Abstract
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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