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The association between sun exposure and multiple sclerosis
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The association between sun exposure and multiple sclerosis

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Content 1





The Association Between Sun Exposure and Multiple Sclerosis


By


Wei Xiong










A Thesis Presented to the  
FACULTY OF THE GRADUATE SCHOOL  
UNIVERSITY OF SOUTHERN CALIFORNIA  
In Partial Fulfillment of the  
Requirements for the Degree  
MASTER OF SCIENCE  
(BIOSTATISTICS)

May 2017














Copyright 2017                                                                   Wei Xiong
2
Table of Contents

DEDICATION…………………………………………………………………………..3

ACKNOWLEDGEMENT………………………………………………………………4

ABSTRACT…………………………………………………………………………….5

INTRODUCTION………………………………………………………………………7

METHODS…………………………………………………………………………....10

RESULTS……………………………………………………………………………..15

DISCUSSION…………………………………………………………………………19

CONCLUSION………………………………………………………………………..22

REFERENCE…………………………………………………………………………23

TABLES……………………………………………………………………………….26

FIGURES……………………………………………………………………………..29























3
DEDICATION
      This work is dedicated to my parents for their endless support and love over
the years.




















4
ACKNOWLEDGEMENT
        I would like to thank my thesis committee chair Dr. Meredith Franklin, who
introduced me to biostatistics, and guided me through this educational journey
with lots of support and help.  
        I would like to thank my thesis committee members Dr. Steven Yong Cen
and Dr. Christianne Lane for their help, guidance and support.
        Lastly, I would like to thank Dr. Mack for introducing me to the biostatistics
master’s program at University of Southern California, and also thank all the
people who helped or supported me in this research project.














5
ABSTRACT
Background: Previous studies have suggested an association between Multiple
Sclerosis (MS) and geographic location, showing higher incidence rates in
regions above 40 degrees latitude in both the northern and southern
hemispheres. One possible explanation is that exposure to greater amounts of
sunlight has a protective effect, suggesting the farther one lives from the equator,
the greater the risk of MS. Using the National Inpatient Sample (NIS) database
and estimates of average global, horizontal, and direct normal solar irradiance,
we investigated the association between exposure to sunlight and outpatient
hospital visits with diagnoses of MS attack between 2000 and 2009 in California,
United States.  
Methods: Spatial statistical methods (variograms and ordinary kriging were
applied to monthly averages of solar irradiance data from 24 locations throughout
California to generate exposure estimates at the locations of the NIS hospitals.
With exposures linked to monthly counts of MS outpatient hospital records,
spatio-temporal Poisson models were used to examine association between
solar irradiance and MS attacks. Age, sex, race (Caucasian, Black, Hispanic,
Asian and other), latitude, longitude, month and year were all considered as
potential confounders and included in the Poisson model.
Results: There was a statistically significant negative association between MS
and global solar irradiance (p=0.02) after adjusting for age, sex, race, latitude,
longitude, month and year but not direct (p=0.09) or diffuse solar irradiance
(p=0.43). The number of MS attacks decreased by 42.7% (95% CI: 7.59%,
6
64.51%) per 1 KWh/m
2
daily increase in global solar irradiance after adjusting for
age, sex, race, latitude, longitude, month and year.  
Conclusion: We found that exposure to sunlight has a protective effect on MS
attacks in California after adjusting for age, sex, race, latitude, longitude, month
and year. These results suggest that moderate exposure to sunlight may be an
effective preventive strategy against MS.




















