Ashley Adanna Lewis

All Publications

• Racial and ethnic disparities in statin adherence: insights from the All of Us Research Program. Frontiers in cardiovascular medicine Escobar, G., Azizi, Z., de Hond, A., Lewis, A. A., Ng, M. Y., Rodriguez, F., Hernandez-Boussard, T. 2025; 12: 1541082

  Abstract

  Statin adherence impacts cardiovascular outcomes, yet disparities persist. Understanding the sociodemographic factors and barriers is crucial for targeted interventions.To investigate the relationship between sociodemographic factors and statin adherence across racial and ethnic groups.This retrospective study examined sociodemographic factors, prescription records, clinical factors, and responses from the Demographic, Drug Exposure, Healthcare Utilization Survey (HUS) in the All of Us (AoU) cohort. Multivariable logistic regression models were used to assess the impact of sociodemographic factors on adherence stratified by race.Adult participants with statin prescription records. Subgroup analyses included those who responded to the HUS.Statin prescription.We calculated percent days covered (PDC) as the proportion of days within a year in which a person prescribed a statin filled a prescription. Adequate adherence was defined as PDC ≥ 80%.Among the 17,029 adults with a statin prescription, the mean PDC was 57%, and 66% had PDC ≤ 80%. In multivariable analyses stratified by race and ethnicity, distinct barriers to adherence emerged. Among the non-Hispanic White participants, barriers to consistent healthcare [odds ratio (OR) = 0.60, 95% CI (0.42-0.87)] and lack of provider identity concordance [OR = 0.83, 95% CI (0.72-0.97)] were associated with lower adherence. In the non-Hispanic Black participants, Medicare [OR = 0.54, 95% CI (0.32-0.90)] and Veterans Affairs insurance [OR = 0.44, 95% CI (0.20-0.96)], as well as financial barriers [OR = 0.69, 95% CI (0.51-0.96)], reduced adherence. Among the Hispanic participants, provider-related anxiety [OR = 0.13, 95% CI (0.02-0.87)], immigrant status [OR = 0.25, 95% CI (0.08-0.72)], and Medicaid coverage [OR = 0.11, 95% CI (0.03-0.45)] predicted lower adherence.Addressing cardiovascular disease disparities requires recognizing unique sociodemographic barriers to statin adherence within racial and ethnic groups. Our findings highlight the need for tailored strategies considering the diverse barriers each group faces. Targeted interventions can bridge adherence gaps and improve cardiovascular outcomes across populations. This approach recognizes that although race and ethnicity may correlate with specific barriers, the underlying social determinants of health often play the key role in statin adherence.

  View details for DOI 10.3389/fcvm.2025.1541082

  View details for PubMedID 41458993

  View details for PubMedCentralID PMC12740900

• Validation of the American Heart Association Predicting Risk of Cardiovascular Disease Events Equations in Diverse Socioeconomic Groups: The All of Us Cohort. Journal of the American Heart Association Lewis, A. A., Bacong, A. M., Palaniappan, L., Hernandez-Boussard, T. 2025: e041549

  Abstract

  In 2023, the American Heart Association PREVENT (Predicting Risk of Cardiovascular Disease Events) equations were introduced as a tool to improve cardiovascular disease (CVD) risk prediction. This study tests their performance in a diverse socioeconomic cohort.We analyzed All of Us participants aged 30 to 79 years without baseline CVD who had required PREVENT input data over a 5.4-year follow-up. Discrimination was assessed using Harrell's C-statistic, with calibration by comparing predicted and observed 5-year CVD rates across 10-year risk deciles. Mean data are ±SD.We examined 9010 individuals (mean age, 63.0±11.0 years; 45.5% male). Racial and ethnic composition was 61.7% non-Hispanic White, 17.2% non-Hispanic Black, 4.5% multiracial/other, 1.3% non-Hispanic Asian, and 11.2% Hispanic or Latino. The "other" race/ethnic category reflects participants who self-identified as "other" in response to the, "Which category describes you?" item in the Basics survey. Over a mean follow-up of 3.6±1.8 years, 9.0% experienced a cardiovascular event. The mean 10-year predicted risks were 0.23±0.17 for total CVD, 0.13±0.10 for atherosclerotic CVD (ASCVD), and 0.19±0.17 for heart failure. The predicted-to-observed rate ratios were 5.3 for CVD and 3.3 for ASCVD. The C statistic for the overall sample was 0.732 (95% CI, 0.718-0.752) for CVD, 0.716 (95% CI, 0.698-0.741) for ASCVD, and 0.777 (95% CI, 0.757-0.800) for heart failure.The PREVENT equations showed strong discrimination across all strata in this national cohort. Overprediction of CVD events likely reflects baseline differences in comorbidity burden between the PREVENT development cohort and this All of Us cohort, particularly due to the exclusion of individuals missing estimated glomerular filtration rate, a variable not routinely collected and likely missing, not at random. Strong discrimination supports potential clinical utility, though further work is needed to improve calibration in this population.

