Risk Management and Healthcare Policy最新文献

Background: Improving health-related quality of life (HRQoL) among hypertensive individuals has emerged as a significant public health issue. However, current research has ignored the individual heterogeneity of perceived social support (PSS) among hypertensive patients. The potential mechanism of delay discounting (DD), living alone, and PSS on HRQoL remains unclear, and further exploration is required.

Aim: This study aimed to ascertain PSS profiles among hypertensive patients and examine the hypotheses that DD mediates the relationship between PSS and HRQoL and that this mediating process is moderated by living alone in hypertensive patients.

Methods: A cross-sectional study was carried out in Jiangsu, China. In total, 1815 hypertensive patients completed socio-demographic and HRQoL questionnaires, a PSS scale, and a DD task. Data analyses included a latent profile analysis, χ²-test, Spearman correlation analysis, and PROCESS macro for regression analysis.

Results: Four potential PSS profiles were identified: lowest (3.2%), moderate-low (26.6%), moderate-high (42.4%), and highest (27.8%). DD mediated the association between PSS and HRQoL. The first half of this mediating process was moderated by living alone.

Conclusion: Our findings indicated that PSS, DD, and living alone significantly influence the HRQoL of individuals with hypertension. Healthcare professionals should consider variations in PSS among hypertensive patients and implement interventions to reduce DD by enhancing PSS, in order to improve the HRQoL of this population.

Purpose: Depression is a major concern in maintenance hemodialysis. However, given the elusive nature of its risk factors and the redundant nature of existing assessment forms for judging depression, further research is necessary. Therefore, this study was devoted to exploring the risk factors for depression in maintenance hemodialysis patients and to developing and validating a predictive model for assessing depression in maintenance hemodialysis patients.

Patients and methods: This cross-sectional study was conducted from May 2022 to December 2022, and we recruited maintenance hemodialysis patients from a multicentre hemodialysis centre. Risk factors were identified by Lasso regression analysis and a Nomogram model was developed to predict depressed patients on maintenance hemodialysis. The predictive accuracy of the model was assessed by ROC curves, area under the ROC (AUC), consistency index (C-index), and calibration curves, and its applicability in clinical practice was evaluated using decision curves (DCA).

Results: A total of 175 maintenance hemodialysis patients were included in this study, and cases were randomised into a training set of 148 and a validation set of 27 (split ratio 8.5:1.5), with a depression prevalence of 29.1%. Based on age, employment, albumin, and blood uric acid, a predictive map of depression was created, and in the training set, the nomogram had an AUC of 0.7918, a sensitivity of 61.9%, and a specificity of 89.2%. In the validation set, the nomogram had an AUC of 0.810, a sensitivity of 100%, and a specificity of 61.1%. The bootstrap-based internal validation showed a c-index of 0.792, while the calibration curve showed a strong correlation between actual and predicted depression risk. Decision curve analysis (DCA) results indicated that the predictive model was clinically useful.

Conclusion: The nomogram constructed in this study can be used to identify depression conditions in vulnerable groups quickly, practically and reliably.

Background: Improving overall and individual health literacy is a major focus of national initiatives in China and similar initiatives globally. However, few studies have examined the identification and improvement of individual health literacy levels, especially among patients.

Purpose: To develop an interpretable method with decision rules to assess the health literacy levels of male patients and identify key factors influencing health literacy levels.

Methods: Using a convenience sampling method, we conducted on-site surveys with 212 male patients of a hospital in China from July 2020 to September 2020. The questionnaire was developed by the Ministry of Health of the People's Republic of China. A total of 206 of the completed surveys were ultimately included for analyses in this study. The rough set theory was used to identify conditional attributes (ie, key factors) and decision attributes (ie, levels of health literacy) and to establish decision rules between them. These rules specifically describe how different combinations of conditional attributes can affect health literacy levels among men.

Results: Basic knowledge, concepts, and health skills are important in identifying whether male patients have health literacy. Health skills, scientific health concepts, healthy lifestyles and behaviors, infectious disease prevention and control literacy, basic medical literacy, and health information literacy can be identified as cognitive behaviors with varying degrees of health literacy among patients.

Conclusion: This model can effectively identify the key factors and decision rules for male patients' health literacy. Simultaneously, it can be applied to clinical nursing practice, making it easier for hospitals to guide male patients to improve their health literacy.

Purpose: Drug shortages directly affect the final stage in the pharmaceutical supply chain, prescription fulfillment in community pharmacies (CPs). This study investigated the current state of drug shortages, their resolution, and influencing factors within CPs.

Methods: A cross-sectional online survey was conducted among pharmacists working at pharmacies in Seoul between 7 and 31 October 2022. The survey gathered data on pharmacies and pharmacists' characteristics, drug distribution, information, communication, and administrative practices. Logistic regression was used to identify the factors influencing these rates. Regression results are presented as odds ratios (OR) and 95% confidence intervals (CIs).

