Space-time Clustering Research Articles

Temporal, spatial and space-time distribution of infections caused by five major enteric pathogens, Ontario, Canada, 2010-2017.

In Canada, enteric diseases pose substantial health and economic burdens. The distribution of these diseases is uneven across both geography and time and understanding these patterns is therefore important for the prevention of future outbreaks. We evaluated temporal, spatial and space-time clustering of laboratory-confirmed cases of Campylobacter spp. (n = 28,728), non-typhoidal Salmonella spp. (n = 22,640), Shiga toxin-producing Escherichia coli (STEC; n = 1340), Yersinia spp. (n = 1674) and Listeria monocytogenes (n = 471) infections, reported between 2010 and 2017 inclusive in Ontario, the most populous province in Canada (population ~ 13,500,000 in 2016). For each enteric pathogen, we calculated the mean incidence rates (IRs) for Ontario's 35 public health unit (PHU) areas and visualized them using choropleth maps. We identified temporal, spatial and space-time high infection rate clusters using retrospective Poisson scan statistics. Campylobacter and Salmonella infections had the highest IRs, while Listeria infections had the lowest. Campylobacter, Salmonella, STEC and Listeria mostly clustered temporally in the spring/summer and sometimes extended into fall, while Yersinia showed a less clear seasonal pattern. The IR visualizations and spatial and space-time scan statistics showed geographic heterogeneity of infection rates with high infection rate clusters detected mainly in PHUs across the southwestern and central-western regions of Ontario for Campylobacter, Salmonella and STEC infections, and mainly in PHUs located in the central-eastern regions for Yersinia and Listeria. A high proportion of cases in some of the significant Salmonella, STEC and Listeria infection clusters were linked to disease outbreaks. Results from this study will inform heightened public health surveillance, and prevention and control programmes, in populations and regions of high infection rates. Further research is needed to determine the pathogen-specific socioeconomic, environmental and agricultural risk factors that may be related to the temporal and spatial disease patterns we observed in our study.

A Multiscale Spatiotemporal Epidemiological Analysis of Neighborhood Correlates of Triple-Negative Breast Cancer.

Women living in disadvantaged neighborhoods present with increased prevalence rates of triple-negative breast cancer (TNBC). This study takes a spatiotemporal epidemiological approach to understand the impact of socioenvironmental contextual factors on TNBC prevalence rates. We analyzed 935 TNBC cases from a major cancer center registry, between 2005 and 2017, to explore spatial and space-time clusters of TNBC prevalence rates at the census tract and neighborhood scales. Spatial regression analysis was performed to examine relationships between nine socioenvironmental factors and TNBC prevalence rates at both ecological scales. We observed spatial clustering of high TNBC prevalence rates along a north-south corridor of Miami-Dade County along Interstate 95, a region containing several majority non-Hispanic Black neighborhoods. Among the ecologic measures, the percent of a region designated as a brownfield was associated with TNBC prevalence rates at the tract-level (β = 4.27; SE = 1.08; P < 0.001) and neighborhood-level (β = 8.61; SE = 2.20; P < 0.001). Our spatiotemporal analysis identified robust patterns of hot spots of TNBC prevalence rates in a corridor of several disadvantaged neighborhoods in the northern half of the county. These patterns of TNBC align with the literature regarding at-risk groups and neighborhood-level effects on TNBC; however, remain to be validated in a population-based sample. Spatial epidemiological approaches can help public health officials and cancer care providers improve place-specific screening, patient care, and understanding of socioenvironmental factors that may shape breast cancer subtype through gene-environment and epigenetic interactions.

