CoChat
Sign Up
Cited by 46

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Disparities in United States hospitalizations for serious infections in patients with and without opioid use disorder: A nationwide observational study

Kim, June-Ho, Fine, Danielle R., Li, Lily, Kimmel, Simeon D., Ngo, Long H., Suzuki, Joji, Price, Christin N., Ronan, Matthew V., Herzig, Shoshana J.·2020·PLOS Medicine
DOI 10.1371/journal.pmed.1003247PMC PMC7413412
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pmed.1003247

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig 1 Probability of discharge to home or a post-acute care facility at any given length of stay for US hospitalizations for serious infections in patients with and without opioid use disorder in 2016. Cumulative incidence curves of length of stay to discharge estimated using a competing risks survival analysis model. The event of interest was defined as discharge to home or a post-acute care facility. Competing risks were defined as patient-directed discharge, transfer to another acute care hospital, or in-hospital death.
Fig 1 Probability of discharge to home or a post-acute care facility at any given length of stay for US hospitalizations for serious infections in patients with and without opioid use disorder in 2016. Cumulative incidence curves of length of stay to discharge estimated using a competing risks survival analysis model. The event of interest was defined as discharge to home or a post-acute care facility. Competing risks were defined as patient-directed discharge, transfer to another acute care hospital, or in-hospital death.CC BY · PubMed Central
Fig 2 Adjusted hazard ratios of length of stay until discharge for US hospitalizations for serious infections in patients with and without opioid use disorder in 2016. Hazard ratios are from the Fine–Gray subdistribution hazard regression model, adjusted for age, sex, race/ethnicity, primary payer, median household income, Elixhauser Comorbidity Index, hospital size, hospital type, hospital region, elective versus non-elective admission, weekday versus weekend admission, and the number of major operating room procedures. The model for serious infections also controlled for infection type. The event of interest was defined as discharge to home or a post-acute care facility. Competing risks were defined as patient-directed discharge, transfer to another acute care hospital, or in-hospital death. Hazards ratios were calculated for (A) the analysis of serious infections, (B) a sensitivity analysis of conditions not usually requiring prolonged intravenous access, and (C) a sensitivity analysis stratifying the primary model by hospitalizations with and without major operating room procedures. Unadjusted hazard ratios are found in S4 Table. aHR, adjusted hazard ratio; OR, operating room; OUD, opioid use disorder.
Fig 2 Adjusted hazard ratios of length of stay until discharge for US hospitalizations for serious infections in patients with and without opioid use disorder in 2016. Hazard ratios are from the Fine–Gray subdistribution hazard regression model, adjusted for age, sex, race/ethnicity, primary payer, median household income, Elixhauser Comorbidity Index, hospital size, hospital type, hospital region, elective versus non-elective admission, weekday versus weekend admission, and the number of major operating room procedures. The model for serious infections also controlled for infection type. The event of interest was defined as discharge to home or a post-acute care facility. Competing risks were defined as patient-directed discharge, transfer to another acute care hospital, or in-hospital death. Hazards ratios were calculated for (A) the analysis of serious infections, (B) a sensitivity analysis of conditions not usually requiring prolonged intravenous access, and (C) a sensitivity analysis stratifying the primary model by hospitalizations with and without major operating room procedures. Unadjusted hazard ratios are found in S4 Table. aHR, adjusted hazard ratio; OR, operating room; OUD, opioid use disorder.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Research works printed but not linked: 49

