Quantifying the risk of type 2 diabetes in East London using the QDScore: a cross-sectional analysis

The impact of sociodemographic determinants and diabetes type-2 on oral health outcomes: An analytical cross-sectional study

OBJECTIVES

This study compared adults with type 2 diabetes (T2DM) and those without diabetes (ND) from East London in terms of sociodemographic characteristics, oral health behaviors, dietary practices, and alcohol and tobacco-related habits.

MATERIALS AND METHODS

A total of 182 participants (n = 91 for each group) were recruited and requested to complete the validated questionnaire with 33 items.

RESULTS

Results showed that the mean ± SD age was 61 ± 11.7 in the T2DM, while 51 ± 11.2 in the ND group. The mean ± SD age at T2DM diagnosis was 43 ± 10. There was a significant gender difference, with more males in the T2DM group (67.7%) and more females in the ND group (64.8%). Asian-British (38.4%) were significantly high in the T2DM group when compared to other ethnicities. 92.3% of T2DM participants were significantly more likely to use medications in comparison to the ND group (29.7%). The T2DM participants' personal statements on general health were fair (34%) and good (46.2%) when compared with the ND group (15.4% and 59.3%, respectively). The majority of T2DM and ND participants (98%) lacked dental insurance. In the T2DM group, 31.8% were receiving benefits, and 39.5% were retired, while 46% of the ND group were full-time employees. Tooth brushing twice a day was slightly less common in T2DM (68%) when compared to the ND group (78%). Nearly half of the participants in both groups failed to carry out interdental cleaning (T2DM = 52%; ND = 47%), and 38.5% of the T2DM group used mouthwash occasionally, while 30% of the ND group had it twice daily. There was a weak association between chewing paan and annual income in ND participants (r = .90, p = .49). There were significant differences in the presence of removable prostheses, juice, and sweetened juice consumptions between the two groups (p < .05).

CONCLUSION

Within the confines of this study, being male, Asian British, retired due to disability, polypharmacy, and the presence of removable prostheses were all significant factors for T2DM.

Application of Spatial Analysis for Electronic Health Records: Characterizing Patient Phenotypes and Emerging Trends

Background

Electronic health records (EHR) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHR in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.

Objective

This study reviews advanced spatial analyses that employed individual-level health data from EHR within the US to characterize patient phenotypes.

Methods

We systematically evaluated English-language peer-reviewed articles from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on time, study design, or specific health domains.

Results

Only 49 articles met the eligibility criteria. These articles utilized diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were relatively underexplored. A noteworthy surge (n = 42, 85.7%) in publications was observed post-2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains, such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were rarely utilized.

Conclusions

This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. Additionally, this review proposes guidelines for harnessing the potential of spatial analysis to enhance the context of individual patients for future clinical decision support.

Understanding the risk of developing weight-related complications associated with different body mass index categories: a systematic review

BACKGROUND

Obesity and overweight are major risk factors for several chronic diseases. There is limited systematic evaluation of risk equations that predict the likelihood of developing an obesity or overweight associated complication. Predicting future risk is essential for health economic modelling. Availability of future treatments rests upon a model's ability to inform clinical and decision-making bodies.  This systematic literature review aimed to identify studies reporting (1) equations that calculate the risk for individuals with obesity, or overweight with a weight-related complication (OWRC), of developing additional complications, namely T2D, cardiovascular (CV) disease (CVD), acute coronary syndrome, stroke, musculoskeletal disorders, knee replacement/arthroplasty, or obstructive sleep apnea; (2) absolute or proportional risk for individuals with severe obesity, obesity or OWRC developing T2D, a CV event or mortality from knee surgery, stroke, or an acute CV event.

METHODS

Databases (MEDLINE and Embase) were searched for English language reports of population-based cohort analyses or large-scale studies in Australia, Canada, Europe, the UK, and the USA between January 1, 2011, and March 29, 2021. Included reports were quality assessed using an adapted version of the Newcastle Ottawa Scale.

