Meghan Podolsky
@meg-po.bsky.social
Research Fellow, Real World Data Research @ BMS | BUSPH
Chronic disease epidemiology, health services research, & HEOR
https://www.linkedin.com/in/meghan-podolsky-099b23145
Chronic disease epidemiology, health services research, & HEOR
https://www.linkedin.com/in/meghan-podolsky-099b23145
Structural factors may impact access to novel GLP-1/GIP RAs.
Future research should assess variations among populations (ex. Medicare, Medicaid) & incorporate add’l sociodemographic characteristics (race, ethnicity, income)
4/
Future research should assess variations among populations (ex. Medicare, Medicaid) & incorporate add’l sociodemographic characteristics (race, ethnicity, income)
4/
January 21, 2025 at 4:33 PM
Structural factors may impact access to novel GLP-1/GIP RAs.
Future research should assess variations among populations (ex. Medicare, Medicaid) & incorporate add’l sociodemographic characteristics (race, ethnicity, income)
4/
Future research should assess variations among populations (ex. Medicare, Medicaid) & incorporate add’l sociodemographic characteristics (race, ethnicity, income)
4/
The top features were included in a multivariable logistic regression model to quantify associations.
Key factors identified included sex, region, plan type, antidepressant use, obesity class, and employer industry.
3/
Key factors identified included sex, region, plan type, antidepressant use, obesity class, and employer industry.
3/
January 21, 2025 at 4:33 PM
The top features were included in a multivariable logistic regression model to quantify associations.
Key factors identified included sex, region, plan type, antidepressant use, obesity class, and employer industry.
3/
Key factors identified included sex, region, plan type, antidepressant use, obesity class, and employer industry.
3/
We used Marketscan Commercial data through 2022 to identify a cohort of pts with obesity without diabetes.
A random forest model was used to pinpoint the most impactful predictors of use, applying SHapley Additive exPlanations (SHAP) values.
2/
A random forest model was used to pinpoint the most impactful predictors of use, applying SHapley Additive exPlanations (SHAP) values.
2/
January 21, 2025 at 4:33 PM
We used Marketscan Commercial data through 2022 to identify a cohort of pts with obesity without diabetes.
A random forest model was used to pinpoint the most impactful predictors of use, applying SHapley Additive exPlanations (SHAP) values.
2/
A random forest model was used to pinpoint the most impactful predictors of use, applying SHapley Additive exPlanations (SHAP) values.
2/