Logit Link Function Research Articles

Determinants of physical activity among female students based on the transtheoretical model

This study investigates factors influencing physical activity based on the Transtheoretical model (TTM) among adolescents. This study was conducted on 745 individuals between the ages of 12 and 16 years and was analyzed using a generalized linear model (GLM) approach with appropriate link functions using both classical and Bayesian frameworks. The results show that in model 1, the probit link function is a more appropriate approach to determine the risk factors for physical activity. In model 2, the logit link function is a more appropriate approach to determine the risk factors for physical activity. In addition, this study highlights a positive association between theatrical relief (OR = 1.08; 95% CI 1.02,1.14) and self-efficacy (OR = 1.06; 95% CI 1.02,1.09) for physical activity, and a negative association between self-relief (OR = 0.82; 95% CI 0.74,0.90), counter-conditioning (OR = 0.85; 95% CI 0.79,0.92), stimulus control (OR = 0.87; 95% CI 0.81,0.93) and helping relationships (OR = 0.94; 95% CI 0.88,0.99) with physical activity among female students. Self-efficacy is crucial for predicting activity levels, suggesting beneficial interventions that combine physical activity with self-efficacy enhancement. Positive thinking and a structured behavior change process are essential for physical activity engagement.

Developing and Comparing Four Families of Bayesian Network Autocorrelation Models for Binary Outcomes: Estimating Peer Effects Involving Adoption of Medical Technologies

ABSTRACTDespite the extensive use of network autocorrelation models in social network analysis, network autocorrelation models for binary dependent variables have received surprisingly scant attention. In this paper, we develop four network autocorrelation models for a binary random variable defined by whether the peer effect (also termed social influence or contagion) acts on latent continuous outcomes leading to an indirect effect under a normal or a logistic distribution or on the probability of the observed outcome itself under a probit or a logit link function defining a direct effect to account for interdependence between outcomes. For all models, we use a Bayesian approach for model estimation under a uniform prior on a transformed peer effect parameter () designed to enhance model computation and compare results to those under the uniform prior for . We use simulation to assess the performance of Bayesian point and interval estimators for each of the four models when the model that generated the data is used for estimation (precision assessment) and when each of the other three models instead generated the data (robustness assessment). We construct a United States New England region patient‐sharing hospital network and apply the four network autocorrelation models to study the adoption of robotic surgery, a new medical technology, among hospitals using a cohort of United States Medicare beneficiaries in 2016 and 2017. Finally, we develop a deviance information criterion for each of the four models to compare their fit to the observed data and use posterior predictive p‐values to assess the models' ability to recover specified features of the data. The results find that although the indirect peer effect of the propensity of peer hospital adoption on that of the focal hospital is positive under both latent response autocorrelation models, the direct peer effect of the peer hospital's probability of adopting robotic surgery on the probability of the focal hospital adopting robotic surgery decreases under both mean autocorrelation data models. However, neither of these associations is statistically significant.

Optimizing mHealth Interventions for Children's Oral Hygiene: A Factorial Trial.

Behavioral interventions can improve children's oral hygiene practices. The multiphase optimization strategy (MOST) offers a framework to prepare, optimize, and evaluate behavioral interventions. This optimization trial tested 3 intervention components-brief motivational interviewing (MI), storytelling videos (STVs), and oral health promotion messages (OHPMs)-in enhancing mother's self-reported brushing of their preschool children's teeth. A total of 128 mothers with children <5 y old were included in a 23 factorial trial. The primary outcome was children's plaque accumulation, assessed using the modified Plaque Index of Silness and Löe, with scores ranging from 0 to 3. The secondary outcome was mothers' self-reported frequency of brushing their children's teeth, categorized as at least once daily or less than once daily. Mothers were randomized to 8 experimental conditions based on combinations of on-off levels of the 3 components. Linear regression and generalized linear regression with logit link function were used to assess the impact of the components and their interactions on plaque score and daily toothbrushing after 3 mo. Multiple imputation was used for missing values. The principle of effect hierarchy guided the selection of components for inclusion in the optimized package, giving priority to main effects and 2-way over 3-way interactions. Plaque was reduced from mean = 1.8 at baseline to mean = 1.5 and daily toothbrushing increased from 50.8% to 69.5% after 3 mo. MI led to non-significantly less plaque and non-significantly more daily toothbrushing. Combining OHPMs and STVs together without MI canceled each other. Neither the main effects nor the 2- or 3-way interactions significantly affected the 2 outcomes. Individual or combined components did not significantly reduce plaque or increase daily toothbrushing. MI had the greatest promise for behavior change, and the m-oral health components need modification before they can be combined with MI in a health promotion package.

