Seasonality of respiratory viruses and bacterial pathogens

Choe, Young June; Smit, Michael; Mermel, Leonard A.

doi:10.1186/s13756-019-0574-7

Cited by 24 publications

(21 citation statements)

References 17 publications

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“…It is of great signi cance to understand the seasonal prevalence of pathogens in LRIs for disease prevention and control as well as vaccination. [15] This study showed that K. pneumoniae was the main pathogen of LRIs. Cephalosporin was often used in the treatment of LRIs because of its small side effects.…”

Section: Discussionmentioning

confidence: 95%

Characteristics of bacterial etiology in lower respiratory tract infection: a surveillance report from a Chinese large teaching hospital from 2015 to 2019

Tian

Chen

et al. 2020

Preprint

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Background: Lower respiratory tract infection (LRI) is a very common clinical disease. The etiological diagnosis of LRI often depends on the result of sputum culture. Sputum is the most common specimen type of LRI in China, but its cultivation result is easily confused by the bacteria colonized in the oral cavity and pharynx. It is very di cult to evaluate the clinical signi cance of sputum culture results both for clinicians and microbiologists. Fortunately, bronchoscope alveolus lavage uid(BALF)is a good specimen, whose culture results can accurately re ect the situation of LRI. By analyzing the culture results and antimicrobial agents sensitivity data of BALF accumulated in this area, we can provide reference for clinicians to experience in the treatment of lower respiratory tract infection.Methods: The accumulated data of BALF culture and antimicrobial susceptibility test in our hospital from January 2015 to October 2019 were reviewed and analyzed.Results: The positive rate of BALF culture in our hospital was 18.3% (3467/18935) in 2015-2019. The most common pathogens were Klebsiella pneumoniae (18.1%, 627/3467), Pseudomonas aeruginosa (16.9%, 587/3467) and Acinetobacter baumannii (14.0%, 485/3467). For the eight most common pathogens (K. pneumoniae, P. aeruginosa, A. baumannii, Staphylococcus aureus, Haemophilus in uenzae, Stenotrophomonas maltophilia, Escherichia coli and S. pneumoniae), 40-70 years old was the highest age of distribution, but for E. coli and S. pneumoniae, 0-5 years old was also the higher age of distribution. The antibiotic resistance rate of K. pneumoniae to imipenem and meropenem was 30.6%

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Section: Discussionmentioning

confidence: 95%

Characteristics of bacterial etiology in lower respiratory tract infection: a surveillance report from a Chinese large teaching hospital from 2015 to 2019

Tian

Chen

et al. 2020

Preprint

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“…In a multivariable model including all weather factors, temperature, relative humidity, precipitation and cloud cover were independently associated with the coronavirus detection rate Seasonality is commonly observed in respiratory infections. Its form and impact depend on the individual pathogen and the climate zone under evaluation [5][6][7][8]. Our study focused on the seasonal non-SARS, non-MERS coronavirus detection rate in Germany, a temperate-climate country.…”

Section: Discussionmentioning

confidence: 99%

Seasonality of Non-SARS, Non-MERS Coronaviruses and the Impact of Meteorological Factors

et al. 2021

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Background: Seasonality is a characteristic of some respiratory viruses. The aim of our study was to evaluate the seasonality and the potential effects of different meteorological factors on the detection rate of the non-SARS coronavirus detection by PCR. Methods: We performed a retrospective analysis of 12,763 respiratory tract sample results (288 positive and 12,475 negative) for non-SARS, non-MERS coronaviruses (NL63, 229E, OC43, HKU1). The effect of seven single weather factors on the coronavirus detection rate was fitted in a logistic regression model with and without adjusting for other weather factors. Results: Coronavirus infections followed a seasonal pattern peaking from December to March and plunged from July to September. The seasonal effect was less pronounced in immunosuppressed patients compared to immunocompetent patients. Different automatic variable selection processes agreed on selecting the predictors temperature, relative humidity, cloud cover and precipitation as remaining predictors in the multivariable logistic regression model, including all weather factors, with low ambient temperature, low relative humidity, high cloud cover and high precipitation being linked to increased coronavirus detection rates. Conclusions: Coronavirus infections followed a seasonal pattern, which was more pronounced in immunocompetent patients compared to immunosuppressed patients. Several meteorological factors were associated with the coronavirus detection rate. However, when mutually adjusting for all weather factors, only temperature, relative humidity, precipitation and cloud cover contributed independently to predicting the coronavirus detection rate.

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“…Since the study was conducted in the first semester, seasonality may have also influenced the results as some diseases (e.g. bacterial and viral infections) have higher incidences in the rainy season, especially in areas of high humidity ( Choe et al., 2019 ; Price et al., 2019 ). In the present study, drugs were not restricted only to essential medicines, thus policy makers should interpret our results with caution.…”

Section: Discussionmentioning

confidence: 99%

Consumption and Lack of Access to Medicines and Associated Factors in the Brazilian Amazon: A Cross-Sectional Study, 2019

2020

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Objective We aimed to investigate the consumption and lack of access to medicines in the adult population of Manaus, Amazonas. Methods A population-based study was conducted in Manaus in 2019. Individuals aged ≥18 years were selected by probabilistic sampling performed in three stages. Study outcomes included the consumption of medicines in the previous fortnight and the lack of access to treatments in those who used any medicine. We calculated the prevalence ratios (PR) for the outcomes with 95% confidence intervals (CI) by Poisson regression with robust variance, considering the complex sampling design. Results Out of the 2,321 participants, 53.2% (95%CI 50.7-55.7%) consumed medicines, of which 14.4% (95% CI 11.9–16.8%) could not obtain appropriate treatments. Analgesics were the most used medicines (557/2,702; 21.4%), whereas antibiotics were the most inaccessible treatments (18/228; 7.9%). Lack of financial resources was the main reason for not accessing treatments (104/228; 45.6%). Consumption was significantly associated with older age (≥60 years: PR = 1.27; 95%CI 1.09–1.49), lower social class (D/E: PR = 0.84; 95%CI 0.72–0.99), lower educational level (p = 0.039), poor health status (PR = 1.30; 95%CI 1.11–1.52), use of health care services (PR = 1.37; 95%CI 1.26–1.49), and chronic diseases (PR = 1.36; 95%CI 1.22–1.52). Lack of access was higher in people with poor health status (PR = 2.46; 95%CI 1.50–4.04) and chronic diseases (PR = 1.84; 95%CI 1.16–2.92). Conclusion Half of Manaus’ population used medicines, which was higher in socially privileged and sicker individuals. Among those, 14 in every 100 could not access drug therapies, which was more frequent in people with poor health and with chronic diseases.

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Seasonality of respiratory viruses and bacterial pathogens

Cited by 24 publications

References 17 publications

Characteristics of bacterial etiology in lower respiratory tract infection: a surveillance report from a Chinese large teaching hospital from 2015 to 2019

Characteristics of bacterial etiology in lower respiratory tract infection: a surveillance report from a Chinese large teaching hospital from 2015 to 2019

Seasonality of Non-SARS, Non-MERS Coronaviruses and the Impact of Meteorological Factors

Consumption and Lack of Access to Medicines and Associated Factors in the Brazilian Amazon: A Cross-Sectional Study, 2019

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