Spatial and temporal analysis of liver cancer mortality in Yunnan province, China, 2015–2019

Feng, Chengcheng; Liu, Jinghua; Ran, Hailiang; Wu, Linxiong; Liang, Xuemeng; Sun, Hao; Xiao, Yuanyuan; Chang, Wei‐Min

doi:10.3389/fpubh.2022.1010752

Cited by 6 publications

(5 citation statements)

References 48 publications

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“…The A positive Moran's I value indicates that air temperatures in neighboring locations tend to have a stronger relationship than those in distant locations and vice versa. [24] [13].…”

Section: Resultsmentioning

confidence: 99%

Calibration Indonesian-Numerical Weather Prediction using Geostatistical Output Perturbation

Sutikno,

Cahyoko,

Putra

et al. 2024

j. meteorologi dan geofisika

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Indonesian-Numerical Weather Prediction (INA-NWP) is a numerical-based weather forecast method that has been developed by the Meteorology, Climatology and Geophysics Agency. However, the forecast is still unable to produce accurate weather forecasts. Geostatistical Output Perturbation (GOP) is a weather forecast method derived from only one deterministic output. GOP takes into consideration the spatial correlation among multiple locations simultaneously. GOP is capable to identify spatial dependency patterns that are associated with error models. This study aims to obtain calibrated forecasts for daily maximum and minimum temperature variables using GOP at 10 meteorological stations in Surabaya and surrounding areas. The stages in performing temperature forecasts using GOP are obtaining regression coefficient estimators, then calculating empirical semivariograms and estimating spatial parameters. Based on several weather forecast indicators, such as RMSE and CRPS, GOP is better than INA-NWP in terms of precision and accuracy.

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“…The A positive Moran's I value indicates that air temperatures in neighboring locations tend to have a stronger relationship than those in distant locations and vice versa. [24] [13].…”

Section: Resultsmentioning

confidence: 99%

Calibration Indonesian-Numerical Weather Prediction using Geostatistical Output Perturbation

Sutikno,

Cahyoko,

Putra

et al. 2024

j. meteorologi dan geofisika

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“…Studies have shown [15][16][17] that the disease burden of liver cancer can be spatially clustered.According to the results of global spatial autocorrelation, the DALY rate of liver cancer in 1990 was distributed randomly, and in 2017 was distributed non randomly, indicating that in recent years, the DALY rate of liver cancer at the provincial level gradually showed spatial autocorrelation and aggregation. The local spatial autocorrelation results show that in 1990, the spatial aggregation of the DALY rate of liver cancer in China was relatively concentrated at the provincial level.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, the prevalence of overweight, diabetes, alcohol consumption and smoking has increased signi cantly in our country. The risk factors of liver cancer seem to be changing 4 , and the task of prevention and treatment of liver cancer still has a long way to go.. Studies have shown 5 that the disease burden of liver cancer has obvious geographical differences.. However, the current epidemiological research on liver cancer should focus on the age of onset, trend analysis and research on related risk factors.…”

