Spatial Scan Statistic Research Articles

Temporal trends and spatial analysis of leprosy surveillance indicators in the municipalities of the state of Mato Grosso, 2008-2022.

In 2022, Mato Grosso (MT, Brazil) reported the highest detection rate of new leprosy cases (66.20 per 100,000 inhabitants) among all Brazilian states. Monitoring of leprosy indicators is an important control strategy in hyperendemic areas. We aimed to describe the temporal trends and identify clusters of municipalities according to leprosy surveillance indicators in MT between 2008 and 2022. Data from the Notifiable Diseases Information System were used to analyze new case detection rate of leprosy (NCDR), new case detection rate of leprosy among children aged <15 years (NCD15), and rate of new cases with grade 2 physical disability (G2DR). Spatial scan statistics with pure spatial analysis and spatial autocorrelation maps were used to analyze the spatial patterns. Joinpoint regression was used to estimate the annual percentage change (APC) in these indicators. The NCDR decreased (APC: -20.2%, 95% confidence interval (CI): -38.7% to -7.4%) between 2019 and 2021. The NCD15 also decreased (APC: -19.2%, 95% CI: -36.4% to -10.3%) between 2018 and 2022. Conversely, the G2DR remained stable throughout the study (APC: 3.2%, 95% CI: -0.1% to 6.7%). Global Moran's index (Moran's I) confirmed the existence of spatial dependence among the municipalities for NCDR (Moran's I=0.348), NCD15 (Moran's I=0.269), and G2DR (Moran's I=0.275). Clusters with high NCDR levels included 13 municipalities in the northern and eastern macroregions, while clusters with high G2DR levels included 12 municipalities in the northwestern, northern, and eastern macroregions. The NCDR and NCD15 decreased, but the G2DR remained stable between 2018 and 2022. The coronavirus disease 2019 (COVID-19) pandemic had a potential negative impact on leprosy case detection, highlighting the need to strengthen leprosy surveillance efforts. The identified clusters of MT municipalities can significantly assist in this task.

Exploring Spatial Variation of Women’s Tetanus Vaccination in Ethiopia: Spatial Analysis of Ethiopian Demographic and Health Survey 2016

Tetanus is a bacterial infection that causes painful muscular contractions throughout the body, usually starting in the jaw. Every year, 15,000 to 30,000 maternal deaths occur due to tetanus related to the delivery process. Exploring variations of women’s tetanus immunization is crucial for programmers’ planning and monitoring. As a result, this study aims to evaluate the spatial distribution of women’s tetanus vaccination coverage and risk areas of women’s tetanus vaccination clustering in Ethiopia. The study used Ethiopian Demographic and Health Survey data, which was conducted in 2016 using the Ethiopia Population and Housing Census 2007 sampling frame. From 15,683 women aged 15 to 49, a sample of 7,193 women was selected using eligible criteria. This study used spatial data analysis. To determine geographical danger zones for tetanus vaccination of women, cluster, hotspot analysis, spatial interpolation, and spatial scan statistics were used. At the regional level of Ethiopia, tetanus vaccination was spatially clustered (Global Moran’s I = 0.066, p = .0001). Western Tigray, eastern SNNP, and northern Oromia were identified as hotspots of poor tetanus vaccine coverage, whereas western Oromia, Afar, and southeast Amhara regions were designated as non-risky areas of Ethiopia. The spatial Bayesian kriging interpolation analysis predicted high-risk areas were the southwestern and eastern parts of Oromia, western Tigray, Harari, and Dire Dawa Western Tigray, the eastern SNNP, southern Amhara, and northern Oromia have been recognized as a significant hotspot. Health workers, programmers, government, and non-governmental organizations in Ethiopia will implement spatially based strategic interventions for women’s tetanus immunization.

