Colombia’s 2016 Peace Agreement is innovative in many ways. Remarkably, the agreement places significant emphasis on gender as a guiding principle. Gender-related measures are at the core of Colombia’s peacebuilding efforts. Nevertheless, six years after, parties have not fully implemented these measures; a narrow understanding of the concept of violence could be one of the reasons behind this. The agreement mainly refers to the physical and dominant understanding of gender-based violence (GBV). However, this understanding is problematic. Environmental and climate-related causes are structural to the Colombian armed conflict, and critical in building peace. Environmental violence points to human-induced activities that cause harms to the environment. Climate violence, one manifestation of environmental violence, is a type of violence that worsens underlying conditions of inequalities through extreme climate conditions. Drawing on the 2016 Colombian Peace Agreement, this article focuses on the experiences of Colombian rural women to assess whether expanding dominant concepts of GBV help implement environmental peacebuilding commitments. Applying an intersectional ecofeminist reading could contribute to acknowledging particular forms of violence embedded in the climate and peace crises in Colombia during the implementation phase of gender-related peace commitments and push towards the recognition of environmental and climate violence as GBV.
The role of climate driving zoonotic diseases’ population dynamics has typically been addressed via retrospective analyses of national aggregated incidence records. A central question in epidemiology has been whether seasonal and interannual cycles are driven by climate variation or generated by socioeconomic factors. Here, we use compartmental models to quantify the role of rainfall and temperature in the dynamics of snakebite, which is one of the primary neglected tropical diseases. We took advantage of space-time datasets of snakebite incidence, rainfall, and temperature for Colombia and combined it with stochastic compartmental models and iterated filtering methods to show the role of rainfall-driven seasonality modulating the encounter frequency with venomous snakes. Then we identified six zones with different rainfall patterns to demonstrate that the relationship between rainfall and snakebite incidence was heterogeneous in space. We show that rainfall only drives snakebite incidence in regions with marked dry seasons, where rainfall becomes the limiting resource, while temperature does not modulate snakebite incidence. In addition, the encounter frequency differs between regions, and it is higher in regions where Bothrops atrox can be found. Our results show how the heterogeneous spatial distribution of snakebite risk seasonality in the country may be related to important traits of venomous snakes’ natural history.
Landslides typify one of the most hazardous natural phenomena fostering economic and even human losses worldwide. Several countries like Colombia, in South America, are hotspots for fatal landslides. In this contribution, we thoroughly reviewed four available databases, articles, grey literature and web resources, in order to build up a new catalogue of fatal landslides in Colombia. We gathered a catalogue of 2351 individual fatal landslides which caused about 37,959 deaths. Of these, we found 11 fatal events in historical times (pre-twentieth century). In modern times (1912-2020), we analysed landslides’ spatial and temporal distribution, finding that in central-western Colombia, particularly in the departments of Caldas, Risaralda, Quindio and Antioquia, these kinds of events are more frequent. Upward trends in these areas and a nationwide increase in the number of events in the last 20 years suggest that fatal landslides are far from being effectively mitigated. Our findings also show a strong correlation between the climate variability phenomenon known as El Nino Southern Oscillation (ENSO) and fatal landslides, particularly during those years when strong La Nina (cold phase of ENSO) events occur. Despite rainfall being the most common trigger for fatal landslides, we observed an increasing trend in anthropogenically related events in the last decade. Finally, we obtained multiple socio-economic indices and ran a statistical analysis at the departmental level in order to assess whether impoverished and vulnerable people are more affected by fatal landslides. We propose that in most cases, departments with low income, high levels of corruption and inequality are usually more affected.
Leptospirosis is an acute febrile disease that mainly affects developing countries with tropical climates. The complexity and magnitude of this disease is attributed to socioeconomic, climatic, and environmental conditions. In this study, in a 10-year period from 2008 to 2017, the relationship between human leptospirosis cases and climatic factors in Cartagena de Indias, Colombia were evaluated. Monthly leptospirosis cases, climatic variables, and macroclimatic phenomena (El Nino and La Nina) were obtained from public datasets. Local climatic factors included temperature (maximum, average, and minimum), relative humidity, precipitation, and the number of precipitation days. Time series graphs were drawn and correlations between cases of leptospirosis and climatic variables considering lags from 0 to 10 months were examined. A total of 360 cases of leptospirosis were reported in Cartagena during the study period, of which 192 (53.3%) were systematically notified between October and December. Several correlations were detected between the number of cases, local climatic variables, and macroclimatic phenomena. Mainly, the increase of cases correlated with increased precipitation and humidity during the La Nina periods. Herein, seasonal patterns and correlations suggest that the climate in Cartagena could favor the incidence of leptospirosis. Our findings suggest that prevention and control of human leptospirosis in Cartagena should be promoted and strengthened, especially in the last quarter of the year.
