A Spatial Signal of Niche Differentiation in Tropical Forests.

AbstractExplaining diversity in tropical forests remains a challenge in community ecology. Theory tells us that species differences can stabilize communities by reducing competition, while species similarities can promote diversity by reducing fitness differences and thus prolonging the time to competitive exclusion. Combined, these processes may lead to clustering of species such that species are niche differentiated across clusters and share a niche within each cluster. Here, we characterize this partial niche differentiation in a tropical forest in Panama by measuring spatial clustering of woody plants and relating these clusters to local soil conditions. We find that species were spatially clustered and the clusters were associated with specific concentrations of soil nutrients, reflecting the existence of nutrient niches. Species were almost twice as likely to recruit in their own nutrient niche. A decision tree algorithm showed that local soil conditions correctly predicted the niche of the trees with up to 85% accuracy. Iron, zinc, phosphorus, manganese, and soil pH were among the best predictors of species clusters.

Areas with high HIV prevalence: A spatial analysis of nationwide claims data in Germany.

OBJECTIVE: We aimed to identify spatial clusters of high HIV prevalence in Germany. METHODS: Using nationwide outpatient claims data comprising information of about 88% of the total German population (N = 72 041 683), we examined spatial variations and spatial clusters of high HIV prevalence at the district level (N = 401). People with HIV were identified using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10 codes) B20, B22, and B24 (HIV disease) documented as 'confirmed'. RESULTS: Among 72 041 683 people with statutory health insurance in Germany in 2021, 72 636 had diagnosed HIV, which corresponds to a prevalence of 101 per 100 000 individuals (0.10%). Of these, 56 895 were males (78%). At a district level, the HIV prevalence varied by a factor of 32 between 13 in a rural district in Bavaria and 417 per 100 000 individuals in the German capital, Berlin. The spatial autocorrelation coefficient was 0.24 (p < 0.0001, Global Moran's I). Several high-prevalence spatial clusters of different sizes were identified, mostly located in western Germany. The largest cluster comprised eight districts in the southern part of Hesse, including the city of Frankfurt and the city of Mainz in Rhineland-Palatinate. The second cluster consisted of four districts in North Rhine-Westphalia, including the cities of Cologne and Düsseldorf. Two districts in southern Germany (Mannheim and Ludwigshafen) formed the third cluster. Only urban districts were observed in spatial clusters of high HIV prevalence. CONCLUSIONS: The current study identified for the first time spatial clusters with high HIV prevalence in Germany. This understanding is of particular importance when planning the general and specialized medical care of patients with HIV and to support preventive measures.

How do spatial scale-dependent urban and forest areas influence regionally seasonal temperatures in mid-latitude South Korean cities?

This study aims to investigate the effects of urban and forest areas measured in three dimensions on seasonal temperature over forty years in South Korean cities. We measure the urban and forest areas at the city, neighborhood, and spatially clustered levels in four periods every ten years. Using Hot Spot Analysis (Getis-Ord Gi*), this study detects the spatially clustered urban and forest areas. We establish a multilevel regression model to explore the relationship between urban and forest areas measured in three dimensions, as well as seasonal temperatures. The study shows that while spatially clustered urban and forest areas have consistent associations with the four seasonal temperatures, urban and forest areas at the city scale have different associations with the seasonal temperature, depending on the season. When spatially clustered urban areas increase by 10 km2, four seasonal temperatures increase by about 0.0016-0.0067 Celsius degree (°C); on the other hand, when spatially clustered forest areas increase by 10 km2, four seasonal temperatures decrease by about 0.0001-0.0016 °C. At the neighborhood level, urban and forest areas are negatively associated with the four seasonal temperatures. The results of this study can be utilized by urban planners and policymakers to establish land use planning or policy by providing evidence of whether land use plans should be established and at what scales to manage regional thermal environments. To alleviate seasonal warming, we recommend increasing forest areas at the neighborhood and spatially clustered levels and controlling the size of spatially clustered urban areas.

Epidemiological characteristics, spatial clusters and monthly incidence prediction of hand, foot and mouth disease from 2017 to 2022 in Shanxi Province, China.

