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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]

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]