How Reliable Are Current Data for Assessing the Actual Prevalence of Chronic Obstructive Pulmonary Disease?
Estimating COPD occurrence is perceived by the scientific community as a matter of increasing interest because of the worldwide diffusion of the disease. We aimed to estimate COPD prevalence by using administrative databases from a city in central Italy for 2002-2006, improving both the sensitivity...
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Published in | PloS one Vol. 11; no. 2; p. e0149302 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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Public Library of Science
22.02.2016
Public Library of Science (PLoS) |
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Online Access | Get full text |
ISSN | 1932-6203 1932-6203 |
DOI | 10.1371/journal.pone.0149302 |
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Abstract | Estimating COPD occurrence is perceived by the scientific community as a matter of increasing interest because of the worldwide diffusion of the disease. We aimed to estimate COPD prevalence by using administrative databases from a city in central Italy for 2002-2006, improving both the sensitivity and the reliability of the estimate.
Multiple sources were used, integrating the hospital discharge register (HDR), clinical charts, spirometry and the cause-specific mortality register (CMR) in a longitudinal algorithm, to reduce underestimation of COPD prevalence. Prevalence was also estimated on the basis of COPD cases confirmed through spirometry, to correct misclassification. Estimating such prevalence relied on using coefficients of validation, derived as the positive predictive value (PPV) for being an actual COPD case from clinical and spirometric data at the Institute of Clinical Physiology of the National Research Council.
We found that sensitivity of COPD prevalence increased by 37%. The highest estimate (4.43 per 100 residents) was observed in the 5-year period, using a 3-year longitudinal approach and combined data from three sources. We found that 17% of COPD cases were misclassified. The above estimate of COPD prevalence decreased (3.66 per 100 residents) when coefficients of validation were applied. The PPV was 80% for the HDR, 82% for clinical diagnoses and 91% for the CMR.
Adjusting the COPD prevalence for both underestimation and misclassification of the cases makes administrative data more reliable for epidemiological purposes. |
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AbstractList | Background Estimating COPD occurrence is perceived by the scientific community as a matter of increasing interest because of the worldwide diffusion of the disease. We aimed to estimate COPD prevalence by using administrative databases from a city in central Italy for 2002–2006, improving both the sensitivity and the reliability of the estimate. Methods Multiple sources were used, integrating the hospital discharge register (HDR), clinical charts, spirometry and the cause-specific mortality register (CMR) in a longitudinal algorithm, to reduce underestimation of COPD prevalence. Prevalence was also estimated on the basis of COPD cases confirmed through spirometry, to correct misclassification. Estimating such prevalence relied on using coefficients of validation, derived as the positive predictive value (PPV) for being an actual COPD case from clinical and spirometric data at the Institute of Clinical Physiology of the National Research Council. Results We found that sensitivity of COPD prevalence increased by 37%. The highest estimate (4.43 per 100 residents) was observed in the 5-year period, using a 3-year longitudinal approach and combined data from three sources. We found that 17% of COPD cases were misclassified. The above estimate of COPD prevalence decreased (3.66 per 100 residents) when coefficients of validation were applied. The PPV was 80% for the HDR, 82% for clinical diagnoses and 91% for the CMR. Conclusions Adjusting the COPD prevalence for both underestimation and misclassification of the cases makes administrative data more reliable for epidemiological purposes. Estimating COPD occurrence is perceived by the scientific community as a matter of increasing interest because of the worldwide diffusion of the disease. We aimed to estimate COPD prevalence by using administrative databases from a city in central Italy for 2002-2006, improving both the sensitivity and the reliability of the estimate. Multiple sources were used, integrating the hospital discharge register (HDR), clinical charts, spirometry and the cause-specific mortality register (CMR) in a longitudinal algorithm, to reduce underestimation of COPD prevalence. Prevalence was also estimated on the basis of COPD cases confirmed through spirometry, to correct misclassification. Estimating such prevalence relied on using coefficients of validation, derived as the positive predictive value (PPV) for being an actual COPD case from clinical and spirometric data at the Institute of Clinical Physiology of the National Research Council. We found that sensitivity of COPD prevalence increased by 37%. The highest estimate (4.43 per 100 residents) was observed in the 5-year period, using a 3-year longitudinal approach and combined data from three sources. We found that 17% of COPD cases were misclassified. The above estimate of COPD prevalence decreased (3.66 per 100 residents) when coefficients of validation were applied. The PPV was 80% for the HDR, 82% for clinical diagnoses and 91% for the CMR. Adjusting the COPD prevalence for both underestimation and misclassification of the cases makes administrative data more reliable for epidemiological purposes. Background Estimating COPD occurrence is perceived by the scientific community as a matter of increasing interest because of the worldwide diffusion of the disease. We aimed to estimate COPD prevalence by using administrative databases from a city in central Italy for 2002–2006, improving both the sensitivity and the reliability of the estimate. Methods Multiple sources were used, integrating the hospital discharge register (HDR), clinical charts, spirometry