scholarly journals A Spatial Analysis Framework to Monitor and Accelerate Progress towards SDG 3 to End TB in Bangladesh

2018 ◽  
Vol 8 (1) ◽  
pp. 14 ◽  
Author(s):  
Ente Rood ◽  
Ahmadul Khan ◽  
Pronab Modak ◽  
Christina Mergenthaler ◽  
Margo van Gurp ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Analytical Framework ◽  
Global Burden ◽  
Case Detection ◽  
Analysis Framework ◽  
Spatial Clusters ◽  
Context Specific ◽  
Simultaneous Autoregressive ◽  
Case Notification

Global efforts to end the tuberculosis (TB) epidemic by 2030 (SDG3.3) through improved TB case detection and treatment have not been effective to significantly reduce the global burden of the TB epidemic. This study presents an analytical framework to evaluate the use of TB case notification rates (CNR) to monitor and to evaluate TB under-detection and under-diagnoses in Bangladesh. Local indicators of spatial autocorrelation (LISA) were calculated to assess the presence and scale of spatial clusters of TB CNR across 489 upazilas in Bangladesh. Simultaneous autoregressive models were fit to the data to identify associations between TB CNR and poverty, TB testing rates and retreatment rates. CNRs were found to be significantly spatially clustered, negatively correlated to poverty rates and positively associated to TB testing and retreatment rates. Comparing the observed pattern of CNR with model-standardized rates made it possible to identify areas where TB under-detection is likely to occur. These results suggest that TB CNR is an unreliable proxy for TB incidence. Spatial variations in TB case notifications and subnational variations in TB case detection should be considered when monitoring national TB trends. These results provide useful information to target and prioritize context specific interventions.

scholarly journals A Novel Efficiency Measure Model for Industrial Land Use Based on Subvector Data Envelope Analysis and Spatial Analysis Method

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Chen ◽  
Rui He ◽  
Qun Wu
Keyword(s):  
Land Use ◽  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Industrial Development ◽  
Efficiency Measure ◽  
Industrial Land ◽  
Envelope Analysis ◽  
Land Use Efficiency ◽  
Industrial Land Use ◽  
Use Efficiency

With the rapid and unbalanced development of industry, a large amount of cultivated land is converted into industrial land with lower efficiency. The existing research is extensively concerned with industrial land use and industrial development in isolation, but little attention has been paid to the relationship between them. To help address this gap, the paper creates a new efficiency measure method for industrial land use combining Subvector Data Envelope Analysis (DEA) with spatial analysis approach. The proposed model has been verified by using the industrial land use data of 30 Chinese provinces from 2001 to 2013. The spatial autocorrelation relationship between industrial development and industrial land use efficiency is explored. Furthermore, this paper examines the effects of industrial development on industrial land use efficiency by spatial panel data model. The results indicate that the industrial land use efficiency and the industrial development level in the provinces of eastern region are higher than those of the western region. The spatial distribution of industrial land use efficiency shows remarkable positive spatial autocorrelation. However, the level of industrial development has obvious negative spatial autocorrelation since 2009. The improvement of industrial development has a significant positive impact on the industrial land use efficiency.

scholarly journals A Randomized Control Trial to Test Effect of Cash Incentives and Training on Active Casefinding for Tuberculosis among Community Health Workers in Nigeria

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1154
Author(s):  
Christie Akwaowo ◽  
Victor Umoh ◽  
Idongesit Umoh ◽  
Eno Usoroh ◽  
Olugbemi Motilewa ◽  
...  
Keyword(s):  
Community Health ◽  
Community Health Workers ◽  
Health Workers ◽  
Case Finding ◽  
P Value ◽  
Case Detection ◽  
High Burden ◽  
Cash Incentives ◽  
Case Notification ◽  
And Training

