Spatial epidemiology provides powerful tools for understanding geographic patterns in health and disease, but methodological and interpretative pitfalls can undermine the validity of spatial analyses. This editorial highlights ten common pitfalls spanning spatial dependence, scale, ecological inference, small-area estimation, spatial confounding, hotspot interpretation, model validation, measurement and statistical uncertainty, and causal interpretation. For each, we provide practical guidance to support more rigorous and reliable spatial epidemiological research. As geospatial data, artificial intelligence, and analytical methods continue to advance, careful spatial reasoning remains essential to ensure that methodological sophistication translates into valid, interpretable, and meaningful public health evidence.
Lyme disease is a growing public health threat driven by interplay of climatic, ecological and social factors. This review adopts an eco-syndemic framework, viewing Lyme disease emergence as the confluence of multiple synergistic drivers rather than a single- cause phenomenon. We synthesize evidence that climate conditions, ecological circumstances and human behavioural patterns inter- sect to amplify Lyme disease risk. By integrating these seemingly separate domains, our review highlights novel insights; for exam- ple, habitat fragmentation and predator loss may magnify the effects of a warming climate on reservoir or host populations, and human activities in suburban green spaces may mediate infection cycles. This holistic perspective is a departure from reductionist explanations and emphasizes the importance of coupled human–environment systems in studies of disease emergence. We discuss how an eco-syndemic frame can improve surveillance and prevention emphasizing One Health approaches that unite environmental management with animal health and public health outreach. Understanding the interactive and non-linear dynamics of Lyme disease drivers is crucial for developing effective interventions and preparing for future tick-borne disease challenges. While selected com- parative evidence from Europe is used where relevant, the review’s primary empirical focus is North America.
This study investigated variations in breast cancer mortality among women in San Luis Potosí, Mexico using an epidemiologicalspatial approach covering the period 2009–2019. Additionally, global and specific survival and its associations with clinical stage, age at diagnosis, marginalization index, recurrence and arsenic concentration in water were evaluated. We conducted a retrospective ecological, study involving1, 626 confirmed breast cancer cases and 264 related deaths registered at a public hospital. Survival analysis was performed using the Kaplan-Meier method and Log-Rank tests. Spatial distribution patterns of municipal average mortality were assessed using Moran’s I and visualized in ArcMap 10.1. At the state-level the mean age at diagnosis among deceased patients was 53.49 years, with 84% presenting at advanced stages (IIB–IV). A progressively increased mortality, particularly in Health Jurisdiction IV and the municipality of Cerro de San Pedro, was observed, with peaks in 2016. The 5-year global survival rate was 83.78%. Factors significantly associated with reduced survival included advanced stage, older age, higher marginalization index and recurrence. Although arsenic exposure was not statistically significant, patients exposed to levels exceeding 0.025 mg/L exhibited longer survival times. The integration of spatial epidemiology with survival analysis underscored critical geographic and sociodemographic disparities and offer valuable insights for targeted healthcare planning and resource allocation, particularly in high-priority regions.
Hypertension poses a major health challenge for the rural elderly in China, but evidence on its spatial patterns and key influencing factors is still limited. This study examined the spatial distribution of hypertension prevalence among rural older adults assessing its associations with dietary, behavioural, socioeconomic and environmental variables. Pearson correlation and maximum entropy (MaxEnt) were used to analyze correlations and identify spatial risk levels. The results showed that hypertension prevalence was negatively correlated with grain intake (r=-0.600, p=0.002) and poultry consumption (r=-0.504,p=0.010) and alcohol use showed the highest contribution among behavioural variables (39.2%) in the MaxEnt model. Climate zones were also associated with prevalence (r=-0.260,p=0.010), with higher risk in temperate and some subtropical regions. The MaxEnt model showed good discriminatory ability (area under the curve (AUC)=0.871) in identifying hypertension high-risk areas mainly in northern and north-eastern rural China. This study provides spatial epidemiological evidence on hypertension among the rural elderly, suggesting that dietary patterns, alcohol consumption and climate conditions may be associated with spatial risk variation. These findings support the use of MaxEnt as an exploratory tool for chronic disease spatial analysis and may provide spatial reference for public health strategies and resource allocation.
