Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

BCAS: A Web-enabled and GIS-based Decision Support System for the Diagnosis and Treatment of Breast Cancer

For decades, geographical variations in cancer rates have been observed but the precise determinants of such geographic differences in breast cancer development are unclear. Various statistical models have been proposed. Applications of these models, however, require that the data be assembled from a variety of sources, converted into the statistical models’ parameters and delivered effectively to researchers and policy makers. A web-enabled and GIS-based system can be developed to provide the needed functionality. This article overviews the conceptual web-enabled and GIS-based system (BCAS), illustrates the system’s use in diagnosing and treating breast cancer and examines the potential benefits and implications for breast cancer research and practice

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling

Vulnerability and Resilience Determinants of under-five mortality changes in Zambia

Trends in under-five mortality were favorable in Zambia in the twelve years following independence (1964-1975), as a result of favorable political and economic context and generous health, education and social policies, largely financed by the exports of copper minerals, the main economic resource of the country. In 1975, the international prices of copper decreased suddenly, and exports of copper continued to diminish in volume. This created a tremendous economic shock to the country, and seriously affected both the state budget and private income. During the long-lasting economic crisis, income per capita was strongly reduced, and most economic indicators collapsed or were strongly reduced as well: imports, agricultural production, private and public consumption, savings, and industrial investments. The health sector was also affected: health expenditures declined, imports of medical drugs and supplies declined, and as a result of declining salaries some physicians left the country. School attendance was reduced somewhat later, in the 1980’s, and had long term effect on the mean level of education of adult women. Under-five mortality increased in the years following the copper crisis, up to a maximum in year 1992, after which under-five mortality went down again, despite a significant impact of HIV/AIDS. A regression model indicates that most of the increase in mortality after discounting for the effect of HIV/AIDS is attributable to the direct and indirect effects of the copper crisis and the declining income. Both trend analysis and regression analysis indicate that mortality in 1992 was more than double what it should have been in the context of a regular health transition and positive economic development. The mortality decline after 1992 seems to be due to the resumption of the health transition, the implementation of new health policies, and continuous investments in health personnel and health infrastructure. These changes occurred in the context of structural adjustment policies. Issues about vulnerability and resilience are discussed in light of economic and political choices made in the earlier periods and recent changes in policies. Key Words: Under-five mortality, Mortality trends, Economic crisis, Copper price, Economic policies, Health policies, Structural adjustment policies, Education, Nutritional status, HIV/AIDS, Malaria, Developing countries, sub-Saharan Africa, Zambia.Zambia, Sub-Saharan Africa, developing countries, malaria, HIV/AIDS, nutritional status, education, structural adjustment policies, health policies, Economic policies, copper price, economic crisis, Mortality trend, Under-five mortality

Community participation in malaria epidemic control in highland areas of southern Oromia, Ethiopia

Background: Satisfactory strategies for the timely and effective control of malaria epidemics have not yet been established in epidemic-prone areas. A devastating malaria epidemic occurred in mid 2000 in four districts of Borena Zone in Oromia Regional State. Objective: To assess and highlight the importance of community participation particularly that of village malaria workers (VMWs) in the control of malaria epidemics. Methods: Epidemic-affected peasant associations (PAs) were initially identified from each of the affected districts. One VMW residing in the PA was selected, and training on health education, diagnosis of suspected malaria cases and treatment by Sulfadoxine-Pyrimethamine (SP), referral of severe cases, source reduction of mosquito breeding sites, registration and reporting of treated cases, consumed antimalarials, registration of deaths and assessment of the overall status of the epidemic in their particular PAs was given for three days. Results: One hundred twenty-four epidemic affected PAs were identified by the study, that and 115 VMWs were deployed to control the epidemic. A total of 72,998 suspected malaria patients were treated by VMWs using SP. Only 11,994 clinical cases of malaria were treated by ordinary health workers at field levels from June–August 2000. A total of 1,323 deaths were reported both by health professionals and the VMWs. Five hundred sixty eight confirmed malaria cases were treated during out patient consultations at Hagere Mariam Hospital during the three month period. In addition, 191 admitted malaria patients and 36 malaria deaths were identified from the Hospital during the June- August 2000 epidemic. The case fatality rate and proportionate mortality ratio for malaria were 20.8% and 90.9% in August, respectively, in the Hospital. Conclusion: Although health professionals of various categories were mobilized, the epidemic covered wide geographical areas and caused high morbidity and mortality within a short period of time. Therefore, mobilizing of the necessary human and material resources, particularly the community itself is extremely important in the control of malaria epidemics. Ethiopian Journal of Health Development Vol. 19(1) 2005: 3-1