Service Dataset Research Articles

Prevalence and demographics of 331 rare diseases and associated COVID-19-related mortality among 58 million individuals: a nationwide retrospective observational study.

The Global Burden of Disease Study has provided key evidence to inform clinicians, researchers, and policy makers across common diseases, but no similar effort with a single-study design exists for hundreds of rare diseases. Consequently, for many rare conditions there is little population-level evidence, including prevalence and clinical vulnerability, resulting in an absence of evidence-based care that was prominent during the COVID-19 pandemic. We aimed to inform rare disease care by providing key descriptors from national data and explore the impact of rare diseases during the COVID-19 pandemic. In this nationwide retrospective observational cohort study, we used the electronic health records (EHRs) of more than 58 million people in England, linking nine National Health Service datasets spanning health-care settings for people who were alive on Jan 23, 2020. Starting with all rare diseases listed in Orphanet (an extensive online resource for rare diseases), we quality assured and filtered down to analyse 331 conditions mapped to ICD-10 or Systemized Nomenclature of Medicine-Clinical Terms that were clinically validated in our dataset. For all 331 rare diseases, we calculated population prevalences, analysed patients' clinical and demographic details, and investigated mortality with SARS-CoV-2. We assessed COVID-19-related mortality by comparing cohorts of patients for each rare disease and rare disease category with controls matched for age group, sex, ethnicity, and vaccination status, at a ratio of two controls per individual with a rare disease. Of 58 162 316 individuals, we identified 894 396 with at least one rare disease and assessed COVID-19-related mortality between Sept 1, 2020, and Nov 30, 2021. We calculated reproducible estimates, adjusted for age and sex, for all 331 rare diseases, including for 186 (56·2%) conditions without existing prevalence estimates in Orphanet. 49 rare diseases were significantly more frequent in female individuals than in male individuals, and 62 were significantly more frequent in male individuals than in female individuals; 47 were significantly more frequent in Asian or British Asian individuals than in White individuals; and 22 were significantly more frequent in Black or Black British individuals than in White individuals. 37 rare diseases were significantly more frequent in the White population compared with either the Black or Asian population. 7965 (0·9%) of 894 396 patients with a rare disease died from COVID-19, compared with 141 287 (0·2%) of 58 162 316 in the full study population. In fully vaccinated individuals, the risk of COVID-19-related mortality was significantly higher for eight rare diseases, with patients with bullous pemphigoid (hazard ratio 8·07, 95% CI 3·01-21·62) being at highest risk. Our study highlights that national-scale EHRs provide a unique resource to estimate detailed prevalence, clinical, and demographic data for rare diseases. Using COVID-19-related mortality analysis, we showed the power of large-scale EHRs in providing insights to inform public health decision making for these often neglected patient populations. British Heart Foundation Data Science Centre, led by Health Data Research UK.

Mitigating DDoS Attacks in Virtual Machine Migration: An In-Depth Security Framework Utilizing Deep Learning and Advanced Encryption Techniques

Safeguarding virtual machines (VMs) during migration is essential to avert Service Level Agreement (SLA) violations. This research article presents a robust security framework that utilizes deep learning and advanced encryption methods to reduce the impact of Distributed Denial of Service (DDoS) attacks during virtual machine migration. The study introduces an Improved Sparrow Search Algorithm-based Deep Neural Network (ISSA-DNN) for the classification of DDoS attacks and utilizes Advanced Encryption Standard-Elliptic Curve Cryptography (AES-ECC) to safeguard virtual machine images. The primary objective is to mitigate the risks associated with VM migration by identifying DDoS attacks and safeguarding VMs using advanced cryptographic techniques. The research employs the Canadian Institute for Cybersecurity Distributed Denial of Service (CICDDoS) dataset, implementing preprocessing procedures like duplication elimination, feature selection via Random Forest, and normalization to improve the precision of the DNN classifier. The ISSA-DNN approach enhances hyperparameter optimization by inverse mutation-based sparrow search, yielding a precise attack classification model. Furthermore, the research incorporates AES-ECC for encrypting VM images, amalgamating AES's computational efficiency with ECCs improved security. In contrast to conventional methods, this hybrid encryption approach enhances throughput and decreases encryption and decryption durations, rendering it appropriate for high-throughput and real-time applications. Experimental findings indicate that the proposed ISSA-DNN attains a classification accuracy of 98.79%, surpassing current state-of-the-art techniques. The AES-ECC encryption technique markedly enhances performance metrics, safeguarding the security of virtual machines during migration. This proactive security policy safeguards sensitive data and guarantees adherence to regulatory standards. In conclusion, the established framework offers a comprehensive solution for mitigating DDoS attacks and safeguarding VM migration via advanced deep learning and encryption methodologies. Integrating ISSA-DNN for attack classification and AES-ECC for encryption offers a robust strategy for improving cybersecurity in cloud environments.

