Dataset Information

Farming tasks and the development of rheumatoid arthritis in the agricultural health study.

ABSTRACT:

Objectives

Farming has been associated with rheumatoid arthritis (RA). Some studies have evaluated the effects of pesticides, but other agricultural exposures may also affect immune response.

Methods

We investigated non-pesticide agricultural exposures in relation to RA in licensed pesticide applicators (n=27 175, mostly male farmers) and their spouses (n=22 231) in the Agricultural Health Study (AHS) cohort (1993-1997) who completed at least one follow-up survey through 2015. Incident RA cases (n=229 applicators and 249 spouses) were identified based on self-report confirmed by use of disease-modifying antirheumatic drugs or medical records. Hazard Ratios (HRs) and 95% Confidence Intervals (CIs) were estimated by Cox proportional hazard models adjusting for applicator status, state, smoking, education and specific pesticide use, allowing estimates to vary by median age when hazards assumptions were not met.

Results

Overall, RA was associated with regularly applying chemical fertilisers (HR=1.50; 95% CI 1.11 to 2.02), using non-gasoline solvents (HR=1.40; 95% CI 1.09 to 1.80), and painting (HR=1.26; 95% CI 1.00 to 1.59). In older applicators (>62 years), RA was associated with driving combines (HR=2.46; 95% CI 1.05 to 5.78) and milking cows (HR=2.56; 95% CI 1.01 to 6.53). In younger participants (≤62 years), RA was inversely associated with raising animals as well as crops (HR=0.68; 95% CI 0.51 to 0.89 vs crops only). Associations with specific crops varied by age: some (eg, hay) were inversely associated with RA in younger participants, while others (eg, alfalfa) were associated with RA in older participants.

Conclusion

These findings suggest several agricultural tasks and exposures may contribute to development of RA.

SUBMITTER: Parks CG

PROVIDER: S-EPMC8275027 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Farming tasks and the development of rheumatoid arthritis in the agricultural health study.

Parks Christine G CG Meyer Armando A Beane Freeman Laura E LE Hofmann Jonathan N JN Sandler Dale P DP

Occupational and environmental medicine 20190213 4

<h4>Objectives</h4>Farming has been associated with rheumatoid arthritis (RA). Some studies have evaluated the effects of pesticides, but other agricultural exposures may also affect immune response.<h4>Methods</h4>We investigated non-pesticide agricultural exposures in relation to RA in licensed pesticide applicators (n=27 175, mostly male farmers) and their spouses (n=22 231) in the Agricultural Health Study (AHS) cohort (1993-1997) who completed at least one follow-up survey through 2015. Inc ...[more]

PMID: 30760605

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Project description:BackgroundFinancial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries.ObjectiveThe aim of this study was to develop an easily implementable workflow that builds a machine learning algorithm capable of accurately identifying patients with rheumatoid arthritis from format-free text fields in electronic health records.MethodsTwo electronic health record data sets were employed: Leiden (n=3000) and Erlangen (n=4771). Using a portion of the Leiden data (n=2000), we compared 6 different machine learning methods and a naïve word-matching algorithm using 10-fold cross-validation. Performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC), and F1 score was used as the primary criterion for selecting the best method to build a classifying algorithm. We selected the optimal threshold of positive predictive value for case identification based on the output of the best method in the training data. This validation workflow was subsequently applied to a portion of the Erlangen data (n=4293). For testing, the best performing methods were applied to remaining data (Leiden n=1000; Erlangen n=478) for an unbiased evaluation.ResultsFor the Leiden data set, the word-matching algorithm demonstrated mixed performance (AUROC 0.90; AUPRC 0.33; F1 score 0.55), and 4 methods significantly outperformed word-matching, with support vector machines performing best (AUROC 0.98; AUPRC 0.88; F1 score 0.83). Applying this support vector machine classifier to the test data resulted in a similarly high performance (F1 score 0.81; positive predictive value [PPV] 0.94), and with this method, we could identify 2873 patients with rheumatoid arthritis in less than 7 seconds out of the complete collection of 23,300 patients in the Leiden electronic health record system. For the Erlangen data set, gradient boosting performed best (AUROC 0.94; AUPRC 0.85; F1 score 0.82) in the training set, and applied to the test data, resulted once again in good results (F1 score 0.67; PPV 0.97).ConclusionsWe demonstrate that machine learning methods can extract the records of patients with rheumatoid arthritis from electronic health record data with high precision, allowing research on very large populations for limited costs. Our approach is language and center independent and could be applied to any type of diagnosis. We have developed our pipeline into a universally applicable and easy-to-implement workflow to equip centers with their own high-performing algorithm. This allows the creation of observational studies of unprecedented size covering different countries for low cost from already available data in electronic health record systems.

Dataset Information

Farming tasks and the development of rheumatoid arthritis in the agricultural health study.

Objectives

Methods

Results

Conclusion

Publications

Farming tasks and the development of rheumatoid arthritis in the agricultural health study.

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