Body mass index relates weight to height differently in women and older adults

This study was partly supported by the University of Manchester’s Health eResearch Centre (HeRC) funded by the Medical Research Council (MRC) Grant MR/K006665/1 and partly funded by the ESRC Obesity eLab Grant (RES-149-25-1076).Background Body mass index (BMI) tends to be higher among shorter adults, especially women. The dependence of BMI–height correlation on age and calendar time may inform us about temporal determinants of BMI. Methods Series of cross-sectional surveys: Health Survey for England, 1992–2011. We study the Benn Index, which is the coefficient in a regression of log(weight) on log(height). This is adjusted for age, gender and calendar time, allowing for non-linear terms and interactions. Results By height quartile, mean BMI decreased with increasing height, more so in women than in men (P < 0.001). The decrease in mean BMI in the tallest compared with the shortest height quartile was 0.77 in men (95% CI 0.69, 0.86) and 1.98 in women (95% CI 1.89, 2.08). Regression analysis of log(weight) on log(height) revealed that the inverse association between BMI and height was more pronounced in older adults and stronger in women than in men, with little change over calendar time. Conclusions Unlike early childhood, where taller children tend to have higher BMI, adults, especially women and older people, show an inverse BMI–height association. BMI is a heterogeneous measure of weight-for-height; height may be an important and complex determinant of BMI trajectory over the life course.Publisher PDFPeer reviewe

Data-driven identification of endophenotypes of Alzheimer's disease progression: implications for clinical trials and therapeutic interventions

Abstract Background Given the complex and progressive nature of Alzheimer’s disease (AD), a precision medicine approach for diagnosis and treatment requires the identification of patient subgroups with biomedically distinct and actionable phenotype definitions. Methods Longitudinal patient-level data for 1160 AD patients receiving placebo or no treatment with a follow-up of up to 18 months were extracted from an integrated clinical trials dataset. We used latent class mixed modelling (LCMM) to identify patient subgroups demonstrating distinct patterns of change over time in disease severity, as measured by the Alzheimer’s Disease Assessment Scale—cognitive subscale score. The optimal number of subgroups (classes) was selected by the model which had the lowest Bayesian Information Criterion. Other patient-level variables were used to define these subgroups’ distinguishing characteristics and to investigate the interactions between patient characteristics and patterns of disease progression. Results The LCMM resulted in three distinct subgroups of patients, with 10.3% in Class 1, 76.5% in Class 2 and 13.2% in Class 3. While all classes demonstrated some degree of cognitive decline, each demonstrated a different pattern of change in cognitive scores, potentially reflecting different subtypes of AD patients. Class 1 represents rapid decliners with a steep decline in cognition over time, and who tended to be younger and better educated. Class 2 represents slow decliners, while Class 3 represents severely impaired slow decliners: patients with a similar rate of decline to Class 2 but with worse baseline cognitive scores. Class 2 demonstrated a significantly higher proportion of patients with a history of statins use; Class 3 showed lower levels of blood monocytes and serum calcium, and higher blood glucose levels. Conclusions Our results, ‘learned’ from clinical data, indicate the existence of at least three subgroups of Alzheimer’s patients, each demonstrating a different trajectory of disease progression. This hypothesis-generating approach has detected distinct AD subgroups that may prove to be discrete endophenotypes linked to specific aetiologies. These findings could enable stratification within a clinical trial or study context, which may help identify new targets for intervention and guide better care

Evaluating the impacts of tiered restrictions introduced in England in December 2020 on covid-19 hospitalisations: a synthetic control study

ABSTRACTObjectivesTo assess the impact of Tier 3 covid-19 restrictions implemented in December 2020 in England on covid-19 hospital admissions compared to Tier 2 restrictions, and its potential variations by neighbourhood deprivation levels and the prevalence of the Alpha variant (B.1.1.7).DesignObservational study utilising a synthetic control approach. Comparison of changes in weekly hospitalisation rates in Tier 3 areas to a synthetic control group derived from Tier 2 areas.SettingEngland between 4thOctober 2020 and 21stFebruary 2021.Participants23 million people under Tier 3 restrictions, compared to a synthetic control group derived from 29 million people under Tier 2 restrictions.InterventionsImplementation of Tier 3 covid-19 restrictions in designated areas on 7thDecember 2020, with additional constraints on indoor and outdoor meetings and the hospitality sector compared to less stringent Tier 2 restrictions.Main Outcome MeasuresWeekly covid-19 related hospital admissions for neighbourhoods in England over a 12-week period following the interventions.ResultsThe introduction of Tier 3 restrictions was associated with a 17% average reduction in hospital admissions compared to Tier 2 areas (95% CI 13% to 21%; 8158 (6286 to 9981) in total)). The effects were similar across different levels of neighbourhood deprivation and prevalence of the Alpha variant (B.1.1.7).ConclusionsRegionally targeted Tier 3 restrictions in England had a moderate but significant effect on reducing hospitalisations. The impact did not exacerbate socioeconomic inequalities during the pandemic. Our findings suggest that regionally targeted restrictions can be effective in managing infectious diseases.SUMMARY BOXESWhat is already known on this topic— Previous studies of localised non-pharmaceutical interventions (NPIs) found that they could be effective in reducing covid-19 transmissions.— covid-19 hospitalisation was a key indicator of healthcare resource dynamics, encompassing supply, demand, burden, and allocation, during the pandemic.— There is a need for a detailed examination of the impact of specific localised restrictions in the UK, such as Tier 3 measures, on hospital admissions to inform targeted public health strategies.What this study adds— This study found that additional localised restrictions on outdoor gatherings and in the hospitality sector were effective in mitigating hospital admissions during the pandemic.How this study might affect research, practice or policy— This study provides evidence for future public health policies and preparedness strategies supporting the use of differential regional restrictions during pandemics.</jats:sec

