Local Indicators Of Spatial Association Research Articles

Association of neighborhood-level superfund sites with atmospheric toxic exposures and increasing cancer risk in the United States: A proof-of-concept geospatial study.

10577 Background: Decades of underinvestment have disproportionately exposed several neighborhoods in the US to hazardous conditions. Superfund is the name given to the environmental program established by the US Congress in 1980, to address abandoned hazardous waste sites. The extent to which Superfund sites have contributed to differences in cancer risk remains unknown. We investigated atmospheric toxic exposures increasing cancer risk by proximity to Superfund sites at the neighborhood level. Methods: In this cross-sectional study of US neighborhoods, census-tract level air toxic cancer risk data from the Environmental Protection Agency for 2019 were linked with census-tract level geolocation data of ~1300 Superfund sites from the Superfund Enterprise Management System database. Linear regression models were utilized to explore association between cancer risk and proximity to Superfunds. Geographically Weighted Regression models explored spatial non-stationarity by modeling spatial relationships that vary across the study area. Bivariate Local Indicators of Spatial Association (LISA) cluster maps were created for superfund site proximity and cancer risk (high-high, low-low, low-high, high-low). Kruskal-Wallis testing compared the distributions of different variables across the four LISA cluster types. Results: Study explored 82,770 census tracts nationwide. For every 10 percentile increase in Superfund proximity, there was an increase of 4% in cancer risk (p < 0.001). This association was strongest in the Intermountain West, South-Southeast, and Northeast regions. High-high clusters (high proximity to Superfund, high cancer risk) had higher percentage of non-White, low-income, unemployed and lower education attainment populations compared to low-low clusters (all p < 0.001). Within high-high clusters, non-White population had the strongest correlation with proximity to Superfunds. Cancer risk was much higher for the top 10% than the bottom 10% of proximity to Superfunds (Table). Conclusions: Our study highlights higher clustering of cancer risk around neighborhoods with existing Superfund sites. Further, we demonstrate the closer a neighborhood is to a Superfund site, the greater the cancer risk is with associated sociodemographic changes. Using a novel approach to geospatial analytics, the results demonstrate the first clear evidence of a dangerous association between Superfunds and healthcare, and encourage the urgent prioritization of Congressional policies that optimize investment in Superfund site clean up for public health. [Table: see text]

Association of Child Growth Failure Indicators With Household Sanitation Practices in India (1998-2021): Spatiotemporal Observational Study.

Undernutrition among children younger than 5 years is a subtle indicator of a country's health and economic status. Despite substantial macroeconomic progress in India, undernutrition remains a significant burden with geographical variations, compounded by poor access to water, sanitation, and hygiene services. This study aimed to explore the spatial trends of child growth failure (CGF) indicators and their association with household sanitation practices in India. We used data from the Indian Demographic and Health Surveys spanning 1998-2021. District-level CGF indicators (stunting, wasting, and underweight) were cross-referenced with sanitation and sociodemographic characteristics. Global Moran I and Local Indicator of Spatial Association were used to detect spatial clustering of the indicators. Spatial regression models were used to evaluate the significant determinants of CGF indicators. Our study showed a decreasing trend in stunting (44.9%-38.4%) and underweight (46.7%-35.7%) but an increasing prevalence of wasting (15.7%-21.0%) over 15 years. The positive values of Moran I between 1998 and 2021 indicate the presence of spatial autocorrelation. Geographic clustering was consistently observed in the states of Madhya Pradesh, Jharkhand, Odisha, Uttar Pradesh, Chhattisgarh, West Bengal, Rajasthan, Bihar, and Gujarat. Improved sanitation facilities, a higher wealth index, and advanced maternal education status showed a significant association in reducing stunting. Relative risk maps identified hotspots of CGF health outcomes, which could be targeted for future interventions. Despite numerous policies and programs, malnutrition remains a concern. Its multifaceted causes demand coordinated and sustained interventions that go above and beyond the usual. Identifying hotspot locations will aid in developing control methods for achieving objectives in target areas.

Reconstruction and normalization of LISA for spatial analysis.