Hand, foot and mouth disease (HFMD) is a common infection in the world, and its epidemics result in heavy disease burdens. Over the past decade, HFMD has been widespread among children in China, with Shanxi Province being a severely affected northern province. Located in the temperate monsoon climate, Shanxi has a GDP of over 2.5 trillion yuan. It is important to have a comprehensive understanding of the basic features of HFMD in those areas that have similar meteorological and economic backgrounds to northern China. We aimed to investigate epidemiological characteristics, identify spatial clusters and predict monthly incidence of HFMD. All reported HFMD cases were obtained from the Shanxi Center for Disease Control and Prevention. Overall HFMD incidence showed a significant downward trend from 2017 to 2020, increasing again in 2021. Children aged < 5 years were primarily affected, with a high incidence of HFMD in male patients (relative risk: 1.316). The distribution showed a seasonal trend, with major peaks in June and July and secondary peaks in October and November with the exception of 2020. Other enteroviruses were the predominant causative agents of HFMD in most years. Areas with large numbers of HFMD cases were primarily in central Shanxi, and spatial clusters in 2017 and 2018 showed a positive global spatial correlation. Local spatial autocorrelation analysis showed that hot spots and secondary hot spots were concentrated in Jinzhong and Yangquan in 2018. Based on monthly incidence from September 2021 to August 2022, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the long short-term memory (LSTM) and seasonal autoregressive integrated moving average (SARIMA) models were 386.58 vs. 838.25, 2.25 vs. 3.08, and 461.96 vs. 963.13, respectively, indicating that the predictive accuracy of LSTM was better than that of SARIMA. The LSTM model may be useful in predicting monthly incidences of HFMD, which may provide early warnings of HFMD epidemics.

Spatial Distributions of Diarrheal Cases in Relation to Housing Conditions in Informal Settlements: A Cross-Sectional Study in Abidjan, Côte d'Ivoire.

In addition to individual practices and access to water, sanitation, and hygiene (WASH) facilities, housing conditions may also be associated with the risk of diarrhea. Our study embraced a broad approach to health determinants by looking at housing deprivation characteristics as exposures of interest and confronting the latter's spatial distribution to that of diarrheal cases. We tested the hypothesis that the risk of diarrhea in informal settlements is not only associated with WASH services, but also with inadequate dwelling characteristics, and that their spatial distributions follow similar patterns. We designed a cross-sectional study and collected primary data through georeferenced household surveys in two informal settlements in Abidjan, Côte d'Ivoire. We used local join count statistics to assess the spatial distribution of events and multiple logistic regressions to calculate adjusted odds ratios between diarrhea and exposures. A total of 567 households were enrolled. We found that constant access to basic WASH services, non-durable building materials, cooking outdoors, and water service discontinuity were associated with higher risks of diarrhea in the general population. The spatial distribution of diarrheal cases coincided with that of dwelling deprivation characteristics. We observed significant heterogeneity within the study sites regarding the spatial distribution of diarrheal cases and deprived dwellings. Along with WASH infrastructure, communities also need dignified housing to effectively prevent diarrhea. We recommend that decision-makers acknowledge a "spectrum" of deprivation within the heterogeneous universe of informal settlements, adopting a site-specific approach based on high-resolution data to address diarrhea and improve people's well-being.

Geographic disparities in COVID-19 testing and outcomes in Florida.

