Scan Statistic Research Articles

Google Trends Assessment of Keywords Related to Smoking and Smoking Cessation During the COVID-19 Pandemic in 4 European Countries: Retrospective Analysis

Background Smoking is a modifiable risk factor for SARS-CoV-2 infection. Evidence of smoking behavior during the pandemic is ambiguous. Most investigations report an increase in smoking. In this context, Google Trends data monitor real-time public information–seeking behavior and are therefore useful to characterize smoking-related interest over the trajectory of the pandemic. Objective This study aimed to use Google Trends data to evaluate the effect of the pandemic on public interest in smoking-related topics with a focus on lockdowns, vaccination campaigns, and incidence. Methods The weekly relative search volume was retrieved from Google Trends for England, Germany, Italy, and Spain from December 31, 2017, to April 18, 2021. Data were collected for keywords concerning consumption, cessation, and treatment. The relative search volume before and during the pandemic was compared, and general trends were evaluated using the Wilcoxon rank-sum test. Short-term changes and hereby temporal clusters linked to lockdowns or vaccination campaigns were addressed by the flexible spatial scan statistics proposed by Takahashi and colleagues. Subsequently, the numbers of clusters after the onset of the pandemic were compared by chi-square test. Results Country-wise minor differences were observed while 3 overarching trends prevailed. First, regarding cessation, the statistical comparison revealed a significant decline in interest for 58% (7/12) of related keywords, and fewer clusters were present during the pandemic. Second, concerning consumption, significantly reduced relative search volume was observed for 58% (7/12) of keywords, while treatment-related keywords exhibited heterogeneous trends. Third, substantial clusters of increased interest were sparsely linked to lockdowns, vaccination campaigns, or incidence. Conclusions This study reports a substantial decline in overall relative search volume and clusters for cessation interest. These results underline the importance of intensifying cessation aid during times of crisis. Lockdowns, vaccination, and incidence had less impact on information-seeking behavior. Other public measures that positively affect smoking behavior remain to be determined.

Spatio-temporal analysis of pulmonary tuberculosis in Astambul District, South Kalimantan, Indonesia 2020-2021

Respiratory tuberculosis (TB) remains a significant global health concern and ranks among the top 10 leading causes of death worldwide, following human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS). Globally, it is estimated that 1.2 billion people are at risk of being infected with tuberculosis, and Indonesia is the country that contributes the third-highest number of TB cases worldwide, behind China and India. Tuberculosis is still a significant issue in South Kalimantan, notably in Banjar Regency. This study is a descriptive study that intends to identify respiratory TB clusters spatially and temporally in Astambul District during 2020–2021 with the SatScan application, which is visualized as a map of respiratory TB clusters using the Quantum GIS 3.28 application. Data on respiratory TB cases collected from the Banjar District Health Office were examined using retrospective space-time scan statistics, employing a Poisson probability model for analysis. This study found 4 respiratory TB clusters, and 2 of them had significant results, namely in 2020 (RR=6.90, p-value=0.000030) and 2021 (RR=5.27, p-value=0.00003). Factors that affect these clusters are population density, the physical condition of houses, humidity levels, and the availability of health facilities.

Flexible scan statistic with a restricted likelihood ratio for optimized COVID-19 surveillance.

Disease surveillance remains important for early detection of new COVID-19 variants. For this purpose, the World Health Organization (WHO) recommends integrating of COVID-19 surveillance with other respiratory diseases. This requires knowledge of areas with elevated risk, which in developing countries is lacking from the routine analyses. Focusing on Ghana, this study employed scan-statistic cluster analysis to uncover the spatial patterns of incidence and Case Fatality Rates (CFR) of COVID-19 based on reports covering the four pandemic waves in Ghana between 12 March 2020 and 28 February 2022. Applying flexible spatial scan statistic with restricted likelihood ratio, we examined the incidence and CFR clusters before and after adjustment for covariates. We used distance to the epicentre, proportion of the population aged ≥ 65, male proportion of the population and urban proportion of the population as the covariates. We identified 56 significant spatial clusters for incidence and 26 for CFR for all four waves of the pandemic. The Most Likely Clusters (MLCs) of incidence occurred in the districts in south-eastern Ghana, while the CFR ones occurred in districts in the central and the northeastern parts of the country. These districts could serve as sites for sentinel or genomic surveillance. Spatial relationships were identified between COVID-19 incidence covariates and the CFR. We observed closeness to the epicentre and high proportions of urban populations increased COVID-19 incidence, whiles high proportions of those aged ≥ 65 years increased the CFR. Accounting for the covariates resulted in changes in the distribution of the clusters. Both incidence and CFR due to COVID-19 were spatially clustered, and these clusters were affected by high proportions of the urban population, high proportions of the male population, high proportions of the population aged ≥ 65 years and closeness to the epicentre. Surveillance should target districts with elevated risk. Long-term control measures for COVID-19 and other contagious diseases should consider improving quality healthcare access and measures to reduce growth rates of urban populations.

