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Population-Based Estimates for the Prevalence of Multiple Sclerosis in the
United States by Race, Ethnicity, Age, Sex, and Geographic Region
Michael Hittle, BS; William J. Culpepper, PhD; Annette Langer-Gould, MD, PhD; Ruth Ann Marrie, MD, PhD;
Gary R. Cutter, PhD; Wendy E. Kaye,PhD; Laurie Wagner, MPH; Barbara Topol, MS; Nicholas G. LaRocca,PhD;
Lorene M. Nelson, PhD, MS; Mitchell T. Wallin, MD, MPH
IMPORTANCE Racial, ethnic, and geographic differences in multiple sclerosis (MS) are
important factors to assess when determining the disease burden and allocating health care
resources.
OBJECTIVE To calculate the US prevalence of MS in Hispanic, non-Hispanic Black (hereafter
referred to as Black), and non-Hispanic White individuals (hereafter referred to as White)
stratified by age, sex, and region.
DESIGN, SETTING, AND PARTICIPANTS A validated algorithm was applied to private, military,
and public (Medicaid and Medicare) administrative health claim data sets to identify adult
cases of MS between 2008 and 2010. Data analysis took place between 2019 and 2022. The
3-year cumulative prevalence overall was determined in each data set and stratified by age,
sex, race, ethnicity, and geography. The insurance pools included 96 million persons from
2008 to 2010. Insurance and stratum-specific estimates were applied to the 2010 US Census
data and the findings combined to calculate the 2010 prevalence of MS cumulated over 10
years. No exclusions were made if a person met the algorithm criteria.
MAIN OUTCOMES AND MEASUREMENTS Prevalence of MS per 100 000 US adults stratified by
demographic group and geography. The 95% CIs were approximated using a binomial
distribution.
RESULTS A total of 744 781 persons 18 years and older were identified with MS with 564 426
cases (76%) in females and 180 355 (24%) in males. The median age group was 45 to 54
years, which included 229 216 individuals (31%), with 101 271 aged 18 to 24 years (14%),
158 997 aged 35 to 44 years (21%), 186 758 aged 55 to 64 years (25%), and 68 539
individuals (9%) who were 65 years or older. White individuals were the largest group,
comprising 577 725 cases (77%), with 80 276 Black individuals (10%), 53 456 Hispanic
individuals (7%), and 33 324 individuals (4%) in the non-Hispanic other category. The
estimated 2010 prevalence of MS per 100 000 US adults cumulated over 10 years was 161.2
(95% CI, 159.8-162.5) for Hispanic individuals (regardless of race), 298.4 (95% CI,
296.4-300.5) for Black individuals, 374.8 (95% CI, 373.8-375.8) for White individuals, and
197.7 (95% CI, 195.6-199.9) for individuals from non-Hispanic other racial and ethnic groups.
During the same time period, the female to male ratio was 2.9 overall. Age stratification in
each of the racial and ethnic groups revealed the highest prevalence of MS in the 45- to
64-year-old age group, regardless of the racial or ethnic classification. With each degree of
latitude, MS prevalence increased by 16.3 cases per 1000 (95% CI, 12.7-19.8; P< .001) in the
unadjusted prevalence estimates, and 11.7 cases per 1000 (95% CI, 7.4-16.1; P< .001) in the
direct adjusted estimates. The association of latitude with prevalence was strongest in
women, Black individuals, and older individuals.
CONCLUSIONS AND RELEVANCE This study found that White individuals had the highest MS
prevalence followed by Black individuals, individuals from other non-Hispanic racial and
ethnic groups, and Hispanic individuals. Inconsistent race and ethnic classifications created
heterogeneity within groups. In the United States, MS affects diverse racial and ethnic
groups. Prevalence of MS increases significantly and nonuniformly with latitude in the United
States, even when adjusted for race, ethnicity, age, and sex. These findings are important for
clinicians, researchers, and policy makers.
JAMA Neurol. doi:10.1001/jamaneurol.2023.1135
Published online May 15, 2023.
Editorial
Supplemental content
Author Affiliations: Author
affiliations are listed at the end of this
article.
Corresponding Author: Mitchell T.
Wallin, MD, MPH, University of
Maryland School of Medicine, VA MS
Center of Excellence–East, 50 Irving
St NW, Washington, DC 20422
(mitchell.wallin@som.umaryland.
edu).
Research
JAMA Neurology | Original Investigation
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Multiple sclerosis (MS) is the most common progres-
sive neurologic disease of youngadults,
1
having a ma-
jor impact on affected individuals as they start their
families and careers. Recent studies have shown acquisition
of the Epstein-Barr virus in adolescence is a major risk factor
for the development of MS by transformation of lympho-
cytes through molecular mimicry.
2,3
A wide variety of disease-
modifying therapies can reduce MS morbidity and prove most
effective when started early in the course of the disease.
4
In the United States, non-White racial and ethnic groups
have dramatically increased their proportion of the popula-
tion over the past 5 decades.
5
Additionally, recent incidence
studies have shown higher rates of MS among Black than
among White individuals, while Hispanic individuals have
moderate rates and Asian and Native American individuals
have the lowest rates.
6,7
Within this demographic backdrop,
the prevalence of MS by race and ethnicity in the United States
has not been adequately assessed. Differences in MS preva-
lence in non-White populationshave been confirmed in stud-
ies in multiple world regions, but there are limited national data
in the United States.