BACKGROUND: Understanding geographic disparities in Coronavirus Disease 2019 (COVID-19) testing and outcomes at the local level during the early stages of the pandemic can guide policies, inform allocation of control and prevention resources, and provide valuable baseline data to evaluate the effectiveness of interventions for mitigating health, economic and social impacts. Therefore, the objective of this study was to identify geographic disparities in COVID-19 testing, incidence, hospitalizations, and deaths during the first five months of the pandemic in Florida.  METHODS: Florida county-level COVID-19 data for the time period March-July 2020 were used to compute various COVID-19 metrics including testing rates, positivity rates, incidence risks, percent of hospitalized cases, hospitalization risks, case-fatality rates, and mortality risks. High or low risk clusters were identified using either Kulldorff's circular spatial scan statistics or Tango's flexible spatial scan statistics and their locations were visually displayed using QGIS. RESULTS: Visual examination of spatial patterns showed high estimates of all COVID-19 metrics for Southern Florida. Similar to the spatial patterns, high-risk clusters for testing and positivity rates and all COVID-19 outcomes (i.e. hospitalizations and deaths) were concentrated in Southern Florida. The distributions of these metrics in the other parts of Florida were more heterogeneous. For instance, testing rates for parts of Northwest Florida were well below the state median (11,697 tests/100,000 persons) but they were above the state median for North Central Florida. The incidence risks for Northwest Florida were equal to or above the state median incidence risk (878 cases/100,000 persons), but the converse was true for parts of North Central Florida. Consequently, a cluster of high testing rates was identified in North Central Florida, while a cluster of low testing rate and 1-3 clusters of high incidence risks, percent of hospitalized cases, hospitalization risks, and case fatality rates were identified in Northwest Florida. Central Florida had low-rate clusters of testing and positivity rates but it had a high-risk cluster of percent of hospitalized cases. CONCLUSIONS: Substantial disparities in the spatial distribution of COVID-19 outcomes and testing and positivity rates exist in Florida, with Southern Florida counties generally having higher testing and positivity rates and more severe outcomes (i.e. hospitalizations and deaths) compared to Northern Florida. These findings provide valuable baseline data that is useful for assessing the effectiveness of preventive interventions, such as vaccinations, in various geographic locations in the state. Future studies will need to assess changes in spatial patterns over time at lower geographical scales and determinants of any identified patterns.

Trend and spatial clustering of medical education in Brazil: an ecological study of time series from 2010 to 2021.

CONTEXT: Studies that analyze the temporal trend and spatial clustering of medical education indicators are scarce, especially in developing countries such as Brazil. This analysis is essential to subsidize more equitable policies for the medical workforce in the states and regions of Brazil. Thus, this study aimed to analyze the temporal trend and identify spatial clusters of medical education indicators in Brazil disaggregated by public and private education, states, and regions. METHODS: A time-series ecological study was conducted using data from the Higher Education Census of the Ministry of Education from 2010 to 2021. The study analyzed vacancy density indicators of active and former students/100,000 population, disaggregated by public and private education, 27 states, and 5 regions in Brazil. Prais-Winsten regression was used for trend analyses of indicators. Hot Spot Analysis (Getis-Ord Gi*) was used to identify spatial clusters of indicators. RESULTS: The number of medical schools increased by 102.2% between 2010 and 2021. A total of 366 medical schools offered 54,870 vacancies at the end of 2021. Vacancy density and active and former students increased significantly in the period, but this increase was greater in private institutions. Most states and regions showed an increasing trend in the indicators, with higher increase percentages in private than in public schools. Hot spot spaces changed over time, concentrated in the southeast, center-west, and north at the end of 2021. Medical education remains uneven in Brazil, with a low provision in regions with low socioeconomic development, academic structure, and health services, represented by regions in the north and northeast. CONCLUSIONS: There is a growing trend in medical education indicators in Brazil, especially in the private sector. Spatial clusters were found predominantly in the southeast, center-west, and north. These results indicate the need for more equitable medical education planning between the regions.

Spatial clustering of low rates of COVID-19 vaccination among children and adolescents and their relationship with social determinants of health in Brazil: a nationwide population-based ecological study.

OBJECTIVE: This study aimed to investigate the spatial clusters of high and low COVID-19 vaccination rates among children and adolescents across Brazilian municipalities and their relationship to social determinants of health. STUDY DESIGN: This is a nationwide population-based ecological study. METHODS: We have obtained for each of the 5570 Brazilian municipalities data on the COVID-19 vaccination rate of children and adolescents by August 16, 2022, the Gini index, the social vulnerability index and the municipal human development index. A Bayesian empirical local model was used to identify fluctuations in the COVID-19 vaccination rates. Spatial clusters were identified using scan spatial statistic tests. The relationship among COVID-19 vaccination rates and social determinants of health was explored by using multiple linear regression models. RESULTS: Overall, 52.1% of children aged 5-11 years and 72.8% of adolescents aged 12-17 years have been fully vaccinated against COVID-19 in Brazil by mid-August 2022. There was spatial dependence on the smoothed rates for both children (I Moran 0.66; P < 0.001) and adolescent (I Moran 0.65; P < 0.001) groups. The lowest rates occurred in municipalities in the North and Northeast regions. Municipalities with a higher Gini Index, higher social vulnerability index and lower municipal human development index were more likely to have a lower COVID-19 vaccination rate for both children and adolescent groups. CONCLUSION: COVID-19 vaccination of children and adolescents was heterogeneously distributed, with spatial clusters of the lowest vaccination rates occurring mainly in municipalities with marked socio-economic disparities and social vulnerability, especially in the North and Northeast regions.