This study employed geospatial approaches to assess the risk and spatial distribution of Dengue Fever (DF) and Dengue Hemorrhagic Fever (DHF) in Phayao Province, Thailand. Epidemiological data from 2016 to 2024, comprising 3,600 reported cases, were analysed alongside demographic and climatic variables. Temporal analysis revealed a major epidemic in 2023 lasting 20 weeks, coinciding with peak rainfall, with adolescents and young adults (13–24 years old) being the most affected group. Spatial autocorrelation (Moran’s I) indicated significant clustering of dengue morbidity rates, with hotspots concentrated in urbanised districts such as Mueang Phayao, Dok Khamtai and Chiang Kham, while Kernel Density Estimation (KDE) highlighted shifts of hotspots toward eastern districts in later years. Local Indicators of Spatial Association (LISA) identified 24 high–high clusters in 2019, predominantly in Mae Chai District. Case-control analysis further revealed that socio-economic conditions, housing environments and inconsistent preventive behaviours influenced dengue incidence, with strong community participation linked to more effective prevention. These findings underscore the spatial heterogeneity of dengue transmission and provide geospatial evidence to guide targeted vector control, strengthen community-based interventions, and support evidence-based public health strategies in northern Thailand.
Inspired by conceptual principles from quantum information theory, a novel classical approach to address temporal delays in spatial data analysis is presented. Current geospatial services face latency challenges due to complex processing chains, which motivate an investigation of whether the quantum-inspired paradigm could offer efficiency gains when implemented using classical hardware. A framework is proposed that incorporates three metaphors derived from quantum concepts: i) application of bit-like representation that mimics Qubit superposition to handle data uncertainty probabilistically; ii) use of probabilistic distributions for handling data uncertainty; and iii) creation of efficient data linkages by establishing pre-computed spatial correlations as an analogue to quantum entanglement. This model suggests potential temporal improvements while acknowledging current classical computing limitations. The proof-of-concept was tested on urban air quality monitoring, integrating data from fixed stations and mobile sensors. Simulation results indicated potential latency reduction while maintaining analytical accuracy (mean error <5.2% in controlled tests). Compared to the standard classical methods, the quantum-inspired metaphor showed efficiency improvements in theory when scaled to appropriate problem sizes, with simulated refresh rates of 250 milliseconds. Error analysis support the usefulness of the system for environmental health applications running on existing classical infrastructure. This research contributes: i) a framework for using quantum- inspired metaphors to address temporal challenges in geospatial analysis; ii) a simulation prototype for air quality monitoring; and iii) preliminary evidence of potential advantages from a bio-inspired approach in GIS processing. The technique may prove valuable for time-sensitive applications with today's technology and could inform future designs for potential quantum computing implementations.
Diabetes prevalence is increasing in Thailand, creating growing demands on the health system. Understanding the spatial distribution of diabetes risk and its association with socioeconomic and healthcare system factors among the diabetes risk population is critical for designing targeted prevention and intervention strategies. We examined the distribution of diabetes risk groups across provinces in Thailand with reference to the spatial association between economic, social and public health service factors based on data from the Ministry of Public Health's Health Data Center (HDC) for the year 2021. The dataset included 22,491,934 individuals across the 76 provinces as well as social, economic and public health services. The methods included Local Indicators of Spatial Association (LISA), Ordinary Least Squares (OLS), Spatial Lag Model (SLM) and Spatial Error Model (SEM). Explanatory variables included average night-time light intensity, average monthly income, hospital-to-population ratio and proportion of the population with health insurance. Major clusters of High-High (HH) diabetes risk were identified by LISA mainly located in the North of Thailand. In all models, the direction and significance of the associations were consistent (p<0.001 for all variables investigated and p<0.01). R2=0.47. The SLM gave the best fit, capturing spatial spill-over effects. Higher night-time light intensity (coefficient = -85.70, p<0.05) and higher monthly income (coefficient = -0.079, p<0.001) were negatively associated with diabetes risk. These inverse relationships implied that greater urbanization and higher socio-economic standing may protect against diabetes risk, possibly through improved access to health infrastructure, improved health education and preventive services. Conversely, the higher hospital-to-population ratios (coefficient = 572.28, p<0.001) and the larger proportions of Civil Servant Medical Benefits Scheme (CSMBS) coverage (coefficient = 226.46, p<0.001) the higher diabetes risk. These counterintuitive findings likely reflect reverse causation, in which provinces with higher disease burden or poor health attract more resources of health care and have increased insurance coverage, a pattern consistent with healthcare service distribution responding to existing health needs rather than preventing occurrence of disease.
