lordif: An R Package for Detecting Differential Item Functioning Using Iterative Hybrid Ordinal Logistic Regression/Item Response Theory and Monte Carlo Simulations

Logistic regression provides a flexible framework for detecting various types of differential item functioning (DIF). Previous efforts extended the framework by using item response theory (IRT) based trait scores, and by employing an iterative process using group-specific item parameters to account for DIF in the trait scores, analogous to purification approaches used in other DIF detection frameworks. The current investigation advances the technique by developing a computational platform integrating both statistical and IRT procedures into a single program. Furthermore, a Monte Carlo simulation approach was incorporated to derive empirical criteria for various DIF statistics and effect size measures. For purposes of illustration, the procedure was applied to data from a questionnaire of anxiety symptoms for detecting DIF associated with age from the Patient-Reported Outcomes Measurement Information System.

Composite scores for executive function items: demographic heterogeneity and relationships with quantitative magnetic resonance imaging

Accurate neuropsychological assessment of older individuals from heterogeneous backgrounds is a major challenge. Education, ethnicity, language, and age are associated with scale level differences in test scores, but item level bias might contribute to these differences. We evaluated several strategies for dealing with item and scale level demographic influences on a measure of executive abilities defined by working memory and fluency tasks. We determined the impact of differential item functioning (DIF). We compared composite scoring strategies on the basis of their relationships with volumetric magnetic resonance imaging (MRI) measures of brain structure. Participants were 791 Hispanic, white, and African American older adults. DIF had a salient impact on test scores for 9% of the sample. MRI data were available on a subset of 153 participants. Validity in comparison with structural MRI was higher after scale level adjustment for education, ethnicity/language, and gender, but item level adjustment did not have a major impact on validity. Age adjustment at the scale level had a negative impact on relationships with MRI, most likely because age adjustment removes variance related to age-associated diseases.

Incidence of postoperative nausea and vomiting following gynecological laparoscopy: A comparison of standard anesthetic technique and propofol infusion

OBJECTIVE

To determine the safety, efficacy, and feasibility of propofol-based anesthesia in gynecological laparoscopies in reducing incidences of postoperative nausea and vomiting compared to a standard anesthesia using thiopentone/isoflurane.

DESIGN

Randomized single-blind (for anesthesia techniques used) and double-blind (for postoperative assessment) controlled trial.

SETTING

Operation theater, postanesthesia recovery room, teaching hospital.

PATIENTS

Sixty ASA (American Society of Anesthesiologists) I and II female patients (aged 20-60 years) scheduled for gynecological laparoscopy were included in the study.

INTERVENTIONS

Patients in Group A received standard anesthesia with thiopentone for induction and maintenance with isoflurane-fentanyl, and those in Group B received propofol for induction and maintenance along with fentanyl. All patients received nitrous oxide, vecuronium, and neostigmine/glycopyrrolate. No patient received elective preemptive antiemetic, but patients did receive it after more than one episode of vomiting.

MEASUREMENTS

Assessment for incidence of postoperative nausea and vomiting as well as other recovery parameters were carried out over a period of 24 hours.

MAIN RESULTS

Six patients (20%) in Group A and seven patients (23.3%) in Group B experienced nausea. Two patients (6.66%) in Group B had vomiting versus 12 (40%) in Group A (p<0.05). Overall, the incidence of emesis was 60% and 30% in Groups A and B, respectively (p<0.05). All patients in Group B had significantly faster recovery compared with those in Group A. No patient had any overt cardiorespiratory complications.

CONCLUSION

Propofol-based anesthesia was associated with significantly less postoperative vomiting and faster recovery compared to standard anesthesia in patients undergoing gynecological laparoscopy.

Determination of relative timing of pubertal maturation through ordinal logistic modeling: evaluation of growth and timing parameters

PURPOSE

(1) To propose a new method using statistical modeling to determine relative timing of pubertal maturation; (2) to validate the new method by evaluating its relationship with pubertal growth and timing parameters, including age at menarche, age onset of areolar maturation, age of peak height velocity, age at attainment of adult height, adult height, peak height velocity, body mass index, and percent body fat; and (3) to contrast the new method with relative timing of menarche on these pubertal parameters.

BACKGROUND

The timing of puberty has a well-known impact on anthropometric and psychosocial outcomes. Multiple methods have been used to determine pubertal timing, but all with limitations. A uniformly applicable method is needed for different study designs and study populations.

METHODS

Using the National Heart Lung and Blood Institute Growth and Health Study data, an ordinal logistic modeling was used to assess relative timing of pubertal maturation.

RESULTS

The proposed method demonstrated good reliability and strong associations with all pubertal timing parameters, also body mass index and percent body fat. Timing was not significantly associated with adult height and peak height velocity.