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.

Time and spatial trends of operated cryptorchidism in France and environmental hypotheses: a nationwide study from 2002 to 2014.

STUDY QUESTION: Is there an evolution in the risk of operated cryptorchidism in France and does local geographical environment appear as an important trigger for this defect? SUMMARY ANSWER: We observed an increase of the risk of operated cryptorchidism in boys under the age of 7 years during the period 2002-2014 and a strong spatial heterogeneity, with the detection of spatial clusters suggesting environmental factors. WHAT IS KNOWN ALREADY: Epidemiologic data on cryptorchidism are scarce and its etiology is poorly understood. As part of the testicular dysgenesis syndrome, cryptorchidism is suspected to be a male genital developmental disorder caused by endocrine disruptor chemical (EDC) exposure during the prenatal period. STUDY DESIGN, SIZE, DURATION: This was a retrospective and descriptive study using data from the French national hospital discharge database, in the 2002-2014 study period. We built an indicator to reflect incident cases of operated cryptorchidism in boys under the age of 7 years in metropolitan France, with an algorithm using specific codes for diseases (ICD-10 codes) and surgical acts (CCAM codes). PARTICIPANTS/MATERIALS, SETTING, METHODS: The study population was composed of 89 382 new cases of operated cases of cryptorchidism in boys under the age of 7 years. We estimated the temporal evolution of the incidence rate. We fitted a spatial disease-mapping model to describe the risk of cryptorchidism at the postcode scale. We used Kulldorff's spatial scan statistic and Tango's flexibly shaped spatial scan statistic to identify spatial clusters. MAIN RESULTS AND THE ROLE OF CHANCE: The estimated increase in the incidence of operated cryptorchidism from 2002 to 2014 was equal to 36.4% (30.8%; 42.1%). Cryptorchidism displayed spatial heterogeneity and 24 clusters (P < 0.0001) were detected. The main cluster was localized in a former coal mining and metallurgic area in northern France, currently an industrial area. The cluster analysis suggests the role of shared socio-economic and environmental factors that may be geographically determined and intertwined. The industrial activities identified in the clusters are potentially the source of persistent environmental pollution by metals, dioxins and polychlorinated biphenyls. LIMITATIONS, REASONS FOR CAUTION: The indicator we used reflects operated cases of cryptorchidism, with an under-evaluation of the health problem. We cannot exclude a possible role of the evolution and local differences in surgical practices in the observed trends. Our inclusion of boys under 7 years of age minimized the biases related to differences in practices according to age. Regarding the environmental hypothesis, this is an exploratory study and should be considered as a hypothesis-generating process for future research studies. WIDER IMPLICATIONS OF THE FINDINGS: To our knowledge, this is the first descriptive study to address nationwide trends of operated cryptorchidism with detection of spatial clusters, with a very large sample allowing great statistical power. Our results generate plausible environmental hypotheses, which need to be further tested. STUDY FUNDING/COMPETING INTEREST(S): This study was entirely funded by Santé publique France, the French National Public Health Agency. All authors declare they have no actual or potential competing financial interest. TRIAL REGISTRATION NUMBER: N/A.

Spatial-temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast.

BACKGROUND: The over-distributed pattern of malaria transmission has led to attempts to define malaria "hotspots" that could be targeted for purposes of malaria control in Africa. However, few studies have investigated the use of routine health facility data in the more stable, endemic areas of Africa as a low-cost strategy to identify hotspots. Here the objective was to explore the spatial and temporal dynamics of fever positive rapid diagnostic test (RDT) malaria cases routinely collected along the Kenyan Coast. METHODS: Data on fever positive RDT cases between March 2018 and February 2019 were obtained from patients presenting to six out-patients health-facilities in a rural area of Kilifi County on the Kenyan Coast. To quantify spatial clustering, homestead level geocoded addresses were used as well as aggregated homesteads level data at enumeration zone. Data were sub-divided into quarterly intervals. Kulldorff's spatial scan statistics using Bernoulli probability model was used to detect hotspots of fever positive RDTs across all ages, where cases were febrile individuals with a positive test and controls were individuals with a negative test. RESULTS: Across 12 months of surveillance, there were nine significant clusters that were identified using the spatial scan statistics among RDT positive fevers. These clusters included 52% of all fever positive RDT cases detected in 29% of the geocoded homesteads in the study area. When the resolution of the data was aggregated at enumeration zone (village) level the hotspots identified were located in the same areas. Only two of the nine hotspots were temporally stable accounting for 2.7% of the homesteads and included 10.8% of all fever positive RDT cases detected. CONCLUSION: Taking together the temporal instability of spatial hotspots and the relatively modest fraction of the malaria cases that they account for; it would seem inadvisable to re-design the sub-county control strategies around targeting hotspots.

