Modeling the impact of prevention policies on future diabetes prevalence in the United States: 2010-2030

Background Although diabetes is one of the most costly and rapidly increasing serious chronic diseases worldwide, the optimal mix of strategies to reduce diabetes prevalence has not been determined. Methods Using a dynamic model that incorporates national data on diabetes prevalence and incidence, migration, mortality rates, and intervention effectiveness, we project the effect of five hypothetical prevention policies on future US diabetes rates through 2030: 1) no diabetes prevention strategy; 2) a “high-risk” strategy, wherein adults with both impaired fasting glucose (IFG) (fasting plasma glucose of 100–124 mg/dl) and impaired glucose tolerance (IGT) (2-hour post-load glucose of 141–199 mg/dl) receive structured lifestyle intervention; 3) a “moderate-risk” strategy, wherein only adults with IFG are offered structured lifestyle intervention; 4) a “population-wide” strategy, in which the entire population is exposed to broad risk reduction policies; and 5) a “combined” strategy, involving both the moderate-risk and population-wide strategies. We assumed that the moderate- and high-risk strategies reduce the annual diabetes incidence rate in the targeted subpopulations by 12.5% through 2030 and that the population-wide approach would reduce the projected annual diabetes incidence rate by 2% in the entire US population. Results We project that by the year 2030, the combined strategy would prevent 4.6 million incident cases and 3.6 million prevalent cases, attenuating the increase in diabetes prevalence by 14%. The moderate-risk approach is projected to prevent 4.0 million incident cases, 3.1 million prevalent cases, attenuating the increase in prevalence by 12%. The high-risk and population approaches attenuate the projected prevalence increases by 5% and 3%, respectively. Even if the most effective strategy is implemented (the combined strategy), our projections indicate that the diabetes prevalence rate would increase by about 65% over the 23 years (i.e., from 12.9% in 2010 to 21.3% in 2030). Conclusions While implementation of appropriate diabetes prevention strategies may slow the rate of increase of the prevalence of diabetes among US adults through 2030, the US diabetes prevalence rate is likely to increase dramatically over the next 20 years. Demand for health care services for people with diabetes complications and diabetes-related disability will continue to grow, and these services will need to be strengthened along with primary diabetes prevention efforts

Who does not gain weight? Prevalence and predictors of weight maintenance in young women

OBJECTIVE: To investigate the prevalence and predictors of weight maintenance over time in a large sample of young Australian women. DESIGN: This population study examined baseline and 4 y follow-up data from the cohort of young women participating in the Australian Longitudinal Study on Women\u27s Health. SUBJECTS: A total of 8726 young women aged 18-23 y at baseline. MEASURES: Height, weight and body mass index (BMI); physical activity; time spent sitting; selected eating behaviours (eg dieting, disordered eating, takeaway food consumption); cigarette smoking, alcohol consumption; parity; and sociodemographic characteristics. RESULTS: Only 44% of the women reported their BMI at follow-up to be within 5% of their baseline BMI (maintainers); 41% had gained weight and 15% had lost weight. Weight maintainers were more likely to be in managerial or professional occupations; to have never married; to be currently studying; and not to be mothers. Controlling for sociodemographic factors, weight maintainers were more likely to be in a healthy weight range at baseline, and to report that they spent less time sitting, and consumed less takeaway food, than women who gained weight. CONCLUSIONS: Fewer than half the young women in this community sample maintained their weight over this 4 y period in their early twenties. Findings of widespread weight gain, particularly among those already overweight, suggest that early adulthood, which is a time of significant life changes for many women, may be an important time for implementing strategies to promote maintenance of healthy weight. Strategies which encourage decreased sitting time and less takeaway food consumption may be effective for encouraging weight maintenance at this life stage.<br /

Deconstructing Weight Management Interventions for Young Adults: Looking Inside the Black Box of the EARLY Consortium Trials.

ObjectiveThe goal of the present study was to deconstruct the 17 treatment arms used in the Early Adult Reduction of weight through LifestYle (EARLY) weight management trials.MethodsIntervention materials were coded to reflect behavioral domains and behavior change techniques (BCTs) within those domains planned for each treatment arm. The analytical hierarchy process was employed to determine an emphasis profile of domains in each intervention.ResultsThe intervention arms used BCTs from all of the 16 domains, with an average of 29.3 BCTs per intervention arm. All 12 of the interventions included BCTs from the six domains of Goals and Planning, Feedback and Monitoring, Social Support, Shaping Knowledge, Natural Consequences, and Comparison of Outcomes; 11 of the 12 interventions shared 15 BCTs in common across those six domains.ConclusionsWeight management interventions are complex. The shared set of BCTs used in the EARLY trials may represent a core intervention that could be studied to determine the required emphases of BCTs and whether additional BCTs add to or detract from efficacy. Deconstructing interventions will aid in reproducibility and understanding of active ingredients

How managers can build trust in strategic alliances: a meta-analysis on the central trust-building mechanisms

