A novel household-based patient outreach pilot program to boost late-season influenza vaccination rates during the COVID-19 pandemic.

BACKGROUND: The objective of this study was to test a novel household-based approach to improve late-season influenza vaccine uptake during the 2020-2021 season, using Epic's MyChart patient portal messages and/or interactive voice response telephone calls. METHODS: This study was a non-blinded, quality improvement program using a block randomized design conducted among patients from Reliant Medical Group clinics residing in a traditional household (≥2 individuals clinically active in the Reliant system living at the same address). Households were randomized 1:1:1 into intervention arms: non-tailored communication (messaging based on CDC's seasonal influenza vaccination campaign), tailored communication (comprehensive communication including reinforcement of the importance of influenza vaccination for high-risk individuals), and standard-of-care control. Influenza vaccination during the program was captured via medical records, and the odds of vaccination among communication arms versus the control arm were assessed. A survey assessing influenza vaccination drivers was administered using MyChart. RESULTS: Influenza vaccination increased by 3.3% during the program period, and no significant differences in vaccination were observed in intervention arms relative to the control arm. Study operationalization faced substantial challenges related to the concurrent COVID-19 pandemic. Compared with vaccinated survey respondents, unvaccinated respondents less frequently reported receiving a recommendation for influenza vaccination from their healthcare provider (15.8% vs. 42.3%, p < 0.001) or awareness that vaccination could protect themselves and higher risk contacts (82.3% vs. 92.6%, p < 0.001). CONCLUSIONS: No significant effects of the interventions were observed. Survey results highlighted the importance of healthcare provider recommendations and the need for increased education around the benefits of vaccination.

Mortality in Stable Coronary Disease in Patients With Intermediate- or High-Risk Myocardial Perfusion Imaging.

The management of patients with stable coronary disease and intermediate- or high-risk features on single photon emission computed tomography myocardial perfusion imaging (SPECT MPI) continues to be controversial as to whether they should be treated with an initial invasive strategy (catheterization and revascularization when feasible) or medical therapy alone to improve mortality. We performed a retrospective observational study of 1,946 patients with intermediate- or high-risk SPECT MPI scans performed over a 6-year period (from 2014 to 2019). Each patient was followed from the time of SPECT MPI to 16 months after the last patient was enrolled. The primary end point was all-cause mortality and the secondary end point cardiovascular mortality. Of the eligible 1,697 patients, 1,144 had an intermediate-risk scan, 553 a high-risk scan, 915 had medical therapy alone, and 782 went on an initial invasive strategy. All patients were divided into the following three groups: combined SPECT MPI (both intermediate- and high-risk), high-risk SPECT MPI, and intermediate-risk SPECT MPI groups. After propensity score matching, there was a statistically significant difference in cardiovascular death (5.9% vs 2.7%; p = 0.038) in the medical therapy cohort compared with initial invasive cohort in the combined SPECT MPI group, but no difference in all-cause death (15.7% vs 13%; p = 0.318). On subgroup analysis, in intermediate-risk SPECT MPI group, there was no significant difference in either all-cause death (13.8 vs 11.7%; p = 0.583) or cardiac death (5.4% vs 2.5%; p = 0.16) in conservative cohort compared with invasive strategy cohort. In high-risk SPECT MPI group, conservative therapy cohort had higher cardiac death (11.7% vs 2.5%; p = 0.002) compared with initial invasive strategy cohort, but there was no significant difference in all-cause death (24.5% vs 15.3%; p = 0.052). In conclusion, this study supports that patients with intermediate- or high-risk SPECT MPI scans when considered together or only with high-risk features, derive a cardiovascular mortality benefit with an initial invasive strategy. Patients who had undergone intermediate-risk SPECT MPI had similar outcomes with either medical therapy alone or initial invasive evaluation.

When the Masks Come Off in Canada and Guatemala: Will the Realities of Racism and Marginalization of Midwives Finally Be Addressed?

This article addresses the effects of COVID-19 in Eastern and Northern Ontario, Canada, with a comparative glimpse at the small province of Totonicapán, Guatemala, with which Canadians have been involved in obstetric and midwifery care in particular over the last 5 years. With universal health care coverage since 1966 and well-integrated midwifery, Canada's system would be considered relatively well set up to deal with a disaster like COVID-19 compared to low resource countries like Guatemala or countries without universal health care insurance (like the USA). However, the epidemic has uncovered the fact that in Ontario, Indigenous, Black, and People of Color (IBPOC), as elsewhere, may have been hardest hit, often not by actually contracting COVID-19, but by suffering secondary consequences. While COVID-19 could be an issue through which health care professionals can come together, there are signs that the medical hierarchies in many hospitals in both Ontario and Totonicapán are taking advantage of COVID-19 to increase interventive measures in childbirth and reduce midwives' involvement in hospitals. Meanwhile, home births are on the rise in both jurisdictions. Stories from a Jamaican Muslim woman in Ottawa, an Indigenous midwifery practice in Northern Ontario, registered midwives in Eastern Ontario, and about the traditional midwives in Guatemala reveal similar as well as unique problems resulting from the lockdowns. While this article is not intended to constitute an exhaustive analysis of social justice and human rights issues in Canada and Guatemala, we do take this opportunity to demonstrate where COVID-19 has become a catalyst that challenges the standard narrative, exposing the old ruts and blind spots of inequality and discrimination that our hierarchies and inadequate data collection-until the epidemic-were managing to ignore. As health advocates, we see signs that this pandemic is resulting in more open debate, which we hope will last long after it is over in both our countries.

Identifying monoclonal gammopathy of undetermined significance in electronic health data.

PURPOSE: Monoclonal gammopathy of undetermined significance (MGUS) is a prevalent yet largely asymptomatic precursor to multiple myeloma. Patients with MGUS must undergo regular surveillance and testing, with few known predictors of progression. We developed an algorithm to identify MGUS patients in electronic health data to facilitate large-scale, population-based studies of this premalignant condition. METHODS: We developed a four-step algorithm using electronic health record and health claims data from men and women aged 50 years or older receiving care from a large, multispecialty medical group between 2007 and 2015. The case definition required patients to have at least two MGUS ICD-9 diagnosis codes within 12 months, at least one serum and/or urine protein electrophoresis and one immunofixation test, and at least one in-office hematology/oncology visit. Medical charts for selected cases were abstracted then adjudicated independently by two physicians. We assessed algorithm validity by positive predictive value (PPV). RESULTS: We identified 833 people with at least two MGUS diagnosis codes; 429 (52%) met all four algorithm criteria. We randomly selected 252 charts for review, including 206 from patients meeting all four algorithm criteria. The PPV for the 206 algorithm-identified charts was 76% (95% CI, 70%-82%). Among the 49 cases deemed to be false positives (24%), 33 were judged to have multiple myeloma or another lymphoproliferative condition, such as lymphoma. CONCLUSIONS: We developed a simple algorithm that identified MGUS cases in electronic health data with reasonable accuracy. Inclusion of additional steps to eliminate cases with malignant disease may improve algorithm performance.