research article spatial analysis of global prevalence of ...department of e rapist assistant,...

Research ArticleSpatial Analysis of Global Prevalence of Multiple SclerosisSuggests Need for an Updated Prevalence Scale

Brett J Wade

Department of Therapist Assistant Okanagan College 1000 KLO Road Kelowna BC Canada V1Y 4X8

Correspondence should be addressed to Brett J Wade bwadeokanaganbcca

Received 8 November 2013 Revised 12 January 2014 Accepted 13 January 2014 Published 16 February 2014

Academic Editor Sten Fredrikson

Copyright copy 2014 Brett J WadeThis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Multiple sclerosis (MS) is a demyelinating disease of the central nervous system with an unknown aetiology MS has a geographicpattern of prevalence with high prevalence rates between 45 degrees and 65 degrees north In much of the northern hemispherethere exists a prevalence gradient with increasing prevalence from south to north While genetics may partially explain thelatitudinal gradient it is not strong enough to exclude exogenous variables Kurtzke initially came up with a three-zone scale forlow medium and high prevalence zones He defined high as 30 or more per 100000 medium as 5ndash29 per 100000 and low asless than 5 per 100000 In this study 131 geographic datasets (geocases) were spatially analyzed to determine whether the existingglobal prevalence scale needed to be updated The mean prevalence rate was 6783100000 with rates ranging from 350100000to 0100000 The results of this study suggest that the commonly referenced scale for global MS prevalence needs to be updatedwith added zones to reflect significantly higher prevalence rates in some areas of the world We suggest a five-zone scale very high(170ndash350) high (70ndash170) medium (38ndash70) low (13ndash38) and very low (0ndash13)

1 Introduction

MS is most common in people of northern European ances-try It has been concluded by many that genetics are animportant factor in disease expression however geneticsalone are not enough to guarantee the development of MSMonozygotic twin studies indicate a clinical concordancerate between twenty and thirty percent [1] compared witha two percent to five percent same-sex fraternal twin rateThe National MS Society states that when a sibling or parenthas MS the chance of developing MS is ldquo1 in 40rdquo [2] Itappears that some environmental factor is required to interactwith genetics to trigger the onset of MS Some environmentalagents that have been studied as a possible cofactor in theexpression of MS include viruses hormones vitamin Ddeficiency UVB deficiency diet smoking and others [3]Studying the effects of environment on disease expression canbe perplexing Factors such as length of time of exposure todifferent factors climate migration and occupation are allpotential confounders

A genetic link toMSmay partially explain the geographicdistribution of the disease as Caucasoid races tended to

migrate towards the temperate climates [4] An interestingfeature of MS is that it seems to have an age of susceptibilityMigration studies have discovered that age of MS risk isaround the age of pubescence [1] If a person moves froma low-risk area to a high-risk area before pubescence theperson will be susceptible to the level of risk in that newhigh-risk area Dean and Elian [5] found that immigrants toEngland from Asia and the Caribbean had an increased riskfor MS if they moved to England prior to 15 years of ageThisfinding adds strength to the argument that some environ-mental variable must have a role to play in the developmentof MS

In 1877 Charcot was the first to publish a paper thatrecognized that the prevalence of MS was not uniform Hediscovered that at the time there appeared to be a higherprevalence of MS in France compared with Germany orEngland [6] Since that time numerous prevalence stud-ies have been published which suggest that a north-southgradient of MS prevalence appears to exist in the northernhemisphere While there is evidence to suggest that thisgradient is disappearing in some countries [7] a recent meta-analysis by Simpson Jr et al [8] shows that on a global scale

Hindawi Publishing CorporationMultiple Sclerosis InternationalVolume 2014 Article ID 124578 7 pageshttpdxdoiorg1011552014124578

2 Multiple Sclerosis International

the latitudinal gradient still exists with exception being inItaly and northern Scandinavia

MS is generally most prevalent in northern geographiclatitudes The highest rates of MS prevalence are generallyfound between the latitudes of 45 degrees north and 65degrees north [9] This same latitudinal prevalence rate canbe found in similar latitudes in the southern hemisphereThedisease is very rare near the equator

