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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]

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]

Purpose: Socially disadvantaged patients may lack self-efficacy to navigate a complex health system making them vulnerable to healthcare inequity. We aimed to develop an Index of social vulnerability that predicts increased risk of negative healthcare events (e.g. emergency hospitalization), independent of chronic disease burden. The analysis illustrates the conceptual and practical steps leading to the development of a pragmatic Index of social vulnerability to limited healthcare self-efficacy. Methods: Using data from a 3-year cohort of 2507 adult primary care patients in Québec (Canada), we applied two complementary structural equation modelling approaches—Partial Least Squares Path Modelling (PLS-PM) and Multiple indicators and Multiple Causes (MIMIC) modelling—to identify a minimal set of social characteristics that could be summed into an Index related to limited healthcare self-efficacy. We then used logistic regression to determine if the Index predicted: hospital emergency department use; hospital admissions; unmet need for care, and others. We privileged parsimony over explanatory capacity in our analytic decisions to make the Index pragmatic for epidemiologic and clinical use. Results: The Individual Social Vulnerability Index is the weighted sum of five indicators: two indicators of social support; educational achievement; financial status; limited language proficiency. The Index predicts increased likelihood of all negative healthcare outcomes except unmet need, with a clear threshold at Index ≥ 2. The effect is independent of chronic disease burden. Conclusion: When social deficits outweigh social assets by two or more (Index ≥ 2), there is an increased risk of negative healthcare events beyond the risk attributable to poor health. The Index is a pragmatic tool to identify a minority of patients who will require additional support to receive equitable healthcare. [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]

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]

Foot care is a common problem for homeless adults in cities, who often receive fragmented foot care services, resulting in increased foot problems, health complications, hospitalisation, limb removal and disabilities. Among the health factors that negatively affect the homeless, foot health is essential, but often neglected. This study employed a descriptive, cross‐sectional research design to assess the foot care of homeless people and develop recommendations for clinical practice. Using Inlow's 60‐s foot screening method, the foot conditions of a purposive sample of 65 homeless adults were examined in British Columbia, Canada, in 2019–2020. Descriptive and inferential statistics were used to analyse the data. Determinants, such as sex and years of homelessness significantly contributed to homeless individuals' risk of developing foot problems. Differences in perceived and observed foot problems were influenced by equitable access to social health and healthcare services. Foot assessment is not well‐covered by homeless health services and should be implemented as part of the standard medical review of homeless patients. Furthermore, foot assessment should be conducted by healthcare professionals, rather than relying on the standard practice of service users' self‐report. Finally, foot care should be based on tailoring assessments and interventions for the individualised needs of homeless service users. Future research must seek ways to integrate homeless individuals' voices in the development, implementation and evaluation of foot care services. [ABSTRACT FROM AUTHOR]