Evidence map›Paper›PMID 29232365›Full record

ArticlePloS one2017

Monitoring compliance with standards of care for chronic diseases using healthcare administrative databases in Italy: Strengths and limitations.

Rosa Gini, Martijn J Schuemie, Alessandro Pasqua, Emanuele Carlini, Francesco Profili, Iacopo Cricelli, Patrizio Dazzi, Valentina Barletta, Paolo Francesconi, Francesco Lapi and 6 more

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Observational
  3. Article
  4. Review
  5. Healthcare resources utilisation in primary progressive multiple sclerosis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2018
    Article
  6. 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

16 authors.

Rosa GiniAgenzia regionale di sanità della Toscana, Osservatorio di epidemiologia, Florence, Italy.ORCID http://orcid.org/0000-0002-6250-877X
Martijn J SchuemieJanssen Research & Development, Epidemiology, Titusville, New Jersey, United States of America.
Alessandro PasquaHealth Search, Italian College of General Practitioners and Primary Care, Florence, Italy.
Emanuele CarliniConsiglio Nazionale delle Ricerche, Istituto di Scienza e Tecnologie dell'Informazione, Pisa, Italy.
Francesco ProfiliAgenzia regionale di sanità della Toscana, Osservatorio di epidemiologia, Florence, Italy.
Iacopo CricelliGenomedics, Florence, Italy.
Patrizio DazziConsiglio Nazionale delle Ricerche, Istituto di Scienza e Tecnologie dell'Informazione, Pisa, Italy.
Valentina BarlettaAgenzia regionale di sanità della Toscana, Osservatorio di epidemiologia, Florence, Italy.
Paolo FrancesconiAgenzia regionale di sanità della Toscana, Osservatorio di epidemiologia, Florence, Italy.
Francesco LapiHealth Search, Italian College of General Practitioners and Primary Care, Florence, Italy.
Andrea DonatiniAssessorato Politiche per la Salute, Bologna, Italy.
Giulia Dal CoAgenzia Nazionale per il Servizi Sanitari Regionali, Rome, Italy.
Modesta ViscaAgenzia Nazionale per il Servizi Sanitari Regionali, Rome, Italy.
Mariadonata BellentaniAgenzia Nazionale per il Servizi Sanitari Regionali, Rome, Italy.
Miriam SturkenboomAgenzia regionale di sanità della Toscana, Osservatorio di epidemiologia, Florence, Italy.
Niek KlazingaAcademic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA recent comprehensive report on healthcare quality in Italy published by the Organization of Economic Co-operation and Development (OECD) recommended that regular monitoring of quality of primary care by means of compliance with standards of care for chronic diseases is performed. A previous ecological study demonstrated that compliance with standards of care could be reliably estimated on regional level using administrative databases. This study compares estimates based on administrative data with estimates based on GP records for the same persons, to understand whether ecological fallacy played a role in the results of the previous study.

methodsWe compared estimates of compliance with diagnostic and therapeutic standards of care for type 2 diabetes (T2DM), hypertension and ischaemic heart disease (IHD) from administrative data (IAD) with estimates from medical records (MR) for the same persons registered with 24 GP's in 2012. Data were linked at an individual level.

results32,688 persons entered the study, 12,673 having at least one of the three diseases according to at least one data source. Patients not detected by IAD were many, for all three conditions: adding MR increased the number of cases of T2DM, hypertension, and IHD by +40%, +42%, and +104%, respectively. IAD had imperfect sensitivity in detecting population compliance with therapies (adding MR increased the estimate, from +11.5% for statins to +14.7% for antithrombotics), and, more substantially, with diagnostic recommendations (adding MR increased the estimate, from +23.7% in glycated hemoglobin tests, to +50.5% in electrocardiogram). Patients not detected by IAD were less compliant with respect to those that IAD correctly identified (from -4.8 percentage points in proportion of IHD patients compliant with a yearly glycated hemoglobin test, to -40.1 points in the proportion of T2DM patients compliant with the same recommendation). IAD overestimated indicators of compliance with therapeutic standards (significant differences ranged from 3.3. to 3.6 percentage points) and underestimated indicators of compliance with diagnostic standards (significant differences ranged from -2.3 to -14.1 percentage points).

conclusionIAD overestimated the percentage of patients compliant with therapeutic standards by less than 6 percentage points, and underestimated the percentage of patients compliant with diagnostic standards by a maximum of 14 percentage points. Therefore, both discussions at local level between GP's and local health unit managers and discussions at central level between national and regional policy makers can be informed by indicators of compliance estimated by IAD, which, based on those results, have the ability of signalling critical or excellent clusters. However, this study found that estimates are partly flawed, because a high number of patients with chronic diseases are not detected by IAD, patients detected are not representative of the whole population of patients, and some categories of diagnostic tests are markedly underrecorded in IAD (up to 50% in the case of electrocardiograms). Those results call to caution when interpreting IAD estimates. Audits based on medical records, on the local level, and an interpretation taking into account information external to IAD, on the central level, are needed to assess a more comprehensive compliance with standards.

Indexed as

Guideline AdherenceChronic DiseaseDiabetes Mellitus, Type 2FemaleHumansHypertensionItalyMaleMyocardial Ischemia

Identifiers

PMID29232365
PMCPMC5726627

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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.