A model of disparities: risk factors associated with COVID-19 infection.

BACKGROUND: By mid-May 2020, there were over 1.5 million cases of (SARS-CoV-2) or COVID-19 across the U.S. with new confirmed cases continuing to rise following the re-opening of most states. Prior studies have focused mainly on clinical risk factors associated with serious illness and mortality of COVID-19. Less analysis has been conducted on the clinical, sociodemographic, and environmental variables associated with initial infection of COVID-19. METHODS: A multivariable statistical model was used to characterize risk factors in 34,503cases of laboratory-confirmed positive or negative COVID-19 infection in the Providence Health System (U.S.) between February 28 and April 27, 2020. Publicly available data were utilized as approximations for social determinants of health, and patient-level clinical and sociodemographic factors were extracted from the electronic medical record. RESULTS: Higher risk of COVID-19 infection was associated with older age (OR 1.69; 95% CI 1.41-2.02, p \u3c 0.0001), male gender (OR 1.32; 95% CI 1.21-1.44, p \u3c 0.0001), Asian race (OR 1.43; 95% CI 1.18-1.72, p = 0.0002), Black/African American race (OR 1.51; 95% CI 1.25-1.83, p \u3c 0.0001), Latino ethnicity (OR 2.07; 95% CI 1.77-2.41, p \u3c 0.0001), non-English language (OR 2.09; 95% CI 1.7-2.57, p \u3c 0.0001), residing in a neighborhood with financial insecurity (OR 1.10; 95% CI 1.01-1.25, p = 0.04), low air quality (OR 1.01; 95% CI 1.0-1.04, p = 0.05), housing insecurity (OR 1.32; 95% CI 1.16-1.5, p \u3c 0.0001) or transportation insecurity (OR 1.11; 95% CI 1.02-1.23, p = 0.03), and living in senior living communities (OR 1.69; 95% CI 1.23-2.32, p = 0.001). CONCLUSION: sisk of COVID-19 infection is higher among groups already affected by health disparities across age, race, ethnicity, language, income, and living conditions. Health promotion and disease prevention strategies should prioritize groups most vulnerable to infection and address structural inequities that contribute to risk through social and economic policy

The impact of a physician-directed health information technology system on diabetes outcomes in primary care: a pre- and post-implementation study

Purpose To determine the impact of a physiciandirected, multifaceted health information technology (HIT) system on diabetes outcomes. Methods A pre/post-interventional study. Setting and participants The setting was Providence Primary Care Research Network in Oregon, with approximately 71 physicians caring for 117 369 patients in 13 clinic locations. The study covered Network patients with diabetes age 18 years and older. Intervention The study intervention included implementation of the CareManagerTM HIT system which augments an electronic medical record (EMR) by automating physician driven quality improvement interventions, including point-of-care decision support and care reminders, diabetes registry with care prompts, performance feedback with benchmarking and access to published evidence and patient educational materials. Measures The primary clinical measures included the change in mean value for low density lipoprotein (LDL) target <100 mg/dL or 2.6 mmol/l, blood pressure (BP) target <130/80 mmHg and glycated haemoglobin (HbA1c) target <7%, and the proportion of patients meeting guideline-recommended targets for those measures. All measures were analysed using closed and open cohort approaches. Results A total of 6072 patients were identified at baseline, 70% of whom were continuously enrolled during the 24-month study. Significant improvements were observed in all diabetes related outcomes except mean HbA1c. LDL goal attainment improved from 32% to 56% (P=0.002), while mean LDL decreased by 13 mg/dL (0.33 mmol/l, P=0.002). BP goal attainment increased significantly from 30% to 52%, with significant decreases in both mean systolic and diastolic BP. The proportion of patients with an HbA1c below 7% was higher at the end of the study (P=0.008). Mean patient satisfaction remained high, with no significant difference between baseline and follow-up. Total Relative Value Units per patient per year significantly increased as a result of an increase in the number of visits in year one and the coding complexity throughout. Conclusion Implementation of a physician-directed, multifaceted HIT system in primary care was associated with significantly improved diabetes process and outcome measures

Effects of Safety Zone Implementation on Perceptions of Safety and Well-being When Caring for COVID-19 Patients.

