Epidemiology and Health

Original Articles

Spatial analysis of human Coxiella burnetii infection and populations of goat and cattle in Korea, 2015-2024: Seung-Bum Kang, Dae Sung Yoo; Epidemiol Health. 2025;47:e2025068. Published online December 9, 2025; DOI: https://doi.org/10.4178/epih.e2025068

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Abstract Summary PDF Supplementary Material: Abstract
OBJECTIVES
Q fever is a bacterial zoonosis that occurs worldwide. Although several studies have reported associations between goat populations and human Q fever outbreaks in Korea, spatial correlation analyses remain limited. Therefore, this study examined the geographic correlation between human Q fever outbreaks and the distributions of goats and cattle in Korea.
METHODS
This study covered a 10-year period (2015-2024), using each of the 250 districts in Korea as the unit of analysis. Data were divided into 2 time periods: 2015-2019 and 2020-2024. Hotspots for the standardized incidence ratio (SIR) were identified using Getis-Ord Gi*. Spatial correlations between SIR and goat and cattle populations were evaluated using a multivariable spatial error model, and the associations between hotspot variables and livestock abundance were assessed using a multivariable Leroux conditional autoregressive model.
RESULTS
SIRs for human Q fever showed significant positive spatial associations with goat populations in 2016 (coefficient=46.52, p<0.01) and 2021 (coefficient=70.97, p<0.01). The associations between goat populations (2016 and 2021) and hotspot classifications were consistent across both periods, with the odds ratio increasing from 1.87 (95% credible interval [CrI], 1.23 to 2.85) in 2015-2019 to 2.33 (95% CrI, 1.55 to 3.64) in 2020-2024. No significant associations were observed between human Q fever and cattle populations.
CONCLUSIONS
Goat populations are becoming more strongly spatially correlated with human Q fever incidence. These findings underscore the need for enhanced preventive management of goat farms to mitigate future outbreaks.; Summary

Korean summary
본 연구는 2015~2024년 국내 사람 큐열 발생과 가축 개체수 간의 공간적 상관관계를 분석한 최초의 연구이다. 분석 결과, 사람 큐열 발생은 염소 개체수와 유의한 양의 상관관계를 보였으며 그 연관성은 최근 더욱 강화된 반면, 소 개체수와는 유의미한 연관성이 나타나지 않았다. 이는 향후 인수공통감염병 발생 억제를 위해 염소 농가에 특화된 예방적 관리와 방역 교육이 필수적임을 시사한다.

Key Message
This study is the first to analyze the spatial correlation between human Q fever outbreaks and livestock populations in South Korea from 2015 to 2024. The results reveal a significant positive spatial association between human Q fever incidence and goat populations, which strengthened over the 10-year period, whereas no such correlation was found with cattle. These findings highlight the urgent need for enhanced preventive management and biosecurity education specifically for goat farms to mitigate future zoonotic outbreak.

Spatial patterns of laboratory-confirmed leptospirosis in north-eastern Peninsular Malaysia, 2016-2023: Hazlienor Mohd Hatta, Kamarul Imran Musa, Nik Mohd Hafiz Mohd Fuzi, Paula Moraga; Epidemiol Health. 2025;47:e2025030. Published online May 29, 2025; DOI: https://doi.org/10.4178/epih.e2025030

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OBJECTIVES
Leptospirosis presents significant public health challenges in endemic regions such as north-eastern Peninsular Malaysia. Spatial analysis is essential for visualising disease incidence and distribution, assessing vulnerability based on geographical and socioeconomic factors, and ultimately informing targeted interventions, optimising resource allocation, and enhancing surveillance strategies. This study aimed to determine the incidence and characterise the spatial distribution of leptospirosis in Kelantan, Malaysia.
METHODS
All laboratory-confirmed leptospirosis cases reported in Kelantan between 2016 and 2023 were extracted from the Communicable Disease Control Information System e-Notifikasi online database. Spatial analyses were performed using the spatstat, spdep, and ggplot2 packages within the RStudio integrated development environment.
RESULTS
The analysis encompassed 1,534 laboratory-confirmed leptospirosis cases. The average crude annual incidence of leptospirosis cases per 1,000 population from 2016 to 2023 was 0.101 (95% confidence interval, 0.038 to 0.164). Incidence varied considerably across districts and subdistricts, initially higher in the north but declining over time, while consistently high and increasing incidence was observed in the southern region. Significant clustering of leptospirosis cases occurred throughout the studied years, except during the coronavirus disease 2019 pandemic. Hotspots were initially prevalent in northern areas but later emerged in south-eastern and southern regions. Significant spatial autocorrelation evolved from high-low to high-high clusters, particularly evident in central and southern regions.
CONCLUSIONS
This study provides valuable local epidemiological and spatial insights into the endemicity of leptospirosis. The findings highlight the need for targeted interventions and continued surveillance to effectively mitigate the leptospirosis burden in endemic areas.; Summary