The local indicators of spatial association (LISA) are important measures for spatial autocorrelation analysis. However, there is an inadvertent fault in the mathematical processes of deriving LISA in literature so that the local Moran and Geary indicators do not satisfy the second basic requirement for LISA: the sum of the local indicators is proportional to a global indicator. This paper aims at reconstructing the calculation formulae of the local Moran indexes and Geary coefficients through mathematical derivation and empirical evidence. Two sets of LISAs were clarified by new mathematical reasoning. One set of LISAs is based on non-normalized weights and non-centralized variable (MI1 and GC1), and the other set is based on row normalized weights and standardized variable (MI2 and GC2). The results show that the first set of LISAs satisfy the above-mentioned second requirement, but the second the set cannot. Then, the third set of LISA was proposed and can be treated as canonical forms (MI3 and GC3). This set of LISAs satisfies the second requirement. The observational data of city population and traffic mileage in Beijing-Tianjin-Hebei region of China were employed to verify the theoretical results. This study helps to clarify the misunderstandings about LISAs in the field of geospatial analysis.

A district-level geospatial analysis of the availability of improved water and sanitation among tribal households in India

ABSTRACT Despite progress in Sustainable Development Goal 6, which envisages clean water and sanitation for all, enormous disparities in access to water and sanitation services between and within countries continue to be a significant challenge. Evidence on the spatial heterogeneity of water, sanitation, and hygiene practices among the Scheduled Tribe (ST) population is limited in India. This study estimates the spatial heterogeneity in ST people's access to improved water and sanitation facilities and its correlates at the district level. Geospatial techniques like Moran's I, univariate and bivariate local indicator of spatial association, and spatial regression models were carried out for 707 districts covered in the fifth round of the National Family Health Survey, 2019–21. Stata was used for descriptive analysis, and ArcMap and GeoDA were used for spatial analysis. Only about half of ST households had access to improved water and sanitation facilities in India. Spatial heterogeneity across districts was evident, with 130 districts from Uttarakhand, Himachal Pradesh, Punjab, Haryana, Delhi, Sikkim, Arunachal Pradesh, Nagaland, Mizoram, and some portion of Karnataka forming the hot spots. Gender and age of the household head, family type, and region were significantly associated with improved water and sanitation facilities among ST households.

Using Local Indicators of Spatial Association to Analyze the Environmental Justice Implications of Ambient Air Pollution in the United States

Using Local Indicators of Spatial Association to Analyze the Environmental Justice Implications of Ambient Air Pollution in the United States

Evaluation of the exposure of the Mediterranean biodiversity to marine litter: the ASI – plastic busters MPAs projects connection

A number of marine species in the Mediterranean are threatened by the presence of several pressure factors, which include climate change, collisions with vessels, entanglement and ingestion of marine litter, especially plastic. Risk reduction policies can only be conceived starting from an accurate analysis of the exposure to such pressure factors. To estimate spatial abundance of both marine species and plastic litter and to assess the exposure risk, a two-stage analysis approach was applied, using aerial survey data from the ACCOBAMS Survey Initiative (ASI), in synergy with the Plastic Busters MPAs (PB MPAs) project. First, a detection function was fitted to observation data to obtain detection probabilities for individuals, then a Generalized Additive Model (GAM) was employed to estimate the spatial distribution of relative abundance, based on survey observations. A bivariate Local Indicator of Spatial Association (LISA) was then applied to the maps of relative abundance to derive risk maps of exposure of marine species to marine litter. The maps, obtained with a spatial resolution of about 10 km, allow us to identify areas with the highest neighboring abundance of taxa and marine litter, in particular for the MPAs studied by the PB MPAs project, which include the North-Western Mediterranean (Pelagos Sanctuary and Tuscan Archipelago), the Ionian and Aegean Sea (Zakynthos), and the Strait of Sicily (Cabrera Archipelago).

Spatio-temporal distribution of hospitalizations for chronic Chagas disease and risk factors associated with in-hospital mortality and surgical intervention in Chile.