7
INTRODUCTION
Multiple Sclerosis (MS) is a chronic neurological disease that affects the central
nervous system (CNS), for which the cause is still largely unknown. There are
about 2.5 million people affected by MS in the world, and 650,000 in the U.S
[1]
.
The etiology of MS is unknown but believed to involve both genetic and
environmental factors; there is no evidence that MS is directly inherited
[2]
. More
than two to three times as many women as men develop MS, suggesting that
hormones may play a significant role in determining susceptibility to MS
[3]
. MS
occurs more commonly in Caucasians, but also occurs in other ethnic groups
including African-Americans, Asians and Hispanics/Latinos
[4]
. Most people are
diagnosed between the ages of 20 and 50 years old, and have been shown to
have, on average, a reduced lifespan of 8 years. By 25 years from disease onset,
65% of patients are non-ambulatory
[5]
. MS is a demyelinating disease that
involves an immune-mediated process; the exact antigen or target that the
immune cells are sensitized to attack is unknown
[6]
. Immunologic factors
involved in MS include abnormal immune-mediated response attacks the myelin
coating around nerve fibers in the central nervous system. Researchers have
identified the immune cells that mount the attack, and are studying the
mechanisms of it to help us learn the cause of MS
[2]
.  
A typical MS attack begins with a flare-up of symptoms, manifested by episodes
of relapse followed by remission. Clinical symptoms of MS include disturbance in
visual acuity, disturbances of walking and using hand ability, bowel and bladder
incontinence, and sensory disturbances
[7]
. Diagnosis of MS is made after
8
reviewing a series of patient’s historical neurological events and excluding other
diseases that could account for the events
[8]
. Four types of MS have been
identified: clinically isolated syndrome (CIS), relapsing-remitting MS (RRMS),
primary progressive MS (PPMS), and secondary progressive MS (SPMS). At
present, there are no effective therapies to treat MS without relapses and still
actively working to find effective treatments for MS.
Some epidemiological studies suggested the positive association between MS
and residential latitude
[9-11]
; and other factors including Vitamin D status
[12]
, viral
infection
[13]
and smoking
[14]
. At higher latitudes (both northern and southern
hemispheres), there is more variability in ambient sunlight throughout the year.
Seasonal Effective Disorder (SAD) is also highly related to latitude due to light
deprivation. Researchers have hypothesized a link between SAD and MS in that
the risk of developing MS is related to impairment of the immune system caused
by light deprivation prior to adulthood
[15]
. More recent studies have suggested
that exposure to sunlight provides a protective effect against developing Multiple
Sclerosis
[16,17]
. Growing evidence suggests that people who live closer to the
equator and are exposed to larger amounts of sunlight year-round tend to have
higher levels of naturally-produced vitamin D, which may protect against immune-
mediated disease since vitamin D supports immune function
[9]
. One study
showed that taking vitamin D was associated with about a 40% reduction in the
risk of developing MS, and this can provide potential explanation for the
association between sunlight and MS attack
[18]
. Studies have also indicated that
the migration from one geographic area to another with more than 5 degrees of
9
latitude may alter the risk of developing MS
[9]
, which is about the distance from
San Diego to San Francisco (736 km).  
We are interested in investigating the possible relationship between MS and
sunlight exposure. California is a good place for this research given the size of its
population and its demographic and geographic diversity.  Geographically,
Southern California has large amounts of sunlight year-round while Northern
California is farther from the equator, has more rain, fog, and relatively lower
amounts of sunlight year-round. Demographically, California has a diverse
population with 38% non-Hispanic white, 39% Hispanic, 13% Asian, and 6%
African American, making it an ideal study population for MS research
[19]
.
In this study, we harness the National Inpatient Sample (NIS) database of
hospital outpatient visits and solar irradiance data to conduct a spatial and
temporal examination of the association between MS attacks and sunlight
exposure in California.









10
METHODS
Participants and study design
This was a retrospective observational study with data abstracted from two
sources. The NIS database is a subset of The Healthcare Cost and Utilization
Project (HCUP) sponsored by the Agency for Healthcare Research and Quality
(AHRQ), and contains data on more than seven million hospital stays each year
[20]
. We used the International Classification of Diseases, 9th Revision (ICD-9) to
ascertain diagnoses. Patients from the hospital between 2000 and 2009 in
California with diagnosis for MS were identified with ICD-9 code 340. Since NIS
only provides the zip code of the hospital, and month and year information for
each hospital stay, we examined monthly counts of MS patients in each zip code
for which there was a hospital. We also abstracted information on age, gender,
and race of patients from the NIS database to include in our study.  