Background: Case detection for Tuberculosis remains low in high burden communities. Community Health Workers (CHWs) are the first point of contact for many Nigerians in the rural areas and have been found useful in active case finding. This study assessed the effect of cash incentives and training on tuberculosis case detection by CHWs in six Local Government Areas in Nigeria. Materials and Methods: A randomised control trial was conducted in three PHC clusters. The intervention Arm (A) received cash incentives for every presumptive case referred. The Training Arm(B) had no cash incentives and the control had neither training nor cash incentives. Case notification rates from the TB program were used to assess the effect of cash incentives on TB case finding. Data was analyzed using Graph Pad Prism. Descriptive data was presented in tables and bivariate data was analyzed using chi square. Mean increases in case notification rates was calculated Statistical significance was set as P=0.05. Results: The intervention identified 394 presumptive TB cases, contributing 30.3% of all presumptive cases notified in the LGAs. Findings also showed an increase of 14.4% (ꭓ2=2.976, P value=0.2258) in case notification rates for the Arm A that received cash incentives alongside training, there was also an increase of 7.4% (ꭓ2= 1.999, P value=0.1575) in Arm B that received Training only. Secondary outcomes indicated a 144.8%(ꭓ2= 4.147, P value=0.1258)  increase in community outreaches conducted in the Arm that were given cash incentives. Conclusion: The study demonstrated an increase in TB control activities of case notification and outreaches among community health workers that received cash incentives and training.  These findings support the use training and cash incentives for CHWs in high burden TB settings to improve TB case detection rates.

Spatial Autocorrelation and Association Measures

2011 ◽  
pp. 1502-1508
Author(s):  
J. Negreiros ◽  
M. Painho ◽  
I. Lopes ◽  
A.C. Costa
Keyword(s):  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Regional Analysis ◽  
Geographical Information ◽  
Association Measures ◽  
Share Data ◽  
Definition Of ◽  
Graphic Database

Several classical statements relating to the definition of GIS can be found in specialized literature such as the GIS International Journal, expressing the idea that spatial analysis can somehow be useful. GIS is successful not only because it integrates data, but it also enables us to share data in different departments or segments of our organizations. I like this notion of putting the world’s pieces back together again (ArcNews, 2000). “GIS is simultaneously the telescope, the microscope, the computer and the Xerox machine of regional analysis and the synthesis of spatial data” (Abler, 1988). “GIS is a system of hardware, software and liveware implemented with the aim of storing, processing, visualizing and analyzing data of a spatial nature. Other definitions are also possible” (Painho, 1999). “GIS is a tool for revealing what is otherwise invisible in geographical information” (Longley, Goodchild, Maguire, & Rhind, 2001). Certainly, GIS is not a graphic database.

K2: A Novel Data Analysis Framework to Understand US Emotions in Space and Time

2019 ◽  
Vol 13 (01) ◽  
pp. 111-133
Author(s):  
Romita Banerjee ◽  
Karima Elgarroussi ◽  
Sujing Wang ◽  
Akhil Talari ◽  
Yongli Zhang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Negative Emotion ◽  
Time Interval ◽  
Temporal Data ◽  
Analysis Framework ◽  
Spatial Clusters ◽  
Change Analysis ◽  
Spatio Temporal ◽  
Temporal Data Analysis ◽  
Very High

Twitter is one of the most popular social media platforms used by millions of users daily to post their opinions and emotions. Consequently, Twitter tweets have become a valuable knowledge source for emotion analysis. In this paper, we present a new framework, K2, for tweet emotion mapping and emotion change analysis. It introduces a novel, generic spatio-temporal data analysis and storytelling framework that can be used to understand the emotional evolution of a specific section of population. The input for our framework is the location and time of where and when the tweets were posted and an emotion assessment score in the range [Formula: see text], with [Formula: see text] representing a very high positive emotion and [Formula: see text] representing a very high negative emotion. Our framework first segments the input dataset into a number of batches with each batch representing a specific time interval. This time interval can be a week, a month or a day. By generalizing existing kernel density estimation techniques in the next step, we transform each batch into a continuous function that takes positive and negative values. We have used contouring algorithms to find the contiguous regions with highly positive and highly negative emotions belonging to each member of the batch. Finally, we apply a generic, change analysis framework that monitors how positive and negative emotion regions evolve over time. In particular, using this framework, unary and binary change predicate are defined and matched against the identified spatial clusters, and change relationships will then be recorded, for those spatial clusters for which a match occurs. We also propose animation techniques to facilitate spatio-temporal data storytelling based on the obtained spatio-temporal data analysis results. We demo our approach using tweets collected in the state of New York in the month of June 2014.