Women's cancers in China: a spatio-temporal epidemiology analysis.

BACKGROUND: Women's cancers, represented by breast and gynecologic cancers, are emerging as a significant threat to women's health, while previous studies paid little attention to the spatial distribution of women's cancers. This study aims to conduct a spatio-temporal epidemiology analysis on breast, cervical and ovarian cancers in China, thus visualizing and comparing their epidemiologic trends and spatio-temporal changing patterns. METHODS: Data on the incidence and mortality of women's cancers between January 2010 and December 2015 were obtained from the National Cancer Registry Annual Report. Linear tests and bar charts were used to visualize and compare the epidemiologic trends. Two complementary spatial statistics (Moran's I statistics and Kulldorff's space-time scan statistics) were adopted to identify the spatial-temporal clusters. RESULTS: The results showed that the incidence and mortality of breast cancer displayed slow upward trends, while that of cervical cancer increase dramatically, and the mortality of ovarian cancer also showed a fast increasing trend. Significant differences were detected in incidence and mortality of breast, cervical and ovarian cancer across east, central and west China. The average incidence of breast cancer displayed a high-high cluster feature in part of north and east China, and the opposite traits occurred in southwest China. In the meantime, the average incidence and mortality of cervical cancer in central China revealed a high-high cluster feature, and that of ovarian cancer in northern China displayed a high-high cluster feature. Besides, the anomalous clusters were also detected based on the space-time scan statistics. CONCLUSION: Regional differences were detected in the distribution of women's cancers in China. An effective response requires a package of coordinated actions that vary across localities regarding the spatio-temporal epidemics and local conditions.

Spatial Distribution of COVID-19 in Nigeria.

BACKGROUND: The first reported case of the novel coronavirus (COVID 19) in Nigeria was on the 27th of February 2020. Since then, the country has witnessed a steady increase in the number of patients confirmed with the disease. As of April 27th 2021, a total of 164,756 confirmed COVID-19 cases were notified making it the fifth-highest number of cases in the African region. This study aims to determine the spatial distribution of COVID-19 in Nigeria, identify clusters and determine factors associated with COVID-19. METHODS: The study used secondary data of COVID-19 cases notified in each of the 36 states and the Federal Capital Territory between 27th February and 9th June, 2020. The Global and Local Moran'sItest were used to identify significant spatial clusters. The negative binomial regression model was used to identify factors associated with COVID-19 and p d" 0.05 was regarded as statistically significant. RESULTS: The Local Moran I identified Lagos State as the significant cluster for COVID-19 in Nigeria at p<0.05. Higher GDP per capita and lower literacy rates were significantly associated with COVID-19 cases reported by the states while population density, BCG coverage and average temperature were not significantly associated. CONCLUSION: The study identified Lagos State as the hotspot for the COVID-19 pandemic in Nigeria. The states with lower literacy rate and higher GDP per capita reported a higher number of COVID-19 cases. Proactive measures are needed to control of the infection in Lagos state while improving the literacy about the disease transmission and control measures.