2 Global Prevalence Zones

Kurtzke [10] designated a three-zone global prevalence ratinghigh zones (30ndash80 per 100000) medium zones (5ndash25 per100000) and low zones (lt5 per 100000) The high zonesfor MS prevalence are generally found in Canada NorthernUnited States most of Northern Europe New ZealandAustralia (south eastern) and IsraelMediumZones includedsouthern Europe southern United States and northernAustralia Low zones includedAsiamost of Africa and SouthAmerica Rosati [11] published a paper which attempted toupdate the global prevalence of MS in the world Rosatirsquosmost notable findings were that there are ldquomany exceptionsto the previously described north-south gradientrdquo and thatMS is very rare among certain races such as Samis TurkmenUzbeks Kazakhs Kyrygzs native Siberians native North andSouth Americans Maoris African blacks Chinese Japaneseand Canadian Hutterites

The high medium and low zones described by Kurtzkeare still valid but a significant number of MS prevalencestudies demonstrating rates well above 100 per 100000 (12ndash35) may suggest that the scale is not accurately capturing thedesignated high zones of MS

3 Method

The search terms ldquomultiple sclerosis and prevalencerdquo andldquomultiple sclerosis and epidemiologyrdquo were applied to thefollowing data bases

(i) Medlinemdash1950-present(ii) MedlinemdashIn-process and other non-indexed cita-

tions(iii) CINAHLmdash1982-present(iv) HealthSTARmdash1966-present(v) Health and Psychosocial instruments 1985ndash2007Where possible studies which used McDonald or Poser

criteria were selected 57 of the prevalence studies usedeither Poser or McDonald criteria The remainder of thestudies used population surveys older diagnostic criteriasuch as Bauer Rose Allison and Millar and Schumacher orunknown diagnostic criteria With the above filters (includ-ing English language studies only) and search criteria appliedwe found 131 prevalence data sets (geocases) that wereanalyzed with descriptive statistics spatial statistical analysistools Table 1 shows a sample of theMS geocases and the CaseAscertainment Methods and Criteria for Diagnosis

Table 2 shows a sample of the area studied in the MSprevalence studies as well as the latitude longitude and

prevalence rates per 100000 The sample data set table pro-vides an example of the center-point latitude and longitudeused for the relevant studies If the study population is definedby a city then the latitude and longitude are those generallyaccepted for the city to the nearest minute When largerregions were studied such as at a province state region orcountry level and a city-level center-point for the area will becalculated and the latitude and longitude of that center-pointto the nearest minute will be used The areas in parenthesesare the approximate center-point of the area

Spatial statistical analyses was completed using the ESRIArcGIS-ArcInfo [12] spatial statistics toolbox [13] The spe-cific analyses contained in each category that were used inthe data analysis are listed below

(i) Geographic Distribution

(a) Median Center(b) Mean Center(c) Central Feature(d) Linear Directional Mean(e) Directional Distribution (Standard Deviational

Ellipse)(f) Standard Distance

(ii) Pattern Analysis

(a) Average Nearest Neighbor(b) HighLow Clustering (Getis-Ord General 119866)(c) Spatial Autocorrelation (Morans 119868)(d) Multi-Distance Spatial Cluster Analysis (Rip-

leyrsquos 119896-function)

(iii) Cluster Mapping

(a) Cluster and Outlier Analysis (Anselin LocalMoranrsquos 119868)

(b) ClusterOutlier Analysis with Rendering(c) Hot Spot Analysis (Getis-Ord Gilowast)(d) Hot Spot Analysis with Rendering

4 Results

Using the search criteria outlined in Methods section wefound 131 geocases that met the criteria Table 3 shows thedescriptive statistics containing the 131 geocases Of note isthe mean global prevalence rate of MS at 6783 per 100000and the maximum value of 350 per 100000 (Canada)

5 Spatial Statistics

To examine the data from the 131 geocases visually Arc GIS931 was used to generate maps on a global level Whatis immediately evident from the maps in Figures 1 and 2is that the majority of the geo-cases represent prevalencestudies from Europe We can see in Figure 1 Hot SpotsMean Center Central Feature and the Standard Distance ofthe 131 geo-cases for MS prevalence in the world The Mean

