Relationship Between Air Pollution and Rheumatic Diseases Development

This systematic review explores the relationship between air pollution and the development of rheumatic diseases. It examines the existing evidence and proposes potential pathways. The study includes various types of rheumatic diseases and analyzes the impact of different air pollutants. The findings suggest a relationship between air pollution and systemic autoimmune rheumatic diseases and juvenile idiopathic arthritis. However, more research is needed to understand the relationship with other types of rheumatic diseases.

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A Systematic Review
Name
Institutional Affiliation

2
PICO elements are critical tools that are used to ask various questions that are related to the
clinical field. This paper would use the PICO elements to identify whether there is an existing
relationship between air pollution and Rheumatic diseases development.
P- Is there a relationship?
I-That exists between
CO-Air pollution and development of Rheumatic Diseases.
Research Question: is there a relationship that exist between Air Pollution and Rheumatic
Diseases Development?
Introduction
The interaction of the environmental and genetic predisposition is hypothesized to cause the
expression of various autoimmune rheumatic infections, for example, juvenile idiopathic
arthritis, rheumatoid arthritis, and systematic autoimmune rheumatic diseases (Chang et al.,
2016). Therefore, it essentially necessary to determine the modified factors of risk, which are
associated with the development and prognosis of such infections.
The research question has a plausible mechanism since air pollutants have a plausible risk factor
for the development of autoimmune infections. Development of RA is significantly associated
with the inhalants such as silica and smoke from tobacco since they have the ability to interact
with the tissues of alveolar directly (Chang et al., 2016). Moreover, air pollutants have been
illustrated through various experiments to directly stimulate inflammatory responses and
consequently alter the microbiome indirectly. Since most of the randomized trials that are used to
directly assess the cause of relationship between air pollutant exposure and various diseases

3
development are not feasible (Chang et al., 2016), it is plausible to prove observational studies
that are used to assess the associations' evidence.
Objective: To identify the observable studies that estimate the link between air pollutants
exposure and selected rheumatic diseases development.
Methods
Search Strategy
MEDLINE (1946 to September 2016) and EMBASE (1980 to 2016, week 37) databases were
searched using MeSH and other keywords to find the case-control, cohort, and case cross-over
studies. This reported the estimation of risks of developing the selected rheumatic infections in
relation to the changes in air pollutants (n=8).
Study Selection
The selection was carried out by assessing the outcome of the rheumatic diseases such as RA,
JIA, and SARDs and the exposure of an individual to various air pollutants, which include;
PM2.5, PM10, SO2 (Sulphur (IV) oxide, NO2, CO, and ozone. This also included a case-control,
cohort design, and a case cross-over. No other language was included apart from the English
language. The study also RR, HR, OR, and 95% (CI) (Tositti, 2017). However, the study
excluded reviews, mechanism studies, case reports, and nonhuman studies. Based on the above
criteria of study selection, Gavin Sun, Glen Hazlewood, and Cheryl Barnabe were identified to
have completed the title, abstracts, and full-text reviews. The records identified in EMBASE
(n=926), identified records in MEDLINE with N=163, and the identified records by searching
when n=1. The records that were available after the duplicates had been removed (n=962). The
records excluded (n=935), not English (n=42), not rheumatic disease (n=311), not air pollution

