Spatial Scan Statistic Research Articles

Spatial heterogeneity of menstrual discriminatory practices against Nepalese women: A population-based study using the 2022 Demographic and Health Survey.

Menstrual discrimination hampers progress toward Sustainable Development Goals. Examining the spatial heterogeneity of menstrual discriminatory practices may present an opportunity for targeted interventions. Here we evaluate geographical disparities in menstrual-related restrictions and assess their association with socio-economic and demographic factors. We used data from the 2022 Nepal Demographic and Health Survey which included 13,065 women aged 15-49 who reported menstruating within the past year. We explored the spatial heterogeneity of menstrual restriction outcomes using the standard Gaussian kernel density approximation method and the spatial scan statistic. The Poisson regression model with robust standard errors was used to assess the association between the different forms of menstrual restriction and the socio-economic, and demographic factors. Overall, the prevalence of women who reported any form of menstrual restriction was 84.8% and was subject to geographical variations ranging from 79.0% in Bagmati to 95.6% in Sudurpashchim. Religious restrictions were the most prevalent (79.8%) followed by household-level restrictions (39.5%) and then Chhaupadi (6.2%). Geographical variations were more prominent for women experiencing Chhaupadi (primary geographical cluster: relative risk = 7.4, p<0.001). Strikingly, women who reside in households led by female household heads were less likely to report experiencing household-level restriction during menstruation (Adjusted prevalence ratio (aPR) = 0.89, [95%CI: 0.84-0.94], p<0.001) whilst those residing in wealthy households were less likely to report experiencing Chhaupadi (aPR = 0.26, [95%CI: 0.17-0.39], p<0.001; among the richest). Our study demonstrated marked geographical micro-variations in menstrual discriminatory practices in Nepal. Policymakers should implement preventive behavioral interventions in the most vulnerable geographic areas to effectively and efficiently reduce the overall prevalence of menstrual discrimination. It is crucial to prioritize the designing and testing of targeted interventions to determine their effectiveness against Chhaupadi in these high-prevalence settings. Additionally, empowering women appears to be a promising strategy for combating menstrual discrimination within the household.

Spatial disparities in cancer incidence and mortality, indicating gaps in prevention and/or care

Abstract Background There is a rich literature on the distribution of cancer incidence and mortality in socioeconomically different regions of the world, but none of the studies has compared the spatial distribution of mortality and morbidity to see if they are consistent with each other. Methods All malignant neoplasms and separately by cervical, colorectal, breast, pancreatic, lung and oral cancers were studied in the Hungarian population aged 25-64 years for the period 2007-2018 at the municipality level by sex. In each case, the spatial distribution of morbidity and mortality and their clusters were compared with each other and with the level of deprivation using disease mapping, spatial regression, risk analysis and spatial scan statistics. Results All-cause cancer morbidity and mortality showed a significant association with deprivation. However, the slope of the increase in mortality with increasing deprivation was much steeper than for morbidity in both sexes. The association between deprivation and morbidity was not consistent across all cancers. No significant association was found for male colorectal cancer (RR: 1.003; 95%CI 0.986-1.019), pancreatic cancer (1.014; 95%CI 0.983-1.045) and female colorectal cancer morbidity (RR: 0.977; 95%CI 0.991-1.029), whereas a negative association was found for breast cancer morbidity (RR: 0.977; 95%CI 0.963-0.991). In contrast, a positive association between deprivation and mortality was found for each type of cancer, although with different RRs. However, disease mapping analyses showed only partial overlap between high-risk morbidity and mortality areas, often independent of deprivation. Conclusions Our results highlight not only the diverse relationship between the epidemiological situation and deprivation, but also the disproportionate relationship between cancer morbidity and mortality, thus identifying geographical regions and sub-regions with populations that require special public health attention. Key messages • Comparative spatial analysis of cancer morbidity and mortality at the municipality level is strongly recommended to identify populations in need of targeted cancer prevention and/or care. • The usefulness of comparative spatial analysis of cancer morbidity and mortality is demonstrated in Hungary, one of the countries with the most unfavourable cancer burden in international comparison.