Dengue Haemorrhagic Fever (DHF) remains a public health burden in Indonesia with substantial provincial variation. We modelled province-level DHF counts in 2023 using Bayesian spatial conditional autoregressive Poisson models with population offsets. Predictors were average annual temperature (per 1°C) and the number of public health workers (province-level count). Spatial dependence was supported by Moran’s I=0.4689 (p=0.021). We fitted models using Besag-York-Mollié (BYM) and Leroux priors via Markov chain Monte Carlo and compared fit using the Deviance Information Criterion (DIC) and the Watanabe–Akaike Information Criterion (WAIC). In the BYM model, temperature was associated with lower risk (RR=0.90; 95% CrI: 0.76 to 1.07), with uncertainty including unity, whereas workforce density was associated with higher reported risk (RR=1.05; 95% CrI: 1.03 to 1.07). Estimates were similar under the Leroux prior (temperature RR=0.89; 95% CrI: 0.74 to 1.07; workforce RR=1.04; 95% CrI: 1.02 to 1.07), and BYM showed marginally better fit. Risk mapping indicated elevated burden in parts of Kalimantan and eastern Indonesia. Findings may inform geographically targeted surveillance and vector control; the workforce association should be interpreted cautiously because it may reflect reporting capacity or reactive deployment.
This study presents the first Knowledge Attitudes, and Practices (KAP) survey on malaria in Djibouti City. It was conducted among 1,344 household heads across nine neighbourhoods in Djibouti City. Composite scores were calculated for each KAP dimension. Analysis of variance and multinomial logistic regression identified socio-demographic predictors and Local Indicators of Spatial Association (LISA) characterised the spatial clustering of the KAP scores. No significant association was found between sociodemographic or economic factors and malaria knowledge. Prevention practices varied notably across neighbourhoods, driven by place of residence, mother tongue, and education-underlining the primacy of spatial determinants. Attitudes were found to be linked to gender and income. Despite high disease awareness, 60% of respondents misidentified transmission routes, nearly two-thirds of respondents failed to adopt effective preventive behaviours, while Long-Lasting Insecticidal Net (LLIN) ownership far exceeded correct use. The gap between awareness and practice suggests that information-deficit approaches have reached their limits; future interventions should target motivational norm-based determinants of behaviour, spatially concentrated in the highest-risk neighbourhoods. Language and cultural barriers require tailored communication strategies beyond standard broadcast campaigns. Strengthened vector control and active surveillance remain essential complements to any behavioural intervention.
Tuberculosis (TB) remains a major public health concern in India, with an estimated 2.69 million cases annually. This study aimed to identify priority TB burden groups in Mysuru District using the Analytic Hierarchy Process (AHP) to support targeted interventions. A retrospective cross-sectional analysis was conducted using 8,459 TB case records reported between 2017 and 2019. Urban areas accounted for most cases (64.8%), with Mysuru City alone contributing 36.5%. Integrating AHP with Geographic Information Systems (GIS), a secondary surveillance approach was used to prioritize high-risk populations and geographic zones. Type of resi- dence, gender, age group, and co-morbidities were selected as key risk criteria based on epidemiological evidence and expert judgement. Relative weights were derived through pair-wise comparisons, with consistency verified using Consistency Ratio (CR), a metric used in the AHP to measure how logically consistent a decision-maker's subjective judgments are when comparing pairs of items. Rankings were assigned based on the highest (4) and lowest (1) proportional burden. The analysis identified urban males aged 40- 59 and 20-39 years as the highest-risk groups, classified as "very high" priority, followed by urban females aged 20-39 years and elderly males as "high" priority. Younger and older age groups constituted moderate to low-risk categories. The findings highlight a disproportionately higher TB burden among urban, male, and working-age populations and demonstrate the utility of AHP in guiding targeted, evidence-based TB control strategies used.
