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Background The European Union has used Healthy Life Years (HLY) as an indicator to monitor the health of its aging populations. Scholarly and popular interest in HLY across countries has grown, particularly regarding the ranking of countries. It is important to note that HLY is based on self-assessments of activity limitations, raising the possibility that it might be influenced by differences in health reporting behaviours between populations, a phenomenon known as differential item functioning (DIF). Methods We estimated DIF-adjusted HLY at age 50 for Belgium, France, Germany, Greece, Italy, the Netherlands, Spain, and Sweden to determine the extent to which differences in HLY might be influenced by reporting heterogeneity across countries. We used anchoring vignettes, taken from the 2004 Survey of Health, Ageing and Retirement in Europe, to estimate DIF-adjusted prevalence rates of activity limitations measured by the Global Activity Limitations Indicator (GALI). The Sullivan method was used to calculate DIF-adjusted HLY. Results Changes in HLY before and after adjustment ranged from a 1.20-year decrease for men in Italy to a 1.61-year increase for women in Spain. Adjustment for DIF produced changes in the rankings of the countries by HLY, with upward and downward movements of up to three positions. Conclusion Our results show that DIF is likely to affect HLY estimates, thereby posing a challenge to the validity of comparisons of HLY across European countries. The findings suggest that HLY should be used to monitor population health status within a country, rather than to make comparisons across countries. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

Background: Obesity-related lifestyle behaviors usually co-exist but few studies have examined their simultaneous relation with body weight. This study aimed to identify the hierarchy of lifestyle-related behaviors associated with being overweight in adults, and to examine subgroups so identified. Methods: Data were obtained from a cross-sectional survey conducted across 60 urban neighborhoods in 5 European urban regions between February and September 2014. Data on socio-demographics, physical activity, sedentary behaviors, eating habits, smoking, alcohol consumption, and sleep duration were collected by questionnaire. Participants also reported their weight and height. A recursive partitioning tree approach (CART) was applied to identify both main correlates of overweight and lifestyle subgroups. Results: In 5295 adults, mean (SD) body mass index (BMI) was 25.2 (4.5) kg/m², and 46.0 % were overweight (BMI ≥25 kg/m²). CART analysis showed that among all lifestyle-related behaviors examined, the first identified correlate was sitting time while watching television, followed by smoking status. Different combinations of lifestyle-related behaviors (prolonged daily television viewing, former smoking, short sleep, lower vegetable consumption, and lower physical activity) were associated with a higher likelihood of being overweight, revealing 10 subgroups. Members of four subgroups with overweight prevalence >50 % were mainly males, older adults, with lower education, and living in greener neighborhoods with low residential density. Conclusion: Sedentary behavior while watching television was identified as the most important correlate of being overweight. Delineating the hierarchy of correlates provides a better understanding of lifestyle-related behavior combinations which may assist in targeting preventative strategies aimed at tackling obesity. [ABSTRACT FROM AUTHOR]