Detecting spatial clusters of human scabies in Tigray, Ethiopia from 2018 to 2023

BackgroundScabies is one of public health concerns among communicable disease in Ethiopia, especially among disadvantaged and the poor. This current study aims to detect the spatiotemporal patterns of scabies in Tigray from 2018 to 2023 using scabies data aggregated at the zonal level. The study also examined the persistent patterns in the spatial variation of scabies incidence across the administrative regions during the study period.MethodWe collected scabies data using a weekly disease surveillance reporting format of the country from 2018 to 2023 across all accessible district health facilities in Tigray region, Ethiopia. We conducted retrospective analyses using both purely spatial and spatiotemporal scan statistic approaches, employing a discrete Poisson probability model to identify statistically significant clusters of high scabies rates throughout the Tigray regional zones in Ethiopia. Our methodology involved the use of Kulldorff’s spatial scan statistic software (SaTScan v10.1.3), R programming software version 4.3.1, and ArcGIS Pro for all analyses.ResultsA total of 101,116 cases of scabies were reported from 2018 to 2023. Our study indicated a spatial heterogeneity in the pattern of scabies across Tigray region as well as its localization among geographically contiguous zones across space, except for the Western zone of Tigray where no data was collected. The detected statistically significant spatial clusters \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:p-value<0.05$$\\end{document} persisted mainly in the Central, Eastern and Northwestern zones of Tigray over the six years of the study period. The highest relative risk (RR) was recorded in year 2021 (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:RR=8.49,\\:LLR=6860.34,p-value=0.01)$$\\end{document}. The central zone had the major clusters of scabies at district level from 2018 to 2023. The heterogeneous distribution of scabies across Tigray could be due to the spatial variations in the determinants of scabies (such as socioeconomic status, demographics, and material deprivation) across the region.ConclusionAn enormous burden of scabies was reported over a period of six years. The present study found localized clusters of high scabies rates at district and zonal levels in Tigray, Ethiopia, possibly due to differences in various determinants of scabies such as access to WASH services. The findings could help the government and health authorities to develop and implement scabies control strategies in Tigray, with a focus on high-risk districts and zones to ensure optimal resource allocation.

High-risk spatiotemporal patterns of leprosy in the southeastern region of Yunnan province from 2010 to 2022: an analysis at the township level

BackgroundDespite being preventable and curable, leprosy remains endemic in some undeveloped regions, including China. Wenshan Zhuang and Miao Autonomous Prefecture (Wenshan prefecture) currently bears the highest leprosy burden in China. In this ecological study, we aimed to analyze the epidemiological characteristics as well as identify and visualize the high-risk townships of Wenshan prefecture using the most updated leprosy data from 2010 to 2022.MethodsGeographical information system combined with spatial scan statistics was used for newly detected leprosy cases abstracted from the Leprosy Management Information System in China. Global Moran’s I index was used to uncover the spatial pattern of leprosy at the township level. Spatial scan statistics, encompassing purely temporal, purely spatial, spatial variation in temporal trends, and space-time analysis, were implemented for detecting the risk clusters.ResultsBetween 2010 and 2022, Wenshan prefecture detected 532 new leprosy cases, comprising 352 (66.17%) males and 180 (33.83%) females. The aggregated time primarily occurred between October 2010 and March 2014. The distribution pattern of newly detected leprosy cases was spatially clustered. We identified four high-risk spatial clusters encompassing 54.51% of the new cases. Furthermore, spatial variation in temporal trends highlighted one cluster as a potential high-risk area. Finally, two space-time clusters were detected, and the most likely cluster was predominantly located in the central and northwest regions of Wenshan prefecture, spanning from January 2010 to September 2013.ConclusionsIn this ecology study, we characterized the epidemiological features and temporal and spatial patterns of leprosy in Wenshan prefecture using the most recent leprosy data between 2010 and 2022. Our findings offer scientific insights into the epidemiological profiles and spatiotemporal dynamics of leprosy in Wenshan prefecture. Clinicians and policymakers should pay particular attention to the identified clusters for the prevention and control of leprosy.

Filling the Treatment Gap: Geographic Expansion of Buprenorphine Providers Across the U.S.

