70 research outputs found

    Public Protests and the Risk of Novel Coronavirus Disease Hospitalizations: A County-Level Analysis from California

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    The objective of this study was to assess the relationship between public protests and county-level, novel coronavirus disease (COVID-19) hospitalization rates across California. Publicly available data were included in the analysis from 55 of 58 California state counties (29 March–14 October 2020). Mixed-effects negative binomial regression models were used to examine the relationship between daily county-level COVID-19 hospitalizations and two main exposure variables: any vs. no protests and 1 or \u3e1 protest vs. no protests on a given county-day. COVID-19 hospitalizations were used as a proxy for viral transmission since such rates are less sensitive to temporal changes in testing access/availability. Models included covariates for daily county mobility, county-level characteristics, and time trends. Models also included a county-population offset and a two-week lag for the association between exposure and outcome. No significant associations were observed between protest exposures and COVID-19 hospitalization rates among the 55 counties. We did not find evidence to suggest that public protests were associated with COVID-19 hospitalization within California counties. These findings support the notion that protesting during a pandemic may be safe, ostensibly, so long as evidence-based precautionary measures are taken

    Evaluation of a social determinants of health screening questionnaire and workflow pilot within an adult ambulatory clinic

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    Background There is increased recognition in clinical settings of the importance of documenting, understanding, and addressing patients’ social determinants of health (SDOH) to improve health and address health inequities. This study evaluated a pilot of a standardized SDOH screening questionnaire and workflow in an ambulatory clinic within a large integrated health network in Northern California. Methods The pilot screened for SDOH needs using an 11-question Epic-compatible paper questionnaire assessing eight SDOH and health behavior domains: financial resource, transportation, stress, depression, intimate partner violence, social connections, physical activity, and alcohol consumption. Eligible patients for the pilot receiving a Medicare wellness, adult annual, or new patient visits during a five-week period (February-March, 2020), and a comparison group from the same time period in 2019 were identified. Sociodemographic data (age, sex, race/ethnicity, and payment type), visit type, length of visit, and responses to SDOH questions were extracted from electronic health records, and a staff experience survey was administered. The evaluation was guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. Results Two-hundred eighty-nine patients were eligible for SDOH screening. Responsiveness by domain ranged from 55 to 67%, except for depression. Half of patients had at least one identified social need, the most common being stress (33%), physical activity (22%), alcohol (12%), and social connections (6%). Physical activity needs were identified more in females (81% vs. 19% in males, p \u3c .01) and at new patient/transfer visits (48% vs. 13% at Medicare wellness and 38% at adult wellness visits, p \u3c .05). Average length of visit was 39.8 min, which was 1.7 min longer than that in 2019. Visit lengths were longer among patients 65+ (43.4 min) and patients having public insurance (43.6 min). Most staff agreed that collecting SDOH data was relevant and accepted the SDOH questionnaire and workflow but highlighted opportunities for improvement in training and connecting patients to resources. Conclusion Use of evidence-based SDOH screening questions and associated workflow was effective in gathering patient SDOH information and identifying social needs in an ambulatory setting. Future studies should use qualitative data to understand patient and staff experiences with collecting SDOH information in healthcare settings

    Placebo Adherence and Its Association with Morbidity and Mortality in the Studies of Left Ventricular Dysfunction

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    A provocative finding from several double-blind clinical trials has been the association between greater adherence to placebo study medication and better health outcomes. We used data from the Studies of Left Ventricular Dysfunction (SOLVD) Treatment Trial (SOLVD-TT) and the SOLVD Prevention Trial (SOLVD-PT) to examine whether such associations could be validated and to examine several sources of bias and potential confounding. Survival analytic methods were used to estimate the association between placebo adherence and several health outcomes, employing a number of modeling techniques to test for the existence of alternative explanations for the association. Higher adherence was defined as having taken ≥75% of prescribed study medication. Higher placebo adherence was associated with improved overall survival in both SOLVD-TT and SOLVD-PT [hazard ratio (HR) = 0.52, 95% confidence interval (CI): 0.35 to 0.79 and HR = 0.52, 95%CI: 0.38 to 0.71, respectively]. Associations were similar for fatal or non-fatal cardiovascular or coronary heart disease events. Adjustment for both modifiable and non-modifiable cardiac risk factors (including age, gender, diabetes, blood pressure, smoking, weight, alcohol use, and levels of education) had minimal effect on the strength of the association. Little evidence of bias was found as an explanation for this relationship. In these two trials, better adherence to placebo was associated with markedly superior health outcomes, including total in-study mortality and incident cardiovascular events. No important confounders were identified. These data suggest there may exist strong but unrecognized determinants of health outcomes for which placebo adherence is a marker

