Multiple Conditions

General practices are facing challenges such as rising patient demand and difficulties recruiting and retaining general practitioners. Greater use of digital technology has been advocated as a way of mitigating some of these challenges and improving patient access. This includes Digital First Primary Care, when a patient's first contact with primary care is through a digital route, either through a laptop or smartphone. The use of Digital First Primary Care has been expedited since COVID-19. There is little evidence of staff experiences of using Digital First Primary Care with more complex patients, such as those with multiple long-term conditions. To understand the experiences of those with multiple long-term conditions of Digital First Primary Care from the perspectives of healthcare professionals and stakeholders. This was a qualitative evaluation, comprised of four distinct work packages: Work package 1: Locating the study within the wider context, engaging with literature, and co-designing the study approach and research questions with patients. Work package 2: Interviews with health professionals working across general practice and key expert topic stakeholders, including academics and policy-makers. Work package 3: Analysis of data and generation of themes, and testing findings with patients. Work package 4: Synthesis, reporting and dissemination. The study commenced in January 2021 and in total 28 interviews were conducted with 14 health professionals and 15 stakeholders between January and August 2022. From the perspective of health professionals, Digital First Primary Care approaches could enable patients to speak with a clinician more quickly than traditional approaches. Those with multiple long-term conditions could submit healthcare readings from home, though health professionals felt patients may struggle navigating digital systems not designed to capture the nuances associated with living with multiple conditions. Clinicians expressed preferences for seeing patients face-to-face, particularly those with multiple long-term conditions, to identify non-verbal cues about a patient's health. Digital First Primary Care approaches provided an opportunity for clinicians to engage with the carers of patients living with multiple long-term conditions, yet there were concerns around obtaining consent and confidentiality. There remain debates among stakeholders about the nature and extent to which Digital First Primary Care impacts on staff workload. At the time of data collection, general practices were facing considerable pressure to deliver care and respond to the COVID-19 pandemic. While it was originally intended that the study would include interviews with patients with multiple long-term conditions and their carers, none of the general practices that took part in the study were willing and/or able to recruit patients and carers in the time available. The rapid implementation of Digital First Primary Care, at a time of immense pressures, meant there has been little time for considering the impact on patients, including those with multiple long-term conditions. The impacts on care continuity depended largely on how surgeries implemented their approaches. Staff and stakeholders felt that Digital First Primary Care, as an additional route for accessing primary care, could be useful for patients with multiple long-term conditions but not at the expense of face-to-face consultations. Future research obtaining patient and carer views of digital-first approaches, understanding the impacts on carers and how approaches are designed with patients with more complex conditions in mind, is essential. This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 16/138/31) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 21. See the NIHR Funding and Awards website for further award information.

To improve the performance of vehicle-grid system under multiple operation conditions and suppress the low-frequency oscillation (LFO) in electrified railways, this paper proposed a control strategy of electric multiple units (EMUs) traction line-side converter (LSC) based on sliding mode control (SMC). First, the accurate model of EMUs is established and then the mathematical model of it in the d-q frame is derived. Then, the application of sliding mode variable structure control strategy in the LSC of EMUs is presented in detail, which includes the design of outer loop voltage sliding mode surface and inner loop current control law. Moreover, the control performance for EMUs based on SMC and traditional linear proportional integral (PI) control is compared under multiple operation conditions in the case of one traction drive unit of EMUs and multi-EMUs, respectively. Finally, experiments are implemented on the hardware in the loop simulation platform based on software StarSim. The experiments results show that, compared with PI control, SMC owns better control performance, such as the lower total harmonic distortion of traction line-side current under multiple conditions, lower voltage fluctuation when braking occurs, better anti-interference ability with the change of system parameters, and can effectively suppress the LFO occurs under operation mode.

