Cyclic patterns of incidence rate for skin malignant melanoma:association with heliogeophysical activity

摘要

Background:Our previous studies revealed cyclicity in the incidence rate of skin malignant melanoma(SMM;ICD9,Dx:172)in the Czech Republic(period T=7.50～7.63 years),UK(T=11.00 years)and Bulgaria(T=12.20 years).Incidences com- pared with the sunspot index Rz(lag-period dT=+2,+4,+6,+10 or+12 years)have indicated that maximal rates are most likely to appear on descending slopes of the 11-year solar cycle,i.e.,out of phase.We summarized and explored more deeply these cyclic variations and discussed their possible associations with heliogeophysical activity(HGA)components exhibiting similar cyclicity. Methods:Annual incidences of SMM from 5 countries(Czech Republic,UK,Bulgaria,USA and Canada)over various time spans during the years 1964～1992 were analyzed and their correlations with cyclic Rz(sunspot number)and aa(planetary geomagnetic activity)indices were summarized.Periodogram regression analysis with trigonometric approximation and phase-correlation analysis were applied.Results:Previous findings on SMM for the Czech Republic,UK and Bulgaria have been validated,and cyclic patterns have been revealed for USA(T=8.63 years,P<0.05)and Canada(Ontario,T=9.91 years,P<0.10).Also,various 'hypercycles'were established(T=45.5,42.0,48.25,34.5 and 26.5 years,respectively)describing long-term cyclic incidence patterns.The association of SMM for USA and Canada with Rz(dT=+6 and+7 years,respectively)and aa(dT=?10 and+9 years,respectively)was described.Possible interactions of cyclic non-photic influences(UV irradiation,Schumann resonance signal,low-frequency geomagnetic fluctuations)with brain waves absorbance,neuronal calcium dynamics,neuro-endocrine axis modulation,melatonin/serotonin disbalance and skin neuro-immunity impairment as likely causal pathways in melanoma ap- pearance,were also discussed.Conclusion:The above findings on cyclicity and temporal association of SMM with cyclic envi- ronmental factors could not only allow for better forecasting models but also lead to a better understanding of melanoma aetiology.