Evidence map›Paper›PMID 31970757›Full record

ArticleHealth services research2020

Measures of care fragmentation: Mathematical insights from population genetics.

Noah A Rosenberg, Donna M Zulman

Abstract read
In one paragraph

Article in Health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. The consequences of medicine shortages on healthcare utilization and care fragmentation.The European journal of health economics : HEPAC : health economics in prevention and care · 2026
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  5. Mathematical bounds on rTheoretical population biology · 2025
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  7. Mathematical bounds onbioRxiv : the preprint server for biology · 2024
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  11. Observational
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Noah A RosenbergDepartment of Biology, Stanford University, Stanford, California.ORCID 0000-0002-1829-8664
Donna M ZulmanDivision of Primary Care and Population Health, Stanford University School of Medicine, Stanford, California.ORCID 0000-0001-8904-2810

Funding

Population genetics for large-scale sequencing studies of diverse populationsR01HG005855 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Noah Rosenberg, Paul A Scheet · 2010 to 2026
$5.8M
HSRD VA I01 HX002127NHGRI NIH HHS R01 HG005855
6 · The paper itself

Abstract

objectiveTo identify novel properties of health care fragmentation measures, drawing on insights from mathematically equivalent measures of genetic diversity. STUDY

designWe describe mathematical relationships between two measures: (a) Breslau's Usual Provider of Care (UPC), the proportion of care with the most frequently visited provider, analogous to the "frequency of the most frequent allele" at a genetic locus; and (b) Bice-Boxerman's Continuity of Care Index (COCI), a measure of care dispersion across multiple providers, analogous to "Nei's estimator of homozygosity" in genetics. PRINCIPAL

findingsJust as the frequency of the most frequent allele places a tight constraint on homozygosity, the proportion of care with the most frequently visited provider (UPC) places lower and upper bounds on dispersion of care (COCI), and vice versa. This property presents the possibility of a normalized COCI given UPC (NCGU) measure, which reflects a bounded range of care dispersion dependent on the number of visits with the most frequently visited provider. Mathematical aspects of UPC and COCI also suggest thresholds for the minimal number of patient visits to use when studying fragmentation.

conclusionsApplying knowledge from population genetics elucidated relationships between care fragmentation measures and produced novel insights for care fragmentation studies.

Indexed as

AdultAgedAged, 80 and overAmbulatory CareContinuity of Patient CareFemaleGenetics, PopulationGenetic VariationHumansMaleMiddle AgedOffice VisitsPatient Acceptance of Health Carecare fragmentationcontinuity of carehealth system outcome modelsutilization

Identifiers

PMID31970757
PMCPMC7080386

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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.