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. If there is no panel dimension to the data, then the causal effect for the subgroup of interest can be bounded from either above or below depending on the condition in question. If panel data are available, then the newly diagnosed can be identified, and their treated outcomes subtracted from the overall effect of interest. I apply these methods to find that the difference-in-discontinuities estimator underestimates the effect of Medicare prescription drug coverage on the uptake of insulin by first-time users by 20%.

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. We focus on key patient subgroups including those with chronic conditions, those living in urban versus rural areas, and different age groups. METHODS: This study examined the universe of claims made by Florida Medicaid beneficiaries (n=2.4 million) between January 2019 and July 2020. Outpatient visits were identified as in-person or telemedicine. Telemedicine visits were classified into audio-visual or audio-only visits. RESULTS: We find that telemedicine offsets much of the decline in in-person outpatient visits among Florida's Medicaid enrollees, however, uptake differs by enrollee type. High utilizers of care and beneficiaries with chronic conditions were significantly more likely to use telemedicine, while enrollees living in rural areas and health professional shortage areas were moderately less likely to use telemedicine. Elderly Medicaid recipients (dual-eligibles) used audio-only telemedicine visits at higher rates than other age groups, and the demand for these consultations is more persistent. CONCLUSIONS: Telemedicine offset the decline in health care utilization among Florida's Medicaid-enrolled population during the novel coronavirus pandemic, with particularly high uptake among those with prior histories of high utilization. Audio-only visits are a potentially important method of delivery for the oldest Medicaid beneficiaries.

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. PAM, JTT, WAG) adopt a one-size-fits-all approach, where a single model is estimated from a number of different protein alignments. Specialist models (e.g. mtREV, rtREV, HIVbetween) can be estimated when a large quantity of data are available for a single organism or gene, and are intended for use on that organism or gene only. Unsurprisingly, specialist models outperform generalist models, but in most instances there simply are not enough data available to estimate them. We propose a method for estimating alignment-specific models of protein evolution in which the complexity of the model is adapted to suit the richness of the data. Our method uses non-negative matrix factorization (NNMF) to learn a set of basis matrices from a general dataset containing a large number of alignments of different proteins, thus capturing the dimensions of important variation. It then learns a set of weights that are specific to the organism or gene of interest and for which only a smaller dataset is available. Thus the alignment-specific model is obtained as a weighted sum of the basis matrices. Having been constrained to vary along only as many dimensions as the data justify, the model has far fewer parameters than would be required to estimate a specialist model. We show that our NNMF procedure produces models that outperform existing methods on all but one of 50 test alignments. The basis matrices we obtain confirm the expectation that amino acid properties tend to be conserved, and allow us to quantify, on specific alignments, how the strength of conservation varies across different properties. We also apply our new models to phylogeny inference and show that the resulting phylogenies are different from, and have improved likelihood over, those inferred under standard models.