Challenges in Translating National and State Reopening Plans Into Local Reopening Policies During the COVID-19 Pandemic

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Pandemic events, such as coronavirus disease 2019 (COVID-19), affect health and economics at national and international scales, but in the United States, health care delivery and public health practice occur at the local level. Transmission control and eventual economic recovery require detailed guidance for communities, cities, metropolitan areas, and states. Our recent experience as consultants on the control and reopening plans for the city of Indianapolis and Marion County, Indiana, illustrated challenges with national plans, highlighted fundamental tensions in identifying the best course for policy, and emphasized gaps in the evidence base and our public health resources

Quantum plasticity and dislocation-induced supersolidity

We suggest that below a certain temperature T_k, the free energy for the creation of kinks-antikinks pairs in the dislocation network of solid He4 becomes negative. The underlying physical mechanism is the related liberation of vacancies which initiate Feynman's permutation cycles in the bulk. Consequently, dislocations should wander and sweep an increasingly larger volume at low temperatures. This phenomenon should lead both to a stiffening of the solid below T_k and to the appearance of a non zero superfluid fraction at a second temperature T_c < T_k.Comment: Longer revised version with more detailed discussion, submitted to EPJ

Semiparametric Bayesian inference in smooth coefficient models

We describe procedures for Bayesian estimation and testing in cross-sectional, panel data and nonlinear smooth coefficient models. The smooth coefficient model is a generalization of the partially linear or additive model wherein coefficients on linear explanatory variables are treated as unknown functions of an observable covariate. In the approach we describe, points on the regression lines are regarded as unknown parameters and priors are placed on differences between adjacent points to introduce the potential for smoothing the curves. The algorithms we describe are quite simple to implement - for example, estimation, testing and smoothing parameter selection can be carried out analytically in the cross-sectional smooth coefficient model. We apply our methods using data from the National Longitudinal Survey of Youth (NLSY). Using the NLSY data we first explore the relationship between ability and log wages and flexibly model how returns to schooling vary with measured cognitive ability. We also examine a model of female labor supply and use this example to illustrate how the described techniques can been applied in nonlinear settings

Community COVID-19 activity level and nursing home staff testing for active SARS-CoV-2 infection in Indiana

Objectives: To assess whether using coronavirus disease 2019 (COVID-19) community activity level can accurately inform strategies for routine testing of facility staff for active severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Design: Cross-sectional study. Setting and Participants: In total, 59,930 nursing home staff tested for active SARS-CoV-2 infection in Indiana. Measures: Receiver operator characteristic curves and the area under the curve to compare the sensitivity and specificity of identifying positive cases of staff within facilities based on community COVID-19 activity level including county positivity rate and county cases per 10,000. Results: The detection of any infected staff within a facility using county cases per 10,000 population or county positivity rate resulted in an area under the curve of 0.648 (95% confidence interval 0.601‒0.696) and 0.649 (95% confidence interval 0.601‒0.696), respectively. Of staff tested, 28.0% were certified nursing assistants, yet accounted for 36.9% of all staff testing positive. Similarly, licensed practical nurses were 1.4% of staff, but 4.7% of positive cases. Conclusions and Implications: We failed to observe a meaningful threshold of community COVID-19 activity for the purpose of predicting nursing homes with any positive staff. Guidance issued by the Centers for Medicare and Medicaid Services in August 2020 sets the minimum frequency of routine testing for nursing home staff based on county positivity rates. Using the recommended 5% county positivity rate to require weekly testing may miss asymptomatic infections among nursing home staff. Further data on results of all-staff testing efforts, particularly with the implementation of new widespread strategies such as point-of-care testing, is needed to guide policy to protect high-risk nursing home residents and staff. If the goal is to identify all asymptomatic SARS-Cov-2 infected nursing home staff, comprehensive repeat testing may be needed regardless of community level activity.This work was supported by the Indiana State Department of Health. We would like to acknowledge the contributions of Matt Foster and Brenda Buroker from the Indiana State Department of Health, and Russ Evans of Probari, Inc. KU is CEO and Founder of Probari, Inc., a program to train nurses to reduce nursing home hospital transfers. No other authors have conflicts of interest to disclose

Infection Fatality Ratios for COVID-19 Among Noninstitutionalized Persons 12 and Older: Results of a Random-Sample Prevalence Study

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic

Attitudes and Experiences of Frontline Nursing Home Staff Towards Coronavirus Testing

The Indiana State Department of Health tested nursing home staff for COVID-19 in June 2020. A survey of staff found many felt physical discomfort, some questioned testing the asymptomatic, but a majority agreed testing is important.This work was supported by the US Department of Health and Human Services, Centers for Medicare and Medicaid Services (Funding Opportunity 1E1CMS331488). The opinions expressed in this article are the authors' own and do not reflect the view of the US Department of Health and Human Services, Centers for Medicare and Medicaid Services. Kathleen T. Unroe is the CEO of Probari, a health care start-up designed to disseminate a successful registered nurse–based clinical care model in NHs

