schliessen

Filtern

 

Bibliotheken

Development and validation of an administrative data algorithm to estimate the disease burden and epidemiology of multiple sclerosis in Ontario, Canada

Background: Few studies have assessed the accuracy of administrative data for identifying multiple sclerosis (MS) patients. Objectives: To validate administrative data algorithms for MS, and describe the burden and epidemiology over time in Ontario, Canada. Methods: We employed a validated search st... Full description

Journal Title: Multiple Sclerosis Journal July 2015, Vol.21(8), pp.1045-1054
Main Author: Widdifield, Jessica
Other Authors: Ivers, Noah M , Young, Jacqueline , Green, Diane , Jaakkimainen, Liisa , Butt, Debra A , O’connor, Paul , Hollands, Simon , Tu, Karen
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 1352-4585 ; E-ISSN: 1477-0970 ; DOI: 10.1177/1352458514556303
Link: https://journals.sagepub.com/doi/full/10.1177/1352458514556303
Zum Text:
SendSend as email Add to Book BagAdd to Book Bag
Staff View
recordid: sage_s10_1177_1352458514556303
title: Development and validation of an administrative data algorithm to estimate the disease burden and epidemiology of multiple sclerosis in Ontario, Canada
format: Article
creator:
  • Widdifield, Jessica
  • Ivers, Noah M
  • Young, Jacqueline
  • Green, Diane
  • Jaakkimainen, Liisa
  • Butt, Debra A
  • O’connor, Paul
  • Hollands, Simon
  • Tu, Karen
subjects:
  • Administrative Database
  • Algorithm
  • Canada
  • Epidemiology
  • Health Data Collection
  • Incidence
  • Multiple Sclerosis
  • Ontario
  • Prevalence
  • Validation Study
  • Medicine
ispartof: Multiple Sclerosis Journal, July 2015, Vol.21(8), pp.1045-1054
description: Background: Few studies have assessed the accuracy of administrative data for identifying multiple sclerosis (MS) patients. Objectives: To validate administrative data algorithms for MS, and describe the burden and epidemiology over time in Ontario, Canada. Methods: We employed a validated search strategy to identify all MS patients within electronic medical records, to identify patients with and without MS (reference standard). We then developed and validated different combinations of administrative data for algorithms. The most accurate algorithm was used to estimate the burden and epidemiology of MS over time. Results: The accuracy of the algorithm of one hospitalisation or five physician billings over 2 years provided both high sensitivity (84%) and positive predictive value (86%). Application of this algorithm to provincial data demonstrated an increasing cumulative burden of MS, from 13,326 patients (0.14%) in 2000 to 24,647 patients in 2010 (0.22%). Age-and-sex...
language: eng
source:
identifier: ISSN: 1352-4585 ; E-ISSN: 1477-0970 ; DOI: 10.1177/1352458514556303
fulltext: fulltext
issn:
  • 1352-4585
  • 13524585
  • 1477-0970
  • 14770970
url: Link


