{"doi":"10.1161/circ.135.suppl_1.mp022","title":"Abstract MP022: Cost-effectiveness of the Non-laboratory Based Framingham Algorithm in Primary Prevention of Cardiovascular Disease: a Simulated Analysis of a Cohort of African American Adults","abstract":"<jats:p>\n            <jats:bold>Background:</jats:bold>\n            The non-laboratory based (non-LB) Framingham algorithm, which substitutes body mass index for lipids, has been validated for screening cardiovascular disease (CVD) risk among African Americans (AA). However, its marginal cost and benefit tradeoffs have not been contrasted with the established laboratory based (LB) Framingham algorithm. This study examines the incremental cost-effectiveness ratio (ICER) of a simulated CVD prevention program guided by the non-LB versus LB Framingham algorithm.\n          </jats:p>\n          <jats:p>\n            <jats:bold>Methods:</jats:bold>\n            We simulated the World Health Organization CVD prevention guidelines on AA participants of the Atherosclerosis Risk in Communities (ARIC) cohort. Treatment intensity was tailored according to absolute CVD risk scores calculated by non-LB and LB Framingham algorithms. Costs were estimated using Medicare fee schedules (diagnostic tests, drugs and visits), Bureau of Labor Statistics (RN wages), and Chapman’s estimates for initial and follow-up costs of managing incident CVD events. Outcomes were assumed to be true positive cases detected at the treatment threshold. ICER was calculated using Drummond’s framework.\n          </jats:p>\n          <jats:p>\n            <jats:bold>Results:</jats:bold>\n            Among 2690 AA (mean age 53.3 ± 5.8 yrs, 59% female) the simulation under the non-LB vs LB Framingham algorithm had an ICER of -$46,240 per true positive case detected. The average 12-year discounted costs under the LB Framingham algorithm were 18.3% higher ($29,991 vs $25,357) and identified 6.5% fewer cases (309 vs 329) compared to the non-LB Framingham algorithm.\n          </jats:p>\n          <jats:p>\n            <jats:bold>Conclusion:</jats:bold>\n            The approach guided by the non-LB Framingham algorithm dominated the LB Framingham strategy with respect to both costs and predictive ability. Consequently, the non-LB Framingham algorithm could potentially provide a lower cost but a highly effective tool to address the high burden of CVD in AA and other resource constrained settings.\n          </jats:p>","journal":"Circulation","year":2017,"id":602574,"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":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1545419,"name":"Susan Leveille","orcid":null,"position":1,"is_corresponding":false},{"id":982370,"name":"Philimon Gona","orcid":"0000-0002-4202-5965","position":2,"is_corresponding":false},{"id":1545420,"name":"Eileen Stuart-Shor","orcid":null,"position":3,"is_corresponding":false},{"id":1545421,"name":"Jerry Cromwell","orcid":null,"position":4,"is_corresponding":false},{"id":1545418,"name":"Jacob Kariuki","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Abstract MP022: Cost-effectiveness of the Non-laboratory Based Framingham Algorithm in Primary Prevention of Cardiovascular Disease: a Simulated Analysis of a Cohort of African American Adults","abstract":"<jats:p>\n            <jats:bold>Background:</jats:bold>\n            The non-laboratory based (non-LB) Framingham algorithm, which substitutes body mass index for lipids, has been validated for screening cardiovascular disease (CVD) risk among African Americans (AA). However, its marginal cost and benefit tradeoffs have not been contrasted with the established laboratory based (LB) Framingham algorithm. This study examines the incremental cost-effectiveness ratio (ICER) of a simulated CVD prevention program guided by the non-LB versus LB Framingham algorithm.\n          </jats:p>\n          <jats:p>\n            <jats:bold>Methods:</jats:bold>\n            We simulated the World Health Organization CVD prevention guidelines on AA participants of the Atherosclerosis Risk in Communities (ARIC) cohort. Treatment intensity was tailored according to absolute CVD risk scores calculated by non-LB and LB Framingham algorithms. Costs were estimated using Medicare fee schedules (diagnostic tests, drugs and visits), Bureau of Labor Statistics (RN wages), and Chapman’s estimates for initial and follow-up costs of managing incident CVD events. Outcomes were assumed to be true positive cases detected at the treatment threshold. ICER was calculated using Drummond’s framework.\n          </jats:p>\n          <jats:p>\n            <jats:bold>Results:</jats:bold>\n            Among 2690 AA (mean age 53.3 ± 5.8 yrs, 59% female) the simulation under the non-LB vs LB Framingham algorithm had an ICER of -$46,240 per true positive case detected. The average 12-year discounted costs under the LB Framingham algorithm were 18.3% higher ($29,991 vs $25,357) and identified 6.5% fewer cases (309 vs 329) compared to the non-LB Framingham algorithm.\n          </jats:p>\n          <jats:p>\n            <jats:bold>Conclusion:</jats:bold>\n            The approach guided by the non-LB Framingham algorithm dominated the LB Framingham strategy with respect to both costs and predictive ability. Consequently, the non-LB Framingham algorithm could potentially provide a lower cost but a highly effective tool to address the high burden of CVD in AA and other resource constrained settings.\n          </jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"21097893","pmcid":null,"openalex_id":"https://openalex.org/W4205890698","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"closed","license":null,"oa_locations":[{"url":"http://journals.lww.com/00003017-201703071-00090","host_type":"publisher"},{"url":"https://doi.org/10.1161/circ.135.suppl_1.mp022","host_type":"journal"}],"fields_of_study":["Cardiovascular Health and Risk Factors"],"mesh_terms":[],"keywords":["Framingham Risk Score","Medicine","Framingham Heart Study","Cohort","Disease","Cardiovascular health","Demography","Internal medicine","Gerontology"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Decent work and economic growth"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-29T19:45:26.468904Z","pmid":null,"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":[]}