Ancestral Origin and Dissemination Dynamics of Reemerging Toxigenic Vibrio cholerae, Haiti.

The 2010 cholera epidemic in Haiti was thought to have ended in 2019, and the Prime Minister of Haiti declared the country cholera-free in February 2022. On September 25, 2022, cholera cases were again identified in Port-au-Prince. We compared genomic data from 42 clinical Vibrio cholerae strains from 2022 with data from 327 other strains from Haiti and 1,824 strains collected worldwide. The 2022 isolates were homogeneous and closely related to clinical and environmental strains circulating in Haiti during 2012-2019. Bayesian hypothesis testing indicated that the 2022 clinical isolates shared their most recent common ancestor with an environmental lineage circulating in Haiti in July 2018. Our findings strongly suggest that toxigenic V. cholerae O1 can persist for years in aquatic environmental reservoirs and ignite new outbreaks. These results highlight the urgent need for improved public health infrastructure and possible periodic vaccination campaigns to maintain population immunity against V. cholerae.

Using spatial video geonarratives to improve nursing care for people who use drugs and experience homelessness: A methodology for nurses.

BACKGROUND: People who are insecurely housed and use drugs are disproportionately affected by drug poisonings. Nurses are uniquely positioned to utilize harm reduction strategies to address the needs of the whole person. Needle debris encompasses drug paraphernalia discarded in public spaces. Studying needle debris provides a strategic opportunity to identify where drugs are being used and target public health strategies accordingly. AIM: Our aim in this article is to illustrate how spatial video geonarratives (SVG) combined GPS technology interviews, and videos of locations with needle debris, can elicit valuable data for nursing research. METHODS: Using SVG required knowledge of how to collect data wearing cameras and practice sessions were necessary. A Miufly camera worn at waist height on a belt provided the stability to walk while interviewing stakeholders. We wore the cameras and conducted go-along interviews with outreach workers, while filming the built environment. Upon completion of data collection, both the interview and GPS information were analysed using Wordmapper software. CONCLUSIONS: This methodology resulted in data presented uniquely in both a visual map and narrative. These data were richer than if a single modality had been used. These data highlighted specific contextual factors that were related to the location of needle debris, which created opportunities for nursing interventions to support people experiencing vulnerability.

Disparities in Pediatric Orthopedic Surgery Care During the COVID-19 Pandemic Pre-vaccine and Post-Vaccine Availability.

BACKGROUND: The COVID-19 pandemic has led to significant disruptions in medical care, resulting in an estimated 40% of US adults avoiding care. However, the return to baseline health care utilization following COVID-19 restrictions within the pediatric orthopedic population remains unexplored. We sought to analyze the visit volume and demographics of pediatric orthopedic patients at 3 timepoints: prepandemic (2019), pandemic (2020), and pandemic post-vaccine availability (2021), to determine the impact of COVID-19 restrictions on our single-center, multisite institution. METHODS: We performed a retrospective cohort study of 6318 patients seeking treatment at our institution from May through August in 2019, 2020, and 2021. Patient age, sex, address, encounter date, and ICD-10 codes were obtained. Diagnoses were classified into fractures and dislocations, non-fracture-related trauma, sports, elective, and other categories. Geospatial analysis comparing incidence and geospatial distribution of diagnoses across the time periods was performed and compared with the Centers for Disease Control (CDC) social vulnerability index (SVI). RESULTS: The total number of pediatric orthopedic visits decreased by 22.2% during the pandemic ( P <0.001) and remained 11.6% lower post-vaccine availability compared with prepandemic numbers ( P <0.001). There was no significant difference in age ( P =0.097) or sex ( P =0.248) of the patients across all 3 timepoints; however, patients seen during the pandemic were more often White race (67.7% vs. 59.3%, P <0.001). Post-vaccine availability, trauma visits increased by 18.2% ( P <0.001) and total fractures remained 13.4% lower than prepandemic volume ( P <0.001). Sports volume decreased during the pandemic but returned to prepandemic volume in the post-vaccine availability period ( P =0.298). Elective visits did not recover to prepandemic volume and remained 13.0% lower compared with baseline ( P <0.001). Geospatial analysis of patient distribution illustrated neighborhood trends in access to care during the COVID-19 pandemic, with fewer patients from high SVI and low socioeconomic status neighborhoods seeking fracture care during the pandemic than prepandemic. Post-vaccine availability, fracture population distribution resembled prepandemic levels, suggesting a return to baseline health care utilization. CONCLUSION: Pediatric orthopedic surgery visit volume broadly decreased during the COVID-19 pandemic and did not return to prepandemic levels. All categories increased in the post-vaccine availability time point except elective visits. Geospatial analysis revealed that neighborhoods with a high social vulnerability index (SVI) were associated with decreased fracture visits during the pandemic, whereas low SVI neighborhoods did not experience as much of a decline. Future research is needed to study these neighborhood trends and more completely characterize factors preventing equitable access to care in the pediatric orthopedic population. LEVEL OF EVIDENCE: Retrospective Study, Level III.

