Research
Cost-Effectiveness of MultifacetedBuilt Environment Interventionsfor Reducing Transmissionof Pathogenic Bacteria inHealthcare Facilities
Marietta M. Squire, MS1, Takeru Igusa, PhD1,Sauleh Siddiqui, PhD1, Gareth K. Sessel, BM BCh, MSc2,and Edward N. Squire Jr., MD, MPH3
AbstractObjectives: The objective of this study is to determine the optimal allocation of budgets for pairs ofalterations that reduce pathogenic bacterial transmission. Three alterations of the built environmentare examined: handwashing stations (HW), relative humidity control (RH), and negatively pressuredtreatment rooms (NP). These interventions were evaluated to minimize total cost of healthcare-associated infections (HAIs), including medical and litigation costs. Background: HAIs are largelypreventable but are difficult to control because of their multiple mechanisms of transmission. More-over, the costs of HAIs and resulting mortality are increasing with the latest estimates at US$9.8 billionannually. Method: Using 6 years of longitudinal multidrug-resistant infection data, we simulated thetransmission of pathogenic bacteria and the infection control efforts of the three alterations usingChamchod and Ruan’s model. We determined the optimal budget allocations among the alterations byrepresenting them under Karush–Kuhn–Tucker conditions for this nonlinear optimization problem.Results: We examined 24 scenarios using three virulence levels across three facility sizes with varyingbudget levels. We found that in general, most of the budget is allocated to the NP or RH alterations ineach intervention. At lower budgets, however, it was necessary to use the lower cost alterations, HWor RH. Conclusions: Mathematical optimization offers healthcare enterprise executives and engi-neers a tool to assist with the design of safer healthcare facilities within a fiscally constrained envi-ronment. Herein, models were developed for the optimal allocation of funds between HW, RH, andnegatively pressured treatment rooms (NP) to best reduce HAIs. Specific strategies vary by facility sizeand virulence.
1 Department of Civil Engineering, Johns Hopkins University, Baltimore, MD, USA2 Outreach Engineering NPC (non-profit company), Johannesburg, South Africa.3 Moss Clinic, Fredericksburg, VA, USA
Corresponding Author:
Marietta M. Squire, MS, Department of Civil Engineering, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218,
USA.
Email: [email protected]
Health Environments Research& Design Journal
2019, Vol. 12(2) 147-161ª The Author(s) 2019
Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/1937586719833360
journals.sagepub.com/home/her
Keywordsinfection control, MRSA, patient safety, airborne transmission, design, hospital hygiene, healthcare-associated infections, hospital, VRE, CRE
Literature Review
Healthcare-Associated Infections
Healthcare-associated infections (HAIs) severely
threaten hospitalized patients who are often
highly susceptible to infection. The numbers are
extremely high. On any given day, 1 in 25 (4%) of
pediatric and adult hospitalized patients will have
at least 1 HAI (Magill et al., 2014). Among those,
1 in 17 will die as a result (Klevens, 2007). Esti-
mates of the annual number of HAIs in U.S. hos-
pitals vary between 400,000 (Zadeh, Sadatsafavi,
& Xue, 2015) and 1.7 million (Klevens et al.,
2007). The estimated annual mortality in the
United States ranges from 75,000 (Lee, 2014) to
99,000 (Klevens et al., 2007).
In 2013, the direct cost of these infections was
US$9.8 billion (US$10.47 billion in 2017; Zim-
lichman et al., 2013). These HAIs had multiple
causes: 33.7% from surgical site infections,
31.6% due to ventilator-associated pneumonia,
18.9% from central line–associated bloodstream
infections, and the remaining 15.8% from Clos-
tridium difficile and other pathogens (Zimlich-
man et al., 2013). HAIs prove most deadly
among older adults and those with immune sys-
tem suppression. Deeming such infections pre-
ventable, the Centers for Medicare and
Medicaid Services have begun withholding pay-
ments for certain infections (Center for Medicare
and Medicaid Service Hospital-Acquired Condi-
tion Reduction Program, 2018). It has been found
that when including indirect costs, HAIs cost
American society US$96 billion to US$147 bil-
lion each year (Marchetti & Rossiter, 2013).
Infections caused by three multidrug-resistantorganisms (MDROs). MDROs, sometimes referred
to as superbugs, are dreaded because they are the
most difficult to treat. In this investigation, three
groups of these MDROs were evaluated:
methicillin-resistant Staphylococcus aureus
(MRSA), carbapenem-resistant
Enterobacteriaceae (CRE), and vancomycin-
resistant Enterococci (VRE).
The first strains of MRSA were discovered in
1961 by a British investigator (Barber, 1961). The
resistance of Staphylococcus aureus to methicil-
lin has expanded to additional antibiotics
(Nichols, 2017). In 1986, vancomycin-resistant
Enterococcus spread rapidly throughout Europe
and the United States (O’Driscoll & Crank, 2015;
Uttley, Collins, Naidoo, & George, 1988). VRE
infects vulnerable patients via surgical wounds
and urinary tract catheters. It also causes colitis
and endocarditis (Cerrud-Rodriguez, Alcaraz-
Alvarez, Chiong, & Ahmed, 2017; Stevens &
Edmond, 2005). Carbapenem-resistant species
in the family, Enterobacteriaceae, CRE were first
identified in November 2012 (Deen & Debbie,
2014). During the course of just 2 years, CRE
spread across 42 states and 200 hospitals in the
United States (Deen & Debbie, 2014).
Benchmark for infection control. Biocontainment
labs and biocontainment units are the gold stan-
dard in infection control against “nightmare
pathogens” such as the Ebola virus. Evaluation
of hospital infection control within the context of
these types of facilities provides an important
frame of reference. Levels 3 and 4 biosafety labs
(i.e., biocontainment labs) and treatment facilities
for highly infectious patients (i.e., biocontainment
units) have rigorous controls and expensive infra-
structure. This infrastructure includes specialized
HVAC systems, water decontamination, and
chemical decontamination of personnel and equip-
ment exiting hot (highly infectious) regions.
There is a spectrum of infection control effi-
cacy, in which biocontainment units lie at the
cost-prohibitive upper limit. Healthcare facilities
must determine where on the spectrum they
should position themselves and evaluate their
infection control measures accordingly.
Three alterations of the built environment intended toreduce HAIs. While many efforts for infection
148 Health Environments Research & Design Journal 12(2)
control have focused on hand hygiene, our inter-
est is in the trade-offs between multiple candidate
interventions; we considered the impact of pairs
of the following three alterations in the built envi-
ronment: handwashing stations (HW), controlled
indoor air hydration (i.e., relative humidity con-
trol or RH), and negatively pressured treatment
rooms (NP). Architects and engineers acknowl-
edge the need for greater insight into the impact
of infrastructure on patient safety (Eames, Tang,
Li, Wilson, 2009; Hamilton, 2013). Close inter-
actions between microbiologists, infection con-
trol experts, engineers, architects, and
healthcare providers are critical to improve hos-
pital design and thereby decrease HAIs (Eames
et al., 2009).
Therefore, our article seeks to answer the fol-
lowing research questions:
1. What is the optimum resource allocation
for implementation of the three candidate
alterations in the built environment among
healthcare facilities of varying sizes and
budgets?
2. How effective is a pair of alterations at
minimizing infections, when compared to
another feasible pair for a specific sce-
nario? The best case for each pair is used
for these comparisons, in which resources
are optimally allocated between the altera-
tions in the pair.
