Local Indicators Of Spatial Association Research Articles

Spatial Patterns and Determinants of Diabetes Mellitus in Indian AdultPopulation: a Secondary Data Analysis from Nationally Representative Surveys.

Diabetes mellitus (DM) is a major public health challenge around the world. It is crucial to understand the geographic distribution of the disease in order to pinpoint high-priority locations and focus intervention on the target populations. Hence, this study was carried out to determine the spatial pattern and determinants of type-2 DM in an Indian population using National Family Health Survey-4 (NFHS-4) and Longitudinal Aging Survey in India (LASI). We have adopted an ecological approach, wherein geospatial analysis was performed using aggregated district-level data from NFHS-4 (613 districts) and LASI survey datasets (632 districts). Moran's I statistic was determined and Local Indicators of Spatial Association (LISA) maps were created to understand the spatial clustering pattern of DM. Spatial regression models were run to determine the spatial factors associated with DM. Prevalence of self-reported DM among males (15-50years) and females (15-49years) was 2.1% [95% confidence interval (CI) 2.0-2.3%] and 1.7% (95% CI 1.6-1.8%), respectively. Prevalence of self-reported DM among males and females aged 45years and above was 12.5% (95% CI 11.5-13.5%) and 10.9% (95% CI 9.8-12%). Positive spatial autocorrelation with significant Moran's I was found for both males and females in both NFHS-4 and LASI data. High-prevalence clustering (hotspots) was maximum among the districts belonging to southern states such as Kerala, Tamil Nadu, Karnataka, and Andhra Pradesh. Northern and central states like Madhya Pradesh, Chhattisgarh, and Haryana mostly had clustering of cold spots (i.e., lower prevalence clustered in the neighboring regions). DM burden in India is spatially clustered. Southern states had the highest level of spatial clustering. Targeted interventions with intersectoral coordination are necessary across the geographically clustered hotspots of DM.

Spatial Accessibility of Bakeries and Supermarkets in Belo Horizonte, Brazil

The recent events and constant global changes show the importance of rethinking city planning. In this context, the 15-Minute City concept got important as it brings people closer to activities and services, through short trips by active modes, being the key to a sustainable city. Based on this concept, this paper analysed the spatial accessibility of residents of Belo Horizonte (Brazil) to two establishments essential to the quality of life: bakeries and supermarkets. The analyses were made through the influence areas, spatial clusters, and the Local Indicators of Spatial Association. The results showed that bakeries are more accessible than supermarkets, which are not accessible to the entire population, especially in low-density and low-income regions. In addition, areas with potential for new projects were identified by the relationship between existing facilities (supermarkets or bakeries) and population density/income. Finally, the results highlight the challenges for developing sustainable cities considering the 15-Minute City concept throughout the territory of Belo Horizonte.

Local Sustainability Performance and Its Spatial Interdependency in Urbanizing Java Island: The Case of Jakarta-Bandung Mega Urban Region

Jakarta–Bandung Mega Urban Region (JBMUR), located in the western part of Java Island, Indonesia, is experiencing rapid regional development which can be observed from its increase in population density, massive changes of land-use from agricultural land into built-up area, rapid development of infrastructure and facilities, and advances in economic activities. Unfortunately, problems related to sustainability emerge along with this rapid regional development, primarily in decrease in environmental quality and social performance, leading to unsustainable development. This study aims: (1) to develop indicators promoting sustainable development at the subdistrict level, named the local sustainability index (LSI), utilizing factor analysis; (2) to observe local-scale spatial interdependency by employing local indicator of spatial association (LISA) statistics; and (3) to identify regional clusters based on LSI scores using K-means clustering method. Our LISA results show that spatial interdependency of local sustainability performances exists between local-scale spatial units: the LSI of a subdistrict is influenced by the sustainability state of the surrounding areas. Meanwhile, the clustering results show that most subdistricts in JBMUR are categorized as members of cluster 1 with low LSI values in economic and social dimensions but moderate in environmental dimensions.