Inequity in treatment access for child mental health services in England: analysis of administrative national data for 2021-2022.

An equitable child mental health service provides access to treatment proportionally to the need of individual demographic groups. Despite qualitative and survey-based evidence of barriers disadvantaging some demographic groups, it is not well understood how these barriers translate into quantifiable inequities. We calculated the treatment access rate for English children aged 6-16 years in 2021-2022, using the patient-level Mental Health Services Data Set and Mental Health of Children and Young People Survey. The number of primary school children in treatment needs to increase nationally by 173%, the number of boys by 65% and the number of children from a White ethnic background by 31%, to achieve equity in treatment access. There was no evidence of inequities by area deprivation. Child mental health services in England should not only increase overall access rates, but also pay more attention to equity in access across different demographic groups.

Characteristics of nursing homes with high rates of invasive methicillin-resistant Staphylococcus aureus infections.

Nursing home residents experience a large burden of invasive methicillin-resistant Staphylococcus aureus (MRSA) infections. Data are limited regarding nursing home characteristics associated with differences in facility-level invasive MRSA rates. We analyzed 2011-2015 data from CDC's Emerging Infections Program (EIP) active population- and laboratory-based surveillance for invasive MRSA cases within seven states. A nursing home-onset case was defined as MRSA cultured from a normally sterile site in a person living in a nursing home 3 days before culture collection. Facility rates were calculated as nursing home-onset cases per 100,000 resident-days. Nursing home resident-day denominators and facility characteristics were obtained from four Centers for Medicare & Medicaid Services (CMS) datasets. A general estimating equations model with a logit link assessed characteristics of the facilities with highest rates comprising 50% of nursing home MRSA cases ("high rates"). The 626 nursing homes in the surveillance area had 2824 invasive MRSA cases; 82% of facilities had at ≥1 case. The 20% of facilities with highest rates (≥3.84 cases/100,000 resident-days) had 50% of nursing home-onset cases. In multivariable regression, facilities with high rates were more likely to have CMS-derived characteristics of presence of a resident with a multidrug-resistant organism; or greater proportions of residents who were male, were short stay (in the facility <100 days), had a nasogastric or percutaneous gastrostomy tube, or require extensive assistance with bed repositioning; and more likely to be in an EIP area with higher hospital-onset MRSA rates. Higher registered nurses staffing levels (hours/resident/day) and higher proportions of White residents were associated with lower rates. Facilities with higher invasive MRSA rates served residents with more clinical and functional care needs. Increasing registered nurse staffing in high-risk facilities might assist with reduction of invasive MRSA rates. These findings could help prioritize nursing homes for future MRSA prevention work.

Cost-effectiveness and readmission rates of laparoscopic vs. open surgery for colorectal cancer: evidence from the health insurance review and assessment service dataset in South Korea.

We aimed to compare and analyze the cost-effectiveness of laparoscopic vs. open colorectal surgery (CRS) for colorectal cancer using health insurance claims data derived from multiple institutions in South Korea as well as the differences in hospital length of stay (LOS) and 30-day readmission rates related to postoperative complications. We retrospectively reviewed the clinical data of patients who underwent curative resection for colorectal cancer between January 1, 2020 and December 31, 2022 using national health insurance claims data in South Korea. We determined the surgical approach based on the presence or absence of treatment material codes specific to laparoscopic surgery, and divided the patients into the laparoscopic-CRS (lap-CRS) and open-CRS groups. A total of 34,779 patients were included [open-CRS: 3,262 patients [9.4%]; lap-CRS: 31,517 patients [90.6%]]. The mean LOS was 14.11 and 11.27 days for the open- and lap-CRS groups, respectively (p < 0.001). The mean medical costs were 9,163 USD and 8,963 USD in the open- and lap-CRS groups, respectively (p < 0.001). A total of 1,192 (3.4%) patients were readmitted within 30 days of discharge, with a rate of 5.4% (176 cases) and 3.2% (1,016 cases) in the open- and lap-CRS groups, respectively (p < 0.001). Open surgery, male sex, and rectal surgery were identified as factors that increased medical cost. According to this South Korean nationwide population-based study, laparoscopic surgery demonstrated a reduction in LOS, medical costs, and readmission rates compared with open surgery in patients with colorectal cancer.

