scholarly journals Use of Activity Space in a Tuberculosis Outbreak: Bringing Homeless Persons Into Spatial Analyses

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Mary Claire Worrell ◽  
Michael Kramer ◽  
Aliya Yamin ◽  
Susan M. Ray ◽  
Neela D. Goswami
Keyword(s):  
Health Department ◽  
Homeless Persons ◽  
Activity Space ◽  
Exposure Science ◽  
Activity Spaces ◽  
Space Analysis ◽  
Location Data ◽  
Density Methods ◽  
Us Cities ◽  
Traditional Surveillance

Abstract Background Tuberculosis (TB) causes significant morbidity and mortality in US cities, particularly in poor, transient populations. During a TB outbreak in Fulton County, Atlanta, GA, we aimed to determine whether local maps created from multiple locations of personal activity per case would differ significantly from traditional maps created from single residential address. Methods Data were abstracted for patients with TB disease diagnosed in 2008–2014 and receiving care at the Fulton County Health Department. Clinical and activity location data were abstracted from charts. Kernel density methods, activity space analysis, and overlay with homeless shelter locations were used to characterize case spatial distribution when using single versus multiple addresses. Results Data were collected for 198 TB cases, with over 30% homeless US-born cases included. Greater spatial dispersion of cases was found when utilizing multiple versus single addresses per case. Activity spaces of homeless and isoniazid (INH)-resistant cases were more spatially congruent with one another than non-homeless and INH-susceptible cases (P < .0001 and P < .0001, respectively). Conclusions Innovative spatial methods allowed us to more comprehensively capture the geography of TB-infected homeless persons, who made up a large portion of the Fulton County outbreak. We demonstrate how activity space analysis, prominent in exposure science and chronic disease, supports that routine capture of multiple location TB data may facilitate spatially different public health interventions than traditional surveillance maps.

Operationalizing Neighborhood Definitions in Health Research

2018 ◽  
Author(s):  
Dustin T. Duncan ◽  
Seann D. Regan ◽  
Basile Chaix
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Global Positioning System ◽  
Health Research ◽  
Self Report ◽  
Activity Space ◽  
Positioning System ◽  
Activity Spaces ◽  
Global Positioning ◽  
Neighborhood Boundaries

Defining neighborhoods for health research continues to be challenging. This chapter discusses different methods to operationalize neighborhood boundaries, including self-report, administrative definitions, geographic information system buffers and activity spaces, including global positioning system (GPS)–defined activity spaces. It discusses the strengths and limitations of each method of examining neighborhood boundaries (e.g., spatial misclassification, technical difficulties, assumptions). Readers are provided with examples of neighborhood definitions frequently applied in the epidemiology and population health literature. In addition, the chapter provides a rigorous overview of theories for selecting neighborhood definitions, including spatial polygamy theory for GPS-defined activity space neighborhoods.

Abstract P317: An Activity Space Approach to Assessing the Food Environments of Food Secure and Insecure Women

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Ilana G Raskind ◽  
Michelle C Kegler ◽  
Michael R Kramer
Keyword(s):  
Food Security ◽  
Food Insecurity ◽  
Spatial Behavior ◽  
Activity Space ◽  
Food Sources ◽  
Food Environments ◽  
Spatial Mobility ◽  
Activity Spaces ◽  
Food Security Status ◽  
Space Approach