CONCLUSIONS

The proposed method is highly feasible, easy to implement, and valid. The study demonstrated important differences between the relationships of relative timing of secondary sexual characteristics and the timing of menarche on pubertal parameters. The study also demonstrates that individuals with early or late timing at one point of time are likely to maintain the same relative timing throughout puberty.

Application of ordinal logistic regression analysis to identify the determinants of illness severity of COVID-19 in China

Corona Virus Disease 2019 (COVID-19) has presented an unprecedented challenge to the health-care system across the world. The current study aims to identify the determinants of illness severity of COVID-19 based on ordinal responses. A retrospective cohort of COVID-19 patients from four hospitals in three provinces in China was established, and 598 patients were included from 1 January to 8 March 2020, and divided into moderate, severe and critical illness group. Relative variables were retrieved from electronic medical records. The univariate and multivariate ordinal logistic regression models were fitted to identify the independent predictors of illness severity. The cohort included 400 (66.89%) moderate cases, 85 (14.21%) severe and 113 (18.90%) critical cases, of whom 79 died during hospitalisation as of 28 April. Patients in the age group of 70+ years (OR = 3.419, 95% CI: 1.596-7.323), age of 40-69 years (OR = 1.586, 95% CI: 0.824-3.053), hypertension (OR = 3.372, 95% CI: 2.185-5.202), ALT >50 μ/l (OR = 3.304, 95% CI: 2.107-5.180), cTnI >0.04 ng/ml (OR = 7.464, 95% CI: 4.292-12.980), myohaemoglobin>48.8 ng/ml (OR = 2.214, 95% CI: 1.42-3.453) had greater risk of developing worse severity of illness. The interval between illness onset and diagnosis (OR = 1.056, 95% CI: 1.012-1.101) and interval between illness onset and admission (OR = 1.048, 95% CI: 1.009-1.087) were independent significant predictors of illness severity. Patients of critical illness suffered from inferior survival, as compared with patients in the severe group (HR = 14.309, 95% CI: 5.585-36.659) and in the moderate group (HR = 41.021, 95% CI: 17.588-95.678). Our findings highlight that the identified determinants may help to predict the risk of developing more severe illness among COVID-19 patients and contribute to optimising arrangement of health resources.

Relationship between sociodemographic factors and selection into UK postgraduate medical training programmes: a national cohort study

INTRODUCTION

Knowledge about allocation of doctors into postgraduate training programmes is essential in terms of workforce planning, transparency and equity issues. However, this is a rarely examined topic. To address this gap in the literature, the current study examines the relationships between applicants' sociodemographic characteristics and outcomes on the UK Foundation Training selection process.

METHODS

A longitudinal, cohort study of trainees who applied for the first stage of UK postgraduate medical training in 2013-2014. We used UK Medical Education Database (UKMED) to access linked data from different sources, including medical school admissions, assessments and postgraduate training. Multivariable ordinal regression analyses were used to predict the odds of applicants being allocated to their preferred foundation schools.

RESULTS

Applicants allocated to their first-choice foundation school scored on average a quarter of an SD above the average of all applicants in the sample. After adjusting for Foundation Training application score, no statistically significant effects were observed for gender, socioeconomic status (as determined by income support) or whether applicants entered medical school as graduates or not. Ethnicity and place of medical qualification were strong predictors of allocation to preferred foundation school. Applicants who graduated from medical schools in Wales, Scotland and Northern Ireland were 1.17 times, 3.33 times and 12.64 times (respectively), the odds of applicants who graduated from a medical school in England to be allocated to a foundation school of their choice.

CONCLUSIONS

The data provide supportive evidence for the fairness of the allocation process but highlight some interesting findings relating to 'push-pull' factors in medical careers decision-making. These findings should be considered when designing postgraduate training policy.

Using Patient Demographics and Statistical Modeling to Predict Knee Tibia Component Sizing in Total Knee Arthroplasty

BACKGROUND

Preoperative planning is important to achieve successful implantation in primary total knee arthroplasty (TKA). However, traditional TKA templating techniques are not accurate enough to predict the component size to a very close range.

METHODS

With the goal of developing a general predictive statistical model using patient demographic information, ordinal logistic regression was applied to build a proportional odds model to predict the tibia component size. The study retrospectively collected the data of 1992 primary Persona Knee System TKA procedures. Of them, 199 procedures were randomly selected as testing data and the rest of the data were randomly partitioned between model training data and model evaluation data with a ratio of 7:3. Different models were trained and evaluated on the training and validation data sets after data exploration.

RESULTS

The final model had patient gender, age, weight, and height as independent variables and predicted the tibia size within 1 size difference 96% of the time on the validation data, 94% of the time on the testing data, and 92% on a prospective cadaver data set.