How effective is a pair of alterations at
minimizing infections, when compared to
another feasible pair for a specific
scenario? The best case for each pair is
used for these comparisons, in which
resources are optimally allocated between
the alterations in the pair.
Specifically, we investigated the cost–benefit
analysis for each intervention (i.e., for each pair
of alterations). The contribution of our study is a
first-of-its-kind toolset that enables evidence-
based decisions for the allocation of budgets to
minimize HAIs. Furthermore, the toolset can be
used to analyze multiple budgets, hospital sizes,
and pathogens.
Alteration #1: Handwashing measures. In 1846,
Ignaz Semmelweis first observed the relation-
ship between infection and healthcare workers’
(HCW) failure to wash their hands (Davis,
2015). Even today, microorganisms may be
transported by the unwashed hands of HCW and
travel from one patient to another. Therefore,
compliance with hand hygiene protocols
remains a critical preventative measure in the
avoidance of HAIs (Beggs, Shepherd, & Kerr,
2009).
Evidence to date shows that handwashing
compliance is increased with convenient place-
ment of hand gel dispensers (Cure & Van Enk,
2015). For example, a paper towel dispenser
which automatically presents a paper towel to
passers-by significantly increased handwashing
compliance from 61.8% to 75.9% (Ford, Boyer,
Menachemi, & Huerta, 2014).
Boyce, Potter-Bynoe, Chenevert, and King
(1997) demonstrated that when treating MRSA
patients, 65% of nurses’ scrubs and uniforms
became contaminated with MRSA. This occurs
during contact with colonized or MRSA-
infected patients. Even without physical contact
with MRSA-infected patients, 42% of nurses
became contaminated from contact with contami-
nated surfaces in MRSA patients’ rooms (Boyce,
Potter-Bynoe, Chenevert, & King, 1997). Fre-
quent handwashing reduces person-to-person and
environment-to-person transmission of MRSA
and other bacteria.
Alteration #2: Control of indoor air hydration inhealthcare facilities. Whereas handwashing is
important for HAIs that are spread by direct con-
tact, RH is important because it affects disper-
sion of bacteria-containing droplets that are
aerosolized by coughing. Investigators found
that CRE can be spread by direct contact with
sinks (Crespo et al., 2004). VRE spreads by
direct contact and through contact with contami-
nated surfaces. MRSA is spread by both of these
routes and airborne transmission (Boswell &
Fox, 2006; Shiomori, Miyamoto, & Makishima,
2001).
Flugge in 1897 evaluated the spread of infection
via aerosols. He determined that droplets emitted
from the nose and mouth contained bacteria (Eames
Squire et al. 149
et al., 2009). Wells showed in 1934 that droplet
movement is dependent on droplet size (Wells,
1934; Tang, Li, Eames, Chan & Ridgway, 2006).
In addition, Arundel, Sterling, Biggin, and Sterling
(1986) demonstrated the control of indoor air
hydration/relative humidity between 40% and
60% is effective at mitigating the transmission of
pathogens through the air. Relative humidity (vapor
equilibrium) impacts the droplet size and therefore
the viability of the infectious microorganism. The
mechanism is further described below.
During a cough, about 3,000 droplets are
expelled from the airways at velocities up to 50
miles per hour (American Lung Association,
2018). Sneezes can reach speeds up to 100 miles
per hour and can include up to 100,000 droplets
(American Lung Association, 2018). MRSA’s
diameter ranges in size from 0.5 to 0.7 mm (Had-
dadin, Fappiano, & Lipsett, 2002). The largest
droplets, comprised of saliva and bacteria, settle
quickly and are deposited on surfaces. The bac-
teria are then released as the droplets evaporate.
Larger droplets settle on surfaces at shorter dis-
tances from the source, thereby reducing the area
of the infectious zone (Tang et al., 2006).
Once the infectious droplet has settled, it can
be effectively removed via surface cleaning. An
RH between 40% and 60% is the optimum level
both to reduce transmission of infection and to
prevent mold formation (Arundel, Sterling, Big-
gin, & Sterling, 1986).
In 1948, Dunklin demonstrated that relative
humidity in the “vicinity of 50%” also impacts
the viability of airborne organisms. This provides
a second mechanism by which relative humidity
affects the airborne transmission of bacteria
(Dunklin & Puck, 1948).
In 1959, Kingdon’s work supported the first
explanation. He showed that high humidities
retard droplet evaporation, hastening their fall
and removal from the atmosphere (Kingdon,
1960). In 1986, Arundel’s review corroborated
Dunklin’s research. Arundel et al. (1986) reported
multiple studies that showed mid-range humid-
ities (40-60%) were more lethal than low or high
humidities to nonpathogenic Escherichia coli,
were effective “with showed” the lethality of
intermediate humidities to Escherichia coli
(40% to 60%), effectiveness at preventing fungi
formation, and that these humidity ranges are the
optimum levels to reduce transmission of infec-
tion (Arundel et al., 1986). For these reasons, we
use this range of RH (i.e., 40–60%) in our models.
Alteration #3: Negatively pressured, high efficiencyparticulate air (HEPA) filtered treatment rooms.Additional built environment controls create a
containment barrier between highly infectious
patients and patients susceptible to infection.
Containment barriers such as negative pressure
and HEPA-filtration, decrease the risk of genetic
recombination between infectious species (Bos-
well & Fox, 2006). Private rooms with negative
pressure and HEPA filtration are likely to protect
both patients and hospital staff.
The Department of Energy requires HEPA-
filtration units to filter particulates greater than
or equal to 0.3 mm (Biosafety in Microbiological
and Biomedical Laboratories, 2009). HEPA fil-
tration is known to trap particles down to at least
0.1 mm; MRSA droplets measure 0.5 mm (Biosaf-
ety in Microbiological and Biomedical Labora-
tories, 2009).
Negative pressure rooms exist to block the air-
borne egress of pathogens. During the 2003 Cana-
dian outbreak of severe acute respiratory
syndrome, 46 patients were successfully isolated
using negative pressure rooms. The deployment
of these isolation measures required augmented
staffing, intense ancillary activity, and wide-
spread collaboration within the facility (Loutfy
et al., 2004).
Two years later, in 2006, the work by Boswell
and Fox further supported the effectiveness of
these measures, by demonstrating that HEPA-
filtration blocked the airborne transmission of
MRSA. Boswell and Fox (2006) demonstrated
that HEPA filtration greatly and significantly
reduces the prevalence of pathogenic bacterial
infections within the rooms of highly infectious
patients. These investigators placed agar plates in
three private intensive care unit (ICU) rooms
occupied by three patients with MRSA infections.
Two of the three patients, considered to be “heavy
shedders,” aerosolized enough MRSA to produce
5.0 colony-forming units per 10 hr of exposure.
Placement of portable HEPA-filtration units in
each room reduced contamination by 75%,
150 Health Environments Research & Design Journal 12(2)
90%, and 96% (Boswell & Fox, 2006). In a 2007
review, Li’s conclusions also supported the effec-
tiveness of negative pressure rooms as he identi-
fied multiple studies that demonstrated how
ventilation in the built environment may be
responsible for airborne transmission (Li et al.,
2007).
Proposed tool to facilitate informed decision-making.The principal goal of this investigation was to
quantify the relative effectiveness of multiple
interventions (each consisting of a pair of altera-
tions). The secondary goal was to optimize cost
and effect for these interventions in various sce-
narios. Total cost consists of both the cost of the
intervention and the cost of infection.