  1. MV Ronan, 2016, Hospitalizations related to opioid abuse/dependence and associated serious infections increased sharply, 2002–12, Health Aff (Millwood), 35, 832, 10.1377/hlthaff.2015.1424 · DOI
  2. AJ Schranz, 2019, Trends in drug use-associated infective endocarditis and heart valve surgery, 2007 to 2017: a study of statewide discharge data, Ann Intern Med, 170, 31, 10.7326/M18-2124 · DOI
  3. L Hartman, 2016, Opiate injection-associated infective endocarditis in the southeastern United States, Am J Med Sci, 352, 603, 10.1016/j.amjms.2016.08.010 · DOI
  4. ME Gray, 2018, Rising rates of injection drug use associated infective endocarditis in Virginia with missed opportunities for addiction treatment referral: a retrospective cohort study, BMC Infect Dis, 18, 532, 10.1186/s12879-018-3408-y · DOI
  5. S Salehi Omran, 2019, National trends in hospitalizations for stroke associated with infective endocarditis and opioid use between 1993 and 2015, Stroke, 50, 577, 10.1161/STROKEAHA.118.024436 · DOI
  6. AG Wurcel, 2016, Increasing infectious endocarditis admissions among young people who inject drugs, Open Forum Infect Dis, 3, ofw157, 10.1093/ofid/ofw157 · DOI
  7. HT D’Couto, 2018, Outcomes according to discharge location for persons who inject drugs receiving outpatient parenteral antimicrobial therapy, Open Forum Infect Dis, 5, ofy056, 10.1093/ofid/ofy056 · DOI
  8. JM Pericàs, 2019, Outpatient parenteral antibiotic treatment (OPAT) for infective endocarditis: a prospective cohort study from the GAMES cohort, Clin Infect Dis, 69, 1690, 10.1093/cid/ciz030 · DOI
  9. O Mansour, 2018, Impact of the implementation of a nurse-managed outpatient parenteral antibiotic therapy (OPAT) system in Baltimore: a case study demonstrating cost savings and reduction in re-admission rates., J Antimicrob Chemother, 73, 3181, 10.1093/jac/dky294 · DOI
  10. J Suzuki, 2018, Outpatient parenteral antimicrobial therapy among people who inject drugs: a review of the literature, Open Forum Infect Dis, 5, ofy194, 10.1093/ofid/ofy194 · DOI
  11. M Vazirian, 2018, Outcomes of outpatient parenteral antimicrobial therapy in patients with injection drug use, Psychosomatics, 59, 490, 10.1016/j.psym.2018.02.005 · DOI
  12. AB Rapoport, 2018, Infectious diseases physicians’ perspectives regarding injection drug use and related infections, United States, 2017, Open Forum Infect Dis, 5, ofy132, 10.1093/ofid/ofy132 · DOI
  13. SE Wakeman, 2017, Barriers to post-acute care for patients on opioid agonist therapy; an example of systematic stigmatization of addiction, J Gen Intern Med, 32, 17, 10.1007/s11606-016-3799-7 · DOI
  14. EM Psaltikidis, 2018, Cost-utility analysis of outpatient parenteral antimicrobial therapy (OPAT) in the Brazilian national health system, Expert Rev Pharmacoecon Outcomes Res, 19, 341, 10.1080/14737167.2019.1541404 · DOI
  15. C Jewell, 2013, Residential addiction treatment for injection drug users requiring intravenous antibiotics: a cost-reduction strategy, J Addict Med, 7, 271, 10.1097/ADM.0b013e318294b1eb · DOI
  16. KC Dewan, 2019, Trends and outcomes of cardiovascular surgery in patients with opioid use disorders, JAMA Surg, 154, 232, 10.1001/jamasurg.2018.4608 · DOI
  17. AG Wurcel, 2019, Drug-associated infective endocarditis trends: what’s all the buzz about?, Ann Intern Med, 170, 68, 10.7326/M18-3026 · DOI
  18. A Tank, 2018, Opioid dependence and prolonged length of stay in lumbar fusion: a retrospective study utilizing the National Inpatient Sample 2003–2014, Spine (Phila Pa 1976), 43, 1739, 10.1097/BRS.0000000000002714 · DOI
  19. SE Rudasill, 2019, Clinical outcomes of infective endocarditis in injection drug users, J Am Coll Cardiol, 73, 559, 10.1016/j.jacc.2018.10.082 · DOI
  20. WJ Freeman, 2006, Healthcare Cost and Utilization Project Statistical Brief #246
  21. JP Fine, 1999, A Proportional hazards model for the subdistribution of a competing risk, J Am Stat Assoc, 94, 496, 10.1080/01621459.1999.10474144 · DOI · Paper page
  22. RJ Gray, 1988, A class of $K$-sample tests for comparing the cumulative incidence of a competing risk, Ann Stat, 16, 1141, 10.1214/aos/1176350951 · DOI
  23. H Pakzad, 2014, Factors associated with longer length of hospital stay after primary elective ankle surgery for end-stage ankle arthritis, J Bone Joint Surg Am, 96, 32, 10.2106/JBJS.K.00834 · DOI
  24. Y Wang, 2014, Factors associated with a prolonged length of stay after acute exacerbation of chronic obstructive pulmonary disease (AECOPD), Int J Chron Obstruct Pulmon Dis, 9, 99, 10.2147/COPD.S51467 · DOI
  25. KH Bowles, 2009, Factors identified by experts to support decision making for post acute referral, Nurs Res, 58, 115, 10.1097/NNR.0b013e318199b52a · DOI
  26. J Ho, 2010, Safe and successful treatment of intravenous drug users with a peripherally inserted central catheter in an outpatient parenteral antibiotic treatment service, J Antimicrob Chemother, 65, 2641, 10.1093/jac/dkq355 · DOI
