scholarly journals Online Map Design for Public Health Decision-Makers

2021 ◽  
Author(s):  
Jonathan Cinnamon ◽  
Claus Rinner ◽  
Michael D. Cusimano ◽  
Sean Marshall ◽  
Tsegaye Bekele ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Health Resources ◽  
Decision Makers ◽  
Data Set ◽  
Public Health Decision ◽  
Web Mapping ◽  
Map Design ◽  
Demographic Risk ◽  
Demographic Risk Factors

Injury places a heavy burden on public-health resources that is not distributed evenly in space, making the mapping of injury and its socio-demographic risk factors an effective tool for prevention planning. In a survey of health-related interactive Web mapping applications we found great variation with respect to content, cartography, and technical aspects. Based on teh survey results, input from a group of potential end users, cartographic design principles, and data-set requirements, we created a Web site with static, animated, and interactive injury maps. We mapped injury rates and possible socio-deomgraphic risk factors for the City of Toronto. Through the three functionally different types of maps, a variety of ways to explore the same public-health data sets could be demostrated. The results highlight the practical options available to public-health analysts and decision makers who wish to expand their data-exploration and decision-support tools with a spatial component.

scholarly journals Online Map Design for Public Health Decision-Makers

2021 ◽  
Author(s):  
Jonathan Cinnamon ◽  
Claus Rinner ◽  
Michael D. Cusimano ◽  
Sean Marshall ◽  
Tsegaye Bekele ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Health Resources ◽  
Decision Makers ◽  
Data Set ◽  
Public Health Decision ◽  
Web Mapping ◽  
Map Design ◽  
Demographic Risk ◽  
Demographic Risk Factors

Injury places a heavy burden on public-health resources that is not distributed evenly in space, making the mapping of injury and its socio-demographic risk factors an effective tool for prevention planning. In a survey of health-related interactive Web mapping applications we found great variation with respect to content, cartography, and technical aspects. Based on teh survey results, input from a group of potential end users, cartographic design principles, and data-set requirements, we created a Web site with static, animated, and interactive injury maps. We mapped injury rates and possible socio-deomgraphic risk factors for the City of Toronto. Through the three functionally different types of maps, a variety of ways to explore the same public-health data sets could be demostrated. The results highlight the practical options available to public-health analysts and decision makers who wish to expand their data-exploration and decision-support tools with a spatial component.

scholarly journals Evaluating Web-based Static, Animated, and Interactive Maps for Injury Prevention

2021 ◽  
Author(s):  
Jonathan Cinnamon ◽  
Claus Rinner ◽  
Michael D. Cusimano ◽  
Sean Marshall ◽  
Tsegaye Bekele ◽  
...  
Keyword(s):  
Public Health ◽  
Health Planning ◽  
Visual Exploration ◽  
Map Comparison ◽  
Interactive Maps ◽  
Map Design ◽  
Health Related ◽  
The Individual ◽  
Demographic Risk ◽  
Demographic Risk Factors

Public health planning can benefit from visual exploration and analysis of geospatial data. Maps and geo-visualization tools must be developed with the user-group in mind. User-needs assessment and usability testing are crucial elements in the iterative process of map design and implementation. This study presents the results of a usability test of static, animated and interactive maps of injury rates and socio-demographic determinants of injury by a sample of potential end-users in Toronto, Canada. The results of the user-testing suggest that different map types are useful for different purposes and for satisfying the varying skill level of the individual user. The static maps were deemed to be easy to use and versatile, while the animated maps could be made more useful if animation controls were provided. The split-screen concepts of the interactive maps was highlighted as particularly effective for map comparison. Overall, interactive maps were identified as the preferred map type for comparing patterns of injury and related socio-demographic risk factors. Information collected from the user-tests is being used to expand and refind the injury webmaps for Toronto, and could inform other public health-related geo-visualization projects.

scholarly journals Predictive Modeling of COVID-19 Case Growth Highlights Evolving Demographic Risk Factors in Tennessee and Georgia

2021 ◽  
Author(s):  
Jamieson D. Gray ◽  
Coleman R. Harris ◽  
Lukasz S. Wylezinski ◽  
Charles F. Spurlock
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Respiratory Infections ◽  
Public Health Intervention ◽  
Direct Consequence ◽  
Determinants Of Health ◽  
Demographic Risk ◽  
Demographic Risk Factors

AbstractThe COVID-19 pandemic has exposed the need to understand the unique risk drivers that contribute to uneven morbidity and mortality in US communities. Addressing the community-specific social determinants of health that correlate with spread of SARS-CoV-2 provides an opportunity for targeted public health intervention to promote greater resilience to viral respiratory infections in the future.Our work combined publicly available COVID-19 statistics with county-level social determinants of health information. Machine learning models were trained to predict COVID-19 case growth and understand the unique social, physical and environmental risk factors associated with higher rates of SARS-CoV-2 infection in Tennessee and Georgia counties. Model accuracy was assessed comparing predicted case counts to actual positive case counts in each county. The predictive models achieved a mean r-squared (R2) of 0.998 in both states with accuracy above 90% for all time points examined. Using these models, we tracked the social determinants of health, with a specific focus on demographics, that were strongly associated with COVID-19 case growth in Tennessee and Georgia counties. The demographic results point to dynamic racial trends in both states over time and varying, localized patterns of risk among counties within the same state.Identifying the specific risk factors tied to COVID-19 case growth can assist public health officials and policymakers target regional interventions to mitigate the burden of future outbreaks and minimize long-term consequences including emergence or exacerbation of chronic diseases that are a direct consequence of infection.