Autism incidence and spatial analysis in more than 7 million pupils in English schools: a retrospective, longitudinal, school registry study

Understanding how certain factors affect autism incidence can help to identify inequities in diagnostic access. We aimed to investigate the incidence of autism in England as a function of geography and sociodemographics, examining spatial distribution across health service boundaries. In this retrospective, longitudinal, school registry study, we sourced data for the years 2014-17 from the summer school census, which is a component of the National Pupil Database, a government registry of pupils under state education in England. Our main outcome was the incidence of autism in the English state-funded education system, defined by the amount of new autism-specific Education, Health and Care Plans or autism-specific special education needs and disability support recorded during each summer school census year since the 2014 baseline. After excluding prevalent cases in 2014, we calculated unadjusted incidence and age-adjusted, sex-adjusted incidence per 100 000 person-years per subsequent school year and by various sociodemographic categories and local authority districts. We report spatial effects using local indicators of spatial association. We used a three-level mixed-effects logistic regression model with two random intercepts (lower-layer super output area [a geographical area in England containing 1000-3000 residents] and pupil identifier) to calculate odds ratios (ORs) for autism incidence, adjusting for age, sex, ethnicity, claimed eligibility for free school meals, ethnic density quintile, Index of Multiple Deprivation quintile, first language spoken at home, and year, with our reference category being White girls without claimed eligibility for free school meals who speak English as their first language. Between 2014 and 2017, our total sample included 31 580 512 person-years and 102 338 newly diagnosed autistic pupils, corresponding to an unadjusted annual autism incidence of 429·1 cases per 100 000 person-years (95% CI 426·4-431·7) and an age-adjusted, sex-adjusted annual incidence of 426·9 cases per 100 000 person-years (423·5-430·4). The adjusted incidence of autism was slightly higher in 2014-15 than in 2015-16 or 2016-17, and, of the age groups, pupils aged 1-3 years, 4-6 years, and 10-12 years had the highest incidence of autism. Adjusted autism incidence in boys was 3·9-times the incidence in girls (668·6 cases per 100 000 person-years [95% CI 662·5-674·6] vs 173·2 cases per 100 000 person-years [170·1-176·3]). Across ethnic groups, adjusted incidence was highest in pupils who had an unclassified ethnicity (599·4 cases per 100 000 person-years [574·5-624·3]) or were Black (466·9 cases per 100 000 person-years [450·8-483·0]). However, in our fully adjusted mixed-effects logistic regression model, we observed lower odds of autism among Asian (OR 0·65 [0·59-0·71]), Black (0·84 [0·77-0·92]), and Chinese (0·62 [0·42-0·92]) girls compared with White girls when these groups had not claimed free school meals and spoke English as a first language. Boys from all ethnicities irrespective of first language spoken and free school meals status had increased odds of autism compared with White girls with no claimed eligibility for free school meals who spoke English as their first language. We also found that claimed free school meal eligibility, first language spoken, sex, and ethnicity differentially impacted the odds of autism. Our spatial analysis showed significant spatial autocorrelation across lower-layer super output areas in England, with 2338 hotspots (high-incidence areas surrounded by other high-incidence areas). The incidence of autism varies across sex, age, ethnicity, and geographical location. Environmental and social factors might interact with autism aetiology. Speaking a language other than English and economic hardship might increase access barriers to autism diagnostic services, autism-specific Education, Health and Care Plans, and school-level support. The Commonwealth Fund, the Institute for Data Valorization, the Fonds de recherche du Québec-Santé, Calcul Quebec, the Digital Research Alliance of Canada, the Wellcome Trust, the Innovative Medicines Initiative, the Autism Centre of Excellence, the Simons Foundation Autism Research Initiative, the Templeton World Charitable Fund, the Medical Research Council, the National Institute for Health and Care Research Cambridge Biomedical Research Centre, and the National Institute for Health and Care Research Applied Research Collaboration East of England-Population Evidence and Data Science.

Who Are the Local Policy Innovators? Cluster Analysis of Municipal Tobacco Control Policies in Massachusetts.

