Dataset Information

Factors predicting poor glycemic control in the first two years of childhood onset type 1 diabetes in a cohort from East London, UK: Analyses using mixed effects fractional polynomial models.

ABSTRACT: BACKGROUND/OBJECTIVE:Poor early glycemic control in childhood onset type 1 diabetes (T1D) is associated with future risk of acute and chronic complications. Our aim was to identify the predictors of higher glycated hemoglobin (HbA1c) within 24?months of T1D diagnosis in children and adolescents. METHODS:Mixed effects models with fractional polynomials were used to analyze longitudinal data of patients <19?years of age, followed from T1D diagnosis for up to 2?years, at three diabetes clinics in East London, United Kingdom. RESULTS:A total of 2209 HbA1c observations were available for 356 patients (52.5% female; 64.4% non-white), followed from within 3?months of diagnosis during years 2005 to 2015, with a mean?±?SD of 6.2?±?2.5 HbA1c observations/participant. The mean age and HbA1c at diagnosis were 8.9?±?4.3?years and 10.7% ±4.3% (or expressed as mmol/mol HbA1c mean?±?SD 92.9?±?23.10?mmol/mol) respectively. Over the 2?years following T1D diagnosis, HbA1c levels were mostly above the National Institute for Health, Care and Excellence (NICE), UK recommendations of 7.5% (<58?mmol/mol). Significant (P?9.5%, ie, >80?mmol/mol), clinic site, non-white ethnicity, and period (pre-year 2011) of diagnosis. Additionally in univariable analyses, frequency of clinic visits, HbA1c at diagnosis, and type of insulin treatment regimen showed association with poor glycemic control (P?

SUBMITTER: Mazarello Paes V

PROVIDER: S-EPMC7028081 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Factors predicting poor glycemic control in the first two years of childhood onset type 1 diabetes in a cohort from East London, UK: Analyses using mixed effects fractional polynomial models.

Mazarello Paes Veena V Barrett Jessica K JK Dunger David B DB Gevers Evelien F EF Taylor-Robinson David C DC Viner Russell M RM Stephenson Terence J TJ

Pediatric diabetes 20191217 2

<h4>Background/objective</h4>Poor early glycemic control in childhood onset type 1 diabetes (T1D) is associated with future risk of acute and chronic complications. Our aim was to identify the predictors of higher glycated hemoglobin (HbA1c) within 24 months of T1D diagnosis in children and adolescents.<h4>Methods</h4>Mixed effects models with fractional polynomials were used to analyze longitudinal data of patients <19 years of age, followed from T1D diagnosis for up to 2 years, at three diabet ...[more]

PMID: 31782879

Similar Datasets

Project description:IntroductionThis paper examines one of the NHS England Pioneers programmes of Integrated Care, which was implemented in three localities in East London, covering the area served by one of the largest hospital groups in the UK and bringing together commissioners, providers and local authorities. The partners agreed to build a model of integrated care that focused on the whole person. This qualitative and participatory evaluation looked at how an ambitious vision translated into the delivery of integrated care on the ground. The study explored the micro-mechanisms of integrated care relationships based on the experience of health and social care professionals working in acute and community care settings.MethodsWe employed a participatory approach, the Researcher in Residence model, whereby the researcher was embedded in the organisations she evaluated and worked alongside managers and clinicians to build collaboration across the full range of stakeholders, develop shared learning, and find common ground through competing interests, while trying to address power imbalances. A number of complementary qualitative methods of data generation were used, including documentary analysis, participant observations, semi-structured interviews, and coproduction workshops with frontline health and social care professionals to interpret the data and develop recommendations.ResultsOur fieldwork exposed persistent organisational fragmentation, despite the dominant rhetoric of integration and efforts to build a shared vision at senior governance levels. The evaluation identified several important themes, including: a growing barrier between acute and community services; a persisting difficulty experienced by health and social care staff in working together because of professional and cultural differences, as well as conflicting organisational priorities and guidelines; and a lack of capacity and support to deliver a genuine multidisciplinary approach in practice, despite the ethos of multiagency being embraced widely.DiscussionBy focusing on professionals' working routines, we detailed how and why action taken by organisational leaders failed to have tangible impact. The inability to align organisational priorities and guidelines on the ground, as well as a failure to acknowledge the impact of structural incentives for organisations to compete at the expense of cooperation, in a context of limited financial and human resources, acted as barriers to more coordinated working. Within an environment of continuous reconfigurations, staff were often confused about the functions of new services and did not feel they had influence on change processes. Investing in a genuine bottom-up approach could ensure that the range of activities needed to generate system-wide cultural transformation reflect the capacity of the organisations and systems and address genuine local needs.LimitationsThe authors acknowledge several limitations of this study, including the focus on one geographical area, East London, and the timing of the evaluation, with several new interventions and programmes introduced more or less simultaneously. Some of the intermediate care services under evaluation were still at pilot stage and some teams were undergoing new reconfigurations, reflecting the fast-pace of change of the past decade. This created confusion at times, for instance when discussing specific roles and activities with participants. We tried to address some of these challenges by organising several workshops with different teams to co-interpret and discuss the findings.

