Spatial Distribution of Health Care Facilities in City of Cape Town, South Africa

[EN] Health care in South Africa is the fourth largest item of government expenditure. Most South Africans do not have medical insurance and therefore seek medical care from public health care facilities such as public hospitals, clinics and community day centres. In City of Cape Town (CoCT), in 201...

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Detalles Bibliográficos
Autor: Er, Sebnem
Tipo de recurso: capítulo de libro
Fecha de publicación:2023
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/201699
Acceso en línea:https://riunet.upv.es/handle/10251/201699
Access Level:acceso abierto
Palabra clave:Health care facilities
Spatial distribution
Geographically weighted poisson regression
Descripción
Sumario:[EN] Health care in South Africa is the fourth largest item of government expenditure. Most South Africans do not have medical insurance and therefore seek medical care from public health care facilities such as public hospitals, clinics and community day centres. In City of Cape Town (CoCT), in 2016 there were 149 health care facilities and in 2023 this number increased to 160 facilities with an annual growth rate of 1.19%. The population in 2016 was 4 million and in 2021 it is estimated as approximately 4.76 million which indicates an annual growth rate of 3.5% between 2016 and 2021. The annual growth rate of health care facilities (primarily representing data applicable to the City of Cape Town’s Environmental Health Department) is clearly not matching the annual growth rate of the population which is potentially a big problem in a fast growing region like CoCT. The main aim of this paper is to analyse the distribution of health care facilities (clinics, hospitals) within the City of Cape Town, South Africa using spatial kernel density estimation methods and explore what factors affect the number of health care facilities within each ward using generalized linear models.