About

Natalia Festa, MD, MHS, is an Assistant Professor in Medicine specializing in Geriatrics at Yale School of Medicine. She trained in Internal Medicine at Massachusetts General Hospital and in Geriatrics at Yale. She completed her MHS as a fellow within the Yale Program on Aging and the National Clinician Scholars Program. Festa earned her undergraduate degree from Harvard College and her MD from Stanford University. Her research focuses on identifying modifiable regulatory and health services factors that can improve person-centered aging outcomes for nursing home residents. She also investigates the identification of dementia using routinely collected healthcare data. Her work includes evaluating emergency preparedness of nursing homes for natural disasters such as hurricanes and wildfires, assessing regional nursing home preparedness and regulatory responsiveness, and exploring the accuracy of diagnosis and health service codes in identifying frailty in Medicare data. Festa's contributions aim to enhance health outcomes for older adults through data-driven approaches and health services research.

Research topics

• Environmental health
• Medicine
• Physical therapy
• Internal medicine
• Gerontology
• Family medicine
• Pediatrics
• Demography

Selected publications

• Evaluating administrative compliance as a predictor of nursing home postdisaster outcomes in the USA

  BMJ Public Health · 2026-01-01

  articleOpen access1st authorCorresponding

  Introduction: Determining whether compliance with the US Centers for Medicare & Medicaid Services (CMS) emergency preparedness standards for nursing homes is a protective factor that improves residents' postdisaster outcomes is vital to informed regulatory oversight. Methods: This retrospective cohort study included 294 CMS-certified nursing homes exposed to Hurricane Michael (October 2018). The exposure was non-compliance with emergency preparedness (E-tag) and/or building code (K-tag) standards from the CMS Life Safety Code survey; we operationalised over 200 unique deficiencies as separate dichotomous indicator variables. We fit generalised linear models and applied elastic net regularisation to identify which regulatory deficiencies were most predictive of adverse postdisaster outcomes (30-day mortality (primary), 30-day hospitalisation and functional decline within 120 days). We selected the best fitting model for each outcome based on the lowest Bayesian Information Criterion and reported incidence rate ratios (IRRs) for retained variables. We performed 10-fold cross-validation and evaluated the predictive accuracy of the best-fitting models using root mean squared error (RMSE), compared with a null (intercept-only) model. Results: Across 294 nursing homes with 21 945 residents with an average age of 81 years, there were 697 deaths, 1316 hospitalisations and 1274 instances of functional decline in the postdisaster period. No emergency preparedness deficiency predicted adverse postdisaster outcomes. In contrast, two building code deficiencies predicted postdisaster functional decline (IRRs 1.21 and 1.51). The best-fitting models demonstrated modest improvements in predictive accuracy compared with the null model for postdisaster mortality (RMSE 1.76 vs 1.79) and functional decline (RMSE 3.35 vs 3.44), although these differences were not statistically significant. Conclusions: Measures of compliance with federal emergency-preparedness standards did not predict postdisaster mortality, hospitalisation or functional decline. These findings indicate a need to better align the measurement and oversight of nursing home emergency preparedness with the complexities of real-world disaster response.

  PublisherOA PDFDOI

• Effects of ambient temperature on hospital admissions and mortality among older adults with and without dementia in South Korea