Multiple Sclerosis International 3

Table 1 Sample table of MS prevalence secondary sources and case ascertainment methods

Region and journal reference Prevalence data (per 100000) Prevalence year Criteria fordiagnosis

Case ascertainmentmethod(s)

Canada [14]

Atlantic Region 350Quebec 180Ontario 230Prairie Region 340British Columbia 240

2000-2001 Self-report

CanadianCommunityHealth Survey11

United States [15]

Western Region 975Midwestern Region 960Northeastern Region 965Southern Region 655

1982ndash1996 Self-report

NationalHealthInterviewSurvey

Russia [16] 35ndash70 2000 Unknown MultipleSources

Table 2 Sample dataset table of latitude and longitude and preva-lence

Country and regioncity (any city inparentheses is an approximate regionalcenter)

Latitudeand

longitude

MSprevalencerate (per100000)

Canada (Beck et al 2005 [14])

Atlantic Provinces (Charlottetown) 46∘141015840N63∘71015840W 350

Quebec (Quebec city) 46∘481015840N71∘141015840W 180

Ontario (Sault Ste Marie) 46∘301015840N84∘201015840W 230

Prairie Provinces (Saskatoon) 52∘61015840N106∘371015840W 340

British Columbia (Prince George) 53∘551015840N122∘501015840W 240

United States (Noonan et al 2002 [15])

Western Region (Salt Lake City) 40∘411015840N111∘531015840W 975

Midwestern Region (Des Moines) 41∘351015840N93∘371015840W 960

Northeastern Region (Albany) 42∘391015840N73∘451015840W 965

Southern Region (Little Rock) 34∘451015840N92∘181015840W 635

Russia (Gusev et al 2002 [16])

Moscow 55∘451015840N37∘421015840E 408

Orel 52∘591015840N36∘51015840E 648

Center of the 131 geo-cases appears to be in northern Africaat approximately 30∘ 221015840N and 11∘ 41015840 E This center merelyrepresents the Mean Center location of the average of the 119909and 119910 values for the geo-cases and should not be confusedwith a geographic center forMS prevalenceThis informationtells us that the majority of studies used in this research werebased on research from the northern hemisphere Similarto Mean Center Central Feature represents a point that is

Table 3Descriptive statistics for 131 geocases ofMSprevalence data

MS prevalence (per 100000)N

Valid 131Missing 0

Mean 678318Std error of mean 618799Median 428700Std deviation 7082478Minimum 000Maximum 35000

the smallest distance to all the other points The CentralFeature in Figure 1 is located in approximately the middle ofItaly at 42∘ 01015840N and 13∘ 11015840 E The Standard Distance whichis the transparent circle encircling all of Europe and Africaagain demonstrates that the majority of the geo-cases arerepresentative of data from European countries

The Gi statistic (hot spot analysis) examines whetherfeatures with high or low values tend to cluster in an areaThisstatistic tool will examine whether neighbouring points havesimilar values if they are similar then they are consideredhot spots A 119885 score is used to determine the statisticalsignificance of the relationship between the neighbours Ahigh positive 119885 score as seen in most of Europe and Canadarepresents a 119885 score which is more than 258 SD from themean The areas that are less than minus258 SD represent areasof low prevalence of MS

Figure 2 shows a spatial analysis done to examine forclusters and outliers Using the Cluster and Outlier Analysis(Anselin Local Morans 119868) tool the data is analyzed forvalues of similar magnitude and those which would be verydissimilar (outliers) This index (Local Morans 119868) then has a119885 score calculated to determine the significance The highpositive 119885 scores (red dots) represent clusters which havesimilar values to values to surrounding points A low negative119885 score (yellow dots) represents an outlier which meansthat the values are significantly (119875 = 005) different fromsurrounding valuesThe significant clusters are in the areas ofCanada United Kingdom and Scandinavia The significant


