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Objective This study aimed to compare the predictive values of the American College of Obstetricians and Gynecologists (ACOG), the National Institute for Health and Care Excellence (NICE), and the Society of Obstetricians and Gynecologists of Canada (SOGC) factor-based models for preeclampsia (PE) screening. Study Design We conducted a secondary analysis of maternal and birth data from 32 hospitals. For each delivery, we calculated the risk of PE according to the ACOG, the NICE, and the SOGC models. Our primary outcomes were PE and preterm PE (PE combined with preterm birth) using the ACOG criteria. We calculated the detection rate (DR or sensitivity), the false positive rate (FPR or 1 − specificity), the positive (PPV) and negative (NPV) predictive values of each model for PE and for preterm PE using receiver operator characteristic (ROC) curves. Results We used 130,939 deliveries including 4,635 (3.5%) cases of PE and 823 (0.6%) cases of preterm PE. The ACOG model had a DR of 43.6% for PE and 50.3% for preterm PE with FPR of 15.6%; the NICE model had a DR of 36.2% for PE and 41.3% for preterm PE with FPR of 12.8%; and the SOGC model had a DR of 49.1% for PE and 51.6% for preterm PE with FPR of 22.2%. The PPV for PE of the ACOG (9.3%) and NICE (9.4%) models were both superior than the SOGC model (7.6%; p < 0.001), with a similar trend for the PPV for preterm PE (1.9 vs. 1.9 vs. 1.4%, respectively; p < 0.01). The area under the ROC curves suggested that the ACOG model is superior to the NICE for the prediction of PE and preterm PE and superior to the SOGC models for the prediction of preterm PE (all with p < 0.001). Conclusion The current ACOG factor-based model for the prediction of PE and preterm PE, without considering race, is superior to the NICE and SOGC models. Key Points Clinical factor-based model can predict PE in approximately 44% of the cases for a 16% false positive. The ACOG model is superior to the NICE and SOGC models to predict PE. Clinical factor-based models are better to predict PE in parous than in nulliparous. [ABSTRACT FROM AUTHOR]

Objective: To compare how different imputation methods affect the estimates and performance of a prediction model for premature mortality. Study Design and Setting: Sex-specific Weibull accelerated failure time survival models were run on four separate datasets using complete case, mode, single and multiple imputation to impute missing values. Six performance measures were compared to access predictive accuracy (Nagelkerke R2, integrated brier score), discrimination (Harrell's c-index, discrimination slope) and calibration (calibration in the large, calibration slope). Results: The highest proportion of missingness for a single variable was 10.86% for the female model and 8.24% for the male model. Comparing the performance measures for complete case, mode, single and multiple imputation: the Nagelkerke R2 values for the female model was 0.1084, 0.1116, 0.1120 and 0.111–0.1120 with the male model exhibited similar variation of 0.1050, 0.1078, 0.1078 and 0.1078–0.1081. Harrell's c-index also demonstrated small variation with values of 0.8666, 0.8719, 0.8719 and 0.8711–0.8719 for the female model and 0.8549, 0.8548, 0.8550 and 0.8550–0.8553 for the male model. Conclusion: In the scenarios examined in this study, mode imputation performed well when using a population health survey compared to single and multiple imputation when predictive performance measures is the main model goal. To generate unbiased hazard ratios, multiple imputation methods were superior. This study shows the need to consider the best imputation approach for a predictive model development given the conditions of missing data and the goals of the analysis. [ABSTRACT FROM AUTHOR]

Purpose: COVID-19 impact on the population's mental health has been reported worldwide. Predicting healthcare workers' mental health and life stress is needed to proactively plan for future emergencies. Design: Statistics Canada has surveyed Canadian healthcare workers and those working in healthcare settings to gauge their perceived mental health and perceived life stress. Setting: A cross-sectional survey of healthcare workers in Canada. Subjects: A sample of 18,139 healthcare workers respondents. Analysis: Eight algorithms, including Logistic Regression, Random Forest (RF), Naive Bayes (NB), K Nearest Neighbours (KNN), Adaptive boost (AdaBoost), Multi-layer perceptron (MLP), XGBoost, and LightBoost. AUC scores, accuracy and precision were measured for all models. Results: XGBoost provided the highest performing model AUC score (AUC = 82.07%) for predicting perceived mental health, and Random Forest performed the best for predicting perceived life stress (AUC = 77.74%). Perceived health, age group of participants, and perceived mental health compared to before the pandemic were found to be the most important 3 features to predict perceived mental health and perceived stress. Perceived mental health compared to before the pandemic was the most important predictor for perceived life stress. Conclusion: Our models are highly predictive of healthcare workers' perceived mental health and life stress. Implementing scalable, non-expensive virtual mental health solutions to address mental health challenges in the workplace could mitigate the impact of workplace conditions on healthcare workers' mental health. [ABSTRACT FROM AUTHOR]