According to the World Health Organization, dengue is a neglected tropical disease. Latin America, specifically Colombia is in alert regarding this arbovirosis as there was a spike in the number of reported dengue cases at the beginning of 2019. Although there has been a worldwide decrease in the number of reported dengue cases, Colombia has shown a growing trend over the past few years. This study performed a Poisson multilevel analysis with mixed effects on STATA® version 16 and R to assess sociodemographic, climatic, and entomological factors that may influence the occurrence of dengue in three municipalities for the period 2010-2015. Information on dengue cases and their sociodemographic variables was collected from the National Public Health Surveillance System (SIVIGILA) records. For climatic variables (temperature, relative humidity, and precipitation), we used the information registered by the weather stations located in the study area, which are managed by the Instituto de Hidrologia, Meteorologia y Estudios Ambientales (IDEAM) or the Corporación Autónoma Regional (CAR). The entomological variables (house index, container index, and Breteau index) were provided by the Health office of the Cundinamarca department. SIVIGILA reported 1921 dengue cases and 56 severe dengue cases in the three municipalities; of them, three died. One out of four cases occurred in rural areas. The age category most affected was adulthood, and there were no statistical differences in the number of cases between sexes. The Poisson multilevel analysis with the best fit model explained the presentation of cases were temperature, relative humidity, precipitation, childhood, live in urban area and the contributory healthcare system. The temperature had the biggest influence on the presentation of dengue cases in this region between 2010 and 2015.
Dengue virus (DENV) is an endemic disease in the hot and humid low-lands of Colombia. We characterize the association of monthly series of dengue cases with indices of El Niño/Southern Oscillation (ENSO) at the tropical Pacific and local climatic variables in Colombia during the period 2007-2017 at different temporal and spatial scales. For estimation purposes, we use lagged cross-correlations (Pearson test), cross-wavelet analysis (wavelet cross spectrum, and wavelet coherence), as well as a novel nonlinear causality method, PCMCI, that allows identifying common causal drivers and links among high dimensional simultaneous and time-lagged variables. Our results evidence the strong association of DENV cases in Colombia with ENSO indices and with local temperature and rainfall. El Niño (La Niña) phenomenon is related to an increase (decrease) of dengue cases nationally and in most regions and departments, with maximum correlations occurring at shorter time lags in the Pacific and Andes regions, closer to the Pacific Ocean. This association is mainly explained by the ENSO-driven increase in temperature and decrease in rainfall, especially in the Andes and Pacific regions. The influence of ENSO is not stationary, given the reduction of DENV cases since 2005, and that local climate variables vary in space and time, which prevents to extrapolate results from one region to another. The association between DENV and ENSO varies at national and regional scales when data are disaggregated by seasons, being stronger in DJF and weaker in SON. Overall, the Pacific and Andes regions control the relationship between dengue dynamics and ENSO at national scale. Cross-wavelet analysis indicates that the ENSO-DENV relation in Colombia exhibits a strong coherence in the 12 to 16-months frequency band, which implies the frequency locking between the annual cycle and the interannual (ENSO) timescales. Results of nonlinear causality metrics reveal the complex concomitant effects of ENSO and local climate variables, while offering new insights to develop early warning systems for DENV in Colombia.
BACKGROUND: The influence of climate on the epidemiology of dengue has scarcely been studied in Cartagena. METHODS: The relationship between dengue cases and climatic and macroclimatic factors was explored using an ecological design and bivariate and time-series analyses during lag and non-lag months. Data from 2008-2017 was obtained from the national surveillance system and meteorological stations. RESULTS: Cases correlated only with climatic variables during lag and non-lag months. Decreases in precipitation and humidity and increases in temperature were correlated with an increase in cases. CONCLUSIONS: Our findings provide useful information for establishing and strengthening dengue prevention and control strategies.