Clusters of hepatitis D in Iraq, 2018 to 2022: A geographic analysis

The regional distribution of hepatitis D is evaluated by the utilization of the disease surveillance tool SaTScan and its spatial scan statistic. The statistic for the spatial scan was used in detecting the area that has a significantly higher prevalence of hepatitis. The spatial scan statistic determined Najaf, Babil, Wasit, Maysan, Dha Qar, Basrah, and Muthanna to be most likely to constitute areas of cluster presence for hepatitis D. The relative risk (RR) for men was 1.49 (P &lt; 0.001) and that for females was 1.46 (P &lt; 0.001). After considering all the parameters under this model, we found that the most probable location for clustering hepatitis in the country is the central areaNajaf, Babil, Wasit, Maysan Dha Qar Muthanna Diyala Baghdad Karbala Salah Ad Din and Tam'mim with a relative risk of 6.65(P &lt; 0.001).

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 NEregion. 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 thepotential clusters, spatio-temporal aggregation, temporal distribution, disease spread, densitymaps, 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),indicatethe 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 diseasecontrol measures in NE Indian states.

Hotspot areas of risky sexual behaviour and associated factors in Ethiopia: Further spatial and mixed effect analysis of Ethiopian demographic health survey.

Sexual behaviour needs to take a central position in the heart of public health policy makers and researchers. This is important in view of its association with Sexually Transmitted Infections (STIs), including HIV. Though the prevalence of HIV/AIDS is declining in Ethiopia, the country is still one of the hardest hit in the continent of Africa. Hence, this study was aimed at identifying hot spot areas and associated factors of risky sexual behavior (RSB). This would be vital for more targeted interventions which can produce a sexually healthy community in Ethiopia. In this study, a cross-sectional survey study design was employed. A further analysis of the 2016 Ethiopia Demographic and Health Survey data was done on a total weighted sample of 10,518 women and men age 15-49 years. ArcGIS version 10.7 and Kuldorff's SaTScan version 9.6 software were used for spatial analysis. Global Moran's I statistic was employed to test the spatial autocorrelation, and Getis-Ord Gi* as well as Bernoulli-based purely spatial scan statistics were used to detect significant spatial clusters of RSB. Mixed effect multivariable logistic regression model was fitted to identify predictors and variables with a p-value ≤0.05 were considered as statistically significant. The study subjects who had RSB were found to account about 10.2% (95% CI: 9.64%, 10.81%) of the population, and spatial clustering of RSB was observed (Moran's I = 0.82, p-value = 0.001). Significant hot spot areas of RSB were observed in Gambela, Addis Ababa and Dire Dawa. The primary and secondary SaTScan clusters were detected in Addis Ababa (RR = 3.26, LLR = 111.59, P<0.01), and almost the entire Gambela (RR = 2.95, LLR = 56.45, P<0.01) respectively. Age, literacy level, smoking status, ever heard of HIV/AIDS, residence and region were found to be significant predictors of RSB. In this study, spatial clustering of risky sexual behaviour was observed in Ethiopia, and hot spot clusters were detected in Addis Ababa, Dire Dawa and Gambela regions. Therefore, interventions which can mitigate RSB should be designed and implemented in the identified hot spot areas of Ethiopia. Interventions targeting the identified factors could be helpful in controlling the problem.

Spatial patterns and sociodemographic predictors of chronic obstructive pulmonary disease in Florida.