ObjectiveWith opioid-related deaths reaching epidemic levels, gaining a better understanding of access to treatment for opioid use disorder (OUD) is critical. Buprenorphine is an effective medication for the treatment of opioid addiction. The analysis is critical post-X-waiver elimination which extended the ability to prescribe buprenorphine for the treatment of OUD to all practitioners with DEA Schedules II-V on their DEA Registration. The primary purpose of the analysis was to explore the geographic patterns of substance use treatment and establish a geographical baseline to assess the removal of the X-Waiver in the future. Data and MethodsWe assessed the expansion of buprenorphine providers across the data from all US counties up to December 31, 2022. We used all certified buprenorphine providers' data from the database of the buprenorphine waiver notification system (BWNS). We used county-level population data from the US Census of American Community Survey (ACS) of 2021 and the CDC's Drug-related mortality data. We implemented spatial scan statistics to identify the spatial clusters using SaTScan. ResultsThe results from our analysis show that Doctor of Medicine/Doctor of Osteopathic Medicine (MD/DO) have the highest numbers of certified providers at 8134 (65.08%) in 2018 and 14525 (57.87%) in 2022. Our analysis shows that the distribution of buprenorphine providers across the counties of the United States was significantly clustered. Higher clusters with RR of more than 1 (p-value <0.001) were found in the states of Washington, Oregon, Northern California, and the Western borders of Montana. Similar clusters of counties with RR more than 1 (p-value <0.001) were found in the northeastern states of Maine, Vermont, and New Hampshire. ConclusionsFrom our analysis, it is evident that buprenorphine-certified providers are clustered in areas of higher drug-related mortality filling a treatment gap. The elimination of the X-waiver will be a significant step towards increasing access to MOUD and this analysis may be used as a geographical baseline to assess in the future.

Spatial distribution and clustering of contagious caprine pleuropneumonia in goats of Amhara region, Ethiopia

A cross-sectional study with a multistage cluster sampling technique was undertaken from January to July 2019 in Amhara region to identify spatial clusters and distributions of contagious caprine pleuropneumonia (CCPP) hotspots. In the study, a total of 2080 goats from 258 flocks across 60 villages in 12 districts were tested for CCPP serostatus using Competitive Enzyme-Linked Immunosorbent Assay (C-ELISA). Villages were taken as an aggregate unit to detect spatial distribution and clustering. Spatial autocorrelation, interpolation, and spatial scan statistics analyses were employed to analyze the spatial patterns and clusters of CCPP serostatus. The overall seroprevalence of CCPP at the animal level was 5.1 % (95 % CI: 3.8–6.6). The spatial distribution of CCPP seropositivity was non-random at the village level (Moran's I: 0.400791, P-value <0.01). Two statistically significant spatial clusters of CCPP seropositive were found: one in West Gojjam and the other encompassing parts of North Wollo, Oromiya Liyu zone, and South Wollo zones. The CCPP seropositivity showed spatial variation across the Amhara region. High CCPP seropositive spots were found in the southwestern and northeastern parts of the Amhara region. Generally, the findings of this study provide valuable insights in developing disease control strategies, prioritizing target intervention areas and measures, and designating CCPP disease-free zones. Applying movement restrictions and vaccinating goats will be important intervention measures to prevent and control CCPP in the region.

Geospatial modelling of ambient air pollutants and chronic obstructive pulmonary diseases at regional scale in Pakistan.

Pakistan is among the South Asian countries mostly vulnerable to the negative health impacts of air pollution. In this context, the study aimed to analyze the spatiotemporal patterns of chronic obstructive pulmonary disease (COPD) incidence and its relationship with air pollutants including aerosol absorbing index (AAI), carbon monoxide, sulfur dioxide (SO2), and nitrogen dioxide. Spatial scan statistics were employed to identify temporal, spatial, and spatiotemporal clusters of COPD. Generalized linear regression (GLR) and random forest (RF) models were utilized to evaluate the linear and non-linear relationships between COPD and air pollutants for the years 2019 and 2020. The findings revealed three spatial clusters of COPD in the eastern and central regions, with a high-risk spatiotemporal cluster in the east. The GLR identified a weak linear relationship between the COPD and air pollutants with R2 = 0.1 and weak autocorrelation with Moran's index = -0.09. The spatial outcome of RF model provided more accurate COPD predictions with improved R2 of 0.8 and 0.9 in the respective years and a very low Moran's I = -0.02 showing a random residual distribution. The RF findings also suggested AAI and SO2 to be the most contributing predictors for the year 2019 and 2020. Hence, the strong association of COPD clusters with some air pollutants highlight the urgency of comprehensive measures to combat air pollution in the region to avoid future health risks.

The distribution of cardiovascular diseases in Tanzania: a spatio-temporal investigation.

Cardiovascular Disease (CVD) is currently the major challenge to people's health and the world's top cause of death. In Tanzania, deaths due to CVD account for about 13% of the total deaths caused by the non-communicable diseases. This study examined the spatio-temporal clustering of CVDs from 2010 to 2019 in Tanzania for retrospective spatio-temporal analysis using the Bernoulli probability model on data sampled from four selected hospitals. Spatial scan statistics was performed to identify CVD clusters and the effect of covariates on the CVD incidences was examined using multiple logistic regression. It was found that there was a comparatively high risk of CVD during 2011-2015 followed by a decline during 2015-2019. The spatio-temporal analysis detected two high-risk disease clusters in the coastal and lake zones from 2012 to 2016 (p<0.001), with similar results produced by purely spatial analysis. The multiple logistic model showed that sex, age, blood pressure, body mass index (BMI), alcohol intake and smoking were significant predictors of CVD incidence.

