Dengue transmission poses significant challenges for public health authorities worldwide due to its susceptibility to various factors, including environmental and climate variability, affecting its incidence and geographic spread. This study focuses on Costa Rica, a country characterized by diverse microclimates nearby, where dengue has been endemic since its introduction in 1993. Using wavelet coherence and clustering analysis, we performed a time-series analysis to uncover the intricate connections between climate, local environmental factors, and dengue occurrences. The findings indicate that multiannual dengue frequency (3 yr) is correlated with the Oceanic Niño Index and the Tropical North Atlantic Index. This association is particularly prominent in cantons located along the North and South Pacific Coast, as well as in the Central cantons of the country. Furthermore, the time series of these climate indices exhibit a leading phase of approximately nine months ahead of dengue cases. Additionally, the clustering analysis uncovers non-contiguous groups of cantons that exhibit similar correlation patterns, irrespective of their proximity or adjacency. This highlights the significance of climate factors in influencing dengue dynamics across diverse regions, regardless of spatial closeness or distance between them. On the other hand, the annual dengue frequency was correlated with local environmental indices. A persistent correlation between dengue cases and local environmental variables is observed over time in the North Pacific and the Central Region of the country’s Northwest, with environmental factors leading by less than three months. These findings contribute to understanding dengue transmission’s spatial and temporal dynamics in Costa Rica, highlighting the importance of climate and local environmental factors in dengue surveillance and control efforts.
For policies and programs aiming at reducing climate risk, it is important to obtain vulnerability information at the sub-national level to identify hotspots. For the case of Costa Rica, no sub-national climate vulnerability index exists to date. To fill this gap, we constructed a climate vulnerability index at the canton level. We ground our work in the conceptual framework that vulnerability is a function of exposure, sensitivity, and adaptive capacity. Making extensive use of geographic information systems and publicly available data, we constructed 13 spatial layers to reflect the multi-dimensionality of vulnerability. Layers reflect for example, changes in climatic extremes, flood risk, vegetation cover, access to infrastructure (road density) and health services (distance to hospitals), as well as various socioeconomic (wealth level, employment rates, remittances, literacy rate) and demographic (infant mortality) characteristics. Following normalization, we constructed an inverse variance weighted index of canton-level climate vulnerability. We confirmed the validity of our climate vulnerability index through correlation with disaster damage data. We find the strongest climate vulnerability not only in the rural, agricultural producing border cantons (Los Chiles, Matina, Talamanca, Buenos Aires), but also for a few central urban cantons (Tibas, San Jose). Projects and interventions in these hot spot cantons may reduce sensitivity through strengthening hydrological infrastructure and economic development, while adaptive capacity may be improved through addressing barriers of remittance transfer, and via public health programs.
Due to the rapid geographic spread of the Aedes mosquito and the increase in dengue incidence, dengue fever has been an increasing concern for public health authorities in tropical and subtropical countries worldwide. Significant challenges such as climate change, the burden on health systems, and the rise of insecticide resistance highlight the need to introduce new and cost-effective tools for developing public health interventions. Various and locally adapted statistical methods for developing climate-based early warning systems have increasingly been an area of interest and research worldwide. Costa Rica, a country with microclimates and endemic circulation of the dengue virus (DENV) since 1993, provides ideal conditions for developing projection models with the potential to help guide public health efforts and interventions to control and monitor future dengue outbreaks. Climate information was incorporated to model and forecast the dengue cases and relative risks using a Bayesian spatio-temporal model, from 2000 to 2021, in 32 Costa Rican municipalities. This approach is capable of analyzing the spatio-temporal behavior of dengue and also producing reliable predictions.
Dengue fever is a vector-borne disease affecting millions yearly, mostly in tropical and subtropical countries. Driven mainly by social and environmental factors, dengue incidence and geographical expansion have increased in recent decades. Therefore, understanding how climate variables drive dengue outbreaks is challenging and a problem of interest for decision-makers that could aid in improving surveillance and resource allocation. Here, we explore the effect of climate variables on relative dengue risk in 32 cantons of interest for public health authorities in Costa Rica. Relative dengue risk is forecast using a Generalized Additive Model for location, scale, and shape and a Random Forest approach. Models use a training period from 2000 to 2020 and predicted climatic variables obtained with a vector auto-regressive model. Results show reliable projections, and climate variables predictions allow for a prospective instead of a retrospective study.
