OBJECTIVE: To investigate the incidence trend and spatial clustering characteristics of scarlet fever in China from 2016 to 2020 to provide evidence for development of regional disease prevention and control strategies. METHODS: The incidence data of scarlet fever in 31 provinces and municipalities in mainland China from 2016 to 2020 were obtained from the Chinese Health Statistics Yearbook and the Public Health Science Data Center led by the Chinese Center for Disease Control and Prevention.The three-dimensional spatial trend map of scarlet fever incidence in China was drawn using ArcGIS to determine the regional trend of scarlet fever incidence.GeoDa spatial autocorrelation analysis was used to explore the spatial aggregation of scarlet fever in China in recent years. RESULTS: From 2016 to 2020, a total of 310 816 cases of scarlet fever were reported in 31 provinces, municipalities directly under the central government and autonomous regions, with an average annual incidence of 4.48/100 000.The reported incidence decreased from 4.32/100 000 in 2016 to 1.18/100 000 in 2020(Z=103.47, P < 0.001).The incidence of scarlet fever in China showed an obvious regional clustering from 2016 to 2019(Moran's I>0, P < 0.05), but was randomly distributed in 2020(Moran's I>0, P=0.16).The incidence of scarlet fever showed a U-shaped distribution in eastern and western regions of China, and increased gradually from the southern to northern regions.Inner Mongolia Autonomous Region and Hebei and Gansu provinces had the High-high (H-H) clusters of scarlet fever in China. CONCLUSION Scarlet fever still has a high incidence in China with an obvious spatial clustering.For the northern regions of China with H-H clusters of scarlet fever, the allocation of health resources and public health education dynamics should be strengthened, and local scarlet fever prevention and control policies should be made to contain the hotspots of scarlet fever.
Humans ; Incidence ; Scarlet Fever/epidemiology* ; China/epidemiology* ; Spatial Analysis ; Cluster Analysis ; Spatio-Temporal Analysis

Objective: To analyze the epidemiology and spatial-temporal distribution characteristics of hand, foot and mouth disease (HFMD) in Shanxi province. Methods: The data of HFMD in Shanxi province from 2009 to 2020 were collected from notifiable disease management information system of Chinese information system for disease control and prevention and analyzed by descriptive epidemiology, Joinpoint regression, spatial autocorrelation analysis and spatio- temporal scanning analysis. Results: A total of 293 477 HFMD cases were reported in Shanxi province from 2009 to 2020, with an average annual incidence of 67.64/100 000 (293 477/433 867 454), severe disease rate of 5.36/100 000 (2 326/433 867 454), severe disease ratio of 0.79%(2 326/293 477), mortality of 0.015/100 000 (66/433 867 454), and fatality rate of 22.49/100 000 (66/293 477). The reported incidence rate, severe disease rate, mortality rate and fatality rate of HFMD showed decreasing trends. The main high-risk groups were scattered children and kindergarten children aged 0-5. The incidence of HFMD had obvious seasonal variation, with two peaks every year: the main peak was during June-July, the secondary peak was during September-October and the peak period is from April to November. A total of 13 942 laboratory cases were confirmed, with a diagnosis rate of 4.75% (13 942/293 477), including 4 438 (35.11%, 4 438/293 477) Enterovirus A71 (EV-A71) positive cases, 4 609 (33.06%, 4 609/293 477) Coxsackievirus A16 (CV-A16) positive cases, and 4 895 (31.83%, 4 895/293 477) other enterovirus positive cases. There was a spatial positive correlation (Moran's I ranged from 0.12 to 0.58, all P<0.05) and the spatial clustering was obvious. High-risk regions were mainly distributed in Taiyuan in central Shanxi province, Linfen and Yuncheng in southern Shanxi province, and Changzhi in southeastern Shanxi province. Spatial-temporal scanning analysis revealed 1 the most likely cluster and 8 secondary likely clusters, of which the most likely cluster (RR=2.65, LLR=22 387.42, P<0.001) located in Taiyuan and Jinzhong city, Shanxi province, including 12 counties (districts), and accumulated from April 1, 2009 to November 30, 2018. Conclusions: There was obvious spatial-temporal clustering of HFMD in Shanxi province, and the epidemic situation was in decline. The key areas were the districts in urban areas and the counties adjacent to it. Meanwhile, the monitoring and classification of other enterovirus types of HFMD should be strengthened.
Child ; Humans ; Hand, Foot and Mouth Disease/epidemiology* ; Spatial Analysis ; Enterovirus Infections ; Spatio-Temporal Analysis ; Cluster Analysis

INTRODUCTIONBreast cancer is a leading tumor with a high mortality in women. This study examined the spatio-temporal distribution of the incidence of female breast cancer in Shenzhen between 2007 and 2012.

