scholarly journals 1001Spatio-temporal epidemiology of mumps and related region factors in Korea

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Kwan Hong ◽  
Hari Hwang ◽  
Byung Chul Chun
Keyword(s):  
Geographical Distribution ◽  
Cumulative Incidence ◽  
Autoregressive Model ◽  
Moran’S I ◽  
P Value ◽  
Moran's I ◽  
Hospital Resources ◽  
Simultaneous Autoregressive Model ◽  
Epidemiological Characteristics ◽  
Simultaneous Autoregressive

Abstract Background Mumps is in Korea's national immunization program, though there are still epidemics, especially in young age. The study's objectives are to establish the epidemiological characteristics of mumps and suggest the predicting factors. Methods We extracted cases from national health insurance data, between 2013 and 2017. Age-specific incidence rate and geographical distribution were evaluated. We tested for spatial autocorrelation by Moran’s I statistics with Delaunary triangular links. Simultaneous autoregressive model for cumulative incidence of mumps using triangular links was used to predict cumulative incidence with region specific factors. Results A total of 219,149 (85.12 per 100,000) were diagnosed and 23,805 (9.25 per 100,000) were hospitalized. Weekly cumulative incidence showed two epidemics every year, between weeks 20-25 and 40-45. Cumulative incidence of ages 10-19 was the highest, 332.21 per 100,000 people, followed by 300.75 per 100,000 people in ages 0-9. Geographical distribution showed clusters of epidemics, and Moran’s I statistics was 0.304 with a p-value <0.01. The Simultaneous autoregressive model estimated the mean age and hospital resources of each region as prediction factors for geographical distribution of mumps. Conclusions Mumps is common in children and peaks in summer and winter. Additionally, there are geographical clusters in epidemics, and the effect of region factors such as mean age and hospital resources are suspected. Key messages Two peaks in age and season appear in mumps in Korea. Clusters of geographical distribution indicate that region factors may affect the incidence.

scholarly journals Spatial Logistic Regression Modeling With Inverse Weighting Distance For Open Unemployment In Districts/Cities On The Island Of Sulawesi

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Broklyn Pippo Marchegiani Baebae ◽  
Nur’eni Nur’eni ◽  
Iman Setiawan
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Moran’S I ◽  
P Value ◽  
Moran's I ◽  
Weighting Matrix ◽  
Inverse Weighting ◽  
Spatial Influence ◽  
Logistic Regression Method

Unemployment is a condition where a person does not have a job, but is looking for a job. To see the unemployment situation in an area, logistic regression analysis can be used. Logistic regression is an analysis used to see the relationship between the response variable (Y) which is binary and the explanatory variable (X) which is categorical or continuous. The application of logistic regression often has a spatial influence on the model. In this study to model the open unemployment rate the spatial logistic regression method is used. Spatial logistic regression is logistic regression analysis by incorporating spatial influences into the model. Spatial dependency testing is used by Moran’s I Test. The weighting matrix used is the distance inverse weighting matrix. The results obtained, the value of Moran's I Test with a p-value of 2.14 x 10-12 <α (0.05), meaning that there is a spatial influence on the level of open unemployment on the island of Sulawesi. So the spatial logistic regression model is obtained as follows : g(x)    = 4,848 0,000002885(X1) 0,0473(X2) 0,006669(X3) 0,04263(X4) 0,269(X5) 0,1642(X6) 1,531(X7) 0,1581(X8) 0,2208(X9) 0,009732(X10) 0,01871(Z) Spatial factors affect the level of open unemployment based on the significance value <α (0.05)

scholarly journals Spatial pattern of perinatal mortality and its determinants in Ethiopia: Data from Ethiopian Demographic and Health Survey 2016

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242499
Author(s):  
Tesfaye Assebe Yadeta ◽  
Bizatu Mengistu ◽  
Tesfaye Gobena ◽  
Lemma Demissie Regassa
Keyword(s):  
Perinatal Mortality ◽  
Health Survey ◽  
Birth Interval ◽  
Healthcare Services ◽  
Demographic And Health Survey ◽  
Sub Saharan Africa ◽  
Moran’S I ◽  
P Value ◽  
Survey Period ◽  
Moran's I

