Main causes of death in Dande, Angola: results from verbal autopsies of deaths occurring during 2009–2012

bACKGROUND - The Dande Health and Demographic Surveillance System (HDSS) located in Bengo province, Angola, covers nearly 65,500 residents living in approximately 19,800 households. This study aims to describe the main causes of deaths (CoD) occurred within the HDSS, from 2009 to 2012, and to explore associations between demographic or socioeconomic factors and broad mortality groups (Group I-Communicable diseases, maternal, perinatal and nutritional conditions; Group II-Non-communicable diseases; Group III-Injuries; IND-Indeterminate). Methods - Verbal Autopsies (VA) were performed after death identification during routine HDSS visits. Associations between broad groups of CoD and sex, age, education, socioeconomic position, place of residence and place of death, were explored using chi-square tests and fitting logistic regression models. Results - From a total of 1488 deaths registered, 1009 verbal autopsies were performed and 798 of these were assigned a CoD based on the 10th revision of the International Classification of Diseases (ICD-10). Mortality was led by CD (61.0%), followed by IND (18.3%), NCD (11.6%) and INJ (9.1%). Intestinal infectious diseases, malnutrition and acute respiratory infections were the main contributors to under-five mortality (44.2%). Malaria was the most common CoD among children under 15 years old (38.6%). Tuberculosis, traffic accidents and malaria led the CoD among adults aged 15–49 (13.5%, 10.5 % and 8.0% respectively). Among adults aged 50 or more, diseases of the circulatory system (23.2%) were the major CoD, followed by tuberculosis (8.2%) and malaria (7.7%). CD were more frequent CoD among less educated people (adjusted odds ratio, 95% confidence interval for none vs. 5 or more years of school: 1.68, 1.04–2.72). Conclusion - Infectious diseases were the leading CoD in this region. Verbal autopsies proved useful to identify the main CoD, being an important tool in settings where vital statistics are scarce and death registration systems have limitations.

Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.