Individual and Neighborhood Vulnerability to the Spanish Flu

个人和社区对西班牙流感的脆弱性

• 批准号: 10644538
• 负责人: John R Logan
• 金额: $ 23.93万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10644538
• 关键词: 1918 influenza pandemic; Address; Affect; Age; Area; Attention; Birth Place; COVID-19; COVID-19 pandemic; Cause of Death; Censuses; Cessation of life; Characteristics; Cities; Communities; Community Health; Coronavirus; Country; Data; Data Set; Death Certificates; Death Rate; Death Records; Dimensions; Ethnic Origin; Event; Fatality rate; Gender; Genetic Transcription; Glean; Health; Household; Individual; Inequality; Influenza; Knowledge; Link; Location; Measurement; Measures; Names; Neighborhoods; New York; New York City; Occupations; Outcome; Parents; Pattern; Persons; Philadelphia; Pneumonia; Public Health; Race; Records; Research; Risk; Risk Factors; Sampling; Severities; Social Class; Societies; Spanish flu; Standardization; Surveys; Survivors; System; Time; Variant; behavior measurement; comparative; contagion; experience; falls; flexibility; flu; future pandemic; health disparity; innovation; mortality; mortality risk; multilevel analysis; outcome disparities; pandemic disease; predictive modeling; previous pandemic; response; social group; spatial relationship; urban area

PROJECT SUMMARY/ABSTRACT This is a study of mortality risk in the deadliest pandemic in the 20th Century, the Spanish flu of 1918, in two of the U.S. cities with the highest number of fatalities, New York and Philadelphia. It has four main aims. Aim 1 is to examine mortality from the Spanish flu at the neighborhood scale in these cities. Aim 2 is to extend the ecological analysis with multilevel models including individual-level predictors. Aim 3 is to compare the patterns of influenza/pneumonia mortality during the pandemic with mortality in the same period in the previous year, and Aim 4 is to compare patterns between cities. Few studies of pandemics have gone beyond analyzing mortality data at the level of whole cities to consider spatial variation within cities, and there have been none that examined risk of mortality in a pandemic using multilevel models. In this project, drawing on individual death records, attention can be directed to specific neighborhoods and mortality risk of their residents. The study will show how personal characteristics such as age, gender, race, nativity, and social class affect risk of mortality in a multilevel model that includes similar characteristics measured at a neighborhood scale and also incorporates spatial relationships among neighborhoods. These aims will be addressed through analysis of a new data set with several innovative components. 1) Transcription of all death certificates during fall 1917 and fall 1918 in New York and Philadelphia including the person’s name, age, race, gender, address, cause of death, date of death, occupation, and parents’ names and place of birth. 2) Geocoding of addresses in a historically accurate GIS system, so that persons can be linked to their neighborhoods. 3) Use of 100% census microdata from 1920 to create measures of neighborhood characteristics, construct a comparison sample of flu survivors, and glean information about the households of those who died. These data will allow the project to probe the independent effects of various individual risk factors while also examining neighborhood differences and spatial patterns in mortality.

项目总结/摘要 这是一项对20世纪世纪最致命的流行病--1918年西班牙流感--的死亡风险的研究， 死亡人数最多的美国城市是纽约和费城。它有四个主要目标。目标1是 在这些城市的社区范围内调查西班牙流感的死亡率。目标二是扩大 生态分析与多层次模型，包括个人水平的预测。目标3是比较 大流行期间流感/肺炎死亡率模式与前一年同期死亡率 目标4是比较城市之间的模式。很少有关于流行病的研究超越了分析 在整个城市一级的死亡率数据，以考虑城市内的空间变化， 使用多层次模型研究了流行病中的死亡风险。在这个项目中， 死亡记录，注意力可以被引导到特定的社区和其居民的死亡风险。的 研究将显示个人特征，如年龄，性别，种族，出生地和社会阶层如何影响风险， 多水平模型中的死亡率，该模型包括在邻域尺度上测量的相似特征， 包含了邻里之间的空间关系。 这些目标将通过分析一个新的数据集与几个创新的组成部分。第一章 1917年秋季和1918年秋季在纽约和费城的所有死亡证明的誊本， 姓名、年龄、种族、性别、地址、死因、死亡日期、职业和父母姓名 和出生地2)在历史上准确的地理信息系统中对地址进行地理编码，以便人们可以 与他们的社区相连。3)使用1920年以来100%的人口普查微观数据， 社区特征，构建流感幸存者的比较样本，并收集有关 死者的家属。这些数据将使该项目能够探索各种因素的独立影响。 个体风险因素，同时还检查死亡率的邻里差异和空间模式。