As far as we are aware, this is the first technique toanalyze budget, pathogen virulence, and facility sizeconcurrently. By using these models, hospital
administrators can assess cost savings that occur
as a by-product of implementing infection control
measures. Informed decision-making bolsters
patient safety, thereby facilitating an improved
environment of care.
Method
Study Design and Facility Size
We considered three facility sizes with the fol-
lowing bed counts, for the treatment region of
each facility: a 134-bed census count for large
hospitals, a 56-bed count for medium hospitals,
and a 13-bed count for small hospitals. Facility
bed count is distributed between private, two-bed,
three-bed, and six-bed rooms (see Supplement).
We assumed the beds in the treatment region of
this investigation are filled by highly immuno-
compromised patients, without time gaps.
Models. This investigation is based on Chamchod
and Ruan’s (2012) baseline model of hospital
infection rates. This model was expanded into
cost-optimization models for each of the three
alterations, HW, RH, and NP. The cost-
optimization models simultaneously account for
budgets, cost of infections, virulence of pathogen,
and cost of the respective alterations. Given a
limited budget, the investigation determines out-
comes for each facility size.
We initially obtained 24 scenarios, which are
summarized in Table 1. We used three virulence
levels (VRE ¼ 0.4, CRE ¼ 0.22, MRSA ¼ 0.08).
We produced 6 scenarios for large hospitals with
3 virulence levels and 1 budgetary level
($600,000), 12 scenarios for medium hospitals
(budgetary level of $180,000 and $250,000), and
6 scenarios for small hospitals (budgetary levels
of $60,000).
Data. We used the longitudinal data from the
MDRO Repository and Surveillance Network
(MRSN) to model the virulence factors, f(Table 1, column C). These longitudinal data
consist of 6 years of MRSA, CRE, and VRE
infections in one representative hospital pro-
vided by the Walter Reed Army Institute of
Research (WRAIR) MRSN. The virulence fac-
tors are derived from the MRSN’s original data,
based upon the highest increase in the rate of a
specific infection, in 2 consecutive years, over a
6-year period (MRSN, 2017). The virulence fac-
tors (f, an input into the models) measure the
rate of progression from colonization to infec-
tion. Direct costs of infections were determined
from Zimlichman et al. (2013). Associated liti-
gation costs (i.e., indirect costs) were also esti-
mated and incorporated. Indirect costs
incorporate decreased productivity, reduction
of wages, premature death, and burden upon
family members who become caretakers
(Marchetti & Rossiter, 2013). Costs of infection
include indirect and direct costs.
Costs for commercial off-the-shelf items used
in the interventions were determined from these
vendors: Johnson & Johnson, Medline Industries
Inc., Purell (HW intervention), Biological Con-
trols, and Qualitair (RH and NP interventions).
This investigation was conducted from a
healthcare-system perspective. Costs are in
2017 US dollars.
The number of contacts per HCW with possi-
bly unwashed hands is eight, based on the litera-
ture supporting the high efficacy of the HW
alteration (Boyce, 2013). The costs for reducing
these contacts with unwashed hands to zero with
Squire et al. 151
Tab
le1.
Outp
uts
From
Optim
izat
ion
Model
sR
un
inT
rade-
Off
Anal
yses
.
Scen
ario
sSc
enar
io#
1,T
rade-
Off
Anal
ysis
ofR
Han
dH
WB
udge
tFr
om
Colu
mn
ESc
enar
io#
2,T
rade-
off
Anal
ysis
ofH
Wan
dN
PB
udge
tFr
om
Colu
mn
E
Cost
of
Infe
ctio
ns
Pat
hV
irule
nce
(f)
Faci
lity
Size
Budge
t$
Rel
ativ
eH
um
idity
(%B
udge
t)H
and
Was
h(%
Budge
t)
Infe
ctio
nW
ithout
Inte
rven
tion
Infe
ctio
nW
ith
Inte
rven
tion
Red
uce
dIn
fect
ion
per
$1,0
00
Han
dW
ash
(%B
udge
t)
Neg
ativ
ePre
ssure
(%B
udge
t)
Infe
ctio
nW
ithout
Inte
rven
tion
Infe
ctio
nW
ith
Inte
rven
tion
Red
uce
dIn
fect
ion
Per
$1,0
00
AB
CD
EF
GH
IJ
KL
MN
O
$400,0
00
MR
SA0.0
8La
rge
Hosp
ital
$600,0
00
$395.2
K,(6
6%
)$152.0
K,(2
5%
)15
infe
ctio
ns
3in
fect
ions
0.0
21
$176.1
K,(2
9%
)$366.4
K,(6
1%
)15
infe
ctio
ns
5in
fect
ions
.017
$250,0
00
CR
E0.2
2La
rge
Hosp
ital
$600,0
00
$433.9
K,(7
2%
)$166.1
K,(2
8%
)36
infe
ctio
ns
8in
fect
ions
0.0
46
$187.3
K,(3
1%
)$412.7
K,(6
9%
)36
infe
ctio
ns
14
infe
ctio
ns
.037
$150,0
00
VR
E0.4
Larg
eH
osp
ital
$600,0
00
$426.4
K,(7
1%
)$173.6
K,(2
9%
)57
infe
ctio
ns
15
infe
ctio
ns
0.0
69
$196.2
K,(3
3%
)$403.8
K,(6
7%
)57
infe
ctio
ns
26
infe
ctio
ns
.051
$400,0
00
MR
SA0.0
8M
ediu
mH
osp
ital
$180,0
00
$33.1
K,(1
8%
)$67.8
K,(3
8%
)5
infe
ctio
ns
2in
fect
ions
0.0
34
$69.7
K,(3
9%
)$0.0
K,(0
%)
5in
fect
ions
2in
fect
ions
.048
$400,0
00
MR
SA0.0
8M
ediu
mH
osp
ital
$250,0
00
$33.1
K,(1
3%
)$67.8
K,(2
7%
)5
infe
ctio
ns
2in
fect
ions
0.0
34
$69.7
K,(2
8%
)$0.0
K,(0
%)
5in
fect
ions
2in
fect
ions
.048
$250,0
00
CR
E0.2
2M
ediu
mH
osp
ital
$180,0
00
$96.8
K,(5
4%
)$79.8
K,(4
4%
)13
infe
ctio
ns
3in
fect
ions
0.0
56
$84.0
K,(4
7%
)$0.0
K,(0
%)
13
infe
ctio
ns
4in
fect
ions
.111
$250,0
00
CR
E0.2
2M
ediu
mH
osp
ital
$250,0
00
$96.8
K,(3
9%
)$79.8
K,(3
2%
)13
infe
ctio
ns
3in
fect
ions
0.0
56
$84.0
K,(3
4%
)$0.0
K,(0
%)
13
infe
ctio
ns
4in
fect
ions
.111
$150,0
00
VR
E4.0
Med
ium
Hosp
ital
$180,0
00
$96.4
K,(5
4%
)$83.6
K,(4
6%
)22
infe
ctio
ns
6in
fect
ionsa
0.0
80
$90.4
K,(5
0%
)$0.0
K,(0
%)
22
infe
ctio
ns
7in
fect
ions
.167
$150,0
00
VR
E4.0
Med
ium
Hosp
ital
$250,0
00
$119.5
K,(4
8%
)$86.3
K,(3
5%
)22
infe
ctio
ns
6in
fect
ionsa
0.0
90
$90.4
K,(3
6%
)$0.0
K,(0
%)
22
infe
ctio
ns
7in
fect
ions
.167
$400,0
00
MR
SA0.0
8Sm
allH
osp
ital
$60,0
00
$0.0
K,(0
%)
$15.1
K,(2
5%
)1
infe
ctio
n1
infe
ctio
n0.0
46
$15.1
K,(2
5%
)$0.0
K,(0
%)
1in
fect
ion
1in
fect
ion
.046
$250,0
00
CR
E0.6
6Sm
allH
osp
ital
$60,0
00
$0.0
K,(0
%)
$18.7
K,(3
1%
)3
infe
ctio
ns
1in
fect
ion
0.1
13
$13.2
K,(3
1%
)$0.0
K,(0
%)
3in
fect
ions
1in
fect
ion
.113
$150,0
00
VR
E4.0
Smal
lH
osp
ital
$60,0
00
$0.0
K,(0
%)
$20.4
K,(3
4%
)5
infe
ctio
ns
2in
fect
ions
0.1
81
$23.4
K,(3
4%
)$0.0
K,(0
%)
5in
fect
ions
2in
fect
ions
.181
Not
e.In
terv
entions
wer
eap
plie
dto
bed
censu
ses
that
vari
edac
cord
ing
tofa
cilit
ysi
ze:la
rge
hosp
ital
s¼
134
bed
s;m
ediu
mhosp
ital
s¼
56
bed
s;sm
allhosp
ital
s¼
13
bed
s.C
olu
mns
A–C¼
par
amet
ers
for
the
multid
rug-
resi
stan
tpat
hoge
n.C
olu
mn
B¼
cost
sofin
fect
ion.C
olu
mn
C¼
viru
lence
fact
or
(f),
det
erm
ined
from
6ye
ars
ofori
ginal
longi
tudin
aldat
a.C
olu
mn
D¼
faci
lity
size
.Colu
mn
E¼
resp
ective
budge
tsfo
rtr
ade-
off
anal
ysis
.
