Differential effects of size-specific particulate matter on frailty transitions among middle-aged and older adults in China: findings from the China Health and Retirement Longitudinal Study (CHARLS), 2015-2018.

BACKGROUND: This study aimed to assess the long-term effects of size-specific particulate matter (PM) on frailty transitions in middle-aged and older Chinese adults. METHODS: We included 13 910 participants ≥45 y of age from the China Health and Retirement Longitudinal Study (CHARLS) for 2015 and 2018 who were classified into three categories in 2015 according to their frailty states: robust, prefrail and frail. Air quality data were obtained from the National Urban Air Quality Real-time Publishing Platform. A two-level logistic regression model was used to examine the association between concentrations of PM and frailty transitions. RESULTS: At baseline, the total number of robust, prefrail and frail participants were 7516 (54.0%), 4324 (31.1%) and 2070 (14.9%), respectively. Significant associations were found between PM concentrations and frailty transitions. For each 10 µg/m3 increase in the 3-y averaged 2.5-µm PM (PM2.5) concentrations, the risk of worsening in frailty increased in robust (odds ratio [OR] 1.06 [95% confidence interval {CI} 1.01 to 1.12]) and prefrail (OR 1.07 [95% CI 1.01 to 1.13]) participants, while the probability of improvement in frailty in prefrail (OR 0.91 [95% CI 0.84 to 0.98]) participants decreased. In addition, the associations of PM10 and coarse fraction of PM with frailty transitions showed similar patterns. CONCLUSIONS: Long-term exposure to PM was associated with higher risks of worsening and lower risks of improvement in frailty among middle-aged and older adults in China.

Discovering risk patterns in people with affective disorder-induced disabilities associated with their healthcare delay.

BACKGROUND: People with affective disorder-induced disabilities (ADIDs) often experience complex needs that delay their healthcare. Discovering hidden patterns in these people for real-world use of health services is essential to improve healthcare delivery. METHODS: A cross-sectional study population (2501 adults with ADIDs) was obtained from the Australian national representative survey of disability in 2015, including 21 demographic, health and social characteristics and healthcare delay information in general practice, specialist and hospital services. The Self-Organising Map Network was used to identify hidden risk patterns associated with healthcare delay and investigate potential predictors of class memberships by means of simple visualisations. RESULTS: While experiencing disability avoidance showed across different healthcare delays, labour force appeared not to have any influence. Approximately 30% delayed their healthcare to general practice services; these were young, single females in great need of psychosocial support and aids for personal activities. Those who delayed their healthcare commonly presented a lack of social connections and a need for contact with family or friends not living in the same household. CONCLUSIONS: The pattern evidence provides an avenue to further develop integrated care strategies with better targeting of people with ADIDs, considering social participation challenges facing them, to improve health service utilisation.

A Realist Evaluation of Case Management Models for People with Complex Health Conditions Using Novel Methods and Tools-What Works, for Whom, and under What Circumstances?

Case management developed from a generalist model to a person-centred model aligned with the evidence-informed evolution of best practice people-centred integrated care. Case management is a multidimensional and collaborative integrated care strategy where the case manager performs a set of interventions/actions to support the person with a complex health condition to progress in their recovery pathway and participate in life roles. It is currently unknown what case management model works in real life for whom and under what circumstances. The purpose of this study was to answer these questions. The study methods used realistic evaluation framework, examined the patterns and associations between case manager actions (mechanisms), the person's characteristics and environment (context), and recovery (outcomes) over 10 years post severe injury. There was mixed methods secondary analysis of data extracted via in-depth retrospective file reviews (n = 107). We used international frameworks and a novel approach with multi-layered analysis including machine learning and expert guidance for pattern identification. The study results confirm that when provided, a person-centred case management model contributes to and enhances the person's recovery and progress towards participation in life roles and maintaining well-being after severe injury.Furthermore, the intensity of case management for people with traumatic brain injury, and the person-centred actions of advising, emotional and motivational support, and proactive coordination contribute to the person achieving their goals. The results provide learnings for case management services on the case management models, for quality appraisal, service planning, and informs further research on case management.

Experts' perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study.

