一個新的研究報告稱：健康統(tǒng)計報告對幫助醫(yī)生和患者有重大意義。這個報告顯示盲目的統(tǒng)計是給醫(yī)療保健和社會帶來負(fù)面影響的重大問題。
政客用統(tǒng)計報告來拉選票，制藥公司用統(tǒng)計報告來銷售產(chǎn)品，媒體用統(tǒng)計報告來做所有的事情，但是他們中很少有人真正懂得健康統(tǒng)計。大多數(shù)醫(yī)生每天都要利用統(tǒng)計數(shù)據(jù)來做醫(yī)療判斷，比如去計算藥物和外科手術(shù)的風(fēng)險，基本統(tǒng)計數(shù)字是必須的。
統(tǒng)計所帶來的結(jié)論有兩面性，研究者稱統(tǒng)計結(jié)果被混淆的原因是統(tǒng)計學(xué)是一般傳遞健康信息，在客戶健康信息的處理中會缺少統(tǒng)計學(xué)的技巧。
這個結(jié)合可以是爆炸性的，英國在九十年代中期一個的報告顯示新的口服避孕藥顯示出不斷增加的血栓風(fēng)險，這在婦女間引發(fā)了恐慌。很多人停止了使用這種避孕方式。在這個報告發(fā)表之后，估計13，000甚至更多的墮胎出現(xiàn)在英國和威爾士。公眾如果被告知這種新藥所帶來的血栓風(fēng)險只是增加了七千分之一或者二，那么這個恐慌就不會發(fā)生了。
這個報告的作者同樣引用研究顯示，當(dāng)數(shù)據(jù)顯示在一個典型的概率模型時，醫(yī)生不能準(zhǔn)確的解讀癌癥篩查數(shù)據(jù)和其他的測試結(jié)果，。比如當(dāng)只顯示一個概率時，一組有經(jīng)驗的醫(yī)師也不能準(zhǔn)確地估計測試結(jié)果正常而實際上患有結(jié)腸癌病人的機(jī)會。而當(dāng)數(shù)據(jù)顯示在一個自然頻率在一個透明的模型中時大多數(shù)醫(yī)生給出了正確的答案。

 

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.