Impact of Medicaid expansion on smoking prevalence and quit attempts among those newly eligible, 2011–2019

Introduction: Low-income populations have higher rates of smoking and are disproportionately affected by smoking-related illnesses. This study assessed the long-term impact of increased coverage for tobacco cessation through Medicaid expansion on past-year quit attempts and prevalence of cigarette smoking. Methods: Using data from CDC's annual Behavioral Risk Factor Surveillance System 2011-2019, we conducted difference-in-difference regression analyses to compare changes in smoking prevalence and past-year quit attempts in expansion states versus non-expansion states. Our sample included non-pregnant adults (18-64 years old) without dependent children with incomes at or below 100% of the Federal Poverty Level (FPL). Results: Regression analyses indicate that Medicaid expansion was associated with reduced smoking prevalence in the first two years post-expansion (β=-0.019, p=0.04), but that this effect was not maintained at longer follow-up periods (β=-0.006, p=0.49). Results of regression analyses also suggest that Medicaid expansion does not significantly impact quit attempts in the short-term (β=-0.013, p=0.52) or at longer term follow-up (β=-0.026, p=0.08). Conclusions: Expanded coverage for tobacco cessation services through Medicaid alone may not be enough to increase quit-attempts or sustain a reduction in overall prevalence of smoking in newly eligible populations over time. Medicaid programs should consider additional strategies, such as public education campaigns and removal of barriers, to support cessation among enrollees

Assessing the Quality Measure for Follow-up Care After Children’s Psychiatric Hospitalizations

OBJECTIVES: Medicaid and Children’s Health Insurance Program plans publicly report quality measures, including follow-up care after psychiatric hospitalization. We aimed to understand failure to meet this measure, including measurement definitions and enrollee characteristics, while investigating how follow-up affects subsequent psychiatric hospitalizations and emergency department (ED) visits. METHODS: Administrative data representing Alabama’s Children’s Health Insurance Program from 2013 to 2016 were used to identify qualifying psychiatric hospitalizations and follow-up care with a mental health provider within 7 to 30 days of discharge. Using relaxed measure definitions, follow-up care was extended to include visits at 45 to 60 days and visits to a primary care provider. Logit regressions estimated enrollee characteristics associated with follow-up care and, separately, the likelihood of subsequent psychiatric hospitalizations and/or ED visits within 30, 60, and 120 days. RESULTS: We observed 1072 psychiatric hospitalizations during the study period. Of these, 356 (33.2%) received follow-up within 7 days and 566 (52.8%) received it within 30 days. Relaxed measure definitions captured minimal additional follow-up visits. The likelihood of follow-up was lower for both 7 days (−18 percentage points; 95% confidence interval [CI] −26 to −10 percentage points) and 30 days (−26 percentage points; 95% CI −35 to −17 percentage points) regarding hospitalization stays of ≥8 days. Meeting the measure reduced the likelihood of subsequent psychiatric hospitalizations within 60 days by 3 percentage points (95% CI −6 to −1 percentage point). CONCLUSIONS: Among children, receipt of timely follow-up care after a psychiatric hospitalization is low and not sensitive to measurement definitions. Follow-up care may reduce the need for future psychiatric hospitalizations and/or ED visits

Adverse Selection in the Children’s Health Insurance Program

This study investigates whether new enrollees in the Alabama Children’s Health Insurance Program have different claims experience from renewing enrollees who do not have a lapse in coverage and from continuing enrollees. The analysis compared health services utilization in the first month of enrollment for new enrollees (who had not been in the program for at least 12 months) with utilization among continuing enrollees. A second analysis compared first-month utilization of those who renew immediately with those who waited at least 2 months to renew. A 2-part model estimated the probability of usage and then the extent of usage conditional on any utilization. Claims data for 826 866 child-years over the period from 1999 to 2012 were used. New enrollees annually constituted a stable 40% share of participants. Among those enrolled in the program, 13.5% renewed on time and 86.5% of enrollees were late to renew their enrollment. In the multivariate 2-part models, controlling for age, gender, race, income eligibility category, and year, new enrollees had overall first-month claims experience that was nearly $29 less than continuing enrollees. This was driven by lower ambulatory use. Late renewals had overall first-month claims experience that was$10 less than immediate renewals. However, controlling for the presence of chronic health conditions, there was no statistically meaningful difference in the first-month claims experience of late and early renewals. Thus, differences in claims experience between new and continuing enrollees and between early and late renewals are small, with greater spending found among continuing and early renewing participants. Higher claims experience by early renewals is attributable to having chronic health conditions