Economic Burden of Community-Acquired Antibiotic-Resistant Urinary Tract Infections: Systematic Review and Meta-Analysis.

Background: Antibiotic resistance (ABR) poses a major burden to global health and economic systems. ABR in community-acquired urinary tract infections (CA-UTIs) has become increasingly prevalent. Accurate estimates of ABR's clinical and economic burden are needed to support medical resource prioritization and cost-effectiveness evaluations of urinary tract infection (UTI) interventions. Objective: This study aims to systematically synthesize the evidence on the economic costs associated with ABR in CA-UTIs, using published studies comparing the costs of antibiotic-susceptible and antibiotic-resistant cases. Methods: We searched the PubMed, Ovid MEDLINE and Embase, Cochrane Review Library, and Scopus databases. Studies published in English from January 1, 2008, to January 31, 2023, reporting the economic costs of ABR in CA-UTI of any microbe were included. Independent screening of titles/abstracts and full texts was performed based on prespecified criteria. A quality assessment was performed using the Integrated Quality Criteria for Review of Multiple Study Designs (ICROMS) tool. Data in UTI diagnosis criteria, patient characteristics, perspectives, resource costs, and patient and health economic outcomes, including mortality, hospital length of stay (LOS), and costs, were extracted and analyzed. Monetary costs were converted into 2023 US dollars. Results: This review included 15 studies with a total of 57,251 CA-UTI cases. All studies were from high- or upper-middle-income countries. A total of 14 (93%) studies took a health system perspective, 13 (87%) focused on hospitalized patients, and 14 (93%) reported UTI pathogens. Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa are the most prevalent organisms. A total of 12 (80%) studies reported mortality, of which, 7 reported increased mortality in the ABR group. Random effects meta-analyses estimated an odds ratio of 1.50 (95% CI 1.29-1.74) in the ABR CA-UTI cases. All 13 hospital-based studies reported LOS, of which, 11 reported significantly higher LOS in the ABR group. The meta-analysis of the reported median LOS estimated a pooled excess LOS ranging from 1.50 days (95% CI 0.71-4.00) to 2.00 days (95% CI 0.85-3.15). The meta-analysis of the reported mean LOS estimated a pooled excess LOS of 2.45 days (95% CI 0.51-4.39). A total of 8 (53%) studies reported costs in monetary terms-none discounted the costs. All 8 studies reported higher medical costs spent treating patients with ABR CA-UTI in hospitals. The highest excess cost was observed in UTIs caused by carbapenem-resistant Enterobacterales. No meta-analysis was performed for monetary costs due to heterogeneity. Conclusions: ABR was attributed to increased mortality, hospital LOS, and economic costs among patients with CA-UTI. The findings of this review highlighted the scarcity of research in this area, particularly in patient morbidity and chronic sequelae and costs incurred in community health care. Future research calls for a cost-of-illness analysis of infections, standardizing therapy-pathogen combination comparators, medical resources, productivity loss, intangible costs to be captured, and data from community sectors and low-resource settings and countries.

Incidence of rectal cancer after colectomy for inflammatory bowel disease: nationwide study.

BACKGROUND: Inflammatory bowel disease increases the risk of colorectal neoplasia. A particular problem arises in patients who have undergone subtotal colectomy leaving a rectal remnant. The risk of future rectal cancer must be accurately estimated and weighed against the risks of further surgery or surveillance. The aim of this study was to estimate the 10-year cumulative incidence of rectal cancer in such patients. METHODS: A nationwide study using England's hospital administrative data was performed. A cohort of patients undergoing subtotal colectomy between April 2002 and March 2014 was identified. A competing risks survival analysis was performed to calculate the cumulative incidence of rectal cancer. The effect of the COVID-19 pandemic on endoscopic surveillance was investigated using time-trend analysis. RESULTS: A total of 8120 patients were included and 61 patients (0.8%) were diagnosed with cancer. The cumulative incidence of rectal cancer was 0.26% (95% c.i. 0.17% to 0.39%), 0.49% (95% c.i. 0.36% to 0.68%), and 0.77% (95% c.i. 0.57% to 1.02%) at 5, 10, and 15 years respectively. A previous diagnosis of colonic dysplasia (HR 3.34, 95% c.i. 1.01 to 10.97; P = 0.047), primary sclerosing cholangitis (HR 5.42, 95% c.i. 1.34 to 21.85; P = 0.018), and elective colectomy (HR 1.83, 95% c.i. 1.11 to 3.02; P = 0.018) was associated with an increased incidence of rectal cancer. Regarding endoscopic surveillance, there was a 43% decline in endoscopic procedures performed in 2020 (333 procedures) compared with 2019 (585 procedures). CONCLUSION: The incidence of rectal cancer after subtotal colectomy is low. Asymptomatic patients without evidence of rectal dysplasia should be carefully counselled on the possible benefits and risks of prophylactic proctectomy.

