Mena Carlos, Sepúlveda Cesar, Fuentes Eduardo, Ormazábal Yony, Palomo Iván
Geomatics Centre, Faculty of Forestry Sciences, University of Talca.
Geospat Health. 2018 May 7;13(1):587. doi: 10.4081/gh.2018.587.
Cardiovascular diseases (CVDs) are the primary cause of death and disability in de world, and the detection of populations at risk as well as localization of vulnerable areas is essential for adequate epidemiological management. Techniques developed for spatial analysis, among them geographical information systems and spatial statistics, such as cluster detection and spatial correlation, are useful for the study of the distribution of the CVDs. These techniques, enabling recognition of events at different geographical levels of study (e.g., rural, deprived neighbourhoods, etc.), make it possible to relate CVDs to factors present in the immediate environment. The systemic literature presented here shows that this group of diseases is clustered with regard to incidence, mortality and hospitalization as well as obesity, smoking, increased glycated haemoglobin levels, hypertension physical activity and age. In addition, acquired variables such as income, residency (rural or urban) and education, contribute to CVD clustering. Both local cluster detection and spatial regression techniques give statistical weight to the findings providing valuable information that can influence response mechanisms in the health services by indicating locations in need of intervention and assignment of available resources.
心血管疾病（CVDs）是全球死亡和残疾的主要原因，识别高危人群以及确定易患区域对于进行充分的流行病学管理至关重要。为空间分析开发的技术，包括地理信息系统和空间统计学，如聚类检测和空间相关性分析，对于研究心血管疾病的分布很有用。这些技术能够在不同地理研究层面（如农村、贫困社区等）识别事件，从而将心血管疾病与周围环境中的因素联系起来。此处呈现的系统性文献表明，这类疾病在发病率、死亡率、住院率以及肥胖、吸烟、糖化血红蛋白水平升高、高血压、体力活动和年龄方面存在聚集现象。此外，诸如收入、居住状况（农村或城市）和教育程度等后天变量也会导致心血管疾病的聚集。局部聚类检测和空间回归技术都为研究结果赋予了统计学权重，提供了有价值的信息，通过指出需要干预的地点和分配可用资源，这些信息能够影响卫生服务中的应对机制。