  International Journal of Epidemiology · 2025-06-11 · 4 citations

  articleOpen access

  BACKGROUND: Dementia is a growing health problem as the global population ages. However, the research on the effects of ambient temperatures on various health outcomes among people with dementia remains limited. This study examined the association between daily temperatures and hospital admissions for dementia among older adults with dementia, as well as their association with all-cause hospital admissions and mortality among individuals with and without dementia. METHODS: This study utilized the National Health Insurance Service-Senior Cohort from 2002 to 2019, consisting of approximately one million older adults in South Korea. Individuals with dementia were identified based on medical claims. Daily mean temperatures were obtained from national monitoring stations and averaged at the province level. We employed a time-stratified case-crossover design to estimate the association between daily temperatures and the risk of hospital admissions and mortality. RESULTS: In the total cohort of 1 057 784 individuals, 78 424 were identified as having dementia. The association between temperature and dementia admissions showed a hockey stick-shaped curve, indicating an elevated risk at higher temperatures. The relative risk (RR) for dementia admissions at the 99th percentile temperature compared to the 50th percentile temperature was 1.36 (95% confidence interval: 1.19-1.57). Individuals with dementia showed more pronounced increases in all-cause hospital admissions and mortality at high temperatures compared to those without dementia. CONCLUSIONS: Our study found that high temperatures increased the risk of hospital admissions for dementia in older adults with dementia. Additionally, those with dementia may experience greater health impacts from extreme temperatures than those without, due to physiological and behavioral vulnerabilities.

  PublisherOA PDFDOI

• Evaluating the Applicability of Federal Regulatory Metrics for Nursing Home Fire Safety and Emergency Preparedness

  Journal of the American Medical Directors Association · 2025-09-11 · 2 citations

  letterOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Comparing Disaster‐Related Surge Staffing Practices Across Rural and Non‐Rural Nursing Homes

  Journal of the American Geriatrics Society · 2025-09-12

  article1st authorCorresponding

  BACKGROUND: Clarifying whether rural and non-rural nursing homes implement different disaster-related staffing approaches is vital to developing targeted preparedness strategies. METHODS: In this retrospective cohort study of 361 nursing homes exposed to Hurricane Michael (2018), we used a comparative interrupted time series design to assess changes in staffing intensity and composition from 30 days before landfall to 7 days after. We examined three outcomes using the Centers for Medicare & Medicaid Services (CMS) Payroll Based Journal: total staffing intensity (hours per resident per day), licensure composition (percentage of registered nurses (RN) and licensed practical nurses (LPN)), and employment composition (percentage of routine staff who are not independent contractors). We evaluated staffing intensity with linear mixed-effects models and licensure and employment composition using linear probability models. We report point estimates and 95% confidence intervals (CI). RESULTS: Of 361 nursing homes, 39.9% were rural. At baseline, rural facilities had 8% (0.92; 95% CI 0.87-0.96) lower total staffing intensity compared to non-rural facilities. In the post-event period, rural facilities increased total staffing by an additional 4% (95% CI 1.02 to 1.06) in the post-event period compared to non-rural facilities. Rural facilities increased their routine staff representation (0.46% points; 95% CI 0.14 to 0.78) but decreased licensed staff representation (-1.00% points; 95% CI -1.39 to 0.61) compared to the post-disaster staffing changes in non-rural facilities. CONCLUSIONS: Distinct disaster-related staffing approaches may heighten baseline staffing differences between rural and non-rural nursing homes. Disaster-related staffing intensity increased in rural facilities, with rural facilities starting from a lower baseline. Although rural facilities may have benefited from greater reliance on routine staff, they were unable to augment licensed nurse staffing. These findings offer foundational evidence regarding the influence of baseline staffing practices and local healthcare workforce conditions on disaster-related staffing strategies.

  PublisherDOI

• Federal inspection timing, not compliance, associated with nursing home post-disaster outcomes

  Health Affairs Scholar · 2025-12-18

  articleOpen access1st authorCorresponding
  PublisherDOI

• Detection of emergency department patients at risk of dementia through artificial intelligence

