Ordinal Regression Models Research Articles

Implementation of a Frailty Care Bundle (FCB) Targeting Mobilisation, Nutrition and Cognitive Engagement to Reduce Hospital Associated Decline in Older Orthopaedic Trauma Patients: Pretest-Posttest Intervention Study.

To implement and evaluate a Frailty Care Bundle (FCB) targeting mobilisation, nutrition, and cognition in older trauma patients to reduce hospital associated decline. We used a two group, pretest-posttest design. The FCB intervention was delivered on two orthopaedic wards and two rehabilitation wards, guided by behaviour change theory (COM-B) to implement changes in ward routines (patient mobility goals, nurse assisted mobilisation, mealtimes, communication). Primary outcomes were patient participants' return to pre-trauma functional capability (modified Barthel Index - mBI) at 6-8 weeks post-hospital discharge and average hospital daily step-count. Statistical analysis compared pre versus post FCB group differences using ordinal regression and log-linear models. We recruited 120 patients (pre n=60 and post n=60), and 74 (pre n=43, post n=36) were retained at follow-up. Median age was 78 years and 83% were female. There was a non-significant trend for higher mBI scores (improved function) in the post compared to pre FCB group (OR 2.29, 95% CI 0.98-5.36), associated with an average 11% increase in step-count. It was feasible, during the Covid-19 pandemic, for multidisciplinary teams to implement elements of the FCB. Clinical facilitation supported teams to prioritise fundamental care above competing demands, but sustainability requires ongoing attention. ISRCTN registry: ISRCTN15145850 (https://doi.org/10.1186/ISRCTN15145850).

The influence of resolution on the predictive power of spatial heterogeneity measures as biomarkers of liver fibrosis

Spatial heterogeneity of cells in liver biopsies can be used as biomarker for disease severity of patients. This heterogeneity can be quantified by non-parametric statistics of point pattern data, which make use of an aggregation of the point locations. The method and scale of aggregation are usually chosen ad hoc, despite values of the aforementioned statistics being heavily dependent on them. Moreover, in the context of measuring heterogeneity, increasing spatial resolution will not endlessly provide more accuracy. The question then becomes how changes in resolution influence heterogeneity indicators, and subsequently how they influence their predictive abilities. In this paper, cell level data of liver biopsy tissue taken from chronic Hepatitis B patients is used to analyze this issue. Firstly, Morisita–Horn indices, Shannon indices and Getis–Ord statistics were evaluated as heterogeneity indicators of different types of cells, using multiple resolutions. Secondly, the effect of resolution on the predictive performance of the indices in an ordinal regression model was investigated, as well as their importance in the model. A simulation study was subsequently performed to validate the aforementioned methods. In general, for specific heterogeneity indicators, a downward trend in predictive performance could be observed. While for local measures of heterogeneity a smaller grid-size is outperforming, global measures have a better performance with medium-sized grids. In addition, the use of both local and global measures of heterogeneity is recommended to improve the predictive performance.

A Survey of Medication Diffusion and Attitudes Among Indian Clinicians Toward Pharmacotherapies for Alcohol Use Disorder.

Alcohol use disorder (AUD) is a significant cause of morbidity and mortality globally. However, uptake of AUD pharmacotherapies among clinicians has remained low. There exists a research gap regarding clinician attitudes and the diffusion of AUD pharmacotherapies among Indian clinicians. We conducted a survey to assess the same. An online survey was sent to clinicians nationwide from February and April 2023 to assess attitudes and practices regarding various AUD medications. Logistic and ordinal regression models evaluate the clinician and caseload factors determining medication diffusion and attitudes. A total of 387 clinicians responded to our survey. Most of them were males (61.7%) and trained psychiatrists (56.3%). The diffusion for acamprosate, naltrexone, and disulfiram was 69.8%, 81.4%, and 58.2%, respectively. Baclofen had the highest diffusion. Female respondents had significantly lower perceived efficacy, acceptability, and safety for disulfiram. Independent practitioners were more likely to prescribe all pharmacotherapies except baclofen. Diffusion for alcohol pharmacotherapies was incomplete. Baclofen had the highest diffusion, probably reflecting its cost and safety profile. Despite its perceived efficacy, safety concerns impacted the diffusion of disulfiram. Female clinicians had lower perceived attitudes toward disulfiram. Specific training for AUD pharmacotherapies is warranted for female clinicians and nonpsychiatrists.

