Research School of Biology, Australian National University, Canberra, ACT, Australia.
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China.
Invest Ophthalmol Vis Sci. 2021 Apr 28;62(5):3. doi: 10.1167/iovs.62.5.3.
Risk factor analysis provides an important basis for developing interventions for any condition. In the case of myopia, evidence for a large number of risk factors has been presented, but they have not been systematically tested for confounding. To be useful for designing preventive interventions, risk factor analysis ideally needs to be carried through to demonstration of a causal connection, with a defined mechanism. Statistical analysis is often complicated by covariation of variables, and demonstration of a causal relationship between a factor and myopia using Mendelian randomization or in a randomized clinical trial should be aimed for. When strict analysis of this kind is applied, associations between various measures of educational pressure and myopia are consistently observed. However, associations between more nearwork and more myopia are generally weak and inconsistent, but have been supported by meta-analysis. Associations between time outdoors and less myopia are stronger and more consistently observed, including by meta-analysis. Measurement of nearwork and time outdoors has traditionally been performed with questionnaires, but is increasingly being pursued with wearable objective devices. A causal link between increased years of education and more myopia has been confirmed by Mendelian randomization, whereas the protective effect of increased time outdoors from the development of myopia has been confirmed in randomized clinical trials. Other proposed risk factors need to be tested to see if they modulate these variables. The evidence linking increased screen time to myopia is weak and inconsistent, although limitations on screen time are increasingly under consideration as interventions to control the epidemic of myopia.
风险因素分析为制定任何疾病的干预措施提供了重要依据。在近视的情况下,已经提出了大量风险因素的证据,但尚未对其混杂因素进行系统测试。为了设计预防性干预措施,风险因素分析理想情况下需要通过因果关系的证明来完成,并确定明确的机制。统计分析常常受到变量共变的影响,因此应该针对孟德尔随机化或随机临床试验中因素与近视之间的因果关系进行论证。当应用这种严格的分析方法时,各种教育压力测量值与近视之间的关联始终存在。然而,更多近距工作与更多近视之间的关联通常较弱且不一致,但已通过荟萃分析得到支持。户外活动时间与近视程度较轻之间的关联更强且更一致,包括荟萃分析的结果也是如此。近距工作和户外活动时间的测量传统上使用问卷调查进行,但越来越多地使用可穿戴的客观设备进行。通过孟德尔随机化证实了受教育年限增加与近视之间的因果关系,而通过随机临床试验证实了户外活动时间增加对预防近视发展的保护作用。其他拟议的风险因素需要进行测试,以确定它们是否调节这些变量。虽然限制屏幕时间作为控制近视流行的干预措施越来越受到关注,但将屏幕时间与近视联系起来的证据仍然薄弱且不一致。
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