BACKGROUND: With increasing trend of internet use in all age groups, whether internet use can prevent frailty in middle-aged and older adults remains unclear.
METHODS: Five cohorts, including Health and Retirement Study (HRS), China Health and Retirement Longitudinal Study (CHARLS), the Survey of Health, Ageing and Retirement in Europe (SHARE), English Longitudinal Study of Aging (ELSA), and Mexican Health and Aging Study (MHAS), were used in this study. Internet use, social isolation, and frailty status was assessed using similar questions. The Generalized estimating equations models, random effects meta-analysis, COX regression, and mediation analysis were utilized.
RESULTS: In the multicohort study, a total of 155,695 participants were included in main analysis. The proportion of internet use was varied across countries, ranging from 5.56% in China (CHARLS) to 83.46% in Denmark (SHARE). According to the generalized estimating equations models and meta-analysis, internet use was inversely associated with frailty, with the pooled ORs (95%CIs) of 0.72 (0.67,0.79). The COX regression also showed that participants with internet use had a lower risk of frailty incidence. Additionally, the association was partially mediated by social isolation and slightly pronounced in participants aged 65 and over, male, not working for payment, not married or partnered, not smoking, drinking, and not co-residence with children.
CONCLUSIONS: Our findings highlight the important role of internet use in preventing frailty and recommend more engagements in social communication and activities to avoid social isolation among middle-aged and older adults.

BACKGROUND: With the aging of populations worldwide, early detection of cognitive impairments has become a research and clinical priority, particularly to enable preventive intervention for dementia. Automated analysis of the drawing process has been studied as a promising means for lightweight, self-administered cognitive assessment. However, this approach has not been sufficiently tested for its applicability across populations.
OBJECTIVE: The aim of this study was to evaluate the applicability of automated analysis of the drawing process for estimating global cognition in community-dwelling older adults across populations in different nations.
METHODS: We collected drawing data with a digital tablet, along with Montreal Cognitive Assessment (MoCA) scores for assessment of global cognition, from 92 community-dwelling older adults in the United States and Japan. We automatically extracted 6 drawing features that characterize the drawing process in terms of the drawing speed, pauses between drawings, pen pressure, and pen inclinations. We then investigated the association between the drawing features and MoCA scores through correlation and machine learning-based regression analyses.
RESULTS: We found that, with low MoCA scores, there tended to be higher variability in the drawing speed, a higher pause:drawing duration ratio, and lower variability in the pen\'s horizontal inclination in both the US and Japan data sets. A machine learning model that used drawing features to estimate MoCA scores demonstrated its capability to generalize from the US dataset to the Japan dataset (R2=0.35; permutation test, P<.001).
CONCLUSIONS: This study presents initial empirical evidence of the capability of automated analysis of the drawing process as an estimator of global cognition that is applicable across populations. Our results suggest that such automated analysis may enable the development of a practical tool for international use in self-administered, automated cognitive assessment.

The programmed death-ligand 1 inhibitor atezolizumab had shown clinical activity against several advanced malignancies.
This phase II, open-label basket study (NCT02458638) was conducted in 16 main cohorts of patients aged ≥18 years with stage III or IV solid tumors. In stage I, 12 patients were enrolled into each cohort. Treatment was atezolizumab 1200 mg intravenously every 3 weeks until loss of clinical benefit or unacceptable toxicity. The primary efficacy endpoint was the non-progression rate (NPR) at 18 weeks in treated, assessable patients. NPR ≤20% was not of interest for development as monotherapy, and NPR ≥40% was defined as the threshold of benefit/success. If ≥3 patients had non-progressive disease in stage I (interim analysis), 13 additional patients could be enrolled into stage II (final analysis). Secondary efficacy and safety endpoints were also evaluated.
Overall, 474 patients were enrolled and treated; 433 were included in the efficacy set. Due partly to slow recruitment because of competing trials and limited efficacy at interim analyses, enrollment was stopped early, including in cohorts that passed stage I boundaries of success. NPR was >20% in five cohorts: cervical cancer {n = 27; NPR 44.4% [95% confidence interval (CI) 25.5% to 64.7%]}; follicular/papillary thyroid cancer [n = 11; 54.5% (95% CI 23.4% to 83.3%)]; thymoma [n = 13; 76.9% (95% CI: 46.2% to 95.0%)]; gastroenteropancreatic (GEP) and lung neuroendocrine tumors [NETs; n = 24; 41.7% (95% CI 22.1% to 63.4%)], and low/intermediate grade carcinoid GEP and lung NETs [n = 12; 58.3% (95% CI 27.7% to 84.8%)]. Treatment-related adverse events occurred in 55.3% of patients overall, and at grade 3, 4, and 5 in 10.3%, 1.7%, and 0.4%, respectively.
Atezolizumab monotherapy was effective in the cervical cancer cohort. The interim benefit threshold was crossed in patients with follicular/papillary thyroid cancer, thymoma, and GEP and lung NETs, but recruitment was stopped before these signals could be confirmed in stage II. Safety was consistent with previous findings.