Colu
mns
Fan
dG
repre
sentth
eoptim
ized
per
centofb
udge
tal
loca
tions
for
Scen
ario
#1
(RH
and
HW
).C
olu
mn
Hre
pre
sents
the
num
ber
ofr
espec
tive
infe
ctio
ns
inSc
enar
io#
1th
atre
sult
withoutth
eim
ple
men
tation
oft
he
inte
rven
tion.C
olu
mn
Ire
pre
sents
the
num
ber
ofin
fect
ions
inSc
enar
io#
1th
atocc
ur
afte
rth
eim
ple
men
tation
ofth
ein
terv
ention.C
olu
mn
Jre
pre
sents
the
effic
iency
ofth
ein
terv
ention.C
olu
mn
Jis
mea
sure
dby
the
reduce
dnum
ber
ofin
fect
ions
per
$1,0
00
spen
t.T
he
most
effic
ient
inte
rven
tion
has
the
low
est
valu
ein
Colu
mn
J.C
olu
mns
Kan
dL
repre
sent
the
optim
ized
per
cent
ofbudge
tal
loca
tions
for
Scen
ario
#2
(HW
and
NP).
Colu
mn
Mre
pre
sents
the
num
ber
ofi
nfe
ctio
ns
inSc
enar
io#
2th
atocc
ur
without
this
inte
rven
tion.C
olu
mn
Nan
nota
tes
the
num
ber
ofi
nfe
ctio
ns
inSc
enar
io#
2af
ter
the
use
oft
he
resp
ective
inte
rven
tion.C
olu
mn
Oal
so
repre
sents
the
effic
iency
oft
he
inte
rven
tion
ofS
cenar
io#
2(H
Wan
dN
P),
whic
his
quan
tifie
das
the
reduce
dnum
ber
ofi
nfe
ctio
ns
per
$1,0
00
spen
t.O
ptim
um
dis
trib
ution
off
unds
that
resu
ltin
min
imiz
atio
nofi
nfe
ctio
ns
and
inte
rven
tion
cost
sca
nocc
ur
without
use
of100%
ofth
ebudge
t.Pat
hoge
ns:
MR
SA¼
met
hic
illin
-res
ista
nt
Stap
hylo
cocc
usau
reus
;H
W¼
han
dw
ashin
gst
atio
ns;
CR
E¼
carb
apen
em-r
esis
tant
Ent
erob
acte
riac
eae;
VR
E¼
vanco
myc
in-r
esis
tant
Ent
eroc
occi
.In
fect
ion
num
ber
sth
atre
sulted
indec
imal
valu
esan
dar
ero
unded
up
toth
enea
rest
valu
e.a T
hes
etw
ova
lues
are
diff
eren
tw
hen
the
dec
imal
isac
counte
dfo
r.
152
the HW alteration were $143,300 (large facility),
$64,512 (medium), and $15,438 (small).
We assumed that without an RH alteration, 1%of those uninfected will be infected by each colo-
nized patient. The cost of reducing this infection
rate to zero for the RH alteration was $725,254
(large facility), $304,216 (medium), and
$89,387 (small). We assumed that without an
NP alteration, 1% of uninfected patients will
be infected by each colonized patient via aero-
solization. The cost of reducing this to zero for
the NP alteration is $4,178,408 (large facility),
$1,462,200 (medium), and $208,920 (small).
Multiple studies demonstrate the effectiveness
of negatively pressured, HEPA-filtered rooms
(Boswell & Fox, 2006).
The investigation determines p values using a
one-way Analysis of Variance (ANOVA); p val-
ues less than .05 were considered to be a sta-
tistically significant. This alteration was
conducted for medical facilities that previously
did not have hand sanitizers placed outside
patient treatment rooms. More details on the
costs incorporated into the alteration costs are
available in the Supplemental Section.
Simulation method. We used MATLAB to analyze
the ordinary differential equations (ODEs) of the
model using the Runge–Kutta technique. Cham-
chod and Ruan’s (2012) baseline differential
equations represent the transmission of patho-
genic bacteria (MRSA, CRE, VRE; see Figure 2,
Supplemental Methods section). Additionally,
Chamchod and Ruan cite and utilize over 12 sci-
entific papers to determine the parameters used in
the baseline transmission model (ODEs). The
input variables (see Figure 2A and 2B; Supple-
mental Methods section) in these differential
equations are used to quantify factors that affect
transmission of infection.
This technique resembles a modified version
of the S-I-R-S (susceptible, infectious, recovered,
susceptible) model. This modeling technique cal-
culates theoretical rates of infection based on the
characteristics of the population at risk. We uti-
lized the MATLAB fmincon solver to determine
the optimal budget allocation for each alteration
in an intervention, to minimize overall cost.
Results
The scenario diagram (see Figure 1) summarizes
the key optimized resource allocation results.
Optimization Results Across Models
Table 1 lists the parameters for the multidrug-
resistant pathogen (Table 1, columns A–C), facil-
ity size (Table 1, column D), and respective budget
trade-off analysis (Table 1, columns F-J and K-O).
Costs of infection (Table 1, column B; Marchetti
& Rossiter, 2013, Zimlichman et al., 2013) were
determined based on literature and frequency of
infection over a 6-year time period (MRSN, 2017).
Our model determined the optimal allocation
of funding for three preventative measures. We
used the model to apportion funds between the
following two trade-off, design scenarios: (see
Table 1) Scenario #1: rooms with relative humid-
ity control and improved HW; Scenario #2:
installation and operation of negative pressure
rooms and improved HW.
Optimized solutions of Scenario #1 (RH and
HW) and Scenario #2 (HW and NP) are presented
in columns F–G, I–J, and K–L, N–O, respectively
(Table 1). Columns I and M in Table 1 calculate the
preintervention, annual number of infections.