Morbidity trend and space-time clusters of COVID-19 occurrence in children and adolescents

ObjectiveTo analyze the morbidity trend and space-time distribution clusters of confirmed COVID-19 cases in children and adolescents. MethodAn ecological study of COVID-19 cases confirmed in the Information System from 2020 to 2022 in the age group from 0 to 19 years old, residents in Mato Grosso municipalities, Brazilian Midwest region. A trend analysis of the monthly morbidity rate of cases/100,000 inhabitants was used, following Prais-Winsten's regression. A space-time distribution of the Bayesian incidence rate per 100,000 inhabitants was performed, in addition to a space-time scan to identify high-risk clusters. ResultsOf all 79,592 COVID-19 cases studied, 51.6% were in females and 44.21% in people aged 15–19 years old. The mean monthly rate was 265.87 cases per 100,000 inhabitants, with a stationary trend in the period analyzed (Monthly Percentage Variation [MPV]) = 12.15; CI95%[MPV]: -0.73;26.70). The morbidity rate due to COVID-19 was higher in the female gender (283.14/100,000 inhabitants) and in the age group from 15 to 19 years old (485.90/100,000 inhabitants). An increasing trend was observed with a greater monthly time variation of 14.42% (CI95%[MPV]: 1.28;29.28)] among those aged from 10 to 14 years old. The primary cluster, which was also the one with the highest Relative Risk (RR = 5,16, p-value = 0.001), included 19 municipalities located in the North health macro-region. ConclusionThe findings indicated a monthly stationary trend in the study population, an increase in the age group from 10 to 14 years old, and areas at a higher risk for the disease in the North health macro-region of the state.

The COVID-19 pandemic and changes in spatio-temporal patterns of suicide: monthly variations among localities in Argentina

The aim of this study was to assess space-time clustering of suicide in Argentina between 2017 and 2020. A spatio-temporal scan statistic using a space-time permutation model was employed to retrospectively detect clusters of total suicides by sex and by age group. From 2017 to 2020, six statistically significant spatio-temporal clusters were identified in Argentina with either more or less suicides than expected. Two clusters extended from May 2019 to July and September 2020, while the remaining four clusters were detected only during 2020. The start of the COVID-19 pandemic seems to have changed the spatio-temporal patterns of suicide in Argentina. Despite the national decrease in the number of suicides during 2020 compared to previous years, the spatio-temporal analysis revealed both areas with decreases and increases in suicide. This heterogeneous scenario highlights the need for the study of local effects behind the emergence of these spatio-temporal suicide clusters.

Detection of Space-Time Clusters and Ambient Temperature Effects on Non-Toxigenic Vibrio Cholerae in Russia from 2005 To 2021

Introduction: The identification of climate temperature-sensitive pathogens and infectious diseases is essential in addressing health risks resulting from global warming. Such research is especially crucial in regions where climate change may have a more significant impact like Russia. Recent studies have reasoned that the abundance of non-toxigenic V. cholerae is environmentally driven and can be part of early global warming signals for Russian territory. The aim of the study is to investigate the spatial-temporal trends and thermo-climatic sensitivity of non-toxigenic V. cholerae abundance in Russia.&#x0D; Methods: This study employed Kulldorff’s space-time statistics to identify persistent clusters of the V. cholerae ctx- isolation and areas for exploring temperature-depended patterns of the vibrio distribution. Correlation analysis was used to identify regions with temperature-driven Vibrio abundance in water samples.&#x0D; Results: The spatial analysis detected 16 persistent (7-8 year) clusters of V. cholerae ctx- across the study period 2005-2021. The number of clusters with RR &gt;1 abandoning from the south to the north and the total number of persistent clusters (9) is greater in the period of 2014(5)-2021 compared with the period 2005-2013 (7). A distinct significant thermo-climatic effect on the abundance of V. cholerae ctx- in water basins was found in three Russian regions with temperate marine (the Kaliningrad region) and sharp continental climatic conditions (the Irkutsk region and the Republic of Sakha). The temperature and Vibrio prevalence trend curves are peaky (the Kaliningrad region and the Republic of Sakha) or bell-shaped (the Irkutsk region) changed and closely followed together.&#x0D; Conclusion: The persistent clusters should become targeted areas to improve sanitation conditions. The study offers valuable outcomes to support simplified empirical evaluations of the potential hazards of vibrio abundance that might be useful locally for public health authorities and globally as a part of Russia's warning system of climate change effects.

