ArticleEuropean journal of public health2019
Estimating disease prevalence from drug utilization data using the Random Forest algorithm.
Article in European journal of public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Identifying diseases in claims data using a machine learning approach - a case from Switzerland.Archives of public health = Archives belges de sante publique · 2025Article
- Using Medication Dispensation Data to Identify Clusters with Similar Prescribing Patterns in Older Adults Living with Dementia.Drugs & aging · 2025Article
- Patient-centred estimation of multimorbidity in chronic disease populations: a novel approach integrating global burden of disease metrics and healthcare administrative data.Population health metrics · 2025Article
- Estimating the burden of dementia and parkinsonism through a novel identification algorithm based on healthcare administrative data.Frontiers in public health · 2025Article
- Validation of Medication Proxies for the Identification of Hospitalizations for Major Adverse Cerebro-Cardiovascular Events.Clinical epidemiology · 2025Article
- Have People Treated With Antihypertensives Been Diagnosed With Hypertension? A Cross-Sectional Study in Stockholm, Sweden.Pharmacoepidemiology and drug safety · 2025Article
- The COVID-19 pandemic and the use of benzodiazepines and benzodiazepine-related drugs in Estonia: an interrupted time-series analysis.Child and adolescent psychiatry and mental health · 2024Article
- Estimating Type 2 Diabetes Prevalence: A Model of Drug Consumption Data.Pharmacy (Basel, Switzerland) · 2024Article
- A study protocol for identifying aging trajectories toward chronic neurodegenerative diseases by means of Marche regional administrative databases - TREND project.Frontiers in public health · 2024Article
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- [Prevention and cost control in the German healthcare sector].Pravention und Gesundheitsforderung · 2023Article
- Neuropsychiatric Comorbidity in Primary Hyperparathyroidism Before and After Parathyroidectomy: A Population Study.World journal of surgery · 2022Article
- Chronic prescription of antidepressant medication in patients with chronic kidney disease with and without kidney replacement therapy compared with matched controls in the Dutch general population.Clinical kidney journal · 2022Article
- Association between health literacy, general psychological factors, and adherence to medical treatment among Danes aged 50-80 years.BMC geriatrics · 2021Article
- Mapping chronic disease prevalence based on medication use and socio-demographic variables: an application of LASSO on administrative data sources in healthcare in the Netherlands.BMC public health · 2021Article
- Article
- Health claims databases used for kidney research around the world.Clinical kidney journal · 2021Review
- Comparing health insurance data and health interview survey data for ascertaining chronic disease prevalence in Belgium.Archives of public health = Archives belges de sante publique · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAggregated claims data on medication are often used as a proxy for the prevalence of diseases, especially chronic diseases. However, linkage between medication and diagnosis tend to be theory based and not very precise. Modelling disease probability at an individual level using individual level data may yield more accurate results.
methodsIndividual probabilities of having a certain chronic disease were estimated using the Random Forest (RF) algorithm. A training set was created from a general practitioners database of 276 723 cases that included diagnosis and claims data on medication. Model performance for 29 chronic diseases was evaluated using Receiver-Operator Curves, by measuring the Area Under the Curve (AUC).
resultsThe diseases for which model performance was best were Parkinson's disease (AUC = .89, 95% CI = .77-1.00), diabetes (AUC = .87, 95% CI = .85-.90), osteoporosis (AUC = .87, 95% CI = .81-.92) and heart failure (AUC = .81, 95% CI = .74-.88). Five other diseases had an AUC >.75: asthma, chronic enteritis, COPD, epilepsy and HIV/AIDS. For 16 of 17 diseases tested, the medication categories used in theory-based algorithms were also identified by our method, however the RF models included a broader range of medications as important predictors.
conclusionData on medication use can be a useful predictor when estimating the prevalence of several chronic diseases. To improve the estimates, for a broader range of chronic diseases, research should use better training data, include more details concerning dosages and duration of prescriptions, and add related predictors like hospitalizations.
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