Web GIS in practice IX: a demonstration of geospatial visual analytics using Microsoft Live Labs Pivot technology and WHO mortality data

The goal of visual analytics is to facilitate the discourse between the user and the data by providing dynamic displays and versatile visual interaction opportunities with the data that can support analytical reasoning and the exploration of data from multiple user-customisable aspects. This paper introduces geospatial visual analytics, a specialised subtype of visual analytics, and provides pointers to a number of learning resources about the subject, as well as some examples of human health, surveillance, emergency management and epidemiology-related geospatial visual analytics applications and examples of free software tools that readers can experiment with, such as Google Public Data Explorer. The authors also present a practical demonstration of geospatial visual analytics using partial data for 35 countries from a publicly available World Health Organization (WHO) mortality dataset and Microsoft Live Labs Pivot technology, a free, general purpose visual analytics tool that offers a fresh way to visually browse and arrange massive amounts of data and images online and also supports geographic and temporal classifications of datasets featuring geospatial and temporal components. Interested readers can download a Zip archive (included with the manuscript as an additional file) containing all files, modules and library functions used to deploy the WHO mortality data Pivot collection described in this paper

Impact of integrative care on cardiovascular disease risk in newly diagnosed type 2 diabetes mellitus patients: A BI-VitalLife Cohort study.

IntroductionType 2 diabetes mellitus (T2DM), a chronic metabolic disorder, significantly increases cardiovascular disease (CVD) risk. Integrative care (IC) offers a personalized health management approach, utilizing various interventions to mitigate this risk. However, the impact of IC on CVD risk in newly diagnosed T2Dm patients remains unclear. This study aims to assess the differences in CVD risk development within 120 months following a new diagnosis of T2DM, using real-world data from Bumrungrad International Hospital and Vitallife Scientific Wellness Center.MethodsThis study utilized the BI-VitalLife Cohort dataset that contains de-identified demographics, vitals, diagnoses and clinical information, laboratory and radiological data, medications, and treatments of more than 2.8 million patients who visited Bumrungrad International Hospital and/or VitalLife Scientific Wellness Center from June 1, 1999, to May 31, 2022. This study focused on newly diagnosed T2DM patients, defined according to American Diabetes Association criteria. We compared CVD risk between the IC and conventional care (CC) groups using the Kaplan-Meier curve and Cox proportional hazard model, adjusted for age, sex, and laboratory values. Propensity score matching was employed to enhance comparability.ResultsOf the 5,687 patients included, 236 were in the IC group and 5,451 in the CC group. The IC group, characterized by a lower age at T2DM diagnosis, showed favorable hematological and metabolic profiles. The Cox proportional hazard ratios revealed a significantly lower CVD risk in the IC group within 120 months post-T2DM diagnosis compared to the CC group, consistent even after adjusting for confounding factors. Propensity score-matched analysis supported these findings.ConclusionPersonalized integrative care may offer a significant advantage in reducing CVD risk among newly diagnosed T2DM patients compared to conventional care, even when considering various confounding factors. This study sheds light on the potential of integrative care in informing treatment strategies for T2DM patients at risk of developing CVD