Geographic Variations of Stroke Hospitalization across France: A Diachronic Cluster Analysis
Background. This study evaluates the clustering of hospitalization rates for stroke and compares this clustering with two different time intervals 2009-2010 and 2012-2013, corresponding to the beginning of the French National Stroke Plan 2010–2014. In addition, these data will be compared with the d...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Stroke Research and Treatment |
| Online Access: | http://dx.doi.org/10.1155/2018/1897569 |
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| author | Yacine Lachkhem Étienne Minvielle Stéphane Rican |
| author_facet | Yacine Lachkhem Étienne Minvielle Stéphane Rican |
| author_sort | Yacine Lachkhem |
| collection | DOAJ |
| description | Background. This study evaluates the clustering of hospitalization rates for stroke and compares this clustering with two different time intervals 2009-2010 and 2012-2013, corresponding to the beginning of the French National Stroke Plan 2010–2014. In addition, these data will be compared with the deployment of stroke units as well as socioeconomic and healthcare characteristics at zip code level. Methods. We used the PMSI data from 2009 to 2013, which lists all hospitalizations for stroke between 2009 and 2013, identified on the most detailed geographic scale allowed by this database. We identify statistically significant clusters with high or low rates in the zip code level using the Getis-Ord statistics. Each of the significant clusters is monitored over time and evaluated according to the nearest stroke unit distance and the socioeconomic profile. Results. We identified clusters of low and high rate of stroke hospitalization (23.7% of all geographic codes). Most of these clusters are maintained over time (81%) but we also observed clusters in transition. Geographic codes with persistent high rates of stroke hospitalizations were mainly rural (78% versus 17%, P < .0001) and had a least favorable socioeconomic and healthcare profile. Conclusion. Our study reveals that high-stroke hospitalization rates cluster remains the same during our study period. While access to the stroke unit has increased overall, it remains low for these clusters. The socioeconomic and healthcare profile of these clusters are poor but variations were observed. These results are valuable tools to implement more targeted strategies to improve stroke care accessibility and reduce geographic disparities. |
| format | Article |
| id | doaj-art-24a7f8fb6379476ebd0eb1cf73beaa01 |
| institution | Kabale University |
| issn | 2090-8105 2042-0056 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Stroke Research and Treatment |
| spelling | doaj-art-24a7f8fb6379476ebd0eb1cf73beaa012025-02-03T01:26:23ZengWileyStroke Research and Treatment2090-81052042-00562018-01-01201810.1155/2018/18975691897569Geographic Variations of Stroke Hospitalization across France: A Diachronic Cluster AnalysisYacine Lachkhem0Étienne Minvielle1Stéphane Rican2Equipe d’Accueil Management des Organisations de Santé, French School of Public Health, avenue du Professeur Léon-Bernard, 35043 Rennes, FranceEquipe d’Accueil Management des Organisations de Santé, French School of Public Health, avenue du Professeur Léon-Bernard, 35043 Rennes, FranceLADYSS, Paris Nanterre University, F92000 Nanterre, FranceBackground. This study evaluates the clustering of hospitalization rates for stroke and compares this clustering with two different time intervals 2009-2010 and 2012-2013, corresponding to the beginning of the French National Stroke Plan 2010–2014. In addition, these data will be compared with the deployment of stroke units as well as socioeconomic and healthcare characteristics at zip code level. Methods. We used the PMSI data from 2009 to 2013, which lists all hospitalizations for stroke between 2009 and 2013, identified on the most detailed geographic scale allowed by this database. We identify statistically significant clusters with high or low rates in the zip code level using the Getis-Ord statistics. Each of the significant clusters is monitored over time and evaluated according to the nearest stroke unit distance and the socioeconomic profile. Results. We identified clusters of low and high rate of stroke hospitalization (23.7% of all geographic codes). Most of these clusters are maintained over time (81%) but we also observed clusters in transition. Geographic codes with persistent high rates of stroke hospitalizations were mainly rural (78% versus 17%, P < .0001) and had a least favorable socioeconomic and healthcare profile. Conclusion. Our study reveals that high-stroke hospitalization rates cluster remains the same during our study period. While access to the stroke unit has increased overall, it remains low for these clusters. The socioeconomic and healthcare profile of these clusters are poor but variations were observed. These results are valuable tools to implement more targeted strategies to improve stroke care accessibility and reduce geographic disparities.http://dx.doi.org/10.1155/2018/1897569 |
| spellingShingle | Yacine Lachkhem Étienne Minvielle Stéphane Rican Geographic Variations of Stroke Hospitalization across France: A Diachronic Cluster Analysis Stroke Research and Treatment |
| title | Geographic Variations of Stroke Hospitalization across France: A Diachronic Cluster Analysis |
| title_full | Geographic Variations of Stroke Hospitalization across France: A Diachronic Cluster Analysis |
| title_fullStr | Geographic Variations of Stroke Hospitalization across France: A Diachronic Cluster Analysis |
| title_full_unstemmed | Geographic Variations of Stroke Hospitalization across France: A Diachronic Cluster Analysis |
| title_short | Geographic Variations of Stroke Hospitalization across France: A Diachronic Cluster Analysis |
| title_sort | geographic variations of stroke hospitalization across france a diachronic cluster analysis |
| url | http://dx.doi.org/10.1155/2018/1897569 |
| work_keys_str_mv | AT yacinelachkhem geographicvariationsofstrokehospitalizationacrossfranceadiachronicclusteranalysis AT etienneminvielle geographicvariationsofstrokehospitalizationacrossfranceadiachronicclusteranalysis AT stephanerican geographicvariationsofstrokehospitalizationacrossfranceadiachronicclusteranalysis |