Aspirin use and knowledge in the community: a population- and health facility based survey for measuring local health system performance

BACKGROUND: Little is known about the relationship between cardiovascular risk, disease and actual use of aspirin in the community. METHODS: The Measuring Disparities in Chronic Conditions (MDCC) study is a community and health facility-based survey designed to track disparities in the delivery of health interventions for common chronic diseases. MDCC includes a survey instrument designed to collect detailed information about aspirin use. In King County, WA between 2011 and 2012, we surveyed 4633 white, African American, or Hispanic adults (45% home address-based sample, 55% health facility sample). We examined self-reported counseling on, frequency of use and risks of aspirin for all respondents. For a subgroup free of CAD or cerebral infarction that underwent physical examination, we measured 10-year coronary heart disease risk and blood salicylate concentration. RESULTS: Two in five respondents reported using aspirin routinely while one in five with a history of CAD or cerebral infarction and without contraindication did not report routine use of aspirin. Women with these conditions used less aspirin than men (65.0% vs. 76.5%) and reported more health problems that would make aspirin unsafe (29.4% vs. 21.2%). In a subgroup undergoing phlebotomy a third of respondents with low cardiovascular risk used aspirin routinely and only 4.6% of all aspirin users had no detectable salicylate in their blood. CONCLUSIONS: In this large urban county where health care delivery should be of high quality, there is insufficient aspirin use among those with high cardiovascular risk or disease and routine aspirin use by many at low risk. Further efforts are needed to promote shared-decision making between patients and clinicians as well as inform the public about appropriate use of routine aspirin to reduce the burden of atherosclerotic vascular disease

Health in times of uncertainty in the eastern Mediterranean region, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

Background: The eastern Mediterranean region is comprised of 22 countries: Afghanistan, Bahrain, Djibouti, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Pakistan, Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, the United Arab Emirates, and Yemen. Since our Global Burden of Disease Study 2010 (GBD 2010), the region has faced unrest as a result of revolutions, wars, and the so-called Arab uprisings. The objective of this study was to present the burden of diseases, injuries, and risk factors in the eastern Mediterranean region as of 2013. Methods: GBD 2013 includes an annual assessment covering 188 countries from 1990 to 2013. The study covers 306 diseases and injuries, 1233 sequelae, and 79 risk factors. Our GBD 2013 analyses included the addition of new data through updated systematic reviews and through the contribution of unpublished data sources from collaborators, an updated version of modelling software, and several improvements in our methods. In this systematic analysis, we use data from GBD 2013 to analyse the burden of disease and injuries in the eastern Mediterranean region specifically. Findings: The leading cause of death in the region in 2013 was ischaemic heart disease (90·3 deaths per 100 000 people), which increased by 17·2% since 1990. However, diarrhoeal diseases were the leading cause of death in Somalia (186·7 deaths per 100 000 people) in 2013, which decreased by 26·9% since 1990. The leading cause of disability-adjusted life-years (DALYs) was ischaemic heart disease for males and lower respiratory infection for females. High blood pressure was the leading risk factor for DALYs in 2013, with an increase of 83·3% since 1990. Risk factors for DALYs varied by country. In low-income countries, childhood wasting was the leading cause of DALYs in Afghanistan, Somalia, and Yemen, whereas unsafe sex was the leading cause in Djibouti. Non-communicable risk factors were the leading cause of DALYs in high-income and middle-income countries in the region. DALY risk factors varied by age, with child and maternal malnutrition affecting the younger age groups (aged 28 days to 4 years), whereas high bodyweight and systolic blood pressure affected older people (aged 60–80 years). The proportion of DALYs attributed to high body-mass index increased from 3·7% to 7·5% between 1990 and 2013. Burden of mental health problems and drug use increased. Most increases in DALYs, especially from non-communicable diseases, were due to population growth. The crises in Egypt, Yemen, Libya, and Syria have resulted in a reduction in life expectancy; life expectancy in Syria would have been 5 years higher than that recorded for females and 6 years higher for males had the crisis not occurred. Interpretation: Our study shows that the eastern Mediterranean region is going through a crucial health phase. The Arab uprisings and the wars that followed, coupled with ageing and population growth, will have a major impact on the region's health and resources. The region has historically seen improvements in life expectancy and other health indicators, even under stress. However, the current situation will cause deteriorating health conditions for many countries and for many years and will have an impact on the region and the rest of the world. Based on our findings, we call for increased investment in health in the region in addition to reducing the conflicts.Ali H Mokdad ... Azmeraw T Amare ... et al