Based on the results in Table 1 and Figure 1, we
can state the following general remarks: Relative
humidity control combined with improved HW pro-
vides the greatest reduction of infections. Cost-
effectiveness and efficiency of interventions are
reflected by a low value for the reduced infections
per $1,000 expended (Table 1, columns J and O).
The values in columns F and G do not always
sum to the values in column E. This is because the
intervention can sometimes achieve the optimum
result (i.e., the greatest reduction in number of
infections) without using all of the available bud-
get. For the same reason, the values in columns K
and L may not sum to the values in column E. The
two extreme examples, one in each scenario,
show that optimization of infection control is
achieved by spending only one quarter (25%) of
the available funds. This is because the cost of
increasing the intervention effort beyond the opti-
mal level was not offset by the associated cost
savings of reduced infections.
Squire et al. 153
RH HW
Budget$600K
HW NP
Highest % 2nd Highest Not Evaluated in Allocated Allocated Scenario
Budget$180K
RH HW
HW NP
Budget$250K
MediumHospital
RH HW
HW NP
Budget$250K
RH HW
RH HW
HW NP
HW NP
Budget$180K
RH HW
Budget$60K
HW NP
SmallHospital
#
#
^,*
^,*
LargeHospital
#, ^, *
#, ^, *
Figure 1. Scenarios run in optimization models. This figure depicts the decision tree processes involved in theinvestigation. RH¼ relative humidity alteration; HW¼ handwashing alteration; NP¼ negative pressure alteration;yellow ¼ large hospital scenarios; blue ¼ medium hospital scenarios; olive green ¼ small hospital scenarios; # ¼with respect to MRSA pathogen; ^ ¼ with respect to CRE pathogen; *¼ with respect to VRE pathogen; MRSA¼methicillin-resistant Staphylococcus aureus; CRE ¼ carbapenem-resistant Enterobacteriaceae; VRE ¼ vancomycin-resistant Enterococci.
154 Health Environments Research & Design Journal 12(2)
Infections
With the implementation of relative humidity
control and handwashing: In a large hospital,
there was a reduction of 12 infections in the case
of MRSA, 28 infections in the case of CRE, and
42 infections in the case of VRE. In a medium
hospital, there was a reduction of 3 infections
in the case of MRSA, 10 infections in the case
A. Ordinary Differential Equations from the Baseline Transmission Model (Chamchod & Ruan, 2012).U = Uncolonized Patient, C = Colonized Patient, I = Infected Patient, H = Uncontaminated Health Care Worker, Hc = Contaminated Health Care Worker
= (1 − − )Λ − − +
= Λ − − − ( + + )
= Λ + − ( + )
= − − +
= + + −
C. Objective (Goal) Function
( , ) = + +
. . ( , ) = + +
Where,
=
=
= 1 ( . . )
= 2 ( . . )
= decision variable/ budget allocation in % for alteration 1 (e.g. HW)
= decision variable/ budget allocation in % for alteration 2 (e.g. RH)
interventionalteration
B. Parameters for Ordinary Differential Equations from the Baseline Transmission Model
(Chamchod & Ruan, 2012).
Parameter DescriptionProbability that a patient is colonized upon admission into healthcare facility. Probability that a patient is infected upon admission into healthcare facility.Probability of colonization after a contact with a contaminated healthcare worker.Probability of contamination after a contact with a colonized patient.Probability of contamination after a contact with an infected patient. Rate of decolonization.
Probability of a successful treatment.
1/ Average duration of treatment of an infected patient.Rate of progression from colonization to infection.
1/ Average duration of contamination (days).
Death rate of an infected patient (for both diseases and related causes).Discharge rate of an infected patient (for both diseases and related causes).
1/ Average length of stay of an colonized patient (days). Total number of contacts patient requires per dayTotal number of healthcare workers
Λ Daily admission rate
D. Constraints for Objective (Goal) Function
( , ) = + ≤
( , ) = + ≤ 1
( , ) = ≥ 0
( , ) = ≥ 0
( , ) = ≤ 1
Figure 2. Mathematical equations for optimization models. (A, top left) Baseline transmission model, ordinarydifferential equations (Chamchod & Ruan, 2012). (B, top right) Parameter definitions (Chamchod & Ruan, 2012).(C, bottom left) Objective (goal) function for optimization of decision variables. (D, bottom right) Constraints forobjective (goal) function and associated dual variables.
Squire et al. 155
of CRE, and 16 infections in the case of VRE.
In a small hospital, there was a reduction of
two infections in the case of CRE and three
infections in the case of VRE (and no reduc-
tion in the case of MRSA).
With the implementation of negative pressure
and handwashing: In a large hospital, there was a
reduction of 10 infections in the case of MRSA,
22 infections in the case of CRE, and 31 infec-
tions in the case of VRE. In a medium hospital,
there was a reduction of 3 infections in the case of
MRSA, 9 infections in the case of CRE, and 15
infections in the case of VRE. In a small hospital,
there was a reduction of two infections in the case
of CRE and three infections in the case of VRE
(and no reduction in the case of MRSA).
Cost Savings From the Interventions
The high cost of these expensive strategies
(negative pressure rooms and relative humidity
control) is justified by the cost savings, as
detailed below:
Among the 12 trials in Scenario # 1 and in the
absence of either of these two alterations (RH and
HW), the model projected an average of 17 infec-
tions (rounded up to the next whole number).
Whereas in the presence of these alterations
(postintervention), the model projected an aver-
age of only five infections. Thus, the difference of
12 infections represents the number of infections
prevented. The distribution of funds between the
two alterations was optimized to maximize the
number of infections prevented. The model fur-
ther projected that spending $1,000 would pre-
vent an average of 0.0688 infections (Table 2,
column E). On average, spending $18,705 would
prevent one infection (total cost expended across
each scenario, divided by total infections
prevented).
Scenario #2: Among the 12 trials in Scenario #
2, without either of the two alterations, the model
again projected an average of 17 infections;
whereas in the presence of the HW and NP altera-
tions, the model projected an average of only 7
infections. Thus, 10 infections were prevented by
optimizing the distribution of funds between
these two alterations. The model further projected
that spending $1,000 would prevent an average of
0.0914 infections (Table 2, column I). On aver-
age, spending $18,406 would prevent one infec-
tion (total cost expended across each scenario,
divided by total infections prevented).
Associations
Certain trends were evident across the three spe-
cies of pathogenic bacteria. The models gener-
ally demonstrated an inverse relationship
between the budget and HW resource allocation,
that is, as the budget decreases, the resource
allocation to HW increases. The budget and
NP resources are directly proportional to one
another, that is, as the budget decreases, NP
resource allocation also decreases.
The models generally demonstrated an
inverse relationship between the budget
and HW resource allocation.
The budget and NP resources are directly
proportional to one another . . .
Another association is that within the
$180,000–$250,000 budget range, increased RH
resource allocation will optimally protect against
increasingly virulent species of bacteria.
Bed Count (i.e., Facility Size)
As the bed count increases, NP resourcing also
increases. Therefore, NP resource allocation is
directly proportional to the bed count.
This investigation determined the prioritiza-
tion order of pairs of alterations, to minimize
infection and cost. Specific examples of unanti-
cipated results are discussed below.
Specific Examples
MRSA and large and medium hospitals. When eval-
uating alterations for MRSA infections in large
and medium hospitals, as the available budget
decreases, RH resource allocation decreases. Spe-
cifically, when a budget of $600 K is used, RH
has priority (see Table 1, column F).