BACKGROUND: In March 2020, the caseload of patients positive for COVID-19 in hospitals began increasing rapidly, creating fear and anxiety among health care workers and concern about supplies of personal protective equipment. OBJECTIVES: To determine if implementing safety zones improves the perceptions of safety, well-being, workflow, and teamwork among hospital staff caring for patients during a pandemic. METHODS: A safety zone process was implemented to designate levels of contamination risk and appropriate activities for certain areas. Zones were designated as hot (highest risk), warm (moderate risk), or cold (lowest risk). Caregivers working in the safety zones were invited to complete a survey regarding their perceptions of safety, caregiver well-being, workflow, and teamwork. Each question was asked twice to obtain caregiver opinions for the periods before and after implementation of the zones. RESULTS: Significant improvements were seen in perceptions of caregiver safety (P \u3c .001) and collaboration within a multidisciplinary staff (P \u3c .001). Significant reductions in perceived staff fatigue (P = .03), perceived cross contamination (P \u3c .001), anxiety (P \u3c .001), and fear of exposure (P \u3c .001) were also seen. Teamwork (P = .23) and workflow (P = .69) were not significantly affected. CONCLUSIONS: Safety zone implementation improved caregivers\u27 perceptions of their safety, their well-being, and collaboration within the multidisciplinary staff but did not improve their perceptions of teamwork or workflow

Association of a multimodal educational intervention for primary care physicians with prescriptions of buprenorphine for opioid use disorders.

Importance: Opioid use disorder (OUD) is a public health crisis in the United States, but only 5% of US physicians have obtained a Drug Addiction Treatment Act (DATA) waiver to prescribe buprenorphine to treat OUD. Increasing the number of primary care physicians (PCPs) who have obtained the waiver and are able to treat patients with OUD is of utmost importance. Objective: To determine whether a multimodal educational intervention of PCPs is associated with an increase in the number of buprenorphine waivers obtained and patients initiated into treatment in a primary care setting. Design, Setting, and Participants: This quality improvement study was conducted in primary health care clinics within a large, integrated health care system. Patients included those who had received a diagnosis of OUD, and had Providence Health Plan Medicare or Medicaid insurance. Included PCPs were divided into 2 groups: those who obtained a DATA waiver after an education intervention (uptake PCPs) vs those who did not obtain a DATA waiver (nonuptake PCPs). The study took place between January 1, 2016, and December 31, 2017. Data analyses were conducted from December 2017 to August 2019. Exposures: Multimodal educational intervention including video, in-person visits to clinical practitioner meetings by physician champions, and a primary care toolkit with training resources and clinic protocols. Main Outcomes and Measures: The number of new uptake clinics where at least 1 PCP obtained a DATA waiver, the number of new PCPs with DATA waivers, the number of patients receiving a buprenorphine prescription, and the number of patients who received 12 or more weeks of treatment. Results: Twenty-seven of 41 invited clinics implemented the intervention, and 620 PCPs were included. The number of PCPs with DATA waivers increased from 5 PCPs (0.8%) to 44 PCPs (7.1%), and the number of clinics with at least 1 buprenorphine prescriber increased from 3 clinics (7.3%) to 17 clinics (41.5%). In total, 213 patients underwent buprenorphine treatment, and 140 patients received 12 or more weeks of treatment. A total of 646 patients had Providence Health Plan Medicare or Medicaid insurance and were eligible for the study (mean [SD] age, 61.7 [16.5] years; 410 [63.5%] women). There was a statistically significant difference in treatment with buprenorphine between patients with uptake PCPs vs patients with nonuptake PCPs (23 patients [16.4%] vs 18 patients [3.5%]; odds ratio, 4.61 [95% CI, 2.32-10.51]; P = .01) after the intervention. Conclusions and Relevance: In this quality improvement study, an educational intervention was associated with an increase in the number of PCPs and clinics that could provide buprenorphine treatment for OUD and with an increase in the patients who were able to access care with medications for OUD

Who was at risk for COVID-19 late in the US pandemic? Insights from a population health machine learning model.

Notable discrepancies in vulnerability to COVID-19 infection have been identified between specific population groups and regions in the USA. The purpose of this study was to estimate the likelihood of COVID-19 infection using a machine-learning algorithm that can be updated continuously based on health care data. Patient records were extracted for all COVID-19 nasal swab PCR tests performed within the Providence St. Joseph Health system from February to October of 2020. A total of 316,599 participants were included in this study, and approximately 7.7% (n = 24,358) tested positive for COVID-19. A gradient boosting model, LightGBM (LGBM), predicted risk of initial infection with an area under the receiver operating characteristic curve of 0.819. Factors that predicted infection were cough, fever, being a member of the Hispanic or Latino community, being Spanish speaking, having a history of diabetes or dementia, and living in a neighborhood with housing insecurity. A model trained on sociodemographic, environmental, and medical history data performed well in predicting risk of a positive COVID-19 test. This model could be used to tailor education, public health policy, and resources for communities that are at the greatest risk of infection