Performance of a negative binomial-GLM in spatial scan statistic: a case study of low-birth weights in Pakistan.

Spatial cluster analyses of health events are useful for enabling targeted interventions. Spatial scan statistic is the stateof- the-art method for this kind of analysis and the Poisson Generalized Linear Model (GLM) approach to the spatial scan statistic can be used for count data for spatial cluster detection with covariate adjustment. However, its use for modelling is limited due to data over-dispersion. A Generalized Linear Mixed Model (GLMM) has recently been proposed for modelling this kind of over-dispersion by incorporating random effects to model area-specific intrinsic variation not explained by other covariates in the model. However, these random effects may exhibit a geographical correlation, which may lead to a potential spatial cluster being undetected. To handle the over-dispersion in the count data, this study aimed to evaluate the performance of a negative binomial- GLM in spatial scan statistic on real-world data of low birth weights in Khyber-Pakhtunkhwa Province, Pakistan, 2019. The results were compared with the Poisson-GLM and GLMM, showing that the negative binomial-GLM is an ideal choice for spatial scan statistic in the presence of over-dispersed data. With a covariate (maternal anaemia) adjustment, the negative binomial-GLMbased spatial scan statistic detected one significant cluster covering Dir lower district. Without the covariate adjustment, it detected two clusters, each covering one district. The district of Peshawar was seen as the most likely cluster and Battagram as the secondary cluster. However, none of the clusters were detected by GLMM spatial scan statistic, which might be due to the spatial correlation of the random effects in GLMM.

Geographic Patterns of Youth Suicide in San Diego County

ObjectiveOur objectives were to evaluate for any cluster patterns of youth suicide deaths and characterize the level of child opportunity in the communities where suicide deaths occurred. MethodsDecedents <18 years were identified from San Diego County Medical Examiner death reports from 2000 to 2020. We mapped decedents’ residential Zone Improvement Plan (ZIP) codes and calculated suicide rates per 10,000 youths. ZIP codes identified in overlapping spatial statistical approaches — the spatial scan statistic and Local Moran with Empirical Bayes (EB) rates — were considered a cluster for the final analysis. We obtained Child Opportunity Index (COI) scores for each ZIP code to determine if there were differences in: 1) ZIP codes with suicide deaths compared to ZIPs with no deaths 2) differences in distribution of suicide death rates across quintiles of COI. ResultsScan statistic identified 25 ZIP codes within a cluster (RR 2.6, P = 0.00066). Local Moran with EB rates identified two ZIP codes as a high-high cluster (P < 0.05). The location identified as a cluster in both approaches was in Alpine. The median COI for ZIP codes with suicide deaths was higher at 63.5 (IQR 38–83) compared to 47 (IQR 22.5–75.5) for ZIP codes without suicide deaths. There was a significant difference in suicide rates between Very Low and Moderate levels of Overall opportunity (P = .013). ConclusionWe identified a cluster of youth suicides in one of the most populous counties in the country. These findings serve to inform policies and prevention programs that aim to mitigate youth suicide mortality.

Prevalence, spatial distribution and risk mapping of Dirofilaria immitis in wild canids in southern Québec, Canada

Domestic dogs (Canis familiaris) and wild canids, including coyotes (Canis latrans) and red foxes (Vulpes vulpes), serve as definitive hosts for Dirofilaria immitis, a parasitic nematode causing the heartworm disease. Understanding infection risks in wildlife reservoirs in relation to environmental factors is crucial for assessing exposure risk in domestic dogs. The regional prevalence of D. immitis infection was estimated in trapped wild coyotes and red foxes across Québec, Canada. Spatial clusters of infection were detected using Kulldorff's spatial scan statistics. A series of logistic regression models predicting the D. immitis status in coyotes were built from heartworm development unit (HDU) estimates and cumulative precipitation variables over various time periods. Between October 2020 and March 2021, 421 coyotes and 284 red foxes were examined for the presence of D. immitis. The parasite was found in 43 coyotes and 1 red fox. A high-risk infection cluster was detected in coyotes in southwestern Québec. The best model included as sole predictor the average cumulative HDU contributing to risk of D. immitis in the three years preceding coyote capture. This model significantly predicted infection status with an area under the curve of 76.1%. The cumulative precipitation had no notable effect in any model. This study highlights a high prevalence of D. immitis in coyotes in Québec with regional differences correlated to temperature-derived predictors. The spatial risk of infection in this population likely represents the environmental risk of exposure to the parasite given that coyotes do not receive preventive treatment compared to domestic dogs. Our findings are important for veterinarians in the application of prevention strategies for heartworm disease in domestic dogs.

