A simulation model of building intervention impacts on indoor environmental quality, pediatric asthma, and costs
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. 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. 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. 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. 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.
A simulation model of building intervention impacts on
indoor environmental quality, pediatric asthma, and costs
Maria Patricia Fabian, ScD,
Gary Adamkiewicz, PhD,
Natasha Kay Stout, PhD,
Megan Sandel, MD,
Jonathan Ian Levy, ScD
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 ev ents 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 effe ct.
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, NO
Asthma is a classic example of a dynamic and nonlinear
disease, with numerous factors inﬂuencing disease course.
United States asthma is among the most common chronic diseases
of childhood across all socioeconomic classes and is the most
frequent cause of hospitalization among children after birth.
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
], and ﬁne particulate matter), and environmental tobacco
Other factors inﬂuencing asthma exacerbations include
access to health care, medication compliance, and respiratory tract
infections, such as rhinovirus.
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
report provided little guidance as to the essential elements of such
Many 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
replacement mattresses and hypoallergenic pillow covers
can reduce dust mite concentrations,
intensive cleaning can
reduce levels of multiple allergens and fungi,
and air cleaners
or source elimination can reduce concentrations of air pollutants.
Some environmental intervention studies were able to demonstrate
statistically signiﬁcant reductions in asthma symptoms or unsched-
uled clinic visits,
but few were able to document signiﬁcant
changes in hospital admissions, emergency department (ED)
visits, or other less frequent outcome measures that are key compo-
nents of asthma-related direct costs.
Without 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 many studies on
bundled interventions. Even the studies with adequate power to
capture some changes in health care use
could not separate
out the inﬂuence of various individual components, an important
input for policymakers looking to invest in only those components
with demonstrated effectiveness. This takes on added importance
when some interventions (eg, tightening the building envelope)
the Department of Environmental Health, Boston University School of Public
the Depart ment of Environmental Health, Harvard School of Public Health;
the Department of Population Medicine, Harvard Medical School and Harvard
Pilgrim Health Care Institute; and
the Department of General Pediatrics, Boston
Medical University School of Medicine.
Supported by award number R21ES017522 from the National Institute of Environmental
Health Sciences. The content is solely the responsibility of the authors and does not
necessarily represent the ofﬁcial views of the National Institut e of Environmental
Health Sciences or the National Institutes of Health.
Disclosure of potential conﬂict of interest: 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: email@example.com.
Ó 2013 American Academy of Allergy, Asthma & Immunology
DEM: Discrete event simulation model
ED: Emergency department
%: Percent predicted forced expiratory volume in 1 second
IPM: Integrated pest management
MEPS: Medical Expenditure Panel Survey
: Nitrogen dioxide
: 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
We previously developed and evaluated a discrete event
simulation model (DEM) of pediatric asthma
to simulate the
effect ofindoor environmental factors, medication compliance, sea-
sonality, and medical history on asthma outcomes (symptom days,
medicationuse,hospitalizations, and ED visits) inlow-income mul-
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
range of building interventions (Fig 1). The model begins with a baseline pop-
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 (NO
and particulate matter <2.5 mm in diam-
]), cockroach allergen (Bla g 1 and Bla g 2), and dampness, which
serves as a proxy for mold. Other common pollutants associated with asthma
exacerbations, such as ozone, mouse, cat, dog, and dust mite allergen, were not
included because we either lacked a critical mass of literature linking the
exposure with percent predicted forced expiratory volume in 1 second
%) or an ability to readily model indoor concentrations. NO
were simulated by using CONTAM (NIST, Gaithersburg, Md; http://
Cockroach allergen was probabilistically
estimated from prior ﬁeld studies. Dampness or mold growth was a function
of sustained relative humidity over time and was affected by showering, occu-
pant breathing, use of the dishwasher, and cooking. Detailed model inputs and
references are provided in Supplement 1 (Tables E1-E4) in this article’s Online
Repository at www.jacionline.org and described elsewhere.
Other time-varying characteristics included age, outdoor temperature,
indoor and outdoor relative humidity, daily random variation in baseline
%, and changes in FEV
% caused by all risk factors. As previously
and summarized in Supplement 2 (Table E5) in this article’s
Online Repository at www.jacionline.org, contaminant concentrations are
then used to predict FEV
%, as done elsewhere in the context of policy models
for asthma medication.
The daily value of FEV
% 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
% and asthma symptoms or serious asthma events (ED visits,
hospitalizations, and clinic visits with prescribed oral steroid bursts).
model was then used to evaluate changes in 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 popul ation 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.
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,
38% used the oven
for supplemental heat in the winter,
34% had a current smoker in the house,
and 13% had a functioning kitchen exhaust fan.
Although we lacked sufﬁ-
cient symptom and severity data to apply the National Heart, Lung, and Blood
Institute’s classiﬁcation guidelines for managing asthma,
we used the FEV
% 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 prescribed medications. 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
Our assumptions regarding asthma medication prescriptions, use, and costs
are shown in Table I stratiﬁed by FEV
% categorization. Although patients
with persistent asthma should be prescribed controller medications,
have found gaps for a number of reasons.
