METHOD: For designing and modeling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. Different Machine learning-based feature ranking techniques were investigated to identify the important MNSI features associated with DSPN diagnosis. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading using the best performing top-ranked MNSI features.
RESULTS: Top-10 ranked features from MNSI features: Appearance of Feet (R), Ankle Reflexes (R), Vibration perception (L), Vibration perception (R), Appearance of Feet (L), 10-gm filament (L), Ankle Reflexes (L), 10-gm filament (R), Bed Cover Touch, and Ulceration (R) were identified as important features for identifying DSPN by Multi-Tree Extreme Gradient Boost model. The nomogram-based prediction model exhibited an accuracy of 97.95% and 98.84% for the EDIC test set and an independent test set, respectively. A DSPN severity score technique was generated for MNSI from the DSPN severity prediction model. DSPN patients were stratified into four severity levels: absent, mild, moderate, and severe using the cut-off values of 17.6, 19.1, 20.5 for the DSPN probability less than 50%, 75%-90%, and above 90%, respectively.
CONCLUSIONS: The findings of this work provide a machine learning-based MNSI severity grading system which has the potential to be used as a secondary decision support system by health professionals in clinical applications and large clinical trials to identify high-risk DSPN patients.
METHODS: Blood lead level, anemia, hepatitis B virus (HBV) infection, tuberculosis infection or disease, and Strongyloides seropositivity data were available for 8148 refugee children (aged < 19 years) from Bhutan, Burma, Democratic Republic of Congo, Ethiopia, Iraq, and Somalia.
RESULTS: We identified distinct health profiles for each country of origin, as well as for Burmese children who arrived in the United States from Thailand compared with Burmese children who arrived from Malaysia. Hepatitis B was more prevalent among male children than female children and among children aged 5 years and older. The odds of HBV, tuberculosis, and Strongyloides decreased over the study period.
CONCLUSIONS: Medical screening remains an important part of health care for newly arrived refugee children in the United States, and disease risk varies by population.
METHODS: 403 female teachers who never or infrequently attended for a Pap test from 40 public secondary schools in Kuala Lumpur were recruited into a cluster randomized trial conducted between January and November 2010. The intervention group participated in a worksite cervical screening initiative whilst the control group received usual care from the existing cervical screening program. Multivariate logistic regression was performed to determine the impact of the intervention program on Pap smear uptake after 24 weeks of followup.
RESULTS: The proportion of women attending for a Pap test was significantly higher in the intervention than in the control group (18.1% versus 10.1%, P value < 0.05) with the worksite screening initiative doubling the Pap smear uptake, adjusted odds ratio 2.44 (95% CI: 1.29-4.62).
CONCLUSION: Worksite health promotion interventions can effectively increase cervical smear uptake rates among eligible workers in middle-income countries. Policy makers and health care providers in these countries should include such interventions in strategies for reducing cervical cancer burden. This trial is registered with IRCT201103186088N1.