Identifying hotspots of type 2 diabetes risk using general practice data and geospatial analysis: an approach to inform policy and practice

Abstract

The prevalence of type 2 diabetes (T2D) is increasing worldwide and there is a need to identify communities with a high-risk profile and to develop appropriate primary care interventions. This study aimed to predict future T2D risk and identify community-level geographic variations using general practices data. The Australian T2D risk assessment (AUSDRISK) tool was used to calculate the individual T2D risk scores using 55693 clinical records from 16 general practices in west Adelaide, South Australia, Australia. Spatial clusters and potential ‘hotspots’ of T2D risk were examined using Local Moran’s I and the Getis-Ord Gi* techniques. Further, the correlation between T2D risk and the socioeconomic status of communities were mapped. Individual risk scores were categorised into three groups: low risk (34.0% of participants), moderate risk (35.2% of participants) and high risk (30.8% of participants). Spatial analysis showed heterogeneity in T2D risk across communities, with significant clusters in the central part of the study area. These study results suggest that routinely collected data from general practices offer a rich source of data that may be a useful and efficient approach for identifying T2D hotspots across communities. Mapping aggregated T2D risk offers a novel approach to identifying areas of unmet need.

Identifying community chronic kidney disease risk profile utilising general practice clinical records and spatial analysis: approach to inform policy and practice

“…Complete data for smoking status, body mass index (BMI) and systolic blood pressure were available for 88% of patients in the GP data set. As in previous studies, 3,7 this pattern of missing data supports the use of multiple imputation (results available from the authors) under the assumption that data are missing at random. However, the majority of missing values for systolic blood pressure (SBP) and BMI were seen in younger participants (<35 years), who usually have a lower risk of CKD compared with people aged greater than 45 years.…”

Arabic version of the Australian type 2 diabetes risk assessment tool (AUSDRISK): translation and validation

“…and the results indicated that the predicted 5-year risk significantly increases with age. Percentages of those at high risk were 0.29%; 1%; 4.96; 7.86 and 16.73% for age groups (18-34); (35-44); (45-54); (55-64); and (≥ 65) respectively [24]. These findings bring to light the simplicity and practicality of the AUSDRISK screening power.…”

The Development of the Municipal Registry of People with Diabetes in Porto Alegre, Brazil

“…Identifying situations of insufficient or inadequate care and alerting professionals and users through digital solutions will also be possible [43][44][45]. In the medium to long term, it will be possible to pair the diabetes registry data with geospatial and socioeconomic data to identify areas of the city of greater epidemiological relevance for strengthening local health services and actions [46,47]. The information produced makes it possible to immediately characterize the population identified with diabetes and thus provide tools for surveillance and epidemiological monitoring of chronic non-communicable diseases at the local level.…”