Spatiotemporal modeling of long-term PM2.5 concentrations and population exposure in Greece, using machine learning and statistical methods.

The lack of high-resolution, long-term PM2.5 observations in Greece and the Eastern Mediterranean hampers the development of spatial models that are crucial for providing representative exposure estimates to health studies. This work presents a spatial modeling approach to address this gap and assess PM2.5 spatial variability for the first time on a national level in Greece, by integrating in situ observations, meteorology, emissions and satellite AOD data among others. A high-resolution (1km2) gridded dataset of PM2.5 concentrations across Greece from 2015 to 2022 was developed, and seven statistical, machine learning, and hybrid models were evaluated under different prediction scenarios. Random Forest (RF) models demonstrated superior performance, (R2=0.73, MAE=2.2μgm-3), validated against ground-based measurements. Winter months consistently showed the highest PM2.5 levels, averaging 16.8μgm-3, over the domain, due to residential biomass burning (BB) and limited atmospheric dispersion. Summer months had the lowest concentrations, averaging 10.3μgm-3, while substantial decreases nationwide were observed during the 2020 COVID-19 lockdown. Population exposure analysis indicated that the entire Greek population was exposed to long-term PM2.5 concentrations exceeding the WHO air quality guideline (AQG) of 5μgm-3. Moreover, the dataset revealed elevated PM2.5 levels across several regions of mainland Greece. Notably, 70% to 90% of the population experience levels exceeding 10μgm-3 in Central and Northern regions of continental Greece like Thessaly, Central Macedonia, and Ioannina. The Ioannina region, which is severely impacted by residential BB, recorded pollution levels up to five times the WHO AQG highlighting the urgent need for targeted interventions. The high-resolution RF model's superior performance for monthly average concentrations, compared to the Copernicus Atmosphere Monitoring Service (CAMS) dataset, renders it a reliable tool for long-term PM2.5 assessment in Greece that can support air quality management and health studies.

Differences in Medicare payment and practice characteristics for orthopedic surgery subspecialties

Background Medicare payment, practice, and patient characteristics give perspective on the lifestyle, challenges, and attractiveness unique to each orthopedic surgery subspecialty. Changes to Medicare reimbursement may also affect health care delivery, warranting policy change. We analyzed differences in orthopedic surgery subspecialty characteristics and Medicare payments. Methods The Centers for Medicare and Medicaid Services data set was filtered by Current Procedural Terminology (CPT) codes unique to each orthopedic surgery subspecialty in 2019. After filtering by unique CPT codes, 100 randomized physicians were validated for each subspecialty: adult reconstruction and arthroplasty, trauma, sports medicine, hand surgery, shoulder and elbow surgery, foot and ankle surgery, and spine surgery. Data gathered included unique procedural codes, number of beneficiaries, services provided, and the total Medicare allowed amount. Differences were assessed with post hoc tests. Subspecialty changes in Medicare payment were assessed using the Physician Fee Schedule and compared using a single-factor analysis of variance. Results On average, hand surgery recorded the most unique CPT codes (106) and the highest volume of beneficiaries (571). Adult reconstruction and arthroplasty had the fewest unique codes (60) and the greatest total Medicare payment ($328,000). Spine surgery had the lowest number of beneficiaries (387) and lowest number of services provided (1752). Lastly, sports medicine provided the largest volume of services (4221). Mean total codes, unique codes, and total patients differed between subspecialties. Varying differences in average Medicare payment, total billed codes, total unique codes, total patients, patient race, patient age, patient sex, and patient health were found for all orthopedic surgery subspecialties in 2019. Conclusions These data may illustrate financial incentives for orthopedic surgery residents to pursue certain subspecialties.

