Implementation of Recommendations for Long-Acting Contraception Among Women Aged 13 to 18 Years in Primary Care

ObjectiveClinical specialty societies recommend long-acting reversible contraceptives (LARCs) as first-line contraception for adolescent women. We evaluated whether a combined educational and process improvement intervention enhanced LARC placement in primary care within an integrated health care system.MethodsThe intervention included journal clubs, live continuing education, point-of-care guidelines, and new patient materials. We conducted a retrospective cohort study across 3 time periods: baseline (January 2013?September 2015), early implementation (October 2015–March 2016), and full implementation (April 2016–June 2017). The primary outcome was the proportion of LARCs placed by primary care clinicians among women aged 13 to 18 years compared with gynecology clinicians.ResultsKaiser Foundation Health Plan of Colorado cared for approximately 20,000 women aged 13 to 18 years in each calendar quarter between 2013 and 2017. Overall, LARC placement increased from 7.0 per 1000 members per quarter at baseline to 13.0 per 1000 during the full intervention. Primary care clinicians placed 6.2% of all LARCs in 2013, increasing to 32.1% by 2017 (P < .001), including 45.5% of contraceptive implants. Clinicians who attended educational sessions were more likely to adopt LARCs than those who did not (17.9% vs 6.4% respectively, P = .009). Neither overall LARC placement rates (relative risk, 1.9; 95% confidence interval, 0.7?5.6) nor contraceptive implant rates (relative risk, 3.0; 95% confidence interval, 0.9?9.8) increased significantly in clinicians who attended educational activities.ConclusionsThis multimodal intervention was associated with increased LARC placement for adolescent women in primary care. The combination of education and process improvement is a promising strategy to promote clinician behavior change.     

South African abortion attitudes from 2007-2016: the roles of religiosity and attitudes toward sexuality and gender equality

ABSTRACT

Abortion is legal in South Africa, but negative abortion attitudes remain common and are poorly understood. We used nationally representative South African Social Attitudes Survey data to analyze abortion attitudes in the case of fetal anomaly and in the case of poverty from 2007 to 2016 (n = 20,711; ages = 16+). We measured correlations between abortion attitudes and these important predictors: religiosity, attitudes about premarital sex, attitudes about preferential hiring and promotion of women, and attitudes toward family gender roles. Abortion acceptability for poverty increased over time (b = 0.05, p < .001), but not for fetal anomaly (b = ?0.008, p = .284). Highly religious South Africans reported lower abortion acceptability in both cases (Odds Ratio (OR)_anomaly = 0.85, p = .015; OR_poverty = 0.84, p = .02). Premarital sex acceptability strongly and positively predicted abortion acceptability (OR_anomaly = 2.63, p < .001; OR_poverty = 2.46, p < .001). Attitudes about preferential hiring and promotion of women were not associated with abortion attitudes, but favorable attitudes about working mothers were positively associated with abortion acceptability for fetal anomaly ((OR_anomaly = 1.09, p = .01; OR_poverty = 1.02, p = .641)). Results suggest negative abortion attitudes remain common in South Africa and are closely tied to religiosity, traditional ideologies about sexuality, and gender role expectations about motherhood.     

Humanitarianism from Below: Sowa Rigpa,the Traditional Pharmaceutical Industry,and Global Health

ABSTRACT

In this article I explore, for the first time, the relationship between Sowa Rigpa (Tibetan medicine) and global health, tracing “the global” in ethical discourses and pharmaceutical innovation practices of Tibetan medical practitioners. I argue that Sowa Rigpa’s engagement with the world and its global health activities outside China can be understood as a form of “humanitarianism from below,” while its industrialization in China aligns with global health in different ways. In providing new insights into recent developments of Sowa Rigpa, I aim to decenter the notion of humanitarianism and contribute to a broader understanding of global health.     

广东省家庭医生式服务试点地区居民健康行为及影响因素分析

目的了解广东省家庭医生式服务试点地区居民健康行为及影响因素,为开展健康教育提供依据。方法2015年7—10月抽样选取1010名家庭医生式服务试点地区居民进行问卷调查,以吸烟、饮酒、早餐、吃水果、锻炼及睡眠等6项行为为调查内容,同时选用P-CAT量表测量基层医疗服务质量。结果 6项行为方式共赋值12分,被调查者平均得分(8.72?0.65)分,得分从高到低依次为吃早餐、限酒、不吸烟、体育锻炼、吃水果和睡眠。多重线性回归分析结果显示:女性、已婚或慢性病患者的健康行为优于各对应组;收入对健康行为有正面影响;基层医疗服务可及性、服务提供综合性对健康行为有正向影响。结论试点地区居民整体健康行为较好。健康教育和健康促进应聚焦于合理膳食、锻炼及睡眠等健康行为;男性、单身或离异及低收入群体是重点关注的对象;提高基层医疗服务可及性和服务提供综合性有利于促进居民的健康行为。     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.