The Impact of COVID-19 on Acute Surgeries in England Among the Under-25s: A Retrospective Study of 61,360 Appendicitis and 15,850 Testicular Torsion Admissions.

BACKGROUND: Little is known about how COVID-19 impacted acute surgical activity for children and young people (CYP) across England. Appendicitis and testicular torsion are common surgical conditions where treatment delays can lead to avoidable complications. We undertook a retrospective national cohort study. PRIMARY AIM: To describe monthly acute surgical activity in CYP during the COVID-19 pandemic. Secondary aim: To investigate evidence of delayed diagnosis and adverse outcomes, describing variations by age and socioeconomic deprivation. METHODS: Acute hospital admissions with appendicitis or testicular pain for those under 18 were extracted using Hospital Episode Statistics. Interrupted time series modelling, Mann-Whitney and Pearson's Chi-Squared tests compared the first 14 pandemic months with the previous five years. Results were stratified by age (0-4s, 5-9s and 10-17s) and appendicitis type (all, simple and complex). RESULTS: Admissions for appendicitis and testicular torsion fell significantly early in the COVID-19 pandemic. The proportion of children with complex appendicitis also increased during this time. Orchidectomy rates rose in April 2020 for the 0-4s (+15.6% (95% CI 7.9-23.3)) and 10-17s (+11.5% (4.9-18.2)), but when the pre-pandemic period was compared with the pandemic period as a whole, there were no overall statistically significant differences in orchidectomy rates between the study periods. Overall, there was a statistically significant rise in the orchidopexy rate during the pandemic period for the 10-17s when compared with the pre-pandemic period (Pre-pandemic: 17.0% vs Pandemic: 20.9%, p < 0.0001). CONCLUSION: A consistent reduction in activity, with short-lived periods of delayed presentations during COVID-19 pandemic peaks, occurred without persisting overall increased complication rates. These results provide useful national context for smaller sized studies that reported complications due to delays in surgery. Future research could examine how reduced activity impacted other healthcare settings and treatment pathways. LEVEL OF EVIDENCE: II.

Has multimorbidity and frailty in adult hospital admissions changed over the last 15 years? A retrospective study of 107 million admissions in England.

BACKGROUND: Few studies have quantified multimorbidity and frailty trends within hospital settings, with even fewer reporting how much is attributable to the ageing population and individual patient factors. Studies to date have tended to focus on people over 65, rarely capturing older people or stratifying findings by planned and unplanned activity. As the UK's national health service (NHS) backlog worsens, and debates about productivity dominate, it is essential to understand these hospital trends so health services can meet them. METHODS: Hospital Episode Statistics inpatient admission records were extracted for adults between 2006 and 2021. Multimorbidity and frailty was measured using Elixhauser Comorbidity Index and Soong Frailty Scores. Yearly proportions of people with Elixhauser conditions (0, 1, 2, 3 +) or frailty syndromes (0, 1, 2 +) were reported, and the prevalence between 2006 and 2021 compared. Logistic regression models measured how much patient factors impacted the likelihood of having three or more Elixhauser conditions or two or more frailty syndromes. Results were stratified by age groups (18-44, 45-64 and 65 +) and admission type (emergency or elective). RESULTS: The study included 107 million adult inpatient hospital episodes. Overall, the proportion of admissions with one or more Elixhauser conditions rose for acute and elective admissions, with the trend becoming more prominent as age increased. This was most striking among acute admissions for people aged 65 and over, who saw a 35.2% absolute increase in the proportion of admissions who had three or more Elixhauser conditions. This means there were 915,221 extra hospital episodes in the last 12 months of the study, by people who had at least three Elixhauser conditions compared with 15 years ago. The findings were similar for people who had one or more frailty syndromes. Overall, year, age and socioeconomic deprivation were found to be strongly and positively associated with having three or more Elixhauser conditions or two or more frailty syndromes, with socioeconomic deprivation showing a strong dose-response relationship. CONCLUSIONS: Overall, the proportion of hospital admissions with multiple conditions or frailty syndromes has risen over the last 15 years. This matches smaller-scale and anecdotal reports from hospitals and can inform how hospitals are reimbursed.

Polypharmacy in primary care: A population-based retrospective cohort study of electronic health records.