Application of spatial scan statistics on retrospective data to detect outbreaks of dengue hemorrhagic fever in Hai An and Duong Kinh districts, Hai Phong, 2020 - 2023

Dengue fever is an endemic disease that can lead to major outbreaks in Vietnam. This study aims to describe application of spatial scan statistics on retrospective data to detect outbreaks of dengue hemorrhagic fever in Hai An and Duong Kinh districts, Hai Phong, 2020 - 2023. The study analyzed aggregate dengue data covering 177 weeks (from Week 1/2020 to Week 20/2023). During the retrospective data period, 360 cases were reported from 14 wards of Hai An and Duong Kinh districts, of which 74 cases formed 5 clusters, with the remaining cases being sporadic. The clusters mainly occurred in November each year. Scan statistics should be systematically applied in monitoring dengue fever outbreaks, as the study results demonstrate that scan techniques can effectively detect clusters in the community.

Discrepancies between the Spatial Distribution of Cancer Incidence and Mortality as an Indicator of Unmet Needs in Cancer Prevention and/or Treatment in Hungary.

There is a rich body of literature on the distribution of cancer incidence and mortality in socioeconomically different world regions, but none of the studies has compared the spatial distribution of mortality and incidence to see if they are consistent with each other. All malignant neoplasms combined and cervical, colorectal, breast, pancreatic, lung, and oral cancers separately were studied in the Hungarian population aged 25-64 years for 2007-2018 at the municipality level by sex. In each case, the spatial distribution of incidence and mortality were compared with each other and with the level of deprivation using disease mapping, spatial regression, risk analysis, and spatial scan statistics. A positive association between deprivation and mortality was found for each type of cancer, but there was no significant association for male colorectal cancer (relative risk (RR) 1.00; 95% credible interval (CI) 0.99-1.02), pancreatic cancer (RR: 1.01; 95%CI 0.98-1.04), and female colorectal cancer incidence (RR: 1.01; 95%CI 0.99-1.03), whereas a negative association for breast cancer (RR: 0.98; 95%CI 0.96-0.99) was found. Disease mapping analyses showed only partial overlap between areas of high incidence and mortality, often independent of deprivation. Our results highlight not only the diverse relationship between cancer burden and deprivation, but also the inconsistent relationship between cancer incidence and mortality, pointing to areas with populations that require special public health attention.

Analysis of management factors influencing Anaplasma marginale transmission

The aim of this study was to identify factors that play a key role in the epidemiology of bovine anaplasmosis by adapting a model primarily developed for cattle babesiosis. A cross-sectional observational study was conducted to study the proportion of calf herds in endemic stability/instability for A. marginale in a semi-arid area of Argentina. The A. marginale inoculation rate (h) was calculated from age-specific seroprevalence using double-antigen sandwich ELISA in 58 herds of 4.5–8.5-month–old calves. Herds were considered to be in endemic instability (EI) at h < 0.005 and, therefore, at risk of anaplasmosis outbreaks. A generalized linear model was performed to explore husbandry practices associated with differences in A. marginale transmission. Additionally, spatial clustering of herds with the same immunological status was analyzed using spatial scan statistics (SatScan, Bernoulli model). Spearman's correlation was used to explore a possible association between A. marginale h and Babesia bovis and B. bigemina h (data obtained in previous works). Almost half (43 %) of the herds were in the EI zone for A. marginale. Calves raised under forage combinations had a greater risk of being in EI (OR = 5.41, CI95 %OR = 1.43–20.41) than those reared exclusively on permanent pastures, where cattle density is higher (P = 0.01). Moreover, calves from herds treated only with pyrethroids to control ticks had more chances of being in EI (OR = 4.16, CI95 %OR = 1.12–15.38) than calves from herds receiving different acaricide combinations (P = 0.03). Calves from herds subjected to more than two treatments against Haematobia irritans had higher odds for EI (OR = 5.69, CI95 %OR = 1.24–26.11) than those from herds using fewer than two treatments (P = 0.02). The spatial analysis revealed no spatial clustering of the immune status of the herds (P = 0.67 and P = 0.74 for low and high incidence rates, respectively). A significant variation between farms was observed in A. marginale h (CV = 90.38 %). The correlation analysis revealed a strong epidemiological link of A. marginale h with B. bovis h (Rho=0.794, P<0.001) and B. bigemina h (Rho=0.839, P<0.001). Given that R. microplus is the only vector of B. bovis and B. bigemina in the region, the results of this work strongly suggest an active and significant role of R. microplus in the transmission of A. marginale.