Chronic obstructive pulmonary disease (COPD) is a chronic, inflammatory respiratory disease that obstructs airflow and decreases lung function and is a leading cause death globally. In the United States (US), the prevalence among adults is 6.2%, but increases with age to 12.8% among those 65 years or older. Florida has one of the largest populations of older adults in the US, accounting for 4.5 million adults 65 years or older. This makes Florida an ideal geographic location for investigating COPD as disease prevalence increases with age. Understanding the geographic disparities in COPD and potential associations between its disparities and environmental factors as well as population characteristics is useful in guiding intervention strategies. Thus, the objectives of this study are to investigate county-level geographic disparities of COPD prevalence in Florida and identify county-level socio-demographic predictors of COPD prevalence. This ecological study was performed in Florida using data obtained from the US Census Bureau, Florida Health CHARTS, and County Health Rankings and Roadmaps. County-level COPD prevalence for 2019 was age-standardized using the direct method and 2020 US population as the standard population. High-prevalence spatial clusters of COPD were identified using Tango's flexible spatial scan statistics. Predictors of county-level COPD prevalence were investigated using multivariable ordinary least squares model built using backwards elimination approach. Multicollinearity of regression coefficients was assessed using variance inflation factor. Shapiro-Wilks, Breusch Pagan, and robust Lagrange Multiplier tests were used to assess for normality, homoskedasticity, and spatial autocorrelation of model residuals, respectively. County-level age-adjusted COPD prevalence ranged from 4.7% (Miami-Dade) to 16.9% (Baker and Bradford) with a median prevalence of 9.6%. A total of 6 high-prevalence clusters with prevalence ratios >1.2 were identified. The primary cluster, which was also the largest geographic cluster that included 13 counties, stretched from Nassau County in north-central Florida to Charlotte County in south-central Florida. However, cluster 2 had the highest prevalence ratio (1.68) and included 10 counties in north-central Florida. Together, the primary cluster and cluster 2 covered most of the counties in north-central Florida. Significant predictors of county-level COPD prevalence were county-level percentage of residents with asthma and the percentage of current smokers. There is evidence of spatial clusters of COPD prevalence in Florida. These patterns are explained, in part, by differences in distribution of some health behaviors (smoking) and co-morbidities (asthma). This information is important for guiding intervention efforts to address the condition, reduce health disparities, and improve population health.

Mapping the spatial distribution of harmful umbilical cord stump care among neonates in Ethiopia: A spatial with multilevel analysis.

The umbilical cord (UC) serves as the main pathway for bacteria to reach the neonate's body, potentially causing local and severe infections, sepsis, and even death. Consequently, neonatal mortality remains a significant public health concern, particularly in Ethiopia. The World Health Organization (WHO) recommends that the umbilical cord stump be kept clean and dry, with the exception of applying topical antiseptics. However, various harmful substances are still applied to the umbilical cord of neonates. Data on the geographical distribution and risk factors for harmful umbilical cord stump (UCS) care are scarce. Therefore, this study aims to fill this gap. A secondary data analysis of the Ethiopian Demographic Health Survey (EDHS 2016) was conducted using a weighted sample of 7,168 live births. ArcGIS version 10.7.1 software was utilized to visualize the spatial distribution of harmful umbilical cord stump (UCS) care practices in Ethiopia. Additionally, a Bernoulli probability model-based spatial scan statistic was applied using Kulldorff's SaTScan version 9.6 software to identify significant clusters of harmful UCS care. A multilevel logistic regression model was used to determine the factors associated with UCS care practices in Ethiopia. Statistical significance was declared at a two-sided P-value of < 0.05. Overall, the prevalence of harmful UCS care in Ethiopia was 15.09% (95% CI: 13.9-16.3), with significant spatial heterogeneity across geographical areas. The hotspot areas of harmful US care were observed in the eastern (Somali) and northern (Tigray and Amhara) parts of Ethiopia. In spatial scan analysis, the most likely primary clusters were observed in South Nation Nationalities and Peoples region (SNNPR), secondary clusters in the Somali, tertiary clusters in Tigray, and the next clusters in the Amhara regions, respectively. In the final multilevel model, maternal age (Adjusted odds ratio/AOR 1.07, 95% CI: 1.02-1.12), institutional delivery (AOR 0.64, 95% CI: 0.42-0.97), female neonates (AOR 1.31, 95% CI: 1.04-1.61), rural residence (AOR 2.18, 95% CI: 1.05-4.52), living in Tigray region (AOR 3.79, 95% CI: 1.38-9.38), living in Somali region (AOR, 2.95% CI: 1.02-8.52), and living in Harari region (AOR 3.51, 95% CI: 1.28-9.60) were identified as a significant factors of harmful US care practice in Ethiopia. In Ethiopia, the distribution of harmful UCS care practices is non-random and highly clustered in the SNNPR, Somalia, Tigray, and Amhara regions. Both individual and community-level factors were significantly associated with the practice. Special emphasis needs to be provided for neonates from those hot-spot areas and to address the identified predictors of harmful umbilical cord stump care practices.

