Bayesian Analysis of Population Health Data
The analysis of population-wide datasets can provide insight on the health status of large populations so that public health officials can make data-driven decisions. The analysis of such datasets often requires highly parameterized models with different types of fixed and random effects to account...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/5/577 |
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| author | Dorota Młynarczyk Carmen Armero Virgilio Gómez-Rubio Pedro Puig |
| author_facet | Dorota Młynarczyk Carmen Armero Virgilio Gómez-Rubio Pedro Puig |
| author_sort | Dorota Młynarczyk |
| collection | DOAJ |
| description | The analysis of population-wide datasets can provide insight on the health status of large populations so that public health officials can make data-driven decisions. The analysis of such datasets often requires highly parameterized models with different types of fixed and random effects to account for risk factors, spatial and temporal variations, multilevel effects and other sources on uncertainty. To illustrate the potential of Bayesian hierarchical models, a dataset of about 500,000 inhabitants released by the Polish National Health Fund containing information about ischemic stroke incidence for a 2-year period is analyzed using different types of models. Spatial logistic regression and survival models are considered for analyzing the individual probabilities of stroke and the times to the occurrence of an ischemic stroke event. Demographic and socioeconomic variables as well as drug prescription information are available at an individual level. Spatial variation is considered by means of region-level random effects. |
| first_indexed | 2024-03-10T13:26:17Z |
| format | Article |
| id | doaj.art-9ec464b0fb414e038f0a8c2e95590285 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T13:26:17Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-9ec464b0fb414e038f0a8c2e955902852023-11-21T09:40:32ZengMDPI AGMathematics2227-73902021-03-019557710.3390/math9050577Bayesian Analysis of Population Health DataDorota Młynarczyk0Carmen Armero1Virgilio Gómez-Rubio2Pedro Puig3Departament de Matemàtiques, Edifici C, Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), 08193 Barcelona, SpainDepartament d’Estadística i Investigació Operativa, Facultat de Ciències Matemàtiques, Universitat de València, Burjassot, 46100 València, SpainDepartment of Mathematics, School of Industrial Engineering, Universidad de Castilla-La Mancha, 02071 Albacete, SpainDepartament de Matemàtiques, Edifici C, Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), 08193 Barcelona, SpainThe analysis of population-wide datasets can provide insight on the health status of large populations so that public health officials can make data-driven decisions. The analysis of such datasets often requires highly parameterized models with different types of fixed and random effects to account for risk factors, spatial and temporal variations, multilevel effects and other sources on uncertainty. To illustrate the potential of Bayesian hierarchical models, a dataset of about 500,000 inhabitants released by the Polish National Health Fund containing information about ischemic stroke incidence for a 2-year period is analyzed using different types of models. Spatial logistic regression and survival models are considered for analyzing the individual probabilities of stroke and the times to the occurrence of an ischemic stroke event. Demographic and socioeconomic variables as well as drug prescription information are available at an individual level. Spatial variation is considered by means of region-level random effects.https://www.mdpi.com/2227-7390/9/5/577bayesian inferencedisease mappingintegrated nested Laplace approximationspatial modelssurvival models |
| spellingShingle | Dorota Młynarczyk Carmen Armero Virgilio Gómez-Rubio Pedro Puig Bayesian Analysis of Population Health Data Mathematics bayesian inference disease mapping integrated nested Laplace approximation spatial models survival models |
| title | Bayesian Analysis of Population Health Data |
| title_full | Bayesian Analysis of Population Health Data |
| title_fullStr | Bayesian Analysis of Population Health Data |
| title_full_unstemmed | Bayesian Analysis of Population Health Data |
| title_short | Bayesian Analysis of Population Health Data |
| title_sort | bayesian analysis of population health data |
| topic | bayesian inference disease mapping integrated nested Laplace approximation spatial models survival models |
| url | https://www.mdpi.com/2227-7390/9/5/577 |
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