8
Prevalence reflects the burden of disease in a population
and is critical for clinical care, resource allocation, and policy
decisions. Historically, the White population in the United
States has had a much higher prevalence of MS compared with
the non-White population.
9
In the 2002 National Health In-
terview Survey, White individuals had a 2-fold higher preva-
lence of MS (96 per 100 000) than did Black individuals (48
per 100 000) and all other racial groups (43 per 100 000).
10
An-
other report in 3 regions of the United States found more vari-
able estimates per 100 000 with Black and White individuals
having the highest prevalence (90.9 and 99.4, respectively),
and Hispanic individuals having a much lower prevalence (56
per 100 000) in the state of Ohio.
11
Aboriginal populations in
the United States
7
and Canada
12
have had significantly lower
MS incidence and prevalence than White individuals, respec-
tively.
Because of the challenges in estimating MS prevalence for
the United States, the National Multiple Sclerosis Society
(NMSS) formed the Multiple Sclerosis Prevalence Workgroup
with the goal of producing a scientifically sound and economi-
cally feasible national MS prevalence estimate. By applying a
validated case algorithm for MS
13
to multiple large adminis-
trative health claim (AHC) data sets,
14
we aimed to generate a
robust national MS prevalence estimate for the adult popula-
tion, stratified by race, ethnicity, age, sex, and geographic re-
gion.
Methods
This study was approved by the institutional review boards at
the Department of Veterans Affairs (VA) Medical Center–
Baltimore, Maryland; University of Maryland Medical Cen-
ter, Baltimore; Stanford University, Stanford, California; and
Quorum Review. Standard contracts and data use agree-
ments were obtained for the analysis of all data sets. Due to
the nature of the study, informed consent was waived.
In the United States, health insurance may be obtained
from several private or public (government)sources, and a pro-
portion of the population is uninsured. The data sets for this
analysis were obtained by the NMSS to represent US private
and government-sponsored insurance programs, reasoning
that nearly all persons with MS (except uninsured people, Na-
tive American individuals exclusively using the Indian Health
Service, and incarcerated people) would receive health ser-
vices through 1 of these programs.
Each data set included the adult population (aged ≥18
years) and their health care use for the years 2008 through
2010. The data sets used in this analysis included Optum’s dei-
dentified Clinformatics Data Mart (CDM) database represent-
ing private health insurance and Medicaid, Medicare and the
VA representing the major government health insurance pro-
grams. Further information on insurance data sets, the MS di-
agnostic algorithm, latitude bands, and state aggregation are
described in the eMethods in the Supplement.
Race and Ethnicity
The AHC data sets varied with respect to the information cap-
tured. Therefore, we developed a common data dictionary and
variable list for this analysis in keeping with recommenda-
tions for retrospective data harmonization. These included a
denominator file for all enrollees, including dates of insur-
ance eligibility, sex, race, ethnicity, year of birth, and geo-
graphic region of residence. It should be noted that Hispanic
is an ethnicity classification and not a race designation. Un-
fortunately, there was not a uniform set of race or ethnicity
codes across all data sets (eTable 1 in the Supplement), even
within the government health insurance programs. We had no
ability to modify the race or ethnicity classifications within AHC
data and therefore had to use categorizations that were the
same across AHC sources: Hispanic, non-Hispanic Black (here-
after referred to as Black), non-Hispanic White (hereafter re-
ferred to as White), and non-Hispanic other (hereafter re-
ferred to as other). The non-Hispanic other category included
individuals who were Asian or Pacific Islander or Native Ha-
waiian, Native American or Alaska Native, and multiracial. The
percentage of patients for which race and ethnicity were un-
known varied between data sources (Medicare 0.2%, VA 3.3%,
Key Points
Question What is the prevalence of multiple sclerosis (MS) in the
United States in Hispanic, non-Hispanic Black, and non-Hispanic
White individuals?
Findings This cohort study found that in 2010 overall MS
prevalence was highest in non-Hispanic White individuals followed
by those who were non-Hispanic Black, members of other
non-Hispanic race and ethnic groups, and Hispanic. Differences in
MS prevalence between racial and ethnic groups varied by US
Census region, and a strong association was observed between
geographic latitude and prevalence.
Meaning Within the United States, where MS prevalence varies
by sex, age, race, ethnicity, and latitude, the most substantial
burden is borne by individuals in non-White and Hispanic racial
and ethnic groups.
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CDM 4.6%, and Medicaid 7.0%). Individuals with unknown
race and ethnicity data from all AHC data sets were included
in the non-Hispanic other category.
In terms of missing race data in US AHC for the years 2008
through 2010, we are in agreement with the proportions cited
in a recent review of large US AHC data sets.
15
In our data set,
overall, Medicare AHC data sets had less than 1% missing race
and ethnicity.Among individuals with MS in the Medicaid AHC
data set, 7% had unknown race and ethnicity.For the CDM data
set, 5% of individuals with MS had unknown values for the race
and ethnicity variable, but this was after Optum applied im-
putation using a proprietary algorithm
16
for persons with un-
certain race and ethnicity data. A recent article indicated that
the proportion of records for which Optum applied this impu-
tation process was 26% for the time period 2000 through 2016,
a percentage that is comparable with that of the National
Healthcare Cost and Utilization Project.
