Miaoqing
Jia.
Health economist studying insurance coverage, medication access, and the policies that decide who gets care.
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/01
D-SNPs and dual eligibles with serious mental illnessNIMH R01 · claims-based analysis comparing FIDE, HIDE, and coordination-only D-SNPs
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/02
GLP-1 access across the commercial-to-Medicare transitionEvent study on coverage transitions, formularies, and continuity of therapy
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/03
Chronic Condition Special Needs Plans (C-SNPs)Enrollment, healthcare utilization, and quality outcomes
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/04
FDA-cleared AI medical devices: adoption and accessDiffusion patterns, insurance coverage, and reimbursement
- 2026 New work in progress on GLP-1 access and C-SNPs.
- 2026 Fluoroquinolone Prescribing to Older Adults published in Antimicrobial Stewardship & Healthcare Epidemiology.
- 2026 Antibiotic Resistance, Drug Prices, and Entry accepted at Economics & Human Biology.
- APR 2025 Joined Weill Cornell Medicine as a Postdoctoral Associate.
A health economist working at the intersection of policy, data, and care.
Trained in applied microeconomics, industrial organization, and causal inference.
I joined the Department of Population Health Sciences at Weill Cornell Medical College in April 2025 after completing my PhD in Economics at Boston University. My research examines how insurance coverage policies — particularly Medicare Advantage, dual eligible special needs plans (D-SNPs), and Part D — affect medication access and health outcomes for populations with complex needs.
Most of my current work uses linked Medicare–Medicaid claims data and the Inovalon dataset to study D-SNP integration models, prescribing responses to regulatory signals, and treatment access for behavioral health populations. I'm supported by an NIMH R01 (MPI: McGinty and Zhang), where I lead the claims-based quantitative analysis on D-SNP enrollment and outcomes among dual eligibles with serious mental illness.
I'm preparing a K99/R00 application on insurance coverage transitions and medication access. My doctoral work, in a different vein, developed theoretical models of pharmaceutical market structures and antibiotic resistance.
Four areas I keep returning to.
My work connects insurance coverage design, pharmaceutical policy, and the health of populations whose care is fragmented across systems.
Medicare program design and dual eligibility
How does the structure of Medicare Advantage and integrated D-SNP enrollment shape healthcare quality and utilization for dual eligible beneficiaries with complex behavioral health needs? Using 20% Medicare claims samples, I evaluate FIDE, HIDE, coordination-only D-SNPs, look-alike plans, non-SNP MA, and traditional fee-for-service.
Machine learning for population health
Applying growth mixture modeling, XGBoost, and unsupervised clustering to large claims, EHR, and geographic datasets — characterizing patient trajectories, predicting acute care use from social needs screening, and identifying neighborhood archetypes for targeted intervention.
Pharmaceutical policy and prescriber response
Using comparative interrupted time series and Medicare Part D Prescriber files, I examine how regulatory signals — FDA black box warnings, scope-of-practice expansions — actually change prescribing behavior for older adults. Recent work covers fluoroquinolones and the rapid rise of GLP-1 prescribing.
Economics of pharmaceutical markets
My doctoral work developed theoretical models of how pharmaceutical market structures affect antibiotic resistance and drug innovation — examining pricing, patents, and usage restrictions as policy levers to balance resistance mitigation with innovation incentives.
Papers and working drafts.
Under review, in progress, in preparation.
Published & Forthcoming / 02
Fluoroquinolone Prescribing to Older Adults Following FDA Black Box Warnings: A Comparative Analysis
Antibiotic Resistance, Drug Prices, and Entry
Under Review / 02
Social Needs Screening and Subsequent Acute Care Utilization in a Large Safety Net Health System
Abstract
Background. Health systems increasingly screen for patients' social needs, but evidence is limited on which specific needs are most associated with subsequent acute care use.
Objective. To examine associations between patient-reported social needs and subsequent acute care utilization in a large safety-net population.
Design. Retrospective cohort study linking 2023 social needs screening data from a large safety-net health system to longitudinal electronic health records from a multi-health system clinical research network.
