This document discusses how spatial and geographic factors can impact the quality of care for older patients. It argues that vulnerability is linked to location, as factors like age, disability, and socioeconomic status are spatially patterned. Analyzing these variables and adverse health events using spatial analysis tools can provide insights. For example, maps showing concentrations of elderly populations or areas with low socioeconomic status relative to locations of contamination events can help identify vulnerable groups and inform quality improvement efforts. The conclusion advocates for more routinely collecting and analyzing patient safety data using spatial analysis to better understand vulnerabilities and compare quality across different locations.

1. Space, place and the quality
of care for older people
Authors:
Hamish Robertson
Nick Nicholas
Joanne Travaglia PhD

2. Background
• Well-established history of natural hazards and
vulnerability studies (30+ years)
• Sociologically nuanced – less so in health sciences –>
social variables often presented as concrete and
theory-free
• Wisner on theory and applied vulnerability analyses –
age, gender, disability, ethnicity (race), class (SES)
• Knowledge production on social factors underpinned
by implicit theory (epistemic vs ontological) regardless
of model
• Vulnerability work in patient safety under-emphasised
and under-theorised (Travaglia, 2009)

3. Space and Place in Health Quality
• Vulnerability is linked to location – vulnerability
factors and hazard events are spatially
patterned
• Location can include broader context and
circumstances (spatial extension and scalar
factors)
• Scale is made explicit in knowledge production
about vulnerabilities
• Implications for PS movement are substantial
and fit within an expanded scientific paradigm

4. Analysis and Visualisation
• Wisner’s vulnerability measures for applied analysis –
see previous slide
• Man-made hazards ie. environmental contamination
events (minus service stations)
• Distance and catchment analysis
• Scale as a concrete analytic variable (quantitative and
qualitative both viable and can be merged)
• Spatial science and patient vulnerability go together eg.
institutional quality and distribution of patients
experiencing adverse events
• Visualisation supports analysis and democratisation of
knowledge (assuming some degree of spatial literacy)
eg. Dartmouth Atlas

5. Age – 65+ and Contamination Events

6. SEIFA Score and Contamination Events

7. Need for Core Activity Assistance - Disability

8. Spatial smoothing and zones of impact

9. Conclusion
• You can only analysis data you collect –
epistemic limitations of any model
• Maps are both theory and data – pluralism is
well-accepted in spatial sciences
• Geographical analysis goes beyond collection
and simple analysis of ‘events’
• Patient safety and vulnerability measures (and
events) can and should be ‘spatialised’
• National, regional and institutional comparisons
benefit from spatial analysis