Identification of Mean Years of Schooling as a Control for RPJMD: A Spatial Autocorrelation Approach

This article will identify the mean years of schooling in East Java as a control for achieving RPJMD. Inequality in the development of education leads to inequalities between the regions of East Java. This is due to the different regional characteristics, it is, therefore, necessary to respond to it by carrying out a regional mapping based on the education indicators listed in the RPJMD of each region using a statistical analysis approach, namely spatial autocorrelation. The variable that becomes the indicator in this study is the Mean Years of Schooling (MYS), the unit of observation being the regencies/cities of East Java. The results of the research that has been conducted can be concluded that the mean years of schooling for the population of East Java Province is seven years where urban areas have a better average length of schooling than in districts, and there are only nine areas in East Java that have MYS exceeding the RPJMD target. In the Global Moran's I test, there is a positive autocorrelation or cluster pattern that exhibits similar characteristics in adjacent locations, and the results of the local Morans’ show that there are nine regions that have spatial relationships with their most significant areas relatives based on the MYS indicator. These areas are Bondowoso Regency, Bangkalan Regency, Pamekasan Regency, Gresik Regency, Jember Regency, Probolinggo Regency, Sampang Regency, Sidoarjo Regency and Surabaya City

1442. Spatiotemporal Clusters of Varicella and the Regional Risks through Bayesian Approach: A National Five-year Cohort Analysis

Background Since varicella epidemics repeatedly occurred in Korea, it is essential to control varicella outbreaks preemptively in the targeted region. Therefore, we aimed to reveal spatiotemporal clusters of varicella and the regional risk factor of varicella incidence at the national level. Methods All varicella cases (defined as ICD-10 codes, B01-B09) from 2013 to 2017 in Korea were extracted from National Health Insurance Service. Of the total, 566,978 cases were realigned spatially by 250 administrative districts of Korea and temporally by a week. Spatial autocorrelation was tested using the global Moran’s I statistics using Monte Carlo simulation. Kulldorff’s prospective space-time scan statistics were used to reveal space-time clusters of varicella. Possible risk factors were extracted from the Korean Statistical Information Service and Community Health Survey of Korea, including hand hygiene perceptions, alcohol and smoking status, the proportion of children under 15 years old, the number of households, and household income by regions. After selecting significant risk factors through non-spatial generalized linear models, a conditional autoregressive spatiotempoal model with Bayesian extension was applied to estimate the regional factors affecting varicella incidence. Results There was spatial autocorrelation using Global Moran’s I statistics (P< 0.01). When the maximum cluster size was limited to 10% of the population, 17 spatiotemporal clusters were detected in specific regions in Korea (figure 1). Low perception of hand hygiene, the high proportion of alcohol drinking and cigarette smoking, high children proportion, low number of familial member, and low household income were associated with varicella spatiotemporal incidence (odds ratio: 0.97, 1.01, 2.31, 1.10, 0.99, 0.99, respectively; 95% credible intervals of all risk factors did not include 1). Figure 1. Space-time prospective clusters of varicella in Korea using varicella incidence from 2013 to 2017. Relative risks ratio of each cluster is described at the point. Conclusion Varicella incidence shows spatiotemporal clustering patterns in specific regions. Since regional factors such as the perception rate of hand hygiene, child proportion, alcohol drinking, cigarette smoking, and low household income affect varicella’s spatiotemporal incidence, strategies for targeted control of high-risk regions are strongly recommended. Disclosures All Authors: No reported disclosures

Spatial-temporal Patterns and Risk Factors for Human Leptospirosis in Thailand, 2012-2018

