糖尿病队列中社会人口学因素的流行:一项回顾性研究

Madurapperumage A. Erandathi, William Yu Chung Wang, Michael Mayo, I. Shafiu
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摘要

探索一个队列的社会人口因素是揭示社会健康状况重要方面的重要阶段。卫生保健部门利用社会人口性质的探索性分析结果来实现各种目的,如制定卫生保健政策、分配充足的资源、提供必要的药物等等。越来越多的证据表明,了解普遍存在的社会人口因素:年龄、种族、性别,揭示了至关重要的信息。因此,本研究旨在通过分析新西兰糖尿病队列的社会人口学细节来揭示这些知识。糖尿病是由于无法控制适当的血糖水平而发生的一种慢性致命疾病,可引起多种急性和慢性并发症。糖尿病成为怀卡托地区的一种高发疾病。分析糖尿病合并并发症的糖尿病患者队列,说明糖尿病患者中糖尿病并发症的发生率。该研究的数据集是从怀卡托地区卫生委员会收集的。本研究旨在报告数据集概况的初始扫描,并将样本的社会人口学细节的结果模式及其与糖尿病并发症的关联可视化。桑基图用于可视化探索性数据分析的结果。数据筛选的结果图表描述性地说明了与人口因素相关的队列特征。毛利人的糖尿病患者比例(0.68)高于其他族裔,但年龄扩张较小(13-95岁),发病年龄较早(20-103岁)。男性(0.61%)比女性(0.55%)更容易患糖尿病。此外,高血压和心血管疾病在怀卡托地区的糖尿病患者中很常见。毛利人男性易患糖尿病。本研究将有助于构建和分析队列的人口学分类,以比较研究疾病在相应阶层中的普遍性。
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Prevalence of sociodemographic factors in a cohort of diabetes mellitus: a retrospective study
Exploring the sociodemographic factors of a cohort is a vital phase in revealing significant aspects of the societal health status. The health care sector utilises the results of exploratory analysis of the sociodemographic nature to fulfil various purposes such as constructing health care policies, allocating adequate resources, imposing necessary medications and many more. A large and growing body of evidence shows that understanding the pervasiveness of sociodemographic factors: age, ethnicity, gender, reveal crucial information. Therefore, this study aims to disclose the knowledge through analysing the sociodemographic details of a New Zealand diabetes cohort. Diabetes mellitus is a chronic fatal disease that occurs due to the inability to control proper blood sugar levels, which causes multitudinous acute and chronic complications. Diabetes became a high prevalence disease in the region of Waikato. Analysing the cohort of diabetes patients associated with complications of diabetes illustrate the prevalence of complications of diabetes among the patients. The dataset of the study has been collected from the Waikato district health board. This study intends to report the initial scanning of the dataset profile with visualising the resulting patterns of sociodemographic details from the samples and their association with complications of diabetes. The Sankey diagrams use to visualise the results of exploratory data analysis. The resulted graphs of the data screening descriptively illustrate the characteristics of the cohort associated with demographic factors. Maori population shows higher percentage (0.68) of diabetes patients than the other ethnicities, while having narrower age expansion (13-95) with early onset age, compared to others (20-103). Males (0.61%) are more vulnerable to diabetes than females (0.55%). Additionally, hypertension and cardiovascular diseases are common among the diabetes patients’ in the Waikato region. Maori male population is highly vulnerable to diabetes. This study will be beneficial in constructing and analysing the demographical categories of the cohort to comparatively study the pervasiveness of the diseases among resulting classes.
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