BACKGROUND: Polypharmacy, prescription of multiple medications to a patient, is a major challenge for health systems. There have been no peer-reviewed studies of polypharmacy prevalence and medication cost at a population level in England. AIMS: To determine prevalence and medication cost of polypharmacy, by patient characteristics. Design and setting: Retrospective cohort study of North West London electronic health records. METHOD: We quantified prevalence and direct cost of polypharmacy (five or more regular medications), stratified by demographics and frailty. We fitted a mixed-effects logistic regression for polypharmacy. RESULTS: Of 1.7 million adults, 167,665 (9.4%) were on polypharmacy. Age and socio-economic deprivation were associated with polypharmacy (OR 9.24 95% CI 8.99 to 9.50, age 65-74 compared with 18-44; OR 0.68 95% CI 0.65 to 0.71, least deprived compared with most). Polypharmacy prevalence increased with frailty (OR 1.53 95% CI 1.53 to 1.54 per frailty component, for White women). Men had higher odds of polypharmacy than women at average frailty (OR 1.26 95% CI 1.24 to 1.28) and with additional frailty components (OR 1.10 95% CI 1.09 to 1.10). Black people had lower odds of polypharmacy at average frailty (OR 0.82 95% CI 0.79 to 0.85, compared with White), but along with other ethnicities, saw greater odds increases with increasing frailty (OR 1.02 95% CI 1.01 to 1.03). Annual medication cost 8.2 times more for those on polypharmacy compared with not (£370.89 and £45.31). CONCLUSION: Demographic characteristics are associated with polypharmacy, after adjusting for frailty. Further research should explore why, to reduce health inequities and optimise cost associated with polypharmacy.

Uptake of COVID-19 vaccines and association with hospitalisation due to COVID-19 in pregnancy: Retrospective cohort study.

OBJECTIVES: To determine demographic and clinical characteristics associated with uptake of COVID-19 vaccines among pregnant women, and quantify the relationship between vaccine uptake and admission to hospital for COVID-19. BACKGROUND: Pregnant women are at increased risk of severe adverse outcomes from COVID-19. Since April 2021, COVID-19 vaccines were recommended for pregnant women in the UK. Despite this, evidence shows vaccine uptake is low. However, this evidence has been based only on women admitted to hospital, or on qualitative or survey-based studies. METHODS: Retrospective cohort study including all pregnancies ending between 18 June 2021 and 22 August 2022, among adult women registered with a Northwest London general practice. Statistical analyses were mixed-effects multiple logistic regression models. We conducted a nested case-control analysis to quantify the relationship between vaccine uptake by end of pregnancy and hospitalisation for COVID-19 during pregnancy. RESULTS: Our study included 47,046 pregnancies among 39,213 women. In 26,724 (57%) pregnancies, women had at least one dose of vaccine by the end of pregnancy. Uptake was lowest in pregnant women aged 18-24 (33%; reference group), Black women compared with White (37%; OR 0.55, 95% CI: 0.51 to 0.60), and women in more deprived areas (50%; reference group). Women with chronic conditions were more likely to receive the vaccine than women without (Asthma OR 1.21, 95% CI: 1.13 to 1.29). Patterns were similar for the second dose. Women admitted to hospital were much less likely to be vaccinated (22%) than those not admitted (57%, OR 0.22, 95% CI: 0.15 to 0.31). CONCLUSIONS: Women who received the COVID-19 vaccine were less likely to be hospitalised for COVID-19 during pregnancy. COVID-19 vaccine uptake among pregnant women is suboptimal, particularly in younger women, Black women, and women in more deprived areas. Interventions should focus on increasing uptake in these groups to improve health outcomes and reduce health inequalities.

Increased 30-day all-cause mortality associated with Gram-negative bloodstream infections in England during the COVID-19 pandemic.

BACKGROUND: Our aim was to assess the impact of COVID-19 pandemic on mortality in patients hospitalised with Gram-negative bloodstream infections (GNBSIs). METHODS: A retrospective cohort study including cases of Escherichia coli, Klebsiella species and Pseudomonas aeruginosa in England (January 2015-December 2021) reported to UKHSA's Second Generation Surveillance System. The outcome was 30-day all-cause mortality. Multivariable logistic regression models were built, and adjusted Odds Ratios (ORs) with 95% confidence intervals were reported. RESULTS: Total E. coli, Klebsiella spp. and P. aeruginosa infections were 206,030, 53,819 and 21,129, respectively. Compared to the pre-pandemic period, odds of death during the pandemic (March 2020 onwards) in E. coli, Klebsiella spp. and P. aeruginosa infections with no COVID-19 infection within 28-days of onset were 1.13 (1.08-1.18), 1.15 (1.07-1.25) and 1.09 (0.97-1.22), while odds in GNBSIs with an associated COVID-19 infection were 2.45 (2.26-2.66), 2.96 (2.62-3.34) and 3.15 (2.61-3.80), respectively. Asian patients with an associated COVID-19 infection were more likely to die during the pandemic compared to White patients (E. coli: OR 1.28 (0.95-1.71); Klebsiella spp. OR 1.59 (1.20-2.11); P. aeruginosa: OR 2.02 (1.23-3.31)). CONCLUSIONS: Patients suffering from a GNBSI had increased risk of death during the pandemic, with the risk higher in patients with an associated COVID-19 infection.

