Background: Since 1987, data regarding dental caries prevalence in the Philippines has been shown to be over 90%.

Objective: This study compared the trends of Filipino web searches regarding toothache and milk tea from 2017 to 2019 through spatio-temporal analyses.

Methods: Google Trends searches for the years 2017, 2018, and 2019 were done using three separate search queries using the parameters "toothache" (TA) and "milk tea" (MT) as search terms, Philippines as location, Health as category, and Web Search as database.

Results: The outcome showed a decreasing trend in searches for toothache and an increasing interest for milk tea web searches from 2017 to 2019. A multiple comparison test showed that searches for MT were significantly more than TA in 2017 (p<0.001), 2018 (p<0.001), and 2019 (p<0.001). Searches for TA during the 2nd, 3rd, and 4th quarter compared to the 1st quarter of the year, in Caraga, Eastern Visayas, Western Visayas and Zamboanga Peninsula compared to Manila, were found to be significantly high.

Conclusion: Filipinos' health-seeking behavior show decreasing interest towards TA and increasing for MT.

Key Words: spatio-temporal analysis, data mining, health-seeking behavior, dental care, Philippines

The velocity of blood in vessels is an important indicator that reflects the microcirculatory status. The core of the measurement technology, which is based on spatiotemporal (ST) image, is to map the cell motion trace to the two-dimensional ST image, and transfer the measurement of flow velocity to the detection of trace orientation in ST image. This paper proposes a trace orientation measurement algorithm is based on Randomized Hough Transformation and projection transformation, and it is able to estimate trace orientation and flow velocity in noisy ST images. Experiments showed that the agreement between the results by manual and by the proposed algorithm reached over 90%.
Algorithms ; Blood Flow Velocity ; Humans ; Microcirculation ; Spatio-Temporal Analysis

OBJECTIVETo study the time-space distribution of viral hepatitis C in Gansu province during 2009-2013, using the time-space statistics.

METHODSUsing Geoda to analysis the univariate Moran's I and univariate local Moran's I while using SaTScan to detect the time-space gathering areas.

RESULTSThere was spatial autocorrelation on incidence of hepatitis C noticed in Gansu during 2009-2013. The hot spots areas were counties as Jinchang, Wuwei, Zhangye and Lanzhou. Cold spot areas would include counties as Dingxi, Longnan, Pingliang, Gannan, Jiuquan, Qingyang, Baiyin and Tianshui. There were time-space gathering areas nitoced, during 2009-2010. Qinzhou and Maiji counties belonged to high incidence gathering areas. Lintao and Linxia were of low incidence gathering areas. In 2011-2013, high incidence gathering area would include counties as Zhangye, Jinchang, Wuwei Lanzhou and Baiyin while low incidence gathering areas would include counties as Dingxi, Tianshui, Pingliang, Longnan and Qingyang.

CONCLUSIONThere appeared time-space gathering of hepatitis C in Gansu province during 2009-2013. High and low gathering areas varied with time and high incidence gathering area mainly distributed in the western and central areas of Gansu province.

China/epidemiology* ; Environmental Monitoring ; Humans ; Spatio-Temporal Analysis ; Tuberculosis/epidemiology*

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

Objective: To analyze the spatial distribution and identify the high risk areas of pulmonary tuberculosis at the township level in Beijing during 2005-2015. Methods: Data on pulmonary tuberculosis cases was collected from the tuberculosis information management system. Global autocorrelation analysis, local indicators of spatial association and Kulldorff's Scan Statistics were applied to map the spatial distribution and detect the space-time clusters of the pulmonary tuberculosis cases during 2005-2015. Results: Spatial analysis on the incidence of pulmonary tuberculosis at the township level demonstrated that the spatial autocorrelation was positive during the study period. The values of Moran's I ranged from 0.224 3 to 0.291 8 with all the P values less than 0.05. Hotspots were primarily distributed in 8 towns/streets as follows: Junzhuang, Wangping, Yongding and Tanzhesi in Mentougou district, Yancun in Fangshan district, Wangzuo town in Fengtai district, Tianqiao street in Xicheng district and Tianzhu town in Shunyi district. Spatiotemporal clusters across the entire study period were identified by using Kulldorff's spatiotemporal scan statistic. The primary cluster was located in Chaoyang and Shunyi districts, including 17 towns/streets, as follows: Cuigezhuang, Maizidian, Dongfeng, Taiyanggong, Zuojiazhuang, Hepingjie, Xiaoguan, Xiangheyuan, Dongba, Jiangtai, Wangjing, Jinzhan, Jiuxianqiao, Laiguangying, Sunhe towns/streets in Chaoyang district, Houshayu and Tianzhu town in Shunyi district, during January to December 2005. Conclusion: Incidence rates of pulmonary tuberculosis displayed spatial and temporal clusterings at the township level in Beijing during 2005-2015, with high risk areas relatively concentrated in the central and southern parts of Beijing.
Beijing ; China ; Cluster Analysis ; Humans ; Incidence ; Spatial Analysis ; Spatio-Temporal Analysis ; Tuberculosis ; Tuberculosis, Pulmonary/ethnology*

It is expected that synaptic construction and electrical characteristics in degenerate retina might be different from those in normal retina. Therefore, we analyzed the retinal waveform recorded with multielectrode array in normal and degenerate retina using principal component analysis (PCA) and independent component analysis (ICA) and compared the results. PCA is a well established method for retinal waveform while ICA has not tried for retinal waveform analysis. We programmed ICA toolbox for spatiotemporal analysis of retinal waveform. In normal mouse, the MEA spatial map shows a single hot spot perfectly matched with PCA-derived ON or OFF ganglion cell response. However in rd/rd mouse, the MEA spatial map shows numerous hot and cold spots whose underlying interactions and mechanisms need further investigation for better understanding.
Animals ; Ganglion Cysts ; Mice* ; Passive Cutaneous Anaphylaxis ; Principal Component Analysis ; Retina ; Retinaldehyde* ; Spatio-Temporal Analysis*

OBJECTIVETo understand the spatial-temporal specific incidence of pulmonary tuberculosis (TB) in Gansu.

METHODSThe county-based incidence of pulmonary TB in Gansu from 2009 to 2013 was used to calculate Moran's I and G statistics, and analyze the spatial-temporal distribution of areas with different pulmonary TB incidences.

RESULTSThe spatial correlation in incidence of pulmonary TB was found in Gansu from 2009 to 2013 (P<0.001), and the hot spot areas were mainly in Hexi area, Linxia, part of Dingxi, while the cold spot areas were in Lanzhou, part of Dingxi, Tianshui, Pingliang and Qingyang. Spatial-temporal analysis showed that the clustering of high pulmonary TB incidence areas were most likely in the Hexi area during 2009-2010 (LLR=3,031.10, RR=2.27, P<0.001), and the clustering of low pulmonary TB incidence areas were most likely in Lanzhou during 2011-2013 (LLR=1,545.52, RR=0.37, P<0.001).

CONCLUSIONThe analysis on spatial and spatial-temporal specific incidences of pulmonary TB in Gansu from 2009 to 2013 indicated that Hexi area is the key area in pulmonary TB prevention and control in Gansu.

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.