Getis-Ord Gi Research Articles

Environmental Cadmium Exposure and Odds of Residing in a Breast Cancer Hotspot in Kentucky

BACKGROUND AND AIM: Breast cancer is the most common invasive cancer diagnosed in US women. Cadmium is a carcinogenic heavy metal pollutant found in cigarettes and as an agricultural and industrial byproduct. Studies of cadmium exposure and breast cancer risk have produced mixed results depending upon methodology. We examined environmental cadmium exposure and risk of residing in a breast cancer hotspot in Kentucky. METHODS: Invasive breast cancer cases diagnosed between 1995-2018 were identified from the Kentucky Cancer Registry (n=66,413) and 68 of the 1,114 KY census-tracts were identified as breast cancer clusters using Getis-Ord Gi* for age-standardized rates. Census-tract cadmium concentrations from the 2014 National Air Toxics Assessment interpolated estimations were linked to cases’ residential addresses. Logistic regression was performed to assess the odds of residing in a hotspot across quartiles of cadmium exposure adjusted for individual-level (demographic, reproductive factors, Appalachian residence, insurance, tobacco use) and neighborhood factors (population density, poverty, and physical inactivity). Effect modification by smoking was assessed. RESULTS:4,134 women with breast cancer resided in hotspots (6.2%), which were located in major cities and southeast Kentucky. The mean age was 61 years. Hotspot cases were more likely than other cases to be black, single, uninsured or have public insurance, live in Appalachia, and reside in more densely populated neighborhoods with greater poverty. The odds of living in a hotspot were 4.2-times higher for those who resided in areas with the highest quartile of cadmium exposure compared to those in the lowest (95% CI: 3.6, 4.8; p-trend 0.0001). Results were similar (Q4 vs Q1: OR=4.4 95% CI 3.6, 5.4) among non-smokers. CONCLUSIONS:The observed associations of environmental cadmium exposure and residing in breast cancer hotspots in Kentucky warrant further research with more appropriate epidemiologic study designs. KEYWORDS: heavy metals, cancer and cancer precursors, environmental epidemiology, female, hotspot, air pollution

Spatial Equity Analysis of Nighttime Pedestrian Safety: Role of Land Use and Alcohol Establishments in Albuquerque, NM

The United States experienced a 53% increase in pedestrian fatalities between 2009 and 2018, with 2018 having a 3.4% increase from 2017. Of the 2018 pedestrian fatalities with known lighting conditions, 76% occurred in dark/nighttime conditions, with 50% occurring between 6:00 and 11:59 p.m. Despite past research exploring several contributing characteristics for nighttime pedestrian crashes, there is limited research that investigates the spatial aspects of land use attributes and sociodemographic factors. Have these nighttime pedestrian collisions been concentrated in certain land uses? Could an establishment with the capacity to serve alcohol invoke a greater risk of pedestrian crashes? Does sociodemographic status correlate with clustering for fatal crashes, severe crashes, or both? To better understand the spatial characteristics of the recent increase in pedestrian collisions, we analyzed crash data from Albuquerque, New Mexico for pedestrian fatalities and severe injuries from 2013 to 2018 relative to lighting condition, land use (with a focus on alcohol establishments), and race/ethnicity on the block group level. We used confidence intervals and Getis-Ord Gi* statistics to verify the statistical integrity of the trends. Findings suggested that pedestrian fatality and severe injury rates were higher within a quarter mile of bars at night and in areas with elevated concentrations of minority populations. Pedestrian fatality and severe injury hot spots appeared to have higher percentages of non-white residents, coupled with lower sidewalk coverage and more arterials or collectors.