The effect of liquid consistency on penetration-aspiration: a Bayesian analysis of two large datasets.

Thickened liquids are commonly recommended to reduce the risk of penetration-aspiration. However, questions persist regarding the impact of bolus consistency on swallowing safety. The common practice of summarizing Penetration-Aspiration Scale (PAS) scores based on worst scores is a bias in prior analyses. The aim of this study was to examine the impact of liquid consistency on PAS scores using a Bayesian multilevel ordinal regression model approach, considering all scores across repeated bolus trials. A second aim was to determine whether PAS scores differed across thickener type within consistency. We analyzed two prior datasets (D1; D2). D1 involved 678 adults with suspected dysphagia (289 female; mean age 69 years, range 20-100). D2 involved 177 adults (94 female; mean age 54 years, range 21-85), of whom 106 were nominally healthy and 71 had suspected dysphagia. All participants underwent videofluoroscopy involving ≥3 boluses of 20% w/v thin liquid barium and of xanthan-gum thickened barium in mildly, moderately and extremely thick consistencies. D2 participants also swallowed trials of slightly thick liquid barium, and starch-thickened stimuli for each thickened consistency. Duplicate blinded rating yielded PAS scores per bolus, with discrepancies resolved by consensus. PAS ratings for a total of 8,185 and 3,407 boluses were available from D1 and D2, respectively. Bayesian models examined PAS patterns across consistencies. We defined meaningful differences as non-overlapping 95% credible intervals (CIs). Across D1 and D2, penetration occurred on 10.87% of trials compared to sensate (0.68%) and silent aspiration (1.54%), with higher rates of penetration (13.47%) and aspiration (3.07%) on thin liquids. For D1, the probability of a PAS score > 2 was higher for thin liquids with weighted PAS scores of 1.57 (CI: 1.48, 1.66) versus mildly (1.26; CI: 1.2, 1.33), moderately (1.1; CI: 1.07, 1.13), and extremely thick liquids (1.04; CI: 1.02, 1.08). D2 results were similar. Weighted PAS scores did not meaningfully differ between thin and slightly thick liquids, or between starch and xanthan gum thickened liquids. These results confirm that the probability of penetration-aspiration is greatest on thin liquids compared to thick liquids, with significant reductions in PAS severity emerging with mildly thick liquids.

Candidate Kidney Protective Strategies for Patients Undergoing Major Abdominal Surgery: A Secondary Analysis of the RELIEF Trial Cohort.

Acute kidney injury (AKI) is common after major abdominal surgery. Selection of candidate kidney protective strategies for testing in large trials should be based on robust preliminary evidence. A secondary analysis of the Restrictive versus Liberal Fluid Therapy in Major Abdominal Surgery (RELIEF) trial was conducted in adult patients undergoing major abdominal surgery and randomly assigned to a restrictive or liberal perioperative fluid regimen. The primary outcome was maximum AKI stage before hospital discharge. Two multivariable ordinal regression models were developed to test the primary hypothesis that modifiable risk factors associated with increased maximum stage of postoperative AKI could be identified. Each model used a separate approach to variable selection to assess the sensitivity of the findings to modeling approach. For model 1, variable selection was informed by investigator opinion; for model 2, the Least Absolute Shrinkage and Selection Operator (LASSO) technique was used to develop a data-driven model from available variables. Of 2,444 patients analyzed, stage 1, 2, and 3 AKI occurred in 223 (9.1%), 59 (2.4%), and 36 (1.5%) patients, respectively. In multivariable modeling by model 1, administration of a nonsteroidal anti-inflammatory drug or cyclooxygenase-2 inhibitor, intraoperatively only (odds ratio, 1.77 [99% CI, 1.11 to 2.82]), and preoperative day-of-surgery administration of an angiotensin-converting enzyme inhibitor or angiotensin receptor blocker compared to no regular use (odds ratio, 1.84 [99% CI, 1.15 to 2.94]) were associated with increased odds for greater maximum stage AKI. These results were unchanged in model 2, with the additional finding of an inverse association between nadir hemoglobin concentration on postoperative day 1 and greater maximum stage AKI. Avoiding intraoperative nonsteroidal anti-inflammatory drugs or cyclooxygenase-2 inhibitors is a potential strategy to mitigate the risk for postoperative AKI. The findings strengthen the rationale for a clinical trial comprehensively testing the risk-benefit ratio of these drugs in the perioperative period.

