Running head: SKIN AND SOFT TISSUE INFECTIONS

Skin and Soft Tissue Infections: Improving Treatments for the Incarcerated Population
Adalivia Resendiz-Casas
Arizona State University

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Abstract

Skin and soft tissue infections (SSTIs) occur at higher rates within correctional facilities due to
the increased risks that are inherent in this population. These infections present at various stages,
requiring different treatment modalities and sometimes require complex treatment. Prompt and
accurate recognition of SSTIs is crucial in selecting appropriate treatment to decrease the
possibility of treatment failure. Literature shows a correlation between diagnosis delay and
increased time and overall cost of care related to delayed diagnosis of SSTIs. These findings
support the implementation of an evidence-based project which aims to determine whether the
utilization of an algorithm for SSTIs can be amplified through increased accessibility.
Keywords: SSTI, skin infections, skin and soft tissue infections, protocols, correctional
facilities, treatment failure

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Skin and Soft Tissue Infections: Improving Treatments for the Incarcerated Population
Skin and soft tissue infections (SSTIs) are a common yet potentially life-threatening
condition. They are difficult to identify and have the potential to be costly to treat or have poor
long-term outcomes if misdiagnosed. In correctional health care facilities, the rate of treatment
failure and recurrence directly causes an increase to overall costs to the health care system,
making this issue important to examine. Populations at greatest risk for SSTIs include the
incarcerated population, who have multiple risk factors which lead to the development of these
infections. When pathogenic organisms enter the protective barrier provided by the skin,
infections can occur which may or may not be self-limiting. Tissue damage is evident when the
area presents with warmth, edema, erythema, and often pain (Aly, 1996). SSTIs often present in
this manner and are categorized into two groups, purulent and non-purulent and then
subcategorized depending on level of severity as mild, moderate, or severe. Treatments for
purulent and non-purulent infections depend on severity as well as patient history, therefore,
established guidelines should be followed to ensure treatments are chosen accordingly.
Currently, guidelines from the Centers for Disease Control and Prevention (CDC) as well
as the Infectious Diseases Society of America (IDSA) provide structured treatment of SSTIs and
are revised periodically based on epidemiological data, statistics, and the most up to date
research. SSTIs continue to afflict individuals despite prevention campaigns. Protocols such as
algorithms can help delineate the various treatment options in place and can assist providers in
caring for these patients before infections become complicated. SSTIs are endemic amongst
incarcerated populations; unfortunately, algorithms are seldom available for the health care
providers to follow (David & Daum, 2010; Dellit et al., 2007; Dhar, 2018; Ellias, A.F.,

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Chaussee, M.S., McDowell, E.J., & Huntington, M.K., 2010; Federal Bureau of Prisons, 2012;
Grief, 2017).
Background and Significance
SSTIs include impetigo, cellulitis, folliculitis, carbuncles, abscesses, pyomyositis, orbital
cellulitis, and necrotizing fasciitis (Popovich & Hota, 2008). Normal skin flora, including
bacteria, are found on the superficial layers of the epidermis typically preventing pathogenic
organisms from colonizing the skin (Aly, 1996); however, these organisms can become
dangerous if they have an entry point into the body. The most common pathogens for skin
infections are Staphylococcus and Streptococcus, with Methicillin-resistant Staphylococcus
aureus (MRSA) being the most common pathogen causing skin infections in the United States
(US), and account for over half of the community-associated SSTIs (Dhar, 2018).
The CDC estimate over 80,000 invasive MRSA infections with resultant 11, 285 deaths
per year world-wide (CDC, 2014). In efforts to further track the number of MRSA infections,
both international and national organizations have encouraged MRSA case tracking by including
this in the National Healthcare Safety Network (NHSN) reportable disease category. In Arizona,
the number of MRSA cases for 2017 was 1,357, which was almost 100 more than the previous
year (Arizona Department of Health Services, 2018). There are factors which predispose
individuals to develop SSTIs including: living in close quarters, previous skin infection, nasal
colonization of MRSA, intravenous drug use, and previous infection (Grief, 2017; Liu et al.,
2008; Raff & Kroshinsky, 2016). Incarcerated individuals are at increased risk for SSTIs due to
the presence of multiple risk factors simultaneously. These include any combination of the
following: living in crowded cells/rooms, higher number of inmates having their anterior nares
colonized with MRSA, personal history of SSTIs with subsequent exposure to antibiotics, not

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finishing their treatment course due to becoming incarcerated, and being exposed to these
organisms due to hospitalizations for other illness (Dober, 2014; Ellias, et al., 2010; Herman,
R.A., Kee, V.R., & Moores, K.G., 2008; Lee et al, 2011).
Problem Statement
The growing problem of SSTIs has surged due to the rising number of pathogens
becoming resistant to established treatments. Multiple drug resistant organisms (MDROs) such
as MRSA can be limited by the implementation of antibiotic stewardship programs, choosing the
proper treatment for each infection per the recommended guideline and properly utilizing
established tools, such as algorithms, for the treatment of infections for incarcerated individuals
(Federal Bureau of Prisons, 2012; Stevens et al., 2014; Wlodaver & May, 2017). Current
research demonstrates that a primary barrier for effective diagnosis and treatment of SSTIs is a
lack of standardized tool use (Lee et. al., 2016; Mullen & O’keefe, 2015).
The incidence of SSTIs raise additional concerns with the incarcerated population. For
example, incarcerated individuals often possess low levels of literacy which would make
initiatives for the reduction of infection difficult to read and comprehend (Grief, 2017). Also,
incarcerated patients pose an additional challenge to providers because of the inability to isolate
patients with known infections due to the mandatory living conditions of shared cells and limited
access to personal hygiene items. This in turn contributes to the spread of infections (Datta &
Juthani-Mehta, 2017). Lastly, inmates face the challenge of obtaining appropriate health care due
to only seeing providers once their condition is deemed urgent according to the nurses’
assessment which can delay the identification of SSTIs leading to rapid progression and
increased complications, including death (Dober, 2014).

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A population-based study in San Francisco demonstrated that 85% of MRSA cases
occurred outside a health care setting (Liu et al., 2008). A retrospective analysis of 986 hospitals
in 33 states and 994 hospitals in 28 states concluded that bacteremia resulting from MRSA has
increased mortality rate twice as high for inpatient treatment (Noskin et al., 2005). As a result, in
the US, the CDC issued guidelines for the treatment of MRSA in October of 2006 (CDC, 2014).
Despite availability of current guidelines for clinical practice, not all organizations housing atrisk individuals have active policies or protocols which may help them determine an appropriate
plan of care for new SSTIs. This is particularly true of jails and prisons, which house a large
number of individuals that have frequent and/or recurrent SSTIs (David & Daum, 2010; Ellias, et
al., 2010; Grief, 2017; Herman, R.A., Kee, V.R., & Moores, K.G.,2008).
There are similarities in the clinical presentation of skin infections and depending on staff
knowledge and experience, there may also be limitations in the ability to adequately identify the
causative organisms in a timely manner; delaying the diagnosis and treatment of SSTIs (Raff &
Kroshinsky, 2016). High staff turn-around, lack of continuity of care due to rotating providers
through various organizations, jail/prison restrictions related to limited content allowed within
charts, length of time elapsed prior to being seen by providers, and limited pharmacological
agents available for in-house treatment pose barriers to implementation of algorithms for
treatment of SSTIs for incarcerated populations.
Careful assessment of incarcerated population risk factors can lead to prompt
identification of SSTIs based on degree of severity during the provider’s initial history taking
and physical examination (Chahine & Sucher, 2015). Provider ordered daily wound care can be
utilized to track the improvement of a SSTI and can also help decrease complications. One such
outcome which would show improvement in the treatment of SSTIs in this population would be

