Local Getis-Ord Gi Research Articles

Racial Residential Segregation and Mental Health During Pregnancy

Research suggests the social, physical, and socioeconomic contexts of residing in segregated neighborhoods may negatively affect mental health. To assess the association between racial residential segregation and prenatal mental health among Asian, Black, Hispanic, and White individuals. This population-based cross-sectional study was conducted in Kaiser Permanente Northern California (KPNC), an integrated health care delivery system. Participants included self-identified Asian, Black, Hispanic, and White pregnant individuals who attended at least 1 prenatal care visit at KPNC between January 1, 2014, and December 31, 2019. Data were analyzed from January 14, 2023, to August 15, 2024. Racial residential segregation, defined by the local Getis-Ord Gi* statistic, was calculated in each racial and ethnic group and categorized as low (<0), medium (0-1.96), or high (>1.96). A positive Gi* statistic indicates overrepresentation (greater clustering or segregation) of the racial and ethnic group in an index census tract and neighboring tracts compared with the larger surrounding geographic area. Prenatal depression and anxiety defined by diagnoses codes documented in the electronic health record between the first day of the last menstrual period and the day prior to birth. Among the 201 115 participants included in the analysis (mean [SD] age, 30.8 [5.3] years; 26.8% Asian, 6.6% Black, 28.0% Hispanic, and 38.6% White), prenatal depression and anxiety were highest in Black individuals (18.3% and 18.4%, respectively), followed by White (16.0% and 18.2%, respectively), Hispanic (13.0% and 14.4%, respectively), and Asian (5.7% and 6.4%, respectively) individuals. Asian (40.8% vs 31.1%) and Black (43.3% vs 22.6%) individuals were more likely to live in neighborhoods with high vs low segregation, while Hispanic individuals were equally likely (34.3% vs 34.7%). High compared with low segregation was associated with greater odds of prenatal depression (adjusted odds ratio [AOR], 1.25 [95% CI, 1.10-1.42]) and anxiety (AOR, 1.14 [95% CI, 1.00-1.29]) among Black individuals. High segregation was associated with lower odds of prenatal depression among Asian (AOR, 0.75 [95% CI, 0.69-0.82]), Hispanic (AOR, 0.88 [95% CI, 0.82-0.94]), and White (AOR, 0.91 [95% CI, 0.86-0.96]) individuals. Similar associations were found for anxiety among Asian (AOR, 0.80 [95% CI, 0.73-0.87]) and Hispanic (AOR, 0.88 [95% CI, 0.82-0.93]) but not White (AOR, 0.95 [95% CI, 0.90-1.00]) individuals. In this cross-sectional study, racial and ethnic residential segregation was associated with worse prenatal mental health for Black individuals but better mental health for Asian, Hispanic, and White individuals. Policies reducing segregation and its impact may improve mental health outcomes in pregnant Black individuals.

Spatial variation of short birth intervals and their determinant factors among reproductive women in Ethiopia using a geographically weighted regression model.

In low- and middle-income nations, a significant proportion of maternal and infant deaths are caused by a short birth interval (SBI). In Ethiopia, it is the main factor contributing to maternal and infant mortality. Understanding the spatial distribution of SBIs, i.e., birth intervals of less than 33 months, and the factors that influence them is important for categorizing and promoting targeted interventions. This study used a geographically weighted regression model to evaluate the factors associated with SBIs in hot areas of Ethiopia. The 2019 Ethiopian Mini Demographic and Health Survey, which is nationally representative, provided the data for this study. The first step in the two-stage cluster design used to collect the data was enumeration areas, and the second stage was households. The survey was conducted between 21 March 2019 and 28 June 2019. A hot spot analysis (local Getis-Ord Gi* statistics) was initially used to investigate spatial variation in SBIs. Geographically weighted regression was used to examine the regional variation in the relationship between SBIs and the factors that cause them. The study indicated that the overall proportion of SBIs among women in Ethiopia was 43.2%. The values for Global Moran's I (Moran's I = 0.773 and p < 0.001) showed the presence of significant SBIs clustering in Ethiopian administrative zones in Ethiopia. High-risk areas of the SBIs include Jarar, Doolo, Shabelle, Afder, Liben, Korahe, Nogob, West Harerge, Guji, Sidama, and Assosa zones. Living in a geographic region with a high proportion of uneducated women, women lacking breastfeeding practices, and followers of Orthodox religions increased the proportion of SBIs. Our full map of hot spots for short birth spacing and the factors that affect them helps in the implementation of precise public health measures for decision-makers.