Chagas disease (CD) is a neglected parasitic zoonotic disease that affects over 6 million people worldwide. We conducted a retrospective study to analyze the spatiotemporal trends and risk factors for hospitalization rates of CD with cardiac and digestive diagnoses in Chile. We used the Mann-Kendall analysis for temporal trends, Global Moran's Index, and Local Indicators of Spatial Association to identify spatial autocorrelation, and regression models to determine the risk factors associated with in-hospital mortality and surgical intervention. Between 2010 and 2020, a total of 654 hospitalizations were reported, corresponding to 527 individuals. The hospitalization rate steadily decreased over the years (t = -0.636; p = 0.009). The Global Moran's I for the study period showed a positive spatial autocorrelation for hospitalization municipality and for residence municipality of CD patients (I = 0.25, p<0.001 and I = 0.45, p<0.001 respectively), indicating a clustering of hospitalizations in northern municipalities. The most frequent diagnosis was a chronic CD with digestive system involvement (55.8%) followed by a chronic CD with heart involvement (44.2%). The highest percentage of hospital discharges was observed among males (56.9%) and in the 60-79 age group (52.7%). In-hospital mortality risk was higher with increasing age (OR = 1.04), and in patients with cardiac involvement (OR = 2.3), whereas factors associated with the risk of undergoing a surgical intervention were sex (OR = 1.6) and diagnosis of CD with digestive involvement (OR = 4.4). The findings of this study indicate that CD is still a significant public health burden in Chile. Efforts should focus on improving access to timely diagnoses and treatment, reducing disease progression and hospitalization burden, and supporting clinicians in preventing complications and deaths.

A spatial analysis of risk factors associated with road collisions in Ciudad Juarez, Mexico and using a geographically weighted regression approach

This study examined changes in the spatial patterns and determinants of road collisions in Ciudad Juarez, Mexico in 2019 and 2020, encompassing the initial period of COVID-19 mobility restrictions. Kernel density estimation and local indicators of spatial association were used to compare collision distributions and identify significant clusters between years. A geographically weighted negative binomial regression model then generated local coefficients to analyze how demographic, socioeconomic, land use, and road network factors influenced collision probability spatially. Results show collisions decreased 13.18% in 2020 but clustered differently, validating restrictions' impact. Population aged 15–64 and industrial land uses significantly increased risk spatially, whereas commercial uses decreased. Lower socioeconomic conditions also correlated with higher risk. Younger populations presented varying collision likelihoods intra-urbanly. This research thus emphasizes how local contexts shape risk factors’ effects and informs data-driven safety policies accounting for place-specific issues. By adopting an explicitly spatial modeling approach, localized risk patterns were uncovered not detectable through traditional methods.

A spatial relationship between density of agricultural land and unmet need for work among elderly in Thailand

Background: Thailand’s aging society is growing as compared to the youngers because of the decreasing birth rate. The transition to an aging society affects the country’s economic system, employment systems, institutional systems, and families. Less productivity of older workers leads to issues like farm succession problems and elderly unemployment. Objectives: The purpose of this study was to assess the proportion of unmet needs for work among the elderly and the spatial association of the density of agricultural land in Thailand. Methods: Data set of 2017 Elderly Population Survey of the National Statistical Office and agricultural area information of the National Statistical Office Thailand were used. A Moran's I and Local Indicators of Spatial Association were used to identify the spatial autocorrelation between unmet needs of work among elderly and density of agriculture land in Thailand. Quantum GIS program and Geo Da program were applied to analyse spatial data. Results: The highest prevalence of unmet needs for work among the elderly in Thailand was in Bueng Kan Province (78.32%) and the lowest was in Lamphun Province (37.20%). Univariate Moran's I analysis showed that there was a concentration of surrounding neighbourhoods with the work needs of the elderly (Hot spot or High-High) in the Samut Prakan Province, and Bueng Kan Province (red area). A high concentration of agricultural land densities with the high unmet work needs of the elderly was found in the Udon Thani and Bueng Kan Province. Conclusion: The findings of this study show the positive and statistically significant relationship in Thai population between agricultural land density and unmet work needs among the elderly. Concern agencies can use the results of this study to establish values and social roles for the elderly in Thailand. Furthermore, this study enables policymakers in the agriculture industry to create and implement an acceptable working environment based on the willingness of elderly.

Historical Hot Spots of Dengue and Zika Viruses to Guide Targeted Vector Control in San Juan, Puerto Rico (2010-2022).