An interpretable neural network-based non-proportional odds model for ordinal regression

This study proposes an interpretable neural network-based non-proportional odds model (N3POM) for ordinal regression. N3POM is different from conventional approaches to ordinal regression with non-proportional models in several ways: (1) N3POM is defined for both continuous and discrete responses, whereas standard methods typically treat the continuous variables as if they were discrete, (2) instead of estimating response-dependent finite-dimensional coefficients of linear models from discrete responses as is done in conventional approaches, we train a non-linear neural network to serve as a coefficient function. Thanks to the neural network, N3POM offers flexibility while preserving the interpretability of conventional ordinal regression. We establish a sufficient condition under which the predicted conditional cumulative probability locally satisfies the monotonicity constraint over a user-specified region in the covariate space. Additionally, we provide a monotonicity-preserving stochastic (MPS) algorithm for effectively training the neural network. We apply N3POM to several real-world datasets.

Assessing support for mental health policies among policy influencers and the general public in Alberta and Manitoba, Canada

BackgroundThere is a need to improve mental health policy in Canada to address the growing population burden of mental illness. Understanding support for policy options is critical for advocacy efforts to improve mental health policy. Our purpose was to describe support for population-level healthy public policies to improve mental health among policy influencers and the general public in Alberta and Manitoba; and, identify associations between levels of support and sociodemographic variables and relative to the Nuffield Bioethics Intervention Ladder framework.MethodsWe used data from the 2019 Chronic Disease Prevention Survey, which recruited a representative sample of the general public in Alberta (n = 1792) and Manitoba (n = 1909) and policy influencers in each province (Alberta n = 291, Manitoba n = 129). Level of support was described for 16 policy options using a Likert-style scale for mental health policy options by province, sample type, and sociodemographic variables using ordinal regression modelling. Policy options were coded using the Nuffield Council on Bioethics Intervention Ladder to classify support for policy options by level of intrusiveness.ResultsPolicy options were categorized as ‘Provide Information’ and ‘Enable Choice’ according to the Nuffield Intervention Ladder. There was high support for all policy options, and few differences between samples or provinces. Strong support was more common among women and among those who were more politically left (versus center). Immigrants were more likely to strongly support most of the policies. Those who were politically right leaning (versus center) were less likely to support any of the mental health policies. Mental health status, education, and Indigenous identity were also associated with support for some policy options.ConclusionsThere is strong support for mental health policy in Western Canada. Results demonstrate a gap between support and implementation of mental health policy and provide evidence for advocates and policy makers looking to improve the policy landscape in Canada.

Empowering Product Teams to Advance Inclusive Language and Mitigate Hateful Speech on Social Media Products

As social media use proliferates, so does hateful language on such platforms among users. How might product teams be empowered to tackle hateful speech and promote inclusive language proactively among users? We developed an intervention that targets the organizational culture of tech companies and their product teams as a driver for change. More specifically, the intervention aimed to advance inclusive language practices within product teams, while introducing product development frameworks for advancing inclusive language and mitigating hateful language among social media product users. For this study, we partnered with a large U.S.-headquartered tech firm that owns several social media products to develop and pilot the intervention. The intervention was implemented with 238 participants—representing employees of the firm across several global locations, with particular representation from the United States—in seven workshops. Forty participants were tracked semilongitudinally across three surveys—prior to the intervention, immediately after, and 4 months after. Two participants participated in in-depth interviews 6 months after the intervention. Survey data were submitted to ordinal regression models that examined difference in workshop attendees’ confidence levels across the three time periods. Findings reveal positive impacts over time among product teams in regard to enhanced agency in advancing inclusive language and mitigating hateful speech among users. More specifically, confidence levels to guide one’s team in preventing or mitigating harmful language among product users significantly increased by 13.6% ( p < .0001) from before the intervention to both postworkshop surveys. Confidence levels to guide one’s team in enhancing inclusive language among users significantly increased by 18.1% ( p < .0001) pre- to postworkshop. Positive correlations are present between the measures. Lastly, qualitative responses in the surveys and interviews express appreciation of learning gained in the workshops, but also the need to continue such interventions as an effort to maintain critical understandings of inclusive language practices.

