Impact of multimorbidity count on all-cause mortality and glycaemic outcomes in people with type 2 diabetes: a systematic review protocol

Introduction: Type 2 diabetes (T2D) is a leading health priority worldwide. Multimorbidity (MM) is a term describing the co-occurrence of two or more chronic diseases or conditions. The majority of people living with T2D have MM. The relationship between MM and mortality and glycaemia in people with T2D is not clear. Methods and analysis: Medline, Embase, Cumulative Index of Nursing and Allied Health Complete, The Cochrane Library, and SCOPUS will be searched with a prespecified search strategy. The searches will be limited to quantitative empirical studies in English with no restriction on publication date. One reviewer will perform title screening and two review authors will independently screen the abstract and full texts using Covidence software, with disagreements adjudicated by a third reviewer. Data will be extracted using a using a Population, Exposure, Comparator and Outcomes framework. Two reviewers will independently extract data and undertake the risk of bias (quality) assessment. Disagreements will be resolved by consensus. A narrative synthesis of the results will be conducted and meta-analysis considered if appropriate. Quality appraisal will be undertaken using the Newcastle-Ottawa quality assessment scale and the quality of the cumulative evidence of the included studies will be assessed using the Grading of Recommendations, Assessment, Development and Evaluation approach. This protocol was prepared in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols guidelines to ensure the quality of our review. Ethics and dissemination: This review will synthesise the existing evidence about the impact of MM on mortality and glycaemic outcomes in people living with T2D and increase our understanding of this subject and will inform future practice and policy. Findings will be disseminated via conference presentations, social media and peer-reviewed publication

Using a Respectful Approach to Child-centred Healthcare (ReACH) in a paediatric clinical trial: A feasibility study

Background There is a growing momentum in paediatric ethics to develop respectful research and healthcare protocols. We developed, tested and refined our ‘Respectful Approach to Child-centred Healthcare’ (ReACH), to underpin respectful participant interactions in a clinical trial. Objective To determine whether a ReACH-based approach is acceptable to children and parents, and effective in obtaining compliance with common healthcare assessments in a clinical trial of healthy 4-6-year-old children. Methods ReACH-based child assessments were evaluated at two baseline clinics and one post-intervention, using mixed methods. Children (n = 49; 46.9% female; mean age = 5.24±0.88 years at baseline) and their parents provided independent evaluation, via customised 5-point Likert scales and qualitative feedback. A dedicated child researcher evaluated adherence to the study ReACH principles. Results Children achieved compliance rates of 95% for body composition (BodPod) assessments; 89% for blood pressure measurements, and 92% (baseline) and 87% (post-intervention) for blood draws. Adherence to ReACH principles during clinic visits was positively associated with child compliance, significantly for baseline BodPod (p = 0.002) and blood test (p = 0.009) clinics. Satisfaction with BodPod protocols was positively associated with compliance, for children at baseline (p = 0.029) and for parents post-intervention (p \u3c 0.001). Parents rated the study itself very highly, with 91.7% satisfied at baseline and 100% post-intervention. Qualitative feedback reflected an enjoyable study experience for both parents and children. Conclusions Adherence to our emerging ReACH approach was associated with high child compliance rates for common healthcare assessments, although no causality can be inferred at this preliminary stage of development. Participants expressed satisfaction with all aspects of the study. Our use of child-centred methods throughout a research intervention appears feasible and acceptable to children and their parents

Whole fat dairy products do not adversely affect adiposity or cardiometabolic risk factors inchildren in the Milky Way study: A double blind randomized controlled pilot study

Background Limited evidence supports the common public health guideline that children \u3e 2 y of age should consume dairy with reduced fat content. Objectives We aimed to investigate the effects of whole-fat compared with reduced-fat dairy intake on measures of adiposity and biomarkers of cardiometabolic risk in healthy 4- to 6-y-old children. Methods The Milky Way Study enrolled 49 children (mean ± SD age: 5.2 ± 0.9 y; 47% girls) who were habitual consumers of whole-fat dairy, then randomly assigned them in a double-blind fashion to remain on whole-fat dairy or switch their dairy consumption to reduced-fat products for 3 mo. Primary endpoints included measures of adiposity, body composition, blood pressure, fasting serum lipids, blood glucose, glycated hemoglobin (HbA1c), and C-reactive protein (CRP) and were assessed at baseline and study end. Pre- and postintervention results were compared using linear mixed models, adjusted for growth, age, and sex. Results Dairy fat intake was reduced by an adjusted (mean ± SEM) 12.9 ± 4.1 g/d in the reduced-fat compared with the whole-fat dairy group (95% CI: –21.2, –4.6 g/d; P = 0.003), whereas dietary energy intakes remained similar (P = 0.936). We found no significant differential changes between dairy groups in any measure of adiposity, body composition, blood pressure, or fasting serum lipids, glucose, HbA1c, and CRP. Conclusions Our results suggest that although changing from whole-fat to reduced-fat dairy products does reduce dairy fat intake, it does not result in changes to markers of adiposity or cardiometabolic disease risk in healthy children

