Legislating Inequity: Structural Racism In Groups Of State Laws And Associations With Premature Mortality Rates.

Most evaluations of health equity policy have focused on the effects of individual laws. However, multiple laws' combined effects better reflect the crosscutting nature of structurally racist legal regimes. To measure the combined effects of multiple laws, we used latent class analysis, a method for detecting unobserved "subgroups" in a population, to identify clusters of US states based on thirteen structural racism-related legal domains in 2013. We identified three classes of states: one with predominantly harmful laws ([Formula: see text]), another with predominantly protective laws ([Formula: see text]), and a third with a mix of both ([Formula: see text]). Premature mortality rates overall-defined as deaths before age seventy-five per 100,000 population-were highest in states with predominantly harmful laws, which included eighteen states with past Jim Crow laws. This study offers a new method for measuring structural racism on the basis of how groups of laws are associated with premature mortality rates.

Assessing Telemedicine Utilization by Using Medicaid Claims Data.

OBJECTIVE: This study characterized telemedicine utilization among Medicaid enrollees by patients' demographic characteristics, geographic location, enrollment type, eligibility category, and clinical conditions. METHODS: This study used 2008-2009 Medicaid claims data from 28 states and the District of Columbia to characterize telemedicine claims (indicated by GT for professional fee claims or Q3014 for facility fees) on the basis of patients' demographic characteristics, geographic location, enrollment type, eligibility category, and clinical condition as indicated by ICD-9 codes. States lacking Medicaid telemedicine reimbursement policies were excluded. Chi-square tests were used to compare telemedicine utilization rates and one-way analysis of variance was used to estimate mean differences in number of telemedicine encounters among subgroups. RESULTS: A total of 45,233,602 Medicaid enrollees from the 22 states with telemedicine reimbursement policies were included in the study, and .1% were telemedicine users. Individuals ages 45 to 64 (16.4%), whites (11.3%), males (8.5%), rural residents (26.0%), those with managed care plans (7.9%), and those categorized as aged, blind, and disabled (28.1%) were more likely to receive telemedicine (p<.001). Nearly 95% of telemedicine claims were associated with a behavioral health diagnosis, of which over 50% were for bipolar disorder and attention-deficit disorder or attention-deficit hyperactivity disorder (29.3% and 23.4%, respectively). State-level variation was high, ranging from .0 to 59.91 claims per 10,000 enrollees (Arkansas and Arizona, respectively). CONCLUSIONS: Despite the touted potential for telemedicine to improve health care access, actual utilization of telemedicine in Medicaid programs was low. It was predominantly used to treat behavioral health diagnoses. Reimbursement alone is insufficient to support broad utilization for Medicaid enrollees.

Missed policy opportunities to advance health equity by recording demographic data in electronic health records.

The science of eliminating health disparities is complex and dependent on demographic data. The Health Information Technology for Economic and Clinical Health Act (HITECH) encourages the adoption of electronic health records and requires basic demographic data collection; however, current data generated are insufficient to address known health disparities in vulnerable populations, including individuals from diverse racial and ethnic backgrounds, with disabilities, and with diverse sexual identities. We conducted an administrative history of HITECH and identified gaps between the policy objective and required measure. We identified 20 opportunities for change and 5 changes, 2 of which required the collection of less data. Until health care demographic data collection requirements are consistent with public health requirements, the national goal of eliminating health disparities cannot be realized.