The relationships between political-economic typology and public service satisfaction: an empirical evidence from Türkiye

PurposeThere are many factors that determine consumers’ satisfaction. Specifically, the current study examines how political-economic views affect the perception of public services. This study also aims to extend the relationship between public service satisfaction and political-economic opinion from an empirical perspective.Design/methodology/approachThe analysis process is carried out with the Microdataset of the Life Satisfaction Survey. Independent sample t-test which compares individual public service satisfaction ratings and ordinal probit regression models assesses the impacts of political-economic attitude on public service satisfaction.FindingsAccording to the findings, household size and gender impact public service satisfaction. Again, service satisfaction diminishes as the education level rises. Moreover, a person’s political-economic view affects their public service satisfaction. The study has the potential to literature in terms of determining society’s political-economic view on public services with these empirical results.Research limitations/implicationsThe study’s use of the state as a point of reference and quantitative and qualitative evaluation of the service exchange between the citizen and the state is a significant difference in the literature. Also, unlike the other studies, this one used microdata to test the relationship between the political-economic type and the level of satisfaction with public services at different levels. It is thought that future studies on the determinants of public service satisfaction, to be carried out in different country samples, will contribute to the field.Practical implicationsThis study has implications on whether the perception of public service varies according to partisanship status.Social implicationsThis study has implications on whether the perception of public service varies according to partisanship status.Originality/valueThis study determines the interaction between the political-economic typology derived from microdata and the satisfaction level variables related to public services offered at different levels. It also evaluates how public service satisfaction differs regarding personality traits and political-economic attitudes.

Health-related risk behaviors among U.S. childhood cancer survivors: a nationwide estimate.

Childhood cancer survivors (CCS) are subject to a substantial burden of treatment-related morbidity. Engaging in health protective behaviors and eliminating risk behaviors are critical to preventing chronic diseases and premature deaths. This study is aimed to provide updated information on currently smoking, physical inactivity, binge drinking patterns and associated factors among CCS using a nationwide dataset. We constructed a sample of CCS (cancer diagnosis at ages < 21y) and healthy controls (matched on age, sex, residency, race/ethnicity) using 2020 Behavioral Risk Factor Surveillance System. We used Chi-square tests and Wilcoxon rank-sum test to examine differences in sociodemographics and clinical characteristics between two groups. Logistic, ordinal regression and multivariable models (conditional models for matching) were used to determine factors associated with risk behaviors. The final sample (18-80y) included 372 CCS and 1107 controls. Compared to controls, CCS had a similar proportion of binge drinking (~ 18%) but higher prevalence of currently smoking (26.6% vs. 14.4%, p < 0.001), physical inactivity (23.7% vs. 17.7%, p = 0.012), and of having 2-or-3 risk behaviors (17.2% vs. 8.1%, p < 0.001). Younger age, lower educational attainment, and having multiple chronic health conditions were associated with engaging in more risk behaviors among CCS. Females, compared to male counterparts, had lower odds of binge drinking (adjusted odds ratio (aOR) = 0.30, 95% confidence interval (CI): 0.16-0.57) among CCS but not in all sample. Having multiple chronic health conditions increased odds of both currently smoking (aOR = 3.52 95%CI: 1.76-7.02) and binge drinking (aOR = 2.13 95%CI: 1.11-4.08) among CCS while it only increased odds of currently smoking in all sample. Our study provided risk behavior information for wide age-range CCS, which is currently lacking. Every one in four CCS was currently smoking. Interventions targeting risk behavior reduction should focus on CCS with multiple chronic health conditions.

