Unseen patterns of preventable emergency care: Emergency department visits for ambulatory care sensitive conditions

OBJECTIVE: Admissions for ambulatory care sensitive conditions (ACSCs) are often used to measure potentially preventable emergency care. Visits to emergency departments with ACSCs may also be preventable care but are excluded from such measures if patients are not admitted. We established the extent and composition of this preventable emergency care. METHODS: We analysed 1,505,979 emergency department visits (5% of the national total) between 1 April 2015 and 31 March 2017 at six hospital Trusts in England, using International Classification of Diseases diagnostic coding. We calculated the number of visits for each ACSC and examined the proportions of these visits that did not result in admission by condition and patient characteristics. RESULTS: 11.1% of emergency department visits were for ACSCs. 55.0% of these visits did not result in hospital admission. Whilst the majority of ACSC visits were for acute rather than chronic conditions (59.4% versus 38.4%), acute visits were much more likely to conclude without admission (70.3% versus 33.4%). Younger, more deprived and ethnic minority patients were less likely to be admitted when they visited the emergency department with an ACSC. CONCLUSIONS: Over half of preventable emergency care is not captured by measures of admissions. The probability of admission at a preventable visit varies substantially between conditions and patient groups. Focussing only on admissions for ACSCs provides an incomplete and skewed picture of the types of conditions and patients receiving preventable care. Measures of preventable emergency care should include visits in addition to admissions

Using survival analysis to improve estimates of life year gains in policy evaluations

Background. Policy evaluations taking a lifetime horizon have converted estimated changes in short-term mortality to expected life year gains using general population life expectancy. However, the life expectancy of the affected patients may differ from the general population. In trials, survival models are commonly used to extrapolate life year gains. The objective was to demonstrate the feasibility and materiality of using parametric survival models to extrapolate future survival in health care policy evaluations. Methods. We used our previous cost-effectiveness analysis of a pay-for-performance program as a motivating example. We first used the cohort of patients admitted prior to the program to compare 3 methods for estimating remaining life expectancy. We then used a difference-in-differences framework to estimate the life year gains associated with the program using general population life expectancy and survival models. Patient-level data from Hospital Episode Statistics was utilized for patients admitted to hospitals in England for pneumonia between 1 April 2007 and 31 March 2008 and between 1 April 2009 and 31 March 2010, and linked to death records for the period from 1 April 2007 to 31 March 2011. Results. In our cohort of patients, using parametric survival models rather than general population life expectancy figures reduced the estimated mean life years remaining by 30% (9.19 v. 13.15 years, respectively). However, the estimated mean life year gains associated with the program are larger using survival models (0.380 years) compared to using general population life expectancy (0.154 years). Conclusions. Using general population life expectancy to estimate the impact of health care policies can overestimate life expectancy but underestimate the impact of policies on life year gains. Using a longer follow-up period improved the accuracy of estimated survival and program impact considerably. </jats:p

How do hospitals respond to payment unbundling for diagnostic imaging of suspected cancer patients?

Payments for some diagnostic scans undertaken in outpatient settings were unbundled from Diagnosis Related Group based payments in England in April 2013 to address under‐provision. Unbundled scans attracted additional payments of between £45 and £748 directly following the reform. We examined the effect on utilization of these scans for patients with suspected cancer. We also explored whether any detected effects represented real increases in use of scans or better coding of activity. We applied difference‐in‐differences regression to patient‐level data from Hospital Episodes Statistics for 180 NHS hospital Trusts in England, between April 2010 and March 2018. We also explored heterogeneity in recorded use of scans before and after the unbundling at hospital Trust‐level. Use of scans increased by 0.137 scans per patient following unbundling, a 134% relative increase. This increased annual national provider payments by £79.2 million. Over 15% of scans recorded after the unbundling were at providers that previously recorded no scans, suggesting some of the observed increase in activity reflected previous under‐coding. Hospitals recorded substantial increases in diagnostic imaging for suspected cancer in response to payment unbundling. Results suggest that the reform also encouraged improvements in recording, so the real increase in testing is likely lower than detected