Leptospirosis is a globally important zoonotic disease. The disease is particularly important in tropical and subtropical countries. Infections in humans can be caused by exposure to infected animals or contaminated soil or water, which are suitable for Leptospira. To explore the cluster area, the Global Moran’s I index was calculated for incidences per 100,000 population at the province level during 2012–2018, using the monthly and annual data. The high-risk and low-risk provinces were identified using the local indicators of spatial association (LISA). The risk factors for leptospirosis were evaluated using a generalized linear mixed model (GLMM) with zero-inflation. We also added spatial and temporal correlation terms to take into account the spatial and temporal structures. The Global Moran’s I index showed significant positive values. It did not demonstrate a random distribution throughout the period of study. The high-risk provinces were almost all in the lower north-east and south parts of Thailand. For yearly reported cases, the significant risk factors from the final best-fitted model were population density, elevation, and primary rice arable areas. Interestingly, our study showed that leptospirosis cases were associated with large areas of rice production but were less prevalent in areas of high rice productivity. For monthly reported cases, the model using temperature range was found to be a better fit than using percentage of flooded area. The significant risk factors from the model using temperature range were temporal correlation, average soil moisture, normalized difference vegetation index, and temperature range. Temperature range, which has strongly negative correlation to percentage of flooded area was a significant risk factor for monthly data. Flood exposure controls should be used to reduce the risk of leptospirosis infection. These results could be used to develop a leptospirosis warning system to support public health organizations in Thailand.

Spatial Heterogeneity Association of HIV Incidence with Socio-economic Factors in Zimbabwe

This study examined the spatial heterogeneity association of HIV incidence and socio-economic factors including poverty severity index,permanently employed females and males, unemployed females, percentage of poor households i.e., poverty prevalence, night lights index, literacy rate,household food security, and Gini index at district level in Zimbabwe.A mix of spatial analysis methods including Poisson model based on original log likelihood ratios (LLR), global Moran’s I, local indicator of spatial association - LISA were employed to determine the HIV hotspots.Geographically Weighted Poisson Regression (GWPR) and semi-parametric GWPR (s-GWPR) were used to determine the spatial association between HIV incidence and socio-economic factors. HIV incidence (number of cases per 1000) ranged from 0.6 (Buhera district) to 13.30 (Mangwe district). Spatial clustering of HIV incidence was observed (Global Moran’s I = - 0.150; Z score 3.038; p-value 0.002). Significant clusters of HIV were observed at district level. HIV incidence and its association with socioeconomic factors varied across the districts except percentage of females unemployed. Intervention programmes to reduce HIV incidence should address the identified socio-economic factors at district level.

Spatial distribution of traditional male circumcision and associated factors in Ethiopia; using multilevel generalized linear mixed effects model

Background Traditional male circumcision (TMC) is primarily associated with a religious or cultural purpose and may lead to complications. To reduce risks of complication and long-term disabilities that may happen from circumcisions that are undertaken in non-clinical settings, information concerning TMC is very important. Therefore, this study is aimed at identifying spatial distribution of TMC and the factors associated with TMC in Ethiopia. Methods A secondary data analysis was conducted among 11,209 circumcised males using data from 2016 Ethiopian Demographic and Health Survey (EDHS). Global Moran’s I statistic was observed to check whether there was a significant clustering of TMC. Primary and secondary clusters of TMC were identified by fitting Bernoulli model in Kulldorff’s SaTScan software. Multilevel Generalized Linear Mixed effects Model (GLMM) was fitted to identify factors associated with TMC. Result The spatial distribution of TMC was nonrandom across the country with Global Moran’s I = 0.27 (p-value < 0.0001). The primary clusters of TMC were identified in the southern part of Oromia and Tigray, northern part of SNNPR, Amhara, Gambella and Benishangul regions. Current age, age at circumcision, ethnicity, religion, place of residence, wealth index, media exposure, sex of household head and age of household head were factors associated with TMC in Ethiopia. Conclusions The spatial distribution of TMC was varied across the country. This variation might be due to the diversity of culture, ethnicity and religion across the regions. Thus, there is a need to rearrange the regulations on standards of TMC practice, conduct training to familiarize operation technique and general hygiene procedures, and launch cross-referral systems between traditional circumcisers and health workers. While undertaking these public health interventions, due attention should be given to the identified clusters and significant factors.

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

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.

