Contributions
Abstract: P344
Type: Poster
Abstract Category: Clinical aspects of MS - 5 Epidemiology
Background: Within the US, the prevalence of multiple sclerosis (MS) is poorly understood and inadequately characterized, but such information is important to support planning of health services and advocacy efforts. We aimed to develop a case definition to identify people with MS using health claims databases, and to apply this definition across the US to generate robust population-based MS prevalence estimates.
Methods: An MS case definition was developed and validated in three independent administrative databases. We applied this definition to identify MS cases between 2008 and 2010 using the following health care databases: Optum, Truven, Department of Veterans Affairs (VA), Kaiser Permanente Southern California (KPSC), Medicare and Medicaid. We estimated the three-year cumulative prevalence, and standardized to the 2010 US population.
Results: Among individuals with at least one health claim for demyelinating disease, the case definition had a sensitivity of 86%, specificity: 76-82%, and positive predictive value: 96-98% when compared to physician-adjudicated diagnoses. The unadjusted cumulative prevalence of MS for 2008-2010 for the private insurance databases was 208 per 100,000 (95% CI: 205-211) for Optum and 208 per 100,000 (95% CI: 207-210) for Truven. The cumulative prevalence for the national VA health care system was 177 per 100,000 (95% CI: 174-181), and for KPSC was 110 per 100,000 (95% CI: 106-114). The female: male ratio for MS prevalence was about 3:1 across databases and a US geographic prevalence gradient was found. A final integrated national MS cumulative prevalence estimate will be generated and stratified by age, sex and geographic region.
Conclusion: The US national cumulative MS prevalence rates for 2008-10 are the highest reported to date and provide a contemporary understanding of the disease burden. Our rigorous algorithm-based approach to estimating prevalence is novel, efficient and has the potential to be used for other chronic conditions.
Disclosure:
Mitchell Wallin: nothing to disclose
William J. Culpepper: nothing to disclose
Jonathan Campbell: nothing to disclose
Lorene Nelson: nothing to disclose
Annette Langer-Gould: nothing to disclose
Ruth Ann Marrie: nothing to disclose (see acknowledgement below)
Gary Cutter: nothing to disclose
Wendy Kaye: nothing to disclose
Laurie Wagner: nothing to disclose
Helen Tremlett: nothing to disclose
Steve Buka: nothing to disclose
Nicholas LaRocca: nothing to disclose
Carson Kai-Sang Leung: nothing to disclose
Piyameth Dilokthornsakul: nothing to disclose
Barbara Topol: nothing to disclose
Lie Hong Chen: nothing to disclose
Helen Tremlett is funded by the Canada Research Chair program and in the last year has received research support from the National Multiple Sclerosis Society, the Canadian Institutes of Health Research, the Multiple Sclerosis Society of Canada and the Multiple Sclerosis Scientific Research Foundation.
Acknowledgement: The results and conclusions are those of the authors and no official endorsement by the Manitoba Centre for Health Policy, Manitoba Health, or other data providers is intended or should be inferred.
Abstract: P344
Type: Poster
Abstract Category: Clinical aspects of MS - 5 Epidemiology
Background: Within the US, the prevalence of multiple sclerosis (MS) is poorly understood and inadequately characterized, but such information is important to support planning of health services and advocacy efforts. We aimed to develop a case definition to identify people with MS using health claims databases, and to apply this definition across the US to generate robust population-based MS prevalence estimates.
Methods: An MS case definition was developed and validated in three independent administrative databases. We applied this definition to identify MS cases between 2008 and 2010 using the following health care databases: Optum, Truven, Department of Veterans Affairs (VA), Kaiser Permanente Southern California (KPSC), Medicare and Medicaid. We estimated the three-year cumulative prevalence, and standardized to the 2010 US population.
Results: Among individuals with at least one health claim for demyelinating disease, the case definition had a sensitivity of 86%, specificity: 76-82%, and positive predictive value: 96-98% when compared to physician-adjudicated diagnoses. The unadjusted cumulative prevalence of MS for 2008-2010 for the private insurance databases was 208 per 100,000 (95% CI: 205-211) for Optum and 208 per 100,000 (95% CI: 207-210) for Truven. The cumulative prevalence for the national VA health care system was 177 per 100,000 (95% CI: 174-181), and for KPSC was 110 per 100,000 (95% CI: 106-114). The female: male ratio for MS prevalence was about 3:1 across databases and a US geographic prevalence gradient was found. A final integrated national MS cumulative prevalence estimate will be generated and stratified by age, sex and geographic region.
Conclusion: The US national cumulative MS prevalence rates for 2008-10 are the highest reported to date and provide a contemporary understanding of the disease burden. Our rigorous algorithm-based approach to estimating prevalence is novel, efficient and has the potential to be used for other chronic conditions.
Disclosure:
Mitchell Wallin: nothing to disclose
William J. Culpepper: nothing to disclose
Jonathan Campbell: nothing to disclose
Lorene Nelson: nothing to disclose
Annette Langer-Gould: nothing to disclose
Ruth Ann Marrie: nothing to disclose (see acknowledgement below)
Gary Cutter: nothing to disclose
Wendy Kaye: nothing to disclose
Laurie Wagner: nothing to disclose
Helen Tremlett: nothing to disclose
Steve Buka: nothing to disclose
Nicholas LaRocca: nothing to disclose
Carson Kai-Sang Leung: nothing to disclose
Piyameth Dilokthornsakul: nothing to disclose
Barbara Topol: nothing to disclose
Lie Hong Chen: nothing to disclose
Helen Tremlett is funded by the Canada Research Chair program and in the last year has received research support from the National Multiple Sclerosis Society, the Canadian Institutes of Health Research, the Multiple Sclerosis Society of Canada and the Multiple Sclerosis Scientific Research Foundation.
Acknowledgement: The results and conclusions are those of the authors and no official endorsement by the Manitoba Centre for Health Policy, Manitoba Health, or other data providers is intended or should be inferred.