OBJECTIVE: Armed conflicts and intimate partner violence (IPV) impose a burden on individual and societal well-being. Given the history of armed conflict in Afghanistan and the high prevalence of IPV, this study aims to examine the influence of armed conflicts on IPV among Afghan women.
METHODS: Multilevel logistic regression models were applied to the 2015 Afghanistan Demographic and Health Survey (N=10 414 women aged 15-49). Armed conflict severity was measured using the conflict index issued by the Office for the Coordination of Humanitarian Affairs, IPV was measured by three types of violence, including emotional, physical and sexual violence. All analyses were conducted by using STATA V.15.1.
RESULTS: Over 52% of women experienced at least one type of IPV, with 33.01%, 49.07%, and 8.99% experiencing emotional, physical, and sexual violence, respectively. The regression results show that armed conflicts were significantly and positively associated with the experience of all types of IPV. In addition, the association between armed conflicts and the experience of emotional IPV was positively moderated by women\'s attitudes towards IPV.
CONCLUSIONS: Our findings suggest that women living in high-conflict regions were more prone to experience IPV, particularly women with positive attitudes towards IPV. Promoting progressive gender roles, women\'s empowerment, awareness of IPV and inclusion of women in conflict resolution will help deal with the issue of IPV.

Sub-Saharan Africa has suffered frequent outbreaks of armed conflict since the end of the Cold War. Although several efforts have been made to understand the underlying causes of armed conflict and establish an early warning mechanism, there is still a lack of a comprehensive assessment approach to model the incidence risk of armed conflict well. Based on a large database of armed conflict events and related spatial datasets covering the period 2000-2019, this study uses a boosted regression tree (BRT) approach to model the spatiotemporal distribution of armed conflict risk in sub-Saharan Africa. Evaluation of accuracy indicates that the simulated models obtain high performance with an area under the receiver operator characteristic curve (ROC-AUC) mean value of 0.937 and an area under the precision recall curves (PR-AUC) mean value of 0.891. The result of the relative contribution indicates that the background context factors (i.e., social welfare and the political system) are the main driving factors of armed conflict risk, with a mean relative contribution of 92.599%. By comparison, the climate change-related variables have relatively little effect on armed conflict risk, accounting for only 7.401% of the total. These results provide novel insight into modelling the incidence risk of armed conflict, which may help implement interventions to prevent and minimize the harm of armed conflict.

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Women and children bear a substantial burden of morbidity and mortality due to armed conflict. Life-saving maternal and child health (MCH) services are low-quality in most conflict-affected regions. Previous studies on armed conflict and MCH services have been mostly cross-sectional, and a causal relationship between armed conflict and MCH services utilisation cannot be inferred.
First, we constructed a utility equation for maternal health-seeking behaviour. Next, we extracted MCH data from the Multiple Indicator Cluster Survey led by the UNICEF. Armed conflict data were obtained from the Uppsala Conflict Data Programme; 55 683 women aged 15-49 from Chad, the Central African Republic, the Democratic Republic of Congo (DRC) and the Republic of Iraq were selected as participants. We fitted a difference-in-differences (DID) model, taking before or after the conflict started as an exposure variable to estimate the effects of armed conflict on maternal health-seeking behaviours.
According to the results of the DID model, in the regional sample, armed conflict had a positive effect on tetanus vaccination (β=0.055, 95% CI 0.004 to 0.106, p<0.05), and had a negative effect on antenatal care at least eight visits (ANC8+) (β=-0.046, 95% CI -0.078 to -0.015, p<0.01). And, the effects of armed conflict on ANC, ANC4+, institutional delivery and early initiation of breast feeding (EIB) were not statistically significant. As for the country sample, we found that armed conflict had a negative effect on EIB (β=-0.085, 95% CI -0.184 to 0.015, p<0.1) in Chad. In Iraq, armed conflict had positive impacts on ANC (β=0.038, 95% CI -0.001 to 0.078, p<0.1) and tetanus vaccination (β=0.059, 95% CI 0.012 to 0.107, p<0.05), whereas it had a negative effect on ANC8+ (β=-0.039, 95% CI -0.080 to 0.002, p<0.1). No statistically significant associations were discovered in DRC based on the DID model.
There might be a mixed effect of armed conflict on maternal health-seeking behaviours. In the absence of humanitarian assistance, armed conflict reduces certain maternal health-seeking behaviours, such as ANC8+. When practical humanitarian health assistance is provided, the damage can be alleviated, and even the prevalence of maternal health-seeking behaviours can be improved, such as tetanus vaccination. Providing humanitarian assistance to conflict-affected regions improved the accessibility of MCH services for women living in those areas. However, the goals of saving lives and alleviating suffering still need to be achieved. In conflict-affected regions, humanitarian assistance on ANC, institutional delivery and breast feeding need strengthening.

Armed conflicts disturb the environment and impair land productivity. Afghanistan has been submerged in conflict for >20 years, affecting the environment dramatically. In this study, we used the Normalised difference vegetation index (NDVI) to investigate vegetation\'s spatial and temporal changes and the potential underpinned mechanisms. We found a 16.44 % increase in NDVI in Afghanistan from 2000 to 2021. The average NDVI growth rate was 11.33 % (within 5 km distance from the armed conflict), higher in the conflict group than in the non-conflict group. People migration may have reduced the human impacts on the environment. The relative contribution of armed conflict to vegetation growth was 3.17 %. Our results showed that the vegetation in Afghanistan increased, confirming the idea that depopulation increase greenness. Despite the reduced variance explained by the war (R2 values around 0.3), our study provides empirical evidence on the linkages between the war and vegetation change in Afghanistan.

This study compares the psychological profile of Internally Displaced Persons (IDPs) and individuals living in host communities in the war-affected setting. We conducted a cross-sectional survey from October-November 2019. Subjects were recruited from six IDPs camps and the surrounding host communities within the metropolis of Maiduguri, Nigeria by convenience sampling. Data were collected using the Hausa version of Depression Anxiety Stress Scale-21, and analysed by logistic regression using adjusted odds ratio (AOR) at 95% Confidence Interval (CI). A total of 562 subjects were recruited. Living in IDP camp was the most significantly predictor of depression, anxiety, and stress. The common predictors were living in an IDP camp, and marital status (separated). Aged 18-29years was a protective factor compared to those ≥50years. Living in IDP camps, separated from partners, lack of education and pre-conflict employment were significant predictors of depression, anxiety and stress.

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Understanding the risk of armed conflict is essential for promoting peace. Although the relationship between climate variability and armed conflict has been studied by the research community for decades with quantitative and qualitative methods at different spatial and temporal scales, causal linkages at a global scale remain poorly understood. Here we adopt a quantitative modelling framework based on machine learning to infer potential causal linkages from high-frequency time-series data and simulate the risk of armed conflict worldwide from 2000-2015. Our results reveal that the risk of armed conflict is primarily influenced by stable background contexts with complex patterns, followed by climate deviations related covariates. The inferred patterns show that positive temperature deviations or precipitation extremes are associated with increased risk of armed conflict worldwide. Our findings indicate that a better understanding of climate-conflict linkages at the global scale enhances the spatiotemporal modelling capacity for the risk of armed conflict.