Geo-Epidemiology of Age-Related Macular Degeneration: New Clues Into the Pathogenesis

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• 作者：Michele Reibaldi^a ; ^{mreibaldi@libero.it" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Antonio Longo^a ; Alfredo Pulvirenti^b ; Teresio Avitabile^a ; Andrea Russo^a ; Salvatore Cillino^c ; Cesare Mariotti^e ; Alessandra Casuccio^dAuthor Vitae
• 刊名：American Journal of Ophthalmology
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：161
• 期：Complete
• 页码：78-93.e2
• 全文大小：3377 K

文摘

To evaluate the demographic, geographic, and race-related variables that account for geographic variability in prevalence rates of age-related macular degeneration (AMD).

Design

Systematic review, meta-regression, and decision-tree analysis.

Methods

A systematic literature review of PubMed, Medline, Web of Science, and Embase databases identified population-based studies on the prevalence of AMD published before May 2014. Only population-based studies that took place in a spatially explicit geographic area that could be geolocalized, and used retinal photographs and standardized grading classifications, were included. Latitude and longitude data (geolocalization) and the mean annual insolation for the area where survey took place were obtained. Age-standardized prevalence rates across studies were estimated using the direct standardization method. Correlations between the prevalence of AMD and longitude and latitude were obtained by regression analysis. A hierarchical Bayesian meta-regression approach was used to assess the association between the prevalence of AMD and other relevant factors. We further investigated the interplay between location and these factors on the prevalence of AMD using regression based on conditional-inference decision trees.

Results

We observed significant inverse correlations between latitude or longitude, and crude or age-standardized prevalence rates, of early and late AMD (P < .001). Metaregression analysis showed that insolation, latitude, longitude, age, and race have a significant effect on the prevalence rates of early and late AMD (P < .001). Decision-tree analysis identified that the most important predictive variable was race for early AMD (P = .002) and insolation for late AMD (P = .001).

Conclusions

Geographic position and insolation are key factors in the prevalence of AMD.