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6 "Spatial analysis"
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Original Article
Spatial analysis of tuberculosis treatment outcomes in Shanghai: implications for tuberculosis control
Jing Zhang, Xin Shen, Chongguang Yang, Yue Chen, Juntao Guo, Decheng Wang, Jun Zhang, Henry Lynn, Yi Hu, Qichao Pan, Zhijie Zhang
Epidemiol Health. 2022;44:e2022045.   Published online May 1, 2022
DOI: https://doi.org/10.4178/epih.e2022045
  • 8,068 View
  • 363 Download
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
Tuberculosis (TB) treatment outcomes are a key indicator in the assessment of TB control programs. We aimed to identify spatial factors associated with TB treatment outcomes, and to provide additional insights into TB control from a geographical perspective.
METHODS
We collected data from the electronic TB surveillance system in Shanghai, China and included pulmonary TB patients registered from January 1, 2009 to December 31, 2016. We examined the associations of physical accessibility to hospitals, an autoregression term, and random hospital effects with treatment outcomes in logistic regression models after adjusting for demographic, clinical, and treatment factors.
RESULTS
Of the 53,475 pulmonary TB patients, 49,002 (91.6%) had successful treatment outcomes. The success rate increased from 89.3% in 2009 to 94.4% in 2016. The successful treatment outcome rate varied among hospitals from 78.6% to 97.8%, and there were 12 spatial clusters of poor treatment outcomes during the 8-year study period. The best-fit model incorporated spatial factors. Both the random hospital effects and autoregression terms had significant impacts on TB treatment outcomes, ranking 6th and 10th, respectively, in terms of statistical importance among 14 factors. The number of bus stations around the home was the least important variable in the model.
CONCLUSIONS
Spatial autocorrelation and hospital effects were associated with TB treatment outcomes in Shanghai. In highly-integrated cities like Shanghai, physical accessibility was not related to treatment outcomes. Governments need to pay more attention to the mobility of patients and different success rates of treatment among hospitals.
Summary
Key Message
Tuberculosis treatment outcomes, a key indicator in the assessment of TB control programs, were associated with spatial autocorrelation and hospital effects in Shanghai; however, they were not associated with physical accessibility to hospitals.
Epidemiologic Investigation
How to improve the human brucellosis surveillance system in Kurdistan Province, Iran: reduce the delay in the diagnosis time
Meysam Olfatifar, Seyed Mehdi Hosseini, Payam Shokri, Soheila Khodakarim, Naghmeh Khadembashi, Sajjad Rahimi Pordanjani
Epidemiol Health. 2020;42:e2020058.   Published online August 10, 2020
DOI: https://doi.org/10.4178/epih.e2020058
  • 10,552 View
  • 178 Download
  • 3 Web of Science
  • 4 Crossref
AbstractAbstract PDF
Abstract
OBJECTIVES
Spatial information makes a crucial contribution to enhancing and monitoring the brucellosis surveillance system by facilitating the timely diagnosis and treatment of brucellosis.
METHODS
An exponential scan statistic model was used to formalize the spatial distribution of the adjusted delay in the diagnosis time of brucellosis (time between onset and diagnosis of the disease) in Kurdistan Province, Iran. Logistic regression analysis was used to compare variables of interest between the clustered and non-clustered areas.
RESULTS
The spatial distribution of clusters of human brucellosis cases with delayed diagnoses was not random in Kurdistan Province. The mean survival time (i.e., time between symptom onset and diagnosis) was 4.02 months for the short spatial cluster, which was centered around the city of Baneh, and was 4.21 months for spatiotemporal clusters centered around the cities of Baneh and Qorveh. Similarly, the mean survival time for the long spatial and spatiotemporal clusters was 6.56 months and 15.69 months, respectively. The spatial distribution of the cases inside and outside of clusters differed in terms of livestock vaccination, residence, sex, and occupational variables.
CONCLUSIONS
The cluster pattern of brucellosis cases with delayed diagnoses indicated poor performance of the surveillance system in Kurdistan Province. Accordingly, targeted and multi-faceted approaches should be implemented to improve the brucellosis surveillance system and to reduce the number of lost days caused by delays in the diagnosis of brucellosis, which can lead to long-term and serious complications in patients.
Summary