Exploring convolutional neural networks and spatial video for on-the-ground mapping in informal settlements.

BACKGROUND: The health burden in developing world informal settlements often coincides with a lack of spatial data that could be used to guide intervention strategies. Spatial video (SV) has proven to be a useful tool to collect environmental and social data at a granular scale, though the effort required to turn these spatially encoded video frames into maps limits sustainability and scalability. In this paper we explore the use of convolution neural networks (CNN) to solve this problem by automatically identifying disease related environmental risks in a series of SV collected from Haiti. Our objective is to determine the potential of machine learning in health risk mapping for these environments by assessing the challenges faced in adequately training the required classification models. RESULTS: We show that SV can be a suitable source for automatically identifying and extracting health risk features using machine learning. While well-defined objects such as drains, buckets, tires and animals can be efficiently classified, more amorphous masses such as trash or standing water are difficult to classify. Our results further show that variations in the number of image frames selected, the image resolution, and combinations of these can be used to improve the overall model performance. CONCLUSION: Machine learning in combination with spatial video can be used to automatically identify environmental risks associated with common health problems in informal settlements, though there are likely to be variations in the type of data needed for training based on location. Success based on the risk type being identified are also likely to vary geographically. However, we are confident in identifying a series of best practices for data collection, model training and performance in these settings. We also discuss the next step of testing these findings in other environments, and how adding in the simultaneously collected geographic data could be used to create an automatic health risk mapping tool.

Addressing the data guardian and geospatial scientist collaborator dilemma: how to share health records for spatial analysis while maintaining patient confidentiality.

BACKGROUND: The utility of being able to spatially analyze health care data in near-real time is a growing need. However, this potential is often limited by the level of in-house geospatial expertise. One solution is to form collaborative partnerships between the health and geoscience sectors. A challenge in achieving this is how to share data outside of a host institution's protection protocols without violating patient confidentiality, and while still maintaining locational geographic integrity. Geomasking techniques have been previously championed as a solution, though these still largely remain an unavailable option to institutions with limited geospatial expertise. This paper elaborates on the design, implementation, and testing of a new geomasking tool Privy, which is designed to be a simple yet efficient mechanism for health practitioners to share health data with geospatial scientists while maintaining an acceptable level of confidentiality. The basic premise of Privy is to move the important coordinates to a different geography, perform the analysis, and then return the resulting hotspot outputs to the original landscape. RESULTS: We show that by transporting coordinates through a combination of random translations and rotations, Privy is able to preserve location connectivity among spatial point data. Our experiments with typical analytical scenarios including spatial point pattern analysis and density analysis shows that, along with protecting spatial privacy, Privy maintains the spatial integrity of data which reduces information loss created due to data augmentation. CONCLUSION: The results from this study suggests that along with developing new mathematical techniques to augment geospatial health data for preserving confidentiality, simple yet efficient software solutions can be developed to enable collaborative research among custodians of medical and health data records and GIS experts. We have achieved this by developing Privy, a tool which is already being used in real-world situations to address the spatial confidentiality dilemma.