CONCLUSION

The study results indicated the statistical model built by ordinal logistic regression can increase the accuracy of tibia sizing information for Persona Knee preoperative templating. This research shows statistical modeling may be used with radiographs to dramatically enhance the templating accuracy, efficiency, and quality. In general, this methodology can be applied to other TKA products when the data are applicable.

Analysis of risk factors affecting driver injury and crash injury with drivers under the influence of alcohol (DUI) and non-DUI

OBJECTIVE

The objective of this research was to study risk factors that significantly influence the severity of crashes for drivers both under and not under the influence of alcohol.

METHODS

Ordinal logistic regression was applied to analyze a crash data set involving drivers under and not under the influence of alcohol in China from January 2011 to December 2014.

RESULTS

Four risk factors were found to be significantly associated with the severity of driver injury, including crash partner and intersection type. Age group was found to be significantly associated with the severity of crashes involving drivers under the influence of alcohol. Crash partner, intersection type, lighting conditions, gender, and time of day were found to be significantly associated with severe driver injuries, the last of which was also significantly associated with severe crashes involving drivers not under the influence of alcohol.

CONCLUSIONS

This study found that pedestrian involvement decreases the odds of severe driver injury when a driver is under the influence of alcohol, with a relative risk of 0.05 compared to the vehicle-to-vehicle group. The odds of severe driver injury at T-intersections were higher than those for traveling along straight roads. Age was shown to be an important factor, with drivers 50-60 years of age having higher odds of being involved in severe crashes compared to 20- to 30-year-olds when the driver was under the influence of alcohol. When the driver was not under the influence of alcohol, drivers suffered more severe injuries between midnight and early morning compared to early nighttime. The vehicle-to-motorcycle and vehicle-to-pedestrian groups experienced less severe driver injuries, and vehicle collisions with fixed objects exhibited higher odds of severe driver injury than did vehicle-to-vehicle impacts. The odds of severe driver injury at cross intersections were 0.29 compared to travel along straight roads. The odds of severe driver injury when street lighting was not available at night were 3.20 compared to daylight. The study indicated that female drivers are more likely to experience severe injury than male drivers when not under the influence of alcohol. Crashes between midnight and early morning exhibited higher odds of severe injury compared to those occurring at other times of day. The identification of risk factors and a discussion on the odds ratio between levels of the impact of the driver injury and crash severity may benefit road safety stakeholders when developing initiatives to reduce the severity of crashes.

Body condition score of Nellore beef cows: a heritable measure to improve the selection of reproductive and maternal traits

Despite the economic importance of beef cattle production in Brazil, female reproductive performance, which is strongly associated with production efficiency, is not included in the selection index of most breeding programmes due to low heritability and difficulty in measure. The body condition score (BCS) could be used as an indicator of these traits. However, so far little is known about the feasibility of using BCS as a selection tool for reproductive performance in beef cattle. In this study, we investigated the sources of variation in the BCS of Nellore beef cows, quantified its association with reproductive and maternal traits and estimated its heritability. BCS was analysed using a logistic model that included the following effects: contemporary group at weaning, cow weight and hip height, calving order, reconception together with the weight and scores of conformation and early finishing assigned to calves at weaning. In the genetic analysis, variance components of BCS were estimated through Bayesian inference by fitting an animal model that also included the aforementioned effects. The results showed that BCS was significantly associated with all of the reproductive and maternal variables analysed. The estimated posterior mean of heritability of BCS was 0.24 (highest posterior density interval at 95%: 0.093 to 0.385), indicating an involvement of additive gene action in its determination. The present findings show that BCS can be used as a selection criterion for Nellore females.

Prevalence of Growth Restriction at Birth for Newborns With Congenital Heart Defects: A Population-Based Prospective Cohort Study EPICARD

Background and Objectives: Congenital heart defects (CHD) and growth restriction at birth are two major causes of childhood and adult morbidity and mortality. The aim of this study was to assess the overall risk of growth restriction at birth, as measured by its imperfect proxy small (< 10th percentile) for gestational age (SGA), for newborns with CHD. Methods: Using data from a population-based cohort of children born with CHD, we assessed the risk of growth restriction at birth using SGA and severe SGA (3rd percentile). To compare the odds of SGA and severe SGA across five specific major CHD, we used ordinal logistic regression using isolated, minor (non-operated) ventricular septal defect (VSD) as the control group. Results: The overall proportion of SGA for "isolated" CHD (i.e., those not associated with other anomalies) was 13% (95% CI, 12-15%), which is 30% higher than what would be expected in the general population (i.e., 10%). The risk of severe SGA was 5% (95% CI, 4-6%) as compared with the expected 3% in the general population. There were substantial differences in the risk of overall SGA and more so severe SGA across the different CHD. The highest risk of SGA occurred for Tetralogy of Fallot (adjusted OR 2.7, 95% CI, 1.3-5.8) and operated VSD (adjusted OR 2.1, 95% CI, 1.1-3.8) as compared with the control group of minor (non-operated) VSD. Conclusion: The overall risks of both SGA and severe SGA were higher in isolated CHD than what would be expected in the general population with substantial differences across the subtypes of CHD. These results may provide a clue for understanding the underlying mechanisms of the relation between alterations in fetal circulation associated with different types of CHD and their effects on fetal growth.