@attributes
ID1421958640
RANK0.07
NO1
SEARCH_ENGINEprimo_central_multiple_fe
SEARCH_ENGINE_TYPEPrimo Central Search Engine
LOCALfalse
PrimoNMBib
record
control
sourcerecordid10_1177_1352458514556303
sourceidsage_s
recordidTN_sage_s10_1177_1352458514556303
sourcesystemPC
dbid
0-MK
1.2E
2.2J
3.2M
4.2N
51~K
631R
731U
831X
931Z
1054M
11AABOD
12AACTG
13AAGMC
14AAMGE
15AAMPI
16AARDL
17AATBZ
18AAUAS
19ABAFQ
20ABAWP
21ABJIS
22ABJUO
23ABNCE
24ABNLC
25ABQXT
26ACDXX
27ACGEY
28ACGZU
29ACJTF
30ACTQU
31ADGDL
32ADZYD
33AECGH
34AEDTQ
35AEKYL
36AERKM
37AERUW
38AEUIJ
39AGWFA
40AIFIH
41AMCVQ
42B3H
43B8R
44B8Z
45B94
46BFDSU
47BKSCU
48DB0
49DB~
50DF0
51DO-
52DV7
53DV9
54J8X
55K.F
56P.B
57Q7L
58Q7U
59Q7X
60Q81
61Q83
62SCNPE
63SDB
64SFC
65SFK
66SFT
67SGO
68SGR
69SGV
70SGZ
71SHG
72SNB
73SPJ
74SPQ
75SPV
76STM
77ZGBWR
pqid1695186532
display
typearticle
titleDevelopment and validation of an administrative data algorithm to estimate the disease burden and epidemiology of multiple sclerosis in Ontario, Canada
creatorWiddifield, Jessica ; Ivers, Noah M ; Young, Jacqueline ; Green, Diane ; Jaakkimainen, Liisa ; Butt, Debra A ; O’connor, Paul ; Hollands, Simon ; Tu, Karen
ispartofMultiple Sclerosis Journal, July 2015, Vol.21(8), pp.1045-1054
identifier
subjectAdministrative Database ; Algorithm ; Canada ; Epidemiology ; Health Data Collection ; Incidence ; Multiple Sclerosis ; Ontario ; Prevalence ; Validation Study ; Medicine
descriptionBackground: Few studies have assessed the accuracy of administrative data for identifying multiple sclerosis (MS) patients. Objectives: To validate administrative data algorithms for MS, and describe the burden and epidemiology over time in Ontario, Canada. Methods: We employed a validated search strategy to identify all MS patients within electronic medical records, to identify patients with and without MS (reference standard). We then developed and validated different combinations of administrative data for algorithms. The most accurate algorithm was used to estimate the burden and epidemiology of MS over time. Results: The accuracy of the algorithm of one hospitalisation or five physician billings over 2 years provided both high sensitivity (84%) and positive predictive value (86%). Application of this algorithm to provincial data demonstrated an increasing cumulative burden of MS, from 13,326 patients (0.14%) in 2000 to 24,647 patients in 2010 (0.22%). Age-and-sex...
languageeng
source
version5
lds50peer_reviewed
links
openurl$$Topenurl_article
openurlfulltext$$Topenurlfull_article
backlink$$Uhttps://journals.sagepub.com/doi/full/10.1177/1352458514556303$$EView_record_in_Sage_(Access_to_full_text_may_be_restricted)
search
creatorcontrib
0Widdifield, Jessica
1Ivers, Noah M
2Young, Jacqueline
3Green, Diane
4Jaakkimainen, Liisa
5Butt, Debra A
6O’connor, Paul
7Hollands, Simon
8Tu, Karen
titleDevelopment and validation of an administrative data algorithm to estimate the disease burden and epidemiology of multiple sclerosis in Ontario, Canada
description

Background: Few studies have assessed the accuracy of administrative data for identifying multiple sclerosis (MS) patients. Objectives: To validate administrative data algorithms for MS, and describe the burden and epidemiology over time in Ontario, Canada. Methods: We employed a validated search strategy to identify all MS patients within electronic medical records, to identify patients with and without MS (reference standard). We then developed and validated different combinations of administrative data for algorithms. The most accurate algorithm was used to estimate the burden and epidemiology of MS over time. Results: The accuracy of the algorithm of one hospitalisation or five physician billings over 2 years provided both high sensitivity (84%) and positive predictive value (86%). Application of this algorithm to provincial data demonstrated an increasing cumulative burden of MS, from 13,326 patients (0.14%) in 2000 to 24,647 patients in 2010 (0.22%). Age-and-sex...