The effect of local hospital waiting times on GP referrals for suspected cancer

Introduction: Reducing waiting times is a major policy objective in publicly-funded healthcare systems. However, reductions in waiting times can produce a demand response, which may offset increases in capacity. Early detection and diagnosis of cancer is a policy focus in many OECD countries, but prolonged waiting periods for specialist confirmation of diagnosis could impede this goal. We examine whether urgent GP referrals for suspected cancer patients are responsive to local hospital waiting times. Method: We used annual counts of referrals from all 6,667 general practices to all 185 hospital Trusts in England between April 2012 and March 2018. Using a practice-level measure of local hospital waiting times based on breaches of the two-week maximum waiting time target, we examined the relationship between waiting times and urgent GP referrals for suspected cancer. To identify whether the relationship is driven by differences between practices or changes over time, we estimated three regression models: pooled linear regression, a between-practice estimator, and a within-practice estimator. Results: Ten percent higher rates of patients breaching the two-week wait target in local hospitals were associated with higher volumes of referrals in the pooled linear model (4.4%; CI 2.4% to 6.4%) and the between-practice estimator (12.0%; CI 5.5% to 18.5%). The relationship was not statistically significant using the within-practice estimator (1.0%; CI -0.4% to 2.5%). Conclusion: The positive association between local hospital waiting times and GP demand for specialist diagnosis was caused by practices with higher levels of referrals facing longer local waiting times. Temporal changes in waiting times faced by individual practices were not related to changes in their referral volumes. GP referrals for diagnostic cancer services were not found to respond to waiting times in the short-term. In this setting, it may therefore be possible to reduce waiting times by increasing supply without consequently increasing demand

Economic analysis of service and delivery interventions in health care

There are well-developed guidelines for economic evaluation of clearly defined clinical interventions, but no such guidelines for economic analysis of service interventions. Distinctive challenges for analysis of service interventions include diffuse effects, wider system impacts, and variability in implementation, costs and effects. Cost-effectiveness evidence is as important for service interventions as for clinical interventions. There is also an important role for wider forms of economic analysis to increase our general understanding of context, processes and behaviours in the care system. Methods exist to estimate the cost-effectiveness of service interventions before and after introduction, to measure patient and professional preferences, to reflect the value of resources used by service interventions, and to capture wider system effects, but these are not widely applied. Future priorities for economic analysis should be to produce cost-effectiveness evidence and to increase our understanding of how service interventions affect, and are affected by, the care system

The effects of structure, process and outcome incentives on primary care referrals to a national prevention programme

From Wiley via Jisc Publications RouterHistory: received 2020-05-22, rev-recd 2021-02-09, accepted 2021-02-17, pub-electronic 2021-03-30, pub-print 2021-06Article version: VoRPublication status: PublishedFunder: Health Services and Delivery Research Programme; Id: http://dx.doi.org/10.13039/501100002001; Grant(s): 16/48/07Abstract: Despite widespread use, evidence is sparse on whether financial incentives in healthcare should be linked to structure, process or outcome. We examine the impact of different incentive types on the quantity and effectiveness of referrals made by general practices to a new national prevention programme in England. We measured effectiveness by the number of referrals resulting in programme attendance. We surveyed local commissioners about their use of financial incentives and linked this information to numbers of programme referrals and attendances from 5170 general practices between April 2016 and March 2018. We used multivariate probit regressions to identify commissioner characteristics associated with the use of different incentive types and negative binomial regressions to estimate their effect on practice rates of referral and attendance. Financial incentives were offered by commissioners in the majority of areas (89%), with 38% using structure incentives, 69% using process incentives and 22% using outcome incentives. Compared to practices without financial incentives, neither structure nor process incentives were associated with statistically significant increases in referrals or attendances, but outcome incentives were associated with 84% more referrals and 93% more attendances. Outcome incentives were the only form of pay‐for‐performance to stimulate more participation in this national disease prevention programme

Is telephone health coaching a useful population health strategy for supporting older people with multimorbidity? : An evaluation of reach, effectiveness and cost-effectiveness using a 'trial within a cohort'