Background The perinatal mortality rate in Ethiopia is among the highest in Sub Saharan Africa. The aim of this study was to identify the spatial patterns and determinants of perinatal mortality in the country using a national representative 2016 Ethiopia Demographic and Health Survey (EDHS) data. Methods The analysis was completed utilizing data from 2016 Ethiopian Demographic and Health Survey. This data captured the information of 5 years preceding the survey period. A total of 7230 women who at delivered at seven or more months gestational age nested within 622 enumeration areas (EAs) were used. Statistical analysis was performed by using STATA version 14.1, by considering the hierarchical nature of the data. Multilevel logistic regression models were fitted to identify community and individual-level factors associated with perinatal mortality. ArcGIS version 10.1 was used for spatial analysis. Moran’s, I statistics fitted to identify global autocorrelation and local autocorrelation was identified using SatSCan version 9.6. Results The spatial distribution of perinatal mortality in Ethiopia revealed a clustering pattern. The global Moran’s I value was 0.047 with p-value <0.001. Perinatal mortality was positively associated with the maternal age, being from rural residence, history of terminating a pregnancy, and place of delivery, while negatively associated with partners’ educational level, higher wealth index, longer birth interval, female being head of household and the number of antenatal care (ANC) follow up. Conclusions In Ethiopia, the perinatal mortality is high and had spatial variations across the country. Strengthening partner’s education, family planning for longer birth interval, ANC, and delivery services are essential to reduce perinatal mortality and achieve sustainable development goals in Ethiopia. Disparities in perinatal mortality rates should be addressed alongside efforts to address inequities in maternal and neonatal healthcare services all over the country.

scholarly journals Clustering Analysis Influenza Disease to Identify Spatio-Temporal Spread Pattern in Thailand

2021 ◽  
pp. 81-89
Keyword(s):  
Population Density ◽  
Clustering Analysis ◽  
Statistical Significance ◽  
Winter Season ◽  
Moran’S I ◽  
P Value ◽  
Moran's I ◽  
Spread Pattern ◽  
Spatio Temporal ◽  
Influenza Outbreaks

The pandemics of influenza in Nonthaburi province was investigated by using autoregression and found the influenza spread pattern by autocorrelation (Moran's I). Population density, temperature, relative humidity, and rainfall are the factors used in the analysis. The influenza quantitative cross-section retrospective research design was employed from 2003-2010. Three seasons are classified as: hot, rainy, and winter season. The study found that influenza outbreaks in the rainy season was R2=0.45 and population density apparently affected the spread of influenza incidence with statistical significance coefficient (p-value <0.05). From the distribution pattern, the highest Moran's I values were related with the highest population density in 4 sub-districts: Suenyai, Taladkhwun, Bangkhen, and Bangkruay sub-district.

scholarly journals Geographical clustering and socioeconomic factors associated with hypoglycemic events requiring emergency assistance in Andalusia (Spain)

2021 ◽  
Vol 9 (1) ◽  
pp. e001731
Author(s):  
Fernando Gomez-Peralta ◽  
Cristina Abreu ◽  
Manuel Benito ◽  
Rafael J Barranco
Keyword(s):  
Geographical Distribution ◽  
Spatial Data ◽  
Geographical Location ◽  
Population Characteristics ◽  
Moran’S I ◽  
Sports Facilities ◽  
Z Score ◽  
Moran's I ◽  
Societal Factors ◽  
Emergency Assistance

