The gap between women's needs when diagnosed with asymptomatic osteoporosis and what is provided by the healthcare system: A qualitative study.

OBJECTIVES: Women diagnosed with asymptomatic osteoporosis need better support to understand the implications of the condition and how to practice self-management in their daily lives. In contrast, physicians report that asymptomatic osteoporosis is not a serious chronic condition and do not pay much attention to the condition compared to other chronic conditions. Therefore, the aim of this study was to investigate the gap between women's needs, when diagnosed with asymptomatic osteoporosis, and what is provided by the healthcare system. METHODS: A secondary analysis of semi-structured interviews with 17 women newly diagnosed with asymptomatic osteoporosis was conducted and combined with semi-structured interviews with six physicians. Giorgi's descriptive phenomenological method was used in the analysis. RESULTS: Two overall themes were identified: different perceptions of asymptomatic osteoporosis and discrepancies in the osteoporosis consultation. Habermas was used as a theoretical approach to discuss the findings. DISCUSSION: We discuss that physicians pay too much attention to the objective world and highlight that there is a need for better inclusion of women's subjective and social worlds, to enable mutual understanding and communicative action in the osteoporosis consultation. This would lead to treatment decisions based on women's needs and support women in their self-management of osteoporosis.

Google Trends and Seasonal Effects in Infodemiology: A Use Case About Obesity.

Google Trends is a free online tool that provides information on the interest on a topic measured as Google searches in the big data era. In this work, we present methods for detection of trends and seasonal effects in online interest for health topics based on Google Trends data. We present data for the term obesity worldwide and in a convenience sample of countries (Denmark, United Kingdom, USA, Japan) over the last five years. Our analysis shows that, despite obesity being one of the global health challenges, there is a decreasing trend in online interest in the topic. We also observed seasonal effects, with less interest for the topic during December and the summer months. Identifying trends and seasonal patterns in online interest can support resource allocation and planning and allows one to evaluate the effectiveness of global and local outreach efforts on public health.

A Predictive Model for Acute Admission in Aged Population.

Acute hospital admission among the elderly population is very common and have a high impact on the health services and the community, as well as on the individuals. Several studies have focused on the possible risk factors, however, predicting who is at risk for acute hospitalization associated with disease and symptoms is still an open research question. In this study, we investigate the use of machine learning algorithms for predicting acute admission in older people based on admission data from individual citizens 70 years and older who were hospitalized in the acute medical unit of Svendborg Hospital in Denmark.