Intensity-based sentiment and topic analysis. The case of the 2020 Aegean Earthquake

After an earthquake, it is necessary to understand its impact to provide relief and plan recovery. Social media (SM) and crowdsourcing platforms have recently become valuable tools for quickly collecting large amounts of first-hand data after a disaster. Earthquake related studies propose using data mining and natural language processing (NLP) for damage detection and emergency response assessment. Using tex-data provided by the Euro-Mediterranean Seismological Centre (EMSC) collected through the LastQuake app for the Aegean Earthquake, we undertake a sentiment and topic analysis according to the intensities reported by their users in the Modified Mercalli Intensity (MMI) scale. There were collected 2,518 comments, reporting intensities from I to X being the most frequent intensity reported III. We use supervised classification according to a rule-set defined by authors and a two-tailed Pearson correlation to find statistical relationships between intensities reported in the MMI by LastQuake app users, polarities, and topics addressed in their comments. The most frequent word among comments was: “Felt.” The sentiment analysis (SA) indicates that the positive polarity prevails in the comments associated with the lowest intensities reported: (I-II), while the negative polarity in the comments is associated with higher intensities (III–VIII and X). The correlation analysis identifies a negative correlation between the increase in the reported MMI intensity and the comments with positive polarity. The most addressed topic in the comments from LastQuake app users was intensity, followed by seismic information, solidarity messages, emergency response, unrelated topics, building damages, tsunami effects, preparedness, and geotechnical effects. Intensities reported in the MMI are significantly and negatively correlated with the number of topics addressed in comments. Positive polarity decreases with the soar in the reported intensity in MMI demonstrated the validity of our first hypothesis, despite not finding a correlation with negative polarity. Instead, we could not prove that building damage, geotechnical effects, lifelines affected, and tsunami effects were topis addressed only in comments reporting the highest intensities in the MMI

New Horizons in the use of routine data for ageing research

The past three decades have seen a steady increase in the availability of routinely collected health and social care data and the processing power to analyse it. These developments represent a major opportunity for ageing research, especially with the integration of different datasets across traditional boundaries of health and social care, for prognostic research and novel evaluations of interventions with representative populations of older people. However, there are considerable challenges in using routine data at the level of coding, data analysis and in the application of findings to everyday care. New Horizons in applying routine data to investigate novel questions in ageing research require a collaborative approach between clinicians, data scientists, biostatisticians, epidemiologists and trial methodologists. This requires building capacity for the next generation of research leaders in this important area. There is a need to develop consensus code lists and standardised, validated algorithms for common conditions and outcomes that are relevant for older people to maximise the potential of routine data research in this group. Lastly, we must help drive the application of routine data to improve the care of older people, through the development of novel methods for evaluation of interventions using routine data infrastructure. We believe that harnessing routine data can help address knowledge gaps for older people living with multiple conditions and frailty, and design interventions and pathways of care to address the complex health issues we face in caring for older people

Early-life socioeconomic position and the accumulation of health-related deficits by midlife in the 1958 British birth cohort study

Reducing population levels of frailty is an important goal, and preventing its development in midadulthood could be pivotal. There is limited evidence on associations between childhood socioeconomic position (SEP) and frailty. Using data on the 1958 British birth cohort (followed from 1958 to 2016; n = 8,711), we aimed to 1) establish the utility of measuring frailty in midlife, by examining associations between a 34-item frailty index at age 50 years (FI50y) and mortality at ages 50-58 years, and 2) examine associations between early-life SEP and FI50y and investigate whether these associations were explained by adult SEP. Hazard ratios for mortality increased with increasing frailty; for example, the sex-adjusted hazard ratio for the highest quintile of FI50y versus the lowest was 4.07 (95% confidence interval (CI): 2.64, 6.25). Lower early-life SEP was associated with higher FI50y. Compared with participants born in the highest social class, the estimated total effect on FI50y was 42.0% (95% CI: 35.5, 48.4) for participants born in the lowest class, with the proportion mediated by adult SEP being 0.45% (95% CI: 0.35, 0.55). Mediation by adult SEP was negligible for other early-life SEP classes. Findings suggest that early-life SEP is associated with frailty and that adult SEP only partially explains this association. Results highlight the importance of improving socioeconomic circumstances across the life course to reduce inequalities in midlife frailty