Coverage Gains After the Affordable Care Act Among the Uninsured in Minnesota.

OBJECTIVES: We determined whether and how Minnesotans who were uninsured in 2013 gained health insurance coverage in 2014, 1 year after the Affordable Care Act (ACA) expanded Medicaid coverage and enrollment. METHODS: Insurance status and enrollment experiences came from the Minnesota Health Insurance Transitions Study (MH-HITS), a follow-up telephone survey of children and adults in Minnesota who had no health insurance in the fall of 2013. RESULTS: ACA had a tempered success in Minnesota. Outreach and enrollment efforts were effective; one half of those previously uninsured gained coverage, although many reported difficulty signing up (nearly 62%). Of the previously uninsured who gained coverage, 44% obtained their coverage through MNsure, Minnesota's insurance marketplace. Most of those who remained uninsured heard of MNsure and went to the Web site. Many still struggled with the enrollment process or reported being deterred by the cost of coverage. CONCLUSIONS: Targeting outreach, simplifying the enrollment process, focusing on affordability, and continuing funding for in-person assistance will be important in the future.

Adaptive COVID-19 Mitigation Strategies: Tradeoffs between Trigger Thresholds, Response Timing, and Effectiveness.

Background. To support proactive decision making during the COVID-19 pandemic, mathematical models have been leveraged to identify surveillance indicator thresholds at which strengthening nonpharmaceutical interventions (NPIs) is necessary to protect health care capacity. Understanding tradeoffs between different adaptive COVID-19 response components is important when designing strategies that balance public preference and public health goals. Methods. We considered 3 components of an adaptive COVID-19 response: 1) the threshold at which to implement the NPI, 2) the time needed to implement the NPI, and 3) the effectiveness of the NPI. Using a compartmental model of SARS-CoV-2 transmission calibrated to Minnesota state data, we evaluated different adaptive policies in terms of the peak number of hospitalizations and the time spent with the NPI in force. Scenarios were compared with a reference strategy, in which an NPI with an 80% contact reduction was triggered when new weekly hospitalizations surpassed 8 per 100,000 population, with a 7-day implementation period. Assumptions were varied in sensitivity analysis. Results. All adaptive response scenarios substantially reduced peak hospitalizations relative to no response. Among adaptive response scenarios, slower NPI implementation resulted in somewhat higher peak hospitalization and a longer time spent under the NPIs than the reference scenario. A stronger NPI response resulted in slightly less time with the NPIs in place and smaller hospitalization peak. A higher trigger threshold resulted in greater peak hospitalizations with little reduction in the length of time under the NPIs. Conclusions. An adaptive NPI response can substantially reduce infection circulation and prevent health care capacity from being exceeded. However, population preferences as well as the feasibility and timeliness of compliance with reenacting NPIs should inform response design. Highlights: This study uses a mathematical model to compare different adaptive nonpharmaceutical intervention (NPI) strategies for COVID-19 management across 3 dimensions: threshold when the NPI should be implemented, time it takes to implement the NPI, and the effectiveness of the NPI.All adaptive NPI response scenarios considered substantially reduced peak hospitalizations compared with no response.Slower NPI implementation results in a somewhat higher peak hospitalization and longer time spent with the NPI in place but may make an adaptive strategy more feasible by allowing the population sufficient time to prepare for changing restrictions.A stronger, more effective NPI response results in a modest reduction in the time spent under the NPIs and slightly lower peak hospitalizations.A higher threshold for triggering the NPI delays the time at which the NPI starts but results in a higher peak hospitalization and does not substantially reduce the time the NPI remains in force.

Impact of the recession on Minnesota's health care market.

Historically, health care spending has not been very sensitive to short-term economic disruptions. However, data show some notable changes in health care spending and utilization since the recession of 2007-2008 both nationally and in Minnesota. This article presents trends emerging from that data and discusses potential reasons for them. It also highlights other factors such as structural changes in health care delivery that were occurring before, during, and after the economic downturn, that might account for the trends.

Minnesota Electronic Health Record Consortium COVID-19 Project: Informing Pandemic Response Through Statewide Collaboration Using Observational Data.

OBJECTIVE: Robust disease and syndromic surveillance tools are underdeveloped in the United States, as evidenced by limitations and heterogeneity in sociodemographic data collection throughout the COVID-19 pandemic. To monitor the COVID-19 pandemic in Minnesota, we developed a federated data network in March 2020 using electronic health record (EHR) data from 8 multispecialty health systems. MATERIALS AND METHODS: In this serial cross-sectional study, we examined patients of all ages who received a COVID-19 polymerase chain reaction test, had symptoms of a viral illness, or received an influenza test from January 3, 2016, through November 7, 2020. We evaluated COVID-19 testing rates among patients with symptoms of viral illness and percentage positivity among all patients tested, in aggregate and by zip code. We stratified results by patient and area-level characteristics. RESULTS: Cumulative COVID-19 positivity rates were similar for people aged 12-64 years (range, 15.1%-17.6%) but lower for adults aged ≥65 years (range, 9.3%-10.7%). We found notable racial and ethnic disparities in positivity rates early in the pandemic, whereas COVID-19 positivity was similarly elevated across most racial and ethnic groups by the end of 2020. Positivity rates remained substantially higher among Hispanic patients compared with other racial and ethnic groups throughout the study period. We found similar trends across area-level income and rurality, with disparities early in the pandemic converging over time. PRACTICE IMPLICATIONS: We rapidly developed a distributed data network across Minnesota to monitor the COVID-19 pandemic. Our findings highlight the utility of using EHR data to monitor the current pandemic as well as future public health priorities. Building partnerships with public health agencies can help ensure data streams are flexible and tailored to meet the changing needs of decision makers.

Population-Level Estimates Of Telemedicine Service Provision Using An All-Payer Claims Database.

In recent years state and federal policies have encouraged the use of telemedicine by formalizing payments for it. Telemedicine has the potential to expand access to timely care and reduce costs, relative to in-person care. Using information from the Minnesota All Payer Claims Database, we conducted a population-level analysis of telemedicine service provision in the period 2010-15, documenting variation in provision by coverage type, provider type, and rurality of patient residence. During this period the number of telemedicine visits increased from 11,113 to 86,238, and rates of use varied extensively by coverage type and rurality. In metropolitan areas telemedicine visits were primarily direct-to-consumer services provided by nurse practitioners or physician assistants and covered by commercial insurance. In nonmetropolitan areas telemedicine use was chiefly real-time provider-initiated services delivered by physicians to publicly insured populations. Recent federal and state legislation that expanded coverage and increased provider reimbursement for telemedicine services could lead to expanded use of telemedicine, including novel approaches in new patient populations.