Sunlight Exposure Data
Radiant energy from the sun received on earth is measured and reported as
solar irradiance. Global Horizontal Irradiance (GHI) is the total amount of solar
energy received by a surface horizontal to the earth’s surface. It is the sum of
Direct Normal Irradiance (DNI), which is the amount of solar energy received by a
surface held perpendicular to the rays that come strictly from the direction of the
sun, and Diffuse Horizontal Irradiance (DHI), which is the amount of solar energy
scattered by molecules and particles in the atmosphere (i.e. it arrives to the
11
surface indirectly). The relationship between GHI, DHI and DNI follows the
equation:
GHI = DHI + DNI * cos (Z)
where Z is the solar zenith angle (Figure 1).
We downloaded solar irradiance data from the National Solar Resource
Database (NSRDB)
[21]
, a product provided by the National Renewable Energy
Laboratory (NERL), which is under the U.S. Department of Energy. The NSRDB
data are a collection of hourly values of GHI, DHI and DNI. While there are 40
sites measuring these data in the U.S., there are many gaps in the records. As
such, the NERL has relied on meteorological statistical models, which
incorporate surface measurements of meteorology, solar radiation, and satellite
observations to make estimates at 1,454 stations across the country. The State
University of New York (SUNY) solar radiation model, which the NERL has
adopted, incorporates Geostationary Operational Environmental Satellite (GOES)
observations of solar irradiance. The SUNY model has been validated
[22]
and is
widely used in environmental sciences
[23]
. This is the first application of these
data in an epidemiological setting.
While there are 24 monitoring locations in the state, they are clustered in
Northern (San Francisco Bay Area) and Southern (Los Angeles to San Diego)
California (Figure 2). Thus, for the purposes of exposure modeling, we stratified
the data into these two regions.  


12
Statistical Methods
Spatio-temporal Methods
To examine the association between hospital admissions for MS and exposure to
sunlight, we spatially and temporally linked the NIS and NSRDB data.
First, we took monthly averages of the SUNY solar irradiances at each location in
order to match the temporal scale of the NIS hospital records. Then, using the
geographic coordinates (latitude and longitude) of the solar stations, we explored
the spatial behavior of the variability in the data with respect to distance using
empirical semivariograms
[24]
. The solar radiation (KW/m
2
) semivariance 𝛾(ℎ)was
determined by:
𝛾 ℎ =
1
2|𝑁 ℎ |
[𝑍 𝑠
!
−𝑍 𝑠
!
]
!
! ! ∈!,!

Where Z(s
i
)-Z(s
j
) is the difference in solar radiation Z at locations s
i
and s
j

(identified by longitude and latitude), h is the distance lag, and N(h) is the number
of pairs for a particular distance lag. Visual inspection of the empirical
semivariogram, plotted as the distance lag (h) versus semivariance  enabled us
to explore the spatial variability between all pairwise locations, and determine
which theoretical semivariogram function best fit the data. We examined the fits
of Gaussian, spherical and exponential theoretical semivariogram functions. This
method was applied to solar stations in Northern and Southern California
separately, and separately for each type of irradiance (global, direct, and diffuse).
Assuming our spatial process of solar irradiance Z(s) is intrinsically stationary (i.e.
has a constant mean, and a semivariogram that only depends on the distances
separating two locations s
i
and s
j
), we used ordinary kriging to interpolate the
13
solar irradiance data in order to generate exposure predictions at the zip codes of
the hospitals for which we had data from the NIS
[25]
. The ordinary kriging
predictor is a weighted average of the data, whereby a predicted value at an
unobserved spatial location s
0
is defined by:
𝑍 𝑠
!
= 𝜆
!
!
!!!
𝑍(𝑠
!
)

where the weights are based on the semivariance . The kriging equation is
solved by minimizing the mean squared prediction error subject to the
unbiasedness constraint
[26]
.
Kriging was applied to Northern and Southern California and each month of data
separately, resulting in an unbiased linear prediction (with standard error) of the
monthly solar irradiance at each hospital location.  

