Applying a Bayesian Spatiotemporal Model to Examine Excess County-Level Cardiovascular Disease Death Rates During the COVID-19 Pandemic.

Amid the COVID-19 pandemic, national cardiovascular disease (CVD) death rates increased, especially among younger adults. County-level variation has not been documented. Using county-level CVD deaths (ICD-10 codes: I00-I99) from the US National Vital Statistics System, we developed a Bayesian multivariate spatiotemporal model to estimate excess CVD death rates in 2020 based on trends from 2010-2019 for adults aged 35-64 and ≥65 years. Among adults aged 35-64 years, 64.7% of counties experienced significant excess CVD death rates. The median county-level CVD death rate in 2020 was 150 per 100,000 persons, which exceeded the predicted rate for 2020 (median excess death rate: 11 per 100,000; median excess rate ratio: 1.08). Among adults aged ≥65 years, 15.2% of counties experienced significant excess CVD death rates. The median county-level CVD death rate was 1,546 per 100,000 in 2020, which exceeded the predicted rate in 2020 (median excess death rate: 48 per 100,000, median excess rate ratio: 1.03). Counties with significant excess death rates in 2020 were geographically dispersed. In 2020, disruptions of county-level CVD death rates were widespread, especially among younger adults, suggesting the continued importance of CVD prevention and treatment in younger adults in communities across the country.

Spatio-temporal distributions of COVID-19 vaccine doses uptake in the Netherlands: a Bayesian ecological modelling analysis.

In the transitioning era towards the COVID-19 endemic, there is still a sizable population that has never been vaccinated against COVID-19 in the Netherlands. This study employs Bayesian spatio-temporal modelling to assess the relative chances of COVID-19 vaccination uptake - first, second, and booster doses - both at the municipal and regional (public health services) levels. Incorporating ecological regression modelling to consider socio-demographic factors, our study unveils a diverse spatio-temporal distribution of vaccination uptake. Notably, the areas located in or around the Dutch main urban area (Randstad) and regions that are more religiously conservative exhibit a below-average likelihood of vaccination. Analysis at the municipal level within public health service regions indicates internal heterogeneity. Additionally, areas with a higher proportion of non-Western migrants consistently show lower chances of vaccination across vaccination dose scenarios. These findings highlight the need for tailored national and local vaccination strategies. Particularly, more regional efforts are essential to address vaccination disparities, especially in regions with elevated proportions of marginalized populations. This insight informs ongoing COVID-19 campaigns, emphasizing the importance of targeted interventions for optimizing health outcomes during the second booster phase, especially in regions with a relatively higher proportion of marginalized populations.

Hospital case fatality and mortality related to Chagas disease in Brazil over two decades.

OBJECTIVE: To analyse hospital case fatality and mortality related to Chagas disease (CD) in Brazil, 2000-2019. METHOD: This is a mixed ecological study with spatial and temporal trends, based on national population data from the Brazilian Ministry of Health - hospital admissions (HA) and death certificates (DC). Records with CD as a primary or secondary cause of death in HA and/or as an underlying or associated cause of death in DC were evaluated. Temporal trends were analysed by Joinpoint regression and the spatial distribution of age- and gender-adjusted rates, spatial moving averages, and standardized morbidity ratios. RESULTS: There were a total of 4,376 HA due to CD resulting in death in Brazil, with a hospital case fatality rate of 0.11/100,000 inhabitants. The Southeast region had the highest rate (63.9%, n = 2,796; 0.17/100,000 inhabitants). The general trend for this indicator in Brazil is upwards (average annual percentage change [AAPC] 7.5; 95% confidence interval [CI] 5.3 to 9.9), with increases in the North, Northeast and Southeast regions. During the same period 122,275 deaths from CD were registered in DC, with a mortality rate of 3.14/100,000 inhabitants. The highest risk of CD-related death was found among men (relative risk [RR] 1.27) and Afro-Brazilians (RR 1.63). There was a downward trend in CD mortality in the country (AAPC - 0.7%, 95%CI -0.9 to -0.5), with an increase in the Northeast region (AAPC 1.1%, 95%CI 0.6 to 1.6). Municipalities with a very high Brazilian Deprivation Index tended to show an increase in mortality (AAPC 2.1%, 95%CI 1.6 to 2.7), while the others showed a decrease. CONCLUSION: Hospital case fatality and mortality due to CD are a relevant public health problem in Brazil. Differences related to gender, ethnicity, and social vulnerability reinforce the need for comprehensive care, and to ensure equity in access to health in the country. Municipalities, states, and regions with indicators that reveal higher morbidity and mortality need to be prioritized.