Geographic disparities and temporal changes of diabetes-related mortality risks in Florida: a retrospective study.

Over the last few decades, diabetes-related mortality risks (DRMR) have increased in Florida. Although there is evidence of geographic disparities in pre-diabetes and diabetes prevalence, little is known about disparities of DRMR in Florida. Understanding these disparities is important for guiding control programs and allocating health resources to communities most at need. Therefore, the objective of this study was to investigate geographic disparities and temporal changes of DRMR in Florida. Retrospective mortality data for deaths that occurred from 2010 to 2019 were obtained from the Florida Department of Health. Tenth International Classification of Disease codes E10-E14 were used to identify diabetes-related deaths. County-level mortality risks were computed and presented as number of deaths per 100,000 persons. Spatial Empirical Bayesian (SEB) smoothing was performed to adjust for spatial autocorrelation and the small number problem. High-risk spatial clusters of DRMR were identified using Tango's flexible spatial scan statistics. Geographic distribution and high-risk mortality clusters were displayed using ArcGIS, whereas seasonal patterns were visually represented in Excel. A total of 54,684 deaths were reported during the study period. There was an increasing temporal trend as well as seasonal patterns in diabetes mortality risks with high risks occurring during the winter. The highest mortality risk (8.1 per 100,000 persons) was recorded during the winter of 2018, while the lowest (6.1 per 100,000 persons) was in the fall of 2010. County-level SEB smoothed mortality risks varied by geographic location, ranging from 12.6 to 81.1 deaths per 100,000 persons. Counties in the northern and central parts of the state tended to have high mortality risks, whereas southern counties consistently showed low mortality risks. Similar to the geographic distribution of DRMR, significant high-risk spatial clusters were also identified in the central and northern parts of Florida. Geographic disparities of DRMR exist in Florida, with high-risk spatial clusters being observed in rural central and northern areas of the state. There is also evidence of both increasing temporal trends and Winter peaks of DRMR. These findings are helpful for guiding allocation of resources to control the disease, reduce disparities, and improve population health.

Community Science Strategies Reveal Distributional Patterns of Treponeme-Associated Hoof Disease in Washington Elk (Cervus canadensis)

Treponeme-associated hoof disease (TAHD) is an emerging disease of conservation concern in elk (Cervus canadensis) in the Northwest USA. Elk with TAHD exhibit characteristic hoof lesions that are often accompanied by lameness and limping. The gold standard approach traditionally used for infectious disease surveillance is laboratory confirmation, which for TAHD is a histologic examination of abnormal elk hooves submitted by wildlife agencies. Diagnostic evaluation affords certainty in confirming TAHD; however, these examinations are also labor and resource intensive, and therefore, not conducive to the collection of sufficient data for epidemiologic investigations. In response, two community science (CS) surveillance strategies have been implemented in Washington State: public observations of limping elk from a web-based reporting tool and hunter reports of hoof abnormalities on harvested elk. Surveillance using CS strategies can be implemented widely and may be useful for describing broad distributional patterns of TAHD, despite their unknown relationship to laboratory-confirmed cases. We described and compared the spatial–temporal distribution of TAHD in western Washington game management units (GMU) using the two CS strategies to assess congruences and discrepancies between observed patterns. We used spatial scan statistics to identify possible core-affected and newly emerging areas at the GMU level. Lastly, we contrasted CS observations against confirmed case data to examine possible delays in TAHD detection and co-occurrence among surveillance strategies. We found public observations of limping elk often predated TAHD confirmations in GMUs by several years, while hunter-reported abnormalities predated confirmations in GMUs by several months. High co-occurrence between the presence of apparent and confirmed cases under different surveillance strategies further supports the use of CS sources. This study capitalizes on wide-reaching CS data to provide new and complementary epidemiological information that can help guide future surveillance, management, and research efforts for this novel elk hoof disease.

