America Addresses Two Epidemics Dataset – Cannabis and Coronavirus and their Interactions: Combined Geospatial and Causal Inference Study

Research Hypothesis - That cannabis may predispose to COVID-19 viral infection due to its immunomodulatory, envorinmental contaminants, vaping and smoking inhalation actions. Data was analyzed by geospatial and causal inference techniques in R. Data was gathered from publicly available on line sources including: Data were downloaded from Publicly available datasets including: • US Census bureau 2019 • Five Year American Community Survey 2013-2018 • National Survey of Drug Use and Health (NSDUH) • NSDUH Resticted Use Data Analysis System (RDAS) • US Department of Transport International Flight Data • Worldometer Covid -19 Dataset Data were collected in the six domains of: • COVID numbers • Fights – numbers of flights and numbers of overseas destionations • Median household income • State ethnic composition • Population and population density • Drug use Inverse probability weights were constructed by inverse probability weighting conducted in package ipw in R. Geospatial weights were constructed in package spdep in R.研究假设——大麻可通过其免疫调节作用、环境污染物关联影响以及雾化与吸烟吸入行为，增加新冠病毒感染的易感性。 本研究采用R语言中的地理空间与因果推断技术对数据进行分析。 数据从以下公开在线来源收集得到： 所有数据均下载自以下公开数据集： • 美国人口普查局2019年数据 • 2013-2018年美国社区调查五年期数据 • 全国药物使用与健康调查（National Survey of Drug Use and Health, NSDUH） • NSDUH受限使用数据分析系统（RDAS） • 美国交通部国际航班数据 • Worldometer新冠疫情数据集 数据涵盖以下六大维度： • 新冠疫情相关数据 • 航班数据——航班数量与海外目的地数量 • 家庭收入中位数 • 各州种族构成 • 人口规模与人口密度 • 药物使用情况 通过R语言ipw包实现的逆概率加权方法构建逆概率权重。 通过R语言spdep包构建地理空间权重。