Mutual Exploration for Missing Data Imputation, QoS Parameter Selection, and QoS Prediction in 5G Networks Using a Novel Skewness Driven Distribution Imputation Algorithm, Pearson Correlation, and XGBoost

Pre-processing is a key stage in the Machine Learning (ML) pipeline. In such a stage, data is prepared and organized for feeding it to the ML models for a prediction task. One of the problems that might incur in this stage is that data may have missing values, requiring that either the data is deleted or imputed with data points that resembles/ correlate with the original one. Imputation is desirable, as having more data to be fed to ML models means the model will have more context and thus better prediction results. From an imputation perspective, since the goal is that the imputed data faithfully relates with the original data, the result of correlation metric is desirable to show that the imputed data and original data are closely correlated. Herein, we present a novel imputation algorithm of Skewness Driven Distribution Imputation (SDDI), and evaluate its efficacy compared to multiple state-of-the-art methods including, K-Nearest Neighbors (KNN), Mean, Mode, Forward and Backward Fill (F&amp;B-Fill) imputation methods. The comparison is done using accuracy metrics, Root Mean Square Error (RMSE), correlation and computation time. Furthermore, a correlation analysis is conducted on the subject 5G Vehicle-to-Everything (V2X) Quality of Service (QoS) dataset, aiming to enhance the understanding of parameter selection in assessing the QoS for 5G networks by providing the comparison regarding the significance in terms of correlation of various parameters in influencing 5G network's QoS. The state-of-the-art Pearson correlation method is used for said purpose. Moreover, we exploit an Extreme Gradient Boosting or XGBoost algorithm which is an ensemble of other techniques and not as complex as deep learning algorithms, to predict QoS, given certain conditions relevant to the 5G network. The comparative analysis of various imputation methods revealed average correlation values (as a measure of faithful data imputation) to be relatively close for Mean, Mode, F&amp;B-Fill, SDDI, and K-Nearest Neighbors imputation methods at 0.161, 0.176, 0.143, 0.143, and 0.196, respectively. In terms of accuracy, all methods achieved high rates, with Mean and Mode at 93%, F&amp;B-Fill at 90%, and both SDDI and KNN at 92%. Notably, in the second part of the research, when only the 15 most correlated features were used, we observed a substantial 60.5% reduction in the amount of data affected, with only a minimal impact of 3% on accuracy, achieving an impressive 93% accuracy. These results highlight the effectiveness of targeted feature selection of parameters for QoS of 5G networks and underscore the potential of our novel SDDI method in maintaining high data integrity while efficiently handling missing data, thereby enhancing the predictive reliability of the XGBoost algorithm.

Vaccine effectiveness against mild and severe covid-19 in pregnant individuals and their infants in England: test negative case-control study

ObjectiveTo estimate real world vaccine effectiveness against symptomatic disease and hospital admission with the delta and omicron variants of the SARS-CoV-2 virus in pregnant individuals, and to estimate the protection...

Transit modal complementarity: measuring the access provided by transfers

AbstractThis research investigates the interactions among transit (public transport) modes by inferring complementarity from a comparison of access provided by various transit modal combinations over a period of 160 years. A unique historical dataset of transit networks and services including buses, trains, and trams, generated for the Greater Sydney region for a period spanning from 1855 to 2015 was used. Access to population was measured for each year by 11 different modal cases formed by combinations of the three transit modes at a spatially disaggregated level as well as the regional level. The changes in access provided by the different modal cases were compared temporally and spatially to infer complementarity among the modes. Throughout the study period, trains, buses, and trams (when available) were found to be highly sub-additive at the regional level. Spatial comparison of complementarity was also demonstrated for Sydney’s Central Business District as an example. Such an investigation of access by modal combinations is a useful planning tool to ensure equitable supply and to investigate transfer benefits and penalties.

