Differences in inpatient performance of public general hospitals following implementation of a points-counting payment based on diagnosis-related group: a robust multiple interrupted time series study in Wenzhou, China.

OBJECTIVES: This study measures the differences in inpatient performance after a points-counting payment policy based on diagnosis-related group (DRG) was implemented. The point value is dynamic; its change depends on the annual DRGs' cost settlements and points of the current year, which are calculated at the beginning of the following year. DESIGN: A longitudinal study using a robust multiple interrupted time series model to evaluate service performance following policy implementation. SETTING: Twenty-two public general hospitals (8 tertiary institutions and 14 secondary institutions) in Wenzhou, China. INTERVENTION: The intervention was implemented in January 2020. OUTCOME MEASURES: The indicators were case mix index (CMI), cost per hospitalisation (CPH), average length of stay (ALOS), cost efficiency index (CEI) and time efficiency index (TEI). The study employed the means of these indicators. RESULTS: The impact of COVID-19, which reached Zhejiang Province at the end of January 2020, was temporary given rapid containment following strict control measures. After the intervention, except for the ALOS mean, the change-points for the other outcomes (p<0.05) in tertiary and secondary institutions were inconsistent. The CMI mean turned to uptrend in tertiary (p<0.01) and secondary (p<0.0001) institutions compared with before. Although the slope of the CPH mean did not change (p>0.05), the uptrend of the CEI mean in tertiary institutions alleviated (p<0.05) and further increased (p<0.05) in secondary institutions. The slopes of the ALOS and TEI mean in secondary institutions changed (p<0.05), but not in tertiary institutions (p>0.05). CONCLUSIONS: This study showed a positive effect of the DRG policy in Wenzhou, even during COVID-19. The policy can motivate public general hospitals to improve their comprehensive capacity and mitigate discrepancies in treatment expenses efficiency for similar diseases. Policymakers are interested in whether the reform successfully motivates hospitals to strengthen their internal impetus and improve their performance, and this is supported by this study.

Urban-Rural Disparities in the Association Between Home- and Community-Based Service Utilization and Levels of Disability Among Chinese Older Adults With Disabilities.

Home- and community-based services (HCBS) are optimal ways to deal with disability problems among older adults. This study aims to analyze urban-rural disparities in the relationship between HCBS utilization and levels of disability among Chinese older adults with disabilities, so as to meet the long-term care needs of them. In applying the Andersen Behavioral Model, bivariate analysis and multivariate regression models were employed using data from 843 older adults with disabilities from the 2018 China Longitudinal Aging Social Survey (CLASS). After adjusting covariates, disability levels among Chinese older adults with disabilities were significantly correlated with HCBS utilization in urban areas but not in rural areas. The urban-rural disparities may be due to the low utilization of HCBS in rural areas (only 11.2%) among older adults with disabilities compared with their urban counterparts (22.7%).

Predicting mild cognitive impairment among Chinese older adults: a longitudinal study based on long short-term memory networks and machine learning.

Background: Mild cognitive impairment (MCI) is a transitory yet reversible stage of dementia. Systematic, scientific and population-wide early screening system for MCI is lacking. This study aimed to construct prediction models using longitudinal data to identify potential MCI patients and explore its critical features among Chinese older adults. Methods: A total of 2,128 participants were selected from wave 5-8 of Chinese Longitudinal Healthy Longevity Study. Cognitive function was measured using the Chinese version of Mini-Mental State Examination. Long- short-term memory (LSTM) and three machine learning techniques, including 8 sociodemographic features and 12 health behavior and health status features, were used to predict individual risk of MCI in the next year. Performances of prediction models were evaluated through receiver operating curve and decision curve analysis. The importance of predictors in prediction models were explored using Shapley Additive explanation (SHAP) model. Results: The area under the curve values of three models were around 0.90 and decision curve analysis indicated that the net benefit of XGboost and Random Forest were approximate when threshold is lower than 0.8. SHAP models showed that age, education, respiratory disease, gastrointestinal ulcer and self-rated health are the five most important predictors of MCI. Conclusion: This screening method of MCI, combining LSTM and machine learning, successfully predicted the risk of MCI using longitudinal datasets, and enables health care providers to implement early intervention to delay the process from MCI to dementia, reducing the incidence and treatment cost of dementia ultimately.

The relationship between home and community-based healthcare services utilization and depressive symptoms in older adults in rural China: a moderated mediation model.

