scholarly journals Epidemiological characteristics and spatiotemporal analysis of hand-foot-mouth diseases from 2010 to 2019 in Zibo city, Shandong, China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lili Liu ◽  
Ling Wang ◽  
Chang Qi ◽  
Yuchen Zhu ◽  
Chunyu Li ◽  
...  
Keyword(s):  
Public Health ◽  
Spatial Autocorrelation ◽  
Hot Spots ◽  
Health Planning ◽  
Statistical Significance ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Female Ratio ◽  
Epidemiological Characteristics ◽  
Children Under 5

Abstract Background Hand-foot-mouth disease (HFMD) is a global public health issues, especially in China. It has threat the health of children under 5 years old. The early recognition of high-risk districts and understanding of epidemic characteristics can facilitate health sectors to prevent the occurrence of HFMD effectively. Methods Descriptive analysis was used to summarize epidemic characteristics, and the spatial autocorrelation analysis and space-time scan analysis were utilized to explore distribution pattern of HFMD and identify hot spots with statistical significance. The result was presented in ArcMap. Results A total of 52,095 HFMD cases were collected in Zibo city from 1 Jan 2010 to 31 Dec 2019. The annual average incidence was 129.72/100,000. The distribution of HFMD was a unimodal trend, with peak from April to September. The most susceptible age group was children under 5 years old (92.46%), and the male-to-female ratio is 1.60: 1. The main clusters were identified in Zhangdian District from 12 April 2010 to 18 September 2012. Spatial autocorrelation analysis showed that the global spatial correlation in Zibo were no statistical significance, except in 2012, 2014, 2015, 2016 and 2018. Cold spots were gathered in Boshan county and Linzi district, while hot spots only in Zhangdian District in 2018, but other years were no significance. Conclusion Hot spots mainly concentrated in the central and surrounding city of Zibo city. We suggest that imminent public health planning and resource allocation should be focused within those areas.

scholarly journals Autokorelasi Spasial Demam Berdarah Dengue di Kecamatan Samarinda Utara, Kota Samarinda

2020 ◽  
Vol 19 (2) ◽  
pp. 119-126
Author(s):  
Syamsir Syamsir ◽  
Andi Daramusseng ◽  
Rudiman Rudiman
Keyword(s):  
Public Health ◽  
Spatial Autocorrelation ◽  
Health Center ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Cluster Sampling ◽  
Spatial Autocorrelation Analysis ◽  
Z Score ◽  
Autocorrelation Analysis ◽  
Moran's I

Latar belakang: Demam Berdarah Dengue (DBD) masih menjadi masalah kesehatan masyarakat. Indonesia menjadi salah satu negara yang setiap tahunnya ditemukan kasus DBD. Program pengendalian DBD masih kurang maksimal karena puskesmas belum mampu memetakan wilayah rentan DBD. Penelitian ini bertujuan untuk mengetahui pola sebaran DBD di Kecamatan Samarinda Utara dengan menggunakan autokorelasi spasial.Metode: Penelitian ini dilaksanakan di kelurahan yang berada pada wilayah kerja Puskesmas Lempake, Kecamatan Samarinda Utara. Sampel penelitian dipilih berdasarkan metode cluster sampling. Berdasarkan kriteria jumlah kasus tertinggi maka kelurahan di Kecamatan Samarinda Utara yang representatif untuk dijadikan cluster pada penelitian ini yaitu kelurahan yang berada pada wilayah kerja Puskesmas Lempake. Analisis yang digunakan pada penelitian ini yaitu Spatial Autocorrelation Analysis dengan menggunakan metode Moran’s I. Spatial Autocorrelation Analysis digunakan untuk mengetahui apakah terdapat hubungan antar titik dan arah hubungannya (postif atau negatif).Hasil: Nilai Z-score atau Z hitung = 3,651181 dengan nilai kritis (Z α/2) sebesar 2,58. Ini menunjukkan bahwa Z-score > Z α/2 (3,6511 > 2,58) sehingga Ho ditolak. Terdapat autokorelasi spasial pada sebaran kasus DBD di wilayah kerja Puskesmas Lempake. Sebaran kasus DBD di wilayah kerja Puskesmas Lempake termasuk kategori clustered atau berkelompok pada lokasi tertentu. Moran’s Index (I) = 0,124420 artinya I > 0. Ini menunjukkan bahwa pola sebaran DBD di wilayah kerja Puskesmaas Lempake merupakan autokorelasi positif.    Simpulan: Pola sebaran kasus DBD di Kecamatan Samarinda Utara yaitu clustered. Autokorelasi spasial yang dihasilkan yaitu autokorelasi positif.  ABSTRACTTitle: Spatial Autocorrelation of Dengue Hemorrhagic Fever  in North Samarinda district, Samarinda CityBackground: Dengue Hemorrhagic Fever (DHF) is still a public health problem. Indonesia is one of the countries where DHF cases are found every year. The DHF control program is still less than optimal because the public health center has not been able to map the DHF vulnerable areas. This study aims to determine the pattern of DHF distribution in the District of North Samarinda by using spatial autocorrelation.Method: This research was conducted in a village located in the working area of the Lempake Health Center, Samarinda Utara district. The research sample was chosen based on the cluster sampling method. Based on the criteria for the highest number of cases, the representative village to be clustered in this study are the village within the working area of the Lempake Health Center. The analysis used in this study is spatial autocorrelation nalysis using the Moran’s I. Spatial autocorrelation Analysis method is used to determine whether there is a relationship between the point and direction of the relationship (positive or negative).Result: Z-score or Z count = 3.651181 with a critical value (Z α / 2) of 2.58. This shows that Z-score> Z α / 2 (3.6511> 2.58) so that Ho is rejected. There is a spatial autocorrelation in the distribution of dengue cases in the working area of the Lempake Health Center. The distribution of dengue cases in the working area of Lempake Health Center is classified as clustered or grouped in certain locations. Moran’s Index (I) = 0.124420 means I> 0. This shows that the pattern of DHF distribution in the work area of Lempake Health Center is a positive autocorrelation.Conclusion: The pattern of distribution of dengue cases in the District of North Samarinda is clustered. The resulting spatial autocorrelation is positive autocorrelation. 