Therefore we simulated the prob-
ability of using a controller medication as a function of FEV
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 FEV
% value during the past
365 days with his or her FEV
% 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-
%, we simpliﬁed this step by using the standard ranges of
% associated with each severity classiﬁcation.
In 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-
the Medical Expenditure Panel Survey (MEPS),
and the 2006 Agency
for Healthcare Research and Quality Healthcare cost and utilization project,
respectively. For the simulation all costs were adjusted to 2009 dollars based
on the Medical Care Consumer Price Index.
Although numerous indirect
costs associated with asthma exacerbations have important economic implica-
such 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-
In 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 potentially offset 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, some implausibly high values for exposures or outcomes were
possible. We established exclusion criteria for outcomes and air pollution
exposures using multiple approaches, including investigating surveillance and
ﬁeld measurement data,
consulting 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 NO
, and 200 mg/m
The cohort of one million children was simulated for the
baseline scenario and for each intervention over a 10-year
horizon. On the basis of the exclusion criteria, 0.83% of simulated
children were excluded. In the absence of interventions, baseline
average yearly health outcomes stratiﬁed by asthma severity
classiﬁcation are presented in Table II. As expected, children with
% 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 into subgroups of 100,000 and calculating SEs 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.
Fig 2 presents the changes in NO
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
control medication Frequency
Cost of medicine per day
>80% SABA 2 d/wk
Low-dose ICS Daily $4.05à
60% to 80% SABA Daily, 2 puffs/d $0.24
Medium-dose ICS Daily $6.46§
<60% SABA Daily, 6 puffs/d $0.72
Medium-dose ICS 1 LABA Daily $8.20k
ICS, Inhaled corticosteroids; LABA, long-acting b
-agonists; SABA, short-acting b
*Based on values reported in the 2010 Red Book
adjusted to 2009 dollars based on the Medical Care Consumer Price Index.
Assumes a cost of $25 per albuterol canister, with 200 doses per canister.
àBased 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.
J ALLERGY CLIN IMMUNOL
VOLUME 133, NUMBER 1
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)
>80% 140 (11) 0.8 (0.6) 0.09 (0.29) 0.02 (0.08) 0.66 (0.57) 578,155 (58%)
60% to 80% 161 (11) 1.1 (0.7) 0.12 (0.20) 0.03 (0.09) 0.96 (0.64) 385,974 (39%)
<60% 174 (14) 1.8 (1.1) 0.18 (0.22) 0.04 (0.10) 1.6 (1.0) 27,547 (3%)
Across all categories 149 (15) 0.9 (0.7) 0.11 (0.19) 0.03 (0.09) 0.80 (0.65) 991,676
*SD across the million children.
FIG 2. Percentage change in pollutant concentrations for each intervention scenario compared with the
baseline scenario: A, NO
; B, PM
. HEPA, High-efﬁciency particulate air.
J ALLERGY CLIN IMMUNOL
80 FABIAN ET AL
exhaust fans) relative to weatherization alone, there remains a net
increase in symptoms given the limited inﬂuence of localized
kitchen exhaust fans on reducing apartment humidity. These
changes in health outcomes correspond with changes in the yearly
cost per asthmatic patient compared with the baseline scenario for
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
0.1 (SE, 0.02) ED visits per year,
and 0.78 serious
events per year.
Many interventions led to signiﬁcant reductions
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 literature
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 NO
, cockroach allergen, and damp homes. HEPA, High-efﬁciency
J ALLERGY CLIN IMMUNOL
VOLUME 133, NUMBER 1
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,
which 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.
The MEPS study
reported an average of $838 (with asthma treatment) and $192
(without asthma treatment) spent on prescription medication
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 alternative data
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 $550
and the health care use cost savings are $175/y
per asthmatic patient (Fig 4), then the payback period is approx-
imately 1.6 to 3 years. Similarly, if the cost of maintaining an IPM
program is $200/y per unit (unpublished data from Boston public
) 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
This 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
assuming 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
if health care use costs were also included. If, in addition to weath-
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 previously
and include the fact that there was 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 based on FEV
% values, although asthma clas-
siﬁcation is far more complex.
In addition, although our model
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
literature values. 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
that could be adjusted over time and reparameterized to be applied
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
could be readily applied to study populations with similar housing
characteristics but different demographics or residents living in
other types of housing. In addition, our cost-beneﬁt comparisons
need to be interpreted with caution in settings in which the entities
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 NO
and is as-
sociated with lung function
), these can be incorporated into the
model. In addition, a comprehensive and decision-relevant model
should be expanded to capture the effect on other diseases, such as
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.
Our work bridges the gap between clinical and environmental
health sciences by providing physicians with information to
increase their understanding of the effect that home
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-
tal quality and pediatric asthma. Results from the model highlight
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
awareness of a multi-intervention approach to control asthma that
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
can affect their asthma symptoms because of changes in ex-
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
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