WBC predicts outcomes in infants undergoing cardiac surgery

Medical Schoolhttps://deepblue.lib.umich.edu/bitstream/2027.42/149406/1/KatieGilbert_1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149406/2/KatieGilbert_2.ppt

Striving and competing and its relationship to self-harm in young adults.

Previous research has found that competitive, insecure striving (striving to avoid inferiority) has strong links with psychopathologies, self-harm and appearance anxiety. However, with rates of self-harm in young people rising, it seems important to explore the link between competitive striving and self-harm in young adults. Ninety-two participants completed a series of questionnaires which measured striving to avoid inferiority, self-harm, psychopathologies, social comparison, goal orientation and self-ideals. The results showed that competitive insecure striving was a key predictor of self-harm, depression, anxiety and stress.Mental Health Research Uni

Using linked administrative data for monitoring and evaluating the Family Nurse Partnership in England: A scoping report

This report, commissioned by the FNP National Unit and undertaken by researchers at UCL and the London School of Hygiene and Tropical Medicine, presents a scoping review of how population-based linkage between data from the Family Nurse Partnership (FNP) in England and administrative datasets from other services could be used to generate evidence for commissioning, service evaluation and research. It addresses the methodological considerations, permission pathways and technical challenges of using data from the FNP linked with routinely collected, administrative data from other public services for population-based analyses, at a national and local authority level. Our ambition, when commissioning this work, was to explore whether linking data from FNP with administrative datasets might help provide a richer view about how the FNP intervention is affecting different cohorts of clients and their child after they have graduated. The report suggests that the potential for data linkage to support ongoing evaluation of a wide range of interventions including FNP at a national level is promising and an important area to explore. It makes a significant contribution to understanding the possibilities and constraints for doing this, which include barriers to data linkage at a local level (which we know is crucial for local commissioners) and the significant investment required to realise the potential of this project. We believe this report offers valuable insights other organisations interested in the delivery of evidence based policy may want to pursue

Utilising identifier error variation in linkage of large administrative data sources.

BACKGROUND: Linkage of administrative data sources often relies on probabilistic methods using a set of common identifiers (e.g. sex, date of birth, postcode). Variation in data quality on an individual or organisational level (e.g. by hospital) can result in clustering of identifier errors, violating the assumption of independence between identifiers required for traditional probabilistic match weight estimation. This potentially introduces selection bias to the resulting linked dataset. We aimed to measure variation in identifier error rates in a large English administrative data source (Hospital Episode Statistics; HES) and to incorporate this information into match weight calculation. METHODS: We used 30,000 randomly selected HES hospital admissions records of patients aged 0-1, 5-6 and 18-19 years, for 2011/2012, linked via NHS number with data from the Personal Demographic Service (PDS; our gold-standard). We calculated identifier error rates for sex, date of birth and postcode and used multi-level logistic regression to investigate associations with individual-level attributes (age, ethnicity, and gender) and organisational variation. We then derived: i) weights incorporating dependence between identifiers; ii) attribute-specific weights (varying by age, ethnicity and gender); and iii) organisation-specific weights (by hospital). Results were compared with traditional match weights using a simulation study. RESULTS: Identifier errors (where values disagreed in linked HES-PDS records) or missing values were found in 0.11% of records for sex and date of birth and in 53% of records for postcode. Identifier error rates differed significantly by age, ethnicity and sex (p < 0.0005). Errors were less frequent in males, in 5-6 year olds and 18-19 year olds compared with infants, and were lowest for the Asian ethic group. A simulation study demonstrated that substantial bias was introduced into estimated readmission rates in the presence of identifier errors. Attribute- and organisational-specific weights reduced this bias compared with weights estimated using traditional probabilistic matching algorithms. CONCLUSIONS: We provide empirical evidence on variation in rates of identifier error in a widely-used administrative data source and propose a new method for deriving match weights that incorporates additional data attributes. Our results demonstrate that incorporating information on variation by individual-level characteristics can help to reduce bias due to linkage error

Probabilistic linkage to enhance deterministic algorithms and reduce data linkage errors in hospital administrative data.