Citations

Citations to this article as recorded by  
  • Asymmetric Effects of Weather-Integrated Human Brucellosis Forecasting System Using a New Nonlinear Autoregressive Distributed Lag Model
    Yongbin Wang, Chenlu Xue, Bingjie Zhang, Yuchun Li, Chunjie Xu, Daniel Diaz
    Transboundary and Emerging Diseases.2024; 2024: 1.     CrossRef
  • Spatio-temporal Analysis of COVID-19: A Global Study
    Sajjad Rahimi Pordanjani, Maryam Mohammadian, Somayeh Derakhshan, Fatemeh Hadavandsiri, Seyed Saeed Hashemi Nazari, Mohammad Hossein Panahi
    Middle East Journal of Rehabilitation and Health Studies.2023;[Epub]     CrossRef
  • Factors Associated With Diagnostic Delays in Human Brucellosis in Tongliao City, Inner Mongolia Autonomous Region, China
    Jingbo Zhai, Ruihao Peng, Ying Wang, Yuying Lu, Huaimin Yi, Jinling Liu, Jiahai Lu, Zeliang Chen
    Frontiers in Public Health.2021;[Epub]     CrossRef
  • Clinical Effect of Doxycycline Combined with Compound Sulfamethoxazole and Rifampicin in the Treatment of Brucellosis Spondylitis
    Xin-Ming Yang, Yong-Li Jia, Ying Zhang, Pei-Nan Zhang, Yao Yao, Yan-Lin Yin, Ye Tian
    Drug Design, Development and Therapy.2021; Volume 15: 4733.     CrossRef
Original Articles
Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014-2017 using a hierarchical Bayesian method and the spatial scan statistic
Erfan Ayubi, Mohammad Barati, Arasb Dabbagh Moghaddam, Ali Reza Khoshdel
Epidemiol Health. 2018;40:e2018032.   Published online July 13, 2018
DOI: https://doi.org/10.4178/epih.e2018032
  • 13,415 View
  • 265 Download
  • 5 Web of Science
  • 7 Crossref
AbstractAbstract PDFSupplementary Material
Abstract
OBJECTIVES
This study aimed to map the incidence of cutaneous leishmaniasis (CL) in Iranian army units (IAUs) and to identify possible spatial clusters.
METHODS
This ecological study investigated incident cases of CL between 2014 and 2017. CL data were extracted from the CL registry maintained by the deputy of health of AJA University of Medical Sciences. The standardized incidence ratio (SIR) of CL was computed with a Besag, York, and Mollié model. The purely spatial scan statistic was employed to detect the most likely highand low-rate clusters and to obtain the observed-to-expected (O/E) ratio for each detected cluster. The statistical significance of the clusters was assessed using the log likelihood ratio (LLR) test and Monte Carlo hypothesis testing.
RESULTS
A total of 1,144 new CL cases occurred in IAUs from 2014 to 2017, with an incidence rate of 260 per 100,000. Isfahan and Khuzestan Provinces were found to have more CL cases than expected in all studied years (SIR>1), while Kermanshah, Kerman, and Fars Provinces were observed to have been high-risk areas in only some years of the study period. The most significant CL cluster was in Kermanshah Province (O/E, 67.88; LLR, 1,200.62; p<0.001), followed by clusters in Isfahan Province (O/E, 6.02; LLR, 513.24; p<0.001) and Khuzestan Province (O/E, 2.35; LLR, 73.71; p<0.001), while low-rate clusters were located in the northeast areas, including Razavi Khorasan, North Khorasan, Semnan, and Golestan Provinces (O/E, 0.03; LLR, 95.11; p<0.001).
CONCLUSIONS
This study identified high-risk areas for CL. These findings have public health implications and should be considered when planning control interventions among IAUs.
Summary

