High prevalence of breast cancer in light polluted areas in urban and rural regions of South Korea: An ecologic study on the treatment prevalence of female cancers based on National Health Insurance data

<div><p>It has been reported that excessive artificial light at night (ALAN) could harm human health since it disturbs the natural bio-rhythm and sleep. Such conditions can lead to various diseases, including cancer. In this study, we have evaluated the association between ALAN and prevalence rates of cancer in females on a regional basis, after adjusting for other risk factors, including obesity, smoking, alcohol consumption rates and PM₁₀ levels. The prevalence rates for breast cancer were found to be significantly associated with ALAN in urban and rural areas. Furthermore, no association was found with ALAN in female lung, liver, cervical, gastric and colon cancer. Despite the limitations of performing ecological studies, this report suggests that ALAN might be a risk factor for breast cancer, even in rural areas.</p></div

Development and External Validation of the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer: Comparison with Two Western Risk Calculators in an Asian Cohort

<div><p>Purpose</p><p>We developed the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer (KPCRC-HG) that predicts the probability of prostate cancer (PC) of Gleason score 7 or higher at the initial prostate biopsy in a Korean cohort (http://acl.snu.ac.kr/PCRC/RISC/). In addition, KPCRC-HG was validated and compared with internet-based Western risk calculators in a validation cohort.</p><p>Materials and Methods</p><p>Using a logistic regression model, KPCRC-HG was developed based on the data from 602 previously unscreened Korean men who underwent initial prostate biopsies. Using 2,313 cases in a validation cohort, KPCRC-HG was compared with the European Randomized Study of Screening for PC Risk Calculator for high-grade cancer (ERSPCRC-HG) and the Prostate Cancer Prevention Trial Risk Calculator 2.0 for high-grade cancer (PCPTRC-HG). The predictive accuracy was assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots.</p><p>Results</p><p>PC was detected in 172 (28.6%) men, 120 (19.9%) of whom had PC of Gleason score 7 or higher. Independent predictors included prostate-specific antigen levels, digital rectal examination findings, transrectal ultrasound findings, and prostate volume. The AUC of the KPCRC-HG (0.84) was higher than that of the PCPTRC-HG (0.79, p<0.001) but not different from that of the ERSPCRC-HG (0.83) on external validation. Calibration plots also revealed better performance of KPCRC-HG and ERSPCRC-HG than that of PCPTRC-HG on external validation. At a cut-off of 5% for KPCRC-HG, 253 of the 2,313 men (11%) would not have been biopsied, and 14 of the 614 PC cases with Gleason score 7 or higher (2%) would not have been diagnosed.</p><p>Conclusions</p><p>KPCRC-HG is the first web-based high-grade prostate cancer prediction model in Korea. It had higher predictive accuracy than PCPTRC-HG in a Korean population and showed similar performance with ERSPCRC-HG in a Korean population. This prediction model could help avoid unnecessary biopsy and reduce overdiagnosis and overtreatment in clinical settings.</p></div

Calibration plots depicting the agreement between predicted and observed probabilities of positive biopsy of the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer (KPCRC-HG), the European Randomized Study of Screening for PC Risk Calculator for high-grade cancer (ERSPCRC-HG), and the Prostate Cancer Prevention Trial Risk Calculator 2.0 for high-grade cancer (PCPTRC-HG).

<p>Calibration plots depicting the agreement between predicted and observed probabilities of positive biopsy of the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer (KPCRC-HG), the European Randomized Study of Screening for PC Risk Calculator for high-grade cancer (ERSPCRC-HG), and the Prostate Cancer Prevention Trial Risk Calculator 2.0 for high-grade cancer (PCPTRC-HG).</p

Simple and multiple logistic regression analyses in the development cohort.

<p>Simple and multiple logistic regression analyses in the development cohort.</p

Comparison of diagnostic accuracy at various threshold probabilities of risk calculators and of a PSA cut-off level of 4.0 ng/mL.

<p>Comparison of diagnostic accuracy at various threshold probabilities of risk calculators and of a PSA cut-off level of 4.0 ng/mL.</p

Relative Risk of Myocardial Infarction per 1°C Change in Temperature above Threshold temperature by Subgroup.

<p>Model adjusted for precipitation, humidity, sea level pressure, and air pollutants (PM10, NO₂) using a spline function.</p><p>RR = Relative risk.</p><p>STEMI: ST elevation myocardial infarction.</p><p>Non-STEMI: Non-ST elevation myocardial infarction.</p><p>Spring: March–May, Summer: June–August, Autumn: September–November, Winter: December–February.</p><p>*<b><i>P</i></b><0.05;</p><p>** <b><i>P</i></b><0.001.</p>a<p>For heat exposure, temperature increase of 1°C above threshold.</p>b<p>For cold exposure, temperature decrease of 1°C below threshold.</p>c<p>Maximum temperature.</p>d<p>Mean temperature.</p>e<p>Minimum temperature.</p>f<p>Threshold temperature.</p>g<p>No threshold effect was identified.</p

Summary Statistics for Temperature and other Meteorological Variables with the Level of Air pollutants in Study Areas.

<p>SD: Standard deviation.</p><p>IQR: Interquartile range.</p

Relative Risk of Myocardial Infarction per 1°C Change in Diurnal Temperature Range (DTR) above the Threshold Temperature in All Regions by Season.

<p>Model adjusted for precipitation, humidity, sea level pressure, and air pollutants (PM10, NO₂) using a spline function.</p><p>RR = Relative risk.</p><p>STEMI: ST elevation myocardial infarction.</p><p>Non-STEMI: Non-ST elevation myocardial infarction.</p><p>Spring: March–May, Summer: June–August, Autumn: September–November, Winter: December–February.</p><p>*<b><i>P</i></b><0.05;</p><p>** <b><i>P</i></b><0.001.</p>a<p>Threshold temperature.</p>b<p>No threshold effect was identified.</p

Relative Risk of Myocardial Infarction per 1°C Change in Successive Daily Temperature Changes by Subgroup.

<p>Model adjusted for precipitation, humidity, sea level pressure, and air pollutants (PM10, NO₂) using a spline function.</p><p>RR = Relative risk.</p><p>STEMI: ST elevation myocardial infarction.</p><p>Non-STEMI: Non-ST elevation myocardial infarction.</p><p>Spring: March–May, Summer: June–August, Autumn: September–November, Winter: December–February.</p><p>*<b><i>P</i></b><0.05;</p><p>** <b><i>P</i></b><0.001.</p>a<p>Temperature rise between consecutive days.</p>b<p>Temperature fall between consecutive days.</p>c<p>No threshold effect was identified.</p

Daily adjusted emergency visit (DAEV) rate for MI according to maximum temperature by regions: A. Combined regions, B. Central region, C. Southern region.

<p>Lower figures showed change of R² values in each temperature by piecewise analysis and maximum R² value was chosen as the inflection point. The maximum R² value of the central region was 30.5°C; however, it did not show a threshold effect.</p