Determinants of households' food insecurity with severity dimensions in Pakistan: Varying estimates using partial proportional odds model

This study is conducted to determine the impact of some selected socioeconomic and demographic factors on households' food insecurity in Pakistan. Data are taken from national-level survey Household Integrated Income and Consumption Survey 2015-2016. Inclusion of gender dimensions of some important factors and estimation of varying estimates at four severity levels of households' food insecurity status using partial proportional odds model may be considered unique features of the study. It is concluded that household's income, employment, agricultural income, donations, parental education level and some households' characteristics are important factors for improving food security in Pakistan. Maternal education, and maternal paid employment compared to paternal education, and paternal paid employment show strong positive effects to improve severe food insecurity. Couple paid employment, livestock ownership and operating agricultural land seem to have the most effective role for improving food security. Social welfare programmes and religious institution of Zakat are helpful to cope with severe food insecurity in Pakistan. Some special efforts or development strategies are recommended to improve food insecurity of rural households and Baluchistan province. Moreover, the observed U-type quadratic impact of household size and the adverse impact of dependency ratio induce effective policies to control high birth rate in Pakistan. Hence, creation of employment opportunities especially for women, appreciation of couple paid employment, easy access to education for women, steps to raise level of education, growth of agricultural sector, rural development, social welfare and development schemes for Baluchistan, and continuation of Benazir Income Support Program are recommended to overcome severe and moderate level food insecurity in Pakistan. Since socioeconomic conditions and food insecurity issues of developing countries and especially in South Asian countries are homogeneous and therefore, the analysis in this study might be relevant to South Asian region.

Sparse Ordinal Logistic Regression and Its Application to Brain Decoding

Brain decoding with multivariate classification and regression has provided a powerful framework for characterizing information encoded in population neural activity. Classification and regression models are respectively used to predict discrete and continuous variables of interest. However, cognitive and behavioral parameters that we wish to decode are often ordinal variables whose values are discrete but ordered, such as subjective ratings. To date, there is no established method of predicting ordinal variables in brain decoding. In this study, we present a new algorithm, sparse ordinal logistic regression (SOLR), that combines ordinal logistic regression with Bayesian sparse weight estimation. We found that, in both simulation and analyses using real functional magnetic resonance imaging (fMRI) data, SOLR outperformed ordinal logistic regression with non-sparse regularization, indicating that sparseness leads to better decoding performance. SOLR also outperformed classification and linear regression models with the same type of sparseness, indicating the advantage of the modeling tailored to ordinal outputs. Our results suggest that SOLR provides a principled and effective method of decoding ordinal variables.

Application of a Bayesian ordinal animal model for the estimation of breeding values for the resistance to Monilinia fruticola (G.Winter) Honey in progenies of peach [Prunus persica (L.) Batsch]

Fruit brown rot caused by Monilinia spp. is the most important fungal disease of stone fruits worldwide. Several phenotyping protocols to accurately characterize and evaluate brown rot infection have been proposed; however, the outcomes from those studies have not led to consistent advances in resistance breeding programs. Breeding for disease resistance is one of the most challenging objectives for crop improvement because disease expression is tetrahedral: it is simultaneously influenced by agent, host, environment, and human management. The present study presents a strategy based on Bayesian inference to analyze a peach breeding progeny for resistance to brown rot, evaluated using a polytomous ordinal scale. A pedigree containing two sources of resistance, one from peach and the other from almond, several commercial cultivars, and two segregating populations were analyzed to estimate the narrow-sense heritability (h² ) and breeding values (EBVs) for brown rot resistance in progenies. Results show promise for genetic improvement of disease resistance and other traits characterized by strong environmental interactions.

The factors of family management affecting asthma control status in school-age children with asthma in China

OBJECTIVE

To identify the factors of family management affecting asthma control status in school-age children with asthma in China.

