Medical geography in public health and tropical medicine: case studies from Brazil

Within the last few decades, the multitude of infrastructural and environmental changes associated with population growth, human migration, and economic development have catalyzed the emergence and re-emergence of many infectious diseases worldwide. The morbidity and mortality associated with these diseases have in turn led to an increased and renewed impetus to gain a better understanding of the etiology, epidemiology, prevention, and control of these diseases in order to achieve better health and well-being, especially for underprivileged populations. Two traditionally separate fields, medical geography and tropical medicine, have recently seen complex and radical paradigm shifts in response to this global situation: medical geography has been developing many new and sophisticated methods of data collection, data manipulation, and spatial analysis that make it more suited for the study of health-related problems; and tropical medicine has been revisiting the fundamental notion that disease is intimately linked to the physical and cultural geographic environments in which humans live. As a result, concepts of medical geography are being more readily employed within tropical disease research, and tropical medicine is embracing geographic methods as a central mainstay in the control, management, and prevention of tropical diseases. As the associations between these two fields continue to grow, a clearer understanding of how they compliment each other will be needed in order to better define their interrelated roles in augmenting human health. This dissertation examines the multifarious relationships that have developed between the fields of medical geography and tropical medicine in recent years by presenting the reader with a brief history of their common origins and a comprehensive review of the techniques and methodologies in medical geography that are frequently employed in tropical disease research. Following this background information, several case studies are investigated that provide examples of how geographic methods can be easily and effectively employed in the analysis of several tropical diseases, including tungiasis, intestinal helminthes, leprosy, and tuberculosis. These case studies demonstrate some of the advantages and disadvantages of current geographic methods employed in health research, and offer a framework for readers who are interested in applying basic geographic concepts to analyze questions of health

Spatial Epidemiology of Birth Defects in the United States and the State of Utah Using Geographic Information Systems and Spatial Statistics

Oral clefts are the most common form of birth defects in the United States (US) and the State of Utah has among the highest prevalence of oral clefts in the nation. The overall objective of this dissertation was to examine the spatial distribution of oral clefts and their linkage with a broad range of demographic, behavioral, social, economic, and environmental risk factors through the application of Geographic Information Systems (GIS) and spatial statistics. Using innovative linked micromaps plots, we investigated the geographic patterns of oral clefts occurrence from 1998 to 2002 and their relationships with maternal smoking rates and proportion of American Indians and Alaskan Natives (AIAN) at large scales across the US. The findings indicated higher oral clefts occurrence in the southwest and the midwest and lower occurrence in the east. Furthermore, these spatial patterns were significantly related to the smoking rates and AIAN. Then at the small area level, hierarchical Bayesian models were built to examine the spatial variation in oral clefts risk in the State of Utah from 1995 to 2004 and to assess association with mothers using tobacco, mothers consuming alcohol during pregnancy, and the proportion of mothers with no high school diploma. Next, multi-scalar spatial clustering and cluster techniques were used to test the hypothesis whether there was spatial clustering of oral clefts anywhere in the State of Utah and whether there were statistically significant local clusters with elevated oral cleft cases. Results generally revealed modest spatial variation in oral clefts risk in the State of Utah, with no pronounced spatial clustering, indicating environmental exposures are unlikely plausible cause of oral clefts. However, a few notable areas within Tri-County Local Health District, Provo/Brigham Young University, and North Orem had a tendency toward elevated oral clefts cases. Investigation of the maternal characteristics of these potential clusters supports the hypotheses that maternal smoking, lower education level, and family history are possible causes of oral clefts. Throughout this dissertation, we demonstrated how birth defects data collected by state and local surveillance systems coupled with GIS and spatial statistics methods can be useful in exploratory etiologic research of birth defects

Global, local and focused geographic clustering for case-control data with residential histories

BACKGROUND: This paper introduces a new approach for evaluating clustering in case-control data that accounts for residential histories. Although many statistics have been proposed for assessing local, focused and global clustering in health outcomes, few, if any, exist for evaluating clusters when individuals are mobile. METHODS: Local, global and focused tests for residential histories are developed based on sets of matrices of nearest neighbor relationships that reflect the changing topology of cases and controls. Exposure traces are defined that account for the latency between exposure and disease manifestation, and that use exposure windows whose duration may vary. Several of the methods so derived are applied to evaluate clustering of residential histories in a case-control study of bladder cancer in south eastern Michigan. These data are still being collected and the analysis is conducted for demonstration purposes only. RESULTS: Statistically significant clustering of residential histories of cases was found but is likely due to delayed reporting of cases by one of the hospitals participating in the study. CONCLUSION: Data with residential histories are preferable when causative exposures and disease latencies occur on a long enough time span that human mobility matters. To analyze such data, methods are needed that take residential histories into account

