The objective of this research is to explore the link between air pollution and multiple sclerosis (MS). MS is a chronic progressive neurological disease of young adulthood that results in significant physical and cognitive disability. Rates of MS in NB are among the highest in the world. Greater exposure to air pollution has been previously implicated as risk factor and basic science studies demonstrate that pollutants can cross the blood brain barrier. We previously conducted a prevalence study and identified regional variation in MS prevalence in NB. To explore this geographic variability, we compared air pollution levels and MS prevalence using data housed at NB-IRDT. We stratified MS cases by geography, into one of the thirty-three Health Council Communities (HCCs), and assigned long-term air pollution levels (i.e. particulate matter <2.5µm (PM2.5), nitrogen dioxide (NO2), sulphur dioxide (SO2) and ozone (O3)), from the Canadian Urban Environmental Health Research Consortium (CANUE) to each HCC. Average pollutant levels were all below established Canadian air quality standards. We found PM2.5 was positively associated with MS prevalence. Our results offer additional evidence for a link between ambient PM2.5 and MS, even in areas with low air pollution levels such as NB.

1. Air Pollution and Multiple Sclerosis Prevalence
in New Brunswick
January 26, 2022
Dr. Sandra Magalhaes
Research Associate
Epidemiologist

2. BACKGROUND

3. Multiple Sclerosis
wikimedia.org wikipedia.org
http://www.my-ms.org/

4. Who Gets MS?
• Peak incidence 30-50 years of age
• Females are two–three times more likely
• Month of birth effect
• Greatest risk in May (northern) and November (southern)
• Latitude gradient
• No consistent association with SES
4

5. Distribution of MS Globally
5

6. Distribution of MS across Canada
6
5.6
1994-2001
9.77 (8.00-11.9)
1995-2010
11.4 (10.7–12.0)
1998-2006
13.3 (12.6, 14.0) in 2007
19.4 (18.5, 20.3) in 2010
16.5 (15.8-17.2)
2001-2013
7.8 (7.6-8.1)
1996 - 2008
16.6 (15.7-17.5)
2001-2014

7. Distribution of MS in NB
14.4
(12.7-16.0)
18.1
(16.1-20.1)
19.7
(17.6-21.8)
19.7
(15.8-23.6)
24.6
(18.6-30.6)
10.9
(8.6-13.2)
12.4
(9.2-15.7)
Incidence
per
100,000
person-years

8. Environmental Risk Factors
• Latitude/Residential Location
• Infectious Mononucleosis/EBV
• Vitamin D
• Sun Exposure
• Body Size
• Smoking
• Air Pollution?
8

9. Evidence for Role of Air Pollution
• Urbanization associated with MS
• Air pollution impacts immune system and brain function
• Studies examining various pollutants identify link with higher MS rates and
disease progression, though results are inconsistent
9

10. RESEARCH OBJECTIVE:
To explore the association
between air pollution and MS in
New Brunswick

11. METHODS

12. NB-IRDT
• Conduct objective, rigorous and evidence-based
research and program evaluation to support
GNB planning and policy development
• Host and provide access to person-level
deidentified program data in a highly secure
research network (Fredericton, Moncton, Saint
John sites) as a data custodian
• Receive, link, host and analyze user-provided
personal information from clinical trials,
observational studies, devices etc.
12

13. Defining Air Pollution Exposure
• Data were available from CANUE (Canadian Urban Environmental Health
Research Consortium; canue.ca)
• Four pollutants were examined
• Particulate matter <2.5µm (PM2.5)
• Nitrogen dioxide (NO2)
• Sulphur dioxide (SO2)
• Ozone (O3)
• Long-term pollutant level obtained for each postal code in NB
• Pollutant levels derived using satellite data and regression models
13

14. Case Identification
• MS cases were identified using data from the Canadian Chronic Disease
Surveillance System (CCDSS)
• CCDSS uses linked administrative data sources in a consistent and
comparable way across jurisdictions, to allow for timely reporting to support
the planning and evaluation of policies and programs

15. CCDSS Multiple Sclerosis Case Algorithm
• 1 MS hospitalization or 5 physician visits in 2 years
• Other algorithms have been tested
• Any 3+ MS claims
• Time dependent definition
• Incorporation of prescription data
Sensitivity Specificity PPV NPV
Saskatchewan:
Al-Sakran
(2018)
91%
(81.2-94.6)
99.0%
(96.4-99.9)
98.9%
(96.1-99.9)
91.7%
(87.2-95.0)
Ontario:
Widdfield
(2015)
84.2%
(79.7-88.8)
100%
(99.9-100)
86.0%
(81.6-90.3)
99.9%
(99.9-100)

16. Geocoding Cases
• Geocoding done using data in the Citizen Database
• Registry of all NB residents who have applied for a Medicare card
• Basic demographic characteristics
• Indicates time eligible and ineligible (and why)
• Address file includes residential history

17. Study Design
• Ecological study design
• Data was aggregated using the 33 New
Brunswick Health Council Communities
(HCCs)
• Air pollutant levels and MS prevalence in 2011
were mapped using ArcGIS and analyzed
using SAS
• Associations between average air pollutant
levels and MS prevalence at the HCC-level
are reported
17

18. RESEARCH HYPOTHESIS:
HCCs with higher air pollutant
levels will have higher prevalence
of MS

19. RESULTS

20. Variation in Air Pollutants in NB
• Some regional variation observed in pollutant levels
• Average pollutant levels in all regions well below established Canadian air
quality standards
20

21. Variation in MS Prevalence
• 1532 prevalent MS cases were
identified in NB in 2011
• Prevalence 261 per 100,000
95%CI: 229-301
• Across NB prevalence of MS cases
varied greatly
• Lowest in Bouctouche and Neguac,
but highest in Campbellton and
Perth-Andover
21

22. Association between Air Pollutants and MS Prevalence
• HCCs with higher PM2.5 have higher prevalence of MS
• When all pollutants were considered together, only PM2.5 was found to be
associated with MS prevalence
• Results remained significant after adjusting for age, sex and income
22
O3
SO2
NO2
PM2.5
Parameter
0.93 (0.86, 1.02)
0.50 (0.16, 1.53)
0.74 (0.62, 0.88)
1.59 (1.31, 1.93)
(95% CI)
Odds Ratio
.15 .5 1 1.5 2
Pollutant Prevalence Ratio
(95%CI)

23. Summary
• Four pollutants were considered and only PM2.5 was associated with higher
MS prevalence
• PM2.5 exposure is suggested to negatively impact immune function and
impairs integrity of the blood-brain barrier
• Strengths: large population-based data sources, prospectively collected,
detailed pollution data, validated case definition
• Limitations: area level analysis, algorithm not a clinical diagnosis (gold
standard), cannot define disease incidence
• Results warrant closer look using individual-level exposure assignment
employing a population-based cohort study
23

24. Thank you!
Questions?
Funding Acknowledgements:
sandra.magalhaes@unb.ca