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.


OBJECTIVE
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 standardised prevalence increased from 133.9 to 207.3 MS patients per 100,000 persons in the population, from 2000 - 2010. During this same period, age-and-sex-standardised incidence varied from 17.9 to 19.4 patients per 100,000 persons.


CONCLUSIONS
MS patients can be accurately identified from administrative data. Our findings illustrated a rising prevalence of MS over time. MS incidence rates also appear to be rising since 2009.
Institute for Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada Jessica.widdifield@utoronto.ca.
Jessica
J
Widdifield
Institute for Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada/ Department of Family and Community Medicine, University of Toronto, Ontario, Canada/Department of Family and Community Medicine, Women's College Hospital, Toronto, Ontario, Canada.
Noah M
NM
Ivers
Institute for Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada.
Jacqueline
J
Young
Institute for Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada.
Diane
D
Green
Institute for Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada/ Department of Family and Community Medicine, University of Toronto, Ontario, Canada/Department of Family and Community Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
Liisa
L
Jaakkimainen
Department of Family and Community Medicine, Scarborough Hospital, University of Toronto, Ontario, Canada.
Debra A
DA
Butt
Department of Neurology, University of Toronto, Ontario, Canada/Department of Neurology, Saint Michael's Hospital, Toronto, Ontario, Canada.
Paul
P
O'Connor
Institute for Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada.
Simon
S
Hollands
Institute for Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada/Department of Family and Community Medicine, University of Toronto, Ontario, Canada/University Health Network, Toronto Western Hospital Family Health Team, Ontario, Canada.
Karen
K
Tu
D000328:Adult; D000367:Age Factors; D000368:Aged; D000465:Algorithms; D015331:Cohort Studies; D017281:Cost of Illness; D003625:Data Collection; D005260:Female; D006760:Hospitalization / Q000191:economics / Q000706:statistics & numerical data; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000191:economics / Q000453:epidemiology*; D009864:Ontario / Q000453:epidemiology; D010820:Physicians / Q000191:economics; D015995:Prevalence; D012015:Reference Standards; D015203:Reproducibility of Results; D012737:Sex Factors
Administrative database; Canada; Ontario; algorithm; epidemiology; health data collection; incidence; multiple sclerosis; prevalence; validation study