EFFECT OF THE DELAY IN THE REPORTS OF COVID-19 CASES ON NEAR REAL-TIME CLUSTERS DETECTION

Abstract. The COVID-19 pandemic has strongly impacted the vast majority of countries in the world. As of today (April 12th, 2023), more than 762 million confirmed cases and nearly 6.9 million deaths are considered widely underestimated. During a pandemic, detecting clusters of patients is crucial to allocate resources and aiding decision-making better as emergent outbreaks continue to grow. However, delays in reporting suspected or confirmed cases can affect the detection of clusters in near real-time. This study aimed to assess whether the delays in reporting COVID-19 in Mexico presented specific Spatiotemporal patterns and whether they significantly affected the detection of clusters. To do this, we used the daily records of the Mexican Ministry of Health for three dates at the beginning and during the increase in cases of the fourth wave (January 2022). We compared the clusters obtained using the data available on the same date and during the following days, including delayed data. We carried out cluster detection using the flexible spatial scan statistic (FlexScan) on the R platform. The results indicate that the spatial distribution of delays was heterogeneous and that delays affect cluster detection.

A Bayesian spatial scan statistic for multinomial data

Spatial scan statistics are commonly used to detect clustering. We present a Bayesian spatial scan statistic for multinomial data. After validating our method with a simulation study, we use it to detect clusters of SARS-CoV-2 infection/immunity in South Carolina.

Spatial variation and clustering of anaemia prevalence in school-aged children in Western Kenya.

Anaemia surveillance has overlooked school-aged children (SAC), hence information on this age group is scarce. This study examined the spatial variation of anaemia prevalence among SAC (5-14 years) in western Kenya, a region associated with high malaria infection rates. A total of 8051 SAC were examined from 82 schools across eight counties in Western Kenya in February 2022. Haemoglobin (Hb) concentrations were assessed at the school and village level and anaemia defined as Hb<11.5g/dl for age 5-11yrs and Hb <12.0g/dl for 12-14yrs after adjusting for altitude. Moran's I analysis was used to measure spatial autocorrelation, and local clusters of anaemia were mapped using spatial scan statistics and local indices of spatial association (LISA). The prevalence of anaemia among SAC was 27.8%. The spatial variation of anaemia was non-random, with Global Moran's I 0.2 (p-value < 0.002). Two significant anaemia cluster windows were identified: Cluster 1 (LLR = 38.9, RR = 1.4, prevalence = 32.0%) and cluster 2 (LLR = 23.6, RR = 1.6, prevalence = 45.5%) at schools and cluster 1 (LLR = 41.3, RR = 1.4, prevalence = 33.3%) and cluster 2 (LLR = 24.5, RR = 1.6, prevalence = 36.8%) at villages. Additionally, LISA analysis identified ten school catchments as anaemia hotspots corresponding geographically to SatScan clusters. Anaemia in the SAC is a public health problem in the Western region of Kenya with some localised areas presenting greater risk relative to others. Increasing coverage of interventions, geographically targeting the prevention of anaemia in the SAC, including malaria, is required to alleviate the burden among children attending school in Western Kenya.