scholarly journals Spatial and Temporal Analysis of Lung Cancer in Shenzhen, 2008–2018

2020 ◽  
Vol 18 (1) ◽  
pp. 26
Author(s):  
Lin Lei ◽  
Anyan Huang ◽  
Weicong Cai ◽  
Ling Liang ◽  
Yirong Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Spatial Autocorrelation ◽  
Incidence Rate ◽  
Cancer Incidence ◽  
Southern China ◽  
Percentage Change ◽  
Spatial Autocorrelation Analysis ◽  
Lung Cancer Incidence ◽  
Annual Percentage Change ◽  
Autocorrelation Analysis

Lung cancer is the most commonly diagnosed cancer in China. The incidence trend and geographical distribution of lung cancer in southern China have not been reported. The present study explored the temporal trend and spatial distribution of lung cancer incidence in Shenzhen from 2008 to 2018. The lung cancer incidence data were obtained from the registered population in the Shenzhen Cancer Registry System between 2008 and 2018. The standardized incidence rates of lung cancer were analyzed by using the joinpoint regression model. The Moran’s I method was used for spatial autocorrelation analysis and to further draw a spatial cluster map in Shenzhen. From 2008 to 2018, the average crude incidence rate of lung cancer was 27.1 (1/100,000), with an annual percentage change of 2.7% (p < 0.05). The largest average proportion of histological type of lung cancer was determined as adenocarcinoma (69.1%), and an increasing trend was observed in females, with an average annual percentage change of 14.7%. The spatial autocorrelation analysis indicated some sites in Shenzhen as a high incidence rate spatial clustering area. Understanding the incidence patterns of lung cancer is useful for monitoring and prevention.

Spatial genetic substructure within natural populations of jack pine (Pinus banksiana)

1991 ◽  
Vol 69 (3) ◽  
pp. 547-551 ◽  
Author(s):  
Chang Yi Xie ◽  
Peggy Knowles
Keyword(s):  
Spatial Autocorrelation ◽  
Pinus Banksiana ◽  
Pollen Dispersal ◽  
Natural Populations ◽  
Jack Pine ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Long Distance ◽  
Genetic Substructure ◽  
Good Agreement

Spatial autocorrelation analysis was used to investigate the geographic distribution of allozyme genotypes within three natural populations of jack pine (Pinus banksiana Lamb.). Results indicate that genetic substructuring within these populations is very weak and the extent differs among populations. These results are in good agreement with those inferred from mating-system studies. Factors such as the species' predominantly outbreeding system, high mortality of selfs and inbreds prior to reproduction, long-distance pollen dispersal, and the absence of strong microhabitat selection may be responsible for the observed weak genetic substructuring. Key words: jack pine, Pinus banksiana, genetic substructure, allozyme, spatial autocorrelation analysis.

scholarly journals Epidemiological Characteristics and Spatiotemporal Analysis of Mumps from 2004 to 2018 in Chongqing, China

2019 ◽  
Vol 16 (17) ◽  
pp. 3052 ◽  
Author(s):  
Zhu ◽  
Zhao ◽  
Ou ◽  
Xiang ◽  
Hu ◽  
...  
Keyword(s):  
Public Health ◽  
Spatial Autocorrelation ◽  
Descriptive Analysis ◽  
Health Resources ◽  
Public Health Problem ◽  
Risk Groups ◽  
Scan Statistics ◽  
Time Space ◽  
High Incidence ◽  
Epidemiological Characteristics

Mumps vaccines have been widely used in recent years, but frequent mumps outbreaks and re-emergence around the world have not stopped. Mumps still remains a serious public health problem with a high incidence in China. The status of mumps epidemics in Chongqing, the largest city in China, is still unclear. This study aimed to investigate the epidemiological and spatiotemporal characteristics of mumps and to provide a scientific basis for formulating effective strategies for its prevention and control. Surveillance data of mumps in Chongqing from January 2004 to December 2018 were collected from the National Notifiable Diseases Reporting Information System. A descriptive analysis was conducted to understand the epidemiological characteristics. Hot spots and spatiotemporal patterns were identified by performing a spatial autocorrelation analysis, a purely spatial scan, and a spatiotemporal scan at the county level based on geographic information systems. A total of 895,429 mumps cases were reported in Chongqing, with an annual average incidence of 36.34 per 100,000. The yearly incidence of mumps decreased markedly from 2004 to 2007, increased sharply from 2007 to 2011, and then tapered with a two-year cyclical peak after 2011. The onset of mumps showed an obvious bimodal seasonal distribution, with a higher peak of mumps observed from April to July of each year. Children aged 5–9 years old, males, and students were the prime high-risk groups. The spatial distribution of mumps did not exhibit significant global autocorrelation in most years, but local indicators of spatial autocorrelation and scan statistics detected high-incidence clusters which were mainly located in the midwestern, western, northeastern, and southwestern parts of Chongqing. The aggregation time frame detected by the purely temporal scan was between March 2009 and July 2013. The incidence of mumps in Chongqing from 2004 to 2018 featured significant spatial heterogeneity and spatiotemporal clustering. The findings of this study might assist public health agencies to develop real-time space monitoring, especially in the clustering regions and at peak periods; to improve immunization strategies for long-term prevention; and to deploy health resources reasonably.

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