Tobacco use is a leading cause of preventable death, yet it is challenging to establish public policy to reduce tobacco use. Massachusetts has been a national leader in tobacco control, and its policy-making patterns can be informative to the country. To identify factors associated with the adoption of tobacco policy within 351 Massachusetts municipalities. We obtained the 2019 Massachusetts municipality-level tobacco control policy information from Massachusetts' Tobacco Automated Fact Sheet Information system and compiled it with data from American Community Survey, Massachusetts Municipal Association, and Massachusetts state government's Web sites. We used k -means clustering method to identify statistical clustering patterns and hotspot analysis (Getis-Ord Gi*) and Local Indicators of Spatial Association to identify geographic clustering patterns. We then performed multinomial logistic regression to identify factors associated with policy clusters. Massachusetts. Three hundred fifty-one municipalities in Massachusetts. Policy clusters-groups of municipalities with similar tobacco control policy behaviors. Based on the k -means analyses, we identified 3 clusters in Massachusetts municipal tobacco control policy behaviors: 54% (N = 191) of municipalities were "Policy Leaders" with a high adoption rate of the 6 tobacco control policies; 18% (N = 63) were "Peer-Influenced Actors" focused on tobacco purchase restrictions for individuals younger than 21 years; and 28% (N = 97) were "Policy Non-Actors," with no tobacco control policies in place. Policy Leaders were geographically clustered in larger cities and the MetroWest region. Policy Non-Actors were clustered in rural areas of Western and Central Massachusetts. Larger municipal population size, higher municipal tax income, and higher percentages of residents voting Democratic were associated with higher policy adoption activities. Local variation in the adoption of tobacco policies may exacerbate inequities in tobacco use and population health. Opportunities remain to implement additional tobacco control regulations at the local level to promote public health.

Mortality due to noninfectious lower respiratory diseases: A spatiotemporal, cross-sectional study.

Background and AimsGeography plays an important role in the incidence of respiratory diseases. The aim of this study was to investigate the epidemiology and geographical distribution of death due to noninfectious lower respiratory diseases (NILRDs).MethodsData related to all death due to NILRD in Kerman Province between 2012 and 2018 were extracted from the National Mortality Registry. The underlying causes of death were extracted from the registry based on the assigned codes from ICD‐10 (International Classification of Diseases 10th Revision) classification. The existence of spatial clusters and outliers was evaluated using local indicators of spatial association statistics.ResultsThe frequency of death due to NILRD was 8005 persons during the 7 years of the study. The main cause of death was chronic lower respiratory disease (54.2%). Other causes of death were, respectively, lung diseases due to external agents (1.09%), other respiratory diseases mainly affecting the interstitium (1.16%), other diseases of pleura (0.57%), and other diseases of the respiratory system (42.13%). The age‐ and sex‐adjusted mortality rates due to NILRD in the north and center of the province increased significantly from 2012 to 2018. Also, the results of cluster analysis identified northern regions as the clustered areas of NILRD.ConclusionsOur findings showed a significant increase in mortality due to NILRD in Kerman Province during the 7 years of the study. To reduce this type of death, health policymakers should have environmental health plans and basic solutions, such as a warning system to reduce the commuting on highly air‐polluted days and to control pollutants, especially in the industrial areas of the north of this province.

Spatial Factors Associated with fall among the Elderly in Thailand

Falls are the leading cause of injury-related visits to emergency departments around the world, including in Thailand, and the primary etiology of accidental deaths in elderly people. Falls among the elderly is an increasing problem, causing a high degree of morbidity, mortality, and use of health care services. This study statistically identified aimed to determine the association of the spatial factors with falls among elderly people in Thailand. The participant consisted of 40,489 elderly people and was conducted using a data set from the national statistical office of Thailand in 2017 and other data. A Moran’s I and local indicators of spatial association (Lisa) were used to identify the spatial autocorrelation between poverty incidence, the proportion of patients with non-communicable diseases, and the population-to- village health volunteers’ ratio with falls among elderly people in Thailand. The results showed that there was spatial global autocorrelation between poverty incidence, the proportion of patients with non-communicable diseases, and the population-to- village health volunteers among elderly people in Thailand with Moran’s I values of 0.176, 0.049, and 0.034, respectively. Therefore, the focus should be on promoting and preventing non-communicable diseases, as well as promoting income-generating jobs for the elderly by closely supervising village health volunteers and elderly caregivers to reduce the risk of falls among the elderly and improve their quality of life.

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

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

Prevalence and distribution pattern of malaria and soil-transmitted helminth co-endemicity in sub-Saharan Africa, 2000-2018: A geospatial analysis.