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the reduction of infections requiring incision and drainage (I&D) treatment, which is usually
reserved for complicated cases (Raff & Kroshinsky, 2016). Another complication of undertreated SSTIs is treatment failure, characterized by no improvement seen after finished course of
antibiotics. Treatment failure can be identified by enlarged area of erythema, increased purulence
production, and systemic symptoms. If treatment failure occurs, the provider can decide on the
necessary course of action to ensure the SSTI is successfully treated, even if it requires inpatient
hospital treatment (Labreche et al., 2013; Noskin et al., 2005; Wlodaver & May, 2017). These
complications have been shown to be reduced by antimicrobial stewardship programs and may
be further improved through implementation of interventions that will allow for the following of
algorithms as they will decrease the number of inappropriate antibiotic treatments (Carrerra, E.,
Pfeffer, I., Zusman, O, Leibovici, L, & Paul, M., 2017; Dellit et al., 2007).
One of five patients experience treatment failure and will likely require additional
interventions at an associated cost of almost $2,000 per patient (Labreche et al., 2013). This does
not include the costs unique to a correctional facility as it relates to prolonged in-house
treatment, staff who must accompany an inmate for off-site treatment, transportation, and any
potential hospital costs for inpatient care, supplies, and medications; expenses which are the
responsibility of correctional health services. An additional cost can also be accrued if the patient
presents with new areas of infection as a result of spreading the infection to other parts of the
body. With early identification and prompt treatment, these costs can all be reduced.
In a correctional facility in the Southwestern US, an SSTI algorithm was recently
instituted to facilitate the early identification and prompt treatment of these type of infections due
to their frequent presentation. Despite a successful presentation to the providers at mandatory
staff meetings, there were several barriers identified which may interfere with the algorithm

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being utilized optimally. One barrier identified was that not all current providers were present
during the presentation. In an effort to further promote the use of the algorithm amongst current
health care providers, the question that must be answered is how to disperse the tool to all new
and rotating staff members. Additionally, patient perspective should be taken into account in
regard to anticipated outcomes vs cost of treatment. In this facility, inmates pay $5 for a nursing
visit, $10 for healthcare provider visit, and $5 per medication ordered. While this may not appear
to be a significant cost, inmates receive no income, may or may not receive money from family,
and must choose between paying for health care costs or buying personal items they might need
from detention services. If providers encounter barriers to implementing current guidelines, the
treatment may be extended and the outcomes may be compromised due to the high prevalence of
MDROs (Carrara et al., 2017). The promotion of current recommendations, including tools such
as algorithms may amplify the implementation of best practices and is key in curtailing the
effects of SSTIs (CDC, 2011), specifically in the incarcerated population.
Absent or inconsistent use of existing tools for the diagnosis and treatment of these types
infections has led to the clinically relevant question: when treating incarcerated adults, does
placing a copy of a pre-existing algorithm in each exam room improve the rate of utilization of
the algorithm for providers for treating individuals with a SSTI diagnosis over a two-week
period?
Search Strategy
In an attempt to establish the current use of algorithms in correctional facilities for the
treatment of SSTIs, an exhaustive search was performed. Literature review was performed after
the following databases were searched: Elton B Stephens Company host (EBSCO host),
PubMed, PsycINFO, and the Cochrane Library. The keywords utilized were “protocol”, “clinical

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judgement”, “skin infections”, “jails”, and “prisons”. There were limits utilized which included
adult population, systematic reviews, meta-analysis, randomized control trials, and publications
within the last five years. Initially, some searches were yielding no results, so it was decided to
include Boolean phrases and MESH terms that may increase the yield. These terms included
“correctional facilities”, “institutions”, “algorithms”, and “improved outcomes”. Inclusion
criteria for the studies included those with adult populations, populations living in close contact,
and those with established protocols/guidelines for treatments provided to this population.
Exclusion criteria included: studies greater than ten years old and population less than eighteen
years old. The Boolean phrase term search on EBSCO host yielded 523 results (Appendix A).
The search was refined to include “improved outcomes” and “guidelines” and thus yielded a
final 17 studies.
The MESH terms method used for PubMed found 15,629 articles (Appendix B). It was
then decided to change the order of the terms and to filter based on type of article published to
reviews, clinical trials, and meta-analysis. This narrowed the findings to 822 and was furthered
refined by choosing the title/abstract as the location for the keywords which resulted in a
manageable 28.
The search done in PsycINFO which included the keyword “algorithm” resulted in 1,134
findings (Appendix C). It was decided to replace this term with “clinical judgement” as this
portion of the topic still needed to be addressed. This reduced the number of publications to 453
and limits on type of publication and year of publication were added which produced a
manageable result of ten.
Performing a search within the Cochrane Library was different than the other databases.
In order to obtain any results, the filters were set to include studies within the last 20 years and

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the keywords utilized were limited to “jail”, “protocol”, and “inmates”. The results were only
twelve (Appendix D). The final analysis included 10 publications that were selected based on
their relevance and high level of statistical significance to the topic at hand.
Evidence Synthesis
A total of ten studies were selected for the literature review. Following evaluation
utilizing the rapid critical appraisal method, data was extracted, summarized, and placed in an
evaluation (Appendix E) and synthesis table (Appendix F). The strength of the studies was
moderate as it included publications of all levels of evidence including: two level I, three level II,
three level III, and two level IV (Melnyk & Fineout-Overholt, 2015). The studies included two
systematic reviews, two retrospective cohort studies, one randomized control trial, three
observational studies with chart review, and one quality improvement project (Appendix F).
Although settings outside of correctional health were utilized, it was due to the lack of
studies performed in this particular setting. Alternate settings were selected due to their inclusion
of prisoners, which allowed to study this specific population. Of the studies, few cited theoretical
frameworks or models, but the ones that did predominantly chose the Multifactorial Causation
Theory or the Levels of Prevention Model. Each publication was chosen due to the discussion,
inclusion, or relevance it had in relation to incarcerated populations. Less than half of the articles
utilized measuring instruments with associated specificities or sensitivities (Appendix E), which
was expected as none of them discussed an accepted protocol/algorithm utilized for the
incarcerated population.
Four of the articles did utilize reliable and valid tools to help demonstrate the statistical
significance of utilizing established tools in the treatment of this population (Appendix E).
Possible bias was noted in two studies, one whose author was employed the funding company