Mathematical Formalization and Applications to Data with Excess of Zeros and Ones of the Unit-Proportional Hazard Inflated Models

In this study, we model the rate or proportion of a specific phenomenon using a set of known covariates. To fit the regression model, which explains the phenomenon within the intervals (0,1), [0,1), (0,1], or [0,1], we employ a logit link function. This approach ensures that the model’s predictions remain within the appropriate range of zero to one. In cases of inflation at zero, one, or both, the logit link function is similarly applied to model the dichotomous Bernoulli-type variable with a multinomial response. The findings demonstrate that the model yields a non-singular information matrix, ensuring valid statistical inference. This ensures the invertibility of the information matrix, allowing for hypothesis testing based on likelihood statistics regarding the parameters in the model. This is not possible with other asymmetric models, such as those derived from the skew-normal distribution, which has a singular information matrix at the boundary of the skewness parameter. Finally, empirical results show the model’s effectiveness in analyzing proportion data with inflation at zero and one, proving its robustness and practicality for analyzing bounded data in various fields of research.

Geessbin: an R package for analyzing small-sample binary data using modified generalized estimating equations with bias-adjusted covariance estimators

BackgroundThe generalized estimating equation (GEE) method is widely used for analyzing longitudinal and clustered data. Although the GEE estimate for regression coefficients and sandwich covariance estimate are consistent regardless of the choice of covariance structure, they are generally biased for small sample sizes. Various researchers have proposed modified GEE methods and covariance estimators to handle small-sample bias.ResultsWe briefly present bias-corrected and penalized GEE methods, along with 11 bias-adjusted covariance estimators. In addition, we focus on analyzing longitudinal or clustered data with binary outcomes using the logit link function and introduce package geessbin in R to implement conventional and modified GEE methods with bias-adjusted covariance estimators. Finally, we illustrate the implementation and detail a usage example of the package. The package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/geessbin/index.html.ConclusionsThe geessbin package provides three GEE estimates with numerous covariance estimates. It is useful for analyzing correlated data such as longitudinal and clustered data. Additionally, the geessbin is designed to be user-friendly, making it accessible to non-statisticians.

Abstract 4137064: High Incidence of Adverse Neonatal Outcomes of Pregnancy in Women with Congenital Heart Disease

Background: Neonates born to women with congenital heart disease (CHD) are at high risk for adverse outcomes. We sought to describe these outcomes and maternal/pregnancy risk factors. Methods: We analyzed linked clinical, administrative, and birth certificate data in 3 geographic areas in Georgia, North Carolina and Utah from 2011 through 2014. Birth certificates provided neonatal, maternal and pregnancy characteristics. Maternal CHD was identified from linked clinical and administrative data and categorized as severe , shunt , valve , or shunt+valve . Neonatal outcomes of interest included preterm delivery (&lt;37 wk), low birth weight (&lt;2500 g) (LBW), small for gestational age (SGA), neonatal intensive care unit admission (NICU), a composite “any poor outcome” (one or more of preterm, SGA, or NICU), and CHD. Neonatal outcomes were summarized using descriptive statistics. Differences by maternal CHD severity were assessed using Kruskal-Wallis and Chi-square tests for continuous and categorical variables, respectively. Association of maternal/pregnancy characteristics and neonatal outcomes were assessed using generalized estimating equations with logit link function clustered on mother and adjusted for mother’s age, birth year, and state. Results: We identified 2,422 liveborn neonates (median gestational age 38 wks, weight 3155 g) born to 1,987 women with CHD (34% severe , 32% non-White, median age 28yrs). Overall, 19% were preterm, 18% LBW and 14% SGA; 25% required NICU and 40% experienced the composite adverse outcome (preterm, SGA, and/or NICU). CHD was present in 4.2%. Infants born to mothers with severe CHD (vs. combined non-severe) were more likely to have an adverse composite outcome [aOR 2.1 (1.7, 2.5)] and CHD [aOR 17.1 (9.3, 31.5)]. Black race [vs. White, aOR 1.8 (1.4, 2.3)], pregestational diabetes (DM) [vs. no, aOR 2.5 (1.3, 5.0)], gestational hypertension (GHTN) [vs. no, aOR 2.1 (1.5, 3.1)], prior preterm birth [vs. no, aOR 2.0 (1.3, 3.1)] and multiples [vs. singleton, aOR 6.7 (3.8, 11.6)] were associated with increased risk of composite adverse outcome. Conclusions: Forty percent of neonates born to women with CHD experienced an adverse neonatal outcome. Risk increased with maternal CHD severity, Black race, GHTN, DM, prior preterm birth, and multiple pregnancy. These findings highlight the need for heightened awareness and surveillance of these risk factors and expert preconception and pregnancy counseling for woman with CHD.