Spatial Distribution Pattern and Influencing Factors of Industrial Pollution Emissions in Yangtze River Economic Belt

The development of Yangtze River economic belt emphasizes the strategy of "no large-scale development, joint efforts in large-scale protection". The restoration of the Yangtze River environment has become an important part of the development of the region. Therefore, it is important to explore the spatial distribution of industrial pollution emissions and its influencing factors in the Yangtze River economic belt. Using industrial SO2, wastewater, and soot emission data from 102 cities in the Yangtze River economic belt, from 2013 to 2017, this study analyzes the spatial distribution of industrial pollution emissions in the Yangtze River economic belt. This was achieved using spatial autocorrelation analysis and Getis-Ord Gi* methods, and by adopting the logarithmic mean Divisia index model to decompose the influencing factors of industrial pollution emissions in the Yangtze River economic belt. The results show that from 2013 to 2017, the emissions of industrial SO2, wastewater, and soot in the Yangtze River economic belt exhibited a downward trend, with a decrease in the number of high-emission cities and an increase in the number of low-emission cities. The spatial correlation of industrial pollutants increased with time, and technological improvements had the most significant inhibitory effect, making it the most important factor affecting industrial pollution emissions in the Yangtze River economic belt.

Evaluation of the Groundwater Safety and Analysis of the Spatial-Temporal Evolution in the Lower Plain of the Liaohe River, Northeast China based on the Improved DRASTIC Model

The deterioration of the groundwater safety may lead to a series of ecological and social problems. In this study, we select relevant hydrogeological and anthropogenic parameters to construct the groundwater safety evaluation method for the lower plain of the Liaohe River based on the improved DRASTIC model. By spatially weighted overlay and Getis-Ord Gi* analysis of groundwater safety distribution maps, the main governance regions and main factors causing groundwater deterioration were identified. On this basis, the evolution trend of the groundwater key management area was quantitatively analyzed using the standard deviation ellipse (SDE) method. The results show that groundwater safety in the north and south of the lower plain of the Liaohe River are continuing to deteriorate. The correlation test between the groundwater safety index and the measured nitrogen concentration verified the scientific accuracy of the proposed groundwater safety evaluation method.

Exploring the relationship between urban form and land surface temperature (LST) in a semi-arid region case study of Ben Guerir city - Morocco

Surface temperature is one of the critical factors used to study microclimate conditions through Land surface Temperature (LST), a widely used data source. This paper tests a classification approach using moderate spatial satellite resolution images to extract surface properties of Local Climate Zones (LCZs) in a semi-arid region against measured LSTs. However, LST is used to study and explore the spatial relationship between temperature and urban morphology. Therefore, it is considered an alternative and reliable data source to complement in-situ measurement, especially in developing countries.The used methodology is based on two statistical approaches: 1) auto-spatial correlation using Getis Ord Gi* statistic, and 2) ANOVA test. Results showed an inversion effect of surface urban heat island - SUHI, where a strong correlation between specific LCZs and the surface temperature was found. The urban classes represented by open-low-rise, compact-low-rise have shown a significant decrease in surface temperature for almost two decades and in both summer and winter seasons compared to non-urban ones.Therefore, the outcomes of this study may have critical implications for urban planners who seek to mitigate SUHI effects in arid and semi-arid urban areas. Also, the approach used in this study can be helpful to follow the urban environment's spatial and temporal development as an application framework of sustainable development.

Developing a geospatial framework for coupled large scale thermal comfort and air quality indices using high resolution gridded meteorological and station based observations

Current bioclimate and air quality indices provide insufficient information about the combined effect on human physiology in outdoor spaces. This work examined, large scale gridded meteorological observations, including air temperature, wind speed, solar radiation, and relative humidity, to derive Universal Thermal Climate Index (UTCI) at hourly intervals along with the air quality index (AQI) derived from Environmental Protection Agency (EPA) observation stations. UTCI and AQI were combined into a single framework using geospatial analytics and a newly developed lookup table approach. High risk areas for heat stress and poor air quality were identified using Moran's I and Getis-Ord GI* statistics. Moderate to strong heat stress was observed during the summer months of 2015–2019, with UTCI ranging from 26 °C to 38 °C. Coastal regions consistently experienced higher UTCI during noon due to higher humidity but the effect subsided with cooler air circulation from the ocean, especially in the morning and evening. Results also indicated the vulnerability of this region due to the combined impact of heat stress and poor air quality based on 95th percentile values. The final products from this analysis can provide valuable insights for urban planning and preventative measures to ensure improved public health in outdoor environments.

Geographic variations in the incidence of congenital hypothyroidism in China: a retrospective study based on 92 million newborns screened in 2013-2018.

