Evidence map›Paper›PMID 28558756›Full record

ArticleBMC health services research2017

Comparison of methods to identify long term care nursing home residence with administrative data.

James S Goodwin, Shuang Li, Jie Zhou, James E Graham, Amol Karmarkar, Kenneth Ottenbacher

Abstract readComparative Study
In one paragraph

Article in BMC health services research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

51 citing papers in PubMed.

  1. Article
  2. Identification of prevalent dementia in Medicare Advantage encounters, compared with rigorous cohort assessments.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
    Article
  3. Incidence and Outcomes of Unplanned Intensive Interventions After Common Inpatient Surgeries: A Population-Based Cohort Study.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2026
    Article
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Impact of traumatic brain injury on recovery over the year following hip fracture among older Medicare beneficiaries.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
    Article
  9. Article
  10. Clinical outcomes following elective lumbar spine surgery in patients living with dementia.The spine journal : official journal of the North American Spine Society · 2026
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Observational
  18. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

James S GoodwinDepartment of Medicine, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA. jsgoodwi@utmb.edu.
Shuang LiSealy Center on Aging, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA.
Jie ZhouSealy Center on Aging, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA.
James E GrahamSealy Center on Aging, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA.
Amol KarmarkarSealy Center on Aging, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA.
Kenneth OttenbacherDivision of Rehabilitation Sciences, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA.

Funding

UTMB Clinical and Translational Science AwardUL1TR001439 · NCATS · UNIVERSITY OF TEXAS MED BR GALVESTON · PI URBAN, RANDALL J · 2015 to 2024
$40.0M
UTMB OAIC Research Education Component (REC)P30AG024832 · NIA · UNIVERSITY OF TEXAS MEDICAL BR GALVESTON · PI JAMES S. GOODWIN, MD, MELISSA M. MORROW · 2005 to 2026
$26.7M
Technique Development CoreP2CHD065702 · NICHD · UNIVERSITY OF TEXAS MED BR GALVESTON · PI GRAHAM, JAMES E. · 2015 to 2019
$5.2M
Patient Centered Outcomes Research in the ElderlyR24HS022134 · AHRQ · UNIVERSITY OF TEXAS MED BR GALVESTON · PI GOODWIN, MD, JAMES S. · 2013 to 2017
$4.8M
Care of the elder hospitalized patient: the role of the hospitalistsR01AG033134 · NIA · UNIVERSITY OF TEXAS MED BR GALVESTON · PI GOODWIN, MD, JAMES S. · 2009 to 2018
$2.2M
Established Investigator Award in Cancer Prevention & ControlK05CA134923 · NCI · UNIVERSITY OF TEXAS MED BR GALVESTON · PI GOODWIN, MD, JAMES S. · 2008 to 2019
$1.0M
Comparing Access and Effectiveness of Post-Acute Care Settings Among Medicare BeneficiariesK01HD086290 · NICHD · VIRGINIA COMMONWEALTH UNIVERSITY · PI KARMARKAR, AMOL · 2016 to 2020
$636k
AHRQ HHS R24 HS022134NCATS NIH HHS UL1 TR001439NCI NIH HHS K05 CA134923NIA NIH HHS P30 AG024832NIA NIH HHS R01 AG033134NICHD NIH HHS K01 HD086290NICHD NIH HHS P2C HD065702
6 · The paper itself

Abstract

backgroundTo compare different methods for identifying a long term care (LTC) nursing home stay, distinct from stays in skilled nursing facilities (SNFs), to the method currently used by the Center for Medicare and Medicaid Services (CMS). We used national and Texas Medicare claims, Minimum Data Set (MDS), and Texas Medicaid data from 2011-2013.

methodsWe used Medicare Part A and B and MDS data either alone or in combination to identify LTC nursing home stays by three methods. One method used Medicare Part A and B data; one method used Medicare Part A and MDS data; and the current CMS method used MDS data alone. We validated each method against Texas 2011 Medicare-Medicaid linked data for those with dual eligibility.

resultsUsing Medicaid data as a gold standard, all three methods had sensitivities > 92% to identify LTC nursing home stays of more than 100 days in duration. The positive predictive value (PPV) of the method that used both MDS and Medicare Part A data was 84.65% compared to 78.71% for the CMS method and 66.45% for the method using Part A and B Medicare. When the patient population was limited to those who also had a SNF stay, the PPV for identifying LTC nursing home was highest for the method using Medicare plus MDS data (88.1%).

conclusionsUsing both Medicare and MDS data to identify LTC stays will lead to more accurate attribution of CMS nursing home quality indicators.

Indexed as

AdultAgedAged, 80 and overEligibility DeterminationFemaleHumansKnowledge ManagementLength of StayLong-Term CareMaleMedicaidMedicareMiddle AgedNursing HomesSkilled Nursing FacilitiesTexasLong term careMedicareMinimum Data SetNursing home

Identifiers

PMID28558756
PMCPMC5450097

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.