Atmospheric CO2 column concentration over Iran: Emissions, GOSAT satellite observations, and WRF-GHG model simulations

Regarding global warming and climate change, carbon dioxide (CO2) is one of the most important greenhouse gases. Simulating CO2 gas at hourly/weekly time intervals and desired vertical resolution is challenging due to the coarse horizontal resolution of global models. In this study, both column-averaged CO2 mixing ratio (XCO2) and vertical cross sections of CO2 mixing ratio were simulated by the Weather Research and Forecast Green House gas (WRF-GHG) model at spatial resolutions of 30 and 10 km for the Middle East region as the first domain, and Iran as the second domain. Simulations consider the primary CO2 sources (anthropogenic, biogenic, fire, and oceanic) and the Copernicus Atmosphere Monitoring Service (CAMS) dataset. XCO2 retrieved from GOSAT satellite observations was employed to evaluate the simulation results of the column-averaged CO2 concentrations in February and August 2010. The evaluations showed that the spatiotemporal variability of meteorological variables was well simulated by WRF-GHG with correlation coefficients r of 0.86–0.92, 0.67–0.75, and 0.76–0.82 for temperature, wind, and relative humidity, respectively, during February and August 2010. The evaluations also indicated that the WRF-GHG simulations outperformed the global model TM3, with mean bias error values of − 0.79 and 0.45 PPMV for WRF-GHG in February and August, respectively. The percentage contribution of net CO2 emissions from human activities in Iran was calculated as (38.33 % and 23.70 %) of the total emissions, respectively, with values of 4.4 and 0.85 kg/km2 in each month. The net emissions contributions of biogenic, fire, and oceanic sources were evaluated in February and August, with biogenic emissions contributing (31.901 % and 27.66 %), biogenic absorption contributing (24.07 % and 46.63 %), fire emissions contributing (5.7 % and 2.064 %), and oceanic emissions contributing (3.23 × 10−6 % and 2.23 × 10−6 %). Large-scale circulations and biogenic activity are responsible for the major features of the spatial and seasonal distribution of CO2 in the area. In February, column mixing ratios are higher in more northern latitudes; in August, they are higher to the south. Furthermore, the simulated vertical cross sections show high CO2 mixing ratios in the mid-lower troposphere and northerly/northeasterly advection in February; the vertical profile is inverted in August with high concentrations in the lower stratosphere associated with southwesterly advection. However, the interaction between the synoptic and sub-synoptic features with the topography determines the precise dispersion and distribution of CO2. Despite the negligible emissions in central and eastern Iran, these factors play an important role in the observed concentrations in February and August. In August, the areas between the 120-day low-level monsoon flow of Sistan in eastern Iran, and the westerlies over western/southwestern Iran and the Zagros Mountain range, interact with the heat-low in the Iranian plains and with the Arabian subtropical high. In February, the dominant winds in western Asia were west winds, with the main source of pollution coming from the northeastern regions flowing towards central and eastern Iran. The equatorward displacement of the polar jet stream and the passage of low-pressure systems from the west in winter cause a temporary reduction in CO2 concentrations.

Implementation of a national rapid prenatal exome sequencing service in England: evaluation of service outcomes and factors associated with regional variation.

Prenatal exome sequencing (pES) can enhance genetic diagnosis of fetuses with structural anomalies and has recently been introduced as a national service in England. We aimed to examine service outcomes such as diagnostic yield (definite final diagnosis), referral rate, and sources of referral, and explore variation in outcomes of pES by individual or service level factors between 01 October 2021 and 30 June 2022. pES testing results from the National Health Service laboratories performing testing were linked to National Congenital Anomaly and Rare Disease Registration Service data and the Maternity Services Data Set and descriptive statistics computed. There were 475,089 women who gave birth in England during the study period. The referral rate for pES was 8.6 (95% CI 7.8, 9.4) per 10,000 maternities. About 59% of those referred proceeded with pES testing and 35% of women who proceeded received a definite final diagnosis with a median turnaround time of 15days. Of those who had pES testing, 64.6% had a live birth, 25.3% underwent termination of pregnancy (median gestational age at termination: 26weeks), and 9.3% had a stillbirth. Among the 85 women who had a definite final diagnosis, 40% had a termination of pregnancy, 18% had a stillbirth, and 42% had a live birth. The corresponding figures among women without a definite final diagnosis were 18%, 5%, and 78%, respectively. Among women who had a termination of pregnancy, the median gestational age at final report was 24.9weeks and 26.2weeks at termination. Variation observed in some of the characteristics and outcomes between regional services were limited by small sample size. This study showed that of those referred, pES testing provided a diagnosis for one in three pregnancies with a fetal anomaly across England during the study period when other tests had been non-informative. Women who opted for a termination of pregnancy underwent the procedure at relatively late gestations. Earlier referral for pES, streamlining pathways, and faster turnaround times may help results to be available at an earlier gestation to allow families more time to make decisions around continuing or terminating their pregnancy. The variation in service outcomes between regional services needs to be investigated further to understand the reasons for these differences.