17
Finally, the VA data
set was missing race or ethnicity for 2% of individuals with MS
while 6% of the total records were missing race or ethnicity.
Prevalence Estimates
To obtain a national US prevalence estimate for MS, we un-
dertook several analytic steps similar to our initial US preva-
lence analysis.
13
The term cumulative prevalence applies to our
case finding approach within data sets in that once an indi-
vidual met the MS case definition for a given year, that per-
son was counted as a case for subsequent years through 2010
if they remained alive and active in the health plan. Cumula-
tive prevalence allows for case ascertainment within a health
insurance plan where there is often sporadic patient follow-
up. This method of case ascertainment effectively represents
a limited-duration (3-year) cumulative prevalence for the year
2010. Ultimately, the prevalence estimate of interest is life-
time prevalence, which is the proportion of a population that
at some point in life (up to the time of assessment) has devel-
oped MS.
In chronic, predominantly relapsing diseases such as MS
that start in early adult life, individuals may forgo contact with
the health system for extended periods. Thus, long periods of
observation (minimum 10 years) are needed to approach life-
time prevalence in the assessment of AHC data sets. As noted
in our methods article,
12
by using AHC data sets available from
Intercontinental Marketing Services, the VA, and the prov-
ince of Manitoba over the period of 2000 through 2016, we de-
termined the proportion of cases missed by using a 3-year vs
10-year cumulative prevalence estimate.On the basis of these
findings, undercount adjustments for the 10-year cumulative
prevalence were required, and we applied these factors to de-
rive estimates for the 2010 prevalence of MS cumulated over
10 years.
13
For the CDM and VA data sets, enrollees who also had Medi-
care coverage were removed from the numerators and de-
nominators within each data set to prevent double counting.
The annual prevalence within a given data set was demar-
cated as all those who met the MS case definition divided by
the annual population at risk, defined as all enrollees 18 years
and older at the beginning of the calendar year and with health
plan eligibility for a total of 6 months within the calendar year.
Because individuals with MS may have variable contact with
the health system, once an enrollee met the case definition and
remained eligible for care, they were considered a case there-
after. Applying the algorithm to each data set, we determined
the prevalence at the end of the 3-year study period by iden-
tifying all persons who met the case definition in any 1 of the
3 study years who were still alive and eligible for care in the
last year of the study period (2010) and dividing this by the
population at risk in 2010.
18,19
Confidence intervals were calculated for the final total
number of cases using binomial CIs: ±1.96 × 兹(NPQ), where
P and Q are the proportions of cases and noncases and N is the
estimated US population in 2010. The 95% CIs were then ad-
justed for the rate per 100 000 with a fixed-effects model to
account for underascertainment due to short duration of fol-
low-up. We adjusted results based on uninsured status for all
major race and ethnicity groups based on population health
insurance estimated from the 2010 American Community
Survey.
20
To carry out analyses examining geographic variation of
MS prevalence, numerator and denominator strata totals were
computed for each state after estimating insurance utiliza-
tion ratios for each state using the 5-year American Commu-
nity Survey for 2007 through 2012.
21
To control for race, eth-
nicity,age, and sex, we applied direct standardization methods
to the crude prevalence proportions using the 2010 US Cen-
sus population as the reference population. Data from the CDM
data set were also aggregated by latitudinal band, with each
band consisting of the entire space within the contiguous
United States existing between each major degree of latitude.
Twenty-four latitudinal bands were used to clearly depict the
north-south gradient. To examine the association between lati-
tude and MS prevalence, we computed Pearson correlation co-
efficients and corresponding 95% CIs.
We conducted the statistical analyses using R version3.6.2
(R Project for Statistical Computing) within RStudio version
1.1.442, SAS version 9.4 (SAS Institute), and SPSS version 22
(IBM). We followedthe Strengthening the Reporting of Obser-
vational Studies in Epidemiology (STROBE) guidelines for
reporting observational studies.
22
Results
A total of 744781 persons 18 years and older were identified
with MS with 564 426 cases (76%) in females and 180 355 (24%)
in males. The median age group was 45 to 54 years, which in-
cluded 229 216 individuals (31%), with 101 271 aged 18 to 24
years (14%), 158997 aged 35 to 44 years (21%), 186758 aged
55 to 64 years (25%), and 68 539 individuals (9%) who were
65 years or older. White individuals were the largest group,
comprising 577 725 cases (77%), with 80 276 Black individu-
als (10%), 53 456 Hispanic individuals (7%), and 33 324 indi-
viduals (4%) in the other category.
The 2010 prevalence for MS per 100 000 US adults cumu-
lated from 2008 to 2010 classified by race, ethnicity, and sex
is displayed in Table 1. Prevalence was found to differ from
highest to lowest in the following order: White individuals,
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Black individuals, individuals from other races, and Hispanic
individuals (Table 1).
The prevalence of MS by age, race, and ethnicity is dis-
played in Table 2. Within age categories, MS prevalence was
highest in White individuals, followed by Black individuals,
and then individuals from other races, with the lowest preva-
lence in Hispanic individuals. The visibly higher prevalence
within midadult life with a slightly lower prevalence in the old-
est age groups is shown in Figure 1. Stratification of racial and
ethnic groups by the 4 US Census regions is shown in eTable 2
in the Supplement. Foreach rac ial and ethnic group,the preva-
lence of MS was higher in the Northeast and Midwest when
compared with the South and West regions. The highest sex
ratios (female to male) were noted in the 45- to 54-year-oldage
group for all racial and ethnic categories as follows: Hispanic
(4.4), Black (3.7), White (3.2), and other (3.6). Separation in
prevalence between women and men was greatest in the
middle adult years in each region of the United States.