METHOD

The cross-sectional descriptive study was conducted among 139 children with asthma and their parents. The age range of the children was 7 to 14 years of age (M_age = 9.85; 76.26% boys). Eight dimensions (Children Identity, View of Condition, Management Mindset, Parental Mutuality, Parenting Philosophy, Management Approach, Family Focus, Future Expectation) of the Family Management Scale for Children with Asthma (FMSCA) were used as factors of family management. The Asthma Control Test (ACT) and the Children Asthma Control Test (C-ACT) were used to measure the asthma control status of children. A parental questionnaire was used to collect information regarding demographic data of familial socioeconomic status, general data about the child, and medical services status (Follow-Up Plan, received manual of asthma education, attended a lecture on asthma) received from medical institutions. A multivariate ordinal logistic regression model was performed.

RESULTS

Factors significantly associated with asthma control were "Follow-Up Plan" (OR, 2.004; 95% CI, 1.009-3.981), "Attended a Lecture on asthma" (OR, 2.586; 95% CI, 1.103-6.066) and two dimensions of the FMSCA, "Children Identity" (OR = 1.133; 95% CI, 1.024-1.254) and "Family Focus" (OR = 1.114; 95% CI, 1.007-1.232).

CONCLUSION

This study shows that asthma control status of school-age children in China is related to the parents' views of their child as having a "normal condition" and the parents' satisfaction with the balance between asthma related management and other aspects of family life.

Risk Factors and Treatment Types for Asthma Severity Among Adult Patients

OBJECTIVE

The aim of this study was to identify the risk factors and treatment types for asthma severity among adult patients by applying a retrospective study design.

MATERIALS AND METHODS

The symptoms of asthma and corresponding medication prescription were addressed by descriptive statistics, and an ordinal logistic regression model was applied to identify the risk factors of asthma severity based on the data obtained from chronic follow-up of 422 adult asthma patients from September 11, 2012, to July 8, 2016, at the University of Gondar Teaching Hospital (UOGTH).

RESULTS

From 422 study units, the more commonly presenting asthma symptoms were coughing and wheezing expressed by 52.13% and 50.9%, respectively. For the treatment type given to the patients, oxygen and prednisolone were highly distributed drugs to the patients in chronic illness, medication and follow-up clinic of the University of Gondar Teaching Hospital (UOGTH) which were expressed by 73.5% and 35.5%, respectively. The proportional odd logit model was used to analyse asthma severity in patients; patients who were female (OR=1.68), a rural resident (OR=1.56), regular physical exercise (OR=2.39), allergen to pet (OR=3.17), had asthma in childhood (OR=2.27), had a family history (OR=1.89), and had depression (OR=2.31) were more likely to increase asthma severity than others, and patients who were in case with regular cooker, dry season was less likely to increase asthma severity.

CONCLUSION

Generally, the study presented the most common asthma symptoms and treatment types correspondingly. The study also showed that demographic, environmental, genetic, and health-related factors have a significant effect on asthma severity.

Factors Associated with Anemia Among Children 6-23 Months of Age in Ethiopia: A Multilevel Analysis of Data from the 2016 Ethiopia Demographic and Health Survey

BACKGROUND

Anemia is disproportionately borne among children in the African regions including Ethiopia. In Ethiopia, there is limited information on the prevalence and factors associated with anemia among children aged 6-23 months. Therefore, the aim of this study was to identify individual- and community-level factors associated with anemia among children 6-23 months of age.

METHODS

The data were obtained from the 2016 Ethiopia Demographic and Health Survey, conducted from January to June 2016. A sample of 2554 children aged 6-23 months was included. Data were analyzed using STATA version 14. A multilevel ordinal logistic regression model was fitted and an adjusted odds ratio with a 95% confidence interval was obtained.

RESULTS

The prevalence of anemia among children 6-23 months of age was 72.3%; 27.5% mild, 41% moderate, and 3.8% severe anemia. child age 18-23 months (AOR: 0.76; 95%CI: 0.61-0.93), female sex (AOR: 0.84; 95%CI: 0.72-0.98), maternal anemia (AOR: 1.53; 95%CI: 1.28-1.82), exclusive breastfeeding (AOR: 0.73; 95%CI: 0.54-0.98), child fever (AOR: 1.41; 95%CI: 1.03-1.93), underweight children (AOR: 1.42; 95%CI: 1.17-1.73) and exposure to either newspaper, radio or television (AOR: 0.78; 95%CI: 0.61-0.99) were the individual-level factors associated with anemia. High community-poverty (AOR: 1.30; 95%CI: 1.01-1.67), living in the regions of Somali (AOR: 2.08; 95%CI: 1.31-3.29), Amhara (AOR: 0.65; 95%CI: 0.45-0.94), Benishangul (AOR: 0.39; 95%CI: 0.25- 0.61) and Harari (AOR: 1.97; 95%CI: 1.18-3.31) were the community-level factors associated with anemia.

CONCLUSION

This study showed that childhood anemia is affected both by the individual- and community-level factors. The strategies of promoting exclusive breastfeeding, addressing maternal anemia, child fever, giving special attention for underweight children, and targeting regions identified to have a high risk of anemia should be strengthened to reduce childhood anemia.