Citations

Citations to this article as recorded by  
  • High-risk spatiotemporal patterns of cutaneous leishmaniasis: a nationwide study in Iran from 2011 to 2020
    Neda Firouraghi, Robert Bergquist, Munazza Fatima, Alireza Mohammadi, Davidson H. Hamer, Mohammad Reza Shirzadi, Behzad Kiani
    Infectious Diseases of Poverty.2023;[Epub]     CrossRef
  • Spatio-temporal visualisation of cutaneous leishmaniasis in an endemic, urban area in Iran
    Neda Firouraghi, Alireza Mohammadi, Davidson H Hamer, Robert Bergquist, Sayyed Mostafa Mostafavi, Ali Shamsoddini, Amene Raouf-Rahmati, Mahmoud Fakhar, Elham Moghaddas, Behzad Kiani
    Acta Tropica.2022; 225: 106181.     CrossRef
  • Spatial patterning of occupational stress and its related factors in Iranian critical care nurses using a hierarchical Bayesian technique
    Morteza Kazemi, Kiavash Hushmandi, Amir Vahedian-Azimi, Majid Moayyed, Leila Karimi, Mohammad Ali Sheikh Beig Goharrizi, Mahmood Salesi, Karim Parastouei, Mehdi Raei
    Work.2022; 72(4): 1409.     CrossRef
  • Socio-Economic Characteristics of Urban Tuberculosis Areas in Petaling, Selangor: A Current Spatial Exploratory Scenario
    N.N.N Mohd Zaini, A.R. Abdul Rasam, C.B. Ahmad
    IOP Conference Series: Earth and Environmental Science.2022; 1067(1): 012041.     CrossRef
  • Cutaneous leishmaniasis based on climate regions in Iran (1998-2021): A systematic review and meta-analysis
    Mehri Rejali, Nadia Mohammadi Dashtaki, Afshin Ebrahimi, Asieh Heidari, MohammadReza Maracy
    Advanced Biomedical Research.2022; 11(1): 120.     CrossRef
  • Molecular Study of Cutaneous Leishmaniasis Species among Soldiers with Dermal Ulcers in Zahedan, Iran
    Sina Sekandarpour, Minoo Shaddel, Zahra Sadat Asadi
    Military Caring Sciences.2021; 7(4): 310.     CrossRef
  • Sensitivity of disease cluster detection to spatial scales: an analysis with the spatial scan statistic method
    Meifang Li, Xun Shi, Xia Li, Wenjun Ma, Jianfeng He, Tao Liu
    International Journal of Geographical Information Science.2019; 33(11): 2125.     CrossRef
Analysis of the relationship between community characteristics and depression using geographically weighted regression
Hyungyun Choi, Ho Kim
Epidemiol Health. 2017;39:e2017025.   Published online June 21, 2017
DOI: https://doi.org/10.4178/epih.e2017025
  • 14,956 View
  • 237 Download
  • 1 Web of Science
  • 4 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
Achieving national health equity is currently a pressing issue. Large regional variations in the health determinants are observed. Depression, one of the most common mental disorders, has large variations in incidence among different populations, and thus must be regionally analyzed. The present study aimed at analyzing regional disparities in depressive symptoms and identifying the health determinants that require regional interventions.
METHODS
Using health indicators of depression in the Korea Community Health Survey 2011 and 2013, the Moran’s I was calculated for each variable to assess spatial autocorrelation, and a validated geographically weighted regression analysis using ArcGIS version 10.1 of different domains: health behavior, morbidity, and the social and physical environments were created, and the final model included a combination of significant variables in these models.
RESULTS
In the health behavior domain, the weekly breakfast intake frequency of 1-2 times was the most significantly correlated with depression in all regions, followed by exposure to secondhand smoke and the level of perceived stress in some regions. In the morbidity domain, the rate of lifetime diagnosis of myocardial infarction was the most significantly correlated with depression. In the social and physical environment domain, the trust environment within the local community was highly correlated with depression, showing that lower the level of trust, higher was the level of depression. A final model was constructed and analyzed using highly influential variables from each domain. The models were divided into two groups according to the significance of correlation of each variable with the experience of depression symptoms.
CONCLUSIONS
The indicators of the regional health status are significantly associated with the incidence of depressive symptoms within a region. The significance of this correlation varied across regions.
Summary
Korean summary
정신질환 중 가장 흔한 우울증의 경우 집단의 특성 간 발생 현황에 차이를 보이고 있어 지역별 접근을 통한 연구가 요구됨에 따라 본 연구에서는 우울증의 지역적 변이요인을 분석하여 지역별 중재가 필요한 건강결정요인을 파악하고자 지역사회건강조사 자료를 이용하여 공간적 지리가중회귀분석을 시행하였다. 본 연구를 통해 지역단위보건관련지표는 지역의 우울증 발생과 유의미한 연관성이 있으며 연관성 우선순위는 지역별 차이가 있음이 밝혀졌다. 지역적 특성에 따른 우선순위를 제시하였음에 본 연구의 의의가 있으며 공중 보건 영역의 다른 사례에 본 연구방법론 및 연구결과 제시 방안을 적용함에 따라 지역의 건강수준향상 프로그램 개발에 유용한 기초자료의 제공을 기대할 수 있다.