Epidemiological Methods
With monthly solar irradiance exposures estimated for each hospital, Poisson
regression was used to determine its association with counts of MS patients in
hospital for MS. Each of the three types of solar radiation, GHI, DNI, DHI, were
separately tested. Possible confounders including sex, race (White, Black,
Asian and Other) and age were included in the model. To conform with the
counts, we computed proportions of sex and race, and mean age for each month
and hospital. Additional confounders including latitude, longitude, month, and
year were evaluated. The Poisson regression model for the counts of MS attacks
14
Y
i
at hospital i (stratified by Northern and Southern California) with adjustment for
confounders X
i
had the form:
log[E(Y
i
| X
i
)] = β
0
+ β X
i

All analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC) and
ArcGIS 10.4.1.


















15
RESULT
Characteristics of study population
The descriptive characteristics are presented in Table 1. There were 20,234
Multiple Sclerosis attacks in California’s NIS database from 2000 to 2009. The
maximum monthly count of MS at a hospital was 19 and the minimum was 0. The
mean age of MS patients is 53.6 years, with the oldest MS patient being 92 and
the youngest 7 years old. The proportion of females was 71.9% (N=14543) and
males 28.1% (N=5692), which is consistent with broad population statistics that
more than two to three times as many women as men develop MS. Apart  
from 424 patients missing race data, 75.5% (N= 14950) of the MS patients are
Caucasian, 11.1% (N=2217) are Black, 9.9% (N=1960) are Hispanic/Latino, 1.7%
(N=331) are Asian and 1.8% (N=352) other races. Compared to the demographic
data of California: 38% non-Hispanic white, 39% Hispanic, 13%
Asian, and 6% African American, our data is consistent with the fact that MS
occurs more commonly in Caucasians.
Characteristics of sunlight exposures
Of the 24 monitoring locations across California, 14 were in Southern California
and 10 in Northern California (Figure 2).  
The mean GHI was 387.5 Wh/m
2
(minimum 146.7 Wh/m
2
, maximum 566.3
Wh/m
2
). In Southern California mean GHI was 405.4 Wh/m
2
and in Northern
California it was 368.4 Wh/m
2
. The mean DNI was 464.3 Wh/m
2
(minimum 232.5
Wh/m
2
, maximum 600.5 Wh/m
2
). In Southern California mean DNI was 480.0
Wh/m
2
and in Northern California it was 447.5 Wh/m
2
. The mean DHI was 132.0
16
Wh/m
2
(minimum 84.7 Wh/m
2
, maximum 206.8 Wh/m
2
). In Southern California
mean DNI was 135.2 Wh/m
2
and in Northern California it was 128.6 Wh/m
2
.
For each of the irradiance types (GHI, DHI, DNI) empirical semivariograms were
computed as were three theoretical semivariograms using the Gaussian,
exponential and spherical models (Figure 3). Examination of the plots suggested
that solar monitoring locations within about 1 degree (approximately 110 km)
exhibited strong spatial correlation, but beyond that distance that correlation
decreased. The best fitting theoretical semivariogram to our data was the
exponential model with sill of 330 kWh/m
2
and range of 1.2 degrees. Thus, we
used the exponential semivariogram model with these spatial parameter values
to describe the spatial semivariance and for use in kriging.
In Figure 4, a 3-D scatterplot shows the kriging predictions for one month of GHI
at the hospitals in Northern California. As described above, kriging with the
exponential function was applied separately to the monthly solar irradiances from
Northern and Southern California. The assumptions of normality of all irradiance
data were checked and they were normally distributed.
Association between solar irradiance and MS  
Our null hypothesis is that GHI, DNI and DHI are not associated with MS attack.
We found that all measures of solar irradiance and MS were statistically
significant. The bivariate association of GHI and MS attack was statistically
significantly (p<0.01), where the number of MS hospital attacks increased by
60% (95% CI: 40%, 70%) per 1KWh/m
2
increase in GHI. DNI was also
statistically significantly associated with MS (p<0.01) and the number of MS
17
hospital attackss increased by 170% (95% CI: 120%, 230%) per 1 KWh/m
2

