Log-linear Research Articles

Comparison of Serum Per- and Polyfluoroalkyl Substances Concentrations in Incumbent and Recruit Firefighters and Longitudinal Assessment in Recruits.

Firefighters are occupationally exposed to per- and polyfluoroalkyl substances (PFAS). This study objective was to compare serum PFAS concentrations in incumbent and recruit firefighters and evaluate temporal trends among recruits. Serum PFAS concentrations were measured in 99 incumbent and 55 recruit firefighters at enrollment in 2015-2016, with follow-up 20 to 37 months later for recruits. Linear and logistic regression and linear mixed-effects models were used for analyses. Fireground exposure impact on PFAS concentrations was investigated using adjusted linear and logistic regression models. Incumbents had lower n-PFOA and PFNA than recruits and most PFAS significantly decreased over time among male recruits. No significant links were found between cumulative fireground exposures and PFAS concentrations. Serum PFAS concentrations were not increased in incumbent firefighters compared with recruits and were not associated with cumulative fireground exposures.

Tumor Volume Growth Rates and Doubling Times during Active Surveillance of IDH-mutant Low-Grade Glioma.

Isocitrate dehydrogenase-mutant (IDH-mt) gliomas are incurable primary brain tumors characterized by a slow-growing phase over several years followed by a rapid-growing malignant phase. We hypothesized that tumor volume growth rate (TVGR) on MRI may act as an earlier measure of clinical benefit during the active surveillance period. We integrated three-dimensional volumetric measurements with clinical, radiologic, and molecular data in a retrospective cohort of IDH-mt gliomas that were observed after surgical resection in order to understand tumor growth kinetics and the impact of molecular genetics. Using log-linear mixed modeling, the entire cohort (n = 128) had a continuous %TVGR per 6 months of 10.46% [95% confidence interval (CI), 9.11%-11.83%] and a doubling time of 3.5 years (95% CI, 3.10-3.98). High molecular grade IDH-mt gliomas, defined by the presence of homozygous deletion of CDKN2A/B, had %TVGR per 6 months of 19.17% (95% CI, 15.57%-22.89%) which was significantly different from low molecular grade IDH-mt gliomas with a growth rate per 6 months of 9.54% (95% CI, 7.32%-11.80%; P < 0.0001). Using joint modeling to comodel the longitudinal course of TVGR and overall survival, we found each one natural logarithm tumor volume increase resulted in more than a 3-fold increase in risk of death (HR = 3.83; 95% CI, 2.32-6.30; P < 0.0001). TVGR may be used as an earlier measure of clinical benefit and correlates well with the WHO 2021 molecular classification of gliomas and survival. Incorporation of TVGR as a surrogate endpoint into future prospective studies of IDH-mt gliomas may accelerate drug development.

Red blood cell distribution width and mortality risk in critically ill cardiovascular patients

BackgroundThe association between red blood cell distribution width (RDW) and mortality risk in critically ill cardiovascular patients has not been well studied. ObjectiveTo examine the association between RDW and 30-day all-cause and cause-specific mortality in critically ill cardiovascular patients. MethodsThis cohort study included 47,266 patients from the eICU database. RDW was categorized as <13.0 %, 13.0–13.4 %, 13.5–13.9 %, 14.0–14.4 %, 14.5–14.9 %, ≥15.0 %. Logistic regression model was used to estimate adjusted odds ratios (ORs), and log-linear regression model was used to examine absolute rate differences (RDs) in mortality risk. Cubic spline curve was used to explore the nonlinear association between changes in RDW and mortality. ResultsA graded association between higher RDW and incremental risk of death was observed. Compared with RDW of <13.0 %, the adjusted odds ratios for all-cause mortality were 1.29 (95 % CI, 1.10 to 1.53) for RDW of 13.5–13.9 %, 1.57 (95 % CI, 1.33 to 1.85) for RDW of 14.0–14.4 %, 1.94 (95 % CI, 1.64 to 2.29) for RDW of 14.5–15.0 %, and 3.15 (95 % CI, 2.74 to 3.63) for RDW of ≥15.0 %. The absolute rate differences for RDW of 13.5–13.9 %, 14.0–14.4 %, 14.5–14.9 %, and ≥15.0 % indicated an additional 6, 13, 14, and 40 deaths per 1000 patients, respectively. RDW was associated with most, but not all, cause-specific deaths. ConclusionRDW was strongly associated with all-cause mortality and most cause-specific mortality in critically ill cardiovascular patients. These findings underscore the importance of this readily available hematologic indicator in mortality risk stratification.

