Climate–health risk in Guatemala: extreme thermal trends and their correlation with dengue, ARIs and FWDs

Authors

DOI:

https://doi.org/10.5377/wani.v1i84.22333

Keywords:

: Climate change adaptation, meteorological conditions, epidemiology, public health, tropical regions

Abstract

Heat waves represent an emerging public health risk in Guatemala, where climatic and territorial heterogeneity intensifies health impacts. The study assessed the spatiotemporal evolution of heat waves and their association with dengue, acute respiratory infections (ARIs), and food- and water-borne diseases (FWDs). Daily maximum temperature series from 33 meteorological stations (1970–2024) and departmental epidemiological records (2014–2024) were analyzed. Heat waves were defined as periods of ≥3 consecutive days with daily maximum temperature above the local monthly 95th percentile. Results show a significant national increase, averaging 0.9 events per decade (p < 0.01), with higher recurrence in Zacapa, Petén, and Escuintla. The maximum temperature was positively associated with dengue (ρ = 0.74), ARIs (ρ = 0.61), and FWDs (ρ = 0.66), and models identified a critical thermal threshold of 32–34 °C. A climate–health vulnerability index integrated climatic, epidemiological, and territorial variables, identifying Guatemala Department as the most vulnerable (IVCS = 0.83). These findings support incorporating extreme-heat metrics into epidemiological surveillance, early warning systems, and territorial prioritization of climate adaptation strategies.

Downloads

Download data is not yet available.
Abstract
5
PDF (Español (España)) 2

References

Alexandersson, H. (1986). A homogeneity test applied to precipitation data. Journal of Climatology, 6(6), 661–675. https://doi.org/10.1002/joc.3370060607

Chan, E. Y. Y., Huang, Z., Lam, H. C. Y., Wong, C. K. P., y Zou, Q. (2019). Health vulnerability index for disaster risk reduction: Application in Belt and Road Initiative (BRI) region. International Journal of Environmental Research and Public Health, 16(3), 380. https://doi.org/10.3390/ijerph16030380

Ebi, K. L., Vanos, J., Baldwin, J. W., Bell, J. E., Hondula, D. M., Errett, N. A., y Berry, P. (2021). Extreme weather and climate change: Population health and health system implications. Annual Review of Public Health, 42, 293–315. https://doi.org/10.1146/annurev-publhealth-012420-105026

Edmonds, H. K., Lovell, J. E., y Lovell, C. A. K. (2020). A new composite climate change vulnerability index. Ecological Indicators, 117, 106529. https://doi.org/10.1016/j.ecolind.2020.106529

Gasparrini, A., Guo, Y., Sera, F., Vicedo-Cabrera, A. M., Huber, V., Tong, S., … Armstrong, B. (2015). Mortality risk attributable to high and low ambient temperature: A multicountry observational study. The Lancet, 386(9991), 369–375. https://doi.org/10.1016/S0140-6736(14)62114-0

Hajat, S., y Kosatky, T. (2010). Heat-related mortality: A review and exploration of heterogeneity. Journal of Epidemiology & Community Health, 64(9), 753–760. https://doi.org/10.1136/jech.2009.087999

Hamed, K. H., y Rao, A. R. (1998). A modified Mann–Kendall trend test for autocorrelated data. Journal of Hydrology, 204(1–4), 182–196. https://doi.org/10.1016/S0022-1694(97)00125-X

Instituto Nacional de Estadística de Guatemala (INE). (2025). Estimación y proyecciones de la población de Guatemala (nacional y departamental). https://www.ine.gob.gt/proyecciones/

Instituto Nacional de Sismología, Vulcanología, Meteorología e Hidrología de Guatemala (INSIVUMEH). (2024). Sección de climatología: Datos de la red de estaciones meteorológicas convencionales de Guatemala. https://insivumeh.gob.gt/?p=92918

Intergovernmental Panel on Climate Change (IPCC). (2021). Climate change 2021: The physical science basis. Cambridge University Press. https://www.ipcc.ch/report/ar6/wg1/

Killick, R., Fearnhead, P., y Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590–1598. https://doi.org/10.1080/01621459.2012.737745

Levy, K., Woster, A. P., Goldstein, R. S., y Carlton, E. J. (2016). Untangling the impacts of climate change on waterborne diseases. Environmental Science & Technology, 50(10), 4905–4922. https://doi.org/10.1021/acs.est.5b06186

Liu-Helmersson, J., Quam, M., Wilder-Smith, A., Stenlund, H., Ebi, K., Massad, E., y Rocklöv, J. (2016). Climate change and Aedes vectors: 21st century projections for dengue transmission in Europe. eBioMedicine, 7, 267–277. https://doi.org/10.1016/j.ebiom.2016.03.046

Liu-Helmersson, J., Stenlund, H., Wilder-Smith, A., y Rocklöv, J. (2014). Vectorial capacity of Aedes aegypti: Effects of temperature and implications for global dengue epidemic potential. PLOS ONE, 9(3), e89783. https://doi.org/10.1371/journal.pone.0089783

Magrin, G., Marengo, J., Boulanger, J. P., Buckeridge, M., Castellanos, E., Poveda, G., y Villamizar, A. (2014). Central and South America. In Climate change 2014: Impacts, adaptation, and vulnerability (pp. 1499–1566). Cambridge University Press.

