Addressing the Midlife Mortality Crisis: Place-Based Modeling, Trend Analysis and Policy Interventions

项目摘要 该项目将通过分析当地的相关因素和导致中年死亡的危险因素来研究中年死亡率的趋势。 1990年至2021年期间，他们在基于地点的背景下死亡。使用特定原因死亡数据，社会- 图形特征和经济趋势，研究将产生中年死亡率风险的轨迹， 动态衍生的空间单位。使这个项目具有创新性和重要性的是（1）死亡率数据， 块，邮政编码或道水平;（2）空间分析，时间序列轨迹确定，和穆尔- （3）采用创新和综合的政策研究方法，由一个 在人口分析、多尺度空间和时间统计以及农村卫生政策方面经验丰富的团队 公式化。这项工作是对关于潜在可修改属性的研究和政策讨论的回应 与中年人死亡率上升有关的地方。几种特定原因的死亡率趋势 注意：自杀率上升，心脏代谢疾病，以及由于药物或酒精滥用，沿着 它们与选定的传染病的相互作用（例如，丙型肝炎和COVID-19）。该项目有五个 具体目标：（1）对于有街区或街区水平死亡率数据的美国各州，研究人员将 创建一个新的微型数据库，其中包含特定原因的死亡率记录和上下文数据，包括cen， 基于统计系统的人口统计数据、就业和商业数据、财产特征和犯罪数据。（二） 该研究利用迭代算法来识别具有共同原因的微单元集群， 具体的死亡率模式，使用社区检测方法，以确定最突出的水平， 影响中年死亡率的过程发生的空间单位。它还评估了空间自相关， 确定死亡率热点和冷点（空间），并进行极端事件检测，以确定热点时刻 （时间）。(3)该项目使用多通道排序对地理单元进行分类，例如， 人口增长或减少，老龄化，或儿童增加，或经济创造了 财富这些轨迹构成了一套基于地点的分类，供以后分析。(4)该项目的 分析将使用时空模型，以估计影响的地方到区域的决定因素的原因， 中年人的社区死亡率，有两个策略：（A）评估不同的基于地点的决定因素的作用， 为了评估中年死亡率差异的理论，并检验具体的健康状况， 假设（例如，经济困境）。(B)确定最佳规模的决定因素与最大的im- 《契约》旨在为针对中年死亡率的具体死亡原因提出政策建议提供信息。 (5)设计政策干预措施对该项目至关重要，该项目首先是政策规划过程， 并在多尺度分析结果的基础上设计针对决定因素的干预策略 死亡率差异和持续死亡率挑战风险最大的地方的特点。

PROJECT SUMMARY This project will study midlife mortality trends by analyzing the local correlates and risk factors of cause of death in their place-based context between 1990 and 2021. Using cause-specific death data, sociodemo- graphic characteristics, and economic trends, the research will generate trajectories of midlife mortality risks for dynamically-derived spatial units. What makes this project innovative and significant is (1) mortality data at the block, zip code or tract level; (2) methods in spatial analysis, time series trajectory determination, and mul- tiscalar statistical analysis; and (3) an innovative and integrated approach to policy research, conducted by a team experienced in demographic analysis, multiscalar spatial and temporal statistics, and rural health policy formulation. This work responds to research and policy discussions regarding potentially-modifiable attributes of places related to rising mortality among middle-aged adults. Several cause-specific mortality trends warrant attention: rising rates of suicides, cardiometabolic diseases, and deaths due to drug or alcohol abuse, along with their interactions with select infectious diseases (e.g., Hepatitis C and COVID-19). The project has five specific aims: (1) For select U.S. states where block- or tract-level mortality data are available, researchers will create a novel micro-scale database with cause-specific mortality records and contextual data including cen- sus-based demographic data, employment and business data, property characteristics, and crime data. (2) The research makes use of an iterative algorithm to identify clusters of micro-units that have common, cause- specific mortality patterns, using community detection methods, in order to identify the most salient levels of spatial units at which processes affecting midlife mortality occur. It also assesses spatial autocorrelation to identify mortality hot- and cold-spots (spatial) and undertakes extreme event detection to identify hot-moments (temporal). (3) The project uses multi-channel sequencing to classify geographic units, for example, where population has grown or diminished, aged, or seen an increase in children, or where the economy has created wealth. These trajectories constitute a set of place-based classifications for later analysis. (4) The project’s analysis will use spatiotemporal models to estimate the effect of local-to-regional determinants of cause-spe- cific mortality in middle-aged adults, with two strategies: (A) Assess the role of different place-based determi- nants of midlife mortality, in order to evaluate theories of midlife mortality differentials and to test specific hy- potheses (e.g., economic distress). (B) Identify the optimal scale at which determinants with the greatest im- pact operate to inform policy recommendations designed to target midlife mortality for specific causes of death. (5) Designing policy interventions is fundamental to the project, which begins with a policy mapping process, and builds on the results of the multiscalar analysis to design intervention strategies targeting the determinants of mortality differentials and the characteristics of places at greatest risk of continued mortality challenges.