New evidence supporting lung cancer screening with low dose CT & surgical implications

IntroductionLung cancer is the leading cause of cancer-death worldwide. The U.S. Preventative Services Task Force (USPTSF) approved screening for current or former smokers aged 55–80 based on the results of the National Lung Screening trial (NLST). Following the NLST, new evidence has emerged from clinical trials and updates to previous trials prior to the anticipated update to the USPSTF guideline. We review the new evidence on lung cancer screening with low dose computed tomography (LDCT) and the surgical implications.MethodsA review of new literature was performed pertaining to lung cancer screening since implementation of UPSTF guidelines. Articles for inclusion were identified by both authors’, then search of the Pubmed and Cochrane database was performed from January 1st, 2013 through February 4th, 2020 using the MeSH search terms: “lung cancer”; “screening”; “low dose CT”. The results of these studies are summarized.ResultsWe identified multiple prospective randomized control trials and meta-analysis since the NLST supporting lung cancer-specific mortality with screening. We identified new nodule classification systems and the development of risk-models which may reduce false positive rates and identify high risk patients not currently eligible for screening. Finally, we discussed the surgical implications of screening.ConclusionNew data supports NLST findings and show ongoing benefit to LDCT for lung cancer screening. Standardized LDCT screening classification has been shown to reduce harm and lower false positive rates. Further study is needed regarding use of risk-modeling. Screening will require an increase in the thoracic workforce to accommodate the amount of surgically operable cancers.     

Using a monotone single-index model to stabilize the propensity score in missing data problems and causal inference

The augmented inverse weighting method is one of the most popular methods for estimating the mean of the response in causal inference and missing data problems. An important component of this method is the propensity score. Popular parametric models for the propensity score include the logistic, probit, and complementary log-log models. A common feature of these models is that the propensity score is a monotonic function of a linear combination of the explanatory variables. To avoid the need to choose a model, we model the propensity score via a semiparametric single-index model, in which the score is an unknown monotonic nondecreasing function of the given single index. Under this new model, the augmented inverse weighting estimator (AIWE) of the mean of the response is asymptotically linear, semiparametrically efficient, and more robust than existing estimators. Moreover, we have made a surprising observation. The inverse probability weighting and AIWEs based on a correctly specified parametric model may have worse performance than their counterparts based on a nonparametric model. A heuristic explanation of this phenomenon is provided. A real-data example is used to illustrate the proposed methods.     

Undocumented Motherhood: Gender,Maternal Identity,and the Politics of Health Care

ABSTRACT

Undocumented Mexican immigrants have had to regularly confront a prohibiting health care system despite alienation, marginalization, and the threat of deportation. In this article, I explore the impact of political exclusion and alienating discourses on the health habitus of undocumented Mexican mothers through the narrative of one mother, Marta Garza, who finds herself at the painful intersection of political and medical alienation. Marta’s narrative reflects an analytical framework that centers undocumented motherhood as a space of necessary resilience and strain, wherein she is forced to advocate for her children’s health despite prohibitive barriers and dangerous potential consequences.     

Efecto de la estrategia de promoción de salud escolar Forma Joven

Objective

To evaluate the impact of the Youth Form Strategy (EFJ, Estrategia Forma Joven) on the attitudes and behaviours of students in the fourth year of compulsory secondary school in Seville, Spain.

Alcance de la implementación en Cataluña de las estrategias de salud reproductiva (2008-2017)

ObjectiveWe analyse how reproductive health strategies have been incorporated into the everyday activities of the services and the resulting transformation of professional and user practices.MethodCartographic research taking a multi-sited ethnographic approach that seeks to reveal the processes of transformation. Data generation techniques featuring participant observation and situated interviews. Discourse analysis of the text corpus using three analytical axes based on three main lines of action promoted by the strategies.ResultsWe identified transformations in: 1) demedicalisation: an increase in midwives’ know-how and autonomy, changes in episiotomy practice and the facilitation of bonding practices; 2) warmth of care: incorporation of women's needs and expectations and improvements in the comfortableness of birth settings, especially in assistance at physiological birth; and 3) participation: actions that foster shared decision-making and the involvement of the persons accompanying women in labour.ConclusionsAbove all, transformation is visible in the incorporation of new attitudes, sensibilities and practices that have developed around the old structures, especially during physiological childbirth. The more technological areas have been less permeable to change. Risk management in decision-making and addressing diversity are identified as areas where transformation is less evident.     

