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Spatialytics in Public Health from Local to Global Scales

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Chatmine CEO, Professor Suchi Gopal lectures at Geographic Perspectives on Infectious Diseases in Humans, Animals, and the Environment symposium hosted by World-Wide Human Geography Data Working Group at Harvard University in Cambridge, MA.

6 Challenges are highlighted in this video with examples of each.

The Original and full video for this series is located at the WWHGD Channel: https://www.youtube.com/channel/UCMSD...
Full Video: https://youtu.be/Nob04KmmLZ0

See More about WWHGD: https://www.wwhgd.org/

Learn More about Chatmine & Our sustainability research: https://riskmetrics4esg.com

Suchi Gopal's academic website can be found: http://people.bu.edu/suchi/

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Spatialytics in Public Health from Local to Global Scales

  1. 1. Professor Suchi Gopal Department of Earth & Environment Center for Remote Sensing, Global Development Policy Center, Pardee Center for the Study of the Longer-Range Future Boston University Marine Program (BUMP) Spatialytics in Public Health from Local to Global Scales. Geographic Perspectives on Infectious Diseases in Humans, Animals, and the Environment Center for Geographic Analysis at Harvard University, Boston June 18, 2019)
  2. 2. 2 • Spatial dependence: The functional relationship between what happens at one point in space and what happens in its immediate neighborhood and beyond. LISA, G and G*. Identify clusters of malaria in East Shao, Ethiopia or Lyme disease in Massachusetts. • Spatial heterogeneity: Relationships between health outcomes and predictors may vary intrinsically across space. Geographically Weighted Regression or AI/ML. Study how environmental exposures, demographic variables, and the built human environment affect healthcare utilization in Kenya Spatialytics in Public Health – Two Spatial Properties Guide Research
  3. 3. 3 • Challenge 1: Spatial information is important as almost all human decisions involve a spatial component • Challenge 2: Explosion in geospatial technologies but spatial data limited at patient scale (finest scale) • Challenge 3: Issues in Access to Data uneven across the Globe • Challenge 4: Multi-disciplinary skillset • Challenge 5: Data Privacy is a big issue – Social Media, Health Records • Challenge 6: Big data challenges Current State - Spatialytics in Public Health
  4. 4. Challenge 1 - Climate Change Impact on Malaria in East Shoa 1.Temperature influences transmission - affects population growth of the mosquito vector and therefore the disease. 2.Malaria is impacted by the interannual variability of temperature in highland regions in Ethiopia. 3.Malaria is now prevalent in higher altitudes in warmer years 4.Climate change will result in an increase of the malaria burden in the densely populated highlands of Africa. 5.Need for better risk models for early warning and public health campaigns. 4
  5. 5. Spatial Clustering of Malaria in East Shoa by Age & Gender 5 • The maps show villages with significant clustering. Statistical hot spots shown in Red. • Significant local clustering in the 2002–03 and 2005–06 time periods, with clusters in northern and southern villages. • Focus anti-malarial interventions such as insecticide treated bed nets (ITNs) and indoor residual spraying of houses (IRS) to those persistent hot spots, • Make timely risk maps and communicate disease risk easily Malaria incidence rate (per 1000 person-years) by age and gender in East Shoa, Ethiopia.
  6. 6. Challenge 2 - Analyzing Access to Healthcare in Kenya 1.Kenya Demographic and Health Survey 2014 (The DHS Program). A total of 40,300 households from 1,612 clusters spread across the country, with 995 clusters in rural areas and 617 in urban area 2.Our analysis is examining the factors underlying healthcare & insurance utilization as well as the distribution of malaria. 6 Every year, malaria causes a million deaths. Every minute, a child dies from malaria.
  7. 7. Healthcare in Kenya – Geographically Weighted Regression: Gender Differentials in Local R2 7
  8. 8. Healthcare in Kenya – Geographically Weighted Regression: Gender Differentials in Total Years of Education Coefficient 9
  9. 9. Challenge 3 & 4 - Studying Heat Related Mortality and Infections in India 10 • While data on heat can be estimated via climate models and station data, its impossible to find mortality stemming from heat related data (under reported). • Heat induced mortality data poor (increased vulnerability of persons with cardiovascular, respiratory, and/or cerebrovascular disease) • The risk of malaria and water-borne diseases, such as jaundice from hepatitis A and gastroenteritis, increases in high temperatures. Comparison of Cumulative Anomalous Heat in India for 1998 and 2015
  10. 10. Annual Cumulative Heat Index Anomalies in India by Station and Year (1998 & 2010) 13
  11. 11. Challenge 5 – Social Media Data & Privacy 16 Text Mining of Social Media Posts – Insights on patients’ topics, sentiments and decisions. Need to protect privacy of the individual in data mining.
  12. 12. Challenge 6 – Spatial Patterns of Prescription Opiates and Opioid Overdose Deaths in the US, 2006-2017 17 • The focus of this study is on the relationship between prescription drug (like oxycodone, hydrocodone, hydromorphone, and oxymorphone) mortality rates and physician prescribing rates along three axes -- space, time, and type of opioids -- using advanced spatial cluster and regression analysis tools at the state level from 2006-2017. • We attempt to identify spatial patterns of similarities and differences in mortality and prescription rates across states and over time, with age, race, and/or gender disaggregation, using advanced spatial panel data models for the purpose of providing area-specific policy recommendations. Source: Theo Brossman, 2019
  13. 13. Questions? THANK YOU suchi@bu.edu People.bu.edu/suchi Chen Xin Dr. Lawrence Were Kenya Team Yaxiong Ma Josh Pitts “Big Data” Analysis

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