Long-term air pollution exposure increases the risk for cardiovascular disease, but little is known about the temporal relationships between exposure and health outcomes. This study aims to estimate the exposure-lag response between air pollution exposure and risk for ischemic heart disease (IHD) and stroke incidence by applying distributed lag non-linear models (DLNMs). Annual mean concentrations of particles with aerodynamic diameter less than 2.5 µm (PM2.5) and black carbon (BC) were estimated for participants in five Swedish cohorts using dispersion models. Simultaneous estimates of exposure lags 1-10 years using DLNMs were compared with separate year specific (single lag) estimates and estimates for lag 1-5- and 6-10-years using moving average exposure. The DLNM estimated no exposure lag-response between PM2.5 total, BC, and IHD. However, for PM2.5 from local sources, a 20% risk increase per 1 µg/m3 for 1-year lag was estimated. A risk increase for stroke was suggested in relation to lags 2-4-year PM2.5 and BC, and also lags 8-9-years BC. No associations were shown in single lag models. Increased risk estimates for stroke in relation to lag 1-5- and 6-10-years BC moving averages were observed. Estimates generally supported a greater contribution to increased risk from exposure windows closer in time to incident IHD and incident stroke.

NMR data from large studies combining multiple cohorts is becoming common in large-scale metabolomics. The data size and combination of cohorts with diverse properties leads to special problems for data processing and analysis. These include alignment, normalization, detection and removal of outliers, presence of strong correlations, and the identification of unknowns. Nonetheless, these challenges can be addressed with suitable algorithms and techniques, leading to enhanced data sets ripe for further data mining.

Alzheimer\'s disease is characterized by hippocampal atrophy. Other factors also influence the hippocampal volume, but their interactive effect has not been investigated before in cognitively healthy individuals. The aim of this study is to evaluate the interactive effect of key demographic and clinical factors on hippocampal volume, in contrast to previous studies frequently investigating these factors in a separate manner. Also, to investigate how comparable the control groups from ADNI, AIBL, and AddNeuroMed are with five population-based cohorts. In this study, 1958 participants were included (100 AddNeuroMed, 226 ADNI, 155 AIBL, 59 BRC, 295 GENIC, 279 BioFiNDER, 398 PIVUS, and 446 SNAC-K). ANOVA and random forest were used for testing between-cohort differences in demographic-clinical variables. Multiple regression was used to study the influence of demographic-clinical variables on hippocampal volume. ANCOVA was used to analyze whether between-cohort differences in demographic-clinical variables explained between-cohort differences in hippocampal volume. Age and global brain atrophy were the most important variables in explaining variability in hippocampal volume. These variables were not only important themselves but also in interaction with gender, education, MMSE, and total intracranial volume. AddNeuroMed, ADNI, and AIBL differed from the population-based cohorts in several demographic-clinical variables that had a significant effect on hippocampal volume. Variability in hippocampal volume in individuals with normal cognition is high. Differences that previously tended to be related to disease mechanisms could also be partly explained by demographic and clinical factors independent from the disease. Furthermore, cognitively normal individuals especially from ADNI and AIBL are not representative of the general population. These findings may have important implications for future research and clinical trials, translating imaging biomarkers to the general population, and validating current diagnostic criteria for Alzheimer\'s disease and predementia stages.

