Evidence map›Paper›PMID 34588267›Full record

ArticleBMJ open2021

Elucidating the association between regional variation in diagnostic frequency with risk-adjusted mortality through analysis of claims data of medicare inpatients: a cross-sectional study.

Linyan Li, George F Chamoun, Nassib G Chamoun, Daniel Sessler, Valérie Gopinath, Vikas Saini

Abstract read
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

6 authors.

Linyan LiSchool of Data Science, City University of Hong Kong, Hong Kong, China.
George F ChamounHealth Data Analytics Institute, Dedham, Massachusetts, USA.
Nassib G ChamounHealth Data Analytics Institute, Dedham, Massachusetts, USA.
Daniel SesslerOutcomes Research, Cleveland Clinic, Cleveland, Ohio, USA.
Valérie GopinathLown Institute, Needham, Massachusetts, USA.
Vikas SainiLown Institute, Needham, Massachusetts, USA vsaini@lowninstitute.org.ORCID 0000-0001-9584-5548

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe validity of risk-adjustment methods based on administrative data has been questioned because hospital referral regions with higher diagnosis frequencies report lower case-fatality rates, implying that diagnoses do not track the underlying health risk. The objective of this study is to test the hypothesis that regional variation of diagnostic frequency in inpatient records is not associated with different coding practices but a reflection of the underlying health risks.

designWe applied two stratification methods to Medicare Analysis and Provider Review data from 2009 through 2014: (1) the number of chronic conditions; and, (2) quartiles of Risk Stratification Index (RSI)-defined risk. After sorting hospital referral regions into quintiles of diagnostic frequency, we examined all-cause mortality.

settingMedicare Analysis and Provider Review administrative database.

participants18 126 301 hospitalised Medicare fee-for-service beneficiaries aged 65 or older who had at least one hospital-based procedure between 2009 and 2014. EXPOSURE: Coding frequency and baseline regional population risk factors by hospital referral region. PRIMARY AND SECONDARY OUTCOMES AND MEASURES: One year all-cause mortality in patients having the same number of chronic conditions or within the same RSI score quartile across US health referral regions, grouped by diagnostic frequency.

resultsNo consistent relationship between diagnostic frequency and mortality in the risk stratum defined by number of chronic conditions was detected. In the strata defined by RSI quartile, there was no decrease in mortality as a function of diagnostic frequency. Instead, adjusted mortality was positively correlated with socioeconomic risk factors.

conclusionsUsing present-on-admission codes only, diagnostic frequency among inpatients with at least one hospital-based procedure appears to be consequent to differences in baseline population health status, rather than diagnostic coding practices. In this population, claims-based risk-adjustment using RSI appears to be useful for assessing hospital outcomes and performance.

Indexed as

InpatientsMedicareAgedCross-Sectional StudiesFee-for-Service PlansHumansRisk AdjustmentUnited Stateshealth economicshealth services administration & managementquality in health care

Identifiers

PMID34588267
PMCPMC8479990

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.