Table 1. 2010Prevalence of Multiple Sclerosis per 100 000 Adults Cumulated Over 10 Years in the United States by Race, Ethnicity, and Sex
Race and
ethnicity
a
Female Male Total
No. of
cases
2010 Cumulative
prevalence (95% CI)
b
No. of
cases
2010 Cumulative
prevalence (95% CI)
b
No. of
cases
2010 Cumulative
prevalence (95% CI)
b
2010 Cumulative prevalence
adjusted for uninsured (95%
CI)
b,c
Hispanic 38 705 235.3 (233.0-237.7) 14 749 88.2 (86.8-89.6) 53454 161.2 (159.8-162.5) 214.4 (212.5-216.1)
Non-Hispanic
Black
62 464 429.8 (426.4-433.2) 17 811 144.0 (141.9-146.2) 80 276 298.4 (296.4-300.5) 358.1 (355.7-360.6)
Non-Hispanic
White
437 404 543.3 (541.7-544.9) 140322 190.6 (189.6-191.6) 577 726 374.8 (373.8-375.8) 423.5 (422.7-424.7)
Non-Hispanic
other
25 851 290.6 (287.0-294.1) 7471 93.9 (91.8-96.0) 33 323 197.7 (195.6-199.9) 247.1 (244.5-245.6)
Total 564 424 468.9 (467.6-470.1) 180 353 163.0 (162.2-163.7) 744 778 322.3 (321.6-323.1) 373.5 (373.1-384.8)-
Abbreviation: AHC, administrative health claim.
a
These categorizations were used because they were the same across AHC data
sources. The non-Hispanic other category included individuals who were Asian
or Pacific Islander or Native Hawaiian, Native American or Alaska Native, and
multiracial and those with unknown race and ethnicity data.
b
Per 100 000 adults.
c
Adjustment based on Artiga et al.
20
Table 2. 2010 MS Prevalence of Multiple Sclerosis per 100 000 Adults Cumulated Over 10 Yearsin the United States by Age, Sex, Race, and Ethnicity
Age, y Sex
Hispanic
a
Non-Hispanic Black
a
Non-Hispanic White
a
Non-Hispanic other
a
No. of
cases
2010 Prevalence
(95% CI)
b
No. of
cases
2010 Prevalence
(95% CI)
b
No. of cases
2010 Prevalence
(95% CI)
b
No. of
cases
2010 Prevalence
(95% CI)
b
18-34 Female 9665 140.1
(137.3-142.8)
10 751 219.5
(215.3-223.6)
46 543 229.1
(227.0-231.1)
5120 158.7
(154.3-163.0)
Male 5799 75.8 (73.8-77.7) 4777 103.2
(100.3-106.1)
17 196 82.6 (81.4-83.9) 1420 46.3 (43.9-48.7)
Total 15 464 106.4
(104.6-107.9)
15 528 163.0
(160.4-165.5)
63 739 155.0
(153.8-156.2)
6540 103.9
(101.4-106.4)
35-44 Female 10 944 307.3
(301.5-313.0)
15 060 556.6
(547.7-565.4)
86 893 692.4
(687.8-697.0)
6190 341.2
(332.7-349.7)
Male 4410 118.4
(114.9-121.9)
4310 179.6
(174.3-185.0)
29 438 233.2
(230.6-235.9)
1752 106.7
(101.7-111.7)
Total 15 354 210.7
(207.4-214.0)
19 370 379.4
(374.1-384.8)
116 331 462.3
(459.5-464.8)
7942 229.8
(224.8-234.9)
45-54 Female 11 079 406.8
(399.2-414.3)
19 913 696.2
(686.5-705.81)
137 780 876.3
(872.1-881.3)
8367 522.3
(511.2-533.5)
Male 2500 91.6 (89.0-95.1) 4723 187.8
(182.5-193.2)
42 743 277.0
(274.4-279.6)
2111 146.7
(140.4-152.9)
Total 13 579 249.0
(244.8-253.2)
24 636 458.3
(452.6-464.1)
180 523 579.6
(577.0-582.3)
10 478 344.5
(338.0-351.1)
55-64 Female 5967 356.2
(347.1-365.2)
12 858 621.4
(610.7-632.13)
117 827 842.3
(837.5-847.1)
5588 470.4
(458.1-482.7)
Male 1659 108.3
(103.1-113.5)
3316 193.0
(186.4-199.6)
37 659 282.9
(280.0-285.7)
1884 179.5
(171.4-187.6)
Total 7626 237.8
(232.4-243.1)
16 174 427.1
(420.5-433.6)
155 486 569.5
(566.7-572.3)
7472 333.9
(326.3-341.5)
≥65 Female 1051 66.3 (62.3-70.3) 3882 194.1
(188.9-200.2)
48 361 269.7
(267.3-272.1)
587 55.0 (50.6-59.5)
Male 382 35.4 (31.8-38.9) 686 62.1 (57.5-66.8) 13 285 115.9
(113.9-117.9)
305 40.3 (35.8-44.9)
Total 1433 53.8 (51.0-56.45) 4568 147.2
(142.9-515.5)
61 646 209.7
(208.1-211.4)
892 48.9 (45.7-52.2)
Abbreviation: AHC, administrative health claim.
a
These categorizations were used because they were the same across AHC data
sources. The non-Hispanic other category included individuals who were Asian
or Pacific Islander or Native Hawaiian, Native American or Alaska Native, and
multiracial and those with unknown race and ethnicity data.
b
Per 100 000 adults.