CONTEXTE: Le premier cas signalé du nouveau coronavirus (COVID 19) au Nigeria a eu lieu le 27 février 2020. Depuis lors, le pays a connu une augmentation constante du nombre de patients confirmés atteints de la maladie. Au 27 avril 2021, un total de 164 756 cas confirmés de COVID-19 ont été notifiés, ce qui en fait le cinquième plus grand nombre de cas dans la région africaine. Cette étude vise à déterminer la distribution spatiale du COVID-19 au Nigeria, à identifier les clusters et à déterminer les facteurs associés au COVID-19. MÉTHODES: L'étude a utilisé des données secondaires de cas de COVID-19 notifiés dans chacun des 36 États et le Territoire de la capitale fédérale entre le 27 février et le 9 juin 2020. Les tests Global et Local de Moran I ont été utilisés pour identifier des clusters spatiaux importants. Le modèle de régression binomiale négative a été utilisé pour identifier les facteurs associés au COVID-19 et p 0,05 a été considéré comme statistiquement significatif. RÉSULTATS: Le Moran local I a identifié l'État de Lagos comme le cluster significatif pour COVID-19 au Nigeria à p<0,05. Un PIB par habitant plus élevé et des taux d'alphabétisation plus faibles étaient significativement associés aux cas de COVID-19 signalés par les États, tandis que la densité de population, la couverture en BCG et la température moyenne n'étaient pas significativement associées. CONCLUSION: L'étude a identifié l'État de Lagos comme le point chaud de la pandémie de COVID-19 au Nigéria. Les États ayant un taux d'alphabétisation plus faible et un PIB par habitant plus élevé ont signalé un nombre plus élevé de cas de COVID-19. Des mesures proactives sont nécessaires pour contrôler l'infection dans l'État de Lagos tout en améliorant les connaissances sur la transmission de la maladie et les mesures de contrôle. Mots-clés: COVID-19, Nigeria, SARS-CoV-2, clusters spatiaux, épidémiologie spatiale, statistiques spatiales.

Understanding the spatio-temporal dynamics of meningitis epidemics outside the belt: the case of the Democratic Republic of Congo (DRC).

BACKGROUND: Bacterial meningitis remains a major threat for the population of the meningitis belt. Between 2004 and 2009, in the countries of this belt, more than 200,000 people were infected with a 10% mortality rate. However, for almost 20 years, important meningitis epidemics are also reported outside this belt. Research is still very poorly developed in this part of the word like in the Democratic Republic of Congo (DRC), which experiences recurrent epidemics. This article describes for the first time the spatio-temporal patterns of meningitis cases and epidemics in DRC, in order to provide new insights for surveillance and control measures. METHODS: Based on weekly suspected cases of meningitis (2000-2012), we used time-series analyses to explore the spatio-temporal dynamics of the disease. We also used both geographic information systems and geostatistics to identify spatial clusters of cases. Both using conventional statistics and the Cleveland's algorithm for decomposition into general trend, seasonal and residuals, we searched for the existence of seasonality. RESULTS: We observed a low rate of biological confirmation of cases (11%) using soluble antigens search, culture and PCR. The main strains found are Streptococcus pneumoniae, Haemophilus influenzae and Neisseria meningitidis (A and C) serogroups. We identified 8 distinct spatial clusters, located in the northeastern and southeastern part of DRC, and in the capital city province, Kinshasa. A low seasonal trend was observed with higher incidence and attack rate of meningitis during the dry season, with a high heterogeneity in seasonal patterns occurring across the different districts and regions of DRC. CONCLUSION: Despite challenges related to completeness of data reporting, meningitis dynamics shows weak seasonality in DRC. This tends to suggest that climatic, environmental factors might be less preponderant in shaping seasonal patterns in central Africa. The characterization of 8 distinct clusters of meningitis could be used for a better sentinel meningitis surveillance and optimization of vaccine strategy in DRC. Improving biological monitoring of suspected cases should be a priority for future eco-epidemiological studies to better understand the emergence and spread of meningitis pathogens, and the potential ecological, environmental drivers of this disease.

Spatio-temporal variation of malaria hotspots in Central Senegal, 2008-2012.

BACKGROUND: In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation. METHODS: This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. RESULTS: The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR = 0.48, IC95%: 0.33-0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown. CONCLUSION: In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed. TRIAL REGISTRATION: The data used in this work were obtained from a clinical trial registered on July 10, 2008 at www.clinicaltrials.gov under NCT00712374.

The Proximity of Spatial Clusters of Low Birth Weight and Risk Factors: Defining a Neighborhood for Focused Interventions.