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and another who was reimbursed for each patient they enrolled in the study. However, upon
further research, it was noted that the studies in question did not support use of specific
medications, instead, they both highlighted the importance of proper identification of infections
in order to reduce length of treatment and cost per patient (Appendix E). The studies which took
place in hospitals focused on the difference between treating as an outpatient versus treatment
failure as a result of inadequate antibiotic therapy leading to hospitalization. The majority of the
studies took place in the US, increasing the generalizability of the findings to the population of
interest since the percentage of incarcerated individuals in the US has increased by 300% since
1980 (Mullen, L. A. & O’keefe, C., 2015).
The sample population focused on incarcerated adults or those with similar
demographics, with a definite inclusion limited to adults greater than 18 years old. The majority
of the studies highlighted the positive outcomes associated with ensuring timely antibiotic
treatment through the use of following established guidelines. As a whole, the studies discussed
challenges that may be present while prescribing medications within correctional facilities, while
three focused on the implementation of antibiotic stewardship programs (Appendix E). Overall,
the study findings established the importance of appropriate antibiotic therapy when treating
infections in incarcerated populations related to overall cost savings and decreased
complications.
Despite available treatment guidelines and the current number of SSTIs worldwide, in
2016, the CDC released a list of organisms which pose even greater threats to public health in an
attempt to highlight the importance of multi-drug resistant organisms (MDROs) worldwide
(CDC, 2014). The facts include both the number of cases reported of the most common MDROs
as well as the number of deaths which are a direct result of these organisms (CDC, 2014). The

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CDC supports preventive efforts, prompt recognition, and aggressive treatments of infections
with a main goal of reduction in mortality. Similarly, the ISDA strives to update guidelines for
the treatment of SSTIs periodically due to the prevalence of MDROs and limited number of
antibiotics which have been found to successfully eradicate these organisms (Stevens et al.,
2014). These guidelines acknowledge the necessity and efficacy of different treatment options
and support them according to the severity of the infection. To best disburse these guidelines,
algorithms are put in place to ensure the same process is being followed by all providers within a
health care organization. An effective approach would include having the algorithm readily
available for providers to reference during each face-to-face encounter and utilizing it for
deciding treatment for every infection (Hashem, N. G., Hidayat, L., Berkowitz, L., &
Venugopalan, V., 2016).
Purpose Statement
An evidence-based project was designed to increase the accessibility of an algorithm for
the treatment of SSTIs in a correctional facility in the Southwestern United States in order to
increase adherence to treatment guidelines for SSTIs. The change to current practice will be
incorporating the tool into each patient-provider visit by placing a copy in the exam rooms in an
attempt to increase its use and thus improve the outcomes of prisoners with a SSTIs diagnosis.
Quality Improvement Model
The Model for Improvement Plan, Do, Study, Act (PDSA) will enable the change to
current clinical practice. This model is utilized in health care as a step driven guide to improve
current practices. It begins by analyzing the current practice and coming up with a proposed
plan to change it. The next step is implementing the plan and gathering the data to be able to
demonstrate the change was implemented. After a pre-determined time has passed the data

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would be reviewed, summarized, and evaluated to ensure no changes are necessary (Langley et
al., 2009). Following this model would facilitate the implementation of a project within a large
organization as it allows for evaluation and critique of current practices with subsequent analysis
of any barriers which may arise before actually implementing the proposed project. Also, the
PDSA is appropriate to use within different facilities as there may be organizational changes
specific to different environments that may not be foreseen but can be addressed after the project
is implemented. The cyclical nature of this model allows for any necessary modifications to be
made if the results of the project are inconsistent with expected results and can allow the
organization to pinpoint what step needs to be changed (Appendix H).
Conceptual Framework
Increasing the use of an established algorithm within correctional facilities would require
three factors to be considered: the setting, the health care providers, and the treatment being
provided. The Social Cognitive theory underpins this project as it focuses on environment,
people, and behavior (Bandura, 2001). This theory supports the idea that these three factors are
constantly influencing one another and they cannot be independent of each other (Appendix G).
Additionally, it supports that the capability of an individual to endorse a particular behavior with
the assumption that they have been given the proper information and possess the knowledge to
perform it adequately. Hence, it would translate into clinical practice by requiring health care
providers to understand, acknowledge, and implement the algorithm for each patient presenting
with a SSTI. This theory would further support a practice change by promoting learning through
the observation of others. Therefore, even in an environment where there are multiple providers
caring for the same patients, following the algorithm would be more likely if providers have
access to the same tool for each encounter. Thus, the logic of altering the environment of the

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visits would increase the provider use of the algorithm and subsequently improve the treatment
of prisoners with SSTIs.
Project Methods
The project was drafted, submitted, and received IRB approval by Arizona State
University as well as support by the facility medical director. The setting for the project was a
correctional facility in the Southwestern United States which houses more than 2,300 prisoners
on a daily basis. This facility was selected as it was part of a larger organization which had an
algorithm developed previously to assist their providers in selecting appropriate treatments for
SSTIs. Despite a receptive attitude amongst the medical director, providers, nurses, and ancillary
staff of the algorithm, it was discovered the use of the algorithm was not being monitored and the
rate of turnover was very high which appeared to be limiting the use of the algorithm.
The project coordinator determined providers were the frontline when implementing the
algorithm correctly and as such they were recruited to participate in the project. A survey was
developed by the project coordinator which would anonymously gather demographic
information, determine whether the providers had knowledge of the existence of the algorithm,
utilize a Likert scale for providers to rate their current use of the algorithm, and to ask providers
what they felt were the current barriers to using the algorithm. In order to secure their
participation, they were assured that their responses to the survey would be anonymous and
tracked with 4-digit identification numbers that only the project coordinator would have access
to. Consent was implied via completion of the survey by providers, thus, this part of the project
consisted of hand-delivering the surveys. The providers were allotted enough time for
completion and after the information was tabulated, it was determined that the main barrier for
utilization consisted of a lack of accessibility.

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From there, the intervention of placing the algorithm into the electronic medical record
was decided. However, due to financial limitations, this option was not feasible. A modification
was made to the initial plan by choosing a more affordable option: making full-size, laminated
copies of the algorithms and placing directly in all exam rooms for providers to have available
for each face-to-face encounter. This proved to be a better option for this facility as the budget to
implement the project consisted of spending a mere $20 US dollars.
In order to determine utilization of the algorithm, it was necessary to perform a
retrospective chart analysis. Prisoner charts were reviewed over a 2-week period pre and post
intervention to identify those who had a SSTIs diagnosis. Each prisoner chart was given a fivedigit identification number on a spreadsheet which was used to keep the necessary data and had
no patient identifiers linked to the original charts. To effectively qualify as having used the
algorithm, the provider had to include the following components: adequate diagnosis, proper
antibiotic selection, and if indicated, wound care orders. Measurement outcomes were obtained
by giving each prisoners’ chart a subject number without including any individual identifiers.
After the data was validated, it was coded and both the survey and chart review results were
analyzed utilizing SPSS version 25. Comparing the pre and post intervention rate of utilization
was done by calculating the number of cases of SSTIs that were properly treated with the
algorithm in each 2-week period before and after the intervention. A tally was also kept of the
total number of SSTIs diagnosed for the duration of the project.
Outcomes/Project Results/Impact
Provider participation only yielded eight participants. Their ages ranged from 29-57 years
old with four identifing as females and four as males. Amongst them, one was a physician, two
were nurse practitioners, and five were physician assistants. Of the eight providers, only three