Application of Geographic Information Systems (GIS) in the Study of Prostate Cancer Disparities: A Systematic Review.

Introduction: PCa is one of the cancers that exhibits the widest disparity gaps. Geographical place of residence has been shown to be associated with healthcare access/utilization and PCa outcomes. Geographical Information Systems (GIS) are widely being utilized for PCa disparities research, however, inconsistencies in their application exist. This systematic review will summarize GIS application within PCa disparities research, highlight gaps in the literature, and propose alternative approaches. Methods: This paper followed the methods of the Cochrane Collaboration and the criteria set of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Articles published in peer-reviewed journals were searched through the PubMed, Embase, and Web of Science databases until December 2022. The main inclusion criteria were employing a GIS approach and examining a relationship between geographical components and PCa disparities. The main exclusion criteria were studies conducted outside the US and those that were not published in English. Results: A total of 25 articles were included; 23 focused on PCa measures as outcomes: incidence, survival, and mortality, while only 2 examined PCa management. GIS application in PCa disparities research was grouped into three main categories: mapping, processing, and analysis. GIS mapping allowed for the visualization of quantitative, qualitative, and temporal trends of PCa factors. GIS processing was mainly used for geocoding and smoothing of PCa rates. GIS analysis mainly served to evaluate global spatial autocorrelation and distribution of PCa cases, while local cluster identification techniques were mainly employed to identify locations with poorer PCa outcomes, soliciting public health interventions. Discussion: Varied GIS applications and methodologies have been used in researching PCa disparities. Multiple geographical scales were adopted, leading to variations in associations and outcomes. Geocoding quality varied considerably, leading to less robust findings. Limitations in cluster-detection approaches were identified, especially when variations were captured using the Spatial Scan Statistic. GIS approaches utilized in other diseases might be applied within PCa disparities research for more accurate inferences. A novel approach for GIS research in PCa disparities could be focusing more on geospatial disparities in procedure utilization especially when it comes to PCa screening techniques. Conclusions: This systematic review summarized and described the current state and trend of GIS application in PCa disparities research. Although GIS is of crucial importance when it comes to PCa disparities research, future studies should rely on more robust GIS techniques, carefully select the geographical scale studied, and partner with GIS scientists for more accurate inferences. Such interdisciplinary approaches have the potential to bridge the gaps between GIS and cancer prevention and control to further advance cancer equity.

Geospatial determinants and spatio-temporal variation of early initiation of breastfeeding and exclusive breastfeeding in Ethiopia from 2011 to 2019, a multiscale geographically weighted regression analysis

BackgroundBreastfeeding offers numerous benefits for infants, mothers, and the community, making it the best intervention for reducing infant mortality and morbidity. The World Health Organization (WHO) recommends initiating breastfeeding within one hour after birth and exclusively breastfeeding for the first six months. This study investigated the trend, spatio-temporal variation, and determinants of spatial clustering of early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) in Ethiopia from 2011 to 2019.MethodsData from the Ethiopian Demographic and Health Survey (EDHS), which was conducted in 2011, 2016, and 2019, were analyzed utilizing a weighted sample of 10,616 children aged 0–23 years for EIBF and 2,881 children aged 0–5 months for EBF. Spatial autocorrelation analysis was used to measure whether EIBF and EBF were dispersed, clustered, or randomly distributed and Kriging interpolation was employed to predict the outcome variables in the unmeasured areas. Spatial scan statistics were used to identify spatial clusters with a high prevalence of cases. Both global and local regression modeling techniques were employed to examine the spatial relationships between the explanatory variables and the dependent variables.ResultsThe trend analysis revealed a notable increase in the prevalence of EIBF from 51.8% in 2011 to 71.9% in 2019. Similarly, the prevalence of EBF increased from 52.7% in 2011 to 58.9% in 2019. Spatial analysis demonstrated significant spatial variation in both EIBF and EBF throughout the country. Cold spots or clusters with a low prevalence of EIBF were observed consistently in the Tigray and Amhara regions, and significant cold spot areas of EBF were observed consistently in the Afar and Somali regions. Multiscale geographically weighted regression analysis revealed significant predictors of spatial variations in EIBF, including the religious affiliation of being a follower of the orthodox religion, parity of 1–2, absence of antenatal care visits, and delivery via cesarean section.ConclusionsDespite the increase in both EIBF and EBF rates over time in Ethiopia, these rates still fall below the national target. To address this issue, the government should prioritize public health programs aimed at improving maternal healthcare service utilization and maternal education. It is essential to integrate facility-level services with community-level services to achieve optimal breastfeeding practices. Specifically, efforts should be made to promote breastfeeding among mothers who have delivered via cesarean section. Additionally, there should be a focus on encouraging antenatal care service utilization and adapting maternal healthcare services to accommodate the mobile lifestyle of pastoralist communities. These steps will contribute to enhancing breastfeeding practices and achieving better outcomes for maternal and child health.

