scholarly journals Specific Urban Elements Identified for Tuberculosis Epidemic in Guangzhou by Using Geographical Detector

2021 ◽  
Author(s):  
HongYan Ren ◽  
Weili Lu ◽  
Xueqiu Li ◽  
Hongcheng Shen
Keyword(s):  
Socioeconomic Factors ◽  
Spatial Scale ◽  
Environmental Variables ◽  
Moran’S I ◽  
Moran's I ◽  
Significance Level ◽  
Targeted Interventions ◽  
Pairwise Interactions ◽  
Geographical Detector ◽  
Q Values

Abstract Background: The prevalence of tuberculosis (TB) in China has heavily affected people’s health for decades, which has been widely investigated for the rural regions and west parts. However, its spatial features in urban areas remain little understood. Thus, this study aims to identify its spatial differentiations and their influencing factors in highly urbanized region on a fine scale.Methods: Together with the TB cases in 2017 obtained from Guangzhou Institute of Tuberculosis Control and Prevention, in total 18 socioeconomic and environmental variables were included in this study. Two spatial analysis tools were respectively applied to select the relative appropriate spatial scale (global Moran’s I), and to identify specific urban factors (the Geographical detector) for this epidemic in the central four districts of Guangzhou.Results: The 2 km × 2 km grid was determined as the most appropriate spatial scale due to its relatively higher spatial autocorrelation (Moran’s I=0.33, Z=4.71). At this spatial level, the TB epidemic in the four central districts was obviously closely associated with most of socioeconomic factors (0.31<r<0.76) at the significance level of 0.01. By contrast, among environmental factors, only the concentration of fine particulate matter (PM2.5) correlated with this epidemic (r=0.21) at the significance level of 0.05. Similarly, according to the q-values derived from geographical detector analysis, socioeconomic factors posed stronger impacts (0.08<q<0.57) on the spatial differentiations of TB prevalence than those of environmental variables (0.06<q<0.27), Furthermore, 153 pairs of variables presented more powerful explanatory abilities for this epidemic’s spatial disparities due to their notable enhancements of q-values (7.3%<sq<311.6%) caused by the pairwise interactions.Conclusion: The spatial heterogeneity of TB prevalence was remarkably influenced by a series of specific urban elements and their pairwise interactions across the central region of Guangzhou. We accordingly suggest that more attentions should be paid to the areas with pairwise interactions of these specific urban elements in this city. This study would provide meaningful clues for local authorities making more targeted interventions on this disease in China’s municipal areas featured by both high urbanization and severe tuberculosis.

scholarly journals Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Simon P. Kigozi ◽  
Ruth N. Kigozi ◽  
Catherine M. Sebuguzi ◽  
Jorge Cano ◽  
Damian Rutazaana ◽  
...  
Keyword(s):  
High Risk ◽  
Incidence Rate ◽  
Health Facility ◽  
Malaria Incidence ◽  
Temporal Patterns ◽  
Health Facilities ◽  
Moran’S I ◽  
Moran's I ◽  
Catchment Population ◽  
Targeted Interventions

Abstract Background As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data, together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda, over a recent 5-year period. Methods Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019, was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index. Results An estimated 38.8 million (95% Credible Interval [CI]: 37.9–40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9–21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7–9.4) to 36.6 (95% CI: 35.7–38.5) across the study period. Strong seasonality was observed, with June–July experiencing highest peaks and February–March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0–50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran’s I = 0.3 (p < 0.001) and districts Moran’s I = 0.4 (p < 0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central – Busoga regions. Conclusion Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.

scholarly journals Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019

2020 ◽  
Author(s):  
Simon Kigozi ◽  
Ruth N Kigozi ◽  
Catherine M Sebuguzi ◽  
Jorge Cano ◽  
Damian Rutazaana ◽  
...  
Keyword(s):  
High Risk ◽  
Incidence Rate ◽  
Health Facility ◽  
Malaria Incidence ◽  
Temporal Patterns ◽  
Health Facilities ◽  
Moran’S I ◽  
Moran's I ◽  
Catchment Population ◽  
Targeted Interventions

Abstract Background. As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data, together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda, over a recent 5-year period.Methods. Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019, was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index.Results. An estimated 38.8 million (95% Credible Interval [CI]: 37.9 – 40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9 - 21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7 – 9.4) to 36.6 (95% CI: 35.7 – 38.5) across the study period. Strong seasonality was observed, with June-July experiencing highest peaks and February-March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0 – 50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran’s I = 0.3 (p<0.001) and districts Moran’s I = 0.4 (p<0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central – Busoga regions.Conclusion. Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.

scholarly journals Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019

2020 ◽  
Author(s):  
Simon Kigozi ◽  
Ruth N Kigozi ◽  
Catherine M Sebuguzi ◽  
Jorge Cano ◽  
Damian Rutazaana ◽  
...  
Keyword(s):  
High Risk ◽  
Incidence Rate ◽  
Health Facility ◽  
Malaria Incidence ◽  
Temporal Patterns ◽  
Health Facilities ◽  
Moran’S I ◽  
Moran's I ◽  
Catchment Population ◽  
Targeted Interventions

