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1.
Age Ageing ; 48(6): 797-802, 2019 11 01.
Article in English | MEDLINE | ID: mdl-31573609

ABSTRACT

BACKGROUND: Frailty is a significant determinant of health care utilisation and associated costs, both of which also increase with proximity to death. What is not known is how the relationships between frailty, proximity to death, hospital use and costs develop in a population aged 85 years and over. METHODS: This study used data from a prospective observational cohort, the Newcastle 85+ Study, linked with hospital episode statistics and death registrations. Using the Rockwood frailty index (cut off <0.25), we analysed the relationship between frailty and mortality, proximity to death, hospital use and hospital costs over 2, 5 and 7 years using descriptive statistics, Kaplan-Meier survival curves, Cox's proportional hazards and negative binomial regression models. RESULTS: Baseline frailty was associated with a more than two-fold increased risk of mortality after 7 years, compared to people who were non-frail. Participants classified as frail spent more time in hospital over 7 years than the non-frail, but this difference declined over time. Baseline frailty was not associated with increased time spent in hospital during the last 90 days of life. CONCLUSION: Evidence continues to accrue on the impact of frailty on emergency health care use. Hospital and community services need to adapt to meet the challenge of introducing new proactive and preventative approaches, designed to achieve benefits in clinical and/or cost effectiveness of frailty management.


Subject(s)
Frailty/mortality , Hospitalization/statistics & numerical data , Aged, 80 and over , England/epidemiology , Female , Frail Elderly/statistics & numerical data , Frailty/epidemiology , Hospital Costs/statistics & numerical data , Humans , Kaplan-Meier Estimate , Male , Patient Acceptance of Health Care/statistics & numerical data , Proportional Hazards Models , Prospective Studies
2.
Lancet Child Adolesc Health ; 3(9): 627-635, 2019 09.
Article in English | MEDLINE | ID: mdl-31281027

ABSTRACT

BACKGROUND: Concerns have been raised about variation in care quality and outcomes among children and young people with epilepsies in England. We aimed to investigate the association between quality of paediatric care, hospital admissions, and all-cause deaths among epilepsy patients. METHODS: In this longitudinal data linkage study of paediatric epilepsy services in England, we linked unit-level data from round 1 (2009-11) and round 2 (2013-14) of the Epilepsy12 national clinical audit, with death registrations from the UK Office for National Statistics and data for unplanned hospital admissions from Hospital Episode Statistics. We investigated the association between unit-level performance in involving a paediatrician with epilepsy expertise, an epilepsy specialist nurse, and a paediatric neurologist (where appropriate) in round 1 and the proportion of adolescents (aged 10-18 years) with epilepsy admitted to each unit who subsequently died during the study period (April 1, 2009, to March 31, 2015). We also investigated whether change in Epilepsy12 performance between the two audit rounds was associated with changes in the standardised ratio of observed-to-expected unplanned epilepsy admissions over the same period. FINDINGS: In 99 units with data for the analyses relating to paediatricians with epilepsy expertise and epilepsy specialist nurses, 134 (7%) of 1795 patients died during the study period, 88 (5%) of whom died after the transition to adult service. In 55 units with data for the analyses relating to paediatric neurologists, 79 (7%) of 1164 patients died, 54 (5%) of whom did so after the transition. In regression models adjusting for population, unit, and hospital activity characteristics, absolute reductions in total mortality risk (6·4 percentage points, 95% CI 0·1-12·7) and mortality risk after transition (5·7 percentage points, 0·6-10·8) were found when comparing units where all versus no eligible patients were seen by a paediatric neurologist. Units where all eligible patients were seen by a paediatric neurologist were estimated to have absolute reductions of 4·6 percentage points (0·3-8·9) in total mortality and of 4·6 percentage points (1·2-8·0) in post-transition mortality, compared with units where no or some eligible patients were seen by a paediatric neurologist. There was no significant association between performance on being seen by an epilepsy specialist nurse or by a paediatrician with epilepsy expertise and mortality. In units where access to an epilepsy specialist nurse decreased, the standardised ratio of epilepsy admissions increased by a mean of 0·21 (0·01-0·42). INTERPRETATION: Among adolescents with epilepsy, greater involvement of tertiary specialists in paediatric care is associated with decreased all-cause mortality in the period after transition to adult services. Reduced access to an epilepsy specialist nurse was associated with an increase in paediatric epilepsy admissions. FUNDING: The Health Foundation.