  View details for DOI 10.1161/JAHA.125.041549

  View details for PubMedID 40932135

• Racial and Ethnic Disparities in Statin Adherence: Insights from the All of Us Research Program. medRxiv : the preprint server for health sciences Escobar, G., Azizi, Z., de Hond, A., Lewis, A. A., Ng, M. Y., Rodriguez, F., Hernandez-Boussard, T. 2025

  Abstract

  Statin adherence impacts cardiovascular outcomes, yet disparities persist. Understanding sociodemographic factors and barriers is crucial for targeted interventions.To investigate the relationship between sociodemographic factors and statin adherence across racial and ethnic groups.This retrospective study examined sociodemographic factors, prescription records, clinical factors, and responses from the Demographic, Drug Exposure, Healthcare Utilization Survey (HUS) in the All of Us (AoU) cohort. Multivariable logistic regression models assessed the impact of sociodemographic factors on adherence stratified by race.Adult participants with statin prescription records. Subgroup analyses included those who responded to the HUS.Statin prescription.Percent days covered (PDC), calculated as the proportion of days within a year in which a person prescribed a statin filled a prescription. Adequate adherence was defined as PDC ≥ 80%.Of the 17,029 participants with a statin prescription, the mean statin PDC was 57%, with 66% reporting a PDC ≤ 80%. Racial and ethnic differences in adherence were observed, with Non-Hispanic White (NHW) participants having a median PDC of 74% (IQR [0.25,0.98]), Non-Hispanic Black (NHB) 49% (IQR [0.25,0.98]), and Hispanic participants 25% (IQR [0.08,0.49]). NHW participants faced employment barriers (OR 0.63, 95% CI [0.46, 0.86]) and provider inaccessibility (OR 0.56, 95% CI [0.40, 0.76]) as significant factors for lower adherence. NHB participants experienced patient anxiety (OR 0.53, 95% CI [0.30, 0.90]) and financial barriers (OR 0.65, 95% CI [0.50, 0.85]), while Hispanic participants showed patient anxiety (OR 0.14, 95% CI [0.02, 0.60]) and immigrant status (OR 0.36, 95% CI [0.17, 0.76]) as significant factors for lower adherence.To address cardiovascular disease disparities, it is crucial to recognize unique sociodemographic barriers to statin adherence within racial and ethnic groups. Our findings highlight the need for tailored strategies considering the diverse barriers each group faces. Targeted interventions can bridge adherence gaps and improve cardiovascular outcomes across populations. This approach recognizes that while race and ethnicity may correlate with specific barriers, it is the underlying SDoH that often play a pivotal role in statin adherence.

  View details for DOI 10.1101/2025.08.26.25334490

  View details for PubMedID 40909854

  View details for PubMedCentralID PMC12407661

• Sequence modeling and design from molecular to genome scale with Evo. Science (New York, N.Y.) Nguyen, E., Poli, M., Durrant, M. G., Kang, B., Katrekar, D., Li, D. B., Bartie, L. J., Thomas, A. W., King, S. H., Brixi, G., Sullivan, J., Ng, M. Y., Lewis, A., Lou, A., Ermon, S., Baccus, S. A., Hernandez-Boussard, T., Re, C., Hsu, P. D., Hie, B. L. 2024; 386 (6723): eado9336

  Abstract

  The genome is a sequence that encodes the DNA, RNA, and proteins that orchestrate an organism's function. We present Evo, a long-context genomic foundation model with a frontier architecture trained on millions of prokaryotic and phage genomes, and report scaling laws on DNA to complement observations in language and vision. Evo generalizes across DNA, RNA, and proteins, enabling zero-shot function prediction competitive with domain-specific language models and the generation of functional CRISPR-Cas and transposon systems, representing the first examples of protein-RNA and protein-DNA codesign with a language model. Evo also learns how small mutations affect whole-organism fitness and generates megabase-scale sequences with plausible genomic architecture. These prediction and generation capabilities span molecular to genomic scales of complexity, advancing our understanding and control of biology.

  View details for DOI 10.1126/science.ado9336

  View details for PubMedID 39541441

• AI and biosecurity: The need for governance. Science (New York, N.Y.) Bloomfield, D., Pannu, J., Zhu, A. W., Ng, M. Y., Lewis, A., Bendavid, E., Asch, S. M., Hernandez-Boussard, T., Cicero, A., Inglesby, T. 2024; 385 (6711): 831-833

  Abstract

  Governments should evaluate advanced models and if needed impose safety measures.

  View details for DOI 10.1126/science.adq1977

  View details for PubMedID 39172825