A Machine Learning Approach to Identifying Wrong-Site Surgeries Using Centers for Medicare and Medicaid Services Dataset: Development and Validation Study (Preprint)

A Machine Learning Approach to Identifying Wrong-Site Surgeries Using Centers for Medicare and Medicaid Services Dataset: Development and Validation Study (Preprint)

Preconception health among migrant women in England: a cross-sectional analysis of maternity services data 2018–19

BackgroundDespite poor perinatal outcomes among migrants in vulnerable situations (eg, asylum seekers), little is known about their health preconception. We investigated preconception health inequalities between migrant women in vulnerable situations and non-migrants. MethodsThis national cross-sectional study used NHS Maternity Services Dataset version 1.5, incorporating English NHS maternity services. Those with antenatal booking appointments between 1/4/2018-31/3/2019 were included (n=652 880). Odds ratios (OR), calculated using logistic regression, compared indicators of preconception health (selected via systematic review) among probable migrants in vulnerable situations (PMVS: first language other than English [FLOTE] with complex social factors [CSF]) to the reference category ‘probable non-migrants not in vulnerable situations’ (English their first language without CSF). CSF include recent migrants, asylum seekers, refugees, difficulty reading/speaking English; alcohol and/or drugs misuse; aged under 20 years; and/or experiencing domestic abuse. University of Warwick ethics committee approved the study. FindingsPMVS represented 3·8% (n=25 070) of the study population, 10·2% (n=66 783) had FLOTE without CSF, 5·6% (n=36 433) spoke English as their first language with CSF, 46·5% (n=303 737) were in the reference category, and 33·8% (n=220 858) had missing migration data (included as ‘missing’ category). PMVS had higher odds of not taking folic acid preconception (OR 2·15, 95% CI 2·06–2·25), previous obstetric complications (OR 1·26, 95% CI 1·21–1·31), hepatitis B (OR 5·40, 95% CI 4·43–6·58), diabetes (OR 1·30, 95% CI 1·17–1·45) and underweight (OR 1·55, 95% CI 1·44–1·68), but lower odds of physical/mental health conditions (OR 0·54, 95% CI 0·52–0·56), smoking (OR 0·86, 95% CI 0·82–0·90) and overweight/obesity (OR 0·73, 95% CI 0·71–0·75). InterpretationInequalities occur across preconception indicators. Lower odds of preconception folic acid have also been identified in studies elsewhere and is a serious concern for migrant maternal health. Limitations include lack of granular data on migration category and missing data. Opportunities exist for more targeted preconception care. FundingMedical Research Council (Grant number MR/W01498X/1)

Real-world effectiveness of primary care psychological therapy for depression and anxiety in patients with coronary heart disease