Geographic Variation in Late-Stage Cervical Cancer Diagnosis

There are stark disparities in cervical cancer burden in the United States, notably by race and ethnicity and geography. Late-stage diagnosis is an indicator of inadequate access to and utilization of screening. To identify geospatial clusters of late-stage cervical cancer at time of diagnosis in Texas. This population-based cross-sectional study used incident cervical cancer data from the Texas Cancer Registry from 2014 to 2018 of female patients aged 18 years or older. Late-stage cervical cancer cases were geocoded at the census tract level (n = 5265) using their residential coordinates (latitude and longitude) at the time of diagnosis. Statistical analysis was performed from April to September 2023. Census tract of residence at diagnosis. Late-stage cervical cancer diagnosis (ie, cases classified by the National Cancer Institute Surveillance, Epidemiology and End Results summary stages 2 to 4 [regional spread] or 7 [distant metastasis]). A Poisson probability-based model of the SaTScan purely spatial scan statistics was applied at the census tract-level to identify geographic clusters of higher (hot spots) or lower (cold spots) proportions than expected of late-stage cervical cancer diagnosis and adjusted for age. Among a total of 6484 female patients with incident cervical cancer cases (mean [SD] age, 48.7 [14.7] years), 2300 (35.5%) were Hispanic, 798 (12.3%) were non-Hispanic Black, 3090 (47.6%) were non-Hispanic White, and 296 (4.6%) were other race or ethnicity. Of the 6484 patients, 2892 with late-stage diagnosis (mean [SD] age, 51.8 [14.4] years were analyzed. Among patients with late-stage diagnosis, 1069 (37.0%) were Hispanic, 417 (14.4%) were non-Hispanic Black, 1307 (45.2%) were non-Hispanic White, and 99 (3.4%) were other race or ethnicity. SaTScan spatial analysis identified 7 statistically significant clusters of late-stage cervical cancer diagnosis in Texas, of which 4 were hot spots and 3 were cold spots. Hot spots included 1128 census tracts, predominantly in the South Texas Plains, Gulf Coast, and Prairies and Lakes (North Texas) regions. Of the 2892 patients with late-stage cervical cancer, 880 (30.4%) were observed within hot spots. Census tract-level comparison of characteristics of clusters suggested that hot spots differed significantly from cold spots and the rest of Texas by proportions of racial and ethnic groups, non-US born persons, and socioeconomic status. In this cross-sectional study examining geospatial clusters of late-stage cervical cancer diagnosis, place-based disparities were found in late-stage cervical cancer diagnosis in Texas. These findings suggest that these communities may benefit from aggressive cervical cancer interventions.

Optimizing the maximum reported cluster size for the multinomial-based spatial scan statistic

BackgroundCorrectly identifying spatial disease cluster is a fundamental concern in public health and epidemiology. The spatial scan statistic is widely used for detecting spatial disease clusters in spatial epidemiology and disease surveillance. Many studies default to a maximum reported cluster size (MRCS) set at 50% of the total population when searching for spatial clusters. However, this default setting can sometimes report clusters larger than true clusters, which include less relevant regions. For the Poisson, Bernoulli, ordinal, normal, and exponential models, a Gini coefficient has been developed to optimize the MRCS. Yet, no measure is available for the multinomial model.ResultsWe propose two versions of a spatial cluster information criterion (SCIC) for selecting the optimal MRCS value for the multinomial-based spatial scan statistic. Our simulation study suggests that SCIC improves the accuracy of reporting true clusters. Analysis of the Korea Community Health Survey (KCHS) data further demonstrates that our method identifies more meaningful small clusters compared to the default setting.ConclusionsOur method focuses on improving the performance of the spatial scan statistic by optimizing the MRCS value when using the multinomial model. In public health and disease surveillance, the proposed method can be used to provide more accurate and meaningful spatial cluster detection for multinomial data, such as disease subtypes.

Ungauged Basin Flood Prediction Using Long Short-Term Memory and Unstructured Social Media Data

Floods are highly perilous and recurring natural disasters that cause extensive property damage and threaten human life. However, the paucity of hydrological observational data hampers the precision of physical flood models, particularly in ungauged basins. Recent advances in disaster monitoring have explored the potential of social media as a valuable source of information. This study investigates the spatiotemporal consistency of social media data during flooding events and evaluates its viability as a substitute for hydrological data in ungauged catchments. To assess the utility of social media as an input factor for flood prediction models, the study conducted time-series and spatial correlation analyses by employing spatial scan statistics and confusion matrices. Subsequently, a long short-term memory model was used to forecast the outflow volume in the Ui Stream basin in South Korea. A comparative analysis of various input factor combinations revealed that datasets incorporating rainfall, outflow models, and social media data exhibited the highest accuracy, with a Nash–Sutcliffe efficiency of 94%, correlation coefficient of 97%, and a minimal normalized root mean square error of 0.92%. This study demonstrated the potential of social media data as a viable alternative for data-scarce basins, highlighting its effectiveness in enhancing flood prediction accuracy.