Selective Dry Cow Therapy: Clinical Field Trial on Prevention and Cure of Intramammary Infections

Intramammary antibiotic dry cow therapy (DCT) at the end of lactation is one of the key measures in the management and control of bovine mastitis. Currently, livestock production is under pressure to reduce antibiotic consumption, emphasizing the need to avoid medicating all cows at dry-off, and instead to treat only infected cows. The study objective was to evaluate IMI cure risk, new IMI risk, and post-calving IMI risk between DCT-treated and untreated quarters over the dry period. The sample totaled 269 cows from 12 Finnish automatic milking system herds entering the dry period between 2019 and 2021. Cows eligible for the study had a final DHI test-day SCC ≥ 100,000 cells/mL and had no clinical signs of mastitis. Based on odd or even identification numbers, cows with an SCC between 100,000 and 250,000 cells/mL were sequentially assigned to either receive DCT at dry-off (DCT100-250) or remain untreated (NoDCT100-250). All cows with an SCC > 250,000 cells/mL received DCT (DCT > 250). No quarters received teat sealants. Farmers collected aseptic quarter-milk samples for microbiological quantitative real-time PCR (qPCR) analysis 0 to 4 d before dry-off and 0 to 4 d after calving. The outcome of the statistical model was the odds of a quarter having an IMI at freshening 0 to 4 d after calving. The statistical tool was a generalized linear mixed-effects model with logit link function and 2-level random intercepts, cows nested within herds. Quarter-level IMI prevalence at dry-off was NoDCT100-250 17.6% (45/256), DCT100-250 22.2% (83/374), and DCT > 250 32.0% (132/413). Quarter-level IMI prevalence at freshening was NoDCT100-250, 36.2% (84/232), DCT100-250, 13.6% (50/369), and DCT > 250, 11.6% (46/397). The untreated quarters in the NoDCT100-250 group had a 1.1 times higher risk of failing to cure over the dry period than did the treated quarters in the DCT100-250 group (unadjusted RR 1.10, 95% CI 0.94-1.28) and a 1.2 times higher risk than the treated quarters in the DCT > 250 group (unadjusted RR 1.15, 95% CI 1.00-1.33). The untreated quarters in the NoDCT100-250 group had a 1.5 times higher risk of having a new IMI over the dry period than did the treated quarters in the DCT100-250 group (unadjusted RR 1.48, 95% CI 1.35-1.63) and a 1.4 times higher risk than the treated quarters in the DCT > 250 group (unadjusted RR 1.35, 95% CI 1.23-1.49). The principal cause of new IMI was NAS, followed by Streptococcus dysgalactiae. Quarters in the NoDCT100-250 group had 6.6 times higher odds for having an IMI at freshening than did quarters in the DCT100-250 group (95% CI 3.52-12.21). According to our findings within the framework of this experiment, selectively DCT-treated cows with an SCC between 100,000 and 250,000 cells/mL at dry-off demonstrated that, although the group-specific cure risks over the dry period were high, untreated quarters had a higher risk of acquiring a new IMI over the dry period than did treated quarters, highlighting the effectiveness of DCT in reducing IMI risks. The prophylactic use of antibiotics, however, is not recommended, and in some countries is even prohibited.

