Dataset Information

Incidence of Clinician-Diagnosed Lyme Disease, United States, 2005-2010.

ABSTRACT: National surveillance provides important information about Lyme disease (LD) but is subject to underreporting and variations in practice. Information is limited about the national epidemiology of LD from other sources. Retrospective analysis of a nationwide health insurance claims database identified patients from 2005-2010 with clinician-diagnosed LD using International Classification of Diseases, Ninth Revision, Clinical Modification, codes and antimicrobial drug prescriptions. Of 103,647,966 person-years, 985 inpatient admissions and 44,445 outpatient LD diagnoses were identified. Epidemiologic patterns were similar to US surveillance data overall. Outpatient incidence was highest among boys 5-9 years of age and persons of both sexes 60-64 years of age. On the basis of extrapolation to the US population and application of correction factors for coding, we estimate that annual incidence is 106.6 cases/100,000 persons and that ≈329,000 (95% credible interval 296,000-376,000) LD cases occur annually. LD is a major US public health problem that causes substantial use of health care resources.

SUBMITTER: Nelson CA

PROVIDER: S-EPMC4550147 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Incidence of Clinician-Diagnosed Lyme Disease, United States, 2005-2010.

Nelson Christina A CA Saha Shubhayu S Kugeler Kiersten J KJ Delorey Mark J MJ Shankar Manjunath B MB Hinckley Alison F AF Mead Paul S PS

Emerging infectious diseases 20150901 9

National surveillance provides important information about Lyme disease (LD) but is subject to underreporting and variations in practice. Information is limited about the national epidemiology of LD from other sources. Retrospective analysis of a nationwide health insurance claims database identified patients from 2005-2010 with clinician-diagnosed LD using International Classification of Diseases, Ninth Revision, Clinical Modification, codes and antimicrobial drug prescriptions. Of 103,647,966 ...[more]

PMID: 26291194

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Project description:An estimated 300,000 cases of Lyme disease occur in the United States annually. Disseminated Lyme disease may result in carditis, arthritis, facial palsy or meningitis, sometimes requiring hospitalization. We describe the epidemiology and cost of Lyme disease-related hospitalizations. We analysed 2005-2014 data from the Truven Health Analytics MarketScan Commercial Claims and Encounters Databases to identify inpatient records associated with Lyme disease based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. We estimated the annual number and median cost of Lyme disease-related hospitalizations in the United States in persons under 65 years of age. Costs were adjusted to reflect 2016 dollars. Of 20,983,165 admission records contained in the inpatient databases during the study period, 2,823 (0.01%) met inclusion criteria for Lyme disease-related hospitalizations. Over half of the identified records contained an ICD-9-CM code for meningitis (n = 614), carditis (n = 429), facial palsy (n = 400) or arthritis (n = 377). Nearly 60% of hospitalized patients were male. The median cost per Lyme disease-related hospitalization was $11,688 (range: $140-$323,613). The manifestation with the highest median cost per stay was carditis ($17,461), followed by meningitis ($15,177), arthritis ($13,012) and facial palsy ($10,491). Median cost was highest among the 15- to 19-year-old age group ($12,991). Admissions occurring in January had the highest median cost ($13,777) for all study years. Based on extrapolation to the U.S. population, we estimate that 2,196 Lyme disease-related hospitalizations in persons under 65 years of age occur annually with an estimated annual cost of $25,826,237. Lyme disease is usually treated in an outpatient setting; however, some patients with Lyme disease require hospitalization, underscoring the need for effective prevention methods to mitigate these serious cases. Information from this analysis can aid economic evaluations of interventions that prevent infection and advances in disease detection.

Project description:BackgroundLyme disease is one of the most commonly reported infectious diseases in the United States (US), accounting for more than [Formula: see text] of all vector-borne diseases in North America.ObjectiveIn this paper, self-reported tweets on Twitter were analyzed in order to predict potential Lyme disease cases and accurately assess incidence rates in the US.MethodsThe study was done in three stages: (1) Approximately 1.3 million tweets were collected and pre-processed to extract the most relevant Lyme disease tweets with geolocations. A subset of tweets were semi-automatically labelled as relevant or irrelevant to Lyme disease using a set of precise keywords, and the remaining portion were manually labelled, yielding a curated labelled dataset of 77, 500 tweets. (2) This labelled data set was used to train, validate, and test various combinations of NLP word embedding methods and prominent ML classification models, such as TF-IDF and logistic regression, Word2vec and XGboost, and BERTweet, among others, to identify potential Lyme disease tweets. (3) Lastly, the presence of spatio-temporal patterns in the US over a 10-year period were studied.ResultsPreliminary results showed that BERTweet outperformed all tested NLP classifiers for identifying Lyme disease tweets, achieving the highest classification accuracy and F1-score of [Formula: see text]. There was also a consistent pattern indicating that the West and Northeast regions of the US had a higher tweet rate over time.ConclusionsWe focused on the less-studied problem of using Twitter data as a surveillance tool for Lyme disease in the US. Several crucial findings have emerged from the study. First, there is a fairly strong correlation between classified tweet counts and Lyme disease counts, with both following similar trends. Second, in 2015 and early 2016, the social media network like Twitter was essential in raising popular awareness of Lyme disease. Third, counties with a high incidence rate were not necessarily related with a high tweet rate, and vice versa. Fourth, BERTweet can be used as a reliable NLP classifier for detecting relevant Lyme disease tweets.

Dataset Information

Incidence of Clinician-Diagnosed Lyme Disease, United States, 2005-2010.

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Incidence of Clinician-Diagnosed Lyme Disease, United States, 2005-2010.

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