The COVID-19 pandemic has generated substantial spatial and social inequalities in mortality, yet within-country council-level variations remain incompletely understood. Scotland offers a useful case, with a severe epidemic but rich administrative data and marked socio-economic gradients. We examined how infection burden, hospital-related indicators and selected area-level social indicators were associated with COVID-19 mortality across mainland Scotland 2020-2021. We assembled a two-year panel for 29 mainland councils, using deaths involving COVID-19, population denominators, COVID-19 testing and hospital activity, council-level indicators of marriage, unemployment, smoking cessation and migration. A Bayesian Poisson log-linear model with conditional autore- gressive random effects was used to estimate area-level relative risks and covariate associations. Given the short panel, the analysis is interpreted primarily as a spatial ecological analysis conducted over two successive pandemic years. Crude mortality showed consistently higher COVID-19 mortality in the central belt than in many northern and rural councils. After adjustment, spatial differences remained but were modest: no council in 2021 had a posterior probability above 0.10 of exceeding the national mean mortality risk. Positive test burden was positively associated with mortality and is interpreted primarily as a proximal epidemiological indicator of infection burden. Social determinants were also important. Higher unemployment was associated with increased risk, whereas higher marriage counts were linked to lower mortality. Smoking quit rates showed a positive association with deaths, likely reflecting residual confounding by underlying deprivation and historical smoking prevalence. Hospital utilisation and migration indicators showed weaker and more uncertain effects. Together, these findings indicate that crude spatial disparities in COVID-19 mortality across mainland Scottish councils became more moderate after adjustment. Given the ecological design and the two-year panel, the findings should be interpreted as area-level associations rather than as evidence of specific local social mechanisms.
Dementia represents a growing public health challenge in Taiwan, particularly within its rapidly aging population. This study employed the Community Readiness Model (CRM) to systematically assess community readiness for dementia prevention across multiple domains. It further examined whether collective efficacy, defined as shared social cohesion and a community's capacity for collective action, is associated with greater readiness for dementia-related prevention efforts. A cross-sectional survey was conducted from March to June 2021 among 3,129 community leaders in 456 communities in Taipei City. A total of 447 valid responses were analyzed, representing 288 communities (63.2% of all communities). A spatial lag regression model was conducted, and spatial spillover effects were further assessed. Spatial lag regressions revealed that willingness to intervene (B=0.256, p<0.0001), social cohesion (B=0.375, p<0.0001), and prior dementia prevention programs (B=1.036, p=0.01) were significantly associated with higher community readiness for dementia prevention. Spill-over patterns - particularly for social cohesion and prior programs - appeared to play a potential, though not fully conclusive, role in shaping readiness across neighbouring communities. Those with higher proportions of residents aged 85+ showed lower readiness, while average income was not a significant predictor. Collective efficacy and prior dementia efforts were associated with higher community readiness. Tailored, community-based strategies that foster social cohesion and proactive engagement while accounting for spatial disparities are considered essential for effective dementia prevention.
Stunting remains a significant public health concern in Indonesia, characterized by wide regional disparities and persistent prevalence in rural and underserved communities. This study applies the Bayesian Spatial Durbin Model (BSDM) to analyze the spatial distribution and interregional dynamics of childhood stunting across 31 provinces in Indonesia. District level data on stunting prevalence were obtained from the community-based health survey RISKESDAS survey of 2023, focusing on three programmatically salient covariates: the proportion of households with adequate housing, the proportion of children under five who received complete basic immunization and the proportion of infants aged 0-5 months who were exclusively breastfed. The BSDM quantifies direct and spatial spill-over effects while accounting for spatial autocorrelation and parameter uncertainty. Results indicate that adequate housing and complete immunization are associated with lower stunting prevalence and that exclusive breastfeeding are directionally protective. The study finds that spatially coordinated investments in housing quality, immunization outreach and infant feeding support accelerated stunting reduction.
Zoning and biosecurity are fundamental tools in veterinary public health and international trade. However, their implementation suffers from fragmented approaches to geospatial representation and uneven adoption across countries. This article discusses outcomes of the G7 Chief Veterinary Officers (CVOs) meeting (Padua, October 2024), where the integration of zoning and biosecurity was highlighted as a possible pathway to strengthen disease control and facilitate trade. Two initiatives, GeoZone and ClassyFarm are used to illustrate this approach. The former advances zoning clarity through a geospatial framework based on ISO TC211 standards, defining spatial data structures and exchange protocols to improve cross-country GIS interoperability, while the latter complements this approach by providing structured biosecurity data collection and risk classification at farm, regional and national levels. We critically discuss the potential and limitations of both systems, and their contribution to the advancement of geospatial epidemiology. By treating zoning and biosecurity as harmonized, data-driven frameworks, these initiatives mark progress towards more a transparent, standardised and globally applicable animal health management.