In Colombia, little is known on the distribution of the Asian mosquito Aedes albopictus, main vector of dengue, chikungunya, and Zika in Asia and Oceania. Therefore, this work sought to estimate its current and future potential geographic distribution under the Representative Concentration Paths (RCP) 2.6 and 8.5 emission scenarios by 2050 and 2070, using ecological niche models. For this, predictions were made in MaxEnt, employing occurrences of A. albopictus from their native area and South America and bioclimatic variables of these places. We found that, from their invasion of Colombia to the most recent years, A. albopictus is present in 47% of the country, in peri-urban (20%), rural (23%), and urban (57%) areas between 0 and 1800 m, with Antioquia and Valle del Cauca being the departments with most of the records. Our ecological niche modelling for the currently suggests that A. albopictus is distributed in 96% of the Colombian continental surface up to 3000 m (p < 0.001) putting at risk at least 48 million of people that could be infected by the arboviruses that this species transmits. Additionally, by 2050 and 2070, under RCP 2.6 scenario, its distribution could cover to nearly 90% of continental extension up to 3100 m (?55 million of people at risk), while under RCP 8.5 scenario, it could decrease below 60% of continental extension, but expand upward to 3200 m (< 38 million of people at risk). These results suggest that, currently in Colombia, A. albopictus is found throughout the country and climate change could diminish eventually its area of distribution, but increase its altitudinal range. In Colombia, surveillance and vector control programs must focus their attention on this vector to avoid complications in the national public health setting.
Climate change has direct effects on the availability and quality of water for human consumption. In order to propose actions aimed at reducing vulnerability caused by water shortages and risk management required due to extreme events, real knowledge of the community`s perception is vital. This study developed in the department of Caldas, in the Colombian Andean region, analysed the perception of the incidence of climate change particularly related to water resources. To achieve this, a survey was used with various actors based on the first National Survey of Public Perception of Climate Change. The results show that the respondents perceive that the availability and quality of water are indeed highly threatened by climate change. As actions for adaptation, they suggested the promotion of the protection of hydrographic basins and a greater control of dumping liquids into surface water sources. Finally, they requested increased opportunities to improve water governance and participation in decision-making bodies regarding climate change, which they see as a fundamental aspect to achieve a real climate empowerment that can lead to action and adaptation in the territories in emerging countries.
Leptospirosis is a disease usually acquired by humans through water contaminated with the urine of rodents that comes into direct contact with the cutaneous lesions, eyes, or mucous membranes. The disease has an important environmental component associated with climatic conditions and natural disasters, such as floods. We analyzed the relationship between rainfall and temperature and the incidence of leptospirosis in the top 30 municipalities with the highest numbers of cases of the disease in the period of 2007 to 2016. It was an ecological study of the time series of cases of leptospirosis, rainfall, and temperature with lags of 0, 1, 2, 3, and 4 weeks. A multilevel negative binomial regression model was implemented to evaluate the relationship between leptospirosis and both meteorological factors. In the 30 evaluated municipalities during the study period, a total of 5136 cases of leptospirosis were reported. According to the implemented statistical model, there was a positive association between the incidence of leptospirosis and rainfall with a lag of 1 week and a negative association with temperature with a lag of 4 weeks. Our results show the importance of short-term lags in rainfall and temperature for the occurrence of new cases of leptospirosis in Colombia.
During the 2012-2016 drought in La Guajira, Colombia, child mortality rates rose to 23.4 out of 1000. Most of these children belonged to the Wayuu indigenous community, the largest and one of the most vulnerable in Colombia. At the municipal level, this study found a significant positive correlation between the average child mortality rate and households with a monthly income of less than USD 100, the number of people without access to health insurance, being part of the indigenous population, being illiterate, lacking sewage systems, living in rural areas, and large households with members younger than 5 years old and older than 65 years old. No correlation was found with households without access to a water source. The stepwise regression analysis showed that households with a monthly income of less than USD 100, no members older than 65 years old, but several children younger than 5 years old, account for 90.4% of the child mortality rate. This study concludes that, if inhabitants had had better incomes or assets, as well as an adequate infrastructure, they could have faced the drought without the observed increase in child mortality.
The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, as well as population counts, income inequality, and education. Our RF model trained on the pooled national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the department’s data. Additionally, the forecast errors of the national RF model were smaller to those of the national pooled ANN model and were increased with the forecast horizon increasing from one-week-ahead (mean absolute error, MAE: 9.32) to 12-weeks ahead (MAE: 24.56). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study demonstrates the potential of RF in dengue forecasting with a feasible approach of using a national pooled model to forecast at finer spatial scales. Furthermore, including sociodemographic predictors is likely to be helpful in capturing longer-term dengue trends.