WHAT FACTORS CONTRIBUTE TO HOSPITAL VARIATION IN OBSTETRIC TRANSFUSION RATES?

Background & Objectives: To explore variation in red blood cell transfusion rates between hospitals, and the extent to which this can be explained. A secondary objective was to assess whether hospital transfusion rates are associated with maternal morbidity. Materials & Methods: Linked hospital discharge and birth data were used to identify births (N=279,145) in hospitals with at least 10 deliveries per annum between 2008-2010 in New South Wales, Australia. To investigate transfusion rates, a series of random effects multilevel logistic regression models were fitted, progressively adjusting for maternal, obstetric and hospital factors. Correlations between hospital transfusion and maternal, neonatal morbidity and readmission rates were assessed. Results: Overall, the transfusion rate was 1.4% (hospital range 0.6 to 2.9) across 89 hospitals. Adjusting for maternal casemix, reduced the variation between hospitals by 26%. Adjustment for obstetric interventions further reduced variation by 8% and a further 39% after adjustment for hospital type. At a hospital level, high transfusion rates were moderately correlated with maternal morbidity (0.56, p=0.01) and low Apgar scores (0.54, p=0.002), but not with readmission rates (0.18, p=0.28). Conclusion: Both casemix and practice differences contributed to the variation in transfusion rates between hospitals. The relationship between outcomes and transfusion rates was variable, however low transfusion rates were not associated with worse outcomes.Partnership Grant from the Australian National Health and Medical Research Council NHMRC (#1027262), the Australian Red Cross and the NSW Clinical Excellence Commission, NHMRC Senior Research Fellowship (#1021025). ARC Future Fellowship (#120100069)

WHAT FACTORS CONTRIBUTE TO HOSPITAL VARIATION IN OBSTETRIC TRANSFUSION RATES?

Background & Objectives: To explore variation in red blood cell transfusion rates between hospitals, and the extent to which this can be explained. A secondary objective was to assess whether hospital transfusion rates are associated with maternal morbidity. Materials & Methods: Linked hospital discharge and birth data were used to identify births (N=279,145) in hospitals with at least 10 deliveries per annum between 2008-2010 in New South Wales, Australia. To investigate transfusion rates, a series of random effects multilevel logistic regression models were fitted, progressively adjusting for maternal, obstetric and hospital factors. Correlations between hospital transfusion and maternal, neonatal morbidity and readmission rates were assessed. Results: Overall, the transfusion rate was 1.4% (hospital range 0.6 to 2.9) across 89 hospitals. Adjusting for maternal casemix, reduced the variation between hospitals by 26%. Adjustment for obstetric interventions further reduced variation by 8% and a further 39% after adjustment for hospital type. At a hospital level, high transfusion rates were moderately correlated with maternal morbidity (0.56, p=0.01) and low Apgar scores (0.54, p=0.002), but not with readmission rates (0.18, p=0.28). Conclusion: Both casemix and practice differences contributed to the variation in transfusion rates between hospitals. The relationship between outcomes and transfusion rates was variable, however low transfusion rates were not associated with worse outcomes.Partnership Grant from the Australian National Health and Medical Research Council NHMRC (#1027262), the Australian Red Cross and the NSW Clinical Excellence Commission, NHMRC Senior Research Fellowship (#1021025). ARC Future Fellowship (#120100069)

Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: a comparison of longitudinal cohort studies