Geographical coverage of SARS-CoV-2 screening and care centers in Haiti: what do national surveillance data tell us?

BACKGROUND: In Haiti, reported incidence and mortality rates for COVID-19 were lower than expected. We aimed to analyze factors at communal and individual level that might lead to an underestimation of the true burden of the COVID-19 epidemic in Haiti during its first two years. METHODS: We analyzed national COVID-19 surveillance data from March 2020 to December 2021, to describe the epidemic using cluster detection, time series, and cartographic approach. We performed multivariate Quasi-Poisson regression models to determine socioeconomic factors associated with incidence and mortality. We performed a mixed-effect logistic regression model to determine individual factors associated with the infection. RESULTS: Among the 140 communes of Haiti, 57 (40.7%) had a COVID-19 screening center, and the incidence was six times higher in these than in those without. Only 22 (15.7%) communes had a COVID-19 care center, and the mortality was five times higher in these than in those without. All the richest communes had a COVID-19 screening center while only 30.8% of the poorest had one. And 75% of the richest communes had a COVID-19 care center while only 15.4% of the poorest had one. Having more than three healthcare workers per 1000 population in the commune was positively associated with the incidence (SIR: 3.31; IC95%: 2.50, 3.93) and the mortality (SMR: 2.73; IC95%: 2.03, 3.66). At the individual level, male gender (adjusted OR: 1.11; IC95%: 1.01, 1.22), age with a progressive increase of the risk compared to youngers, and having Haitian nationality only (adjusted OR:2.07; IC95%: 1.53, 2.82) were associated with the infection. CONCLUSIONS: This study highlights the weakness of SARS-CoV-2 screening and care system in Haiti, particularly in the poorest communes, suggesting that the number of COVID-19 cases and deaths were probably greatly underestimated.

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

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

Mortality due to visceral leishmaniasis in Brazil by municipalities, 2001-2018: a spatial-temporal analysis of estimates from the Global Burden of Disease study.

OBJECTIVES: In the American regions, Brazil accounts for 97% of visceral leishmaniasis (VL) cases, with a case fatality rate of approximately 10%. This study aimed to investigate the VL mortality distribution in Brazil and identify high-priority and high-risk areas for intervention strategies. STUDY DESIGN: This was an ecological study that analysed the spatial-temporal patterns of VL mortality in Brazilian municipalities. METHODS: Age-standardised VL mortality rates from the Global Burden of Disease study from 2001 to 2018 were used. The distribution of mortality in the municipalities was assessed, and subsequently the Local Index of Spatial Autocorrelation (LISA) analysis was conducted to identify contiguous areas with high mortality rates. Scan analysis identified clusters of high spatial-temporal risks. RESULTS: The highest mortality rates and clusters were in municipalities located in the Northeast region and in the states of Tocantins and Roraima (North region), Mato Grosso do Sul (Central-West region), and Minas Gerais (Southeast region). According to LISA, there was an increase in the number of municipalities classified as high priority from the first 3-year period (n = 434) to the last 3-year period (n = 644). The spatio-temporal analysis identified 21 high-risk clusters for VL mortality. CONCLUSION: Areas with a high risk of VL mortality should prioritise preventing transmission, invest in early diagnosis and treatment, and promote the training of healthcare professionals.

[Space-time heterogeneity of measles, mumps, and rubella vaccination indicators in children from BrazilHeterogeneidad espaciotemporal de los indicadores de inmunización con la vacuna triple viral en la población infantil de Brasil]. / Heterogeneidade espaço-temporal dos indicadores de imunização da vacina tríplice viral em crianças no Brasil.

Objective: To evaluate immunization coverage and dropout rates of the measles, mumps, and rubella vaccine across Brazilian regions. Methods: This ecological study employing a space-time approach used data from the National Immunization Program (PNI) and the Live Births Information System (SINASC). We estimated the annual variation (2014 to 2021) in immunization coverage and dropout rate of the measles, mumps, and rubella vaccine in children aged 12 and 15 months in the 5 570 Brazilian municipalities. Statistical analysis was conducted for the municipalities contained in each Brazilian region using the space-time scan technique, considering the Poisson probabilistic model and the hypothesis that municipalities with lower immunization coverage or high dropout rates would form space-time clusters. Results: From 2014 to 2021, 38.3% and 12.9% of municipalities achieved a coverage ≥ 95.0% in the first and second doses respectively; 53.6% of municipalities had a high dropout rate (20.0% to 49.9%) and 37.2% had a very high dropout rate. The Northeast region had primary clusters for low coverage of the first (2018 to 2021) and second vaccine doses (2020 to 2021) with higher relative risks (RR) compared to other primary clusters. The RR for high dropout rate was elevated in all Brazilian regions (1.57 to 26.23). Conclusions: In some Brazilian regions, the dropout rate has been high since 2014, signaling a risk of measles resurgence. In turn, space-time analysis indicated lower vaccination coverage in 2020, suggesting the influence of the COVID-19 pandemic.

Objetivo: Evaluar la cobertura de vacunación y las tasas de incumplimiento del esquema de vacunación triple viral en las macrorregiones de Brasil. Métodos: En este estudio ecológico, con un enfoque espaciotemporal, se utilizaron datos del Programa Nacional de Inmunización y del Sistema de Información sobre Recién Nacidos Vivos. Se estimó la variación anual de la cobertura de vacunación y la tasa de incumplimiento del esquema de vacunación triple viral en niños de 12 y 15 meses en 5570 municipios brasileños (en el período 2014-2021). El análisis estadístico se realizó en el conjunto de municipios de cada macrorregión brasileña mediante la técnica de barrido espaciotemporal, con un modelo probabilístico de Poisson y con la hipótesis de que los municipios con menor cobertura de vacunación o tasas altas de incumplimiento formarían conglomerados espaciotemporales. Resultados: En el período 2014-2021, el 38,3% y el 12,9% de los municipios alcanzó una cobertura ≥95,0% para la primera y la segunda dosis de la vacuna triple viral, respectivamente; el 53,6% de los municipios tuvo una tasa de incumplimiento alta (del 20,0% al 49,9%) y el 37,2% una tasa extremadamente alta. En la zona del nordeste se observaron los conglomerados primarios de cobertura baja de la primera y la segunda dosis de la vacuna triple viral (administradas en los períodos 2018-2021 y 2020-2021, respectivamente) con un mayor riesgo relativo (RR), en comparación con los demás conglomerados primarios. Se observó un RR alto de tener una tasa elevada de incumplimiento en todas las macrorregiones de Brasil (de entre 1,57 y 26,23). Conclusiones: En algunas macrorregiones, la tasa de incumplimiento había sido elevada desde el 2014, lo cual indica un riesgo de resurgimiento del sarampión. A su vez, en el análisis espaciotemporal se observó una menor cobertura de vacunación en el 2020, lo que denota la influencia de la pandemia de COVID-19.

Assessment of seasonal variations in soil heavy metal concentrations and potential health risks in Gujarat, India.

This study investigates the prevalence, distribution, and ecological consequences of 21 heavy metals (Ag, Al, As, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Li, Mg, Mn, Na, Ni, Pb, Sr, and Zn) in the soils between the Mahi and Dhadhar rivers in Gujarat, India. It aims to assess the seasonal variations in soil contamination and the potential human health risk associated with the heavy metal exposure through ingestion, inhalation, and dermal contact pathways. Soil samples were collected from 57 sites across three distinct seasons and analyzed using indices such as the geo-accumulation index, contamination factor, pollution load index, ecological risk factor, and global potential ecological risk. Human health risks were also evaluated for carcinogenic and non-carcinogenic effects. The findings reveal significant soil contamination, especially during the Monsoon and Post-Monsoon seasons, with heavy metals like Ag, Cd, Cr, Cu, Co, Ni, and Pb posing considerable ecological threats. Cr and Ni were identified as presenting the highest carcinogenic risks, while Fe and Cr posed major non-carcinogenic challenges. Seasonal variations significantly influenced heavy metal concentrations and distribution patterns. The study highlights the urgent need for comprehensive policies and sustainable practices to mitigate soil contamination and protect environmental health. It emphasizes the critical role of human activities, such as industrial, agricultural, and mining operations, in soil degradation and calls for increased community awareness and action to address these challenges.

Spatio-temporal dynamics and distributional trend analysis of African swine fever outbreaks (2020-2021) in North-East India.

African swine fever (ASF) is a highly contagious, notifiable, and fatal hemorrhagic viral disease affecting domestic and wild pigs. The disease was reported for the first time in India during 2020, resulted in serious outbreaks and economic loss in North-Eastern (NE) parts, since 47% of the Indian pig population is distributed in the NE region. The present study focused on analyzing the spatial autocorrelation, spatio-temporal patterns, and directional trend of the disease in NE India during 2020-2021. The ASF outbreak data (2020-2021) were collected from the offices of the Department of Animal Husbandry and Veterinary Services in seven NE states of India to identify the potential clusters, spatio-temporal aggregation, temporal distribution, disease spread, density maps, and risk zones. Between 2020 and 2021, a total of 321 ASF outbreaks were recorded, resulting in 59,377 deaths. The spatial pattern analysis of the outbreak data (2020-2021) revealed that ASF outbreaks were clustered in 2020 (z score = 2.20, p < .01) and 2021 (z score = 4.89, p < .01). Spatial autocorrelation and Moran's I value (0.05-0.06 in 2020 and 2021) revealed the spatial clustering and spatial relationship between the outbreaks. The hotspot analysis identified districts of Arunachal Pradesh, Assam and districts of Mizoram, Tripura as significant hotspots in 2020 and 2021, respectively. The spatial-scan statistics with a purely spatial and purely temporal analysis revealed six and one significant clusters, respectively. Retrospective unadjusted, temporal, and spatially adjusted space-time analysis detected five, five, and two statistically significant (p < .01) clusters, respectively. The directional trend analysis identified the direction of disease distribution as northeast-southwest (2020) and north-south (2021), indicate the possibility of ASF introduction to India from China. The high-risk zones and spatio-temporal pattern of ASF outbreaks identified in the present study can be used as a guide for deploying proper prevention, optimizing resource allocation and disease control measures in NE Indian states.

Implementing Geospatial Science and Technology to Get to Zero New HIV Infections.

PURPOSE OF REVIEW: Tremendous advancements have been made in HIV treatment and prevention during the last 40 years that zero new HIV cases has become an attainable goal declared by international agencies. However, new cases of HIV infection persist. RECENT FINDINGS: The emerging field of geospatial science is positioned to play key role in the reduction of continued HIV incidence through technology-driven interventions and innovative research that gives insights into at-risk populations. As these methods become more utilized, findings consistently show the important role of location and environment plays in HIV incidence and treatment adherence. This includes distance to HIV provider, locations of where HIV transmissions occurs compared to where people with HIV reside, and how geospatial technology has been leveraged to identify unique insights among varying groups of those at increased risk for HIV, among others. Given these insights, leveraging geospatial technology would play a prominent role in achieving zero new cases of HIV infections.

Social disparities and inequalities in healthcare access and expenditures among Iranians exposed to sulfur mustard: a national study using spatio-temporal analysis.

BACKGROUND: Sulfur Mustard (SM) is a chemical warfare agent that has serious short-term and long-term effects on health. Thousands of Iranians were exposed to SM during the eight-year Iran-Iraq conflict and permanently injured while the socioeconomic imbalance in their healthcare utilization (HCU) and health expenditures remains. This study aims to describe the HCU of SM-exposed survivors in Iran from 2018 to 2021; identify high-risk areas; and apply an inequality analysis of utilization regarding the socioeconomic groups to reduce the gap by controlling crucial determinants. METHODS: From Oct 2018 to June 2021, the Veterans and Martyrs Affairs Foundation (VMAF) recorded 58,888 living war survivors with eye, lung, and skin ailments. After cleaning the dataset and removing junk codes, we defined 11 HCU-related variables and predicted the HCU for the upcoming years using Bayesian spatio-temporal models. We explored the association of individual-level HCU and determinants using a Zero-inflated Poisson (ZIP) model and also investigated the provincial hotspots using Local Moran's I. RESULTS: With ≥ 90% confidence, we discovered eleven HCU clusters in Iran. We discovered that the expected number of HCU 1) rises with increasing age, severity of complications in survivors' eyes and lungs, wealth index (WI), life expectancy (LE), and hospital beds ratio; and 2) decreases with growing skin complications, years of schooling (YOS), urbanization, number of hospital beds, length of stay (LOS) in bed, and bed occupancy rate (BOR). The concentration index (CInd) of HCU and associated costs in age and wealth groups were all positive, however, the signs of CInd values for HCU and total cost in YOS, urbanization, LOS, and Hospital beds ratio groups were not identical. CONCLUSIONS: We observed a tendency of pro-rich inequity and also higher HCU and expenditures for the elderly population. Finally, health policies should tackle potential socioeconomic inequities to reduce HCU gaps in the SM-exposed population. Also, policymakers should allocate the resources according to the hotspots of HCU.

Spatio-Temporal Analysis of Marine Water Quality Data Based on Cross-Recurrence Plot (CRP) and Cross-Recurrence Quantitative Analysis (CRQA).

In recent years, with the frequency of marine disasters, water quality has become an important environmental problem for researchers, and much effort has been put into the prediction of marine water quality. The temporal and spatial correlation of marine water quality parameters directly determines whether the marine time-series data prediction task can be completed efficiently. However, existing research has only focused on the correlation analysis of marine data in a certain area and has ignored the temporal and spatial characteristics of marine data in complex and changeable marine environments. Therefore, we constructed a spatio-temporal dynamic analysis model of marine water quality based on a cross-recurrence plot (CRP) and cross-recurrence quantitative analysis (CRQA). The time-series data of marine water quality were first mapped to high-dimensional space through phase space reconstruction, and then the dynamic relationship among various factors affecting water quality was visually displayed through CRP. Finally, their correlation was quantitatively explained by CRQA. The experimental results showed that our scheme demonstrated well the dynamic correlation of various factors affecting water quality in different locations, providing important data support for the spatio-temporal prediction of marine water quality.

Spatial diffusion of COVID-19 in Algeria during the third wave.

The differential geographic impact of the third wave of COVID-19 is unknown in Algeria. We thus analyze the spatiotemporal variations of cases and deaths of COVID-19 in Algeria, between January and mid-August 2021. Cases and deaths due to COVID-19 were aggregated at the wilaya (province) level. The space-time permutation scan statistic was applied retrospectively to identify spatial-temporal clusters of COVID-19 cases and deaths. We detected 14 spatio-temporal clusters of COVID-19 cases, with only one high risk cluster. Among the 13 low risk clusters, 7 clusters emerged before the start of the third wave and were mostly located in wilayas with lower population density compared to the clusters that emerged during the third wave. For deaths, the largest geographic low-risk cluster emerged in southern Algeria, between April and early July 2021. Northern and coastal wilayas should be prioritized when allocating resources and implementing various quarantine and isolation measures to slow viral transmission.

Stroke Hospital Admissions during the COVID-19 Outbreak in São Paulo, Brazil.

BACKGROUND: Globally, stroke remains an important cause of death and long-term disability, and the impact of coronavirus disease (COVID-19) on the health system may have impaired stroke care. Previous studies suggest significant reduction in hospital admissions for stroke after COVID-19 onset as patients may hesitate seeking medical help due to fear of exposure. METHODS: This cross-sectional study included cases of hospital admissions for stroke, identified from the Hospital Information System of the Unified Health System (Sistema Único de Saúde), which contains official and public data in Brazil. Data were collected in duplicate, then categorized according to the International Classification of Diseases, tenth revision (ICD-10), considering codes I60-I69. Linear regression was used to estimate the variation in hospital admissions for stroke in the city of São Paulo (SP) - the largest and most populous city in Brazil and Latin America, between January and June of each analyzed year (2017-2020). The percentage variation between June and January 2020 was also compared. The level of significance was set at 5%, and the statistical program used was Stata, version 14.0. RESULTS: In the city of SP, during the first wave of COVID-19, from January to June 2020, there were registered decreases in absolute numbers and mean monthly admissions for stroke. Compared to January 2020, data from June 2020 showed 17% reduction in hospitalizations for intracerebral hemorrhage, 32% for cerebral infarction, 26% for stroke unspecified, and 47% for other cerebrovascular diseases. CONCLUSION: We argue for policies aimed at improving stroke care and developing awareness campaigns regarding the importance of early diagnosis and treatment, as even in less severe presentations, stroke can trigger an increase in mortality, cost, and long-term disability.

Measles epidemic in Southern Vietnam: an age-stratified spatio-temporal model for infectious disease counts.

Measles resurged in Vietnam between 2018 and 2020, especially in the Southern region. The proportion of children with measles infection showed quite some variation at the provincial level. We applied a spatio-temporal endemic-epidemic modelling framework for age-stratified infectious disease counts using measles surveillance data collected in Southern Vietnam between 1 January 2018 and 30 June 2020. We found that disease transmission within age groups was greatest in young children aged 0-4 years whereas a relatively high between-group transmission was observed in older age groups (5-14 years, 15-24 years and 25+ years groups). At the provincial level, spatial transmission followed an age-dependent distance decay with measles spread mainly depending on local and neighbouring transmission. Our study helped to clarify the measles transmission dynamics in a more detailed fashion with respect to age strata, time and space. Findings from this study may help determine proper strategies in measles outbreak control including promotion of age-targeted intervention programmes in specific areas.

Prospective spatial-temporal clusters of COVID-19 in local communities: case study of Kansas City, Missouri, United States.

Kansas City, Missouri, became one of the major United States hotspots for COVID-19 due to an increase in the rate of positive COVID-19 test results. Despite the large numbers of positive cases in Kansas City, MO, the spatial-temporal analysis of data has been less investigated. However, it is critical to detect emerging clusters of COVID-19 and enforce control and preventive policies within those clusters. We conducted a prospective Poisson spatial-temporal analysis of Kansas City, MO data to detect significant space-time clusters of COVID-19 positive cases at the zip code level in Kansas City, MO. The analysis focused on daily infected cases in four equal periods of 3 months. We detected temporal patterns of emerging and re-emerging space-time clusters between March 2020 and February 2021. Three statistically significant clusters emerged in the first period, mainly concentrated in downtown. It increased to seven clusters in the second period, spreading across a broader region in downtown and north of Kansas City. In the third period, nine clusters covered large areas of north and downtown Kansas City, MO. Ten clusters were present in the last period, further extending the infection along the State Line Road. The statistical results were communicated with local health officials and provided the necessary guidance for decision-making and allocating resources (e.g., vaccines and testing sites). As more data become available, statistical clustering can be used as a COVID-19 surveillance tool to measure the effects of vaccination.

Geographical inequalities in the decreasing 28-day mortality following incident acute myocardial infarction: a Danish register-based cohort study, 1987-2016.

BACKGROUND: Mortality following acute myocardial infarction (AMI) has decreased in western countries for decades; however, it remains unknown whether the decrease is distributed equally across the population independently of residential location. This study investigated whether the observed decreasing 28-day mortality following an incident AMI in Denmark from 1987 to 2016 varied geographically at municipality level after accounting for sociodemographic characteristics. METHODS: A register-based cohort study design was used to investigate 28-day mortality among individuals with an incident AMI. Global spatial autocorrelation (within sub-periods) was analysed at municipality level using Moran's I. Analysis of spatio-temporal autocorrelation before and after adjusting for sociodemographic characteristics was performed using logistic regression and conditional autoregressive models with inference in a Bayesian setting. RESULTS: In total, 368,839 individuals with incident AMI were registered between 1987 and 2016 in Denmark; 128,957 incident AMIs were fatal. The 28-day mortality decreased over time at national level with an odds ratio of 0.788 (95% credible interval (0.784, 0.792)) per 5-year period after adjusting for sociodemographic characteristics. The decrease in the 28-day mortality was geographically unequally distributed across the country and in a geographical region in northern Jutland, the 28-day mortality decreased significantly slower (4-12%) than at national level. CONCLUSIONS: During the period from 1987 to 2016, the 28-day mortality following an incident AMI decreased substantially in Denmark. However, in a local geographical region, the 28-day mortality decreased significantly slower than in the rest of the country both before and after adjusting for sociodemographic differences. Efforts should be made to keep geographical trend inequalities in the 28-day mortality to a minimum.

Homicide rates are spatially associated with built environment and socio-economic factors: a study in the neighbourhoods of Toronto, Canada.

OBJECTIVES: Homicide rate is associated with a large variety of factors and therefore unevenly distributed over time and space. This study aims to explore homicide patterns and their spatial associations with different socioeconomic and built-environment conditions in 140 neighbourhoods of the city of Toronto, Canada. METHODS: A homicide dataset covering the years 2012 to 2021 and neighbourhood-based indicators were analysed using spatial techniques such as Kernel Density Estimation, Global/Local Moran's I and Kulldorff's SatScan spatio-temporal methodology. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) were used to analyse the spatially varying correlations between the homicide rate and independent variables. The latter was particularly suitable for manifested spatial variations between explanatory variables and the homicide rate and it also identified spatial non-stationarities in this connection. RESULTS: The adjusted R2 of the MGWR was 0.53, representing a 4.35 and 3.74% increase from that in the linear regression and GWR models, respectively. Spatial and spatio-temporal high-risk areas were found to be significantly clustered in downtown and the north-western parts of the city. Some variables (e.g., the population density, material deprivation, the density of commercial establishments and the density of large buildings) were significantly associated with the homicide rate in different spatial ways. CONCLUSION: The findings of this study showed that homicide rates were clustered over time and space in certain areas of the city. Socioeconomic and the built environment characteristics of some neighbourhoods were found to be associated with high homicide rates but these factors were different for each neighbourhood.

The spatio-temporal dynamics of infant mortality in Ecuador from 2010 to 2019.

The infant mortality rate (IMR) is still a key indicator in a middle-income country such as Ecuador where a slightly increase up to 11.75 deaths per thousand life births has been observed in 2019. The purpose of this study is to propose and apply a prioritization method that combines clusters detection (Local Indicators of Spatial Association, LISA) and a monotonic statistic depicting time trend over 10 years (Mann-Kendall) at municipal level. Annual national databases (2010 to 2019) of live births and general deaths are downloaded from National Institute of Statistics and Censuses (INEC). The results allow identifying a slight increase in the IMR at the national level from 9.85‰ in 2014 to 11.75‰ in 2019, neonatal mortality accounted for 60% of the IMR in the last year. The LISA analysis allowed observing that the high-high clusters are mainly concentrated in the central highlands. At the local level, Piñas, Cuenca, Ibarra and Babahoyo registered the highest growth trends (0.7,1). The combination of techniques made it possible to identify eight priority counties, half of them pertaining to the highlands region, two to the coastal region and two to the Amazon region. To keep infant mortality at a low level is necessary to prioritize critical areas where public allocation of funds should be concentrated and formulation of policies.

Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network.

Solar irradiance forecasting is fundamental and essential for commercializing solar energy generation by overcoming output variability. Accurate forecasting depends on historical solar irradiance data, correlations between various meteorological variables (e.g., wind speed, humidity, and cloudiness), and influences between the weather contexts of spatially adjacent regions. However, existing studies have been limited to spatiotemporal analysis of a few variables, which have clear correlations with solar irradiance (e.g., sunshine duration), and do not attempt to establish atmospheric contextual information from a variety of meteorological variables. Therefore, this study proposes a novel solar irradiance forecasting model that represents atmospheric parameters observed from multiple stations as an attributed dynamic network and analyzes temporal changes in the network by extending existing spatio-temporal graph convolutional network (ST-GCN) models. By comparing the proposed model with existing models, we also investigated the contributions of (i) the spatial adjacency of the stations, (ii) temporal changes in the meteorological variables, and (iii) the variety of variables to the forecasting performance. We evaluated the performance of the proposed and existing models by predicting the hourly solar irradiance at observation stations in the Korean Peninsula. The experimental results showed that the three features are synergistic and have correlations that are difficult to establish using single-aspect analysis.