Abstract Background Depression and anxiety are often inadequately treated in patients with coronary heart disease (CHD), despite mental health issues being associated with poorer quality of life, higher cardiovascular morbidity, and higher mortality. Talking therapies, such as cognitive behavioural therapy, are the recommended first-line treatment for depression and anxiety symptoms in CHD patients. However, the evidence for their effectiveness was considered low in a recent Cochrane review, and trial participants may not represent patients treated in routine clinical settings. Purpose To assess the effectiveness of routinely-delivered talking therapies in patients with CHD from nationwide primary care talking therapy services in England, and to compare their treatment outcomes to patients without a CHD diagnosis. Methods Healthcare records from 1.9 million patients who completed a course of treatment (≥2 treatment sessions) via NHS Talking Therapies for anxiety and depression (NHS TTad) between 2012-2019 were linked to Hospital Episode Statistics, the Mental Health Services Data Set, and death records. Standard NHS TTad outcome indicators (recovery, reliable improvement, and reliable deterioration) were examined in three groups, with (1) any CHD (n=41,317; ICD-10=I20-I25), (2) acute coronary syndromes (ACS, n=29,991; I21, I22, I20.0), and (3) chronic CHD (n=40,524; I20.1-I20.9, I23-I25) at baseline. Effect sizes for pre-post treatment changes in depression and anxiety symptom measures (PHQ-9 and GAD-7) were calculated using the adapted Cohen’s d for within-subjects design (dav). Outcomes were also compared to those of propensity-score matched control groups without the relevant diagnoses, using logistic regression. Results After completing a course of talking therapy, 49.8% (95%CI=49.3;50.3) of patients with CHD recovered from depression or anxiety, 69.4% (95%CI=69.0;69.9) saw reliable improvement in symptoms, and 7.3% (95%CI=7.0;7.5) experienced reliable deterioration in symptoms. There were large reductions in both depression (dav=-1.02) and anxiety symptoms (dav=-1.03) on average. Compared to a control sample of patients with no diagnosis of CHD, those with a CHD diagnosis were less likely to recover (OR=0.87[95%CI=0.84;0.89]) or reliably improve (OR=0.84[95%CI=0.81;0.87]) and more likely to reliably deteriorate (OR=1.28[95%CI=1.21;1.36]), when matched on age, sex, ethnicity, deprivation, baseline depression and anxiety symptom scores, self-reported long-term health condition, and waiting times between referral, assessment, and treatment. The findings were consistent for patients with ACS and those with chronic CHD. Conclusions Nearly half of CHD patients who received primary care talking therapy recovered, in line with the UK Government target of 50% recovery for the services. Nevertheless, compared to otherwise similar patients without CHD, those with CHD at baseline had worse psychological treatment outcomes.

Brexit and the Services Trade—A Longitudinal Analysis

This study investigates the impact of Brexit uncertainty on the UK's services sector trade from 2016 to 2019 revealing a statistically significant negative impact that varied across sectors and destinations. Using the OECD-WTO Balanced Trade in Services dataset and synthetic difference in differences methodology, the findings indicate an annual shortfall of US$23.7/£18.5 billion in UK services exports compared to a scenario where the UK remained in the EU, translating to a 5.65 percent reduction. The bilateral data suggests an 8.5 percent average decline, with larger exports to countries like Germany and the US being less affected than smaller bilateral exports. The UK bilateral exports to the EU declined by 6.21 percent, while Ireland’s exports expanded by 21.4 percent. The study highlights that small firms were disproportionately impacted, leading to a decline in trade openness and export activities. Methodological sensitivity is underscored, with various approaches estimating Brexit’s impact differently. Sectoral analysis shows significant declines in Travel, Insurance, Finance, Telecom, Business and Cultural Services. Ireland notably benefited post-Brexit, experiencing a 14.75 percent annual increase in services exports due to business relocations. The study emphasizes the critical yet underexplored consequences of economic disintegration on international trade in services, providing essential evidence for future UK-EU trade relations post-Brexit.

Trends in Phototherapy Utilization, Payments, and Geographic Distribution: An Analysis of United States Medicare Part B From 2000 to 2021.

Trends in phototherapy utilization in the context of new therapeutics have not been evaluated. This study aimed to evaluate phototherapy utilization, payments, and geographic distribution for Medicare beneficiaries from 2000 to 2021. A longitudinal analysis of the Medicare Part B National Summary Data Files was conducted to obtain phototherapy utilization and payment amounts. Medicare Physician & Other Practitioners by Provider and Service datasets were used to determine provider type and geographic distribution. Between 2000 and 2021, total Medicare phototherapy volume increased from 335,152 to 621,850, correlating with a 3.0% annual growth rate (5.0% between 2000 and 2015, -3.1% between 2016 and 2021). Ultraviolet B phototherapy represented the majority of use, rising from 68.1% in 2000 to 78.0% in 2021. Psoralens plus ultraviolet A decreased by 11.3% annually, while laser treatments increased by 17.3%. Between 2013 and 2021, the number of clinics offering phototherapy declined; facilities were available in only 9.9% of counties, mainly clustered along both coasts and east of the Mississippi. Overall, phototherapy continues to be an important therapeutic option. Limited access in non-urban areas remains a significant challenge, and further work is necessary to both evaluate this impact and reduce disparities.

Outcomes and Healthcare Resource Utilization in Patients with COVID-19 Treated with Nirmatrelvir-Ritonavir: Real-World Data Analysis.