Background: Cohorts such as UK Biobank are increasingly used to study multimorbidity; however, there are concerns that lack of representativeness may lead to biased results. This study aims to compare associations between multimorbidity and adverse health outcomes in UK Biobank and a nationally representative sample. Methods and findings: These are observational analyses of cohorts identified from linked routine healthcare data from UK Biobank participants (n = 211,597 from England, Scotland, and Wales with linked primary care data, age 40 to 70, mean age 56.5 years, 54.6% women, baseline assessment 2006 to 2010) and from the Secure Anonymised Information Linkage (SAIL) databank (n = 852,055 from Wales, age 40 to 70, mean age 54.2, 50.0% women, baseline January 2011). Multimorbidity (n = 40 long-term conditions [LTCs]) was identified from primary care Read codes and quantified using a simple count and a weighted score. Individual LTCs and LTC combinations were also assessed. Associations with all-cause mortality, unscheduled hospitalisation, and major adverse cardiovascular events (MACEs) were assessed using Weibull or negative binomial models adjusted for age, sex, and socioeconomic status, over 7.5 years follow-up for both datasets. Multimorbidity was less common in UK Biobank than SAIL (26.9% and 33.0% with ≥2 LTCs in UK Biobank and SAIL, respectively). This difference was attenuated, but persisted, after standardising by age, sex, and socioeconomic status. The association between increasing multimorbidity count and mortality, hospitalisation, and MACE was similar between both datasets at LTC counts of ≤3; however, above this level, UK Biobank underestimated the risk associated with multimorbidity (e.g., mortality hazard ratio for 2 LTCs 1.62 (95% confidence interval 1.57 to 1.68) in SAIL and 1.51 (1.43 to 1.59) in UK Biobank, hazard ratio for 5 LTCs was 3.46 (3.31 to 3.61) in SAIL and 2.88 (2.63 to 3.15) in UK Biobank). Absolute risk of mortality, hospitalisation, and MACE, at all levels of multimorbidity, was lower in UK Biobank than SAIL (adjusting for age, sex, and socioeconomic status). Both cohorts produced similar hazard ratios for some LTCs (e.g., hypertension and coronary heart disease), but UK Biobank underestimated the risk for others (e.g., alcohol-related disorders or mental health conditions). Hazard ratios for some LTC combinations were similar between the cohorts (e.g., cardiovascular conditions); however, UK Biobank underestimated the risk for combinations including other conditions (e.g., mental health conditions). The main limitations are that SAIL databank represents only part of the UK (Wales only) and that in both cohorts we lacked data on severity of the LTCs included. Conclusions: In this study, we observed that UK Biobank accurately estimates relative risk of mortality, unscheduled hospitalisation, and MACE associated with LTC counts ≤3. However, for counts ≥4, and for some LTC combinations, estimates of magnitude of association from UK Biobank are likely to be conservative. Researchers should be mindful of these limitations of UK Biobank when conducting and interpreting analyses of multimorbidity. Nonetheless, the richness of data available in UK Biobank does offers opportunities to better understand multimorbidity, particularly where complementary data sources less susceptible to selection bias can be used to inform and qualify analyses of UK Biobank

A comprehensive evaluation of colonic mucosal isolates of Sutterella wadsworthensis from inflammatory bowel disease

Peer reviewedPublisher PD

Prediction modelling for trauma using comorbidity and 'true' 30-day outcome

BACKGROUND: Prediction models for trauma outcome routinely control for age but there is uncertainty about the need to control for comorbidity and whether the two interact. This paper describes recent revisions to the Trauma Audit and Research Network (TARN) risk adjustment model designed to take account of age and comorbidities. In addition linkage between TARN and the Office of National Statistics (ONS) database allows patient's outcome to be accurately identified up to 30 days after injury. Outcome at discharge within 30 days was previously used. METHODS: Prospectively collected data between 2010 and 2013 from the TARN database were analysed. The data for modelling consisted of 129 786 hospital trauma admissions. Three models were compared using the area under the receiver operating curve (AuROC) for assessing the ability of the models to predict outcome, the Akaike information criteria to measure the quality between models and test for goodness-of-fit and calibration. Model 1 is the current TARN model, Model 2 is Model 1 augmented by a modified Charlson comorbidity index and Model 3 is Model 2 with ONS data on 30 day outcome. RESULTS: The values of the AuROC curve for Model 1 were 0.896 (95% CI 0.893 to 0.899), for Model 2 were 0.904 (0.900 to 0.907) and for Model 3 0.897 (0.896 to 0.902). No significant interaction was found between age and comorbidity in Model 2 or in Model 3. CONCLUSIONS: The new model includes comorbidity and this has improved outcome prediction. There was no interaction between age and comorbidity, suggesting that both independently increase vulnerability to mortality after injury