Human Brucellosis in Iraq: Spatiotemporal Data Analysis From 2007-2018.

Brucellosis is both endemic and enzootic in Iraq, resulting in long-term morbidity for humans as well as economic loss. No previous study of the spatial and temporal patterns of brucellosis in Iraq was done to identify potential clustering of cases. This study aims to detect the spatial and temporal distribution of human brucellosis in Iraq and identify any changes that occurred from 2007 to 2018. A descriptive, cross-sectional study was conducted using secondary data from the Surveillance Section at the Communicable Diseases Control Center, Public Health Directorate, Ministry of Health in Iraq. The trends of cases by sex and age group from 2007 to 2018 were displayed. The seasonal distribution of the cases from 2007 to 2012 was graphed. We calculated the incidence of human brucellosis per district per year and used local Getis-Ord Gi* statistics to detect the spatial distribution of the data. The data were analyzed using Microsoft Excel and GeoDa software. A total of 51,508 human brucellosis cases were reported during the 12-year study period, with some missing data for age groups. Human brucellosis persisted annually in Iraq across the study period with no specific temporal clustering of cases. In contrast, spatial clustering was predominant in northern Iraq. There were significant differences in the geographic distribution of brucellosis. The number of cases is the highest in the north and northeast regions of the country, which has borders with nearby countries. In addition, people in these areas depend more on locally made dairy products, which can be inadequately pasteurized. Despite the lack of significant temporal clustering of cases, the highest number of cases were reported during summer and spring. Considering these patterns when allocating resources to combat this disease, determining public health priorities, and planning prevention and control strategies is important.

Malaria seroepidemiology in very low transmission settings in the Peruvian Amazon

Despite progress towards malaria reduction in Peru, measuring exposure in low transmission areas is crucial for achieving elimination. This study focuses on two very low transmission areas in Loreto (Peruvian Amazon) and aims to determine the relationship between malaria exposure and proximity to health facilities. Individual data was collected from 38 villages in Indiana and Belen, including geo-referenced households and blood samples for microscopy, PCR and serological analysis. A segmented linear regression model identified significant changes in seropositivity trends among different age groups. Local Getis-Ord Gi* statistic revealed clusters of households with high (hotspots) or low (coldspots) seropositivity rates. Findings from 4000 individuals showed a seropositivity level of 2.5% (95%CI: 2.0%-3.0%) for P. falciparum and 7.8% (95%CI: 7.0%-8.7%) for P. vivax, indicating recent or historical exposure. The segmented regression showed exposure reductions in the 40–50 age group (β1 = 0.043, p = 0.003) for P. vivax and the 50–60 age group (β1 = 0.005, p = 0.010) for P. falciparum. Long and extreme distance villages from Regional Hospital of Loreto exhibited higher malaria exposure compared to proximate and medium distance villages (p < 0.001). This study showed the seropositivity of malaria in two very low transmission areas and confirmed the spatial pattern of hotspots as villages become more distant.

Investigating inequalities in HIV testing in sub-Saharan Africa: spatial analysis of cross-sectional population-based surveys in 25 countries

ObjectivesWe aim to explore spatial variations in socioeconomic inequalities in HIV testing uptake in sub-Saharan Africa (SSA) at different geographical scales to identify potential geographical hotspots of inequalities. Additionally, to...

Residence in Hispanic/Latino Immigrant Neighborhoods, Away-From-Home Food Consumption, and Diet Quality: The Hispanic Community Health Study/Study of Latinos.

