Evidence map›Paper›PMID 32749490›Full record

ArticleJAMA2020

Association of Diagnosis Coding With Differences in Risk-Adjusted Short-term Mortality Between Critical Access and Non-Critical Access Hospitals.

Cyrus M Kosar, Lacey Loomer, Kali S Thomas, Elizabeth M White, Orestis A Panagiotou, Momotazur Rahman

Abstract read
In one paragraph

Article in JAMA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. A Scoping Review of Inpatient Medical and Surgical Quality Outcomes at Critical Access Hospitals.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2026
    Article
  3. Two decades of trends in nontraumatic intracerebral hemorrhage care: A nationwide analysis.Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia · 2025
    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.

Cyrus M KosarDepartment of Health Services, Policy, and Practice, Brown University, Providence, Rhode Island.
Lacey LoomerDepartment of Health Services, Policy, and Practice, Brown University, Providence, Rhode Island.
Kali S ThomasDepartment of Health Services, Policy, and Practice, Brown University, Providence, Rhode Island.
Elizabeth M WhiteDepartment of Health Services, Policy, and Practice, Brown University, Providence, Rhode Island.
Orestis A PanagiotouDepartment of Health Services, Policy, and Practice, Brown University, Providence, Rhode Island.
Momotazur RahmanDepartment of Health Services, Policy, and Practice, Brown University, Providence, Rhode Island.

Funding

VARIATIONS IN LIFE'S FINAL CHAPTERP01AG027296 · NIA · BROWN UNIVERSITY · PI AMAL N. TRIVEDI, Kali St. Marie Thomas · 2007 to 2026
$38.9M
Rural-Urban Disparity in Post-Acute CareR21AG053712 · NIA · BROWN UNIVERSITY · PI RAHMAN, MD MOMOTAZUR · 2017 to 2018
$425k
Nursing Home Selection of Medicare Advantage Patients and Its Implications on Health OutcomesR03AG050002 · NIA · BROWN UNIVERSITY · PI RAHMAN, MD MOMOTAZUR · 2016 to 2017
$163k
Disparity in Incidence and Health Outcomes between Rural and Urban Patients with Alzheimers disease and related disordersR03AG054687 · NIA · BROWN UNIVERSITY · PI RAHMAN, MD MOMOTAZUR · 2017 to 2018
$163k
NIA NIH HHS P01 AG027296NIA NIH HHS R03 AG050002NIA NIH HHS R03 AG054687NIA NIH HHS R21 AG053712
6 · The paper itself

Abstract

Importance: Critical access hospitals (CAHs) provide care to rural communities. Increasing mortality rates have been reported for CAHs relative to non-CAHs. Because Medicare reimburses CAHs at cost, CAHs may report fewer diagnoses than non-CAHs, which may affect risk-adjusted comparisons of outcomes. Objective: To assess serial differences in risk-adjusted mortality rates between CAHs and non-CAHs after accounting for differences in diagnosis coding. Design, Setting, and Participants: Serial cross-sectional study of rural Medicare Fee-for-Service beneficiaries admitted to US CAHs and non-CAHs for pneumonia, heart failure, chronic obstructive pulmonary disease, arrhythmia, urinary tract infection, septicemia, and stroke from 2007 to 2017. The final date of follow-up was December 31, 2017. Exposure: Admission to a CAH vs non-CAH. Main Outcomes and Measures: Discharge diagnosis count including trends from 2010 to 2011 when Medicare expanded the allowable number of billing codes for hospitalizations, and combined in-hospital and 30-day postdischarge mortality adjusted for demographics, primary diagnosis, preexisting conditions, and with vs without further adjustment for Hierarchical Condition Category (HCC) score to understand the contribution of in-hospital secondary diagnoses. Results: There were 4 094 720 hospitalizations (17% CAH) for 2 850 194 unique Medicare beneficiaries (mean [SD] age, 76.3 [11.7] years; 55.5% women). Patients in CAHs were older (median age, 80.1 vs 76.8 years) and more likely to be female (58% vs 55%). In 2010, the adjusted mean discharge diagnosis count was 7.52 for CAHs vs 8.53 for non-CAHs (difference, -0.99 [95% CI, -1.08 to -0.90]; P < .001). In 2011, the CAH vs non-CAH difference in diagnoses coded increased (P < .001 for interaction between CAH and year) to 9.27 vs 12.23 (difference, -2.96 [95% CI, -3.19 to -2.73]; P < .001). Adjusted mortality rates from the model with HCC were 13.52% for CAHs vs 11.44% for non-CAHs (percentage point difference, 2.08 [95% CI, 1.74 to 2.42]; P < .001) in 2007 and increased to 15.97% vs 12.46% (difference, 3.52 [95% CI, 3.09 to 3.94]; P < .001) in 2017 (P < .001 for interaction). Adjusted mortality rates from the model without HCC were not significantly different between CAHs and non-CAHs in all years except 2007 (12.19% vs 11.74%; difference, 0.45 [95% CI, 0.12 to 0.79]; P = .008) and 2010 (12.71% vs 12.28%; difference, 0.42 [95% CI, 0.07 to 0.77]; P = .02). Conclusions and Relevance: For rural Medicare beneficiaries hospitalized from 2007 to 2017, CAHs submitted significantly fewer hospital diagnosis codes than non-CAHs, and short-term mortality rates adjusted for preexisting conditions but not in-hospital comorbidity measures were not significantly different by hospital type in most years. The findings suggest that short-term mortality outcomes at CAHs may not differ from those of non-CAHs after accounting for different coding practices for in-hospital comorbidities.

Indexed as

Clinical CodingHospital MortalityHospitals, RuralAgedAged, 80 and overChronic DiseaseCross-Sectional StudiesFee-for-Service PlansFemaleHospitalizationHumansMaleMedicarePatient Discharge SummariesRisk AdjustmentUnited States

Identifiers

PMID32749490
PMCPMC7403917

What OpenQuestion holds

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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.