Conectar
by Longgang Zhao, Yuan Wang, Eric Mishio Bawa, Zichun Meng, Jingkai Wei, Sarah Newman-Norlund, Tushar Trivedi, Hatice Hasturk, Roger D. Newman-Norlund, Julius Fridriksson, Anwar T. Merchant
BackgroundCognitive impairment has multiple risk factors spanning several domains, but few studies have evaluated risk factor clusters. We aimed to identify naturally occurring clusters of risk factors of poor cognition among middle-aged and older adults and evaluate associations between measures of cognition and these risk factor clusters.
MethodsWe used data from the National Health and Nutrition Examination Survey (NHANES) III (training dataset, n = 4074) and the NHANES 2011–2014 (validation dataset, n = 2510). Risk factors were selected based on the literature. We used both traditional logistic models and support vector machine methods to construct a composite score of risk factor clusters. We evaluated associations between the risk score and cognitive performance using the logistic model by estimating odds ratios (OR) and 95% confidence intervals (CI).
ResultsUsing the training dataset, we developed a composite risk score that predicted undiagnosed cognitive decline based on ten selected predictive risk factors including age, waist circumference, healthy eating index, race, education, income, physical activity, diabetes, hypercholesterolemia, and annual visit to dentist. The risk score was significantly associated with poor cognitive performance both in the training dataset (OR Tertile 3 verse tertile 1 = 8.15, 95% CI: 5.36–12.4) and validation dataset (OR Tertile 3 verse tertile 1 = 4.31, 95% CI: 2.62–7.08). The area under the receiver operating characteristics curve for the predictive model was 0.74 and 0.77 for crude model and model adjusted for age, sex, and race.
ConclusionThe model based on selected risk factors may be used to identify high risk individuals with cognitive impairment.
The COVID-19 pandemic disrupted healthcare services, leading to the cancellation of non-urgent tests, screenings and procedures, a shift towards remote consultations, stalled childhood immunisations and clinic closures which had detrimental effects across the healthcare system. This study investigates the impact of the COVID-19 pandemic on clinical admissions and healthcare quality in the Peel, York and Toronto regions within the Greater Toronto Area (GTA).
In a cross-sectional study, the negative impact of the pandemic on various healthcare sectors, including preventive and primary care (PPC), the emergency department (ED), alternative level of care (ALC) and imaging, procedures and surgeries is investigated. Study questions include assessing impairments caused by the COVID-19 pandemic and discovering hotspots and critical subregions that require special attention to recover. The measuring technique involves comparing the number of cases during the COVID-19 pandemic with before that, and determining the difference in percentage. Statistical analyses (Mann-Whitney U test, analysis of variance, Dunn’s test) is used to evaluate sector-specific changes and inter-relationships.
This work uses primary data which were collected by the Black Creek Community Health Centre. The study population was from three regions of GTA, namely, the city of Toronto, York and Peel. For all health sectors, the sample size was large enough to have a statistical power of 0.95 to capture 1% variation in the number of cases during the COVID-19 pandemic compared with before that.
All sectors experienced a significant decline in patient volume during the pandemic. ALC admissions surged in some areas, while IPS patients faced delays. Surgery waitlists increased by an average of 9.75%, and completed IPS procedures decreased in several subregions.
The COVID-19 pandemic had a universally negative impact on healthcare sectors across various subregions. Identification of the hardest-hit subregions in each sector can assist health officials in crafting recovery policies.
Current formulations of ready-to-use therapeutic foods (RUTFs) to treat severe acute malnutrition (SAM) in children focus on nutrient density and quantity. Less attention is given to foods targeting gut microbiota metabolism and mucosal barrier functions. Heat-stabilised rice bran contains essential nutrients, prebiotics, vitamins and unique phytochemicals that have demonstrated favourable bioactivity to modulate gut microbiota composition and mucosal immunity. This study seeks to examine the impact of RUTF with rice bran on the microbiota during SAM treatment, recovery and post-treatment growth outcomes in Jember, Indonesia. Findings are expected to provide insights into rice bran as a novel food ingredient to improve SAM treatment outcomes.
A total of 200 children aged 6–59 months with uncomplicated SAM (weight-for-height z-scores (WHZ)
The findings of this trial will be submitted to peer-reviewed journals and will be presented at relevant conferences. Ethics approval obtained from the Medical and Health Research Ethical Committee at the Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Madain Yogyakarta Ref. No.: KE/FK/0546/EC/2022 and KE/FK/0703/EC/2023 and from Colorado State University IRB#1823, OHRP FWA00000647.
FreshRSS 