Rural health disparities in chronic heart disease

The mediating effect of uncertainty in illness between heart failure symptoms and health-related quality of life among rural patients with heart failure: A multi-center cross-sectional study

BACKGROUND

The health-related quality of life (HRQoL) of patients with heart failure (HF) in rural settings in China remains unclear. Limited studies explored the mediating effect of uncertainty in illness between heart failure symptoms and HRQoL in this population.

OBJECTIVES

To explore the status of HRQoL in rural patients with HF; assess the impact of HF symptoms and uncertainty in illness on HRQoL; and examine the mediating effect of uncertainty in illness on the relationship between symptoms and HRQoL in rural patients with HF.

METHODS

Overall, 298 rural patients with HF were recruited from five township hospitals of Taishan and Jinzhong City in China between November 2021 and August 2022. Three variables, namely HF symptoms, uncertainty in illness, and HRQoL were measured using three validated scales.

RESULTS

The average score of HRQoL in rural patients with HF was 43.19. Of the participants, 60.4 %, 35.23 %, and 4.37 % exhibited poor, moderate, and good HRQoL, respectively. The HF symptoms (β = -0.47) and uncertainty in illness (β = -0.34) directly influenced HRQoL. Moreover, the HF symptoms also indirectly affected HRQoL through uncertainty in illness (β = -0.07). The indirect effect accounted for 12.96 % of the total effect of HF symptoms on HRQoL.

CONCLUSION

Rural patients with HF exhibited poor HRQoL. In this population, HF symptoms and uncertainty in illness were negatively associated with HRQoL. Uncertainty in illness mediated the relationship between HF symptoms and HRQoL. Tailored healthcare services should be developed for the rural population to alleviate HF symptoms, reduce uncertainty in illness, and enhance their HRQoL.

Digital Transformation in the Diagnostics and Therapy of Cardiovascular Diseases: Comprehensive Literature Review

BACKGROUND

The digital transformation of our health care system has experienced a clear shift in the last few years due to political, medical, and technical innovations and reorganization. In particular, the cardiovascular field has undergone a significant change, with new broad perspectives in terms of optimized treatment strategies for patients nowadays.

OBJECTIVE

After a short historical introduction, this comprehensive literature review aimed to provide a detailed overview of the scientific evidence regarding digitalization in the diagnostics and therapy of cardiovascular diseases (CVDs).

METHODS

We performed an extensive literature search of the PubMed database and included all related articles that were published as of March 2022. Of the 3021 studies identified, 1639 (54.25%) studies were selected for a structured analysis and presentation (original articles: n=1273, 77.67%; reviews or comments: n=366, 22.33%). In addition to studies on CVDs in general, 829 studies could be assigned to a specific CVD with a diagnostic and therapeutic approach. For data presentation, all 829 publications were grouped into 6 categories of CVDs.

RESULTS

Evidence-based innovations in the cardiovascular field cover a wide medical spectrum, starting from the diagnosis of congenital heart diseases or arrhythmias and overoptimized workflows in the emergency care setting of acute myocardial infarction to telemedical care for patients having chronic diseases such as heart failure, coronary artery disease, or hypertension. The use of smartphones and wearables as well as the integration of artificial intelligence provides important tools for location-independent medical care and the prevention of adverse events.

CONCLUSIONS

Digital transformation has opened up multiple new perspectives in the cardiovascular field, with rapidly expanding scientific evidence. Beyond important improvements in terms of patient care, these innovations are also capable of reducing costs for our health care system. In the next few years, digital transformation will continue to revolutionize the field of cardiovascular medicine and broaden our medical and scientific horizons.

Care delivery team composition effect on hospitalization risk in African Americans with congestive heart failure

The care delivery team (CDT) is critical to providing care access and equity to patients who are disproportionately impacted by congestive heart failure (CHF). However, the specific clinical roles that are associated with care outcomes are unknown. The objective of this study was to examine the extent to which specific clinical roles within CDTs were associated with care outcomes in African Americans (AA) with CHF. Deidentified electronic medical record data were collected on 5,962 patients, representing 80,921 care encounters with 3,284 clinicians between January 1, 2014 and December 31, 2021. Binomial logistic regression assessed associations of specific clinical roles and the Mann Whitney-U assessed racial differences in outcomes. AAs accounted for only 26% of the study population but generated 48% of total care encounters, the same percentage of care encounters generated by the largest racial group (i.e., Caucasian Americans; 69% of the study population). AAs had a significantly higher number of hospitalizations and readmissions than Caucasian Americans. However, AAs had a significantly higher number of days at home and significantly lower care charges than Caucasian Americans. Among all CHF patients, patients with a Registered Nurse on their CDT were less likely to have a hospitalization (i.e. 30%) and a high number of readmissions (i.e., 31%) during the 7-year study period. When stratified by heart failure phenotype, the most severe patients who had a Registered Nurse on their CDT were 88% less likely to have a hospitalization and 50% less likely to have a high number of readmissions. Similar decreases in the likelihood of hospitalization and readmission were also found in less severe cases of heart failure. Specific clinical roles are associated with CHF care outcomes. Consideration must be given to developing and testing the efficacy of more specialized, empirical models of CDT composition to reduce the disproportionate impact of CHF.