Investigating tuberculosis transmission using spatial methods

BACKGROUND: Tuberculosis remains a leading infectious cause of death worldwide. Reducing transmission requires an increased focus on local control measures informed by spatial data. Effective use of spatial methods will improve understanding of tuberculosis transmission and support outbreak investigations. METHODS: I conducted a systematic literature review to describe spatial methods that have been used in previous outbreak investigations (Chapter 2). I developed and evaluated a novel interactive mapping tool, written using the R programming language (Chapter 3). Using multinomial logistic regression and spatial scan statistics, I investigated molecular and spatial clustering of tuberculosis in London (Chapter 4). I described the evolution of a large outbreak of drug-resistant tuberculosis in London in space and time (Chapter 5). Through three case studies, I assessed the utility of a novel spatial tool, geographic profiling, which aims to identify the locations of sources of infectious disease using locations of linked cases (Chapter 6). I analysed the spatial accessibility of tuberculosis services in London using travel time data (Chapter 7). KEY FINDINGS: • Spatial methods provide an important complementary tool to epidemiological analyses, but are currently under-used (less than half a percent of published outbreak investigations used spatial methods). • Large numbers of tuberculosis cases in London have resulted from local transmission, with more than one in ten cases part of large clusters. • Social complexity and area-level deprivation are associated with transmission of tuberculosis in large clusters. • Geographic profiling may assist with epidemiological investigations of infectious diseases in some circumstances by prioritising areas for investigation. • Pan-London commissioning could improve tuberculosis services by enhancing spatial accessibility. CONCLUSIONS: Spatial methods provide many valuable contributions to investigations of tuberculosis. Development of new tools and wider use of existing methods could limit the public health impacts of infectious disease outbreaks

A Spatial Inquiry of Infant Low Birthweight and Cancer Mortality in East Baton Rouge Parish, Louisiana.

Infant low birthweight rate in East Baton Rouge Parish is higher compared to the nation. Cancer is one of the most serious health problems in Louisiana. A few studies have indicated a relationship between the two health outcomes and their environment, but conclusions from these studies have not always been consistent. The spatial and temporal scales of the data and the methods used in these studies contribute to the inconsistent and uncertain results. The spatial patterns of infant low birthweight and cancer mortality in East Baton Rouge Parish from 1993 to 1996 and their relationships with environmental factors at three geographic scales (census blocks, block group, and tract) were investigated. Spatial clusterings of each health outcome were tested using spatial autocorrelation and correlograms, scan statistic, and geographic analysis machine. In searching for the presence of geographical clues for cancer mortality, this study revealed that male lung cancer exhibited significant positive spatial autocorrelation and there was geographically distinct cluster of black male lung cancer deaths in the western part of the parish. Stepwise regression analyses revealed that the spatial clusters were characterized by low per capita income and high percentage of persons below poverty. Spatial correlogram results suggested that infant low birthweight have significant positive spatial autocorrelation at the census tract scale. Both cluster detection techniques resulted in the same spatial clusters, which were centered in the Midwestern part of the parish. The clusters were characterized by low per capita income, high percentage of population below poverty, low median value house, and low median rent. The logistic regression analysis results show that infant low birthweight was associated with race, sex of the newborn, age, marital status, gestation, prenatal visits, multiple birth, and tobacco use. When comparing across spatial scales, there were no considerable variations of the geographic area of the most likely clusters of infant low birthweight. For black male lung cancer, there was a minor variation of the geographic area of the most likely dusters among the two scales, though the clusters were generally located at the south of the petrochemical manufacturing corridor along the Mississippi River

