RESUMEN
While modelling and simulation are powerful techniques for exploring complex phenomena, if they are not coupled with suitable real-world data any results obtained are likely to require extensive validation. We consider this problem in the context of search game modelling, and suggest that both demographic and behaviour data are used to configure certain model parameters. We show this integration in practice by using a combined dataset of over 150,000 individuals to configure a specific search game model that captures the environment, population, interventions and individual behaviours relating to winter health service pressures. The presence of this data enables us to more accurately explore the potential impact of service pressure interventions, which we do across 33,000 simulations using a computational version of the model. We find government advice to be the best-performing intervention in simulation, in respect of improved health, reduced health inequalities, and thus reduced pressure on health service utilisation.
RESUMEN
BACKGROUND: Carers of people who are involuntarily admitted to hospital report feeling isolated and unsupported by services. The Independent Review of the Mental Health Act (MHA) recommended that carers be supported. However, no research has directly explored what type of support carers would find most helpful when a relative/friend is involuntary admitted. AIMS: To explore carers' experiences and views around the support they want to receive when their relative/friend is involuntarily admitted under the MHA. METHOD: A total of 22 one-to-one interviews with carers were conducted online at three sites across England. Audio recordings of the interviews were transcribed, and data were analysed with thematic analysis. RESULTS: Four main themes were identified: (a) heterogeneity in the current support for carers, (b) information about mental health and mental health services, (c) continuous support, and (d) peer support and guidance. Carers reported receiving support from professionals, peers and relatives, but this was unstructured, and the extent of support varied across carers. Carers reported wanting more information about mental health services, and for this information to be consistent. Carers also reported wanting emotional support from a single, continuous person, helping them establish a more personal and sincere connection. Peers were also identified as important in the provision of carer support, allowing carers to feel reassured and understood in their experience. CONCLUSIONS: The support received by carers is currently unstructured. To meet the MHA review recommendations, carers of patients who are involuntarily admitted should be allocated a named contact person, ideally with lived experience, to offer information and personal continuity of support.
RESUMEN
BACKGROUND: Digital health interventions have become increasingly common across health care, both before and during the COVID-19 pandemic. Health inequalities, particularly with respect to ethnicity, may not be considered in frameworks that address the implementation of digital health interventions. We considered frameworks to include any models, theories, or taxonomies that describe or predict implementation, uptake, and use of digital health interventions. OBJECTIVE: We aimed to assess how health inequalities are addressed in frameworks relevant to the implementation, uptake, and use of digital health interventions; health and ethnic inequalities; and interventions for cardiometabolic disease. METHODS: SCOPUS, PubMed, EMBASE, Google Scholar, and gray literature were searched to identify papers on frameworks relevant to the implementation, uptake, and use of digital health interventions; ethnically or culturally diverse populations and health inequalities; and interventions for cardiometabolic disease. We assessed the extent to which frameworks address health inequalities, specifically ethnic inequalities; explored how they were addressed; and developed recommendations for good practice. RESULTS: Of 58 relevant papers, 22 (38%) included frameworks that referred to health inequalities. Inequalities were conceptualized as society-level, system-level, intervention-level, and individual. Only 5 frameworks considered all levels. Three frameworks considered how digital health interventions might interact with or exacerbate existing health inequalities, and 3 considered the process of health technology implementation, uptake, and use and suggested opportunities to improve equity in digital health. When ethnicity was considered, it was often within the broader concepts of social determinants of health. Only 3 frameworks explicitly addressed ethnicity: one focused on culturally tailoring digital health interventions, and 2 were applied to management of cardiometabolic disease. CONCLUSIONS: Existing frameworks evaluate implementation, uptake, and use of digital health interventions, but to consider factors related to ethnicity, it is necessary to look across frameworks. We have developed a visual guide of the key constructs across the 4 potential levels of action for digital health inequalities, which can be used to support future research and inform digital health policies.