Hispanics/Latinos are disproportionately burdened by nutrition-related diseases but immigrants appear healthier than their US-born counterparts. Neighborhoods characterized by high Hispanic/Latino immigrant segregation may provide environments to support healthier diets. To examine whether or not Hispanic/Latino immigrant segregation is associated with frequency of away-from-home food consumption and diet quality in a large, diverse sample of Hispanic/Latino adults. Cross-sectional baseline data from the Hispanic Community Health Study/Study of Latinos were analyzed (2008-2011). Residential addresses were geocoded and linked to census tract-level 2008-2012 American Community Survey data. Hispanic/Latino immigrant segregation was characterized using the local Getis-Ord Gi∗ statistic, a spatial clustering measure that quantifies the extent to which demographically similar neighborhoods group together. Participants were 15,661 adults in the Hispanic Community Health Study/Study of Latinos, a population-based study of Hispanic/Latinos aged 18 to 74 years from 4 US regions (Bronx, NY; Chicago, IL; Miami, FL; and San Diego, CA). Away-from-home food consumption was assessed using a modified dietary behavior questionnaire. Diet quality was assessed using the Alternate Healthy Eating Index 2010 (range= 0 to 110) from two 24-hour recalls. Multilevel linear and logistic regression with multilevel weights were used to estimate associations between Hispanic/Latino immigrant segregation (low, medium, or high) with Alternate Healthy Eating Index 2010 score, and away-from-home food consumption (≥3 vs <3 times/week) in separate models, respectively. The mediating role of neighborhood poverty and whether or not associations differed by nativity were also assessed. Higher levels of segregation were associated with higher adjusted mean Alternate Healthy Eating Index 2010 scores; estimates were further magnified after accounting for neighborhood poverty (low segregation: reference category; medium segregation: β= 2.43, 95% CI 1.10 to 3.77; and high segregation: β= 1.63, 95% CI .43 to 2.82). Associations were strongest among the foreign-born compared with the US-born. There was no association between segregation and away-from-home food consumption. These results highlight the potential role of Hispanic/Latino immigrant neighborhoods in supporting healthy diets among residents, especially immigrants.

Mortality from neglected tropical ciseases in Brazil in the 21st Century: Analysis of spatial and temporal trends and associated factorsMortalidad por enfermedades tropicales desatendidas en Brasil en el siglo XXI: análisis de tendencias espaciales y temporales y factores asociados

To analyze the spatial-temporal distribution and factors associated with mortality from neglected tropical diseases (NTDs) in Brazil from 2000 to 2019. We performed an ecological study to analyze NTD-related deaths recorded in the Ministry of Health Mortality Information System (SIM). For the temporal analysis, the joinpoint method was used. Spatial dependence was analyzed using global Moran and local Getis-Ord Gi* indices. Four non-spatial and spatial regression models were used to identify factors associated with mortality. The mean mortality rate from NTDs in Brazil during the study period was 3.32 deaths per 100 000 inhabitants, with the highest rate (8.68 deaths per 100 000 inhabitants) recorded in the Midwest. The most prevalent causes of death were Chagas disease (n = 94 781; 74.9%) and schistosomiasis (n = 10 271; 8.1%). There was a 1.24% reduction (95%CI = -1.6; -0.9; P < 0.001) in NTD-related mortality in Brazil per year. A high/high spatial distribution pattern and hotspots were observed in municipalities in the states of Goiás, Minas Gerais, Bahia, Tocantins, and Piauí. The indicators "population in households with density > 2 people per bedroom" (β = -0.07; P = 0.00) and "municipal human development index" (β = -3.36; P = 0.08) were negatively associated with the outcome, while the "index of social vulnerability" (β = 2.74; P = 0.05) was positively associated with the outcome. Lower human development and higher social vulnerability are associated with higher mortality from NTDs, which should guide NTD prevention and control efforts.

0233 Racial/ethnic Residential Segregation - a Component of Structural Racism - and Sleep Health by Neighborhood-level Poverty

