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Related Concept Videos

What are Estimates?01:06

What are Estimates?

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
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Estimation of k and VD of Aminoglycosides01:20

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Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration01:28

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Glomerular filtration rate (GFR) can be estimated from serum creatinine using the modification of diet in renal disease (MDRD) formula or the chronic kidney disease–epidemiology collaboration (CKD–EPI) equation. Both methods are widely used in clinical practice to assess kidney function and guide treatment decisions.The MDRD equation does not require weight or height measurements and is normalized to the body surface area of 1.73 m², considered the average adult surface area.
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Estimation of the Physical Quantities01:05

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Estimating disease burden using Internet data.

Riyi Qiu, Mirsad Hadzikadic, Sha Yu1

  • 1The University of North Carolina at Charlotte, USA.

Health Informatics Journal
|November 30, 2018
PubMed
Summary

Internet search and social media data show some correlation with disease burden. Google searches best predicted disease burden for 39 conditions, highlighting potential for public health insights.

Keywords:
Google searchTwitterWikipediadata miningdisease burdenleast absolute shrinkage and selection operatorprevalencetreatment cost

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Area of Science:

  • Digital epidemiology
  • Public health informatics
  • Health services research

Background:

  • Disease burden data is crucial for public health assessment, intervention evaluation, policy formulation, and resource allocation.
  • Traditional disease burden metrics may not fully capture real-world population health dynamics.
  • The rise of internet and social media presents novel data sources for health research.

Purpose of the Study:

  • To investigate the correlation between internet usage data (Google search volume, Wikipedia page views, Twitter mentions) and disease burden (prevalence, treatment cost).
  • To assess the predictive power of digital data for disease burden across a wide range of diseases.
  • To explore the utility of digital footprints in understanding population health trends.

Main Methods:

  • Analysis of disease burden data for 1633 diseases over an 11-year period.
  • Correlation analysis between disease burden metrics and internet/social media data (Google Trends, Wikipedia page views, Twitter data).
  • Application of least absolute shrinkage and selection operator (LASSO) for disease burden prediction.

Main Results:

  • Google search volume showed strong correlations with disease burden for 39 diseases (e.g., viral hepatitis, diabetes mellitus, multiple sclerosis, hemorrhoids).
  • Wikipedia page views and Twitter mentions correlated with disease burden for 15 and 7 diseases, respectively.
  • The predictive accuracy varied, emphasizing the need to consider disease-specific characteristics.

Conclusions:

  • Internet search data, particularly Google Trends, can serve as a valuable proxy for certain aspects of disease burden.
  • Wikipedia and Twitter data offer complementary insights but show weaker correlations.
  • Accurate analysis requires consideration of disease characteristics like chronicity, severity, public familiarity, and stigma.