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Figure 2 reveals the 10-year cumulative prevalence per
100 000 for MS in 2010 by race, ethnicity, age, and US Census
region. In reference to the 18- to 34-year-old group, Black and
White females had the most dramatic increase in prevalence
per 100 000 persons to the 45- to 54-year-old group goingf rom
219.5 (95% CI, 215.3-226.3)to 696.2 (95% CI, 686.5-705.8) and
229.1 (95% CI, 227.0-231.1) to 876.3 (95% CI, 872.1-881.3), re-
spectively. In contrast, other females and Hispanic females had
Figure 1. 2010 Prevalence of Multiple Sclerosis per 100 000 Adults Cumulated Over10 Years in the United
States by Age, Sex, Race, and Ethnicity
1000
800
600
400
200
0
Prevalence, No. per 100
000
Age group, y
18-34 35-44 45-54 55-64 ≥65
Female non-Hispanic White
Male non-Hispanic White
Female non-Hispanic Black
Male non-Hispanic Black
Female Hispanic
Male Hispanic
Non-Hispanic other female
Non-Hispanic other male
Figure 2. 2010 Prevalence of Multiple Sclerosis per 100 000 Adults Cumulated Over10 Years in the United States by Age, Sex, Race, Ethnicity, and US
Geographic Census Region: Northeast (Region 1), Midwest (Region 2), South (Region3), and West (Region 4)
1000
800
600
400
200
1200
1000
800
600
400
200
800
600
400
200
1000
800
600
400
200
1200
0
Prevalence, No. per 100
000
Age group, y
18-34 35-44 45-54 55-64 ≥65
Northeast
A
1200
1000
0
Prevalence, No. per 100
000
Age group, y
18-34 35-44 45-54 55-64 ≥65
Midwest
B
0
Prevalence, No. per 100
000
Age group, y
18-34 35-44 45-54 55-64 ≥65
South
C
1200
0
Prevalence, No. per 100
000
Age group, y
18-34 35-44 45-54 55-64 ≥65
West
D
Female non-Hispanic White
Male non-Hispanic White
Female non-Hispanic Black
Male non-Hispanic Black
Female Hispanic
Male Hispanic
Non-Hispanic other female
Non-Hispanic other male
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less dramatic increases between the 18- to 34-year-old and 45-
to 54-year-old groups with overall growth of 363.3 and 266.7,
respectively. Compared with females, males had more mod-
est increases in MS prevalence with most racial and ethnic
groups peaking at older ages. For example, MS prevalence
among White men was 82.6 (95% CI, 81.4-83.9) for 18- to 34-
year-olds and increased to 282.9 (95% CI, 280.0-285.7) for 55-
to 64-year-olds. Hispanic men had the slowest increase in MS
age-specific prevalence starting from 82.6 (95% CI, 81.4-
83.9) for 18- to 34-year-olds and growing only to 118.4 (95%
CI, 114.9-121.9) for 35- to 44-year-olds. Prevalence estimates
for individual MS data sets with the complete race and eth-
nicity variables (as represented in their data sets) are pre-
sented in eTables 3-6 in the Supplement.
Figure 3 depicts age-, sex-, race-, and ethnicity-adjusted
prevalence estimates by state and by latitudinalband. Figure 3A
clearly shows higher prevalence estimates in the northern lati-
tudes, with the highest estimates occurring in the mountain
states. Weobser ved a strong association between latitude and
prevalence in unadjusted prevalence with r= 0.80 (95% CI,
0.42-0.77) and direct standardized prevalence with r=0.62
(95% CI, 12.7-19.8). Unless otherwise stated, prevalence is ex-
pressed as cases per 100 000. With each degree of latitude, the
unadjusted prevalence increased by 16.2 cases (95% CI, 12.7-
19.8), and the direct adjusted prevalence increased by 11.7c ases
(95% CI, 7.4-16.1). Figure 3B shows the prevalence estimates
per latitudinal band, as adjusted by age, sex, race, and ethnic-
ity. The Pearson correlation coefficient between latitude and
MS prevalence was r= 0.82(95% CI, 0.62-0.92). Witheach de-
gree of latitude, prevalence increased by 7.8 cases (5.4-10.1)
in both the unadjusted and direct standardized data.
Discussion
In this national population-based cohort study of MS preva-
lence, we found that the distribution of MS in the United States
has become more racially and ethnically diverse. White indi-
viduals continued to have the highest prevalence of MS fol-
lowed by Black individuals, individuals from other races, and
Hispanic individuals. Age-specific prevalence peaked in the 45-
to 54-year-old group for women of every racial and ethnic
group. With the exceptionof prevalence for Hispanic men peak-
ing in the 35- to 44-year-old group, the remainder of the male
racial groups peaked at age 55 to 64 years. The relationship be-
tween latitude and MS prevalence was observed in unad-
justed and standardized data across insurance sources and
within all of the demographic strata examined (sex, age, and
race).
