Hospital readmission rates: signal of failure or success?

Hospital readmission rates are increasingly used as signals of hospital performance and a basis for hospital reimbursement. However, their interpretation may be complicated by differential patient survival rates. If patient characteristics are not perfectly observable and hospitals differ in their mortality rates, then hospitals with low mortality rates are likely to have a larger share of un-observably sicker patients at risk of a readmission. Their performance on readmissions will then be underestimated. We examine hospitals' performance relaxing the assumption of independence between mortality and readmissions implicitly adopted in many empirical applications. We use data from the Hospital Episode Statistics on emergency admissions for fractured hip in 290,000 patients aged 65 and over from 2003 to 2008 in England. We find evidence of sample selection bias that affects inference from traditional models. We use a bivariate sample selection model to allow for the selection process and the dichotomous nature of the outcome variables

Comparison of two prognostic models in trauma outcome

BACKGROUND: The Trauma Audit and Research Network (TARN) in the UK publicly reports hospital performance in the management of trauma. The TARN risk adjustment model uses a fractional polynomial transformation of the Injury Severity Score (ISS) as the measure of anatomical injury severity. The Trauma Mortality Prediction Model (TMPM) is an alternative to ISS; this study compared the anatomical injury components of the TARN model with the TMPM. METHODS: Data from the National Trauma Data Bank for 2011-2015 were analysed. Probability of death was estimated for the TARN fractional polynomial transformation of ISS and compared with the TMPM. The coefficients for each model were estimated using 80 per cent of the data set, selected randomly. The remaining 20 per cent of the data were used for model validation. TMPM and TARN were compared using calibration curves, measures of discrimination (area under receiver operating characteristic curves; AUROC), proximity to the true model (Akaike information criterion; AIC) and goodness of model fit (Hosmer-Lemeshow test). RESULTS: Some 438 058 patient records were analysed. TMPM demonstrated preferable AUROC (0·882 for TMPM versus 0·845 for TARN), AIC (18 204 versus 21 163) and better fit to the data (32·4 versus 153·0) compared with TARN. CONCLUSION: TMPM had greater discrimination, proximity to the true model and goodness-of-fit than the anatomical injury component of TARN. TMPM should be considered for the injury severity measure for the comparative assessment of trauma centres

REACH: a mixed-methods study to investigate the measurement, prediction and improvement of retention and engagement in outpatient HIV care

BACKGROUND Antiretroviral therapy (ART) benefits individuals living with human immunodeficiency virus (HIV) through reduced morbidity and mortality, and brings public health gains through a reduction in HIV transmission. People living with human immunodeficiency virus (PLWH) need to know their HIV status and engage in HIV care in order for these individual and public health benefits to be realised. OBJECTIVE To explore, describe and understand HIV outpatient attendance in PLWH, in order to develop cost-effective interventions to optimise engagement in care. DESIGN A mixed-methods study incorporating secondary analysis of data from the UK Collaborative HIV Cohort (UK CHIC) study and primary data collection. METHODS Phase 1 – an engagement-in-care (EIC) algorithm was developed to categorise patients as in care or out of care for each month of follow-up. The algorithm was used in group-based trajectory analysis to examine patterns of attendance over time and of the association between the proportion of months in care before ART initiation and post-ART mortality and laboratory test costs. Phase 2 – a cross-sectional survey was conducted among patients attending seven London HIV clinics. Regular attenders (all appointments attended in past year), irregular attenders (one or more appointments missed in past year) and non-attenders (recent absence of ≥ 1 year) were recruited. A ‘retention risk tool’ was developed to identify those at risk of disengaging from care. Individual in-depth interviews and focus groups were conducted with PLWH. Phase 3 – key informant interviews were conducted with HIV service providers. Interventions were developed from the findings of phases 2 and 3. RESULTS Plots from group-based trajectory analysis indicated that four trajectories best fitted the data. Higher EIC is associated with reduced mortality but the association between EIC before starting ART, and post-ART mortality [relative hazard (RH) per 10% increase in EIC 0.29, 95% confidence interval (CI) 0.18 to 0.47] was attenuated after adjustment for fixed covariates and post-ART cluster of differentiation 4 counts and viral loads (RH 0.74, 95% CI 0.42 to 1.30). Small differences were found in pre-ART EIC and the costs of post-ART lab tests. The final model for the retention risk tool included age at diagnosis, having children, recreational drug use, drug/alcohol dependency, insufficient money for basic needs and use of public transport to get to the clinic. Quantitative and qualitative data showed that a range of psychological, social and economic issues were associated with disengagement from care. The negative impact of stigma on attendance was highlighted. Interventions were proposed that support a holistic approach to care including peer support, address stigma by holding clinics in alternative locations and involve training staff to encourage attendance. CONCLUSIONS The study shows the adverse health impacts of disengaging from HIV care and demonstrates the importance of the wider health and social context in managing HIV effectively. Although phase 1 analysis was based on UK data, phases 2 and 3 were limited to London. The interventions proposed are supported by the data but their cost-effectiveness requires testing. Future research is needed to evaluate the interventions, to validate our retention risk tool across populations and settings, and to fully analyse the economic costs of disengaging from HIV care