Zika, chikungunya and co-occurrence in Brazil: space-time clusters and associated environmental–socioeconomic factors

Chikungunya and Zika have been neglected as emerging diseases. This study aimed to analyze the space-time patterns of their occurrence and co-occurrence and their associated environmental and socioeconomic factors. Univariate (individually) and multivariate (co-occurrence) scans were analyzed for 608,388 and 162,992 cases of chikungunya and Zika, respectively. These occurred more frequently in the summer and autumn. The clusters with the highest risk were initially located in the northeast, dispersed to the central-west and coastal areas of São Paulo and Rio de Janeiro (2018–2021), and then increased in the northeast (2019–2021). Chikungunya and Zika demonstrated decreasing trends of 13% and 40%, respectively, whereas clusters showed an increasing trend of 85% and 57%, respectively. Clusters with a high co-occurrence risk have been identified in some regions of Brazil. High temperatures are associated with areas at a greater risk of these diseases. Chikungunya was associated with low precipitation levels, more urbanized environments, and places with greater social inequalities, whereas Zika was associated with high precipitation levels and low sewage network coverage. In conclusion, to optimize the surveillance and control of chikungunya and Zika, this study’s results revealed high-risk areas with increasing trends and priority months and the role of socioeconomic and environmental factors.

Epidemiology of listeriosis in a region in central Italy from 2010 to 2019: Estimating the real incidence and space-time analysis for detecting cluster of cases.

BackgroundContamination and transmission of different Listeria monocytogenes strains along food chain are a serious threat to public health and food safety. Understanding the distribution of diseases in time and space-time is fundamental in the epidemiological study and in preventive medicine programs. The aim of this study is to estimate listeriosis incidence along 10-years period and to perform space-time cluster analysis of listeriosis cases in Marche Region, Italy. MethodsThe number of observed listeriosis cases/year was derived from regional data of surveillance of notifiable diseases and hospital discharge form. The capture and recapture method (C-R method) was applied to estimate the real incidence of listeriosis cases in Marche Region and the space-time scan statistics analysis was performed to detect clusters of space-time of listeriosis cases and add precision to the conventional epidemiological analysis. ResultsThe C-R method estimation of listeriosis cases was 119 in the 10- year period (2010–2019), with an average of 31.93 % of unobserved cases (lost cases). The estimated mean annual incidence of listeriosis was 0.77 per 100,000 inhabitants (95 %CI 0.65–0.92), accounting for 6.07 % of additional listeriosis cases per year than observed cases. Using the scan statistic, the two most likely clusters were identified, one of these was statistically significant (p < 0.05). The underdiagnosis and under-reporting in addition to listeriosis incidence variability suggested that the surveillance system of Marche Region should be improved. ConclusionsThis study provides evidence of the ability of space-time cluster analysis to complement traditional surveillance of food-borne diseases and to understand the local risk factors by implementing timely targeted interventions.

A web-based analytical framework for the detection and visualization space-time clusters of COVID-19

ABSTRACT The COVID-19 pandemic has had a profound impact worldwide and continues to spread due to various mutations of the virus. Many governmental and nonprofit agencies at different levels have quickly developed COVID-19 dashboards to disseminate information on the pandemic to the public. However, most of these systems have mainly distributed “plain” information (e.g. cases, death counts, vaccination), and rarely provided insights that can be gained from spatiotemporal analyses, such as the detection of emerging clusters. The results from these analyses hold tremendous potential for health policymakers as they try to identify ways to slow down transmission. We present a web-based geographic framework to detect and visualize space-time clusters of COVID-19. Our tightly coupled framework integrates the prospective space-time scan statistics and local indicators of spatial association (LISA) with novel 2D and 3D interactive visuals in a cyber environment (http://159.223.164.41/app/). We illustrate the applicability of our approach using COVID-19 data for the continental US. Our framework is portable to other regions that may experience infectious diseases but is also flexible to handle data of different spatial and temporal granularities. This paper fits within an effort to integrate space-time analytics for the monitoring of infectious diseases in web environment, ultimately improving health surveillance systems.

