METHODS: We obtained random urine samples from 9,275 cases of acute first stroke and 9,726 matched controls from 27 countries and estimated the 24-hour sodium and potassium excretion, a surrogate for intake, using the Tanaka formula. Using multivariable conditional logistic regression, we determined the associations of estimated 24-hour urinary sodium and potassium excretion with stroke and its subtypes.
RESULTS: Compared with an estimated urinary sodium excretion of 2.8-3.5 g/day (reference), higher (>4.26 g/day) (odds ratio [OR] 1.81; 95% confidence interval [CI], 1.65-2.00) and lower (<2.8 g/day) sodium excretion (OR 1.39; 95% CI, 1.26-1.53) were significantly associated with increased risk of stroke. The stroke risk associated with the highest quartile of sodium intake (sodium excretion >4.26 g/day) was significantly greater (P < 0.001) for intracerebral hemorrhage (ICH) (OR 2.38; 95% CI, 1.93-2.92) than for ischemic stroke (OR 1.67; 95% CI, 1.50-1.87). Urinary potassium was inversely and linearly associated with risk of stroke, and stronger for ischemic stroke than ICH (P = 0.026). In an analysis of combined sodium and potassium excretion, the combination of high potassium intake (>1.58 g/day) and moderate sodium intake (2.8-3.5 g/day) was associated with the lowest risk of stroke.
CONCLUSIONS: The association of sodium intake and stroke is J-shaped, with high sodium intake a stronger risk factor for ICH than ischemic stroke. Our data suggest that moderate sodium intake-rather than low sodium intake-combined with high potassium intake may be associated with the lowest risk of stroke and expected to be a more feasible combined dietary target.
METHODS: We conducted a prospective cohort study, between March 27, 2004 and November 2, 2022, in 279 ICUs of 95 hospitals in 44 cities in 9 Asian countries (China, India, Malaysia, Mongolia, Nepal, Pakistan, Philippines, Sri Lanka, Thailand, Vietnam).
RESULTS: 153,717 patients, followed during 892,996 patient-days, acquired 3,369 VAPs. We analyzed 10 independent variables. Using multiple logistic regression we identified following independent VAP RFs= Age, rising VAP risk 1% per year (aOR=1.01; 95%CI=1.00-1.01, Prisk 7% daily (aOR=1.07; 95%CI=1.06-1.07, Prisk.
CONCLUSIONS: Some identified VAP RFs are unlikely to change= age, gender, ICU type, facility ownership, country income level. Based on our results, we recommend limit use of tracheostomy, reducing LOS, reducing the MV/DU ratio, and implementing an evidence-based set of VAP prevention recommendations.
METHODS: From January 1, 2014, to February 12, 2022, we conducted a prospective cohort study. To estimate CAUTI incidence, the number of UC days was the denominator, and CAUTI was the numerator. To estimate CAUTI RFs, we analyzed 11 variables using multiple logistic regression.
RESULTS: 84,920 patients hospitalized for 499,272 patient days acquired 869 CAUTIs. The pooled CAUTI rate per 1,000 UC-days was 3.08; for those using suprapubic-catheters (4.11); indwelling-catheters (2.65); trauma-ICU (10.55), neurologic-ICU (7.17), neurosurgical-ICU (5.28); in lower-middle-income countries (3.05); in upper-middle-income countries (1.71); at public-hospitals (5.98), at private-hospitals (3.09), at teaching-hospitals (2.04). The following variables were identified as CAUTI RFs: Age (adjusted odds ratio [aOR] = 1.01; 95% CI = 1.01-1.02; P risk are higher for older patients, women, hospitalized at trauma-ICU, neurologic-ICU, neurosurgical-ICU, and public facilities. All of them are unlikely to change.
CONCLUSIONS: It is suggested to focus on reducing the length of stay and the Urinary catheter device utilization ratio, avoiding suprapubic catheters, and implementing evidence-based CAUTI prevention recommendations.
MATERIALS AND METHODS: Pure tone audiometry test was conducted on 263 residents of a rural village who were not exposed to noise. The pack-years of smoking were computed from the subjects' smoking history. The association between pack-years and hearing impairment was assessed. The combined effect of smoking and age on hearing impairment was determined based on prevalence rate ratio.
