背景:在印度等低收入和中等收入国家,人们对利用数字健康技术支持高血压管理有很大的兴趣。尚未研究医疗保健基础设施的潜力和更广泛的背景来支持印度的此类举措。我们评估了现有的医疗保健基础设施,以支持数字健康干预措施,并检查了流行病学,社会经济,以及涵盖印度29个州和联邦领土的544个地区的医疗基础设施的地理背景相关。
方法:该研究是对印度第四区级家庭和设施调查(DLHS-4;2012-2014)的横断面分析,最新的具有全国代表性的地区级医疗保健基础设施数据。设施是分析的单位,和分析说明了州内的聚类。主要结果是实施高血压管理计划的医疗保健系统基础设施。域包括诊断(功能BP仪器),药物(抗高血压药物库存),必要的临床工作人员(例如,工作人员护士,医务人员,药剂师),和特定于IT的基础设施(常规电源,互联网连接,计算机可用性)。根据印度公共卫生标准对基础设施指标进行了描述性分析,我们进行了logistic回归,以评估流行病学和地理环境(暴露)与医疗系统综合测量之间的关联.
结果:分析了来自32,215个政府机构的数据。在最低层的子中心中,30%有一些IT基础设施,在最高级别的地区医院,92%拥有IT基础设施。在中级初级保健中心和社区保健中心,IT基础架构的可用性分别为28%和51%,分别。除了分中心,基本员工的可用性低于IT基础架构的可用性。除了地区医院,更高的血压水平,身体质量指数,城市居民与更有利的基础设施相关。按地区,印度西部的地区倾向于拥有最好的医疗设施。
结论:与印度的顶级医疗设施相比,中低端医疗设施更缺乏支持数字健康干预措施的IT基础设施。人员配备的差距通常大于有形基础设施,这表明,在IT基础设施之外,基本工作人员的短缺严重制约了数字卫生干预措施的采用。这些数据为州和地区一级的规划提供了早期基准。
BACKGROUND: There is substantial interest in leveraging digital health technology to support hypertension management in low- and middle-income countries such as India. The potential for healthcare infrastructure and broader context to support such initiatives in India has not been examined. We evaluated existing healthcare infrastructure to support digital health interventions and examined epidemiologic, socioeconomic, and geographical contextual correlates of healthcare infrastructure in 544 districts covering 29 states and union territories across India.
METHODS: The study was a cross-sectional analysis of India\'s Fourth District Level Household and Facility Survey (DLHS-4; 2012-2014), the most up-to-date nationally representative district-level healthcare infrastructure data. Facilities were the unit of analysis, and analyses accounted for clustering within states. The main outcome was healthcare system infrastructural context to implement hypertension management programs. Domains included diagnostics (functional BP instrument), medications (anti-hypertensive medication in stock), essential clinical staff (e.g., staff nurse, medical officer, pharmacist), and IT specific infrastructure (regular power supply, internet connection, computer availability). Descriptive analysis was conducted for infrastructure indicators based on the Indian Public Health Standards, and logistic regression was conducted to estimate the association between epidemiologic and geographical context (exposures) and the composite measure of healthcare system.
RESULTS: Data from 32,215 government facilities were analyzed. Among lowest-tier subcenters, 30% had some IT infrastructure, while at the highest-tier district hospitals, 92% possessed IT infrastructure. At mid-tier primary health centres and community health centres, IT infrastructure availability was 28 and 51%, respectively. For all but sub-centres, the availability of essential staff was lower than the availability of IT infrastructure. For all but district hospitals, higher levels of blood pressure, body mass index, and urban residents were correlated with more favorable infrastructure. By region, districts in Western India tended towards having the best prepared health facilities.
CONCLUSIONS: IT infrastructure to support digital health interventions is more frequently lacking at lower and mid-tier healthcare facilities compared with apex facilities in India. Gaps were generally larger for staffing than physical infrastructure, suggesting that beyond IT infrastructure, shortages in essential staff impose significant constraints to the adoption of digital health interventions. These data provide early benchmarks for state- and district-level planning.