  27. GM Allison, 2014, Prediction model for 30-day hospital readmissions among patients discharged receiving outpatient parenteral antibiotic therapy, Clin Infect Dis, 58, 812, 10.1093/cid/cit920 · DOI
  28. A Beieler, 2019, Outpatient parenteral antimicrobial therapy in vulnerable populations—people who inject drugs and the homeless, J Hosp Med, 14, 105, 10.12788/jhm.3138 · DOI
  29. DJ Buehrle, 2017, Risk factors associated with outpatient parenteral antibiotic therapy program failure among intravenous drug users, Open Forum Infect Dis, 4, ofx102, 10.1093/ofid/ofx102 · DOI
  30. P Tattevin, 2019, Outpatient parenteral antibiotic treatment (OPAT) for infective endocarditis: insights from the real life, Clin Infect Dis, 69, 1701, 10.1093/cid/ciz027 · DOI
  31. K Iversen, 2019, Partial oral versus intravenous antibiotic treatment of endocarditis, N Engl J Med, 380, 415, 10.1056/NEJMoa1808312 · DOI
  32. HW Boucher, 2019, Partial oral therapy for osteomyelitis and endocarditis—is it time?, N Engl J Med, 380, 487, 10.1056/NEJMe1817264 · DOI
  33. H-K Li, 2019, Oral versus intravenous antibiotics for bone and joint infection, N Engl J Med, 380, 425, 10.1056/NEJMoa1710926 · DOI
  34. H Englander, 2018, Lessons learned from the implementation of a medically enhanced residential treatment (MERT) model integrating intravenous antibiotics and residential addiction treatment, Subst Abus, 39, 225, 10.1080/08897077.2018.1452326 · DOI
  35. LR Marks, 2018, Addiction medicine consultations reduce readmission rates for patients with serious infections from opioid use disorder, Clin Infect Dis, 68, 1935, 10.1093/cid/ciy924 · DOI
  36. Department of Health and Human Services, 2018, Department of Health and Human Services
  37. US Department of Justice, 2018, U.S. Attorney’s Office settles disability discrimination allegations at skilled nursing facility
  38. JM Glasgow, 2010, risk of 30-day mortality and hospital readmission, J Gen Intern Med, 25, 926, 10.1007/s11606-010-1371-4 · DOI
  39. ACH Chan, 2004, HIV-positive injection drug users who leave the hospital against medical advice: the mitigating role of methadone and social support, J Acquir Immune Defic Syndr, 35, 56, 10.1097/00126334-200401010-00008 · DOI
  40. L Ti, 2015, Factors associated with leaving hospital against medical advice among people who use illicit drugs in Vancouver, Canada, PLoS ONE, 10, e0141594, 10.1371/journal.pone.0141594 · DOI · Paper page
  41. ES Rosenthal, 2016, Suboptimal addiction interventions for patients hospitalized with injection drug use-associated infective endocarditis, Am J Med, 129, 481, 10.1016/j.amjmed.2015.09.024 · DOI · Paper page
  42. J Suzuki, 2016, Medication-assisted treatment for hospitalized patients with intravenous-drug-use related infective endocarditis., Am J Addict, 25, 191, 10.1111/ajad.12349 · DOI
  43. P Trowbridge, 2017, Addiction consultation services—linking hospitalized patients to outpatient addiction treatment, J Subst Abuse Treat, 79, 1, 10.1016/j.jsat.2017.05.007 · DOI
  44. SE Wakeman, 2017, Inpatient addiction consultation for hospitalized patients increases post-discharge abstinence and reduces addiction severity, J Gen Intern Med, 32, 909, 10.1007/s11606-017-4077-z · DOI
  45. H Englander, 2019, Tools to support hospital-based addiction care: core components, values, and activities of the improving addiction care team, J Addict Med, 13, 85, 10.1097/ADM.0000000000000487 · DOI
  46. C Peterson, 2018, US hospital discharges documenting patient opioid use disorder without opioid overdose or treatment services, 2011–2015, J Subst Abuse Treat, 92, 35, 10.1016/j.jsat.2018.06.008 · DOI
  47. AC Miller, 2018, Many opportunities to record, diagnose, or treat injection drug-related infections are missed: a population-based cohort study of inpatient and emergency department settings, Clin Infect Dis, 68, 1166, 10.1093/cid/ciy632 · DOI
  48. J Tsai, 2013, Latent homeless risk profiles of a national sample of homeless veterans and their relation to program referral and admission patterns, Am J Public Health, 103, S239, 10.2105/AJPH.2013.301322 · DOI
  49. MA Bachhuber, 2015, Screening for homelessness among individuals initiating medication-assisted treatment for opioid use disorder in the Veterans Health Administration, J Opioid Manag, 11, 459, 10.5055/jom.2015.0298 · DOI

Cited by

Sorted by citations

Metadata

Authors
Kim, J.-H., Fine, D. R., Li, L., Kimmel, S. D., Ngo, L. H., Suzuki, J., Price, C. N., Ronan, M. V., & Herzig, S. J.
ORCID
Venue
PLOS Medicine
Publisher
Public Library of Science (PLoS)
Published
2020-08-07
Volume
17
Issue
8
Pages
e1003247
ISSN
1549-1676
DOI
10.1371/journal.pmed.1003247
PMC
PMC7413412
Cited by
46
References
2
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • Health Resources and Services Administration (T32HP10251)
  • National Institute on Drug Abuse (R25DA033211, K23DA042326)
  • National Institute of Allergy and Infectious Diseases (5T32AI05207409)
  • National Institute on Aging (K23AG042459)
  • Agency for Healthcare Research and Quality (R01HS026215)

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review