健康统计学的意义

一个新的研究报告称：健康统计报告对帮助医生和患者有重大意义。这个报告显示盲目的统计是给医疗保健和社会带来负面影响的重大问题。
政客用统计报告来拉选票，制药公司用统计报告来销售产品，媒体用统计报告来做所有的事情，但是他们中很少有人真正懂得健康统计。大多数医生每天都要利用统计数据来做医疗判断，比如去计算药物和外科手术的风险，基本统计数字是必须的。
统计所带来的结论有两面性，研究者称统计结果被混淆的原因是统计学是一般传递健康信息，在客户健康信息的处理中会缺少统计学的技巧。
这个结合可以是爆炸性的，英国在九十年代中期一个的报告显示新的口服避孕药显示出不断增加的血栓风险，这在妇女间引发了恐慌。很多人停止了使用这种避孕方式。在这个报告发表之后，估计13，000甚至更多的堕胎出现在英国和威尔士。公众如果被告知这种新药所带来的血栓风险只是增加了七千分之一或者二，那么这个恐慌就不会发生了。
这个报告的作者同样引用研究显示，当数据显示在一个典型的概率模型时，医生不能准确的解读癌症筛查数据和其他的测试结果，。比如当只显示一个概率时，一组有经验的医师也不能准确地估计测试结果正常而实际上患有结肠癌病人的机会。而当数据显示在一个自然频率在一个透明的模型中时大多数医生给出了正确的答案。

 

Politicians use them to persuade voters, drug companies to sell products, and the media spin them in all kinds of ways, but few really understand health statistics. Even most doctors, who must make daily decisions based on numerical data - for instance, to calculate the risks of a drug or surgical intervention - lack the basic statistical literacy required.

The problem of statistical illiteracy has two sides, the researchers say. It is caused by the confusing ways statistics are ordinarily presented in health communication, coupled with a lack of statistical thinking skills among consumers of health information.

The combination can be explosive. A mid-1990s report in Britain showed that new oral contraceptive pills posed a twofold (or 100 percent) increased risk of blood clots led to a panic among women. Many stopped using this form of birth control. There were an estimated 13,000 more abortions in England and Wales in the year following the report. Had the public been informed that the absolute risk with the new pills only increased from one in 7,000 women having a blood clot to two in 7,000, the panic would not have occurred.

Health statistics are often presented in relative risks (percentages), which are generally large numbers that capture people's attention. Percentages are misleading though when facts like base rates are left out. Absolute risk figures (like “one in 7,000") are much more understandable, and less easy to sensationalise.
The authors of the report also cite studies showing the inability of doctors to accurately interpret data from cancer screenings or other test results, when the data were presented in the typical form of conditional probabilities. For example, when presented with conditional probabilities, a group of experienced physicians was unable to accurately estimate the chances a patient with a positive test result actually had colorectal cancer, given the known sensitivity and false-positive rate of the test used (estimates ranged from a one percent to a 99 percent chance, with most guessing around 50 percent). Most physicians gave the right answer (a less than ten percent chance) when the data were presented in the more transparent form of natural frequencies.