Identifying multi-resolution clusters of diseases in ten million patients with multimorbidity in primary care in England.

BACKGROUND: Identifying clusters of diseases may aid understanding of shared aetiology, management of co-morbidities, and the discovery of new disease associations. Our study aims to identify disease clusters using a large set of long-term conditions and comparing methods that use the co-occurrence of diseases versus methods that use the sequence of disease development in a person over time. METHODS: We use electronic health records from over ten million people with multimorbidity registered to primary care in England. First, we extract data-driven representations of 212 diseases from patient records employing (i) co-occurrence-based methods and (ii) sequence-based natural language processing methods. Second, we apply the graph-based Markov Multiscale Community Detection (MMCD) to identify clusters based on disease similarity at multiple resolutions. We evaluate the representations and clusters using a clinically curated set of 253 known disease association pairs, and qualitatively assess the interpretability of the clusters. RESULTS: Both co-occurrence and sequence-based algorithms generate interpretable disease representations, with the best performance from the skip-gram algorithm. MMCD outperforms k-means and hierarchical clustering in explaining known disease associations. We find that diseases display an almost-hierarchical structure across resolutions from closely to more loosely similar co-occurrence patterns and identify interpretable clusters corresponding to both established and novel patterns. CONCLUSIONS: Our method provides a tool for clustering diseases at different levels of resolution from co-occurrence patterns in high-dimensional electronic health records, which could be used to facilitate discovery of associations between diseases in the future.

Having multiple long-term conditions is linked to worse health, poorer quality of life, and difficulties accessing healthcare. Identifying groups, or 'clusters' of diseases that are more likely to occur together in one person may help healthcare services to better meet the needs of those with multiple conditions. Our study aims to identify clusters of similar diseases, based not only on the diseases someone has now, but on the order in which they developed them. We compare a range of methods and find that our strategy performs best at explaining diseases that are already known to be linked, whilst also identifying new clusters of diseases. These methods could be used in future to better understand how diseases occur together, which could help the design of more efficient healthcare services.

The impact of the COVID-19 pandemic on 397 631 elective dental admissions among the under-25s in England: a retrospective study.

BACKGROUND: COVID-19 caused widespread disruptions to health services worldwide, including reductions in elective surgery. Tooth extractions are among the most common reasons for elective surgery among children and young people (CYP). It is unclear how COVID-19 affected elective dental surgeries in hospitals over multiple pandemic waves at a national level. METHODS: Elective dental tooth extraction admissions were selected using Hospital Episode Statistics. Admission trends for the first 14 pandemic months were compared with the previous five years and results were stratified by age (under-11s, 11-16s, 17-24s). RESULTS: The most socioeconomically deprived CYP comprised the largest proportion of elective dental tooth extraction admissions. In April 2020, admissions dropped by >95%. In absolute terms, the biggest reduction was in April (11-16s: -1339 admissions, 95% CI -1411 to -1267; 17-24s: -1600, -1678 to -1521) and May 2020 (under-11s: -2857, -2962 to -2752). Admissions differed by socioeconomic deprivation for the under-11s (P < 0.0001), driven by fewer admissions than expected by the most deprived and more by the most affluent during the pandemic. CONCLUSION: Elective tooth extractions dropped most in April 2020, remaining below pre-pandemic levels throughout the study. Despite being the most likely to be admitted, the most deprived under-11s had the largest reductions in admissions relative to other groups.

Comparing natural language processing representations of coded disease sequences for prediction in electronic health records.

OBJECTIVE: Natural language processing (NLP) algorithms are increasingly being applied to obtain unsupervised representations of electronic health record (EHR) data, but their comparative performance at predicting clinical endpoints remains unclear. Our objective was to compare the performance of unsupervised representations of sequences of disease codes generated by bag-of-words versus sequence-based NLP algorithms at predicting clinically relevant outcomes. MATERIALS AND METHODS: This cohort study used primary care EHRs from 6 286 233 people with Multiple Long-Term Conditions in England. For each patient, an unsupervised vector representation of their time-ordered sequences of diseases was generated using 2 input strategies (212 disease categories versus 9462 diagnostic codes) and different NLP algorithms (Latent Dirichlet Allocation, doc2vec, and 2 transformer models designed for EHRs). We also developed a transformer architecture, named EHR-BERT, incorporating sociodemographic information. We compared the performance of each of these representations (without fine-tuning) as inputs into a logistic classifier to predict 1-year mortality, healthcare use, and new disease diagnosis. RESULTS: Patient representations generated by sequence-based algorithms performed consistently better than bag-of-words methods in predicting clinical endpoints, with the highest performance for EHR-BERT across all tasks, although the absolute improvement was small. Representations generated using disease categories perform similarly to those using diagnostic codes as inputs, suggesting models can equally manage smaller or larger vocabularies for prediction of these outcomes. DISCUSSION AND CONCLUSION: Patient representations produced by sequence-based NLP algorithms from sequences of disease codes demonstrate improved predictive content for patient outcomes compared with representations generated by co-occurrence-based algorithms. This suggests transformer models may be useful for generating multi-purpose representations, even without fine-tuning.

