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BackgroundEmergence of the coronavirus disease (COVID-19) caught the world off guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their health care systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policy makers to make informed decisions during a rapidly evolving pandemic. ObjectiveThe goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina and the Charlotte Metropolitan Region, and to incorporate the effect of a public health intervention to reduce disease spread while accounting for unique regional features and imperfect detection. MethodsThree SIR models were fit to infection prevalence data from North Carolina and the greater Charlotte Region and then rigorously compared. One of these models (SIR-int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases. We computed longitudinal total estimates of the susceptible, infected, and removed compartments of both populations, along with other pandemic characteristics such as the basic reproduction number. ResultsPrior to March 26, disease spread was rapid at the pandemic onset with the Charlotte Region doubling time of 2.56 days (95% CI 2.11-3.25) and in North Carolina 2.94 days (95% CI 2.33-4.00). Subsequently, disease spread significantly slowed with doubling times increased in the Charlotte Region to 4.70 days (95% CI 3.77-6.22) and in North Carolina to 4.01 days (95% CI 3.43-4.83). Reflecting spatial differences, this deceleration favored the greater Charlotte Region compared to North Carolina as a whole. A comparison of the efficacy of intervention, defined as 1 – the hazard ratio of infection, gave 0.25 for North Carolina and 0.43 for the Charlotte Region. In addition, early in the pandemic, the initial basic SIR model had good fit to the data; however, as the pandemic and local conditions evolved, the SIR-int model emerged as the model with better fit. ConclusionsUsing local data and continuous attention to model adaptation, our findings have enabled policy makers, public health officials, and health systems to proactively plan capacity and evaluate the impact of a public health intervention. Our SIR-int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.

ObjectivesSome studies suggested more COVID-19-associated hospitalizations among racial and ethnic minorities. To inform public health practice, the COVID-19-associated Hospitalization Surveillance Network (COVID-NET) quantified associations between race/ethnicity, census tract socioeconomic indicators, and COVID-19-associated hospitalization rates.MethodsUsing data from COVID-NET population-based surveillance reported during March 1-April 30, 2020 along with socioeconomic and denominator data from the US Census Bureau, we calculated COVID-19-associated hospitalization rates by racial/ethnic and census tract-level socioeconomic strata.ResultsAmong 16,000 COVID-19-associated hospitalizations, 34.8% occurred among non-Hispanic White (White) persons, 36.3% among non-Hispanic Black (Black) persons, and 18.2% among Hispanic or Latino (Hispanic) persons. Age-adjusted COVID-19-associated hospitalization rate were 151.6 (95% Confidence Interval (CI): 147.1-156.1) in census tracts with >15.2%-83.2% of persons living below the federal poverty level (high-poverty census tracts) and 75.5 (95% CI: 72.9-78.1) in census tracts with 0%-4.9% of persons living below the federal poverty level (low-poverty census tracts). Among White, Black, and Hispanic persons living in high-poverty census tracts, age-adjusted hospitalization rates were 120.3 (95% CI: 112.3-128.2), 252.2 (95% CI: 241.4-263.0), and 341.1 (95% CI: 317.3-365.0), respectively, compared with 58.2 (95% CI: 55.4-61.1), 304.0 (95%: 282.4-325.6), and 540.3 (95% CI: 477.0-603.6), respectively, in low-poverty census tracts.ConclusionsOverall, COVID-19-associated hospitalization rates were highest in high-poverty census tracts, but rates among Black and Hispanic persons were high regardless of poverty level. Public health practitioners must ensure mitigation measures and vaccination campaigns address needs of racial/ethnic minority groups and people living in high-poverty census tracts.

Most reported cases of coronavirus disease 2019 (COVID-19) in children aged

Background: Inappropriate prescribing of antibiotics poses an urgent public health threat. Limited research has examined factors associated with antibiotic prescribing practices in outpatient settings. The goals of this study were to explore elements influencing provider decisions to prescribe antibiotics, identify provider recommendations for interventions to reduce inappropriate antibiotic use, and inform the clinical management of patients in the outpatient environment for infections that do not require antibiotics. Methods: This was a qualitative study using semi-structured interviews with key informants. Seventeen outpatient providers (10 medical doctors and 7 advanced care practitioners) within a large healthcare system in Charlotte, North Carolina, participated. Interviews were audio recorded, transcribed, and analyzed for themes. Results: Primary barriers to reducing inappropriate antibiotic prescribing included patient education and expectations, system-level factors, and time constraints. Providers indicated they would be interested in having system-wide, evidence-based guidelines to inform their prescribing decisions and that they would also be receptive to efforts to improve their awareness of their own prescribing practices. Results further suggested that providers experience a high demand for antibiotic prescriptions; consequently, patient education around appropriate use would be beneficial. Conclusions: Findings suggest that antibiotic prescribing in the outpatient setting is influenced by many pressures, including patient demand and patient satisfaction. Training on appropriate antibiotic prescribing, guideline-based decision support, feedback on prescribing practices, and patient education are recommended interventions to improve levels of appropriate prescribing. [ABSTRACT FROM AUTHOR]