Using a novel strategy to identify the clustered regions of associations between short-term exposure to temperature and mortality and evaluate the inequality of heat- and cold-attributable burdens: A case study in the Sichuan Basin, China

BackgroundFew studies have focused on the spatially clustered regions in the association between short-term exposure to temperature and mortality, which is important for identifying high-susceptibility population and enhancing the prevention of high/low temperatures. Previous studies have explored the association inequality, but no study has evaluated the inequalities of temperature-attributable burdens, which may be more meaningful for reducing temperature-related regional inequality. MethodsTaking the Sichuan Basin (SCB), an economically imbalanced area with high humidity and four distinctive seasons, as an example, we used a novel multi-stage strategy to investigate the two issues. First, distributed lag nonlinear models were independently constructed to obtain the county-level associations between daily temperature and cardiorespiratory mortality. Then, an estimation-error-based spatial scan statistic was used to detect the association-clustered regions. Third, multivariate meta-regression incorporating the identified clustered regions and socioeconomic and natural factors was used to obtain stable county-specific associations, based on which the heat- and cold-attributable deaths were mapped and their inequalities were evaluated using concentration indices and Lorenz curves. ResultsOn average, a U-shaped temperature-mortality association was examined. A significantly association-clustered region was detected (P = 0.017), in which heat and cold temperatures presented significantly stronger associations than those in the non-clustered region, particularly for heat temperatures. The cold-attributable deaths (3.5%) were substantially more than the heat-attributable deaths (0.5%). Both presented severe inequalities over counties. Significant temperature-attributable inequalities were also found over per-capital public budget, urbanization rate, employment rate and per-capital GDP. The directions of inequalities over GDP and urbanization rate were opposite between heat and cold temperatures. ConclusionsOur analysis provided the first evidence about the clustering of temperature-mortality associations and the inequality of cold- and heat-attributable burdens. Significantly association-clustered regions and heavy temperature-attributable inequalities were found in the SCB. Rural people bore heavier cold-attributable but less heat-attributable mortality risk than urban people, suggesting that different policies should be designed to reduce the temperature-attributable inequalities for heat and cold temperatures and different regions. This novel strategy can provide an interesting new perspective in the association between environmental exposure and human health.

Exploring the spatial pattern of animal bites in Iran (2021–2022)

ObjectiveKnowledge of the spatial pattern of animal bites can be helpful for targeted resource allocation and to develop and deliver effective intervention programs. The aim of this study was to explore the spatial pattern of the animal bites in Iran during 2021–2022. MethodsAnimal bite cases from all provinces and counties in Iran were obtained from a nationally based registry. Global Moran's I was applied to check spatial autocorrelation. The spatially adjusted standardized incidence ratios (SIRs) were estimated using the Besag, York, and Mollie (BYM) model. Spatial clusters were identified by local indicators of spatial association (LISA) and purely spatial scan statistic. ResultsA total of 260,470 animal bites were registered during the study period. There was a positive spatial autocorrelation (global Moran's I=0.27, p-value=0.001). The majority of SIRs greater than 1.00 was found in counties in the northern belt of Iran (e.g., observed greater than expected animal bites). LISA found that approximately 7% of counties in the north and northeast, 18% in the west and south, and 3% in the central part of Iran were significant hot spots, cold spots, and spatial outliers (p-value≤0.05). Spatial scan statistic detected primary hot spot cluster in the counties in the Mazandaran and Alborz provinces (Relative Risk=2.56, p-value<0.001), while primary cold spot cluster involved counties in Kurdistan and Kermanshah province (0.37, <0.001). ConclusionAnimal bites were unevenly distributed in Iran. Further prevention and control programs as well as appropriately resource allocation are needed in order to reduce the observed animal bites spatial disparity.

Leveraging data from a private recreational fishing application to begin to understand potential impacts from offshore wind development

Abstract The development of offshore wind energy in the United States necessitates a sound understanding of trade-offs across ocean uses. Location data on private recreational fishing have been a glaring gap in understanding how society uses marine resources, despite its economic importance. In this study, we use a novel data set to start to fill that knowledge gap. We employ a flexible restricted likelihood spatial scan statistic on data from Fish Rules, a smartphone application, which provides georeferenced species-level regulations, to understand whether species-level data of user queries are clustered spatially. Originally developed for epidemiological studies of disease clusters, the flexible scan statistic employed in this study uses a Bernoulli likelihood ratio test to assess the size, number, and significance of clusters in presence/absence data for recreational species. We use a second data set of known fishing locations to validate that the clusters identify private recreational fishing activity. We then discuss the analysis in the context of wind lease areas in the region, highlighting its value in supporting management decision-making. The results suggest that Fish Rules data identify areas with a high likelihood of being private angler fishing locations and can assess differential impacts of offshore wind development on private recreational fishing activities.