Physical activity attitudes, intentions and behaviour among 18-25 year olds: a mixed method study

Peer reviewedPublisher PD

Forecasting Tunisian type 2 diabetes prevalence to 2027: validation of a simple model.

BACKGROUND: Most projections of type 2 diabetes (T2D) prevalence are simply based on demographic change (i.e. ageing). We developed a model to predict future trends in T2D prevalence in Tunisia, explicitly taking into account trends in major risk factors (obesity and smoking). This could improve assessment of policy options for prevention and health service planning. METHODS: The IMPACT T2D model uses a Markov approach to integrate population, obesity and smoking trends to estimate future T2D prevalence. We developed a model for the Tunisian population from 1997 to 2027, and validated the model outputs by comparing with a subsequent T2D prevalence survey conducted in 2005. RESULTS: The model estimated that the prevalence of T2D among Tunisians aged over 25 years was 12.0% in 1997 (95% confidence intervals 9.6%-14.4%), increasing to 15.1% (12.5%-17.4%) in 2005. Between 1997 and 2005, observed prevalence in men increased from 13.5% to 16.1% and in women from 12.9% to 14.1%. The model forecast for a dramatic rise in prevalence by 2027 (26.6% overall, 28.6% in men and 24.7% in women). However, if obesity prevalence declined by 20% in the 10 years from 2013, and if smoking decreased by 20% over 10 years from 2009, a 3.3% reduction in T2D prevalence could be achieved in 2027 (2.5% in men and 4.1% in women). CONCLUSIONS: This innovative model provides a reasonably close estimate of T2D prevalence for Tunisia over the 1997-2027 period. Diabetes burden is now a significant public health challenge. Our model predicts that this burden will increase significantly in the next two decades. Tackling obesity, smoking and other T2D risk factors thus needs urgent action. Tunisian decision makers have therefore defined two strategies: obesity reduction and tobacco control. Responses will be evaluated in future population surveys

Toward optimal implementation of cancer prevention and control programs in public health: A study protocol on mis-implementation

Abstract Background Much of the cancer burden in the USA is preventable, through application of existing knowledge. State-level funders and public health practitioners are in ideal positions to affect programs and policies related to cancer control. Mis-implementation refers to ending effective programs and policies prematurely or continuing ineffective ones. Greater attention to mis-implementation should lead to use of effective interventions and more efficient expenditure of resources, which in the long term, will lead to more positive cancer outcomes. Methods This is a three-phase study that takes a comprehensive approach, leading to the elucidation of tactics for addressing mis-implementation. Phase 1: We assess the extent to which mis-implementation is occurring among state cancer control programs in public health. This initial phase will involve a survey of 800 practitioners representing all states. The programs represented will span the full continuum of cancer control, from primary prevention to survivorship. Phase 2: Using data from phase 1 to identify organizations in which mis-implementation is particularly high or low, the team will conduct eight comparative case studies to get a richer understanding of mis-implementation and to understand contextual differences. These case studies will highlight lessons learned about mis-implementation and identify hypothesized drivers. Phase 3: Agent-based modeling will be used to identify dynamic interactions between individual capacity, organizational capacity, use of evidence, funding, and external factors driving mis-implementation. The team will then translate and disseminate findings from phases 1 to 3 to practitioners and practice-related stakeholders to support the reduction of mis-implementation. Discussion This study is innovative and significant because it will (1) be the first to refine and further develop reliable and valid measures of mis-implementation of public health programs; (2) bring together a strong, transdisciplinary team with significant expertise in practice-based research; (3) use agent-based modeling to address cancer control implementation; and (4) use a participatory, evidence-based, stakeholder-driven approach that will identify key leverage points for addressing mis-implementation among state public health programs. This research is expected to provide replicable computational simulation models that can identify leverage points and public health system dynamics to reduce mis-implementation in cancer control and may be of interest to other health areas