Trust is an important driver of superior alliance performance. Alliance managers are influential in this regard because trust requires active involvement, commitment and the dedicated support of the key actors involved in the strategic alliance. Despite the importance of trust for explaining alliance performance, little effort has been made to systematically investigate the mechanisms that managers can use to purposefully create trust in strategic alliances. We use Parkhe’s (1998b) theoretical framework to derive nine hypotheses that distinguish between process-based, characteristic-based and institutional-based trust-building mechanisms. Our meta-analysis of 64 empirical studies shows that trust is strongly related to alliance performance. Process-based mechanisms are more important for building trust than characteristic- and institutional-based mechanisms. The effects of prior ties and asset specificity are not as strong as expected and the impact of safeguards on trust is not well understood. Overall, theoretical trust research has outpaced empirical research by far and promising opportunities for future empirical research exist

A Machine Learning Trainable Model to Assess the Accuracy of Probabilistic Record Linkage

Record linkage (RL) is the process of identifying and linking data that relates to the same physical entity across multiple heterogeneous data sources. Deterministic linkage methods rely on the presence of common uniquely identifying attributes across all sources while probabilistic approaches use non-unique attributes and calculates similarity indexes for pair wise comparisons. A key component of record linkage is accuracy assessment — the process of manually verifying and validating matched pairs to further refine linkage parameters and increase its overall effectiveness. This process however is time-consuming and impractical when applied to large administrative data sources where millions of records must be linked. Additionally, it is potentially biased as the gold standard used is often the reviewer’s intuition. In this paper, we present an approach for assessing and refining the accuracy of probabilistic linkage based on different supervised machine learning methods (decision trees, naïve Bayes, logistic regression, random forest, linear support vector machines and gradient boosted trees). We used data sets extracted from huge Brazilian socioeconomic and public health care data sources. These models were evaluated using receiver operating characteristic plots, sensitivity, specificity and positive predictive values collected from a 10-fold cross-validation method. Results show that logistic regression outperforms other classifiers and enables the creation of a generalized, very accurate model to validate linkage results

Informing patients of familial diabetes mellitus risk: How do they respond? A cross-sectional survey

<p>Abstract</p> <p>Background</p> <p>A strong family history of type 2 diabetes mellitus (DM) confers increased DM risk. This survey analysis determined whether patients who were informed by their doctors of familial DM risk acknowledged that risk and took steps to reduce it.</p> <p>Methods</p> <p>We conducted an analysis of the National <it>Health Styles 2004 </it>mail survey. All non-diabetic participants who responded to the question of whether their doctor had or had not informed them of their familial DM risk (<it>n </it>= 3,323) were compared for their risk-reducing behaviour and attitude to DM risk.</p> <p>Results</p> <p>Forty-one percent (<it>n </it>= 616) of the question responders that had DM family histories were informed by their doctors of their familial risk; the chance of being informed increased with the number of relatives that had the disease. Members of the informed group were more likely than those in the non-informed group to report lifestyle changes to prevent DM (odds ratio [OR] 4.3, 95% confidence interval [CI] 3.5–5.2) and being tested for DM (OR 2.9, 95% CI 2.4–3.6), although no significant improvement occurred in their U.S.-recommended exercise activity (OR 0.9, 95% CI 0.7–1.1). Overall, informed responders recognised both their familial and personal DM risk; most discussed diabetes with their family (69%), though less so with friends (42%); however, 44% of them still did not consider themselves to be at risk.</p> <p>Conclusion</p> <p>Responders who were informed by their doctors of being at familial DM risk reported greater incidences of lifestyle changes, DM screening, and awareness of risk than non-informed responders. Doctors were more likely to inform patients with stronger DM family histories. Identifying this higher risk group, either in isolation or in combination with other recognised risk factors, offers doctors the opportunity to target limited health promotion resources efficiently for primary DM prevention.</p

Acute Sleep Deprivation and Circadian Misalignment Associated with Transition onto the First Night of Work Impairs Visual Selective Attention

Background: Overnight operations pose a challenge because our circadian biology promotes sleepiness and dissipates wakefulness at night. Since the circadian effect on cognitive functions magnifies with increasing sleep pressure, cognitive deficits associated with night work are likely to be most acute with extended wakefulness, such as during the transition from a day shift to night shift. Methodology/Principal Findings: To test this hypothesis we measured selective attention (with visual search), vigilance (with Psychomotor Vigilance Task [PVT]) and alertness (with a visual analog scale) in a shift work simulation protocol, which included four day shifts followed by three night shifts. There was a nocturnal decline in cognitive processes, some of which were most pronounced on the first night shift. The nighttime decrease in visual search sensitivity was most pronounced on the first night compared with subsequent nights (p = .04), and this was accompanied by a trend towards selective attention becoming ‘fast and sloppy’. The nighttime increase in attentional lapses on the PVT was significantly greater on the first night compared to subsequent nights (p<.05) indicating an impaired ability to sustain focus. The nighttime decrease in subjective alertness was also greatest on the first night compared with subsequent nights (p<.05). Conclusions/Significance: These nocturnal deficits in attention and alertness offer some insight into why occupational errors, accidents, and injuries are pronounced during night work compared to day work. Examination of the nighttime vulnerabilities underlying the deployment of attention can be informative for the design of optimal work schedules and the implementation of effective countermeasures for performance deficits during night work