Disparities in Anti-emetic Prophylaxis Care Processes are Predicted by Patient Neighborhood: A Retrospective Cohort and Geospatial Analysis.

Social Determinants of Health (SDoH) continue to drive persistent disparities in perioperative care. Our team has previously demonstrated racial and socioeconomic disparities in perioperative processes, notably in the administration of antiemetic prophylaxis, in several large perioperative registries. Given how neighborhoods are socially segregated in the US, we examined geospatial clustering of perioperative antiemetic disparities. We conducted a retrospective cohort study of anesthetic records from the University of Utah Hospital with 19,477 patients meeting inclusion criteria. We geocoded patient home addresses and combined them with the Census Block Group(CBG) level neighborhood disadvantage (ND), a composite index of from the National Neighborhood Data Archive (NaNDA). We stratified our patients by antiemetic risk score and calculated the number of anti-emetic interventions. We utilized Poisson Spatial Scan Statistics, implemented in SaTScan, to detect geographic clusters of under-treatment. We identified one significant cluster (p < .001) of undertreated perioperative antiemetic prophylaxis cases. The relative risk (RR) of the whole cluster is 1.44, implying that patients within the cluster are 1.44 times more likely to receive fewer antiemetics after controlling for antiemetic risk. Patients from more disadvantaged neighborhoods were more likely to receive below median antiemetic prophylaxis after controlling for risk. To our knowledge, this is the first geospatial cluster analysis of perioperative process disparities; we leveraged innovative geostatistical methods and identified a spatially defined, geographic cluster of patients whose home address census-tract level neighborhood deprivation index predicted disparities in risk adjusted antiemetic prophylaxis.

Epidemiological characteristics and spatio-temporal clusters of human brucellosis in Inner Mongolia, 2010–2021

BackgroundBrucellosis poses a significant public health challenge in China. Inner Mongolia, characterized by its developed livestock industry, is the most severe endemic area for human brucellosis. This study aims to describe the epidemiology, explore the spatial–temporal distribution patterns, and clustering characteristics of human brucellosis in Inner Mongolia.MethodsData on human brucellosis cases from 2010 to 2021 were obtained from the Centers for Disease Control and Prevention in Inner Mongolia. Spatial autocorrelation analysis was used to identify high-risk areas, while spatial–temporal scan statistics were employed to detect changes in clusters over time.ResultsA total of 153,792 brucellosis cases were reported in Inner Mongolia from 2010 to 2021, with an average annual incidence rate of 52.59 per 100,000 persons. The incidence showed a decreasing trend from 2010 to 2016, followed by a significant increase from 2016 to 2021. The disease exhibited distinct seasonality, peaking in spring and summer (March to August). Middle-aged individuals, males, and farmers/herdsmen had higher incidence rates. Spatially, incidence rates decreased from north to south and from the central and eastern regions to the west. Clear spatial clusters were observed during 2010–2013 and 2016–2021 in the global Moran’s I test. Local spatial autocorrelation analysis revealed that high-high clusters expanded from the central and eastern regions towards the west over time. Spatio-temporal scan analysis further indicated that high-risk clusters were primarily concentrated in the central and eastern regions, with a continuous expansion towards the west and south, leading to an increasingly broad geographical spread.ConclusionHuman brucellosis cases in Inner Mongolia exhibit spatio-temporal clustering, with spatial concentration in the central and eastern regions, but also observed expansion towards the western and southern regions. The most of cases occur between March and August each year. For high-risk areas and populations, more timely and effective prevention and control measures should be implemented to mitigate the spread of brucellosis and protect public health.

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.