Limited understanding exists about the interactions between malaria and soil-transmitted helminths (STH), their potential geographical overlap and the factors driving it. This study characterised the geographical and co-clustered distribution patterns of malaria and STH infections among vulnerable populations in sub-Saharan Africa (SSA). We obtained continuous estimates of malaria prevalence from the Malaria Atlas Project (MAP) and STH prevalence surveys from the WHO-driven Expanded Special Project for the Elimination of NTDs (ESPEN) from Jan 1, 2000, to Dec 31, 2018. Although, MAP provides datasets on the estimated prevalence of Plasmodium falciparum at 5km x 5km fine-scale resolution, we calculated the population-weighted prevalence of malaria for each implementation unit to ensure that both malaria and STH datasets were on the same spatial resolution. We incorporated survey data from 5,935 implementation units for STH prevalence and conducted the prevalence point estimates before and after 2003. We used the bivariate local indicator of spatial association (LISA analysis) to explore potential co-clustering of both diseases at the implementation unit levels among children aged 2-10 years for P. falciparum and 5-14 years for STH, living in SSA. Our analysis shows that prior to 2003, a greater number of SSA countries had a high prevalence of co-endemicity with P.falciparium and any STH species than during the period from 2003-2018. Similar prevalence and distribution patterns were observed for the co-endemicity involving P.falciparum-hookworm, P.falciparum-Ascaris lumbricoides and P.falciparum-Trichuris trichiura, before and after 2003. We also observed spatial variations in the estimates of the prevalence of P. falciparum-STH co-endemicity and identified hotspots across many countries in SSA with inter-and intra-country variations. High P. falciparum and high hookworm co-endemicity was more prevalent in West and Central Africa, whereas high P. falciparum with high A. lumbricoides and high P. falciparum with high T. trichiura co-endemicity were more predominant in Central Africa, compared to other sub-regions in SSA. Wide spatial heterogeneity exists in the prevalence of malaria and STH co-endemicity within the regions and within countries in SSA. The geographical overlap and spatial co-existence of malaria and STH could be exploited to achieve effective control and elimination agendas through the integration of the vertical control programmes designed for malaria and STH into a more comprehensive and sustainable community-based paradigm.

Geographical Inequality on Cataract Surgery Uptake in 200,000 Australians: Findings from the "45 and Up Study".

Using a geographical information system (GIS), we investigated the spatiotemporal evolution of a cataract surgery service and its association with socioeconomic factors and private insurance, based on 10-year real-world medical claim data in an Australian population. The data collected cover a decade (2007–2016) from the “45 and Up Study”. A total of 234,201 participants within the cataract surgery service were grouped into 88 Statistical Area Level 3 (SA3s) according to their residential postcodes in New South Wales Australia. We analyzed the spatiotemporal variations and geographical distribution inequality in cataract surgery incidence and its respect to socioeconomic status (SES) and private health insurance coverage by Spearman correlation analysis and Moran's I test. Then these variations were intuitive displayed by six-quartile maps and a local indicator of spatial association (LISA) maps based on GIS. The average cumulative age-gender-standardized of the incidence of cataract surgery (ICS) was 8.85% (95% CI, 5.33–15.6). Spatial variation was significant (univariate Moran's I = 0.45, P = 0.001) with incidence gradually decreasing from the coastal regions to the north-western inland regions, suggesting inequality in the cataract surgery service across the state of New South Wales. Notably, clustering of the low incidence areas had gradually disappeared over the decade, suggesting that the cataract surgery service has improved over time. Low scores on the “index of socioeconomic disadvantages” (IRSD) and high private health insurance coverage were significantly associated with a higher incidence of cataract surgery (bivariate Moran's I = −0.13 and 0.23, P < 0.01; Spearman correlation r = 0.25 and −0.25, P = 0.02), which is displayed on the map visually and obviously. Spatiotemporal variations in the incidence of cataract surgery are significant, but the low incidence area had gradually disappeared over time. High socioeconomic status and private insurance contribute to a higher incidence of cataract surgery in Australia.