Developing A Geospatial Protocol For Coral Epizootiology

This dissertation explores how geographic information systems (GIS) and spatial statistics, specifically the techniques used to map, detect, and spatially analyze disease epidemics, could be used to advance our understanding of coral reef health. Given that different types of spatial analysis, as well as different parameter settings within each analysis, can produce noticeably different results, poor selection or improper use of a given technique would likely lead to inaccurate representations of the spatial distribution and false interpretations of the disease. For this reason, I performed a comprehensive review of the following types of exploratory spatial data analysis (ESDA): mapping and visualization methods; centrographic and distance-based point pattern analyses; spatial kernel density estimates (KDE) using single and dual versions of adaptive and fixed-distance KDEs in which the fixed-distance KDEs were performed using bandwidths calculated using 12 different estimation methods; SaTScan’s spatial scan statistic using both the Bernoulli and Poisson probability models; and last, local and global versions of the Moran’s I and Getis-ord G spatial autocorrelation statistics. Each technique was applied to an artificial dataset with known cluster locations in order to determine which methods provided the most accurate results. These results were then used to develop different geospatial analytical protocols based on the types of coral data available, noting that the most meaningful results would be produced using local spatial statistics to analyze data of diseased colonies and colonies from the underlying coral population at risk. Last, I applied the techniques from one of the protocols to data from a 2004 White-Band Disease (WBD) outbreak on a population of Acropora palmata corals in the US Virgin Islands. The results of this work represent the first application of geospatial analytical techniques in visualizing the spatial nature of a coral disease and provides important information about the epizootiology of this particular outbreak. Specifically, the results indicated that WBD prevalence was low with numerous significant disease clusters occurring throughout the study area, suggesting WBD may be caused by a ubiquitous stressor. The material presented in this dissertation will provide researchers with the necessary tools and information needed to perform the most accurate geospatial analysis possible based on the coral data available

Development and Applications of Similarity Measures for Spatial-Temporal Event and Setting Sequences

Similarity or distance measures between data objects are applied frequently in many fields or domains such as geography, environmental science, biology, economics, computer science, linguistics, logic, business analytics, and statistics, among others. One area where similarity measures are particularly important is in the analysis of spatiotemporal event sequences and associated environs or settings. This dissertation focuses on developing a framework of modeling, representation, and new similarity measure construction for sequences of spatiotemporal events and corresponding settings, which can be applied to different event data types and used in different areas of data science. The first core part of this dissertation presents a matrix-based spatiotemporal event sequence representation that unifies punctual and interval-based representation of events. This framework supports different event data types and provides support for data mining and sequence classification and clustering. The similarity measure is based on the modified Jaccard index with temporal order constraints and accommodates different event data types. This approach is demonstrated through simulated data examples and the performance of the similarity measures is evaluated with a k-nearest neighbor algorithm (k-NN) classification test on synthetic datasets. These similarity measures are incorporated into a clustering method and successfully demonstrate the usefulness in a case study analysis of event sequences extracted from space time series of a water quality monitoring system. This dissertation further proposes a new similarity measure for event setting sequences, which involve the space and time in which events occur. While similarity measures for spatiotemporal event sequences have been studied, the settings and setting sequences have not yet been considered. While modeling event setting sequences, spatial and temporal scales are considered to define the bounds of the setting and incorporate dynamic variables along with static variables. Using a matrix-based representation and an extended Jaccard index, new similarity measures are developed to allow for the use of all variable data types. With these similarity measures coupled with other multivariate statistical analysis approaches, results from a case study involving setting sequences and pollution event sequences associated with the same monitoring stations, support the hypothesis that more similar spatial-temporal settings or setting sequences may generate more similar events or event sequences. To test the scalability of STES similarity measure in a larger dataset and an extended application in different fields, this dissertation compares and contrasts the prospective space-time scan statistic with the STES similarity approach for identifying COVID-19 hotspots. The COVID-19 pandemic has highlighted the importance of detecting hotspots or clusters of COVID-19 to provide decision makers at various levels with better information for managing distribution of human and technical resources as the outbreak in the USA continues to grow. The prospective space-time scan statistic has been used to help identify emerging disease clusters yet results from this approach can encounter strategic limitations imposed by the spatial constraints of the scanning window. The STES-based approach adapted for this pandemic context computes the similarity of evolving normalized COVID-19 daily cases by county and clusters these to identify counties with similarly evolving COVID-19 case histories. This dissertation analyzes the spread of COVID-19 within the continental US through four periods beginning from late January 2020 using the COVID-19 datasets maintained by John Hopkins University, Center for Systems Science and Engineering (CSSE). Results of the two approaches can complement with each other and taken together can aid in tracking the progression of the pandemic. Overall, the dissertation highlights the importance of developing similarity measures for analyzing spatiotemporal event sequences and associated settings, which can be applied to different event data types and used for data mining, sequence classification, and clustering