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reported having knowledge of the existence of the algorithm prior to the intervention and even
then, only two of them reported using it “sometimes” for the treatment of SSTIs. Overall, the
provider responses indicated that if they could see the algorithm during visits, they would use it
more often. After the intervention, the same providers were surveyed and seven of the eight
endorsed the algorithm as a tool they used “frequently” when treating SSTIs.
The total number of charts reviewed was 97. For statistical analysis, the Pearson Chi
Square two-tailed test was utilized as it is suitable for unpaired data sets when testing a null
hypothesis, with p< 0.05 indicating significance. If the p-value is larger than the significance
level, you fail to reject the null hypothesis because there is not enough evidence to conclude that
the variables are associated (Keller & Kelvin, 2013). The results were calculated via SPSS
version 25 and yielded p= 0.137, showing non-significance.
Prior to intervention, there were 55 SSTIs diagnosed, 18 of which met algorithm criteria;
therefore, the rate of utilization was 32.7%. For the post intervention period, there were 42 SSTIs
diagnosed with 20 meeting algorithm criteria. This showed an increase of 14.9% use of the
algorithm amongst all providers. This was clinically significant because it correlated with the
provider responses post-intervention of increased use of the algorithm.
Additionally, it was noted that there was a 23% decrease in total number of SSTIs
diagnosed during the post intervention period. This was clinically significant as it could be
related to adequate initial treatment leading to a decrease in number of treatment failures
(Carrera et al., 2017; Kamath, et al., 2018; Labreche et al., 2013; Lane et al., 2016).

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Discussion

Demonstrating the positive effects of utilizing algorithms for the treatment of SSTIs can
promote a change to the current practice within correctional facilities. The change to current
practice will ensure correctional health care providers are following the most current guidelines
when treating SSTIs which will be supported with a marked reduction in treatment time,
decrease in complications, and decreased costs. These benefits will positively impact the
prisoners, the providers, and the correctional facilities who choose to implement this change.
This project intervention has minimal costs when compared to the long-term savings that will be
seen by the facilities and organizations who do not currently follow the practice of utilizing
algorithms in the treatment of SSTIs.
Limitations of this project consisted of small sample sizes and short intervention period,
both can be explained by the setting of the project and the amount of time it takes to receive IRB
approval in these types of facilities. Future studies should be done in larger facilities, over a
longer period of time, and be based on the most up-to-date guidelines. Sustainability of the
project would consist of monitoring for any updates made to current recommendations in the
treatment of SSTIs and then changing the algorithm accordingly. These updates are only
necessary every couple of years, when the CDC or IDSA release their new guidelines, and this
can be delegated to the education coordinator of each facility which minimizes the costs and
maximizes its savings. The algorithm will be presented to all providers during the on-boarding
process to ensure they are educated about its existence and availability.
Conclusions
Despite current treatment guidelines in regard to SSTIs in the general population, there
exists no established protocol for implementation of these guidelines for special populations such

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as those in correctional facilities. The studies suggest a need for providers to be consistent when
treating infections, particularly when it involves resistant pathogens due to the complexity of
their treatment. The research does not discuss whether the guidelines from one organization
would be better than another, it simply supports that utilizing guidelines is recommended as it
decreases the risk of complications and promotes improved outcomes. The research reviewed
indicates that health care providers working in correctional facilities should implement SSTI
treatment guidelines using a standard approach, which can easily be done when following a preestablished algorithm. If the implementation of algorithms continues to be promoted and results
are recorded, this practice can be adopted throughout all correctional facilities in the U.S.

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Journal of Antimicrobial Chemotherapy, 71(11), 3268-3275.
Hassan, L., Edge, D., Senior, J., & Shaw, J. (2015). Accounting for psychotropic medication
changes in prisons: patient and doctor perspectives. Qualitative Health Research, 25(7),
954-965. DOI: http://dx.doi.org.ezproxy1.lib.asu.edu/10.1177/1049732314554094.
Herman, R.A., Kee, V.R., & Moores, K.G. (2008). Etiology and treatment of communityassociated-methicillin-resistant Staphylococcus aureus. American Journal of HealthSystem Pharmacy, 65(2), 219-225.
Kamath, J., Zhang, W., Kesten, K., Wakai, S., Shelton, D., & Trestman, R. (2013). Algorithmdriven pharmacological management of bipolar disorder in Connecticut prisons.
International Journal of Offender Therapy and Comparative Criminology, 57(2), 251264. DOI: http://dx.doi.org.exproxy1.lib.asu.edu/10.1037/t01554-000.
Kamath, R.S., Sudhakar, D., Gardner, J.G., Hemmige, V., Safar, H., & Musher, D.M. (2018).
Guidelines vs actual management of skin and soft tissue infections in the emergency
department. Open Forum Infectious Diseases, 4(1), 1-6.
Keller, S.P. & Kelvin, E.A. (2013). Munro’s statistical methods for health care research (6th
ed.). Philadelphia, PA: Wolters Kluwer/Lippincott Williams & Wilkins.
Labreche, M.J., Lee, G.C., Attridge, R.T., Mortensen, E.M., Koeller, J., Du, L.C…. Frei, C.R.
(2013). Treatment failure and costs in patients with methicillin-resistant Staphylococcus
aureus (MRSA) skin and soft tissue infections: a South Texas Ambulatory Research
Network (STARNet) study. The Journal of the American Board of Family Medicine,
26(5), 508-517.

SKIN AND SOFT TISSUE INFECTIONS

22

Lane, S., Johnston, K., Sulham, K. A., Syed, I., Pollack, C. V., Holland, T., & Nathwani, D.
(2016). Identification of patient characteristics influencing setting of care decisions for
patients with acute bacterial skin and skin structure infections: results of a discrete choice
experiment. Clinical therapeutics, 38(3), 531-544.
Langley, G.J., Moen, R.D., Nolan, K.M., Nolan, T.W., Norman, C.L., & Provost, L.P. (2009).
The improvement guide-A practical approach to enhancing organizational performance
(2nd ed.). San Francisco, CA: Jossey-Bass Publishers.
Lee, C.J., Sankaran, S., Mukherjee, D.V., Apa, Z.L., Hafer, C.A., Wright, L., … Lowy, F.D.
(2011). Staphylococcus aureus oropharyngeal carriage in a prison population. Clinical
Infectious Diseases, 52(6), 775-778.
Liu, C., Graber, C.J., Karr, M., Diep, B.A., Basuino, L., Schwartz, B.S., … Chambers, H.F.
(2008). A population-based study of the incidence and molecular epidemiology of
methicillin-resistant staphylococcus aureus disease in San Francisco, 2004-2005.
Clinical Infectious Diseases, 46(11), 1637-1646. DOI: https://doi.org/10.1086/587893.
Melnyk, B.M., & Fineout-Overholt, E. (2015). Evidence-based practice in nursing and
healthcare: A guide to best practice (3rd ed.). Lippincott, Williams & Wilkins.
Mullen, L. A. & O’keefe, C. (2015). Management of skin and soft tissue infections in a county
correctional facility: A quality improvement project. Journal of Correctional Health
Care, 21(4), 335-364.
Noskin, G.A., Rubin, R.J., Schentag, J.J., Kluytmans, J., Hedblom, E.C., Smulders, M., …
Gemmen, E. (2005). The burden of Staphylococcus aureus infections on hospitals in the
United States: An analysis of the 2000 and 2001 Nationwide Inpatient Sample Database.
Archives of Internal Medicine, 165(15), 1756-1761. DOI: 10.1001/archinte.165.15.1756.