scholarly journals Job morale of physicians in low-income and middle-income countries: a systematic literature review of qualitative studies

BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e028657 ◽  
Author(s):  
Alina Sabitova ◽  
Sana Zehra Sajun ◽  
Sandra Nicholson ◽  
Franziska Mosler ◽  
Stefan Priebe
Keyword(s):  
Low Income ◽  
Grey Literature ◽  
Well Being ◽  
Analytical Framework ◽  
Working Environment ◽  
Reference List ◽  
Middle Income ◽  
Staff Shortage ◽  
Middle Income Countries ◽  
Context Specific

ObjectivesTo systematically review the available literature on physicians’ and dentists’ experiences influencing job motivation, job satisfaction, burnout, well-being and symptoms of depression as indicators of job morale in low-income and middle-income countries.DesignThe review was reported following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for studies evaluating outcomes of interest using qualitative methods. The framework method was used to analyse and integrate review findings.Data sourcesA primary search of electronic databases was performed by using a combination of search terms related to the following areas of interest: ‘morale’, ‘physicians and dentists’ and ‘low-income and middle-income countries’. A secondary search of the grey literature was conducted in addition to checking the reference list of included studies and review papers.ResultsTen papers representing 10 different studies and involving 581 participants across seven low-income and middle-income countries met the inclusion criteria for the review. However, none of the studies focused on dentists’ experiences was included. An analytical framework including four main categories was developed: work environment (physical and social), rewards (financial, non-financial and social respect), work content (workload, nature of work, job security/stability and safety), managerial context (staffing levels, protocols and guidelines consistency and political interference). The job morale of physicians working in low-income and middle-income countries was mainly influenced by negative experiences. Increasing salaries, offering opportunities for career and professional development, improving the physical and social working environment, implementing clear professional guidelines and protocols and tackling healthcare staff shortage may influence physicians’ job morale positively.ConclusionsThere were a limited number of studies and a great degree of heterogeneity of evidence. Further research is recommended to assist in scrutinising context-specific issues and ways of addressing them to maximise their utility.PROSPERO registration numberCRD42017082579.

scholarly journals The Application Of Local Indicators For Categorical Data (LICD) In The Spatial Analysis Of Economic Development

2014 ◽  
Vol 17 (4) ◽  
pp. 203-220 ◽  
Author(s):  
Michał Pietrzak ◽  
Justyna Wilk ◽  
Roger S. Bivand ◽  
Tomasz Kossowski
Keyword(s):  
Economic Development ◽  
Spatial Analysis ◽  
Categorical Data ◽  
Spatial Clustering ◽  
Spatial Clusters ◽  
Global Test ◽  
Spatial Dependencies ◽  
Taxonomic Methods ◽  
Development Levels ◽  
Growth Centres

The paper makes an attempt to apply local indicators for categorical data (LICD) in the spatial analysis of economic development. The first part discusses the tests which examine spatial autocorrelation for categorical data. The second part presents a two-stage empirical study covering 66 Polish NUTS 3 regions. Firstly, we identify classes of regions presenting different economic development levels using taxonomic methods of multivariate data analysis. Secondly, we apply a join-count test to examine spatial dependencies between regions. It examines the tendency to form the spatial clusters. The global test indicates general spatial interactions between regions, while local tests give detailed results separately for each region. The global test detects spatial clustering of economically poor regions but is statistically insignificant as regards well-developed regions. Thus, the local tests are also applied. They indicate the occurrence of five spatial clusters and three outliers in Poland. There are three clusters of wealth. Their development is based on a diffusion impact of regional economic centres. The areas of eastern and north western Poland include clusters of poverty. The first one is impeded by the presense of three indiviual growth centres, while the second one is out of range of diffusion influence of bigger agglomerations.

