Abstract 216: Describing Geographically High-Risk Areas for PAD in the Netherlands: Socio-Economic Profile, Treatment Rates and Cardiovascular Prognosis
| Autor: | Tijmen Koëter, Patrick W Vriens, Moniek van Zitteren, Jan M Heyligers, Desiree H Burger, Maarten Lijkwan, Koen van Hees, Maria Nooren, Kim G Smolderen |
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| Rok vydání: | 2014 |
| Předmět: | |
| Zdroj: | Circulation: Cardiovascular Quality and Outcomes. 7 |
| ISSN: | 1941-7705 1941-7713 |
| DOI: | 10.1161/circoutcomes.7.suppl_1.216 |
| Popis: | Introduction: Geospatial mapping technology has been previously successfully used in cardiac disease to identify geographical areas where at-risk patients live in terms of their socio-economic background and cardiovascular outcomes. This methodology has not been applied for peripheral arterial disease (PAD). By doing so, we could identify vulnerable subpopulations that may benefit from more aggressive secondary prevention and follow-up. Methods: We are introducing the Geographically High-Risk Areas for PAD (GAP) study in the Netherlands as a pilot project to leverage the use of geospatial mapping technology in a national outpatient database focusing on patients with PAD. The pilot project reports on 816 patients with newly identified PAD (>Rutherford 1) identified at the regional vascular clinics between March 2006 and November 2011 in the city of Tilburg, The Netherlands. Using the ESRI ArcGIS software, we will address the following specific aims: 1) to geo-map patients’ residential location based on their zip code;2) to describe patients’ socioeconomic characteristics based on data obtained from the Central Bureau for Statistics in the Netherlands and patient interviews;3) to geo-map their interventional procedures (endovascular and/or surgical); and 4) to geo-map patients’ cardiovascular outcomes. As an exploratory aim, we will evaluate the association between having a more vulnerable socioeconomic profile, and undergoing more interventional procedures, and having an adverse prognosis, respectively. Results: As an example and to test feasibility, we created a density map with the occurrence of newly identified PAD in the Tilburg area (Figure a), as well as several overviews of maps containing socioeconomic variables (e.g. Figure b - number of patients with PAD on welfare) and cardiovascular risk factors (e.g. Figure c - BMI categories distribution among patients with PAD). Conclusion: Using the geospatial mapping methodology in the GAP pilot project in Tilburg, The Netherlands, we will be able to leverage the use of this technology in larger national databases to better identify patients with PAD who are at risk of increased health care utilization and adverse outcomes. This information will be instrumental to help improve prevention and care for PAD in collaboration with local care providers. |
| Databáze: | OpenAIRE |
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