Collaborating toward equity in Pennsylvania: The Age-Friendly Care,PA project

Objective

To collaboratively implement the age-friendly health systems framework, known as the 4Ms: What Matters, Medication, Mentation, and Mobility, at The Primary Health Network (PHN), a federally qualified health center.

Data Sources

Data were collected from PHN electronic medical records (EMRs) for individuals over age 65 from December 30, 2019 to December 24, 2021 and from Project ECHO© attendance and evaluation surveys.

Study Design

The telementoring educational program, Project ECHO©, was used to engage PHN health care professionals working in rural areas of Pennsylvania to incorporate the 4Ms into their practice starting with the annual wellness visit (AWV). Project ECHO© was launched at three primary care sites. After 18 months, it was then disseminated to an additional 18 sites creating pilot and comparison groups. Outcomes included codesigned patient process metrics using EMR data and project ECHO© participant data.

Data Collection Methods

EMR data were generated by system reports created by PHN's quality assurance program manager. Project ECHO© data were collected and managed using REDCap electronic data capture tools. Outcomes were aggregated, analyzed for trends over time, and compared between groups.

Principal Findings

All nine process outcomes increased from baseline to follow-up at the three initial sites, ranging from 4% to 43% g. At year two, the three initial sites had higher rates on AWVs (pilot 24%, comparison 12%; p < 0.0001), Advance Care Planning (New on file, pilot 8%, comparison 2%; Discussed with patient, pilot 18%, comparison 13%; Patient declined, pilot 0%, comparison 0%; p = 0.0001), Dementia Screening (pilot 24%, comparison 12%; p < 0.0001), Fall Risk Management (pilot 43%, comparison 10%; p < 0.0001), and Mobility Goal (pilot 19%, comparison 9%; p < 0.0001); and lower rates on High-Risk Medication Elimination (pilot 54%, comparison, 63%, p < 0.02).

Conclusions

Access to high-quality geriatric care for rural older adults can be improved by increasing health care professionals' knowledge of the 4Ms, beginning with its incorporation into the AWV.     

RNA sequencing steps toward the first line

• DNA is the sequence that codes for proteins.
• Messenger RNA is transcribed from the DNA sequence of genes and translated into protein.
• It can be difficult to predict how a change in the DNA sequence will affect messenger RNA and protein quantity and quality.
• DNA translocation changes can cause the joining of sequences from two different genes or different parts of the same gene.
• DNA sequencing is often used clinically to predict how DNA changes might affect proteins.
• Alternatively, RNA sequencing can be used as a more direct measure of the effect of DNA changes on the protein products.
• This sequencing is important for identifying changes in cancer that may indicate response to targeted therapy, prognosis, or diagnosis.

     

The impact of neighborhoods and friendships on interracial anxiety among medical students and residents: A report from the medical student CHANGES study

Objective

To examine the experience of interracial anxiety among health professionals and how it may affect the quality of their interactions with patients from racially marginalized populations. We explored the influence of prior interracial exposure—specifically through childhood neighborhoods, college student bodies, and friend groups—on interracial anxiety among medical students and residents. We also examined whether levels of interracial anxiety change from medical school through residency.

Data Source

Web-based longitudinal survey data from the Medical Student Cognitive Habits and Growth Evaluation Study.

Study Design

We used a retrospective longitudinal design with four observations for each trainee. The study population consisted of non-Black US medical trainees surveyed in their 1st and 4th years of medical school and 2nd and 3rd years of residency. Mixed effects longitudinal models were used to assess predictors of interracial anxiety and assess changes in interracial anxiety scores over time.

Principal Findings

In total, 3155 non-Black medical trainees were followed for 7 years. Seventy-eight percent grew up in predominantly White neighborhoods. Living in predominantly White neighborhoods and having less racially diverse friends were associated with higher levels of interracial anxiety among medical trainees. Trainees' interracial anxiety scores did not substantially change over time; interracial anxiety was highest in the 1st year of medical school, lowest in the 4th year, and increased slightly during residency.

Conclusions

Neighborhood and friend group composition had independent effects on interracial anxiety, indicating that premedical racial socialization may affect medical trainees' preparedness to interact effectively with diverse patient populations. Additionally, the lack of substantial change in interracial anxiety throughout medical training suggests the importance of providing curricular tools and structure (e.g., instituting interracial cooperative learning activities) to foster the development of healthy interracial relationships.     

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.