A model for national assessment of barriers for implementing digital technology interventions to improve hypertension management in the public health care system in India.
A model for national assessment of barriers for implementing digital technology interventions to improve hypertension management in the public health care system in India.
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DOI：
10.1186/s12913-021-06999-9
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发表时间：
2021-10-15
影响因子：
2.8
通讯作者：
Tandon N
中科院分区：
文献类型：
--
作者：
Patel SA;Vashist K;Jarhyan P;Sharma H;Gupta P;Jindal D;Venkateshmurthy NS;Pfadenhauer L;Mohan S;Tandon N
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非常感兴趣。印度医疗基础设施和更广泛的背景支持此类倡议的潜力尚未得到考察。我们评估了支持数字健康干预的现有医疗基础设施，并检查了覆盖印度29个邦和联邦地区的544个区的医疗基础设施的流行病学、社会经济和地理背景相关性。这项研究是对印度第四次地区级家庭和设施调查(DLHS-4；2012-2014)的横断面分析，这是最新的具有全国代表性的地区级医疗基础设施数据。设施是分析的单位，分析考虑了国家内部的集群。主要结果是医疗保健系统基础设施背景下实施高血压管理计划。领域包括诊断(功能性BP仪器)、药物(抗高血压药物库存)、基本临床工作人员(例如护士、医务人员、药剂师)和IT特定基础设施(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描述性分析，并进行Logistic回归，以估计流行病学和地理环境(暴露)与医疗保健系统综合衡量之间的关联。分析了来自32,215个政府设施的数据。在最低级别的子中心中，30%拥有一些IT基础设施，而在最高级别的地区医院中，92%拥有IT基础设施。在中端初级保健中心和社区保健中心，信息技术基础设施的可用性分别为28%和51%。除分中心外，所有中心基本工作人员的供应情况都低于信息技术基础设施的供应情况。除了地区医院，对于所有的医院来说，较高的血压、体重指数和城市居民水平与更有利的基础设施相关。按地区划分，西印度各区往往拥有准备最充分的卫生设施。与印度的顶尖设施相比，中低端医疗机构更缺乏支持数字健康干预的IT基础设施。人员配备方面的缺口一般大于有形基础设施，这表明除了信息技术基础设施外，基本工作人员短缺对采用数字保健干预措施构成了重大制约。这些数据为州和区级规划提供了早期基准。网上版载有补充材料，可在10.1186/s12913-021-06999-9查阅。
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. 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. 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. 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. The online version contains supplementary material available at 10.1186/s12913-021-06999-9.
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DOI：
10.1186/1748-5908-4-50
发表时间：
2009-08-07
期刊：
Implementation science : IS
影响因子：
--
作者：
Damschroder LJ;Aron DC;Keith RE;Kirsh SR;Alexander JA;Lowery JC
通讯作者：
Lowery JC
影响因子：
8.1
作者：
Elias, Maya Annie;Pati, Manoj Kumar;Srinivas, Prashanth N
通讯作者：
Srinivas, Prashanth N
影响因子：
8.1
作者：
Kujawski, Stephanie A.;Leslie, Hannah H.;Kruk, Margaret E.
通讯作者：
Kruk, Margaret E.
影响因子：
2.3
作者：
Meador, Margaret;Lewis, Joy H.;Jackson, Chanaye
通讯作者：
Jackson, Chanaye
预防和控制高血压：JACC健康促进系列。
影响因子：
24
作者：
Carey RM;Muntner P;Bosworth HB;Whelton PK
通讯作者：
Whelton PK