Probable extinction of influenza B/Yamagata and its public health implications: a systematic literature review and assessment of global surveillance databases.

Early after the start of the COVID-19 pandemic, the detection of influenza B/Yamagata cases decreased globally. Given the potential public health implications of this decline, in this Review, we systematically analysed data on influenza B/Yamagata virus circulation (for 2020-23) from multiple complementary sources of information. We identified relevant articles published in PubMed and Embase, and data from the FluNet, Global Initiative on Sharing All Influenza Data, and GenBank databases, webpages of respiratory virus surveillance systems from countries worldwide, and the Global Influenza Hospital Surveillance Network. A progressive decline of influenza B/Yamagata detections was reported across all sources, in absolute terms (total number of cases), as positivity rate, and as a proportion of influenza B detections. Sporadically reported influenza B/Yamagata cases since March, 2020 were mostly vaccine-derived, attributed to data entry errors, or have yet to be definitively confirmed. The likelihood of extinction necessitates a rapid response in terms of reassessing the composition of influenza vaccines, enhanced surveillance for B/Yamagata, and a possible change in the biosafety level when handling B/Yamagata viruses in laboratories.

Association between work sick-leave absenteeism and SARS-CoV-2 notifications in the Netherlands during the COVID-19 epidemic.

BACKGROUND: Alternative data sources for surveillance have gained importance in maintaining coronavirus disease 2019 (COVID-19) situational awareness as nationwide testing has drastically decreased. Therefore, we explored whether rates of sick-leave from work are associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) notification trends and at which lag, to indicate the usefulness of sick-leave data for COVID-19 surveillance. METHODS: We explored trends during the COVID-19 epidemic of weekly sick-leave rates and SARS-CoV-2 notification rates from 1 June 2020 to 10 April 2022. Separate time series were inspected visually. Then, Spearman correlation coefficients were calculated at different lag and lead times of zero to four weeks between sick-leave and SARS-CoV-2 notification rates. We distinguished between four SARS-CoV-2 variant periods, two labour sectors and overall, and all-cause sick-leave versus COVID-19-specific sick-leave. RESULTS: The correlation coefficients between weekly all-cause sick-leave and SARS-CoV-2 notification rate at optimal lags were between 0.58 and 0.93, varying by the variant period and sector (overall: 0.83, lag -1; 95% CI [0.76, 0.88]). COVID-19-specific sick-leave correlations were higher than all-cause sick-leave correlations. Correlations were slightly lower in healthcare and education than overall. The highest correlations were mostly at lag -2 and -1 for all-cause sick-leave, meaning that sick-leave preceded SARS-CoV-2 notifications. Correlations were highest mostly at lag zero for COVID-19-specific sick-leave (coinciding with SARS-CoV-2 notifications). CONCLUSION: All-cause sick-leave might offer an earlier indication and evolution of trends in SARS-CoV-2 rates, especially when testing is less available. Sick-leave data may complement COVID-19 and other infectious disease surveillance systems as a syndromic data source.

Preparing for the upcoming 2022/23 influenza season: A modelling study of the susceptible population in Australia, France, Germany, Italy, Spain and the United Kingdom.

We analysed the influenza epidemic that occurred in Australia during the 2022 winter using an age-structured dynamic transmission model, which accounts for past epidemics to estimate the population susceptibility to an influenza infection. We applied the same model to five European countries. Our analysis suggests Europe might experience an early and moderately large influenza epidemic. Also, differences may arise between countries, with Germany and Spain experiencing larger epidemics, than France, Italy and the United Kingdom, especially in children.

Effects of capitation payment on utilization and claims expenditure under National Health Insurance Scheme: a cross-sectional study of three regions in Ghana.

INTRODUCTION: Ghana introduced capitation payment under National Health Insurance Scheme (NHIS), beginning with pilot in the Ashanti region, in 2012 with a key objective of controlling utilization and related cost. This study sought to analyse utilization and claims expenditure data before and after introduction of capitation payment policy to understand whether the intended objective was achieved. METHODS: The study was cross-sectional, using a non-equivalent pre-test and post-test control group design. We did trend analysis, comparing utilization and claims expenditure data from three administrative regions of Ghana, one being an intervention region and two being control regions, over a 5-year period, 2010-2014. We performed multivariate analysis to determine differences in utilization and claims expenditure between the intervention and control regions, and a difference-in-differences analysis to determine the effect of capitation payment on utilization and claims expenditure in the intervention region. RESULTS: Findings indicate that growth in outpatient utilization and claims expenditure increased in the pre capitation period in all three regions but slowed in post capitation period in the intervention region. The linear regression analysis showed that there were significant differences in outpatient utilization (p = 0.0029) and claims expenditure (p = 0.0003) between the intervention and the control regions before implementation of the capitation payment. However, only claims expenditure showed significant difference (p = 0.0361) between the intervention and control regions after the introduction of capitation payment. A difference-in-differences analysis, however, showed that capitation payment had a significant negative effect on utilization only, in the Ashanti region (p < 0.007). Factors including availability of district hospitals and clinics were significant predictors of outpatient health care utilization. CONCLUSION: We conclude that outpatient utilization and related claims expenditure increased in both pre and post capitation periods, but the increase in post capitation period was at slower rate, suggesting that implementation of capitation payment yielded some positive results. Health policy makers in Ghana may, therefore, want to consider capitation a key provider payment method for primary outpatient care in order to control cost in health care delivery.