Introduction COVID-19 is an unprecedented public health threat in modern times, especially for older adults or those with chronic illness. Beyond the threat of infection, the pandemic may also have longer-term impacts on mental and physical health. The COVID-19 & Chronic Conditions (‘C3’) study offers a unique opportunity to assess psychosocial and health/healthcare trajectories over 5 years among a diverse cohort of adults with comorbidities well-characterised from before the pandemic, at its onset, through multiple surges, vaccine rollouts and through the gradual easing of restrictions as society slowly returns to ‘normal’. Methods and analysis The C3 study is an extension of an ongoing longitudinal cohort study of ‘high-risk’ adults (aged 23–88 at baseline) with one or more chronic medical conditions during the COVID-19 pandemic. Five active studies with uniform data collection prior to COVID-19 were leveraged to establish the C3 cohort; 673 adults in Chicago were interviewed during the first week of the outbreak. The C3 cohort has since expanded to include 1044 participants across eight survey waves (T 1 –T 8 ). Four additional survey waves (T 9 –T 12 ) will be conducted via telephone interviews spaced 1 year apart and supplemented by electronic health record and pharmacy fill data, for a total of 5 years of data post pandemic onset. Measurement will include COVID-19-related attitudes/behaviours, mental health, social behaviour, lifestyle/health behaviours, healthcare use, chronic disease self-management and health outcomes. Mental health trajectories and associations with health behaviours/outcomes will be examined in a series of latent group and mixed effects modelling, while also examining mediating and moderating factors. Ethics and dissemination This study was approved by Northwestern University’s Feinberg School of Medicine Institutional Review Board (STU00215360). Results will be published in international peer-reviewed journals and summaries will be provided to the funders of the study.
Abstract Objective To document disparities in registration and use of an online patient portal among older adults. Materials and methods Data from 534 older adults were linked with information from the Northwestern Medicine Electronic Data Warehouse on patient portal registration and use of functions (secure messaging, prescription reauthorizations, checking test results, and monitoring vital statistics). Age, gender, race, education, self-reported chronic conditions, and the Newest Vital Sign health literacy measure were available from cohort data. Results Most patients (93.4%) had a patient portal access code generated for them, and among these 57.5% registered their accounts. In multivariable analyses, White patients (P < .001) and college graduates were more likely to have registered their patient portal (P = .015). Patients with marginal (P = .034) or adequate (P < .001) health literacy were also more likely to have registered their patient portal. Among those registering their accounts, most had messaged their physician (90%), checked a test result (96%), and ordered a reauthorization (55%), but few monitored their vital statistics (11%). Adequate health literacy patients were more likely to have used the messaging function (P = .003) and White patients were more likely to have accessed test results (P = .004). Higher education was consistently associated with prescription reauthorization requests (all P < .05). Discussion Among older American adults, there are stark health literacy, educational, and racial disparities in the registration, and subsequent use of an online patient portal. These population sub-group differences may exacerbate existing health disparities. Conclusions If patient portals are implemented, intervention strategies are needed to monitor and reduce disparities in their use.
Objectives To examine the effect of the relationship between literacy and other individual‐level factors on having an advance directive ( AD ). Design Face‐to‐face structured interview. Setting Participants were recruited from an academic general internal medicine clinic and one of four federally qualified health centers in Chicago. Participants Seven hundred eighty‐four adults aged 55 to 74. Measurements Assessment of participant literacy, sociodemographic factors, and having an AD for medical care. Results One‐eighth (12.4%) of participants with low literacy, 26.6% of those with marginal literacy, and 49.5% of those with adequate literacy reported having an AD ( P < .001). In multivariable analyses, literacy and race were independently associated with less likelihood of having an AD . Specifically, participants with limited literacy (risk ratio ( RR ) = 0.45, 95% confidence interval ( CI ) = 0.22–0.95) and African Americans ( RR = 0.64, 95% CI = 0.47–0.88) were less likely to have an AD . Exploratory analyses showed that there was not a significant interaction between the effect of literacy and race. Conclusion Limited literacy and African‐American race were significant risk factors for not having an AD in this cohort of older adults. Literacy and race probably represent two separate but important causal pathways that need to be understood to improve how the healthcare system ascertains and protects individuals' advance care preferences.
Abstract Many older adults manage multiple chronic conditions requiring adherence to multidrug regimens, yet half are non-adherent, increasing their risk of hospitalization for poorly controlled chronic conditions. Few studies have investigated whether caregivers support medication-related behaviors of community-dwelling older adults. We interviewed 97 patient-caregiver dyads participating in a cognitive aging cohort study to identify factors associated with caregiver assistance in managing multidrug regimens. Patients completed a neuropsychological battery covering five cognitive domains. Health literacy and patient activation were measured using the Newest Vital Sign and Consumer Health Activation Index, respectively. Caregivers reported their medication-related involvement. Predictors of involvement in medication-related tasks were examined using logistic regression models. Patients were on average 71 years old, managing 4 comorbidities and prescribed 5 medications. The majority were female (73%) and identified as Black (46%) or White (47%). Caregivers’ mean age was 65 years; half were female (53%), were predominantly spouses (57%) or children (26%), and lived with the patient (61%). 31% of caregivers ordered patients’ prescribed medications, 40% helped manage their medications, and 50% spoke with the patient’s clinician about their clinical care. Cognitive impairment (OR 2.60, 95% CI 1.08-6.25), limited health literacy (OR 2.97, 95% CI 1.26-6.97), and ≥3 comorbidities (OR 2.14, 95% CI 1.06-9.30) were associated with medication management assistance. Patient activation, gender, cohabitation, or relationship were not associated. These findings suggest that caregivers are assisting with older adults’ medication management and should be included in clinical discussions about medication management, especially among patients with cognitive impairment, low health literacy or multimorbidities.
