Trends in and drivers of Healthcare Expenditure in the English NHS : a retrospective analysis

Background: In England, rises in healthcare expenditure consistently outpace growth in both GDP and total public expenditure. To ensure the National Health Service (NHS) remains financially sustainable, relevant data on healthcare expenditure are needed to inform decisions about which services should be delivered, by whom and in which settings. Methods: We analyse routine data on NHS expenditure in England over 9 years (2008/09 to 2016/17). To quantify the relative contribution of the different care settings to overall healthcare expenditure, we analyse trends in 14 healthcare settings under three broad categories: Hospital Based Care (HBC), Diagnostics and Therapeutics (D&T) and Community Care (CC). We exclude primary care and community mental health services settings due to a lack of consistent data. We employ a set of indices to aggregate diverse outputs and to disentangle growth in healthcare expenditure that is driven by activity from that due to cost pressures. We identify potential drivers of the observed trends from published studies. Results: Over the 9-year study period, combined NHS expenditure on HBC, D&T and CC rose by 50.2%. Expenditure on HBC rose by 54.1%, corresponding to increases in both activity (29.2%) and cost (15.7%). Rises in expenditure in inpatient (38.5%), outpatient (57.2%), and A&E (59.5%) settings were driven predominately by higher activity. Emergency admissions rose for both short-stay (45.6%) and long-stay cases (26.2%). There was a switch away from inpatient elective care (which fell by 5.1%) and towards day case care (34.8% rise), likely reflecting financial incentives for same-day discharges. Growth in expenditure on D&T (155.2%) was driven by rises in the volume of high cost drugs (270.5%) and chemotherapy (110.2%). Community prescribing grew by 45.2%, with costs falling by 24.4%. Evidence on the relationship between new technologies and healthcare expenditure is mixed, but the fall in drug costs could reflect low generic prices, and the use of health technology assessment or commercial arrangements to inform pricing of new medicines

Using Bayesian optimization and wavelet decomposition in GPU for arterial blood pressure estimation

Continuous monitoring of arterial blood pressure (ABP) of patients in hospital is currently carried out in an invasive way, which could represent a risk for them. In this paper, a noninvasive methodology to optimize ABP estimators using electrocardiogram and photoplethysmography signals is proposed. For this, the XGBoost machine learning model, optimized with Bayesian techniques, is executed in a Graphics Processing Unit, which drastically reduces execution time. The methodology is evaluated using the MIMIC-III Waveform Database. Systolic and diastolic pressures are estimated with mean absolute error values of 15.85 and 11.59 mmHg, respectively, similar to those of the state of the art. The main advantage of the proposed methodology with respect to others of the current state of the art is that it allows the optimization of the estimator model to be performed automatically and more efficiently at the computational level for the data available. Clinical Relevance— This approach has the advantage of using noninvasive methods to continuously monitor patient's arterial blood pressure, reducing the risk for patientsAgencia Gallega de Innovación | Ref. IN845D-2020/29Agencia Gallega de Innovación | Ref. IN607B-2021/1

Comparative Analysis of Molecular Allergy Features of Seed Proteins from Soybean (Glycine max) and Other Legumes Extensively Used for Food

Food allergies due to eating habits, pollution, and other factors are a growing problem in Western nations as well as developing countries. Symptoms of food allergies include changes in the respiratory and digestive systems. Legumes are a potential solution to the enormous demands for healthy, nutritive, and sustainable food. However, legumes also contain families of proteins that can cause food allergies. Some of these legumes include peanut, pea, chickpea, soy, and lupine. It has been shown that processing can alter the allergenicity of legumes since thermic and enzymatic resistance can affect these properties. Cross-reactivity (CR) is an allergy feature of some allergen proteins when the immune system recognizes part of the common share sequences (epitopes) in these allergic proteins. The research about molecular allergy includes comparisons of immunoglobulin E (IgE) and T-cell epitopes, assessment of three-dimensional structure and comparison of secondary structure elements, post-transduction modifications analysis by bioinformatic approach, and post-transduction modifications affecting epitopes properties may facilitate molecular tools to predict protein allergic behavior establishing prevention measurements that could promote the use of legumes and other seeds. This chapter provides an overview of the structural features of the main allergen proteins from legumes and their allergenic potential

Ancestry-related assortative mating in Latino populations

Examination of ancestry-informative genetic markers shows that Puerto Rican and Mexican populations have shown strong assortative mating that continues to this day