Spatial and temporal distribution characteristics research of esophageal cancer in China

Objectives: To explore the spatial distribution characteristics, trend changes, and spatial clustering of esophageal cancer among residents in China at the county (city, district) scale, a spatial epidemiological approach was used, with the aim of providing localized evidence for the prevention and treatment of esophageal cancer in China. Methods: The data source was the incidence (crude rate) and mortality (crude rate) of esophageal cancer from 2005 to 2016 in the 2008-2019 edition of China Cancer Registration Annual Report published by the National Cancer Center. The Joinpoint model was used for time trend analysis. The tumor registration area in 2016 was selected as the study area for spatial feature analysis, with a total of 487 counties (cities and districts), covering 27.6% of the national population. Spatial autocorrelation analysis was performed to reveal spatial distribution characteristics by using Arcgis 10.6 software, and spatial scanning statistics was used to analyze spatial clustering characteristics by using SaTScan 9.5 software. The log-likelihood ratio (LLR) and relative risk (RR) were calculated in different windows, and the region with the largest LLR value represented the most likely cluster. Results: From 2005 to 2016, the incidence and mortality rate of esophageal cancer in China showed a trend of increasing at first and then decreasing. The incidence and mortality rate of esophageal cancer in 2016 were characterized by spatial positive correlation. High incidence and high mortality were mainly concentrated in the areas through which the Huaihe River flowed. The primary clusters (taking high incidence rate as an example LLR=6 374.41, RR=2.37, P＜0.001) were mainly distributed in Jiangsu, Anhui and Shandong in eastern China and eastern Henan and southern Hebei in central China, and secondary clusters (taking high incidence rate as an example LLR=1 971.19, RR=1.91, P＜0.001) in Gansu, Ningxia Hui Autonomous Region, Shaanxi, Sichuan and other central and western regions. Conclusions: The incidence and mortality of esophageal cancer in China have decreased since 2010. The disease burden of esophageal cancer has obvious spatial differences, and measures should be taken according to local conditions in high-risk cluster areas such as the Huaihe River basin.

Insights into canine rabies vaccination Disparities in Sierra Leone: A cross-sectional household study.

Annually, Sierra Leone records an estimated 301 human fatalities due to rabies. Canine vaccination is crucial for rabies prevention and control efforts. However, considerable variability exists in vaccination rates. Reasons for this variation remain unclear. We conducted a cross-sectional study across 2,558 dog-owning households (HHs) to provide insights into factors influencing canine vaccination for targeted prevention and control towards elimination by 2030. First, we described dog ownership practices, then built a probabilistic model to understand factors associated with dog vaccination, and finally used a spatial scan statistic to identify spatial clusters where vaccination rates were low. Our results indicated that only 14% (358/2,558) of participating HHs had fully vaccinated their dogs against rabies. The probability of dog vaccination increased when comparing civil servants to private workers/artisans, with an Odds Ratio(OR) of 1.14 (95% credible interval (Crl) of 0.82-1.56), residing in locations with a veterinary establishment vs. none (OR = 6.43, 95% Crl (4.97-8.35), providing care to dogs vs. allowing dogs to roam freely (OR = 2.38, 95% Crl(1.80-3.17) and owning a single dog vs multiple dogs (OR = 1.20, 95 Crl (0.92-1.56). Conversely, there was a decrease in the estimated probability of vaccination when comparing dog owners located in rural vs. urban areas (OR = 0.58, CrI 95% (0.43-0.78). Latent understanding, a measure of overall understanding of rabies virus, which we estimated using participant education levels and responses to questions about rabies epidemiology, was also an important predictor of vaccination probability (OR = 1.44, 95% Crl (1.04-2.07). The spatial analysis identified high-risk clusters for low vaccination in the cities of Moyamba, with a radius of 40 km, a relative risk (RR) of 1.10, and Bo, with a radius of 19.9 km with RR of 1.11. These data do not support Sierra Leone reaching the 2030 goal of human rabies elimination caused by dogs. Our study highlights a critical need for public outreach and education, improved vaccination rates, increased accessibility to veterinary services, and targeted interventions in Bo and Moyamba to support rabies prevention and control efforts.