4
(n=564), wrong study design (n=8), not describing risk of rheumatic disease development (n=10)
(Tositti, 2017). The next to full-texted assessed for eligibility (n=27), the records excluded
(n=19), not rheumatic disease (n=2), wrong study design (n=8), no estimate of association
described (n=9). Finally, the studies included in the qualitative summary (n=8).
Data Extraction and Assessment of Study Quality
Two authors, Gavin Sun and Cheryl Barnabe performed the data extraction in duplicate followed
by developing a standard reporting form necessary to extract important information from every
study conducted (De Roos et al., 2013). This included the calendar years of study, country of
study, criteria of diagnosing the rheumatic disease assessed, and the number of patients in the
control study groups. After, which the design of study and methods of air pollutant levels
assessment methods were also extracted. The study also extracted the estimates and their error
margins. The quality of the studies in relations to the objective of the study was assessed in
duplicate by Gavin Sun and Cheryl Barnabe using the scale of Newcastle-Ottawa. The case-
control studies quality was assessed using the four domains of selection, three domains of
exposure, and two domains of comparability. The four domains of selection included a definition
of the case, case representativeness, controls of selection, and controls definition (De Roos et al.,
2013). The two domains of comparability included study control for the most pertinent factor
and any available additional factor. The three domains of exposure included exposure
ascertainment, similar ascertainment method for cases and controls, and the rate of nonresponse.
The qualities of cohort studies were assessed in four domains of selection, two comparability
domains, and three outcome domains (Ziade, Bouzamel, Abi Karam, Mrad-Nakhle, & Farah,
2018). The four domains of selection included representativeness of exposed cohort, non-
exposed cohort selection, exposure ascertainment, and a demonstration of the absence of

5
outcome of interest at the beginning of the study. The two comparability domains included
control study for the most important factor and other additional factors (De Roos et al., 2013).
The three outcome domains included outcome assessment method, a period of follow-up, and a
cohort of follow-up adequacy. The points were assigned depending on the specified quality
levels within every domain to at most 9 points.
Results
While using the “more specific” filters, the findings were characteristics of various selected
terms related to the environment, changing the world, and pollution, which produced a number
of potentially important articles greater than 40%. On the other hand, using the "more sensitive”
filter was based on a combination of all the terms related to the topic of study. The “more
specific” filter recorded the lowest specificity at 67.4% highest sensitivity (82.5%), and 1.9 NNR
to finding the significance of an article when contrasted to gold measurement standards. While
“more sensitive” filter yielded 3.3 NNR, the highest sensitivity at 98.5% and lowest specificity at
48%.
Disease
studied
Author
and
year
Country
or
region
Type
of
study
Sample A case
definition for
diagnosis of
rheumatic
disease
Year
of
study
Air
pollutants
studied
Exposure determination
Method
Rheumat
oid
Chang
et al.,
Taiwan Coho Population
at risk
Administrative
data, 1 ICD-9-
2000- NO2, Monitoring sites

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arthritis 2016 rt NO2
exposure,
n=247,
419, with
n=376
Cases;
PM2.5
exposure,
n=244,
413, with
n=236
cases
CM code for
RA
2010 PM2.5
De
Roos et
al.,
2014
British,
Columbi
a,
Canada
Neste
d
case-
contr
ol
Controls
n=19, 066
cases, n=1
911
Administrative
data, 2 ICD-9
codes for RA
with minimum
1 visit to a
physician
specialist
1994-
2002
NO2,
SO2,
PM2.5,
PM10,
CO, NO,
black
carbon,
ozone
The land use regression method
for black carbon, PM2.5,
NO2, NO. Inverse
weighting method
for PM10, NO, SO2,
Ozone, CO
Ziade
et al,
2018
Sweden Case-
contr
ols
Controls,
n=2, 536
cases,
Rheumatologist
history and
1996-
2008
NO2,
SO2,
Land use regression

7
n=1497 exam PM10
Tositti
et
al,.2017
USA Coho
rt
Population
at risk,
n=111,
425 cases,
n=858
Self-report and
medical chart
review
1986-
2006
NO2,
SO2,
PM2.5,
PM10
Land use regression
Systemic
autoimm
une
rheumati
c disease
Bernats
ky et
al.,
2016
Quebec
and
Alberta,
Canada
Coho
rt
Estimated
cases,
n=30, 330
Alberta
estimated
population
at risk
n=3, 053,
980
estimated
cases,
n=8, 180
Administrative
data, 2 ICD-9
codes for
SARD or 1
ICD-9 code for
SARD by a
rheumatologist
or 1 instance of
hospitalization
Quebe
c,
1996-
2011
Albert
a,
1993-
2007
PM2.5 Satellite-derived data of
exposure levels at
location of residence
at the time of diagnosis
Bernats
ky et
al.,
2015
Calgary,
Alberta,
Canada
Coho
rt
Not
provided
Administrative
data, 2 ICD-9
codes for
SARD or 1
ICD-9 code for
1993-
2007
PM2.5,
NO2
Land use regression