The utility of Zip4 codes in spatial epidemiological analysis.

There are many public health situations within the United States that require fine geographical scale data to effectively inform response and intervention strategies. However, a condition for accessing and analyzing such data, especially when multiple institutions are involved, is being able to preserve a degree of spatial privacy and confidentiality. Hospitals and state health departments, who are generally the custodians of these fine-scale health data, are sometimes understandably hesitant to collaborate with each other due to these concerns. This paper looks at the utility and pitfalls of using Zip4 codes, a data layer often included as it is believed to be "safe", as a source for sharing fine-scale spatial health data that enables privacy preservation while maintaining a suitable precision for spatial analysis. While the Zip4 is widely supplied, researchers seldom utilize it. Nor is its spatial characteristics known by data guardians. To address this gap, we use the context of a near-real time spatial response to an emerging health threat to show how the Zip4 aggregation preserves an underlying spatial structure making it potentially suitable dataset for analysis. Our results suggest that based on the density of urbanization, Zip4 centroids are within 150 meters of the real location almost 99% of the time. Spatial analysis experiments performed on these Zip4 data suggest a far more insightful geographic output than if using more commonly used aggregation units such as street lines and census block groups. However, this improvement in analytical output comes at a spatial privy cost as Zip4 centroids have a higher potential of compromising spatial anonymity with 73% of addresses having a spatial k anonymity value less than 5 when compared to other aggregations. We conclude that while offers an exciting opportunity to share data between organizations, researchers and analysts need to be made aware of the potential for serious confidentiality violations.

Prevalence and geographic features of patients eligible for extracorporeal cardiopulmonary resuscitation.

OBJECTIVE: This study sought to identify Out of Hospital Cardiac Arrests (OHCA) eligible for Extracorporeal Cardiopulmonary Resuscitation (ECPR), use Geographic Information Systems (GIS) to investigate geographic patterns, and investigate if correlation between ECPR candidacy and Social Determinants of Health (SDoH) exist. METHODS: This study is of emergency medical service (EMS) runs for OHCA to an urban medical center from January 1, 2016 to December 31, 2020. All runs were filtered to inclusion criteria for ECPR: age 18-65, initial shockable rhythm, and no return of spontaneous circulation within initial defibrillations. Address level data were mapped in a GIS. Cluster detection assessed for granular areas of high concentration. The Center for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) was overlaid. The SVI ranges from 0-1 with higher values indicating increasing social vulnerability. RESULTS: There were 670 EMS transports for OHCA during the study period. 12.7% (85/670) met inclusion criteria for ECPR. 90% (77/85) had appropriate addresses for geocoding. Three geographic clusters of events were detected. Two were residential areas and one was concentrated over a public use area of downtown Cleveland. The SVI for these locations was 0.79, indicative of high social vulnerability. Nearly half (32/77, 41.5%) occurred in neighborhoods with the highest level of social vulnerability (SVI ≥ 0.9). CONCLUSION: A significant proportion of OHCAs were eligible for ECPR based on prehospital criteria. Utilizing GIS to map and analyze ECPR patients provided insights into the locations of these events and the SDoH that may be driving risk in these places.

Spatial Syndromic Surveillance and COVID-19 in the U.S.: Local Cluster Mapping for Pandemic Preparedness.

Maps have become the de facto primary mode of visualizing the COVID-19 pandemic, from identifying local disease and vaccination patterns to understanding global trends. In addition to their widespread utilization for public communication, there have been a variety of advances in spatial methods created for localized operational needs. While broader dissemination of this more granular work is not commonplace due to the protections under Health Insurance Portability and Accountability Act (HIPAA), its role has been foundational to pandemic response for health systems, hospitals, and government agencies. In contrast to the retrospective views provided by the aggregated geographies found in the public domain, or those often utilized for academic research, operational response requires near real-time mapping based on continuously flowing address level data. This paper describes the opportunities and challenges presented in emergent disease mapping using dynamic patient data in the response to COVID-19 for northeast Ohio for the period 2020 to 2022. More specifically it shows how a new clustering tool developed by geographers in the initial phases of the pandemic to handle operational mapping continues to evolve with shifting pandemic needs, including new variant surges, vaccine targeting, and most recently, testing data shortfalls. This paper also demonstrates how the geographic approach applied provides the framework needed for future pandemic preparedness.

