Suppr超能文献

美国东南部两个医疗系统在肺癌筛查利用方面的差异。

Disparities in lung cancer screening utilization at two health systems in the Southeastern USA.

作者信息

Niranjan Soumya J, Rivers Desiree, Ramachandran Rekha, Murrell JEdward, Curry Kayleigh C, Mubasher Mohammed, Flenaugh Eric, Dransfield Mark T, Bae Sejong, Scarinci Isabel C

机构信息

Department of Health Services Administration, Birmingham, USA.

Morehouse School of Medicine, Atlanta, USA.

出版信息

Cancer Causes Control. 2025 Feb;36(2):135-145. doi: 10.1007/s10552-024-01929-6. Epub 2024 Oct 15.

Abstract

PURPOSE

Low-dose computed tomography lung cancer screening is effective for reducing lung cancer mortality. It is critical to understand the lung cancer screening practices for screen-eligible individuals living in Alabama and Georgia where lung cancer is the leading cause of cancer death. High lung cancer incidence and mortality rates are attributed to high smoking rates among underserved, low income, and rural populations. Therefore, the purpose of this study is to define sociodemographic and clinical characteristics of patients who were screened for lung cancer at an Academic Medical Center (AMC) in Alabama and a Safety Net Hospital (SNH) in Georgia.

METHODS

A retrospective cohort study of screen-eligible patients was constructed using electronic health records between 2015 and 2020 seen at an Academic Medical Center (AMC) and a Safety Net Hospital (SNH) separately. Chi-square tests and Student t tests were used to compare screening uptake across patient demographic and clinical variables. Bivariate and multivariate logistic regressions determined significant predictors of lung cancer screening uptake.

RESULTS

At the AMC, 67,355 were identified as eligible for LCS and 1,129 were screened. In bivariate analyses, there were several differences between those who were screened and those who were not screened. Screening status in the site at Alabama-those with active tobacco use are significantly more likely to be screened than former smokers (OR: 3.208, p < 0.01). For every 10-unit increase in distance, the odds of screening decreased by about 15% (OR: 0.848, p < 0.01). For every 10-year increase in age, the odds of screening decrease by about 30% (OR: 0.704, p < 0.01). Each additional comorbidity increases the odds of screening by about 7.5% (OR: 1.075, p < 0.01). Those with both private and public insurance have much higher odds of screening compared to those with only private insurance (OR: 5.403, p < 0.01). However, those with only public insurance have lower odds of screening compared to those with private insurance (OR: 0.393, p < 0.01). At the SNH-each additional comorbidity increased the odds of screening by about 11.9% (OR: 1.119, p = 0.01). Notably, those with public insurance have significantly higher odds of being screened compared to those with private insurance (OR: 2.566, p < 0.01).

CONCLUSION

The study provides evidence that LCS has not reached all subgroups and that additional targeted efforts are needed to increase lung cancer screening uptake. Furthermore, disparity was noticed between adults living closer to screening institutions and those who lived farther.

摘要

目的

低剂量计算机断层扫描肺癌筛查对于降低肺癌死亡率有效。了解居住在阿拉巴马州和佐治亚州符合筛查条件的个体的肺癌筛查情况至关重要,在这两个州肺癌是癌症死亡的主要原因。肺癌的高发病率和死亡率归因于服务不足、低收入和农村人口中的高吸烟率。因此,本研究的目的是确定在阿拉巴马州的一家学术医疗中心(AMC)和佐治亚州的一家安全网医院(SNH)接受肺癌筛查的患者的社会人口统计学和临床特征。

方法

分别使用2015年至2020年期间在一家学术医疗中心(AMC)和一家安全网医院(SNH)的电子健康记录,构建了一项针对符合筛查条件患者的回顾性队列研究。使用卡方检验和学生t检验比较不同患者人口统计学和临床变量的筛查接受情况。二元和多元逻辑回归确定了肺癌筛查接受情况的显著预测因素。

结果

在AMC,有67355人被确定符合低剂量计算机断层扫描肺癌筛查条件,其中1129人接受了筛查。在二元分析中,接受筛查者和未接受筛查者之间存在一些差异。在阿拉巴马州的医疗机构——当前吸烟者接受筛查的可能性显著高于既往吸烟者(比值比:3.208,p < 0.01)。距离每增加10个单位,筛查的几率降低约15%(比值比:0.848,p < 0.01)。年龄每增加10岁,筛查的几率降低约30%(比值比:0.704,p < 0.01)。每增加一种合并症,筛查的几率增加约7.5%(比值比:1.075,p < 0.01)。同时拥有私人保险和公共保险的人接受筛查的几率比仅拥有私人保险的人高得多(比值比:5.403,p < 0.01)。然而,仅拥有公共保险的人接受筛查的几率比拥有私人保险的人低(比值比:0.393,p < 0.01)。在SNH,每增加一种合并症,筛查的几率增加约11.9%(比值比:1.119,p = 0.01)。值得注意的是,拥有公共保险的人接受筛查的几率比拥有私人保险的人显著更高(比值比:2.566,p < 0.01)。

结论

该研究提供了证据表明低剂量计算机断层扫描肺癌筛查尚未覆盖所有亚组,需要额外的针对性努力来提高肺癌筛查的接受率。此外,注意到居住在距离筛查机构较近的成年人与居住较远的成年人之间存在差异。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验