Mortalidad asociada con las temporadas de mayor circulación de los virus de la influenza en Bogotá, Colombia, 1997-2005

Porras-Ramírez, Alexandra; Mendoza, Alejandro; Moreno‐Montoya, José; Cotes, Karol; López, Juan Diego; Herrera, Diana; Rey, Gloria; Hoz, Fernando de la

doi:10.1590/s1020-49892009001100008

Cited by 16 publications

(2 citation statements)

References 17 publications

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“…Two additional factors may have influenced the low incidence and mortality rates: weather and demographic profile. In Colombia, peaks of respiratory virus transmission and related mortality occur mostly during rainy seasons ( Porras Ramírez et al, 2009 , Cotes et al, 2012 ). However, since October 2019, the country has experienced an unusually longer dry season that has extended through June 2020 (El Tiempo, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Is Colombia an example of successful containment of the 2020 COVID-19 pandemic? A critical analysis of the epidemiological data, March to July 2020

Hoz-Restrepo¹,

Alvis-Zakzuk²,

Gomez³

et al. 2020

International Journal of Infectious Diseases

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Background: Colombia detected its first coronavirus disease 2019 (COVID-19) case on March 2, 2020. From March 22 to April 25, it implemented a national lockdown that, apparently, allowed the country to keep a low incidence and mortality rate up to mid-May. Forced by the economic losses, the government then opened many commercial activities, which was followed by an increase in cases and deaths. This paper presents a critical analysis of the Colombian surveillance data in order to identify strengths and pitfalls of the control measures. Methods: A descriptive analysis of PCR-confirmed cases between March and July 25 was performed. Data were described according to the level of measurement. Incidence and mortality rates of COVID-19 were estimated by age, sex, and geographical area. Sampling rates for suspected cases were estimated by geographical area, and the potential for case underestimation was assessed using sampling differences. Results: By July 25, Colombia (population 50 372 424) had reported 240 745 cases and 8269 deaths (case fatality rate of 3.4%). A total of 1 370 271 samples had been analyzed (27 405 samples per million people), with a positivity rate of 17%. Sampling rates per million varied by region from 2664 to 158 681 per million, and consequently the incidence and mortality rates also varied. Due to geographical variations in surveillance capacity, Colombia may have overlooked up to 82% of the actual cases. Conclusion: Colombia has a lower case and mortality incidence compared to other South American countries. This may be an effect of the lockdown, but may also be attributed, to some extent, to geographical differences in surveillance capacity. Indigenous populations with little health infrastructure have been hit the hardest.

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Section: Discussionmentioning

confidence: 99%

Is Colombia an example of successful containment of the 2020 COVID-19 pandemic? A critical analysis of the epidemiological data, March to July 2020

Hoz-Restrepo¹,

Alvis-Zakzuk²,

Gomez³

et al. 2020

International Journal of Infectious Diseases

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“…31 For example, seasonality in the incidence of influenza is marked at higher latitudes in temporal relationship with the astronomical seasons; 32 in our city, this incidence is more closely related to the rainy seasons. 33,34 Also, the other factors such as ambient temperature and pluviosity might influence the light exposure of pregnant women. In our results, the months with the highest and lowest probability of MS (September and March) were preceded by the dry seasons, which could reflect differences in the light-seeking behavior of pregnant women.…”

Section: Discussionmentioning

confidence: 99%

No seasonality in the risk of multiple sclerosis in an equatorial country: A case–control ecological study

et al. 2022

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Background: Multiple sclerosis risk has been shown to have seasonal variations that are more pronounced in higher latitudes. However, this phenomenon has not been adequately studied near the Equator. Objective: To explore the risk of multiple sclerosis associated with month, season of birth, and sunlight exposure variables in Colombia. Methods: In this case–control study, 668 multiple sclerosis cases were matched to 2672 controls by sex and age. Association of multiple sclerosis with each month/season of birth and sunlight exposure variables was estimated with multilevel mixed-effects logistic regression and ecological regression models, respectively. Seasonality in the births of multiple sclerosis was assessed with a non-parametric seasonality test. Results: We found a higher probability of multiple sclerosis in September (0.25; 95% confidence interval (CI) = 0.21–0.31) and lower in March (0.15; 95% CI = 0.10–0.18), which turned non-significant after a multiple comparisons test. Sunlight exposure variables had no significant effect on the risk of MS, and the tests of seasonality in the births of MS did not show significant results. Conclusion: Our results show no seasonality in the risk of multiple sclerosis near the Equator, supporting the hypothesis that this phenomenon is latitude dependent.

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Síndrome de muerte súbita y otras muertes infantiles súbitas e inesperadas: uso de un modelo logarítmico para analizar el comportamiento epidemiológico en Bogotá y en Colombia, 2005 y 2010

et al. 2015

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Mortalidad asociada con las temporadas de mayor circulación de los virus de la influenza en Bogotá, Colombia, 1997-2005

Cited by 16 publications

References 17 publications

Is Colombia an example of successful containment of the 2020 COVID-19 pandemic? A critical analysis of the epidemiological data, March to July 2020

Is Colombia an example of successful containment of the 2020 COVID-19 pandemic? A critical analysis of the epidemiological data, March to July 2020

No seasonality in the risk of multiple sclerosis in an equatorial country: A case–control ecological study

Síndrome de muerte súbita y otras muertes infantiles súbitas e inesperadas: uso de un modelo logarítmico para analizar el comportamiento epidemiológico en Bogotá y en Colombia, 2005 y 2010

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