Background:Although congenital hypothyroidism (CH) has been widely studied in Western countries, CH incidence at different administrative levels in China during the past decade remains unknown. This study aimed to update the incidence and revealed the spatial pattern of CH incidence in the mainland of China, which could be helpful in the planning and implementation of preventative measures.Methods:The data used in our study were derived from 245 newborns screening centers that cover 30 provinces of the Chinese Newborn Screening Information System. Spatial auto-correlation was analyzed by Global Moran I and Getis-Ord Gi statistics at the provincial level. Kriging interpolation methods were applied to estimate a further detailed spatial distribution of CH incidence at city level throughout the mainland of China, and Kulldorff space scanning statistical methods were used to identify the spatial clusters of CH cases at the city level.Results:A total of 91,921,334 neonates were screened from 2013 to 2018 and 42,861 cases of primary CH were identified, yielding an incidence of 4.66 per 10,000 newborns screened (95% confidence interval [CI]: 4.62–4.71). Neonates in central (risk ratio [RR] = 0.84, 95% CI: 0.82–0.85) and western districts (RR = 0.71, 95% CI: 0.69–0.73) had lower probability of CH cases compared with the eastern region. The CH incidence indicated a moderate positive global spatial autocorrelation (Global Moran I value = 0.394, P < 0.05), and the CH cases were significantly clustered in spatial distribution. A most likely city-cluster (log-likelihood ratio [LLR] = 588.82, RR = 2.36, P < 0.01) and 25 secondary city-clusters of high incidence were scanned. The incidence of each province and each city in the mainland of China was estimated by kriging interpolation, revealing the most affected province and city to be Zhejiang Province and Hangzhou city, respectively.Conclusion:This study offers an insight into the space clustering of CH incidence at provincial and city scales. Future work on environmental factors need to focus on the effects of CH occurrence.

Hotspot Analysis of Structure Fires in Urban Agglomeration: A Case of Nagpur City, India

Fire Service is the fundamental civic service to protect citizens from irrecoverable, heavy losses of lives and property. Hotspot analysis of structure fires is essential to estimate people and property at risk. Hotspot analysis for the peak period of last decade, using a GIS-based spatial analyst and statistical techniques through the Kernel Density Estimation (KDE) and Getis-Ord Gi* with Inverse Distance Weighted (IDW) interpolation is performed, revealing fire risk zones at the city ward micro level. Using remote sensing, outputs of hotspot analysis are integrated with the built environment of Land Use Land Cover (LULC) to quantify the accurate built-up areas and population density of identified fire risk zones. KDE delineates 34 wards as hotspots, while Getis-Ord Gi* delineates 17 wards within the KDE hotspot, the central core areas having the highest built-up and population density. A temporal analysis reveals the maximum fires on Thursday during the hot afternoon hours from 12 noon to 5 p.m. The study outputs help decision makers for effective fire prevention and protection by deploying immediate resource allocations and proactive planning reassuring sustainable urban development. Furthermore, updating the requirement of the National Disaster Management Authority (NDMA) to build urban resilient infrastructure in accord with the Smart City Mission.

Spatial variation and disparity in female breast cancer relative survival in the United States.

Breast cancer is the most common cancer among women in the United States. However, data on spatial disparities in survival for breast cancer are limited in the country. This study estimated 5-year relative survival (RS) of female breast cancer and examined the spatial variations across the contiguous United States. Women newly diagnosed with breast cancer in 2003-2010 in the United States were identified from the National Cancer Database and followed up through 2016. The crude 5-year RS at the county level was estimated and adjusted for patients' key sociodemographic and clinical factors. To account for spatial effects, the RS estimates were smoothed using the Bayesian spatial survival model. A local spatial autocorrelation analysis with the Getis-Ord Gi* statistics was applied to identify geographic clusters of low or high RS. Clusters of low RS were identified in more than 15 states covering 671 counties, mostly in the southeast and southwest regions, including Georgia, Alabama, Mississippi, Louisiana, Arkansas, Oklahoma, and Texas. Approximately 30% of these clusters can be explained by patients' characteristics: Race, insurance, and stage at diagnosis appeared to be the major attributable factors. Significant spatial disparity in female breast cancer survival was found, with low RS clusters identified in Georgia, Alabama, Mississippi, Louisiana, Arkansas, Oklahoma, and Texas. Policies and interventions that focus on serving Black women, improvements in insurance coverage, and early detection in these areas could potentially mitigate the spatial disparities.