Abstract Introduction Racial/ethnic residential segregation may contribute to sleep disparities by concentrating poverty and impairing sleep among minoritized groups. Yet, due to racism, equitable access to sleep-promoting resources may not occur in integrated neighborhoods. Further, social/cultural factors within ethnic enclaves could be protective. Methods To investigate whether relationships between racial/ethnic residential segregation and sleep vary by census tract-level poverty among US adults, we linked nationally-representative National Health Interview Survey data (2011-2017) to 2012 and 2017 American Community Survey census tract-level data. We used the local Getis-Ord Gi* statistic to categorize residential segregation (high, medium, low [reference]), considering differences between racial/ethnic composition within a census tract to the surrounding county. Survey-weighted, Poisson regressions with robust variance estimation were stratified by race/ethnicity and approximate census tract-level poverty tertile (high [&amp;gt;16.9% of residents below poverty level], medium [&amp;gt;7.9%-16.9%], low [0%-7.9%]) to estimate prevalence ratios (PRs) and 95% confidence intervals (CIs) for self-reported sleep health measures. Results Among 126,539 participants (mean age±SE=46±0.1 years), non-Hispanic (NH)-Black and Latino adults were most likely to report short sleep (41%) and non-restorative sleep (37%), respectively. Racial/ethnic residential segregation was most common among NH-Black (38%), followed by non-Mexican Latino (34%) and NH-Asian adults (34%), and was least prevalent among NH-White adults (17%) but was not associated with sleep among Mexican, non-Mexican Latino, or NH-Black adults. Among NH-Asian adults, high segregation was marginally associated with a higher prevalence of short sleep (&amp;lt; 7-hours vs. recommended [7-9 hours]) in low-poverty neighborhoods (PR=1.17 [95% CI:0.97-1.42]) but was associated with recommended sleep in medium-poverty neighborhoods (PRshort sleep=0.79 [0.63-0.99]). Among NH-White adults, high residential segregation was associated with more restorative sleep only in high-poverty (PR=1.13 [1.06-1.20]) and low-poverty (PR=1.03 [1.00-1.07]) neighborhoods. Conclusion Associations between racial/ethnic residential segregation and sleep health varied by neighborhood-level poverty among NH-Asian and NH-White adults. The lack of differences among most minoritized groups may be related to the close proximity of higher-income and lower-income neighborhoods due to structural racism. Future studies including spatial analyses are warranted. The findings/conclusions in this research are those of the authors and do not necessarily represent the views of the Research Data Center, National Center for Health Statistics, or CDC. Support (if any)

TYPHOON DAMAGE MAPPING USING NORMALIZED DIFFERENCED SPATIAL AUTOCORRELATION AND PLANETSCOPE IMAGE

Abstract. Damages to built and natural environments are essentially changes that needs to be detected and quantified. This is particularly true for the change detection approach. While the use of vegetation indices is effective for such assessment in natural or vegetated areas, the use of built-up indices does not yield useful results. This is because there is usually no significant reduction in materials. However, typhoon-damaged buildings are usually characterized by a change in form, shape, color, and texture. In this study, we examined the use of local spatial autocorrelation (LSA) to evaluate the level of damage to buildings. In particular, the local Moran’s I was used, and an index called the normalized difference spatial autocorrelation (NDSA) was developed. Similar with other indices, the values are within the range of −1 to 1. NDSA using local Getis-Ord Gi and local Geary’s C were also generated. With the observation that LSA generally decreases due to damages (especially to manmade structures), positive NDSA identifies the damage areas. The magnitude of the values corresponded to the level or degree of damage sustained as interpreted from very high-resolution satellite image. It was noted that the manual tagging of damages had missed buildings which are clearly damaged or destroyed based on the visual comparison of pre- and post-typhoon satellite images. This illustrates the value of the NDSA not only to assess damage on its own but also for guiding manual tagging from image and prioritizing post-disaster needs assessment and recovery operation.

Severe Fever With Thrombocytopenia Syndrome in Southeastern China, 2011-2019.

IntroductionSevere fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease, and the number of cases has increased in recent years in Zhejiang Province, China. However, whether the seasonal distribution, geographic distribution, and demographic characteristics of SFTS have changed with the increase of incidence was unclear.Materials and MethodsData on SFTS cases in Zhejiang Province and tick density in Daishan County from 2011 to 2019 were collected. The changing epidemiological characteristics of SFTS including seasonal distribution, geographical distribution, and demographic features were analyzed using descriptive statistical methods, Global Moran's I, local Getis-Ord Gi* statistic, and spatial scan statistic.ResultsA total of 463 SFTS cases including 53 (11.45%) deaths were reported from 2011 to 2019 in Zhejiang Province, and the annual number of cases showed increasing tendency. SFTS cases were reported in almost half of the counties (40/89) of Zhejiang Province. Elderly farmers accounted for most cases and the proportion of farmers has increased. Most cases (81.21%) occurred during April and August. The interval from illness onset to confirmation was significantly shortened (Z = 5.194, p < 0.001). The majority of cases were reported in Zhoushan City from 2011 to 2016, but most cases were reported in Taizhou City since 2017.DiscussionWe observed dynamic changes in the seasonal distribution, geographical distribution, and demographic features of SFTS, and comprehensive intervention measures, such as clearance of breeding sites, killing of tick adults, and health education should be strengthened in farmers of the key areas according to the changed epidemiological characteristics.