SKIN AND SOFT TISSUE INFECTIONS

23

Popovich, K.J., & Hota, B. (2008). Treatment and prevention of community-associated
methicillin-resistant staphylococcus aureus skin and soft tissue infections. Dermatologic
Therapy, 21(1), 167-179.
Raff, A.B. & Kroshinsky, D. (2016). Cellulitis: A review. The Journal of the American Medical
Association, 316(3), 325-337. DOI: 10.1001/jama.2016.8825.
Stevens D.L., Bisno A.L., Chambers H.F., Dellinger, E.P., Goldstein, E.J., Gorbach, S.L., …
Wade, J.C. (2014). Practice guidelines for the diagnosis and management of skin and soft
tissue infections: 2014 update by the Infectious Diseases Society of America. Clinical
Infectious Diseases, 59(4), e10-e52.
Talan, D. A., Salhi, B. A., Moran, G. J., Mower, W. R., Hsieh, Y., Krishnadasan, A. & Rothman,
R. E. (2014). Factors associated with decision to hospitalize emergency department
patients with skin and soft tissue infections. Western Journal of Emergency Medicine, 1,
89-97.
Wlodaver, C.G. & May, C. (2017). Antibiotic stewardship for non-infectious disease
physicians: Focusing on when to withhold, modify, and discontinue antibiotics. The
Journal of the Oklahoma State Medical Association, 110(1), 8-12.

SKIN AND SOFT TISSUE INFECTIONS

24
Appendix A

EBSCO host search

SKIN AND SOFT TISSUE INFECTIONS

25
Appendix B

PubMed search

SKIN AND SOFT TISSUE INFECTIONS

26
Appendix C

PsycINFO search

SKIN AND SOFT TISSUE INFECTIONS

27
Appendix D

Cochrane Library search

SKIN AND SOFT TISSUE INFECTIONS

28
Appendix E

Table 1
Evaluation Table
Citation

Carrara et al.
(2017).
Determinants of
inappropriate
empirical
antibiotic
treatment: a
systematic
review and
meta-analysis.
Funding: Israeli
Ministry of
Science,
Technology &
Space research
grant
Conflicts/bias:
none

Theory/conceptual
framework

Multifactorial
causation theory

Study Design/
Method

Sample/setting

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis
(stats used)

Systematic
review

N: 168 studies

IV1: prevalence
of MDRO

Chart review

Singlecovariate
analysis

n= 73,595

S: studies cited
Purpose: assess from multiple
the determinants countries
of inappropriate
IC: > 18 yrs
ABX tx
old,
documented
bacterial
infections
EC: studies
with < 50 pts,
studies where
infections were
deemed to need
long-term
ABX tx

IV2: IEAT

DV: association
between IV’s
and mortality

Multi-covariate
analysis

Findings/Results Level/Quality
of Evidence;
Decision for
practice/
application to
practice
IV1: 1.64-5.10
LOE: I
ES2.88
- large sample
CI: 95%
size
IV2: 1.05-1.13

CI: 95%
Analyses were
DV: 25%, CI
performed
95%, (22-27%)
using
Comprehensive
Meta-Analysis
Software v.3

- Review of
studies done
prior to 2010.
-prospective
and
retrospective
analysis of
studies done
over 8 yrs
Application:
heterogenous
patients
demonstrating
high correlation

ABX: antibiotics, CF: correctional facility, C-SS: cross-sectional study, CS: control study, DV: dependent variable, EC: exclusion criteria, ED: emergency department, HCP: healthcare provider,
I&D: Incision and drainage, IC: inclusion criteria, IEAT: inappropriate empirical antibiotic treatment, INPT: inpatient treatment, IV: independent variable, LOE: level of evidence, MDRO:
multidrug resistant organism, MRSA: Methicillin resistant staphylococcus aureus, n: sample size/people N: sample size/studies, NI: not identified in the study, Obs: observation, OTPT: outpatient
treatment, QIP: quality improvement project, RCT: randomized control trial, S: setting, SR: systematic review, SSTI: skin and soft tissue infections, TX: treatment, TXF: treatment failure, US:
United States, $: cost in US dollars, ↑: increase, ↓: decrease

SKIN AND SOFT TISSUE INFECTIONS

29

Country: Israel

Citation

Drobniewski, F.
(2015).
Systematic
review, metaanalysis and
economic
modelling of
molecular
diagnostic tests
for antibiotic
resistance in
tuberculosis.
Funding: The
National
Institute for
Health Research
Health
Technology
Assessment
programme

between
MDROs and
IEAT.
Theory/conceptual
framework

Levels of
prevention model

Study Design/
Method

Sample/setting

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis
(stats used)

Systematic
review

N: 6

Total cost of TX

n: NI

IV1: molecular
testing for ABX
resistance

IC: >18 yrs
old, presence
of MDRO
infection

IV2: treatment
time delay
effects on
outcome

Probabilistic
sensitivity
analysis of 4
different tests

EC: infection
w/ no MDRO

DV: cost
effectiveness r/t
early
identification of
ABX resistance

Purpose:
determine the
efficacy of rapid
molecular tests
for drug
resistance to
determine if
their accuracy
would impact
clinical practice

Quality-adjusted
life-years

Findings/Results Level/Quality
of Evidence;
Decision for
practice/
application to
practice
IV1: specificity LOE: I
of tests was
99.6%, 98.2%,
-large sample
99.7%, &
size
98.4%.
-analyzes tests
IV2: QALY was with high
reliability and
↓ 3.5%
specificity
Application:
initial testing
decreases
delays in
treatment and
improves
outcomes

Conflicts/bias:
none
ABX: antibiotics, CF: correctional facility, C-SS: cross-sectional study, CS: control study, DV: dependent variable, EC: exclusion criteria, ED: emergency department, HCP: healthcare provider,
I&D: Incision and drainage, IC: inclusion criteria, IEAT: inappropriate empirical antibiotic treatment, INPT: inpatient treatment, IV: independent variable, LOE: level of evidence, MDRO:
multidrug resistant organism, MRSA: Methicillin resistant staphylococcus aureus, n: sample size/people N: sample size/studies, NI: not identified in the study, Obs: observation, OTPT: outpatient
treatment, QIP: quality improvement project, RCT: randomized control trial, S: setting, SR: systematic review, SSTI: skin and soft tissue infections, TX: treatment, TXF: treatment failure, US:
United States, $: cost in US dollars, ↑: increase, ↓: decrease

SKIN AND SOFT TISSUE INFECTIONS

30

Country: United
Kingdom
Citation

Hashem, N.G.
(2016).
Management of
skin and soft
tissue infections
at a community
teaching hospital
using a severity
of illness tool.

Theory/conceptual
framework

Levels of
prevention model

Study Design/
Method

Sample/setting

Major Variables
& Definitions

Measurement/
Instrumentation

Retrospective
cohort study.

N= 200

IV1: SSTI w/
varying degree
of severity

IV1: CREST
scoring based on
tool

IV2: ability to
recognize SSTI
early

IV2: SEWS
Scoring (have
Class I leastClass IV most
severe)

Purpose: Assess
the treatment of
the SSTI based
on a tool which
grades the
severity of the
infection.

