Handgrip strength and risk of cognitive outcomes: new prospective study and meta-analysis of 16 observational cohort studies

Handgrip strength (HGS), a measure of muscular strength, might be a risk indicator for cognitive functioning, but the evidence is not consistent. Using a new prospective study and meta-analysis of published observational cohort studies, we aimed to evaluate the prospective associations of HGS with poor cognitive outcomes including cognitive impairment, dementia and Alzheimer’s disease (AD). Handgrip strength, measured using a Martin-Balloon-Vigorimeter, was assessed at baseline in a population-based sample of 852 men and women with good cognitive function in the Kuopio Ischemic Heart Disease cohort. Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated for cognitive outcomes. Relevant published studies were sought in MEDLINE, Embase and Web of Science from inception until October 2021 and pooled using random effects meta-analysis. During a median follow-up of 16.6 years, 229 dementia cases were recorded. Comparing extreme tertiles of HGS, the multivariable adjusted HR (95% CI) for dementia, AD and vascular dementia was 0.77 (0.55–1.07), 0.75 (0.52–1.10) and 0.49 (0.16–1.48), respectively. In a meta-analysis of 16 population-based prospective cohort studies (including the current study) comprising 180,920 participants, the pooled multivariable adjusted relative risks (95% CIs) comparing the top vs bottom thirds of HGS levels were as follows: 0.58 (0.52–0.65) for cognitive impairment; 0.37 (0.07–1.85) for cognitive decline; 0.73 (0.62–0.86) for dementia; 0.68 (0.53–0.87) for AD; and 0.48 (0.32–0.73) for vascular dementia. GRADE quality of evidence ranged from low to very low. Meta-analysis of aggregate prospective data suggests that HGS may be a risk indicator for poor cognitive outcomes such as cognitive impairment, dementia and AD. Systematic review registration: PROSPERO 2021: CRD42021237750.

Associations between sociodemographic factors, health spending, disease burden, and life expectancy of older adults (70 + years old) in 22 countries in the Western Pacific Region, 1995–2019: estimates from the Global Burden of Disease (GBD) Study 2019

The needs of the aging populations are putting increasing burden on healthcare particularly in the Western Pacific Region (WPR), which is the home of aging economies such as Hong Kong and Japan alongside rapid increases in older people in low- and middle-income countries (LMICs). However, little is known about the associations between sociodemographic factors, disease burden, and life expectancy in WPR. The current study conducted secondary analysis on the Global Burden of Disease (GBD) estimates of populations aged 70 years and older in WPR countries between 1995 and 2019. Correlation tests, linear mixed regressions and generalized additive mixture models were run to examine the associations of interest. Unsupervised machine learning was conducted to segment the data automatically, at cluster analysis. The sociodemographic development index was found an important factor to the disease burden in terms of Years Lived with Disability (YLD) rate and mortality rate among older adults. YLD rate and mortality rate of non-communicable diseases (NCDs) attributable to different risk groups were significant contributors to higher expectation of Lost Healthy Years (LHE) and shorter life expectancy respectively in the context of rising YLD rate, particularly in LMICs. Three clusters of countries with similar characteristics were identified. NCDs were the most significant contributors to shorter life expectancy within which the time living in poor health did not improve. Better management of NCDs in aging populations should be emphasized in all countries. The country clusters may help in a more regional strategic planning.

Modeling of nursing care-associated airborne transmission of SARS-CoV-2 in a real-world hospital setting

Respiratory transmission of SARS-CoV-2 from one older patient to another by airborne mechanisms in hospital and nursing home settings represents an important health challenge during the COVID-19 pandemic. However, the factors that influence the concentration of respiratory droplets and aerosols that potentially contribute to hospital- and nursing care-associated transmission of SARS-CoV-2 are not well understood. To assess the effect of health care professional (HCP) and patient activity on size and concentration of airborne particles, an optical particle counter was placed (for 24 h) in the head position of an empty bed in the hospital room of a patient admitted from the nursing home with confirmed COVID-19. The type and duration of the activity, as well as the number of HCPs providing patient care, were recorded. Concentration changes associated with specific activities were determined, and airway deposition modeling was performed using these data. Thirty-one activities were recorded, and six representative ones were selected for deposition modeling, including patient’s activities (coughing, movements, etc.), diagnostic and therapeutic interventions (e.g., diagnostic tests and drug administration), as well as nursing patient care (e.g., bedding and hygiene). The increase in particle concentration of all sizes was sensitive to the type of activity. Increases in supermicron particle concentration were associated with the number of HCPs (r = 0.66; p < 0.05) and the duration of activity (r = 0.82; p < 0.05), while submicron particles increased with all activities, mainly during the daytime. Based on simulations, the number of particles deposited in unit time was the highest in the acinar region, while deposition density rate (number/cm2/min) was the highest in the upper airways. In conclusion, even short periods of HCP-patient interaction and minimal patient activity in a hospital room or nursing home bedroom may significantly increase the concentration of submicron particles mainly depositing in the acinar regions, while mainly nursing activities increase the concentration of supermicron particles depositing in larger airways of the adjacent bed patient. Our data emphasize the need for effective interventions to limit hospital- and nursing care-associated transmission of SARS-CoV-2 and other respiratory pathogens (including viral pathogens, such as rhinoviruses, respiratory syncytial virus, influenza virus, parainfluenza virus and adenoviruses, and bacterial and fungal pathogens).