Dengue viruses (DENV) continue to cause large outbreaks in tropical countries, while chikungunya and Zika (ZIKV) viruses have added complexity to Aedes-borne disease prevention and control efforts. Because these viruses are transmitted by the same vectors in urban areas, it is useful to understand if sequential outbreaks caused by these viruses have commonalities, such as similar seasonal and spatial patterns, that would help anticipate and perhaps prevent future outbreaks. We explored and analyzed the heterogeneity of confirmed cases of DENV (2010-2014 and 2015-2022) and ZIKV (2016-2017) during outbreaks in the San Juan metropolitan area of Puerto Rico to explore their degree of overlap and prioritize areas for Aedes aegypti control. Deidentified, georeferenced case data were aggregated into grid cells (500 × 500 m) within a geographical information system of the study area and analyzed to calculate the degree of overlap between outbreaks. Spatial autocorrelations using local indicators of spatial associations were conducted to identify significant disease case hot spots and correlations between outbreaks. We found that 75% of cases during the three transmission periods were concentrated in 25% of the total number of grid cells covering the study area. We also found significant clustering of cases during each outbreak, enabling identification of consistent disease hot spots. Our results showed 85% spatial overlap between cases of ZIKV in 2015-2017 and DENV in 2010-2014 and 97% overlap between DENV cases in 2010-2014 and 2015-2022. These results reveal urban areas at greater risk of future arbovirus outbreaks that should be prioritized for vector control.

Confounded Local Inference: Extending Local Moran Statistics to Handle Confounding

Local statistical analysis has long been of interest to social and environmental scientists who analyze geographic data. Research into local spatial statistics experienced a step-change in the mid-1990s, which provided a large class of local statistical methods and models. The local Moran statistic is one commonly used local indicator of spatial association, able to detect both areas of similarity and observations that are very dissimilar from their surroundings. From this, many further local statistics have been developed to characterize spatial clusters and outliers. These statistics have seen limited adoption because they do not sufficiently model the relationships involved in confounded spatial data, where the analyst seeks to understand the local spatial structure of a given outcome variable that is influenced by one or more additional factors. Recent innovations used to do joint multivariate local analysis also do not model this kind of conditional local structure in data. This article provides tools to rigorously characterize confounded local inference and a new and different class of multivariate conditional local Moran statistics that can account for confounding. To do this, we return to the Moran scatterplot as the critical tool for local Moran-style covariance statistics. Extending this concept, a new method is available directly from a “Moran-form” multiple regression. We show the empirical and theoretical properties of this statistic, show how some existing heuristic approaches arise naturally from this framework, and show how the use of conditional inference can change interpretations in an empirical analysis of rent and housing stock in a rapidly changing neighborhood.

Spatial analysis of food and water-borne diseases in Ahmedabad, India: Implications for urban public health planning

Spatial analysis of infectious diseases can play an important role in mapping the spread of diseases and can support policy making at local level. Moreover, identification of disease clusters based on local geography and landscape forms the basis for disease control and prevention. Therefore, this study aimed to examine the spatial-temporal variations, hotspot areas, and potential risk factors of infectious diseases (including Viral Hepatitis, Typhoid and Diarrhea) in Ahmedabad city of India. We used Moran's I and Local Indicators of Spatial Association (LISA) mapping to detect spatial clustering of diseases. Spatial and temporal regression analysis was used to identify the association between disease incidence and spatial risk factors. The Moran's I statistics identified presence of positive spatial autocorrelation within the considered diseases, with Moran's I from 0.09 for typhoid to 0.21 for diarrhea (p < 0.001). This indicates a clustering of affected wards for each disease, suggesting that cases were not randomly distributed across the city. LISA mapping demonstrated the clustering of hotspots in central regions of the city, especially towards the east of the river Sabarmati, highlighting key geographical areas with elevated disease risk. The spatial clusters of infectious diseases were consistently associated with slum population density and illiteracy. Furthermore, temporal analysis suggested illiteracy rates could increase risk of viral hepatitis by 13 % (95 % Confidence Interval (CI): 1.01–1.26) and of diarrhea by 18 % (95 % CI: 1.07–1.31). Significant inverse association was also seen between viral hepatitis incidence and the distance of wards from rivers. Conclusively, the study highlight the impact of socio-economic gradients, such as slum population density (indicative of poverty) and illiteracy, on the localized transmission of water and foodborne infections. The evident social stratification between impoverished and affluent households emerges as a notable contributing factor and a potential source of differences in the dynamics of infectious diseases in Ahmedabad.

Construction of China's food security evaluation index system and spatiotemporal evolution.