Integrating supply and demand factors for estimating ecosystem services scarcity value and its response to urbanization in typical mountainous and hilly regions of south China

In the process of rapid urbanization, the decline in ecosystems' physical supply value is a direct result of the increasing demands of human development. The ecosystem services value combined with supply and demand factors can be used to obtain the ecosystem services scarcity value (ESSV). These ecosystems with larger ESSVs warrant increased protection, or at the very least a development plan to ensure a balance between their continued existence and human usage. However, urbanization and other developmental effects have often caused the ESSV of those regions to be disregarded, which produces an imbalance in the ecosystem. This paper evaluated the spatial differentiation of ESSV based on heterogeneous units in the Yuebei region, China. Then, a spatial response analysis of the ESSV to urbanization according to the spatial correlation model and Getis-Ord Gi* was conducted. The results showed that the ecosystem service physical supply value declined due to urban sprawl in 1990–2015, but the ESSV increased sharply during the same time with its acceleration being particularly strong after 2000. Areas with higher ESSV were concentrated in the northern region due to a higher prevalence of woodland. Urbanization accelerated significantly after 2000, and it is found that similar tendencies of ESSV and urbanization occurred over the same period. Surprisingly, the ESSV was negatively correlated with urbanization in local regions, and their interaction continued to increase over time. The spatial hotspot map of the ESSV and urbanization also formed an opposite pattern. In addition, the grid number of High-Low and Low-High types is more than half of the total number of aggregated grids. According to the results, understanding the variation in the ESSV clearly due to urbanization and identifying their interaction in local regions can provide a more specific direction for future resource adjustment and ecosystem service improvements.

Neighborhood racial/ethnic segregation and cognitive decline in older adults

Social determinants of health, including neighborhood factors, play a key role in the health of diverse older adults. However, few longitudinal studies have examined the role of neighborhood racial/ethnic segregation on cognitive decline in diverse samples. We examined older non-Hispanic White (NHW), Black, and Latino participants evaluated at an Alzheimer's Disease Research Center. Neighborhood racial/ethnic segregation was measured using the Getis-Ord Gi* statistic, a spatial measure of clustering that was created for Latino and Black clustering separately. Cognitive outcomes included episodic memory, semantic memory, and executive function. We used mixed effects multivariable regression models to evaluate associations between segregation and cognitive function and decline. We had 452 individuals: 46% NHW, 26% Black, and 21% Latino in 309 census tracts with an average of 5.2 years of follow-up data (range 0.6–15.0). In analyses that adjusted for a variety of covariates (including neighborhood SES), individuals in neighborhoods with a higher clustering of Latino residents (higher Gi* statistic) had slower declines over time on semantic memory and those in neighborhoods with a higher clustering of Black residents had slower declines over time on episodic memory. In race/ethnicity-stratified adjusted analyses: for Black participants, the association between clustering and cognition was present for episodic memory and executive function, showing lower baseline scores in highly clustered Black and Latino neighborhoods, respectively. There was no association with cognitive change. Among Latino participants, highly clustered Latino neighborhoods were associated with lower baseline scores in semantic memory, but slower declines in episodic memory; Latinos living in neighborhoods with a greater clustering of Black residents also had slower declines in episodic memory. Among NHWs, residing in neighborhoods with a higher clustering of Latino residents was associated with slower declines over time on semantic memory. Segregated neighborhoods may be differentially associated with cognitive outcomes depending on individual race/ethnicity.

