| Literature DB >> 31671121 |
Johannes P Borde1,2, Klaus Kaier3, Philip Hehn3, Merle M Böhmer4, Teresa M Kreusch5, Gerhard Dobler6.
Abstract
BACKGROUND: Little is known regarding the changing seasonality of infections with the tick-borne encephalitis virus (TBEV) and the incidence of the resulting disease over the last two decades. Seasonal patterns have to our knowledge not previously been systematically investigated and are poorly understood. We investigate emerging seasonal changes in clinical aspects like potentially increasing hospitalization during the year, variations in clinical symptoms and disease severity during the season and seasonal dynamics of fatal outcomes.Entities:
Mesh:
Year: 2019 PMID: 31671121 PMCID: PMC6822726 DOI: 10.1371/journal.pone.0224044
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Overview study population (N = 6,073) and dataset, general epidemiology.
| mean | SD | ||
|---|---|---|---|
| Demographics | |||
| age | 46.6 | 19.4 | |
| male | 3857 | 63.5% | |
| female | 2211 | 36.4% | |
| Symptoms | |||
| no CNS symptoms | 3122 | 51.4% | |
| CNS symptoms | 2800 | 46.1% | |
| missing | 151 | 2.5% | |
| Hospitalization | |||
| hospitalized | 4844 | 79.8% | |
| not hospitalized | 1129 | 18.6% | |
| missing | 100 | 1.6% | |
| Outcome | |||
| survived | 6022 | 99.2% | |
| fatal | 25 | 0.4% | |
| missing | 26 | 0.4% | |
| Myelitis | |||
| no myelitis | 5885 | 96.9% | |
| myelitis | 188 | 3.1% | |
| missing | 0 | 0.0% | |
Fig 1Seasonal analysis—It is shown, that there was no detectable seasonal variation regarding the variables myelitis, CNS and fatal outcome (Fig 1).
However, there is a significant seasonal trend regarding hospitalization, which increases until August (d) It is shown that there was no detectable seasonal variation regarding the variable fatal outcome (c). In (e), female patients who died, had a later onset of symptoms than their male counterparts. Predicted probabilities and corresponding 95% confidence intervals (shaded area) based on the results of logistic regression models with the time point in year at which TBEV infection related symptoms were detected included as a continuous but non-linear covariate. Non-linearity was modelled using restricted cubic splines with knot locations based on Harrell's recommended percentiles [14].
Fig 2For the early years (2001–2009), an autumn decrease of CNS and myelitis was detected.
For the later years (2010–2018), however, there is a steep increase in CNS and myelitis occurrence in autumn. Predicted probabilities and corresponding 95% confidence intervals (shaded area) based on the results of logistic regression models with the time point in year at which TBEV infection related symptoms were detected included as a continuous but non-linear covariate. Non-linearity was modelled using restricted cubic splines with knot locations based on Harrell's recommended percentiles [14].