The Geographic Context of Racial Disparities in Aggressive Endometrial Cancer Subtypes: Integrating Social and Environmental Aspects to Discern Biological Outcomes.

The number of Endometrial Carcinoma (EC) diagnoses is projected to increase substantially in coming decades. Although most ECs have a favorable prognosis, the aggressive, non-endometrioid subtypes are disproportionately concentrated in Black women and spread rapidly, making treatment difficult and resulting in poor outcomes. Therefore, this study offers an exploratory spatial epidemiological investigation of EC patients within a U.S.-based health system's institutional cancer registry (n = 1748) to search for and study geographic patterns. Clinical, demographic, and geographic characteristics were compared by histotype using chi-square tests for categorical and t-tests for continuous variables. Multivariable logistic regression evaluated the impact of risks on these histotypes. Cox proportional hazard models measured risks in overall and cancer-specific death. Cluster detection indicated that patients with the EC non-endometrioid histotypes exhibit geographic clustering in their home address, such that congregate buildings can be identified for targeted outreach. Furthermore, living in a high social vulnerability area was independently associated with non-endometrioid histotypes, as continuous and categorical variables. This study provides a methodological framework for early, geographically targeted intervention; social vulnerability associations require further investigation. We have begun to fill the knowledge gap of geography in gynecologic cancers, and geographic clustering of aggressive tumors may enable targeted intervention to improve prognoses.

The Use of Spatial Video to Map Dynamic and Challenging Environments: A Case Study of Cholera Risk in the Mujoga Relief Camp, D.R.C.

In this paper, we provide an overview of how spatial video data collection enriched with contextual mapping can be used as a universal tool to investigate sub-neighborhood scale health risks, including cholera, in challenging environments. To illustrate the method's flexibility, we consider the life cycle of the Mujoga relief camp set up after the Nyiragongo volcanic eruption in the Democratic Republic of Congo on 22 May 2021. More specifically we investigate how these methods have captured the deteriorating conditions in a camp which is also experiencing lab-confirmed cholera cases. Spatial video data are collected every month from June 2021 to March 2022. These coordinate-tagged images are used to make monthly camp maps, which are then returned to the field teams for added contextual insights. At the same time, a zoom-based geonarrative is used to discuss the camp's changes, including the cessation of free water supplies and the visible deterioration of toilet facilities. The paper concludes by highlighting the next data science advances to be made with SV mapping, including machine learning to automatically identify and map risks, and how these are already being applied in Mujoga.

Mapping Mobility: Utilizing Local-Knowledge-Derived Activity Space to Estimate Exposure to Ambient Air Pollution among Individuals Experiencing Unsheltered Homelessness.

Individuals experiencing homelessness represent a growing population in the United States. Air pollution exposure among individuals experiencing homelessness has not been quantified. Utilizing local knowledge mapping, we generated activity spaces for 62 individuals experiencing homelessness residing in a semi-rural county within the United States. Satellite derived measurements of fine particulate matter (PM2.5) were utilized to estimate annual exposure to air pollution experienced by our participants, as well as differences in the variation in estimated PM2.5 at the local scale compared with stationary monitor data and point location estimates for the same period. Spatial variation in exposure to PM2.5 was detected between participants at both the point and activity space level. Among all participants, annual median PM2.5 exposure was 16.22 µg/m3, exceeding the National Air Quality Standard. Local knowledge mapping represents a novel mechanism to capture mobility patterns and investigate exposure to air pollution within vulnerable populations. Reliance on stationary monitor data to estimate air pollution exposure may lead to exposure misclassification, particularly in rural and semirural regions where monitoring is limited.

Spatial Video and EpiExplorer: A Field Strategy to Contextualize Enteric Disease Risk in Slum Environments.