Space-time cluster analysis of anemia in pregnant women in the province of Khyber Pakhtunkhwa, Pakistan (2014-2020).

Anaemia is a common public-health problem affecting about two-thirds of pregnant women in developing countries. Spacetime cluster analysis of anemia cases is important for publichealth policymakers to design evidence-based intervention strategies. This study discovered the potential space-time clusters of anemia in pregnant women in Khyber Pakhtunkhwa Province, Pakistan, from 2014 to 2020 using space-time scan statistic (SatScan). The results show that the most likely cluster of anemia was seen in the rural areas in the eastern part of the province covering five districts from 2017 to 2019. However, three secondary clusters in the West and one in the North were still active, signifying important targets of interest for public-health interventions. The potential anemia clusters in the province's rural areas might be associated with the lack of nutritional education in women and lack of access to sufficient diet due to financial constraints.

Distribution of West Nile virus cases in horses reveals different spatiotemporal patterns in eastern and western Canada.

West Nile virus (WNV) became notifiable in horses in 2003 in Canada and has been reported every year since. The objective of this study was to describe the spatiotemporal distribution of WNV in horses between 2003 and 2020 in Canada. The 848 symptomatic and laboratory-confirmed WNV cases in horses reported to the Canadian Food Inspection Agency between 2003 and 2020. Canada was divided into eastern and western regions for analysis. For each case, location and date of notification were captured. Triennial maps were made to describe the spatiotemporal distribution and expansion of reported cases. The association between year and latitude of cases was investigated with simple linear regressions, and space-time clusters were detected with a permutation scan test. Most of the western region showed an extended distribution of WNV cases from 2003 to 2005 and a high recurrence of cases at the census division level. In the eastern region, the expansion of cases was gradual, with new infected census divisions mostly contiguous to previous ones. There was no association between year and latitude of cases. Six spatiotemporal clusters were detected. This study confirmed the endemicity of WNV in parts of both regions with local peaks in risk varying in time. Prevention and control efforts should focus on previously infected areas based on the spatiotemporal regional distribution patterns. Incursions of WNV to new areas should also be anticipated. These findings could also contribute to enhancing monitoring and prevention of WNV infections in an integrated surveillance system.

Sporadic Shiga Toxin-Producing Escherichia coli-Associated Pediatric Hemolytic Uremic Syndrome, France, 2012-2021.

Shiga toxin-producing Escherichia coli-associated pediatric hemolytic uremic syndrome (STEC-HUS) remains an important public health risk in France. Cases are primarily sporadic, and geographic heterogeneity has been observed in crude incidence rates. We conducted a retrospective study of 1,255 sporadic pediatric STEC-HUS cases reported during 2012-2021 to describe spatiotemporal dynamics and geographic patterns of higher STEC-HUS risk. Annual case notifications ranged from 109 to 163. Most cases (n = 780 [62%]) were in children <3 years of age. STEC serogroups O26, O80, and O157 accounted for 78% (559/717) of cases with serogroup data. We identified 13 significant space-time clusters and 3 major geographic zones of interest; areas of southeastern France were included in >5 annual space-time clusters. The results of this study have numerous implications for outbreak detection and investigation and research perspectives to improve knowledge of environmental risk factors associated with geographic disparities in STEC-HUS in France.