Food security is a vital material foundation for a nation's development and has been a topic of significant concern on the international stage in recent years. With a population exceeding 1.4 billion, China is not only a major producer but also a substantial consumer of food. Ensuring food security in China is not only a top priority for its socio-economic development but also a driving force in maintaining the stability of the global food supply chain and reducing the number of hungry people worldwide. However, a lack of comprehensive research into the Chinese food security system remains. This study addresses this gap by constructing a comprehensive evaluation framework encompassing four dimensions: food supply, accessibility, production stability, and sustainability. Utilizing the Moran's Index and generating LISA (Local Indicators of Spatial Association) maps, we analyze the spatial correlations of food security. The Dagum Gini coefficient and kernel density estimation are applied to assess heterogeneity and spatial disparities. Furthermore, this research employs the Exponential Smoothing (ETS) model to forecast food security trends. The findings reveal that the overall composite food security score exhibited fluctuations, initially increasing and reaching its peak of 0.407 in 2003, followed by a subsequent sharp decline after 2019. Spatially, food security exhibits correlations, with the Huang-Huai-Hai Plain and Northeast regions consistently showing high-high clustering. In contrast, the Western and Southern regions exhibit low-low clustering at specific periods. The Dagum Gini coefficient indicates that overall food security disparities are relatively small. However, these disparities have gradually expanded in recent years, with inter-group differences becoming predominant after 2005. As indicated by the kernel density estimation, the dynamic distribution of food security initially widens and then narrows, suggesting a shift from dispersed to concentrated data distribution. This phenomenon is accompanied by polarization and convergence trends, particularly evident after 2015. According to the ETS model, the study forecasts a substantial risk of declining food security in China over the next decade, largely influenced by the ongoing pandemic. In conclusion, this research provides a comprehensive assessment of the changing status of food security in China. It offers early warnings through predictive analysis, addressing the existing research gaps in the field of food security.

KONUT İŞLEM HACİMLERİ ARACILIĞIYLA KONUT PİYASASININ MEKANSAL ANALİZİ: MENTEŞE, MUĞLA

As there is a reciprocal relationship between the patterns of investor decisions, local and central government strategies, household mobility, and housing transactions have repercussions on both macro and micro scales. Housing transactions within a metropolitan region can alter certain neighbourhood dynamics through the cycle of housing sales regarding urban development patterns, socio-economic characteristics of the residents, tenure type, neighbourhood, and housing quality. Even though the connection between neighbourhood dynamics and housing transactions is undeniable for spatial disci-plines, there are very limited studies for that matter. This study aims to explore the housing market dynamics of Menteşe via housing transactions. It employs three different datasets (Open Street Map (i), Household, housing and office data collected by the Turkish Statistical Institute (ii), Title deed transaction statistics by The Directorate of Land Registry and Cadastre (iii)). For spatial heterogeneity, Moran’s I, and spatial randomness, a local indicator of spatial association (LISA) has been applied. The findings revealed the role of centrality and the importance of mixed land use in housing markets even though all the neighborhoods on the periphery are very close to the city center in terms of distance.

Are National Breast and Cervical Cancer Early Detection Program Recipients Providing Services in Counties Heavily Burdened by Breast and Cervical Cancer?

Alignment of National Breast and Cervical Cancer Early Detection Program (NBCCEDP) clinical services with the spatial distribution of breast and cervical cancer burden is essential to maximizing programmatic impact and addressing cancer disparities. This study identified spatial clustering of breast and cervical cancer burden scores and assessed whether and to what extent NBCCEDP clinical services were associated with clusters for the 5-year period, 2015-2019. We examined burden scores for spatial clustering using Local Indicators of Spatial Association (LISA) tests in GeoDA. We then used t-tests to compare the NBCCEDP 5-year average percentage of eligible women served clinical breast and cervical cancer services between hotspot (high burden) and coolspot clusters. There was statistically significant spatial clustering in the pattern of breast and cervical cancer burden scores across counties, with hotspot clusters mostly observed in the Southern region, Idaho and Nevada. For both breast and cervical cancer, higher percentages of eligible women received breast and cervical cancer clinical services in coolspot clusters compared to hotspot clusters during each year from 2015-2019. NBCCEDP clinical services can help reduce breast and cervical cancer burden. Yet, during 2015-2019, increased service delivery was not aligned with the spatial distribution of counties with greater breast and cervical cancer burdens. NBCCEDP recipients may improve their impact on breast and cervical cancer burden by prioritizing and consistently increasing service delivery in cancer burden hotspot clusters if they have not already maximized their resources in these areas.

