Care Sensitive Admissions Research Articles

Quantile regression for overdispersed count data: a hierarchical method

Generalized Poisson regression is commonly applied to overdispersed count data, and focused on modelling the conditional mean of the response. However, conditional mean regression models may be sensitive to response outliers and provide no information on other conditional distribution features of the response. We consider instead a hierarchical approach to quantile regression of overdispersed count data. This approach has the benefits of effective outlier detection and robust estimation in the presence of outliers, and in health applications, that quantile estimates can reflect risk factors. The technique is first illustrated with simulated overdispersed counts subject to contamination, such that estimates from conditional mean regression are adversely affected. A real application involves ambulatory care sensitive emergency admissions across 7518 English patient general practitioner (GP) practices. Predictors are GP practice deprivation, patient satisfaction with care and opening hours, and region. Impacts of deprivation are particularly important in policy terms as indicating effectiveness of efforts to reduce inequalities in care sensitive admissions. Hierarchical quantile count regression is used to develop profiles of central and extreme quantiles according to specified predictor combinations.

External validation of the Vulnerable Elder\u2019s Survey for predicting mortality and emergency admission in older community-dwelling people: a prospective cohort study

BackgroundProspective external validation of the Vulnerable Elder’s Survey (VES-13) in primary care remains limited. The aim of this study is to externally validate the VES-13 in predicting mortality and emergency admission in older community-dwelling adults.MethodsDesign: Prospective cohort study with 2 years follow-up (2010–2012). Setting: 15 General Practices (GPs) in the Republic of Ireland. Participants: n = 862, aged ≥70 years, community-dwellers Exposure: VES-13 calculated at baseline, where a score of ≥3 denoted high risk. Outcomes: i) Mortality; ii) ≥1 Emergency admission and ≥1 ambulatory care sensitive (ACS) admission over 2 years. Statistical analysis: Descriptive statistics, model discrimination (c-statistic) and sensitivity/specificity.ResultsOf 862 study participants, a total of 246 (38%) were classified as vulnerable at baseline. Fifty-three (6%) died during follow-up and 246 (29%) had an emergency admission. At the VES-13 cut-point of ≥3 denoting high-risk model discrimination was poor for mortality (c-statistic: 0.61 (95% CI 0.54, 0.67), ≥1 emergency admission (c-statistic: 0.59 (95% CI 0.56, 0.63) and ≥1 ACS emergency admission (c-statistic: 0.63 (95% CI 0.60, 0.67).ConclusionsIn this study the VES-13 demonstrated relatively limited predictive accuracy in predicting mortality and emergency admission. External validation studies examining the tool in different health settings and healthier populations are needed and represent an interesting area for future research.

Evaluation of Ambulatory Geriatric Rehabilitation (EAGER): study protocol of a matched cohort study based on claims data

BackgroundAmbulatory geriatric rehabilitation (AGR) is a community based outpatient intervention which aims to improve physical function, maintain independent living of geriatric patients, avoiding hospitalisation and institutionalisation. It should therefore reduce health care costs. The objective of our study is to evaluate the effectiveness of AGR for frail elderly patients insured by the statutory health insurance AOK Nordost compared to care as usual. Outcome variables are progression to higher nursing care levels, admission to nursing home, incident fractures, hospital admission, ambulatory care sensitive hospital admissions, days spent in hospital, and health care costs.MethodsThis matched cohort study aims to estimate average treatment effects of AGR. For this purpose we will compare patients receiving AGR with matched patients receiving care as usual. Patients in the intervention group were treated between the years 2009 and 2013 from three centres in Mecklenburg-Vorpommern, Germany. Matching will be conducted using propensity score techniques. Claims data will be provided by the statutory health insurance AOK Nordost. The primary outcomes are the progression of nursing care levels, incident fractures, and admission to nursing home. Secondary outcomes are total and ambulatory care sensitive hospital admissions, and health care costs from the statutory health insurance perspective. Data will be analysed using appropriate regression models.DiscussionThis study aims to quantify the effectiveness of AGR. Results will be important for providers of AGR, policy makers and stakeholders to make informed decisions on whether to continue, modify or extend AGR.Trial registrationGerman Clinical Trials Register (DRKS) S00008926, registered 29.07.2015