8
SARD by a
rheumatologist
or 1 instance of
hospitalization
Juvenile
idiopathi
c arthritis
Zeft et
al.,
2015
USA Cohort
(case-
crossover)
Cases,
n=338
Cohort (case-
crossover)
1993-
2006
PM2.5 Cohort (case-
crossover)
Selected
Zeft et
al.,
2014
USA
and
Canada
Coho
rt
(case-
cross
over)
Not
mentioned
Not mentioned Not
mentio
ned
PM2.5 Not mentioned
Conclusion
Various pieces of evidence are there to support the relationship between exposure to air
pollutants and SARDs and JIA developments. However, there is no clear evidence on the
relationship between air pollutants and other types of rheumatic diseases.
Gaps in the evidence and proposed pathway
Sensitivity and specificity were used to evaluate the overall performance of the research topic
according to the gold standard that was obtained from the systematic literature reviews. The
possible method of evoking the two filters in PubMed could be by entering the URLs (Chang et

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al., 2016). The abstract was used to formulate the two search filters I also formulated the two
search filters using the abstracts. However, the sampled articles body and quality of every study
were evaluated. Thus; I could not count for the loss of certain data indicated in the articles
without a significant summary. Therefore, I suggest that for a further research study, the search
filters should not exclude the studies that are not indexed as animal studies in PubMed (Chang et
al., 2016). Moreover, I suggest that for further studies, a study should be restricted to PubMed
since all the medical database have their own syntax and critical terms that should be separately
studied.

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References
Bernatsky, S., Smargiassi, A., & Barnabe, C. (2016). Fine particulate air pollution and systemic
autoimmune rheumatic disease in two Canadian provinces. Environmental Research, 146,
85-91.
Bernatsky, S., Smargiassi, S., & Johnson, M. (2015). Fine particulate air pollution, nitrogen
dioxide, and systemic autoimmune rheumatic disease in Calgary, Alberta. Environmental
Research,, 140, 474-478.
Chang, K. H., Hsu, C. C., & Muo, C. H. (2016). “Air pollution exposure increases the risk of
rheumatoid arthritis: a longitudinal and nationwide study,,” Environment International,
94, 495-499.
De Roos, A. J., Koehoorn, M., Tamburic, L., Davies, H. W., & Brauer, M. (2014). Proximity to
traffic, ambient air pollution, and community noise in relation to incident rheumatoid
arthritis,,” Environmental Health Perspectives, 122, 1075-1080.
Tositti, L. (2017). The Relationship Between Health Effects and Airborne Particulate
Constituents. Clinical Handbook of Air Pollution-Related Diseases, 33-54.
doi:10.1007/978-3-319-62731-1_3
Zeft, A., Burns, J., & Yeung, R. (2015). “A5.2 Kawasaki disease and exposure to fine particulate
air pollution, Annals of the Rheumatic Diseases, 74, A47.

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Zeft, A. S., Prahalad, S., & Schneider, R. (2014). Systemic juvenile idiopathic arthritis and
exposure to fine particulate air pollution, Arthritis, and Rheumatology, 66, S136.
Ziade, N., Bouzamel, M., Abi Karam, G., Mrad-Nakhle, M., & Farah, W. (2018). Weather, air
pollution and rheumatic diseases: a prospective and correlational study of the influence of
weather and air pollution on the perception of joint pain in patients with chronic
rheumatic diseases. Osteoarthritis and Cartilage, 26, S212-S213.
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