The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Vital Signs: Epidemiology of Sepsis: Prevalence of Health Care Factors and Opportunities for Prevention
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In the citing paper
“The next most common comorbidities are highly networked conditions strongly associated with social stress and economic marginalization including diabetes mellitus, hypertension, cardiovascular disease (CVD) and stroke, obesity, chronic kidney disease and chronic obstructive pulmonary disease (COPD)”
From On race, human variation, and who gets and dies of sepsis (2022) · cites this paper as Novosad et al., 2016
Experimental — accuracy not yet measuredIn the citing paper
“The leading underlying factor is age ‐ with infants under 1 year of age and adults over the age of 55 years strongly overrepresented in the annual case count”
From On race, human variation, and who gets and dies of sepsis (2022) · cites this paper as Novosad et al., 2016
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Citations
Cites
The epidemiology of sepsis in the United States from 1979 through 2000
Martin, G. S., Mannino, D. M., Eaton, S., & Moss, M. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa022139 · Cited by 5,841
Research works printed but not linked: 13
- Cohen, 2015, Sepsis: a roadmap for future research., Lancet Infect Dis, 15, 581, 10.1016/S1473-3099(15)70112-X · DOI
- Angus, 2001, Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care., Crit Care Med, 29, 1303, 10.1097/00003246-200107000-00002 · DOI · Paper page
- Singer, 2016, The third international consensus definitions for sepsis and septic shock (Sepsis-3)., JAMA, 315, 801, 10.1001/jama.2016.0287 · DOI · Paper page
- Gaieski, 2013, Benchmarking the incidence and mortality of severe sepsis in the United States., Crit Care Med, 41, 1167, 10.1097/CCM.0b013e31827c09f8 · DOI
- Epstein, 2016, Varying estimates of sepsis mortality using death certificates and administrative codes—United States, 1999–2014., MMWR Morb Mortal Wkly Rep, 65, 342, 10.15585/mmwr.mm6513a2 · DOI
- Seymour, 2016, Assessment of clinical criteria for sepsis. For the third international consensus definitions for sepsis and septic shock (Sepsis-3)., JAMA, 315, 762, 10.1001/jama.2016.0288 · DOI · Paper page
- McCormick K. National trends of severe sepsis and septic shock among Centers for Medicaid & Medicare Services beneficiaries using administrative claims data, incidence and mortality, 2008. Presented at the annual Society for Healthcare Epidemiology of America Spring Conference, Atlanta, GA, May 18–21, 2016.
- Ani, 2015, Variations in organism-specific severe sepsis mortality in the United States: 1999–2008., Crit Care Med, 43, 65, 10.1097/CCM.0000000000000555 · DOI
- Lagu, 2012, Hospitalizations, costs, and outcomes of severe sepsis in the United States 2003 to 2007., Crit Care Med, 40, 754, 10.1097/CCM.0b013e318232db65 · DOI · Paper page
- Moore, 2015, Effect of use of 13-valent pneumococcal conjugate vaccine in children on invasive pneumococcal disease in children and adults in the USA: analysis of multisite, population-based surveillance., Lancet Infect Dis, 15, 301, 10.1016/S1473-3099(14)71081-3 · DOI
- Williams, 2016, Surveillance of vaccination coverage among adult populations—United States, 2014., MMWR Surveill Summ, 65, 10.15585/mmwr.ss6501a1 · DOI
- Foppa, 2015, Deaths averted by influenza vaccination in the U.S. during the seasons 2005/06 through 2013/14., Vaccine, 33, 3003, 10.1016/j.vaccine.2015.02.042 · DOI
- Wise, 2013, National estimates of central line-associated bloodstream infections in critical care patients., Infect Control Hosp Epidemiol, 34, 547, 10.1086/670629 · DOI
Cited by
Sorted by citations
Prediction of sepsis patients using machine learning approach: a meta-analysis
Islam, M. M., Nasrin, T., Walther, B. A., Wu, C.-C., Yang, H.-C., & Li, Y.-C. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.12.027 · Cited by 259
Stefanova, D., Raychev, A., Deville, J., Humphries, R., Campeau, S., Ruchala, P., Nemeth, E., Ganz, T., & Bulut, Y. (2018) · Infection and Immunity · doi:10.1128/iai.00253-18 · Cited by 68
On race, human variation, and who gets and dies of sepsis
Brinkworth, J. F., & Shaw, J. G. (2022) · American Journal of Biological Anthropology · doi:10.1002/ajpa.24527 · Cited by 9
Risk of subsequent sepsis within 90 days after a hospital stay by type of antibiotic exposure.
Baggs, J., Jernigan, J. A., Halpin, A. L., Epstein, L., Hatfield, K. M., & McDonald, L. C. (2018) · Clinical Infectious Diseases · doi:10.1093/cid/cix947 · Cited by 190
Self-citation
Metadata
- Authors
- Novosad, S. A., Sapiano, M. R. P., Grigg, C., Lake, J., Robyn, M., Dumyati, G., Felsen, C., Blog, D., Dufort, E., Zansky, S., Wiedeman, K., Avery, L., Dantes, R. B., Jernigan, J. A., Magill, S. S., Fiore, A., & Epstein, L.
- Venue
- MMWR. Morbidity and Mortality Weekly Report
- Published
- 2016-08-26
- Volume
- 65
- Issue
- 33
- Pages
- 864-869
- ISSN
- 0149-2195
- 1545-861X
- Cited by
- 144
- References
- 1
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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