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Google trend analysis of climatic zone based Indian severe seasonal sensitive population

机译：基于气候区的印度严重季节性敏感人群谷歌趋势分析

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BACKGROUND:Our earlier Google Trend (GT) Analytics study reported that the worldwide human population severely subject to four seasonal (sensitive) comorbid lifestyle diseases (SCLD) such as asthma, obesity, hypertension and fibrosis. The human population subject to seasonal variability in these four diseases activity referred as "severe seasonal sensitive population". In India, the estimated burden of these four seasonal diseases is more than 350 million as on the year 2018. It is a growing crisis for India with a projected disease burden of 500 million in the year 2025. This study was aimed to decipher the genuine SCLD seasonal trends in the entire Indian population using GT and validate these trends in Indian climatic zones.METHODS:GT is used to study the temporal trends in web search using weekly Relative Search Volume (RSV) for the period 2004 to 2017. The relative search volume (RSV) of the four-severe seasonal comorbid diseases namely Asthma, Hypertension, Obesity and Fibrosis were collected with and without obesity as the reference. The RSV were collected using the GT selection options as (i) Whole India (ii) Jammu and Kashmir (Cold zone) (iii) Rajasthan (Hot and Dry zone) (iii) West Bengal (Hot and Humid zone) and (iv) Uttar Pradesh state (Composite zone). The time series analysis was carried out to find seasonal patterns, comorbidity, trends and periodicity in the entire India and four of its states (zones).RESULTS:Our analysis of entire India (2004-2017) revealed high significant seasonal patterns and comorbidity in all the four diseases of SCLD. The positive tau values indicated strong positive seasonal trends in the SCLD throughout the period (Table). The auto correlation analysis revealed that these diseases were subjected to 3, 4 and 6?months period seasonal variations. Similar seasonal patterns and trends were also observed in all the four Indian temperature zones. Overall study indicated that SCLD seasonal search patterns and trends are highly conserved in India even in drastic Indian climatic zones.CONCLUSIONS:The clinical outcome arise out of these observations could be of immense significance in handling the major chronic life style diseases asthma, hypertension, obesity and fibrosis. The possible strong comorbid relationship among asthma, hypertension, obesity and fibrosis may be useful to segregate Indian seasonal sensitive population. In disease activity-based chronotherapy, the search interest of segment of the population with access to Internet may be used as an indicator for public health sectors in the early detection of SCLD from a specific country or a region. As this disease population could be highly subject to the adverse effect of seasons in addition to life style and other environmental factors. Our study necessitates that these Indian populations need special attention from the Indian health care sectors.
机译：背景：我们之前的谷歌趋势（GT）分析研究报告称，全球人口严重受到四季季节性（敏感）的患者（SCLD），如哮喘，肥胖，高血压和纤维化。在这四种疾病活动中，人口受到季节性变异性的影响，该疾病活动称为“严重季节性敏感人口”。在印度，这四种季节性疾病的估计负担达到2018年的3.5亿。印度危机增长，预计疾病负担在2025年的预计疾病负担。该研究旨在破译真实的SCLD季节性趋势在整个印度人人口中使用GT并验证印度气候Zones的这些趋势。方法：GT用于研究2004年至2017年期间的每周相对搜索卷（RSV）的网络搜索时间趋势。相对搜索四次严重的季节性疾病的体积（RSV）包括哮喘，高血压，肥胖症和纤维化，并在没有肥胖作为参考。使用GT选择选项收集RSV，如（i）全印度（ii）jammu和克什米尔（寒冷区）（iii）拉贾斯坦（炎热和干燥区）（iii）西孟加拉邦（炎热和潮湿区）和（iv）北方邦州（复合区）。进行时间序列分析，以寻找整个印度和四个国家（地区）的季节性模式，合并症，趋势和周期性。结果：我们对整个印度的分析（2004-2017）揭示了高层季节性模式和合并症SCLD的所有四种疾病。正极价值的阳性价值在整个时期（表）中表明了SCLD的强烈正季节趋势。自动相关分析显示，这些疾病受到3,4和6个月期间的季节性变化。在所有四个印度温度区域也观察到类似的季节性模式和趋势。整体研究表明，即使在激烈的印度气候区，SCLD季节性搜索模式和趋势也在印度高度保守。结论：在这些观察结果中出现的临床结果可能具有巨大意义，在处理主要的慢性生活方式哮喘，高血压，肥胖方面可能具有巨大意义和纤维化。哮喘，高血压，肥胖和纤维化之间可能的强烈同伴关系可能有助于隔离印度季节性敏感人群。在基于疾病活动的计时中，可以使用获得互联网的人口部分的搜索利息作为公共卫生部门，在特定国家或地区的早期检测SCLD中的公共卫生部门的指标。由于这种疾病群体可以高度受到季节的不利影响，除了生活方式和其他环境因素。我们的研究需要这些印度人需要特别关注印度医疗部门。

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