subject
0Administrative Database
1Algorithm
2Canada
3Epidemiology
4Health Data Collection
5Incidence
6Multiple Sclerosis
7Ontario
8Prevalence
9Validation Study
10Medicine
general
0English
1SAGE Publications
210.1177/1352458514556303
3Sage Journals (Sage Publications)
4SAGE Clinical Medicine (Sage Publications)
5SAGE Health Sciences (Sage Publications)
6SAGE STM (Sage Publications)
7SAGE Palliative Medicine and Chronic Care (Sage Publications)
8SAGE Pharmacology and Biomedical (Sage Publications)
9SAGE Neurology (Sage Publications)
10SAGE Mental Health (Sage Publications)
11SAGE Communication and Media Studies (Sage Publications)
12SAGE Journals (Sage Publications)
sourceidsage_s
recordidsage_s10_1177_1352458514556303
issn
01352-4585
113524585
21477-0970
314770970
rsrctypearticle
creationdate2015
addtitleMultiple Sclerosis Journal
searchscope
0sage_full
1sage162
2sage24
3sage131
4sage214
5sage252
6sage213
7sage21
8sage128
9sage210
10sage249
11sage32
12sage254
13sage27
14sage44
15sage219
16sage139
17sage255
18sage29
19sage194
20sage269
21sage198
22sage220
23sage183
24sage270
25sage221
26sage216
27sage134
28sage28
29sage273
30sage257
31sage175
32sage135
33sage133
34sage26
35sage138
36sage268
37sage272
38sage256
39sage264
40sage136
41sage22
42sage129
43sage211
44sage250
45sage263
46sage30
47sage25
48sage215
49sage132
50sage253
51sage282
52sage18
53sage42
54sage40
55sage239
56sage130
57sage23
58sage54
59sage212
60sage251
61sage31
62sage19
63sage11
64sage208
65sage206
66sage20
67sage127
68sage209
69sage248
70sage35
71sage126
72sage207
73sage34
74sage33
75sage247
76sage218
scope
0sage_full
1sage162
2sage24
3sage131
4sage214
5sage252
6sage213
7sage21
8sage128
9sage210
10sage249
11sage32
12sage254
13sage27
14sage44
15sage219
16sage139
17sage255
18sage29
19sage194
20sage269
21sage198
22sage220
23sage183
24sage270
25sage221
26sage216
27sage134
28sage28
29sage273
30sage257
31sage175
32sage135
33sage133
34sage26
35sage138
36sage268
37sage272
38sage256
39sage264
40sage136
41sage22
42sage129
43sage211
44sage250
45sage263
46sage30
47sage25
48sage215
49sage132
50sage253
51sage282
52sage18
53sage42
54sage40
55sage239
56sage130
57sage23
58sage54
59sage212
60sage251
61sage31
62sage19
63sage11
64sage208
65sage206
66sage20
67sage127
68sage209
69sage248
70sage35
71sage126
72sage207
73sage34
74sage33
75sage247
76sage218
alttitleMult Scler
lsr44$$EView_record_in_Sage_(Access_to_full_text_may_be_restricted)
tmp01
0Sage Journals (Sage Publications)
1SAGE Clinical Medicine (Sage Publications)
2SAGE Health Sciences (Sage Publications)
3SAGE STM (Sage Publications)
4SAGE Palliative Medicine and Chronic Care (Sage Publications)
5SAGE Pharmacology and Biomedical (Sage Publications)
6SAGE Neurology (Sage Publications)
7SAGE Mental Health (Sage Publications)
8SAGE Communication and Media Studies (Sage Publications)
9SAGE Journals (Sage Publications)
tmp02
0-MK
1.2E
2.2J
3.2M
4.2N
51~K
631R
731U
831X
931Z
1054M
11AABOD
12AACTG
13AAGMC
14AAMGE
15AAMPI
16AARDL
17AATBZ
18AAUAS
19ABAFQ
20ABAWP
21ABJIS
22ABJUO
23ABNCE
24ABNLC
25ABQXT
26ACDXX
27ACGEY
28ACGZU
29ACJTF
30ACTQU
31ADGDL
32ADZYD
33AECGH
34AEDTQ
35AEKYL
36AERKM
37AERUW
38AEUIJ
39AGWFA
40AIFIH
41AMCVQ