Deforestation and fragmentation trends of seasonal dry tropical forest in Ecuador: impact on conservation

BackgroundFragmentation and deforestation are one of the greatest threats to forests, and these processes are of even more concern in the tropics, where the seasonal dry forest is possibly one of the most threatened ecosystems with the least remaining surface area.MethodsThe deforestation and fragmentation patterns that had occurred in Ecuadorian seasonal dry forests between 1990 and 2018 were verified, while geographic information systems and land cover shapes provided by the Ecuadorian Ministry of the Environment were employed to classify and evaluate three types of seasonal dry forests: deciduous, semi-deciduous, and transition. The study area was tessellated into 10 km2 hexagons, in which six fragmentation parameters were measured: number of patches, mean patch size, median patch size, total edge, edge density and reticular fragmentation index (RFI). The RFI was also measured both outside and inside protected natural areas (unprotected, national protected areas and protected forest). Moreover, the areas with the best and worst conservation status, connectivity and risk of disappearance values were identified by means of a Getis-Ord Gi* statistical analysis.ResultsThe deforestation of seasonal dry forests affected 27.04% of the original surface area still remaining in 1990, with an annual deforestation rate of − 1.12% between 1990 and 2018. The RFI has increased by 11.61% as a result of the fact that small fragments of forest have tended to disappear, while the large fragments have been fragmented into smaller ones. The semi-deciduous forest had the highest levels of fragmentation in 2018. The three categories of protection had significantly different levels of fragmentation, with lower RFI values in national protected areas and greater values in protected forests.ConclusionsThe seasonal dry forest is fragmenting, deforesting and disappearing in some areas. An increased protection and conservation of the Ecuadorian seasonal dry forest is, therefore, necessary owing to the fact that not all protection measures have been effective.

Study on Coupled Relationship between Urban Air Quality and Land Use in Lanzhou, China

The intensification of global urbanization has exacerbated the negative impact of atmospheric environmental factors in urban areas, thus threatening the sustainability of future urban development. In order to ensure the sustainability of urban atmospheric environments, exploring the changing laws of urban air quality, identifying highly polluted areas in cities, and studying the relationship between air quality and land use have become issues of great concern. Based on AQI data from 340 air quality monitoring stations and urban land use data, this paper uses inverse distance weight (IDW), Getis-Ord Gi*, and a negative binomial regression model to discuss the spatiotemporal variation of air quality in the main urban area of Lanzhou and its relationship with urban land use. The results show that urban air quality has characteristics of temporal and spatial differentiation and spatially has characteristics of agglomeration of cold and hot spots. There is a close relationship between urban land use and air quality. Industrial activities, traffic pollution, and urban construction activities are the most important factors affecting urban air quality. Green spaces can reduce urban pollution. The impact of land use on air quality has a seasonal effect.

Spatial clusters of daily tobacco consumption before and after a smoke-free policy implementation.

This study assessed the spatial dependence of daily tobacco consumption and how it is spatially impacted by individual and neighborhood socioeconomic determinants, and tobacco consumption facilities before and after a smoke-free implementation. Individual data was obtained from the Bus Santé, a cross-sectional survey in Geneva. Spatial clusters of high and low tobacco consumption were assessed using Getis-Ord Gi*. Daily tobacco consumption was not randomly clustered in Geneva and may be impacted by tobacco consumption facilities independently of socioeconomic factors and a smoking ban. Spatial analysis should be considered to highlight the impact of smoke-free policies and guide public health interventions.

Spatial Distribution of Road Traffic Accident at Hawassa City Administration, Ethiopia

Globally, road traffic accidents (RTAs) are the leading killer of young people and are projected to be the 7th leading cause of death by 2030. This study is aimed at analyzing the spatial distribution of road traffic accident and identifying hotspot areas across Kebeles (smallest administrative division in Ethiopia) of Hawassa city administration in Ethiopia. Secondary data on daily traffic accident record from October 2013 to June 2018 was obtained from Hawassa city administration police department. The spatial clustering and hotspots identification were carried through Moran's I and Getis-Ord Gi* statistics. Data analysis was conducted using GeoDa 1.16.0.0 and ArcGIS 10.2 softwares. Drivers within age group of 18-30 years, who were hired by private business owners and who had no driving license committed the highest number of traffic accidents. The majority of traffic accidents were caused due to careless driving, failure to give priority for pedestrian, high speed and driver failure to give priority for each other. In addition, about 82.01% of traffic accidents were recorded on asphalts road and 11.51% by gravel road. Spatial clustering of road traffic accidents for accidents occurred on gravel road and in sunny weather conditions found to be significant. Different hotspot areas were identified for gravel type of road and sunny weather condition. The concerned government bodies involved in policymaking are recommended to give special attention to young driver who were hired by private business owners. Interventions to mitigate the occurrence of traffic accident would take in to account the identified hotspot areas.