Background: Nirmatrelvir-ritonavir was granted emergency use authorization in Israel in January 2022 to treat high-risk patients with mild-to-moderate COVID-19. The aim of the study was to assess the association between nirmatrelvir-ritonavir treatment and COVID-19-related hospitalization and healthcare resource utilization (HCRU) in a country with a high level of vaccinations compared to patients who were offered treatment and declined. Methods: The Maccabi Healthcare Services dataset was used to identify high-risk SARS-CoV-2-positive adults from January to February 2022 who received nirmatrelvir-ritonavir within 5 days of symptom onset (treatment group) or who were offered nirmatrelvir-ritonavir treatment and declined it (reference group). COVID-19-related hospitalizations and all-cause mortality and HCRU within 30 days were compared between treatment and reference groups using inverse probability of treatment weighting. Results: Treatment and reference groups included 3460 (median age, 68.4 years) and 1654 (70.2 years) patients, respectively. Patients with ≥1 dose of COVID-19 vaccine accounted for 89.5% (treatment group) and 72.1% (reference group) of the total. Treatment was associated with a lower risk of COVID-19-related hospitalization (adjusted OR, 0.59 [95% CI, 0.41,0.83]). Results were similar by age group (18-64/≥65 years) and among patients with/without vaccination in the prior 180 days. There were 11 (0.3%) versus 11 (0.7%) deaths in the treatment and reference groups, respectively. Treated patients had lower inpatient HCRU and greater less intensive outpatient HCRU (e.g., telemedicine and emergency room visits). Conclusions: Nirmatrelvir-ritonavir treatment was associated with a reduced risk of COVID-19-related hospitalization and a shift to less intensive outpatient HCRU. Comparison with a reference group of nirmatrelvir-ritonavir-eligible patients who declined treatment enabled an unbiased outcome assessment. Real-world data gathered during the Omicron BA.1 variant wave of COVID-19 in Israel support the continued use of nirmatrelvir-ritonavir for high-risk adults of all ages, regardless of previous vaccinations.

Comparing Refugee Resettlement Services: A New Global Dataset and Typology

Abstract The number of countries participating in resettlement has increased substantially in recent years: since 2010, one-third of resettlement countries officially began offering permanent homes to refugees. The services provided to refugees upon resettlement are surprisingly distinct, even among countries with similar political and economic contexts. Despite the importance of these services, existing scholarship offers surprisingly little comparison of resettlement policies globally. To address this gap, this multi-method project analyses refugee resettlement services in 26 countries that are responsible for 99.9 per cent of all resettled refugees, from 1951 to present. The study introduces an original Global Resettlement Services dataset with 42 indicators that provide a current, reliable, and easily accessible overview of resettlement services in each country. Cluster analysis of these data helped to construct a typology that captures four distinct “types” of refugee resettlement: Limited Provision, Cultural Transition, Core Services, and Flexible Support. The typology provides a framework to help policy-makers, academics, and practitioners increase collaboration, design innovative programmes, and improve existing resettlement services. This study offers a comprehensive analysis of the different approaches to resettling refugees worldwide, with significant implications for advancing theory and improving global refugee resettlement policies.

Emotional dialogue generation model of electronic commerce intelligent customer service based on topic expansion

Current electronic commerce (e-commerce) customer service systems often rely on retrieval-based methods, resulting in impersonal and emotionally flat responses. These systems struggle to handle diverse expressions and emotional nuances in user queries, leading to poor user experience. In this paper, we applied artificial intelligence techniques, notably the Gated Recurrent Unit model, to generate emotionally resonant dialogues in e-commerce customer service systems. We integrate intent recognition, emotion recognition, and topic expansion for accurate intent identification and nuanced responses. The model is trained on a customer service dataset from Jing Dong, a leading Chinese e-commerce platform. Through extensive experiments, we conducted comparative analyses against benchmark model Gated Recurrent Unit neural network. The results demonstrate that our model significantly outperforms existing approaches, achieving fluency, rationality, and emotionality scores of 62%, 58% and 61% respectively. These findings indicate that our approach generates responses with greater consistency to user intent and richer emotional content. The significance of these results lies in their potential to enhance user experience in e-commerce customer service interactions by providing more personalized and emotionally resonant automated responses.