{"doi":"10.1002/ajim.23485","title":"An algorithm for quantitatively estimating occupational endotoxin exposure in the biomarkers of exposure and effect in agriculture study: II. Application to the study population","abstract":"BACKGROUND: We developed an algorithm to quantitatively estimate endotoxin exposure for farmers in the Biomarkers of Exposure and Effect in Agriculture (BEEA) Study. METHODS: The algorithm combined task intensity estimates derived from published data with questionnaire responses on activity duration to estimate task-specific cumulative endotoxin exposures for 13 tasks during four time windows, ranging from \"past 12 months\" to \"yesterday/today.\" We applied the algorithm to 1681 participants in Iowa and North Carolina. We examined correlations in endotoxin metrics within- and between-task. We also compared these metrics to prior day full-shift inhalable endotoxin concentrations from 32 farmers. RESULTS: The highest median task-specific cumulative exposures were observed for swine confinement, poultry confinement, and grind feed. Inter-quartile ranges showed substantial between-subject variability for most tasks. Time window-specific metrics of the same task were moderately-highly correlated. Between-task correlation was variable, with moderately-high correlations observed for similar tasks (e.g., between animal-related tasks). Prior day endotoxin concentration increased with the total metric and with task metrics for swine confinement, clean other animal facilities, and clean grain bins. SIGNIFICANCE: This study provides insight into the variability and sources of endotoxin exposure among farmers in the BEEA study and summarizes exposure estimates for future investigations in this population.","journal":"American Journal of Industrial Medicine","year":2023,"id":378559,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9456,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":104588,"name":"Laura E. Beane Freeman","orcid":"0000-0003-1294-4124","position":1,"is_corresponding":false},{"id":444149,"name":"Sarah J. Locke","orcid":"0000-0002-4827-222X","position":2,"is_corresponding":false},{"id":444150,"name":"Pabitra R Josse","orcid":"0000-0003-4506-7682","position":3,"is_corresponding":false},{"id":951235,"name":"Shuai Xie","orcid":"0000-0002-0426-7613","position":4,"is_corresponding":false},{"id":674605,"name":"Susan Marie Viet","orcid":null,"position":5,"is_corresponding":false},{"id":444148,"name":"Jean‐François Sauvé","orcid":"0000-0002-4995-4388","position":6,"is_corresponding":false},{"id":104585,"name":"Gabriella Andreotti","orcid":"0000-0002-1096-3324","position":7,"is_corresponding":false},{"id":317029,"name":"Peter S. Thorne","orcid":"0000-0002-5045-0929","position":8,"is_corresponding":false},{"id":241832,"name":"Jonathan N. Hofmann","orcid":"0000-0002-1043-5812","position":9,"is_corresponding":false},{"id":417503,"name":"Melissa C. Friesen","orcid":"0000-0002-1695-3282","position":0,"is_corresponding":true}],"reference_count":19,"raw_metadata":null,"created_at":"2026-07-19T01:16:48.594070Z","pmid":"37087683","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}