Assigning disease clusters to people: A cohort study of the implications for understanding health outcomes in people with multiple long-term conditions.

Background: Identifying clusters of co-occurring diseases may help characterise distinct phenotypes of Multiple Long-Term Conditions (MLTC). Understanding the associations of disease clusters with health-related outcomes requires a strategy to assign clusters to people, but it is unclear how the performance of strategies compare. Aims: First, to compare the performance of methods of assigning disease clusters to people at explaining mortality, emergency department attendances and hospital admissions over one year. Second, to identify the extent of variation in the associations with each outcome between and within clusters. Methods: We conducted a cohort study of primary care electronic health records in England, including adults with MLTC. Seven strategies were tested to assign patients to fifteen disease clusters representing 212 LTCs, identified from our previous work. We tested the performance of each strategy at explaining associations with the three outcomes over 1 year using logistic regression and compared to a strategy using the individual LTCs. Results: 6,286,233 patients with MLTC were included. Of the seven strategies tested, a strategy assigning the count of conditions within each cluster performed best at explaining all three outcomes but was inferior to using information on the individual LTCs. There was a larger range of effect sizes for the individual LTCs within the same cluster than there was between the clusters. Conclusion: Strategies of assigning clusters of co-occurring diseases to people were less effective at explaining health-related outcomes than a person's individual diseases. Furthermore, clusters did not represent consistent relationships of the LTCs within them, which might limit their application in clinical research.

Effect of timeframes to define long term conditions and sociodemographic factors on prevalence of multimorbidity using disease code frequency in primary care electronic health records: retrospective study.

Objective: To determine the extent to which the choice of timeframe used to define a long term condition affects the prevalence of multimorbidity and whether this varies with sociodemographic factors. Design: Retrospective study of disease code frequency in primary care electronic health records. Data sources: Routinely collected, general practice, electronic health record data from the Clinical Practice Research Datalink Aurum were used. Main outcome measures: Adults (≥18 years) in England who were registered in the database on 1 January 2020 were included. Multimorbidity was defined as the presence of two or more conditions from a set of 212 long term conditions. Multimorbidity prevalence was compared using five definitions. Any disease code recorded in the electronic health records for 212 conditions was used as the reference definition. Additionally, alternative definitions for 41 conditions requiring multiple codes (where a single disease code could indicate an acute condition) or a single code for the remaining 171 conditions were as follows: two codes at least three months apart; two codes at least 12 months apart; three codes within any 12 month period; and any code in the past 12 months. Mixed effects regression was used to calculate the expected change in multimorbidity status and number of long term conditions according to each definition and associations with patient age, gender, ethnic group, and socioeconomic deprivation. Results: 9 718 573 people were included in the study, of whom 7 183 662 (73.9%) met the definition of multimorbidity where a single code was sufficient to define a long term condition. Variation was substantial in the prevalence according to timeframe used, ranging from 41.4% (n=4 023 023) for three codes in any 12 month period, to 55.2% (n=5 366 285) for two codes at least three months apart. Younger people (eg, 50-75% probability for 18-29 years v 1-10% for ≥80 years), people of some minority ethnic groups (eg, people in the Other ethnic group had higher probability than the South Asian ethnic group), and people living in areas of lower socioeconomic deprivation were more likely to be re-classified as not multimorbid when using definitions requiring multiple codes. Conclusions: Choice of timeframe to define long term conditions has a substantial effect on the prevalence of multimorbidity in this nationally representative sample. Different timeframes affect prevalence for some people more than others, highlighting the need to consider the impact of bias in the choice of method when defining multimorbidity.

Impact of the COVID-19 pandemic on the clinical management trends for acute appendicitis among the under-25s: a retrospective study.

