Using administrative data from a national shared services database to target the delivery of homeless services in the Dublin Region
| Autor: | Pathie Maphosa, Declan Redmond, Bernie O'Donoghue Hynes, Richard Waldron |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Consumption (economics)
Typology education.field_of_study Information Systems and Management Housing First Database Population Health Informatics Disease cluster computer.software_genre Outreach Geography lcsh:HB848-3697 Extensive data lcsh:Demography. Population. Vital events Service user education computer Information Systems Demography |
| Zdroj: | International Journal of Population Data Science, Vol 3, Iss 2 (2018) |
| ISSN: | 2399-4908 |
| DOI: | 10.23889/ijpds.v3i2.491 |
| Popis: | BackgroundPASS is a national shared services database that captures live information on service user interactions with all state funded NGO and local authority homeless services. In the Dublin region, which accommodates in excess of 70% of the nationalhomeless population, this data was mined and cleansed in order to carry out a k-mean cluster analysis. ObjectiveThe objective was to determine the rate of movement through homeless services and the consumption of resources of different clusters cohorts and to compare these findings with other regions internationally. MethodsFollowing extensive data preparation, the Kuhn and Culhane (1998) k-mean cluster analysis was applied in 2017 to five years of PASS data (2012-2016) and results categorised to align to their typology of homelessness. FindingsResults for Dublin showed patterns similar to those reported in the US, Canada and Denmark, with approximately 80% of services users transitioning quickly through services. These transitional service users accounted for just over one third of total bed-nights while the remaining 20% of episodic and long-term service users accounted for almost two thirds of the bed-nights over the five years. Uniquely, the analysis also considered the patterns of engagement of people sleeping rough and results revealed similar but more extreme patterns with 86% of those rough sleeping accounting for only 28% of outreach contacts with a small number adults accounting for over 70% of all street contacts. ConclusionThe results from the analysis of administrative data were used to inform operations so appropriate ‘Housing First’ responses were developed for those episodically or chronically experiencing homelessness and engaged in sleeping rough. |
| Databáze: | OpenAIRE |
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