Validation of Medical Service Insurance Claims as a Surrogate for Ascertaining Vitiligo Cases Article Swipe
Madelaine Bell
,
Harvey Lui
,
Tim Lee
,
Sunil Kalia
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1587917/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1587917/v1
Background: The epidemiology of vitiligo, especially its disease burden on the healthcare system, can be assessed indirectly by analyzing health insurance claims data. Validating this approach is integral to ensuring accurate case identification and cohort characterization. Objectives: The primary aim of this study was to develop and validate an indirect measure of vitiligo ascertainment using health insurance claims data. These data were used secondarily to identify demographic characteristics, body site involvement, vitiligo subtypes, disease associations, and treatments. Methods: This study assessed the validity of identifying vitiligo from billing claims within a Canadian provincial universal health insurance program, versus vitiligo cases accrued from direct medical chart reviews. Claims-based algorithms combining ICD-9-CM diagnostic code 709 with treatment-specific data were derived and tested to identify vitiligo patients. This was compared against cases arising from the manual review of medical records of 606 patient with a diagnostic code for “dyschromia” (ICD-9-CM diagnostic code 709) from January 1 to December 31, 2016. Results: Based on the chart reviews, 204 (33.7%) patients were confirmed to have vitiligo. 42 separate claims-based algorithms combining ICD-9-CM diagnostic code 709 with treatment data specific to vitiligo were modelled and individually tested to evaluate their accuracy for vitiligo ascertainment. One algorithm achieved a sensitivity, specificity, PPV and NPV of 86.8% (95% CI 82.1-91.4), 92.5% (95% CI 90.0-95.1), 85.5% (95% CI 80.7-90.3), and 93.2% (95% CI 90.8-95.7), respectively. There was a 2.2 female to male ratio. The most common medical treatments were tacrolimus (74.5%) and topical corticosteroids (54.3%). Hypertension (24.2%) and hypothyroidism (19.6%) were the predominant co-morbidities associated with vitiligo. Conclusions: Health insurance claims data can be used to indirectly ascertain vitiligo for epidemiologic purposes with relatively high diagnostic performance between 85.5-93.2%.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-1587917/v1
- https://www.researchsquare.com/article/rs-1587917/latest.pdf
- OA Status
- green
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224758978
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4224758978Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-1587917/v1Digital Object Identifier
- Title
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Validation of Medical Service Insurance Claims as a Surrogate for Ascertaining Vitiligo CasesWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-25Full publication date if available
- Authors
-
Madelaine Bell, Harvey Lui, Tim Lee, Sunil KaliaList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-1587917/v1Publisher landing page
- PDF URL
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https://www.researchsquare.com/article/rs-1587917/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-1587917/latest.pdfDirect OA link when available
- Concepts
-
Vitiligo, Medicine, Diagnosis code, Epidemiology, Cohort, Medical record, Chart, Family medicine, Dermatology, Internal medicine, Population, Environmental health, Statistics, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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21Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.86.8% | 209 |
| abstract_inverted_index.92.5% | 213 |
| abstract_inverted_index.93.2% | 222 |
| abstract_inverted_index.Based | 159 |
| abstract_inverted_index.There | 227 |
| abstract_inverted_index.These | 60 |
| abstract_inverted_index.cases | 100, 129 |
| abstract_inverted_index.chart | 105, 162 |
| abstract_inverted_index.data. | 23, 59 |
| abstract_inverted_index.study | 43, 80 |
| abstract_inverted_index.their | 194 |
| abstract_inverted_index.using | 55 |
| abstract_inverted_index.Health | 260 |
| abstract_inverted_index.burden | 9 |
| abstract_inverted_index.claims | 22, 58, 89, 262 |
| abstract_inverted_index.cohort | 35 |
| abstract_inverted_index.common | 237 |
| abstract_inverted_index.direct | 103 |
| abstract_inverted_index.female | 231 |
| abstract_inverted_index.health | 20, 56, 95 |
| abstract_inverted_index.manual | 133 |
| abstract_inverted_index.ratio. | 234 |
| abstract_inverted_index.review | 134 |
| abstract_inverted_index.tested | 120, 191 |
| abstract_inverted_index.versus | 98 |
| abstract_inverted_index.within | 90 |
| abstract_inverted_index.(19.6%) | 251 |
| abstract_inverted_index.(24.2%) | 248 |
| abstract_inverted_index.(33.7%) | 165 |
| abstract_inverted_index.(74.5%) | 242 |