Introduction: Community food environments (FE) are an important correlate of diet- and weight-related CV health. Conventional approaches to measuring the FE focus on residential neighborhoods, and do not assess the full extent of food sources regularly encountered and used. Further, little attention has been given to how individual diet-related experiences, like food insecurity, may interact with features of the FE to affect health. To address these limitations, we use an activity space approach, defined by the locations women routinely visit, to measure FE exposure and use, and assess differences by food security status. Hypothesis: Food-related spatial behavior and features of the FE differ between a) conventional and activity space definitions, and b) food secure and insecure women. Methods: We present initial results (n=51) from an ongoing clinic-based study of low-income African American women in Atlanta, GA. Data are collected in-person using a Google Map-powered activity space questionnaire. USDA’s 10-item adult scale is used to measure food insecurity. Retail FE data are from Dun & Bradstreet. ArcGIS 10.5 was used to define three environments: residential census tract (CT), and convex hull polygons of overall and food-specific activity spaces. We tested differences, by food security status, in mean behaviors and FE features with one-way ANOVAs. Results: Eighty-eight percent of women were food insecure. Food insecure women were lower income, less often employed, and less often had access to a car. CTs contained fewer supermarkets (μ=1.2 SD =1.4) and fast food restaurants (μ=3.9 SD =3.2) than activity spaces (μ=7.9 SD =7.0; μ=55.5 SD =44.1, respectively). On average, 6.7% ( SD =13.5) of utilized food sources fell within CT bounds, while 53.4% ( SD =35.5) fell within activity spaces. Compared to food secure women, food insecure women had smaller overall (μ=329.8km 2 SD =340.4 vs. μ=548.3km 2 SD =422.4; p =0.16) and food-specific (μ=48.1km 2 SD =74.3 vs. μ=85.6km 2 SD =106.4; p =0.28) activity spaces, and a smaller proportion of their utilized supermarkets fell within their activity spaces (μ=60.9% SD =42.4 vs. μ=81.9% SD =21.4; p =0.24). FE features did not differ by food security status. Conclusions: Conventional FE definitions likely underestimate the number of food sources women encounter, and do not capture the majority of sources used. Smaller activity spaces among food insecure women suggest that routine spatial mobility may be constrained by factors like transportation access. Still, food insecure women more often traveled outside of their activity spaces to utilize supermarkets, suggesting a dual burden of constrained spatial mobility and access. Interestingly, FE features did not differ by food security status. In planned future analyses, any observed differences in diet and weight may indicate variation in how women interact with the FE, rather than differences in exposure.

scholarly journals Activity space-based measures of the food environment and their relationships to food purchasing behaviours for young urban adults in Canada

2018 ◽  
Vol 21 (11) ◽  
pp. 2103-2116 ◽  
Author(s):  
Michael J Widener ◽  
Leia M Minaker ◽  
Jessica L Reid ◽  
Zachary Patterson ◽  
Tara Kamal Ahmadi ◽  
...  
Keyword(s):  
Food Environment ◽  
Activity Space ◽  
Trajectory Data ◽  
Food Retail ◽  
Food Purchasing ◽  
Activity Spaces ◽  
Food Purchases ◽  
Food Shopping ◽  
Food Retailers ◽  
Urban Adults

AbstractObjectiveTo examine the potential links between activity spaces, the food retail environment and food shopping behaviours for the population of young, urban adults.DesignParticipants took part in the Canada Food Study, which collected information on demographics, food behaviour, diet and health, as well as an additional smartphone study that included a seven-day period of logging GPS (global positioning system) location and food purchases. Using a time-weighted, continuous representation of participant activity spaces generated from GPS trajectory data, the locations of food purchases and a geocoded food retail data set, negative binomial regression models were used to explore what types of food retailers participants were exposed to and where food purchases were made.SettingToronto, Montreal, Vancouver, Edmonton and Halifax, Canada.SubjectsYoung adults aged 16–30 years (n 496). These participants were a subset of the larger Canada Food Study.ResultsDemographics, household food shopper status and city of residence were significantly associated with different levels of exposure to various types of food retailers. Food shopping behaviours were also statistically significantly associated with demographics, the activity space-based food environment, self-reported health and city of residence.ConclusionsThe study confirms that food behaviours are related to activity space-based food environment measures, which provide a more comprehensive accounting of food retail exposure than home-based measures. In addition, exposure to food retail and food purchasing behaviours of an understudied population are described.