Geospatial analysis of severe road traffic accidents in Singapore in 2013-2014.

Injury is a significant cause of mortality and morbidity. We aimed to investigate which areas in Singapore have a significantly higher incidence of road traffic accidents (RTA) resulting in severe injuries (Tier 1), which is defined as an Injury Severity Score (ISS) greater than 15, and to develop a spatiotemporal model. Data was obtained from the National Trauma Registry. The RTA locations were geomapped onto the Singapore map, and spatial statistical techniques were used to identify hotspots with the Getis-Ord Gi* algorithm. From 1 January 2013 to 31 December 2014, there were 35,673 people who were injured as a result of RTAs and 976 Tier 1 RTA victims. A total of 920 people were included in the geospatial analysis. Another 56 were involved in RTAs that did not occur within Singapore or had missing location data and thus were not included. 745 (81.0%) were discharged alive, whereas 175 (19.0%) did not survive to discharge (median ISS 38.00, interquartile range 30.00-48.00). Most of the Tier 1 RTA victims were motorcycle riders (50.1%, n = 461), pedestrians (21.8%, n = 201) and cyclists (9.9%, n = 91). The majority were male and aged 20-40 years, and there was a peak occurrence at 0600-0759 hours. Nine hotspots were identified (p < 0.01). Information from studying hotspots of RTAs, especially those resulting in severe injuries, can be used by multiple agencies to direct resources efficiently.

Spatial and Temporal Characteristics of Urban Tourism Travel by Taxi—A Case Study of Shenzhen

Tourism networks are an important research part of tourism geography. Despite the significance of transportation in shaping tourism networks, current studies have mainly focused on the “daily behavior” of urban travel at the expense of tourism travel, which has been regarded as an “exceptional behavior”. To fill this gap, this study proposes a framework for exploring the spatial and temporal characteristics of urban tourism travel by taxi. We chose Shenzhen, a densely populated mega-city in China with abundant tourism resources, as a case study. First, we extracted tourist trips from taxi trajectories and used kernel density estimation to analyze the spatial aggregation characteristics of tourist trip origins. Second, we investigated the spatial dependence of tourist trips using local spatial autocorrelation analysis (Getis-Ord Gi*). Third, we explored the correlations between the tourist trip origins and urban geographic contextual factors (e.g., catering services and transportation facilities) using a geographically weighted regression model. The results show the following: (1) the trends between the coverage of tourist travel networks and the volume of tourist trips are similar; (2) the spatial interaction intensity of urban tourism has grouping and hierarchical characteristics; and (3) the spatial distribution of tourist trips by taxi is uneven and influenced by the distribution of urban morphology, tourism resources, and the preferences of taxi pick-up passengers. Our proposed framework and revealed spatial and temporal patterns have implications for urban tourism traffic planning, tourism product development, and tourist flow control in tourist attractions.

Do malnutrition, pre-existing morbidities, and poor household environmental conditions aggravate susceptibility to Coronavirus disease (COVID-19)? A study on under-five children in India