Comprehensive Knowledge, Attitudes, Behaviors, and Associated Factors of HIV/AIDS in Gondar City Public Health Facilities Among HIV Testing and Counselling Service Users, Northwest Ethiopia, 2022; an Ordinal Logistic Regression Analysis

BACKGROUND

The primary cause of HIV transmission is a failure to deliver proper HIV/AIDS information, which is a major public health issue in low- and middle-income nations. While global health coverage continues, there is still little understanding of HIV/AIDS in Ethiopia, and there is a data gap. As a result, the aim of this study was to investigate the factors that influence comprehensive HIV/AIDS knowledge among HIV testing and counselling (HTC) service users in Gondar, northwest Ethiopia.

METHODS

A facility-based cross-sectional study was conducted from June to July 2022. About 423 clients were included. Data on HIV/AIDS knowledge, attitude, and behavior were collected. Stata 17 was used to analyze. A pre-tested questionnaires were used. Using ordinal logistic regression model, variables having p values of 0.2 and <0.05 were chosen as a candidate variable in binary and final model.

RESULTS

The total knowledge, favorable attitude, and good behaviors of HIV/AIDS were approximately 258 (61%), 220 (52%), and 210 (49.5%), respectively. Being male (AOR = 1.39, 95% CI = 1.28-3.01), age from 31 to 35 years (AOR = 2.13, 95% CI = 1.88-4.18), and above 36 years old (AOR = 2.21, 95% CI = 1.98-3.44), primary (AOR = 1.21, 95% CI = 1.67-3.20), and higher education (AOR = 1.19, 95% CI = 1.03-3.01), employed (AOR = 1.33, 95% CI = 1.09, 3.37), and media exposure (AOR = 2.02, 95% CI = 1.21, 3.61) were factors associated with comprehensive knowledge of HIV/AIDS, respectively.

CONCLUSION

The study participants' knowledge, attitudes, and behaviors regarding HIV/AIDS were rather low. Since HIV/AIDS is still a major public health issue, if the concerned body uses and designs media effectively, encouraging females to attend school, it will have a better chance of combating HIV/AIDS by increasing their knowledge.

Statistical learning in preclinical drug proarrhythmic assessment

Torsades de pointes (TdP) is an irregular heart rhythm characterized by faster beat rates and potentially could lead to sudden cardiac death. Much effort has been invested in understanding the drug-induced TdP in preclinical studies. However, a comprehensive statistical learning framework that can accurately predict the drug-induced TdP risk from preclinical data is still lacking. We proposed ordinal logistic regression and ordinal random forest models to predict low-, intermediate-, and high-risk drugs based on datasets generated from two experimental protocols. Leave-one-drug-out cross-validation, stratified bootstrap, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The potential outlier drugs identified by our models are consistent with their descriptions in the literature. Our method is accurate, interpretable, and thus useable as supplemental evidence in the drug safety assessment.

An Investigation of Age-Related Differential Item Functioning in the EQ-5D-5L Using Item Response Theory and Logistic Regression

OBJECTIVES

In economic evaluations, quality of life is measured using patient-reported outcome measures (PROMs), such as the EQ-5D-5L. A key assumption for the validity of PROMs data is measurement invariance, which requires that PROM items and response options are interpreted the same across respondents. If measurement invariance is violated, PROMs exhibit differential item functioning (DIF), whereby individuals from different groups with the same underlying health respond differently, potentially biasing scores. One important group of healthcare consumers who have been shown to have different views or priorities over health is older adults. This study investigates age-related DIF in the EQ-5D-5L using item response theory (IRT) and ordinal logistic regression approaches.

METHODS

Multiple-group IRT models were used to investigate DIF, by assessing whether older adults aged 65+ years and younger adults aged 18 to 64 years with the same underlying health had different IRT parameter estimates and expected item and EQ-5D-5L level sum scores. Ordinal logistic regression was also used to examine whether DIF resulted in meaningful differences in expected EQ level sum scores. Effect sizes examined whether DIF indicated meaningful score differences.

RESULTS

The anxiety/depression item exhibited meaningful DIF in both approaches, with older adults less likely to report problems. Pain/discomfort and mobility exhibited DIF to a lesser extent.

CONCLUSIONS

When using the EQ-5D-5L to evaluate interventions and make resource allocation decisions, scoring bias due to DIF should be controlled for to prevent inefficient service provision, where the most cost-effective services are not provided, which could be detrimental to patients and the efficiency of health budgets.

