scholarly journals Spatial heterogeneity in discontinuation of modern spacing method in districts of India

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Soumya Ranjan Nayak ◽  
Sanjay K. Mohanty ◽  
Bidhubhusan Mahapatra ◽  
Umakanta Sahoo
Keyword(s):  
Impact Analysis ◽  
Quality Care ◽  
Unmet Need ◽  
Moran’S I ◽  
P Value ◽  
Modern Contraceptive ◽  
Discontinuation Rate ◽  
Moran's I ◽  
Modern Contraceptive Method ◽  
Spacing Method

Abstract Background Despite six decades of official family planning programme, the use of modern contraceptive method remained low in India. The discontinuation of modern spacing method (DMSM) has also increased from 42.3% in 2005−06 to 43.6% during 2015–16. Discontinuation rate is higher for Injectable (51%), followed by condom (47%), pill (42%) and lowest in IUD (26%). Methods Data from NFHS-4 (2015–16) comprising of 601,509 households, 699,686 women and a sample of 119,548 episode of modern spacing method was used for the analysis. Multiple decrement life table has used to estimate 12-month discontinuation rate of modern spacing methods (DMSM). Moran’s I statistics, Bivariate LISA cluster map has used to understand the spatial correlates and clustering the DMSM. OLS model and impact analysis has used to assess the significant associated covariates with discontinuation. Result The 12-month DMSM in India is 43.5%; largely due to desire for becoming pregnant and method failure. The high discontinuation rate was observed in most of the southern (62%) and central (46%) regions of India. DMSM has significantly and spatially associated with neighbouring districts of India (Moran’s I = 0.47, p-value = 0.00). The prevalence of modern spacing method is negatively associated with discontinuation in the neighbouring districts of India. The unmet need (β = 0.84, 95% CI 0.55–1.14), desire of children (β = 0.26, 95% CI − 0.05–0.57) and female sterilization (β = 0.54, 95% CI 0.14–0.95) were three main contributing factor to DMSM. Conclusion Districts of high DMSM need programmatic intervention. More attention for counselling to client, health worker outreach to user and better quality care services will stimulate non-user of contraception.

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 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.

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 &lt;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.

Spatial Distribution and Predictors of Domestic Violence Among Women Aged 15-49 in Ethiopia: Analysis of EDHS 2016 Dataset.

2020 ◽  
Author(s):  
Elias Ali Seid ◽  
Tesfahun Melese ◽  
Kassahun Alemu
Keyword(s):  
Domestic Violence ◽  
Spatial Distribution ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Spatial Clustering ◽  
Public Health Problem ◽  
Wife Beating ◽  
Moran’S I ◽  
P Value ◽  
Moran's I

Abstract Introduction: Violence against women particularly that is commited by an intimate partner is becoming a social and public health problem across the world. Studies from different countries shows that the spatial variation in distribution of domestic violence was commonly attributed by neighborhood level predictors. Despite the importance of spatial techniques, studies that employ it in Ethiopia are limited. Therefore, the aim of this study is to determine the spatial distribution and predictors of domestic violence among women aged 15-49 in Ethiopia by using EDHS 2016 dataset. Methods: Secondary data from EDHS 2016 was used to determine the spatial distribution of domestic violence in Ethiopia. Spatial auto-correlation statistics (both Global and Local Moran’s I) was used to assess the spatial distribution of domestic violence cases in Ethiopia. Spatial locations of significant clusters were identified by using Kuldorff’s Sat Scan version 9.4 software. Finally, binary logistic regression and generalized linear mixed model were fitted to identify predictors of domestic violence. Result: The study found that spatial clustering of domestic violence cases in Ethiopia with Moran’s I value of 0.26, Z score of 8.26, and P-value < 0.01. The Sat Scan analysis find out 24 significant locations of domestic violence clusters. Among this, 10 are primary clusters with RR 2.18, LLR of 39.55, and P-value < 0.01. The output from regression analysis identifies low economic status, husband/partner alcohol use, witnessing family violence as a child, marital controlling behaviors, and community acceptance of wife-beating as significant predictors of domestic violence.Conclusion and Recommendation: There is spatial clustering of d domestic violence cases in Ethiopia. Areas with a high burden of the problem should get priority for intervention. Comprehensive and collaborative action should be taken by involving stakeholders at different levels. Specific activities may include Organizing media on awareness creation and continuous education on how to maintain a stable relationship between couples and employing long term and intensive effort for transforming culture and social norms that encourage violence against woman are among the major ones.

Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I

2017 ◽  
Vol 8 (4) ◽  
Author(s):  
Matheus Supriyanto Rumetna ◽  
Eko Sediyono ◽  
Kristoko Dwi Hartomo
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Spatial Autocorrelation ◽  
Land Use Changes ◽  
Geographical Information ◽  
Moran’S I ◽  
Complete Spatial Randomness ◽  
Moran's I ◽  
Global Moran’S I ◽  
Spatial Randomness

Abstract. Bantul Regency is a part of Yogyakarta Special Province Province which experienced land use changes. This research aims to assess the changes of shape and level of land use, to analyze the pattern of land use changes, and to find the appropriateness of RTRW land use in Bantul District in 2011-2015. Analytical methods are employed including Geoprocessing techniques and analysis of patterns of distribution of land use changes with Spatial Autocorrelation (Global Moran's I). The results of this study of land use in 2011, there are thirty one classifications, while in 2015 there are thirty four classifications. The pattern of distribution of land use change shows that land use change in 2011-2015 has a Complete Spatial Randomness pattern. Land use suitability with the direction of area function at RTRW is 24030,406 Ha (46,995406%) and incompatibility of 27103,115 Ha or equal to 53,004593% of the total area of Bantul Regency.Keywords: Geographical Information System, Land Use, Geoprocessing, Global Moran's I, Bantul Regency. Abstrak. Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I. Kabupaten Bantul merupakan bagian dari Provinsi Daerah Istimewa Yogyakarta yang mengalami perubahan tata guna lahan. Penelitian ini bertujuan untuk mengkaji perubahan bentuk dan luas penggunaan lahan, menganalisis pola sebaran perubahan tata guna lahan, serta kesesuaian tata guna lahan terhadap RTRW yang terjadi di Kabupaten Bantul pada tahun 2011-2015. Metode analisis yang digunakan antara lain teknik Geoprocessing serta analisis pola sebaran perubahan tata guna lahan dengan Spatial Autocorrelation (Global Moran’s I). Hasil dari penelitian ini adalah penggunaan tanah pada tahun 2011, terdapat tiga puluh satu klasifikasi, sedangkan pada tahun 2015 terdapat tiga puluh empat klasifikasi. Pola sebaran perubahan tata guna lahan menunjukkan bahwa perubahan tata guna lahan tahun 2011-2015 memiliki pola Complete Spatial Randomness. Kesesuaian tata guna lahan dengan arahan fungsi kawasan pada RTRW adalah seluas 24030,406 Ha atau mencapai 46,995406 % dan ketidaksesuaian seluas 27103,115 Ha atau sebesar 53,004593 % dari total luas wilayah Kabupaten Bantul. Kata Kunci: Sistem Informasi Georafis, tata guna lahan, Geoprocessing, Global Moran’s I, Kabupaten Bantul.

Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

2012 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Asra Hosseini
Keyword(s):  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Smart Cities ◽  
Moran’S I ◽  
Moran's I ◽  
Urban Planner ◽  
Universal Feature ◽  
Autocorrelation Method ◽  
Selection Of

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature of urban areas. This study explored issue of measuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% of neighbourhoods' area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined. 

Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

2012 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Asra Hosseini
Keyword(s):  
Spatial Autocorrelation ◽  
Smart Cities ◽  
Moran’S I ◽  
Moran's I ◽  
Urban Planner ◽  
Universal Feature ◽  
Autocorrelation Method ◽  
Selection Of ◽  
Monotonic Relation

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature ofurban areas. This study explored issue ofmeasuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% ofneighbourhoods, area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined.

scholarly journals Geographical variations and associated factors of defaulting from immunization among children aged 12 to 23 months in Ethiopia: using spatial and multilevel analysis of 2016 Ethiopian Demographic and Health Survey

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Mukemil Awol ◽  
Zewdie Aderaw Alemu ◽  
Nurilign Abebe Moges ◽  
Kemal Jemal
Keyword(s):  
Multilevel Analysis ◽  
Health Survey ◽  
Associated Factors ◽  
Demographic And Health Survey ◽  
Community Level ◽  
Scan Statistics ◽  
Moran’S I ◽  
Moran's I ◽  
Depth Analysis ◽  
Global Moran’S I

Abstract Background In Ethiopia, despite the considerable improvement in immunization coverage, the burden of defaulting from immunization among children is still high with marked variation among regions. However, the geographical variation and contextual factors of defaulting from immunization were poorly understood. Hence, this study aimed to identify the spatial pattern and associated factors of defaulting from immunization. Methods An in-depth analysis of the 2016 Ethiopian Demographic and Health Survey (EDHS 2016) data was used. A total of 1638 children nested in 552 enumeration areas (EAs) were included in the analysis. Global Moran’s I statistic and Bernoulli purely spatial scan statistics were employed to identify geographical patterns and detect spatial clusters of defaulting immunization, respectively. Multilevel logistic regression models were fitted to identify factors associated with defaulting immunization. A p value < 0.05 was used to identify significantly associated factors with defaulting of child immunization. Results A spatial heterogeneity of defaulting from immunization was observed (Global Moran’s I = 0.386379, p value < 0.001), and four significant SaTScan clusters of areas with high defaulting from immunization were detected. The most likely primary SaTScan cluster was seen in the Somali region, and secondary clusters were detected in (Afar, South Nation Nationality of people (SNNP), Oromiya, Amhara, and Gambella) regions. In the final model of the multilevel analysis, individual and community level factors accounted for 56.4% of the variance in the odds of defaulting immunization. Children from mothers who had no formal education (AOR = 4.23; 95% CI: 117, 15.78), and children living in Afar, Oromiya, Somali, SNNP, Gambella, and Harari regions had higher odds of having defaulted immunization from community level. Conclusions A clustered pattern of areas with high default of immunization was observed in Ethiopia. Both the individual and community-level characteristics were statistically significant factors of defaulting immunization. Therefore, the Federal Ethiopian Ministry of Health should prioritize the areas with defaulting of immunization and consider the identified factors for immunization interventions.

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