Reproducibility, reliability and validity of population-based administrative health data for the assessment of cancer non-related comorbidities

Patients with comorbidities do not receive optimal treatment for their cancer, leading to lower cancer survival. Information on individual comorbidities is not straightforward to derive from population-based administrative health datasets. We described the development of a reproducible algorithm to...

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Vydané v:PloS one Ročník 12; číslo 3; s. e0172814
Hlavní autori: Maringe, Camille, Fowler, Helen, Rachet, Bernard, Luque-Fernandez, Miguel Angel
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 06.03.2017
Public Library of Science (PLoS)
Predmet:
Algorithms
Analysis
Breast cancer
Cancer
Cancer therapies
Care and treatment
Chemotherapy
Chronic obstructive lung disease
Chronic obstructive pulmonary disease
Comorbidity
Complications and side effects
Data collection
Diagnosis
England - epidemiology
Epidemiology
Female
Health
Hospitals
Humans
Hygiene
Laryngeal cancer
Longitudinal Studies
Lung cancer
Lung diseases
Male
Medical diagnosis
Medical prognosis
Medical records
Medical research
Medicine
Medicine and Health Sciences
Mortality
Neoplasms - epidemiology
Neoplasms - mortality
Obstructive lung disease
Patients
People and places
Physical Sciences
Population
Population Surveillance
Population-based studies
Proportional Hazards Models
Reliability analysis
Reproducibility
Reproducibility of Results
Research and Analysis Methods
Researchers
Retrospective Studies
Studies
Validity
United Kingdom
ISSN:1932-6203, 1932-6203
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Shrnutí:Patients with comorbidities do not receive optimal treatment for their cancer, leading to lower cancer survival. Information on individual comorbidities is not straightforward to derive from population-based administrative health datasets. We described the development of a reproducible algorithm to extract the individual Charlson index comorbidities from such data. We illustrated the algorithm with 1,789 laryngeal cancer patients diagnosed in England in 2013. We aimed to clearly set out and advocate the time-related assumptions specified in the algorithm by providing empirical evidence for them. Comorbidities were assessed from hospital records in the ten years preceding cancer diagnosis and internal reliability of the hospital records was checked. Data were right-truncated 6 or 12 months prior to cancer diagnosis to avoid inclusion of potentially cancer-related comorbidities. We tested for collider bias using Cox regression. Our administrative data showed weak to moderate internal reliability to identify comorbidities (ICC ranging between 0.1 and 0.6) but a notably high external validity (86.3%). We showed a reverse protective effect of non-cancer related Chronic Obstructive Pulmonary Disease (COPD) when the effect is split into cancer and non-cancer related COPD (Age-adjusted HR: 0.95, 95% CI:0.7-1.28 for non-cancer related comorbidities). Furthermore, we showed that a window of 6 years before diagnosis is an optimal period for the assessment of comorbidities. To formulate a robust approach for assessing common comorbidities, it is important that assumptions made are explicitly stated and empirically proven. We provide a transparent and consistent approach useful to researchers looking to assess comorbidities for cancer patients using administrative health data.
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Competing Interests: The authors have declared that no competing interests exist.
Conceptualization: BR MALF HF CM.Data curation: MALF HF CM.Formal analysis: MALF HF CM.Funding acquisition: BR.Investigation: MALF HF CM.Methodology: MALF HF CM.Project administration: BR MALF HF CM.Resources: BR.Software: BR MALF HF CM.Supervision: MALF BR.Validation: MALF HF CM.Visualization: MALF HF CM.Writing – original draft: MALF HF CM.Writing – review & editing: BR MALF HF CM.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0172814