Large-scale metabolomics studies involving thousands of samples present multiple challenges in data analysis, particularly when an untargeted platform is used. Studies with multiple cohorts and analysis platforms exacerbate existing problems such as peak alignment and normalization. Therefore, there is a need for robust processing pipelines that can ensure reliable data for statistical analysis. The COMBI-BIO project incorporates serum from ∼8000 individuals, in three cohorts, profiled by six assays in two phases using both 1H NMR and UPLC-MS. Here we present the COMBI-BIO NMR analysis pipeline and demonstrate its fitness for purpose using representative quality control (QC) samples. NMR spectra were first aligned and normalized. After eliminating interfering signals, outliers identified using Hotelling\'s T2 were removed and a cohort/phase adjustment was applied, resulting in two NMR data sets (CPMG and NOESY). Alignment of the NMR data was shown to increase the correlation-based alignment quality measure from 0.319 to 0.391 for CPMG and from 0.536 to 0.586 for NOESY, showing that the improvement was present across both large and small peaks. End-to-end quality assessment of the pipeline was achieved using Hotelling\'s T2 distributions. For CPMG spectra, the interquartile range decreased from 1.425 in raw QC data to 0.679 in processed spectra, while the corresponding change for NOESY spectra was from 0.795 to 0.636, indicating an improvement in precision following processing. PCA indicated that gross phase and cohort differences were no longer present. These results illustrate that the pipeline produces robust and reproducible data, successfully addressing the methodological challenges of this large multifaceted study.

BACKGROUND: A large number of economic evaluations have already confirmed the cost-effectiveness of different human papillomavirus (HPV) vaccination strategies. Standard analyses might not capture the full economic value of novel vaccination programs because the cost-effectiveness paradigm fails to take into account the value of active management. Management decisions can be seen as real options, a term used to refer to the application of option pricing theory to the valuation of investments in nonfinancial assets in which much of the value is attributable to flexibility and learning over time.
OBJECTIVE: The aim of this article was to discuss the potential advantages shown by using the payoff method in the valuation of the cost-effectiveness of competing HPV immunization programs.
METHODS: This was the first study, to the best of our knowledge, to use the payoff method to determine the real option values of 4 different HPV vaccination strategies targeting female subjects aged 12, 15, 18, and 25 years. The payoff method derives the real option value from the triangular payoff distribution of the project\'s net present value, which is treated as a triangular fuzzy number. To inform the real option model, cost-effectiveness data were derived from an empirically calibrated Bayesian model designed to assess the cost-effectiveness of a multicohort HPV vaccination strategy in the context of the current cervical cancer screening program in Italy. A net health benefit approach was used to calculate the expected fuzzy net present value for each of the 4 vaccination strategies evaluated.
RESULTS: Costs per quality-adjusted life-year gained seemed to be related to the number of cohorts targeted: a single cohort of girls aged 12 years (€10,955 [95% CI, -1,021 to 28,212]) revealed the lowest cost among the 4 alternative strategies evaluated. The real option valuation challenged the cost-effectiveness dominance of a single cohort of 12-year-old girls. The simultaneous vaccination of 2 cohorts of girls aged 12 and 15 years yielded a real option value (€17,723) equivalent to that attributed to a single cohort of 12-year-old girls (€17,460).
CONCLUSIONS: The payoff method showed distinctive advantages in the valuation of the cost-effectiveness of competing health care interventions, essentially determined by the replacement of the nonfuzzy numbers that are commonly used in cost-effectiveness analysis models, with fuzzy numbers as an input to inform the real option pricing method. The real option approach to value uncertainty makes policy making in health care an evolutionary process and creates a new \"space\" for decision-making choices.