睾丸

Marsha A. Elkhunovich, T. Kang, C. Brennan, Kathryn Pade, Rashida T. Campwala, Jessica H Rankin, Kristin Berona
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引用次数: 3

摘要

劳博士及其同事的研究结果对那些从事健康促进的人来说是令人鼓舞的。然而,他们的发现并不一定能解决公众真正关心的问题。许多人问他们的医生如何降低心脏病发作的风险,但他们真正关心的是如何活得更长、更健康。如果这些心脏病的显著减少与同样令人印象深刻的寿命延长相匹配,我们可以全心全意地提倡所推荐的措施。有人认为,在健康年份,这种增长可能更为温和。几年前,我试图用计算机建模来解决这个问题我使用了1984年澳大利亚的年龄死亡率,并假设(a)降低血清胆固醇浓度会将心脏死亡率自然降低到这个较低水平的男性死亡率;(b)胆固醇浓度的相对风险在所有年龄段都是相同的;(c)降低血清胆固醇浓度不影响其他原因导致的死亡率。这一建模工作产生了两个主要结果。首先是死亡原因的巨大变化。在目前的血清胆固醇浓度范围内,该模型预测47%的死亡是由于心脏病,27%的死亡是由于癌症,26%的死亡是由于其他原因。这与当时的实际数字很接近。如果所有胆固醇浓度降低10%,该模型预测42%的死亡将是由于心脏病,30%的死亡是由于癌症,28%的死亡是由于其他原因,而平均寿命将增加一年。将所有人的胆固醇浓度降低到目前最低的五分之一的浓度范围内,将导致33%的死亡是由于心脏病,34%的人死于癌症,33%的人死于其他原因,而平均寿命将增加三年。将平均胆固醇浓度降低10%是一个可以实现的目标,但这仅仅是多活一年。大幅度降低胆固醇浓度并不是一个实际的目标。计算机建模不如数据分析,应该用来产生假设,而不是检验它们。Law和他的同事们利用他们的数据发现了胆固醇浓度变化导致的死亡原因的变化。通过一些额外的分析,他们的数据也可以用来显示降低胆固醇浓度对寿命的影响。我能说服他们做这些分析来回答有关寿命变化的问题吗?这些问题对执业临床医生和健康教育者来说很重要(我敢说至关重要)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Testicular
EDrTOR,-M R Law and colleagues' findings' 2 are encouraging for those engaged in health promotion. Their findings do not, however, necessarily solve the real concerns of the public. Many people ask their doctor how to reduce the risk of heart attacks, but their real concern is how to live a longer and healthier life. If these major reductions in heart disease were matched by an equally impressive lengthening of the lifespan we could wholeheartedly advocate the measures recommended. There have been suggestions that the gain in healthy years may be more modest. Several years ago I tried to solve this problem by computer modelling.3 I used age specific mortality for Australia for 1984 and assumed that (a) lowering the serum cholesterol concentration would reduce the cardiac mortality to that ofmen naturally at this lower level; (b) the relative risks for cholesterol concentrations were the same at all ages; and (c) lowering the serum cholesterol concentration did not affect mortality from other causes. There were two main results from this modelling exercise. The first was a dramatic change in the causes of death. With the present range of serum cholesterol concentrations the model predicted that 47% of deaths would be due to heart disease, 27% to cancer, and 26% to other causes. This is close to the actual figures at the time. If all cholesterol concentrations were reduced by 10% the model predicted that 42% of deaths would be due to heart disease, 30% to cancer, and 28% to other causes while the median lifespan would be increased by one year. Reducing the cholesterol concentration of all people to within the range of the present lowest fifth of concentrations would result in 33% of deaths being due to heart disease, 34% to cancer, and 33% to other causes while the median lifespan would be increased by three years. A reduction in the mean cholesterol concentration by 10% is an achievable goal, but the gain is only one extra year of life. The major reduction in cholesterol concentrations is not a practical goal. Computer modelling is inferior to analysis of data and should be used to generate hypotheses rather than test them. Law and colleagues have used their data to find the changes in the causes of death with changes in cholesterol concentrations. With little extra analysis their data could also be used to show the effect of reduced cholesterol concentrations on lifespan. Could I persuade them to do the analyses to answer questions about changes in lifespan, which are important (dare I say vital) for practising clinicians and health educators?
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