and the cause-specific mortality register (CMR) in a longitudinal algorithm, to reduce underestimation of COPD prevalence. Prevalence was also estimated on the basis of COPD cases confirmed through spirometry, to correct misclassification. Estimating such prevalence relied on using coefficients of validation, derived as the positive predictive value (PPV) for being an actual COPD case from clinical and spirometric data at the Institute of Clinical Physiology of the National Research Council. Results We found that sensitivity of COPD prevalence increased by 37%. The highest estimate (4.43 per 100 residents) was observed in the 5-year period, using a 3-year longitudinal approach and combined data from three sources. We found that 17% of COPD cases were misclassified. The above estimate of COPD prevalence decreased (3.66 per 100 residents) when coefficients of validation were applied. The PPV was 80% for the HDR, 82% for clinical diagnoses and 91% for the CMR. Conclusions Adjusting the COPD prevalence for both underestimation and misclassification of the cases makes administrative data more reliable for epidemiological purposes. Estimating COPD occurrence is perceived by the scientific community as a matter of increasing interest because of the worldwide diffusion of the disease. We aimed to estimate COPD prevalence by using administrative databases from a city in central Italy for 2002-2006, improving both the sensitivity and the reliability of the estimate.BACKGROUNDEstimating COPD occurrence is perceived by the scientific community as a matter of increasing interest because of the worldwide diffusion of the disease. We aimed to estimate COPD prevalence by using administrative databases from a city in central Italy for 2002-2006, improving both the sensitivity and the reliability of the estimate.Multiple sources were used, integrating the hospital discharge register (HDR), clinical charts, spirometry and the cause-specific mortality register (CMR) in a longitudinal algorithm, to reduce underestimation of COPD prevalence. Prevalence was also estimated on the basis of COPD cases confirmed through spirometry, to correct misclassification. Estimating such prevalence relied on using coefficients of validation, derived as the positive predictive value (PPV) for being an actual COPD case from clinical and spirometric data at the Institute of Clinical Physiology of the National Research Council.METHODSMultiple sources were used, integrating the hospital discharge register (HDR), clinical charts, spirometry and the cause-specific mortality register (CMR) in a longitudinal algorithm, to reduce underestimation of COPD prevalence. Prevalence was also estimated on the basis of COPD cases confirmed through spirometry, to correct misclassification. Estimating such prevalence relied on using coefficients of validation, derived as the positive predictive value (PPV) for being an actual COPD case from clinical and spirometric data at the Institute of Clinical Physiology of the National Research Council.We found that sensitivity of COPD prevalence increased by 37%. The highest estimate (4.43 per 100 residents) was observed in the 5-year period, using a 3-year longitudinal approach and combined data from three sources. We found that 17% of COPD cases were misclassified. The above estimate of COPD prevalence decreased (3.66 per 100 residents) when coefficients of validation were applied. The PPV was 80% for the HDR, 82% for clinical diagnoses and 91% for the CMR.RESULTSWe found that sensitivity of COPD prevalence increased by 37%. The highest estimate (4.43 per 100 residents) was observed in the 5-year period, using a 3-year longitudinal approach and combined data from three sources. We found that 17% of COPD cases were misclassified. The above estimate of COPD prevalence decreased (3.66 per 100 residents) when coefficients of validation were applied. The PPV was 80% for the HDR, 82% for clinical diagnoses and 91% for the CMR.Adjusting the COPD prevalence for both underestimation and misclassification of the cases makes administrative data more reliable for epidemiological purposes.CONCLUSIONSAdjusting the COPD prevalence for both underestimation and misclassification of the cases makes administrative data more reliable for epidemiological purposes. |
Author | Prediletto, Renato Faustini, Annunziata Protti, Maria Angela Romanelli, Anna Maria Raciti, Mauro Fornai, Edo |
AuthorAffiliation | University of Rochester Medical Center, UNITED STATES 1 CNR, Institute of Clinical Physiology, Pisa, Italy 2 Fondazione Gabriele Monasterio CNR-Regione Toscana, Pisa, Italy 3 Department of Epidemiology, Regional Health Service, Lazio Region, Rome, Italy |
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BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26901166$$D View this record in MEDLINE/PubMed |
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Copyright | 2016 Romanelli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2016 Romanelli et al 2016 Romanelli et al |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceived and designed the experiments: AF AMR RP. Analyzed the data: MR MAP AR AF. Wrote the paper: AF AR. Acquired and Analyzed Spirometric Data: EF. Competing Interests: The authors have declared that no competing interests exist. |
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SubjectTerms | Adult Aged Charts Chronic illnesses Chronic obstructive pulmonary disease Computer and Information Sciences Epidemiology Estimates Estimation Female Health risk assessment Heart failure Hospitals Humans Italy - epidemiology Lung diseases Male Medicine and Health Sciences Middle Aged Mortality Obstructive lung disease People and Places Physiology Prevalence Primary care Pulmonary Disease, Chronic Obstructive - epidemiology Pulmonary Disease, Chronic Obstructive - physiopathology Registries Research and Analysis Methods Retrospective Studies Sensitivity Studies |
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Title | How Reliable Are Current Data for Assessing the Actual Prevalence of Chronic Obstructive Pulmonary Disease? |
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