INVESTIGATING THE RISK FACTORS AFFECTING THE OCCURRENCE, FREQUENCY, AND SEVERITY OF LARGE TRUCK ACCIDENTS IN AL-NAJAF GOVERNORATE, IRAQ

In spite of the established literature-based evidence regarding the consequences of large truck accidents, limited body of research has been done on the characteristics and risk factors of such road accidents in Iraqi cities and governorates. According to national statistics, there has been a steady increase in the number of trucks and truck-related crashes over the past ten years. This paper aims to investigate the characteristics and risk factors associated with accidents involving large trucks in Al-Najaf governorate. A sample of 400 truck drivers were randomly selected and interviewed to collect the needed accident data. Four generalized linear models have been built; ordinal regression model for total injuries, binary logistic model for fatal accident occurrence, multinomial logit model for accident frequency, and ordinal regression model for accident cost. The analysis results revealed several influential predictors including truck driver age, education level, type of collision, truck speed, truck type, and street lighting condition. The obtained findings should be enlightening and helpful for government organizations looking to promote safety measures for sustainable freight truck transport.

Modeling Zero-inflated Count Data Using Generalized Poisson and Ordinal Logistic Regression Models in Medical Research.

In medical research, the study's design and statistical methods are pivotal, as they guide interpretation and conclusion. Selecting appropriate statistical models hinges on the distribution of the outcome measure. Count data, frequently used in medical research, often exhibit over-dispersion or zero inflation. Occasionally, count data are considered ordinal (with a maximum outcome value of 5), and this calls for the application of ordinal regression models. Various models exist for analyzing over-dispersed data such as negative binomial, generalized Poisson (GP), and ordinal regression model. This study aims to examine whether the GP model is a superior alternative to the ordinal logistic regression (OLR) model, specifically in the context of zero-inflated Poisson models using both simulated and real-time data. Simulated data were generated with varied estimates of regression coefficients, sample sizes, and various proportions of zeros. The GP and OLR models were compared using fit statistics. Additionally, comparisons were made using real-time datasets. The simulated results consistently revealed lower bias and mean squared error values in the GP model compared to the OLR model. The same trend was observed in real-time datasets, with the GP model consistently demonstrating lower standard errors. Except when the sample size was 1000 and the proportions of zeros were 30% and 40%, the Bayesian information criterion consistently favored the GP model over the OLR model. This study establishes that the proposed GP model offers a more advantageous alternative to the OLR model. Moreover, the GP model facilitates easier modeling and interpretation when compared to the OLR model.

Association Between Body Mass Index and Functional Outcomes in Patients With Intracerebral Hemorrhage.

Evidence of the so-called "obesity paradox," which refers to the protective effect and survival benefit of obesity in patients with spontaneous intracerebral hemorrhage (ICH), remains controversial. This study aims to determine the association between body mass index (BMI) and functional outcomes in patients with ICH and whether it is modified by race/ethnicity. Included individuals were derived from the Ethnic/Racial Variations of Intracerebral Hemorrhage study, which prospectively recruited 1,000 non-Hispanic White, 1,000 non-Hispanic Black, and 1,000 Hispanic patients with spontaneous ICH. Only patients with available BMI were included. The primary outcome was 90-day mortality. Secondary outcomes were mortality at discharge, modified Rankin Scale (mRS), Barthel Index, and self-reported health status measures at 90 days. Associations between BMI and ICH outcomes were assessed using univariable and multivariable logistic, ordinal, and linear regression models, as appropriate. Sensitivity analyses after excluding frail patients and by patient race/ethnicity were performed. A total of 2,841 patients with ICH were included. The median age was 60 years (interquartile range 51-73). Most patients were overweight (n = 943; 33.2%) or obese (n = 1,032; 36.3%). After adjusting for covariates, 90-day mortality was significantly lower among overweight and obese patients than their normal weight counterparts (adjusted odds ratio [aOR] = 0.71 [0.52-0.98] and aOR = 0.70 [0.50-0.97], respectively). Compared with patients with BMI <25 kg/m2, those with BMI ≥25 kg/m2 had better 90-day mRS (aOR = 0.80 [CI 0.67-0.95]), EuroQoL Group 5-Dimension (EQ-5D) (aβ = 0.05 [0.01-0.08]), and EQ-5D VAS (aβ = 3.80 [0.80-6.98]) scores. These differences persisted after excluding withdrawal of care patients. There was an inverse relationship between BMI and 90-day mortality (aOR = 0.97 [0.96-0.99]). Although non-Hispanic White patients had significantly higher 90-day mortality than non-Hispanic Black and Hispanic (26.6% vs 19.5% vs 18.0%, respectively; p < 0.001), no significant interactions were found between BMI and race/ethnicity. No significant interactions between BMI and age or sex for 90-day mortality were found, whereas for 90-day mRS, there was a significant interaction with age (pinteraction = 0.004). We demonstrated that a higher BMI is associated with decreased mortality, improved functional outcomes, and better self-reported health status at 90 days, thus supporting the paradoxical role of obesity in patients with ICH. The beneficial effect of high BMI does not seem to be modified by race/ethnicity or sex, whereas age may play a significant role in patient functional outcomes.