OBJECTIVE: To describe the COVID-19 pandemic's impact on acute appendicitis management on children and young people (CYP). DESIGN: Retrospective cohort study. SETTING: All English National Health Service hospitals. PATIENTS: Acute appendicitis admissions (all, simple, complex) by CYP (under-5s, 5-9s, 10-24s). EXPOSURE: Study pandemic period: February 2020-March 2021. Comparator pre-pandemic period: February 2015-January 2020. MAIN OUTCOME MEASURES: Monthly appendicectomy and laparoscopic appendicectomy rate trends and absolute differences between pandemic month and the pre-pandemic average. Proportions of appendicitis admissions comprising complex appendicitis by hospital with or without specialist paediatric centres were compared. RESULTS: 101 462 acute appendicitis admissions were analysed. Appendicectomy rates fell most in April 2020 for the 5-9s (-18.4% (95% CI -26.8% to -10.0%)) and 10-24s (-28.4% (-38.9% to -18.0%)), driven by reductions in appendicectomies for simple appendicitis. This was equivalent to -54 procedures (-68.4 to -39.6) and -512 (-555.9 to -467.3) for the 5-9s and 10-24s, respectively. Laparoscopic appendicectomies fell in April 2020 for the 5-9s (-15.5% (-23.2% to -7.8%)) and 10-24s (-44.8% (-57.9% to -31.6%) across all types, which was equivalent to -43 (-56.1 to 30.3) and -643 (-692.5 to -593.1) procedures for the 5-9s and 10-24s, respectively. A larger proportion of complex appendicitis admissions were treated within trusts with specialist paediatric centres during the pandemic. CONCLUSIONS: For CYP across English hospitals, a sharp recovery followed a steep reduction in appendicectomy rates in April 2020, due to concerns with COVID-19 transmission. This builds on smaller-sized studies reporting the immediate short-term impacts.

Treating insomnia in people who are incarcerated: a feasibility study of a multicomponent treatment pathway.

Around 60% of people who are incarcerated have insomnia; 6-10 times more prevalent than the general population. Yet, there is no standardized, evidence-based approach to insomnia treatment in prison. We assessed the feasibility of a treatment pathway for insomnia in a high-secure prison to inform a future randomized controlled trial (RCT) and initial efficacy data for sleep and mental health outcomes. We used a within-participants pre-post design. The stepped-care pathway included: self-management with peer support, environmental aids, and cognitive behavioral therapy for insomnia (CBTi). Assessment measures for insomnia, well-being, mood, anxiety, suicidality, overall health, sleepiness, fatigue, and cognitive functioning were administered at baseline and pathway exit. Feasibility criteria included eligibility to participate, CBTi uptake, and assessment completion. Forty-two adult males who are incarcerated were approached of which 95.2% were eligible. Of those deemed eligible, most participated (36/40, 90.0%). Most who completed baseline completed post-assessments (28/36, 77.8%) and of these, most showed improvements in their subjective sleep (27/28, 96.4%). Large reductions were found from pre- to posttreatment in insomnia severity (d = -1.81, 95% CI: 8.3 to 12.9) and 57.0% reported no clinically significant insomnia symptoms at post-assessment. There was no overall change in actigraphy-measured sleep. Large treatment benefits were found for depression, anxiety, well-being, and cognitive functioning, with a medium benefit on suicidal ideation. The treatment pathway for insomnia in prison was feasible and may be an effective treatment for insomnia in people who are incarcerated, with additional promising benefits for mental health. A pragmatic RCT across different prison populations is warranted. This paper is part of the Sleep and Circadian Health in the Justice System Collection.

A service evaluation of passive remote monitoring technology for patients in a high-secure forensic psychiatric hospital: a qualitative study.

BACKGROUND: Technology has the potential to remotely monitor patient safety in real-time that helps staff and without disturbing the patient. However, staff and patients' perspectives on using passive remote monitoring within an inpatient setting is lacking. The study aim was to explore stakeholders' perspectives about using Oxehealth passive monitoring technology within a high-secure forensic psychiatric hospital in the UK as part of a wider mixed-methods service evaluation. METHODS: Semi-structured interviews were conducted with staff and patients with experience of using Oxehealth technology face-to-face within a private room in Broadmoor Hospital. We applied thematic analysis to the data of each participant group separately. Themes and sub-themes were integrated, finalised, and presented in a thematic map. Design, management, and analysis was meaningfully informed by both staff and patients. RESULTS: Twenty-four participants were interviewed (n = 12 staff, n = 12 patients). There were seven main themes: detecting deterioration and improving health and safety, "big brother syndrome", privacy and dignity, knowledge and understanding, acceptance, barriers to use and practice issues and future changes needed. Oxehealth technology was considered acceptable to both staff and patients if the technology was used to detect deterioration and improve patient's safety providing patient's privacy was not invaded. However, overall acceptance was lower when knowledge and understanding of the technology and its camera was limited. Most patients could not understand why both physical checks through bedroom windows, and Oxehealth was needed to monitor patients, whilst staff felt Oxehealth should not replace physical checks of patients as reassures staff on patient safety. CONCLUSIONS: Oxehealth technology is considered viable and acceptable by most staff and patients but there is still some concern about its possible intrusive nature. However, more support and education for new patients and staff to better understand how Oxehealth works in the short- and long-term could be introduced to further improve acceptability. A feasibility study or pilot trial to compare the impact of Oxehealth with and without physical checks may be needed.

Identifying potential biases in code sequences in primary care electronic healthcare records: a retrospective cohort study of the determinants of code frequency.