Migration timing and marine space use of an anadromous Arctic fish (Arctic Char, Salvelinus alpinus) revealed by local spatial statistics and network analysis.

The ice-free season (typically late-June to early-October) is crucial for anadromous species of fish in the Arctic, including Arctic Char (Salvelinus alpinus), which must acquire adequate resources for growth, reproduction, and survival during a brief period of feeding in the marine environment. Arctic Char is an important food fish for Inuit communities across the Arctic. Understanding drivers and patterns of migration in the marine environment is thus essential for conservation and management of the species. We used passive acoustic telemetry to characterize migration patterns of 51 individual anadromous Arctic Char during the ice-free season in the marine environment of Coronation Gulf (Nunavut, Canada; 2019-2022). Based on recent genetic evidence, some tagged individuals were likely Dolly Varden (Salvelinus malma malma), a closely related species to Arctic Char. Using local Getis G* and network analysis, we described movement patterns and identified high-use locations in the marine environment. We also related freshwater overwintering location to migration timing and movement pattern. Comparing groups of fish that overwintered in distinct locations, we found: (i) limited evidence that marine movements were associated with overwintering location; (ii) minor differences in use of marine space; and, (iii) timing of freshwater return differed significantly between overwintering groups, and was related to length and difficulty of the migratory pathway in freshwater. Results from both network analysis and local Getis G* revealed that, regardless of overwintering location, coastal locations were highly used by fish. Overwintering locations, and the migratory routes to access overwintering locations, affect the timing of freshwater return. Preference of fish for coastal marine locations is likely due to abundance of forage and patterns in break-up of sea ice. Similarities in marine space use and movement patterns present challenges for managing this and other mixed stock fisheries of anadromous Salvelinus spp. Absences or periods of time when fish were not detected prevented comprehensive assessment of movement patterns. Local Getis G*, a local indicator of spatial association, is a helpful tool in identifying locations associated with absences in acoustic telemetry arrays, and is a complementary method to network analysis.

Spatial associations between chronic kidney disease and socio-economic factors in Thailand.

Chronic kidney disease (CKD) is a persistent, progressive condition characterized by gradual decline of kidney functions leading to a range of health issues. This research used recent data from the Ministry of Public Health in Thailand and applied spatial regression and local indicators of spatial association (LISA) to examine the spatial associations with night-time light, Internet access and the local number of health personnel per population. Univariate Moran's I scatter plot for CKD in Thailand's provinces revealed a significant positive spatial autocorrelation with a value of 0.393. High-High (HH) CKD clusters were found to be predominantly located in the North, with Low-Low (LL) ones in the South. The LISA analysis identified one HH and one LL with regard to Internet access, 15 HH and five LL clusters related to night-time light and eight HH and five LL clusters associated with the number of health personnel in the area. Spatial regression unveiled significant and meaningful connections between various factors and CKD in Thailand. Night-time light displayed a positive association with CKD in both the spatial error model (SEM) and the spatial lag model (SLM), with coefficients of 3.356 and 2.999, respectively. Conversely, Internet access exhibited corresponding negative CKD associations with a SEM coefficient of - 0.035 and a SLM one of -0.039. Similarly, the health staff/population ratio also demonstrated negative associations with SEM and SLM, with coefficients of -0.033 and -0.068, respectively. SEM emerged as the most suitable spatial regression model with 54.8% according to R2. Also, the Akaike information criterion (AIC) test indicated a better performance for this model, resulting in 697.148 and 698.198 for SEM and SLM, respectively. These findings emphasize the complex interconnection between factors contributing to the prevalence of CKD in Thailand and suggest that socioeconomic and health service factors are significant contributing factors. Addressing this issue will necessitate concentrated efforts to enhance access to health services, especially in urban areas experiencing rapid economic growth.

How can tracking and tracing systems give us a look at the dark side of the tobacco market?