Analysis of Effluent Management among Medium and Large Scale Agro-Allied Industries in South West, Nigeria

The study analysed effluent management among medium and large scale agro-allied industries in South west, Nigeria. Multi-stage sampling techniques was employed in selecting 287 MLS agro-allied industries (MSAAI=222; LSAAI=65). Data for this study was obtained from primary sources using a semi-structured questionnaire. Descriptive and relevant inferential statistics such as one-way ANOVA, Pearson’s chi-square test and ordinal Probit regression model were used for data analysis. The study revealed a minimum, maximum and average daily end of pipe volume of 430kg, 8000kg and 3084.21kg, respectively, for medium scale agro-allied industries and 520kg, 15,000kg and 3,534.72kg, respectively, for large scale agro-allied industries with an average proportion (53.6% and 49.0%) of medium and large scale agro-allied industries generating between 1001kg and 3000kg of effluent per day during agro-allied industrial processes. The result also indicated the quantity of end-of-pipe effluents recycled to be 436.08kg and 695.54kg for medium and large scale agro-allied industries, respectively which from the independent t-test statistics, there was a significant difference between the mean quantity of effluent recycled by medium scale agro-allied industries in comparison with the mean quantity of effluent recycled by large scale agro-allied industries (t=12.021; p&lt;0.05). Based on comparing effluent treatment levels/systems between M&amp;LSAI in South west Nigeria. The result revealed an average proportion (57.7%) of medium scale agro-allied industries to have disposed of their end-of-pipe without treatment, while 42.3% treated their end-of-pipe mainly through ponding or sedimentation (20.3%), aeration/membrane filtration (10.4%), chlorination and neutralisation or physicochemical treatment (4.5%) etc. By contrast, a very few proportion (15.4%) of large scale agro-allied industries disposed of their end-of-pipe without treatment, while majority (84.6%) treated end-of-pipe mainly through ponding or sedimentation (18.5%), aeration/membrane filtration (29.2%), chlorination and neutralisation or physicochemical treatment (15.4%) etc. The ordinal Probit regression model of socio-institutional predictors influencing effluent treatment level among M&amp;LSAI in South west Nigeria showed significant variables viz: frequency of visit by waste management agency (-0.2605122 and -0.0849263); M&amp;LSAI income (-1.94e-12 and 4.66e-14); membership of association (-0.7487836 and -0.5136997); and waste management satisfaction level (-0.2552832 and -0.1250288) respectively. The study recommended activities that increase industrial income, membership in associations and orientation on the anti-satisfaction with the status quo of effluent management to promote enhanced effluent treatment level/practices. How to Cite: Ude Kingsley David, Okoye C. U., Arene C. J., et. al., 2024. "Analysis of Effluent Management among Medium and Large Scale Agro-Allied Industries in South West, Nigeria." Journal of Agriculture and Crops, vol. 9, pp. 30-41.

The Association between Self-Care Activities and Depression in Adult Patients with Type 2 Diabetes in Saudi Arabia: A Cross-Sectional Web-Based Survey Study.

This study examined the level of adherence to self-care behaviors among individuals with type 2 diabetes in Saudi Arabia and its connection with depression and demographic factors. A cross-sectional survey was conducted among diabetes patients using the Patient Health Questionnaire (PHQ-9) to measure depression and the Summary of Diabetes Self-Care Activities (SDSCA) to evaluate diabetes self-care activities. Among the 252 participants who completed the survey, 43.2% were older than 55 and 59% were men. The ordinal regression model showed an association between the PHQ-9 and SDSCA scores with an OR of 0.83 (95% CI: 0.71 to 0.96, p = 0.013). The PHQ-9 score was significantly associated with blood sugar monitoring (OR: 0.90 [95% CI: 0.82 to 0.99, p = 0.003]), exercise (OR: 0.88 [95% CI: 0.79 to 0.98, p = 0.002]), and diet (OR: 0.94 [95% CI: 0.85 to 1.03, p = 0.045]). Of all the diabetes-related factors, only a history of hospitalization and receiving diabetes education were found to be associated with improved self-care behaviors. In conclusion, a negative association was found between PHQ-9 scores and the SDSCA mean score and most daily diabetic self-care behavior components.

