流行病学通常被称为公共卫生的“基石”，它是一门研究疾病的分布和决定因素，健康状况，或人群间的活动和应用于控制健康问题的学科。由于流行病学与现实生活息息相关，并更好地评估公共卫生项目和政策，学生将理解流行病学的研究方法，通过这一门课所学到的理论知识应用到当今的公共健康问题。本课程通过流行病学的视框，探讨了心血管疾病和传染病等公共卫生问题，对地区情况和全球情况都进行了讨论。 翻译: Yi Zhou

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來自 The University of North Carolina at Chapel Hill 的課程

流行病学：基础公共卫生科学

1187 個評分

流行病学通常被称为公共卫生的“基石”，它是一门研究疾病的分布和决定因素，健康状况，或人群间的活动和应用于控制健康问题的学科。由于流行病学与现实生活息息相关，并更好地评估公共卫生项目和政策，学生将理解流行病学的研究方法，通过这一门课所学到的理论知识应用到当今的公共健康问题。本课程通过流行病学的视框，探讨了心血管疾病和传染病等公共卫生问题，对地区情况和全球情况都进行了讨论。 翻译: Yi Zhou

從本節課中

Measures of Association

This module introduces measures of association and confidence intervals.

- Dr. Karin YeattsClinical Associate Professor

Department of Epidemiology, UNC Gillings School of Global Public Health - Dr. Lorraine AlexanderClinical Associate Professor, Director of Distance Learning (North Carolina Institute for Public Health)

Department of Epidemiology, UNC Gillings School of Global Public Health

[MUSIC]

Welcome to Week 4, Confidence Intervals Example.

This segment will focus on an example of calculating

the measure of risk and the associated 95% confidence intervals.

Many of the world's used electronics such as cell phones,

TVs and computers get shipped to cities in China for recycling.

Both children and adults work to separate out rare metals in the electronic devices.

Some of the children have very high metal exposures to lead and cadmium.

Our example is adapted from a study by Yang and Ahn published in 2013 entitled,

Effects of Lead and Cadmium Exposure from Electronic Waste on Child Physical Growth.

In our fictitious example, we will look at a cohort

of children in Guru, China who recycle metal and electronics.

This is a diagram of the cohort study.

We start with a cohort children ages 5 years old who

are working on recycling electronics with an n equal to 1,725.

Then we measure the concentrations of the metals in their blood at baseline.

Based on their blood concentrations of metals, we then classify them into

children with high metal concentrations in their blood where their n equals 589 and

low metal concentrations where the number is 1,136.

We then follow both groups of children over 10 years, and

measure their physical growth, measured by shorter height, at age 15.

This is how you would then set up a 2 by 2

table to calculate the risk of decreased growth, or shorter height in

children with high levels of metal concentrations in their blood compared to

childrens with low level concentrations of metals in their blood at baseline.

Now, I would like you to calculate the risk ratio of

decreased growth or shorter height,

potentially related to heavy metal exposure.

The answer is 0.139219 divided by

0.025528 which is equal to 5.45.

The interpretation of this risk ratio is, children with high

metal exposures at age 5 were 5.45 times as likely

to have decreased physical growth, or shorter height over a

10 year period, compared with children with low metal exposure.

Now, how do you calculate the 95%

confidence intervals associated with this risk ratio?

One possibility is with this free software.

And you could either use Open Epi or

EpiSheet and the 2 links are provided here.

So using the free software, we plug in the numbers to

our 2 by 2 table and calculate the 95% confidence interval.

In this example, for the risk ratio of

5.45, the 95% confidence interval is 3.614 to 8.23.

Is this risk ratio estimate statistically significant?

What do you think?

The answer is yes.

This risk ratio is statistically significant because the 95%

confidence interval does not include the null value of 1.