Capsule Commentary on Zullig et al., Primary Care Providers’ Acceptance of Pharmacists’ Recommendations to Support Optimal Medication Management for Patients with Diabetic Kidney Disease

Cancer: Personal, Professional, and Practice Impact

Recurrent Sinopulmonary Infections in a Patient Whose HIV Masked Common Variable Immunodeficiency Abstract It is generally accepted that persons infected with human immunodeficiency virus (HIV) are at an increased risk of infection due to direct destruction of CD4+ lymphocytes and subsequently impaired cell-mediated immunity. Typically, HIV infection is associated with immunoglobulin elevations, but quantitative deficiencies in immunoglobulins have also been rarely described. We present an unusual case of common variable immunodeficiency (CVID) in a HIV-positive patient with recurrent severe respiratory infections. We review epidemiology, clinical presentation, and treatment of primary immunoglobulin deficiency. We also review the relationship between immunoglobulin deficiency and HIV and highlight the importance of recognizing the coexistence of two distinct immunodeficiency syndromes.

What Is a Medication-Related Problem? A Qualitative Study of Older Adults and Primary Care Clinicians Abstract Background Older adults often take multiple medications, leading to a myriad of medication-related problems. Addressing these problems requires thoughtful approaches that align with patients’ perspectives and experiences. Objective To (1) identify and categorize medication-related problems from the patient perspective and (2) understand patient and clinician attitudes toward these problems and experiences with addressing these problems. Design Qualitative, semi-structured interviews with patients and focus groups with physicians and pharmacists. Participants Twenty older adults recruited from an academic medical center and from a community senior center; 14 primary care physicians and 6 pharmacists affiliated with an academic medical center. Approach Hybrid deductive-inductive thematic analysis. Key Results Older adults identified a variety of medication-related problems that could be classified into four broad categories: (1) obtaining medications (e.g., problems with cost and insurance coverage); (2) taking medications (e.g., organization and remembering to take pills); (3) medication effects, including side effects and concerns over lack of effectiveness; and (4) communication and care coordination, including information related to medications. Many of the problems described by older adults were framed within the person’s socioemotional context, including the impact of medications on interpersonal relationships, emotional wellbeing, and activities that add meaning and quality to life. In contrast, clinicians almost exclusively focused on discrete medication issues without reference to this larger context and expressed relatively little interest in learning more about their patients’ perspectives. Conclusions Older adults experience medication-related problems as inseparable from their broader life context. Incorporating the social and emotional context of medications and related communication into a problem-focused framework can guide clinicians in specific actions and interventions to address medication-related problems from the patient perspective.

Prevalence of Patient-Reported Social Risk Factors and Receipt of Assistance in Federally Funded Health Centers

Worse Mental Health Among More-Acculturated and Younger Immigrants Experiencing Discrimination: California Health Interview Survey, 2015–2016 Abstract Background Experiences of discrimination harm mental and physical health, with the strongest penalty on mental health. Among immigrants, it remains unclear how acculturation—the process by which immigrants acquire the beliefs and practices of a host culture—influences the mental health burden of navigating discrimination. On the one hand, acculturation can be associated with upward social mobility. Conversely, the acculturative process may increase exposure to, and recognition of, discrimination. Objectives We examined the relationship between discrimination and mental illness across racial/ethnic groups, and pathways by which acculturation and age relate to the discrimination-mental health relationship. Design A secondary data analysis using population data from the 2015–2016 California Health Interview Survey. Main Measures The Kessler 6-item Psychological Distress Scale (K6) assessed symptoms of psychological distress, with K6 score ≥ 13 associated with severe mental illness. Discrimination was measured using a self-reported measure of lifetime experience of unfair treatment in getting medical care. We used a 5-point acculturation index (constructed by measures of nativity, years living in the USA, and home language use). A weighted logistic regression model predicted mental illness as a function of discrimination. We ran mediational analysis using the Karlson-Holm-Breen method and used predictive margins to present predicted probabilities of mental illness for people reporting discrimination at different acculturation and age levels. Key Results There were independent effects on mental illness associated with increased discrimination (OR 3.85, 95% CI = 2.46, 6.03, p < 0.001) and increased acculturation (OR 1.72, 95% CI = 1.24, 2.38, p = 0.001), including when stratified across racial/ethnic groups. Higher levels of acculturation led to a significant increase in discrimination’s association with mental illness. There was a higher probability of mental illness in younger age groups than in older age groups. Conclusions While discrimination is associated with poor mental health, a stronger link between discrimination and mental illness exists among younger immigrants and immigrants with increased acculturation. Health practitioners should not overlook the mental health needs of younger immigrants and immigrants who may seem more integrated into US society.