Is car traffic close to saturation in France?

After decades of uninterrupted growth, car traffic temporarily levelled off during the 2000′s in France, before coming back to growth after 2008. However, there are reasons to believe that car traffic growth is potentially limited, among which completion of the diffusion process for car ownership, limited travel time budgets, decreasing returns of additional car travel. In this study, we implement a disaggregate model at the household level to project future car ownership and car mileage per household in France, along with total car traffic, accounting for demographic growth. Our model relies on a sequential approach using panel data, with an ordered response probit model for the number of vehicles, including saturation thresholds by vehicle rank, and a log-linear model for car mileage, conditional on the number of vehicles, both with correlated random effects. A time trend is also incorporated to account for the decreasing household car mileage that was noticed over the last twenty years, which is not entirely explained by decreasing household size and the gradual decline of income growth. Assuming this trend to go on, total car traffic in France would grow only a little by 2050, despite demographic growth, and start decreasing from 2040. We discuss these results by questioning the structural nature of this trend.

Modelling inactivation kinetics of free and encapsulated probiotic cells in millet biscuit under different baking conditions

The rising popularity of probiotic food in the diet for improved health benefits leads to the development of new probiotic functional foods. In general, biscuit is a long-shelf-life snack product that can be consumed straight from the pack without further processing. Although the development of probiotic bakery products is an innovative approach to market expansion, the infusion of probiotics in biscuits to produce probiotic biscuits has not been explored because of the complexity of the baking process. Therefore, this study aimed to evaluate the impact of baking conditions (160, 180, 200, and 220 °C) on the viability of free and encapsulated probiotic Lactobacillus acidophilus NCDC 016 cells by adding them into biscuit dough separately and baking for up to 600 sec. The cells were encapsulated using 20 % maltodextrin and 8.51 % gum arabic as a wall material and spray drying at an inlet and outlet air temperature of 150 and 55 ± 2 °C, respectively. At different baking temperatures (160, 180, 200, and 220 °C), the viability of probiotic (free and encapsulated) cells, the physicochemical properties of biscuits, and the inactivation kinetics of cells were examined by withdrawing samples every 120 sec. The survivability of encapsulated cells was observed to be higher than free cells at 160 and 180 °C for 600 sec. The moisture content and water activity were found to be higher and lower, respectively for encapsulated probiotic biscuits than for the biscuit containing free cells. The observed results of higher cell viability at 200 °C, 360 sec (5.38 log CFU/g) than at 180 °C, 600 sec (5.02 log CFU/g) can be explained by the time–temperature combination. Thus, producing the probiotic biscuit at baking conditions of 200 °C and 360 min is possible, providing the cell viability of 5 log CFU/g of probiotic biscuit. Further, the inactivation kinetics of cells were predicted by log-linear, Weibull, log-logistic, Gompertz, and Buchanan models. Under all baking conditions, the log-linear model was the best model for describing the data of encapsulated and free cells.

Anxiety and depression among women with newly diagnosed vulvar cancer - A nationwide longitudinal study.

Our objective was to investigate the trajectories of anxiety, depression, emotional and social functioning in women with newly diagnosed vulvar cancer from the time of diagnosis to 12 months after treatment. A further aim was to identify risk factors for high levels of anxiety. PROVE (PROspective Vulvar Cancer Evaluation) is a nationwide longitudinal cohort study investigating quality of life in women with newly diagnosed vulvar cancer by the following validated patient-reported outcome measures at diagnosis, and 3 and 12 months after treatment: The Hospital Anxiety and Depression Scale, the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30, and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Vulvar module VU34. Mean scores, changes over time and associations were analyzed by generalized estimated equations and log-linear regression models, adjusted for possible confounders. Between 2019 and 2021, 105 (69%) women completed the questionnaires at all three time points. At diagnosis, 42% of the women reported elevated anxiety levels, decreasing significantly to 30% during the first 12 months. Insomnia, persisting vulvar symptoms and high information needs were significantly associated with a high level of anxiety (relative risk [RR] 2.1, 95% CI 1.2-3.7 for insomnia; RR 2.8, 95% CI 1.7-4.6 for vulvar symptoms, RR 2.7, 95% CI 1.5-4.9 for information needs). We found a trend towards a higher level of anxiety in younger women (<65 years: RR 1.5, 95% CI 1.0-2.5). Participants reported a low and stable prevalence of depression (14%) and high social functioning throughout the study period. Women with newly diagnosed vulvar cancer report a high level of anxiety at diagnosis. Despite a significant improvement, anxiety remains widely prevalent during the first year of follow-up. Targeting insomnia, vulvar symptoms and unmet needs may decrease anxiety during surveillance.