Results: Of the 1200 pharmacists approached, 713 participated, yielding a response rate of 59.4%. After excluding incomplete responses, data from 671 respondents were analyzed. Pharmacies with higher prescription drug sales demonstrated a lower OR for drug shortages (OR=0.66, 95% CI=0.60-0.72) compared to those with lower sales volumes. Resolution rates were significantly higher when pharmacies were located near clinics (OR=3.30, 95% CI=2.3-4.74) and general hospitals (OR=3.45, 95% CI=2.35-5.07) compared to those without nearby medical facilities. Additionally, good communication with prescribers increased the resolution rates (OR=1.46, 95% CI=1.26-1.69).

Conclusion: This study examines the influence of pharmacy purchasing power on drug shortages, identifying proximity to healthcare facilities and communication with prescribers as factors affecting the resolution rates. These findings provide valuable insights for pharmacists, policymakers, and future researchers to optimize drug supply chain management and mitigate shortages in community settings.

Purpose: The use of multi-source precursor data to predict the epidemic risk level would aid in the early and timely identification of the epidemic risk of infectious diseases. To achieve this, a new comprehensive big data fusion assessment method must be developed.

Methods: With the help of the Functional Resonance Analysis Method (FRAM) model, this paper proposes a risk portrait for the whole process of a pandemic spreading. Using medical, human behaviour, internet and geo-meteorological data, a hierarchical multi-source dataset was developed with three function module tags, ie, Basic Risk Factors (BRF), the Spread of Epidemic Threats (SET) and Risk Influencing Factors (RIF).

Results: Using the dynamic functional network diagram of the risk assessment functional module, the FRAM portrait was applied to pandemic case analysis in Wuhan in 2020. This new-format FRAM portrait model offers a potential early and rapid risk assessment method that could be applied in future acute public health events.

Purpose: The aim of this study was to analyze hospital-discharged acute myocardial infarction (AMI) patients in Korea (2006-2020) to understand how pre-existing conditions affect mortality rates.

Participants and methods: This study utilized the 2006-2020 Korean National Hospital Discharge In-depth Injury Survey data. A weighted frequency analysis estimated discharged AMI patients and calculated age-standardized discharge and mortality rates, Charlson Comorbidity Index (CCI) score distribution, and general patient characteristics. Weighted logistic regression analysis examined influencing mortality factors.

Results: There were 486,464 AMI patients (143,751 female), with AMI-related mortality rates at 7.5% (36,312): 5.7% for males (19,190) and 11.8% for females (17,122). The highest mortality rate was among individuals aged 70-79 years (25%). Factors influencing mortality included sex, insurance type, admission route, hospital bed count, region, operation status, and CCI score. Mortality risk at discharge was 1.151 times higher in females than males (95% CI: 1.002-1.322), 0.787 times lower among those with national health insurance than Medicaid recipients (95% CI 0.64-0.967), 2.182 times higher among those admitted via the emergency department than the outpatient department (95% CI 1.747-2.725), and 3.402 times higher in patients with a CCI score of 3 points than those with 0 points (95% CI 1.263-9.162).

Conclusion: The number of discharged AMI patients and related mortality rates increased, underscoring the need for proactive management of chronic diseases, particularly for those with higher CCI scores.

Purpose: To control medical costs and regulate the behavior of providers, China has formed an original widely piloted case-based payment under the regional global budget, called the Diagnosis-Intervention Packet (DIP). This study aimed to evaluated the impact of the DIP payment reform on medical costs, quality of care, and medical service capacity in a less-developed pilot city in Northwest China.

Patients and methods: We used the de-identified case-level discharge data of hospitalized patients from January 2021 to June 2022 in pilot and control cities located in the same province. We performed difference-in-differences (DID) analysis to examine the differential impact of the DIP reform for the entire sample and between secondary and tertiary hospitals.

Results: The DIP payment reform resulted in a significant decrease of total expenditure per case in the entire sample (5.5%, P < 0.01) and tertiary hospitals (9.3%, P < 0.01). In-hospital mortality rate decreased significantly in tertiary hospitals (negligible in size, P < 0.05), as did all-cause readmission rate within 30 days in the entire sample (1.1 percentage points, P < 0.01) and secondary hospitals (1.4 percentage points, P < 0.01). Proportion of severe patients increased significantly in the entire sample (1.2 percentage points, P < 0.05) and tertiary hospitals (2.5 percentage points, P < 0.01). We did not find the DIP reform was associated with a significant change in relative weight per case.

Conclusion: The DIP payment reform in the less-developed pilot city achieved short-term success in controlling medical costs without sacrificing the quality of care for the entire sample. Compared with secondary hospitals, tertiary hospitals experienced a greater decline in medical costs and received more severe patients. These findings hold lessons for less developed countries or areas to implement case-based payments and remind them of the variations between different levels of hospitals.