160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

180∘

180∘

80∘N

60∘N

40∘N

20∘N

0∘

0∘

20∘S

40∘S

60∘S

80∘S

80∘N

60∘N

40∘N

20∘N

20∘S

40∘S

60∘S

80∘S

N

SW E

The world

Figure 1 World map of MS prevalence hot spots Mean Center Central Feature and Standard Distance using 131 geocases

160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

180∘

180∘

80∘N

60∘N

40∘N

20∘N

0∘

0∘

20∘S

40∘S

60∘S

80∘S

80∘N

60∘N

40∘N

20∘N

20∘S

40∘S

60∘S

80∘S

N

SW E

The world

Figure 2 World map of MS prevalence analysis of clusters and outliers using 131 geocases


160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

180∘

180∘

80∘N

60∘N

40∘N

20∘N

0∘

0∘

20∘S

40∘S

60∘S

80∘S

80∘N

60∘N

40∘N

20∘N

20∘S

40∘S

60∘S

80∘S

N

SW E

The world

Figure 3 World map of MS prevalence with cluster rendering and directional distribution using 131 geocases

outliers are found in Eastern Europe South America andAfrica

Figure 3 shows cluster analysis with rendering and adirectional distribution of MS prevalence In addition toseeing the clusters with color-weighting the directionaldistribution (Standard Deviational Ellipse) shows where themajority of the geocases are in Europe and trending towardsCanada and south-east Asia

6 Pattern Analysis

Using ArcGIS 93 a statistical test of pattern analysis wasperformed to quantify the relationships of the geocasesWhile mapping the geocases as shown in Figures 1ndash3 givesa sense of patterns statistically testing using pattern analysisquantifies the probabilities that the visual relationships didnot merely occur by chance The four pattern analyses per-formed on the 131 geocases were Average Nearest NeighbourDistance HighLow Clustering Spatial Autocorrelation andMultidistance Spatial Cluster Analysis

Average Nearest Neighbour this statistical tool measuresthe distance between the centroid of a geocase and itsnearest neighbourrsquos geocase centroid These distances arethen averaged to determine if a cluster exists relative to arandom distribution An index ratio is created (observeddistance divided by expected distance) An index less than 1 istrending towards a cluster whereas an index greater than 1 istrending towards dispersionTheAverage Nearest Neighbour

test results with our 131 geocases revealed that significantclustering exists 067 (index) 119885 = minus729 SD 119875 lt 001

HighLow Clustering (Getis-Ord General 119866) this testdetermines how concentrated high or low the geocase valuesare in the area of our global mapThe results of this statisticaltool revealed significant high clustersGeneral119866 index= 004119885 = 716 SD 119875 lt 001 The input variable used for this testwas MS prevalence

Spatial AutocorrelationMorans 119868 this test will determinehow similar the geocases are based on both their location andtheir values (MS prevalence) As with the Getis-Ord General119866 it uses an index to determine the degree of clustering Aswith the previous 2 tests a score and a 119875 value are calculatedA Moranrsquos 119868 index near 10 trends to clustering whereas minus10trends towards dispersion The results of this test for the131 geocases demonstrate significant clustering Moranrsquos 119868index = 043 119885 = 1342 SD 119875 lt 001

Multidistance Spatial Cluster Analysis Ripleyrsquos 119896-Function this tool as with the previous two tools measuresthe degree of clustering Unlike the previous two tools thistool analyzes clusters over a range of distances For the testof the 131 geocases we chose 10 distance bands over whichto have the tool analyze clusters in each of the 10 distancebands If the observed number of clusters is higher thanthe expected number of clusters then the test is significantfor clustering in that band The index used is the Ripleyrsquos119896-Function and is expressed as 119871 (119889) The higher (and morepositive) the Ripleyrsquos 119896-Function means that the geocases are


Table 4 Kurtzkersquos versus proposed global MS prevalence scale

(a) Kurtzkersquos global MS prevalence scale

Classification Prevalence rate (per 100000) Countries

High 30ndash80 Most of Europe most of United States Canada NewZealand Israel Cyprus and south-eastern Australia

Medium 5ndash25Southern Europe most of Australia most of Russiafrom the Urals into Siberia as well as the Ukraine SouthAfrica possibly much of the Caribbean region andSouth America