Examining Group Differences in Mathematics Achievement: Explanatory Item Response Model Application

Students take many different exams throughout their educational lives. In these exams, various individual and item characteristics can affect the responses of individuals to the items. In this study, it was aimed to examine the effects of person and item predictors on the mathematics common exam results of 365 9th grade students with explanatory item response models. Gender and school type as person variables and cognitive domain, content domain and booklet type as item variables were added to the models due to their widespread inclusion in the literature. When the predicted item parameters were examined, it was seen that the smallest parameter values were obtained for all items with the Rasch model. When the model data fit values of four different models were examined, it was concluded that the latent regression and latent regression linear logistic test models showed better fit than the Rasch model. By adding person and item predictors to the model, the parameters obtained for each variable group were compared, and differences were observed between the groups for school type, cognitive domain, and content domain variables. It was concluded that the item parameters did not differ for the variables of gender and booklet type. It is thought that it would be beneficial to use these models more widely in studies to be conducted in the field of education and psychology, since they provide more detailed information about the reasons for the differences in the estimated parameters.

Exposure to surrounding greenness and well-being in Portugal, a cross sectional study

Abstract Background Positive mental health effects of nature have been studied before with relevant associations between the two easily found in literature. However, there is still a lack of population based studies that focus on the effect that the amount of surrounding greenness might have on well-being. Objectives This study aims to evaluate the effects of exposure to surrounding greenness in the residential area with well-being based on the Mental Health Inventory 5-items (MHI-5) score from 0-100 (less to more well-being). Methods We used data from participants of the 2015 National Health Examination Survey (INSEF) which uses complex sampling methods to represent the Portuguese population and includes 4911 participants. Data regarding socioeconomic status, urbanization level, education, sex, age, postal code and well-being score was extracted. We then calculated the average surrounding greenness using the Normalized Difference Vegetation Index (NDVI) in a 300m and 1km buffer around each participant's residency and divided it by tertiles (from less green to most green). Log linear regression models controlling for confounder effects were applied. Results For the 300m buffer living in the 2nd tertile of greeness compared to the 1st tercile decreased the average well-being score by around 4% (ExpB=0.969, 95% p &amp;lt; 0.05). Living in the 3rd tercile (most green) had less to almost no effect (ExpB=0.989, 95% p &amp;lt; 0.05). Sensitivity analysis in a 1km buffer and the stratified analysis for sex and urbanization, also show a 4% decrease in well-being in the 2nd tertile and little to no effect in the 3rd. Discussion This study has found an association that is contrary to most literature on this subject, the study design might be subject to selection biases and misclassification of exposure. Further analysis should be done with relation to qualitative aspects of green space, accessibility to green areas and the roles of social cohesion and physical activity. Key messages • Population based study done in Portugal regarding the association between well-being and surrounding greenness. • Findings point to a negative association between increased surrounding greeness and well-being.

Sustainability of food purchases according to sociodemographic factors

Abstract While the nutritional quality and environmental impact of diets are widely assessed, different production modes (organic vs. conventional) remain understudied as for sustainability. The present study examines the associations between different dimensions of sustainability of food purchases and sociodemographic factors. Households (n = 462) were included in this cross-sectional study using quota sampling in Montpellier Metropolitan Area. Food items acquired over one month were registered in a food supply diary, and the expenditure share of each food group (including organic food) was estimated. Validated indexes estimated scores reflecting the nutritional quality and the environmental impact of food purchases according to these expenditure shares. Associations were tested for each sustainability dimension using linear and multinomial logistic regression models adjusted for sociodemographic factors (e.g. age, education, living area). The age of the household's reference person was consistently related to the sustainability dimensions of food purchases, albeit in different ways. While older age was associated with better nutritional quality (estimate for age &amp;gt;50 years vs. age between 30-50 [95%CI]=0.91[0.29;1.54]), it was associated with a higher environmental impact (estimate for age &amp;gt;50 years vs. age between 30-50 [95%CI]=1.84[0.95;2.73]). Households with a highly educated reference person (OR master's degree vs. lower education levels [95%CI]=1.59 [1.02;2.47] had a larger share of organic foods and those living around the city of Montpellier (OR vs. living in the city centre [95%CI]=0.44[0.24;0.81]) had a smaller share. Our findings highlight that sociodemographic factors relate to sustainability dimensions in different ways. In addition, we address organic food purchases, which provide insights into different aspects of sustainability. Taken together, the findings suggest that there are various levers for promoting healthy and sustainable diets at the consumer level. Key messages • Sustainability of food purchases relates to sociodemographic factors (i.e. age, education, and living location); different dimensions may be addressed. • Mode of food production (i.e., organic) may provide insights in the context of sustainable diets.

