Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning.

In this work, we examine magnetic resonance imaging (MRI) and ultrasound (US) appointments at the Diagnostic Imaging (DI) department of a pediatric hospital to discover possible relationships between selected patient features and no-show or long waiting room time endpoints. The chosen features include age, sex, income, distance from the hospital, percentage of non-English speakers in a postal code, percentage of single caregivers in a postal code, appointment time slot (morning, afternoon, evening), and day of the week (Monday to Sunday). We trained univariate Logistic Regression (LR) models using the training sets and identified predictive (significant) features that remained significant in the test sets.

Framingham ten-year general cardiovascular disease risk: agreement between BMI-based and cholesterol-based estimates in a South Asian convenience sample.

The goal of this analysis was to determine the agreement between body mass index-based and cholesterol-based ten-year Framingham general cardiovascular disease risk scores among a convenience sample of 773 South Asian Canadian adults attending community-based screening clinics. Scores were calculated using age, systolic blood pressure, antihypertensive use, current smoking, diabetes, and total cholesterol and high density lipoprotein (for cholesterol-based risk) or height and weight (for body mass index-based risk). Mean risk score differences (body mass index-based risk minus cholesterol-based risk) were estimated using paired t-tests.

Enhancing cultural competence for improved access to quality care.

The Hospital for Sick Children (SickKids) is situated in one of the most diverse cities in the world. This is reflected in the patient population it serves. In 2009, the hospital embarked on a quality improvement initiative to address the existing evidence base on health disparities and to enhance health equity through cultural competence programming.