increase in DNI. Finally, DHI was statistically significantly associated with MS
(p<0.01) and the number of MS hospital attacks increased by 360% (95% CI:
150%, 760%) per 1 KWh/m
2
increase in DHI.  
Age, sex and race, latitude, longitude, month and year were all considered as
potential confounders. Pearson correlation among potential confounders showed
that there were no two variables strongly correlated with each other (Table 2).
Age, sex, race, latitude, longitude, month and year all independently altered the
main effect of interest by more than 10% and thus all were included in the final
Poisson regression model. The percent changes of regression coefficient for the
main effect between solar irradiance and MS are presented in Table 3.
Table 4 a-c shows the negative association between GHI, DNI and DHI with MS
after adjusting for baseline age, sex, race, latitude, longitude month and year. We
rejected the null hypothesis and concluded that GHI was statistically significantly
associated with MS after adjusting for age, sex, race, latitude, longitude, month
and year (p=0.02). After adjustment, the number of MS hospital attacks
decreased by 42.7% (95% CI: 7.59%, 64.5%) per 1 KWh/m
2
increase in GHI.
Compared to males, the number of MS attacks for females increased by 0.84%
(95% CI: -3.35%, 4.88%), but was not statistically significant. Compared to
Caucasians, the number of MS attacks for African Americans was higher by
3.15% (95% CI: -2.63%, 8.61%), for Hispanic/Latinos was lower by 37.7% (95%
CI: 29.7%, 46.2%), for Asians was lower by 17.4% (95% CI: 1.92%, 36.3%), and
for other races was 27.1% lower (95% CI: 10.3%, 46.2%) after adjusting for other
18
dependent variables. Overall, race was a statistically significant confounder
(F=4.89, p=0.03).
The number of MS attacks decreased by 9.64% (95% CI: 7,68%, 11.6%) with
one degree increase in latitude after adjusting for other dependent variables.
Neither DHI nor DNI was statistically significantly associated with MS attack after
adjusting for age, sex, race, latitude, longitude, month and year (p=0.09 and
p=0.43, respectively). Nevertheless we observed that in both cases the number
of MS attacks decreased with increasing irradiance. Specifically, MS attacks
decreased by 21.7% (95% CI: -4.31%, 41.3%) per 1 KWh/m
2
increase in DHI and
by 48.2% (95% CI: -160.8%, 89.7%) per 1 KWh/m
2
increase in DNI.  













19
DISCUSSION  
We examined whether sunlight exposure exhibited a potential protective effect on
Multiple Sclerosis attacks through the use of a large databases of hospital
attacks and solar irradiance data in California. We chose to study California for its
population, which is large and demographically diverse, and for is geographic
and meteorological variability.  
Our results show that with greater exposure to sunlight, there is a marked
decrease in MS after adjustment for population characteristics, and spatial and
temporal confounders. These results were statistically significant for GHI (-
42.7.Z%, p=0.02) and while not statistically significant, the association showed a
similar pattern for DHI (-21.7%) and DNI (-48.2%). That we find this result with
GHI is not surprising, as the horizontal and indirect forms of irradiance are not as
reliable as the global irradiance
[27]
. DNI has been found to have much greater
bias than measured GHI, resulting in the modeled GHI being overestimated by
10-40% and the modeled DNI being overestimated up to 275% on a monthly
average basis.
[27]