Citations

Citations to this article as recorded by  
  • A geographically weighted artificial neural network
    Julian Hagenauer, Marco Helbich
    International Journal of Geographical Information Science.2022; 36(2): 215.     CrossRef
  • Spatial Dependence in Local Suicide Ideation and Actual Suicide among the Elderly: A Comparative Study between Men and Women
    Taewan Kim, Hee-Jung Jun
    Journal of Korea Planning Association.2021; 56(4): 49.     CrossRef
  • Geographic Disparities in Stress Levels during the COVID-19 Pandemic in Kuwait
    Mohammad Alnasrallah, Ibrahim Alshehab
    Papers in Applied Geography.2020; 6(4): 449.     CrossRef
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    Ho-Jung Kim, Iyn-Hyang Lee
    Korean Journal of Clinical Pharmacy.2018; 28(4): 308.     CrossRef
Exploring neighborhood inequality in female breast cancer incidence in Tehran using Bayesian spatial models and a spatial scan statistic
Erfan Ayubi, Mohammad Ali Mansournia, Ali Ghanbari Motlagh, Alireza Mosavi-Jarrahi, Ali Hosseini, Kamran Yazdani
Epidemiol Health. 2017;39:e2017021.   Published online May 17, 2017
DOI: https://doi.org/10.4178/epih.e2017021
  • 15,650 View
  • 229 Download
  • 11 Web of Science
  • 13 Crossref
AbstractAbstract PDF
Abstract
OBJECTIVES
The aim of this study was to explore the spatial pattern of female breast cancer (BC) incidence at the neighborhood level in Tehran, Iran.
METHODS
The present study included all registered incident cases of female BC from March 2008 to March 2011. The raw standardized incidence ratio (SIR) of BC for each neighborhood was estimated by comparing observed cases relative to expected cases. The estimated raw SIRs were smoothed by a Besag, York, and Mollie spatial model and the spatial empirical Bayesian method. The purely spatial scan statistic was used to identify spatial clusters.
RESULTS
There were 4,175 incident BC cases in the study area from 2008 to 2011, of which 3,080 were successfully geocoded to the neighborhood level. Higher than expected rates of BC were found in neighborhoods located in northern and central Tehran, whereas lower rates appeared in southern areas. The most likely cluster of higher than expected BC incidence involved neighborhoods in districts 3 and 6, with an observed-to-expected ratio of 3.92 (p<0.001), whereas the most likely cluster of lower than expected rates involved neighborhoods in districts 17, 18, and 19, with an observed-to-expected ratio of 0.05 (p<0.001).
CONCLUSIONS
Neighborhood-level inequality in the incidence of BC exists in Tehran. These findings can serve as a basis for resource allocation and preventive strategies in at-risk areas.
Summary