42B3H
43B8R
44B8Z
45B94
46BFDSU
47BKSCU
48DB0
49DB~
50DF0
51DO-
52DV7
53DV9
54J8X
55K.F
56P.B
57Q7L
58Q7U
59Q7X
60Q81
61Q83
62SCNPE
63SDB
64SFC
65SFK
66SFT
67SGO
68SGR
69SGV
70SGZ
71SHG
72SNB
73SPJ
74SPQ
75SPV
76STM
77ZGBWR
startdate20150701
enddate20150731
lsr40Multiple Sclerosis Journal, July 2015, Vol.21 (8), pp.1045-1054
doi10.1177/1352458514556303
citationpf 1045 pt 1054 vol 21 issue 8
lsr30VSR-Enriched:[pqid]
sort
titleDevelopment and validation of an administrative data algorithm to estimate the disease burden and epidemiology of multiple sclerosis in Ontario, Canada
authorWiddifield, Jessica ; Ivers, Noah M ; Young, Jacqueline ; Green, Diane ; Jaakkimainen, Liisa ; Butt, Debra A ; O’connor, Paul ; Hollands, Simon ; Tu, Karen
creationdate20150700
lso0120150700
facets
frbrgroupid1126197839732463224
frbrtype5
newrecords20170608
languageeng
topic
0Administrative Database
1Algorithm
2Canada
3Epidemiology
4Health Data Collection
5Incidence
6Multiple Sclerosis
7Ontario
8Prevalence
9Validation Study
10Medicine
collectionSage Journals (Sage Publications)
prefilterarticles
rsrctypearticles
creatorcontrib
0Widdifield, Jessica
1Ivers, Noah M
2Young, Jacqueline
3Green, Diane
4Jaakkimainen, Liisa
5Butt, Debra A
6O’connor, Paul
7Hollands, Simon
8Tu, Karen
jtitleMultiple Sclerosis Journal
creationdate2015
toplevelpeer_reviewed
delivery
delcategoryRemote Search Resource
fulltextfulltext
addata
aulast
0Widdifield
1Ivers
2Young
3Green
4Jaakkimainen
5Butt
6O’connor
7Hollands
8Tu
aufirst
0Jessica
1Noah M
2Jacqueline
3Diane
4Liisa
5Debra A
6Paul
7Simon
8Karen
auinitJ
auinit1J
au
0Widdifield, Jessica
1Ivers, Noah M
2Young, Jacqueline
3Green, Diane
4Jaakkimainen, Liisa
5Butt, Debra A
6O’connor, Paul
7Hollands, Simon
8Tu, Karen
atitleDevelopment and validation of an administrative data algorithm to estimate the disease burden and epidemiology of multiple sclerosis in Ontario, Canada
jtitleMultiple Sclerosis Journal
risdate201507
volume21
issue8
spage1045
epage1054
pages1045-1054
issn1352-4585
eissn1477-0970
formatjournal
genrearticle
ristypeJOUR
abstract

Background: Few studies have assessed the accuracy of administrative data for identifying multiple sclerosis (MS) patients. Objectives: To validate administrative data algorithms for MS, and describe the burden and epidemiology over time in Ontario, Canada. Methods: We employed a validated search strategy to identify all MS patients within electronic medical records, to identify patients with and without MS (reference standard). We then developed and validated different combinations of administrative data for algorithms. The most accurate algorithm was used to estimate the burden and epidemiology of MS over time. Results: The accuracy of the algorithm of one hospitalisation or five physician billings over 2 years provided both high sensitivity (84%) and positive predictive value (86%). Application of this algorithm to provincial data demonstrated an increasing cumulative burden of MS, from 13,326 patients (0.14%) in 2000 to 24,647 patients in 2010 (0.22%). Age-and-sex...

copLondon, England
pubSAGE Publications
doi10.1177/1352458514556303
lad01Multiple Sclerosis Journal
date2015-07