scholarly journals Prioritizing COVID-19 tests based in Participatory Surveillance and Spatial Scanning

2020 ◽  
Author(s):  
OB Leal-Neto ◽  
FAS Santos ◽  
JY Lee ◽  
JO Albuquerque ◽  
WV Souza
Keyword(s):  
Spatial Data ◽  
Surveillance System ◽  
Statistical Significance ◽  
Health Department ◽  
P Value ◽  
Priority Areas ◽  
Participatory Surveillance ◽  
The City ◽  
Traditional Surveillance

AbstractObjectivesThis study aimed to identify, describe and analyze priority areas for Covid-19 testing combining participatory surveillance and traditional surveillance.DesignIt was carried out a descriptive transversal study in the city of Caruaru, Pernambuco state, Brazil, within the period of 20/02/2020 to 05/05/2020. Data included all official reports for influenza-like illness notified by the municipality health department and the self-reports collected through the participatory surveillance platform Brasil Sem Corona.MethodsWe used linear regression and loess regression to verify a correlation between Participatory Surveillance (PS) and Traditional Surveillance (TS). Also a spatial scanning approach was deployed in order to identify risk clusters for Covid-19.ResultsIn Caruaru, the PS had 861 active users, presenting an average of 1.2 reports per user per week. The platform Brasil Sem Corona started on March 20th and since then, has been officially used by the Caruaru health authority to improve the quality of information from the traditional surveillance system. Regarding the respiratory syndrome cases from TS, 1,588 individuals were positive for this clinical outcome. The spatial scanning analysis detected 18 clusters and 6 of them presented statistical significance (p-value < 0.1). Clusters 3 and 4 presented an overlapping area that was chosen by the local authority to deploy the Covid-19 serology, where 50 individuals were tested. From there, 32% (n=16) presented reagent results for antibodies related to Covid-19.ConclusionParticipatory surveillance is an effective epidemiological method to complement the traditional surveillance system in response to the COVID-19 pandemic by adding real-time spatial data to detect priority areas for Covid-19 testing.

scholarly journals Modeling the Factors Influencing the Activity Spaces of Bikeshare around Metro Stations: A Spatial Regression Model

2018 ◽  
Vol 10 (11) ◽  
pp. 3949 ◽  
Author(s):  
Xinwei Ma ◽  
Yanjie Ji ◽  
Yuchuan Jin ◽  
Jianbiao Wang ◽  
Mingjia He
Keyword(s):  
Spatial Regression ◽  
Central Business District ◽  
Ordinary Least Squares ◽  
Model Fit ◽  
Activity Space ◽  
Spatial Error ◽  
Activity Spaces ◽  
Local Residents ◽  
Business District ◽  
Social Demographic

Metro-bikeshare integration is considered a green and efficient travel model. To better understand bikeshare as a feeder mode to the metro, this study explored the factors that influence the activity spaces of bikeshare around metro stations. First, metro-bikeshare transfer trips were recognized by matching bikeshare smartcard data and metro smartcard data. Then, standard deviation ellipse (SDE) was used for the calculation of the metro-bikeshare activity spaces. Moreover, an ordinary least squares (OLS) regression and a spatial error model (SEM) were established to reveal the effects of social-demographic, travel-related, and built environment factors on the activity spaces of bikeshare around metro stations, and the SEM outperformed OLS significantly in terms of model fit. Results show that the average metro-bikeshare activity space on weekdays is larger than that on weekends. The proportion of local residents promotes the increase in activity space on weekends, while a high density of road and metro impedes the activity space on weekdays. Additionally, with increased job density, the activity space becomes smaller significantly throughout the week. Also, both on weekdays and weekends, the closer to the central business district (CBD), the smaller the activity space. This study can offer meaningful guidance to policymakers and city planners aiming to make the bikeshare distribution more reasonable.

scholarly journals Integrating activity spaces in health research: Comparing the VERITAS activity space questionnaire with 7-day GPS tracking and prompted recall