Project description:BackgroundThe multivariable fractional polynomial (MFP) approach combines variable selection using backward elimination with a function selection procedure (FSP) for fractional polynomial (FP) functions. It is a relatively simple approach which can be easily understood without advanced training in statistical modeling. For continuous variables, a closed test procedure is used to decide between no effect, linear, FP1, or FP2 functions. Influential points (IPs) and small sample sizes can both have a strong impact on a selected function and MFP model.MethodsWe used simulated data with six continuous and four categorical predictors to illustrate approaches which can help to identify IPs with an influence on function selection and the MFP model. Approaches use leave-one or two-out and two related techniques for a multivariable assessment. In eight subsamples, we also investigated the effects of sample size and model replicability, the latter by using three non-overlapping subsamples with the same sample size. For better illustration, a structured profile was used to provide an overview of all analyses conducted.ResultsThe results showed that one or more IPs can drive the functions and models selected. In addition, with a small sample size, MFP was not able to detect some non-linear functions and the selected model differed substantially from the true underlying model. However, when the sample size was relatively large and regression diagnostics were carefully conducted, MFP selected functions or models that were similar to the underlying true model.ConclusionsFor smaller sample size, IPs and low power are important reasons that the MFP approach may not be able to identify underlying functional relationships for continuous variables and selected models might differ substantially from the true model. However, for larger sample sizes, a carefully conducted MFP analysis is often a suitable way to select a multivariable regression model which includes continuous variables. In such a case, MFP can be the preferred approach to derive a multivariable descriptive model.

Project description:Deprived communities in many cities are exposed to higher levels of outdoor air pollution, and there is increasing evidence of similar disparities for indoor air pollution exposure. There is a need to understand the drivers for this exposure disparity in order to develop effective interventions aimed at improving population health and reducing health inequities. With a focus on London, UK, this paper assembles evidence to examine why indoor exposure to PM2.5, NOx and CO may disproportionately impact low-income groups. In particular, five factors are explored, namely: housing location and ambient outdoor levels of pollution; housing characteristics, including ventilation properties and internal sources of pollution; occupant behaviours; time spent indoors; and underlying health conditions. Evidence is drawn from various sources, including building physics models, modelled outdoor air pollution levels, time-activity surveys, housing stock surveys, geographical data, and peer-reviewed research. A systems framework is then proposed to integrate these factors, highlighting how exposure to high levels of indoor air pollution in low-income homes is in large part due to factors beyond the control of occupants, and is therefore an area of systemic inequality.Policy relevanceThere is increasing public and political awareness of the impact of air pollution on public health. Strong scientific evidence links exposure to air pollution with morbidity and mortality. Deprived communities may be more affected, however, with limited evidence on how deprivation may influence their personal exposure to air pollution, both outdoors and indoors. This paper describes different factors that may lead to low-income households being exposed to higher levels of indoor air pollution than the general population, using available data and models for London (i.e. living in areas of higher outdoor air pollution, in poor-quality housing, undertaking more pollution-generating activities indoors and spending more time indoors). A systems approach is used to show how these factors lead to systemic exposure inequalities, with low-income households having limited opportunities to improve their indoor air quality. This paper can inform actions and public policies to reduce environmental health inequalities, considering both indoor and outdoor air.

Dataset Information

Factors predicting poor glycemic control in the first two years of childhood onset type 1 diabetes in a cohort from East London, UK: Analyses using mixed effects fractional polynomial models.

Publications

Factors predicting poor glycemic control in the first two years of childhood onset type 1 diabetes in a cohort from East London, UK: Analyses using mixed effects fractional polynomial models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets