Spatial Variation In Temporal Trends Research Articles

The COVID-19 pandemic in Brazil: space-time approach of cases, deaths, and vaccination coverage (February 2020 – April 2024)

ObjectiveTo assess the evolution of the COVID-19 pandemic in Brazil and its macro-regions, considering disease incidence and mortality rates, as well as identifying territories with still rising disease indices and evaluating vaccine coverage and population adherence to COVID-19 immunization.MethodsAn ecological study conducted in Brazil with COVID-19 cases and deaths reported between February 2020 and April 2024, obtained through the Coronavirus Panel. Historical series were constructed from incidence and mortality rates to assess the pandemic’s evolution, and temporal trends were estimated using the Seasonal Trend Decomposition using Loess (STL) method. The Spatial Variation in Temporal Trends (SVTT) technique was employed to identify clusters with significant variations in temporal trends. Vaccination was analyzed considering the percentage of vaccinated and unvaccinated population in each municipality of the country.ResultsBrazil recorded a total of 38,795,966 cases and 712,038 deaths from COVID-19 during the study period. Incidence and mortality rates showed three waves of the disease, with a fourth wave of smaller amplitude. Four clusters with significant case growth and two with increased deaths were identified. Vaccine coverage varied among municipalities, with some regions showing low vaccination rates and others with high immunization adherence.ConclusionThe study provided a comprehensive overview of coronavirus behavior in Brazil, and its results highlight the ongoing importance of vaccination and the need to direct efforts and resources to areas of higher risk.

High-risk spatiotemporal patterns of cutaneous leishmaniasis: a nationwide study in Iran from 2011 to 2020

BackgroundCutaneous leishmaniasis (CL) is a wide-reaching infection of major public health concern. Iran is one of the six most endemic countries in the world. This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020, detecting high-risk zones, while also noting the movement of high-risk clusters.MethodsOn the basis of clinical observations and parasitological tests, data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education. Utilizing spatial scan statistics, we investigated the disease’s purely temporal, purely spatial, spatial variation in temporal trends and spatiotemporal patterns. At P = 0.05 level, the null hypothesis was rejected in every instance.ResultsIn general, the number of new CL cases decreased over the course of the 9-year research period. From 2011 to 2020, a regular seasonal pattern, with peaks in the fall and troughs in the spring, was found. The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country [relative risk (RR) = 2.24, P < 0.001)]. In terms of location, six significant high-risk CL clusters covering 40.6% of the total area of the country were observed, with the RR ranging from 1.87 to 9.69. In addition, spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency. Finally, five space-time clusters were found. The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period affecting many regions of the country.ConclusionsOur study has revealed significant regional, temporal, and spatiotemporal patterns of CL distribution in Iran. Over the years, there have been multiple shifts in spatiotemporal clusters, encompassing many different parts of the country from 2011 to 2020. The results reveal the formation of clusters across counties that cover certain parts of provinces, indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries. Such analyses, at a finer geographical scale, such as county level, might provide more precise results than analyses at the scale of the province.Graphical

Aglomerados de risco para ocorrência de hanseníase e as incapacidades em menores de 15 anos em Cuiabá: um estudo geoespacial

ABSTRACT Objective This study aimed to analyze the spatial distribution of leprosy and disabilities in children under 15 years of age in Cuiabá. Methods Ecological study carried out in the city of Cuiabá, Mato Grosso, Brazil. The study population consisted of leprosy cases in children under 15 years old notified in the Notifiable Diseases Information System, between 2008 and 2018. Based on residential addresses, cases were georeferenced. In the analysis of the spatial distribution of the cases, the estimation of the Kernel density was used and, later, the statistics of spatial, spatio-temporal and Spatial Variation in Temporal Trends were applied. Results 514 cases of leprosy were reported in children under 15 years of age in Cuiabá, with a percentage of 10.1% of cases with degree of physical disability 1 and 2.3% with degree of physical disability 2 at the time of diagnosis. With the techniques of spatial and spatio-temporal scanning, clusters of risk for leprosy were identified in the North, West, East and South regions of Cuiabá, and with the technique of Spatial Variation in Temporal Trends, a cluster was identified in the West region of Cuiabá. Conclusion In Cuiabá, cases of leprosy in children under 15 years of age with disabilities were distributed throughout the urban area of the city, with the highest density of cases in the North and West regions, followed by the East region. The clusters with the highest Relative Risk were identified in the East and West regions, characterized by having low and medium income levels

Clusters of risk for the occurrence of leprosy and disabilities in children under 15 years of age in Cuiabá: a geospatial study

This study aimed to analyze the spatial distribution of leprosy and disabilities in children under 15 years of age in Cuiabá. Ecological study carried out in the city of Cuiabá, Mato Grosso, Brazil. The study population consisted of leprosy cases in children under 15 years old notified in the Notifiable Diseases Information System, between 2008 and 2018. Based on residential addresses, cases were georeferenced. In the analysis of the spatial distribution of the cases, the estimation of the Kernel density was used and, later, the statistics of spatial, spatio-temporal and Spatial Variation in Temporal Trends were applied. 514 cases of leprosy were reported in children under 15 years of age in Cuiabá, with a percentage of 10.1% of cases with degree of physical disability 1 and 2.3% with degree of physical disability 2 at the time of diagnosis. With the techniques of spatial and spatio-temporal scanning, clusters of risk for leprosy were identified in the North, West, East and South regions of Cuiabá, and with the technique of Spatial Variation in Temporal Trends, a cluster was identified in the West region of Cuiabá. In Cuiabá, cases of leprosy in children under 15 years of age with disabilities were distributed throughout the urban area of the city, with the highest density of cases in the North and West regions, followed by the East region. The clusters with the highest Relative Risk were identified in the East and West regions, characterized by having low and medium income levels.

Spatial, spatio-temporal, and origin-destination flow analyses of patients with severe acute respiratory syndrome hospitalized for COVID-19 in Southeastern Brazil, 2020-2021

Brazil experienced one of the fastest increasing numbers of coronavirus disease (COVID-19) cases worldwide. The Sao Paulo State (SPS) reported a high incidence, particularly in Sao Paulo municipality. This study aimed to identify clusters of incidence and mortality of hospitalized patients with severe acute respiratory syndrome for COVID-19 in the SPS, in 2020-2021, and describe the origin flow pattern of the cases. Cases and mortality risk area clusters were identified through different analyses (spatial clusters, spatio-temporal clusters, and spatial variation in temporal trends) by weighting areas. Ripley's K12-function verified the spatial dependence between the cases and infrastructure. There were 517,935 reported cases, with 152,128 cases resulting in death. Of the 470,441 patients hospitalized and residing in the SPS, 357,526 remained in the original municipality, while 112,915 did not. Cases and death clusters were identified in the Sao Paulo metropolitan region (SPMR) and Baixada Santista region in the first study period, and in the SPMR and the Campinas, Sao Jose do Rio Preto, Barretos, and Sorocaba municipalities during the second period. We highlight the priority areas for control and surveillance actions for COVID-19, which could lead to better outcomes in future outbreaks.

Risk-prone territories for spreading tuberculosis, temporal trends and their determinants in a high burden city from São Paulo State, Brazil

ObjectivesTo identify risk-prone areas for the spread of tuberculosis, analyze spatial variation and temporal trends of the disease in these areas and identify their determinants in a high burden city.MethodsAn ecological study was carried out in Ribeirão Preto, São Paulo, Brazil. The population was composed of pulmonary tuberculosis cases reported in the Tuberculosis Patient Control System between 2006 and 2017. Seasonal Trend Decomposition using the Loess decomposition method was used. Spatial and spatiotemporal scanning statistics were applied to identify risk areas. Spatial Variation in Temporal Trends (SVTT) was used to detect risk-prone territories with changes in the temporal trend. Finally, Pearson's Chi-square test was performed to identify factors associated with the epidemiological situation in the municipality.ResultsBetween 2006 and 2017, 1760 cases of pulmonary tuberculosis were reported in the municipality. With spatial scanning, four groups of clusters were identified with relative risks (RR) from 0.19 to 0.52, 1.73, 2.07, and 2.68 to 2.72. With the space–time scan, four clusters were also identified with RR of 0.13 (2008–2013), 1.94 (2010–2015), 2.34 (2006 to 2011), and 2.84 (2014–2017). With the SVTT, a cluster was identified with RR 0.11, an internal time trend of growth (+ 0.09%/year), and an external time trend of decrease (− 0.06%/year). Finally, three risk factors and three protective factors that are associated with the epidemiological situation in the municipality were identified, being: race/brown color (OR: 1.26), without education (OR: 1.71), retired (OR: 1.35), 15 years or more of study (OR: 0.73), not having HIV (OR: 0.55) and not having diabetes (OR: 0.35).ConclusionThe importance of using spatial analysis tools in identifying areas that should be prioritized for TB control is highlighted, and greater attention is necessary for individuals who fit the profile indicated as “at risk” for the disease.

High-resolution analysis of observed thermal growing season variability over northern Europe

Strong historical and predicted future warming over high-latitudes prompt significant effects on agricultural and forest ecosystems. Thus, there is an urgent need for spatially-detailed information of current thermal growing season (GS) conditions and their past changes. Here, we deployed a large network of weather stations, high-resolution geospatial environmental data and semi-parametric regression to model the spatial variation in multiple GS variables (i.e. beginning, end, length, degree day sum [GDDS, base temperature + 5 °C]) and their intra-annual variability and temporal trends in respect to geographical location, topography, water and forest cover, and urban land use variables over northern Europe. Our analyses revealed substantial spatial variability in average GS conditions (1990–2019) and consistent temporal trends (1950–2019). We showed that there have been significant changes in thermal GS towards earlier beginnings (on average 15 days over the study period), increased length (23 days) and GDDS (287 °C days). By using a spatial interpolation of weather station data to a regular grid we predicted current GS conditions at high resolution (100 m × 100 m) and with high accuracy (correlation ≥ 0.92 between observed and predicted mean GS values), whereas spatial variation in temporal trends and interannual variability were more demanding to predict. The spatial variation in GS variables was mostly driven by latitudinal and elevational gradients, albeit they were constrained by local scale variables. The proximity of sea and lakes, and high forest cover suppressed temporal trends and inter-annual variability potentially indicating local climate buffering. The produced high-resolution datasets showcased the diversity in thermal GS conditions and impacts of climate change over northern Europe. They are valuable in various forest management and ecosystem applications, and in adaptation to climate change.

Spatial inequality, characteristics of internal migration, and pulmonary tuberculosis in China, 2011\u20132017: a spatial analysis

BackgroundHuman migration facilitate the spread of tuberculosis (TB). Migrants face an increased risk of TB infection. In this study, we aim to explore the spatial inequity of sputum smear-positive pulmonary TB (SS + PTB) in China; and the spatial heterogeneity between SS + PTB and internal migration.MethodsNotified SS + PTB cases in 31 provinces in mainland China were obtained from the national web-based PTB surveillance system database. Internal migrant data were extracted from the report on China’s migrant population development. Spatial autocorrelations were explored using the global Moran’s statistic and local indicators of spatial association. The spatial variation in temporal trends was performed using Kulldorff’s scan statistic. Fixed effect and spatial autoregressive models were used to explore the spatial inequity between SS + PTB and internal migration.ResultsA total of 2 380 233 SS + PTB cases were reported in China between 2011 and 2017, of which, 1 716 382 (72.11%) were male and 663 851 (27.89%) were female. Over 70% of internal migrants were from rural households and had lower income and less education. The spatial variation in temporal trend results showed that there was an 9.9% average annual decrease in the notification rate of SS + PTB from 2011 to 2017; and spatial clustering of SS + PTB cases was mainly located in western and southern China. The spatial autocorrelation results revealed spatial clustering of internal migration each year (2011–2017), and the clusters were stable within most provinces. Internal emigration, urban-to-rural migration and GDP per capita were significantly associated with SS + PTB, further, internal emigration could explain more variation in SS + PTB in the eastern region in mainland. However, internal immigration and rural-to-urban migration were not significantly associated with SS + PTB across China.ConclusionsOur study found the spatial inequity between SS + PTB and internal migration. Internal emigration, urban-to-rural migration and GDP per capita were statistically associated with SS + PTB; the negative association was identified between internal emigration, urban-to-rural migration and SS + PTB. Further, we found those migrants with lower income and less education, and most of them were from rural households. These findings can help stakeholders to implement effective PTB control strategies for areas at high risk of PTB and those with high rates of internal migration.

