Probabilistic classification of acute myocardial infarction from multiple cardiac markers

Logistic regression and Gaussian mixture model (GMM) classifiers have been trained to estimate the probability of acute myocardial infarction (AMI) in patients based upon the concentrations of a panel of cardiac markers. The panel consists of two new markers, fatty acid binding protein (FABP) and glycogen phosphorylase BB (GPBB), in addition to the traditional cardiac troponin I (cTnI), creatine kinase MB (CKMB) and myoglobin. The effect of using principal component analysis (PCA) and Fisher discriminant analysis (FDA) to preprocess the marker concentrations was also investigated. The need for classifiers to give an accurate estimate of the probability of AMI is argued and three categories of performance measure are described, namely discriminatory ability, sharpness, and reliability. Numerical performance measures for each category are given and applied. The optimum classifier, based solely upon the samples take on admission, was the logistic regression classifier using FDA preprocessing. This gave an accuracy of 0.85 (95% confidence interval: 0.78–0.91) and a normalised Brier score of 0.89. When samples at both admission and a further time, 1–6 h later, were included, the performance increased significantly, showing that logistic regression classifiers can indeed use the information from the five cardiac markers to accurately and reliably estimate the probability AMI

Spatial and temporal variations of malaria epidemic risk in Ethiopia: factors involved and implications.

The aim of this study was to describe spatial and temporal variations in malaria epidemic risk in Ethiopia and to examine factors involved in relation to their implications for early warning and interpretation of geographical risk models. Forty-eight epidemic episodes were identified in various areas between September 1986 and August 1993 and factors that might have led to the events investigated using health facility records and weather data. The study showed that epidemics in specific years were associated with specific geographical areas. A major epidemic in 1988 affected the highlands whereas epidemics in 1991 and 1992 affected highland-fringe areas on the escarpments of the Rift Valley and in southern and north-western parts of the country. Malaria epidemics were significantly more often preceded by a month of abnormally high minimum temperature in the preceding 3 months than based on random chance, whereas frequency of abnormally low minimum temperature prior to epidemics was significantly lower than expected. Abnormal increases of maximum temperature and rainfall had no positive association with the epidemics. A period of low incidence during previous transmission seasons might have aggravated the events, possibly due to low level of immunity in affected populations. Epidemic risk is a dynamic phenomenon with changing geographic pattern based on temporal variations in determinant factors including weather and other eco-epidemiological characteristics of areas at risk. Epidemic early warning systems should take account of non-uniform effects of these factors by space and time and thus temporal dimensions need to be considered in spatial models of epidemic risks

Forecasting malaria incidence from historical morbidity patterns in epidemic-prone areas of Ethiopia: a simple seasonal adjustment method performs best.

The aim of this study was to assess the accuracy of different methods of forecasting malaria incidence from historical morbidity patterns in areas with unstable transmission. We tested five methods using incidence data reported from health facilities in 20 areas in central and north-western Ethiopia. The accuracy of each method was determined by calculating errors resulting from the difference between observed incidence and corresponding forecasts obtained for prediction intervals of up to 12 months. Simple seasonal adjustment methods outperformed a statistically more advanced autoregressive integrated moving average method. In particular, a seasonal adjustment method that uses mean deviation of the last three observations from expected seasonal values consistently produced the best forecasts. Using 3 years' observation to generate forecasts with this method gave lower errors than shorter or longer periods. Incidence during the rainy months of June-August was the most predictable with this method. Forecasts for the normally dry months, particularly December-February, were less accurate. The study shows the limitations of forecasting incidence from historical morbidity patterns alone, and indicates the need for improved epidemic early warning by incorporating external predictors such as meteorological factors

Advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling

Mathematical simulation models for transmission and control of lymphatic filariasis are useful tools for studying the prospects of lymphatic filariasis elimination. Two simulation models are currently being used. The first, EPIFIL, is a population-based, deterministic model that simulates average trends in infection intensity over time. The second, LYMFASIM, is an individual-based, stochastic model that simulates acquisition and loss of infection for each individual in the simulated population, taking account of individual characteristics. For settings like Pondicherry (India), where Wuchereria bancrofti infection is transmitted by Culex quinquefasciatus, the models give similar predictions of the coverage and number of treatment rounds required to bring microfilaraemia prevalence below a level of 0.5%. Nevertheless, published estimates of the duration of mass treatment required for elimination differed, due to the use of different indicators for elimination (EPIFIL: microfilaraemia prevalence < 0.5% after the last treatment; LYMFASIM: reduction of microfilaraemia prevalence to zero, within 40 years after the start of mass treatment). The two main challenges for future modelling work are: 1) quantification and validation of the models for other regions, for investigation of elimination prospects in situations with other vector-parasite combinations and endemicity levels than in Pondicherry; 2) application of the models to address a range of programmatic issues related to the monitoring and evaluation of ongoing control programmes. The models' usefulness could be enhanced by several extensions; inclusion of different diagnostic tests and natural history of disease in the models is of particular relevance

The Long Term Effect of Current and New Interventions on the New Case Detection of Leprosy: A Modeling Study

Leprosy is a contagious disease that will remain prevalent, despite the declining number of patients worldwide over the last century. With approximately 250,000 new cases detected annually, leprosy is far from being eradicated. Leprosy can be treated with drugs after disease detection

Irreversible Effects of Ivermectin on Adult Parasites in Onchocerciasis Patients in the Onchocerciasis Control Programme in West Africa

Ivermectin is an effective drug for the treatment of human onchocerciasis, a disease caused by the parasitic filarial nematode Onchocerca volvulus. When humans are treated, the microfilariae normally found in the skin are rapidly and very nearly completely eliminated. Nonetheless, after a delay, microfilariae gradually reappear in the skin. This study is concerned with the causes of this delay. Hypotheses are tested by comparing the results of model calculations with skin microfilaria counts collected from 114 patients during a trial of five annual treatments in the focus area of Asubende, Ghana. The results obtained strongly suggest that annual treatment with ivermectin causes an irreversible decline in microfilariae production of ∼30%/treatment. This result has important implications for public health strategies designed to eliminate onchocerciasis as a significant health hazar