Epidemiological insights and geographic clusters for COVID-19 in Taiwan using a mixture scan statistic

The COVID-19 pandemic has posed unprecedented public health challenges worldwide, necessitating a comprehensive understanding of its transmission dynamics. This study examines the correlation between COVID-19 transmission and various risk factors, focusing on the impact of population structure and socio-economic conditions in Taiwan. By analyzing official government databases, we explore how factors such as population density, dependency ratios, and socio-economic environment influence the spread of COVID-19. Our findings highlight that densely populated areas, along with regions characterized by higher child dependency ratios and a significant number of low- and middle-income households, exhibit higher transmission rates. This research underscores the importance of considering socio-economic disparities and healthcare access in developing effective public health strategies. Furthermore, we utilize a mixture scan statistic to identify disease hotspots, taking into account spatial correlation and covariate effects. This approach can detect clusters based on known risk factors and help to assess possible unknown geographic risks, facilitating targeted interventions and resource allocation. Our study contributes to the broader understanding of COVID-19 transmission dynamics, offering insights into the importance of integrating socio-economic factors and spatial analysis in pandemic response efforts.

A Mixture Scan Statistic for Semi-continuous Data with Covariates and Spatial Correlation

A Mixture Scan Statistic for Semi-continuous Data with Covariates and Spatial Correlation

Spatial distribution and determinants of early postnatal discharge among vaginally delivered mothers in Ethiopia: spatial and multilevel analysis of Ethiopian demographic survey data

BackgroundPostnatal hospital care is aimed to detect any complications for both mother and child. Postnatal care should start as early as possible, especially during the first 24 hours. No study...

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.

Multiscale scanning with nuisance parameters

Abstract We develop a multiscale scanning method to find anomalies in a d-dimensional random field in the presence of nuisance parameters. This covers the common situation that either the baseline-level or additional parameters such as the variance are unknown and have to be estimated from the data. We argue that state of the art approaches to determine asymptotically correct critical values for multiscale scanning statistics will in general fail when such parameters are naively replaced by plug-in estimators. Instead, we suggest to estimate the nuisance parameters on the largest scale and to use (only) smaller scales for multiscale scanning. We prove a uniform invariance principle for the resulting adjusted multiscale statistic, which is widely applicable and provides a computationally feasible way to simulate asymptotically correct critical values. We illustrate the implications of our theoretical results in a simulation study and in a real data example from super-resolution STED microscopy. This allows us to identify interesting regions inside a specimen in a pre-scan with controlled family-wise error rate.

An epidemiological and spatiotemporal analysis of visceral leishmaniasis in West Pokot, Kenya, between 2018 and 2022

BackgroundVisceral leishmaniasis (VL) remains a significant public health concern in West Pokot County, Kenya, where a large outbreak between 2020 and 2022 emphasised the need for improved VL control strategies. However, these measures are partially hampered by limited insight into the geographical distribution of cases and localised outbreaks of the disease. This study aimed to describe the epidemiology and spatiotemporal patterns of VL in West Pokot between 2018 and 2022, in order to map the spread of VL transmission and identify regions that should be prioritised for control interventions.MethodsVL patient demographics and village of residence were retrieved from admission records of Kacheliba Sub-County Hospital in West Pokot, Kenya. The temporal trend in VL admissions between 2018 and 2022 was analysed using seasonal decomposition analysis. To describe the spatial distribution of VL cases, geographic coordinates of villages of residence were collected from pre-established databases, and VL incidence was mapped at the sub-location level. Hotspot analysis was performed per study year to identify villages with high VL incidence, and scan statistics were applied to detect spatiotemporal clusters of VL cases during the study period.ResultsA total of 1948 VL patients were reported between 2018 and 2022. The annual number of cases increased from 245 in 2019 to 598 in 2022, and VL admissions were generally higher at the start of the wet seasons. 70% of the VL cases could be georeferenced, and mapping of VL incidence revealed high case rates in the east of West Pokot during the complete study period. The eastern villages Lotongot and Chepaywat were marked as VL hotspots at a 99% confidence level in all study years. In addition, five significant spatiotemporal clusters were detected in the east and north, suggestive of local VL outbreaks in these regions.ConclusionsThe increase in VL hospital admissions during the study period stresses the need for enhanced VL control and outbreak mitigation in West Pokot. These control measures should be focused on the hotspot regions in the east of the county.

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.

Application of spatiotemporal scan statistics in cases of intentional homicides in northern Brazil

The crime of intentional homicide in Brazil is worrying. In the northern region, this type of crime has been growing since 2020. In this sense, we decided to apply Kuldorff's prospective space-time scan statistics in order to identify emerging counties. We conclude that there are two emerging clusters of high relative risk (Amazonas and Pará) that require rapid intervention and two clusters of low relative risk (Acre, Roraima, Amazonas, Amapá and Pará) that do not require urgent intervention. Some characteristics of these two clusters are presented: radius, population, relative risk, likelihood ratio.