Geographical epidemiology of health and overall deprivation in England, its changes and persistence from 2004 to 2015: a longitudinal spatial population study

Background - Socioeconomic deprivation is a key determinant for health. In England, the Index of Multiple Deprivation (IMD) is a widely used composite measure of deprivation. However, little is known about its spatial clustering or persistence across time. Methods - Data for overall IMD and its health domain were analysed for 2004–2015 at a low geographical area (average of 1500 people). Levels and temporal changes were spatially visualised for the whole of England and its 10 administrative regions. Spatial clustering was quantified using Moran’s I, correlations over time were quantified using Pearson’s r. Results - Between 2004 and 2015 we observed a strong persistence for both overall (r=0.94) and health-related deprivation (r=0.92). At the regional level, small changes were observed over time, but with areas slowly regressing towards the mean. However, for the North East, North West and Yorkshire, where health-related deprivation was the highest, the decreasing trend in health-related deprivation reversed and we noticed increases in 2015. Results did not support our hypothesis of increasing spatial clustering over time. However, marked regional variability was observed in both aggregate deprivation outcomes. The lowest autocorrelation was seen in the North East and changed very little over time, while the South East had the highest autocorrelation at all time points. Conclusions - Overall and health-related deprivation patterns persisted in England, with large and unchanging health inequalities between the North and the South. The spatial aspect of deprivation can inform the targeting of health and social care interventions, particularly in areas with high levels of deprivation clustering

Management of antipsychotics in primary care: Insights from healthcare professionals and policy makers in the United Kingdom

Introduction Antipsychotic medication is increasingly prescribed to patients with serious mental illness. Patients with serious mental illness often have cardiovascular and metabolic comorbidities, and antipsychotics independently increase the risk of cardiometabolic disease. Despite this, many patients prescribed antipsychotics are discharged to primary care without planned psychiatric review. We explore perceptions of healthcare professionals and managers/directors of policy regarding reasons for increasing prevalence and management of antipsychotics in primary care. Methods Qualitative study using semi-structured interviews with 11 general practitioners (GPs), 8 psychiatrists, and 11 managers/directors of policy in the United Kingdom. Data was analysed using thematic analysis. Results Respondents reported competency gaps that impaired ability to manage patients prescribed antipsychotic medications, arising from inadequate postgraduate training and professional development. GPs lacked confidence to manage antipsychotic medications alone; psychiatrists lacked skills to address cardiometabolic risks and did not perceive this as their role. Communication barriers, lack of integrated care records, limited psychology provision, lowered expectation towards patients with serious mental illness by professionals, and pressure to discharge from hospital resulted in patients in primary care becoming ‘trapped’ on antipsychotics, inhibiting opportunities to deprescribe. Organisational and contractual barriers between services exacerbate this risk, with socioeconomic deprivation and lack of access to non-pharmacological interventions driving overprescribing. Professionals voiced fears of censure if a catastrophic event occurred after stopping an antipsychotic. Facilitators to overcome these barriers were suggested. Conclusions People prescribed antipsychotics experience a fragmented health system and suboptimal care. Several interventions could be taken to improve care for this population, but inadequate availability of non-pharmacological interventions and socioeconomic factors increasing mental distress need policy change to improve outcomes. The role of professionals’ fear of medicolegal or regulatory censure inhibiting antipsychotic deprescribing was a new finding in this study

Novel United Kingdom prognostic model for 30-day mortality following transcatheter aortic valve implantation

Objective Existing clinical prediction models (CPM) for short-term mortality after transcatheter aortic valve implantation (TAVI) have limited applicability in the UK due to moderate predictive performance and inconsistent recording practices across registries. The aim of this study was to derive a UK-TAVI CPM to predict 30-day mortality risk for benchmarking purposes. Methods A two-step modelling strategy was undertaken: first, data from the UK-TAVI Registry between 2009 and 2014 were used to develop a multivariable logistic regression CPM using backwards stepwise regression. Second, model-updating techniques were applied using the 2013–2014 data, thereby leveraging new approaches to include frailty and to ensure the model was reflective of contemporary practice. Internal validation was performed by bootstrapping to estimate in-sample optimism-corrected performance. Results Between 2009 and 2014, up to 6339 patients were included across 34 centres in the UK-TAVI Registry (mean age, 81.3; 2927 female (46.2%)). The observed 30-day mortality rate was 5.14%. The final UK-TAVI CPM included 15 risk factors, which included two variables associated with frailty. After correction for in-sample optimism, the model was well calibrated, with a calibration intercept of 0.02 (95% CI −0.17 to 0.20) and calibration slope of 0.79 (95% CI 0.55 to 1.03). The area under the receiver operating characteristic curve, after adjustment for in-sample optimism, was 0.66. Conclusion The UK-TAVI CPM demonstrated strong calibration and moderate discrimination in UK-TAVI patients. This model shows potential for benchmarking, but even the inclusion of frailty did not overcome the need for more wide-ranging data and other outcomes might usefully be explored