Nutritional inequalities among under-five children: a geospatial analysis of hotspots and cold spots in 73 low- and middle-income countries

BackgroundChild undernutrition is a severe health problem in the developing world, which affects children’s development in the long term. This study analyses the extent and patterns of under-five child undernutrition using Demographic and Health Surveys (DHS) for 73 low- and middle-income countries (LMICs).MethodsFirst, we mapped the prevalence of undernutrition in the developing world. Second, using the LISA (a local indicator of spatial association) technique, we analyzed the geographical patterns in undernutrition to highlight the localized hotspots (regions with high undernutrition prevalence surrounded by similar other regions), cold spots (regions with low undernutrition prevalence surrounded by similar other regions), and outliers (regions with high undernutrition surrounded by low undernutrition and vice versa). Third, we used Moran’s I to find global patterns in child undernutrition.ResultsWe find that South Asia has the highest under-five child undernutrition rates. The intra-country nutritional inequalities are highest in Burundi (stunting), Kenya (wasting), and Madagascar (underweight). The local indicator of spatial association (LISA) analysis suggests that South Asia, Middle East and North Africa (MENA) region, and Sub-Saharan Africa are undernutrition hotspots and Europe and Central Asia and Latin America, and the Caribbean are undernutrition cold spots (regions with low undernutrition surrounded by similar other regions). Getis Ord-Gi* estimates generally support LISA analysis. Moran’s I and Geary’s C gave similar results about the global patterns of undernutrition. Geographically weighted regressions suggest that several socioeconomic indicators significantly explain child undernutrition.ConclusionsWe found a significant within and across country variation in stunting, wasting and underweight rates among the under-five children’s population. The geospatial analysis also suggested that stunting, wasting, and underweight patterns exhibit clear regional patterns, underscoring the need for coordinated interventions at the regional level.

Spatiotemporal variations and driving forces of per capita carbon emissions from energy consumption in China

The carbon emissions vary over space and time in China, as well as driving forces. It is particularly important to analyze the spatiotemporal variations and driving factors of China’s per capita carbon emissions. This study adopted global Moran’ I and local indicators of spatial association to analyze the spatial autocorrelation of per capita carbon emissions in China during 2004–2019 and discussed the driving factors of per capita carbon emissions by geographically and temporally weighted regression model. The results demonstrated a positive spatial correlation between inter-provincial per capita carbon emissions, but this correlation has gradually decreased since 2008. The High-High clusters were concentrated in the Bohai Economic Rim and the Low-Low clusters were mainly located in the south. Driving factors of per capita carbon emission at the provincial level have spatiotemporal heterogeneity. From 2004 to 2019, per capita GDP, urbanization rate, and energy intensity are the main contributors to per capita carbon emissions, and the role of per capita GDP is weakening, while urbanization rate and energy intensity change in the opposite direction. Foreign direct investment is the main disincentive in most regions. These findings provided a reference for emission reduction policies implemented in different regions.

Spatial equity of modal access gap to multiple destination types across Chicago

This study examines the Modal Access Gap (MAG) between transit and automobile to employment, groceries, hospitals, and schools in 15-min, 30-min, 45-min, and 60-min travel-time thresholds in the City of Chicago. We use automobile and transit access data from the Metropolitan Chicago Accessibility Explorer and augment it with data from the American Community Survey and the Smart Location Database. We employ a Spatial Lag Model to explore sociodemographic and built-environment correlates of MAG and the bivariate local indicator of spatial association to create cluster maps to offer a way to assess the spatial equity of MAG as it relates to carless households. The findings indicate that: (1) regardless of the travel-time threshold, the automobile has an advantage over transit in providing access to opportunities, (2) block groups with low MAG are concentrated and clustered in the Central Business District, (3) Millennials and car-free households are more likely to reside in areas with lower accessibility gap to employment, groceries, hospitals, and schools for 30-min and 60-min travel-time thresholds, and (4) areas with high access gap and a high proportion of carless households have a higher percentage of African Americans and low-income households. We recommend using the bivariate spatial autocorrelation analysis to classify areas according to the gap in accessibility and proportion of households without vehicles. This classification is then used to prioritize different planning actions for high-high, high-low, low-high, and low-low combinations of MAG and the proportion of carless households. We also show that this spatial identification, at least in the case of Chicago, captures racial and economic differences in the underlying population and can help address inequities in accessibility, particularly in high access gap, high carless areas.

