Department of Geography, University of Connecticut, Storrs, Connecticut, USA.
Int J Health Geogr. 2009 Sep 24;8:52. doi: 10.1186/1476-072X-8-52.
Disparities in health outcomes across communities are a central concern in public health and epidemiology. Health disparities research often links differences in health outcomes to other social factors like income. Choropleth maps of health outcome rates show the geographical distribution of health outcomes. This paper illustrates the use of cumulative frequency map legends for visualizing how the health events are distributed in relation to social characteristics of community populations. The approach uses two graphs in the cumulative frequency legend to highlight the difference between the raw count of the health events and the raw count of the social characteristic like low income in the geographical areas of the map. The approach is applied to mapping publicly available data on low birth weight by town in Connecticut and Lyme disease incidence by town in Connecticut in relation to income. The steps involved in creating these legends are described in detail so that health analysts can adopt this approach.
The different health problems, low birth weight and Lyme disease, have different cumulative frequency signatures. Graphing poverty population on the cumulative frequency legends revealed that the poverty population is distributed differently with respect to the two different health problems mapped here.
Cumulative frequency legends can be useful supplements for choropleth maps. These legends can be constructed using readily available software. They contain all of the information found in standard choropleth map legends, and they can be used with any choropleth map classification scheme. Cumulative frequency legends effectively communicate the proportion of areas, the proportion of health events, and/or the proportion of the denominator population in which the health events occurred that falls within each class interval. They illuminate the context of disease through graphing associations with other variables.
社区间健康结果的差异是公共卫生和流行病学的核心关注点。健康差异研究通常将健康结果的差异与收入等其他社会因素联系起来。健康结果率的专题地图显示了健康结果的地理分布。本文说明了使用累积频率地图图例来直观地展示健康事件与社区人口社会特征之间的分布关系。该方法在累积频率图例中使用两个图形来突出显示健康事件的原始计数与地理区域中社会特征(如低收入)的原始计数之间的差异。该方法应用于绘制康涅狄格州城镇的低出生体重和康涅狄格州城镇的莱姆病发病率与收入的公开数据。详细描述了创建这些图例的步骤,以便健康分析师可以采用这种方法。
不同的健康问题,低出生体重和莱姆病,具有不同的累积频率特征。在累积频率图例上绘制贫困人口揭示了贫困人口在这两个不同的健康问题映射方面的分布方式不同。
累积频率图例可以作为专题地图的有用补充。这些图例可以使用现成的软件构建。它们包含标准专题地图图例中发现的所有信息,并且可以与任何专题地图分类方案一起使用。累积频率图例通过与其他变量的关联来有效地传达落在每个类别间隔内的区域比例、健康事件比例和/或健康事件发生的分母人口比例。它们通过绘制与其他变量的关联来阐明疾病的背景。