IntroductionThe geographical distribution of hypoglycemic events requiring emergency assistance was explored in Andalusia (Spain), and potentially associated societal factors were determined.Research design and methodsThis was a database analysis of hypoglycemia requiring prehospital emergency assistance from the Public Company for Health Emergencies (Empresa Pública de Emergencias Sanitarias (EPES)) in Andalusia during 2012, which served 8 393 159 people. Databases of the National Statistics Institute, Basic Spatial Data of Andalusia and System of Multiterritorial Information of Andalusia were used to retrieve spatial data and population characteristics. Geographic Information System software (QGIS and GeoDA) was used for analysis and linkage across databases. Spatial analyses of geographical location influence in hypoglycemic events were assessed using Moran’s I statistics, and linear regressions were used to determine their association with population characteristics.ResultsThe EPES attended 1 137 738 calls requesting medical assistance, with a mean hypoglycemia incidence of 95.0±61.6 cases per 100 000 inhabitants. There were significant differences in hypoglycemia incidence between basic healthcare zones attributable to their geographical location in the overall population (Moran’s I index 0.122, z-score 7.870, p=0.001), women (Moran’s I index 0.088, z-score 6.285, p=0.001), men (Moran’s I index 0.076, z-score 4.914, p=0.001) and aged >64 years (Moran’s I index 0.147, z-score 9.753, p=0.001). Hypoglycemia incidence was higher within unemployed individuals (β=0.003, p=0.001) and unemployed women (β=0.005, p=0.001), while lower within individuals aged <16 years (β=−0.004, p=0.040), higher academic level (secondary studies) (β=−0.003, p=0.004) and women with secondary studies (β=−0.005, p<0.001). In subjects aged >64 years, lower rate of hypoglycemia was associated with more single-person homes (β=−0.008, p=0.022) and sports facilities (β=−0.342, p=0.012).ConclusionsThis analysis supports the geographical distribution of hypoglycemia in the overall population, both genders and subjects aged >64 years, which was affected by societal factors such as unemployment, literacy/education, housing and sports facilities. These data can be useful to design specific prevention programs.

scholarly journals What do we know about the behavior of animal rabies in Chile through the last years?

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Raul Alegria-Moran ◽  
Daniela Miranda ◽  
Alonso Parra ◽  
Lisette Lapierre
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Association ◽  
Winter Season ◽  
Central Zone ◽  
Domestic Dog ◽  
Moran’S I ◽  
P Value ◽  
Moran's I ◽  
Epidemiological Characterization

ObjectiveThis study aims to analyze the evolution of the epidemiologicalbehavior of rabies in Chile during the period 2003 to 2013, throughthe epidemiological characterization of a number of variables anddescription of spatial and temporal patterns of animal cases.IntroductionRabies is a zoonotic disease caused by an RNA virus from thefamily Rhabdoviridae, genus Lyssavirus. Worldwide distributed,control of rabies has been considered to be particularly amenable toa “One Health” strategy (1). In Chile, rabies was considered endemicin domestic dog population until the late 1960s, when a surveillanceprogram was established, decreasing the number of human casesrelated to canine variants until the year 1972 (2). Rabies is recognizedas a endemic infection in chiropterans of Chile and prompted thesurveillance of the agent in this and other species (3).MethodsAn epidemiological characterization of the registered cases fromthe National Program for Prevention and Control of Rabies wascarried. During the period 2003-2013, 927 cases were reported.Descriptive statistics and descriptive mapping, recording origin of thesample, number of cases per region, animal reservoir implicated andviral variant were performed. A spatial autocorrelation analysis wascarried using Moran’s I indicator for the detection of spatial clusters(4), using the Local Indicators of Spatial Association (LISA) statistics(5), at national and regional level of aggrupation (north, central andsouth zone). Temporal descriptive analysis was carried.Results927 positive cases were recorded. 920 (99.2%) cases came frompassive surveillance, while 7 (0.8%) cases by active surveillance, totalpositivity was 77.02% and 1.37% respectively. Positivity was reportedmainly in the central zone (88.1%), mainly in Valparaiso (19.1%),Metropolitana (40.6%) (Figure 1), Maule (11.8%) regions concentratedin urban centers. Main positive reservoirs were bats (99.8%),specificallyTadarida brasiliensisand viral variant 4 was the mostcommonly diagnosed. LISA test gives a Moran’s I indicator of 0.1537(p-value = 0.02) for the central zone (Table 1). Rabies tend to decreasein fall and winter season (2.9 cases vs 13 cases during summer).ConclusionsWildlife rabies in bats remains endemic in Chile, concentrated inurban areas. The main reservoirs are insectivorous bats. There is asignificant spatial autocorrelation of animal rabies cases in the centralzone of Chile. Results are relevant to the design of preventive andcontrol measures.

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