Section: Introductionmentioning

confidence: 99%

Spatial epidemiological analysis of the burden of liver cancer in China

Guo

Qin

Lin

et al. 2023

Preprint

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Purpose To analyze the spatial distribution characteristics and clustering of the disability-adjusted life years (DALY) rate of liver cancer in 1990 and 2017 in China based on provincial administrative divisions, and to explore its possible influencing factors, in order to provide a new scientific perspective for the prevention and control of liver cancer.Methods The DALY rate data of liver cancer at the provincial level in China were collected, the DALY rate of thematic map was segmented by natural discontinuity method, the global autocorrelation of the DALY rate was analyzed by Moran's I, the local autocorrelation of the DALY rate was analyzed by Getis-Ord-Gi*, and the influencing factors related to the DALY rate were analyzed by the least squares regression model.Results The DALY rate of liver cancer in China generally showed an increasing trend. The DALY rate of liver cancer in China increased from 649.16/100,000 in 1990 to 789.61/100,000 in 2017, increasing by 21.64%. The DALY rate increased in 22 provinces and decreased in 9 provinces. In 2017, the distribution of DALY rate in all provinces showed heterogeneity, with the highest DALY rate in Guangxi (1,363.37/100,000) and the lowest in Beijing (315.78/100,000). In 2017, the DALY rate of liver cancer in China was more extensive. The low and low clustering were mainly concentrated in Inner Mongolia and Hebei. The low and high clustering in Yunnan, Guizhou and Guangdong were surrounded by the high clustering in neighboring provinces, and the high clustering was mainly concentrated in Hunan and Jiangxi. The results of the least square regression model showed that the per capita years of education, hepatitis B incidence and the proportion of population over 65 years old had an impact on the DALY rate of liver cancer (P < 0.05). The incidence of hepatitis B and the proportion of population over 65 years old were positively correlated with the DALY rate of liver cancer, and the per capita years of education was negatively correlated with the DALY rate. GDP had no effect on the DALY rate of HCC (P > 0.05).Conclusion The DALY rate of liver cancer in China showed an overall increasing trend. In 2017, the DALY rate of liver cancer in China had a spatial aggregation in the whole country, and the per capita years of education, the incidence of hepatitis B and the proportion of population over 65 years old had an impact on the DALY rate of liver cancer in space.

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“…When the Moran's I value is less than 0, it indicates negative spatial autocorrelation. When the Moran's I value is equal to 0, it indicates a random distribution and no spatial autocorrelation 22 .…”

Section: Global Spatial Autocorrelationmentioning

confidence: 99%

Epidemiological characteristics and Spatiotemporal Analysis of Occupational Noise-induced Deafness from 2006 to 2022 in Guangdong, China (Preprint)

Zhou,

Huang,

Chen

et al. 2024

Preprint

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BACKGROUND Occupational noise-induced deafness (ONID) has replaced occupational poisoning as the second most common occupational disease in China since 2015. However, there is a limited number of literatures on epidemiological characteristics of legally diagnosed ONID. OBJECTIVE We conducted a comprehensive analysis of the epidemiological and spatiotemporal characteristics of ONID in Guangdong Province from 2006 to 2022, with the aim of providing a scientific foundation for policy formulation and health resource allocation. METHODS Surveillance data of ONID cases in Guangdong Province from 2006, to 2022, were obtained from the “Occupational Diseases and Health Hazard Factors Monitoring Information System”. Joinpoint regression analysis was applied to access the long-term trends in cases of ONID for the period 2006-2022. Global spatial autocorrelation analysis was performed to measure the overall degree of similarity of the attribute values of spatially adjacent or neighboring regional units. The local indicators of spatial autocorrelation (LISA) plots were then employed to identify the local clusters of ONID in Guangdong. RESULTS It has been reported that there were 3,761 ONID cases in Guangdong Province from 2006 to 2022, showing a significantly increased trend in cases across the entire study period (average annual percentage change:21.9, 95% CI:18.7, 35.1). The Moran's I values for the period of 2006 to 2022 ranged from 0.202 to 0.649 (all P < 0.001), indicating a positive spatial correlation of ONID across regions each year in Guangdong Province. Fifteen high-high clustering were notably concentrated in specific counties within the Pearl River Delta. CONCLUSIONS ONID in Guangdong Province has experienced a dramatic increase followed by a stabilization from 2006 to 2022. The distribution of ONID shows spatiotemporal clustering. Our results could help prioritize the allocation of resources for targeted prevention and control measures for ONID.

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Spatial and temporal analysis of liver cancer mortality in Yunnan province, China, 2015–2019

Cited by 6 publications

References 48 publications

Calibration Indonesian-Numerical Weather Prediction using Geostatistical Output Perturbation

Calibration Indonesian-Numerical Weather Prediction using Geostatistical Output Perturbation

Spatial epidemiological analysis of the burden of liver cancer in China

Epidemiological characteristics and Spatiotemporal Analysis of Occupational Noise-induced Deafness from 2006 to 2022 in Guangdong, China (Preprint)

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