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BACKGROUND: In Sub-Saharan African countries which often lack high-quality vital registration data, estimates of under-five mortality (U5M) rely heavily on full birth histories (FBHs) collected in surveys and model age patterns of mortality calibrated against vital statistics from other populations. Health and Demographic Surveillance Systems (HDSS) are al-ternate sources of population-based data in much of sub-Saharan Africa, which are less formally utilized in estimation. OBJECTIVE: The objective of this study is to compare the age pattern of U5M in African HDSS with FBHs from the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), the Human Mortality Database (HMD), and model age patterns. METHODS: We examined the relative levels of neonatal, post neonatal, infant, and child mortality across data sources. We directly compared estimates for DHS and MICS subnational regions with HDSS, and used OLS regression to identify data attributes that correlated with the disparity between estimates. RESULTSH: DSS and FBH data suggests that African populations have higher levels of child mortality and lower infant mortality than the historic record. This age pattern is most explicit for Western African populations, but also characterizes data for other sub regions. The comparison between HDSS and FBH suggests that FBH child mortality is biased downward. The comparison is less conclusive for neonatal and infant mortality. CONTRIBUTION: This study questions the practice of using model age patterns derived from largely high-income settings for inferring or correcting U5M estimates for African populations. It also highlights the considerable uncertainty around the consistency of HDSS and FBH estimates of U5M.

The experience gathered from normal industrial operations allows us to associate its degrading conditions with the potential for an accident. Such association is the basis for the definition of the system risk and appropriate safety measures. If a skilled operator observes further degrading conditions, his/her mind quickly learns from this new experience, derives an updated risk level, and tunes the safety measures. Similarly, safety management techniques aim to construct a risk model while learning from past and new observations with the purpose to warn of an imminent accident. However, the model can be tested only in hindsight, after the occurrence (or the missed occurrence) of an accident. How can we generalise and model the risk analysis learning process? How can we optimise its configuration towards new observations? This study discusses these issues of meta-learning for safety management by considering the case study of a drive-off scenario involving an oil and gas drilling rig, for which a risk assessment approach based on machine learning is developed. The results indicate the way forward for a generalisation of risk analysis learning processes and their optimisation.

There are limited data on the impact of antiretroviral therapy (ART) on population-level adult mortality in sub-Saharan Africa. We analysed data for 2000-14 from the Rakai Community Cohort Study (RCCS) in Uganda, where free ART was scaled up after 2004. Using non-parametric and parametric (Weibull) survival analysis, we estimated trends in average person-years lived between exact ages 15 and 50, per capita life-years lost to HIV, and the mortality hazards of people living with HIV (PLHIV). Between 2000 and 2014, average adult life-years lived before age 50 increased significantly, from 26.4 to 33.5 years for all women and from 28.6 to 33.8 years for all men. As of 2014, life-years lost to HIV had declined significantly, to 1.3 years among women and 0.4 years among men. Following the roll-out of ART, mortality reductions among PLHIV were initially larger in women than men, but this is no longer the case.

We have introduced a methodology based on Markov chain for modeling domino effects in chemical plants. The graphical structure and modeling features offered by this chain make it possible to intuitively model the temporal and spatial escalation of domino effects. Accommodating conditional dependencies and complex interactions among the involved units, Markov chain can be used 1) to estimate the probability and duration of potential domino effects at different levels; 2) to identify the most probable sequence of events leading to a domino effect; and 3) to foresee whether a domino effect would terminate or escalate to the next level(s). The application of the methodology has been demonstrated via a hypothetical chemical storage plant.

Chemical distribution is described from a product lifecycle perspective. The impact of the economic decline in 2009 on Belgian chemical distribution is given in figures. In 2010 the sector recovered sharply. Although the individual companies performed very differently, overall, 2010 was even better than 2008. Increasingly complex legislation on chemical products has initiated a consolidation trend in the sector which in turn has brought on trends in outsourcing and inventory management.

Due to the higher workload it produces, reducing the size of operational teams in the chemical process industry can have a negative effect on the ability to control abnormal situations, fatigue, etc. A lack of qualified operational personnel in unusual conditions and the resulting lack of process control can trigger a series of internal or external accidents, eventually leading to a major accident. This paper suggests a practical method to evaluate the safety critical staffing levels required to meet performance specifications for safety critical activities. For single plants as well as for clusters of chemical plants, the method also enables consultants and inspectors to consequently apply principles to assess those manning levels representing the last but one line of defense in the prevention of major accidents.

Empirical research on major accident safety in the second largest chemical cluster worldwide, the Antwerp port area, supports the design of a meta-technical framework for optimizing external domino prevention. First, the majority of Seveso top tier companies have expressed a willingness to cooperate more intensively to protect themselves against potential off-site major accidents. Second, Hazop What-if analysis and the Risk Matrix are interesting building blocks for such a framework and are frequently used risk analysis techniques at Seveso lower tier and Seveso top tier companies. The developed framework, called Hazwim, integrates these three complementary techniques into an effective standardized risk analysis framework for the prevention of external domino accidents in an industrial area. The main strengths of Hazwim are its completeness and its cost-effectiveness. The combination of techniques on the one hand and qualitative and quantitative data on the other, offers a comprehensive up-to-date list of cross-company domino hazards and recommended actions in the area under consideration. The Hazwim framework offers support to prevention managers and safety policy makers concerning external domino prevention.