OBJECTIVES: To determine whether the frequency of diagnostic codes for long-term conditions (LTCs) in primary care electronic healthcare records (EHRs) is associated with (1) disease coding incentives, (2) General Practice (GP), (3) patient sociodemographic characteristics and (4) calendar year of diagnosis. DESIGN: Retrospective cohort study. SETTING: GPs in England from 2015 to 2022 contributing to the Clinical Practice Research Datalink Aurum dataset. PARTICIPANTS: All patients registered to a GP with at least one incident LTC diagnosed between 1 January 2015 and 31 December 2019. PRIMARY AND SECONDARY OUTCOME MEASURES: The number of diagnostic codes for an LTC in (1) the first and (2) the second year following diagnosis, stratified by inclusion in the Quality and Outcomes Framework (QOF) financial incentive programme. RESULTS: 3 113 724 patients were included, with 7 723 365 incident LTCs. Conditions included in QOF had higher rates of annual coding than conditions not included in QOF (1.03 vs 0.32 per year, p<0.0001). There was significant variation in code frequency by GP which was not explained by patient sociodemographics. We found significant associations with patient sociodemographics, with a trend towards higher coding rates in people living in areas of higher deprivation for both QOF and non-QOF conditions. Code frequency was lower for conditions with follow-up time in 2020, associated with the onset of the COVID-19 pandemic. CONCLUSIONS: The frequency of diagnostic codes for newly diagnosed LTCs is influenced by factors including patient sociodemographics, disease inclusion in QOF, GP practice and the impact of the COVID-19 pandemic. Natural language processing or other methods using temporally ordered code sequences should account for these factors to minimise potential bias.

Factors associated with enrolment into a national COVID-19 pulse oximetry remote monitoring programme in England: a retrospective observational study.

BACKGROUND: Hypoxaemia is an important predictor of severity in individuals with COVID-19 and can present without symptoms. The COVID Oximetry @home (CO@h) programme was implemented across England in November, 2020, providing pulse oximeters to higher-risk people with COVID-19 to enable early detection of deterioration and the need for escalation of care. We aimed to describe the clinical and demographic characteristics of individuals enrolled onto the programme and to assess whether there were any inequalities in enrolment. METHODS: This retrospective observational study was based on data from a cohort of people resident in England recorded as having a positive COVID-19 test between Oct 1, 2020, and May 3, 2021. The proportion of participants enrolled onto the CO@h programmes in the 7 days before and 28 days after a positive COVID-19 test was calculated for each clinical commissioning group (CCG) in England. Two-level hierarchical multivariable logistic regression with random intercepts for each CCG was run to identify factors predictive of being enrolled onto the CO@h programme. FINDINGS: CO@h programme sites were reported by NHS England as becoming operational between Nov 21 and Dec 31, 2020. 1 227 405 people resident in 72 CCGs had a positive COVID-19 test between the date of programme implementation and May 3, 2021, of whom 19 932 (1·6%) were enrolled onto the CO@h programme. Of those enrolled, 14 441 (72·5%) were aged 50 years or older or were identified as clinically extremely vulnerable (ie, having a high-risk medical condition). Higher odds of enrolment onto the CO@h programme were found in older individuals (adjusted odds ratio 2·21 [95% CI 2·19-2·23], p<0·001, for those aged 50-64 years; 3·48 [3·33-3·63], p<0·001, for those aged 65-79 years; and 2·50 [2·34-2·68], p<0·001, for those aged ≥80 years), in individuals of non-White ethnicity (1·35 [1·28-1·43], p<0·001, for Asian individuals; 1·13 [1·04-1·22], p=0·005, for Black individuals; and 1·17 [1·03-1·32], p=0·015, for those of mixed ethnicity), in those who were overweight (1·31 [1·26-1·37], p<0·001) or obese (1·69 [1·63-1·77], p<0·001), or in those identified as clinically extremely vulnerable (1·58 [1·51-1·65], p<0·001), and lower odds were reported in those from the least socioeconomically deprived areas compared with those from the most socioeconomically deprived areas (0·75 [0·69-0·81]; p<0·001). INTERPRETATION: Nationally, uptake of the CO@h programme was low, with clinical judgment used to determine eligibility. Preferential enrolment onto the pulse oximetry monitoring programme was observed in people known to be at the highest risk of developing severe COVID-19. FUNDING: NHS England, National Institute for Health Research, and The Wellcome Trust.

Evaluating the impact of a pulse oximetry remote monitoring programme on mortality and healthcare utilisation in patients with COVID-19 assessed in emergency departments in England: a retrospective matched cohort study.