This study assessed spatial accessibility to fixed mammography centres across Oklahoma State, USA using the Two-Step Floating Catchment Area (2SFCA) and the Enhanced Two-Step Floating Catchment Area (E2SFCA) methods to identify areas with limited or no access. For this analysis, we used data from the mammography facilities database of the US Food and Drug Administration verified by direct contact with the facility and the U.S. Census block group population and demographics for women aged 40 years and older. Analyses were stratified by urban areas; large rural areas; and small rural areas. Accessibility scores were calculated using the 2SFCA method with 30-minute drive times and the E2SFCA method with drive times of 10, 20 and 30 minutes weighted by distance decay. Block groups were categorized into quartiles based on accessibility scores. Among 940,994 eligible women, 10% lived in areas with no access. Small rural regions faced the greatest barriers. Spatial disparities were linked to racial and socioeconomic differences: non-Hispanic American Indian/Alaska Native and non-Hispanic White populations were more likely to reside in noaccess zones, while Black and Hispanic populations clustered in high-access urban areas. Spatial analysis reveals significant rural disparities in mammography access. Mobile machines should prioritize underserved rural regions to improve equity.
This study investigates spatial disparities in cancer and lung-cancer mortality across Europe through an integrative geospatial epidemiological framework. Using age-standardised Eurostat mortality data for 2022 at the NUTS-2 level, we combine Getis-Ord Gi* and Anselin Local Moran's I to detect statistically significant hot/cold spots, while multivariate regressions incorporate environmental and topographic predictors. Results reveal pronounced east-west and urban-rural gradients: persistent high-mortality clusters span Central and Eastern Europe, where historical industrialisation, elevated smoking prevalence, and structural healthcare gaps converge. By contrast, Southern European regions - Portugal, western Spain, and southern Greece - are associated with lower observed mortality levels, plausibly reflecting favourable behavioural profiles, environmental conditions, and healthcare accessibility. Spatial outliers identify territories where localised factors, such as air-pollution peaks or differential diagnostic capacity, modify broader regional patterns. Overall, the findings highlight geography as a structuring context for exposure, vulnerability, and access to care, rather than as a direct causal driver of cancer risk, and demonstrate the value of spatial epidemiology for territorial health governance, environmental monitoring, and urban planning. Policy relevance is twofold. First, the evidence supports region-specific interventions aligned with the Sustainable Development Goals (SDG) - especially SDG 3 (health), SDG 10 (reduced inequalities), and SDG 11 (sustainable cities). Second, the spatial outputs provide a robust empirical basis for informing the health-equity ambitions of Europe's Beating Cancer Plan and the environmental-justice agenda of the European Green Deal. By bridging granular geospatial evidence with EU-wide priorities, the study underscores the need for place-based, equity-oriented frameworks in cancer prevention and control across heterogeneous European landscapes.
In the State of Mexico, several venomous snakes have low median lethal doses, which therefore pose serious health risks. We anal- ysed the epidemiology of snakebites from 2003 to 2024 and examined their relationship with demographic, socioeconomic, and bio- logical factors. Incidence rates and demographic characteristics were calculated, and Getis-Ord Gi* statistics were used to identify snakebite hotspots. We also applied Non-Metric Multi-Dimensional Scaling (NMDS) to explore associations between hotspot cate- gories and socioeconomic conditions. The potential distribution of 14 venomous snake species was modelled to estimate venomous snake diversity across municipalities. A total of 3,972 cases were reported, with an increasing trend over time. Most bites occurred in summer, affecting mainly males aged 25-44. Hotspot analysis identified 27 municipalities as hotspots, 50 as not significant and 48 as coldspots. Southern municipalities showed higher snakebite incidence. Coldspot areas had higher educational attainment and greater employment in services and tertiary sectors, despite similar snake diversity to hotspots. These findings can guide public health strategies, particularly regarding the allocation of antivenoms in regional hospitals.