Prenatal particulate matter exposure is linked with neurobehavioral development in early life

Abstract Background Particulate matter (PM) is a major environmental health concern and there is growing evidence that early exposure to PM can have adverse effects on neurobehavioral development in children, including attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). In this study, we hypothesized that prenatal PM2.5 exposure is linked with changes in neurobehavioral development in early life. Methods This study was part of the prospective ENVIRONAGE birth cohort and included 85 mother-newborn pairs. The Neonatal Behavioral Assessment Scale (NBAS) was administered to 1-2-month-olds to evaluate newborn's behavior. In addition, 392 children aged 4-6 years of the ENVIRONAGE cohort were included. The Strengths and Difficulties Questionnaire (SDQ) was used to assess the child's behavior. PM2.5 concentrations were determined by a high-resolution spatial-temporal method based on the maternal residential address. Multiple linear and multinomial logistic regression models were used to link prenatal PM2.5 exposure and neurobehavioral development in newborns and children, respectively. Results A 5 µg/m3 increase in PM2.5 concentration in early pregnancy was linked with lower scores for the NBAS range of state cluster (-0.22; 95% CI: -0.42 to -0.02; p = 0.03) in newborns aged 1-2 months old, as well as with higher odds of a child having peer problems (OR 3.90; 95% CI: 1.39 to 10.88; p = 0.01), higher total difficulties scores (OR 1.67; 95% CI: 1.01 to 2.78; p = 0.047), and abnormal prosocial behavior (OR 0.49; 95% CI: 0.25 to 0.98; p = 0.04) at age 4-6 years. Conclusions This study provides indications that prenatal PM2.5 exposure influences neurobehavioral development, both in newborns and in early childhood. This evidence suggests that reducing exposure to PM2.5 during pregnancy may be an important public health strategy for promoting optimal neurodevelopment in early life. Key messages • In utero PM2.5 exposure is related to changes in neurobehavior in newborns. • Prenatal PM2.5 exposure is linked with higher chances of abnormal behavior in children aged 4-6 years old.

Cystic echinococcosis in humans and ruminants in Portugal: a one health approach

Abstract Background Cystic echinococcosis or hydatid disease is an important parasitic zoonosis for public health, although neglected. Despite being a mandatory notification disease both in humans and animals, data in Portugal are almost absent. A collaborative One Health retrospective nationwide study was conducted, bringing together the expertise of public health doctors, veterinarians and epidemiologists. Methods Human hospitalizations caused by cystic echinococcosis and slaughterhouse records of echinococcosis from bovine, ovine and caprine species were obtained. Poisson loglinear regression model was used. Occurrence maps were generated combining animal and human cases. Results From 2008 to 2022, 298 cases of echinococcosis were identified in post-mortem inspection in Portuguese slaughterhouses (bovines n = 125, ovines=288 cases, caprines n = 14). Between 2008 and 2018, cystic echinococcosis caused 582 hospitalizations in Portuguese public hospital, with an average length of 11 (± 15.66) days, resulting in 13 deaths (2.23%). Alentejo was the region with the highest hospitalizations. For every infected animal, a 7% increase in the incidence rate of human hospitalization was found (p = 0.002, Exp(B)=1.070, 95% CI: 1.025 - 1.117). Residing in the Alentejo increased the incidence rate of human hospitalizations by 5.3 times when compared to other regions (p = 0.000, Exp(B)=5.290, 95% CI: 3.835 - 7.296). For every 100,000 person-years, there was a total of 0.528 hospitalizations and the risk of death from echinococcosis was 8 times higher for those living in the Alentejo (p &amp;lt; 0.001, Exp(B)=8.259, 95% CI: 2.427 - 28.106). Conclusions Living in the Alentejo region has shown to be a high-risk factor for echinococcosis. This work gives an accurate recognition of the situation in Portugal, allowing the development of effective health control and education programs to face an important but neglected zoonotic disease under a One Health approach. Key messages • Considering that echinococcosis is still one of the 20 neglected tropical diseases of global health importance, is critical to map and monitor its prevalence and trends. • This study highlights the impact of this neglected zoonosis in humans and livestock, allowing the identification of the most problematic areas for echinococcosis in Portugal.