The aim of this study was to evaluate heat exposure, dehydration, and kidney function in rice workers over the course of three months, in Guanacaste, Costa Rica. We collected biological and questionnaire data across a three-month-period in male field (n = 27) and other (n = 45) workers from a rice company where chronic kidney disease of unknown origin (CKDu) is endemic. We used stepwise forward regression to determine variables associated with estimated glomerular filtration rate eGFR at enrollment and/or change in eGFR, and Poisson regression to assess associations with incident kidney injury (IKI) over the course of three months. Participants were 20−62 years old (median = 40 in both groups). Dehydration was common (≥37%) in both groups, particularly among other workers at enrollment, but field workers were more exposed to heat and had higher workloads. Low eGFR (<60 mL/min/1.73 m2) was more prevalent in field workers at enrollment (19% vs. 4%) and follow-up (26% vs. 7%). Field workers experienced incident kidney injury (IKI) more frequently than other workers: 26% versus 2%, respectively. Age (β = −0.71, 95%CI: −1.1, −0.4), current position as a field worker (β = −2.75, 95%CI: −6.49, 0.99) and past work in construction (β = 3.8, 95%CI: −0.1, 7.6) were included in the multivariate regression model to explain eGFR at enrollment. The multivariate regression model for decreased in eGFR over three month included current field worker (β = −3.9, 95%CI: −8.2, 0.4), current smoking (β= −6.2, 95%CI: −13.7−1.3), dehydration (USG ≥ 1.025) at both visits (β= −3.19, 95%CI: −7.6, 1.2) and pain medication at follow-up (β= −3.2, 95%CI: −8.2, 1.95). Current fieldwork [IR (incidence rate) = 2.2, 95%CI 1.1, 5.8) and being diabetic (IR = 1.8, 95%CI 0.9, 3.6) were associated with IKI. Low eGFR was common in field workers from a rice company in Guanacaste, and being a field worker was a risk factor for IKI, consistent with the hypothesis that occupational heat exposure is a critical risk factor for CKDu in Mesoamerica.
Este reporte corresponde al capítulo de la guía de la CMNUCC bajo el título: Programas que comprenden medidas para facilitar la adecuada adaptación al cambio climático.
La Convención Marco de Naciones Unidas sobre el Cambio Climático (CMNUCC) establece que los países firmantes, deben informar periódicamente a la Conferencia de las Partes (CP) sobre tres puntos básicos por medio de las Comunicaciones Nacionales (CN):
Fuentes de emisión y absorción de gases de efecto invernadero
Información relevante para el logro del objetivo de la Convención
Programas nacionales sobre mitigación y que faciliten la adecuada adaptación al cambio.
Con el fin de facilitar el reporte de la información en una forma transparente, comparable y flexible, la secretaría de la CMNUCC ha preparado instrumentos que guían la elaboración de las CN (UNFCC, 2004). Estas guías han servido de marco para adecuar la información de vulnerabilidad y adaptación de sectores relevantes para la economía y la sociedad costarricense, con el fin de que sirvan como plataforma de conocimiento para que el país inicie el camino de la adaptación ante el cambio climático con un sentido de desarrollo y aprovechamiento de oportunidades.
El Sistema de Alerta Temprana en Incendios Forestales (SATIF) permite evaluar los distintos elementos que afectan la probable ocurrencia y el potencial comportamiento del fuego; así mismo es de importancia para planificar la prevención y el control de incendios, ayudando a una mejor asignación de los recursos.
El SATIF, se basa únicamente en el cálculo de las siguientes variables meteorológicas: Temperatura (ºC),
Humedad Relativa (%), Velocidad del Viento (km/h), Lluvia (mm).