Subject(s)
Epilepsy/mortality , Epilepsy/therapy , Hospital Units/standards , Quality of Health Care , Adolescent , Child , Datasets as Topic , England/epidemiology , Hospitalization , Humans , Longitudinal Studies , Transition to Adult Care
3.
BMJ Open ; 8(7): e022339, 2018 07 10.
Article in English | MEDLINE | ID: mdl-29991633

ABSTRACT

OBJECTIVES: To develop a method for calculating age-specific hospital catchment populations (HCPs) for children and young people (CYP) in England. To show how these methods allow geographical variation in hospital activity to be investigated and addressed more effectively. DESIGN: Retrospective, secondary analysis of existing national datasets. SETTING: Inpatient care of CYP (0-18 years) in England. PARTICIPANTS: Hospital Episode Statistics (HES) data were accessed for all inpatient admissions (elective and emergency) for CYP from birth to 18 years, 364 days, for 2011/2012-2014/2015. In 2014/2015, 857 112 admissions were analysed, from an eligible population of approximately 11.9 million CYP. OUTCOME MEASURES: For each hospital Trust, the catchment population of CYP was calculated; Trust-level admission rates per thousand per year were then calculated for admissions due to (1) any diagnostic code, (2) primary diagnosis of epilepsy and (3) epilepsy listed as primary diagnosis or comorbidity. RESULTS: Estimated 2014/2015 HCPs for CYP ranged from 268 558 for Barts Health NHS Trust to around 30 000 for the smallest acute general paediatric services and below 10 000 for many Trusts providing specialist services. As expected, the composition of HCPs was fairly consistent for age breakdown but levels of deprivation varied widely. After standardising for population characteristics, admission rates with a primary diagnosis of epilepsy ranged from 14.3 to 157.7 per 100 000 per year (11.0-fold variation) for Trusts providing acute general paediatric services. All-cause admission rates showed less variation, ranging from 4033 to 11 681 per 100 000 per year (2.9-fold variation). CONCLUSIONS: Use of age-specific catchment populations allows variation in hospital activity to be linked to specific teams and care pathways. This provides an evidence base for initiatives to tackle unwarranted variation in healthcare activity and health outcomes.


Subject(s)
Catchment Area, Health , Epilepsy/epidemiology , Episode of Care , Hospitalization/statistics & numerical data , Adolescent , Child , Child, Preschool , Cross-Sectional Studies , Databases, Factual , England/epidemiology , Female , Hospitals , Humans , Infant , Infant, Newborn , Male , Retrospective Studies
4.
Lancet ; 391(10132): 1775-1782, 2018 05 05.
Article in English | MEDLINE | ID: mdl-29706364

ABSTRACT

BACKGROUND: Older people are increasing users of health care globally. We aimed to establish whether older people with characteristics of frailty and who are at risk of adverse health-care outcomes could be identified using routinely collected data. METHODS: A three-step approach was used to develop and validate a Hospital Frailty Risk Score from International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnostic codes. First, we carried out a cluster analysis to identify a group of older people (≥75 years) admitted to hospital who had high resource use and diagnoses associated with frailty. Second, we created a Hospital Frailty Risk Score based on ICD-10 codes that characterised this group. Third, in separate cohorts, we tested how well the score predicted adverse outcomes and whether it identified similar groups as other frailty tools. FINDINGS: In the development cohort (n=22 139), older people with frailty diagnoses formed a distinct group and had higher non-elective hospital use (33·6 bed-days over 2 years compared with 23·0 bed-days for the group with the next highest number of bed-days). In the national validation cohort (n=1 013 590), compared with the 429 762 (42·4%) patients with the lowest risk scores, the 202 718 (20·0%) patients with the highest Hospital Frailty Risk Scores had increased odds of 30-day mortality (odds ratio 1·71, 95% CI 1·68-1·75), long hospital stay (6·03, 5·92-6·10), and 30-day readmission (1·48, 1·46-1·50). The c statistics (ie, model discrimination) between individuals for these three outcomes were 0·60, 0·68, and 0·56, respectively. The Hospital Frailty Risk Score showed fair overlap with dichotomised Fried and Rockwood scales (kappa scores 0·22, 95% CI 0·15-0·30 and 0·30, 0·22-0·38, respectively) and moderate agreement with the Rockwood Frailty Index (Pearson's correlation coefficient 0·41, 95% CI 0·38-0·47). INTERPRETATION: The Hospital Frailty Risk Score provides hospitals and health systems with a low-cost, systematic way to screen for frailty and identify a group of patients who are at greater risk of adverse outcomes and for whom a frailty-attuned approach might be useful. FUNDING: National Institute for Health Research.


Subject(s)
Electronic Health Records/statistics & numerical data , Frail Elderly/statistics & numerical data , Geriatric Assessment/methods , Outcome Assessment, Health Care , Aged , Aged, 80 and over , Databases, Factual , Female , Hospitalization/statistics & numerical data , Humans , Male , Predictive Value of Tests , Retrospective Studies , Risk Factors
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