Geospatial approach to investigate spatial clustering and hotspots of blood lead levels in children within Kabwe, Zambia

BackgroundCommunities around Kabwe, Zambia are exposed to lead due to deposits from an old lead (Pb) and zinc (Zn) mining site. Children are particularly more vulnerable than adults, presenting with greatest risk of health complications. They have increased oral uptake due to their hand to mouth activities. Spatial analysis of childhood lead exposure is useful in identifying specific areas with highest risk of pollution. The objective of the current study was to use a geospatial approach to investigate spatial clustering and hotspots of blood lead levels in children within Kabwe. MethodsWe analysed existing data on blood lead levels (BLL) for 362 children below the age of 15 from Kabwe town. We used spatial autocorrelation methods involving the global Moran's I and local Getis-Ord Gi*statistic in ArcMap 10.5.1, to test for spatial dependency among the blood lead levels in children using the household geolocations. ResultsBLL in children from Kabwe are spatially autocorrelated with a Moran's Index of 0.62 (p < 0.001). We found distinct hotspots (mean 51.9 μg/dL) in communities close to the old lead and zinc-mining site, lying on its western side. Whereas coldspots (mean 7 μg/dL) where observed in areas distant to the mine and traced on the eastern side. This pattern suggests a possible association between observed BLL and distance from the abandoned lead and zinc mine, and prevailing winds. ConclusionUsing geocoded data for households, we found clustering of childhood blood lead and identified distinct hotspot areas with high lead levels for Kabwe town. The geospatial approach used is especially valuable in resource-constrained settings like Zambia, where the precise identification of high risk locations allows for the initiation of targeted remedial and treatment programs.

A description of spatial-temporal patterns of the novel COVID-19 outbreak in the neighbourhoods' scale in Tehran, Iran.

Background: Analyzing and monitoring the spatial-temporal patterns of the new coronavirus disease (COVID-19) pandemic can assist local authorities and researchers in detecting disease outbreaks in the early stages. Because of different socioeconomic profiles in Tehran's areas, we will provide a clear picture of the pandemic distribution in Tehran's neighbourhoods during the first months of its spread from February to July 2020, employing a spatial-temporal analysis applying the geographical information system (GIS). Disease rates were estimated by location during the 5 months, and hot spots and cold spots were highlighted. Methods: This study was performed using the COVID-19 incident cases and deaths recorded in the Medical Care Monitoring Centre from February 20, to July 20, 2020. The local Getis-Ord Gi* method was applied to identify the hotspots where the infectious disease distribution had significantly clustered spatially. A statistical analysis for incidence and mortality rates and hot spots was conducted using ArcGIS 10.7 software. Results: The addresses of 43,000 Tehrani patients (15,514 confirmed COVID-19 cases and 27,486 diagnosed as probable cases) were changed in its Geo-codes in the GIS. The highest incidence rate from February to July 2020 was 48 per 10,000 and the highest 5-month incidence rate belonged to central and eastern neighbourhoods. According to the Cumulative Population density of patients, the higher number is estimated by more than 2500 people in the area; however, the lower number is highlighted by about 500 people in the neighborhood. Also, the results from the local Getis-Ord Gi* method indicate that COVID-19 has formed a hotspot in the eastern, southeast, and central districts in Tehran since February. We also observed a death rate hot spot in eastern areas. Conclusion: Because of the spread of COVID-19 disease throughout Tehran's neighborhoods with different socioeconomic status, it seems essential to pay attention to health behaviors to prevent the next waves of the disease. The findings suggest that disease distribution has formed a hot spot in Tehran's eastern and central regions.

