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Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors in Health Insurance Data Using Bayesian Spatial Modelling

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Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors in Health Insurance Data Using Bayesian Spatial Modelling. / Kauhl, Boris; König, Jörg; Wolf, Sandra.

in: INT J ENV RES PUB HE, Jahrgang 20, Nr. 5, 4375, 28.02.2023.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Kauhl, B, König, J & Wolf, S 2023, 'Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors in Health Insurance Data Using Bayesian Spatial Modelling', INT J ENV RES PUB HE, Jg. 20, Nr. 5, 4375. https://doi.org/10.3390/ijerph20054375

APA

Kauhl, B., König, J., & Wolf, S. (2023). Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors in Health Insurance Data Using Bayesian Spatial Modelling. INT J ENV RES PUB HE, 20(5), [4375]. https://doi.org/10.3390/ijerph20054375

Vancouver

Kauhl B, König J, Wolf S. Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors in Health Insurance Data Using Bayesian Spatial Modelling. INT J ENV RES PUB HE. 2023 Feb 28;20(5). 4375. https://doi.org/10.3390/ijerph20054375

Bibtex

@article{84b9655924574ec7bbfbe235ba13e408,
title = "Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors in Health Insurance Data Using Bayesian Spatial Modelling",
abstract = "The onset of COVID-19 across the world has elevated interest in geographic information systems (GIS) for pandemic management. In Germany, however, most spatial analyses remain at the relatively coarse level of counties. In this study, we explored the spatial distribution of COVID-19 hospitalizations in health insurance data of the AOK Nordost health insurance. Additionally, we explored sociodemographic and pre-existing medical conditions associated with hospitalizations for COVID-19. Our results clearly show strong spatial dynamics of COVID-19 hospitalizations. The main risk factors for hospitalization were male sex, being unemployed, foreign citizenship, and living in a nursing home. The main pre-existing diseases associated with hospitalization were certain infectious and parasitic diseases, diseases of the blood and blood-forming organs, endocrine, nutritional and metabolic diseases, diseases of the nervous system, diseases of the circulatory system, diseases of the respiratory system, diseases of the genitourinary and symptoms, and signs and findings not classified elsewhere.",
keywords = "Male, Humans, Female, COVID-19, Bayes Theorem, Hospitalization, Insurance, Health, Risk Factors",
author = "Boris Kauhl and J{\"o}rg K{\"o}nig and Sandra Wolf",
year = "2023",
month = feb,
day = "28",
doi = "10.3390/ijerph20054375",
language = "English",
volume = "20",
journal = "INT J ENV RES PUB HE",
issn = "1660-4601",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "5",

}

RIS

TY - JOUR

T1 - Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors in Health Insurance Data Using Bayesian Spatial Modelling

AU - Kauhl, Boris

AU - König, Jörg

AU - Wolf, Sandra

PY - 2023/2/28

Y1 - 2023/2/28

N2 - The onset of COVID-19 across the world has elevated interest in geographic information systems (GIS) for pandemic management. In Germany, however, most spatial analyses remain at the relatively coarse level of counties. In this study, we explored the spatial distribution of COVID-19 hospitalizations in health insurance data of the AOK Nordost health insurance. Additionally, we explored sociodemographic and pre-existing medical conditions associated with hospitalizations for COVID-19. Our results clearly show strong spatial dynamics of COVID-19 hospitalizations. The main risk factors for hospitalization were male sex, being unemployed, foreign citizenship, and living in a nursing home. The main pre-existing diseases associated with hospitalization were certain infectious and parasitic diseases, diseases of the blood and blood-forming organs, endocrine, nutritional and metabolic diseases, diseases of the nervous system, diseases of the circulatory system, diseases of the respiratory system, diseases of the genitourinary and symptoms, and signs and findings not classified elsewhere.

AB - The onset of COVID-19 across the world has elevated interest in geographic information systems (GIS) for pandemic management. In Germany, however, most spatial analyses remain at the relatively coarse level of counties. In this study, we explored the spatial distribution of COVID-19 hospitalizations in health insurance data of the AOK Nordost health insurance. Additionally, we explored sociodemographic and pre-existing medical conditions associated with hospitalizations for COVID-19. Our results clearly show strong spatial dynamics of COVID-19 hospitalizations. The main risk factors for hospitalization were male sex, being unemployed, foreign citizenship, and living in a nursing home. The main pre-existing diseases associated with hospitalization were certain infectious and parasitic diseases, diseases of the blood and blood-forming organs, endocrine, nutritional and metabolic diseases, diseases of the nervous system, diseases of the circulatory system, diseases of the respiratory system, diseases of the genitourinary and symptoms, and signs and findings not classified elsewhere.

KW - Male

KW - Humans

KW - Female

KW - COVID-19

KW - Bayes Theorem

KW - Hospitalization

KW - Insurance, Health

KW - Risk Factors

U2 - 10.3390/ijerph20054375

DO - 10.3390/ijerph20054375

M3 - SCORING: Journal article

C2 - 36901384

VL - 20

JO - INT J ENV RES PUB HE

JF - INT J ENV RES PUB HE

SN - 1660-4601

IS - 5

M1 - 4375

ER -