A semiparametric accelerated failure time-based mixture cure tree

The mixture cure rate model (MCM) is the most widely used model for the analysis of survival data with a cured subgroup. In this context, the most common strategy to model the cure probability is to assume a generalized linear model with a known link function, such as the logit link function. However, the logit model can only capture simple effects of covariates on the cure probability. In this article, we propose a new MCM where the cure probability is modeled using a decision tree-based classifier and the survival distribution of the uncured is modeled using an accelerated failure time structure. To estimate the model parameters, we develop an expectation maximization algorithm. Our simulation study shows that the proposed model performs better in capturing nonlinear classification boundaries when compared to the logit-based MCM and the spline-based MCM. This results in more accurate and precise estimates of the cured probabilities, which in-turn results in improved predictive accuracy of cure. We further show that capturing nonlinear classification boundary also improves the estimation results corresponding to the survival distribution of the uncured subjects. Finally, we apply our proposed model and the EM algorithm to analyze an existing bone marrow transplant data.

Poderes partidarios y reclutamiento ministerial. El caso argentino

Objective/context: Party recruitment is a crucial governance tool in contemporary democracies. Presidential and parliamentary heads of government distribute ministerial portfolios to party members in exchange for essential political resources, such as legislative support. This article explores the relationship between government formation and political parties, with parties that are not very institutionalized. In particular, it examines the extent to which arguments about presidential cabinet formation hold up when the hierarchical level of party ministers is considered. Methodology: This statistical study uses a generalized linear model (GLM) with a binomial distribution and a logit link function with robust standard errors. It aims to carry out causality exploration to test the original indicators of the proposed dependent variables. Conclusions: Hierarchy and partisan powers are relevant factors to complement existing explanations of ministerial recruitment strategies. Originality: Comparative research on government formation and its connection to political parties has made significant progress, focusing on the distinction between ministers affiliated and not affiliated with a party. However, this dichotomous approach neglects consideration of the hierarchical position within the party structure, a fundamental aspect of the governing party model underlying parliamentary democracies. This article presents a trichotomous measure that considers the hierarchical level of ministers.

217 Effect of within-litter competition on lamb survivability

Abstract The objective of this study was to evaluate the influence of lamb and littermate competition on lamb survivability to weaning. Litter sex composition and litter birth weight variation were the primary considerations for influencing competition. Records from maternal dual-purpose (Polypay, Rambouillet, and Targhee) and terminal breeds [Suffolk and Paternal Composite (PC; 3/8 Columbia, 3/8 Suffolk, 1/4 Texel)] were used, with 18,654 single and 56,324 twin records from all breeds and 11,082 triplet records from the 3 maternal breeds. Records were reported from 1980 to 2023, except for the PC, which began inter se mating in 2009. Lamb survivability to 90 d was analyzed using a Bayesian logistic regression model with the logit link function. Lamb survivability was assumed binomially distributed and influenced by age of dam, birth year, lamb sex, and sex of littermates as fixed effects. The relationship among littermates was also accounted for. Least squares means were transformed from the log odds scale to the probability scale. Birth weight was assumed normally distributed and influenced by the same fixed effects as lamb survival and was analyzed using a linear mixed model. Lamb survivability was also analyzed using the same model described above but including mean litter weight and individual birth weight deviation as linear covariates. Accounting for mean litter birth weight and individual birth weight deviation removed the birth weight differences between the sexes, leaving only true sex differences between lambs for survivability. For single births, females tended to have greater survivability than males when adjusted for birth weight for all breeds but was only significant (P &amp;lt; 0.05) for Rambouillet and Targhee. For maternal breeds, females in twin litters had significantly greater survivability (P &amp;lt; 0.05) than their male counterparts when adjusted for birth weight; differences in terminal breeds were not significant (Table 1). Males in twin litters had heavier birth weights than females (P &amp;lt; 0.05) for all breeds. Triplet born females for all litter-sex combinations had significantly higher survival than males (P &amp;lt; 0.05) for Polypay when adjusted for birth weight. Females in female-female-male (FFM) litters had 76.4% survival versus 58.9% for males while females in female-male-male (FMM) litters had 75.9% versus 66.7% for males. When lamb survivability was adjusted for birth weight, Rambouillet females in FFM litters had 72.2% survival compared with 59.9% for males (P &amp;lt; 0.05) and females in FMM litters had 73.0% survival compared with 63.8% for males. Similar significant differences were observed for FFM and FMM Targhee litters. This study suggests lamb performance is influenced by its littermate(s). Changes in postnatal management of mixed-sex litters may benefit overall lamb survival. USDA is an equal opportunity provider and employer.