RESULTS

Of the 60 included studies, the majority used European cohorts. Twenty-nine reported a risk prediction equation for developing an additional complication. The most common risk prediction equations were logistic regression models that did not differentiate between body mass index (BMI) groups (particularly above 40 kg/m²) and lacked external validation. The remaining included studies (31 studies) reported the absolute or proportional risk of mortality (29 studies), or the risk of developing T2D in a population with obesity and with prediabetes or normal glucose tolerance (NGT) (three studies), or a CV event in populations with severe obesity with NGT or T2D (three studies). Most reported proportional risk, predominantly a hazard ratio.

CONCLUSION

More work is needed to develop and validate these risk equations, specifically in non-European cohorts and that distinguish between BMI class II and III obesity. New data or adjustment of the current risk equations by calibration would allow for more accurate decision making at an individual and population level.

Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health

There is an ongoing demand for data on population health, for reasons of resource allocation, future planning and crucially to address inequalities in health between people and between populations. Although there are regular sources of data at coarse spatial scales, such as countries or large sub-national units such as states, there is often a lack of good quality health data at the local level. One method to develop reliable estimates of population health outcomes is spatial microsimulation, an approach that has its roots in economic studies. Here, we share a review of this method for estimating health in populations, explaining the different approaches available and examples where the method is applied successfully for creating both static and dynamic populations. Recent notable advances in the method that allow uncertainty to be represented are highlighted, along with the evolving approaches to validation that are an ongoing challenge in small-area estimation. The summary serves as a primer for academics new to the area of research as well as an overview for non-academic researchers who consider using these models for policy evaluations.

Could social prescribing contribute to type 2 diabetes prevention in people at high risk? Protocol for a realist, multilevel, mixed methods review and evaluation

INTRODUCTION

Social prescribing is an innovation being widely adopted within the UK National Health Service policy as a way of improving the management of people with long-term conditions, such as type 2 diabetes (T2D). It generally involves linking patients in primary care with non-medical community-based interventions. Despite widespread national support, evidence for the effectiveness of social prescribing is both insufficient and contested. In this study, we will investigate whether social prescribing can contribute to T2D prevention and, if so, when, how and in what circumstances it might best be introduced.

METHODS AND ANALYSIS

We will draw on realist evaluation to investigate the complex interpersonal, organisational, social and policy contexts in which social prescribing relevant to T2D prevention is implemented. We will set up a stakeholder group to advise us throughout the study, which will be conducted over three interconnected stages. In stage 1, we will undertake a realist review to synthesise the current evidence base for social prescribing. In stage 2, we will investigate how social prescribing relevant to people at high risk of T2D 'works' in a multiethnic, socioeconomically diverse community and any interactions with existing T2D prevention services using qualitative, quantitative and realist methods. In stage 3 and building on previous stages, we will synthesise a 'transferable framework' that will guide implementation and evaluation of social prescribing relevant to T2D prevention at scale.

ETHICS AND DISSEMINATION

National Health Service ethics approval has been granted (reference 20/LO/0713). This project will potentially inform the adaptation of social prescribing services to better meet the needs of people at high risk of T2D in socioeconomically deprived areas. Findings may also be transferable to other long-term conditions. Dissemination will be undertaken as a continuous process, supported by the stakeholder group. Tailored outputs will target the following audiences: (1) service providers and commissioners; (2) people at high risk of T2D and community stakeholders; and (3) policy and strategic decision makers.

PROSPERO REGISTRATION NUMBER

CRD42020196259.

HEROIC: a 5-year observational cohort study aimed at identifying novel factors that drive diabetic kidney disease: rationale and study protocol

INTRODUCTION

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease worldwide and a major cause of premature mortality in diabetes mellitus (DM). While improvements in care have reduced the incidence of kidney disease among those with DM, the increasing prevalence of DM means that the number of patients worldwide with DKD is increasing. Improved understanding of the biology of DKD and identification of novel therapeutic targets may lead to new treatments. A major challenge to progress has been the heterogeneity of the DKD phenotype and renal progression. To investigate the heterogeneity of DKD we have set up The East and North London Diabetes Cohort (HEROIC) Study, a secondary care-based, multiethnic observational study of patients with biopsy-proven DKD. Our primary objective is to identify histological features of DKD associated with kidney endpoints in a cohort of patients diagnosed with type 1 and type 2 DM, proteinuria and kidney impairment.