Supermarket Proximity and Risk of Hypertension, Diabetes, and CKD: A Retrospective Cohort Study

Living in environments with low access to food may increase the risk of chronic diseases. We investigated the association of household distance to the nearest supermarket (as a measure of food access) with the incidence of hypertension, diabetes, and chronic kidney disease (CKD) in a metropolitan area of the United States. Retrospective cohort study. 777,994 individuals without hypertension, diabetes, or CKD at baseline within the HealthLNK Data Repository, which contains electronic health records from 7 health care institutions in Chicago, Illinois. Zip code-level average distance between households and nearest supermarket. Incidence of hypertension, diabetes, and CKD based on presence of ICD-9 code and/or blood pressure≥140/90mm Hg, hemoglobin A1c≥6.5%, and eGFR<60mL/min/1.73m2, respectively. Average distance to nearest supermarket was aggregated from street-level metrics for 56 Chicagoland zip codes. The cumulativeincidence of hypertension, diabetes, and CKD from 2007-2012 was calculated for each zip code in patients free of these diseases in 2006. Spatial analysis of food access and disease incidence was performed using bivariate local indicator of spatial association (BiLISA) maps and bivariate local Moran I statistics. The relationship between supermarket access and outcomes was analyzed using logistic regression. Of 777,994 participants, 408,608 developed hypertension, 51,380 developed diabetes, and 56,365 developed CKD. There was significant spatial overlap between average distance to supermarket and incidence of hypertension and diabetes but not CKD. Zip codes with large average supermarket distances and high incidence of hypertension and diabetes were clustered in southern and western neighborhoods. Models adjusted only for neighborhood factors (zip code-level racial composition, access to vehicles, median income) revealed significant associations between zip code-level average distance to supermarket and chronic disease incidence. Relative to tertile 1 (shortest distance), ORs in tertiles 2 and 3, respectively, were 1.27 (95% CI, 1.23-1.30) and 1.38 (95% CI, 1.33-1.43) for diabetes, 1.03 (95% CI, 1.02-1.05) and 1.04 (95% CI, 1.02-1.06) for hypertension, and 1.18 (95% CI, 1.15-1.21) and 1.33 (95% CI, 1.29-1.37) for CKD. Models adjusted for demographic factors and health insurance showed significant and positive association with greater odds of incident diabetes (tertile 2: 1.29 [95% CI, 1.26-1.33]; tertile 3: 1.35 [95% CI, 1.31-1.39]) but lesser odds of hypertension (tertile 2: 0.95 [95% CI, 0.94-0.97]; tertile 3: 0.91 [95% CI, 0.89-0.92]) and CKD (tertile 2: 0.80 [95% CI, 0.78-0.82]; tertile 3: 0.73 [95% CI, 0.72-0.76]). After adjusting for both neighborhood and individual covariates, supermarket distance remained significantly associated with greater odds of diabetes and lesser odds of hypertension, but there was no significant association with CKD. Unmeasured neighborhood and social confounding variables, zip code-level analysis, and limited individual-level information. There are significant disparities in supermarket proximity and incidence of hypertension, diabetes, and CKD in Chicago, Illinois. The relationship between supermarket access and chronic disease is largely explained by individual- and neighborhood-level factors.

The epidemiology of diphtheria in Haiti, December 2014-June 2021: A spatial modeling analysis.

BackgroundHaiti has been experiencing a resurgence of diphtheria since December 2014. Little is known about the factors contributing to the spread and persistence of the disease in the country. Geographic information systems (GIS) and spatial analysis were used to characterize the epidemiology of diphtheria in Haiti between December 2014 and June 2021.MethodsData for the study were collected from official and open-source databases. Choropleth maps were developed to understand spatial trends of diphtheria incidence in Haiti at the commune level, the third administrative division of the country. Spatial autocorrelation was assessed using the global Moran’s I. Local indicators of spatial association (LISA) were employed to detect areas with spatial dependence. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were built to identify factors associated with diphtheria incidence. The performance and fit of the models were compared using the adjusted r-squared (R2) and the corrected Akaike information criterion (AICc).ResultsFrom December 2014 to June 2021, the average annual incidence of confirmed diphtheria was 0.39 cases per 100,000 (range of annual incidence = 0.04–0.74 per 100,000). During the study period, diphtheria incidence presented weak but significant spatial autocorrelation (I = 0.18, p<0.001). Although diphtheria cases occurred throughout Haiti, nine communes were classified as disease hotspots. In the regression analyses, diphtheria incidence was positively associated with health facility density (number of facilities per 100,000 population) and degree of urbanization (proportion of urban population). Incidence was negatively associated with female literacy. The GWR model considerably improved model performance and fit compared to the OLS model, as indicated by the higher adjusted R2 value (0.28 v 0.15) and lower AICc score (261.97 v 267.13).ConclusionThis study demonstrates that GIS and spatial analysis can support the investigation of epidemiological patterns. Furthermore, it shows that diphtheria incidence exhibited spatial variability in Haiti. The disease hotspots and potential risk factors identified in this analysis could provide a basis for future public health interventions aimed at preventing and controlling diphtheria transmission.

