Data

Spatial Analysis Data of Population-based Breast Screening Participation in South Australia Dataset

University of South Australia
Prof Gelareh Farshid (Principal investigator) AsPr Theo Niyonsenga (Associated with) Dr Andrew Holmes (Associated with) Dr Deborah Van Gaans (Associated with) Dr Neil Coffee (Associated with)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://researchoutputs.unisa.edu.au/11541.1/58a83e9e-cb1a-471c-b7fa-8b7a79adb048&rft.title=Spatial Analysis Data of Population-based Breast Screening Participation in South Australia Dataset&rft.identifier=http://research.unisa.edu.au/dataset/269355&rft.publisher=University of South Australia&rft.description=To develop a new and unique geodatabase to identify locational disparities in population based breast cancer screening participation in South Australia according to place of residence. The small area geodatabase resulting from this project will provide a platform for advanced research into breast screening participation, geographic and spatial differentials in screening rates, and investigate possible predictors of these differentials. Specific analyses at the smallest geographic areas available will be undertaken where feasible to precisely identify social and demographic disparities and area level risk factors that warrant new service response. Spatial analysis will be undertaken at the smallest area possible, ideally the statistical area 1 (SA1) level. In addition, we shall assess small area predictors of screening participation with measures that include age, education, socioeconomic status, ethnic composition, family status and housing, as well as screening service access (presence and distance to screening service). Changes in screening participation over time will be investigated and assessed in relation to variation in BreastScreen SA program delivery including service pathways and local availability and timing for mobile screening units.Deidentified BreastScreen SA data will be geocoded and linked to census data. Age specific population denominators will be constructed for area of residence. BreastScreen SA participation rates will be standardised by the direct method. Area level sociodemographic predictors including median income and education, ethnicity, SEIFA and other census variable descriptors will be used to create a database for this project. Spatial visualisation will be undertaken for age specific and age standardised participation rates by small area of residence characteristics (e.g., rural/urban) and compared by small area socioeconomic and other measures. Spatial models will identify spatial variations and clusters of screening participation at the area level. Advanced models will assess secular trends in screening participation according to spatial, temporal, and spatiotemporal interaction dimensions, both at State and small area level. &rft.creator=Prof Gelareh Farshid&rft.date=2021&rft.coverage=129,-38.5 129,-26 141,-26 141,-38.5&rft_subject=Public Health and Health Services not elsewhere classified&rft_subject=MEDICAL AND HEALTH SCIENCES&rft_subject=PUBLIC HEALTH AND HEALTH SERVICES&rft_subject=Epidemiology&rft_subject=Health Informatics&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=LIBRARY AND INFORMATION STUDIES&rft_subject=Breast Screening&rft_subject=Public Health Service&rft_subject=Cancer&rft_subject=Health -- Health - General -- Cancer&rft.type=dataset&rft.language=English Access the data
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Contact Information

[email protected]

Full description

To develop a new and unique geodatabase to identify locational disparities in population based breast cancer screening participation in South Australia according to place of residence. The small area geodatabase resulting from this project will provide a platform for advanced research into breast screening participation, geographic and spatial differentials in screening rates, and investigate possible predictors of these differentials. Specific analyses at the smallest geographic areas available will be undertaken where feasible to precisely identify social and demographic disparities and area level risk factors that warrant new service response. Spatial analysis will be undertaken at the smallest area possible, ideally the statistical area 1 (SA1) level. In addition, we shall assess small area predictors of screening participation with measures that include age, education, socioeconomic status, ethnic composition, family status and housing, as well as screening service access (presence and distance to screening service). Changes in screening participation over time will be investigated and assessed in relation to variation in BreastScreen SA program delivery including service pathways and local availability and timing for mobile screening units.
Deidentified BreastScreen SA data will be geocoded and linked to census data. Age specific population denominators will be constructed for area of residence. BreastScreen SA participation rates will be standardised by the direct method. Area level sociodemographic predictors including median income and education, ethnicity, SEIFA and other census variable descriptors will be used to create a database for this project. Spatial visualisation will be undertaken for age specific and age standardised participation rates by small area of residence characteristics (e.g., rural/urban) and compared by small area socioeconomic and other measures. Spatial models will identify spatial variations and clusters of screening participation at the area level. Advanced models will assess secular trends in screening participation according to spatial, temporal, and spatiotemporal interaction dimensions, both at State and small area level.
Reuse Information

Existing data was sourced from:
local : DSET_EXISTING_DATA
Breast Screen SA, Australian Bureau of Statistics, LocationSA

The following software (and version) was used to generate or capture the data:
local : DSET_SW_DATA_CAPTURE
ArcGIS, Excel, Microsoft Access, SQL Server, R, Stata

The following instruments/equipment were used to generate or capture the data:
local : DSET_INST_DATA_CAPTURE
ArcGIS

The following software (and version) was used to analyse the data:
local : DSET_SW_DATA_ANALYSIS
ArcGIS, Excel, Microsoft Access, SQL Server, R, Stata

Available: 2021

Data time period: 2017

This dataset is part of a larger collection

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129,-38.5 129,-26 141,-26 141,-38.5

135,-32.25

Subjects
Breast Screening | Cancer | Epidemiology | Health -- Health - General -- Cancer | Health Informatics | Information and Computing Sciences | Library and Information Studies | Medical and Health Sciences | Public Health and Health Services | Public Health Service | Public Health and Health Services Not Elsewhere Classified |

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