Low lt5 Asia most of Africa and South America (Venezuelaand Colombia)

(b) Proposed global MS prevalence scale


Very high 170ndash350 Canada Sweden (Varmland) Finland (Seinajoki andVaasa) Scotland and most of Ireland

High 70ndash170

Most of United States Norway Sweden (Vasterbotten)Denmark Finland (Uusimaa) Iceland EnglandIreland (Wexford) Germany Austria SwitzerlandBelgium Italy Turkey Slovenia Croatia CzechRepublic Luxembourg and Netherlands

Medium 38ndash70

Southern United States Russia most Australia NewZealand (south) Faroe Islands Poland Estonia SpainGreece Hungary Serbia and Montenegro BulgariaBelarus Israel Latvia Lithuania Moldova Portugaland Ukraine

Low 13ndash38

Australia (Queensland) New Zealand (north)Kazakhstan Romania India Jordan Saudi ArabiaMartinique Argentina South Africa Brazil BahrainBarbados Lebanon Malta Morocco Slovakia TunisiaUnited Arab Emirates and Uruguay

Very low 0ndash13

Japan Mexico China Taiwan Malaysia ThailandKuwait Panama Colombia Afghanistan AlgeriaBahrain Cameroon Chile Costa Rica Cuba EcuadorGuatemala Guinea Honduras Iraq Malawi MongoliaNicaragua Nigeria Paraguay Peru Qatar Republic ofKorea Singapore Benin and Senegal

more clustered The results of this test reveal that all distanceintervals from 744 to 7444 are significantly clustered exceptat 7444 which is slightly dispersed 119871(119889) = 7204 diff =minus240Themost clustered distance was at 2978 119871(119889) = 5128diff = 2150

7 Discussion

Although Kurtzke did revisit the global prevalence scale forMS when he reexamined more recent European data theoriginal three-zone scale of HighMedium and Low remainsthe same When our 131 prevalence studies were examinedover forty percent of the prevalence rates were over 50 per100000 It is clear that Kurtzkersquos [10] original divisions ofMS prevalence into high zones (30ndash80 per 100000) mediumZones (5ndash25 per 100000) and low zones (lt5 per 100000)may not be granular enough to account for more recentprevalence studies showing MS prevalence rates in Canadaover 300 per 100000 As a result of this research it is

recommended that the global prevalence scale be expandedinto a five-zone scale based on the results from the spatialanalyses in Figures 1ndash3 The prevalence scales generated byArcGIS divided the five prevalence zones into (rounded) 0ndash13 13ndash38 38ndash70 70ndash170 170ndash350 per 100000 The categoriesrepresenting these more granular divisions for global MSprevalence zones should be very high (170ndash350 per 100000)high (70ndash170 per 100000) medium (38ndash70 per 100000) low(13ndash38 per 100000) and very low (0ndash13 per 100000)

It is necessary to expand from a three-zone classifica-tion system to a five-zone classification system to avoidgrouping together countries with prevalence rates which areincomparable In a three-zone classification countries suchas EnglandGermany and Switzerlandwhich have prevalencerates between 100 and 150 per 100000 would likely be rankedas ldquohighrdquo and in the same category as countries such asCanada Norway Sweden Scotland and Ireland which haveprevalence rates ranging between 150 and 350 per 100000 Asimilar argument is made for adding a ldquovery lowrdquo category as


to not group countries such as Malaysia Thailand and mostof the African countries with a zero to very low prevalencerates with countries such as Argentina Uruguay and thenorth island of New Zealand which have prevalence ratesbetween 13 and 38 per 100000

Whether due to actual increases in MS incidence rates orbetter diagnostic practices in some countries it is likely thatin the future the proposed five-zone classification scale forglobal MS prevalence will be even more important to captureand effectively compare countries with higher MS prevalencerates than previously reported Table 4 compares Kurtzkersquos[10] global MS prevalence scale and the new Proposed globalMS prevalence scale

Conflict of Interests

The author declares that there is no conflict of interests regar-ding the publication of this paper

