Background: Healthcare 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. Methods: Disease-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. Results: Raw 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). Conclusions: COPD and asthma prevalence may be estimated from HCU data, albeit with high systematic uncertainty. Validation is recommended in this setting.

Biffi, A., Comoretto, R., Arfè, A., Scotti, L., Merlino, L., Vaghi, A., et al. (2017). Can healthcare utilization data reliably capture cases of chronic respiratory diseases? A cross-sectional investigation in Italy. BMC PULMONARY MEDICINE, 17(1) [10.1186/s12890-016-0362-6].

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

BIFFI, ANNALISA
Primo
;
COMORETTO, ROSANNA IRENE
Secondo
;
SCOTTI, LORENZA;PESCI, ALBERTO;DE MARCO, ROBERTO;CORRAO, GIOVANNI
;
2017

Abstract

Background: Healthcare 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. Methods: Disease-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. Results: Raw 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). Conclusions: COPD and asthma prevalence may be estimated from HCU data, albeit with high systematic uncertainty. Validation is recommended in this setting.
Articolo in rivista - Articolo scientifico
Algorithms; Asthma; Chronic obstructive pulmonary disease; Healthcare utilization database; Prevalence;
English
2017
17
1
20
open
Biffi, A., Comoretto, R., Arfè, A., Scotti, L., Merlino, L., Vaghi, A., et al. (2017). Can healthcare utilization data reliably capture cases of chronic respiratory diseases? A cross-sectional investigation in Italy. BMC PULMONARY MEDICINE, 17(1) [10.1186/s12890-016-0362-6].
File in questo prodotto:
File Dimensione Formato  
10281-145615.pdf

accesso aperto

Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Dimensione 394.24 kB
Formato Adobe PDF
394.24 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/145615
Citazioni
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 12
Social impact