Spatial Analysis of Beaked Whale Foraging During Two 12 kHz Multibeam Echosounder Surveys

To add to the growing information about the effect of multibeam echosounder (MBES) operation on marine mammals, a study was conducted to assess the spatial foraging effort of Cuvier’s beaked whales during two MBES surveys conducted in January of 2017 and 2019 off of San Clemente Island, California. The MBES surveys took place on the Southern California Antisubmarine Warfare Range (SOAR), which contains an array of 89 hydrophones covering an area of approximately 1800 km2over which foraging beaked whales were detected. A spatial autocorrelation analysis of foraging effort was conducted using the Moran’s I (global) and the Getis-Ord Gi∗(local) statistics, to understand the animals’ spatial use of the entire SOAR, as well as smaller areas, respectively, within the SOARBefore, During, andAfterthe two MBES surveys. In both years, the global Moran’s I statistic suggested significant spatial clustering of foraging events on the SOAR during all analysis periods (Before, During, andAfter). In addition, a Kruskal-Wallis (comparison) test of both years revealed that the number of foraging events across analysis periods were similar within a given year. In 2017, the local Getis-Ord Gi∗analysis identified hot spots of foraging activity in the same general area of the SOAR during all analysis periods. This local result, in combination with the global and comparison results of 2017, suggest there was no obvious period-related change detected in foraging effort associated with the 2017 MBES survey at the resolution measurable with the hydrophone array. In 2019, the foraging hot spot area shifted from the southernmost corner of the SOARBefore, to the centerDuring, and was split between the two locationsAfterthe MBES survey. Due to the pattern of period-related spatial change identified in 2019, and the lack of change detected in 2017, it was unclear whether the change detected in 2019 was a result of MBES activity or some other environmental factor. Nonetheless, the results strongly suggest that the level of detected foraging during either MBES survey did not change, and most of the foraging effort remained in the historically well-utilized foraging locations of Cuvier’s beaked whales on the SOAR.

A spatial assessment of high-resolution drainage characteristics and roadway safety during wet conditions

Much of the research regarding traffic crashes considers various geometric roadway features; however, ever-evolving urban watersheds and climate change increasingly impact roadway conditions. Little research has focused on the relationship between high-resolution drainage characteristics and the spatial distribution of crashes. This study incorporated local environmental and drainage risk factors in assessing network safety performance using spatial analysis techniques. Kernel density surfaces and the Local Getis Ord Gi* statistics were used to identify and visualize locations prone to experiencing crashes during wet conditions. Spatial regression modelling was used to link crashes to environmental and traffic risk factors across a citywide network. Proof-of-concept for this framework is demonstrated in the City of Philadelphia, Pennsylvania using publicly available spatial data. The results of this study show a relationship between local drainage, environmental characteristics, and wet crash distribution, providing novel insight into roadway safety during wet conditions.

Abstract P208: The Differential Impact Of Residential Segregation On Gestational Hypertension Development Among Minority Women

Introduction: Higher levels of residential segregation have been associated with poorer cardiometabolic health profiles among women. Still, it remains unclear whether segregation may differentially impact the development of gestational hypertension (gHTN) among an ethnically-diverse cohort of pregnant women. We used birth record data from 2003-2009 and data from the 2000 US Census to determine whether racial and economic segregation are associated with gHTN among a diverse cohort of child-bearing women in the greater Philadelphia area. Methods: We quantified racial and economic segregation using sociodemographic data from the US Census and the local Getis-Ord (Gi*) spatial statistic. The Gi* produces a spatially-weighted z-score for each census tract reflecting the degree of clustering of racially-similar neighborhoods in an area relative to the surrounding Philadelphia region. We categorized each type of segregation as low (Gi*&lt;0), moderate (Gi*0-1.96), or high (Gi*&gt;1.96), and assigned these to each woman by her census tract of residence. Gestational hypertension was defined in the birth record data as the development of pregnancy-induced hypertension or preeclampsia. We used hierarchical generalized linear mixed effect models to obtain risk ratios and differences (per 1000 women) for the relationships between each form of residential segregation and gHTN. All models were stratified by maternal race/ethnicity, and sequentially adjusted for maternal sociodemographics, health behaviors, medical histories, and neighborhood-level characteristics. Results: Our sample consisted of 220,897 Non-Hispanic (NH) Black (26%), NH White (64%), and Hispanic (10%) women, of whom 4% developed gHTN. However, a much greater proportion of NH Black women both developed gHTN and lived in high segregation neighborhoods compared to NH Whites and Hispanics. After adjustment, NH Black women in moderate and high economic segregation areas had 16% higher risk (RR=1.16, 95% CI: 1.03-1.31) and 23% higher risk (RR=1.23, 95% CI: 1.08-1.39) of gHTN, respectively, compared to NH Black women living in low segregation areas. NH Black women in highly racially segregated neighborhoods saw an additional 9 cases of gHTN (per 1000 women) compared to NH Black women living in more racially integrated neighborhoods (RD=8.47, 95% CI: 3.14-13.80). Among NH White and Hispanic women, economic segregation was not associated with gHTN, and only marginally significant findings were observed for racial segregation. Conclusions: In our diverse sample of child-bearing women from the greater Philadelphia area, higher levels of racial and economic segregation were associated with greater risk of gHTN among NH Black women. Future work should seek to delineate the specific pathways by which neighborhoods differentially impact individual level cardiovascular health based upon race.