BackgroundThe novel Coronavirus disease 2019 (2019-nCoV) outbreak, caused by severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2), has become the worst serious global risk to humanity in the last century and linked with various risk factors. ObjectiveTo find out the risk zone associated with Coronavirus disease among children under-five age using malnourished status, pre-existing morbidity conditions, poor household environmental conditions, and also with case fatality rate (CFR) and active case rate (ACR) of COVID-19 in India. Data sources & methodsData was collected from the 4th round of the National Family Health Survey (NFHS)-4, 2015–16, and CFR and ACR of COVID-19 related data collected from the Ministry of Health and Family Welfare (MoHFW) on 18th May 2020. Mean, standard deviation, and Z-score statistical methods have been employed to identify the risk factors zone and Hot Spot analysis (Getis-Ord Gi) has been done. ResultsThe states and union territories (UTs) which have a high composite vulnerability score (CVS) of COVID-19 among under-five children are in Meghalaya (CVS = 1), Uttar Pradesh (CVS = 0.93), Jharkhand (CVS = 0.86), Bihar (CVS = 0.74), Madhya Pradesh (CVS = 0.74), and Odisha (CVS = 0.55). The states and UTs which have low composite vulnerability score of COVID-19 among under-five children are in Sikkim (CVS = -0.90), Daman & Diu (CVS = -0.76) Lakshadweep (CVS = -0.74), Kerala (CVS = -0.72), Chandigarh (CVS = -0.71). The COVID-19 high-risk zones (hot spot: 99% Confidence interval [CI]) were observed in Madhya Pradesh, Uttar Pradesh, Jharkhand, Bihar, and Meghalaya states of India, which are spatially high clustered and the low-risk zones (cold spot: 95% CI) were observed in Kerala, Mizoram states of India. ConclusionsWell-built public health measures, including rapidly searching in high focus areas and testing of COVID-19, should be performed in vulnerable regions of COVID-19.

Identification of TB space-time clusters and hotspots in Ouest département, Haiti, 2011-2016.

Haiti has the highest incidence rate of TB in the Western Hemisphere, with an estimated 170 cases per 100,000 in 2019. Since 2010, control efforts have focused on targeted case-finding activities in urban areas, implementation of rapid molecular diagnostics at high-volume TB centers, and improved reporting. TB analyses are rarely focused on lower geographic units; thus, the major goal was to determine if there were focal areas of TB transmission from 2011 to 2016 at operational geographic levels useful for the National TB Control Program (PNLT). We created a geocoder to locate TB cases at the smallest geographic level. Kulldorff's space-time permutation scan, Anselin Moran's I, and Getis-Ord Gi* statistics were used to determine the spatial distribution and clusters of TB. With 91% of cases linked using the geocoder, TB clusters were identified each year. Getis-Ord Gi* analysis revealed 14 distinct spatial clusters of high incidences in the Port-au-Prince metropolitan area. One hundred retrospective space-time clusters were detected. Our study confirms the presence of TB hotspots in the Ouest département, with most clusters in the Port-au-Prince metropolitan area. Results will help the PNLT and its partners better design case-finding strategies for these areas.

Assessment of intention to use contraceptive methods with spatial distributions and associated factors among women in Ethiopia: evidence from EDHS 2016

BackgroundModern contraceptive methods have immense influences on the health of mothers and their children. Using contraceptive methods is seen to control family size and unnecessary pregnancies. Considering different factors like resources and various cultural aspects, assessing the intention to use contraception might bring areas with these problems into the light for intervention.MethodsWe analyzed the cross-sectional survey data from EDHS 2016, which comprised 5651 reproductive-age women. Spatial autocorrelation was checked with global Moran’s statistics, at ±1 for dispersion and clustering. Aselin Local moran’s statistics also indicated types of clusters. Hot spot(Getis-Ord Gi) statistics further used to measure autocorrelation over different spatial locations. The significance level was checked by calculating Z-score and hot and cold spots indicated the variation in intention to use contraceptives per catchments. Interpolation was also applied to see the number of intents to use contraceptive areas other than the sampled using ordinary Kriging spatial interpolation. We used Kulldorff’s SatScan for specific local clustering and the Bernoulli model test was applied to check significance. Individual and community-level factors were examined using multilevel logistic regression. Due to the clustering nature of data where p-value< 0.05 signaled associations. The disproportional nature of data was adjusted using sampling weights.ResultFrom the total sample of women, the intention to use contraceptive methods was 2366.08(44.11%) and was highly clustered in North and Western Ethiopia. The mean number of children was (4.5 ± 2.90); age at first cohabitation was (16.9 ± 3.99); the ideal number of children was (4.77 ± 2.00). Age and the ideal number of children were negatively associated with the use of contraception. Primary education, number of children, counseling at health facilities, and age at first cohabitation were negatively associated.ConclusionWe observed various distributions among regions. Educational status and various socio-cultural including working with the religious organization might need serious considerations to increase the intention to use contraceptive methods. Besides the efforts done, policy decisions might need to consider this finding and uphill the intervention against the negatively associated socio-cultural and demographic variables in outplayed areas.