Spatial clusters for potentially preventable hospitalisations (PPHs) for chronic conditions and access to allied health in South Western Sydney

Problem

A key role of primary care is to support patients to live well with their chronic condition in the community. Potentially preventable hospitalisations (PPHs) for chronic conditions are increasing in Australia. A recent study in South Western Sydney Local Health District (SWLHD) found six hotspots of PPHs which were associated with socioeconomic disadvantage but also higher access to primary care (general practice) services. These services were largely bulk-billing general practices . Many PPHs for chronic conditions may be amenable to allied health interventions and yet access to affordable allied health professionals in SWSLHD is lower than for more affluent areas in Sydney. The aim of this study is to use geospatial analysis techniques to determine whether there is an association between access to primary and community allied health services and hotspots of PPHs for chronic conditions in the SWSLHD area

Approach

The main study involves the comparison of two sets of data:1. Hospital admission data for PPHs for chronic conditions for all age groups from all public hospitals in SWSLHD for a 12-month period (2018) available from NSW Health.2. The location and availability of allied health services (dietetics, exercise physiology, occupational therapy, physiotherapy, psychology, social work and speech pathology) in primary and community health in SWSLHD and South Western Sydney Primary Health Network (SWSPHN). The following data is required for all services: geographic location (suburb & postcode), opening hours, out of pocket cost of services (private or community health), and languages spoken by allied health professionals.The allied health service data for 2019 was obtained from National Health Services Directory. The data accuracy and completeness checked against the directory at SWSPHN and community health services (SWSLHD) publically available information.The data will be cleaned and coded. Summary statistics will be produced for all variables in the data. Inferential or regression models will be implemented to investigate relationships.

Findings

Cross checking of the allied health service data for data accuracy and completeness is underway. Summary data for PPHs and allied health services will be presented.

Consequences

Cross checking of the allied health service data has revealed some challenges. This has implications for patients and providers trying to access allied health services to support people to live well with chronic disease.