METHODS AND ANALYSIS

HEROIC is a longitudinal observational study that aims to recruit 500 patients with DKD at high-risk of renal and cardiovascular events. Demographic, clinical and laboratory data will be collected and assessed annually for 5 years. Renal biopsy tissue will be collected and archived at recruitment. Blood and urine samples will be collected at baseline and during annual follow-up visits. Measured glomerular filtration rate (GFR), echocardiography, retinal optical coherence tomography angiography and kidney and cardiac MRI will be performed at baseline and twice more during follow-up. The study is 90% powered to detect an association between key histological and imaging parameters and a composite of death, renal replacement therapy or a 30% decline in estimated GFR.

ETHICS AND DISSEMINATION

Ethical approval has been obtained from the Bloomsbury Research Ethics Committee (REC 18-LO-1921). Any patient identifiable data will be stored on a password-protected National Health Services N3 network with full audit trail. Anonymised imaging data will be stored in a ISO27001-certificated data warehouse.Results will be reported through peer-reviewed manuscripts and conferences and disseminated to participants, patients and the public using web-based and social media engagement tools as well as through public events.

Identifying hotspots of type 2 diabetes risk using general practice data and geospatial analysis: an approach to inform policy and practice

The prevalence of type 2 diabetes (T2D) is increasing worldwide and there is a need to identify communities with a high-risk profile and to develop appropriate primary care interventions. This study aimed to predict future T2D risk and identify community-level geographic variations using general practices data. The Australian T2D risk assessment (AUSDRISK) tool was used to calculate the individual T2D risk scores using 55693 clinical records from 16 general practices in west Adelaide, South Australia, Australia. Spatial clusters and potential 'hotspots' of T2D risk were examined using Local Moran's I and the Getis-Ord Gi* techniques. Further, the correlation between T2D risk and the socioeconomic status of communities were mapped. Individual risk scores were categorised into three groups: low risk (34.0% of participants), moderate risk (35.2% of participants) and high risk (30.8% of participants). Spatial analysis showed heterogeneity in T2D risk across communities, with significant clusters in the central part of the study area. These study results suggest that routinely collected data from general practices offer a rich source of data that may be a useful and efficient approach for identifying T2D hotspots across communities. Mapping aggregated T2D risk offers a novel approach to identifying areas of unmet need.

Implementation of public health genomics in Pakistan

There has been considerable recent progress in the implementation of public health genomics policy throughout the developed world. However, in the developing world, genetic services still remain limited, or unavailable to most. Here, we discuss challenges and opportunities related to the implementation of public health genomics in developing countries. We focus on Pakistan, a country with one of the world's highest rates of inter-family marriages and prevalence of inherited genetic conditions. Pakistan still lacks a national newborn screening programme, clinical genetic testing services, or public health genomics framework. The medical infrastructure in Pakistan, characterized by limited publicly-funded health services and a significant burden of infectious disease, may contribute to de-prioritization of genetic health services. In addition, there are a number of societal, cultural and religious factors to consider. Recently a number of large research studies have been conducted in populations of Pakistani descent, mostly in collaboration with major US, UK and European institutions. Some of these have yielded high-impact scientific findings, but have yet to translate into public health outcomes in Pakistan. Before the benefits of genomics can be realized in developing countries, the first initial steps towards strategic prioritization, resourcing, and long-term goal setting are required. We propose some practical recommendations and possible first steps forward.

Is Strongyloides seropositivity associated with diabetes mellitus? A retrospective case-control study in an East London NHS Trust

BACKGROUND

The association between diabetes and Strongyloides stercoralis remains controversial. We conducted a case-control study examining the association between diabetes and Strongyloides seropositivity in a large UK centre.