Religiosity and Patient Activation Among Hospital Survivors of an Acute Coronary Syndrome Abstract Background Optimum management after an acute coronary syndrome (ACS) requires considerable patient engagement/activation. Religious practices permeate people’s lives and may influence engagement in their healthcare. Little is known about the relationship between religiosity and patient activation. Objective To examine the association between religiosity and patient activation in hospital survivors of an ACS. Design Secondary analysis using baseline data from Transitions, Risks, and Actions in Coronary Events: Center for Outcomes Research and Education (TRACE-CORE) Study. Participants A total of 2067 patients hospitalized for an ACS at six medical centers in Central Massachusetts and Georgia (2011–2013). Main Measures Study participants self-reported three items assessing religiosity—strength and comfort from religion, making petition prayers, and awareness of intercessory prayers for health. Patient activation was assessed using the 6-item Patient Activation Measure (PAM-6). Participants were categorized as either having low (levels 1 and 2) or high (levels 3 and 4) activation. Results The mean age of study participants was 61 years, 33% were women, and 81% were non-Hispanic White. Approximately 85% derived strength and comfort from religion, 61% prayed for their health, and 89% received intercessory prayers for their health. Overall, 58% had low activation. Reports of a great deal (aOR, 2.02; 95% CI, 1.44–2.84), and little/some (aOR, 1.45; 95% CI, 1.07–1.98) strength and comfort from religion were associated with high activation, as were receipt of intercessions (aOR, 1.48; 95% CI, 1.07–2.05). Praying for one’s health was associated with low activation (aOR, 0.78; 95% CI, 0.61–0.99). Conclusions Most ACS survivors acknowledge religious practices toward their recovery. Strength and comfort from religion and intercessory prayers for health were associated with high patient activation. Petition prayers for health were associated with low activation. Healthcare providers should use knowledge about patient’s religiosity to enhance patient engagement in their care.

A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score ABSTRACT Background Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for creating predictive clinical applications. Objective Evaluate a state-of-the-art machine learning model for predicting mortality at the triage level and, by validating this automatic tool, improve the categorization of patients in the ED. Design An institutional review board (IRB) approval was granted for this retrospective study. Information of consecutive adult patients (ages 18–100) admitted at the emergency department (ED) of one hospital were retrieved (January 1, 2012–December 31, 2018). Features included the following: demographics, admission date, arrival mode, referral code, chief complaint, previous ED visits, previous hospitalizations, comorbidities, home medications, vital signs, and Emergency Severity Index (ESI). The following outcomes were evaluated: early mortality (up to 2 days post ED registration) and short-term mortality (2–30 days post ED registration). A gradient boosting model was trained on data from years 2012–2017 and examined on data from the final year (2018). The area under the curve (AUC) for mortality prediction was used as an outcome metric. Single-variable analysis was conducted to develop a nine-point triage score for early mortality. Key Results Overall, 799,522 ED visits were available for analysis. The early and short-term mortality rates were 0.6% and 2.5%, respectively. Models trained on the full set of features yielded an AUC of 0.962 for early mortality and 0.923 for short-term mortality. A model that utilized the nine features with the highest single-variable AUC scores (age, arrival mode, chief complaint, five primary vital signs, and ESI) yielded an AUC of 0.962 for early mortality. Conclusion The gradient boosting model shows high predictive ability for screening patients at risk of early mortality utilizing data available at the time of triage in the ED.

Capsule Commentary on Shapiro et al., Impact of a Patient-Centered Behavioral Economics Intervention on Hypertension Control in a Highly Disadvantaged Population: a Randomized Trial

Capsule Commentary on Hsien et al., “Getting Everyone on the Same Page”: Interprofessional Team Training to Develop Shared Mental Models on Interprofessional Rounds