Risk models and scores for type 2 diabetes: Systematic review

This article is published under a Creative Commons Attribution Non Commercial (CC BY-NC 3.0) licence that allows reuse subject only to the use being non-commercial and to the article being fully attributed (http://creativecommons.org/licenses/by-nc/3.0).Objective - To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice. Design - Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion - criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes. Data sources - Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact. Data extraction - Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes. Results - 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as “simple” or “easily implemented,” although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse. Conclusion - Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk “hotspots” for targeted public health interventions.Tower Hamlets, Newham, and City and Hackney primary care trusts and National Institute of Health Research

Predictors of Late Stage Cervical Cancer Diagnoses and Disparities in the U.S. (A Closer Look at the Interactions Between Characteristics of Access, Women & Place)

Background: Approximately 51% of women with cervical cancer (CVC) are diagnosed at a late stage (regional or distant), an outcome associated with increased morbidity and mortality.African American, and Hispanic women,and women residing in specific geographic regions of the (US) are among those most heavily burdened by late stage CVC.The cause(s) of these disparities are multifaceted and not well understood. However, the most significant predictor of late stage CVC diagnosis and disparities is current screening, which is largely impacted by access to care. Thus, the goal of this study was to identify access-related predictors of late stage CVC and develop a comprehensive understanding of where and why racial and geographic disparities in late stage CVC occur. Methods: This three-part study examined primary CVC cases diagnosed between the years of 2005-2014, from the United States Cancer Statistics (USCS) database. The final sample included 120,325 CVC cases within 43 states and their 2,357constituent counties. First, Empirical Bayes LISA clustering methods were applied to identify clusters of counties considered to be high risk for late stage CVC “hotspots” during two 5-year time periods (pre- and post-2010). Second, a series of T-tests were conducted to determine whether various contextual and compositional factors were significantly different in hotspots versus other places. Third, two Generalized Linear Mixed Models (GzLMM), using data from person and county levels, were estimated to identify predictors of late stage CVC diagnosis and racial or ethnic disparities among women with CVC in the US. Lastly, a General Linear Mixed Model (GLMM) using data from county- and state –levels was estimated to examine predictors of higher proportions of late stage CVC among counties. Results: Primary care physician shortage areas, Planned Parenthood (PPH) clinics, area-level poverty rates, area-level uninsured rates, percent of immigrants from other countries, state CVC screening mandates and prevalence of self-insured employer health plans were all statistically significant predictors of access to care associated with late stage CVC diagnoses and geographic disparities. We also found that PPH clinics play an important role in reducing the odds of late stage CVC among Hispanic women with CVC. Conclusion: Access to CVC screening plays a significant role in the etiological pathway to late stage CVC diagnoses and disparities. Given that significant access barriers occurred at various ecological and geographical levels, it is recommend that future research and intervention efforts begin to focus on multilevel and/or spatial approaches. Without further exploration of the factors impacting late stage CVC diagnoses, CVC mortality rates will remain high and at a disproportionately higher rate for women in various geographical areas and among African American and Hispanic women

A case study of survival and presentation of gastroesophageal cancer in local neighbourhoods

This thesis presents a quantitative case study on incidence, survival and presentation of patients diagnosed with gastroesophageal cancer to evaluate whether where people live affects how they present and survive with a gastroesophageal cancer diagnosis. The focus research evolved from studies on gastroesophageal cancer’s ‘geographic affiliation’ and a desire to review whether patient and population attributes could be harnessed to reveal potential ‘hotspots’ to inform targeted health intervention strategies. As the most crucial stage for intervention was associated with patients detecting symptoms early enough for intervention, the focus of this case study was narrowed to survival and presentation.This research analysed data from 2785 patients who presented to a regional referral specialist cancer treatment centre between the years 2000 and 2013. Cohort analysis revealed common attributes and survival, and data were merged with demographic information in a geographic information system to present findings in mapped format.Descriptive analysis revealed an association between later stage presentation and reduced survival outcome. Emergency presentations tended to have worse outcomes. Survival deteriorated with advancing age. Gastroesophageal cancer diagnoses in the under 54 age group was more common in lower socioeconomic groups and survival outcomes were marginally lower than in those patients from the least deprived areas. Spatial analysis revealed variation in incidence, presentation and survival across the region. Though this case study revealed several new findings on gastroesophageal cancer presentation and survival, there remains no single solution to informing and encouraging earlier diagnosis interventions. Though presenting data at finer scales of resolution is more clinically relevant, it threatens patient confidentiality