The aim of this work is to present possible applications of the systems of tobacco traceability for guiding local enforcement against illicit trade. The proposed three-step strategy relies on a robust regression technique and Local Moran's I, a local indicator of spatial association, and aims at identifying retail outlets with significantly low sales compared to normal market conditions, which can indicate illegal sales activities. The ability of the method to produce alerts pointing to areas subject to illicit trade is tested on synthetic data in terms of precision and accuracy in different scenarios. Other metrics are also provided. Our approach performs well under different metrics and across various levels of illicit trade prevalence, achieving a precision of 94% under the main scenario and method parametrisation. The proposed strategy provides high-quality leads for investigations into geographical areas disproportionately susceptible to illicit trade, potentially unveiling any form of illegal sales, including those involving products that have never entered the legal supply chain. Therefore, it can be a valuable tool for law enforcement agencies to tackle illegal sales activities. The findings of this study support also the argument in favour of expanding tobacco traceability systems downstream to the full length of the supply chain.

A district-level geospatial analysis of anaemia prevalence among rural men in India, 2019-21

BackgroundDespite its considerable impact on health and productivity, anemia among men has received limited attention. In a country as diverse as India, characterized by extensive geographic variations, there is a pressing need to investigate the nuanced spatial patterns of anemia prevalence among men. The identification of specific hotspots holds critical implications for policymaking, especially in rural areas, where a substantial portion of India’s population resides.MethodsThe study conducted an analysis on a sample of 61,481 rural men from 707 districts of India, utilizing data from the National Family Health Survey-5 (2019-21). Various analytical techniques, including Moran’s I, univariate LISA (Local Indicators of Spatial Association), bivariate LISA, and spatial regression models such as SLM (Spatial Lag Model), and SEM (Spatial Error Model) were employed to examine the geographic patterns and spatial correlates of anaemia prevalence in the study population.ResultsIn rural India, three out of every ten men were found to be anemic. The univariate Moran’s I value for anaemia was 0.66, indicating a substantial degree of spatial autocorrelation in anaemia prevalence across the districts in India. Cluster and outlier analysis identified five prominent ‘hotspots’ of anaemia prevalence across 97 districts, primarily concentrated in the eastern region (encompassing West Bengal, Jharkhand, and Odisha), the Dandakaranya region, the Madhya Pradesh-Maharashtra border, lower Assam, and select districts in Jammu and Kashmir. The results of SLM revealed significant positive association between anaemia prevalence at the district-level and several key factors including a higher proportion of Scheduled Tribes, men in the 49–54 years age group, men with limited or no formal education, individuals of the Muslim faith, economically disadvantaged men, and those who reported alcohol consumption.ConclusionsSubstantial spatial heterogeneity in anaemia prevalence among men in rural India suggests the need for region-specific targeted interventions to reduce the burden of anaemia among men in rural India and enhance the overall health of this population.

Epidemiological features of seasonal influenza transmission among 11 climate zones in Chinese Mainland

BackgroundPrevious studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes. However, research on seasonal influenza activities based on climate zones are still in lack. This study aims to utilize the ecological-based Köppen Geiger climate zones classification system to compare the spatial and temporal epidemiological characteristics of seasonal influenza in Chinese Mainland and assess the feasibility of developing an early warning system.MethodsWeekly influenza cases number from 2014 to 2019 at the county and city level were sourced from China National Notifiable Infectious Disease Report Information System. Epidemic temporal indices, time series seasonality decomposition, spatial modelling theories including Moran’s I and local indicators of spatial association were applied to identify the spatial and temporal patterns of influenza transmission.ResultsAll climate zones had peaks in Winter-Spring season. Arid, desert, cold (BWk) showed up the first peak. Only Tropical, savannah (Aw) and Temperate, dry winter with hot summer (Cwa) zones had unique summer peak. Temperate, no dry season and hot summer (Cfa) zone had highest average incidence rate (IR) at 1.047/100,000. The Global Moran’s I showed that average IR had significant clustered trend (z = 53.69, P < 0.001), with local Moran’s I identified high-high cluster in Cfa and Cwa. IR differed among three age groups between climate zones (0–14 years old: F = 26.80, P < 0.001; 15–64 years old: F = 25.04, P < 0.001; Above 65 years old: F = 5.27, P < 0.001). Age group 0–14 years had highest average IR in Cwa and Cfa (IR = 6.23 and 6.21) with unique dual peaks in winter and spring season showed by seasonality decomposition.ConclusionsSeasonal influenza exhibited distinct spatial and temporal patterns in different climate zones. Seasonal influenza primarily emerged in BWk, subsequently in Cfa and Cwa. Cfa, Cwa and BSk pose high risk for seasonal influenza epidemics. The research finds will provide scientific evidence for developing seasonal influenza early warning system based on climate zones.