Participants. 20,337 adults with a primary care visit who completed social needs screening in 2023.
Main Measures. Positive screening for nine social needs domains (e.g., food insecurity, transportation-related delayed care). Outcomes were any hospitalization, any ED visit, preventable hospitalization, and preventable ED visit, using multivariable logistic regression adjusting for demographics, comorbidities, and neighborhood social conditions.
Key Results. Overall, 15.9% screened positive for ≥1 social need; the most prevalent were food insecurity (5.9%) and problems with residence (5.6%). After adjustment, positive screening for transportation-related delayed care was associated with a 6.8-percentage-point increase in the probability of ≥1 hospitalization, an 8.4-point increase in ≥1 ED visit, a 1.2-point increase in ≥1 preventable hospitalization, and a 4.2-point increase in ≥1 preventable ED visit. Housing-related problems were associated with a 4.1-point increase in ≥1 ED visit.
Conclusions. In a safety-net setting, transportation barriers showed the strongest and most consistent associations with subsequent acute care use, suggesting targeted transportation support and care-navigation strategies may be critical components of social care interventions.
Social Determinants of Health Clustering Analysis Identifies Five Neighborhood Archetypes for Cardiovascular Disease Prevention in New York City
Working Papers / 02
Nurse Practitioner and Physician Assistant Prescribing of GLP-1 Agonists to Older Adults, 2013–2023
Addressing the Externalities of Medicine Overconsumption
In Preparation / 02
Association between Enrollment in Integrated Dual Eligible Special Needs Plans and Quality and Utilization of Healthcare among Dual Eligibles with Serious Mental Illnesses
Using Machine Learning to Characterize Patient Trajectories after Hospice Live Discharge among Medicare Beneficiaries
What's on my desk right now.
Active grants, ongoing analyses, and what's coming next.
Integrated D-SNPs and healthcare quality for dual eligibles with SMI
Leading the claims-based quantitative analysis comparing FIDE, HIDE, coordination-only D-SNPs, look-alike plans, non-SNP MA, and traditional fee-for-service. The first outcome evaluation of HIDE and FIDE SNPs since they emerged in 2021.
GLP-1 access across the commercial-to-Medicare transition
An event study examining how GLP-1 medication use, out-of-pocket spending, and continuity of therapy change when individuals age into Medicare from commercial insurance — a coverage transition with sharply different formularies, prior authorization, and cost-sharing structures.
Chronic Condition Special Needs Plans (C-SNPs)
Evaluating enrollment, healthcare utilization, and quality outcomes among Medicare beneficiaries enrolled in C-SNPs — a less-studied corner of the SNP landscape designed for beneficiaries with severe or disabling chronic conditions.
Adoption and access for FDA-cleared AI medical devices
Examining diffusion patterns of FDA-cleared AI/ML-enabled medical devices and what insurance and reimbursement structures mean for patient access — including how Medicare and commercial coverage decisions shape uptake across specialties.
K99/R00 — Insurance coverage transitions and medication access
Building toward an independent research program on how insurance coverage policies and transitions affect medication access for chronic conditions — with a particular focus on GLP-1 access and behavioral health populations.
Methods I keep coming back to
Difference-in-differences with staggered adoption, instrumental variables for plan choice, and how to draw causal inferences from claims data when enrollment in any given plan is anything but random.
A life outside the claims data.
Cats, travel, and the parts of myself that don't fit on a CV.
The cats
My most loyal research assistants — they don't read drafts, but they sit on them. Vital contributors to thinking-time and to the warmth of any home office.
The trips
I keep a running list of places that surprised me — back streets, small museums, unexpected meals. Travel is how I remember that my data is made of people.
Quiet hours
I love creative writing, listening to music, cooking, baking, and arranging flowers. The same care I bring to a careful research design, I bring to a Sunday loaf or a bouquet on the kitchen table.
Get in touch.
Always happy to talk about D-SNPs, claims data, GLP-1s, identification strategies, or recommend a city to visit.
For research collaborations, please email directly. I aim to respond within a few days.