References

[1] A Green and E Waubant ldquoGenetics and epidemiology of mul-tiple sclerosisrdquo CONTINUUM Lifelong Learning in Neurologyvol 13 no 5 pp 63ndash85 2007

[2] ldquoSecure Mobile Payment based on Super SET protocolrdquo inProceedings of the IEEE International Conference on AdvancedComputer Control (ICACC rsquo10) Z Wan and Q Wang Eds pp640ndash643 Shenyang China March 2010

[3] H Coo and K J Aronson ldquoA systematic review of severalpotential non-genetic risk factors for multiple sclerosisrdquo Neu-roepidemiology vol 23 no 1-2 pp 1ndash12 2004

[4] P T Donnan J D E Parratt S V Wilson R B Forbes J IOrsquoRiordan and R J Swingler ldquoMultiple sclerosis in TaysideScotland detection of clusters using a spatial scan statisticrdquoMultiple Sclerosis vol 11 no 4 pp 403ndash408 2005

[5] G Dean andM Elian ldquoAge at immigration to England of Asianand Caribbean immigrants and the risk of developing multiplesclerosisrdquo Journal ofNeurologyNeurosurgery andPsychiatry vol63 no 5 pp 565ndash568 1997

[6] A D Sadovnick and G C Ebers ldquoEpidemiology of multiplesclerosis a critical overviewrdquo Canadian Journal of NeurologicalSciences vol 20 no 1 pp 17ndash29 1993

[7] A Ascherio and K L Munger ldquoEnvironmental risk factorsfor multiple sclerosismdashpart I the role of infectionrdquo Annals ofNeurology vol 61 no 4 pp 288ndash299 2007

[8] S Simpson Jr L Blizzard P Otahal I Van Der Mei and BTaylor ldquoLatitude is significantly associated with the prevalenceof multiple sclerosis a meta-analysisrdquo Journal of NeurologyNeurosurgery and Psychiatry vol 82 no 10 pp 1132ndash1141 2011

[9] I P Carlyle ldquoMultiple sclerosis a geographical hypothesisrdquoMedical Hypotheses vol 49 no 6 pp 477ndash486 1997

[10] J F Kurtzke ldquoA reassessment of the distribution of multiplesclerosismdashpart 1rdquo Acta Neurologica Scandinavica vol 51 no 2pp 110ndash136 1975

[11] G Rosati ldquoThe prevalence of multiple sclerosis in the world anupdaterdquo Neurological Sciences vol 22 no 2 pp 117ndash139 2001

[12] ArcInfo 2011 httpwwwesricomsoftwarearcgisarcinfoindexhtml

[13] An overview of the Spatial Statistics toolbox 2011 httpedndocesricomarcobjects92NETsharedgeoprocessingspatialstatistics toolboxan overview of the spatial statistics tool-boxhtm

[14] C A Beck L M Metz L W Svenson and S B PattenldquoRegional variation of multiple sclerosis prevalence in CanadardquoMultiple Sclerosis vol 11 no 5 pp 516ndash519 2005

[15] C W Noonan S J Kathman and M C White ldquoPrevalenceestimates for MS in the United States and evidence of anincreasing trend for womenrdquo Neurology vol 58 no 1 pp 136ndash138 2002

[16] E I Gusev I A Zavalishin A N Boıko N L Khoroshilovaand A P Iakovlev ldquoEpidemiological characteristics of multiplesclerosis in Russiardquo Zhurnal nevrologii i psikhiatrii imeni S SKorsakova supplement 3ndash6 2002 (Russian)

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the latitudinal gradient still exists with exception being inItaly and northern Scandinavia

MS is generally most prevalent in northern geographiclatitudes The highest rates of MS prevalence are generallyfound between the latitudes of 45 degrees north and 65degrees north [9] This same latitudinal prevalence rate canbe found in similar latitudes in the southern hemisphereThedisease is very rare near the equator

