Improving prevalence estimation through data fusion: methods and validation
Estimation of health prevalences is usually performed with a single survey. Some attempts have been made to integrate more than one source of data. We propose here to validate this approach through data fusion. Data Fusion is the process of integrating two sources of data into one combined file. It...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/80031 |
| Acceso en línea: | https://hdl.handle.net/2117/80031 https://dx.doi.org/10.1186/s12911-015-0169-z |
| Access Level: | acceso abierto |
| Palabra clave: | Combinatorial probabilities Population surveys Prevalences Diabetes Cardio vascular diseases Multiple imputation Sequential regression Probabilitats Classificació AMS::60 Probability theory and stochastic processes::60C05 Combinatorial probability Àrees temàtiques de la UPC::Matemàtiques i estadística::Probabilitat |
| Sumario: | Estimation of health prevalences is usually performed with a single survey. Some attempts have been made to integrate more than one source of data. We propose here to validate this approach through data fusion. Data Fusion is the process of integrating two sources of data into one combined file. It allows us to take even greater advantage of existing information collected in databases. Here, we use data fusion to improve the estimation of health prevalences for two primary health factors: cardiovascular diseases and diabetes. |
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