Evidence map›Paper›PMID 37356820›Full record

ArticleHealth services research2023

Informing a home time measure reflective of quality of life: A data driven investigation of time frames and settings of health care utilization.

Paul A Dennis, Karen M Stechuchak, Courtney H Van Houtven, Kasey Decosimo, Cynthia J Coffman, Janet M Grubber, Jennifer H Lindquist, Nina R Sperber, S Nicole Hastings, Megan Shepherd-Banigan and 2 more

Abstract read
In one paragraph

Article in Health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Trial
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  5. Article
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  8. Article
  9. The association between social risks and days at home for older veterans.Journal of the American Geriatrics Society · 2024
    Article
  10. Article
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

12 authors.

Paul A DennisCenter of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA.
Karen M StechuchakCenter of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA.
Courtney H Van HoutvenCenter of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA.ORCID 0000-0002-0783-1611
Kasey DecosimoCenter of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA.ORCID 0000-0002-0070-2454
Cynthia J CoffmanCenter of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA.
Janet M GrubberCenter of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA.
Jennifer H LindquistCenter of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA.
Nina R SperberCenter of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA.
S Nicole HastingsCenter of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA.
Megan Shepherd-BaniganCenter of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA.ORCID 0000-0002-4020-8936
Brystana G KaufmanCenter of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA.ORCID 0000-0001-6553-0406
Valerie A SmithCenter of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA.ORCID 0000-0001-5170-9819

Funding

HSR&D Merit Review Research Career Scientist AwardIK6HX003396 · VA · DURHAM VA MEDICAL CENTER · PI VAN HOUTVEN, COURTNEY HAROLD · 2021 to 2025
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HSRD VA IK6 HX003396
6 · The paper itself

Abstract

objectiveTo evaluate short- and long-term measures of health care utilization-days in the emergency department (ED), inpatient (IP) care, and rehabilitation in a post-acute care (PAC) facility-to understand how home time (i.e., days alive and not in an acute or PAC setting) corresponds to quality of life (QoL). DATA SOURCES: Survey data on community-residing veterans combined with multipayer administrative data on health care utilization. STUDY

designVA or Medicare health care utilization, quantified as days of care received in the ED, IP, and PAC in the 6 and 18 months preceding survey completion, were used to predict seven QoL-related measures collected during the survey. Elastic net machine learning was used to construct models, with resulting regression coefficients used to develop a weighted utilization variable. This was then compared with an unweighted count of days with any utilization. PRINCIPAL

findingsIn the short term (6 months), PAC utilization emerged as the most salient predictor of decreased QoL, whereas no setting predominated in the long term (18 months). Results varied by outcome and time frame, with some protective effects observed. In the 6-month time frame, each weighted day of utilization was associated with a greater likelihood of activity of daily living deficits (0.5%, 95% CI: 0.1%-0.9%), as was the case with each unweighted day of utilization (0.6%, 95% CI: 0.3%-1.0%). The same was true in the 18-month time frame (for both weighted and unweighted, 0.1%, 95% CI: 0.0%-0.3%). Days of utilization were also significantly associated with greater rates of instrumental ADL deficits and fair/poor health, albeit not consistently across all models. Neither measure outperformed the other in direct comparisons.

conclusionsThese results can provide guidance on how to measure home time using multipayer administrative data. While no setting predominated in the long term, all settings were significant predictors of QoL measures.

Indexed as

MedicareQuality of LifeAgedHospitalizationHumansPatient Acceptance of Health CareUnited Statesdays alive and not at homehealth care utilizationhome timemachine learningpatient-centered outcomesquality of lifeveterans

Identifiers

PMID37356820
PMCPMC10622302

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.