Penetration Thresholds of Porcine Limbs for Low Sectional Density Projectiles in High-Rate Impact.

With similar prevalence to injuries from fires, stings, and natural disasters, soft tissue injuries may occur from fireworks, industrial accidents, or other explosives. Surgeons are less familiar with treating high-velocity penetration from small debris, which may increase the chance of infection and subsequent fatality. Penetration risk curves have been developed to predict V50, the velocity with 50% probability of penetration, for various sized projectiles. However, there has been limited research using nonmetallic materials to achieve lower density projectiles less than 1 g cm-2, such as sand or rocks. To emulate the size and density of these energized particles, 14 ball bearings of stainless steel, silicon nitride, or Delrin acetal plastic ranging from 1.59 mm (1/16") to 9.53 mm (3/8") with sectional densities between 0.3 g cm-2 and 5 g cm-2 were launched toward porcine legs at a range of velocities to determine the penetration thresholds. High-speed videography was captured laterally at 40 kHz and impact velocity was captured using a physics-based tracking software. A generalized linear model with repeated measures and a logit link function was used to predict probability of penetration for each projectile. A total of 600 impacts were conducted to achieve at least 15 penetrating impacts for each projectile over a range of velocities. Higher impact velocities were required to penetrate the skin as sectional density of the projectile decreased, and the relationship between velocity and sectional density exhibited an exponential relationship (V50, $ = 184.6*S{D^{ - 0.385}}$, R2 = 0.95) with substantial change for nonlinearity in sectional densities ranging from 0.3 g cm-2 to 1 g cm-2. Compared to previous studies, the empirical relationship was consistent in the linear region (2-5 g cm-2), and novel experimentation filled in the gaps for sectional densities less than 1 g cm-2, which expressed more nonlinearity than previously estimated. For low-density projectiles with diameters of 1.59 (1/16") or 3.18 (1/8"), 32 impacts were lodged into the epidermis but did not penetrate through the dermis; however, penetration was defined as displacement into or through the dermis. These experimental results may be used to develop and validate finite element simulations of low-density projectile impacts to address complex, multivariate loading conditions for the development of protective clothing to reduce wounding and subsequent infection rates.

Multiplex plasma protein assays as a diagnostic tool for lung cancer.

Lack of the established noninvasive diagnostic biomarkers causes delay in diagnosis of lung cancer (LC). The aim of this study was to explore the association between inflammatory and cancer-associated plasma proteins and LC and thereby discover potential biomarkers. Patients referred for suspected LC and later diagnosed with primary LC, other cancers, or no cancer (NC) were included in this study. Demographic information and plasma samples were collected, and diagnostic information was later retrieved from medical records. Relative quantification of 92 plasma proteins was carried out using the Olink Immuno-Onc-I panel. Association between expression levels of panel of proteins with different diagnoses was assessed using generalized linear model (GLM) with the binomial family and a logit-link function, considering confounder effects of age, gender, smoking, and pulmonary diseases. The analysis showed that the combination of five plasma proteins (CD83, GZMA, GZMB, CD8A, and MMP12) has higher diagnostic performance for primary LC in both early and advanced stages compared with NC. This panel demonstrated lower diagnostic performance for other cancer types. Moreover, inclusion of four proteins (GAL9, PDCD1, CD4, and HO1) to the aforementioned panel significantly increased the diagnostic performance for primary LC in advanced stage as well as for other cancers. Consequently, the collective expression profiles of select plasma proteins, especially when analyzed in conjunction, might have the potential to distinguish individuals with LC from NC. This suggests their utility as predictive biomarkers for identification of LC patients. The synergistic application of these proteins as biomarkers could pave the way for the development of diagnostic tools for early-stage LC detection.

