Dementia epidemiology, health service utilization and treatment costs among American Indian and Alaska Native Elders

项目总结 随着美国人口老龄化，阿尔茨海默氏症患者的数量和 预计到2050年，相关痴呆症(ADRD)将增加两倍。尽管生命的预期寿命 美国印第安人和阿拉斯加原住民(AI/ANS)比其他美国人矮得多， 在过去的几十年里，随着AI/AN老年人的扩展，这一数字急剧增加 预计人口将继续增长。与大多数美国人相比，人工智能/人工智能携带着 痴呆症的许多已确定的可改变的危险因素造成的不成比例的负担，例如 糖尿病、肥胖症和吸烟。然而，我们对这一人群中痴呆症的了解 仍然受到严格的限制。到目前为止，只有少数研究对痴呆症进行了研究。 在人工智能/ANS中，具有关于该人群痴呆症患病率或发病率的可靠数据 是不存在的。鉴于我们对人工智能/人工智能中的痴呆症的了解存在如此多的差距，a 迫切需要系统的研究来确定痴呆症在这方面的问题有多严重 人口及其对印度卫生系统的影响，以实现更精确的规划和 确定支持相关卫生保健政策所需资源的优先顺序。在这里，利用16 印度卫生服务(IHS)数据项目中可获得的多年纵向数据 近30%的IHS服务人口(约640,000人)的现有IHS电子健康数据 AI/ANS)，我们建议解决AI/ANS之间关于痴呆症的四个主要知识差距 以下列具体目标为指导：(1)估计痴呆症的患病率、发病率和 按个人和社区特征划分的AI/AN老年人的共患率，例如 年龄、性别、地点和县级社会经济地位；(2)该协会的特征 痴呆症和潜在的可改变的危险因素之间的关系，如糖尿病、肥胖、烟草 使用、酗酒、创伤性脑损伤和抑郁；(3)评估 包括住院患者在内的卫生服务利用方式中的痴呆和共病状况 门诊和药房服务；以及(4)检查提供治疗的成本 患有痴呆症的AI/ANS，以及影响这些费用的因素，如共病状况。我们的 拟议的分析将为流行病学提供相对准确和完整的图景 基于大量不同地理位置的样本，在人工智能/人工智能中诊断为痴呆症的比例，以及 有助于了解痴呆症和相关的卫生服务利用情况 生活在弱势社区的极度缺乏服务的人口。我们的发现承诺 为未来关于痴呆症的研究奠定基础，以满足人工智能/ANS和 其他类似的服务不足的人群。

PROJECT SUMMARY As the US population ages, the number of people living with Alzheimer’s Disease and related dementias (ADRD) is projected to triple by 2050. Although the life expectancy of American Indian and Alaska Natives (AI/ANs) is much shorter than that of other US populations, it has dramatically increased in the last few decades, with the expansion of the AI/AN elder population expected to continue. Compared to most US populations, AI/ANs bear a disproportionate burden of many established modifiable risk factors for dementia, such as diabetes, obesity, and smoking. However, our knowledge about dementia in this population remains to be severely limited. So far, only a handful of studies have examined dementia among AI/ANs, with reliable data on the prevalence or incidence of dementia in this population being nonexistent. Given so many gaps in our knowledge about dementia among AI/ANs, a systematic investigation is urgently needed to identify how problematic dementia is in this population and its impact on the Indian health system to enable more precise planning and prioritization of resources needed to support relevant health care policy. Here, leveraging on 16 years of longitudinal data available in the Indian Health Service (IHS) Data Project, a synthesis of existing IHS electronic health data for nearly 30% of the IHS service population (~ 640,000 AI/ANs), we propose to address 4 major knowledge gaps about dementia among AI/ANs as guided by the following specific aims: (1) Estimate dementia prevalence, incidence, and comorbidity rates among AI/AN elders by individual and community characteristics, such as age, gender, location, and county level socioeconomic status; (2) Characterize the association between dementia and potentially modifiable risk factors, such as diabetes, obesity, tobacco use, binge drinking, traumatic brain injuries and depression; (3) Evaluate the influence of dementia and comorbidity status on health service utilization patterns, including inpatient, outpatient, and pharmacy services; and (4) Examine the costs of providing treatment among AI/ANs with dementia, and factors that influence these costs such as comorbidity status. Our proposed analyses will provide a relatively accurate and complete picture for the epidemiology of diagnosed dementia among AI/ANs based on a large and geographically diverse sample, and contribute to the understanding of dementia and associated health service utilization in an extremely underserved population living in disadvantaged communities. Our findings promise to lay groundwork for future studies on dementia to address both the needs of AI/ANs and those of other similarly underserved populations.