Key Message
• This study examined leptospirosis patterns in Kelantan, Malaysia, from 2016 to 2023, highlighting distinct regional varia- tions in disease incidence. • Case densities were high in the northern region, whereas incidence demonstrated higher risk in the central and southern regions. • The study underscores the importance of considering both geographic location and population dynamics when planning interventions and allocating resources for disease control. • The evident spatial clustering highlights the need for targeted public health interventions.

Geospatial analysis of neonatal mortality in north-eastern India: a multilevel Bayesian approach: Vidhi Jain, Kh. Jitenkumar Singh, Deboshree Das, Shefali Gupta, Gunjan Singh; Epidemiol Health. 2025;47:e2025021. Published online April 27, 2025; DOI: https://doi.org/10.4178/epih.e2025021

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OBJECTIVES
Neonatal mortality remains a significant public health issue in India. This study investigates spatial patterns and contributing factors to neonatal mortality in the north-eastern states, identifying hotspot regions and spatial variations.
METHODS
A sample of 34,222 mothers from India’s National Family Health Survey (NFHS-5, 2019-21) in the north-eastern states was analysed. Descriptive and bivariate analyses were conducted alongside Bayesian multilevel logistic regression using integrated nested Laplace approximation to model neonatal mortality. Spatial hotspot analysis using Getis-Ord Gi* statistics identified clusters of high neonatal mortality, while geographically weighted regression (GWR) was used to examine spatial variations in the relationships between neonatal mortality and contributing factors.
RESULTS
The neonatal mortality rate in the north-eastern states declined from 45 to 21 per 1,000 live births (NFHS-1 to NFHS-5) but remains higher than the national average. Assam reported the highest mortality (42.16%), whereas Sikkim had the lowest (0.87%). Higher mortality was observed among male infants, mothers with advanced age, low maternal education, and mothers who attended less than 5 antenatal care (ANC) visits. Spatial analysis identified hotspots in Assam, Meghalaya, and Tripura. GWR indicated that areas with less than 5 ANC visits had the strongest association with neonatal mortality. Bayesian multilevel analysis highlighted spatial variations of up to 51% across districts in northeast India.
CONCLUSIONS
This study underscores spatial disparities in neonatal mortality across north-eastern India. Addressing childcare practices and healthcare access in hotspot regions is essential for improving new-born health outcomes. The findings provide critical insights for policymakers to develop targeted interventions aimed at reducing neonatal mortality in these underserved areas.; Summary

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

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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.

Citations

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Spatial Epidemiological Approach to Tuberculosis Treatment Outcomes in a Tertiary-Level Hospital: A Retrospective Analysis
Luis Eduardo Del Moral Trinidad, Gilberto Silva Bañuelos, Esteban Gonzalez-Diaz, Melva Guadalupe Herrera Godina
Tropical Medicine and Infectious Disease.2026; 11(2): 57. CrossRef
Tuberculosis treatment outcomes and associated factors among patients treated at Bosaso TB Hospital, Bosaso, Somalia: A five-year retrospective study
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Examining the efficacy of treatment outcomes for patients with pulmonary tuberculosis in Western China: A retrospective study in a region of high incidence
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Michael Asare-Baah, Tan M. Luong, Ernest Kwarteng, Charles Domotey, Nellie Arthur, Magalie Zoungrana, Sheila Mireku-Apah, Honesty Ganu, Michael Amo Omari, Adelaide Sackey, Awewura Kwara, Jane Sandra Afriyie-Mensah, Marie Nancy Séraphin
Scientific Reports.2025;[Epub] CrossRef

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

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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

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Spatiotemporal epidemiology of brucellosis in Iran from 2009 to 2018: A mixed ecological study
Sajjad Rahimi Pordanjani, Ghobad Moradi, Behrad Pourmohammadi, Elaheh Mazaheri, Farshid Farivar, Somayeh Derakhshan
Journal of Research in Medical Sciences.2025;[Epub] CrossRef
Incidence trend analysis of human brucellosis in the context of human development: an ecological study using joinpoint regression in Iran
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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;[Epub] 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
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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

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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

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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

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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.

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Impacts of Social Environments on Neighborhood Depression Incidence: Fully Accounting for Spatial Effects
Peter Congdon, Esmail Abdul-Fattah
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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

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Abstract 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

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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

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Abstract 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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