A Multi-Level Analysis of Help-Seeking Behaviour of Male Victims of Intimate Partner Violence

Globally, many men are victims of intimate partner violence but not many seek help to stop the violence. This study sought to identify the factors associated with the help-seeking behavior of male victims of intimate partner violence (IPV) in Uganda. This was done using ordinary and mixed-effects regression models with logit, probit, and complementary log-log link functions and secondary data from the 2016 Uganda Demographic and Health Survey. Most males (70.6 percent) never sought help after experiencing intimate partner violence. Marital status, listening to the radio, physically hurt partner, experienced physical injury, number of control issues, father ever beat mother, and severity of violence were significantly associated with the help-seeking behavior of male victims of IPV. There is a need to come up with interventions that encourage males to report IPV, especially among the married, and also sensitize males not to wait until they have experienced physical injuries or any other severe violence before they seek help. Given that the sampling methodology used in demographic and health surveys introduces nesting in the data, researchers should consider using multilevel models.

Machine learning prediction of hypertension and diabetes in twin pregnancies using characteristics at prenatal care entry: a nationwide study.

To develop a prediction model for hypertensive disorders in pregnancy (HDP) and gestational diabetes (GDM) in twin pregnancies utilizing characteristics at the prenatal care entry level. Cross-sectional study using the US national live birth data between 2016 and 2021. The association of all prenatal candidate variables with HDP and GDM was tested with uni- and multi-variable logistic regression analyses. Prediction models were built with generalized linear models using the logit link function and classification and regression tree approach (XGboost) machine learning (ML) algorithm. Performance was assessed with repeated 2-fold cross-validation and performance metrics we considered were area under the curve (AUC). P value <0.001 was considered statistically significant. A total of 707,198 twin pregnancies were included in the HDP analysis and 723,882 twin pregnancies for the GDM analysis. The incidence of HDP and GDM significantly increased from 12.2% in 2016 to 15.4% in 2021 and from 8.1% in 2016 to 10.7% in 2021, respectively. Factors that increase the risk of HDP in twin gestations are maternal age <20, age≥35, infertility, prepregnancy DM, non-Hispanic Black population, obesity, and those with Medicaid insurance (p<0.001). Factors that more than doubled the risk are obesity class II and III (p<0.001). Factors that increase the risk of GDM in twin gestations are age <25, age≥30, history of infertility, prepregnancy hypertension, non-Hispanic Asian population, non-US nativity, and obesity (p<0.001). Factors that more than doubled the risk are maternal age ≥ 30 years, non-Hispanic Asian, and class I, II, and III maternal obesity ( p<0.001). For both HDP and GDM, the performance of the ML and logistic regression model was mostly similar with negligible difference in terms of all tested performance domains. The AUC of the final ML model for HDP and GDM were 0.62±0.004, and 0.67±0.004, respectively. The incidence of HDP and GDM in twin gestations is increasing. The predictive accuracy of the machine learning model for both HDP and GDM in twin gestations is similar to that of the logistic regression model. Both models had modest performance, well-calibrated, and neither had a poor fit. This article is protected by copyright. All rights reserved.

Tangible or intangible satisfiers? Comparative study of visitor satisfaction in a nature-based tourism destination in the pre- and during-COVID pandemic