Bivariate spatial statistics applied to precipitation and off-season corn yield in the state of Paraná, Brazil

ABSTRACT The state of Paraná is one of the largest corn producers in Brazil, cultivating in two growing seasons. This study evaluated bivariate correlations between precipitation and the second-crop corn yield between the first 10 days of April and the first 10 days of October using spatial statistics. The spatial analysis of yield was evaluated using geostatistical models and validated by cross-validation. Spatial analysis was performed using the local indicator of spatial association and a cross-semivariogram, and the co-dispersion coefficient was used to validate the correlation between variables. Cross-validation identified the exponential model as the best fit, indicating a spatial dependence of 317.26 km. Spatial associations between municipalities were significant throughout the state of Paraná, Brazil. The cross-semivariogram allowed fitting the wave, spherical, Gaussian, and exponential models, with a spatial correlation of 37.04 to 570.38 km between the precipitations and yield. Moreover, the co-dispersion coefficient validated the correlation between the two variables for all 10-day periods.

Spatial-temporal evolution characteristics and drivers of carbon emission intensity of resource-based cities in china

As the key object of carbon emission reduction, resource-based cities’ carbon emission problems are related to the achievement of China’s goals to peak carbon emission and achieve carbon neutrality. In this paper, 115 resource-based cities with abundant natural resources in China were studied, and spatial analysis techniques such as LISA (Local Indicators of Spatial Association) time path and spatial-temporal transition were used to explore their spatial divergence pattern and spatio-temporal evolution characteristics of carbon emission intensity from 2000 to 2019, while geodetector model was used further to reveal their drivers and impacts on the environment. It is found that 1) the carbon emission intensity of resource-based cities shows a significant decreasing trend, with significant differences in carbon emission intensity and its decreasing rate in different development stages and resource-type cities. The overall trend of growing cities, declining cities, mature cities and regenerating cities decreases in order. The carbon emission intensity of cities in the energy, forest industry, general, metal and non-metal categories gradually decrease. The spatial pattern of carbon emission intensity has strong stability, with an overall spatial distribution of high in the north and low in the south. 2) The spatial structure of carbon emission intensity in resource-based cities has strong stability, dependence and integration, with the stability gradually increasing from north to south and the path dependence and locking characteristics of the carbon emission intensity pattern slightly weakened. 3) The spatial divergence of carbon emission intensity in resource-based cities is the result of the action of multiple factors, among which the level of financial investment, urban economic density, urban population density, urban investment intensity and energy use efficiency are the dominant factors. 4) The leading drivers of carbon emission intensity are different in cities at different development stages and with various resources, and grasping the characteristics of carbon emission intensity changes and drivers of various resource-based cities can better provide targeted countermeasures for resource-based cities to achieve carbon emission reduction targets and sustainable development.

Temporal Variations and Spatial Clusters of Dengue in Thailand: Longitudinal Study before and during the Coronavirus Disease (COVID-19) Pandemic.

The efforts towards effective control of the COVID-19 pandemic may affect the incidence of dengue. This study aimed to investigate temporal variations and spatial clusters of dengue in Thailand before and during the COVID-19 pandemic. Reported dengue cases before (2011–2019) and during (2020–2021) the COVID-19 pandemic were obtained from the national disease surveillance datasets. The temporal variations were analyzed using graphics, a seasonal trend decomposition procedure based on Loess, and Poisson regression. A seasonal ARIMA model was used to forecast dengue cases. Spatial clusters were investigated using the local indicators of spatial associations (LISA). The cyclic pattern showed that the greatest peak of dengue cases likely changed from every other year to every two or three years. In terms of seasonality, a notable peak was observed in June before the pandemic, which was delayed by one month (July) during the pandemic. The trend for 2011–2021 was relatively stable but dengue incidence decreased dramatically by 7.05% and 157.80% on average in 2020 and 2021, respectively. The forecasted cases in 2020 were slightly lower than the reported cases (2.63% difference), whereas the forecasted cases in 2021 were much higher than the actual cases (163.19% difference). The LISA map indicated 5 to 13 risk areas or hotspots of dengue before the COVID-19 pandemic compared to only 1 risk area during the pandemic. During the COVID-19 pandemic, dengue incidence sharply decreased and was lower than forecasted, and the spatial clusters were much lower than before the pandemic.

Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022.

The Omicron variant was first reported to the World Health Organization (WHO) from South Africa on November 24, 2021; this variant is spreading rapidly worldwide. No study has conducted a spatiotemporal analysis of the morbidity of Omicron infection at the country level; hence, to explore the spatial transmission of the Omicron variant among the 220 countries worldwide, we aimed to the analyze its spatial autocorrelation and to conduct a multiple linear regression to investigate the underlying factors associated with the pandemic. This study was an ecological study. Data on the number of confirmed cases were extracted from the WHO website. The spatiotemporal characteristic was described in a thematic map. The Global Moran Index (Moran's I) was used to detect the spatial autocorrelation, while the local indicators of spatial association (LISA) were used to analyze the local spatial correlation characteristics. The joinpoint regression model was used to explore the change in the trend of the Omicron incidence over time. The association between the morbidity of Omicron and influencing factors were analyzed using multiple linear regression. This study was an ecological study. Data on the number of confirmed cases were extracted from the WHO website. The spatiotemporal characteristic was described in a thematic map. The Global Moran Index (Moran's I) was used to detect the spatial autocorrelation, while the LISA were used to analyze the local spatial correlation characteristics. The joinpoint regression model was used to explore the change in the trend of the Omicron incidence over time. The association between the morbidity of Omicron and influencing factors were analyzed using multiple linear regression. The value of Moran's I was positive (Moran's I = 0.061, Z‐score = 3.772, p = 0.007), indicating a spatial correlation of the morbidity of Omicron at the country level. From November 26, 2021 to February 26, 2022; the morbidity showed obvious spatial clustering. Hotspot clustering was observed mostly in Europe (locations in High–High category: 24). Coldspot clustering was observed mostly in Africa and Asia (locations in Low−Low category: 32). The result of joinpoint regression showed an increasing trend from December 21, 2021 to January 26, 2022. Results of the multiple linear regression analysis demonstrated that the morbidity of Omicron was strongly positively correlated with income support (coefficient = 1.905, 95% confidence interval [CI]: 1.354–2.456, p < 0.001) and strongly negatively correlated with close public transport (coefficient = −1.591, 95% CI: −2.461 to −0.721, p = 0.001). Omicron outbreaks exhibited spatial clustering at the country level worldwide; the countries with higher disease morbidity could impact the other countries that are surrounded by and close to it. The locations with High–High clustering category, which referred to the countries with higher disease morbidity, were mainly observed in Europe, and its adjoining country also showed high spatial clustering. The morbidity of Omicron increased from December 21, 2021 to January 26, 2022. The higher morbidity of Omicron was associated with the economic and policy interventions implemented; hence, to deal with the epidemic, the prevention and control measures should be strengthened in all aspects.

A Social Vulnerability Index for Air Pollution and Its Spatially Varying Relationship to PM2.5 in Uganda

Fine particulate matter (PM2.5) is a ubiquitous air pollutant that is harmful to human health. Social vulnerability indices (SVIs) are calculated to determine where vulnerable populations are located. We developed an SVI for Uganda to identify areas with high vulnerability and exposure to air pollution. The 2014 national census was used to create the SVI. Mean PM2.5 at the subcounty level was estimated using global PM2.5 estimates. The mean PM2.5 for Kampala at the parish level was estimated using low-cost PM2.5 sensors and spatial interpolation. A local indicator of spatial association (LISA) was performed to determine significant spatial clusters of social vulnerability, and a bivariate analysis was performed to identify where significant associations were between SVI and annual PM2.5 mean concentrations. The LISA results showed significant clustering of high SVI in the northern and western regions of the country. The spatial bivariate analysis showed positive linear associations between SVI and PM2.5 concentration in subcounties in the northern, western, and central regions of Uganda, as well as in certain northern parishes in Kampala. Our approach identified areas facing both high social vulnerability and air pollution levels. These areas can be prioritized for health interventions and policy to reduce the impact of ambient PM2.5.