The impact of humanitarian emergencies on the prevalence of violence against children: an evidence-based ecological framework.

Little is known about the patterns and mechanisms by which humanitarian emergencies may exacerbate violence against children. In this article, we propose using the ecological framework to examine the impact of humanitarian emergencies on interpersonal violence against children. We consider the literature that supports this framework and suggest future directions for research to fill identified gaps in the framework. The relationship between humanitarian emergencies and violence against children depends on risk factors at multiple levels, including a breakdown of child protection systems, displacement, threats to livelihoods, changing gender roles, changing household composition, overcrowded living conditions, early marriage, exposure to conflict or other emergency events, and alcohol abuse. The empirical evidence supporting the proposed emergency/violence framework is limited by cross-sectional study designs and a propensity to predominantly examine individual-level determinants of violence, especially exposure to conflict or emergency events. Thus, there is a pressing need to contextualize the relationship between conflict or emergency events and violence against children within the wider ecological and household dynamics that occur during humanitarian emergencies. Ultimately, this will require longitudinal observations of children, families and communities from before the emergency through recovery and improvements to ongoing global surveillance systems. More complete data will enable the humanitarian community to design effective, appropriate and well-targeted interventions.

Documenting the Effects of Armed Conflict on Population Health.

War and other forms of armed conflict have profound adverse effects on population health. It is important to document these effects to inform the general public and policy makers about the consequences of armed conflict, provide services to meet the needs of affected populations, protect human rights and document violations of international humanitarian law, and help to prevent future armed conflict. Documentation can be accomplished with surveillance, epidemiological surveys, and rapid assessment. Challenges include inadequate or absent data systems, social breakdown, forced migration, reporting biases, and the fog of war. The adverse effects of the Iraq War on population health demonstrate how the effects of armed conflict on population health can be documented. We recommend the establishment of an independent mechanism, operated by the United Nations or a multilateral organization, to investigate and document the effects of armed conflict on population health.

Model Selection in Historical Research Using Approximate Bayesian Computation.

FORMAL MODELS AND HISTORY: Computational models are increasingly being used to study historical dynamics. This new trend, which could be named Model-Based History, makes use of recently published datasets and innovative quantitative methods to improve our understanding of past societies based on their written sources. The extensive use of formal models allows historians to re-evaluate hypotheses formulated decades ago and still subject to debate due to the lack of an adequate quantitative framework. The initiative has the potential to transform the discipline if it solves the challenges posed by the study of historical dynamics. These difficulties are based on the complexities of modelling social interaction, and the methodological issues raised by the evaluation of formal models against data with low sample size, high variance and strong fragmentation. CASE STUDY: This work examines an alternate approach to this evaluation based on a Bayesian-inspired model selection method. The validity of the classical Lanchester's laws of combat is examined against a dataset comprising over a thousand battles spanning 300 years. Four variations of the basic equations are discussed, including the three most common formulations (linear, squared, and logarithmic) and a new variant introducing fatigue. Approximate Bayesian Computation is then used to infer both parameter values and model selection via Bayes Factors. IMPACT: Results indicate decisive evidence favouring the new fatigue model. The interpretation of both parameter estimations and model selection provides new insights into the factors guiding the evolution of warfare. At a methodological level, the case study shows how model selection methods can be used to guide historical research through the comparison between existing hypotheses and empirical evidence.