A Descriptive Analysis of Childhood Lead Poisoning in Jefferson County, Kentucky – Louisville’s Silent Epidemic

Introduction In this study we provide an epidemiological description of Elevated Blood Lead Levels (EBLLs) in the pediatric population of Jefferson County, KY. To provide a more thorough epidemiological description of this issue, we put together the largest known pediatric blood lead dataset for Jefferson County and evaluated the data using the Center for Disease Control and Prevention’s current Blood Lead Referent Value (BLRV) of &gt; 3.5 mcg/dL. Methods We collected 111,065 rows of pediatric blood lead data from the Louisville Metro Department of Public Health and Wellness. Descriptive and inferential statistics were used to explore the magnitude of this issue. Unadjusted Risk Ratios (RR) were also calculated by specific demographics. Geographic cluster analysis was produced by the Kulldorff spatial scan statistic. Results There are 74,014 unique children in the blood lead dataset, of which 9,823 had at least one blood lead test &gt; 3.5 mcg/dL. Black children had a 77% increased risk for an EBLL compared to white children (Unadjusted Risk Ratio: 1.77; 95% CI: 1.61, 1.95). Additionally, children who live in the northwest corner of Jefferson County, have an estimated 9.37-fold increased risk for an EBLL compared to children who live elsewhere in the county. Discussion The non-random distribution of EBLLs in Jefferson County is likely due to the distribution of Pre-1950 housing which is a known risk factor for remnant environmental lead. This issue may be a consequence of the systematic financial disinvestment of these neighborhoods via redlining during the 20th century. Childhood lead poisoning remains a critical public health issue in Jefferson County, KY.

SARS-CoV-2 seroprevalence among Beninese pregnant women in the third year of the pandemic

BackgroundPregnant women are a vulnerable population to COVID-19 given an increased susceptibility to severe SARS-CoV-2 infection and pregnancy complications. However, few SARS-CoV-2 serological surveys have been performed among this population to assess the extent of the infection in sub-Saharan countries. The objectives of this study were to determine SARS-CoV-2 seroprevalence among Beninese pregnant women, to identify spatial seropositivity clusters and to analyse factors associated with the infection.MethodsA cross-sectional study including women in their third trimester of pregnancy attending the antenatal care (ANC) clinics at Allada (south Benin) and Natitingou (north Benin) was conducted. Rapid diagnostic tests (RDT) for detection of IgG/IgM against the SARS-CoV-2 spike protein were performed using capillary blood. Seroprevalence of SARS-CoV-2 antibodies and associations between SARS-CoV-2 serostatus and maternal characteristics were analyzed by multivariate logistic regression. Spatial analyses were performed using the spatial scan statistics to identify spatial clusters of SARS-CoV-2 infection.ResultsA total of 861 pregnant women were enrolled between May 4 and June 29, 2022. 58/861 (6.7%) participants reported having received COVID-19 vaccine. None of the participants had been diagnosed with COVID-19 during their pregnancy. SARS-CoV-2 antibodies were detected in 607/802 (75.7%; 95% CI 72.56%–78.62%) of unvaccinated participants. Several urban and rural spatial clusters of SARS-CoV-2 cases were identified in Allada and one urban spatial cluster was identified in Natitingou. Unvaccinated participants from Allada with at least one previous morbidity were at a three-times higher risk of presenting SARS-CoV-2 antibodies (OR = 2.89; 95%CI 1.19%-7.00%).ConclusionThree out of four pregnant women had SARS-CoV-2 antibodies, suggesting a high virus circulation among pregnant women in Benin, while COVID-19 vaccination coverage was low. Pregnant women with comorbidities may be at increased risk of SARS-CoV-2 infection. This population should be prioritized for COVID-19 diagnosis and vaccination in order to prevent its deleterious effects.Trial registrationNCT06170320 (retrospectively registered on December 21, 2023).