2 Global Prevalence Zones

Kurtzke [10] designated a three-zone global prevalence ratinghigh zones (30ndash80 per 100000) medium zones (5ndash25 per100000) and low zones (lt5 per 100000) The high zonesfor MS prevalence are generally found in Canada NorthernUnited States most of Northern Europe New ZealandAustralia (south eastern) and IsraelMediumZones includedsouthern Europe southern United States and northernAustralia Low zones includedAsiamost of Africa and SouthAmerica Rosati [11] published a paper which attempted toupdate the global prevalence of MS in the world Rosatirsquosmost notable findings were that there are ldquomany exceptionsto the previously described north-south gradientrdquo and thatMS is very rare among certain races such as Samis TurkmenUzbeks Kazakhs Kyrygzs native Siberians native North andSouth Americans Maoris African blacks Chinese Japaneseand Canadian Hutterites

The high medium and low zones described by Kurtzkeare still valid but a significant number of MS prevalencestudies demonstrating rates well above 100 per 100000 (12ndash35) may suggest that the scale is not accurately capturing thedesignated high zones of MS

3 Method

The search terms ldquomultiple sclerosis and prevalencerdquo andldquomultiple sclerosis and epidemiologyrdquo were applied to thefollowing data bases

(i) Medlinemdash1950-present(ii) MedlinemdashIn-process and other non-indexed cita-

tions(iii) CINAHLmdash1982-present(iv) HealthSTARmdash1966-present(v) Health and Psychosocial instruments 1985ndash2007Where possible studies which used McDonald or Poser

criteria were selected 57 of the prevalence studies usedeither Poser or McDonald criteria The remainder of thestudies used population surveys older diagnostic criteriasuch as Bauer Rose Allison and Millar and Schumacher orunknown diagnostic criteria With the above filters (includ-ing English language studies only) and search criteria appliedwe found 131 prevalence data sets (geocases) that wereanalyzed with descriptive statistics spatial statistical analysistools Table 1 shows a sample of theMS geocases and the CaseAscertainment Methods and Criteria for Diagnosis

Table 2 shows a sample of the area studied in the MSprevalence studies as well as the latitude longitude and

prevalence rates per 100000 The sample data set table pro-vides an example of the center-point latitude and longitudeused for the relevant studies If the study population is definedby a city then the latitude and longitude are those generallyaccepted for the city to the nearest minute When largerregions were studied such as at a province state region orcountry level and a city-level center-point for the area will becalculated and the latitude and longitude of that center-pointto the nearest minute will be used The areas in parenthesesare the approximate center-point of the area

Spatial statistical analyses was completed using the ESRIArcGIS-ArcInfo [12] spatial statistics toolbox [13] The spe-cific analyses contained in each category that were used inthe data analysis are listed below

(i) Geographic Distribution

(a) Median Center(b) Mean Center(c) Central Feature(d) Linear Directional Mean(e) Directional Distribution (Standard Deviational

Ellipse)(f) Standard Distance

(ii) Pattern Analysis

(a) Average Nearest Neighbor(b) HighLow Clustering (Getis-Ord General 119866)(c) Spatial Autocorrelation (Morans 119868)(d) Multi-Distance Spatial Cluster Analysis (Rip-

leyrsquos 119896-function)

(iii) Cluster Mapping

(a) Cluster and Outlier Analysis (Anselin LocalMoranrsquos 119868)

(b) ClusterOutlier Analysis with Rendering(c) Hot Spot Analysis (Getis-Ord Gilowast)(d) Hot Spot Analysis with Rendering

4 Results

Using the search criteria outlined in Methods section wefound 131 geocases that met the criteria Table 3 shows thedescriptive statistics containing the 131 geocases Of note isthe mean global prevalence rate of MS at 6783 per 100000and the maximum value of 350 per 100000 (Canada)

5 Spatial Statistics

To examine the data from the 131 geocases visually Arc GIS931 was used to generate maps on a global level Whatis immediately evident from the maps in Figures 1 and 2is that the majority of the geo-cases represent prevalencestudies from Europe We can see in Figure 1 Hot SpotsMean Center Central Feature and the Standard Distance ofthe 131 geo-cases for MS prevalence in the world The Mean





Canada [14]




United States [15]