A small number of published reports have examined MS
prevalence by race in the United States. One of the earliest stud-
ies used data from the National Health Interview Survey (1989-
1994) to calculate MS prevalence estimates per 100 000 adults
in the United States for White (96) and Black individuals (48)
and those from all other racial groups (43).
10
These estimates
were considerably low overall compared with those per-
formed in the same time period by other groups.
23
A recent
study assessed MS prevalence with the Behavioral Risk Fac-
tors Surveillance System in 4 states.
24
For 2015-2016, the MS
prevalence estimates for Black, Hispanic, and White respon-
dents were 741, 349, and 824 per 100 000, respectively. While
yielding higher overall prevalence estimates with a tele-
phone survey approach, the race and ethnicity proportions
were similar to those of the current study. Using the Kaiser
Permanente Southern California data set, investigators re-
cently reported a similarly high MS prevalence per 100 000 in
Black (225.8) and White individuals (237.7) and significantly
lower in Asian (22.6) and Hispanic persons (69.9) in Southern
California.
25
Only a single study estimated MS prevalence in the United
States by race and ethnicity and compared estimates be-
tween geographic regions.
11
In 1998, regional MS prevalence
studies were carried out using record reviews from neurol-
ogy practices and nursing homes in 2 counties (1 in Ohio and 1
in Texas).
11
In Ohio, the estimated prevalence of MS was 90.9
for Black individuals, 56.0 for Hispanic individuals, and 99.4
Figure 3. Maps of Direct Age-, Sex-, Race-, and Ethnicity-Adjusted Prevalence of Multiple Sclerosis (MS)per 100 000 Cumulated Over 10 Years by
Latitude in the Contiguous United States, 2008-2010
332 to 616
Direct adjusted MS prevalence per 100
000
291 to 332
254 to 291
235 to 254
208 to 235
167 to 208
Choropleth map
A
Adjusted MS prevalence per 100
000
250
Flordia
Georgia
South
Carolina
North
Carolina
Alabama
Mississippi
LouisianaTexas
Arkansas
Oklahoma
New Mexico
Arizona
Utah
Colorado
Kansas
Nebraska
Missouri
Illinois
Michigan
Indiana
Ohio
Pennsylvania
New York
Maine
Vermont
New
Hampshire
Massachusetts
Rhode Island
Connecticut
New Jersey
Maryland
Delaware
West
Virgina Virgina
Kentucky
Tennessee
Wisconsin
Iowa
Minnesota
South Dakota
Wyoming
Idaho
Oregon
Washington
Montana North Dakota
Nevada
California
300 350 400 450
Latitude band analyses
B
The choropleth map (A) represents state-aggregated analyses. The latitude band analyses used Optum Clinformatics Data Mart data only.
Research Original Investigation Population-Based Estimates for the Prevalence of Multiple Sclerosis in the United States
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for White individuals, whereas in Texas, MS prevalence was
22.1, 11.2, and 56.0, respectively. A north-south geographic gra-
dient was observed for the overall and race-specific MS preva-
lence estimates with Ohio having 2- to 5-fold higher ratios com-
pared with Texas. The authors noted that case finding in
neurology practices might have resulted in underascertain-
ment among non-White and Hispanic individuals in the lower
socioeconomic strata in Texas.
The relatively high prevalence ratios we haveobser vedfor
racial and ethnic minority groups can be attributed to several
factors. First, the incidence rates of MS for Black individuals
in the United States have been highest of all racial and ethnic
groups for the past 3 decades.
6,7
Based on longitudinal cohort
data from the US military, the differential MS risk in Black com-
pared with White individuals has been growing since the
1940s.
7
Additionally, MS incidence rates for Hispanic indi-
viduals in the United States, many of whom are recent immi-
grants, are higher than in their home country.
26
Second, some
of the increase in MS prevalence in racial and ethnic minority
groups may be due to improved access to health care and
greater recognition of an MS diagnosis within the magnetic
resonance imaging era.
27
Third, despite recent reductions in
life expectancy related to the COVID-19 and opiate overdose
pandemics, overall life spans have been slowly increasing for
all racial and ethnic groups in the United States over the past
50 years.
28
Because our report included most government-
sponsored health insurance programs, we had an opportu-
nity to include non-White cases to the greatest extent pos-
sible over the life span. However, a recent study found more
than 80% of patients with MS have seen neurologists even in
regions with lower socioeconomic status.
29
A final issue to note
is we did not assess the prevalence of MS in children, and this
should be considered when our findings are compared with
those reported in other populations. If we use our age-
stratified rates for 2010 (low and high estimates), they fall
within the range of the 2006 MS prevalence estimates in Mani-
toba, Canada, for all 10-year age groups.
30
The prevalence of MS in Black and Hispanic individuals
may be underestimated in this analysis for several reasons.
First, large privately insured health AHC data sets have race
and ethnicity that is unknown for approximately 25% or more
of their patient population.
17
These AHC databases thereby rely
on imputation of unknown race and ethnicity data to fill the
gap using methods that have been shown to be least reliable
for non-White individuals. For example, concordance of race
information derived from electronic health records with the
CDM data set revealed moderate to low positive predictive
value for Black (40%-74%) and Hispanic (52%-77%) individu-
als compared with individuals who were White (94%-95%).
