Laura A. Hatfield, PhD
Dr. Hatfield is an Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School's Department of Health Care Policy. Her methods research focuses on causal inference in non-randomized settings, especially using difference-in-differences, and quantifying variation in health care utilization, outcomes, and quality using clustering and hierarchical Bayesian models. She co-leads the Health Policy Data Science Lab (with Dr. Sherri Rose) and leads the Data & Methods Core of a National Institute on Aging-funded Program Project titled, “Improving Medicare in an Era of Change.” She is the PI of an AHRQ-funded R01 entitled “Examining payment and delivery model impacts on health equity using novel quasi-experimental causal inference methods.”
Recent honors include the 2023 Mid-Career Achievement Award from the Health Policy Statistics Section (HPSS) of the American Statistical Association and the 2022 James F. Burgess Methods Article of the Year award for her paper Birds of a Feather Flock Together: Comparing controlled pre-post designs.
Dr. Hatfield earned her BS in genetics from Iowa State University and her MS and PhD in biostatistics from the University of Minnesota.
Dr. Hatfield’s thoughts and advice on the Harvard’s Health Policy PhD program are here.
Methods: difference-in-differences
To evaluate an intervention that was not randomized, we quantify the change in outcomes of the treated group and compare this to the change over the same period in an untreated comparison group. This controlled pre-post design is known as a difference-in-differences study. To make causal conclusions, we must believe that the comparison group's change represents what would have happened in the intervention group had the intervention not occurred.
+ Papers and talks
A new transition matrix estimator for diff-in-diff with categorical outcomes [paper | thread]
Some versions of comparative interrupted time series models imply the same counterfactual construction as diff-in-diff with group-specific trends [paper | thread]
Controlling for covariates in diff-in-diff regression estimation should account for the causal model [paper | thread]
Matching on time-varying covariates can lead to bias when treated and control come from different populations [paper | thread], but fix bias when treated units are selected on transient differences [commentary | reply]
Testing for parallel pre-trends: passing is not as good as you think and failing is not as bad as you think [preprint | slides]
Methods: clustering and modeling to understand variance
It can be difficult to summarize the diversity of patients’ experiences of health care. Dr. Hatfield’s research explores two avenues for reducing complex patterns into digestible summaries: clustering and structured covariance estimation.
+ Papers and talks
We can apply clustering to longitudinal trajectories to illustrate variation in patient experiences [paper | slides | website]
Linear combinations of Kronecker products allow flexible dimension reduction for "medium-dimensional" hierarchical models [paper | slides]; using hierarchical models of health plan quality yields personalized ratings [paper | slides]
Applications: program and policy evaluations
Payers and providers and constantly innovating in pursuit of the triple aim of improving health outcomes, quality of care, and health care spending. These interventions are rarely randomized, so evaluations rely on quasi-experimental designs, especially difference-in-differences. Dr. Hatfield has evaluated price transparency tools, accountable care organizations, patient-centered medical homes, Medicaid expansion, hospital global budgets, and telehealth.
+ Papers and talks
Medicare accountable care organizations (ACOs) demonstrate small spending decreases, concentrated in independent primary care groups (versus hospital-integrated groups), that grow over time [paper]
Hospital global budgets in Maryland: little evidence of utilization changes in urban [paper] or rural [paper] programs
A patient-centered medical home with financial incentives showed little evidence of impacts on utilization or spending [paper]
A phone-based change-in-condition monitoring intervention did not reduce hospitalizations for home care recipients [protocol | results]
Physician-facing price transparency at ordering makes little difference in spending or utilization [adult paper | peds paper]; consumer-facing price transparency tools are rarely used [paper] and did not reduce spending among California public employees and retirees [paper] and two large employers [paper]
Applications: documenting variation
To identify policy targets and evaluate the success of interventions, we need evidence on the sources of variation in important outcomes. Dr. Hatfield has documented variation in health care spending, utilization, outcomes, and coverage at patient, provider, facility, and geographic levels.
+ Papers and talks
Only 1/3 of patients with mental health care needs have substantive mental health discussions with providers at wellness visits [paper]
Medicare beneficiaries face high out-of-pocket health care spending that is projected to grow and especially impacts near-poor seniors [paper | slides]
Families of patients with cancer report better outcomes with hospice [paper]; older women with ovarian cancer receive intensive end-of-life care despite high hospice enrollment [paper]
Hospitalized patients cared for by their own PCPs versus hospitalists stay slightly longer, but are more likely to be discharged home and less likely to die within 30 days after discharge [paper]
In the US, there is large variation in the patterns of care for patients with advanced cancer [paper | slides], treatment for depression in children and adolescents [paper], and health insurance coverage gaps around pregnancy and delivery [paper]
We also see large variation in use of implantable cardiac electric devices to treat heart failure [paper]. Current electrical leads for implantable devices rarely fail [paper | meta-analysis]; evidence is still needed on choosing the best treatment in implantable device therapy for heart failure [paper]