Association of Socio-Demographic Factors, Social Determinants of Health, and Weekly Physical Activity in an Urban Hospital in Northwest Indiana

Background: Engaging in regular physical activity has been proven to have beneficial health effects such as preventing chronic diseases and improving mental health. Recent studies have demonstrated correlations between socio-demographic factors and physical activity levels. This study determined the associations between socio-demographic factors, social determinants of health and the amount of weekly physical activity in patients occupying an urban underserved area.&#x0D; Methods: This study retrospectively analyzed a dataset generated by St. Mary Medical Center from EPIC™ with demographic characteristics and physical activity levels partitioned by time per week for adult inpatient visits from January 2021 to March 2023. Patients were stratified into physical activity levels based upon published guidelines: inactive (no physical activity), insufficiently active (&lt;150 minutes per week) or sufficiently active (≥150 minutes per week). Data analysis was conducted in SPSS 28.0 using tests of association including Kruskal Wallis H and multivariate ordinal regression model. This study was exempted by Indiana University Human Research Protection Program (IRB # 14040).&#x0D; Results: The sample of individuals from the dataset who answered physical activity questions was comprised of 1498 patients. There was a statistically significant difference in physical activity level by age group (p&lt;0.001), sex (p&lt;0.05), insurance category (p&lt;0.001), and social connections risk score (p&lt;0.001); with race (p=0.057) and language (p=0.054) approaching significance. Multivariate analysis showed that age was the only significant factor when accounting for all variables, with higher age groups reporting lower proportions of physically active individuals.&#x0D; Conclusion: Determining how socio-demographic factors influence physical activity levels will direct efforts to form and implement new interventions in the Northwest Indiana urban area and support community health initiatives. This data makes it possible to inform practitioners of the demographics that are at risk of being insufficiently active and having them direct those patients to programs in place to help bridge the lapse.

Association of mild traumatic brain injury, post-traumatic stress disorder, and other comorbidities on photosensitivity.

Photosensitivity is common after mild traumatic brain injury. However, this study demonstrates that photosensitivity is also impacted by common comorbidities that often occur with mild traumatic brain injury. Understanding how physical and psychological traumas impact photosensitivity can help improve provider care to trauma survivors and guide novel therapeutic interventions. This study aimed to characterize the association between mild traumatic brain injury and common comorbidities on photosensitivity in post-9/11 veterans. Existing data from the Translational Research Center for TBI and Stress Disorders cohort study were analyzed including traumatic brain injury history and post-traumatic stress disorder clinical diagnostic interviews; sleep quality, anxiety, and depression symptoms self-report questionnaires; and photosensitivity severity self-report from the Neurobehavioral Symptom Inventory. Analysis of covariance and multiple ordinal regression models were used to assess associations between mild traumatic brain injury and common comorbidities with photosensitivity severity. Six hundred forty-one post-9/11 veterans were included in this study. An initial analysis showed that both mild traumatic brain injury and current post-traumatic stress disorder diagnosis were independently associated with higher photosensitivity ratings compared with veterans without either condition, with no interaction observed between these two conditions. Results of the ordinal regression models demonstrated positive associations between degree of photosensitivity and the number of mild traumatic brain injuries during military service and current post-traumatic stress disorder symptom severity, particularly hyperarousal symptoms, even when controlling for other factors. In addition, the degree of sleep disturbances and current anxiety symptoms were both positively associated with photosensitivity ratings, whereas depression symptoms, age, and sex were not. Repetitive mild traumatic brain injury, post-traumatic stress disorder, anxiety, and sleep disturbances were all found to significantly impact photosensitivity severity and are therefore important clinical factors that eye care providers should consider when managing veterans with a history of deployment-related trauma reporting photosensitivity symptoms.