Space-time clustering of COVID-19 cases in the United States veteran population

To assess the distribution and clustering of coronavirus disease 2019 (COVID-19) testing and incidence over space and time, U.S. Department of Veteran's Affairs (VA) data were used to describe where and when veterans experienced highest proportions of test positivity. Data for 6,342,455 veterans who utilized VA services between January 1, 2018, and September 30, 2021, were assessed for COVID-19 testing and test positivity. Testing and positivity proportions by county were mapped and focused-cluster tests identified significant clustering around VA facilities. Spatial cluster analysis also identified where and when veterans experienced highest proportions of test positivity. Within the veterans study population and our time window, 21.3% received at least one COVID-19 test, and 20.4% of those tested had at least one positive test. There was statistically significant clustering of testing around VA facilities, revealing regional variation in testing practices. Veterans experienced highest test positivity proportions between November 2020 and January 2021 in a cluster of states in the Midwest, compared to those who received testing outside of the identified cluster (RR: 3.45). Findings reflect broad regional trends in COVID-19 positivity which can inform VA policy and resource allocation. Additional analysis is needed to understand patterns during Delta and Omicron variant periods.

Space-time clusters of cardiovascular mortality and the role of heatwaves and cold spells in the city of São Paulo, Brazil

The effects extreme air temperature events are related with an increase in cardiovascular mortality among vulnerable groups worldwide. Therefore, we identify spatiotemporal mortality clusters associated with diseases of the cardiovascular system among people ≥ 65 years in São Paulo, from 2006 to 2015, and investigate whether high-risk mortality clusters occurred during or following extreme air temperature events. To detect the clusters, we used daily mortality data and a retrospective space-time scan analysis with a discrete Poisson model. Extreme air temperature events were defined by daily mean temperatures, below the 10th percentile for cold spells and above the 90th percentile for heatwaves, with two or more consecutive days. We found statistically significant high-risk mortality clusters located in the peripheral areas. The spatiotemporal clusters of risk areas for cardiovascular and ischemic heart disease occurred during or following cold spell events, whereas those for stroke and ischemic stroke events were related to heatwaves.

Multi-source data assessment and multi-factor analysis of urban carbon emissions: A case study of the Pearl River Basin, China

The Pearl River Basin (PRB) is of strategic importance to China's economic development and ecological sustainability, while carbon emissions (CES) are currently exerting immense pressure on the region. This study attempts to study the spatial-temporal evolution and driving factors of CES to achieve regional joint emission reduction. Through the integration of multi-source data, this study has developed a coupled model for calculating CES from both carbon sources and sinks. The space-time patterns and clustering characteristics of CES in the PRB were analyzed at multiple scales. And the main driving factors of CES were revealed from two scales of urban and basin, respectively. The results show that CES exhibited an increasing trend, with cities with high CES mainly located in the downstream of the PRB. The CES between neighboring cities exhibited strong spatial dynamics and dependence, resulting in a collaborative development trend of CES with adjacent cities. The social consumption level, land use degree, and fiscal expenditure were significant drivers of CES. Additionally, the interaction impact between influencing factors on CES exerted more significantly than any single factor. This study improved the methods of calculating CES with multi-source data and provided support for different-type cities to formulate targeted CES reduction policies.

Correction. Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach..

In the Article titled "Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach." published November 6th, 2017, in Vol 12(2) of Geospatial Health, an author's name was misspelled. The fifth author's name should be "Alamgir". Reference: Ullah S, Daud H, Dass SC, Khan HN, Khalil A, 2017. Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach. Geospatial Health, 12:567. https://doi.org/10.4081/gh.2017.567.

Space-Time Patterns of Poultry Pathogens in the USA: A Case Study of Ornithobacterium rhinotracheale and Pasteurella multocida in Turkey Populations.