Drivers of Health Care Expenditure

Since the NHS was established in 1948, growth in health care expenditure (HCE) has outpaced the rise in both GDP and in total public expenditure. Known drivers of HCE growth include demographic factors, income and wealth effects, technology and cost pressures. To identify the challenges and opportunities for developing a model of healthcare demand, this report addressed two research questions: 1.What are the drivers of past trends in health care expenditure and how much has each of the drivers contributed to past increases in expenditure? 2.How much has each type of service contributed to past trends in health care expenditure and why have there been different trends for different types of care? We set out a conceptual framework for understanding drivers of HCE, placing it in the broader context of underlying drivers of demand and macroeconomic trends. We reviewed studies from higher-income countries published over the last decade, and analysed datasets compiled in-house of cost and volume of care by different settings. We linked data on HCE trends to relevant, setting-specific evidence from the literature review. We identified 52 studies using aggregate data and 54 individual-level studies. The relative contribution of different drivers could not be quantified due to heterogeneity in study methodologies. Aggregate studies using longer panels of data show that the relationship between HCE and its drivers is non-linear, varies over time and varies cross countries. These studies mostly find a strong, positive relationship between HCE and technological progress. Individual-level studies usually rely on observational, non-experimental data from administrative databases, such as claims data or registers, or on survey data or cohort studies. Trends in HCE from 2008/9 to 2016/17 reveal that the largest rises were in high cost drugs (231%), chemotherapy (113%) and attendances at A&E (60%) or outpatient departments (57%). Most evidence on the drivers of HCE related to hospital care, but we found no studies explaining the factors behind the rise in expenditure on chemotherapy or high cost drugs. We conclude by presenting four lessons that could inform decisions on building a projections model of health care expenditure

Spatiotemporal progression of ubiquitin-proteasome system inhibition after status epilepticus suggests protective adaptation against hippocampal injury.

BACKGROUND: The ubiquitin-proteasome-system (UPS) is the major intracellular pathway leading to the degradation of unwanted and/or misfolded soluble proteins. This includes proteins regulating cellular survival, synaptic plasticity and neurotransmitter signaling; processes controlling excitability thresholds that are altered by epileptogenic insults. Dysfunction of the UPS has been reported to occur in a brain region- and cell-specific manner and contribute to disease progression in acute and chronic brain diseases. Prolonged seizures, status epilepticus, may alter UPS function but there has been no systematic attempt to map when and where this occurs in vivo or to determine the consequences of proteasome inhibition on seizure-induced brain injury. METHOD: To determine whether seizures lead to an impairment of the UPS, we used a mouse model of status epilepticus whereby seizures are triggered by an intra-amygdala injection of kainic acid. Status epilepticus in this model causes cell death in selected brain areas, in particular the ipsilateral CA3 subfield of the hippocampus, and the development of epilepsy after a short latent period. To monitor seizure-induced dysfunction of the UPS we used a UPS inhibition reporter mouse expressing the ubiquitin fusion degradation substrate ubiquitin(G76V)-green fluorescent protein. Treatment with the specific proteasome inhibitor epoxomicin was used to establish the impact of proteasome inhibition on seizure-induced pathology. RESULTS AND CONCLUSIONS: Our studies show that status epilepticus induced by intra-amygdala kainic acid causes select spatio-temporal UPS inhibition which is most evident in damage-resistant regions of the hippocampus, including CA1 pyramidal and dentate granule neurons then appears later in astrocytes. In support of this exerting a beneficial effect, injection of mice with the proteasome inhibitor epoxomicin protected the normally vulnerable hippocampal CA3 subfield from seizure-induced neuronal death in the model. These studies reveal brain region- and cell-specific UPS impairment occurs after seizures and suggest UPS inhibition can protect against seizure-induced brain damage. Identifying networks or pathways regulated through the proteasome after seizures may yield novel target genes for the treatment of seizure-induced cell death and possibly epilepsy

Expression of SMARCD1 interacts with age in association with asthma control on inhaled corticosteroid therapy.

