Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis.
Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis.
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DOI：
10.1136/bmjopen-2021-056292
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发表时间：
2022-11-17
期刊：
影响因子：
2.9
通讯作者：
Baladandayuthapani, Veerabhadran
中科院分区：
文献类型：
--
作者：
Bhattacharyya, Rupam;Burman, Anik;Singh, Kalpana;Banerjee, Sayantan;Maity, Subha;Auddy, Arnab;Rout, Sarit Kumar;Lahoti, Supriya;Panda, Rajmohan;Baladandayuthapani, Veerabhadran
COVID-19对各国的影响不同，卫生基础设施和其他相关脆弱性指标在确定其传播程度方面发挥着作用。一个地理区域对COVID-19的脆弱性一直是一个感兴趣的话题，特别是在印度等低收入和中等收入国家，以评估其对发病率、流行率或死亡率的多因素影响。本研究旨在构建一个统计分析管道来计算这些脆弱性指数，并调查它们与流行病增长指标的关联。利用印度国家调查中公开报告的社会经济、人口、健康和流行病学观察数据，我们计算了不同地理和空间行政区域的多个主题决议的背景COVID-19脆弱性指数（cVIs）。然后将这些cVIs用于贝叶斯回归模型，以评估其对COVID-19传播指标的影响。本研究使用了印度奥里萨邦的地区级指标和病例计数数据。我们使用瞬时R（估计的COVID-19时变繁殖数的时间平均值）作为模型中的主要结果变量。我们的观察性研究以奥里萨邦的30个地区为重点，将住房和卫生条件、COVID-19防范和流行病学因素确定为与COVID-19脆弱性相关的重要指标。在第一波成功地将COVID-19控制在合理水平之后，第二波COVID-19进一步侵入奥里萨邦的腹地和外围地区，给这些地区本已不足的公共卫生系统增加了负担。更好地了解导致COVID-19脆弱性的因素将有助于政策制定者优先考虑资源和区域，从而为当前和未来制定更有效的缓解战略。
COVID-19 has differentially affected countries, with health infrastructure and other related vulnerability indicators playing a role in determining the extent of its spread. Vulnerability of a geographical region to COVID-19 has been a topic of interest, particularly in low-income and middle-income countries like India to assess its multifactorial impact on incidence, prevalence or mortality. This study aims to construct a statistical analysis pipeline to compute such vulnerability indices and investigate their association with metrics of the pandemic growth. Using publicly reported observational socioeconomic, demographic, health-based and epidemiological data from Indian national surveys, we compute contextual COVID-19 Vulnerability Indices (cVIs) across multiple thematic resolutions for different geographical and spatial administrative regions. These cVIs are then used in Bayesian regression models to assess their impact on indicators of the spread of COVID-19. This study uses district-level indicators and case counts data for the state of Odisha, India. We use instantaneous R (temporal average of estimated time-varying reproduction number for COVID-19) as the primary outcome variable in our models. Our observational study, focussing on 30 districts of Odisha, identified housing and hygiene conditions, COVID-19 preparedness and epidemiological factors as important indicators associated with COVID-19 vulnerability. Having succeeded in containing COVID-19 to a reasonable level during the first wave, the second wave of COVID-19 made greater inroads into the hinterlands and peripheral districts of Odisha, burdening the already deficient public health system in these areas, as identified by the cVIs. Improved understanding of the factors driving COVID-19 vulnerability will help policy makers prioritise resources and regions, leading to more effective mitigation strategies for the present and future.
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