The Evaluation of Ordinal Regression Model's Performance Through the Implementation of Multilayer Feed-Forward Neural Network: A Case Study of Hypertension

Background Hypertension, or high blood pressure, is a common medical condition that affects a significant portion of the global population. It is a major risk factor for cardiovascular diseases (CVD), stroke, and kidney disorders. Objective The objective of this study is to create and validate a model that combines bootstrapping, ordered logistic regression, and multilayer feed-forward neural networks (MLFFNN) to identify and analyze the factors associated with hypertension patients who also have dyslipidemia. Material and methods A total of 33 participants were enrolled from the Hospital Universiti Sains Malaysia (USM) for this study. In this study, advanced computational statistical modeling techniques were utilized to examine the relationship between hypertension status and several potential predictors. The RStudio (Posit, Boston, MA) software and syntax were implemented to establish the relationship between hypertension status and the predictors. Results The statistical analysis showed that the developed methodology demonstrates good model fitting through the value of predicted mean square error (MSE), mean absolute deviance (MAD), and accuracy. To evaluate model fitting, the data in this study was divided into distinct training and testing datasets. The findings revealed that the results strongly support the superior predictive capability of the hybrid model technique. In this case, five variables are considered: marital status, smoking status, systolic blood pressure, fasting blood sugar levels, and high-density lipoprotein levels. It is important to note that all of them affect the hazard ratio: marital status (β1, -17.12343343; p < 0.25), smoking status (β2, 1.86069121; p < 0.25), systolic blood pressure (β3, 0.05037332; p < 0.25), fasting blood sugar (β4, -0.53880322; p < 0.25), and high-density lipoprotein (β5, 5.38065556; p < 0.25). Conclusion This research aims to develop and extensively evaluate the hybrid approach. The statistical methods employed in this study using R language show that regression modeling surpasses R-squared values in predicting the mean square error. The study's conclusion provides strong evidence for the superiority of the hybrid model technique.

Modeling injury severity of crashes involving golf carts: A case study of The Villages, Florida

OBJECTIVE

Crashes involving golf carts (GCs) have been on an increasing trend in recent years, particularly in the United States. This study focuses on analyzing GC crashes in the Florida community known as The Villages, one of the largest GC-oriented communities in the nation and worldwide. The objective was to evaluate the injury severity of crashes involving GCs in a retirement community where GCs are a common mode of transportation.

METHODS

The ordinal logistic regression (OLR) and Decision Tree Ensemble (DTE) models were used to analyze the injury severity of 616 GC-related crashes. Models' accuracy parameters were used to check their reliability.

RESULTS

The analysis revealed that GC crash severity is influenced by various factors. Factors found to be significant by the OLR model in determining injury severity include ejection of one or more occupants from the GC, the extent of damage to the GC, GC speed prior to the crash, roadway characteristics (including divided roadways, traffic control devices, paved shoulders, and T-intersections), and roll-over incidents. The OLR model demonstrated an overall accuracy of approximately 71% in predicting injury severity. The DTE model performed better, with an overall accuracy of 78%. The OLR model's findings were supported by the DTE model, which identified estimated GC speed, occupant(s) ejection from the GC, estimated GC vehicle damage, intersection type, and type of shoulder as the most important factors influencing GC crash severity.

CONCLUSIONS

Understanding these factors is vital for transportation agencies to develop effective strategies to reduce the severity of GC crashes, ensuring the safety of GC users. This study provides recommendations to transportation agencies on measures to improve the safety of GCs.

Extension of an iterative hybrid ordinal logistic regression/item response theory approach to detect and account for differential item functioning in longitudinal data

Many constructs are measured using multi-item data collection instruments. Differential item functioning (DIF) occurs when construct-irrelevant covariates interfere with the relationship between construct levels and item responses. DIF assessment is an active area of research, and several techniques are available to identify and account for DIF in cross-sectional settings. Many studies include data collected from individuals over time; yet appropriate methods for identifying and accounting for items with DIF in these settings are not widely available. We present an approach to this problem and apply it to longitudinal Modified Mini-Mental State Examination (3MS) data from English speakers in the Canadian Study of Health and Aging. We analyzed 3MS items for DIF with respect to sex, birth cohort and education. First, we focused on cross-sectional data from a subset of Canadian Study of Health and Aging participants who had complete data at all three data collection periods. We performed cross-sectional DIF analyses at each time point using an iterative hybrid ordinal logistic regression/item response theory (OLR/IRT) framework. We found that item-level findings differed at the three time points. We then developed and applied an approach to detecting and accounting for DIF using longitudinal data in which covariation within individuals over time is accounted for by clustering on person. We applied this approach to data for the "entire" dataset of English speaking participants including people who later dropped out or died. Accounting for longitudinal DIF modestly attenuated differences between groups defined by educational attainment. We conclude with a discussion of further directions for this line of research.