Brand communities are of great interest to marketers, and motivating participation in these communities has been explored from many different perspectives. The current study examined how men and women differ in the motives (i.e., entertainment, receiving and sharing information, social integration, self-discovery, and status enhancement) behind their behavior rather than the more often discussed difference between men and women regarding actual behavior and attitudes. A survey sample (N = 195) was collected to explore this, and the data were analyzed with PLS-SEM. The model of participation motives for women was compared to the model for men, and differences (and similarities) were analyzed. The findings indicate that the genders have different motivations to participate in these brand communities. Specifically, the self-discovery and sharing information motives were significantly more important for males, while the social integration motive was significantly more important for females. However, the genders had no differences regarding entertainment, receiving information, and status enhancement motives. [ABSTRACT FROM AUTHOR]

To identify profiles of aging by combining psychological distress, cognition and functional disability, and their associated factors. Data were drawn from the Étude sur la Santé des Aînés-Services study and included 1585 older adults. Sociodemographic, psychosocial, lifestyle and health factors were informed from structured interviews. Group-based multi-trajectory modeling and multinomial logistic regression were used to identify aging profiles and correlates. Sampling weights were applied to account for the sampling plan. The weighted sample size was 1591. Three trajectories were identified: a favorable (79.0%), intermediate (14.5%), and severe scenario (6.5%). Factors associated with the severe scenario were older age, male gender, lower education, the presence of anxiety disorders, low physical activity, and smoking. Membership in the intermediate scenario was associated with daily hassles, physical disorders, anxiety and depression, antidepressant/psychotherapy use, low physical activity, and no alcohol use. High social support was protective against less favorable profiles. Symptoms of anxiety and depression and high burden of physical disorders were associated with less favorable trajectories. Modifiable lifestyle factors have a significant effect on healthy aging. Assessment and management of anxio-depressive symptoms are important in older adults. Clinical interventions including access to psychotherapy and promotion of healthier lifestyles should be considered. [ABSTRACT FROM AUTHOR]

Aims: Male driving while impaired (DWI) offenders are at heightened risk for engaging in risky driving. Males in a depressed mood are also more prone to alcohol misuse, which may further contribute to risky driving. This manuscript investigates the predictive potential of combined depressed mood and alcohol misuse on risky driving outcomes 3 and 9 years after baseline in male DWI offenders. Methods: At baseline, participants completed questionnaires assessing depressed mood (Major Depression scale of the Millon Clinical Multiaxial Inventory-III), alcohol misuse (Alcohol Use Disorders Identification Test), and sensation-seeking (Sensation Seeking Scale-V). Risky driving data (Analyse des comportements routiers; ACR3) were collected at follow-up 3 years after baseline. Driving offence data were obtained for 9 years after baseline. Results: There were 129 participants. As 50.4% of the sample were missing ACR3 scores, multiple imputation was conducted. In the final regression model, R2  = 0.34, F (7,121) = 8.76, P  < 0.001, alcohol misuse significantly predicted ACR3, B  = 0.56, t  = 1.96, P  = 0.05. Depressed mood, however, did not significantly predict ACR3 and sensation-seeking was not a significant moderator. Although the regression model predicting risky driving offences at Year 9 was significant R2  = 0.37, F (10,108) = 6.41, P  < 0.001, neither depressed mood nor alcohol misuse was a significant predictor. Conclusions: These findings identify alcohol misuse as a predictor of risky driving 3 years after baseline among male DWI offenders. This enhances our prediction of risky driving, extending beyond the widely researched acute impacts of alcohol by exploring chronic patterns. [ABSTRACT FROM AUTHOR]

Simple Summary: Diagnosing most cancers at an earlier stage improves outcomes because treatments are more effective and often less invasive. This study looked at the health patterns of adults enrolled in Alberta's Tomorrow Project before they were diagnosed with cancer to identify factors related to cancers that are caught early or late. These identified factors were then tested in a similar group of adults from the British Columbia Generations Project who also subsequently developed cancer, to see how well they could predict stage at diagnosis. The confirmed health patterns can be used to improve cancer prevention programs and activities to find cancer earlier in people who are at most risk of being diagnosed with late-stage cancer. Risk prediction models for cancer stage at diagnosis may identify individuals at higher risk of late-stage cancer diagnoses. Partial proportional odds risk prediction models for cancer stage at diagnosis for males and females were developed using data from Alberta's Tomorrow Project (ATP). Prediction models were validated on the British Columbia Generations Project (BCGP) cohort using discrimination and calibration measures. Among ATP males, older age at diagnosis was associated with an earlier stage at diagnosis, while full- or part-time employment, prostate-specific antigen testing, and former/current smoking were associated with a later stage at diagnosis. Among ATP females, mammogram and sigmoidoscopy or colonoscopy were associated with an earlier stage at diagnosis, while older age at diagnosis, number of pregnancies, and hysterectomy were associated with a later stage at diagnosis. On external validation, discrimination results were poor for both males and females while calibration results indicated that the models did not over- or under-fit to derivation data or over- or under-predict risk. Multiple factors associated with cancer stage at diagnosis were identified among ATP participants. While the prediction model calibration was acceptable, discrimination was poor when applied to BCGP data. Updating our models with additional predictors may help improve predictive performance. [ABSTRACT FROM AUTHOR]