{"doi":"10.1093/aje/kwae335","title":"US county-level food insecurity and COVID-19 mortality: a Bayesian spatial analysis with R-INLA","abstract":"Abstract The objective of our study was to evaluate the relationship between county-level food insecurity (FI) and the risk of Coronavirus-2019 (COVID-19) mortality in the United States during the early pandemic (March 25, 2020-December 25, 2021), while accounting for geographic contributions to that risk. Bayesian Intrinsic Conditional Autoregressive negative binomial regression models with spatially-structured and unstructured random effects were used to model the posited relationship between FI and COVID-19 mortality and to account for geographic heterogeneity and residual sources of variance. The model parameters’ posterior distributions, means, and credible intervals (CrIs) were approximated with Laplace’s approximation via the R-INLA package. Using data from the Johns Hopkins Coronavirus Resource Center, we found that a standard deviation increase in county-level FI prevalence (4%) was associated with a 1.11-fold increased risk of county-level COVID-19 mortality (95% CrI, 1.02-1.20), but only in sensitivity analyses. We found no significant association when the analysis was performed with COVID-19 mortality data from the Centers for Disease Control and Prevention. County-level FI was positively and modestly associated with COVID-19 mortality within the first 2 years of the pandemic. Data sources and quality impacted the results.","journal":"American Journal of Epidemiology","year":2024,"id":502798,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9338,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1352230,"name":"Maurício Campos","orcid":"0000-0002-9351-7193","position":1,"is_corresponding":false},{"id":393069,"name":"Sara McLafferty","orcid":"0000-0001-8413-7562","position":2,"is_corresponding":false},{"id":247987,"name":"May A. Beydoun","orcid":"0000-0002-1050-4523","position":3,"is_corresponding":false},{"id":440017,"name":"Francesca Gany","orcid":"0000-0003-0684-2367","position":4,"is_corresponding":false},{"id":404089,"name":"Anna E. Arthur","orcid":"0000-0002-5208-4336","position":5,"is_corresponding":false},{"id":306867,"name":"Rebecca L. Smith","orcid":"0000-0002-8343-794X","position":6,"is_corresponding":false},{"id":1081627,"name":"Christian A. Maino Vieytes","orcid":"0009-0001-8052-0203","position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":null,"created_at":"2026-07-19T02:10:23.709105Z","pmid":"39218432","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":[]}