Funding: none
Conflict/bias:
none
Country: US

Citation

Theory/conceptual
framework

Study Design/
Method

S: community
hospital in
Brooklyn,
NYC
IC: patients
with SSTI, >18
yrs old
EC: those
admitted w/
cellulitis r/t
other disease
processes, <18
yrs old
Sample/setting

DV: levels of
treatment

DV: measured
based on route of
antimicrobial
given

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis
(stats used)

ANOVA,
Fischer’s Exact
test
P<0.05

Data Analysis
(stats used)

Findings/Results Level/Quality
of Evidence;
Decision for
practice/
application to
practice
IV1: P= <0.05
LOE: III

IV2: P= <0.05

DV: compares
overtreatment
w/
undertreatment,
P=<0.05

Can utilize
CREST tool in
clinical practice
at low cost
Application: established
tools providers
can ensure
adequate TX is
selected.

Findings/Results Level/Quality
of Evidence;
Decision for
practice/
application to

ABX: antibiotics, CF: correctional facility, C-SS: cross-sectional study, CS: control study, DV: dependent variable, EC: exclusion criteria, ED: emergency department, HCP: healthcare provider,
I&D: Incision and drainage, IC: inclusion criteria, IEAT: inappropriate empirical antibiotic treatment, INPT: inpatient treatment, IV: independent variable, LOE: level of evidence, MDRO:
multidrug resistant organism, MRSA: Methicillin resistant staphylococcus aureus, n: sample size/people N: sample size/studies, NI: not identified in the study, Obs: observation, OTPT: outpatient
treatment, QIP: quality improvement project, RCT: randomized control trial, S: setting, SR: systematic review, SSTI: skin and soft tissue infections, TX: treatment, TXF: treatment failure, US:
United States, $: cost in US dollars, ↑: increase, ↓: decrease

SKIN AND SOFT TISSUE INFECTIONS
Hassan, L.
(2015)
Accounting for
Psychotropic
Medication
Changes in
Prisons: Patient
and Doctor
Perspectives.

Grounded Theory

Purpose:
To validate
importance for
continuity and
equivalence of
care for
prisoners upon
being detained.

Funding:
Department of
health
Conflict/bias:
undertaken as
part of a work
program offered
by the Offender
Health Research
Network
Country: United
Kingdom
Citation

Kamath, J.
(2013).
Algorithm-

Observational,
interview

31
n=17
S: four prisons
in England
IC: >18 yrs
old,
incarcerated
individuals
EC: juvenile
inmates

IV1: patient
perspective
IV2: HCP
perspective

IV1/2: Interviews
of each
participant
utilizing
Foucauldian
disclosure
analysis.

DV: use of
psychotropic
medications

IV1/2: Interviews
were transcribed,
checked against
audio recording.

Nvivo version
8

IV1: 4 common
themes amongst
subjects
IV2: shared
views regarding
barriers that are
present
DV: correlated
hx of psych
diagnosis

practice
LOE: IV
-small number
of participants
-intensive
analysis of
interviews
Application:
demonstrates
difficulty of pts
receiving meds
while
incarcerated

Utilized
discursive
approach,
rooted in social
constructionism

Theory/conceptual
framework

Roy’s Adaptation
Model

Study Design/
Method

Sample/setting

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis
(stats used)

Open-Label
Trial, RCT

n=40, but only
29 completed

IV: Bipolar
Disorder
Symptom Scale

IV1: Bipolar
Disorder
Symptom Scale

Paired t-tests

Findings/Results Level/Quality
of Evidence;
Decision for
practice/
application to
practice
IV1: p <.001
LOE: II

ABX: antibiotics, CF: correctional facility, C-SS: cross-sectional study, CS: control study, DV: dependent variable, EC: exclusion criteria, ED: emergency department, HCP: healthcare provider,
I&D: Incision and drainage, IC: inclusion criteria, IEAT: inappropriate empirical antibiotic treatment, INPT: inpatient treatment, IV: independent variable, LOE: level of evidence, MDRO:
multidrug resistant organism, MRSA: Methicillin resistant staphylococcus aureus, n: sample size/people N: sample size/studies, NI: not identified in the study, Obs: observation, OTPT: outpatient
treatment, QIP: quality improvement project, RCT: randomized control trial, S: setting, SR: systematic review, SSTI: skin and soft tissue infections, TX: treatment, TXF: treatment failure, US:
United States, $: cost in US dollars, ↑: increase, ↓: decrease

SKIN AND SOFT TISSUE INFECTIONS
Driven
Pharmacological
Management of
Bipolar Disorder
in Connecticut
Prisons.

Purpose: To
establish
whether
evidence-based
algorithms can
aid in reducing
polypharmacy

Funding:
National
Institute of
Mental Health

32
S: Connecticut
prisons
IC: >18 yrs
old,
incarcerated
adults
EC: hx of
schizophrenia,
hx of brain
injury/head
trauma

Conflict/bias:
none

IV2: Global
clinical status
IV3: Comorbid
symptomatology
IV4: QoL
DV: reduction of
polypharmacy

Kamath, R.S.
(2018).
Guidelines vs
Actual
Management of
Skin and Soft

IV1: Brief
Psychiatric
Rating Scale
(BPRS)

IV2: p <.001
Algorithm subanalysis

IV2: Clinical
Global
Impression (CGI)

Theory/conceptual
framework

Multifactorial
causation theory

Study
Design/Method

Sample/setting

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis
(stats used)

Retrospective
analysis

n= 240

IV1: baseline
characteristics of
SSTI used to
decide TX

Chart analysis

Stata 12

Purpose:

S: Michael E.
Debakey
Veterans
Affairs

IV2: clinical

IV3: p <.002
IV4: p=1.88

IV2: Global
Assessment of
Functioning scale
(GAF)
IV3: Short Form
36 Health Survey
(SF-36)

Country: US

Citation

(BDSS)

Application:
supports
adaptation of
algorithms by
correctional
facilities
through
effectively
identified
barriers for the
incarcerated
population
Findings/Results Level/Quality
of Evidence;
Decision for
practice/
application to
practice
IV1: p < .001
LOE: III

X² test
Fisher’s exact
test

Delineates
importance of
medication
algorithm to
help decrease
variation in
clinical
practices and
reduce costs.

