Comparing Rates of Multiple Chronic Conditions at Primary Care and Mental Health Visits to Community Health Centers Versus Private Practice Providers

Effects of a medication adherence app among medically underserved adults with chronic illness: a randomized controlled trial

For individuals living with a chronic illness who require use of long-term medications, adherence is a vital aspect of successful symptom management and outcomes. This study investigated the effect of a smartphone app on adherence, self-efficacy, knowledge, and medication social support in a medically underserved adult population with various chronic illnesses. Participants were randomized to a group who used the app for one month or a control group provided with a printed medication list. Compared to the control group, participants receiving the intervention had significantly greater medication adherence (Cohen's d = -0.52, p = .014) and medication self-efficacy (Cohen's d = 0.43, p = .035). No significant effects were observed related to knowledge or social support. The findings suggest use of the app could positively impact chronic disease management in a medically underserved population in the United States.

Engaging Interdisciplinary Innovation Teams in Federally Qualified Health Centers

To foster bottom-up innovations, health care organizations are leveraging interdisciplinary frontline innovation teams. These teams include workers across hierarchical levels and professional backgrounds, pooling diverse knowledge sources to develop innovations that improve patient and worker experiences and care quality, equity, and costs. Yet, these frontline innovation teams experience barriers, such as time constraints, being new to innovation, and team-based role hierarchies. We investigated the practices that such teams in federally qualified health centers (FQHCs) used to overcome these barriers. Our 20-month study of two FQHC innovation teams provides one of the first accounts of how practices that sustained worker engagement in innovation and supported their ideas to implementation evolve over time. We also show the varied quantity of engagement practices used at different stages of the innovation process. At a time when FQHCs face pressure to innovate amid staff shortages, our study provides recommendations to support their work.

The impact of dual VA-Medicare use on a data-driven clinical management tool for older Veterans with multimorbidity

BACKGROUND

Healthcare systems are increasingly turning to data-driven approaches, such as clustering techniques, to inform interventions for medically complex older adults. However, patients seeking care in multiple healthcare systems may have missing diagnoses across systems, leading to misclassification of resulting groups. We evaluated the impact of multi-system use on the accuracy and composition of multimorbidity groups among older adults in the Veterans Health Administration (VA).

METHODS

Eligible patients were VA primary care users aged ≥65 years and in the top decile of predicted 1-year hospitalization risk in 2018 (n = 558,864). Diagnoses of 26 chronic conditions were coded using a 24-month lookback period and input into latent class analysis (LCA) models. In a random 10% sample (n = 56,008), we compared the resulting model fit, class profiles, and patient assignments from models using only VA system data versus VA with Medicare data.

RESULTS

LCA identified six patient comorbidity groups using VA system data. We labeled groups based on diagnoses with higher within-group prevalence relative to the average: Substance Use Disorders (7% of patients), Mental Health (15%), Heart Disease (22%), Diabetes (16%), Tumor (14%), and High Complexity (10%). VA with Medicare data showed improved model fit and assigned more patients with high accuracy. Over 70% of patients assigned to the Substance, Mental Health, High Complexity, and Tumor groups using VA data were assigned to the same group in VA with Medicare data. However, 41.9% of the Heart Disease group and 14.7% of the Diabetes group were reassigned to a new group characterized by multiple cardiometabolic conditions.

CONCLUSIONS

The addition of Medicare data to VA data for older high-risk adults improved clustering model accuracy and altered the clinical profiles of groups. Accessing or accounting for multi-system data is key to the success of interventions based on empiric grouping in populations with dual-system use.

Prevalent Multimorbidity Combinations Among Middle-Aged and Older Adults Seen in Community Health Centers

BACKGROUND

Multimorbidity (≥ 2 chronic diseases) is associated with greater disability and higher treatment burden, as well as difficulty coordinating self-management tasks for adults with complex multimorbidity patterns. Comparatively little work has focused on assessing multimorbidity patterns among patients seeking care in community health centers (CHCs).

OBJECTIVE

To identify and characterize prevalent multimorbidity patterns in a multi-state network of CHCs over a 5-year period.

DESIGN

A cohort study of the 2014-2019 ADVANCE multi-state CHC clinical data network. We identified the most prevalent multimorbidity combination patterns and assessed the frequency of patterns throughout a 5-year period as well as the demographic characteristics of patient panels by prevalent patterns.

PARTICIPANTS

The study included data from 838,642 patients aged ≥ 45 years who were seen in 337 CHCs across 22 states between 2014 and 2019.

