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Objective: Long waiting times for elective hospital treatments are common in many countries. This study seeks to address a deficit in the literature concerning the effect of long waits on the wider consumption of healthcare resources. Methods: We carried out a retrospective treatment-control study in a healthcare system in South West England from 15 June 2021 to 15 December 2021. We compared weekly contacts with health services of patients waiting over 18 weeks for treatment ('Treatments') and people not on a waiting list ('Controls'). Controls were matched to Treatments based on age, sex, deprivation and multimorbidity. Treatments were stratified by the clinical specialty of the awaited hospital treatment, with healthcare usage assessed over various healthcare settings. Wilcoxon signed-rank tests assessed whether there was an increase in healthcare utilisation and bootstrap resampling was used to estimate the magnitude of any differences. Results: A total of 44,616 patients were waiting over 18 weeks (the constitutional target in England) for treatment during the study period. There was an increase (p < 0.0004) in healthcare utilisation for all specialties. Patients in the Cardiothoracic Surgery specialty had the largest increase, with 17.9 [interquartile-range: 4.3, 33.8] additional contacts with secondary care and 17.3 [-1.1, 34.1] additional prescriptions per year. Conclusion: People waiting for treatment consume higher levels of healthcare than comparable individuals not on a waiting list. These findings are relevant for clinicians and managers in better understanding patient need and reducing harm. Results also highlight the possible 'false economy' in failing to promptly resolve long elective waits. [ABSTRACT FROM AUTHOR]

Objective: To compare the original synthetic control (OSC) method with alternative approaches (Generalized [GSC], Micro [MSC], and Bayesian [BSC] synthetic control methods) and re‐evaluate the impact of a significant restructuring of urgent and emergency care in Northeast England, which included the opening of the UK's first purpose‐built specialist emergency care hospital. Data Sources: Simulations and data from Secondary Uses Service data, a single comprehensive repository for patient‐level health care data in England. Study Design: Hospital use of individuals exposed and unexposed to the restructuring is compared. We estimate the impact using OSC, MSC, BSC, and GSC applied at the general practice level. We contrast the estimation methods' performance in a Monte Carlo simulation study. Data Collection/Extraction Methods: Hospital activity data from Secondary Uses Service for patients aged over 18 years registered at a general practice in England from April 2011 to March 2019. Principal Findings: None of the methods dominated all simulation scenarios. GSC was generally preferred. In contrast to an earlier evaluation that used OSC, GSC reported a smaller impact of the opening of the hospital on Accident and Emergency (A&E) department (also known as emergency department or casualty) visits and no evidence for any impact on the proportion of A&E patients seen within 4 h. Conclusions: The simulation study highlights cases where the considered methods may lead to biased estimates in health policy evaluations. GSC was found to be the most reliable method of those considered. Considering more disaggregated data over a longer time span and applying GSC indicates that the specialist emergency care hospitals in Northumbria had less impact on A&E visits and waiting times than suggested by the original evaluation which applied OSC to more aggregated data. [ABSTRACT FROM AUTHOR]

To evaluate referral patterns to secondary care and categorise referrals by complexity level. To assess compliance with commissioning guidelines for cases accepted for treatment in secondary care, comparing complexity to Index of Orthodontic Treatment Need (IOTN) data. Service evaluation of all referrals during study period recorded prospectively. The standard was set that >85% of cases offered treatment in secondary care should be complexity level 3b. Seven NHS orthodontic departments within a regional clinical effectiveness group in secondary care in England. All patients seen as new referrals during the three-month study period. Data recorded at clinic appointment on data collection proforma including referral information, complexity, IOTN and outcome of the first appointment. Data were collated on a spreadsheet and simple statistics were applied. A total of 493 patients were included in data analysis. Median waiting time for a new patient appointment was 11.0 weeks. For the whole study group, 53.8% were IOTN 5, 30.8% IOTN 4, 9.7% IOTN 3; complexity levels were 54.2% complexity 3b, 37.1% 3a and 6.7% 2. Of the patients, 30.0% were offered treatment in secondary care at their first attendance; of these, 74.3 % were IOTN 5, 93.2% were complexity level 3b. The gold standard has been met in the region as a whole and at each individual unit. There is a large discrepancy between the IOTN score and complexity level in those patients offered orthodontic treatment in secondary care. Routine recording of complexity level is recommended for all patients seen in the secondary care setting. [ABSTRACT FROM AUTHOR]

Purpose: Early Intervention Psychosis [EIP] services have gained traction internationally, but are currently undergoing various forms of reconfiguration. In England, such services are now mandated to ensure 50% of accepted referrals commence care within 14 days, but no empirical evidence exists. We sought to estimate waiting times to EIP services in a large, representative epidemiological cohort in England, and investigate possible reasons for any variation.Methods: We estimated median waiting time from referral to acceptance by EIP services and investigated whether this varied by clinical, demographic or neighbourhood-level factors, amongst 798 participants, 16-35 years old, presenting to six EIP services over 3.5 years in a defined catchment area serving 2.5 million people. We used parametric survival analysis to inspect variation in waiting times (in days).Results: Median waiting time was 15 days (interquartile range 7-30), although this varied across services (p < 0.01). Waiting times increased over the case ascertainment period by an average of 4.3 days (95% CI 1.3, 6.2; p < 0.01). Longer waiting times were associated with greater diagnostic uncertainty, indexed by an organic presentation (+ 9.1 days; 95% CI 1.9, 16.6; p < 0.01), polysubstance abuse (+ 2.6; 0.6, 3.9; p < 0.01), absence of psychotic disorder (+1.8; -0.1, 3.0; p = 0.05) and insidious onset (+1.8; -0.1, 3.0; p = 0.06). Waiting times did not vary by most demographic or neighbourhood-level characteristics.Conclusions: EIP services operate close to new waiting time standards in England, with little systematic variation by sociodemographic position. However, waiting times increased over the study period, coinciding with substantial service reorganisation. Longer waiting times associated with greater diagnostic uncertainty highlight opportunities to reduce delays in certain clinical groups at initial referral. [ABSTRACT FROM AUTHOR]

Abstract: Aim: To investigate if a relationship exists between hospital waiting time to major amputation and outcome. Method: All patients undergoing major lower limb amputation in England between April 2002 and March 2006 were identified from the Hospital Episodes Statistics (HES) data. Amputations related to trauma or malignancy were excluded. The length of wait (LOW), from date of admission to date of major amputation was calculated. A two-level regression model was used to investigate if LOW had a significant effect on recovery time and in-hospital mortality. Results were adjusted for age, sex, Charlson score, Social Deprivation, mode of intervention (bypass/angioplasty/no intervention) and mode of admission (emergency/elective). Results: 14 168 major amputations were identified. 12 884 (90.9%) had no intervention prior to amputation on that admission. Length of Wait (LOW) significantly prolonged recovery in men (Exponential Estimate 1.01 1.01–1.02 p < 0.0001) and women (EE 1.02 1.01–1.02 p < 0.0001) and increased in-hospital mortality in men (OR 1.02 1.02–1.03 p < 0.0001). Risk of in-hospital death increased by 2% for each day waited. Conclusion: Delays in decision making or in getting a patient into the operating theatre have a negative effect on patient outcome in terms of overall length of stay and mortality after major lower limb amputation. [Copyright &y& Elsevier]