How Are BMI, Nutrition, and Physical Exercise Related? An Application of Ordinal Logistic Regression

BACKGROUND

This paper performs a detailed ordinal logistic regression study in an evaluation of a survey at a university in South Texas, USA. We show that, for categorical data in our case, ordinal logistic regression works well.

METHODS

The survey was designed according to the guidelines in diet and lifestyle from the American Heart Association and the United States Department of Agriculture and was sent out to all registered students at Texas A&M International University in Laredo, Texas. Data analysis included 601 students' results from the survey. Data analysis was conducted in Rstudio.

RESULTS

The results showed that, compared with students who do not have enough whole grain food and exercise, those who have enough in both tend to have normal BMIs. As age increases, BMI tends to be out of the normal range.

CONCLUSIONS

Because BMI in this research has three categories, applying an ordinal logistic regression model to describe the relationship between an ordered categorical response variable and more explanatory variables has several advantages compared with other models, such as the linear regression model.

Caregiver burden and its sociodemographic determinants in family caregivers of patients with schizophrenia attending a psychiatric tertiary hospital in South Africa

Background

Chronic mental illnesses such as schizophrenia affect patients' functioning, making caregiving necessary although burdensome.

Aim

This study aimed to determine caregiver burden and its sociodemographic determinants in family caregivers of patients with schizophrenia attending a Psychiatric Outpatient Department (POD).

Setting

Tertiary hospital in Northern Pretoria, South Africa.

Methods

In this cross-sectional study conducted over 3 months, 300 consecutive family caregivers who attended the POD were administered a 22-item Zarit Burden Interview (ZBI-22), which has a score of 0-88, with higher values indicating more burden. Their sociodemographic characteristics were ascertained. Linear and ordinal logistic regression analyses were performed to identify determinants or predictors of total and severe burdens, respectively.

Results

Most caregivers were aged 46.0 ± 14 years, females (62%), parents (39%), of low-income status (93.7%), had secondary education (70%), resided with the patient (87%), and helped with all troublesome activities (95.3%). The median ZBI-22 score was 19.0 (interquartile range: 13.0-30.5). The determinants of both total and severe burdens were: caregiver age ≥ 50 years adjusted odds ratio (aOR): 2.55, confidence interval (CI): 1.49-4.36; residential area farther away from the hospital aOR: 1.76, CI: 1.3-2.99; increasing months of caregiving aOR: 1.0, CI: 1.001-1.009, p = 0.006; and not having another family member that needs care aOR: 0.43, CI: 0.24-0.78.

Conclusion

Having mental healthcare facilities close to residential areas and assisting caregivers aged ≥ 50 years who have multiple family members who need care may alleviate the burden.

Contribution

Predicting total and severe caregiver burdens contemporaneously is effective for identifying potential burden interventions.

The Association between Active Mobility and Subjective Wellbeing during COVID-19 in MENA Countries

Objective: To augment the international scientific approach to raising public mental health through active lifestyle among adults, we added the evidence of the association between physical activity and subjective wellbeing in the Middle East and North Africa region by emphasizing the mediator—COVID-19. This study aimed to identify the correlations between active mobility and subjective wellbeing during the COVID-19 pandemic in a sample from Tabriz, Iran, which has not yet been tackled in previous study. Methods: We finalized an online survey (N = 603) from adults between 5 June and 15 July 2021. This group reported their individual and socio-economic characteristics and their perception features and location (e.g., home, work) during COVID-19. The paper developed three ordinal logistic regression (OLR) models to examine the association between active mobility types such as commute, non-commute, frequency of active travel to parks and services per week, and different subjective wellbeing including: 1- life satisfaction, 2- feeling energetic, and 3- peaceful mind while controlling for socio-economic variables (e.g., age, gender, education, job, and income) and objective commute distance. We also incorporated the most relevant objective (street length, land-use mix, number of intersections, and building and population density) and subjective (perceived distance to different services, perceived walking places, and perceived facilities attractiveness) factors. Results: Positive response relationships between four types of physical activity levels and subjective wellbeing scores were demonstrated in all of the three developed models (with significant levels of 0.05, and 0.1) with appropriate model fits, which confirmed the existing literature. However, these relationships showed different patterns (varied significant levels) for each type of subjective wellbeing. In addition, the factors including street length (p value: 0.004), perceived walkable places (p value: 0.021, 0.068, 0.017, and 0.025) (positively), population density, and perceived distance to shopping malls (p value: 0.076, <0.0001, and 0.059 (negatively) were associated with different indicators of subjective wellbeing. Conclusion: As for the implication of our study, special actions by urban authorities such as increasing mixed-use and creating attractive places will be required to enhance the walkability of the neighborhoods. Moreover, notifying the adults regarding the benefits of physical activity is much more needed.