Ren for
developing the initial DEM code, and Kadin Tseng and Daniel Kamalic for
their help running the models at the Boston University Scientific Computer
d Environmental changes in the homes of asthmatic patients
posure to indoor environmental pollutants.
d Clinicians should take a multi-intervention approach to
asthma control, which includes both medication and envi-
ronmental interventions, to reduce asthma-associated pol-
d Results emphasize the importance of responsibly imple-
menting energy-saving interventions with a focus on in-
door environmental quality and health and highlight the
short payback periods for key interventions, such as
IPM, repair or installation of exhaust fans, and indoor
1. Frey U. Predicting asthma control and exacerbations: chronic asthma as a complex
dynamic model. Curr Opin Allergy Clin Immunol 2007;7:223-30.
2. Merrill CT, Elixhauser A. Hospitalization in the United States, 2002: HCUP Fact
Book No. 6. Rockville (MD): Agency for Healthcare Research and Quality;
3. Institute of Medicine. Clearing the air: asthma and indoor exposures. Washington
(DC): National Academy Press; 2000.
4. Sharma HP, Hansel NN, Matsui E, Diette GB, Eggleston P, Breysse P. Indoor en-
vironmental influences on children’s asthma. Pediatr Clin North Am 2007;54:
5. Johnston NW, Sears MR. Asthma exacerbations. 1: Epidemiology. Thorax 2006;
6. National Heart, Lung, and Blood Institute. National Asthma Education and Preven-
tion Program Expert Panel Report 3: guidelines for the diagnosis and management
of asthma. Bethesda (MD): National Heart, Lung, and Blood Institute/National In-
stitutes of Health; 2007.
7. Crocker DD, Kinyota S, Dumitru GG, Ligon CB, Herman EJ, Ferdinands JM, et al.
Effectiveness of home-based, multi-trigger, multicomponent interventions with an
environmental focus for reducing asthma morbidity a community guide systematic
review. Am J Prev Med 2011;41(suppl 1):S5-32.
J ALLERGY CLIN IMMUNOL
VOLUME 133, NUMBER 1
FABIAN ET AL 83
8. Peters JL, Levy JI, Muilenberg ML, Coull BA, Spengler JD. Efficacy of integrated Download full-text
pest management in reducing cockroach allergen concentrations in urban public
housing. J Asthma 2007;44:455-60.
9. Brugge D, Rioux C, Groover T, Peters J, Kosheleva A, Levy JI. Dust mites: using
data from an intervention study to suggest future research needs and directions.
Rev Environ Health 2007;22:245-54.
10. Adgate JL, Ramachandran G, Cho SJ, Ryan AD, Grengs J. Allergen levels in inner
city homes: baseline concentrations and evaluation of intervention effectiveness.
J Expo Sci Environ Epidemiol 2008;18:430-40.
11. Lanphear BP, Hornung RW, Khoury J, Yolton K, Lierl M, Kalkbrenner A. Effects
of HEPA air cleaners on unscheduled asthma visits and asthma symptoms for chil-
dren exposed to secondhand tobacco smoke. Pediatrics 2011;127:93-101.
12. Kattan M, Stearns SC, Crain EF, Stout JW, Gergen PJ, Evans R 3rd, et al. Cost-ef-
fectiveness of a home-based environmental intervention for inner-city children
with asthma. J Allergy Clin Immunol 2005;116:1058-63.
13. Levy JI, Brugge D, Peters JL, Clougherty JE, Saddler SS. A community-based par-
ticipatory research study of multifaceted in-home environmental interventions for
pediatric asthmatics in public housing. Soc Sci Med 2006;63:2191-203.
14. Weiss KB, Sullivan SD. The health economics of asthma and rhinitis. I. Assessing
the economic impact. J Allergy Clin Immunol 2001;107:3-8.
15. Law AM, Kelton WD. Simulation modeling and analysis. Boston: McGraw-Hill
Higher Education; 2000.
16. Fabian MP, Stout NK, Adamkiewicz G, Geggel A, Ren C, Sandel M, et al. The
effects of indoor environmental exposures on pediatric asthma: a discrete event
simulation model. Environ Health 2012;11:66.
17. Fabian MP, Adamkiewicz G, Levy JI. Simulating indoor concentrations of NO(2)
and PM(2.5) in multi-family housing for use in health-based intervention model-
ing. Indoor Air 2012;22:12-23.
18. Fuhlbrigge AL, Bae SJ, Weiss ST, Kuntz KM, Paltiel AD. Cost-effectiveness of in-
haled steroids in asthma: impact of effect on bone mineral density. J Allergy Clin
19. Paltiel AD, Fuhlbrigge AL, Kitch BT, Liljas B, Weiss ST, Neumann PJ, et al. Cost-
effectiveness of inhaled corticosteroids in adults with mild-to-moderate asthma:
results from the asthma policy model. J Allergy Clin Immunol 2001;108:39-46.
20. Wu AC, Paltiel AD, Kuntz KM, Weiss ST, Fuhlbrigge AL. Cost-effectiveness of
omalizumab in adults with severe asthma: results from the Asthma Policy Model.
J Allergy Clin Immunol 2007;120:1146-52.
21. Fuhlbrigge AL, Weiss ST, Kuntz KM, Paltiel AD. Forced expiratory volume in
1 second percentage improves the classification of severity among children with
asthma. Pediatrics 2006;118:e347-55.
22. Brugge D, Rice PW, Terry P, Howard L, Best J. Housing conditions and respiratory
health in a Boston public housing community. New Solut 2001;11:149-64.