scholarly journals Autokorelasi Spasial untuk Identifikasi Pola Hubungan Kemiskinan di Jawa Timur

2012 ◽  
Vol 3 (1) ◽  
pp. 217
Author(s):  
Rokhana Dwi Bekti
Keyword(s):  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Moran’S I ◽  
Poor People ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Moran's I ◽  
Relationship Of ◽  
The Relationship

Spatial autocorrelation is a spatial analysis to determine the relationship pattern or correlation among some locations (observation). On the poverty case of East Java, this method will provide important information for analyze the relationship of poverty characteristics in each district or cities. Therefore, in this research performed spatial autocorrelation analysis on the data of East Java’s poverty. The method used is moran's I test and Local Indicator of Spatial Autocorrelation (LISA). The analysis showed that by the moran's I test, there is spatial autocorrelation found in the percentage of poor people amount in East Java, both in 2006 and 2007. While by LISA, obtained the conclusion that there is a significant grouping of district or cities.

scholarly journals Prevalence and spatial distribution characteristics of human echinococcosis in China

2021 ◽  
Vol 15 (12) ◽  
pp. e0009996
Author(s):  
Li-Ying Wang ◽  
Min Qin ◽  
Ze-Hang Liu ◽  
Wei-Ping Wu ◽  
Ning Xiao ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Autocorrelation ◽  
Prevention And Control ◽  
Disease Prevalence ◽  
Tibet Plateau ◽  
Spatial Clusters ◽  
Autonomous Region ◽  
And Control ◽  
Qinghai Tibet Plateau ◽  
Human Echinococcosis

Background Echinococcosis is a zoonotic parasitic disease caused by larval stages of cestodes belonging to the genus Echinococcus. The infection affects people’s health and safety as well as agropastoral sector. In China, human echinococcosis is a major public health burden, especially in western China. Echinococcosis affects people health as well as agricultural and pastoral economy. Therefore, it is important to understand the prevalence status and spatial distribution of human echinococcosis in order to advance our knowledge of basic information for prevention and control measures reinforcement. Methods Report data on echinococcosis were collected in 370 counties in China in 2018 and were used to assess prevalence and spatial distribution. SPSS 21.0 was used to obtain the prevalence rate for CE and AE. For statistical analyses and mapping, all data were processed using SPSS 21.0 and ArcGIS 10.4, respectively. Chi-square test and Exact probability method were used to assess spatial autocorrelation and spatial clustering. Results A total of 47,278 cases of echinococcosis were recorded in 2018 in 370 endemic counties in China. The prevalence rate of human echinococcosis was 10.57 per 10,000. Analysis of the disease prevalence showed obvious spatial positive autocorrelation in globle spatial autocorrelation with two aggregation modes in local spatial autocorrelation, namely high-high and low-high aggregation areas. The high-high gathering areas were mainly concentrated in northern Tibet, western Qinghai, and Ganzi in the Tibetan Autonomous Region and in Sichuan. The low-high clusters were concentrated in Gamba, Kangma and Yadong counties of Tibet. In addition, spatial scanning analysis revealed two spatial clusters. One type of spatial clusters included 71 counties in Tibet Autonomous Region, 22 counties in Qinghai, 11 counties in Sichuan, three counties in Xinjiang Uygur Autonomous Region, two counties in Yunnan, and one county in Gansu. In the second category, six types of spatial clusters were observed in the counties of Xinjiang Uygur Autonomous Region, and the Qinghai, Gansu, and Sichuan Provinces. Conclusion This study showed a serious prevalence of human echinococcosis with obvious spatial aggregation of the disease prevalence in China. The Qinghai-Tibet Plateau is the "hot spot" area of human echinococcosis in China. Findings from this study indicate that there is an urgent need of joint strategies to strengthen efforts for the prevention and control of echinococcosis in China, especially in the Qinghai-Tibet Plateau.

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