Spatiotemporal clustering and meteorological factors affected scarlet fever incidence in mainland China from 2004 to 2017.

To analyze the spatiotemporal dynamic distribution and detect the related meteorological factors of scarlet fever from an ecological perspective, which could provide scientific information for effective prevention and control of this disease. The data on scarlet fever cases in mainland China were downloaded from the Data Center of the China Public Health Science, while monthly meteorological data were extracted from the official website of the National Bureau of Statistics. Global Moran's I, local Getis-Ord Gi⁎ hotspot statistics, and Kulldorff's retrospective space-time scan statistical analysis were used to detect the spatial and spatiotemporal clusters of scarlet fever across all settings. A spatial panel data model was conducted to estimate the impact of meteorological factors on scarlet fever incidence. Scarlet fever in China had obvious spatial, temporal, and spatiotemporal clustering, high-incidence spatial clusters were located mainly in the north and northeast of China. Nine spatiotemporal clusters were identified. A spatial lag fixed effects panel data model was the best fit for regression analysis. After adjusting for spatial individual effects and spatial autocorrelation (ρ=0.5623), scarlet fever incidence was positively associated with a one-month lag of average temperature, precipitation, and total sunshine hours (all P-values < 0.05). Each 10°C, 2cm, and 10h increase in temperature, precipitation, and sunshine hours, respectively, was associated with a 6.41% increment and 1.04% and 1.41% decrement in scarlet fever incidence, respectively. The incidence of scarlet fever in China showed an upward trend in recent years. It had obvious spatiotemporal clustering, with the high-risk areas mainly concentrated in the north and northeast of China. Areas with high temperature and with low precipitation and sunshine hours tended to have a higher scarlet fever incidence, and we should pay more attention to prevention and control in these places.

Landscape disturbance impacts on Attalea butyracea palm distribution in central Panama

BackgroundIncreased Attalea butyracea palm propagation, notable for its role as key habitat for the primary Chagas disease vector in Panama, has been linked to landscape disturbance in single-palm observations in this region. Close proximity of these palms to human dwellings is proposed to increase risk of Chagas disease transmission from sylvatic transmission cycles to domestic transmission involving human populations. This study examines the relationship between landscape disturbance and mature A. butyracea spatial distribution, density, and proximity to human populations and vector and reservoir species’ movement corridors at a regional scale in a 300 km2 heterogeneous tropical landscape in central Panama.MethodsWe remotely identified the locations of over 50,000 mature A. butyracea palms using high-resolution WorldView2 satellite imagery. A local Getis-Ord Gi* spatial analysis identified significant clusters of aggregated palms. Associations between palm and cluster abundance and a landscape disturbance gradient, derived from official Panama land cover data, were tested using Chi-square tests for Homogeneity and Z-test for proportions. Kruskall-Wallis non-parametric analysis of variance tests were run to assess whether palm cluster area varied by disturbance level, or whether disturbance was associated with proximity of palms and palm clusters to susceptible populations or vector movement corridors.ResultsOur findings indicate a regional relationship between landscape disturbance and A. butyracea occurrence. We observe a significant increase in both individual and clustered A. butyracea in secondary forest, but a reduction of palms in agricultural settings. We do not detect evidence of any reduction in abundance of palms in residential settings. The majority of residential and commercial buildings in our study area are within vector flight distance of potential vector habitat in palm crowns.ConclusionsWe observe probable anthropogenic elimination of A. butyracea palms in agricultural, but not residential, settings. Even in heavily deforested regions, significant concentrations of mature palms remain in close proximity to human establishments.