BACKGROUND: Studies have shown a close association between home and community-based healthcare services (HCBHS) utilization and depressive symptoms in older adults. However, no studies have explored the underlying mechanism of this relationship in rural China. This study was designed to evaluate the roles of instrumental activities of daily living (IADL) and marital status in the association between HCBHS utilization and depressive symptoms in Chinese rural older adults. METHODS: Data were obtained from the 2018 China Health and Retirement Longitudinal Study, and 5,981 rural respondents (≥ 60 years old) were included. Depression scores were calculated using the ten-item Center for Epidemiological Studies Depression Scale. Moderated mediation analysis was carried out applying Hayes' PROCESS macro (Model 7). RESULTS: HCBHS utilization had a direct and negative effect on depressive symptoms. Furthermore, marital status moderated the association between HCBHS utilization and IADL, which belonged to the indirect influence of the first half on the association between HCBHS utilization and depressive symptoms. HCBHS utilization was associated with IADL in single but not in married respondents. CONCLUSION: The results demonstrated that marital status moderated the indirect relationship between HCBHS utilization and depressive symptoms, with HCBHS utilization being negatively associated with IADL among single but not married respondents. The government should focus on rural older adults, especially those who are single and have poor IADL function, and improve the provision of HCBHS to alleviate depressive symptoms.

Demand prediction of medical services in home and community-based services for older adults in China using machine learning.

Background: Home and community-based services are considered an appropriate and crucial caring method for older adults in China. However, the research examining demand for medical services in HCBS through machine learning techniques and national representative data has not yet been carried out. This study aimed to address the absence of a complete and unified demand assessment system for home and community-based services. Methods: This was a cross-sectional study conducted on 15,312 older adults based on the Chinese Longitudinal Healthy Longevity Survey 2018. Models predicting demand were constructed using five machine-learning methods: Logistic regression, Logistic regression with LASSO regularization, Support Vector Machine, Random Forest, and Extreme Gradient Boosting (XGboost), and based on Andersen's behavioral model of health services use. Methods utilized 60% of older adults to develop the model, 20% of the samples to examine the performance of models, and the remaining 20% of cases to evaluate the robustness of the models. To investigate demand for medical services in HCBS, individual characteristics such as predisposing, enabling, need, and behavior factors constituted four combinations to determine the best model. Results: Random Forest and XGboost models produced the best results, in which both models were over 80% at specificity and produced robust results in the validation set. Andersen's behavioral model allowed for combining odds ratio and estimating the contribution of each variable of Random Forest and XGboost models. The three most critical features that affected older adults required medical services in HCBS were self-rated health, exercise, and education. Conclusion: Andersen's behavioral model combined with machine learning techniques successfully constructed a model with reasonable predictors to predict older adults who may have a higher demand for medical services in HCBS. Furthermore, the model captured their critical characteristics. This method predicting demands could be valuable for the community and managers in arranging limited primary medical resources to promote healthy aging.

Developing a Capsule Clinic-A 24-Hour Institution for Improving Primary Health Care Accessibility: Evidence From China.

Telehealth is an effective combination of medical service and intelligent technology. It can improve the problem of remote access to medical care. However, an imbalance in the allocation of health resources still occurs. People spend more time and money to access higher-quality services, which results in inequitable access to primary health care (PHC). At the same time, patients' usage of telehealth services is limited by the equipment and their own knowledge, and the PHC service suffers from low usage efficiency and lack of service supply. Therefore, improving PHC accessibility is crucial to narrowing the global health care coverage gap and maintaining health equity. In recent years, China has explored several new approaches to improve PHC accessibility. One such approach is the capsule clinic, an emerging institution that represents an upgraded version of the internet hospital. In coordination with the United Nations, the Yinzhou district of Ningbo city in Zhejiang, China, has been testing this new model since 2020. As of October 2022, the number of applications in Ningbo was 15, and the number of users reached 12,219. Unlike internet hospitals, the entire process-from diagnosis to prescription services-can be completed at the capsule clinic. The 24-hour telehealth service could also solve transportation problems and save time for users. Big data analysis can accurately identify regional populations' PHC service needs and improve efficiency in health resource allocation. The user-friendly, low-cost, and easily accessible telehealth model is of great significance. Installation of capsule clinics would improve PHC accessibility and resolve the uneven distribution of health resources to promote health equity.