Why do people fail to turn good intentions into action? : The role of executive control processes in the translation of healthy eating intentions into action in young Scottish adults

Non peer reviewedPublisher PD

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 /

Impact of the population at risk of diabetes on projections of diabetes burden in the United States: an epidemic on the way

Aims/hypothesis The aim of this study was to make projections of the future diabetes burden for the adult US population based in part on the prevalence of individuals at high risk of developing diabetes. Materials and methods Models were created from data in the nationally representative National Health and Nutrition Examination Survey (NHANES) II mortality survey (1976–1992), the NHANES III (1988–1994) and the NHANES 1999–2002. Population models for adults (>20 years of age) from NHANES III data were fitted to known diabetes prevalence in the NHANES 1999–2002 before making future projections. We used a multivariable diabetes risk score to estimate the likelihood of diabetes incidence in 10 years. Estimates of future diabetes (diagnosed and undiagnosed) prevalence in 2011, 2021, and 2031 were made under several assumptions. Results Based on the multivariable diabetes risk score, the number of adults at high risk of diabetes was 38.4 million in 1991 and 49.9 million in 2001. The total diabetes burden is anticipated to be 11.5% (25.4 million) in 2011, 13.5% (32.6 million) in 2021, and 14.5% (37.7 million) in 2031. Among individuals aged 30 to 39 years old who are not currently targeted for screening according to age, the prevalence of diabetes is expected to rise from 3.7% in 2001 to 5.2% in 2031. By 2031, 20.2% of adult Hispanic individuals are expected to have diabetes. Conclusions/interpretation The prevalence of diabetes is projected to rise to substantially greater levels than previously estimated. Diabetes prevalence within the Hispanic community is projected to be potentially overwhelming

A Portable Wireless Particulate Sensor System for Continuous Real-Time Environmental Monitoring

Airborne particulate matter has been shown to be associated with morbidity and mortality, and may interfere with certain sensitive experiment. Understanding the levels and movements of particulate matter in an enclosed space can lead to a reduction in the impact of this material on health and experimental results. A system of environmental sensors including particulate matter, selected gasses, humidity, temperature, and pressure can be used to assist in tracking air movement, providing real-time mapping of potential contaminants as they move through a space. In this paper we present a system that is capable of sensing these environmental factors, collecting data from multiple dispersed nodes and presenting the aggregated information in real-time. The highly modular system is based on a flexible and scalable framework developed for use in aircraft cabin environments. Use of this framework enables the deployment of a custom suite of sensors with minimal development effort. Individual nodes communicate using a self-organizing mesh network and can be powered from a variety of sources, bringing a high level of flexibility in the arrangement and distribution of the sensor array. Sensor data is transmitted to a coordinator node, which then passes the time-correlated information to a server-hosted database through a choice of wired or wireless networks. Presentation software is used to either monitor the real-time data stream, or to extract records of interest from the database. A reference implementation has been created for the National Institutes of Health consisting of a custom optical particle counter and off-the-shelf sensors for CO2, CO, temperature, humidity, pressure, and acoustic noise. The total environmental sensing system provides continuous, real-time data in a readable format that can be used to analyze ambient air for events of interest

Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning

In this paper, we present the current state-of-the-art of decision making (DM) and machine learning (ML) and bridge the two research domains to create an integrated approach of complex problem solving based on human and computational agents. We present a novel classification of ML, emphasizing the human-in-the-loop in interactive ML (iML) and more specific on collaborative interactive ML (ciML), which we understand as a deep integrated version of iML, where humans and algorithms work hand in hand to solve complex problems. Both humans and computers have specific strengths and weaknesses and integrating humans into machine learning processes might be a very efficient way for tackling problems. This approach bears immense research potential for various domains, e.g., in health informatics or in industrial applications. We outline open questions and name future challenges that have to be addressed by the research community to enable the use of collaborative interactive machine learning for problem solving in a large scale