    Migraine polygenic risk score associates with efficacy of migraine-specific drugs

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    Objective To assess whether the polygenic risk score (PRS) for migraine is associated with acute and/or prophylactic migraine treatment response. Methods We interviewed 2,219 unrelated patients at the Danish Headache Center using a semistructured interview to diagnose migraine and assess acute and prophylactic drug response. All patients were genotyped. A PRS was calculated with the linkage disequilibrium pred algorithm using summary statistics from the most recent migraine genome-wide association study comprising ∼375,000 cases and controls. The PRS was scaled to a unit corresponding to a twofold increase in migraine risk, using 929 unrelated Danish controls as reference. The association of the PRS with treatment response was assessed by logistic regression, and the predictive power of the model by area under the curve using a case-control design with treatment response as outcome. Results A twofold increase in migraine risk associates with positive response to migraine-specific acute treatment (odds ratio [OR] = 1.25 [95% confidence interval (CI) = 1.05–1.49]). The association between migraine risk and migraine-specific acute treatment was replicated in an independent cohort consisting of 5,616 triptan users with prescription history (OR = 3.20 [95% CI = 1.26–8.14]). No association was found for acute treatment with non–migraine-specific weak analgesics and prophylactic treatment response. Conclusions The migraine PRS can significantly identify subgroups of patients with a higher-than-average likelihood of a positive response to triptans, which provides a first step toward genetics-based precision medicine in migraine

    Improving Reliability of Scheduling Post-Acute Follow-Up Care, Implementing One Element of Project RED

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    Background: The New England Journal of Medicine reports that 1 in 5 discharged Medicare patients is readmitted to a hospital within 30 days. Readmissions are costly to hospitals and are a major disruption to a patient’s life, creating financial and emotional strain. Project Re-Engineered Discharge (RED), a nationally recognized program to reduce preventable hospital readmissions, was implemented at Sutter Health’s California Pacific Medical Center (CPMC) beginning in late 2013. The most challenging of the 12 components of the intervention is the coordination and timely completion of follow-up care with a primary care physician after hospital discharge. Methods: To ensure that each patient going home from the hospital would have a post-acute care appointment scheduled, we developed Discharge Planner, a web-based software application to support the required multistep, multidisciplinary workflow. We then piloted the tool in a single unit at CPMC. We measured fidelity by monitoring whether case managers were launching the application and recorded the proportion of patients with appointments scheduled at the time of discharge. We used patient electronic health records to measure the proportion of patients who attended follow-up appointments postdischarge. Finally, we used provider surveys to determine user acceptability. Results: The app was opened an average of 40 times/day for a single hospital unit during business hours and \u3c 5 times/day on weekends. Follow-up appointment scheduling increased from \u3c 20% during the 4 months “pre-go-live” to \u3e 50% during the 5-month “post-go-live” period. Only 20%–30% of patients kept their scheduled appointments throughout the study period, with no change during the “post-go-live” period. User acceptance scores were highly favorable (on a scale from 1 to 100, average scores were 73 overall and 87 among those case managers who rely most heavily on the application). Conclusion: The application was highly successful at accomplishing its primary goal, scheduling follow-up appointments, and it has been accepted into the workflow. However, patients still do not appear to be keeping their follow-up appointments. Our next step is to uncover strategies to better measure kept appointments and to identify factors that prevent patients from keeping their appointments
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