Nature-based destinations started to become objects of travel for various visitor segments during the COVID-19 pandemic. The changing environment and the travel conditions influence overall visitor satisfaction and its determinants. This study identifies the role of tangible and intangible factors in forming overall visitor satisfaction with a stay in a nature-based tourism destination and their changes due to the COVID-19 pandemic. A Generalized Linear Model with a logit link function was used to identify the significant satisfiers and to determine the factors that play a critical role in visitor satisfaction in the during-COVID era compared to the pre-COVID period. The results show that intangible satisfiers play a more significant role than tangible ones in the COVID time. Surprisingly, natural features are insignificant in explaining overall satisfaction at a nature-based destination in the during-COVID in comparison with the pre-COVID period. In contrast, active sports options have become a significant satisfier in the COVID era. This comparison results contribute to the current theory, and the findings indicate the practical implications for Destination Management Organisations regarding sustainable tourism development. Management implications•Destination Management Organisations (DMO) in nature-based destinations should become more conscious of changing visitor structures concerning destination satisfaction.•DMOs could focus more on promoting activities such as active sports options and tourism infrastructure than natural features regarding current visitor preferences; however, sustainability should be a fundamental goal of competitive tourism destinations.•There is a need to carefully deal with the expanding tourism infrastructure development and prevent uncontrolled growth of commercial activities and attractions that would act against the interests of residents and endanger the value of the natural environment.•DMOs should educate the changing visitors about the importance of responsible tourism.•DMOs should focus on environmental-friendly activities which do not negatively impact nature in terms of using land and natural areas for commercial activities and construction.•DMOs should make efforts to attract their environmental sensitive pre-COVID visitors and target new segments searching for health and sustainability, such as the Lifestyle of Health and Sustainability (LOHAS) segment and the Free Independent Travellers (FIT).

Tactical Situations and Playing Styles as Key Performance Indicators in Soccer.

The game of soccer is complex and unpredictable, demanding multifaceted strategies for success. Performance analysis has evolved, focusing on key performance indicators (KPIs) to determine the factors that most significantly influence a team's success or failure during matches. Traditional performance analysis methods have emphasized quantifiable data like physical exertion and basic play events but often neglected the subtler tactical dimensions that could significantly impact game outcomes. This study aimed to fill the gap in the current literature by creating a comprehensive framework that incorporates tactical situations as KPIs. The objective was to examine whether specific playing styles adopted by teams in various tactical situations and phases of the game could predict the outcome of matches. A dataset comprising all First Division Championship matches from 11 different European countries for the 2021-2022 season was analyzed. Variables representing tactical situations were correlated with match outcomes using a Generalized Estimating Equation framework. The model was specified with a binomial distribution and a logit link function. Statistical significance was determined using Wald χ2 tests with a significance level set at p < 0.05. The study's findings revealed that possession style, counterattacking during offensive transitions, and a balanced aggressive defensive strategy significantly increase a team's chances of victory. It also showed that successful teams tend to focus on central attacks, minimize crossing, and execute strategic plays that lead to final attempts on goal with minimal ball possession. The above findings demonstrate that adopting certain tactical approaches significantly influences soccer match outcomes, highlighting the importance of considering tactical aspects as KPIs.

Association between sociodemographic factors, clinic characteristics and mental health screening rates in primary care.

Screening for mental health problems has been shown to be effective to detect depression and initiate treatment in primary care. Current guidelines recommend periodic screening for depression and anxiety. This study examines the association of patient sociodemographic factors and clinic characteristics on mental health screening in primary care. In this retrospective cohort study, electronic medical record (EMR) data from a 14-month period from 10/15/2021 to 12/14/2022 were analyzed. Data were retrieved from 18 primary care clinics from the Corewell Health healthcare system in West Michigan. The main outcome was documentation of any Patient Health Questionnaire (PHQ-4/PHQ-9/GAD-7) screening in the EMR within the 14-month period at patient level. General linear regression models with logit link function were used to assess adjusted odds ratio (aOR) of having a documented screening. In total, 126,306 unique patients aged 16 years or older with a total of 291,789 encounters were included. The prevalence of 14-month screening was 79.8% (95% CI, 79.6-80.0). Regression analyses revealed higher screening odds for patients of smaller clinics (<5,000 patients, aOR 1.88; 95% CI 1.80-1.98 vs. clinics >10.000 patients), clinics in areas with mental health provider shortages (aOR 1.69; 95% CI 1.62-1.77), frequent visits (aOR 1.80; 95% CI, 1.78-1.83), and having an annual physical / well child visit encounter (aOR 1.52; 95% CI, 1.47-1.57). Smaller positive effect sizes were also found for male sex, Black or African American race, Asian race, Latinx ethnicity (ref. White/Caucasians), and having insurance through Medicaid (ref. other private insurance). The 14-month mental health screening rates have been shown to be significantly lower among patients with infrequent visits seeking care in larger clinics and available mental health resources in the community. Introducing and incentivizing mandatory mental health screening protocols in annual well visits, are viable options to increase screening rates.

