Zero-Premium Health Insurance Plans Became More Prevalent In Federal Marketplaces In 2018.

The Affordable Care Act established two federally funded subsidies-cost-sharing reductions and premium tax credits-available in the health insurance Marketplaces. In 2018 federal payments to insurers for cost-sharing reductions were terminated. Insurers responded by increasing plan premiums to account for the loss of these payments. Premiums for silver plans were increased more than those for other metal tiers because cost-sharing reductions are available only in silver plans, while premium tax credits can be applied across different metal tiers. One consequence of greater premium increases for silver plans was the increased availability and selection of plans with zero premiums for consumers. We examined the magnitude of this issue using plan selections through the federal Marketplaces during the open enrollment periods before (2017) and after (2018) the termination of payments. We found that zero-premium plan availability increased by 18.3 percentage points, selection increased by 7.9 percentage points, and selection conditional on having a zero-premium plan available increased by 8.8 percentage points. Were federal cost-sharing reduction payments to be restored, a reduction in availability and selection of zero-premium plans would likely occur, and more consumers could lose access to the plans.

Estimating the Cost of Providing Foundational Public Health Services.

OBJECTIVE: To estimate the cost of resources required to implement a set of Foundational Public Health Services (FPHS) as recommended by the Institute of Medicine. STUDY DESIGN: A stochastic simulation model was used to generate probability distributions of input and output costs across 11 FPHS domains. We used an implementation attainment scale to estimate costs of fully implementing FPHS. DATA COLLECTION/EXTRACTION METHODS: We use data collected from a diverse cohort of 19 public health agencies located in three states that implemented the FPHS cost estimation methodology in their agencies during 2014-2015. PRINCIPAL FINDINGS: The average agency incurred costs of $48 per capita implementing FPHS at their current attainment levels with a coefficient of variation (CV) of 16 percent. Achieving full FPHS implementation would require $82 per capita (CV=19 percent), indicating an estimated resource gap of $34 per capita. CONCLUSIONS: Substantial variation in costs exists across communities in resources currently devoted to implementing FPHS, with even larger variation in resources needed for full attainment. Reducing geographic inequities in FPHS may require novel financing mechanisms and delivery models that allow health agencies to have robust roles within the health system and realize a minimum package of public health services for the nation.

Time-Series Analysis of the Impact of Prescription Drug Monitoring Programs on Heroin Treatment Admissions.

BACKGROUND: Prescription drug abuse has become a major issue in the United States in recent years. Prescription drug monitoring programs (PDMPs) are designed to help health care providers to prevent such abuses. There may be unintended effects of these programs. Specifically, PDMPs may move prescription opioid users to begin use of heroin. OBJECTIVES: This article aims to evaluate the impact of PDMPs on heroin abuse across several different states through use of treatment admissions records obtained from the Treatment Episode Data Set. METHODS: Operational dates and other characteristics of state PDMPs were obtained from the Prescription Drug Monitoring Program Training and Technical Assistance Center. Data for the dependent variable were collected from the Treatment Episodes Data Set from 1992 to 2012. Interrupted time-series analyses using autoregressive integrated moving average modeling were used to estimate the effect of presence of an operational PDMP on the number of admissions reporting heroin as their primary drug being used. RESULTS: The relationship between heroin admissions and prescription opioid admissions was significant for the average data (ß = 0.41, p = 0.0017) and the 5-year data (ß = 0.5, p = 0.036), both showing positive associations between heroin and prescription drug admissions in states in the post PDMP implementation period. Conclusions/Importance: The study found a positive relationship that between heroin and prescription opioid admissions post PDMP implementation. Future research should attempt to identify what this relationship means and how this information can be used to improve opioid policy.