METHODS

Between January 2013 and October 2016, cases and controls were identified by positive and negative Strongyloides serology, respectively. Demographic, clinical and microbiological data were retrospectively collected. Multivariate logistic regression analysis was performed.

RESULTS

Over the study period, 532 samples were serologically tested for Strongyloides. After exclusion of duplicates and cases with missing data, 100 (22.3%; 95% CI 18.5-26.4%) out of 449 tested positive. Of seropositive cases, the mean age was 57 years (SD 16), 71 (71%) were male, 94 (94%) were migrants and 92 (92%) had eosinophilia.Univariate logistic regression analysis demonstrated a significant association between Strongyloides seropositivity and age (OR 1.04, 95% CI 1.02-1.05), male sex (OR 2.22, 95% CI 1.37-3.59), migration (OR 5.36, 95% CI 2.27-12.67), eosinophilia (OR 4.36, 95% CI 2.04-9.33) and diabetes (OR 3.52, 95% CI 2.19-5.66). In multivariate analysis, there remained a significant association between diabetes and Strongyloides seropositivity (OR 1.81, 95% CI 1.04-3.16).

CONCLUSIONS

We demonstrated a high rate of Strongyloides seropositivity in our East London cohort and a significant association with diabetes.

Development and validation of QDiabetes-2018 risk prediction algorithm to estimate future risk of type 2 diabetes: cohort study

Objectives To derive and validate updated QDiabetes-2018 prediction algorithms to estimate the 10 year risk of type 2 diabetes in men and women, taking account of potential new risk factors, and to compare their performance with current approaches.Design Prospective open cohort study.Setting Routinely collected data from 1457 general practices in England contributing to the QResearch database: 1094 were used to develop the scores and a separate set of 363 were used to validate the scores.Participants 11.5 million people aged 25-84 and free of diabetes at baseline: 8.87 million in the derivation cohort and 2.63 million in the validation cohort.Methods Cox proportional hazards models were used in the derivation cohort to derive separate risk equations in men and women for evaluation at 10 years. Risk factors considered included those already in QDiabetes (age, ethnicity, deprivation, body mass index, smoking, family history of diabetes in a first degree relative, cardiovascular disease, treated hypertension, and regular use of corticosteroids) and new risk factors: atypical antipsychotics, statins, schizophrenia or bipolar affective disorder, learning disability, gestational diabetes, and polycystic ovary syndrome. Additional models included fasting blood glucose and glycated haemoglobin (HBA1c). Measures of calibration and discrimination were determined in the validation cohort for men and women separately and for individual subgroups by age group, ethnicity, and baseline disease status.Main outcome measure Incident type 2 diabetes recorded on the general practice record.Results In the derivation cohort, 178 314 incident cases of type 2 diabetes were identified during follow-up arising from 42.72 million person years of observation. In the validation cohort, 62 326 incident cases of type 2 diabetes were identified from 14.32 million person years of observation. All new risk factors considered met our model inclusion criteria. Model A included age, ethnicity, deprivation, body mass index, smoking, family history of diabetes in a first degree relative, cardiovascular disease, treated hypertension, and regular use of corticosteroids, and new risk factors: atypical antipsychotics, statins, schizophrenia or bipolar affective disorder, learning disability, and gestational diabetes and polycystic ovary syndrome in women. Model B included the same variables as model A plus fasting blood glucose. Model C included HBA1c instead of fasting blood glucose. All three models had good calibration and high levels of explained variation and discrimination. In women, model B explained 63.3% of the variation in time to diagnosis of type 2 diabetes (R²), the D statistic was 2.69 and the Harrell's C statistic value was 0.89. The corresponding values for men were 58.4%, 2.42, and 0.87. Model B also had the highest sensitivity compared with current recommended practice in the National Health Service based on bands of either fasting blood glucose or HBA1c. However, only 16% of patients had complete data for blood glucose measurements, smoking, and body mass index.Conclusions Three updated QDiabetes risk models to quantify the absolute risk of type 2 diabetes were developed and validated: model A does not require a blood test and can be used to identify patients for fasting blood glucose (model B) or HBA1c (model C) testing. Model B had the best performance for predicting 10 year risk of type 2 diabetes to identify those who need interventions and more intensive follow-up, improving on current approaches. Additional external validation of models B and C in datasets with more completely collected data on blood glucose would be valuable before the models are used in clinical practice.

