Spatial Accessibility of Primary Care in the Dual Public-Private Health System in Rural Areas, Malaysia.
Jabrullah Ab HamidMuhamad Hanafiah JuniRosliza Abdul ManafSharifah Norkhadijah Syed IsmailPoh Ying LimPublished in: International journal of environmental research and public health (2023)
Disparities in access to health services in rural areas represent a global health issue. Various external factors contribute to these disparities and each root requires specific remedial action to alleviate the issue. This study elucidates an approach to assessing the spatial accessibility of primary care, considering Malaysia's dual public-private system specifically in rural areas, and identifies its associated ecological factors. Spatial accessibility was calculated using the Enhance 2-Step Floating Catchment Area (E2SFCA) method, modified as per local context. Data were secondary sourced from Population and Housing Census data and administrative datasets pertaining to health facilities and road network. The spatial pattern of the E2SFCA scores were depicted using Hot spot Analysis. Hierarchical multiple linear regression and geographical weight regression were performed to identify factors that affect E2SFCA scores. Hot spot areas revolved near the urban agglomeration, largely contributed by the private sector. Distance to urban areas, road density, population density dependency ratios and ethnic composition were among the associated factors. Accurate conceptualization and comprehensive assessment of accessibility are crucial for evidence-based decision making by the policymakers and health authorities in identifying areas that need attention for a more specific and localized planning and development.
Keyphrases
- healthcare
- primary care
- global health
- public health
- mental health
- health insurance
- decision making
- electronic health record
- affordable care act
- big data
- health information
- body mass index
- working memory
- human health
- general practice
- health promotion
- rna seq
- risk assessment
- mental illness
- machine learning
- climate change
- mass spectrometry
- data analysis
- weight gain
- dna methylation
- artificial intelligence
- deep learning
- body weight
- drug induced