{"doi":"10.1016/j.ajpc.2022.100370","title":"County-level variation in cardioprotective antihyperglycemic prescribing among medicare beneficiaries","abstract":"Background: Cardioprotective antihyperglycemic agents, SGLT2 inhibitors (SGLT2i) and GLP-1 receptor agonists (GLP1RA), improve outcomes of patients with type 2 diabetes, but adoption has been limited. Differences across individuals have been noted but area-level variation is unknown. Objectives: Given healthcare access and sociodemographic differences, we evaluated whether SGLT2i and GLP-1RA utilization varies across US counties. Methods: We linked 2019 Medicare Part D national prescription data with county-level demographic measures from the Agency for Health Quality and Research. We compared the number of beneficiaries receiving prescriptions for any cardioprotective antihyperglycemic to the number receiving metformin prescriptions across US counties. In multivariable linear regression with SGLT2i-to-metformin and GLP1RA-to-metformin prescriptions as outcomes, we evaluated county factors associated with use of cardioprotective agents while adjusting for sociodemographic measures, region, and cardiometabolic risk factor prevalence. Results: < 0.01). A higher median age of county residents, rural location, and lower prevalence of diabetes were associated with lower SGLT2i prescribing. Similarly, more advanced age of county residents, rural location, proportion of Hispanic individuals, and household income and lower education levels were associated with lower GLP-1RA prescribing. Prescribing was higher in the Northeast and lower in the West as compared with the Midwest for both classes. Conclusion: There was large variation by county in cardioprotective antihyperglycemic prescribing, with a pattern of lower use in Black-predominant and rural counties, highlighting the critical need to investigate equity in uptake of novel therapeutic agents.","journal":"American Journal of Preventive Cardiology","year":2022,"id":271963,"datarank":0.6951717279573215,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"self_citation_contribution":0.38474240361923057,"citation_network_contribution":0.310429324338091,"self_endowment_contribution":0.38474240361923057,"citer_contribution":0.310429324338091,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":10,"citers_with_citation_signal":9,"citers_with_endowment":9,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9589,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":418194,"name":"Arash Aghajani Nargesi","orcid":"0000-0002-6182-5271","position":1,"is_corresponding":false},{"id":355852,"name":"Utibe R. Essien","orcid":"0000-0002-4494-5028","position":2,"is_corresponding":false},{"id":807985,"name":"Veer Sangha","orcid":"0000-0002-8524-1203","position":3,"is_corresponding":false},{"id":550685,"name":"Zhenqiu Lin","orcid":"0000-0002-3089-141X","position":4,"is_corresponding":false},{"id":1077,"name":"Harlan M. Krumholz","orcid":"0000-0003-2046-127X","position":5,"is_corresponding":false},{"id":74880,"name":"Rohan Khera","orcid":"0000-0001-9467-6199","position":6,"is_corresponding":false},{"id":905043,"name":"Jonathan Hanna","orcid":"0000-0003-0339-7050","position":0,"is_corresponding":true}],"reference_count":39,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:27:43.806977Z","pmid":"35968531","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":[]}