Bridging the Gaps: Investigating the Complex Impact of the COVID-19 Pandemic on Tuberculosis Records in Brazil.

This study aimed to analyze the temporal evolution, spatial distribution, and impact of the COVID-19 pandemic on tuberculosis records in a northeastern state of Brazil. This is an ecological study involving all diagnoses of Tuberculosis (TB) in residents of the state of Pernambuco/Brazil. Data were extracted from the National System of Notifiable Diseases. A pre-pandemic COVID-19 temporal analysis (2001-2019), a spatial analysis before (2015-2019) and during the first two pandemic years (2020-2021), and the impact of the COVID-19 pandemic on cases of TB diagnoses in Pernambuco in the years 2020 and 2021 were performed. Inflection point regression models, Global and Local Moran's statistics, and spatial scan statistics were used. In the period from 2001 to 2019, 91,225 cases of TB were registered in Pernambuco (48.40/100,000 inhabitants), with a tendency of growth starting in 2007 (0.7% per year; p = 0.005). In the pre-pandemic period (2015-2019), 10.8% (n = 20) of Pernambuco municipalities had TB incidence rates below 10/100,000. In 2020, this percentage reached 27.0% (n = 50) and in 2021 it was 17.8% (n = 33). Risk clusters were identified in the eastern region of the state, with five clusters in the pre-pandemic period and in 2021 and six in 2020. In the first year of the pandemic, an 8.5% reduction in the number of new TB cases was observed. In 2021, the state showed a slight increase (1.1%) in the number of new TB cases. The data indicate that the COVID-19 pandemic may have caused a reduction in the number of new TB case reports in the state of Pernambuco, Brazil.

Malaria in Burkina Faso: A comprehensive analysis of spatiotemporal distribution of incidence and environmental drivers, and implications for control strategies.

The number of malaria cases worldwide has increased, with over 241 million cases and 69,000 more deaths in 2020 compared to 2019. Burkina Faso recorded over 11 million malaria cases in 2020, resulting in nearly 4,000 deaths. The overall incidence of malaria in Burkina Faso has been steadily increasing since 2016. This study investigates the spatiotemporal pattern and environmental and meteorological determinants of malaria incidence in Burkina Faso. We described the temporal dynamics of malaria cases by detecting the transmission periods and the evolution trend from 2013 to 2018. We detected hotspots using spatial scan statistics. We assessed different environmental zones through a hierarchical clustering and analyzed the environmental and climatic data to identify their association with malaria incidence at the national and at the district's levels through generalized additive models. We also assessed the time lag between malaria peaks onset and the rainfall at the district level. The environmental and climatic data were synthetized into indicators. The study found that malaria incidence had a seasonal pattern, with high transmission occurring during the rainy seasons. We also found an increasing trend in the incidence. The highest-risk districts for malaria incidence were identified, with a significant expansion of high-risk areas from less than half of the districts in 2013-2014 to nearly 90% of the districts in 2017-2018. We identified three classes of health districts based on environmental and climatic data, with the northern, south-western, and western districts forming separate clusters. Additionally, we found that the time lag between malaria peaks onset and the rainfall at the district level varied from 7 weeks to 17 weeks with a median at 10 weeks. Environmental and climatic factors have been found to be associated with the number of cases both at global and districts levels. The study provides important insights into the environmental and spatiotemporal patterns of malaria in Burkina Faso by assessing the spatio temporal dynamics of Malaria cases but also linking those dynamics to the environmental and climatic factors. The findings highlight the importance of targeted control strategies to reduce the burden of malaria in high-risk areas as we found that Malaria epidemiology is complex and linked to many factors that make some regions more at risk than others.