BACKGROUND: The pseudonymisation algorithm used to link together episodes of care belonging to the same patients in England (HESID) has never undergone any formal evaluation, to determine the extent of data linkage error. OBJECTIVE: To quantify improvements in linkage accuracy from adding probabilistic linkage to existing deterministic HESID algorithms. METHODS: Inpatient admissions to NHS hospitals in England (Hospital Episode Statistics, HES) over 17 years (1998 to 2015) for a sample of patients (born 13/28th of months in 1992/1998/2005/2012). We compared the existing deterministic algorithm with one that included an additional probabilistic step, in relation to a reference standard created using enhanced probabilistic matching with additional clinical and demographic information. Missed and false matches were quantified and the impact on estimates of hospital readmission within one year were determined. RESULTS: HESID produced a high missed match rate, improving over time (8.6% in 1998 to 0.4% in 2015). Missed matches were more common for ethnic minorities, those living in areas of high socio-economic deprivation, foreign patients and those with 'no fixed abode'. Estimates of the readmission rate were biased for several patient groups owing to missed matches, which was reduced for nearly all groups. CONCLUSION: Probabilistic linkage of HES reduced missed matches and bias in estimated readmission rates, with clear implications for commissioning, service evaluation and performance monitoring of hospitals. The existing algorithm should be modified to address data linkage error, and a retrospective update of the existing data would address existing linkage errors and their implications

Changes in first entry to out-of-home care from 1992 to 2012 among children in England.

Placement in out-of-home care (OHC) indicates serious childhood adversity and is associated with multiple adverse outcomes. Each year 0.5% of children in England live in OHC but evidence is lacking on the cumulative proportion who enter during childhood and how this varies over time. We measured the proportion of children born between 1992 and 2011 who entered OHC, including variation in rates of entry over time, and explored the determinants of these changes using decomposition methods. We also described changes in placement type, duration and stability. By age 18, 3.3% of children born 1992-94 entered OHC. This proportion varied by ethnicity (1.6% of White vs. 4.5% of Black children born 2001-03 entered OHC by age 9, 95% CI [1.5-1.7] and [4.4-4.6], p<0.001) and increased over time (0.8% of children born 2009-11 entered OHC by age 1 vs. 0.5% born 1992-94, 95% CI [0.7-0.9] and [0.4-0.6], p<0.001). This overall increase was driven primarily by the increased rate of entry among White children and not by concurrent changes in the population's ethnic composition. The proportion of children entering OHC in England is increasing and characteristics of the care they receive are changing with earlier intervention and longer, more stable placements. Further research is required to understand the reasons for these changes in practice and whether they are cost-effective, sustainable, and improve outcomes for children and society

Newborn Length of Stay and Risk of Readmission.

BACKGROUND: Evidence on the association between newborn length of hospital stay (LOS) and risk of readmission is conflicting. We compared methods for modelling this relationship, by gestational age, using population-level hospital data on births in England between 2005-14. METHODS: The association between LOS and unplanned readmission within 30 days of postnatal discharge was explored using four approaches: (i) modelling hospital-level LOS and readmission rates; (ii) comparing trends over time in LOS and readmission; (iii) modelling individual LOS and adjusted risk of readmission; and (iv) instrumental variable analyses (hospital-level mean LOS and number of births on the same day). RESULTS: Of 4 667 827 babies, 5.2% were readmitted within 30 days. Aggregated data showed hospitals with longer mean LOS were not associated with lower readmission rates for vaginal (adjusted risk ratio (aRR) 0.87, 95% confidence interval (CI) 0.66, 1.13), or caesarean (aRR 0.89, 95% CI 0.72, 1.12) births. LOS fell by an average 2.0% per year for vaginal births and 3.4% for caesarean births, while readmission rates increased by 4.4 and 5.1% per year respectively. Approaches (iii) and (iv) indicated that longer LOS was associated with a reduced risk of readmission, but only for late preterm, vaginal births (34-36 completed weeks' gestation). CONCLUSIONS: Longer newborn LOS may benefit late preterm babies, possibly due to increased medical or psychosocial support for those at greater risk of potentially preventable readmissions after birth. Research based on observational data to evaluate relationships between LOS and readmission should use methods to reduce the impact of unmeasured confounding