Seasonal patterns of dengue fever in rural Ecuador: 2009-2016.
Season is a major determinant of infectious disease rates, including arboviruses spread by mosquitoes, such as dengue, chikungunya, and Zika. Seasonal patterns of disease are driven by a combination of climatic or environmental factors, such as temperature or rainfall, and human behavioral time tren...
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ftdoajarticles:oai:doaj.org/article:e22800c438d54db982bcddf3fed426a8 2023-05-15T15:17:45+02:00 Seasonal patterns of dengue fever in rural Ecuador: 2009-2016. Rachel Sippy Diego Herrera David Gaus Ronald E Gangnon Jonathan A Patz Jorge E Osorio 2019-05-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0007360 https://doaj.org/article/e22800c438d54db982bcddf3fed426a8 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0007360 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0007360 https://doaj.org/article/e22800c438d54db982bcddf3fed426a8 PLoS Neglected Tropical Diseases, Vol 13, Iss 5, p e0007360 (2019) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2019 ftdoajarticles https://doi.org/10.1371/journal.pntd.0007360 2022-12-31T09:21:49Z Season is a major determinant of infectious disease rates, including arboviruses spread by mosquitoes, such as dengue, chikungunya, and Zika. Seasonal patterns of disease are driven by a combination of climatic or environmental factors, such as temperature or rainfall, and human behavioral time trends, such as school year schedules, holidays, and weekday-weekend patterns. These factors affect both disease rates and healthcare-seeking behavior. Seasonality of dengue fever has been studied in the context of climatic factors, but short- and long-term time trends are less well-understood. With 2009-2016 medical record data from patients diagnosed with dengue fever at two hospitals in rural Ecuador, we used Poisson generalized linear modeling to determine short- and long-term seasonal patterns of dengue fever, as well as the effect of day of the week and public holidays. In a subset analysis, we determined the impact of school schedules on school-aged children. With a separate model, we examined the effect of climate on diagnosis patterns. In the first model, the most important predictors of dengue fever were annual sinusoidal fluctuations in disease, long-term trends (as represented by a spline for the full study duration), day of the week, and hospital. Seasonal trends showed single peaks in case diagnoses, during mid-March. Compared to the average of all days, cases were more likely to be diagnosed on Tuesdays (risk ratio (RR): 1.26, 95% confidence interval (CI) 1.05-1.51) and Thursdays (RR: 1.25, 95% CI 1.02-1.53), and less likely to be diagnosed on Saturdays (RR: 0.81, 95% CI 0.65-1.01) and Sundays (RR: 0.74, 95% CI 0.58-0.95). Public holidays were not significant predictors of dengue fever diagnoses, except for an increase in diagnoses on the day after Christmas (RR: 2.77, 95% CI 1.46-5.24). School schedules did not impact dengue diagnoses in school-aged children. In the climate model, important climate variables included the monthly total precipitation, an interaction between total precipitation and monthly ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 13 5 e0007360 |
institution |
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ftdoajarticles |
language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
spellingShingle |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Rachel Sippy Diego Herrera David Gaus Ronald E Gangnon Jonathan A Patz Jorge E Osorio Seasonal patterns of dengue fever in rural Ecuador: 2009-2016. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Season is a major determinant of infectious disease rates, including arboviruses spread by mosquitoes, such as dengue, chikungunya, and Zika. Seasonal patterns of disease are driven by a combination of climatic or environmental factors, such as temperature or rainfall, and human behavioral time trends, such as school year schedules, holidays, and weekday-weekend patterns. These factors affect both disease rates and healthcare-seeking behavior. Seasonality of dengue fever has been studied in the context of climatic factors, but short- and long-term time trends are less well-understood. With 2009-2016 medical record data from patients diagnosed with dengue fever at two hospitals in rural Ecuador, we used Poisson generalized linear modeling to determine short- and long-term seasonal patterns of dengue fever, as well as the effect of day of the week and public holidays. In a subset analysis, we determined the impact of school schedules on school-aged children. With a separate model, we examined the effect of climate on diagnosis patterns. In the first model, the most important predictors of dengue fever were annual sinusoidal fluctuations in disease, long-term trends (as represented by a spline for the full study duration), day of the week, and hospital. Seasonal trends showed single peaks in case diagnoses, during mid-March. Compared to the average of all days, cases were more likely to be diagnosed on Tuesdays (risk ratio (RR): 1.26, 95% confidence interval (CI) 1.05-1.51) and Thursdays (RR: 1.25, 95% CI 1.02-1.53), and less likely to be diagnosed on Saturdays (RR: 0.81, 95% CI 0.65-1.01) and Sundays (RR: 0.74, 95% CI 0.58-0.95). Public holidays were not significant predictors of dengue fever diagnoses, except for an increase in diagnoses on the day after Christmas (RR: 2.77, 95% CI 1.46-5.24). School schedules did not impact dengue diagnoses in school-aged children. In the climate model, important climate variables included the monthly total precipitation, an interaction between total precipitation and monthly ... |
format |
Article in Journal/Newspaper |
author |
Rachel Sippy Diego Herrera David Gaus Ronald E Gangnon Jonathan A Patz Jorge E Osorio |
author_facet |
Rachel Sippy Diego Herrera David Gaus Ronald E Gangnon Jonathan A Patz Jorge E Osorio |
author_sort |
Rachel Sippy |
title |
Seasonal patterns of dengue fever in rural Ecuador: 2009-2016. |
title_short |
Seasonal patterns of dengue fever in rural Ecuador: 2009-2016. |
title_full |
Seasonal patterns of dengue fever in rural Ecuador: 2009-2016. |
title_fullStr |
Seasonal patterns of dengue fever in rural Ecuador: 2009-2016. |
title_full_unstemmed |
Seasonal patterns of dengue fever in rural Ecuador: 2009-2016. |
title_sort |
seasonal patterns of dengue fever in rural ecuador: 2009-2016. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2019 |
url |
https://doi.org/10.1371/journal.pntd.0007360 https://doaj.org/article/e22800c438d54db982bcddf3fed426a8 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 13, Iss 5, p e0007360 (2019) |
op_relation |
https://doi.org/10.1371/journal.pntd.0007360 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0007360 https://doaj.org/article/e22800c438d54db982bcddf3fed426a8 |
op_doi |
https://doi.org/10.1371/journal.pntd.0007360 |
container_title |
PLOS Neglected Tropical Diseases |
container_volume |
13 |
container_issue |
5 |
container_start_page |
e0007360 |
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1766347996195192832 |