Valachovic Edward, Zurbenko Igor
School of Public Health, State University of New York at Albany, Rensselaer, NY 12144, USA.
Biomed Res Int. 2014;2014:538574. doi: 10.1155/2014/538574. Epub 2014 Jul 14.
Skin cancer is diagnosed in more than 2 million individuals annually in the United States. It is strongly associated with ultraviolet exposure, with melanoma risk doubling after five or more sunburns. Solar activity, characterized by features such as irradiance and sunspots, undergoes an 11-year solar cycle. This fingerprint frequency accounts for relatively small variation on Earth when compared to other uncorrelated time scales such as daily and seasonal cycles. Kolmogorov-Zurbenko filters, applied to the solar cycle and skin cancer data, separate the components of different time scales to detect weaker long term signals and investigate the relationships between long term trends. Analyses of crosscorrelations reveal epidemiologically consistent latencies between variables which can then be used for regression analysis to calculate a coefficient of influence. This method reveals that strong numerical associations, with correlations >0.5, exist between these small but distinct long term trends in the solar cycle and skin cancer. This improves modeling skin cancer trends on long time scales despite the stronger variation in other time scales and the destructive presence of noise.
在美国，每年有超过200万人被诊断出患有皮肤癌。它与紫外线暴露密切相关，经历五次或更多次晒伤后，患黑色素瘤的风险会加倍。以辐照度和太阳黑子等特征为表征的太阳活动，会经历一个为期11年的太阳周期。与其他不相关的时间尺度（如日周期和季节周期）相比，这种指纹频率在地球上造成的变化相对较小。将科尔莫戈罗夫-祖尔本科滤波器应用于太阳周期和皮肤癌数据，可分离不同时间尺度的成分，以检测较弱的长期信号，并研究长期趋势之间的关系。互相关分析揭示了变量之间在流行病学上一致的延迟，然后可用于回归分析以计算影响系数。该方法表明，太阳周期和皮肤癌这些微小但明显的长期趋势之间存在强数值关联，相关性大于0.5。尽管在其他时间尺度上变化更强且存在破坏性噪声，但这仍有助于在长时间尺度上对皮肤癌趋势进行建模。