BACKGROUND: To identify the impact of enrolment onto a national pulse oximetry remote monitoring programme for COVID-19 (COVID-19 Oximetry @home; CO@h) on health service use and mortality in patients attending Emergency Departments (EDs). METHODS: We conducted a retrospective matched cohort study of patients enrolled onto the CO@h pathway from EDs in England. We included all patients with a positive COVID-19 test from 1 October 2020 to 3 May 2021 who attended ED from 3 days before to 10 days after the date of the test. All patients who were admitted or died on the same or following day to the first ED attendance within the time window were excluded. In the primary analysis, participants enrolled onto CO@h were matched using demographic and clinical criteria to participants who were not enrolled. Five outcome measures were examined within 28 days of first ED attendance: (1) Death from any cause; (2) Any subsequent ED attendance; (3) Any emergency hospital admission; (4) Critical care admission; and (5) Length of stay. RESULTS: 15 621 participants were included in the primary analysis, of whom 639 were enrolled onto CO@h and 14 982 were controls. Odds of death were 52% lower in those enrolled (95% CI 7% to 75%) compared with those not enrolled onto CO@h. Odds of any ED attendance or admission were 37% (95% CI 16% to 63%) and 59% (95% CI 32% to 91%) higher, respectively, in those enrolled. Of those admitted, those enrolled had 53% (95% CI 7% to 76%) lower odds of critical care admission. There was no significant impact on length of stay. CONCLUSIONS: These findings indicate that for patients assessed in ED, pulse oximetry remote monitoring may be a clinically effective and safe model for early detection of hypoxia and escalation. However, possible selection biases might limit the generalisability to other populations.

Characteristics associated with influenza vaccination uptake in pregnancy: a retrospective cohort study.

BACKGROUND: Pregnant women are at increased risk from influenza, yet maternal influenza vaccination levels remain suboptimal. AIM: To estimate associations between sociodemographic and health characteristics and seasonal influenza vaccination uptake among pregnant women, and to understand trends over time to inform interventions to improve vaccine coverage. DESIGN AND SETTING: Retrospective cohort study using linked electronic health records of women in North West London with a pregnancy overlapping an influenza season between September 2010 and February 2020. METHOD: A multivariable mixed-effects logistic regression model was used to identify associations between characteristics of interest and the primary outcome of influenza vaccination. RESULTS: In total, 451 954 pregnancies, among 260 744 women, were included. In 85 376 (18.9%) pregnancies women were vaccinated against seasonal influenza. Uptake increased from 8.4% in 2010/11 to 26.4% in 2017/18, dropping again to 21.1% in 2019/20. Uptake was lowest among women aged 15-19 years (11.9%; reference category) or ≥40 years (15.2%; odds ratio [OR] 1.17, 95% confidence interval [CI] = 1.10 to 1.24); of Black (14.1%; OR 0.55, 95% CI = 0.53 to 0.57) or unknown ethnicity (9.9%; OR 0.42, 95% CI = 0.39 to 0.46); who lived in more deprived areas (OR least versus most deprived [reference category] 1.16, 95% CI = 1.11 to 1.21); or with no known risk factors for severe influenza. CONCLUSION: Seasonal influenza vaccine uptake in pregnant women increased in the decade before the COVID-19 pandemic, but remained suboptimal. Targeted approaches are recommended to reducing inequalities in access to vaccination and should focus on women of Black ethnicity, younger and older women, and women living in deprived areas.

Patient outcomes following emergency admission to hospital for COVID-19 compared with influenza: retrospective cohort study.

BACKGROUND: We examine differences in posthospitalisation outcomes, and health system resource use, for patients hospitalised with COVID-19 during the UK's first pandemic wave in 2020, and influenza during 2018 and 2019. METHODS: This retrospective cohort study used routinely collected primary and secondary care data. Outcomes, measured for 90 days follow-up after discharge were length of stay in hospital, mortality, emergency readmission and primary care activity. RESULTS: The study included 5132 patients admitted to hospital as an emergency, with COVID-19 and influenza cohorts comprising 3799 and 1333 patients respectively. Patients in the COVID-19 cohort were more likely to stay in hospital longer than 10 days (OR 3.91, 95% CI 3.14 to 4.65); and more likely to die in hospital (OR 11.85, 95% CI 8.58 to 16.86) and within 90 days of discharge (OR 7.92, 95% CI 6.20 to 10.25). For those who survived, rates of emergency readmission within 90 days were comparable between COVID-19 and influenza cohorts (OR 1.07, 95% CI 0.89 to 1.29), while primary care activity was greater among the COVID-19 cohort (incidence rate ratio 1.30, 95% CI 1.23 to 1.37). CONCLUSIONS: Patients admitted for COVID-19 were more likely to die, more likely to stay in hospital for over 10 days and interact more with primary care after discharge, than patients admitted for influenza. However, readmission rates were similar for both groups. These findings, while situated in the context of the first wave of COVID-19, with the associated pressures on the health system, can inform health service planning for subsequent waves of COVID-19, and show that patients with COVID-19 interact more with healthcare services as well as having poorer outcomes than those with influenza.