Respiratory infections caused by Ornithobacterium rhinotrachealis (ORT) and Pasteurella multocida (PM) bacteria are significant threats to the poultry industry by causing economic losses and welfare issues. Due to characterization difficulties and underutilization of epidemiological tools, description of the spatio-temporal spread of these diseases in the field is limited. The objectives of this retrospective observational cross-sectional study were to (a) investigate the existence of space-time clusters (hotspots); and (b) investigate the association between genetic similarity and spatial proximity for both pathogens using molecular typing and a recently developed Core-Genome Multilocus Sequencing Typing (cgMLST) scheme. ORT (n = 103) and PM (n = 69) isolates from confirmed disease outbreaks from one commercial company between 2013 and 2021 were obtained from a veterinary diagnostic laboratory, characterized using a cgMLST scheme and visualized using a minimum spanning tree. Spatio-temporal cluster analysis using SaTScanTM and a Spearman's rank correlation were performed to investigate clustering and any association between allelic diversity and geospatial distance. The cgMLST sequencing revealed three allelic clusters for ORT and thirteen clusters for PM. The spatio-temporal analysis revealed two significant clusters for PM, one with a 259.3 km cluster containing six cases between May and July 2018 and a 9 km cluster containing five cases between February 2019 and February 2021. No spatio-temporal clusters were found for ORT. A weak negative correlation between allelic diversity and geospatial distance was observed for ORT (r = -0.04, p < 0.01) and a weak positive correlation was observed for PM (r = 0.11, p < 0.01). This study revealed regional spatio-temporal clusters for PM in commercial turkey sites between 2018 and 2021 and provided additional insight into bacterial strain subgroups and the geographical spread of ORT and PM over time.

Evaluating co-occurring space-time clusters of depression and suicide-related outcomes before and during the COVID-19 pandemic

Rapidly emerging research on the mental health consequences of the COVID-19 pandemic shows increasing patterns of psychological distress, including anxiety and depression, and self-harming behaviors, particularly during the early months of the pandemic. Yet, few studies have investigated the spatial and temporal changes in depressive disorders and suicidal behavior during the pandemic. The objective of this retrospective analysis was to evaluate geographic patterns of emergency department admissions for depression and suicidal behavior in North Carolina before (March 2017-February 2020) and during the COVID-19 pandemic (March 2020 - December 2021). Univariate cluster detection examined each outcome separately and multivariate cluster detection was used to examine the co-occurrence of depression and suicide-related outcomes in SatScan; the Rand index evaluated cluster overlap. Cluster analyses were adjusted for age, race, and sex. Findings suggest that the mental health burden of depression and suicide-related outcomes remained high in many communities throughout the pandemic. Rural communities exhibited a larger increase in the co-occurrence of depression and suicide-related ED visits during the pandemic period. Results showed the exacerbation of depression and suicide-related outcomes in select communities and emphasize the need for targeted and sustained mental health interventions throughout the many phases of the COVID-19 pandemic.

Epidemiology and Evolutionary Dynamics of High Pathogenicity Avian Influenza (HPAI) H5N1 in Bangladesh

Understanding disease clustering and transmission patterns improves the prevention and control of disease. Herein, we described the epizootic characteristics and spatiotemporal dynamics of High Pathogenicity Avian Influenza (HPAI) H5N1 outbreaks as well as clade diversity and phylodynamics of H5N1 over time and across host species in Bangladesh. We used Moran’s I, Geary’s C, Getis-Ord Gi∗, and a space-time permutation model to analyze the spatiotemporal patterns of H5N1 outbreaks. We used Bayesian phylogenetic analysis to generate a time-scaled maximum clade credibility (MCC) tree. Our study revealed nine HPAI H5N1 epizootic waves between 2007 and 2020 that invariably peaked in the wintertime. After vaccination of poultry against H5N1 was introduced in 2012, the incidence of HPAI H5N1 outbreaks and poultry mortality decreased significantly over time. Nonetheless, our research revealed that the virus continued circulating unabatedly in Bangladesh. The various spatiotemporal analyses were identified up to nine space-time clusters across Bangladesh, with the most significant clustering and hotspots of H5N1 outbreaks in and around the district of Dhaka. Since 2007, four H5N1 clades have been detected in Bangladesh, with only clade 2.3.2.1a continuing to circulate since 2011, which was followed up by the reassorted clade 2.3.2.1a in 2012. The HA gene of the H5N1 clade 2.3.2.1a has been reassorted into at least nine subgroups (R1–R9). After 2016, the first eight groups disappeared, with only the R9 group remaining. Spatiotemporal patterns and phylodynamics of H5N1 outbreaks are crucial for developing targeted and appropriate HPAI control and prevention measures. We recommended intensive monitoring of biosecurity measures and disease records in high-priority areas, along with assessing vaccine efficacy to better control HPAI outbreaks in Bangladesh.

