Unknown

Dataset Information

0

GeographicalDifference, Rural-urban Transition and Trend in Stroke Prevalence in China: Findings from a National Epidemiological Survey of Stroke in China.


ABSTRACT: Accurate and up-to-date provincial and regional-level stroke prevalence estimates are important for research planning and targeted strategies for stroke prevention and management. However, recent and comprehensive evaluation is lacking over the past 30 years in China. This study aimed to examine the geographical variations in stroke prevalence based on data from the National Epidemiological Survey of Stroke in China (NESS-China) and demonstrate urban-rural transition and trend over three decades. The stroke prevalence (prevalence day, August 31, 2013) was estimated using the world standard population. The stroke prevalence was 873.4 per 100,000 population, and varied from 218.0 in Sichuan to 1768.9 in Heilongjiang. Stroke prevalence exhibited a noticeable north-south gradient (1097.1, 917.7, and 619.4 in the north, middle, and the south, respectively; P?

SUBMITTER: Ru X 

PROVIDER: S-EPMC6874659 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

altmetric image

Publications

GeographicalDifference, Rural-urban Transition and Trend in Stroke Prevalence in China: Findings from a National Epidemiological Survey of Stroke in China.

Ru Xiaojuan X   Wang Wenzhi W   Sun Haixin H   Sun Dongling D   Fu Jie J   Ge Siqi S   Wang Limin L   Wang Linhong L   Jiang Bin B  

Scientific reports 20191122 1


Accurate and up-to-date provincial and regional-level stroke prevalence estimates are important for research planning and targeted strategies for stroke prevention and management. However, recent and comprehensive evaluation is lacking over the past 30 years in China. This study aimed to examine the geographical variations in stroke prevalence based on data from the National Epidemiological Survey of Stroke in China (NESS-China) and demonstrate urban-rural transition and trend over three decades  ...[more]

Similar Datasets

| S-EPMC9409540 | biostudies-literature
| PRJEB59322 | ENA
2015-10-26 | GSE53522 | GEO
| S-EPMC6615079 | biostudies-literature
2015-10-26 | E-GEOD-53522 | biostudies-arrayexpress
| S-EPMC9252928 | biostudies-literature
| S-EPMC6383236 | biostudies-literature
| S-EPMC7223184 | biostudies-literature
| S-EPMC7670952 | biostudies-literature
2017-01-07 | GSE87825 | GEO