METHODSThe data on breast cancer incidence were obtained from the Shenzhen Cancer Registry System. To describe the temporal trend, the average annual percentage change (AAPC) was analyzed using a joinpoint regression model. Spatial autocorrelation and a retrospective spatio-temporal scan approach were used to detect the spatio-temporal cluster distribution of breast cancer cases.

RESULTSBreast cancer ranked first among different types of cancer in women in Shenzhen between 2007 and 2012 with a crude incidence of 20.0/100,000 population. The age-standardized rate according to the world standard population was 21.1/100,000 in 2012, with an AAPC of 11.3%. The spatial autocorrelation analysis showed a spatial correlation characterized by the presence of a hotspot in south-central Shenzhen, which included the eastern part of Luohu District (Donghu and Liantang Streets) and Yantian District (Shatoujiao, Haishan, and Yantian Streets). Five spatio-temporal cluster areas were detected between 2010 and 2012, one of which was a Class 1 cluster located in southwestern Shenzhen in 2010, which included Yuehai, Nantou, Shahe, Shekou, and Nanshan Streets in Nanshan District with an incidence of 54.1/100,000 and a relative risk of 2.41; the other four were Class 2 clusters located in Yantian, Luohu, Futian, and Longhua Districts with a relative risk ranging from 1.70 to 3.25.

CONCLUSIONSThis study revealed the spatio-temporal cluster pattern for the incidence of female breast cancer in Shenzhen, which will be useful for a better allocation of health resources in Shenzhen.

No abstract available.
Spatial Analysis

To investigate the transmission pattern of geographical area and temporal trends of the 2010~2011 foot-and-mouth disease (FMD) outbreaks in Korea, and to explore temporal intervals at which spatial clustering of FMD cases space-time analysis based on georeferenced database of 3,575 burial sites, from 30 November 2010 to 23 February 2011, was performed. The cases represent approximately 98.1% of all infected farms (n = 3,644) during the same period. Descriptive maps of spatial patterns of the outbreaks were generated by ArcGIS. Spatial Scan Statistics, using SaTScan software, was applied to investigate geographical clusters of FMD cases across the country. Overall, spatial heterogeneity was identified, and the transmission pattern was different by province. Cattle have more clusters in number but smaller in size, as compared to the swine population. In addition, spatiotemporal analysis and the comparison of clustering patterns between the first 7 days and days 8 to 14 of the outbreak revealed that the strongest spatial clustering was identified at the 7-day interval, although clustering over longer intervals (8~14 days) was also observed. We further discussed the importance of time period elapsed between FMD-suspected notice and the date of confirmation, and emphasized the necessity of region-specific and species-specific control measures.
Animals ; Burial ; Cattle ; Disease Outbreaks ; Foot-and-Mouth Disease ; Geographic Information Systems ; Korea ; Population Characteristics ; Republic of Korea ; Spatial Analysis ; Spatio-Temporal Analysis ; Swine