Intracerebral hemorrhage mortality in individuals with atrial fibrillation: a nationwide analysis of mortality trends in the United States.

Atrial fibrillation (AF) is a risk factor for intracerebral hemorrhage (ICH), both with and without use of anticoagulation. Limited data exists on mortality trends and disparities related to this phenomenon. We aimed to assess ICH mortality trends and disparities based on demographic factors in individuals with atrial fibrillation in the United States (US). Our cross-sectional analysis utilized mortality data from the CDC database through death certificate queries from the years 1999 to 2020 in the US. We queried for all deaths with ICH as the underlying cause of death and atrial fibrillation as the multiple causes of death. Mortality data was obtained for overall population and demographic subpopulations based on sex, race and ethnicity, and geographic region. Trend analysis and average annual-mortality percentage change (AAPC) were completed using log-linear regression models. ICH age-adjusted mortality rate (AAMR) in patients with AF increased from 0.27 (95% CI 0.25-0.29) in 1999 to 0.30 (95% CI 0.29-0.32) in 2020. A higher mortality rate was observed in males (AAMR 0.33) than in females (AAMR 0.26). The highest mortality was found in Asian/Pacific Islander (AAMR: 0.32) populations, followed by White (AAMR: 0.30), Black (AAMR: 0.15), and American Indian/Alaska Native (AAMR: 0.11) populations. Southern (AAPC: 1.3%) and non-metropolitan US regions (AAPC: + 1.9%) had the highest increase in annual mortality change. Our findings highlight the disparities in ICH mortality in patients with AF. Further investigation is warranted to confirm these findings and evaluate for contributors to the observed disparities.

Exploring the relationship between traditional bibliometrics and Altmetric scores in the primary care literature

AbstractThere is some evidence that Altmetric scores correlate with citations in medical research, but this is not consistent across different specialties. No previous studies have examined the association between Altmetric score and citations amongst primary care research journals. The aim of this study was therefore to describe this association. We identified the ten most frequently cited articles published in the top 15 highest impact factor primary care research journals. Article and journal metrics were extracted and summarized using descriptive statistics. We used Spearman's correlation coefficient (rs) and log–log linear regression modelling to examine the relationship between citations and Altmetric score. 150 articles were included with a median of 36.5 (IQR 20–59; range 5–811) citations. We found a positive association between citations and Altmetric score (rs = 0.519; p &lt; 0.001). A unit increase in log Altmetric score was associated with increased log citations [0.175 (95% CI 0.091–0.259, p &lt; 0.001)] in an adjusted linear regression model. The regression findings indicate that increasing Altmetric score by 10% was associated with a 1.68% increase in citation rate. This has implications for how authors, academic institutions and primary care research journals approach dissemination of articles.

Exploring Heart Failure Mortality Trends and Disparities in Women: A Retrospective Cohort Analysis

Heart failure (HF) remains a significant cause of morbidity and mortality in women. Population-level analyses shed light on existing disparities and promote targeted interventions. We evaluated HF-related mortality data in women in the United States to identify disparities based on race/ethnicity, urbanization level, and geographic region. We conducted a retrospective cohort analysis utilizing the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research database to identify HF-related mortality in the death files from 1999 to 2020. Age-adjusted HF mortality rates were standardized to the 2000 US population. We fit log-linear regression models to analyze mortality trends. Age-adjusted HF mortality rates in women have decreased significantly over time, from 97.95 in 1999 to 89.19 in 2020. Mortality mainly downtrended from 1999 to 2012, followed by a significant increase from 2012 to 2020. Our findings revealed disparities in mortality rates based on race and ethnicity, with the most affected population being non-Hispanic Black (age-adjusted mortality rates [AAMR] 90.36), followed by non-Hispanic White (AAMR 83.25), American Indian/Alaska Native (AAMR 64.27), and Asian/Pacific Islander populations (AAMR 37.46). We also observed that nonmetropolitan (AAMR 103.36) and Midwestern (AAMR 90.45) regions had higher age-adjusted mortality rates compared with metropolitan (AAMR 78.43) regions and other US census regions. In conclusion, significant differences in HF mortality rates were observed based on race/ethnicity, urbanization level, and geographic region. Disparities in HF outcomes persist and efforts to reduce HF-related mortality rates should focus on targeted interventions that address social determinants of health, including access to care and socioeconomic status.