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.

Maternal mortality in Brazil: an analysis of temporal trends and spatial clustering.

This article aims to analyze spatial and temporal patterns of maternal mortality in Brazil during the period 2010-2020 and identify related socioeconomic indicators. We conducted an ecological study of the maternal mortality ratio (MMR) in Brazil's municipalities using secondary data. Temporal analysis was performed using the joinpoint method. Bayesian statistics, spatial autocorrelation, the Getis Ord Gi* technique and the scan statistic were used to identify spatial clusters, and multiple non-spatial and spatial regression models were used to assess the association between factors and the MMR. There was an increase in the MMR in 2020 and an increase in deaths in the North and Southeast. Clusters were found in Amazonas, Tocantins, Piauí, Maranhão, Bahia and Mato Grosso do Sul. The following indicators were negatively associated with the MMR: cesarean section rate, Municipal Human Development Index, and per capita household income of people who are vulnerable to poverty. The MMR was stable up to 2019, followed by a sharp rise in 2020 coinciding with the onset of the Covid-19 pandemic in the country. It is essential that efforts to reduce maternal mortality in Brazil extend beyond the promotion of improvements in antenatal, childbirth and postpartum care to address the social determinants of the problem.

Drought‐Rich Periods Are More Likely Than Flood‐Rich Periods in Brazil

AbstractStreamflow exhibits persistent decadal variability; however, it is unclear if the magnitude and spatial extent of these variabilities are symmetric for droughts and floods. Here, we examine drought‐rich and flood‐rich periods in 319 streamflow gauges in Brazil from 1940 to 2020. Drought‐ and flood‐rich periods are detected by computing annual streamflow minima and maxima time series and using scan statistics to verify if events exceeding a threshold follow a Bernoulli process. We contrast streamflow time clustering with rainfall, evaporation, water abstraction, the Atlantic Multidecadal Oscillation (AMO), and the Pacific Decadal Oscillation (PDO). We detected a higher spatial frequency of drought‐ than flood‐rich periods. For 5‐year return period thresholds, drought‐rich periods are observed in 81% of the basins, 16.7 times the false positive rate (4.8%) and 4.7 times flood‐rich periods (17%). This asymmetry is linked with sharp increases in water abstractions since the 1990s and a higher prevalence of rainfall‐poor periods (41% of gauges) compared to rainfall‐rich (22% of gauges), which we interpret as being further amplified into drought‐rich periods due to an interannual persistence of water storage deficits. Brazil experienced a dry period until the 1960s, extensive flooding in the 1980s, and record low flows from the 2000s onward. Drought and flood‐rich periods are well aligned with rainfall clustering, water abstractions, the AMO and PDO. Droughts‐rich periods are more frequent in shorter time scales (several years to one decade) and flood‐rich periods in longer time scales (a few decades). Our findings highlight the nonlinearity and asymmetry of drought and flood change at decadal scales.

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.

Utilization of publicly available data to summarize spatio-temporal patterns of fish health events of Atlantic salmon (Salmo salar) reported by marine finfish industries in British Columbia (BC), Canada.

Atlantic salmon aquaculture companies in British Columbia (BC) must report fish health events to Fisheries and Oceans Canada (DFO) as part of their licensing conditions. Our study aimed to summarize these fish health events reported by Atlantic salmon sites in BC to identify spatial and spatio-temporal clusters. We conducted descriptive, retrospective global, and local cluster analyses using Moran's I and scan statistics. Between 2016 and 2022, 265 fish health events were reported. The annual incidence ranged from 5.60 (95% CI: 3.90-7.80) to 6.86 (95% CI: 4.70-9.60) health events per 100 active site-months. The most common events were yellow mouth (60.75%; 161/265) and salmonid rickettsial septicaemia (SRS) (15.47%; 41/265). The Moran's I index was positive and significant for yellow mouth, SRS, and overall fish health events at different distance bands. Most of the spatial and spatio-temporal clusters were identified in the west-central and southwestern parts of Vancouver Island. Our study hypothesizes that management practices, environmental conditions, and water quality parameters may have influenced the increased reporting of fish health events in these regions. Overall, the study highlights the potential of publicly available data for practical risk mapping in understanding the patterns of farmed Atlantic salmon diseases in BC.