IV2: p= .033

Though setting
is not
correctional
facility,

ABX: antibiotics, CF: correctional facility, C-SS: cross-sectional study, CS: control study, DV: dependent variable, EC: exclusion criteria, ED: emergency department, HCP: healthcare provider,
I&D: Incision and drainage, IC: inclusion criteria, IEAT: inappropriate empirical antibiotic treatment, INPT: inpatient treatment, IV: independent variable, LOE: level of evidence, MDRO:
multidrug resistant organism, MRSA: Methicillin resistant staphylococcus aureus, n: sample size/people N: sample size/studies, NI: not identified in the study, Obs: observation, OTPT: outpatient
treatment, QIP: quality improvement project, RCT: randomized control trial, S: setting, SR: systematic review, SSTI: skin and soft tissue infections, TX: treatment, TXF: treatment failure, US:
United States, $: cost in US dollars, ↑: increase, ↓: decrease

SKIN AND SOFT TISSUE INFECTIONS
Tissue
Infections in the
Emergency
Department.

establish what
percent of
patients are
being treated
according to
guidelines

Funding: none
Conflict/bias:
none
Country: US

33
Medical Center
in Houston
IC: >18 yrs
old, dx of SSTI
EC: 26 pt’s due
to alternate
principal dx, to
ED for f/u visit

findings
Used to decide
TX
IV3: OTPT
IV4: INPT

Univariate
logistic
regression

IV3: 39.7%

Multivariate
logistic
regression

IV4: 60.3%

HosmerLemeshow test

DV: established
guidelines
DV2: HCP
decision to of tx

Citation

Labreche, M.
(2013).
Treatment
failure and costs
in patients with
methicillinresistant
Staphylococcus
aureus (MRSA)
skin and soft
tissue infections:
A South Texas

Theory/conceptual
framework

Study Design/
Method

Multifactorial
causation theory

Observational
cohort study

Purpose:
Measure the
prevalence of
TXF and
additional $ in
pts w/ SSTI

Sample/setting

Major Variables
& Definitions

N: 13 clinics

IV1: incidence
of TXF

n= 265
S: South Texas
region
IC: > 18 yrs
old, dx w/
SSTI

IV2: $
associated w/
TXF

DV: Additional
costs of TXF r/t
rates of TXF for

Measurement/
Instrumentation

Data Analysis
(stats used)

JMP 9.0
software
Medical chart
reviews

Chi-square test

Follow-up forms

Fisher’s Exact
test
t-test

population
included is only
adults dx w
SSTI.
Application:
Highlight the
low percentage
of SSTI being
TX w/ proper
meds and
setting
according to
guidelines.

Findings/Results Level/Quality
of Evidence;
Decision for
practice/
application to
practice
IV1: p >0.05
LOE: II
IV2: ~$2,000/pt

DV: ↑ costs

-Serves to
highlight the
need for
adequate
stratification of
infections at
baseline
Application:

ABX: antibiotics, CF: correctional facility, C-SS: cross-sectional study, CS: control study, DV: dependent variable, EC: exclusion criteria, ED: emergency department, HCP: healthcare provider,
I&D: Incision and drainage, IC: inclusion criteria, IEAT: inappropriate empirical antibiotic treatment, INPT: inpatient treatment, IV: independent variable, LOE: level of evidence, MDRO:
multidrug resistant organism, MRSA: Methicillin resistant staphylococcus aureus, n: sample size/people N: sample size/studies, NI: not identified in the study, Obs: observation, OTPT: outpatient
treatment, QIP: quality improvement project, RCT: randomized control trial, S: setting, SR: systematic review, SSTI: skin and soft tissue infections, TX: treatment, TXF: treatment failure, US:
United States, $: cost in US dollars, ↑: increase, ↓: decrease

SKIN AND SOFT TISSUE INFECTIONS
Ambulatory
Research
Network
(STARNet)
study.

34
EC: pregnancy,
incarceration,
impaired
decisionmaking
capacity

Funding: Pfizer
and the U.S.
National
Institutes of
Health

OTPT

Wilcoxon
Rank-Sum test

Will serve to
demonstrate the
links between
inadequate
selection of
antibiotics as
well as support
the financial
implications of
TXF as a result
of IEAT with
an emphasis on
MRSA
infections

Conflicts/bias:
clinics were
compensated
$50 for each pt
they enrolled
Country: US
=Citation

Lane, S. (2016)
Identification of
patient
characteristics
influencing
setting of care
decisions for
patients with
acute bacterial

Theory/Conceptual Study
framework
Design/Method

Multifactorial
causation theory

Observational
cross-sectional
study.
Purpose:
Understand the
factors which
may influence

Sample/setting

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis
(stats used)

n= 400

IV1: SSTI w/
varying degree
of severity

IV1: evaluation
of presenting
criteria
determines level
of infection

Multinomial
Model

IV2:
demographic

Mixed effects
Model

S: US, United
Kingdom (50
MD from ED,
50 MD from
ID, 50 NP)
IC: >18 yrs old

IV2: different

Findings/Results Level/Quality
of Evidence;
Decision for
practice/
application to
practice
IV1: p= 0.02
LOE: Level II

CI: 95%
IV2: age < 75
had decreased

Convenience
sample was
utilized and
may not
represent
general
population,

ABX: antibiotics, CF: correctional facility, C-SS: cross-sectional study, CS: control study, DV: dependent variable, EC: exclusion criteria, ED: emergency department, HCP: healthcare provider,
I&D: Incision and drainage, IC: inclusion criteria, IEAT: inappropriate empirical antibiotic treatment, INPT: inpatient treatment, IV: independent variable, LOE: level of evidence, MDRO:
multidrug resistant organism, MRSA: Methicillin resistant staphylococcus aureus, n: sample size/people N: sample size/studies, NI: not identified in the study, Obs: observation, OTPT: outpatient
treatment, QIP: quality improvement project, RCT: randomized control trial, S: setting, SR: systematic review, SSTI: skin and soft tissue infections, TX: treatment, TXF: treatment failure, US:
United States, $: cost in US dollars, ↑: increase, ↓: decrease

SKIN AND SOFT TISSUE INFECTIONS
skin and skin
structure
infections:
Results of a
discrete choice
experiment.

the setting in
which SSTI
should be
treated.

35

EC: < 18 yrs
old,

age groups

criteria

DV: OTPT tx vs
INPT tx

DV: included in
information
gathering

probability of
outpt tx (0.280.61)

Online survey, 3
part

Funding: The
Medicines
Company.

Application:
Identifying key
factors that
affect TX.
Highlights
importance of
severity
stratification in
selecting TX.

Conflicts/Bias:
The author
works for the
Medicines
Company.
Country: United
Kingdom
Citation

Mullen &
O’keefe. (2015).
Management of
skin and soft
tissue infections
in a county
correctional
facility: A
quality

Theory/conceptual
framework

Study Design/
Method

Sample/setting

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis
(stats used)

Pender’s Health
Promotion Model

Quality
improvement
project

n= 132

IV: incidence of
SSTI

Baseline needs
assessment

Chart review

Purpose:
examine current
management

creating slight
bias, and could
affect
interpretation
of results

S: DCDC
IC: inmates,
prescribed
multiple ABX
for suspected
MRSA

DV: TX of SSTI
utilizing new
tool/assessment

60-day outcome
interview

Findings/Results Level/Quality
of Evidence;
Decision for
practice/
application to
practice
-improving
LOE: IV
screening
- limited to one
protocol helped correctional
identify
facility
infection sooner
- ample subject
inclusion
-making data
collection
simple increased Application:

ABX: antibiotics, CF: correctional facility, C-SS: cross-sectional study, CS: control study, DV: dependent variable, EC: exclusion criteria, ED: emergency department, HCP: healthcare provider,
I&D: Incision and drainage, IC: inclusion criteria, IEAT: inappropriate empirical antibiotic treatment, INPT: inpatient treatment, IV: independent variable, LOE: level of evidence, MDRO:
multidrug resistant organism, MRSA: Methicillin resistant staphylococcus aureus, n: sample size/people N: sample size/studies, NI: not identified in the study, Obs: observation, OTPT: outpatient
treatment, QIP: quality improvement project, RCT: randomized control trial, S: setting, SR: systematic review, SSTI: skin and soft tissue infections, TX: treatment, TXF: treatment failure, US:
United States, $: cost in US dollars, ↑: increase, ↓: decrease