On semiparametric accelerated failure time models with time-varying covariates: A maximum penalised likelihood estimation.

The accelerated failure time (AFT) model offers an important and useful alternative to the conventional Cox proportional hazards model, particularly when the proportional hazards assumption for a Cox model is violated. Since an AFT model is basically a log-linear model, meaningful interpretations of covariate effects on failure times can be made directly. However, estimation of a semiparametric AFT model imposes computational challenges even when it only has time-fixed covariates, and the situation becomes much more complicated when time-varying covariates are included. In this paper, we propose a penalised likelihood approach to estimate the semiparametric AFT model with right-censored failure time, where both time-fixed and time-varying covariates are permitted. We adopt the Gaussian basis functions to construct a smooth approximation to the nonparametric baseline hazard. This model fitting method requires a constrained optimisation approach. A comprehensive simulation study is conducted to demonstrate the performance of the proposed method. An application of our method to a motor neuron disease data set is provided.

Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data

Water depth estimation is paramount in various domains, including navigation, environmental monitoring, and resource management. Traditional depth measurement methods, such as bathymetry, can often be expensive and time-consuming, especially in remote or inaccessible areas. This study delves into the application of machine learning techniques, specifically focusing on the Baidu Easy DL model for water depth estimation leveraging satellite imagery. Utilizing Sentinel-2 satellite data over Rushikonda Beach in India and processing it into remote sensing reflectance using ACOLITE software, this research compares the performance of several machine learning algorithms, including the Stumpf model, Log-Linear model, and the Baidu Easy DL model, for accurate depth estimation. The results indicate that the Easy-DL model outperforms traditional methods, particularly excelling in the 0–11 m depth range. This study showcases the substantial potential of machine learning in remote sensing, offering robust water depth estimates, even in complex coastal environments. Furthermore, it underscores the critical role of comprehensive training datasets and ensemble learning techniques in enhancing accuracy. This research opens avenues for the further exploration of machine learning applications in remote sensing and highlights the promising prospects of online model APIs when streamlining remote sensing data processing.

Trends in glucagon-like peptide 1 receptor agonist use, 2014 to 2022

BackgroundRecent Food and Drug Administration approvals of glucagon-like peptide 1 (GLP-1) receptor agonists linked to substantial weight loss have generated interest in demand projections. However, a longitudinal analysis in a large, diverse, current, real-world database has not been published. ObjectivesThe study objective was to determine user frequency of GLP-1 receptor agonist products overall and by type 2 diabetes (T2D), cardiovascular disease (CVD), and overweight or obese status. Secondary objective was monthly growth rate estimation by product since first appearance in University of California Health. MethodsThis retrospective cohort study included patients who were dispensed a GLP-1 receptor agonist from 2014 to 2022 in the University of California Health Data Warehouse. Exponential growth rates were estimated using a log-linear regression model. ResultsBetween 2014 and 2018, only Trulicity and Victoza exceeded 5000 annual users. Ozempic users increased from 569 in 2019 to 7667 in 2020. Use accelerated with more than 13,310 users in 2021 to surpass Trulicity. Ozempic count was 22,891 in 2022. Wegovy rose from 989 in 2021 to 2992 in 2022. Mounjaro increased to 1508 users in 2022. Although generally similar trends were observed for T2D, CVD, and overweight or obese subgroups, the ascent of Ozempic as most frequently used was more apparent in the overweight or obese group. The monthly growth rates were 83.9% for Ozempic, 119.2% for Wegovy, 84.8% for Rybelsus, 53.3% for Saxenda, 12.9% for Adlyxin, 78.8% for Trulicity, and 254.3% for Mounjaro. ConclusionThis first cohort study of weight loss–associated GLP-1 receptor agonists in a large, diverse, state-wide health system demonstrated a rapid increased use that represents a clear and likely durable transition in utilization for this category. Informed decision making and longitudinal studies are needed to ensure evidence-concordant prescribing and supply stability.

