Building new standards to prevent harm from medication errors in children with cancer

• Children with cancer receive many medications outside the hospital administered by their caregivers.
• The study by Walsh et al. shows the number and types of medication errors in these patients. The study includes data from three different centers.
• Importantly, the study shows the types of errors that cause harm. The authors describe how the harmful errors can be prevented.
• We suggest ways these results can be used to identify which patients and families will benefit from additional attention. Providing more help at clinic and in the home may help prevent harmful medication errors in children with cancer.

     

The social vulnerability metric (SVM) as a new tool for public health

Objective

To derive and validate a new ecological measure of the social determinants of health (SDoH), calculable at the zip code or county level.

Data Sources and Study Setting

The most recent releases of secondary, publicly available data were collected from national U.S. health agencies as well as state and city public health departments.

Study Design

The Social Vulnerability Metric (SVM) was constructed from U.S. zip-code level measures (2018) from survey data using multidimensional Item Response Theory and validated using outcomes including all-cause mortality (2016), COVID-19 vaccination (2021), and emergency department visits for asthma (2018). The SVM was also compared with the existing Centers for Disease Control and Prevention's Social Vulnerability Index (SVI) to determine convergent validity and differential predictive validity.

Data Collection/Extraction Methods

The data were collected directly from published files available to the public online from national U.S. health agencies as well as state and city public health departments.

Principal Findings

The correlation between SVM scores and national age-adjusted county all-cause mortality was r = 0.68. This correlation demonstrated the SVM's robust validity and outperformed the SVI with an almost four-fold increase in explained variance (46% vs. 12%). The SVM was also highly correlated (r ≥ 0.60) to zip-code level health outcomes for the state of California and city of Chicago.

Conclusions

The SVM offers a measurement tool improving upon the performance of existing SDoH composite measures and has broad applicability to public health that may help in directing future policies and interventions. The SVM provides a single measure of SDoH that better quantifies associations with health outcomes.