SKIN AND SOFT TISSUE INFECTIONS
improvement
project.

within DCDC
compared to
current best
clinical
practices and
guidelines

Funding: none
Conflicts/bias:
none

36
EC: diagnosed
w/ SSTI prior
to incarceration

process vs
guidelines

identification
-increasing
hygiene was
done through
DVD showings
within housing
units

Country: US

Citation

Talan, D.A.
(2014). Factors
associated with
decision to
hospitalize
emergency
department
patients with
skin and soft
tissue infections.
Funding:
Centers for

-implementing
documentation
templates can
increase early
identification
Theory/conceptual
framework

Multifactorial
causation theory

Study Design/
Method

Sample/setting

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis
(stats used)

Retrospective
cohort study

n= 619

IV1: A, prior
infection

Physicians filled
out surveys
asking them for
the reasons for
admission/clinical
characteristics of
infection at the
time of
presentation

Statpages, 2way
contingency
table analysis

Purpose:
Identify any
factors that may
determine the
need for a pt w/
SSTI to be
hospitalized

S: 12 ED in the
US
IC: > 18 yrs
old, diagnosis
of SSTI
EC: < 18 yrs
old

IV2: hx of
previous OTPT
IV3: TXF
DV1: Admitted
with SSTI or
DV2: discharged

Chi-Square
binary
recursive
partitioning
CI: 95%

combining data
from each
facility and
comparing with
current
guidelines can
help develop
protocol for
each
organization

Findings/Results Level/Quality
of Evidence;
Decision for
practice/
application to
practice
IV1: CI 2.31LOE: III
5.22
-addresses
important
ES: 3.47
variables
IV2: NI
-variable
sample size
IV3: CI 1.383.72
Quality of
ES: 1.76
evidence: safe
method
Application:

ABX: antibiotics, CF: correctional facility, C-SS: cross-sectional study, CS: control study, DV: dependent variable, EC: exclusion criteria, ED: emergency department, HCP: healthcare provider,
I&D: Incision and drainage, IC: inclusion criteria, IEAT: inappropriate empirical antibiotic treatment, INPT: inpatient treatment, IV: independent variable, LOE: level of evidence, MDRO:
multidrug resistant organism, MRSA: Methicillin resistant staphylococcus aureus, n: sample size/people N: sample size/studies, NI: not identified in the study, Obs: observation, OTPT: outpatient
treatment, QIP: quality improvement project, RCT: randomized control trial, S: setting, SR: systematic review, SSTI: skin and soft tissue infections, TX: treatment, TXF: treatment failure, US:
United States, $: cost in US dollars, ↑: increase, ↓: decrease

SKIN AND SOFT TISSUE INFECTIONS
Disease Control
Conflicts/bias:
none
Country: US

37
with SSTI

provides
reasons for
providers to
establish IP vs
OP tx

ABX: antibiotics, CF: correctional facility, C-SS: cross-sectional study, CS: control study, DV: dependent variable, EC: exclusion criteria, ED: emergency department, HCP: healthcare provider,
I&D: Incision and drainage, IC: inclusion criteria, IEAT: inappropriate empirical antibiotic treatment, INPT: inpatient treatment, IV: independent variable, LOE: level of evidence, MDRO:
multidrug resistant organism, MRSA: Methicillin resistant staphylococcus aureus, n: sample size/people N: sample size/studies, NI: not identified in the study, Obs: observation, OTPT: outpatient
treatment, QIP: quality improvement project, RCT: randomized control trial, S: setting, SR: systematic review, SSTI: skin and soft tissue infections, TX: treatment, TXF: treatment failure, US:
United States, $: cost in US dollars, ↑: increase, ↓: decrease

SKIN AND SOFT TISSUE INFECTIONS

38
Appendix F

Table 2
Synthesis Table
Study
Characteristics

Carrara

Drobniewski Hashem

Hassan

Kamath, J.

Kamath, R.S.

Labreche

Lane

Mullen

Talan

Year
LOE
Conflict/bias
Location
Design

2017
I
None
Israel
SR

2015
I
None
UK
SR

2016
III
None
US
Retro CS

2016
IV
Yes
UK
Obs Int

2013
II
None
US
RCT

2013
II
Yes
US
Obs study

2016
II
Yes
UK
Obs C-SS

2015
IV
None
US
QIP

2014
III
None
US
Retro CS

N
Setting

73,595
Other

56
Other

200
17
community CF
hospital

2018
III
None
US
Retro
analysis
240
Large
hospital

265
community
hospital

400
Large
hospital

132
CF

619
ED

IV’s
Abx
Stewardship
program
Severity tool
algorithm
Follow
guidelines
Outcomes
IEAT
TXF
INPT
OTPT
I&D

40
CF

x

x

x

x
x
x

x
x
x
x
x

x

X

x
x

x
x
x
x
x
x

x

x

x
x
x

x
x
x

x
x
x

x
x
x

ABX: antibiotics, CF: correctional facility, C-SS: cross-sectional study, CS: control study, DV: dependent variable, EC: exclusion criteria, ED: emergency department, HCP: healthcare provider,
I&D: Incision and drainage, IC: inclusion criteria, IEAT: inappropriate empirical antibiotic treatment, INPT: inpatient treatment, IV: independent variable, LOE: level of evidence, MDRO:
multidrug resistant organism, MRSA: Methicillin resistant staphylococcus aureus, n: sample size/people N: sample size/studies, NI: not identified in the study, Obs: observation, OTPT: outpatient
treatment, QIP: quality improvement project, RCT: randomized control trial, S: setting, SR: systematic review, SSTI: skin and soft tissue infections, TX: treatment, TXF: treatment failure, US:
United States, $: cost in US dollars, ↑: increase, ↓: decrease

SKIN AND SOFT TISSUE INFECTIONS
Noncompliance
Cost
TX time
Mortality

x

x

↑↑
x

39
x

x
x

↑

↑↑

↑

↑↑

↑

↑↑

↑

ABX: antibiotics, CF: correctional facility, C-SS: cross-sectional study, CS: control study, DV: dependent variable, EC: exclusion criteria, ED: emergency department, HCP: healthcare provider,
I&D: Incision and drainage, IC: inclusion criteria, IEAT: inappropriate empirical antibiotic treatment, INPT: inpatient treatment, IV: independent variable, LOE: level of evidence, MDRO:
multidrug resistant organism, MRSA: Methicillin resistant staphylococcus aureus, n: sample size/people N: sample size/studies, NI: not identified in the study, Obs: observation, OTPT: outpatient
treatment, QIP: quality improvement project, RCT: randomized control trial, S: setting, SR: systematic review, SSTI: skin and soft tissue infections, TX: treatment, TXF: treatment failure, US:
United States, $: cost in US dollars, ↑: increase, ↓: decrease

SKIN AND SOFT TISSUE INFECTIONS

40
Appendix G

Social Cognitive Theory
(Bandura, 1986)

SKIN AND SOFT TISSUE INFECTIONS

41

Appendix H
PDSA Quality Improvement Model