Strengths Model for Youth (SM-Y) Case Management: Initial Findings on Youth Outcomes.

Case management is a widely utilized service in both youth and adult outpatient community mental health settings. Despite its widespread use, previous findings suggest that youth case management often lacks empirically tested models or frameworks. This article presents the results of a pilot study that involved adapting the Strengths Model, an adult case management model, for the child and adolescent outpatient community mental health population. The newly adapted model, known as the Strengths Model for Youth (SM-Y), was implemented in an urban community mental health center across five different youth case management teams. To assess changes over time in youth receiving SM-Y case management, marginal maximum likelihood multilevel modeling with adaptive Gaussian quadrature methods was applied. The study focused on three domains: socialization, education, and hospitalization. Utilizing the logit link function and Bernoulli conditional distribution due to the binary nature of the outcome data, three individual trajectories were drawn for socialization, education, and hospitalization. Positive findings indicated increases in socialization and educational performance among children and adolescents.

Predictors of neonatal mortality in Ethiopia: Cross sectional study using 2005, 2010 and 2016 Ethiopian demographic health survey datasets.

Ethiopia is among the countries that have highest neonatal mortality in the world. Despite efforts to reduce neonatal mortality, the country has faced challenges in achieving national and global targets. The study aims to determine the trends and predictors of change in neonatal mortality in Ethiopia for the past 15 years. The study used Ethiopian Demographic Health Survey Datasets (EDHS) from 2005, 2011, and 2016. All live births of reproductive-age women in Ethiopia were included in the study. Multivariate decomposition analysis for the nonlinear response variable (MVDCMP) based on the logit link function was employed to determine the relative contribution of each independent variable to the change in neonatal mortality over the last 15 years. The neonatal mortality rate has decreased by 11 per 1,000 live births, with an annual reduction rate of 2.8% during the survey period. The mortality rate increased in the pastoralist regions of the country from 31 per 1,000 live births to 36 per 1,000 live births, compared to the city and agrarian regions. Maternal ANC visits in 2005 and 2016 (AOR [95%CI] = 0.10 [0.01, 0.81]; 0.01 [0.02, 0.60]) were significantly associated with decreased neonatal mortality. In addition, the decomposition analysis revealed that increased birth interval of more than 24 months and early breastfeeding initiation contributed to the reduction of neonatal mortality by 26% and 10%, respectively, during the survey period. The study found that neonatal mortality is a public health problem in the country, particularly in pastoralist communities. Tailor made maternal and child healthcare interventions that promote early breastfeeding initiation, increased birth intervals and ANC utilization should be implemented to reduce neonatal mortality, particularly in pastoralist communities.

From periphery immunity to central domain through clinical interview as a new insight on schizophrenia

Identifying disease predictors through advanced statistical models enables the discovery of treatment targets for schizophrenia. In this study, a multifaceted clinical and laboratory analysis was conducted, incorporating magnetic resonance spectroscopy with immunology markers, psychiatric scores, and biochemical data, on a cohort of 45 patients diagnosed with schizophrenia and 51 healthy controls. The aim was to delineate predictive markers for diagnosing schizophrenia. A logistic regression model was used, as utilized to analyze the impact of multivariate variables on the prevalence of schizophrenia. Utilization of a stepwise algorithm yielded a final model, optimized using Akaike’s information criterion and a logit link function, which incorporated eight predictors (White Blood Cells, Reactive Lymphocytes, Red Blood Cells, Glucose, Insulin, Beck Depression score, Brain Taurine, Creatine and Phosphocreatine concentration). No single factor can reliably differentiate between healthy patients and those with schizophrenia. Therefore, it is valuable to simultaneously consider the values of multiple factors and classify patients using a multivariate model.