Who dies where, when and why? Modelling determinants and space-time risk of infant, child and adult mortality in rural South Africa, 1992-2008

Ph.D., University of the Witwatersrand, Faculty of Health Sciences, 201

Spatial clusters of autism births and diagnoses point to contextual drivers of increased prevalence

Autism prevalence has risen dramatically over the past two decades in California. Although often suggested to have been crucial to the rise of autism, environmental and social contextual drivers of diagnosis have not been extensively examined. Identifying the spatial patterning of autism cases at birth and at diagnosis can help clarify which contextual drivers are affecting autism's rising prevalence. Children with autism not co-morbid with mental retardation served by the California Department of Developmental Services during the period 1992–2005 were matched to California's Birth Master Files. We search for spatial clusters of autism at time of birth and at time of diagnosis using a spatial scan approach that controls for key individual-level risk factors. We then test whether indicators of neighborhood-level diagnostic resources are associated with the diagnostic clusters and assess the extent of clustering by autism symptom severity through a multivariate scan. Finally, we test whether children who move into neighborhoods with higher levels of resources are more likely to receive an autism diagnosis relative to those who do not move with regard to resources. Significant birth and diagnostic clusters of autism are observed independent of key individual-level risk factors. While the clusters overlap, there is a strong positive association between the diagnostic clusters and neighborhood-level diagnostic resources. In addition, children with autism who are higher functioning are more likely to be diagnosed within a cluster than children with autism who are lower functioning. Most importantly, children who move into a neighborhood with more diagnostic resources than their previous residence are more likely to subsequently receive an autism diagnosis than children whose neighborhood resources do not change. We identify birth and diagnostic clusters of autism in California that are independent of individual-level autism risk factors. Our findings implicate a causal relationship between neighborhood-level diagnostic resources and spatial patterns of autism incidence but do not rule out the possibility that environmental toxicants have also contributed to autism risk

Geoinformatic methodologies and quantitative tools for detecting hotspots and for multicriteria ranking and prioritization: application on biodiversity monitoring and conservation