31
Thus, the CDM data set in our analysis was subject to misclas-
sification of Black and Hispanic individuals and potentially
other groups. Second, as cited in our Methods, the unknown
race category for the Medicare, Medicaid, and VA AHC data-
bases was placed in the “other” race and ethnicity category.
Although the percentage of race and ethnicity data that was
unknown was small (<5%), this reclassification within each
AHC data set may have diluted the prevalence estimates for
Black and Hispanic individuals with MS. Finally, rates of uti-
lization in health care systems are generally lower for Black and
Hispanic patients, which may diminish the ability to identify
people with MS as cases.
16
Strengths and Limitations
Limitations in this analysis included the lack of consistency
in the coding of race and ethnicity throughout the AHC data
sets. Unknown race was part of all AHCs and was grouped
within the other race category, thus creating an imprecise cat-
egory and an inability to determine the prevalence of MS in
other standard groups with the US Census, including Asian,
Native American, and Alaska Native individuals and multira-
cial groups with MS. CDM imputed unknown race based on an
internal algorithm, and this approach likely resulted in some
misclassification.
32,33
In addition, Hispanic was considered a
race by CDM, so some Black Hispanic individuals were likely
grouped with Hispanic and non-Black individuals. Because not
all AHC data sources had an Asian category, we could not es-
timate MS prevalence separately for Asian individuals, and they
were included in the “other” group. We did not include data
for children, the Indian Health Service, the US prison system,
or undocumented US residents in our prevalence estimates.
These segments of the population are relatively small, or in the
case of children, would contribute few cases,
34
and many in-
dividuals could be detected by other health systems, includ-
ing the Medicare insurance program, at some point later in life.
Strengths of our analysis included the large sample size
(which captured one-third of the US population), the use of a
validated MS case-finding algorithm, broad health care sys-
tem representation, and a population-based approach thatcon-
sidered the complexity of the US health care system.
Conclusions
Our contemporary assessment of the US national prevalence
estimate for MS stratified by race and ethnicity revealed that
the burden of MS is highest in White individuals followed by
Black individuals, those from other races, and Hispanic indi-
viduals (of any race). Northern regions of the United States con-
tinue to have a higher prevalence of MS across racial and eth-
nic groups. Additional analyses are needed to examine
climatological, demographic, infectious, and other factors that
may contribute to this geographic variation. In the United
States, MS has become more prevalent and demographically
diverse. These data are important for clinicians, researchers,
and policy makers.
ARTICLE INFORMATION
Accepted for Publication: February 4, 2023.
Published Online: May 15, 2023.
doi:10.1001/jamaneurol.2023.1135
Open Access: This is an open access article
distributed under the terms of the CC-BY License.
© 2023 Hittle M et al. JAMA Neurology.
Population-Based Estimates for the Prevalence of Multiple Sclerosis in the United States Original Investigation Research
jamaneurology.com (Reprinted) JAMA Neurology Published online May 15, 2023 E7
jamanetwork/2023/neu/05_15_2023/noi230024pap PAGE: right 7 SESS: 20 OUTPUT: Apr 17 16:12 2023
Author Affiliations: Stanford University School of
Medicine, Stanford, California (Hittle, Topol,
Nelson); Department of Veterans Affairs Multiple
Sclerosis Center of Excellence, Baltimore, Maryland
(Culpepper, Wallin);University of Maryland School
of Medicine, Baltimore (Culpepper, Wallin);
Southern California Permanente Medical Group,
Pasadena (Langer-Gould); Department of Internal
Medicine, Max Rady College of Medicine, Rady
Faculty of Health Sciences, University of Manitoba,
Winnipeg, Manitoba, Canada (Marrie); Department
of Community Health Sciences, Max Rady College
of Medicine, Rady Faculty of Health Sciences,
University of Manitoba, Winnipeg, Manitoba,
Canada (Marrie); University of Alabama at
Birmingham (Cutter); McKing Consulting
Corporation, Atlanta, Georgia (Kaye, Wagner);
National Multiple Sclerosis Society, New York, New
York (LaRocca).
Author Contributions: Drs Nelson and Wallin had
full access to all of the data in the study and take
responsibility for the integrity of the data and the
accuracy of the data analysis. Drs Nelson and Wallin
are co–senior authors.
Concept and design: Hittle, Culpepper,
Langer-Gould, Marrie, Cutter,LaRocca, Nelson,
Wallin.
Acquisition, analysis, or interpretation of data:
Hittle, Culpepper, Langer-Gould,Cutter, Kaye,
Wagner,Topol, LaRocca, Nelson, Wallin.
Drafting of the manuscript: Hittle, Topol, LaRocca,
Nelson, Wallin.
Critical revision of the manuscript for important
intellectual content: All authors.
Statistical analysis: Hittle, Culpepper,Cutter, Topol,
LaRocca, Nelson.
Obtained funding: LaRocca, Nelson, Wallin.
Administrative, technical, or material support:
Culpepper, Cutter, Kaye, Wagner, LaRocca, Nelson,
Wallin.
Supervision: Culpepper, Nelson, Wallin.