| abstract_inverted_index.January | 152 |
| abstract_inverted_index.accrued | 101 |
| abstract_inverted_index.against | 128 |
| abstract_inverted_index.arising | 130 |
| abstract_inverted_index.between | 279 |
| abstract_inverted_index.billing | 88 |
| abstract_inverted_index.derived | 118 |
| abstract_inverted_index.develop | 46 |
| abstract_inverted_index.disease | 8, 74 |
| abstract_inverted_index.measure | 51 |
| abstract_inverted_index.medical | 104, 136, 238 |
| abstract_inverted_index.patient | 140 |
| abstract_inverted_index.primary | 39 |
| abstract_inverted_index.records | 137 |
| abstract_inverted_index.system, | 13 |
| abstract_inverted_index.topical | 244 |
| abstract_inverted_index.(54.3%). | 246 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Canadian | 92 |
| abstract_inverted_index.December | 155 |
| abstract_inverted_index.ICD-9-CM | 110, 177 |
| abstract_inverted_index.Methods: | 78 |
| abstract_inverted_index.Results: | 158 |
| abstract_inverted_index.accuracy | 195 |
| abstract_inverted_index.accurate | 31 |
| abstract_inverted_index.achieved | 201 |
| abstract_inverted_index.approach | 26 |
| abstract_inverted_index.assessed | 16, 81 |
| abstract_inverted_index.compared | 127 |
| abstract_inverted_index.ensuring | 30 |
| abstract_inverted_index.evaluate | 193 |
| abstract_inverted_index.identify | 66, 122 |
| abstract_inverted_index.indirect | 50 |
| abstract_inverted_index.integral | 28 |
| abstract_inverted_index.modelled | 188 |
| abstract_inverted_index.patients | 166 |
| abstract_inverted_index.program, | 97 |
| abstract_inverted_index.purposes | 273 |
| abstract_inverted_index.reviews, | 163 |
| abstract_inverted_index.reviews. | 106 |
| abstract_inverted_index.separate | 173 |
| abstract_inverted_index.specific | 184 |
| abstract_inverted_index.validate | 48 |
| abstract_inverted_index.validity | 83 |
| abstract_inverted_index.vitiligo | 53, 72, 86, 99, 123, 186, 197, 270 |
| abstract_inverted_index.(ICD-9-CM | 147 |
| abstract_inverted_index.algorithm | 200 |
| abstract_inverted_index.analyzing | 19 |
| abstract_inverted_index.ascertain | 269 |
| abstract_inverted_index.combining | 109, 176 |
| abstract_inverted_index.confirmed | 168 |
| abstract_inverted_index.insurance | 21, 57, 96, 261 |
| abstract_inverted_index.patients. | 124 |
| abstract_inverted_index.subtypes, | 73 |
| abstract_inverted_index.treatment | 182 |
| abstract_inverted_index.universal | 94 |
| abstract_inverted_index.vitiligo, | 5 |
| abstract_inverted_index.vitiligo. | 171, 258 |
| abstract_inverted_index.Validating | 24 |
| abstract_inverted_index.algorithms | 108, 175 |
| abstract_inverted_index.associated | 256 |
| abstract_inverted_index.diagnostic | 111, 143, 148, 178, 277 |
| abstract_inverted_index.especially | 6 |
| abstract_inverted_index.healthcare | 12 |
| abstract_inverted_index.indirectly | 17, 268 |
| abstract_inverted_index.provincial | 93 |
| abstract_inverted_index.relatively | 275 |
| abstract_inverted_index.tacrolimus | 241 |
| abstract_inverted_index.treatments | 239 |
| abstract_inverted_index.80.7-90.3), | 220 |
| abstract_inverted_index.82.1-91.4), | 212 |
| abstract_inverted_index.85.5-93.2%. | 280 |
| abstract_inverted_index.90.0-95.1), | 216 |
| abstract_inverted_index.90.8-95.7), | 225 |
| abstract_inverted_index.Background: | 1 |
| abstract_inverted_index.Objectives: | 37 |
| abstract_inverted_index.demographic | 67 |
| abstract_inverted_index.identifying | 85 |
| abstract_inverted_index.performance | 278 |
| abstract_inverted_index.predominant | 254 |
| abstract_inverted_index.secondarily | 64 |
| abstract_inverted_index.treatments. | 77 |
| abstract_inverted_index.Claims-based | 107 |
| abstract_inverted_index.Conclusions: | 259 |
| abstract_inverted_index.Hypertension | 247 |
| abstract_inverted_index.claims-based | 174 |
| abstract_inverted_index.epidemiology | 3 |
| abstract_inverted_index.individually | 190 |
| abstract_inverted_index.involvement, | 71 |
| abstract_inverted_index.sensitivity, | 203 |
| abstract_inverted_index.specificity, | 204 |
| abstract_inverted_index.ascertainment | 54 |
| abstract_inverted_index.associations, | 75 |
| abstract_inverted_index.epidemiologic | 272 |
| abstract_inverted_index.respectively. | 226 |
| abstract_inverted_index.ascertainment. | 198 |
| abstract_inverted_index.co-morbidities | 255 |
| abstract_inverted_index.hypothyroidism | 250 |
| abstract_inverted_index.identification | 33 |
| abstract_inverted_index.corticosteroids | 245 |
| abstract_inverted_index.characteristics, | 68 |
| abstract_inverted_index.“dyschromia” | 146 |
| abstract_inverted_index.characterization. | 36 |
| abstract_inverted_index.treatment-specific | 115 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5089195756 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I141945490 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.04473213 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |