貌似不科学的,不全是糟粕

贵圈

政府都对党
注册
2014-10-21
消息
32,821
荣誉分数
6,152
声望点数
373
How your birth month determines if you will get sick: Researchers reveal the ailments you are most at risk from
  • Scientists compared 1,688 diseases to birth dates of 1.7m New Yorkers
  • Found 55 diseases that correlated with the season of birth
  • April babies at highest risk of angina, November bronchitis and ADHD
The month you were born does have an impact on how likely you are to become ill, researchers have claimed.

They created software to scour birth and medical records to look for links.

After using the algorithm to examine New York City medical databases, they found 55 diseases that correlated with the season of birth.

0BFC168D00000578-3117205-image-a-9_1433873958441.jpg



+2
Researchers found viral infections and bronchitis are most likely among those born in November.

HOW THEY DID IT
Columbia's scientists to compare 1,688 diseases against the birth dates and medical histories of 1.7 million patients treated at NewYork-Presbyterian Hospital/CUMC between 1985 and 2013.

The study ruled out more than 1,600 associations and confirmed 39 links previously reported in the medical literature.

The researchers also uncovered 16 new associations, including nine types of heart disease, the leading cause of death in the United States.

Overall, the Columbia University study indicated people born in May had the lowest disease risk, and those born in October the highest.

'Lifetime disease risk is affected by birth month,' the researchers wrote in in the Journal of American Medical Informatics Association.

'Seasonally dependent early developmental mechanisms may play a role in increasing lifetime risk of disease. '

'This data could help scientists uncover new disease risk factors,' said author Nicholas Tatonetti.

The researchers now plan to replicate their study with data from several other locations in the U.S. and abroad to see how results vary with the change of seasons and environmental factors in those places.

By identifying what's causing disease disparities by birth month, the researchers hope to figure out how they might close the gap.

Earlier research on individual diseases such as ADHD and asthma suggested a connection between birth season and incidence, but no large-scale studies had been undertaken.

RELATED ARTICLES
SHARE THIS ARTICLE
Share
4.2k shares
This motivated Columbia's scientists to compare 1,688 diseases against the birth dates and medical histories of 1.7 million patients treated at NewYork-Presbyterian Hospital/CUMC between 1985 and 2013.

The study ruled out more than 1,600 associations and confirmed 39 links previously reported in the medical literature.


Scientists find your birth month can predict disease risk

video-undefined-297D475C00000578-918_636x358.jpg


2975CE6600000578-0-image-a-1_1433869608488.jpg



+2
Researchers examined New York City medical databases and found 55 diseases that correlated with the season of birth. This data visualization maps the statistical relationship between birth month and disease incidence in the electronic records of 1.7 million New York City patients.

The researchers also uncovered 16 new associations, including nine types of heart disease, the leading cause of death in the United States.

The researchers performed statistical tests to check that the 55 diseases for which they found associations did not arise by chance.

'It's important not to get overly nervous about these results because even though we found significant associations the overall disease risk is not that great,' notes Dr. Tatonetti. 'The risk related to birth month is relatively minor when compared to more influential variables like diet and exercise.'

The new data are consistent with previous research on individual diseases.

For example, the study authors found that asthma risk is greatest for July and October babies.

An earlier Danish study on the disease found that the peak risk was in the months (May and August) when Denmark's sunlight levels are similar to New York's in the July and October period.

For ADHD, the Columbia data suggest that around one in 675 occurrences could relate to being born in New York in November.

This result matches a Swedish study showing peak rates of ADHD in November babies.

The researchers also found a relationship between birth month and nine types of heart disease, with people born in March facing the highest risk for atrial fibrillation, congestive heart failure, and mitral valve disorder.

One in 40 atrial fibrillation cases may relate to seasonal effects for a March birth.

A previous study using Austrian and Danish patient records found that those born in months with higher heart disease rates -March through June -had shorter life spans.

'Faster computers and electronic health records are accelerating the pace of discovery,' said the study's lead author, Mary Regina Boland, a graduate student at Columbia.

'We are working to help doctors solve important clinical problems using this new wealth of data.'

 
5678四个月份最牛逼了
 
胡扯吧 看星象把
 
季节影响胎儿完全可能。年份影响胎儿都有可能。关键是如何找到其中的关联。
 
季节影响胎儿完全可能。年份影响胎儿都有可能。关键是如何找到其中的关联。
大数据 :buttrock:
 
季节影响胎儿完全可能。年份影响胎儿都有可能。关键是如何找到其中的关联。
there might be correlation between the two but correlation is not causality. if you keep a record of raining days and a particular stock daily price changes for a few years, you might be find a correlation coefficient larger than or smaller than 0.5 as well but it doesn't imply causality. 很多完全不相干的事务都可以找到大于或小于0.5的关联系数。
 
there might be correlation between the two but correlation is not causality. if you keep a record of raining days and a particular stock daily price changes for a few years, you might be find a correlation coefficient larger than or smaller than 0.5 as well but it doesn't imply causality. 很多完全不相干的事务都可以找到大于或小于0.5的关联系数。

科普一下。在生物学中的相关性如何表述,测量。相关性大小有啥意义。相关性与因果关系有舍区别。你这一说0.5,LZ一下现原形。不懂。
 
there might be correlation between the two but correlation is not causality. if you keep a record of raining days and a particular stock daily price changes for a few years, you might be find a correlation coefficient larger than or smaller than 0.5 as well but it doesn't imply causality. 很多完全不相干的事务都可以找到大于或小于0.5的关联系数。

你举的例子还是不能说服人的。
stockmarket完全可能受到天气影响。
至于如何影响需要大数据:buttrock:
但是上来就否认其中可能的因果关系?
有违科学谨慎的格律。
有大数据了再说吧。
 
科普一下。在生物学中的相关性如何表述,测量。相关性大小有啥意义。相关性与因果关系有舍区别。你这一说0.5,LZ一下现原形。不懂。
correlation and causality are totally two different things. correlation doesn't imply causality. There are lots of cases where the two variables have some sort of correlations but only a very few have a true causality. People often think correlation is the proof of causality but it is not. It is a logical fallacy. Making a causal claim based on correlations is never accepted in scientific studies. I suggest you check it out what correlation coefficient means before making a statement. Googling " correlation doesn't imply causality or causation" might get you start on the right track.
 
Correlation 是否能作为指针?至少说因果必然显示出相关。不相关则可以淘汰存在因果的可能性。因此,相关至少是发现证明有因果的潜力。因此大幅度缩减数据需要。很多医学发现不是这么做的吗?有相关就有可能存在因果关系。
 
这么巧,昨天刚好看到这个视频。这算是把大数据用在医学研究上的先驱吧?

科学不科学的,至少要先尊重事实。现在至少有数据摆在那里,至于什么原因,有待进一步研究。人家至少发现这个现象了。我感觉,大数据的收集研究,会给我们打开一个完全不同的新奇有趣的新世界。
 
后退
顶部