RESULTS: There was a statistically significant trend in the number of pack-years of smoking and age as risk factors for hearing impairment. The prevalence rates of hearing impairment for nonsmokers aged 40 years and younger, smokers aged 40 years and younger, nonsmokers older than 40 years of age, and smokers older than 40 years of age were 6.9%, 11.9%, 29.7%, and 51.3%, respectively. The prevalence rate ratio for nonsmokers aged 40 years and younger, smokers aged 40 years and younger, nonsmokers older than 40 years of age, and smokers older than 40 years of age (nonsmokers aged 40 years and younger as a reference group) was 1, 1.7, 4.3, and 7.5, respectively. The prevalence rate ratios showed a multiplicative effect of smoking and age on hearing impairment.
CONCLUSION: Age and smoking are risk factors for hearing impairment. It is clear that smoking and age have multiplicative adverse effects on hearing impairment.
METHOD: A cross-sectional study among pharmacy students. Data were analyzed with Chi-square to find difference at p value < 0.05.
RESULTS: The majority of students (83.07%) responded showing a difference in gender and race. Students showed low willingness (9.2%) to assist patients and low confidence (36.1%) in their education about HIV/AIDS patients. Students recommended HIV testing for health care professionals (69.4%) and patients (75.9%) before surgical procedures. Students knew little about Post Exposure Prophylaxis (18.5%) or about the time for HIV to develop into AIDS (57.4%). About 40% of students were unaware of the inability of antivirals to treat HIV/AIDS. Students had low awareness for opportunistic infections (18.5%), and low agreement on competency to treat and counsel HIV patients (12.9%).
CONCLUSION: The study highlighted students' misconceptions, negative attitudes, and risk perceptions towards HIV/AIDS.
METHODS: Relative mortality and mortality rate advancement periods (RAPs) were estimated by Cox proportional hazards models for the population-based prospective cohort studies from Europe and the U.S. (CHANCES [Consortium on Health and Ageing: Network of Cohorts in Europe and the U.S.]), and subsequently pooled by individual participant meta-analysis. Statistical analyses were performed from June 2013 to March 2014.
RESULTS: A total of 489,056 participants aged ≥60 years at baseline from 22 population-based cohort studies were included. Overall, 99,298 deaths were recorded. Current smokers had 2-fold and former smokers had 1.3-fold increased mortality compared with never smokers. These increases in mortality translated to RAPs of 6.4 (95% CI=4.8, 7.9) and 2.4 (95% CI=1.5, 3.4) years, respectively. A clear positive dose-response relationship was observed between number of currently smoked cigarettes and mortality. For former smokers, excess mortality and RAPs decreased with time since cessation, with RAPs of 3.9 (95% CI=3.0, 4.7), 2.7 (95% CI=1.8, 3.6), and 0.7 (95% CI=0.2, 1.1) for those who had quit <10, 10 to 19, and ≥20 years ago, respectively.
CONCLUSIONS: Smoking remains as a strong risk factor for premature mortality in older individuals and cessation remains beneficial even at advanced ages. Efforts to support smoking abstinence at all ages should be a public health priority.
METHODS: Data were used from children and adolescents aged 8-19 years in six pooled childhood cohorts (19,261 participants, collected between 1972 and 2010) to create reference standards for fasting glucose and total cholesterol. Using the models for glucose and cholesterol as well as previously published reference standards for body mass index and blood pressure, clinical cardiovascular health charts were developed. All models were estimated using sex-specific random-effects linear regression, and modeling was performed during 2020-2022.
RESULTS: Models were created to generate charts with smoothed means, percentiles, and standard deviations of clinical cardiovascular health for each year of childhood. For example, a 10-year-old girl with a body mass index of 16 kg/m2 (30th percentile), blood pressure of 100/60 mm Hg (46th/50th), glucose of 80 mg/dL (31st), and total cholesterol of 160 mg/dL (46th) (lower implies better) would have a clinical cardiovascular health percentile of 62 (higher implies better).
CONCLUSIONS: Clinical cardiovascular health charts based on pediatric data offer a standardized approach to express clinical cardiovascular health as an age- and sex-standardized percentile for clinicians to assess cardiovascular health in childhood to consider preventive approaches at early ages and proactively optimize lifetime trajectories of cardiovascular health.