Given the substantial impact of herpes zoster on health and quality of life, and its considerable economic burden, prevention through vaccination is a priority. We aimed to evaluate the effectiveness of the herpes zoster vaccines (recombinant zoster vaccine [RZV] and zoster vaccine live [ZVL]) against incident herpes zoster and postherpetic neuralgia in older adults. We did a systematic review and meta-analysis of studies assessing the effectiveness of herpes zoster vaccines in adults aged 50 years or older, compared with no vaccination or another vaccine. We searched published literature on MEDLINE, Embase, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, ProQuest Central, and Dimensions, as well as unpublished studies, grey literature, and the reference lists of included studies. Observational studies published in any language between May 25, 2006, and Dec 31, 2020, were included. Eligible studies were appraised for methodological quality using standardised critical appraisal instruments from the Joanna Briggs Institute, and data were extracted from selected studies using a standardised tool. Random-effects meta-analysis models were used to estimate pooled vaccine effectiveness for outcomes of interest (herpes zoster, herpes zoster ophthalmicus, and postherpetic neuralgia) among clinically and methodologically comparable studies, with a fixed-effects model also used for herpes zoster ophthalmicus. Vaccine effectiveness was also assessed in people with comorbidities. As a post-hoc analysis, a forward citation search was done on Jan 31, 2021. This study is registered on PROSPERO, CRD42021232383. Our search identified 1240 studies, of which 1162 were excluded based on title and abstract screening. A further 56 articles were excluded on reading the full text. 22 studies (21 cohort studies and one case-control study, involving 9 536 086 participants and 3·35 million person-years in the USA, UK, Canada, and Sweden) were included in the quantitative analysis. Of these, 13 articles were included in the meta-analysis. The overall quality of evidence was very low for all outcomes. The pooled vaccine effectiveness for ZVL against herpes zoster in adults was 45·9% (95% CI 42·2-49·4; seven studies). The vaccine effectiveness for ZVL against postherpetic neuralgia was 59·7% (58·4-89·7; three studies) and against herpes zoster ophthalmicus (in a fixed-effects model) was 30·0% (20·5-38·4; two studies). ZVL was effective in preventing herpes zoster in people with comorbidities, including diabetes (vaccine effectiveness 49·8%, 45·1-54·1; three studies), chronic kidney disease (54·3%, 49·0-59·1; four studies), liver disease (52·9%, 41·6-62·1; two studies), heart disease (52·3%, 45·0-58·7; two studies), and lung disease (49·0%, 32·2-66·2; two studies). In a post-hoc analysis of two studies from the USA published after 2020, the pooled vaccine effectiveness for RZV against herpes zoster in adults was 79·2% (57·6-89·7). Substantial heterogeneity (I²≥75%) was observed in 50% of the meta-analyses. ZVL and RZV are effective in preventing herpes zoster in routine clinical practice. ZVL also reduces the risk of postherpetic neuralgia. Selection bias and confounding by unmeasured variables are inherent challenges of observational studies based on large health-care databases. Nevertheless, these findings will reassure policy makers, health practitioners, and the public that the vaccinations currently available for herpes zoster vaccination programmes are effective at preventing herpes zoster and related complications. None.

Biometric recognition can be used to improve gesture-based interfaces by automatically identifying operators. Traditional palm biometric recognition techniques depend on palm appearance features, but these features are not available in an operating theater where gloves are worn. We propose a depth-based solution for palm biometric recognition. Based on the depth image, our system automatically segments the user's palm and extracts finger dimensions. The finger dimensions are further scaled according to the sensed depth to obtain the true finger dimensions, which are then used as features to characterize the palm. Finally, a modified $k$ -nearest neighbors algorithm that assigns class labels based on the centroid displacement of each class in the neighboring points is applied to recognize the palm based on the geometric features. An accuracy of 96.24% was achieved for the biometric recognition of 4057 gloved palm samples captured at different angles and depths from 27 users. This accuracy is comparable with those of other state-of-the-art classification algorithms and demonstrates that biometric recognition may be viable for settings with gloved hands such as surgery.