Yewale Sushil, Chaudhary Navendu, Miriam Demi, Bhor Shital, Dange Nimisha, Shah Nikhil, Khadilkar Vaman, Khadilkar Anuradha
Department of Growth and Pediatric Endocrinology, Hirabai Cowasji Jehangir Medical Research Institute, Jehangir Hospital, Pune, Maharashtra, India.
Symbiosis Institute of Geo-Informatics (SIG), Symbiosis International (Deemed) University, Pune, Maharashtra, India.
J Pediatr Endocrinol Metab. 2024 Nov 28;38(1):29-36. doi: 10.1515/jpem-2024-0401. Print 2025 Jan 29.
Geographic Information System (GIS) mapping, is a novel way to provide insights into spatial distribution of type 1 diabetes (T1D) and associations between T1D outcomes and potential predictors. We aimed to explore GIS in children with T1D, and identify predictors of poor glycemic control.
Design: Cross-sectional; Participants: 402 children and youth (187 boys) with T1D. Place of residence (coordinates) of participants were geocoded in GIS. They were divided into two groups living in urban or peri-urban areas using ArcGIS Pro. The characteristics of urban/peri-urban living were linked to sociodemographic and biochemical data and spatial autocorrelation analysis was performed. Association between glycemic control and distance to our unit was studied.
Mean age was 13.2 ± 4.7 years; 196 children were living in urban areas, 206 in peri-urban areas. There was significant difference in HbA between groups (Urban 9.9 (9.7, 10.2) %, Peri-urban 10.5 (10.1, 10.8) %) (p=0.004); mean difference 0.5 (0.1, 1.0) with poorer glycemic control and higher prevalence of vitamin D sufficiency in peri-urban and higher prevalence of hypothyroidism in urban areas. There was significant correlation between glycemic control (HbA) and distance to our unit r=0.108 (0.023, 0.218) (p=0.031). Individuals with an HbA ≥9.5 were residing farther away (58.9 (49.4, 68.5) km) as compared to those with HbA <9.5 (44.5 (35.1, 53.9) km) (p<0.05).
Children with T1D when grouped using GIS had differences in glycemic control and comorbidities; peri-urban participants and those residing further away from our unit had poorer glycemic control. Future efforts may be aimed at identifying centers and channelizing resources towards children showing poor glycemic control, thus optimizing disease management.
地理信息系统（GIS）绘图是一种提供1型糖尿病（T1D）空间分布以及T1D结局与潜在预测因素之间关联见解的新方法。我们旨在探讨GIS在T1D儿童中的应用，并确定血糖控制不佳的预测因素。
设计：横断面研究；参与者：402名T1D儿童和青少年（187名男孩）。参与者的居住地点（坐标）在GIS中进行地理编码。使用ArcGIS Pro将他们分为居住在城市或城郊地区的两组。将城市/城郊生活特征与社会人口统计学和生化数据相关联，并进行空间自相关分析。研究血糖控制与到我们单位距离之间的关联。
平均年龄为13.2±4.7岁；196名儿童居住在城市地区，206名居住在城郊地区。两组之间的糖化血红蛋白（HbA）存在显著差异（城市地区9.9（9.7，10.2）%，城郊地区10.5（10.1，10.8）%）（p = 0.004）；平均差异为0.5（0.1，1.0），城郊地区血糖控制较差且维生素D充足率较高，城市地区甲状腺功能减退患病率较高。血糖控制（HbA）与到我们单位的距离之间存在显著相关性r = 0.108（0.023，0.218）（p = 0.031）。糖化血红蛋白≥9.5%的个体居住得更远（58.9（49.4，68.5）公里），而糖化血红蛋白<9.5%的个体居住距离为44.5（35.1，53.9）公里（p<0.05）。
使用GIS分组时，T1D儿童在血糖控制和合并症方面存在差异；城郊参与者以及居住在离我们单位较远的儿童血糖控制较差。未来的努力可能旨在确定中心，并将资源导向血糖控制不佳的儿童，从而优化疾病管理。
