Obesity and associated adverse health outcomes among US military members and veterans: Findings from the millennium cohort study

Obesity ◽  
2016 ◽  
Vol 24 (7) ◽  
pp. 1582-1589 ◽  
Author(s):  
Toni Rush ◽  
Cynthia A. LeardMann ◽  
Nancy F. Crum-Cianflone
Addiction ◽  
2011 ◽  
Vol 107 (2) ◽  
pp. 323-330 ◽  
Author(s):  
Tapio Paljärvi ◽  
Pia Mäkelä ◽  
Kari Poikolainen ◽  
Sakari Suominen ◽  
Josip Car ◽  
...  

BMC Medicine ◽  
2011 ◽  
Vol 9 (1) ◽  
Author(s):  
Aron S Buchman ◽  
Sue E Leurgans ◽  
Patricia A Boyle, ◽  
Julie A Schneider, ◽  
Steven E Arnold ◽  
...  

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Li Yuan ◽  
Xiaoming Zhang ◽  
Na Guo ◽  
Zhen Li ◽  
Dongmei Lv ◽  
...  

Abstract Background Previous studies on the relationship between cognitive impairment and adverse outcomes among geriatric inpatients are not representative of older inpatients in China because of insufficient sample sizes or single-center study designs. The purpose of our study was to examine the prevalence of cognitive impairment and the relationship between cognitive impairment and 1-year adverse health outcomes in older inpatients. Methods This study was a large-scale multi-center cohort study conducted from October 2018 to February 2020. Six tertiary hospitals across China were selected using a two-stage cluster sampling method, and eligible older inpatients were selected for the baseline survey and follow-up. The Mini Cognitive Scale and the FRAIL scale were used to screen for cognitive impairment and frailty, respectively. The EuroQol-5 Dimension-5 Level questionnaire was used to assess health-related quality of life (HRQoL). We used a generalized estimating model to evaluate the relationship between cognitive impairment and adverse outcomes. Results The study included 5008 men (58.02%) and 3623 women (41.98%), and 70.64% were aged 65–75 years, and 26.27% were aged 75–85 years. Cognitive impairment was observed in 1756 patients (20.35%). There were significant differences between participants with cognitive impairment and those with normal cognitive function for age, gender, surgery status, frailty, depression, handgrip strength and so on. After adjusting for multiple covariates, compared with patients with normal cognitive function, the odds ratio for 1-year mortality was 1.216 (95% confidence interval [CI]: 1.076–1.375) and for 1-year incidence of frailty was 1.195 (95% CI: 1.037–1.376) in patients with cognitive impairment. Similarly, the regression coefficient of 1-year HRQoL was − 0.013 (95% CI: − 0.024−− 0.002). In the stratified analysis, risk of adverse outcome within 1 year was higher in older patients with cognitive impairment aged over 75 years than those aged 65–74 years. Conclusions We revealed that cognitive impairment was highly correlated with occurrence of 1-year adverse health outcomes (death, frailty, and decreased HRQoL) in older inpatients, which provides a basis for formulating effective intervention measures. Trial registration Chinese Clinical Trial Registry, ChiCTR1800017682, registered 09 August 2018.


2018 ◽  
Vol 108 ◽  
pp. 47-52 ◽  
Author(s):  
Claire D. Madigan ◽  
Toby Pavey ◽  
Amanda J. Daley ◽  
Kate Jolly ◽  
Wendy J. Brown

2019 ◽  
Vol 22 (4) ◽  
pp. 205-212 ◽  
Author(s):  
Jacqueline R. Burt ◽  
Judith Godin ◽  
Josée Filion ◽  
Manuel Montero-Odasso ◽  
Kenneth Rockwood ◽  
...  

BackgroundFrailty is characterized by increased vulnerability to adverse health outcomes. The prevalence of frailty across neurodegenerative disorders (NDD) is largely unknown. Symptoms of frailty and NDD overlap, calling into question a tautology in some frailty instruments. Our objectives were 1) to construct a Frailty Index (FI) independent of NDD symptoms, and 2) to estimate frailty prevalence in a broad NDD cohort using both the Frailty Phenotype (FP) and the constructed FI as measures.MethodsData from the Canadian COMPASS-ND cohort study were assessed for applicability to FI construction. Frailty status accord-ing to FI and FP criteria were ascertained for each participant. Results81 items were selected for the FI. In the cohort (150 participants; 46% women; mean age 73.6±7.0; 10 NDD subgroups), frailty was identified in 11% and 14% of participants according to the FI and FP, respectively. The difference between estimates was not significant. The FP classified most participants (84%) as pre-frail. ConclusionThe presence of frailty elements, regardless of whether they are part of NDD, is likely to influence health status. Given the FP identified a large proportion of the cohort as pre-frail or frail, it is likely worthwhile to identify frailty in the context of NDD.


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