CRE and large hospitals. As the budget decreases
from $600,000, RH resourcing decreases. At
156 Health Environments Research & Design Journal 12(2)
Tab
le2.
Anal
ysis
and
Sign
ifica
nce
ofT
rade-
Offs
Bet
wee
nT
hre
eIn
terv
entions.
Scen
ario
sSc
enar
io#
1,R
elat
ive
Hum
idity
Contr
olan
dH
andw
ashin
gSt
atio
ns
Scen
ario
#2,H
andw
ashin
gSt
atio
ns
and
Neg
ativ
ePre
ssure
Tri
al#
Rel
ativ
eH
um
idH
and
Was
hIn
fect
ion
With
Inte
rven
tion
Red
uce
dIn
fect
ion
per
$1,0
00
Han
dW
ash
Neg
ativ
ePre
ssure
Infe
ctio
nW
ith
Inte
rven
tion
Red
uce
dIn
fect
ion
per
$1,0
00
AB
CD
EF
GH
I
1to
266%
25%
3in
fect
ions
0.0
21
29%
61%
5in
fect
ions
0.0
17
3to
472%
28%
8in
fect
ions
0.0
46
31%
69%
14
infe
ctio
ns
0.0
37
5to
671%
29%
15
infe
ctio
ns
0.0
69
33%
67%
26
infe
ctio
ns
0.0
51
7to
818%
38%
2in
fect
ions
0.0
34
39%
0%
2in
fect
ions
0.0
48
9to
10
13%
27%
2in
fect
ions
0.0
34
28%
0%
2in
fect
ions
0.0
48
11
to12
54%
44%
3in
fect
ions
0.0
56
47%
0%
4in
fect
ions
0.1
11
13
to14
39%
32%
3in
fect
ions
0.0
56
34%
0%
4in
fect
ions
0.1
11
15
to16
54%
46%
6in
fect
ionsa
0.0
90
50%
0%
7in
fect
ions
0.1
67
17
to18
48%
35%
6in
fect
ionsa
0.0
80
36%
0%
7in
fect
ions
0.1
67
19
to20
0%
25%
1in
fect
ion
0.0
46
25%
0%
1in
fect
ion
0.0
46
21
to22
0%
31%
1in
fect
ion
0.1
13
31%
0%
1in
fect
ion
0.1
13
23
to24
0%
34%
2in
fect
ions
0.1
81
34%
0%
2in
fect
ions
0.1
81
Mea
ns,
stan
dar
ddev
iations
36.2
%(+
28.5
%)
32.8
%(+
6.9
%)
4.3
infe
ctio
ns
(+4.0
infe
ctio
ns)
0.0
688
(+0.0
439)
34.7
%(+
7.4
%)
16.4
%(+
29.7
%)
6.2
infe
ctio
ns
(+7.2
infe
ctio
ns)
0.0
914
(+0.0
58)
Diff
eren
cein
mea
ns,
pva
lues
3.4
%p¼
.000135
bp¼
.000291
b18.3
%p¼
.001219
bp¼
.004359
b
Not
e.T
able
dep
icts
the
stat
istica
lan
alys
isfo
rT
rial
s1–24:m
eans
and
stan
dar
ddev
iations
(Colu
mns
B,C
,D
,E,F,
G,H
,an
dI)
,diff
eren
cein
mea
ns
(colu
mns
Can
dG
),an
das
soci
ated
pva
lues
(colu
mns
D,E,H
,an
dI)
.p
Val
ues
wer
eca
lcula
ted
usi
ng
aone-
way
AN
OV
Ast
atis
tica
lte
st.T
he
pva
lue,
p¼
.000135,dem
onst
rate
sst
atis
tica
lsi
gnifi
cance
bet
wee
nth
ebudge
tper
centa
geal
loca
tions
(Sce
nar
io#
1,c
olu
mns
Ban
dC
)an
dth
enum
ber
sofin
fect
ions
post
inte
rven
tion
(Sce
nar
io#
1,c
olu
mn
D).
The
pva
lue,
p¼
.000291,d
emonst
rate
sst
atis
tica
lsig
nifi
cance
bet
wee
nth
ebudge
tper
centa
geal
loca
tions
(Sce
nar
io#
1,co
lum
ns
Ban
dC
)an
dth
enum
ber
ofre
duce
din
fect
ions
per
$1,0
00
expen
ded
(Sce
nar
io#
1,co
lum
nE).
The
pva
lue,
p¼
.00129,dem
onst
rate
sst
atis
tica
lsi
gnifi
cance
bet
wee
nth
ebudge
tper
centa
geal
loca
tions
(Sce
nar
io#
2,co
lum
ns
Fan
dG
)an
dth
enum
ber
sofin
fect
ions
post
inte
rven
tion
(Sce
nar
io#
2,co
lum
nH
).T
he
pva
lue,
p¼
.004359,d
emonst
rate
sst
atis
tica
lsig
nifi
cance
bet
wee
nm
easu
res
(acr
oss
the
valu
es)
for
the
budge
tper
centa
geal
loca
tions
(Sce
nar
io#
2,c
olu
mns
Fan
dG
)an
dth
enum
ber
ofre
duce
din
fect
ions
per
$1,0
00
expen
ded
(Sce
nar
io#
2,co
lum
nI)
.p
Val
ues
less
than
.05
wer
edet
erm
ined
tohav
ea
stat
istica
llysi
gnifi
cant
diff
eren
ce.
a Rep
rese
nts
diff
eren
tva
lues
when
acco
unting
for
dec
imal
s.bSt
atis
tica
llysi
gnifi
cant
bas
edon
aone-
way
AN
OV
Ate
st.
157
$600,000, RH is at 72% (in Scenario #1) and NP
is at 69% (in Scenario #2); thus, the delta
between NP and RH, between the scenarios,
is 3% (see Table 1, columns F and L). In these
instances, delta designates an absolute differ-
ence; more specifically, it is the absolute per-
centage difference in allocated funds between
any two alterations.
Statistical analysis. In Table 2, we provide a brief
statistical analysis of all of our design scenarios to
identify global trends in our results. Columns B,
C, D, and E show a summary of the model results
for Scenario #1. Averaging over all trials, our
model proposed investing 36.2% in relative
humidity control and 32.8% in improved HW,
which produced a mean difference of 3.4%. The
average number of infections postintervention,
across the 24 trials of Scenario #1, was 5 infec-
tions (rounded up from 4.3), with a p ¼ .000135.
Using a one-way ANOVA across the budget allo-
cation percentages (Table 1, columns F and G)
and the reduced infections per $1,000 (Table 1,
column J) results in p ¼ .000291.
Similarly, Table 2, columns F, G, H, and I
show a summary of model results for Scenario
#2. The model proposed investing an average of
16.4% of the allocated budget in negative pres-
sure rooms and 34.7% in improved HW, which
produced a mean difference of 18.3%. The aver-
age reduction of infections across the trials of
Scenario #2 was seven infections (rounded up
from 6.2), with p ¼ .00122. Using a one-way
ANOVA statistical analysis across the budget
allocation percentages (Table 1, columns K and
L) and the reduced infections per $1,000 (Table 1,
column O) results in p ¼ .00436.
Discussion
Our research questions focus on optimizing cost
and effectiveness of these interventions in various
scenarios (see Figures 2; Supplemental Results)
and on comparing the combined effectiveness of
a pair of alterations to another feasible pair. The
overall reduction of infections is greater in Sce-
nario #1 (RH and HW) than in Scenario #2 (HW
and NP).
