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      Estimating Prevalence, Demographics, and Costs of ME/CFS Using Large Scale Medical Claims Data and Machine Learning

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          Abstract

          Techniques of data mining and machine learning were applied to a large database of medical and facility claims from commercially insured patients to determine the prevalence, gender demographics, and costs for individuals with provider-assigned diagnosis codes for myalgic encephalomyelitis (ME) or chronic fatigue syndrome (CFS). The frequency of diagnosis was 519–1,038/100,000 with the relative risk of females being diagnosed with ME or CFS compared to males 1.238 and 1.178, respectively. While the percentage of women diagnosed with ME/CFS is higher than the percentage of men, ME/CFS is not a “women's disease.” Thirty-five to forty percent of diagnosed patients are men. Extrapolating from this frequency of diagnosis and based on the estimated 2017 population of the United States, a rough estimate for the number of patients who may be diagnosed with ME or CFS in the U.S. is 1.7 million to 3.38 million. Patients diagnosed with CFS appear to represent a more heterogeneous group than those diagnosed with ME. A machine learning model based on characteristics of individuals diagnosed with ME was developed and applied, resulting in a predicted prevalence of 857/100,000 ( p > 0.01), or roughly 2.8 million in the U.S. Average annual costs for individuals with a diagnosis of ME or CFS were compared with those for lupus (all categories) and multiple sclerosis (MS), and found to be 50% higher for ME and CFS than for lupus or MS, and three to four times higher than for the general insured population. A separate aspect of the study attempted to determine if a diagnosis of ME or CFS could be predicted based on symptom codes in the insurance claims records. Due to the absence of specific codes for some core symptoms, we were unable to validate that the information in insurance claims records is sufficient to identify diagnosed patients or suggest that a diagnosis of ME or CFS should be considered based solely on looking for presence of those symptoms. These results show that a prevalence rate of 857/100,000 for ME/CFS is not unreasonable; therefore, it is not a rare disease, but in fact a relatively common one.

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          A community-based study of chronic fatigue syndrome.

          Most previous estimates of the prevalence of chronic fatigue syndrome (CFS) have derived largely from treated populations, and have been biased by differential access to health care treatment linked with sex, ethnic identification, and socioeconomic status. To assess the point prevalence of CFS in an ethnically diverse random community sample. A sample of 28,673 adults in Chicago, Ill, was screened by telephone, and those with CFS-like symptoms were medically evaluated. MAIN OUTCOME MEASURES AND ANALYSES: Self-report questionnaires, psychiatric evaluations, and complete medical examinations with laboratory testing were used to diagnose patients with CFS. Univariate and multivariate statistical techniques were used to delineate the overall rate of CFS in this population, and its relative prevalence was subcategorized by sex, ethnic identification, age, and socioeconomic status. There was a 65.1% completion rate for the telephone interviews during the first phase of the study. Findings indicated that CFS occurs in about 0.42% (95% confidence interval, 0.29%-0.56%) of this random community-based sample. The highest levels of CFS were consistently found among women, minority groups, and persons with lower levels of education and occupational status. Chronic fatigue syndrome is a common chronic health condition, especially for women, occurring across ethnic groups. Earlier findings suggesting that CFS is a syndrome primarily affecting white, middle-class patients were not supported by our findings.
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            Prevalence and incidence of chronic fatigue syndrome in Wichita, Kansas.

            Chronic fatigue syndrome (CFS) is a debilitating illness with no known cause or effective therapy. Population-based epidemiologic data on CFS prevalence and incidence are critical to put CFS in a realistic context for public health officials and others responsible for allocating resources and for practicing physicians when examining and caring for patients. We conducted a random digit-dialing survey and clinical examination to estimate the prevalence of CFS in the general population of Wichita, Kan, and a 1-year follow-up telephone interview and clinical examination to estimate the incidence of CFS. The survey included 33 997 households representing 90 316 residents. This report focuses on 7162 respondents aged 18 to 69 years. Fatigued (n = 3528) and randomly selected nonfatigued (n = 3634) respondents completed telephone questionnaires concerning fatigue, other symptoms, and medical history. The clinical examination included the Diagnostic Interview Schedule for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, laboratory testing, and a physical examination. The overall weighted point prevalence of CFS, adjusted for nonresponse, was 235 per 100,000 persons (95% confidence interval, 142-327 per 100,000 persons). The prevalence of CFS was higher among women, 373 per 100,000 persons (95% confidence interval, 210-536 per 100,000 persons), than among men, 83 per 100,000 persons (95% confidence interval, 15-150 per 100,000 persons). Among subjects nonfatigued and fatigued for less than 6 months, the 1-year incidence of CFS was 180 per 100,000 persons (95% confidence interval, 0-466 per 100,000 persons). Chronic fatigue syndrome constitutes a major public health problem. Longitudinal follow-up of this cohort will be used to further evaluate the natural history of this illness.
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              A Community-Based Study of Chronic Fatigue Syndrome

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                Author and article information

                Contributors
                Journal
                Front Pediatr
                Front Pediatr
                Front. Pediatr.
                Frontiers in Pediatrics
                Frontiers Media S.A.
                2296-2360
                08 January 2019
                2018
                : 6
                : 412
                Affiliations
                [1] 1Optum Enterprise Analytics, UnitedHealth Group , Minneapolis, MN, United States
                [2] 2Optum Technology, UnitedHealth Group , Minneapolis, MN, United States
                [3] 3Innovation, Research, and Development, UnitedHealth Group , Minneapolis, MN, United States
                [4] 4Bateman Horne Center , Salt Lake City, UT, United States
                [5] 5Bureau of Infectious Disease and Laboratory Sciences, Massachusetts Department of Public Health , Boston, MA, United States
                [6] 6Hunter-Hopkins Center , Charlotte, NC, United States
                [7] 7Children's Center Chronic Fatigue Clinic, Johns Hopkins University School of Medicine , Baltimore, MD, United States
                [8] 8Massachusetts ME/CFS & FM Association , Quincy, MA, United States
                Author notes

                Edited by: Kette D. Valente, University of São Paulo, Brazil

                Reviewed by: Leonardo Emberti Gialloreti, Università degli Studi di Roma Tor Vergata, Italy; Filippo M. Santorelli, Fondazione Stella Maris (IRCCS), Italy

                *Correspondence: Charmian Proskauer charmian.proskauer@ 123456verizon.net

                This article was submitted to Pediatric Neurology, a section of the journal Frontiers in Pediatrics

                Article
                10.3389/fped.2018.00412
                6331450
                30671425
                705ce6da-4dd7-4fba-8f6b-f49f752344a8
                Copyright © 2019 Valdez, Hancock, Adebayo, Kiernicki, Proskauer, Attewell, Bateman, DeMaria, Lapp, Rowe and Proskauer.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 29 October 2018
                : 11 December 2018
                Page count
                Figures: 5, Tables: 12, Equations: 0, References: 30, Pages: 14, Words: 9783
                Categories
                Pediatrics
                Original Research

                me/cfs,myalgic encephalomyelitis,chronic fatigue syndrome,prevalence,costs,machine learning,data mining

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