The American Academy of Health Behavior 2017 Annual Scientific Meeting: Health Behavior Research in the Age of Personalized Medicine

The American Academy of Health Behavior (AAHB) hosted its Annual Scientific Meeting at Loews Ventana Canyon in Tucson, AZ March 19-22, 2017. The theme of the meeting was “Health Behavior Research in the Age of Personalized Medicine.” This publication describes the meeting theme, podium presentations and workshop, and includes the refereed abstracts presented at the 2017 Annual Scientific Meeting

Predicting the epidemic: a study of diabetes risk profiling in a multi-ethnic inner city population

PhDType 2 diabetes has increased in prevalence globally in recent years, mainly due to obesity. Many other risk factors are well known. Identifying those at high risk of type 2 diabetes may guide targeted interventions aimed at reducing risk. Type 2 diabetes risk prediction is a complex science. The first half of this thesis presents a quantitative and qualitative systematic review of 145 risk prediction models and scores. Many are available; few are usable in real life clinical practice. Seven have high potential to be used with routine data (such as electronic primary care records). The second half of this thesis describes the use of one of the risk prediction scores locally, the QDScore, on a dataset of 519,288 electronic primary care records in East London, UK to calculate the ten year risk of developing type 2 diabetes. Ten percent of the population were at high risk (defined as a ten year risk of greater than 20%). Ethnicity and deprivation were key factors responsible for increasing risk, and there was overlap with cardiovascular morbidity. A sub-section of these data were mapped to explore the feasibility of using geospatial mapping to convey the risk of non-communicable disease in a public health setting. Previous research has focussed on targeting individuals with pre-diabetes (e.g. Impaired Fasting Glucose) and screening for undiagnosed diabetes. Going a step further back and identifying those at risk of type 2 diabetes is theoretically possible due to the wide availability of prediction algorithms, and such an approach is potentially achievable locally using electronic primary care records. This produces important descriptive data 3 to aid the interventions of general practitioners, public health specialists and urban planners. Future research should focus on interventions which reduce risk of type 2 diabetes in otherwise healthy adults

A systematic review of the physical health impacts from non-occupational exposure to wildfire smoke

Background: Climate change is likely to increase the threat of wildfires, and little is known about how wildfires affect health in exposed communities. A better understanding of the impacts of the resulting air pollution has important public health implications for the present day and the future. Method: We performed a systematic search to identify peer-reviewed scientific studies published since 1986 regarding impacts of wildfire smoke on health in exposed communities. We reviewed and synthesized the state of science of this issue including methods to estimate exposure, and identified limitations in current research. Results: We identified 61 epidemiological studies linking wildfire and human health in communities. The U.S. and Australia were the most frequently studied countries (18 studies on the U.S., 15 on Australia). Geographic scales ranged from a single small city (population about 55,000) to the entire globe. Most studies focused on areas close to fire events. Exposure was most commonly assessed with stationary air pollutant monitors (35 of 61 studies). Other methods included using satellite remote sensing and measurements from air samples collected during fires. Most studies compared risk of health outcomes between 1) periods with no fire events and periods during or after fire events, or 2) regions affected by wildfire smoke and unaffected regions. Daily pollution levels during or after wildfire in most studies exceeded U.S. EPA regulations. Levels of PM10, the most frequently studied pollutant, were 1.2 to 10 times higher due to wildfire smoke compared to non-fire periods and/or locations. Respiratory disease was the most frequently studied health condition, and had the most consistent results. Over 90% of these 45 studies reported that wildfire smoke was significantly associated with risk of respiratory morbidity.Conclusion: Exposure measurement is a key challenge in current literature on wildfire and human health. A limitation is the difficulty of estimating pollution specific to wildfires. New methods are needed to separate air pollution levels of wildfires from those from ambient sources, such as transportation. The majority of studies found that wildfire smoke was associated with increased risk of respiratory and cardiovascular diseases. Children, the elderly and those with underlying chronic diseases appear to be susceptible. More studies on mortality and cardiovascular morbidity are needed. Further exploration with new methods could help ascertain the public health impacts of wildfires under climate change and guide mitigation policies

