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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]

Objectives Damage accrual in SSc can be tracked using the Scleroderma Clinical Trials Consortium Damage Index (DI). Our goal was to develop a prediction model for damage accrual in SSc patients with early disease. Methods Using patients with <2 years disease duration from Canada and Australia as a derivation cohort, and from the Netherlands as a validation cohort, we used group-based trajectory modelling (GBTM) to determine 'good' and 'bad' latent damage trajectories. We developed a prediction model from this analysis and applied it to patients from derivation and validation cohorts. We plotted the actual DI trajectories of the patients predicted to be in 'good' or 'bad' groups. Results We found that the actual trajectories of damage accumulation for lcSSc and dcSSc were very different, so we studied each subset separately. GBTM found two distinct trajectories in lcSSc and three in dcSSc. We collapsed the two worse trajectories in the dcSSc into one group and developed a prediction model for inclusion in either 'good' or 'bad' trajectories. The performance of models using only baseline DI and sex was excellent with ROC AUC of 0.9313 for lcSSc and 0.9027 for dcSSc. Using this model, we determined whether patients would fall into 'good' or 'bad' trajectory groups and then plotted their actual trajectories which showed clear differences between the predicted 'good' and 'bad' cases in both derivation and validation cohorts. Conclusions A simple model using only cutaneous subset, baseline DI and sex can predict damage accumulation in early SSc. [ABSTRACT FROM AUTHOR]

Study Question: Can we derive adequate models to predict the probability of conception among couples actively trying to conceive?Summary Answer: Leveraging data collected from female participants in a North American preconception cohort study, we developed models to predict pregnancy with performance of ∼70% in the area under the receiver operating characteristic curve (AUC).What Is Known Already: Earlier work has focused primarily on identifying individual risk factors for infertility. Several predictive models have been developed in subfertile populations, with relatively low discrimination (AUC: 59-64%).Study Design, Size, Duration: Study participants were female, aged 21-45 years, residents of the USA or Canada, not using fertility treatment, and actively trying to conceive at enrollment (2013-2019). Participants completed a baseline questionnaire at enrollment and follow-up questionnaires every 2 months for up to 12 months or until conception. We used data from 4133 participants with no more than one menstrual cycle of pregnancy attempt at study entry.Participants/materials, Setting, Methods: On the baseline questionnaire, participants reported data on sociodemographic factors, lifestyle and behavioral factors, diet quality, medical history and selected male partner characteristics. A total of 163 predictors were considered in this study. We implemented regularized logistic regression, support vector machines, neural networks and gradient boosted decision trees to derive models predicting the probability of pregnancy: (i) within fewer than 12 menstrual cycles of pregnancy attempt time (Model I), and (ii) within 6 menstrual cycles of pregnancy attempt time (Model II). Cox models were used to predict the probability of pregnancy within each menstrual cycle for up to 12 cycles of follow-up (Model III). We assessed model performance using the AUC and the weighted-F1 score for Models I and II, and the concordance index for Model III.Main Results and the Role Of Chance: Model I and II AUCs were 70% and 66%, respectively, in parsimonious models, and the concordance index for Model III was 63%. The predictors that were positively associated with pregnancy in all models were: having previously breastfed an infant and using multivitamins or folic acid supplements. The predictors that were inversely associated with pregnancy in all models were: female age, female BMI and history of infertility. Among nulligravid women with no history of infertility, the most important predictors were: female age, female BMI, male BMI, use of a fertility app, attempt time at study entry and perceived stress.Limitations, Reasons For Caution: Reliance on self-reported predictor data could have introduced misclassification, which would likely be non-differential with respect to the pregnancy outcome given the prospective design. In addition, we cannot be certain that all relevant predictor variables were considered. Finally, though we validated the models using split-sample replication techniques, we did not conduct an external validation study.Wider Implications Of the Findings: Given a wide range of predictor data, machine learning algorithms can be leveraged to analyze epidemiologic data and predict the probability of conception with discrimination that exceeds earlier work.Study Funding/competing Interest(s): The research was partially supported by the U.S. National Science Foundation (under grants DMS-1664644, CNS-1645681 and IIS-1914792) and the National Institutes for Health (under grants R01 GM135930 and UL54 TR004130). In the last 3 years, L.A.W. has received in-kind donations for primary data collection in PRESTO from FertilityFriend.com, Kindara.com, Sandstone Diagnostics and Swiss Precision Diagnostics. L.A.W. also serves as a fibroid consultant to AbbVie, Inc. The other authors declare no competing interests.Trial Registration Number: N/A. [ABSTRACT FROM AUTHOR]