{"doi":"10.1016/j.ajic.2020.12.002","title":"Evaluation of hospital nurse-to-patient staffing ratios and sepsis bundles on patient outcomes","abstract":"•New York state was one of the first to require implementation of sepsis bundles.•Patient-to-nurse staffing ratios vary considerably across hospitals in New York.•Sepsis outcomes vary despite bundle requirements because of nurse staffing.•Requiring minimum nurse staffing along with sepsis bundles may improve outcomes. BackgroundDespite nurses’ responsibilities in recognition and treatment of sepsis, little evidence documents whether patient-to-nurse staffing ratios are associated with clinical outcomes for patients with sepsis.MethodsUsing linked data sources from 2017 including MEDPAR patient claims, Hospital Compare, American Hospital Association, and a large survey of nurses, we estimate the effect of hospital patient-to-nurse staffing ratios and adherence to the Early Management Bundle for patients with Severe Sepsis/Septic Shock SEP-1 sepsis bundles on patients’ odds of in-hospital and 60-day mortality, readmission, and length of stay. Logistic regression is used to estimate mortality and readmission, while zero-truncated negative binomial models are used for length of stay.ResultsEach additional patient per nurse is associated with 12% higher odds of in-hospital mortality, 7% higher odds of 60-day mortality, 7% higher odds of 60-day readmission, and longer lengths of stay, even after accounting for patient and hospital covariates including hospital adherence to SEP-1 bundles. Adherence to SEP-1 bundles is associated with lower in-hospital mortality and shorter lengths of stay; however, the effects are markedly smaller than those observed for staffing.DiscussionImproving hospital nurse staffing over and above implementing sepsis bundles holds promise for significant improvements in sepsis patient outcomes. Despite nurses’ responsibilities in recognition and treatment of sepsis, little evidence documents whether patient-to-nurse staffing ratios are associated with clinical outcomes for patients with sepsis. Using linked data sources from 2017 including MEDPAR patient claims, Hospital Compare, American Hospital Association, and a large survey of nurses, we estimate the effect of hospital patient-to-nurse staffing ratios and adherence to the Early Management Bundle for patients with Severe Sepsis/Septic Shock SEP-1 sepsis bundles on patients’ odds of in-hospital and 60-day mortality, readmission, and length of stay. Logistic regression is used to estimate mortality and readmission, while zero-truncated negative binomial models are used for length of stay. Each additional patient per nurse is associated with 12% higher odds of in-hospital mortality, 7% higher odds of 60-day mortality, 7% higher odds of 60-day readmission, and longer lengths of stay, even after accounting for patient and hospital covariates including hospital adherence to SEP-1 bundles. Adherence to SEP-1 bundles is associated with lower in-hospital mortality and shorter lengths of stay; however, the effects are markedly smaller than those observed for staffing. Improving hospital nurse staffing over and above implementing sepsis bundles holds promise for significant improvements in sepsis patient outcomes. For nearly a decade, New York state has been a leader in efforts to reduce high rates of mortality for people with sepsis. In 2013, after the highly publicized death of a 12-year-old boy with sepsis, New York enacted Rory's Regulations,1New York State Department of Health. Rory's regulations. Available at:https://www.health.ny.gov/facilities/public_health_and_health_planning_council/meetings/2013-02-07/docs/13-01.pdf. Accessed December 14, 2020.Google Scholar which required hospitals to implement evidence-based protocols for the screening, early diagnosis, and timely treatment of patients with severe sepsis and/or septic shock. Included in Rory's Regulations was the public reporting of hospital adherence to protocols. More recently, hospitals in all states followed suit when the Centers for Medicare and Medicaid Services (CMS) implemented a similar public r","journal":"American Journal of Infection Control","year":2020,"id":57695,"datarank":0.6554171778700533,"base_score":4.3694478524670215,"endowment":4.3694478524670215,"self_citation_contribution":0.6554171778700533,"citation_network_contribution":0.0,"self_endowment_contribution":0.6554171778700533,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":78,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9607,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":233760,"name":"Douglas M. Sloane","orcid":"0000-0003-2541-9783","position":1,"is_corresponding":false},{"id":233763,"name":"Matthew D. McHugh","orcid":"0000-0002-1263-0697","position":2,"is_corresponding":false},{"id":301706,"name":"Jeannie P. Cimiotti","orcid":"0000-0003-0134-7797","position":3,"is_corresponding":false},{"id":303281,"name":"Kathryn A. Riman","orcid":null,"position":4,"is_corresponding":false},{"id":233762,"name":"Brendan Martin","orcid":"0000-0003-2063-7407","position":5,"is_corresponding":false},{"id":234723,"name":"Maryann Alexander","orcid":null,"position":6,"is_corresponding":false},{"id":233759,"name":"Linda H. Aiken","orcid":"0000-0001-8004-3630","position":7,"is_corresponding":false},{"id":233758,"name":"Karen B. Lasater","orcid":"0000-0002-5834-1954","position":0,"is_corresponding":true}],"reference_count":18,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T21:06:48.611399Z","pmid":"33309843","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":[]}