The overall reduction of infections is
greater in Scenario #1 (RH and HW) than
in Scenario #2 (HW and NP).
The results demonstrate an inverse relation-
ship between the budget and handwashing
alterations, with the exception of the small hos-
pital MRSA scenario (see Table 1). As the
budget increased, handwashing resourcing
decreased. The benefit of HW is significant,
but maximum benefit can be obtained with a
relatively small investment. This effect is sup-
ported by a study undertaken in ICU environ-
ments (Jayaraman et al., 2014), which
demonstrates that when handwashing compli-
ance is at an extremely high level, the benefits
plateau.
The benefit of HW is significant, but
maximum benefit can be obtained with a
relatively small investment.
For any of the three bacterial species, and
within the $180,000 to $250,000 budget range,
there is an inverse relationship between the
budget and resource allocation for the relative
humidity alteration (see Table 1). The NP
alteration is directly proportional to the budget.
These findings are likely due to the expense of
these alterations as well as their impact on
mitigating transmission of pathogenic bacteria
from a colonized to an uncolonized patient.
The RH and HW scenarios best contain
multidrug-resistant bacteria. The HW and NP
alterations generally had the largest allocation
of resources for the larger sized healthcare
facilities (see Figure 1).
These results can be generalized to facilities
(or sections thereof) of various sizes by simply
modifying the bed count in the model. The
associated up-front costs can also be adjusted
in the models as needed. Methods described in
this article can be used to allocate resources in
the face of epidemics or facility outbreaks.
Finally, these methods can be used to inform
decision-making when unexpected funding
becomes available.
158 Health Environments Research & Design Journal 12(2)
Limitations
The infectious disease data derive from a
single, representative, medium-sized hospital.
Incorporation of data from multiple healthcare
facilities would likely improve the ability to
apply these determinations to a diversity of facil-
ities. This investigation only evaluated three
multidrug-resistant pathogenic bacteria and does
not include other causes of HAIs that may not
necessarily be due to an MDRO, such as C. dif-
ficile. An additional limitation is that the impact
of the bacterial infection by age-group is not
evaluated.
Conclusion
In this study, we developed a novel mathematical
approach that integrates hospital design and
infection control within a budget framework.
We used original longitudinal data, representing
6 years of observation in a single facility. The
budget allocations are representative of funding
that a facility, based on its size, would likely
receive for annual repairs and renovations. In this
research, we have determined how cost-effective
resource allocation enhances the built environ-
ment to provide effective infection control to
reduce the transmission of three bacterial patho-
gens from one human host to another.
We have conducted this analysis for 24 sce-
narios. Each scenario represents a unique combi-
nation of facility size, facility budget, pathogen
virulence, and alteration pairing. Each of these
pairs is derived from combinations of HW, neg-
ative pressure rooms, and control of relative
humidity between 40% and 60%.
Determinations from these models can inform
infection control strategies as architects, owners,
engineers, and medical providers plan, design,
and renew healthcare facilities. With this infor-
mation, designers and planners can exercise
novel insights as to the interactions between the
built environment, expenditure of resources, and
HAIs. In doing so, they are better prepared to
address the containment of multidrug-resistant
pathogens that are endemic in healthcare facili-
ties today.
With this information, designers and
planners can exercise novel insights as to
the interactions between the built
environment, expenditure of resources,
and HAIs. In doing so, they are better
prepared to address the containment of
multidrug-resistant pathogens that are
endemic in healthcare facilities today.
Implications for Practice
� This knowledge better prepares healthcare
facilities for the containment of multidrug-
resistant pathogens.
� These mathematical optimization tools can
better inform design decisions made by
healthcare executives and project delivery
teams, helping bolster patient safety and
enhance environments of care.
� These findings contribute to knowledge that
can shape best practices among hospital
design, including better use of RH control
and negatively pressured patient rooms, as
well as greater compliance of existing HW
practices.
Authors’ Note
The article has been reviewed by the Walter Reed
Army Institute of Research. There are no objec-
tions to its presentation. The opinions or asser-
tions contained herein are the private views of the
authors and are not to be construed as official or
reflecting the views of the Department of the
Army or the Department of Defense.
Acknowledgment
The Walter Reed Army Institute of Research
Multidrug-Resistant Organism Repository and
Surveillance Network provided the 6-year period,
original, longitudinal, infectious disease data.
The authors would like to thank Major Anthony
Jones and Lieutenant Colonel Mary Hinkle of the
Walter Reed Army Institute of Research, for their
feedback and collaboration throughout the inves-
tigation. Alexandros Moissiadis and Ramyani
Sengupta contributed extensively in the model
Squire et al. 159
development and data collection phase. Eili
Klein, assistant professor of Emergency Medi-
cine, Johns Hopkins, contributed helpful feed-
back to the investigation.
Declaration of Conflicting Interests
The authors declared no potential conflicts of
interest with respect to the research, authorship,
and/or publication of this article.
Funding
The authors disclosed receipt of the following
financial support for the research, authorship,
and/or publication of this article: This study was
funded by the U.S. Army Medical Command,
U.S. Army Medical Department Center and
Schools and Johns Hopkins University.
Supplemental Material
Supplemental material for this article is available
online.
References
Arundel, A. V., Sterling, E. M., Biggin, J. H., & Ster-
ling, T. D. (1986). Indirect health effects of relative
humidity in indoor environments. Environmental
Health Perspectives, 65, 351–361.
American Lung Association. (2018). How fast is a
sneeze versus a cough? Cover your mouth either
way! Retrieved from http://www.lung.org/about-
us/blog/2016/05/sneeze-versus-cough.html
Barber, M. (1961). Methicillin-resistant staphylococci.
Journal of Clinical Pathology, 14, 385–393.
Beggs, C., Shepherd, S., & Kerr, K. (2009). How does
healthcare worker hand hygiene behaviour impact upon
the transmission of MRSA between patients? An anal-
ysis using a Monte Carlo model. BMC Infectious Dis-
eases, 2, 1–9. Retrieved from https://bmcinfectdis.
biomedcentral.com/articles/10.1186/1471-2334-9-64
Biosafety in Microbiological and Biomedical Labora-
tories. (2009). HHS Publication No. (CDC) 21-
1112. U.S. Department of Health and Human
Services, Public Health Service, Centers for Dis-
ease Control and Prevention, National Institutes of
Health. Government Printing Office. 292, 323.
Retrieved from https://www.cdc.gov/biosafety/pub
lications/bmbl5/index.htm
Boswell, T. C., & Fox, P. C. (2006). Reduction in
MRSA environmental contamination with a
portable HEPA-filtration unit. The Journal of Hos-
pital Infection, 63(1), 47–54.
Boyce, J. M., Potter-Bynoe, G., Chenevert, C., & King,
T. (1997). Environmental contamination due to
methicillin-resistant Staphylococcus aureus: Possi-
ble infection control implications. Infection Control
& Hospital Epidemiology, 18(9), 622–627.
Boyce, J. M. (2013). Update on hand hygiene. Amer-
ican Journal of Infection Control, 41(5 Suppl),
S94–S96.
