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Estimates suggest that 1 in 100 people in the UK live with facial scarring. Despite this incidence, psychological support is limited.
The aim of this study was to strengthen the case for improving such support by determining the incidence and risk factors for anxiety and depression disorders in patients with facial scarring.
A matched cohort study was performed. Patients were identified via secondary care data sources, using clinical codes for conditions resulting in facial scarring. A diagnosis of anxiety or depression was determined by linkage with the patient's primary care general practice data. Incidence was calculated per 1000 person-years at risk (PYAR). Logistic regression was used to determine risk factors.
Between 2009 and 2018, 179 079 patients met the study criteria and were identified as having a facial scar, and matched to 179 079 controls. The incidence of anxiety in the facial scarring group was 10.05 per 1000 PYAR compared with 7.48 per 1000 PYAR for controls. The incidence of depression in the facial scarring group was 16.28 per 1000 PYAR compared with 9.56 per 1000 PYAR for controls. Age at the time of scarring, previous history of anxiety or depression, female gender, socioeconomic status and classification of scarring increased the risk of both anxiety disorders and depression.
There is a high burden of anxiety disorders and depression in this patient group. Risk of these mental health disorders is very much determined by factors apparent at the time of injury, supporting the need for psychological support.
New approaches are needed to safely reduce emergency admissions to hospital by targeting interventions effectively in primary care. A predictive risk stratification tool (PRISM) identifies each registered patient's risk of an emergency admission in the following year, allowing practitioners to identify and manage those at higher risk. We evaluated the introduction of PRISM in primary care in one area of the United Kingdom, assessing its impact on emergency admissions and other service use.
We conducted a randomized stepped wedge trial with cluster-defined control and intervention phases, and participant-level anonymized linked outcomes. PRISM was implemented in eleven primary care practice clusters (total thirty-two practices) over a year from March 2013. We analyzed routine linked data outcomes for 18 months.
We included outcomes for 230,099 registered patients, assigned to ranked risk groups.
Overall, the rate of emergency admissions was higher in the intervention phase than in the control phase: adjusted difference in number of emergency admissions per participant per year at risk, delta = .011 (95 percent Confidence Interval, CI .010, .013). Patients in the intervention phase spent more days in hospital per year: adjusted delta = .029 (95 percent CI .026, .031). Both effects were consistent across risk groups.
Primary care activity increased in the intervention phase overall delta = .011 (95 percent CI .007, .014), except for the two highest risk groups which showed a decrease in the number of days with recorded activity.
Introduction of a predictive risk model in primary care was associated with increased emergency episodes across the general practice population and at each risk level, in contrast to the intended purpose of the model. Future evaluation work could assess the impact of targeting of different services to patients across different levels of risk, rather than the current policy focus on those at highest risk.
Emergency admissions to hospital are a major financial burden on health services. In one area of the United Kingdom (UK), we evaluated a predictive risk stratification tool (PRISM) designed to support primary care practitioners to identify and manage patients at high risk of admission. We assessed the costs of implementing PRISM and its impact on health services costs. At the same time as the study, but independent of it, an incentive payment (‘QOF’) was introduced to encourage primary care practitioners to identify high risk patients and manage their care.
We conducted a randomized stepped wedge trial in thirty-two practices, with cluster-defined control and intervention phases, and participant-level anonymized linked outcomes. We analysed routine linked data on patient outcomes for 18 months (February 2013 – September 2014). We assigned standard unit costs in pound sterling to the resources utilized by each patient. Cost differences between the two study phases were used in conjunction with differences in the primary outcome (emergency admissions) to undertake a cost-effectiveness analysis.
We included outcomes for 230,099 registered patients. We estimated a PRISM implementation cost of GBP0.12 per patient per year.
Costs of emergency department attendances, outpatient visits, emergency and elective admissions to hospital, and general practice activity were higher per patient per year in the intervention phase than control phase (adjusted δ = GBP76, 95 percent Confidence Interval, CI GBP46, GBP106), an effect that was consistent and generally increased with risk level.
Despite low reported use of PRISM, it was associated with increased healthcare expenditure. This effect was unexpected and in the opposite direction to that intended. We cannot disentangle the effects of introducing the PRISM tool from those of imposing the QOF targets; however, since across the UK predictive risk stratification tools for emergency admissions have been introduced alongside incentives to focus on patients at risk, we believe that our findings are generalizable.
A predictive risk stratification tool (PRISM) to estimate a patient's risk of an emergency hospital admission in the following year was trialled in general practice in an area of the United Kingdom. PRISM's introduction coincided with a new incentive payment (‘QOF’) in the regional contract for family doctors to identify and manage the care of people at high risk of emergency hospital admission.
Alongside the trial, we carried out a complementary qualitative study of processes of change associated with PRISM's implementation. We aimed to describe how PRISM was understood, communicated, adopted, and used by practitioners, managers, local commissioners and policy makers. We gathered data through focus groups, interviews and questionnaires at three time points (baseline, mid-trial and end-trial). We analyzed data thematically, informed by Normalisation Process Theory (1).
All groups showed high awareness of PRISM, but raised concerns about whether it could identify patients not yet known, and about whether there were sufficient community-based services to respond to care needs identified. All practices reported using PRISM to fulfil their QOF targets, but after the QOF reporting period ended, only two practices continued to use it. Family doctors said PRISM changed their awareness of patients and focused them on targeting the highest-risk patients, though they were uncertain about the potential for positive impact on this group.
Though external factors supported its uptake in the short term, with a focus on the highest risk patients, PRISM did not become a sustained part of normal practice for primary care practitioners.
Objectives: There has been a rapid growth in the use of patient-assessed outcomes (PAOs) that are measured in the assessment of health technologies. The process of collection of such measures can be costly, and there may be problems associated with the ability of the patient to complete them. The use of electronically stored routine data may reduce costs and overcome the problems associated with patient completion. The feasibility of using routine data surrogates for the UK Inflammatory Bowel Disease Questionnaire (UKIBDQ) and the Short Form 36 (SF-36) was examined.
Methods: Clinical terms and codes for the UKIBDQ and SF-36 questions were identified, and data from electronic routine sources were sought on patients participating in a randomized controlled trial. The presence or absence of relevant symptoms was used to generate surrogate scores, which were compared with the original scores.
Results: Most questions in the UKIBDQ and SF-36 were codable but only one third of the terms were recorded routinely in electronic form. The surrogate total IBDQ score had reasonable reliability (Kuder–Richardson coefficient = 0.51), but this reliability could not be determined for the SF-36. Intraclass correlations between routine and designed data were poor to weak.
Conclusions: Although electronic routine data sources had the capacity to develop surrogate measures for patient assessed outcomes, there was evidence of wide underutilization of coding systems leading to an underreporting of symptoms. This finding is consistent with previous literature where only poor correlations were illustrated between patient assessed outcomes and surrogate scoring of symptoms.
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