BACKGROUND: Low birth weight (LBW) is associated with significant mortality and morbidity and remains a significant preventable problem. Risk factors include socioeconomic, demographics, and characteristics of the environment. Spatial analysis can uncover unusual frequencies of health problems in neighborhoods, eventually leading to insights for targeted interventions. OBJECTIVES: This study's goals were to 1. Evaluate the geographic distribution of spatial clusters of LBW births and maternal risk factors. 2. Determine the spatial relationship between risk factors and LBW. METHODS: This study obtained data on LBW newborns and risk factors from 19,013 births over 5 years (2012-2016) for Escambia County Census Tracts, extracted from FloridaCharts.com. Software was used to detect significant spatial clusters; these clusters were then plotted on a map. Poisson regression determined the statistical relationship between Census Tract risk factors and LBW. A separate analysis of the LBW cluster controlling for risk factors was also performed. RESULTS: All risk factor clusters resided in similar locations as the LBW cluster. The multiple Poisson regression model containing all risk factors fully explained the LBW cluster. On bivariate Poisson regression all risk factors in the Census Tract were significantly related to LBW whereas in multivariable Poisson regression, the proportion of births to African American women in the Census Tract remained significant after adjusting for other risk factors (p < 0.001). CONCLUSIONS FOR PRACTICE: Clusters of LBW and risk factors were located in the same region of the county, with the proportion of births to African American women in the Census Tract remaining significant on multiple Poisson Regression. Targeted interventions should be directed at the geographic level.

Spatial Clusters of Breast Cancer Mortality and Incidence in the Contiguous USA: 2000-2014.

BACKGROUND: Clusters of breast cancer with varied incidence or mortality are known to exist. No national scale of analysis of geographical variation in breast cancer incidence has been published before for the contiguous USA. METHODS: This was a spatial cluster analysis of incidence and mortality data on breast cancer in the contiguous USA at the county resolution. Data for the years 2000-2014 were downloaded and analyzed with the software SaTScan with the goal to identify significant spatial clusters of breast cancer. Regression analysis was used to then adjust breast cancer incidence and mortality for several key risk factors such as age, smoking, particulate matter air pollution, physical inactivity, urban living, education level, and race. RESULTS: Spatial clusters of counties for higher than expected breast cancer incidence and also for breast cancer mortality were identified. All identified clusters have p < 0.05. The mortality clusters show the mean breast cancer rates inside the cluster, while the incidence clusters show the relative risk inside each cluster. This is the first study of the contiguous USA for breast cancer mortality and incidence together. The clustering for mortality is quite different from the clustering for incidence. Using the software JOINPOINT, it is shown that the annual US downward trend for breast cancer mortality slowed down in recent years. CONCLUSIONS: There exist several significant clusters in the contiguous USA, both for breast cancer incidence and for breast cancer mortality. Some of the clusters persisted even after adjusting for several key risk factors. These geographic areas warrant further investigation to potentially identify additional local concerns or needs to further address female breast cancer in those specific sites.

Spatial Clusters and Non-spatial Predictors of Tick-Borne Disease Diagnosis in Indiana.

The purpose of this study was two-fold. First, we sought to identify spatial clusters of self-reported tick-borne disease (TBD) diagnosis in Indiana. Secondly, we determined the significant predictors of self-reported TBD diagnosis in a sample of Indiana residents. Study participants were selected from existing online panels maintained by Qualtrics and completed a cross-sectional survey (n = 3003). Our primary outcome of interest was self-reported TBD diagnosis (Yes/No). Cases and background population were aggregated to the county level. We used a purely spatial discrete Poisson model in SatScan® to determine significant clusters of high-risk TBD diagnosis counties. We also used X2 tests in bivariate analyses, to identify potential predictor variables for inclusion in an initial model, and backward elimination selection method to identify the final model. Two clusters of counties with significant high relative risk of self-reported TBD diagnosis in the southeast and southwest of Indiana were detected. Males in Indiana were more likely to self-report TBD diagnosis compared to females. Study participants who conducted a thorough tick check after being outdoors were significantly less likely to report TBD diagnosis compared to those who did not. Increased positive perceptions of TBD personal protective measures were associated with reduced self-reported TBD diagnosis. Older study participants were less likely to self-report TBD diagnosis compared to younger participants. The identification of two clusters of TBD diagnosis in southern Indiana is consistent with a northern spread of TBDs and suggests a need for continued surveillance of the counties in the vicinity of the observed clusters. Future studies should be designed to identify risk factors for TBD diagnosis in the affected counties of Indiana.