Conflict of Interest Disclosures: Mr Hittle reported
grants from the National Institutes of Health (NIH)
and personal fees from Institute for Clinical
Research during the conduct of the study.Dr
Culpepper reported grants from National Multiple
Sclerosis Society (NMSS) during the conduct of the
study,suppor t from the Veterans Health
Administration MS Center of Excellence, and being
a member of the NMSS Health Care Delivery and
Policy Research study section. Dr Langer-Gould
reported being principal investigator for 2
industry-sponsored phase 3 clinical trials (Biogen
Idec, Hoffman-LaRoche) and 1 industry-sponsored
observation study (Biogen Idec) and grant support
from the NIH, National Institute of Neurological
Disorders and Stroke (NINDS), Patient-Centered
Outcomes Research Institute, and NMSS. Dr Marrie
reported being co-investigator on trials sponsored
by Roche and Biogen outside the submitted work;
support from the Waugh Family Chair in Multiple
Sclerosis; research funding from Canadian Institutes
of Health Research, Research Manitoba, Multiple
Sclerosis Society of Canada, Multiple Sclerosis
Scientific Foundation, Consortium of MS Centers,
and NMSS; and serving on the editorial board of
Neurology. Dr Cutter reported being a member of
data and safety monitoring boards for Applied
Therapeutics, AI Therapeutics, AstraZeneca, Avexis
Pharmaceuticals, AMO Pharmaceuticals, Apotek,
Biolinerx, Brainstorm Cell Therapeutics, Bristol
Myers Squibb/Celgene, CSL Behring, Galmed
Pharmaceuticals, Green Valley Pharma, Horizon
Pharmaceuticals, Immunic, Karuna Therapeutics,
Mapi Pharmaceuticals, Modigenetech/Prolor,
Merck, Merck/Pfizer, Mitsubishi Tanabe Pharma
Holdings, Opko Biologics, Prothena Biosciences,
Novartis, Regeneron, Neurim, Sanofi-Aventis,Reata
Pharmaceuticals, Receptos/Celgene, Teva
Pharmaceuticals, National Heart, Lung, and Blood
Institute (protocol review committee), Eunice
Kennedy Shriver National Institute of Child Health
and Human Development (Obstetric-Fetal
Pharmacology Research Center oversight
committee), University of Texas Southwestern,
University of Pennsylvania, and Visioneering
Technologies; being a member of consulting or
advisory boards for Atara Biotherapeutics, Argenix,
Bioeq, Consortium of MS Centers (grant),
Genzyme, Genentech, Innate Therapeutics,
Klein-Buendel Incorporated, Medimmune, Medday,
Novartis, Opexa Therapeutics, Roche, Savara,
Somahlution, TevaPharmaceuticals, Transparency
Life Sciences, and TG Therapeutics; receiving
personal fees from Alexion, Antisense
Therapeutics, Biogen, Clinical Trial Solutions,
Entelexo Biotherapeutics, Genzyme, Genentech,
GW Pharmaceuticals, Immunic, Immunosis,
Klein-Buendel Incorporated, Merck/Serono,
Novartis, Perception Neurosciences, Protalix
Biotherapeutics, Regeneron, Roche, and SAB
Biotherapeutics; and receiving personal fees from
Pythagoras (company owned for consulting)
outside the submitted work. Dr Kaye reported
funding from the Agency for ToxicSubstances and
Disease Registry,NMSS, and Association for the
Accreditation of Human Research Protection
Programs. Dr Wagner reported funding from the
Agency for ToxicSubstances and Disease Registry
and NMSS. Dr LaRocca reported being previously
employed full-time by the NMSS. Dr Nelson
reported grants from the Centers for Disease
Control and Prevention, NIH, and NMSS; contracts
from the Agency for ToxicSubstances and Diseases
Registry; compensation for serving as a consultant
to Acumen; and being on a data monitoring
committee for Neuropace. Dr Wallin reported
serving on data safety monitoring boards for the
NIH NINDS; being a member of the NMSS Health
Care Delivery and Policy Research study section;
and receiving funding support from the NMSS and
Department of Veterans Affairs Merit Review
Research Program. No other disclosures were
reported.
Funding/Support: This study was funded by a
grant from the National Multiple Sclerosis Society
(HC-1508-05693).Data for this project were
accessed using the Stanford Center for Population
Health Sciences Data Core, which is supported by a
National Institutes of Health National Center for
Advancing TranslationalScience Clinical and
Translational Science Award (UL1 TR001085) and
internal Stanford funding.
Role of the Funder/Sponsor:The study sponsor,
National Multiple Sclerosis Society (NMSS), had a
role in the design and conduct of the study and in
the collection, management, analysis, and
interpretation of the data but did not have a role in
the preparation, review,or approval of the
manuscript or decision to submit the manuscript for
publication. The datasets for this study were
purchased and are owned by the NMSS.
Disclaimer: The content is solely the responsibility
of the authors and does not necessarily represent
the official views of the National Institutes of
Health.
Data Sharing Statement: See Supplement 2.
Additional Contributions: We thank the following
co-investigators and members of the US Multiple
Sclerosis Prevalence Workgroup for their
contributions, including primary founding members
(Stephen Buka, ScD; Jon Campbell, PhD; Albert Lo,
MD, PhD, and Helen Tremlett, PhD) and
contributing members (Piyameth Dilokthornsakul,
PhD; Lie Chen, DrPH; Robert McBurney,PhD, and
Oleg Muravov, PhD).
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