BACKGROUND: Health experts including planners and policy-makers face complex decisions in diverse and constantly changing healthcare systems. Visual analytics may play a critical role in supporting analysis of complex healthcare data and decision-making. The purpose of this study was to examine the real-world experience that experts in mental healthcare planning have with visual analytics tools, investigate how well current visualisation techniques meet their needs, and suggest priorities for the future development of visual analytics tools of practical benefit to mental healthcare policy and decision-making. METHODS: Health expert experience was assessed by an online exploratory survey consisting of a mix of multiple choice and open-ended questions. Health experts were sampled from an international pool of policy-makers, health agency directors, and researchers with extensive and direct experience of using visual analytics tools for complex mental healthcare systems planning. We invited them to the survey, and the experts' responses were analysed using statistical and text mining approaches. RESULTS: The forty respondents who took part in the study recognised the complexity of healthcare systems data, but had most experience with and preference for relatively simple and familiar visualisations such as bar charts, scatter plots, and geographical maps. Sixty-five percent rated visual analytics as important to their field for evidence-informed decision-making processes. Fifty-five percent indicated that more advanced visual analytics tools were needed for their data analysis, and 67.5% stated their willingness to learn new tools. This was reflected in text mining and qualitative synthesis of open-ended responses. CONCLUSIONS: This exploratory research provides readers with the first self-report insight into expert experience with visual analytics in mental healthcare systems research and policy. In spite of the awareness of their importance for complex healthcare planning, the majority of experts use simple, readily available visualisation tools. We conclude that co-creation and co-development strategies will be required to support advanced visual analytics tools and skills, which will become essential in the future of healthcare.

Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning.

BACKGROUND: Decision-making in mental health systems should be supported by the evidence-informed knowledge transfer of data. Since mental health systems are inherently complex, involving interactions between its structures, processes and outcomes, decision support systems (DSS) need to be developed using advanced computational methods and visual tools to allow full system analysis, whilst incorporating domain experts in the analysis process. In this study, we use a DSS model developed for interactive data mining and domain expert collaboration in the analysis of complex mental health systems to improve system knowledge and evidence-informed policy planning. METHODS: We combine an interactive visual data mining approach, the self-organising map network (SOMNet), with an operational expert knowledge approach, expert-based collaborative analysis (EbCA), to develop a DSS model. The SOMNet was applied to the analysis of healthcare patterns and indicators of three different regional mental health systems in Spain, comprising 106 small catchment areas and providing healthcare for over 9 million inhabitants. Based on the EbCA, the domain experts in the development team guided and evaluated the analytical processes and results. Another group of 13 domain experts in mental health systems planning and research evaluated the model based on the analytical information of the SOMNet approach for processing information and discovering knowledge in a real-world context. Through the evaluation, the domain experts assessed the feasibility and technology readiness level (TRL) of the DSS model. RESULTS: The SOMNet, combined with the EbCA, effectively processed evidence-based information when analysing system outliers, explaining global and local patterns, and refining key performance indicators with their analytical interpretations. The evaluation results showed that the DSS model was feasible by the domain experts and reached level 7 of the TRL (system prototype demonstration in operational environment). CONCLUSIONS: This study supports the benefits of combining health systems engineering (SOMNet) and expert knowledge (EbCA) to analyse the complexity of health systems research. The use of the SOMNet approach contributes to the demonstration of DSS for mental health planning in practice.

Enhanced anaerobic gas production of waste activated sludge pretreated by pulse power technique.

An electric pulse-power reactor consisting of one coaxial electrode and multiple ring electrodes was developed to solubilize waste activated sludge (WAS) prior to anaerobic digestion. By pretreatment of WAS, the soluble chemical oxygen demand (SCOD)/total chemical oxygen demand (TCOD) ratio and exocelluar polymers (ECP) content of WAS increased 4.5 times and 6.5 times, respectively. SEM images clearly showed that pulse-power pretreatment of WAS was found to result in destruction of sludge cells. Batch-anaerobic digestion of pulse-power treated sludge showed 2.5 times higher gas production than that of untreated sludge. Solubilized sludge cells by pulse-power pretreatment would be readily utilized for anaerobic microorganisms to produce anaerobically-digested gas. Slow or lagged gas production in the initial anaerobic digestion stage of pulse-power pretreated sludge implied that the methane-forming stage of anaerobic digestion would be the rate-limiting step for anaerobic digestion of pulse-power pretreated sludge.