Latitudeand

longitude














Moscow 55∘451015840N37∘421015840E 408

Orel 52∘591015840N36∘51015840E 648




Valid 131Missing 0






160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

180∘

180∘

80∘N

60∘N

40∘N

20∘N

0∘

0∘

20∘S

40∘S

60∘S

80∘S

80∘N

60∘N

40∘N

20∘N

20∘S

40∘S

60∘S

80∘S

N

SW E

The world


160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

180∘

180∘

80∘N

60∘N

40∘N

20∘N

0∘

0∘

20∘S

40∘S

60∘S

80∘S

80∘N

60∘N

40∘N

20∘N

20∘S

40∘S

60∘S

80∘S

N

SW E

The world



160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

180∘

180∘

80∘N

60∘N

40∘N

20∘N

0∘

0∘

20∘S

40∘S

60∘S

80∘S

80∘N

60∘N

40∘N

20∘N

20∘S

40∘S

60∘S

80∘S

N

SW E

The world




6 Pattern Analysis

















High 70ndash170


Medium 38ndash70


Low 13ndash38


Very low 0ndash13



7 Discussion









References






















of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of




















Canada [14]




United States [15]







Latitudeand

longitude














Moscow 55∘451015840N37∘421015840E 408

Orel 52∘591015840N36∘51015840E 648




Valid 131Missing 0






160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

180∘

180∘

80∘N

60∘N

40∘N

20∘N

0∘

0∘

20∘S

40∘S

60∘S

80∘S

80∘N

60∘N

40∘N

20∘N

20∘S

40∘S

60∘S

80∘S

N

SW E

The world


160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

180∘

180∘

80∘N

60∘N

40∘N

20∘N

0∘

0∘

20∘S

40∘S

60∘S

80∘S

80∘N

60∘N

40∘N

20∘N

20∘S

40∘S

60∘S

80∘S

N

SW E

The world



160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

180∘

180∘

80∘N

60∘N

40∘N

20∘N

0∘

0∘

20∘S

40∘S

60∘S

80∘S

80∘N

60∘N

40∘N

20∘N

20∘S

40∘S

60∘S

80∘S

N

SW E

The world




6 Pattern Analysis

















High 70ndash170


Medium 38ndash70


Low 13ndash38


Very low 0ndash13



7 Discussion









References






















of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

180∘

180∘

80∘N

60∘N

40∘N

20∘N

0∘

0∘

20∘S

40∘S

60∘S

80∘S

80∘N

60∘N

40∘N

20∘N

20∘S

40∘S

60∘S

80∘S

N

SW E

The world


160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

180∘

180∘

80∘N

60∘N

40∘N

20∘N

0∘

0∘

20∘S

40∘S

60∘S

80∘S

80∘N

60∘N

40∘N

20∘N

20∘S

40∘S

60∘S

80∘S

N

SW E

The world



160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

180∘

180∘

80∘N

60∘N

40∘N

20∘N

0∘

0∘

20∘S

40∘S

60∘S

80∘S

80∘N

60∘N

40∘N

20∘N

20∘S

40∘S

60∘S

80∘S

N

SW E

The world




6 Pattern Analysis

















High 70ndash170


Medium 38ndash70


Low 13ndash38


Very low 0ndash13



7 Discussion









References






















of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

160∘W 140

∘W 120∘W 100

∘W 80∘W 60

∘W 40∘W 20

∘W 0∘

20∘E 40

∘E 60∘E 80

∘E 100∘E 120

∘E 140∘E 160

∘E 180∘

180∘

180∘

80∘N

60∘N

40∘N

20∘N

0∘

0∘

20∘S

40∘S

60∘S

80∘S

80∘N

60∘N

40∘N

20∘N

20∘S

40∘S

60∘S

80∘S

N

SW E

The world




6 Pattern Analysis

















High 70ndash170


Medium 38ndash70


Low 13ndash38


Very low 0ndash13



7 Discussion









References






















of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of


























High 70ndash170


Medium 38ndash70


Low 13ndash38


Very low 0ndash13



7 Discussion









References






















of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of





















References






















of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of





















of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
















research article spatial analysis of global prevalence of ...department of e rapist assistant,...

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