基于中医体质辨识的糖尿病风险评估模型建立与验证

背景　中医体质因素在糖尿病的发生发展过程中具有重要作用，但目前糖尿病的预测预警模型仅涵盖一般人口学资料、客观检查指标、生活方式等内容。在糖尿病风险评估模型中纳入中医体质辨识内容，对有针对性地防治糖尿病的发生发展具有重要意义。目的　根据健康体检数据建立基于中医体质辨识的糖尿病风险模型并对其进行验证。方法　于2016年1月-2018年12月，以2014-2015年某省级综合性医院健康管理中心体检数据为训练集数据（n=30 951），对是否患糖尿病进行单因素和多因素Logistic回归分析，纳入有意义的影响因素指标建立糖尿病风险评估模型；以2016-2017年的健康体检数据作为测试集数据（n=24 061），采用受试者工作特征曲线（ROC）对模型进行验证。结果　训练集人群中，患有糖尿病者1 315例（4.25%），未患糖尿病者29 636例（95.75%）。多因素Logistic回归分析结果显示，logit（P）（糖尿病患病情况）=-4.632-0.198×（女）+0.864×（年龄45~59岁）+1.684×（年龄≥60岁）+0.635×（高血压）+0.149×（超重）+0.376×（肥胖）-0.531×（偏轻）-0.234×（淋巴细胞百分数偏高）+0.279×（淋巴细胞百分数偏低）+0.304×（红细胞计数异常）-0.430×（红细胞比容偏低）+0.722×（平均红细胞血红蛋白浓度异常）+0.532×（血小板分布宽度异常）+1.016×（癌胚抗原异常）-0.406×（尿酸异常）+1.341×（肌酐偏低）+0.488×（血尿素氮偏高）+0.473×（三酰甘油异常）+0.257×（总胆固醇偏高）+0.544×（高密度脂蛋白偏低）+0.290×（总蛋白异常）+0.395×（丙氨酸氨基转移酶异常）+0.362×（谷氨酰转肽酶异常）+0.993×（阴虚质）+1.016×（气虚质）+0.601×（痰湿质）。模型验证结果显示，训练集ROC曲线下面积（AUC）为0.792，95%CI为0.779~0.816（P<0.05），最佳截断值为0.405，灵敏度为0.771，特异度为0.690；测试集验证准确率达到95.69%，Kappa=0.636（P<0.001）。结论　初步构建了糖尿病风险评估模型，且此模型具有较高诊断效应。中医体质辨识作为重要的影响因素纳入糖尿病发病风险评估的模型中来，可以提高其预测能力，为糖尿病的中医药早期防治提供一定的依据。     

胰腺囊性肿瘤恶性风险预测模型研究

目的 明确与恶性胰腺囊性肿瘤（PCN）相关的术前危险因素，建立准确的预测模型，并予以验证。方法 纳入2013年1月至2020年5月复旦大学附属华东医院经术后病理检查证实的114例PCN病例，分为模型组（n=80）和验证组（n=34）。回顾性分析模型组术前的临床资料并探索与恶性PCN相关的影响因素，建立PCN恶性风险预测模型，绘制受试者工作特征（ROC）曲线和校正曲线评价模型，最后基于验证组数据对模型进行临床验证。结果 单因素回归分析提示临床症状、CA19-9水平升高、中性粒细胞淋巴细胞比值（NLR）、淋巴细胞单核细胞比值（LMR）、肿瘤最大直径、胰管扩张和实性成分与恶性PCN显著相关，进一步行多因素回归分析确定了NLR≥2.146、CA19-9水平升高、胰管扩张是恶性PCN的独立预测因素。基于多因素回归分析结果建立恶性PCN预测模型，绘制模型的ROC曲线，计算AUC为0.921（95%CI 0.863~0.979），Youden指数最大时取得最佳临界预测值为0.203，此时相对应的特异度为83.3%，敏感度为92.9%，准确率为85%。同时校正曲线显示模型具有较好的拟合度，最后代入验证组数据显示模型预测准确率为82.4%，特异度81.2%，敏感度100%。结论 CA19-9水平升高、NLR升高以及胰管扩张是恶性PCN的高危因素，基于此建立的恶性胰腺囊性肿瘤的预测模型具有较好的准确率，可为今后的临床诊疗提供辅助参考。