O measure of progress in reducing negative attitudes toward people with mental illness is the extent to which members of the general public who report having a mental health condition seek specialist care. To examine this issue more closely, we analyzed adult responses to the 2010 edition of the HealthStyles Survey. The survey is a large, private proprietary, national mail panel marketing survey conducted annually to assess attitudes and beliefs about chronic and infectious disease and behaviors, exposure to health information and to health communication campaigns, and self-reported symptoms, risk factors, diseases, and disorders (1). The survey is designed to be representative of the U.S. population, and the 2010 survey data were weighted to match the 2009 Current Population Survey estimates for age, sex, marital status, education, and race-ethnicity. The 2010 survey was mailed to 6,255 respondents; the response rate was 67% (4,184 individuals age 18 and over). Most respondents agreed with statements that a person with mental illness would improve if given treatment and support (68%) and that treatment can help people with mental illness lead a normal life (65%). A subset of respondents (24%) self-reported having one or more of eight listed mental health conditions in the past year. Among those with a self-reported mental health condition, approximately 42% reported having more than one mental health condition. For those with either a single condition or multiple conditions, depression and anxiety were the most prevalent, accounting for almost 80% of all conditions in both groups. Figure 1 presents data about respondents with one or more self-reported mental health conditions who reported visiting a mental health specialty provider in the previous year and the extent of these visits. Only 17% of those with one self-reported condition and 46% of those with multiple self-reported conditions saw a mental health provider during the previous year. Further, among those who visited a mental health provider, most made fewer than six visits. Among those with a single condition who sought treatment, 79% reported six or fewer visits. Among those with multiple conditions, 58% reported six or fewer visits. Reported Mental Health Specialty Care in the 2010 HealthStyles Survey

Over the past few years there has been an explosion of interest in the concept of multimorbidity, the existence of multiple longterm conditions in one individual. A search of Medline for papers on multimorbidity or the related term comorbidity reveals that the number of publications on this topic has increased more than twenty fold in the past 20 years (Figure 1). Figure 1 Number of publications on multimorbidity or comorbidity by year. This level of interest of multimorbidity reflects a growing tension between two opposing trends. Medicine is increasingly focused on helping people to manage longterm conditions rather than treating acute illness, and many people have multiple longterm conditions, and yet medical services are becoming ever more specialised and fragmented. Attention has been paid to improving vertical integration across primary and secondary care using disease pathways for individual conditions such as diabetes or heart failure, but this can be at the expense of holistic coordinated care for people with multiple conditions. Thanks to all the research over the past two decades we have learnt a great deal about the epidemiology of multimorbidity, the consequences for patients and healthcare professionals of designing systems based on a single disease paradigm, and the adverse impact of multimorbidity on health. We now understand that multimorbidity is extremely common,1 and is particularly prevalent in deprived areas.2 Qualitative studies have shown that providing care for one disease at a time can be inconvenient, inefficient, and unsatisfactory, both for patients with multimorbidity and for healthcare professionals.3,– …

Clinicians are caring for an increasing number of older patients with multiple diseases in the face of uncertainty concerning the benefits and harms associated with guideline-directed interventions. Understanding how primary care clinicians approach treatment decision making for these patients is critical to the design of interventions to improve the decision-making process. Focus groups were conducted with 40 primary care clinicians (physicians, nurse practitioners, and physician assistants) in academic, community, and Veterans Affairs-affiliated primary care practices. Participants were given open-ended questions about their approach to treatment decision making for older persons with multiple medical conditions. Responses were organized into themes using qualitative content analysis. The participants were concerned about their patients' ability to adhere to complex regimens derived from guideline-directed care. There was variability in beliefs regarding, and approaches to balancing, the benefits and harms of guideline-directed care. There was also variability regarding how the participants involved patients in the process of decision making, with clinicians describing conflicts between their own and their patients' goals. The participants listed a number of barriers to making good treatment decisions, including the lack of outcome data, the role of specialists, patient and family expectations, and insufficient time and reimbursement. The experiences of practicing clinicians suggest that they struggle with the uncertainties of applying disease-specific guidelines to their older patients with multiple conditions. To improve decision making, they need more data, alternative guidelines, approaches to reconciling their own and their patients' priorities, the support of their subspecialist colleagues, and an altered reimbursement system.