Abstract Background. As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda over a recent 5-year period.Methods. Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019 was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index.Results. An estimated 38.8 million (95% Credible Interval [CI]: 37.9 – 40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9 - 21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7 – 9.4) to 36.6 (95% CI: 35.7 – 38.5) across the study period. Strong seasonality was observed, with June-July experiencing highest peaks and February-March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0 – 50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran’s I = 0.3 (p<0.001) and districts Moran’s I = 0.4 (p<0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central – Busoga regions.Conclusion. Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.

scholarly journals Spatial distribution and determinants of health loss from Kashin-Beck disease in Bin County, Shaanxi Province, China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jing Wang ◽  
Xiaoya Wang ◽  
Hairong Li ◽  
Linsheng Yang ◽  
Yingchun Li ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Life Quality ◽  
Poverty Rate ◽  
Shaanxi Province ◽  
Moran’S I ◽  
Moran's I ◽  
Targeted Interventions ◽  
Health Loss ◽  
Significant Difference ◽  
Beck Disease

Abstract Background Kashin-Beck disease (KBD) is one of the major endemic diseases in China, which severely impacts the physical health and life quality of people. A better understanding of the spatial distribution of the health loss from KBD and its influencing factors will help to identify areas and populations at high risk so as to plan for targeted interventions. Methods The data of patients with KBD at village-level were collected to estimate and analyze the spatial pattern of health loss from KBD in Bin County, Shaanxi Province. The years lived with disability (YLDs) index was applied as a measure of health loss from KBD. Spatial autocorrelation methodologies, including Global Moran’s I and Local Moran’s I, were used to describe and map spatial clusters of the health loss. In addition, basic individual information and environmental samples were collected to explore natural and social determinants of the health loss from KBD. Results The estimation of YLDs showed that patients with KBD of grade II and patients over 50 years old contributed most to the health loss of KBD in Bin County. No significant difference was observed between two genders. The spatial patterns of YLDs and YLD rate of KBD were clustered significantly at both global and local scales. Villages in the southwestern and eastern regions revealed higher health loss, while those in the northern regions exhibited lower health loss. This clustering was found to be significantly related to organically bound Se in soil and poverty rate of KBD patients. Conclusions Our results suggest that future treatment and prevention of KBD should focus on endemic areas with high organically bound Se in soil and poor economic conditions. The findings can also provide important information for further exploration of the etiology of KBD.

scholarly journals Spatial Autocorrelation in Health Loss of Kashin-Beck Disease and Relationship to Environmental Factors: A Cross-Sectional Study in Bin County, Shaanxi Province, China

2020 ◽  
Author(s):  
Jing Wang ◽  
Xiaoya Wang ◽  
Hairong Li ◽  
Linsheng Yang ◽  
Yingchun Li ◽  
...  
Keyword(s):  
Spatial Autocorrelation ◽  
Life Quality ◽  
Shaanxi Province ◽  
Moran’S I ◽  
Cross Sectional ◽  
Moran's I ◽  
Targeted Interventions ◽  
Health Loss ◽  
Significant Difference ◽  
Beck Disease

Abstract Background: Kashin-Beck disease (KBD) is one of the major endemic diseases in China, which severely impacts the physical health and life quality of people. A better understanding of the spatial distribution of the health loss from KBD and its influencing factors will help to identify areas and populations at high risk so as to plan for targeted interventions. Methods: The data of patients with KBD at village-level were collected to estimate and analyze the spatial pattern of health loss from KBD in Bin County, Shaanxi Province. The years lived with disability (YLDs) index was applied as a measure of health loss from KBD. Spatial autocorrelation methodologies, including Global Moran’s I and Local Moran’s I, were used to describe and map spatial clusters of the health loss from KBD. In addition, selenium concentrations in soil and wheat samples in Bin County were determined to detect their relationships with the distribution of health loss of KBD. Results: The estimation of YLDs for KBD showed that patients with KBD of grade II and patients over 50 years old contributed the most to the health loss in Bin County. There was no significant difference between the two genders. The spatial patterns of YLDs and YLD rate of KBD were clustered significantly at both global and local scales. Villages in the southwestern and eastern regions revealed higher health loss, while those in the northern regions exhibited lower health loss. This clustering was found to be closely related to organically bound Se in soil and poverty rate of KBD patients. Conclusions: Our results suggest that future treatment and prevention of KBD should focus on endemic areas with high organically bound Se in soil and poor economic conditions. The method of estimating the health loss of KBD with YLDs can be useful for KBD surveillance for public health officials.

scholarly journals Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019

2020 ◽  
Author(s):  
Simon Kigozi ◽  
Ruth N Kigozi ◽  
Catherine M Sebuguzi ◽  
Jorge Cano ◽  
Damian Rutazaana ◽  
...  
Keyword(s):  
High Risk ◽  
Incidence Rate ◽  
Health Facility ◽  
Malaria Incidence ◽  
Temporal Patterns ◽  
Health Facilities ◽  
Moran’S I ◽  
Moran's I ◽  
Catchment Population ◽  
Targeted Interventions

Abstract Background: As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda over a recent 5-year period.Methods: Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019 was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index.Results: An estimated 38.8 million (95% Credible Interval [CI]: 37.9–40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9–21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7–9.4) to 36.6 (95% CI: 35.7–38.5) across the study period. Strong seasonality was observed, with June-July experiencing highest peaks and February-March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0–50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran’s I = 0.3 (p < 0.001) and districts Moran’s I = 0.4 (p < 0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central – Busoga regions.Conclusion: Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.

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.

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