Abstract
BackgroundSocioeconomic status (SES) is an important determinant of health, and SES data is an important confounder to control for in epidemiology and health services research. Individual level SES measures are cumbersome to collect and susceptible to biases, while area level SES measures may have insufficient granularity. The ‘Singapore Housing Index’ (SHI) is a validated, building level SES measure that bridges individual and area level measures. However, determination of the SHI has previously required periodic data purchase and manual parsing. In this study, we describe a means of SHI determination for public housing buildings with open government data, and validate this against the previous SHI determination method.MethodsGovernment open data sources (e.g. data.gov.sg, Singapore Land Authority OneMAP API, Urban Redevelopment Authority API) were queried using custom Python scripts. Data on residential public housing block address and composition from the HDB Property Information dataset (data.gov.sg) was matched to postal code and geographical coordinates via OneMAP API calls. The SHI was calculated from open data, and compared to the original SHI dataset that was curated from non-open data sources in 2018.ResultsTen thousand seventy-seven unique residential buildings were identified from open data. OneMAP API calls generated valid geographical coordinates for all (100%) buildings, and valid postal code for 10,012 (99.36%) buildings. There was an overlap of 10,011 buildings between the open dataset and the original SHI dataset. Intraclass correlation coefficient was 0.999 for the two sources of SHI, indicating almost perfect agreement. A Bland-Altman plot analysis identified a small number of outliers, and this revealed 5 properties that had an incorrect SHI assigned by the original dataset. Information on recently transacted property prices was also obtained for 8599 (85.3%) of buildings.ConclusionSHI, a useful tool for health services research, can be accurately reconstructed using open datasets at no cost. This method is a convenient means for future researchers to obtain updated building-level markers of socioeconomic status for policy and research.
Highlights
Socioeconomic status (SES) is a well-established determinant of health and is relevant to medical research and public health policy [1]
We describe the use of application programming interfaces (APIs) and other public-domain datasets provided by the Singapore Government and other government sources (Singapore Land Authority, Urban Redevelopment Authority, Ministry of Health, Attorney-General’s Chambers), to reconstruct the Singapore Singapore Housing Index’ (SHI)
We examined the overlap between the 10,077 buildings included in the open dataset, and the original dataset by Wong et al, which had 10,012 unique HDB residential buildings
Summary
Socioeconomic status (SES) is a well-established determinant of health and is relevant to medical research and public health policy [1]. Obtaining individual-level SES measures (e.g. education, income and occupation) is challenging, as they are considered sensitive information. They are subject to more stringent confidentiality protections and regulatory barriers with respect to collection and sharing of data. Socioeconomic status (SES) is an important determinant of health, and SES data is an important confounder to control for in epidemiology and health services research. The ‘Singapore Housing Index’ (SHI) is a validated, building level SES measure that bridges individual and area level measures. We describe a means of SHI determination for public housing buildings with open government data, and validate this against the previous SHI determination method
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