We examined MS attacks, which represent acute flaring of symptoms in patients
who already have MS. Our results are in line with those of previous studies that
have found a negative association between MS and residential latitude
[9-11]
. A
previous study found that with migration of 5 degrees latitude, there was an
improvement in the risk of developing MS
[9]
. While we cannot make any
conclusions regarding the risk of onset of MS, we observed a decrease in the
20
number of MS attacks within our study region, which spans a similar distance of
approximately 5 degrees latitude (736 km).  
Various biological mechanisms may be at play in sunlight’s protective effect.
Ultraviolet radiation (UVR), a component of sunlight, is a primary source of
natural Vitamin D, which is an essential nutrient for musculoskeletal health and
has been shown to be protective against several diseases and cancer
[28]
.  In a
population-based case control study, subjects who had 25-hydroxy-vitamin D
levels less than 50 nM/l had an increased risk for MS (OR 1.4, 95% CI 1.2–1.7)
[28]
. It is likely that it is UVR providing a protective effect by boosting the body’s
vitamin D deficiency. Bäärnhielm et al. further concluded from their study that
UVR exposure may also exert a protective effect against developing MS via other
pathways than those involving vitamin D.  Namely, separating UVR into UVA and
UVB components, other studies have found that UVB appears to up-regulate the
secretion of TNF-a, IL-10, and regulatory T cells
[29]
, and UVA radiation has a
complex dose-related immunomodulating effect where the underlying mechanism
is not fully investigated
[30]
. Sunlight also plays a role in the melatonin-serotonin
cycle, which has a direct impact on SAD
[31]
. When exposed to sunlight in the
morning, nocturnal melatonin levels are triggered to produce earlier in the
evening, meaning that sleep will occur more easily. Sunlight also increases
serotonin during the day, resulting in improved mood. The link between SAD and
MS has been made
[15]
, suggesting an additional benefit in that greater sunlight
exposure could reduce MS attacks.
21
According to the 2006 World Health Organization (WHO) report, a large annual
disease burden of 3.3 billion disability-adjusted life
years (DALYs) worldwide may result from low levels of sunlight exposure,
including disorders of the musculoskeletal system and various autoimmune
diseases and cancers
[32]
. While greater sunlight exposure would result in DALY
improvement, it is important to note that excessive doses of sunlight can be
harmful. Namely, UVB radiation can penetrate the skin and contribute to skin
cancer via the generation of DNA-damaging molecules.  
Limitations of this study include the geographic region of analysis, measurement
error, and the availability of more detailed health characteristics of the study
population. While California has a varying geography and climate, future studies
should compare locations with greater span than 5 degrees latitude to include
areas that experience much less sunlight in winter. Measurement error may arise
from imperfect spatial interpolation of the available solar data to the hospitals.
The NIS database lacks information on some potentially important confounders
including smoking status, socioeconomic status and family history. This
retrospective data is not at individual level but at hospital level, thus it based on
the assumption of uniformity in hospital levels. Despite these limitations, this
study is still one of the first population-based studies of association between solar
radiation and MS in California.



22
CONCLUSION  
Our study is the first research of association between MS and solar radiation in
California. Our results demonstrated that there are negative significant
associations between MS and global horizontal irradiance (p=0.02) after
adjusting for confounders age, sex, race, latitude, longitude, month and year
California. People live in places which have higher sunlight exposure tend to
have lower risk of having MS attack than people live in place which have lower
sunlight exposure. Further research will be conducted to analysis the association
between Multiple Sclerosis and solar radiation in United States and other areas
of the world and provide preventive strategy for MS.



















23
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24
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26
TABLES
Table 1: Descriptive characteristics of the study population
Descriptive characteristics  Mean (SD) or Number (%)
Age (years)  53.6 (14.4)
Gender  
Male  5692 (28.1%)
Female  14543 (71.9%)
Race  
Caucasian  14950 (75.5%)
Black  2217 (11.1%)
Hispanic/Latino  1960 (9.9%)
Asian  331 (1.7%)
Other  352 (1.8%)
GHI (Wh/m
2
)  387.5 (103.7)
Southern California (Wh/m
2
)  405.3 (87.9)
Northern California (Wh/m
2
)  368.4 (115.4)
DNI (Wh/m
2
)  464.3 (65.7)
Southern California (Wh/m2)  480.0 (47.1)
Northern California (Wh/m2)  447.5 (76.3)
DHI (Wh/m
2
)  132.0 (22.2)
Southern California (Wh/m
2
)  135.2 (23.3)
Northern California (Wh/m
2
)  128.6 (20.4)