Temporal trends in areas at risk for concomitant tuberculosis in a hyperendemic municipality in the Amazon region of Brazil

BackgroundAlthough preventable and curable, tuberculosis (TB) still occurs in poor or developing countries, mainly in metropolitan regions of larger cities. The disease is a serious public health problem, and is directly linked to social issues. We analyzed temporal trend variations in areas at risk for concomitant TB, and characterized the clinical and epidemiological profiles of cases in a hyperendemic municipality in the Amazon region of Brazil.MethodsThis ecological study was performed in the municipality of Manaus, in northern Brazil. The population comprised cases with concomitant pulmonary and extrapulmonary TB, registered on the Notifiable Diseases Information System (SINAN), between January 1, 2009 and December 31, 2018. For risk cluster detection, spatial and spatiotemporal scanning statistical techniques were used. The Spatial Variation in Temporal Trends (SVTT) approach was used to detect and infer clusters for significantly different time trends.ResultsBetween 2009 and 2018, 873 concomitant TB cases were registered in Manaus. By using purely spatial scanning statistics, we identified two risk clusters. The relative risk (RR) of the clusters was 2.21 (95% confidence interval [CI]: 1.57–2.88; P = 0.0031) and 2.03 (95% CI: 1.58–2.58; P = 0.0029). Using space-time scanning, we identified a risk cluster with an RR of 3.57 (95% CI: 2.84–4.41; P = 0.014), between 2017 and 2018. For SVTT analyses, three clusters with spatial variations were detected in the significant temporal trends: SVTT 1 (P = 0.042), SVTT 2 (P = 0.046) and SVTT 3 (P = 0.036).ConclusionsIn Brazil, several TB-determining factors such as race/color, gender, low educational level and low income overlap in needy urban areas and communities, demonstrating that it is unlikely to reach the goals, agreed and launched with the END TB Strategy within the deadlines of international agreements, if there is no reduction in existing inequities determinants and risk of illness in the country.

Tuberculosis among South American immigrants in São Paulo municipality: an analysis in space and time.

To identify spatial and temporal, and spatial variation in temporal trends in clusters of tuberculosis (TB) among South American migrants residing in São Paulo municipality, SP, Brazil, between 2006 and 2013. An ecological descriptive study was conducted using data obtained from official TB reports and the 2010 Brazilian demographic census. Clusters were identified using scan statistics and SaTScan software; those with P < 0.05 were considered statistically significant. Social and economic characteristics of residents within the clusters were investigated. One high-risk spatial cluster (relative risk [RR] 4.46, P < 0.001) for TB was identified. Bolivian immigrants comprised the majority of immigrants residing in this area. One purely temporal high-risk cluster was identified between 2011 and 2013 (RR 1.55, P = 0.001). In one of the spatial variation in temporal trends clusters, the annual increase in TB incidence was 17.54% inside the cluster and 5.17% outside. We found areas of high risk for TB among South American immigrants. These areas and those with increasing trends of TB incidence must be prioritised by TB control programmes.

A decade of adverse drug events in Portuguese hospitals: space-time clustering and spatial variation in temporal trends

BackgroundThe aim of this study is to identify the distribution by municipalities of adverse drug events (ADE) in Portugal, including adverse drug reactions (ADR) and accidental poisoning by drugs (AP), on municipality/years ADE rate clustering. Also we identify areas with different trends in time.MethodsWe used a national dataset of public hospital discharges in Continental Portugal from 2004 to 2013. Events were identified based on codes: from E930 to E949.9 (ADR) and from E850 to E858.9 (AP). Space-time clustering and spatial variation in temporal trends methods were applied in three different time-periods: globally, by year and grouped in 2 classes (periods of 5 years).ResultsA total of 9,320,076 patients were discharged within this period, with 133,688 patients (1.46%) having at least one ADE, 4% of them related with AP. Critical space-time identified clusters (p < 0.001) were the municipalities from Lisbon metropolitan area and Centro region area. The global rate increased at a 7.8% mean annual percentage change, with high space-time heterogeneity and variation in time trends clusters (p < 0.001). For whole period, 2004–2013, all clusters presented increasing trends. However when analyzed by period of 5 years we identified two clusters with decreasing trends in time in 2004–2008.ConclusionThe impact of ADE is huge, with widely variations within country and in time, and represents an increasing challenge. Future research using individual and contextual risk factors are urgently needed to understand this spatiotemporal variability in order to promote local tailored and updated actions of prevention.