Center for Medicare and Medicaid Service Hospital-
Acquired Condition Reduction Program. (2018). Center
for Medicare and Medicaid Services policy document in
reference to payment policy for HAIs. Retrieved from
http://www.cms.gov/Medicare/Medicare-Fee-for-Ser
vice-Payment/HospitalAcqCond/index.html
Cerrud-Rodriguez, R. C., Alcaraz-Alvarez, D., Chiong,
B. B., & Ahmed, A. (2017). Vancomycin-resistant
Enterococcus faecium bacteraemia as a complica-
tion of Kayexalate (sodium polystyrene sulfonate,
SPS) in sorbitol-induced ischaemic colitis. British
Medical Journal Case Reports, 2017, 1–5. doi:10.
1136/bcr-2017-221790
Chamchod, F., & Ruan, S. (2012). Modeling methicillin-
resistant Staphylococcus aureus in Hospitals: Trans-
mission dynamics, antibiotic usage and its history.
Theoretical Biology and Medical Modelling, 9, 1–15.
Cure, L., & Van Enk, R. (2015). Effect of hand saniti-
zer location on hand hygiene compliance. American
Journal of Infection Control, 43(9), 917–921.
Crespo, M. P., Woodford, N., Sinclair, A., Kaufmann, ME.
, Turton, J., Glover, J., . . . Livermore, DM. (2004).
Outbreak of carbapenem-resistant Pseudomonas aer-
uginosa producing VIM-8, a novel metallo-beta lacta-
mase, in a tertiary care center in Cali, Colombia.
Journal of Clinical Microbiology, 42(11), 5094–5101.
Davis, R. (2015, January). The doctor who championed
hand-washing and briefly saved lives. Health News
from, National Public Radio. Retrieved from
https://www.npr.org/sections/health-shots/2015/01/
12/375663920/the-doctor-who-championed-hand-
washing-and-saved-women-s-lives.
Deen, R., & Debbie, D. (2014). Carbapenem-resistant
Enterobacteriaceae: Deadly superbugs on the rise.
Prevention Strategist, Fall, 2014, 44–46.
Dunklin, E. W., & Puck, T. T. (1948). The lethal effect
of relative humidity on air-borne bacteria. Journal
of Experimental Medicine, 87(2), 87–101.
160 Health Environments Research & Design Journal 12(2)
Eames, I., Tang, J., Li, Y., & Wilson, P. (2009). Air-
borne Transmission of disease in hospitals. Journal
of the Royal Society Interface, 6(6), S697–S702.
Ford, W., Boyer, B. T., Menachemi, N., & Huerta, T.
R. (2014). Increasing hand washing compliance
with a simple visual cue. American Journal of Pub-
lic Health, 104(10), 1851–1856.
Haddadin, A. S, Fappiano, S. A., & Lipsett, P. A. (2002).
Methicillin resistant Staphylococcus aureus (MRSA)
in the intensive care unit. Postgraduate Medical
Journal, British Medical Journal, 78(921), 385–392.
Hamilton, D. K. (2013). Design and infection: A call for
greaterprogress throughresearch.HealthEnvironments
Research & Design Journal, 7(1 Suppl), 140–142.
Jayaraman, S. P., Klompas, M., Bascon, M., Liu, X.,
Piszcz, R., Rogers, S. O., & Askari, R. (2014). Hand
hygiene compliance does not predict rates of resis-
tant infections in critically Ill surgical patients. Sur-
gical Infections, 15(5), 533–539.
Kingdon, K. H. (1960). Relative Humidity and Air-
borne infections. AJRCCM, 8(4), 504–512.
Klevens, R. M., Edwards, J. R., Richards, C. L., Horan,
T. C., Gaynes, R. P., Pollock, D. A., & Cardo, D. M.
(2007). Estimating health care-associated infections
and deaths in U.S. Hospitals. Public Health
Reports, 122(2), 160–166.
Li, Y., Leung, G. M., Tang, J. W., Yang, X., Chao, C.
Y., Lin, J. Z., . . . Yuen, P. L. (2007). Role of ven-
tilation in airborne transmission of infectious agents
in the built environment—A multidisciplinary sys-
tematic review. Indoor Air, 17(1), 2–18.
Lee, R. (2014, March). Hospital infections account for
75,000 deaths in the US annually. Tech Times.
Retrieved from https://www.techtimes.com/arti
cles/4872/20140327/hospital-infections-account-
for-75-000deaths-in-the-u-s-annually.htm
Loutfy, M. R., Wallington, T., Rutledge, T., Mederski,
B., Rose, K., Kwolek, S., . . . Berall, G. (2004).
Hospital preparedness and SARS. Emerging Infec-
tious Diseases, 10(5), 771–776.
Magill, S. S., Edwards, J. R., Bamberg, W., Beldavs, Z.
G., Dumyati, G., Kainer, M. A., . . . Fridkin, S. K.
(2014). Multistate point-prevalence survey of
health care–associated infections. New England
Journal of Medicine, 370(13), 1198–1208.
Marchetti, A., & Rossiter, R. (2013). Economic burden
of healthcare-associated infection in US acute care
hospitals: Societal perspective. Journal of Medical
Economics, 16(12), 1399–1404.
Multidrug-Resistant Organism Repository and Surveil-
lance Network (MRSN) of Walter Reed Army Insti-
tute of Research. (2017). [MRSA, CRE, VRE
infectious disease data, 2012-2017, from one repre-
sentative hospital]. Unpublished data set.
Nichols,H. (2017, November). All you need to know about
MRSA. Medical News Today. Retrieved from https://
www.medicalnewstoday.com/articles/10634.php
O’Driscoll, T., & Crank, C. W. (2015). Vancomycin-
resistant enterococcal infections: Epidemiology,
clinical manifestations, and optimal management.
Infection and Drug Resistance, 8, 217–230.
Richards, C. L., Horan, T. C., Gaynes, R. P., Pollock,
D. A., & Cardo, D. M. (2007). Estimating health
care-associated infections and deaths in U.S. hospi-
tals, 2002. Public Health Rep. 122(2), 160–166.
Shiomori, T., Miyamoto, H., & Makishima, K. (2001).
Significance of airborne transmission of
methicillin-resistant Staphylococcus aureus in an
otolaryngology head and neck surgery. Archives
of Otolaryngology—Head & Neck Surgery,
127(6), 644–648.
Stevens, M. P., & Edmond, M. B. (2005). Endocarditis
due to vancomycin-resistant enterococci: Case
report and review of the literature. Clinical Infec-
tious Diseases, 41(8), 1134–1142.
Tang, J. W., Li, Y., Eames, I., Chan, P. K. S., & Ridg-
way, G. L. (2006). Factors involved in the aerosol
transmission of infection and control of ventilation
in healthcare premises. Journal of Hospital Infec-
tion, 64(2), 100–114.
Uttley, A. H., Collins, C. H., Naidoo, J., & George, R.
C. (1988). Vancomycin-resistant enterococci. Lan-
cet, 1, 57–58.
Zadeh, R., Sadatsafavi, H., & Xue, R. (2015).
Evidence-based & value-based decision making
about healthcare: An economic evaluation of safety
& quality outcomes. Health Environment Research
& Design Journal, 8, 58–76.
Wells, W. F. (1934). On Air-borne Infection. Study II.
Droplets and Droplet Nuclei. American Journal of
Epidemiology 20(3), 611–618.
Zimlichman, E., Henderson, D., Tamir, O., Franz,
C., Song, P., Yamin, C. K., . . . Bates, D. W.
(2013). Health care-associated infections: A
meta-analysis of costs and financial impact on
the U.S. Health Care System. Journal of Amer-
ican Medical Association Internal Medicine,
173(22), 2039–2046.
Squire et al. 161