Image based deep learning in 12-lead ECG diagnosis

Background

The electrocardiogram is an integral tool in the diagnosis of cardiovascular disease. Most studies on machine learning classification of electrocardiogram (ECG) diagnoses focus on processing raw signal data rather than ECG images. This presents a challenge for models in many areas of clinical practice where ECGs are printed on paper or only digital images are accessible, especially in remote and regional settings. This study aims to evaluate the accuracy of image based deep learning algorithms on 12-lead ECG diagnosis.

Methods

Deep learning models using VGG architecture were trained on various 12-lead ECG datasets and evaluated for accuracy by testing on holdout test data as well as data from datasets not seen in training. Grad-CAM was utilized to depict heatmaps of diagnosis.

Results

The results demonstrated excellent AUROC, AUPRC, sensitivity and specificity on holdout test data from datasets used in training comparable to the best signal and image-based models. Detection of hidden characteristics such as gender were achieved at a high rate while Grad-CAM successfully highlight pertinent features on ECGs traditionally used by human interpreters.

Discussion

This study demonstrates feasibility of image based deep learning algorithms in ECG diagnosis and identifies directions for future research in order to develop clinically applicable image based deep-learning models in ECG diagnosis.

Health care resource utilization and costs associated with atrial fibrillation and rural-urban disparities

BACKGROUND: Atrial fibrillation (AF) imposes substantial health care and economic burden on health care systems and patients. Previous studies failed to examine health care resource utilization (HCRU) and costs among patients with incident AF and potential disparity with regard to geographic location. OBJECTIVES: To examine HCRU and costs among patients with incident AF compared with patients without AF and examine whether a geographic disparity exists. METHODS: This was a retrospective cohort study. We selected patients with AF and patients without AF from IBM/Watson MarketScan Research Databases 2014-2019. HCRU and costs were collected 12 months following an AF index date. We used 2-part models with bootstrapping to obtain the marginal estimates and CIs. Rural status was identified based on Metropolitan Statistical Area. We adjusted for age, sex, plan type, US region, and comorbidities. RESULTS: Among 156,732 patients with AF and 3,398,490 patients without AF, patients with AF had 9.04 (95% CI = 8.96-9.12) more outpatient visits, 0.82 (95% CI = 0.81-0.83) more emergency department (ED) visits, 0.33 (95% CI = 0.33-0.34) more inpatient admission, and $15,095 (95% CI = 14,871-15,324) higher total costs, compared with patients without AF. Among patients with AF, rural patients had 1.99 fewer (95% CI = -2.26 to -1.71) outpatient visits and 0.05 (95% CI = 0.02-0.08) more ED visits than urban patients. Overall, rural patients with AF had decreased total costs compared with urban patients (mean = $751; 95% CI = -1,227 to -228). CONCLUSIONS: Incident AF was associated with substantial burden of health care resources and an economic burden, and the burden was not equally distributed across patients in urban vs rural settings. DISCLOSURES: Dr Hansen reports grants from the National Science Foundation during the conduct of the study.

The need for a complex systems approach in rural health research

On a global scale, many major rural health issues have persisted for decades despite the introduction of new health interventions and public health policies. Although research efforts have generated valuable new knowledge about the aetiology of health, disease and health inequities in rural communities, rural health systems remain to be some of the most deprived and challenged in both the developing and developed world. While the reasons for this are many, a significant factor contributing to the current state of play is the pressing need for methodological innovation and relevant scientific approaches that have the capacity to support the translation of novel solutions into 'real world' rural contexts. Fortunately, complex systems approaches, which have seen an increase in popularity in the wider public health literature, could provide answers to some of the most resilient rural health problems in recent times. The purpose of this article is to promote the value and utility of a complex systems approach in rural health research. We explain the benefits of a complex systems approach and provide a background to the complexity sciences, including the main characteristics of complex systems. Two popular computational methods are described. The next step for rural health research involves exploring how a complex systems approach can help with the identification and evaluation of new and existing solutions to policy-resistant rural health issues. This includes generating awareness around the analytical trade-offs that occur between the use of traditional scientific methods and complex systems approaches.

Behavior change, health, and health disparities 2021: Rural addiction and health

This Special Issue of Preventive Medicine (PM) is the 8th in a series on behavior change, health, and health disparities. This is a topic of critical importance to improving U.S. population health. There is broad consensus that personal behavior patterns or lifestyle such as substance abuse, poor food choices, physical inactivity, and non-adherence with medical regimens are among the most important modifiable causes of chronic disease and premature death and contributors to recent decreases in U.S. longevity. While no U.S region is free of these problems, they disproportionately impact rural communities. As in prior Special Issues in this series, we devote considerable space to the ongoing U.S. opioid epidemic while also examining selected issues in rural health disparities involving tobacco use, cancer, and cardiovascular disease. Across each of these topics we have recruited contributions from accomplished investigators, clinicians, and policymakers to acquaint readers with recent advances while also noting knowledge gaps and unresolved challenges.