Abstracts of the 20th College of Medicine Research Dissemination Conference

No Abstract

Measuring Community Mobility in Older Adults with Parkinson’s Disease Using A Wearable GPS Sensor And Self-report Assessment Tools

Community mobility (CM) is an important instrumental activity of daily living associated with quality of life and independence. Measuring the CM of older adults, particularly those with gait disorders such as Parkinson’s disease (PD), is an important way to understand how to help people maintain mobility in the real life setting. CM is measured using self-report measures and emergent technologies, such as wearable Global Positioning System (GPS) sensors. However, the measurement properties of most available assessments have not been compared within a PD population to determine their appropriateness and identify any feasibility issues. The primary objective of this project was to compare a novel instrumented measure (Wireless Isoinertial Measurement unit with GPS; WIMU-GPS) with a self-report diary and the Life Space Assessment (LSA). To accomplish this aim, a review of literature was first conducted to show that the validity and reliability between mobility measures were seldom assessed in existing comparison studies. Then, seventy people with early to mid-stage PD (67.4 ± 6.5 years, 67.1% men) wore the WIMU-GPS and completed the self-report diaries and LSA for a 14 day period. Moderate agreements were observed between WIMuGPS and diary for trip frequency and duration (Intraclass correlation coefficient [ICC] = 0.71, 95% CI = 0.51 to 0.82; 0.67, 95% CI = 0.42 to 0.82, respectively). Disagreement between these two measures was higher for duration, particularly among individuals who regularly worked or volunteered. Convergent validity and good reliability was attained for trip frequency (Spearman correlation coefficient [rs] = 0.69, 95% CI = 0.52 to 0.81; ICC = 0.714, 95% CI = 0.51 to 0.82) and duration outside (rs = 0.43, 95% CI = 0.18 to 0.62; ICC = 0.674, 95% CI = 0.42 to 0.82) measured by the WIMU-GPS and diary. However, convergent validity was not observed between WIMU-GPS recordings and LSA reported life space size (rs = 0.39, 95% CI = 0.14 to 0.60). The LSA exhibited ceiling effects and discrimination issues. It should be avoided as a CM measure when it is feasible to use the WIMU-GPS and diary instead. The secondary objective was to determine the utility and feasibly of using WIMU-GPS to quantify different dimensions of CM in people with PD (PwP). Using a subset of participants, it was first determined that sampling error was minimized in non-discrete continuous outcomes, such as “time outside” and “area size”, when daily WIMU-GPS recordings lasted at least 600 minutes. A shorter recording minimum of at least 500 minutes per day was also suitable for discrete outcomes, such as “trip count” and “hotspot count”. The sample size precluded the determination of the optimal number of days of recording. However, data from at least seven distinct days of recording is required to capture the natural fluctuations in CM between days of the week. From a practical standpoint, a minimum of seven distinct recording days were best attained if the WIMU-GPS was worn for at least eight days. Next, the new minimum GPS recording length was adopted in a larger subset of the sample to show that PwP were regularly in the community, and they preferred vehicular travel over walking when travelling to a destination. Distances walked by PwP increased when they perceived higher levels of PD-related impact on emotional wellbeing (Pearson correlation [r] = 0.40, p \u3c 0.01) and bodily discomfort (r = 0.30, p = 0.03). Complementary diary data also showed PwP were making regular weekly visits to medical facilities. Finally, the body of work described in this Dissertation culminated in a series of practical recommendations for those interested in the CM of an older PD population or wishing to use GPS sensors for assessing real-life CM. The results of this Dissertation also are useful resources for the development of needed standards on how mobility measurements should be compared, and on the study design, data collection, and reporting of health data using GPS sensors