Examining the spread of Aquatic Invasive Species, Bythothephes longimanus, In Inland Lakes Across Ontario

The purpose of this study is to is to examine the secondary spread of Bythothephes longimanus, commonly known as spiny water flea, across inland lakes in Ontario, and potentially determine predictors for the its invasion. Data for 190 inland lakes across 84 quaternary watersheds in Ontario were included in the database. Global Moran's I was used to analyze the spatial autocorrelation of the variables, and McFadden's Rho-Squared was used to determine if a variable was a predictor of invasion. Three independent variables, out of 28, were found to be good predictors of invasion: (1) mean temperature of watersheds during summer (MNTMPWSSU), (2) mean precipitation for watersheds during spring (MNPCPWSSP), and (3) mean precipitation for watersheds during summer (MNPCPWSSU). Of the three, mean precipitation for watersheds during summer was determined to be the best predictor.

Examining the spread of Aquatic Invasive Species, Bythothephes longimanus, In Inland Lakes Across Ontario

The purpose of this study is to is to examine the secondary spread of Bythothephes longimanus, commonly known as spiny water flea, across inland lakes in Ontario, and potentially determine predictors for the its invasion. Data for 190 inland lakes across 84 quaternary watersheds in Ontario were included in the database. Global Moran's I was used to analyze the spatial autocorrelation of the variables, and McFadden's Rho-Squared was used to determine if a variable was a predictor of invasion. Three independent variables, out of 28, were found to be good predictors of invasion: (1) mean temperature of watersheds during summer (MNTMPWSSU), (2) mean precipitation for watersheds during spring (MNPCPWSSP), and (3) mean precipitation for watersheds during summer (MNPCPWSSU). Of the three, mean precipitation for watersheds during summer was determined to be the best predictor.

Spatial Unevenness of Formation, Remediation and Persistence of Post-Agricultural Brownfields

The fall of the Iron Curtain created a vacuum upon which large-scale collectivized agriculture was largely abandoned. Post-agricultural brownfields emerge in multiple manners across national, regional and local levels. While these sites remain rarely explored, we aimed to better understand the spatial consequences of the formation, persistence and reuse of these sites. The regions of South Bohemia and South Moravia in the Czech Republic are used to show the location of post-agricultural brownfields identified in 2004 through 2018. Using Global Moran’s I test we have found that post-agricultural brownfields existing in 2004, long-term brownfields in 2018 and brownfields established between 2004 and 2018 are spatially clustered, but remediated brownfields between 2004 and 2018 are not. Next, the Anselin’s Local Moran’s I test identified where the spatial clusters exist. The clusters identified were examined for differences in their social, economic and environmental development by the means of logistic regression. The results show that the brownfields initially identified in 2004 are concentrated in regions with lower quality agricultural land while simultaneously located in the hinterlands of regional urban centers. In contrast, peripheral regions most often contained long-term brownfields. Brownfield sites identified after 2004 occurred in regions with higher agricultural quality of land and where corn usually grows.

The external impacts of historic landmarks and buildings on townhouse prices in Vietnam

Purpose This paper aims to explore the external spillover effects of landmarks and buildings with historic preservation designation in Vietnam, a country marked with a unique property right regime and market transparency. The study contributes to the existing debate over the impact of distance to historic preservation sites and landmarks and property prices. Design/methodology/approach The study examines property data of 274 attached townhouses in Ho Chi Minh City, Vietnam and estimates the spillover effects of historic preservation on property prices collected during 2018–2019. The authors test for spatial autocorrelation by using the Global Moran’s I and Lagrange Multiplier diagnostics and deploy different spatial regression models including SAR, SEM and SDM. Findings The authors find that there is a premium on the prices of townhouses near formally designated landmarks and buildings. This premium decreases monotonically away from the historic sites. However, this paper also demonstrates that there is a non-linear (U-shape) relationship between housing premium and the distance to the nearest historic building. Originality/value This study is the first to take advantage of the surveyed property data to study the external impacts of historic preservation designation on housing prices in Vietnam. The study also contributes to the ongoing scholarly debate over the direction of the impacts. The study suggests that similar to other amenities, the price effect of designation tends to fade away after a certain distance.