Disease risk associated with contaminated water, poor sanitation, and hygiene in informal settlement environments is conceptually well understood. From an analytical perspective, collecting data at a suitably fine scale spatial and temporal granularity is challenging. Novel mobile methodologies, such as spatial video (SV), can complement more traditional epidemiological field work to address this gap. However, this work then poses additional challenges in terms of analytical visualizations that can be used to both understand sub-neighborhood patterns of risk, and even provide an early warning system. In this paper, we use bespoke spatial programming to create a framework for flexible, fine-scale exploratory investigations of simultaneously-collected water quality and environmental surveys in three different informal settlements of Port-au-Prince, Haiti. We dynamically mine these spatio-temporal epidemiological and environmental data to provide insights not easily achievable using more traditional spatial software, such as Geographic Information System (GIS). The results include sub-neighborhood maps of localized risk that vary monthly. Most interestingly, some of these epidemiological variations might have previously been erroneously explained because of proximate environmental factors and/or meteorological conditions.

Fine Scale Replicable Risk Mapping in an Informal Settlement: A Case Study of Mathare, Nairobi.

Slums and informal settlements continue to pose considerable health challenges, mostly associated with the unavailability of basic amenities and proper waste management. While mapping where risks occur, such as the location of features associated with disease is obviously beneficial, the spatial data required is frequently not available, especially on a continuous basis. In this paper, we employ a robust, cost-effective, and efficient means of monitoring for these types of environments, using the Mathare SIS in Kenya as an illustration. We show how spatial videos can be used to capture microenvironments around homes or other key features such as toilets and water points, to show localized environmental risks such as standing water and mud. We also show the utility of this approach to capture longitudinal change. The objective of this paper is to illustrate how this method can map changes in the spatial variability of health risks in a challenging environment.

Appreciating the complexity of localized malaria risk in Ghana: Spatial data challenges and solutions.

Various factors have been associated with the ongoing high prevalence of malaria in Ghana. Among these are poor sanitation, low socioeconomic status (SES), building construction and other proximate micro environmental risks, and individual behaviors. What makes the curbing of malaria more challenging, is that for many of the most impacted areas there is little data for modeling or predictions, which are needed, as risk is not homogenous at the sub-neighborhood scale. In this study we use available local surveillance data combined with novel on-the-ground fine scale environmental data collection, to gain an initial understanding of malaria risk for the Teshie township of Accra, Ghana. Mapped environmental risk factors include open drains, stagnant water and trash. Overlaid onto these were clinical data of reported malaria cases collected between 2012 and 2016 at LEKMA hospital. We then enrich these maps with local context using a new method for malaria research, spatial video geonarratives (SVGs). These SVGs provide insights into the underlying spatial-social patterns of risks, to reveal where traditional data collection is lacking, and how and where to develop local intervention strategies.

Geographic monitoring for early disease detection (GeoMEDD).

Identifying emergent patterns of coronavirus disease 2019 (COVID-19) at the local level presents a geographic challenge. The need is not only to integrate multiple data streams from different sources, scales, and cadences, but to also identify meaningful spatial patterns in these data, especially in vulnerable settings where even small numbers and low rates are important to pinpoint for early intervention. This paper identifies a gap in current analytical approaches and presents a near-real time assessment of emergent disease that can be used to guide a local intervention strategy: Geographic Monitoring for Early Disease Detection (GeoMEDD). Through integration of a spatial database and two types of clustering algorithms, GeoMEDD uses incoming test data to provide multiple spatial and temporal perspectives on an ever changing disease landscape by connecting cases using different spatial and temporal thresholds. GeoMEDD has proven effective in revealing these different types of clusters, as well as the influencers and accelerators that give insight as to why a cluster exists where it does, and why it evolves, leading to the saving of lives through more timely and geographically targeted intervention.

The Use of Geonarratives to Add Context to Fine Scale Geospatial Research.