MAIN MEASURES

Prevalent multimorbidity patterns of somatic, mental health, and mental-somatic combinations of 22 chronic diseases based on the U.S. Department of Health and Human Services Multiple Chronic Conditions framework: anxiety, arthritis, asthma, autism, cancer, cardiac arrhythmia, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), congestive heart failure, coronary artery disease, dementia, depression, diabetes, hepatitis, human immunodeficiency virus (HIV), hyperlipidemia, hypertension, osteoporosis, post-traumatic stress disorder (PTSD), schizophrenia, substance use disorder, and stroke.

KEY RESULTS

Multimorbidity is common among middle-aged and older patients seen in CHCs: 40% have somatic, 6% have mental health, and 24% have mental-somatic multimorbidity patterns. The most frequently occurring pattern across all years is hyperlipidemia-hypertension. The three most frequent patterns are various iterations of hyperlipidemia, hypertension, and diabetes and are consistent in rank of occurrence across all years. CKD-hyperlipidemia-hypertension and anxiety-depression are both more frequent in later study years.

CONCLUSIONS

CHCs are increasingly seeing more complex multimorbidity patterns over time; these most often involve mental health morbidity and advanced cardiometabolic-renal morbidity.

Practice Environment and Workforce Outcomes of Nurse Practitioners in Community Health Centers

The nurse practitioner (NP) workforce in community health centers (CHCs) increases access to primary care for underserved populations. Working with medically complex patients, high workloads, and low resources in the CHC setting, CHC NPs may be susceptible to poor workforce outcomes. This study uses NP survey data collected from 6 US states to describe and assess the relationship between CHC NP practice environment and burnout, job satisfaction, and turnover intention. CHC NPs rated their practice environments favorably, and more than 89% of CHC NPs reported satisfaction with their job. Better rating of NPs' relationship with CHC administration was associated with improved job satisfaction and decreased turnover intention.

Multimorbidity

Multimorbidity (two or more coexisting conditions in an individual) is a growing global challenge with substantial effects on individuals, carers and society. Multimorbidity occurs a decade earlier in socioeconomically deprived communities and is associated with premature death, poorer function and quality of life and increased health-care utilization. Mechanisms underlying the development of multimorbidity are complex, interrelated and multilevel, but are related to ageing and underlying biological mechanisms and broader determinants of health such as socioeconomic deprivation. Little is known about prevention of multimorbidity, but focusing on psychosocial and behavioural factors, particularly population level interventions and structural changes, is likely to be beneficial. Most clinical practice guidelines and health-care training and delivery focus on single diseases, leading to care that is sometimes inadequate and potentially harmful. Multimorbidity requires person-centred care, prioritizing what matters most to the individual and the individual's carers, ensuring care that is effectively coordinated and minimally disruptive, and aligns with the patient's values. Interventions are likely to be complex and multifaceted. Although an increasing number of studies have examined multimorbidity interventions, there is still limited evidence to support any approach. Greater investment in multimorbidity research and training along with reconfiguration of health care supporting the management of multimorbidity is urgently needed.

Evaluating Primary Care Providers' Readiness for Delivering Genetic and Genomic Services to Underserved Populations

INTRODUCTION

Increased genomics knowledge and access are advancing precision medicine and care delivery. With the translation of precision medicine across health care, genetics and genomics will play a greater role in primary care services. Health disparities and inadequate representation of racial and ethnically diverse groups threaten equitable access for those historically underserved. Health provider awareness, knowledge, and perceived importance are important determinants of the utilization of genomic applications.

METHODS

We evaluated the readiness of primary care providers at a Federally Qualified Health Center, the Community Health Center, Inc. (CHCI) for delivering genetic and genomic testing to underserved populations. Online survey questions focused on providers' education and training in basic and clinical genetics, familiarity with current genetic tests, and needs for incorporating genetics and genomics into their current practice.

RESULTS

Fifty of 77 (65%) primary care providers responded to the survey. Less than half received any training in basic or clinical genetics (40%), were familiar with specific genetic tests (36%), or felt confident with collecting family health history (44%), and 70% believed patients would benefit from genetic testing.

CONCLUSION

Despite knowledge gaps, respondents recognized the value and need to bring these services to their patients, though would like more education on applying genetics and genomics into their practice, and more training about discussing risk factors associated with race or ethnicity. We provide further evidence of the need for educational resources and standardized guidelines for providers caring for underserved populations to optimize appropriate use and referral of genetic and genomic services and to reduce disparities in care.