Citations

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  • Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand
    Chawarat Rotejanaprasert, Kawin Chinpong, Andrew B. Lawson, Peerut Chienwichai, Richard J. Maude
    BMC Medical Research Methodology.2024;[Epub]     CrossRef
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    P.M.M. Bermudi, A.C.G. Pellini, C.S.G. Diniz, A.G. Ribeiro, B.S. de Aguiar, M.A. Failla, F. Chiaravalloti Neto
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    Revista Brasileira de Epidemiologia.2023;[Epub]     CrossRef
  • Intra-urban spatial variability of breast and cervical cancer mortality in the city of São Paulo: analysis of associated factors
    Breno Souza de Aguiar, Alessandra Cristina Guedes Pellini, Elizabeth Angélica Salinas Rebolledo, Adeylson Guimarães Ribeiro, Carmen Simone Grilo Diniz, Patricia Marques Moralejo Bermudi, Marcelo Antunes Failla, Oswaldo Santos Baquero, Francisco Chiaravall
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    Gene Reports.2022; 26: 101481.     CrossRef
  • The Effect of Religious–Spiritual Psychotherapy on Illness Perception and Inner Strength among Patients with Breast Cancer in Iran
    Safoora Davari, Isaac Rahimian Boogar, Siavash Talepasand, Mohamad Reza Evazi
    Journal of Religion and Health.2022; 61(6): 4302.     CrossRef
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    BMC Cancer.2021;[Epub]     CrossRef
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    Yuwei Wang, Wang Gao
    IOP Conference Series: Earth and Environmental Science.2020; 568(1): 012009.     CrossRef
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    Mohammad Khalid Hossain, Qingmin Meng
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    Massimiliano Agovino, Maria Carmela Aprile, Antonio Garofalo, Angela Mariani
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  • Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014-2017 using a hierarchical Bayesian method and the spatial scan statistic
    Erfan Ayubi, Mohammad Barati, Arasb Dabbagh Moghaddam, Ali Reza Khoshdel
    Epidemiology and Health.2018; 40: e2018032.     CrossRef
Ecological context of infant mortality in high-focus states of India
Laishram Ladusingh, Ashish Kumar Gupta, Awdhesh Yadav
Epidemiol Health. 2016;38:e2016006.   Published online March 5, 2016
DOI: https://doi.org/10.4178/epih.e2016006
  • 19,253 View
  • 199 Download
  • 8 Web of Science
  • 8 Crossref
AbstractAbstract PDF
Abstract
OBJECTIVES
This goal of this study was to shed light on the ecological context as a potential determinant of the infant mortality rate in nine high-focus states in India.
METHODS
Data from the Annual Health Survey (2010-2011), the Census of India (2011), and the District Level Household and Facility Survey 3 (2007-08) were used in this study. In multiple regression analysis explanatory variable such as underdevelopment is measured by the non-working population, and income inequality, quantified as the proportion of households in the bottom wealth quintile. While, the trickle-down effect of education is measured by female literacy, and investment in health, as reflected by neonatal care facilities in primary health centres.
RESULTS
A high spatial autocorrelation of district infant mortality rates was observed, and ecological factors were found to have a significant impact on district infant mortality rates. The result also revealed that non-working population and income inequality were found to have a negative effect on the district infant mortality rate. Additionally, female literacy and new-born care facilities were found to have an inverse association with the infant mortality rate.
CONCLUSIONS
Interventions at the community level can reduce district infant mortality rates.
Summary

Citations

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