To evaluate how UK guidelines for individual health conditions consider coexisting multiple long term conditions and to propose improvements to guideline development processes so that guidelines appropriately account for and consider coexisting multiple long term conditions. Analysis and recommendations from review of single condition guidelines. Clinical guidelines developed by the National Institute for Health and Care Excellence (NICE), UK, 1 January 2013 to 31 December 2024. 56 clinical guidelines developed by NICE covering a broad range of long term conditions. The extent to which guideline recommendations consider multiple long term conditions and coexisting conditions, distinguishing between concordant conditions (those affecting the same organ system as the index condition) and discordant conditions (those affecting different systems). All but one of the NICE guidelines (n=55, 98%) included some advice on managing the index condition in the presence of coexisting conditions, and 50 (89%) guidelines offered general guidance on tailoring care. Only 11 (20%) guidelines, however, explicitly referred to multiple long term conditions, and none included a dedicated section on multiple long term conditions or how care should be adapted in this context. 19 (34%) guidelines featured sections looking at specific coexisting conditions or coexisting conditions generally. Coverage of coexisting conditions varied widely across categories of conditions, with mental health guidelines dealing with the most coexisting conditions (median 10, interquartile range (IQR) 4.5-14.75) in contrast with guidelines on cancer and eye disease covering the fewest conditions (median 3, 1-4.5; median 3, 2.25-2.75, respectively). Of the 397 possible concordant pairings, 120 (30%) were referenced, whereas of the 3859 possible discordant pairings, 259 (7%) were referenced, indicating greater coverage of same system combinations. Data on the composition of guideline committees showed wide variation in size, disciplinary diversity, inclusion of generalist clinicians (eg, general practitioners, general physicians, or others with no single specialty focus), and public contributors, although lived experience of multiple long term conditions was rarely specified. Despite widespread acknowledgement of coexisting or multiple long term conditions, NICE guidelines are predominantly condition specific and offer limited tailored support for the care of multiple long term conditions. Recommendations rarely considered common condition clusters or the cumulative effect of multiple long term conditions. Structured improvements, such as clearer guidance on adapting care, broader cross condition referencing, and more transparent inclusion of lived experience could enhance the relevance and usability of guidelines for clinicians managing patients with multiple long term conditions.

The objective of this study was to assess the impact of health-related quality of life (HRQOL) across prevalent chronic conditions, individually and comorbid, in school-aged children in the Netherlands. 5301 children aged 4–11 years from the Dutch Health Interview Survey were included. Parents completed questionnaires regarding child and parental characteristics. HRQOL of children was measured using the Child Health Questionnaire Parent Form 28 (CHQ-PF28). Independent-t tests were used to assess differences in the mean scores of the CHQ-PF28 summary scales and profile scales between children with a prevalent chronic condition (excluding or including children with multiple chronic conditions) and children without a chronic condition. Cohen’s effect sizes (d) were calculated to assess the clinical significance of difference. The mean age of children was 7.55 (SD 2.30) years; 50.0% were boys. In children without any chronic condition, the mean score of physical summary scale (PhS) was 58.53 (SD 4.28) and mean score of the psychosocial summary scale (PsS) was 53.86 (SD 5.87). Generally, PhS and/or PsS scores in children with only one condition were lower (p<0.05) than for children without chronic conditions. When children with multiple conditions were included, mean scores of CHQ-PF28 summary and profile scales were generally lower than when they were excluded. The present study shows important information regarding the impact of prevalent chronic conditions on HRQOL in a representative population-based sample of school-aged children in the Netherlands. The information could be used for developing a more holistic approach to patient care and a surveillance framework for health promotion.

Few studies have assessed the effect of multiple health conditions among patients with heart disease, particularly the economic implications of having multiple conditions. This study used a microsimulation model, Health&WealthMOD, to assess the effect of comorbidities on the labor force participation of 45-64-year-old Australians with heart disease, and the indirect economic costs to these individuals and government. For most comorbid conditions, there is a significant increase in the chance of an individual being out of the labor force, relative to those with heart disease alone. For example, individuals with heart disease and arthritis have more than 6-fold the odds of being out of the labor force relative to those with heart disease alone (OR 6.64, 95% CI: 2.46-17.95). People with heart disease and ≥1 comorbidities also receive a significantly lower income, pay less in taxation and receive more in government transfer payments than those with heart disease alone. It is important to consider whether an individual with heart disease also has other health conditions, as individuals with comorbidities have inferior financial situations and are a greater burden on government finances than those with only heart disease. (Circ J 2014; 78: 644-648).

To examine what influences self-management priorities for individuals with multiple long-term conditions and how this changes over time. A longitudinal qualitative study using semi-structured interviews completed with 21 participants with more than one chronic condition. The study demonstrates the impact of multiple conditions on many aspects of people's illness management. Narratives illuminated how individual's condition priorities changed at pivotal points and altered their engagement with self-management practices. This is influenced by contact with health professionals and how people framed illness and lifestyle changes. Medication management was a central point where individuals took control of their conditions. Additional conditions were more readily accommodated if people established a cognitive link between existing management practices. Thus, multiple conditions were not inevitably experienced as an increasing burden but subject to considerable flux and change. Prioritizing one condition over another at a particular time together with a transfer and amalgamation of practices appears to facilitate accommodation of multiple conditions and ease the burden of everyday management. These findings have implications for how we understand the variable nature of multimorbidity experience and management for patients. Clinicians might usefully engage with patients' understanding to reduce complexity, and enhance engagement of condition management.

