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Observational Studies01:11

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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
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Seasoning of wood is a crucial process aimed at reducing and stabilizing the moisture content within the wood to prevent future shrinkage, structural damage, or aesthetic issues once the wood is used in construction. Wood naturally swells when it absorbs moisture and contracts as it dries.
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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
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Variation01:19

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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Seasonal Variation in Human Adenovirus Conjunctivitis: A 30-Year Observational Study.

Jennifer Lee1, Richard A Bilonick1,2, Eric G Romanowski1,3

  • 1a Department of Ophthalmology and Visual Sciences , University of Pittsburgh Medical Center , Pittsburgh , Pennsylvania , USA.

Ophthalmic Epidemiology
|August 18, 2018
PubMed
Summary

Adenovirus conjunctivitis cases peak in summer (July-September) and are lowest in spring (April-June). This study highlights seasonal trends in the USA, aiding outbreak prediction and prevention strategies.

Keywords:
Conjunctivitisadenovirusepidemic keratoconjunctivitisobservational studyseasonality

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Area of Science:

  • Ophthalmology
  • Epidemiology
  • Virology

Background:

  • Adenovirus conjunctivitis, a common eye infection, exhibits seasonal patterns.
  • Understanding these trends is crucial for public health interventions.

Purpose of the Study:

  • To measure and analyze the seasonal trends of adenovirus conjunctivitis.
  • To establish the longest time-series data on adenovirus conjunctivitis seasonality in the USA.

Main Methods:

  • Reviewed 926 positive cases of HAdV conjunctivitis from March 1987 to December 2016.
  • Adjusted daily case counts for population changes and analyzed seasonal trends by quarter using B-spline modeling.

Main Results:

  • Adenovirus conjunctivitis cases were 45% higher in the third quarter (July-September) compared to the second quarter (April-June).
  • No statistically significant difference was found when comparing the third quarter to the first or fourth quarters.

Conclusions:

  • The study identified a distinct seasonal pattern, with the highest incidence from July to September.
  • This research is the first and longest time-series analysis of adenovirus conjunctivitis seasonality in the USA.
  • Knowledge of these seasonal trends can inform outbreak management, reduce unnecessary antibiotic use, and improve disease prevention.