Diabetes mellitus, a chronic metabolic disorder characterized by elevated blood glucose, remains a pressing public health challenge in the United States. This study aims to identify spatial clusters of diabetes and examine associated factors at a granular scale using the state of Alabama. Data on diabetes prevalence, socioeconomic, environmental and behavioural risk factors were extracted at the census tract level from the CDC PLACES Project. Moran's I and Getis-Ord Gi* were first used to assess the spatial autocorrelation and spatial clusters of diabetes, respectively. Due to the existence of spatial autocorrelation (Moran's I = 0.275, p<0.001) of diabetes prevalence, three additional spatial statistical techniques, including the Spatial Lag Model (SLM), the Spatial Error Model (SEM) and Geographically Weighted Regression (GWR), were used to examine its associated factors while detecting the local spatial variations. Several significant clusters of high diabetes prevalence were found in most counties in the middle, known as the Black Belt. The GWR model (R2 = 0.921 & AICc = 2414.0) outperformed SLM and SEM and was therefore used to explore the strong spatial heterogeneity in the associated risk factors. Statistically significant predictors identified were smoking, drinking, obesity, poverty, and age 65+. These localized findings enable governments to develop interventions targeting risk factors to address diabetes prevalence in the state of Alabama.
Plateau State is one of Nigeria's 14 states with a high Tuberculosis (TB) burden. In this state and its capital city, Jos Metropolis, TB cases have been on the increase. There are no reported studies on the spatial mapping of TB cases from Jos Metropolis. Thus, it is not known how TB hotspots and clusters may contribute to the propagation of area-wide TB transmission in this area and the implications for prevention and control. The objective of this study was to determine the spatial pattern of TB cases in Jos Metropolis from 2019-2022 based on existing TB data in the treatment registers and their residential addresses. We geolocated the cases to the nearest Polling Unit (PU) in their Electoral Ward (EW) using the Global positioning System (GPS) coordinates obtained from the Polling Unit Locator (PUL) on the website of the Independent National Electoral Commission (INEC). In ArcGIS Pro (version 3.5.3) environment, TB hotspots were determined. Using the SaTScan software (version 10.3.2), a retrospective purely spatial analysis was carried out to identify purely spatial TB clusters based on the discrete Poisson model. A total of 4,897 TB cases were mapped. Significant TB hotspots (Z-score >1.96 and p-value <0.05) and primary TB clusters were found for each of the study years. The hotspots and clusters were located in the northern part of the Jos Metropolis, particularly the more centrally located areas. We found both a potential for future increase in TB cases and a spread to other areas of the Jos Metropolis from these TB hotspots and clusters in the northern part of the metropolis. Hence, there is an urgent need for a targeted TB screening and treatment, resource allocation and health education campaigns in the identified EWs.
Public health is a key component of the United Nations Sustainable Development Goals (SDGs) and is central to the all-round development of individuals. Based on panel data from 31 provinces in China between 2010 and 2023, this article adopts the Population Mortality Rate (PMR) as a measure of public health and employs the Generalized Additive Model (GAM) from machine learning to systematically investigate the nonlinear effects of multidimensional factors-including medical resources, environmental pollution, socioeconomic conditions, and technology-on public health. The results of the study show that China's PMR exhibits an overall upward trend, with a spatially uneven distribution and significant regional disparities, as mortality rates in the central and western regions are generally higher than those in the eastern coastal provinces. All influencing factors show significant associations with the PMR. The effects of all influencing factors on mortality exhibit complex nonlinear characteristics, with their impacts varying considerably across different value ranges. Specifically, the influence of medical resources exhibits critical thresholds, such as the Number of Urban Practicing (assistant) Physicians per 10,000 people (NUPP) reaching its maximum health benefit at approximately 40; while the relationship between environmental indicators and mortality reveals potential complex confounding mechanisms, as evidenced by the negative association observed between Sulphur Dioxide Emissions (SDE) and mortality rates within the observed range; whereas the health benefits of basic resources and developmental factors, such as Per Capita Water Resources (PCWR), stabilize after crossing a specific threshold (20,000 cubic meters per person). Finally, some practical policy recommendations are put forward.