Pulmonary tuberculosis space-time clustering and spatial variation in temporal trends in Portugal, 2000-2010: an updated analysis.

Portugal, a medium- to low-level endemic country (21·6 cases/100 000 population in 2012), has one of the highest European Union tuberculosis (TB) incidences. Although incidence is declining progressively, the country's heterogeneity in both regional endemics and their evolution suggests the importance of a better understanding of subnational epidemiology to customize TB control efforts. We aimed to update knowledge on municipality-years pulmonary TB incidence clustering, identify areas with different time trends, and show the potential of combining complementary clustering methods in control of infectious diseases. We used national surveillance municipality-level data (mainland Portugal, 2000-2010). Space-time clustering and spatial variation in temporal trends methods were applied. Space-time critical clusters identified (P < 0·001) were still the Lisbon and Oporto regions. The global incidence declined at a 5·81% mean annual percentage change, with high space-time heterogeneity and distinct time trend clusters (P < 0·001). Municipalities with incidences declining more rapidly belonged to critical areas. In particular, the Oporto trend cluster had a consistent -8·98% mean annual percentage change. Large space-time heterogeneities were identified, with critical incidences in the greater Lisbon and Oporto regions, but declining more rapidly in these regions. Oporto showed a consistent, steeper decrease and could represent a good example of local control strategy. Combining results from these approaches gives promise for prospects for infectious disease control and the design of more effective, focused interventions.

A Hierarchical Model for Estimating Long‐Term Trend of Atrazine Concentration in the Surface Water of the Contiguous U.S.

AbstractAtrazine is a herbicide frequently detected in both surface and groundwater in the United States (U.S.), but its spatiotemporal distribution and concentration trends have only been analyzed recently at regional or local scales. We employed a Bayesian hierarchical modeling approach to assess spatial and seasonal variation in atrazine concentration trends between 1990 and 2010 for the contiguous U.S. A Markov chain Monte Carlo simulation algorithm was used to address the problem of left‐censored data (i.e., atrazine concentration values below method reporting levels). We observed opposing temporal trends in the northern (flat or decreasing) and southern (increasing) regions of the U.S. This spatial variation in temporal trends can be partially explained by the relative amount of cropland in the region. Flat or decreasing trends in the north are more likely in regions with high cropland coverage while positive trends in the south are more likely in regions with low cropland coverage.

Monitoring temporal trends in spatially structured populations: how should sampling effort be allocated between space and time?

Estimating temporal trends in spatially structured populations has a critical role to play in understanding regional changes in biological populations and developing management strategies. Designing effective monitoring programmes to estimate these trends requires important decisions to be made about how to allocate sampling effort among spatial replicates (i.e. number of sites) and temporal replicates (i.e. how often to survey) to minimise uncertainty in trend estimates. In particular, the optimal mix of spatial and temporal replicates is likely to depend upon the spatial and temporal correlations in population dynamics. Although there has been considerable interest in the ecological literature on understanding spatial and temporal correlations in species’ population dynamics, little attention has been paid to its consequences for monitoring design. We address this issue using model‐based survey design to identify the optimal allocation of sampling effort among spatial and temporal replicates for estimating population trends under different levels of spatial and temporal correlation. Based on linear trends, we show that how we should allocate sampling effort among spatial and temporal replicates depends crucially on the spatial and temporal correlations in population dynamics, environmental variation, observation error and the spatial variation in temporal trends. When spatial correlation is low and temporal correlation is high, the best option is likely to be to sample many sites infrequently, particularly when observation error and/or spatial variation in temporal trends are high. When spatial correlation is high and temporal correlation is low, the best option is likely to be to sample few sites frequently, particularly when observation error and/or spatial variation in temporal trends are low. When abundances are spatially independent, it is always preferable to maximise spatial replication. This provides important insights into how spatio‐temporal monitoring programmes should be designed to estimate temporal trends in spatially structured populations.