Spatio-temporal dynamic of malaria in Ouagadougou, Burkina Faso, 2011-2015.

BACKGROUND: Given the scarcity of resources in developing countries, malaria treatment requires new strategies that target specific populations, time periods and geographical areas. While the spatial pattern of malaria transmission is known to vary depending on local conditions, its temporal evolution has yet to be evaluated. The aim of this study was to determine the spatio-temporal dynamic of malaria in the central region of Burkina Faso, taking into account meteorological factors. METHODS: Drawing on national databases, 101 health areas were studied from 2011 to 2015, together with weekly meteorological data (temperature, number of rain events, rainfall, humidity, wind speed). Meteorological factors were investigated using a principal component analysis (PCA) to reduce dimensions and avoid collinearities. The Box-Jenkins ARIMA model was used to test the stationarity of the time series. The impact of meteorological factors on malaria incidence was measured with a general additive model. A change-point analysis was performed to detect malaria transmission periods. For each transmission period, malaria incidence was mapped and hotspots were identified using spatial cluster detection. RESULTS: Malaria incidence never went below 13.7 cases/10,000 person-weeks. The first and second PCA components (constituted by rain/humidity and temperatures, respectively) were correlated with malaria incidence with a lag of 2 weeks. The impact of temperature was significantly non-linear: malaria incidence increased with temperature but declined sharply with high temperature. A significant positive linear trend was found for the entire time period. Three transmission periods were detected: low (16.8-29.9 cases/10,000 person-weeks), high (51.7-84.8 cases/10,000 person-weeks), and intermediate (26.7-32.2 cases/10,000 person-weeks). The location of clusters identified as high risk varied little across transmission periods. CONCLUSION: This study highlighted the spatial variability and relative temporal stability of malaria incidence around the capital Ouagadougou, in the central region of Burkina Faso. Despite increasing efforts in fighting the disease, malaria incidence remained high and increased over the period of study. Hotspots, particularly those detected for low transmission periods, should be investigated further to uncover the local environmental and behavioural factors of transmission, and hence to allow for the development of better targeted control strategies.

Epidemiological features and spatial clusters of hand, foot, and mouth disease in Qinghai Province, China, 2009-2015.

BACKGROUND: Hand, Foot, and Mouth Disease (HFMD) is most frequently caused by Enterovirus71 (EV-A71) or Coxsackie virus A16 (CV-A16), infants and young children are at greatest risk. Describing the epidemiology of HFMD can help develop and better target interventions, including the use of pediatric EV-A71 vaccination. METHODS: We obtained data from the national surveillance system for HFMD cases with onset dates from 2009 to 2015. We defined probable cases as patient with skin papular or vesicular rashes on the hands, feet, mouth, or buttocks and confirmed cases as patients with the above symptoms along with laboratory-based enterovirus detection. We generated overall and age-specific annual incidence rates and described the temporal variability and seasonality of HFMD in Qinghai Province. We identified spatial clustering of HFMD incidence at the county level using the Local Indicator of Spatial Associationand an alpha level of 0.05. RESULTS: During the study period, 14,480 HFMD probable or confirmed cases were reported in Qinghai Province. Of the 2158 (14.9%) with laboratory confirmation, 924 (42.6%) were caused by CV-A16 and 830 (38.2%) were caused by EV-A71. The majority (89%) of all case-patients were ≤ 5 years of age and male (61.5%). The overall mean annual HFMD incidence rate was 36.4 cases per 100,000 populations, while the incidence rate for children ≤5 years of age was 379.5 cases per 100,000. Case reports peaked during the months of May through July. HFMD was predominantly caused by EV-A71, except in 2010 and 2014 when CV-A16 was the predominant causative agent. High incidence rates of HFMD were clustered (Moran's I = 0.59, P < 0.05) in the eastern region of the province. CONCLUSION: HFMD remains an important cause of childhood disease in Qinghai Province, occurring in an acyclical pattern of increased incidence, primarily due to CV-A16 circulation every three years. Incidence is also seasonal and tends to spatially cluster in the eastern region of the province. Since approximately 40% of confirmed HFMD cases were due to EV-A71, EV-A71 vaccination is likely to have a positive impact on the HFMD disease burden. Routine analysis of local surveillance data is crucial for describing disease occurrence and changes in etiology.