  Alzheimer s & Dementia · 2025-06-01 · 6 citations

  articleOpen access

  INTRODUCTION: The study aimed to develop and validate the Emergency Department Dementia Algorithm (EDDA) to detect dementia among older adults (65+) and support clinical decision-making in the emergency department (ED). METHODS: In a multisite retrospective study of 759,665 ED visits, electronic health record data from Yale New Haven Health (2014-2022) were used to train three supervised and semi-unsupervised positive-unlabeled machine learning models (XGBoost, Random Forest, LASSO). A separate test set of 400 ED encounters underwent adjudicated chart review for validation. RESULTS: EDDA achieved an area under the receiver-operating characteristic curve (AUROC) of 0.85 in the test set and 0.93 in the validation set. Positive-unlabeled learning improved performance. Agreement between EDDA and clinician-adjudicated dementia diagnoses was moderate (kappa = 0.50), with 17% of EDDA-positive patients having undiagnosed probable dementia. DISCUSSION: EDDA enhances dementia detection in the ED, with potential for real-time implementation to improve patient outcomes and care transitions. HIGHLIGHTS: Developed a machine learning algorithm using electronic health record data to detect dementia in the emergency department (ED). Algorithm designed to balance detection accuracy with ease of ED implementation. Parsimonious model with limited but predictive variables selected for rapid ED use. Focused on real-time application, optimizing ED workflows, and clinician support. Aims to enhance ED dementia detection, patient safety, and care coordination.

  PublisherOA PDFDOI

• Short-term association of non-optimum ambient temperature with dementia-related hospital admissions in South Korea

  ISEE Conference Abstracts · 2024-07-31

  articleOpen access
  PublisherDOI

• Enhancing the quality and reproducibility of research: How to work effectively with medical and data librarians

  Journal of the American Geriatrics Society · 2024-01-13 · 5 citations

  articleOpen access

  The authors declare no conflicts of interest. Supplementary File S1. Search strategies used to create Figure 1. These search strategies were used to extract data from Scopus used to create Figure 1. Supplementary File S2. Python code used to create Figure 1. This code was written to transform data and create the visualization for Figure 1. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

  PublisherOA PDFDOI

• Nursing home infection control strategies during the <scp>COVID</scp> ‐19 pandemic

  Journal of the American Geriatrics Society · 2023-05-22 · 2 citations

  articleOpen access1st authorCorresponding

  BACKGROUND: The American Rescue Plan Act of 2021 awarded $500 million toward scaling "strike teams" to mitigate the impact of Coronavirus Disease 2019 (COVID-19) within nursing homes. The Massachusetts Nursing Facility Accountability and Support Package (NFASP) piloted one such model during the first weeks of the pandemic, providing nursing homes financial, administrative, and educational support. For a subset of nursing homes deemed high-risk, the state offered supplemental, in-person technical infection control support. METHODS: Using state death certificate data and federal nursing home occupancy data, we examined longitudinal all-cause mortality per 100,000 residents and changes in occupancy across NFASP participants and subgroups that varied in their receipt of the supplemental intervention. RESULTS: Nursing home mortality peaked in the weeks preceding the NFASP, with a steeper increase among those receiving the supplemental intervention. There were contemporaneous declines in weekly occupancy. The potential for temporal confounding and differential selection across NFASP subgroups precluded estimation of causal effects of the intervention on mortality. CONCLUSIONS: We offer policy and design suggestions for future strike team iterations that could inform the allocation of state and federal funding. We recommend expanded data collection infrastructure and, ideally, randomized assignment to intervention subgroups to support causal inference as strike team models are scaled under the direction of state and federal agencies.

  PublisherOA PDFDOI

• Promise and peril of claims-based dementia ascertainment in causal inference

  BMJ evidence-based medicine · 2023-05-02 · 5 citations

  articleOpen access1st authorCorresponding

  Festa and colleagues highlight underrecognized factors that may bias research, policy, and population health strategies predicated upon claims-based ascertainment of Alzheimer’s Disease and Related Dementias within the United States.

  PublisherOA PDFDOI

Frequent coauthors

• John Hsu

  Massachusetts General Hospital

  19 shared

• Joseph P. Newhouse

  Harvard University

  15 shared

• Thomas M. Gill

  Yale University

  11 shared

• Max Weiss

  Massachusetts General Hospital

  10 shared

• Mary Price

  Massachusetts General Hospital

  8 shared

• Sharon‐Lise T. Normand

  Harvard University

  8 shared

• Nicole M. Benson

  McLean Hospital

  8 shared

• Fernando Mendoza

  Stanford University

  7 shared

Education

• M.D., Internal Medicine

  Massachusetts General Hospital

• Other

  Yale