Is the emergency department used as a substitute or a complement to primary care in Medicaid?

Policies to decrease low-acuity emergency department (ED) use have traditionally assumed that EDs are a substitute for unavailable primary care (PC). However, such policies can exacerbate ED overcrowding, rather than ameliorate it, if patients use EDs to complement, rather than substitute, their PC use. We tested whether Medicaid managed care enrolees visit the ED for nonemergent and PC treatable conditions to substitute for or to complement PC. Based on consumer choice theory, we modelled county-level monthly ED visit rate as a function of PC supply and used 2012-2015 New York Statewide Planning and Research Cooperative System (SPARCS) outpatient data and non-linear least squares method to test substitution vs complementarity. In the post-Medicaid expansion period (2014-2015), ED and PC are substitutes state-wide, but are complements in highly urban and poorer counties during nights and weekends. There is no evidence of complementarity before the expansion (2012-2013). Analyses by PC provider demonstrate that the relationship between ED and PC differs depending on whether PC is provided by physicians or advanced practice providers. Policies to reduce low-acuity ED use via improved PC access in Medicaid are likely to be most effective if they focus on increasing actual appointment availability, ideally by physicians, in areas with low PC provider supply. Different aspects of PC access may be differently related to low-acuity ED use.

Unclassified drug overdose deaths in the opioid crisis: emerging patterns of inequity.

OBJECTIVE: Examine whether individual, geographic, and economic phenotypes predict missing data on specific drug involvement in overdose deaths, manifesting inequities in overdose mortality data, which is a key data source used in measuring the opioid epidemic. MATERIALS AND METHODS: We combined national data sources (mortality, demographic, economic, and geographic) from 2014-2016 in a multi-method analysis of missing drug classification in the overdose mortality records (as defined by the use of ICD-10 T50.9 on death certificates). We examined individual disparities in decedent-level multivariate logistic regression models, geographic disparities in spatial analysis (heat maps), and economic disparities in a combination of temporal trend analyses (descriptive statistics) and both decedent- and county-level multivariate logistic regression models. RESULTS: Our analyses consistently found higher rates of unclassified overdoses in decedents of female gender, White race, non-Hispanic ethnicity, with college education, aged 30-59 and those from poorer counties. Despite the fact that unclassified drug overdose death rates have reduced over time, gaps persist between the richest and poorest counties. There are also striking geographic differences both across and within states. DISCUSSION: Given the essential role of mortality data in measuring the scale of the opioid epidemic, it is important to understand the individual and community inequities underlying the missing data on specific drug involvements. Knowledge of these inequities could enhance our understanding of the opioid crisis and inform data-driven interventions and policies with more equitable resource allocations. CONCLUSION: Multiple individual, geographic, and economic disparities underlie unclassified overdose deaths, with important implications for public health informatics and addressing the opioid crisis.

Did Medicaid expansion matter in states with generous Medicaid?

OBJECTIVES: It is unclear whether the Medicaid expansion under the Affordable Care Act had an effect on coverage in states with relatively generous pre-expansion Medicaid eligibility levels. We examined the effect of the Medicaid expansions on Medicaid coverage in 4 generous states: New York, Vermont, Massachusetts, and Delaware. STUDY DESIGN: We used the American Community Survey (2011-2016) to estimate effects on coverage among nonelderly adults with incomes up to 138% of the federal poverty level. METHODS: We estimated differences in differences (DID) in marginal probabilities following probit models, comparing New York, Vermont, Massachusetts, and Delaware with nonexpansion states on the East Coast. RESULTS: There is strong evidence of the effect in New York: DID estimates ranged from 3.3 to 5.2 percentage points. There is weak or no evidence of coverage gains in the other 3 states. Pronounced effects were found among the racial/ethnic majority (white, non-Hispanic white, and nonblack populations) in New York, as well as the working poor and previously eligible in New York and Massachusetts. CONCLUSIONS: Even in states with relatively generous pre-expansion Medicaid programs, the expansion can produce nontrivial coverage gains, as evidenced by New York. Our findings of spillover effects may indicate the relative importance and success of a simplified enrollment process and increased media coverage in boosting enrollment in Medicaid. Our subgroup analyses highlight a potential need to improve access to office-based care to accommodate the growing population of the working poor on Medicaid and potential changes in the Medicaid risk pool served by managed care organizations and subsequent decreases in capitated payments.