Cyclic patterns of incidence rate for skin malignant melanoma: association with heliogeophysical activity
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 compared with the sunspot index Rz (lag-period d T=+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 (d T=+6 and +7 years, respectively) and aa (d T=-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 appearance, were also discussed.Conclusion
The above findings on cyclicity and temporal association of SMM with cyclic environmental factors could not only allow for better forecasting models but also lead to a better understanding of melanoma aetiology.