Limited research has examined racial/ethnic differences in diabetes care and outcomes among primary care patients. This study examined racial/ethnic differences in diabetes care and outcomes among an ambulatory patient population and explored patient perceptions of the patient-provider relationship to inform strategies to improve care delivery. Using data from 62,149 adults with diabetes who received care within Atrium Health in 2013, regression models assessed associations between race/ethnicity and the following outcomes: glycated hemoglobin (HbA1c) tests, low density lipoprotein (LDL) and blood pressure (BP) screening, foot and eye exams, and HbA1c, LDL, and BP control. Eleven patients with diabetes and uncontrolled hypertension participated in three focus groups about their perceptions of the patient-provider relationship. Compared to non-Hispanic Whites, non-Hispanic Blacks had 22% to 73% higher odds of receiving screenings (HbA1c, LDL, BP, foot and eye exams; p<0.05) and 25% to 36% lower odds of HbA1c, LDL or BP control (p<0.05). Focus group participants indicated that good communication and continuity in their relationships with providers played a key role in management of their diabetes. Findings suggest that strategies to strengthen partnerships between patients and their clinical teams, increase self-management support and utilize shared-decision making may help to improve outcomes for minority patients. [ABSTRACT FROM AUTHOR]

Pregnant women might be at increased risk for severe coronavirus disease 2019 (COVID-19) (1,2). The COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) (3) collects data on hospitalized pregnant women with laboratory-confirmed SARS-CoV-2, the virus that causes COVID-19; to date, such data have been limited. During March 1-August 22, 2020, approximately one in four hospitalized women aged 15-49 years with COVID-19 was pregnant. Among 598 hospitalized pregnant women with COVID-19, 54.5% were asymptomatic at admission. Among 272 pregnant women with COVID-19 who were symptomatic at hospital admission, 16.2% were admitted to an intensive care unit (ICU), and 8.5% required invasive mechanical ventilation. During COVID-19-associated hospitalizations, 448 of 458 (97.8%) completed pregnancies resulted in a live birth and 10 (2.2%) resulted in a pregnancy loss. Testing policies based on the presence of symptoms might miss COVID-19 infections during pregnancy. Surveillance of pregnant women with COVID-19, including those with asymptomatic infections, is important to understand the short- and long-term consequences of COVID-19 for mothers and newborns. Identifying COVID-19 in women during birth hospitalizations is important to guide preventive measures to protect pregnant women, parents, newborns, other patients, and hospital personnel. Pregnant women and health care providers should be made aware of the potential risks for severe COVID-19 illness, adverse pregnancy outcomes, and ways to prevent infection., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. Evan J. Anderson reports grants from MedImmune, Regeneron, PaxVax, Pfizer, GSK, Merck, Novavax, Sanofi-Pasteur, Micron, and Janssen, and personal fees from AbbVie, Pfizer, Sanofi Pasteur and Kentucky BioProcessing, Inc. outside the submitted work. William Schaffner reports personal fees from VBI Vaccines, outside the submitted work. No other potential conflicts of interest were disclosed.

The 2017-18 U.S. influenza season was notable for its high severity, with approximately 45 million illnesses and 810,000 influenza-associated hospitalizations throughout the United States (1). The purpose of the investigation reported here was to create a state-level estimate of the number of persons in Utah who became ill with influenza disease during this severe national seasonal influenza epidemic and to create a sustainable system for making timely updates in future influenza seasons. Knowing the extent of influenza-associated illness can help public health officials, policymakers, and clinicians tailor influenza messaging, planning, and responses for seasonal influenza epidemics or during pandemics. Using national methods and existing influenza surveillance and testing data, the influenza burden (number of influenza illnesses, medical visits for influenza, and influenza-associated hospitalizations) in Utah during the 2016-17 and 2017-18 influenza seasons was estimated. During the 2016-17 season, an estimated 265,000 symptomatic illnesses affecting 9% of Utah residents occurred, resulting in 125,000 medically attended illnesses and 2,700 hospitalizations. During the 2017-18 season, an estimated 338,000 symptomatic illnesses affecting 11% of Utah residents occurred, resulting in 160,000 medically attended illnesses and 3,900 hospitalizations. Other state or county health departments could adapt similar methods in their jurisdictions to estimate the burden of influenza locally and support prompt public health activities., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. Mr. McCaffrey reports grants from the Council of State and Territorial Epidemiologists during the conduct of the study. Dr. Pavia reports personal fees from Antimicrobial Therapy Inc, WebMD, Genentech, Merck, and Sequirius outside the submitted work. No other potential conflicts of interest were disclosed.

In September 2015, a Wyoming woman was admitted to a local hospital with a 5-day history of progressive weakness, ataxia, dysarthria, and dysphagia. Because of respiratory failure, she was transferred to a referral hospital in Utah, where she developed progressive encephalitis. On day 8 of hospitalization, the patient's family told clinicians they recalled that, 1 month before admission, the woman had found a bat on her neck upon waking, but had not sought medical care. The patient's husband subsequently had contacted county invasive species authorities about the incident, but he was not advised to seek health care for evaluation of his wife's risk for rabies. On October 2, CDC confirmed the patient was infected with a rabies virus variant that was enzootic to the silver-haired bat (Lasionycteris noctivagans). The patient died on October 3. Public understanding of rabies risk from bat contact needs to be improved; cooperation among public health and other agencies can aid in referring persons with possible bat exposure for assessment of rabies risk.