Objectives: To help promote early detection of cognitive impairment in primary care, MyCog Mobile was designed as a cognitive screener that can be self-administered remotely on a personal smartphone. We explore the potential utility of MyCog Mobile in primary care by comparing MyCog Mobile to a commonly used screener, Mini-Cog. Methods: A sample of 200 older adults 65+ years (mean age = 72.56 years), completed the Mini-Cog and MyCog Mobile, which includes 2 memory measures and 2 executive functioning measures. A logistic regression model was conducted to predict failing Mini-Cog scores (≤2) based on MyCog Mobile measures. Results: A total of 20 participants earned a Mini-Cog score ≤2. MyCog Mobile demonstrated an AUC of 0.83 (95% bootstrap CI [0.75, 0.95]), sensitivity of 0.76 (95% bootstrap CI [0.63, 0.97]), and specificity of .88 (95% bootstrap CI [0.63, 0.10]). The subtest Name Matching from MyFaces and MySorting were the only significant predictors of failed Mini-Cogs. Conclusions: MyCog Mobile demonstrated sensitivity and specificity to identify participants who failed the Mini-Cog, and may show promise as a screening tool for cognitive impairment in older adults. Further research is necessary to establish the clinical utility of MyCog Mobile in a larger sample using documented clinical diagnoses.
There are over 15 million children who have cardiac anomalies around the world, resulting in a significant morbidity and mortality. Early recognition and treatment can improve the outcomes and lengthen life-expectancy of these patients. The NIH and WHO have promoted guidelines for screening for congenital cardiac anomalies using ultrasound in rural environments.Our study took place in Bocas Del Toro, Panama where a mobile clinic was established for community healthcare screening and ultrasonographic evaluation by medical student volunteers and volunteer clinical faculty. This was a non-blinded, investigational study utilizing a convenience sample of pediatric patients presenting for voluntary evaluation. Seven first-year medical students were recruited for the study. These students underwent a training program for advanced cardiac ultrasound instruction, termed "Pediatric Echocardiography Cardiac Screening (PECS)".Ten patients were enrolled in the study. Nine patients had adequate images as defined by the PECS criteria and were all classified as normal cardiac pathology by the medical students, resulting in a sensitivity and specificity of 100%. A single patient was identified by medical students as having a pathologic pulmonic stenosis. This was confirmed as correct by a blinded ultrasonographer.In this pilot study, the first-year medical students were able to correctly identify pediatric cardiac anatomy and pathology in rural Panama after undergoing a 12-hour ultrasound PECS training session. We believe that with this knowledge, minimally trained practitioners can be used to screen for cardiac anomalies in rural Panama using ultrasound.
Many college students have nutrient poor and energy dense diets and are also more likely to experience poor body image, which can result in unsafe dieting behaviors for the purpose of managing weight. Intuitive eating is an alternative approach to dieting that focuses on physiological hunger and fullness cues, while eating for both satisfaction and health without restriction of any foods. This study examined the association between intuitive eating and diet quality in a college population. College students, aged 18-56 years, completed an online survey which assessed intuitive eating using the Intuitive Eating Scale-2 (IES-2) and diet quality using the Starting The Conversation (STC) simplified food frequency instrument. IES-2 total score was positively correlated with higher overall diet quality and was negatively correlated with fast food and chip consumption. Eating for physical rather than emotional reasons and body-food choice congruence IES-2 subscales were positively correlated with diet quality while the unconditional permission to eat subscale was negatively correlated with diet quality. Strategies that focus on eating for health and well-being and minimize emotional eating are associated with higher overall diet quality and may be incorporated in dietary interventions among college students aimed at promoting healthy behaviors.
Abstract INTRODUCTION Early detection of patients with cognitive impairment may facilitate care for individuals in this population. Natural language processing (NLP) is a potential approach to identifying patients with cognitive impairment from electronic health records (EHR). METHODS We used three machine learning algorithms (logistic regression, multilayer perceptron, and random forest) using clinical terms extracted by NLP to predict cognitive impairment in a cohort of 199 patients. Cognitive impairment was defined as a mini-mental status exams (MMSE) score <24. RESULTS NLP identified 69 (35%) patients with cognitive impairment and ICD codes identified 44 (22%). Using MMSE as a reference standard, NLP sensitivity was 35%, specificity 66%, precision 41%, and NPV 61%. The random forest method had the best test parameters; sensitivity 95%, specificity 100%, precision 100%, and NPV 97% DISCUSSION NLP can identify adults with cognitive impairment with moderate test performance that is enhanced with machine learning.