Scavenging patterns of an inbred wolf population in a landscape with a pulse of human‐provided carrion

AbstractScavenging is an important part of food acquisition for many carnivore species that switch between scavenging and predation. In landscapes with anthropogenic impact, humans provide food that scavenging species can utilize. We quantified the magnitude of killing versus scavenging by gray wolves (Canis lupus) in Scandinavia where humans impact the ecosystem through hunter harvest, land use practices, and infrastructure. We investigated the cause of death of different animals utilized by wolves, and examined how the proportion of their consumption time spent scavenging was influenced by season, wolf social affiliation, level of inbreeding, density of moose (Alces alces) as their main prey, density of brown bear (Ursus arctos) as an intraguild competitor, and human density. We used data from 39 GPS‐collared wolves covering 3198 study days (2001–2019), including 14,205 feeding locations within space–time clusters, and 1362 carcasses utilized by wolves. Most carcasses were wolf‐killed (80.5%) while a small part had died from other natural causes (1.9%). The remaining had either anthropogenic mortality causes (4.7%), or the cause of death was unknown (12.9%). Time spent scavenging was higher during winter than during summer and autumn. Solitary wolves spent more time scavenging than pack‐living individuals, likely because individual hunting success is lower than pack success. Scavenging time increased with the mean inbreeding coefficient of the adult wolves, possibly indicating that more inbred individuals resort to scavenging, which requires less body strength. There was weak evidence for competition between wolves and brown bears as well as a positive relationship between human density and time spent scavenging. This study shows how both intrinsic and extrinsic factors drive wolf scavenging behavior, and that despite a high level of inbreeding and access to carrion of anthropogenic origin, wolves mainly utilized their own kills.

Geographic variation in HCV treatment penetration among people who inject drugs in Baltimore, MD.

We evaluated geographic heterogeneity in hepatitis C virus (HCV) treatment penetration among people who inject drug (PWID) across Baltimore, MD since the advent of direct-acting antivirals (DAAs) using space-time clusters of HCV viraemia. Using data from a community-based cohort of PWID, the AIDS Linked to the IntraVenous Experience (ALIVE) study, we identified space-time clusters with higher-than-expected rates of HCV viraemia between 2015 and 2019 using scan statistics. We used Poisson regression to identify covariates associated with HCV viraemia and used the regression-fitted values to detect adjusted space-time clusters of HCV viraemia in Baltimore city. Overall, in the cohort, HCV viraemia fell from 77% in 2015 to 64%, 49%, 39% and 36% from 2016 to 2019. In Baltimore city, the percentage of census tracts where prevalence of HCV viraemia was ≥85% dropped from 57% to 34%, 25%, 22% and 10% from 2015 to 2019. We identified two clusters of higher-than-expected HCV viraemia in the unadjusted analysis that lasted from 2015 to 2017 in East and West Baltimore and one adjusted cluster of HCV viraemia in West Baltimore from 2015 to 2016. Neither differences in age, sex, race, HIV status, nor neighbourhood deprivation were able to explain the significant space-time clusters. However, residing in a cluster with higher-than-expected viraemia was associated with age, sex, educational attainment and higher levels of neighbourhood deprivation. Nearly 4 years after DAAs became available, HCV treatment has penetrated all PWID communities across Baltimore city. While nearly all census tracts experienced improvements, change was more gradual in areas with higher levels of poverty.