Identifying the impact of health insurance on subgroups with changing rates of diagnosis.

Expanded access to health care often leads to new diagnoses for previously undetected conditions. New diagnoses make it difficult to identify the causal effect of expanding health insurance on individuals with particular diagnoses: the newly diagnosed in the treatment group are likely to differ in unobserved ways from the control group. This paper provides two methods for dealing with this problem depending on the data available to the researcher and diagnosis-specific knowledge.

Using Administrative Data to Examine Telemedicine Usage Among Medicaid Beneficiaries During the Coronavirus Disease 2019 Pandemic.

Background: The coronavirus disease 2019 (COVID-19) pandemic necessitated the replacement of in-person physician consultations with telemedicine. During the pandemic, Medicaid covered the cost of telemedicine visits.

Objectives: The aim was to measure the adoption of telemedicine during the pandemic.

Non-negative matrix factorization for learning alignment-specific models of protein evolution.

Models of protein evolution currently come in two flavors: generalist and specialist. Generalist models (e.g.