Developing the health effect assessment of landscape (HEAL) Tool: Assessing the health effects of community greenspace morphology design on non-communicable diseases

Neighborhood greenspace benefits health, yet few tools are available for estimating the health consequences of community greenspace design alternatives, especially prior to the implementation of a landscape design plan. Herein we present a machine learning based tool for predicting the prevalence of non-communicable diseases based on landscape design maps at the community scale. We achieve this based on data collected in five major metropolises in the United States. By using high-resolution satellite imagery and remote sensing technologies, landscape spatial design characteristics were extracted through spatial pattern analysis, allowing the measurement of verdancy, fragmentation, connectedness, aggregation, and shape of greenspaces. We established a model to estimate the prevalence of poor mental health, coronary heart disease, stroke, diabetes, chronic obstructive pulmonary disease, and physical inactivity at the census tract level by adopting a combination of random forest decision tree algorithm and spatial Gaussian process models. Model accuracy was found to be significantly higher than ordinary regression models. The model accounted for very high levels of variance in the specified morbidities; viz., poor mental health (97%), heart disease (93%), stroke (93%), diabetes (95%), COPD (94%), and physical inactivity (98%). This tool is implemented using a freely available programming language (R), and we offer the model accessible to the public. This tool enables urban planners and landscape designers to assess and compare the health effects of different greenspace design plans prior to implementation, thus providing policymakers and designers with evidence-rich alternatives during the health-promoting greenspace planning process.

What affects the promoting intention of mobile banking services? Insights from mining consumer reviews

Unlike adoption intention, the post-adoption intention of mobile banking (m-banking) services has not been extensively investigated. Most extant studies predicted usage continuance intention as a post-adoption behaviour. However, existing consumers' promoting intention is yet to be explored. This study attempts to address the concerns of technology-resisting consumers in India who seek signals from existing consumers which can change their perception towards m-banking adoption. We adopt the Expectation Disconfirmation Theory and Thatcher's framework to understand the cognitive factors induced by m-banking services which affect consumers' post-exposure behaviour. Using natural language processing, we identify the relevant factors by mining a total of around 157 thousand and 190 thousand consumer reviews from Google play store and Apple app store on PhonePe and Paytm respectively, two extensively used m-banking apps in India. We craft a unique framework by mapping these factors to theoretical lexicons. We use multiple ordinal regression models to confirm how these factors affect the promoting intention of m-banking services. We find perception of resources, internet anxiety from cyberthreats, technology-trusting performance, and intention significantly influence the promoting intention for PhonePe, whereas, technology-trusting expectation too impact the promoting intention for Paytm. Apart from the technology-resisting consumers, our findings can also help the mobile banking service providers improving their services, enhancing markets, and gaining more customers.

Knowledge, attitudes, and practices about HIV and other sexually transmitted infections among High School students in Southern Italy: A cross-sectional survey.

High School students, recognized as a high-risk group for sexually transmitted infections (STIs), were the focal point of an educational campaign in Southern Italy to share information and good practices about STIs and HIV/AIDS. A baseline survey comprising 76 items was conducted via the REDCap platform to assess students' initial knowledge, attitudes, and practices (KAP) related to STIs and HIV/AIDS. Sociodemographic variables were also investigated. The association between variables and KAP score was assessed by Kruskal-Wallis' or Spearman's test, as appropriate. An ordinal regression model was built to estimate the effect size, reported as odds ratio (OR) with a 95% confidence interval (CI), for achieving higher KAP scores among students features. On a scale of 0 to 29, 1702 participants achieved a median KAP score of 14 points. Higher scores were predominantly reported by students from classical High Schools (OR 3.19, 95% C.I. 1.60-6.33, p<0.001). Additionally, elevated scores were associated with sexually active students (OR 1.48, 95% C.I. 1.12-1.96, p = 0.01), those vaccinated against Human Papilloma Virus (OR 2.47, 95% C.I. 1.89-3.24, p<0.001), those who had used emergency contraception (OR 1.56, 95% C.I. 1.09-2.24, p = 0.02, Table 2) and those obtaining information from TikTok (OR 1.62, 95% C.I. 1.14-2.30, p = 0.01). Conversely, being heterosexual was associated with an overall lower score (OR 0.48, 95% C.I. 0.32-0.73, p<0.001). High School students, often due to early sexual debut, seek information about HIV and STIs independently using social channels. However, the overall level of knowledge, attitudes, and practices remains low. Urgent school-based interventions are needed for this age group.