scholarly journals Evaluating Web-based Static, Animated, and Interactive Maps for Injury Prevention

2021 ◽  
Author(s):  
Jonathan Cinnamon ◽  
Claus Rinner ◽  
Michael D. Cusimano ◽  
Sean Marshall ◽  
Tsegaye Bekele ◽  
...  
Keyword(s):  
Public Health ◽  
Health Planning ◽  
Visual Exploration ◽  
Map Comparison ◽  
Interactive Maps ◽  
Map Design ◽  
Health Related ◽  
The Individual ◽  
Demographic Risk ◽  
Demographic Risk Factors

Public health planning can benefit from visual exploration and analysis of geospatial data. Maps and geo-visualization tools must be developed with the user-group in mind. User-needs assessment and usability testing are crucial elements in the iterative process of map design and implementation. This study presents the results of a usability test of static, animated and interactive maps of injury rates and socio-demographic determinants of injury by a sample of potential end-users in Toronto, Canada. The results of the user-testing suggest that different map types are useful for different purposes and for satisfying the varying skill level of the individual user. The static maps were deemed to be easy to use and versatile, while the animated maps could be made more useful if animation controls were provided. The split-screen concepts of the interactive maps was highlighted as particularly effective for map comparison. Overall, interactive maps were identified as the preferred map type for comparing patterns of injury and related socio-demographic risk factors. Information collected from the user-tests is being used to expand and refind the injury webmaps for Toronto, and could inform other public health-related geo-visualization projects.

scholarly journals Online Map Design for Public-Health Decision Makers

2009 ◽  
Vol 44 (4) ◽  
pp. 289-300 ◽  
Author(s):  
Jonathan Cinnamon ◽  
Claus Rinner ◽  
Michael D. Cusimano ◽  
Sean Marshall ◽  
Tsegaye Bekele ◽  
...  
Keyword(s):  
Public Health ◽  
Decision Makers ◽  
Public Health Decision ◽  
Map Design ◽  
Health Decision

Clinical and Demographic Risk Factors for COVID-19 during Delivery Hospitalizations in New York City

2021 ◽  
Author(s):  
Desmond Sutton ◽  
Timothy Wen ◽  
Anna P. Staniczenko ◽  
Yongmei Huang ◽  
Maria Andrikopoulou ◽  
...  
Keyword(s):  
Risk Factors ◽  
New York ◽  
New York City ◽  
Logistic Regression ◽  
York City ◽  
Demographic Data ◽  
Outcome Data ◽  
Demographic Risk ◽  
Demographic Risk Factors ◽  
Zip Code

Objective This study was aimed to review 4 weeks of universal novel coronavirus disease 2019 (COVID-19) screening among delivery hospitalizations, at two hospitals in March and April 2020 in New York City, to compare outcomes between patients based on COVID-19 status and to determine whether demographic risk factors and symptoms predicted screening positive for COVID-19. Study Design This retrospective cohort study evaluated all patients admitted for delivery from March 22 to April 18, 2020, at two New York City hospitals. Obstetrical and neonatal outcomes were collected. The relationship between COVID-19 and demographic, clinical, and maternal and neonatal outcome data was evaluated. Demographic data included the number of COVID-19 cases ascertained by ZIP code of residence. Adjusted logistic regression models were performed to determine predictability of demographic risk factors for COVID-19. Results Of 454 women delivered, 79 (17%) had COVID-19. Of those, 27.9% (n = 22) had symptoms such as cough (13.9%), fever (10.1%), chest pain (5.1%), and myalgia (5.1%). While women with COVID-19 were more likely to live in the ZIP codes quartile with the most cases (47 vs. 41%) and less likely to live in the ZIP code quartile with the fewest cases (6 vs. 14%), these comparisons were not statistically significant (p = 0.18). Women with COVID-19 were less likely to have a vaginal delivery (55.2 vs. 51.9%, p = 0.04) and had a significantly longer postpartum length of stay with cesarean (2.00 vs. 2.67days, p < 0.01). COVID-19 was associated with higher risk for diagnoses of chorioamnionitis and pneumonia and fevers without a focal diagnosis. In adjusted analyses, including demographic factors, logistic regression demonstrated a c-statistic of 0.71 (95% confidence interval [CI]: 0.69, 0.80). Conclusion COVID-19 symptoms were present in a minority of COVID-19-positive women admitted for delivery. Significant differences in obstetrical outcomes were found. While demographic risk factors demonstrated acceptable discrimination, risk prediction does not capture a significant portion of COVID-19-positive patients. Key Points

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