Chi ha la responsabilità di gestire un’area protetta non solo deve essere consapevole dei problemi ambientali dell’area ma dovrebbe anche avere a disposizione dati aggiornati e appropriati strumenti metodologici per esaminare accuratamente ogni singolo problema. In effetti, il decisore ambientale deve organizzare in anticipo le fasi necessarie a fronteggiare le prevedibili variazioni che subirà la pressione antropica sulle aree protette. L’obiettivo principale della Tesi è di natura metodologica e riguarda il confronto tra differenti metodi statistici multivariati utili per l’individuazione di punti critici nello spazio e per l’ordinamento degli “oggetti ambientali” di studio e quindi per l’individuazione delle priorità di intervento ambientale. L’obiettivo ambientale generale è la conservazione del patrimonio di biodiversità. L’individuazione, tramite strumenti statistici multivariati, degli habitat aventi priorità ecologica è solamente il primo fondamentale passo per raggiungere tale obiettivo. L’informazione ecologica, integrata nel contesto antropico, è un successivo essenziale passo per effettuare valutazioni ambientali e per pianificare correttamente le azioni volte alla conservazione. Un’ampia serie di dati ed informazioni è stata necessaria per raggiungere questi obiettivi di gestione ambientale. I dati ecologici sono forniti dal Ministero dell’Ambiente Italiano e provengono al Progetto “Carta della Natura” del Paese. I dati demografici sono invece forniti dall’Istituto Italiano di Statistica (ISTAT). I dati si riferiscono a due aree geografiche italiane: la Val Baganza (Parma) e l’Oltrepò Pavese e Appennino Ligure-Emiliano. L’analisi è stata condotta a due differenti livelli spaziali: ecologico-naturalistico (l’habitat) e amministrativo (il Comune). Corrispondentemente, i risultati più significativi ottenuti sono: 1. Livello habitat: il confronto tra due metodi di ordinamento e determinazione delle priorità, il metodo del Vettore Ideale e quello della Preminenza, tramite l’utilizzo di importanti metriche ecologiche come il Valore Ecologico (E.V.) e la Sensibilità Ecologica (E.S.), fornisce dei risultati non direttamente comparabili. Il Vettore Ideale, non essendo un procedimento basato sulla ranghizzazione dei valori originali, sembra essere preferibile nel caso di paesaggi molto eterogenei in senso spaziale. Invece, il metodo della Preminenza probabilmente è da preferire in paesaggi ecologici aventi un basso grado di eterogeneità intesa nel senso di differenze non troppo grandi nel E.V. ed E.S. degli habitat. 2. Livello comunale: Al fine di prendere delle decisioni gestionali ed essendo gli habitat solo delle suddivisioni naturalistiche di un dato territorio, è necessario spostare l’attenzione sulle corrispondenti unità amministrative territoriali (i Comuni). Da questo punto di vista, l’introduzione della demografia risulta essere un elemento centrale oltre che di novità nelle analisi ecologico-ambientali. In effetti, l’analisi demografica rende il risultato di cui al punto 1 molto più realistico introducendo altre dimensioni (la pressione antropica attuale e le sue tendenze) che permettono l’individuazione di aree ecologicamente fragili. Inoltre, tale approccio individua chiaramente le responsabilità ambientali di ogni singolo ente territoriale nei riguardi della difesa della biodiversità. In effetti un ordinamento dei Comuni sulla base delle caratteristiche ambientali e demografiche, chiarisce le responsabilità gestionali di ognuno di essi. Un’applicazione concreta di questa necessaria quanto utile integrazione di dati ecologici e demografici viene discussa progettando una Rete Ecologica (E.N.). La Rete cosi ottenuta infatti presenta come elemento di novità il fatto di non essere “statica” bensì “dinamica” nel senso che la sua pianificazione tiene in considerazione il trend di pressione antropica al fine di individuare i probabili punti di futura fragilità e quindi di più critica gestione.Who has the responsibility to manage a conservation zone, not only must be aware of environmental problems but should have at his disposal updated databases and appropriate methodological instruments to examine carefully each individual case. In effect he has to arrange, in advance, the necessary steps to withstand the foreseeable variations in the trends of human pressure on conservation zones. The essential objective of this Thesis is methodological that is to compare different multivariate statistical methods useful for environmental hotspot detection and for environmental prioritization and ranking. The general environmental goal is the conservation of the biodiversity patrimony. The individuation, through multidimensional statistical tools, of habitats having top ecological priority, is only the first basic step to accomplish this aim. Ecological information integrated in the human context is an essential further step to make environmental evaluations and to plan correct conservation actions. A wide series of data and information has been necessary to accomplish environmental management tasks. Ecological data are provided by the Italian Ministry of the Environment and they refer to the Map of Italian Nature Project database. The demographic data derives from the Italian Institute of Statistics (ISTAT). The data utilized regards two Italian areas: Baganza Valley and Oltrepò Pavese and Ligurian-Emilian Apennine. The analysis has been carried out at two different spatial/scale levels: ecological-naturalistic (habitat level) and administrative (Commune level). Correspondingly, the main obtained results are: 1. Habitat level: comparing two ranking and prioritization methods, Ideal Vector and Salience, through important ecological metrics like Ecological Value (E.V.) and Ecological Sensitivity (E.S.), gives results not directly comparable. Being not based on a ranking process, Ideal Vector method seems to be used preferentially in landscapes characterized by high spatial heterogeneity. On the contrary, Salience method is probably to be preferred in ecological landscapes characterized by a low degree of heterogeneity in terms of not large differences concerning habitat E.V. and E.S.. 2. Commune level: Being habitat only a naturalistic partition of a given territory, it is necessary, for management decisions, to move towards the corresponding administrative units (Communes). From this point of view, the introduction of demography is an essential element of novelty in environmental analysis. In effect, demographic analysis makes the goal at point 1 more realistic introducing other dimensions (actual human pressure and its trend) which allows the individuation of environmentally fragile areas. Furthermore this approach individuates clearly the environmental responsibility of each administrative body for what concerns the biodiversity conservation. In effect communes’ ranking, according to environmental/demographic features, clarify the responsibilities of each administrative body. A concrete application of this necessary and useful integration of ecological and demographic data has been developed in designing an Ecological Network (E.N.).The obtained E.N. has the novelty to be not “static” but “dynamic” that is the network planning take into account the demographic pressure trends in the individuation of the probable future fragile points