2018 ◽  
Vol 25 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Kestens ◽  
Benoit Thierry ◽  
Martine Shareck ◽  
Madeleine Steinmetz-Wood ◽  
Basile Chaix
Keyword(s):  
Health Research ◽  
Activity Space ◽  
Gps Tracking ◽  
Activity Spaces

scholarly journals Community transmission of multidrug-resistant tuberculosis is associated with activity space overlap in Lima, Peru

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
David P. Bui ◽  
Shruthi S. Chandran ◽  
Eyal Oren ◽  
Heidi E. Brown ◽  
Robin B. Harris ◽  
...  
Keyword(s):  
Multidrug Resistant ◽  
Activity Space ◽  
Estimation Methods ◽  
Spatial Proximity ◽  
Utilization Distribution ◽  
Activity Spaces ◽  
Resistant Tuberculosis ◽  
Multidrug Resistant Tuberculosis ◽  
Community Transmission ◽  
Community Controls

Abstract Background Transmission of multidrug-resistant tuberculosis (MDRTB) requires spatial proximity between infectious cases and susceptible persons. We assess activity space overlap among MDRTB cases and community controls to identify potential areas of transmission. Methods We enrolled 35 MDRTB cases and 64 TB-free community controls in Lima, Peru. Cases were whole genome sequenced and strain clustering was used as a proxy for transmission. GPS data were gathered from participants over seven days. Kernel density estimation methods were used to construct activity spaces from GPS locations and the utilization distribution overlap index (UDOI) was used to quantify activity space overlap. Results Activity spaces of controls (median = 35.6 km2, IQR = 25.1–54) were larger than cases (median = 21.3 km2, IQR = 17.9–48.6) (P = 0.02). Activity space overlap was greatest among genetically clustered cases (mean UDOI = 0.63, sd = 0.67) and lowest between cases and controls (mean UDOI = 0.13, sd = 0.28). UDOI was positively associated with genetic similarity of MDRTB strains between case pairs (P < 0.001). The odds of two cases being genetically clustered increased by 22% per 0.10 increase in UDOI (OR = 1.22, CI = 1.09–1.36, P < 0.001). Conclusions Activity space overlap is associated with MDRTB clustering. MDRTB transmission may be occurring in small, overlapping activity spaces in community settings. GPS studies may be useful in identifying new areas of MDRTB transmission.

scholarly journals Spatial Characteristics and Activity Space Pattern Analysis of Dhaka City, Bangladesh

Urban Science ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 36 ◽  
Author(s):  
Naila Sharmeen ◽  
Douglas Houston
Keyword(s):  
Land Use ◽  
Travel Behavior ◽  
Size Structure ◽  
Spatial Behavior ◽  
Activity Space ◽  
Dhaka City ◽  
Activity Spaces ◽  
Space Pattern ◽  
Personal Attitudes ◽  
Integrate Activity

Although a handful of studies have begun to integrate activity space within travel behavior analysis in the European and United States (U.S.) contexts, few studies have measured the size, structure, and implications of human activity spaces in the context of developing countries. To identify the effects of land-use characteristics, socio-demographics, individual trip characteristics, and personal attitudes on the travel-activity based spatial behavior of various population groups in Dhaka city, Bangladesh, a household-based travel diary pilot survey (for two weekdays) was conducted for 50 randomly selected households in the winter of 2017. The study focused on two separate subareas: one taken from Dhaka North City Corporation, and another taken from Dhaka South City Corporation. Two methods—shortest-path network and road network buffer—were used for calculating activity space in a geographic information system (GIS). The daily activity areas for individual respondents ranged from 0.37 to 6.18 square miles. Land-use mix was found to be a significant predictor of activity space size for the residents. Larger activity space was recorded for the residents of one subarea over another due to less land-use diversity. The pilot data showed some specific socio-economic and travel differences across the two study subareas (car ownership, income, modal share, distance traveled, trip duration).

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