Group of Happy Women In a study published in Menopause, researchers created two Cox models using time-dependent covariates associated with age at natural menopause to predict the timing of menopause in women. “The suggested approach for predicting the age at natural menopause could provide a useful and easily accessible tool for assessing the time to approaching natural menopause in middle-aged women,” Matti Hyvärinen, MSc, a doctoral researcher in the Gerontology Research Center and faculty of sport and health sciences at the University of Jyväskylä in Finland, told Healio. “This information can be useful for clinicians when making decisions related to the use of hormonal contraception and treatments for menopausal symptoms in perimenopausal women.” Hyvärinen and colleagues obtained data page from women participating in the Estrogenic Regulation of Muscle Apoptosis study. All participants were aged 47 to 55 years and living in Jyväskylä, Finland. Of 279 women in the study, age at natural menopause was determined for 105 participants who were perimenopausal at baseline, remained through the follow-up period until they were postmenopausal, and completed a monthly diary detailing their menstrual bleeding. Age at natural menopause was defined as the age when the last reported bleeding period began. Researchers identified 32 covariates describing characteristics associated with age at natural menopause or reported to fluctuate during menopause transition. The covariates included blood-based biomarkers, body composition variables, objectively measured physical activity, menstrual cycle characteristics and self-reported data, including gynecologic history, menopause symptoms. Also included were lifestyle and socioeconomic information, such as education level, relationship status and physical activity habits. Two Cox regression models were created, with the first model using all 32 predictors and the second model using only self-reported variables and BMI as candidate covariates. The set of seven predictors with the best predictive performance were chosen for the final models. Both models demonstrated good predictive performance for age at natural menopause. The first model with all candidate predictors had a median C index of 0.76 (95% CI, 0.71-0.81), whereas the model using only self-reported candidate predictors and BMI had a median C index of 0.7 (95% CI, 0.65-0.75). The mean absolute error was 0.56 helpful site years in the first model and 0.62 years in the second model. Both figures were smaller than the mean absolute error of 1.58 years in the predicted sample mean. Higher estradiol and follicle-stimulating hormone levels, irregular menstrual bleeding and vasomotor symptoms were strong indicators of approaching natural menopause. Although these associations were consistent with earlier findings, the researchers had a few contradictory results. “One interesting finding was that participants tended to increase their alcohol consumption when approaching the menopause,” Hyvärinen said. “Thus, higher alcohol consumption was a significant predictor of the approaching menopause in our models.


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subscribe to Quality Magazine Quality 4.0 integrates the features from Industry 4.0 with traditional quality tools to achieve operational excellence, improved overall performance, and innovation. Quality 4.0 combines people, processes, and technologies to accomplish these goals, along with complete digitalization of quality management systems theory. Quality 4.0 is not a substitute for traditional quality tools but rather builds and improves upon them.¹ According to the American Society for Quality (ASQ), quality can have two meanings: The characteristics of a product or service that bear on its ability to satisfy stated or implied needs. A product or service free of deficiencies. The first meaning is clearly related to how good the product/service is in the eyes of the customers and the second aspect is the degree of conformance.² Since Quality 4.0 was announced in 2011, it feels like a Sisyphean task to get quality professionals engaged with it. In a 2018 study on Quality 4.0 by the American Society for Quality and the Boston Consulting Group, only 16% of organizations surveyed have implemented Quality 4.0 initiatives, and 63% have not started planning.³ Furthermore in a study published by the World Economic Forum in “The Future of Jobs Report 2018”, a new human-machine frontier within existing tasks has already begun. In 2018, the total task hours performed by humans was 71%. That is projected to drop to 58% by 2022. This is an 18% decrease in total task hours performed by humans. In contrast, 29% of total task hours performed by machines in 2018 is projected to increase to 42% by 2022, a 49% increase.⁴ I believe these circumstances affect the slow progress with Quality 4.0. In addition, a recent study by LNS Research revealed that a large portion of quality professionals do not understand the technologies of Industry 4.0.⁵ I have asked during local ASQ meetings and at work if people knew what Internet of Things (IoT), cloud technology, and cyber-physical systems (CPS) are, and I get blank stares back. Do you think quality professionals can lead the way with Quality 4.0? I believe we can, but we need to focus on some new ways forward. In the Total Quality Management (TQM) Journal,⁶ several articles were analyzed for successful implementation of Quality 4.0. We recommend these guidelines: Effective vertical, horizontal and end to end integration through Quality 4.0 ASQ has recently formed a Technical Communities Council (TCC) Committee Quality 4.0. There is an opportunity to prepare quality professionals with the skills to enable Quality 4.0 today. Some ideas: Outreach through social/print media, myASQ Quality 4.0 involvement with Quality Book of Knowledge (QBOK) peer group Provide industry and academia linkages for Q 4.0 General awareness with ASQ TV, YouTube, LinkedIn, and new groups Bottom line: If we want to move forward with Quality 4.0 activities it is imperative to study new technologies such as blockchain, IoT, CPS, AR, VR, 3D printing, nanotechnology, and gene editing, to name just a few. One valuable resource we all have available is the use of our public library.