BACKGROUND: Innovative ways of delivering care are needed to improve outcomes for older people with multimorbidity. Health coaching involves 'a regular series of phone calls between patient and health professional to provide support and encouragement to promote healthy behaviours'. This intervention is promising, but evidence is insufficient to support a wider role in multimorbidity care. We evaluated health coaching in older people with multimorbidity. METHODS: We used the innovative 'Trials within Cohorts' design. A cohort was recruited, and a trial was conducted using a 'patient-centred' consent model. A randomly selected group within the cohort were offered the intervention and were analysed as the intervention group whether they accepted the offer or not. The intervention sought to improve the skills of patients with multimorbidity to deal with a range of long-term conditions, through health coaching, social prescribing and low-intensity support for low mood. RESULTS: We recruited 4377 older people, and 1306 met the eligibility criteria (two or more long-term conditions and moderate 'patient activation'). We selected 504 for health coaching, and 41% consented. More than 80% of consenters received the defined 'dose' of 4+ sessions. In an intention-to-treat analysis, those selected for health coaching did not improve on any outcome (patient activation, quality of life, depression or self-care) compared to usual care. We examined health care utilisation using hospital administrative and self-report data. Patients selected for health coaching demonstrated lower levels of emergency care use, but an increase in the use of planned services and higher overall costs, as well as a quality-adjusted life year (QALY) gain. The incremental cost per QALY was £8049, with a 70-79% probability of being cost-effective at conventional levels of willingness to pay. CONCLUSIONS: Health coaching did not lead to significant benefits on the primary measures of patient-reported outcome. This is likely related to relatively low levels of uptake amongst those selected for the intervention. Demonstrating effectiveness in this design is challenging, as it estimates the effect of being selected for treatment, regardless of whether treatment is adopted. We argue that the treatment effect estimated is appropriate for health coaching, a proactive model relevant to many patients in the community, not just those seeking care. TRIAL REGISTRATION: International Standard Randomised Controlled Trial Number ( ISRCTN12286422 )

Mapping the disease-specific LupusQoL to the SF-6D

Purpose To derive a mapping algorithm to predict SF-6D utility scores from the non-preference-based LupusQoL and test the performance of the developed algorithm on a separate independent validation data set. Method LupusQoL and SF-6D data were collected from 320 patients with systemic lupus erythematosus (SLE) attending routine rheumatology outpatient appointments at seven centres in the UK. Ordinary least squares (OLS) regression was used to estimate models of increasing complexity in order to predict individuals’ SF-6D utility scores from their responses to the LupusQoL questionnaire. Model performance was judged on predictive ability through the size and pattern of prediction errors generated. The performance of the selected model was externally validated on an independent data set containing 113 female SLE patients who had again completed both the LupusQoL and SF-36 questionnaires. Results Four of the eight LupusQoL domains (physical health, pain, emotional health, and fatigue) were selected as dependent variables in the final model. Overall model fit was good, with R2 0.7219, MAE 0.0557, and RMSE 0.0706 when applied to the estimation data set, and R2 0.7431, MAE 0.0528, and RMSE 0.0663 when applied to the validation sample. Conclusion This study provides a method by which health state utility values can be estimated from patient responses to the non-preference-based LupusQoL, generalisable beyond the data set upon which it was estimated. Despite concerns over the use of OLS to develop mapping algorithms, we find this method to be suitable in this case due to the normality of the SF-6D data

Framework for identification and measurement of spillover effects in policy implementation: intended non-intended targeted non-targeted spillovers (INTENTS)

BACKGROUND: There is increasing awareness among researchers and policymakers of the potential for healthcare interventions to have consequences beyond those initially intended. These unintended consequences or "spillover effects" result from the complex features of healthcare organisation and delivery and can either increase or decrease overall effectiveness. Their potential influence has important consequences for the design and evaluation of implementation strategies and for decision-making. However, consideration of spillovers remains partial and unsystematic. We develop a comprehensive framework for the identification and measurement of spillover effects resulting from changes to the way in which healthcare services are organised and delivered. METHODS: We conducted a scoping review to map the existing literature on spillover effects in health and healthcare interventions and used the findings of this review to develop a comprehensive framework to identify and measure spillover effects. RESULTS: The scoping review identified a wide range of different spillover effects, either experienced by agents not intentionally targeted by an intervention or representing unintended effects for targeted agents. Our scoping review revealed that spillover effects tend to be discussed in papers only when they are found to be statistically significant or might account for unexpected findings, rather than as a pre-specified feature of evaluation studies. This hinders the ability to assess all potential implications of a given policy or intervention. We propose a taxonomy of spillover effects, classified based on the outcome and the unit experiencing the effect: within-unit, between-unit, and diagonal spillover effects. We then present the INTENTS framework: Intended Non-intended TargEted Non-Targeted Spillovers. The INTENTS framework considers the units and outcomes which may be affected by an intervention and the mechanisms by which spillover effects are generated. CONCLUSIONS: The INTENTS framework provides a structured guide for researchers and policymakers when considering the potential effects that implementation strategies may generate, and the steps to take when designing and evaluating such interventions. Application of the INTENTS framework will enable spillover effects to be addressed appropriately in future evaluations and decision-making, ensuring that the full range of costs and benefits of interventions are correctly identified