Chronic non-communicable diseases (CNCDs) are an urgent public health issue in China, especially among older adults. Hence, self-management is crucial for disease progression and treatment. Electronic health (e-health) literacy and self-efficacy positively correlate with self-management. However, we know little about their underlying mechanisms in older adults with CNCDs.To explore the factors that influence chronic disease self-management (CDSM) and verify self-efficacy as the mediator between e-health literacy and self-management behavior in older patients with CNCDs.This cross-sectional study included 289 older patients with CNCDs from Hunan province, China, between July and November 2021. E-health literacy, self-efficacy, social support, and CDSM data were collected through questionnaires. The influence of each factor on CDSM was explored with multiple linear regression analysis. Intermediary effects were computed via a structural equation model.The total CDSM score in the patients was 29.39 ± 9.60 and only 46 (15.92%) patients used smart healthcare devices. The regression analysis showed e-health literacy, self-efficacy, and social support were the factors that affected CDSM. Furthermore, the structural equation model revealed that self-efficacy directly affected CDSM (β = 0.45, P < 0.01), whereas e-health literacy affected it directly (β = 0.42, P < 0.01) and indirectly (β = 0.429, P < 0.01) through self-efficacy.This study revealed that self-management among older patients with CNCDs is at a low level, and few of them use smart healthcare devices. Self-efficacy plays a partial intermediary role between e-health literacy and self-management in older patients with CNCDs. Thus, efforts to improve their CDSM by targeting e-health literacy may be more effective when considering self-efficacy. 

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Associations between e-health literacy and chronic disease self-management in older Chinese patients with chronic non-communicable diseases: a mediation analysis

http://www.thelancet.com/article/S2214109X22004582/pdf

Ensuring the right to health along the life course

Chronic diseases, because of insidious onset and long latent period, have become the major global disease burden. However, the current chronic disease diagnosis methods based on genetic markers or imaging analysis are challenging to promote completely due to high costs and cannot reach universality and popularization. This study analyzed massive data from routine blood and biochemical test of 32 448 patients and developed a novel framework for cost-effective chronic disease prediction with high accuracy (AUC 87.32%). Based on the best-performing XGBoost algorithm, 20 classification models were further constructed for 17 types of chronic diseases, including 9 types of cancers, 5 types of cardiovascular diseases and 3 types of mental illness. The highest accuracy of the model was 90.13% for cardia cancer, and the lowest was 76.38% for rectal cancer. The model interpretation with the SHAP algorithm showed that CREA, R-CV, GLU and NEUT% might be important indices to identify the most chronic diseases. PDW and R-CV are also discovered to be crucial indices in classifying the three types of chronic diseases (cardiovascular disease, cancer and mental illness). In addition, R-CV has a higher specificity for cancer, ALP for cardiovascular disease and GLU for mental illness. The association between chronic diseases was further revealed. At last, we build a user-friendly explainable machine-learning-based clinical decision support system (DisPioneer: http://bioinfor.imu.edu.cn/dispioneer) to assist in predicting, classifying and treating chronic diseases. This cost-effective work with simple blood tests will benefit more people and motivate clinical implementation and further investigation of chronic diseases prevention and surveillance program.

A computational framework of routine test data for the cost-effective chronic disease prediction

Abstract Objective Physician density is a crucial element of a well-functioning health system. Previous research has investigated factors affecting country-level physician supply. To date, however, no evidence has been provided about the patterns of convergence in physician density among countries. This paper thus tested club convergence in physician density in 204 countries worldwide from 1990 to 2019. A nonlinear time-varying factor model was adopted to identify potential clubs, wherein groups of countries tend to converge towards the same level of physician density. Our primary purpose was to document the potential long-lasting disparity in future global physician distribution. Results Despite physician density increasing in all regions globally from 1990 to 2019, we found no evidence in favor of the hypothesis of global convergence. Conversely, the clustering algorithm successfully identified three main patterns (i.e., three final clubs). With few exceptions, the results indicated an uneven physician distribution between the majority of North and Sub-Saharan African countries (where physician density would remain well below the estimated threshold of at least 70% of the Universal Health Coverage Services Index) and the rest of the world. These findings support the WHO's global strategy to reverse the chronic under-investment in human resources for health. 

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Physician density: will we ever close the gap?

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9989322

Stroke Is an Emergency, Even During a Pandemic

/pdf/assessing-performance-of-the-healthcare-access-and-quality-3bx9s62w.pdf

Assessing performance of the Healthcare Access and Quality Index, overall and by select age groups, for 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019

Jun Wang

Papers

Assessing performance of the Healthcare Access and Quality Index, overall and by select age groups, for 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019