Epidemiologic Methods ; Spatial Analysis

PURPOSE: To investigate the spatial distribution of diabetes-related lower limb amputations and analyze the relationship between the spatial distribution of diabetes-related lower limb amputations and regional factors. MATERIALS AND METHODS: This study was performed based on the data from the Korean Health Insurance Review and Assessment Service, in 2016. The unit of analysis was the administrative districts of city·gun·gu. The dependent variable was the age- and sex-adjusted incidence of diabetes-related lower limb amputations and the regional variables were selected to represent two aspects: socioeconomic factors, and health and medical factors. Along with traditional ordinary least square (OLS) regression analysis, geographically weighted regression (GWR) was applied for spatial analysis. RESULTS: The age- and sex-adjusted incidence of diabetes-related lower limb amputation varied according to region. OLS regression showed that the incidence of diabetes-related lower limb amputation had significant relationships with the health and medical factors (number of healthcare institution and doctors per 100,000 population). In GWR, the effects of regional factors were not consistent. CONCLUSION: The spatial distribution of the incidence of diabetes-related lower limb amputations and the effects of regional factors varied according to the regions. The regional characteristics should be considered when establishing health policy related to diabetic foot care.
Amputation ; Delivery of Health Care ; Diabetes Mellitus ; Diabetic Foot ; Health Policy ; Incidence ; Insurance, Health ; Lower Extremity ; Socioeconomic Factors ; Spatial Analysis ; Spatial Regression

OBJECTIVES: Achieving national health equity is currently a pressing issue. Large regional variations in the health determinants are observed. Depression, one of the most common mental disorders, has large variations in incidence among different populations, and thus must be regionally analyzed. The present study aimed at analyzing regional disparities in depressive symptoms and identifying the health determinants that require regional interventions. METHODS: Using health indicators of depression in the Korea Community Health Survey 2011 and 2013, the Moran's I was calculated for each variable to assess spatial autocorrelation, and a validated geographically weighted regression analysis using ArcGIS version 10.1 of different domains: health behavior, morbidity, and the social and physical environments were created, and the final model included a combination of significant variables in these models. RESULTS: In the health behavior domain, the weekly breakfast intake frequency of 1-2 times was the most significantly correlated with depression in all regions, followed by exposure to secondhand smoke and the level of perceived stress in some regions. In the morbidity domain, the rate of lifetime diagnosis of myocardial infarction was the most significantly correlated with depression. In the social and physical environment domain, the trust environment within the local community was highly correlated with depression, showing that lower the level of trust, higher was the level of depression. A final model was constructed and analyzed using highly influential variables from each domain. The models were divided into two groups according to the significance of correlation of each variable with the experience of depression symptoms. CONCLUSIONS: The indicators of the regional health status are significantly associated with the incidence of depressive symptoms within a region. The significance of this correlation varied across regions.
Breakfast ; Depression* ; Depressive Disorder ; Diagnosis ; Health Behavior ; Health Equity ; Health Surveys ; Incidence ; Korea ; Mental Disorders ; Myocardial Infarction ; Spatial Analysis ; Spatial Regression* ; Tobacco Smoke Pollution

OBJECTIVES: Achieving national health equity is currently a pressing issue. Large regional variations in the health determinants are observed. Depression, one of the most common mental disorders, has large variations in incidence among different populations, and thus must be regionally analyzed. The present study aimed at analyzing regional disparities in depressive symptoms and identifying the health determinants that require regional interventions.METHODS: Using health indicators of depression in the Korea Community Health Survey 2011 and 2013, the Moran's I was calculated for each variable to assess spatial autocorrelation, and a validated geographically weighted regression analysis using ArcGIS version 10.1 of different domains: health behavior, morbidity, and the social and physical environments were created, and the final model included a combination of significant variables in these models.RESULTS: In the health behavior domain, the weekly breakfast intake frequency of 1-2 times was the most significantly correlated with depression in all regions, followed by exposure to secondhand smoke and the level of perceived stress in some regions. In the morbidity domain, the rate of lifetime diagnosis of myocardial infarction was the most significantly correlated with depression. In the social and physical environment domain, the trust environment within the local community was highly correlated with depression, showing that lower the level of trust, higher was the level of depression. A final model was constructed and analyzed using highly influential variables from each domain. The models were divided into two groups according to the significance of correlation of each variable with the experience of depression symptoms.CONCLUSIONS: The indicators of the regional health status are significantly associated with the incidence of depressive symptoms within a region. The significance of this correlation varied across regions.
Breakfast ; Depression ; Depressive Disorder ; Diagnosis ; Health Behavior ; Health Equity ; Health Surveys ; Incidence ; Korea ; Mental Disorders ; Myocardial Infarction ; Spatial Analysis ; Spatial Regression ; Tobacco Smoke Pollution

Breast Neoplasms ; Spatial Analysis ; Women ; Malaysia