Table 2: Pairwise Pearson correlation between potential confounders

Age Gender Race Month Year Longitude Latitude
Age 1.00      
Gender -0.04 1.00      
Race -0.22 0.01 1.00    
Month 0.01 -0.00 0.01 1.00    
Year 0.05 0.02 0.04 0.00 1.00  
Longitude -0.04 -0.01 0.07 0.01 -0.02 1.00  
Latitude 0.04 0.02 0.04 -0.01 0.03 -0.83 1.00





27
Table 3: Change of SUNY global, direct and diffuse after adjusting for
potential confounders

Variables SUNY global SUNY direct SUNY diffuse
Age -21% -43% -51%
Gender -32% -42% -51%
Race -20% -40% -54%
Month, Year 742% -15% 715%
Longitude,
Latitude
-24% -36% -82%
   



Table 4a: Poisson regression model of Global Horizontal Irradiance and MS
attack
 
Variables Estimated Parameter
(95% CI)
Standard
Error
P-Value
GHI (KWh/m
2
) -0.56 (-1.04, -0.01) 0.24 0.02
Gender    
Baseline (male)    
Female 0.01 (-0.03, 0.05) 0.02 0.69
Age (years) -0.002 (-0.003, -0.001) <0.01 0.01
Race    
Baseline(Caucasian)    
Black 0.032 (-0.026, 0.09) 0.03 0.28
Hispanic -0.32 (-0.38, -0.26) 0.03 <0.01
Asian -0.16 (-0.31, -0.02) 0.08 0.03
Other -0.24 (-0.38, -0.10) 0.07 <0.01
Month 0.16 (0.03, 0.30) 0.07 0.02
Year -0.14 (-0.2, -0.08) 0.03 <0.01
Longitude -0.13 (0.15, -0.11) 0.01 <0.01
Latitude -0.09 (-0.11, -0.07) 0.01 <0.01












28
Table 4b: Poisson regression model of Direct Normal Irradiance and MS
attack
 
Variables Estimated Parameter
(95% CI)
Standard
Error
P-Value
DNI (KWh/m
2
) -0.25 (-0.54, 0.03) 0.15 0.08
Gender    
Baseline (male)    
Female 0.01 (-0.03, 0.05) 0.02 0.68
Age (years) -0.002 (-0.003, -0.001) <0.01 0.01
Race    
Baseline(Caucasian)    
Black 0.03 (-0.03, 0.09) 0.03 0.31
Hispanic -0.32 (-0.38, -0.26) 0.03 <0.01
Asian -0.16 (-0.31, -0.02) 0.07 0.03
Other -0.24 (-0.38, -0.10) 0.07 <0.01
Month 0.04 (-0.03, 0.11) 0.04 0.27
Year -0.14 (-0.2, -0.08) 0.03 <0.01
Longitude -0.09 (-0.11,-0.08) 0.01 <0.01
Latitude -0.13 (-0.15, -0.11) 0.01 <0. 01


Table 4c: Poisson regression model of Diffuse Horizontal Irradiance and
MS attack  

Variables Estimated
Parameter (95%
CI)
Standard
Error
P-
Value
DHI (KWh/m
2
) -0.56 (-2.2, 1.05) 0.82 0.50
Gender    
Baseline (male)    
Female 0.01(-0.03,0.05) 0.02 0.72
Age (years) -0.002 (-0.003, -
0.001)
<0.01 <0.01
Race    
Baseline(Caucasian)    
Black 0.03 (-0.03,0.09) 0.03 0.27
Hispanic -0.32 (-0.38, -0.26) 0.03 <0.01
Asian -0.16 (-0.31,-0.02) 0.07 0.03
Other -0.24 (-0.39, -0.1) 0.07 <0.01
Month 0.06 (-0.06, 0.17) 0.06 0.35
Year -0.14 (-0.20,-0.07) 0.03 <0. 01
Longitude -0.10 (-0.11,-0.08) 0.01 <0.01
Latitude -0.13 (-0.15,-0.11) 0.01 <0.01
   