Prevention of diabetes in Bangladeshis in East London: experiences and views of young people

BACKGROUND AND OBJECTIVES

Type 2 diabetes is common amongst Bangladeshis, and prevention strategies are needed. Little is known about the views of younger people concerning diabetes prevention and the risk factors. We aimed to explore the experience and views on the prevention of diabetes amongst young Bangladeshis in Tower Hamlets.

METHODS

Semistructured interviews involving 40 young Bangladeshis.

RESULTS

Participants were aware of diabetes being a major health issue and its link with poor diet. Many had a relative with diabetes, and some had negative experiences, such as suffering poor control, complications, or hypoglycemia. Knowledge of diabetes was predominantly gleaned from school. Many felt that older generations were at higher risk due to lack of exercise and reliance on traditional diets. Participants recognized that the Westernized diets also increased the risk of diabetes. Views on prevention of diabetes were strong, including increasing diabetes awareness in schools, rewards for healthier lifestyles, reducing costs of exercise, reducing advertising of poorly nutritious foods, and tackling the proliferation of fast food outlets.

CONCLUSIONS

Young Bangladeshi people showed good knowledge of diabetes and its causes and have cogent ideas on its prevention. The views of young people should be considered when developing diabetes prevention strategies at the local and national level.

Development and Validation of a Risk-Score Model for Type 2 Diabetes: A Cohort Study of a Rural Adult Chinese Population

Some global models to predict the risk of diabetes may not be applicable to local populations. We aimed to develop and validate a score to predict type 2 diabetes mellitus (T2DM) in a rural adult Chinese population. Data for a cohort of 12,849 participants were randomly divided into derivation (n = 11,564) and validation (n = 1285) datasets. A questionnaire interview and physical and blood biochemical examinations were performed at baseline (July to August 2007 and July to August 2008) and follow-up (July to August 2013 and July to October 2014). A Cox regression model was used to weigh each variable in the derivation dataset. For each significant variable, a score was calculated by multiplying β by 100 and rounding to the nearest integer. Age, body mass index, triglycerides and fasting plasma glucose (scores 3, 12, 24 and 76, respectively) were predictors of incident T2DM. The model accuracy was assessed by the area under the receiver operating characteristic curve (AUC), with optimal cut-off value 936. With the derivation dataset, sensitivity, specificity and AUC of the model were 66.7%, 74.0% and 0.768 (95% CI 0.760–0.776), respectively. With the validation dataset, the performance of the model was superior to the Chinese (simple), FINDRISC, Oman and IDRS models of T2DM risk but equivalent to the Framingham model, which is widely applicable in a variety of populations. Our model for predicting 6-year risk of T2DM could be used in a rural adult Chinese population.

Use of Electronic Health Records and Geographic Information Systems in Public Health Surveillance of Type 2 Diabetes: A Feasibility Study

Background

Data routinely collected in electronic health records (EHRs) offer a unique opportunity to monitor chronic health conditions in real-time. Geographic information systems (GIS) may be an important complement in the analysis of those data.

Objective

The aim of this study was to explore the feasibility of using primary care EHRs and GIS for population care management and public health surveillance of chronic conditions, in Portugal. Specifically, type 2 diabetes was chosen as a case study, and we aimed to map its prevalence and the presence of comorbidities, as well as to identify possible populations at risk for cardiovascular complications.

Methods

Cross-sectional study using individual-level data from 514 primary care centers, collected from three different types of EHRs. Data were obtained on adult patients with type 2 diabetes (identified by the International Classification of Primary Care [ICPC-2] code, T90, in the problems list). GISs were used for mapping the prevalence of diabetes and comorbidities (hypertension, dyslipidemia, and obesity) by parish, in the region of Lisbon and Tagus Valley. Descriptive statistics and multivariate logistic regression were used for data analysis.