Chronotype is associated with eating behaviors, physical activity and overweight in school-aged children

BackgroundA later chronotype has been found to be associated with unhealthy habits and diseases, such as an unhealthy diet and metabolic syndrome in adults. Little is known about the association between chronotype, eating habits, physical activity and obesity. Thus, this study aimed to explore the relationships between chronotype, eating behaviors, physical activity, and overweight in Chinese school-aged children.MethodsData from this study was based on 952 schoolchildren (10–12 y) from six primary schools that participated in China. Anthropometric measurements of height and body weight were performed. Information about sleeping habits, dietary behaviors, and other lifestyle behaviors was gathered using a self-administered questionnaire. Multiple linear regression analysis or multivariable logistic regression model was performed to assess the associations between chronotype, eating behaviors, physical activity, and overweight.ResultsNearly 70% (69.9%) of the participants had a self-reported morning chronotype. Multiple linear regression analysis revealed chronotype score was positively associated with physical activities (all P values < 0.001) and sleep duration (all P values < 0.001) and negatively associated with BMI, meal time, eating jet lag and social jet lag (all P values < 0.001). Multivariable logistic regression analysis showed that compared to morning types, non-morning types individuals were more likely to be overweight (OR = 1.593, P value < 0.05), and had more frequent consumption of fast food (OR = 1.616, P value < 0.05), but less frequent consumption of milk (OR = 0.716, P value < 0.05), less time taking part in moderate (OR = 1.356, P value < 0.05) or muscle strengthening (OR = 1.393, 1.877, P value < 0.05) physical activity.ConclusionsThis study indicates that early chronotype children are more active, have healthier dietary habits, get more sleep, have shorter social jet lag, and are less likely to be overweight than non-early chronotype children. Our findings suggest that later chronotype may be a potential indicator in the early detection of overweight, unhealthy eating, and physical inactivity behaviors.Plain english summaryChronotype has been found to have an important impact on individual’s health. In the present study, we conducted a cross-sectional study to investigate the association between chronotype, eating behaviors, physical activity, and overweight in school-aged children. The findings showed that children with early chronotype is associated with more active, healthier dietary behaviors, longer sleep duration, short social jet lag, and a lower risk of overweight.

Classical iterative proportional scaling of log-linear models with rational maximum likelihood estimator

In this work we investigate multipartition models, the subset of log-linear models for which one can perform the classical iterative proportional scaling (IPS) algorithm to numerically compute the maximum likelihood estimate (MLE). Multipartition models include families of models such as hierarchical models and balanced, stratified staged trees. We define a sufficient condition, called the Generalized Running Intersection Property (GRIP), on the matrix representation of a multipartition model under which the classical IPS algorithm produces the exact MLE in one cycle. In this case, the MLE is a rational function of the data. Additionally we connect the GRIP to the toric fiber product and to previous results for hierarchical models and balanced, stratified staged trees. This leads to a characterization of balanced, stratified staged trees in terms of the GRIP.

The association of serum neurofilament light chains with early symptoms related to Parkinson's disease: A cross-sectional study

Neurofilament light chains (NfL), released with neural axon injury, is considered as a potential biomarker for Parkinson's disease (PD). The relationship between NfL and PD has been studied mainly in diagnosed patients. Few large-scale studies analyze the association between NfL levels and multiple non-motor symptoms linked to early PD in the general population. Therefore, this study aims to determine the association of NfL with early symptoms of PD, and effectively respond to the development of early symptoms of PD. We examined the relationship between serum NfL and early non-motor symptoms of PD (smell dysfunction, sleep problems, cognitive function) and serum Klotho levels in the general population using data from the 2013–2014 National Health and Nutrition Examination Survey (NHANES). The relationship between serum NfL and early symptoms of PD in 1125 participants was analyzed by multiple linear regression and logistic regression models. The results showed a significant association between serum NfL and early symptoms of PD. There was a significant positive correlation between NfL and smell dysfunction, short sleep and long sleep. There was a significant negative correlation between NfL and Klotho levels and cognitive function test results. Further, we observed gender and age differences in the association of NfL with early symptoms of PD. Our study demonstrate that elevated serum NfL levels are positively associated with an increased risk of early PD-related symptoms, suggesting that serum NfL can be a promising biomarker for early PD.