23. Digenis-Bury EC, Brooks DR, Chen L, Ostrem M, Horsburgh CR. Use of a
population-based survey to describe the health of Boston public housing residents.
Am J Public Health 2008;98:85-91.
24. Hynes H, Brugge D, Watts J, Lally J. Public health and the physical environment in
Boston Public Housing: a community-based survey and action agenda. Plann Pract
25. Kattan M, Mitchell H, Eggleston P, Gergen P, Crain E, Redline S, et al. Character-
istics of inner-city children with asthma: the National Cooperative Inner-City
Asthma Study. Pediatr Pulmonol 1997;24:253-62.
26. Levy JI, Welker-Hood LK, Clougherty JE, Dodson RE, Steinbach S, Hynes HP.
Lung function, asthma symptoms, and quality of life for children in public housing
in Boston: a case-series analysis. Environ Health 2004;3:13.
27. P.D.R.. Red book: pharmacy’s fundamental reference. Montvale (NJ): PDR Net-
28. American Academy of Pediatrics. 2007/08 AAP Medicaid Reimbursement Survey.
Elk Grove Village (IL): American Academy of Pediatrics; 2008.
29. Barnett SB, Nurmagambetov TA. Costs of asthma in the United States: 2002-2007.
J Allergy Clin Immunol 2011;127:145-52.
30. Stranges E, Merrill CT, Steiner CA. Hospital stays related to asthma in chil-
dren, 2006. Rockville (MD): Agency for Healthcare Research and Quality;
31. US Department of Labor. Medical care consumer price index and the consumer
price index. Washington (DC): US Department of Labor; 2013.
32. Burke JM, Zufall MJ, Ozkaynak H. A population exposure model for particulate
matter: case study results for PM(2.5) in Philadelphia, PA. J Expo Anal Environ
33. Lee K, Levy JI, Yanagisawa Y, Spengler JD, Billick IH. The Boston residential ni-
trogen dioxide characterization study: classification and prediction of indoor NO2
exposure. J Air Waste Manag Assoc 1998;48:736-42.
34. Levy JI, Lee K, Spengler JD, Yanagisawa Y. Impact of residential nitrogen dioxide
exposure on personal exposure: an international study. J Air Waste Manag Assoc
35. Long CM, Suh HH, Catalano PJ, Koutrakis P. Using time- and size-resolved par-
ticulate data to quantify indoor penetration and deposition behavior. Environ Sci
36. Centers for Disease Control and Prevention. National Hospital Discharge Survey:
2007 Summary. Atlanta: Centers for Disease Control; 2010. Available at: http://
37. Centers for Disease Control and Prevention. Summary Health Statistics for US
Children, National Health Interview Survey 2009. Atlanta: Centers for Disease
Control; 2010. Available at: http://www.cdc.gov/nchs/data/series/sr_10/sr10_247.
38. Centers for Disease Control and Prevention. Ambulatory medical care utilization
estimates for 2007. Atlanta: Centers for Disease Control; 2011. Available at: http://
www.cdc.gov/nchs/data/series/sr_13/sr13_169.pdf. Contract no. 169.
39. Hynes HP, Brugge D, Osgood ND, Snell J, Vallarino J, Spengler J. ‘‘Where does
the damp come from?’’ Investigations into the indoor environment and respiratory
health in Boston public housing. J Public Health Policy 2003;24:401-26.
40. Sarpong EM. Health expenditures among children with reported treatment for
asthma, United States, 1997-1998 and 2007-2008. Rockville (MD): Agency for
Healthcare Research and Quality; 2011.
cessed March 20, 2012.
41. Homewyse. Cost of kitchen exhaust fans. Available at: http://www.homewyse.com/
costs/cost_of_kitchen_exhaust_fans.html. Accessed September 25, 2012.
42. Community Access Partnership of Stafford County. Weatherization. Dover (NH):
Community Access Partnership of Stafford County. Available at: http://www.
straffordcap.org/programs/weatherization. Accessed March 16, 2011.
43. LBL. Home energy saver. Available at: http://hes.lbl.gov/consumer/. Accessed
February 29, 2012.
44. Spahn JD, Cherniack R, Paull K, Gelfand EW. Is forced expiratory volume in one
second the best measure of severity in childhood asthma? Am J Respir Crit Care
45. Zhang J, Lioy PJ. Ozone in residential air: concentrations, I/O ratios, indoor chem-
istry, and exposures. Indoor Air 1994;4:95-105.
46. US Environmental Protection Agency. US Environmental Protection Agency air
quality criteria for ozone and related photochemical oxidants. Research Triangle
Park (NC): US Environmental Protection Agency Office of Research and Develop-
ment; 2006. Report no. EPA 600/R-05/004aF.
47. Howden-Chapman P, Pierse N, Nicholls S, Gillespie-Bennett J, Viggers H, Cun-
ningham M, et al. Effects of improved home heating on asthma in community
dwelling children: randomised controlled trial. BMJ 2008;337:a1411.
48. Morgan WJ, Crain EF, Gruchalla RS, O’Connor GT, Kattan M, Evans R 3rd, et al.
Results of a home-based environmental intervention among urban children with
asthma. N Engl J Med 2004;351:1068-80.
49. Takaro TK, Krieger J, Song L, Sharify D, Beaudet N. The Breathe-Easy Home: the
impact of asthma-friendly home construction on clinical outcomes and trigger
exposure. Am J Public Health 2011;101:55-62.
(Statistical brief #332). Available at:
J ALLERGY CLIN IMMUNOL
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