In the industrial production process, the remaining useful life (RUL) of the machine part is the key factor to determine the product quality, so it is important to predict the RUL of the machine part for industrial manufacturing. With the development of intelligent manufacturing, data-driven RUL prediction has become very popular. When the training dataset and the test dataset are distributed similarly, the traditional machine learning prediction method is very effective. However, in actual production, the operation conditions of the machine part used for training and testing may be different, resulting in different distribution of data sets. In this paper, we propose a domain adaptive SAE-LSTM (DASL) model for RUL prediction of the machine part to solve this problem. The DASL model contains sparse autoencoder (SAE) and Long Short-Term Memory (LSTM) with domain adaptive mechanism. The latent features extracted by SAE from source dataset and target dataset are transformed to reproducing kernel Hilbert space (RKHS) and the distribution discrepancy is reduced by using maximum mean discrepancy (MMD). Then the latent features are input into the LSTM to predict the RUL. What is more, the case where both source data and target data are data containing multiple conditions is also considered. The proposed model is tested on Foxconn tool wear dataset and PHM Challenging 2012 dataset. The results show that the method has a better improvement. In most experiments, this method outperforms other state-of-arts' methods.

Aiming at the current challenges in converting electrical parameters to indicator diagrams, a subspace-aided indicator diagram estimation approach is proposed to establish a data-driven mapping model from electrical to force parameters, which helps avoid the need for analyzing the mechanism model of tower-type pumping systems. Specifically, the lifting technique is adopted based on the subspace method to construct the space of the electrical parameter signals, addressing the correspondence between input and output signals, while preventing the loss of effective information. Then, a recursive indicator diagram estimation approach is proposed, utilizing the updating/downdating of the Cholesky decomposition to enable online updating of the data-driven mapping model. In addition, for tower-type pumping systems operating under multiple conditions, a gap metric indicator is developed as a test statistic to determine the switching of operating conditions. The effectiveness of the proposed methods is verified through experimental measurements from tower-type pumping systems in actual oil wells.

Liquid chromatography is a cornerstone analytical technique for separating, quantifying, and purifying components from complex mixtures. To accelerate method screening, we herein introduce an uncertainty-aware graph-based neural network that predicts retention times across multiple column chemistries and buffer pH conditions. Specifically, the multiple condition retention time model, MC-Retention, incorporates explicit column and buffer descriptors and is trained on a newly assembled data set produced by screening 480 analytes under eight chromatography conditions. By being able to accurately model multiple chromatographic methods simultaneously, MC-Retention can substantially reduce the time and resources required for LC method screening and development. With an aggregate R2 of 0.86 and a mean absolute error (MAE) of 15.5 s during cross-validation, MC-Retention's respectable performance is greatly enhanced for analytes made present during training for one of the four column chemistries, when those analytes are otherwise not associated with the columns being validated. This selective training of analytes for a single column chemistry reduces error by 50%, with an R2 of 0.95 and MAE of 8.2 s in aggregate. Additionally, we demonstrate that MC-Retention effectively identifies optimal conditions for separating amide bond-forming reaction components, while also supplying calibrated uncertainty estimates for its retention time predictions.

Temporal gene expression data contain ample information to characterize gene function and are now widely used in bio-medical research. A dense temporal gene expression usually shows various patterns in expression levels under different biological conditions. The existing literature investigates the gene trajectory using the mean function. However, temporal gene expression curves usually show a strong degree of heterogeneity under multiple conditions. As a result, rates of change for gene expressions may be different in non-central locations and a mean function model may not capture the non-central location of the gene expression distribution. Further, the mean regression model depends on the normality assumptions of the error terms of the model, which may be impractical when analyzing gene expression data. In this research, a linear quantile mixed model is used to find the trajectory of gene expression data. This method enables the changes in gene expression over time to be studied by estimating a family of quantile functions. A statistical test is proposed to test the similarity between two different gene expressions based on estimated parameters using a quantile model. Then, the performance of the proposed test statistic is examined using extensive simulation studies. Simulation studies demonstrate the good statistical performance of this proposed test statistic and show that this method is robust against normal error assumptions. As an illustration, the proposed method is applied to analyze a dataset of 18 genes in P. aeruginosa, expressed in 24 biological conditions. Furthermore, a minimum Mahalanobis distance is used to find the clustering tree for gene expressions.

Global Pneumococcal Disease and Policies for Control

IntroductionOne in five pregnant women has multiple pre-existing long-term conditions in the UK. Studies have shown that maternal multiple long-term conditions are associated with adverse outcomes. This observational study aims...