BackgroundGlobal gene expression levels are known to be highly dependent upon gross demographic features including age, yet identification of age-related genomic indicators has yet to be comprehensively undertaken in a disease and treatment-specific context.MethodsWe used gene expression data from CD4+ lymphocytes in the Asthma BioRepository for Integrative Genomic Exploration (Asthma BRIDGE), an open-access collection of subjects participating in genetic studies of asthma with available gene expression data. Replication population participants were Puerto Rico islanders recruited as part of the ongoing Genes environments & Admixture in Latino Americans (GALA II), who provided nasal brushings for transcript sequencing. The main outcome measure was chronic asthma control as derived by questionnaires. Genomic associations were performed using regression of chronic asthma control score on gene expression with age in years as a covariate, including a multiplicative interaction term for gene expression times age.ResultsThe SMARCD1 gene (SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily D member 1) interacted with age to influence chronic asthma control on inhaled corticosteroids, with a doubling of expression leading to an increase of 1.3 units of chronic asthma control per year (95% CI [0.86, 1.74], p = 6 × 10- 9), suggesting worsening asthma control with increasing age. This result replicated in GALA II (p = 3.8 × 10- 8). Cellular assays confirmed the role of SMARCD1 in glucocorticoid response in airway epithelial cells.ConclusionFocusing on age-dependent factors may help identify novel indicators of asthma medication response. Age appears to modulate the effect of SMARCD1 on asthma control with inhaled corticosteroids

Whole-Genome Sequencing of Individuals from a Founder Population Identifies Candidate Genes for Asthma

Asthma is a complex genetic disease caused by a combination of genetic and environmental risk factors. We sought to test classes of genetic variants largely missed by genome-wide association studies (GWAS), including copy number variants (CNVs) and low-frequency variants, by performing whole-genome sequencing (WGS) on 16 individuals from asthma-enriched and asthma-depleted families. The samples were obtained from an extended 13-generation Hutterite pedigree with reduced genetic heterogeneity due to a small founding gene pool and reduced environmental heterogeneity as a result of a communal lifestyle. We sequenced each individual to an average depth of 13-fold, generated a comprehensive catalog of genetic variants, and tested the most severe mutations for association with asthma. We identified and validated 1960 CNVs, 19 nonsense or splice-site single nucleotide variants (SNVs), and 18 insertions or deletions that were out of frame. As follow-up, we performed targeted sequencing of 16 genes in 837 cases and 540 controls of Puerto Rican ancestry and found that controls carry a significantly higher burden of mutations in IL27RA (2.0% of controls; 0.23% of cases; nominal p = 0.004; Bonferroni p = 0.21). We also genotyped 593 CNVs in 1199 Hutterite individuals. We identified a nominally significant association (p = 0.03; Odds ratio (OR) = 3.13) between a 6 kbp deletion in an intron of NEDD4L and increased risk of asthma. We genotyped this deletion in an additional 4787 non-Hutterite individuals (nominal p = 0.056; OR = 1.69). NEDD4L is expressed in bronchial epithelial cells, and conditional knockout of this gene in the lung in mice leads to severe inflammation and mucus accumulation. Our study represents one of the early instances of applying WGS to complex disease with a large environmental component and demonstrates how WGS can identify risk variants, including CNVs and low-frequency variants, largely untested in GWAS

Identification of CFTR variants in Latino patients with cystic fibrosis from the Dominican Republic and Puerto Rico

BackgroundIn cystic fibrosis (CF), the spectrum and frequency of CFTR variants differ by geography and race/ethnicity. CFTR variants in White patients are wellâ described compared with Latino patients. No studies of CFTR variants have been done in patients with CF in the Dominican Republic or Puerto Rico.MethodsCFTR was sequenced in 61 Dominican Republican patients and 21 Puerto Rican patients with CF and greater than â â â â 60â mmol/L sweat chloride. The spectrum of CFTR variants was identified and the proportion of patients with 0, 1, or 2 CFTR variants identified was determined. The functional effects of identified CFTR variants were investigated using clinical annotation databases and computational prediction tools.ResultsOur study found 10% of Dominican patients had two CFTR variants identified compared with 81% of Puerto Rican patients. No CFTR variants were identified in 69% of Dominican patients and 10% of Puerto Rican patients. In Dominican patients, there were 19 identified CFTR variants, accounting for 25 out of 122 disease alleles (20%). In Puerto Rican patients, there were 16 identified CFTR variants, accounting for 36 out of 42 disease alleles (86%) in Puerto Rican patients. Thirty CFTR variants were identified overall. The most frequent variants for Dominican patients were p.Phe508del and p.Ala559Thr and for Puerto Rican patients were p.Phe508del, p.Arg1066Cys, p.Arg334Trp, and p.I507del.ConclusionsIn this first description of the CFTR variants in patients with CF from the Dominican Republic and Puerto Rico, there was a low detection rate of two CFTR variants after full sequencing with the majority of patients from the Dominican Republic without identified variants.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153634/1/ppul24549.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153634/2/ppul24549_am.pd