There has been a move towards using mixed method approaches in geospatial research to gain context in understanding health related social patterns and processes. The central premise is that official data is often too reductionist and misses' nuances that can help explain causality. One example is the geonarrative, a spatially relevant commentary or interview that can be mapped by content and/or location. While there have been several examples of geonarratives being used by researchers, there is no commonly available software that can easily transfer the associated text into spatial data. Having a standardized software platform is vital if these methods are to be used across different disciplines. This paper presents an overview of a solution, Wordmapper (WM), which is a standalone software developed to process geonarratives from a transcription and associated global positioning system (GPS) path. Apart from querying textual narrative data, Wordmapper facilitates qualitative coding which could be used to extract latent contextual information from the narratives. In order to improve interoperability, Wordmapper provides spatialized narrative data in formats, such as ESRI shape files, Keyhole Markup Language (KML), and Comma Separated Values (CSV). A case study based on five different spatial video geonarratives (SVG) collected to assess the human impacts following the 2011 Joplin, Missouri are used for illustration.

Micro-Space Complexity and Context in the Space-Time Variation in Enteric Disease Risk for Three Informal Settlements of Port au Prince, Haiti.

Diffusion of cholera and other diarrheal diseases in an informal settlement is a product of multiple behavioral, environmental and spatial risk factors. One of the most important components is the spatial interconnections among water points, drainage ditches, toilets and the intervening environment. This risk is also longitudinal and variable as water points fluctuate in relation to bacterial contamination. In this paper we consider part of this micro space complexity for three informal settlements in Port au Prince, Haiti. We expand on more typical epidemiological analysis of fecal coliforms at water points, drainage ditches and ocean sites by considering the importance of single point location fluctuation coupled with recording micro-space environmental conditions around each sample site. Results show that spatial variation in enteric disease risk occurs within neighborhoods, and that while certain trends are evident, the degree of individual site fluctuation should question the utility of both cross-sectional and more aggregate analysis. Various factors increase the counts of fecal coliform present, including the type of water point, how water was stored at that water point, and the proximity of the water point to local drainage. Some locations fluctuated considerably between being safe and unsafe on a monthly basis. Next steps to form a more comprehensive contextualized understanding of enteric disease risk in these environments should include the addition of behavioral factors and local insight.

Spatial Video Health Risk Mapping in Informal Settlements: Correcting GPS Error.

Informal settlements pose a continuing health concern. While spatial methodologies have proven to be valuable tools to support health interventions, several factors limit their widespread use in these challenging environments. One such technology, spatial video, has been used for fine-scale contextualized mapping. In this paper, we address one of the limitations of the technique: the global positioning system (GPS) coordinate error. More specifically, we show how spatial video coordinate streams can be corrected and synced back to the original video to facilitate risk mapping. Past spatial video collections for the Mathare informal settlement of Kenya are used as an illustration as these data had been previously discarded because of excessive GPS error. This paper will describe the bespoke software that makes these corrections possible, and then will go on to investigate patterns in the coordinate error.

Community context and sub-neighborhood scale detail to explain dengue, chikungunya and Zika patterns in Cali, Colombia.

BACKGROUND: Cali, Colombia has experienced chikungunya and Zika outbreaks and hypoendemic dengue. Studies have explained Cali's dengue patterns but lack the sub-neighborhood-scale detail investigated here. METHODS: Spatial-video geonarratives (SVG) with Ministry of Health officials and Community Health Workers were collected in hotspots, providing perspective on perceptions of why dengue, chikungunya and Zika hotspots exist, impediments to control, and social outcomes. Using spatial video and Google Street View, sub-neighborhood features possibly contributing to incidence were mapped to create risk surfaces, later compared with dengue, chikungunya and Zika case data. RESULTS: SVG captured insights in 24 neighborhoods. Trash and water risks in Calipso were mapped using SVG results. Perceived risk factors included proximity to standing water, canals, poverty, invasions, localized violence and military migration. These risks overlapped case density maps and identified areas that are suitable for transmission but are possibly underreporting to the surveillance system. CONCLUSION: Resulting risk maps with local context could be leveraged to increase vector-control efficiency- targeting key areas of environmental risk.