Results

We identified 205,068 individuals with the diagnosis of type 2 diabetes, corresponding to a prevalence of 5.6% (205,068/3,659,868) in the study population. The mean age of these patients was 67.5 years, and hypertension was present in 71% (144,938/205,068) of all individuals. There was considerable variation in diagnosed comorbidities across parishes. Diabetes patients with concomitant hypertension or dyslipidemia showed higher odds of having been diagnosed with cardiovascular complications, when adjusting for age and gender (hypertension odds ratio [OR] 2.16, confidence interval [CI] 2.10-2.22; dyslipidemia OR 1.57, CI 1.54-1.60).

Conclusions

Individual-level data from EHRs may play an important role in chronic disease surveillance, namely through the use of GIS. Promoting the quality and comprehensiveness of data, namely through patient involvement in their medical records, is crucial to enhance the feasibility and usefulness of this approach.

How diabetes risk assessment tools are implemented in practice: a systematic review

This review aimed to explore the extent of the use of diabetes risk assessment tools and to determine influential variables associated with the implementation of these tools. CINAHL, Google Scholar, ISI Citation Indexes, PubMed, and Scopus were searched from inception to January 2013. Studies that reported the use of diabetes risk assessment tools to identify individuals at risk of diabetes were included. Of the 1719 articles identified, 24 were included. Follow-up of high risk individuals for diagnosis of diabetes was conducted in 5 studies. Barriers to the uptake of diabetes risk assessment tools by healthcare practitioners included (1) attitudes toward the tools; (2) impracticality of using the tools and (3) lack of reimbursement and regulatory support. Individuals were reluctant to undertake self-assessment of diabetes risk due to (1) lack of perceived severity of type 2 diabetes; (2) impracticality of the tools; and (3) concerns related to finding out the results. The current use of non-invasive diabetes risk assessment scores as screening tools appears to be limited. Practical follow up systems as well as strategies to address other barriers to the implementation of diabetes risk assessment tools are essential and need to be developed.

Automated Risk Assessment for Stroke in Atrial Fibrillation (AURAS-AF)--an automated software system to promote anticoagulation and reduce stroke risk: study protocol for a cluster randomised controlled trial

Background

Patients with atrial fibrillation (AF) are at significantly increased risk of stroke. Oral anticoagulants (OACs) substantially reduce this risk, with gains seen across the spectrum of baseline risk. Despite the benefit to patients, OAC prescribing remains suboptimal in the United Kingdom (UK). We will investigate whether an automated software system, operating within primary care electronic medical records, can improve the management of AF by identifying patients eligible for OAC therapy and increasing uptake of this treatment.

Methods/Design

We will conduct a cluster randomised controlled trial, involving general practices using the Egton Medical Information Systems (EMIS) Web clinical system. We will randomise practices to use an electronic software tool or to continue with usual care. The tool will a) produce (and continually refresh) a list of patients with AF who are eligible for OAC therapy - practices will invite these patients to discuss therapy at the start of the trial - and b) generate electronic screen reminders in the medical records of those eligible, appearing throughout the trial. The software will run for 6 months in 23 intervention practices. A total of 23 control practices will manage their AF register in line with the usual care offered. The primary outcome is change in proportion of eligible patients with AF who have been prescribed OAC therapy after six months. Secondary outcomes are incidence of stroke, transient ischaemic attack, other major thromboembolism, major haemorrhage and reports of inappropriate OAC prescribing in the data collection sample - those deemed eligible for OACs. We will conduct a process evaluation in parallel with the randomised trial. We will use qualitative methods to examine patient and practitioner views of the intervention and its impact on primary care practice, including its time implications.

Discussion

AURAS-AF will investigate whether a simple intervention, using electronic primary care records, can improve OAC uptake in a high risk group for stroke. Given previous concerns about safety, especially surrounding inappropriate prescribing, we will also examine whether electronic reminders safely impact care in this clinical area.

Trial registration

http://ISRCTN 55722437