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One solution is to include fish intake and a biomarker of MeHg exposure in the same analytical model, but resulting estimates do not reflect the independent impact of accumulated MeHg or fish exposure. In fish-eating populations, this can be addressed by separating MeHg exposure into fish intake and average mercury content of the consumed fish. We assessed the joint association of prenatal MeHg exposure (maternal hair mercury level) and fish intake (among fish-eating mothers) with neurodevelopment in 361 children aged 8 years from the New Bedford Cohort (New Bedford, Massachusetts; born in 1993-1998). Neurodevelopmental assessments used standardized tests of IQ, language, memory, and attention. Covariate-adjusted regression assessed the association of maternal fish consumption, stratified by tertile of estimated average fish mercury level, with neurodevelopment. Associations between maternal fish intake and child outcomes were generally beneficial for those in the lowest average fish mercury tertile but detrimental in the highest average fish mercury tertile, where, for example, each serving of fish was associated with 1.3 fewer correct responses (95% CI, \u22122.2 to \u22120.4) on the Boston Naming Test. Standard analyses showed no outcome associations with hair mercury level or fish intake.<\/jats:p>\n                  <jats:p>This article is part of a Special Collection on Environmental Epidemiology.<\/jats:p>","DOI":"10.1093\/aje\/kwae149","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T03:33:31Z","timestamp":1718940811000},"page":"172-184","source":"Crossref","is-referenced-by-count":5,"title":["A novel approach to assessing the joint effects of mercury and fish consumption on neurodevelopment in the New Bedford Cohort"],"prefix":"10.1093","volume":"194","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2538-762X","authenticated-orcid":false,"given":"Sally W","family":"Thurston","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Computational Biology, University of Rochester Medical Center , Rochester, NY 14642 ,","place":["United States"]},{"name":"Department of Environmental Medicine, University of Rochester Medical Center , Rochester, NY 14642 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6713-2257","authenticated-orcid":false,"given":"David","family":"Ruppert","sequence":"additional","affiliation":[{"name":"School of Operations Research and Information Engineering, Cornell University , Ithaca, NY 14853 ,","place":["United States"]},{"name":"Department of Statistics and Data Science, Cornell University , Ithaca, NY 14853 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5966-1036","authenticated-orcid":false,"given":"Susan A","family":"Korrick","sequence":"additional","affiliation":[{"name":"Channing Division of Network Medicine, Brigham and Women\u2019s Hospital and Harvard Medical School , Boston, MA 02115 ,","place":["United States"]},{"name":"Department of Environmental Health , Harvard T.H. 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