29
FIGURES

Figure 1: The relationship between Global Horizontal Irradiance (GHI),
Diffuse Horizontal Irradiance (DHI) and Direct Normal Irradiance (DNI)

























30

Figure 2a: Map of monitoring and hospital locations in Southern California  







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! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! (
! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! (
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G Hospitals
! ( Solar Stations
¯
0 10 20 30 40 5
Km
31

Figure 2b: Map of monitoring and hospital locations in Northern California






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! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! (
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! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! ( ! (
G Hospitals
! ( Solar Stations
¯
0 10 20 30 40 5
Km
32
Figure 3: Graphs of the empirical and three theoretical variograms
(Gaussian, exponential and spherical) for global horizontal irradiance (a),
direct normal irradiance (b) and diffuse horizontal irradiance (c) in Southern
California. Y axis: semivariance (kW/m
2
) X axis: distance (degrees).

Figure 3a  


















33
Figure 3b






















34
Figure 3c






















35
Figure 4: 3-D scatterplot of estimated global irradiance in Southern
California 
Abstract (if available)
Abstract Background: Previous studies have suggested an association between Multiple Sclerosis (MS) and geographic location, showing higher incidence rates in regions above 40 degrees latitude in both the northern and southern hemispheres. One possible explanation is that exposure to greater amounts of sunlight has a protective effect, suggesting the farther one lives from the equator, the greater the risk of MS. Using the National Inpatient Sample (NIS) database and estimates of average global, horizontal, and direct normal solar irradiance, we investigated the association between exposure to sunlight and outpatient hospital visits with diagnoses of MS attack between 2000 and 2009 in California, United States. ❧ Methods: Spatial statistical methods (variograms and ordinary kriging were applied to monthly averages of solar irradiance data from 24 locations throughout California to generate exposure estimates at the locations of the NIS hospitals. With exposures linked to monthly counts of MS outpatient hospital records, spatio-temporal Poisson models were used to examine association between solar irradiance and MS attacks. Age, sex, race (Caucasian, Black, Hispanic, Asian and other), latitude, longitude, month and year were all considered as potential confounders and included in the Poisson model. ❧ Results: There was a statistically significant negative association between MS and global solar irradiance (p=0.02) after adjusting for age, sex, race, latitude, longitude, month and year but not direct (p=0.09) or diffuse solar irradiance (p=0.43). The number of MS attacks decreased by 42.7% (95% CI: 7.59%, 64.51%) per 1 KWh/m² daily increase in global solar irradiance after adjusting for age, sex, race, latitude, longitude, month and year. ❧ Conclusion: We found that exposure to sunlight has a protective effect on MS attacks in California after adjusting for age, sex, race, latitude, longitude, month and year. These results suggest that moderate exposure to sunlight may be an effective preventive strategy against MS. 
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Asset Metadata
Creator Xiong, Wei (author) 
Core Title The association between sun exposure and multiple sclerosis 
School Keck School of Medicine 
Degree Master of Science 
Degree Program Biostatistics 
Publication Date 04/13/2017 
Defense Date 04/13/2017 
Publisher University of Southern California (original), University of Southern California. Libraries (digital) 
Tag multiple sclerosis,OAI-PMH Harvest,ordinary kriging,Poisson model,sun exposure 
Language English
Contributor Electronically uploaded by the author (provenance) 
Advisor Franklin, Meredith (committee chair), Cen, Steven Yong (committee member), Lane, Christianne (committee member) 
Creator Email weixiong@usc.edu,weixiongstat@gmail.com 
Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-353400 
Unique identifier UC11255826 
Identifier etd-XiongWei-5175.pdf (filename),usctheses-c40-353400 (legacy record id) 
Legacy Identifier etd-XiongWei-5175.pdf 
Dmrecord 353400 
Document Type Thesis 
Rights Xiong, Wei 
Type texts
Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection) 
Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law.  Electronic access is being provided by the USC Libraries in agreement with the a... 
Repository Name University of Southern California Digital Library
Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA
Tags
multiple sclerosis
ordinary kriging
Poisson model
sun exposure