Marengo, J. A., Costa, M. C., Cunha, A. P., Espinoza, J.-C., Jimenez, J. C., Libonati, R., Miranda, V., Trigo, I. F., Sierra, J. P., Geirinhas, J. L., Ramos, A. M., Skansi, M., Molina-Carpio, J., y Salinas, R. (2025). Climatological patterns of heatwaves during winter and spring 2023 and trends for the period 1979–2023 in central South America. Frontiers in Climate, 7, Article 1529082. https://doi.org/10.3389/fclim.2025.1529082

Ministerio de Agricultura, Ganadería y Alimentación (MAGA). (2021). Determinación de la cobertura vegetal y uso de la tierra a escala 1:50,000 de la República de Guatemala, año 2020. Dirección de Información Geográfica, Estratégica y Gestión de Riesgos.

Ministerio de Salud Pública y Asistencia Social (MSPAS). (2025). Datos abiertos de enfermedades. https://datosabiertos.mspas.gob.gt/

Mordecai, E. A., Cohen, J. M., Evans, M. V., Gudapati, P., Johnson, L. R., Lippi, C. A., Miazgowicz, K., Murdock, C. C., Rohr, J. R., y Ryan, S. J. (2017). Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models. PLOS Neglected Tropical Diseases, 11(4), e0005568. https://doi.org/10.1371/journal.pntd.0005568

Morin, C. W., Comrie, A. C., y Ernst, K. C. (2013). Climate and dengue transmission: Evidence and implications. Environmental Health Perspectives, 121(11–12), 1264–1272. https://doi.org/10.1289/ehp.1306556

Ochoa-Orozco, W., Rivera, P., y Herrera, E. (2022). Comportamiento meteorológico durante la sequía de medio verano en Guatemala. Ciencia, Tecnología y Salud, 9(2), 150–165. https://doi.org/10.36829/63CTS.v9i2.1284

Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), 1–24. https://doi.org/10.1002/qj.49710845502

Perkins, S. E., y Alexander, L. V. (2013). On the measurement of heat waves. Journal of Climate, 26(13), 4500–4517. https://doi.org/10.1175/JCLI-D-12-00383.1

R Core Team. (2026). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/

Reckien, D. (2018). What is in an index? Construction method, data metric, and weighting scheme determine the outcome of composite social vulnerability indices in New York City. Regional Environmental Change, 18(5), 1439–1451. https://doi.org/10.1007/s10113-017-1273-7

Russo, S., Dosio, A., Graversen, R. G., Sillmann, J., Carrao, H., Dunbar, M. B., y Vogt, J. V. (2014). Magnitude of extreme heat waves in present climate and their projection in a warming world. Journal of Geophysical Research: Atmospheres, 120(24), 12,500–12,512. https://doi.org/10.1002/2014JD022098

Ryan, S. J., Carlson, C. J., Mordecai, E. A., y Johnson, L. R. (2019). Global expansion and redistribution of Aedes-borne virus transmission risk with climate change. PLOS Neglected Tropical Diseases, 13(3), e0007213. https://doi.org/10.1371/journal.pntd.0007213

Wood, S. N. (2017). Generalized additive models: An introduction with R (2nd ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781315370279

World Health Organization. (2018). COP24 special report: Health and climate change. WHO. https://www.who.int/publications/i/item/cop24-special-report-health-climate-change

Zúñiga, S. P., Castillo, L. C., Gonzalez, N., Araque, J., Hernández, F., Ramos, J., Nuñez, N., Vasquez, F., Barrientos, A., Bolaños, E. J. A., Santos, E., Motta, M. L., Ambikan, A., & Neogi, U. (2025). Temporal trends and public health implications of dengue in Guatemala: A decade of challenges and emerging threats (2013–2024). IJID Regions, 15, 100667. https://doi.org/10.1016/j.ijregi.2025.100667

Published

2026-03-24

How to Cite

Ochoa-Orozco, W. A., Araque-Pérez , J. J., & Barrios Garrido, G. E. (2026). Climate–health risk in Guatemala: extreme thermal trends and their correlation with dengue, ARIs and FWDs. Wani, (84). https://doi.org/10.5377/wani.v1i84.22333

Issue

Section

Natural Resources and Environment

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.