Geospatial intelligence and health analitycs: Its application and utility in a city with high tuberculosis incidence in Brazil

Gehlen, Mirela; Nicola, Maria Rita Castilhos; Costa, Elis Regina Dalla; Cabral, Vagner Kunz; Quadros, Everton Luís Luz de; Chaves, Caroline O.; Lahm, Regis Alexandre; Nicolella, Alberto Domiziano Rita; Rossetti, Maria L.R.; Silva, Denise Rossato

doi:10.1016/j.jiph.2019.03.012

Cited by 20 publications

(16 citation statements)

References 24 publications

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“…In addition, these were pre-decided locations such as home, work, etc. Unlike this study, they did not use the dynamic daily timeline to track the exact visited places [17,45,46].…”

Section: Discussionmentioning

confidence: 99%

Epidemic investigations within an arm’s reach – role of google maps during an epidemic outbreak

2020

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Epidemics such as novel Coronavirus 2019 (COVID-19) can be contained and the rate of infection reduced by public health measures such as epidemiologic inquiries and social distancing. Epidemiologic inquiry requires resources and time which may not be available or reduced when the outbreak is excessive. We evaluated the use of Google Maps Timeline (GMTL) for creating spatial epidemiologic timelines. The study compares locations, routes, and means of transport between GMTL and user recall for 17 suitable users who were recruited during March 2020. They were interviewed about their timeline using the Timeline Follow-Back (TLFB) method which was then compared to their GMTL and discrepancies between both methods were addressed. Interviewer conclusions were divided into categories: (1) participant recalled, (2) no recall (until shown). Categories were subdivided by GMTL accuracy: [a] GMTL accurate, [b] GMTL inaccurate, [c] GMTL data missing. A total of 362 locations were compared. Participants recalled 322 (88.95% SD = 8.55) locations compared with 40 (11.05%, SD = 2.05) locations not recalled. There were 304 locations found accurate on GMTL (83.98%, SD = 9.49), 29 (8.01%, SD = 1.11) inaccurate locations, and 29 (8.01%, SD = 0.54) missing locations. The total discrepancy between GMTL and TLFB recall was 95 cases (26.24%, SD = 3.25). Despite variations between users, Google Maps with GMTL technology may be useful in identifying potentially exposed individuals in a pandemic. It is especially useful when resources are limited. Further research is required with a larger number of users who are undergoing a real epidemiologic investigation to corroborate findings and establish further recommendations.

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“…In addition, these were pre-decided locations such as home, work, etc. Unlike this study, they did not use the dynamic daily timeline to track the exact visited places [17,45,46].…”

Section: Discussionmentioning

confidence: 99%

Epidemic investigations within an arm’s reach – role of google maps during an epidemic outbreak

2020

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“…The study suggested looking into the continuous high mortality in a county and analyzing it to determine the involved factors [31]. Another study looked into mapping health issues, such as tuberculosis, and the correlation with human development such as food access, income, education, and health [32]. The study concluded that there was a connection between human development and tuberculosis [32], where neighborhoods with less than the average income and education had higher tuberculosis rates [32].…”

Section: Introductionmentioning

confidence: 99%

“…Another study looked into mapping health issues, such as tuberculosis, and the correlation with human development such as food access, income, education, and health [32]. The study concluded that there was a connection between human development and tuberculosis [32], where neighborhoods with less than the average income and education had higher tuberculosis rates [32]. A further study illustrated the investigation of type 2 diabetes per county level by machine learning [33].…”

Section: Introductionmentioning

confidence: 99%

Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities

Almalki

Gokaraju

Mehta

et al. 2021

IJGI

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Food access is a major key component in food security, as it is every individual’s right to proper access to a nutritious and affordable food supply. Low access to healthy food sources influences people’s diet and activity habits. Guilford County in North Carolina has a high ranking in low food security and a high rate of health issues such as high blood pressure, high cholesterol, and obesity. Therefore, the primary objective of this study was to investigate the geospatial correlation between health issues and food access areas. The secondary objective was to quantitatively compare food access areas and heath issues’ descriptive statistics. The tertiary objective was to compare several machine learning techniques and find the best model that fit health issues against various food access variables with the highest performance accuracy. In this study, we adopted a food-access perspective to show that communities that have residents who have equitable access to healthy food options are typically less vulnerable to health-related disasters. We propose a methodology to help policymakers lower the number of health issues in Guilford County by analyzing such issues via correlation with respect to food access. Specifically, we conducted a geographic information system mapping methodology to examine how access to healthy food options influenced health and mortality outcomes in one of the largest counties in the state of North Carolina. We created geospatial maps representing food deserts—areas with scarce access to nutritious food; food swamps—areas with more availability of unhealthy food options compared to healthy food options; and food oases—areas with a relatively higher availability of healthy food options than unhealthy options. Our results presented a positive correlation coefficient of R2 = 0.819 among obesity and the independent variables of transportation access, and population. The correlation coefficient matrix analysis helped to identify a strong negative correlation between obesity and median income. Overall, this study offers valuable insights that can help health authorities develop preemptive preparedness for healthcare disasters.

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“…The epidemiology of tuberculosis is influenced by geographical and social factors. The social and economic dimensions of a country are based on the health, the level of education and the standard of living of the population in general [5]. Therefore, the Social Determinants of Health (SDH) are understood as socioeconomic, cultural, ethnic-racial, psychological, environmental and behavioral factors that influence the occurrence of health problems and their risk factors in the population.…”

Section: Introductionmentioning

confidence: 99%

Social inequalities associated with the onset of tuberculosis in disease-prone territories in a city from northeastern Brazil

Andrade

Arroyo

Yamamura

et al. 2021

J Infect Dev Ctries

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Introduction: Tuberculosis remains a major health problem worldwide, killing thousand adults and children every year mainly in developing countries as Brazil. The disease is socially determined, caused mainly by inequalities as overcrowding, bad conditions of housing, unemployment, and limited access to health care. The aim of this study was to identify the social inequalities associated with the onset of tuberculosis in disease-prone territories in a city from the Northeast. Methodology: This was an ecological study, which has gathered patients diagnosed with tuberculosis through secondary data source in a city from the northeast of Brazil. The GAMLSS statistical model has been applied considering as response variable the count of Tuberculosis cases and the independent variable, the social conditions. The double Poisson distribution was considered in the analysis. The best model fitted was selected according the Akaike information criterion value. For all tests, the p value < 0.05 was considered as statistically significant. Results: 460 patients with diagnosis of tuberculosis were identified, which represents an incidence of 36.3 cases/100,000 in males and 20.7 cases/100,000 in females. Regarding social inequality associated with tuberculosis, income (households with per capita income between 1/8 and 3 minimum wages), gender and age (Proportion of males under 15 years of age) were associated with the disease. Conclusions: The findings evidenced the social determinants associated with tuberculosis, with a greater occurrence of the disease in areas with mostly male children and low-income families, these issues must be managed within and beyond the health sector, which is mandatory for the Tuberculosis elimination.

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Geospatial intelligence and health analitycs: Its application and utility in a city with high tuberculosis incidence in Brazil

Cited by 20 publications

References 24 publications

Epidemic investigations within an arm’s reach – role of google maps during an epidemic outbreak

Epidemic investigations within an arm’s reach – role of google maps during an epidemic outbreak

Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities

Social inequalities associated with the onset of tuberculosis in disease-prone territories in a city from northeastern Brazil

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