Geospatial analysis to assess distribution patterns and predictive models for endangered plant species to support management decisions: a case study in the Balearic Islands

Species distribution modelling (SDM) has been used to support biodiversity management in recent years. However, the use of SDM at small scales with geolocation systems to obtain high-accuracy location data remains unexplored. In this study, we focused on Euphorbia fontqueriana, an endangered species to Mallorca (western Mediterranean basin), and we aimed to assess the spatial distribution patterns, and to generate a distribution map of the habitat suitability in a wider area. A differential GPS was used to geolocate all the ramets. We used the global Moran’s I index and local Getis Ord-Gi* method to assess the spatial patterns. We pre-selected derived topographic variables that were generated from LiDAR data (elevation, slope, northness and eastness), the connectivity index, normalised difference vegetation index, and soil type, as the environmental (predictor) variables. In addition, we ran the Maxent model using 1603 occurrence locations and seven environmental variables at a resolution of 2 × 2 m in grid size. The population consisted of 1625 ramets that were clustered (global Moran’s I index = 0.161, z score = 16.599, p value < 0.001) in several hotspots (i.e. areas with high plant density that were surrounded by areas with high plant densities). The Maxent model, which showed a good performance (AUC training score = 0.977), generated a habitat suitability map that displayed zones of high suitability in other areas away from the natural geographical area. Finally, we discuss the usefulness of this study to guide management purposes.

A spatio-temporal analysis of influenza-like illness in Iran from 2011 to 2016.

Background: Investigating the spatial aspects of the disease can help decision-makers and researchers better understand the pattern of the disease, and is also very important in the implementation of the disease control programs. Given the vast area of Iran, as well as the diverse geographical and climate conditions of the country, using the geographical information system (GIS) is a suitable method for the study of influenza. In this study, we provide a clear picture of the distribution of the influenza-like illness (ILI) in Iranian provinces through the years from 2011 to 2016 by applying a spatio-temporal analysis, using geographic information system (GIS). Disease rates by location and year are estimated, and then hot spots and cold spots are distinguished. Methods: This study was conducted using the ILI incidence rate data recorded in the Iranian Influenza Surveillance System from August 2011 to August 2016. The Choropleth map method and the various equal interval and natural break classifications were used. The local Getis-Ord Gi* method was then used to identify the hot spots and regions where, for some reason, the distribution of the disease had significantly clustered spatially. Statistical analyses were done using the ArcGIS 10.2 software. Results: This study indicates that the highest ILI rate belongs to the period from August 2014 to August 2015 with a rate of 180.26 (95%CI: 177.65 to 182.9) per 100,000 people. The results show that the highest 5-year mean of ILI rate belongs to Zanjan, Markazi, Lorestan, Ilam, North Khorasan, and South Khorasan provinces. Also, results from the local Getis-Ord Gi* method show that ILI has formed a hot spot between the years 2011 and 2013 on the eastern borders of Iran and afterward during the years 2014 to 2016 in the western regions of the country. Conclusion: Given the importance of influenza and its huge economic burden on the society, identifying the hot spot regions can help better manage the disease. This study indicates the distribution of the disease has formed a hot spot in the western regions of the country.

Статистика Getis-Ord Gi* при обработке многолетних данных содержания углеводородов в бассейне реки Большой Балык

Balyk river basin for the period 2006–2017 using ArcGIS Pro statistical analysis tools are presented in this article. The information basis of the research is the local environment monitoring data of license areas of Ugra, Khanty-Mansiysk Autonomous Okrug, RF. The research was implemented in two stages. At the first stage, pollution hot spots were revealed basing on the calculation of local Getis-Ord Gi* index for each year. The calculation was made taking into account the mutual location of sampling points and value of the neighborhood. At the second stage hot spots genesis for 12 years was analyzed via modelling space-and-time cube. Clustering time series of hydrocarbons average annual concentration according to the Getis-Ord Gi* indicator made it possible to determine the places of one-off pollution, most likely associated with oil spills, and to track pollutants transportation along the current. The location of the increasing river ecosystem pollution was also determined. The obtained results enable bringing out basic zones of permanent high hydrocarbon concentrations and places of periodic discharges into the river basin.