Evidence map›Paper›PMID 28103865›Full record

ArticleBMC pulmonary medicine2017

Can healthcare utilization data reliably capture cases of chronic respiratory diseases? a cross-sectional investigation in Italy.

A Biffi, R Comoretto, A Arfè, L Scotti, L Merlino, A Vaghi, A Pesci, R de Marco, G Corrao, “CRD Real-World Evidence” scientific board

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pulmonary medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Observational
  3. Observational
  4. Article
  5. Article
  6. Article
  7. Article
  8. Reduced Lung Function in Midlife and Cognitive Impairment in the Elderly.American journal of respiratory and critical care medicine · 2019
    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

10 authors.

A BiffiLaboratory of Healthcare Research and Pharmacoepidemiology, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi, 8, Edificio U7, 20126, Milan, Italy.
R ComorettoLaboratory of Healthcare Research and Pharmacoepidemiology, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi, 8, Edificio U7, 20126, Milan, Italy.
A ArfèLaboratory of Healthcare Research and Pharmacoepidemiology, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi, 8, Edificio U7, 20126, Milan, Italy.
L ScottiLaboratory of Healthcare Research and Pharmacoepidemiology, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi, 8, Edificio U7, 20126, Milan, Italy.
L MerlinoOperative Unit of Territorial Health Services, Region Lombardia, Milan, Italy.
A VaghiDivision of Pneumology, "Guido Salvini" Hospital, Garbagnate Milanese, Italy.
A PesciDepartment of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy.
R de MarcoUnit of Epidemiology and Medical Statistics, University of Verona, Verona, Italy.
G CorraoLaboratory of Healthcare Research and Pharmacoepidemiology, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi, 8, Edificio U7, 20126, Milan, Italy. giovanni.corrao@unimib.it.
“CRD Real-World Evidence” scientific board

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealthcare utilization data are increasingly used for chronic disease surveillance. Nevertheless, no standard criteria for estimating prevalence of high-impact diseases, such as chronic obstructive pulmonary disease (COPD) and asthma, are available. In this study an algorithm for recognizing COPD/asthma cases from HCU data is developed and implemented in the HCU databases of the Italian Lombardy Region (about 10 million residents). The impact of diagnostic misclassification for reliably estimating prevalence was also assessed.

methodsDisease-specificdrug codes, hospital discharges together with co-payment exemptions when available, and a combination of them according with patient's age, were used to create the proposed algorithm. Identified cases were considered for prevalence estimation. An external validation study was also performed in order to evaluate systematic uncertainty of prevalence estimates.

resultsRaw prevalence of COPD and asthma in 2010 was 3.6 and 3.3% respectively. According to external validation, sensitivity values were 53% for COPD and 39% for asthma. Adjusted prevalence estimates were respectively 6.8 and 8.5% for COPD (among person aged 40 years or older) and asthma (among person aged 40 years or younger).

conclusionsCOPD and asthma prevalence may be estimated from HCU data, albeit with high systematic uncertainty. Validation is recommended in this setting.

Indexed as

Databases, FactualAdolescentAdultAgedAged, 80 and overAlgorithmsAsthmaChildChild, PreschoolChronic DiseaseCross-Sectional StudiesFemaleHumansInfantInfant, NewbornItalyAlgorithmsAsthmaChronic obstructive pulmonary diseaseHealthcare utilization databasePrevalence

Identifiers

PMID28103865
PMCPMC5248488

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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.