Project Tycho: US disease data of past century

1. Large scale historical data for public health: do climate and demographics explain disease patterns? Wilbert van Panhuis Dan Bain Erin Jenkins, Xi Zhang, Yongxu Huang Patrick Manning

2. Project Tycho: US disease data of past century • Use of integrating disease, climate and demographic data • Compilation of demographic data • Climate data and seasonality of measles and polio

3. A project to digitize and render computable public health data from around the world and to provide open access to these data. • Goal: Increased use of public health data for decision making • Vision: Centralized, coordinated access to disaggregated public health data • Strategy: • Set example using data already in public domain • Demonstrate value through analyses for decision making • Establish collaborations to link data and enhance data use • Explore barriers to Open Access in interdisciplinary context • Create international guidelines for public health data sharing

4. Tycho Brahe 1546 – 1601 Danish nobleman who made accurate and comprehensive observations of the positions of the stars and planets. After his death, Tycho’s assistant Johannes Kepler used these data to derive the laws of planetary motion.

5. Digitization: 2 years, 200M keystrokes Year 2 Year 1 35,000 files

6. Web interface in beta testing Measles, London 1944 1966 Measles, Pittsburgh www.tycho.pitt.edu 1906 1953

7. Project Tycho: US disease data of past century • Use of integrating disease, climate and demographic data • Compilation of demographic data • Climate data and seasonality of measles and polio

8. Demographic drivers of disease patterns Timing of peak activity Crude birth rate (/1000) Science, 01-28-2000 Science, 07-17-2009

9. Climate drivers of disease patterns % polio cases per month by latitude: 1956-57, 1965-69 35-70ºN 10-35N 10ºN-10ºS 10-25ºS 25-55ºS Science, 01-28-2000 WHSQ,1979 AJE,1979

10. Integrating different data sets • Data transformation required: • Max. and min. temperature per day -> per week • Precipitation per day -> per week • Decennial census data -> interpolations per year • Assume no change within years

11. Project Tycho: US disease data of past century • Use of integrating disease, climate and demographic data • Compilation of demographic data • Climate data and seasonality of measles and polio

12. Demographic data: ICPSR and others Sources: ICPSR: Decennial census (state and county), City-county data books, US Census Bureau: State populations by year (interpolations) State Health Departments: State variables by year (eg birth rates)

13. Interpolations of state population Census higher Difference by state Difference by year Linear higher Difference of linear interpolation and census interpolated data - Linear overestimates between 1940-1950 - Similar variance across states

14. Yearly birth and death rates for states Crude birth rate (/1000) Crude death rate (/1000)

15. Project Tycho: US disease data of past century • Use of integrating disease, climate and demographic data • Compilation of demographic data • Climate data and seasonality of measles and polio

16. Climate data: sources PRISM: data by month for weather stations NCDC: climate indicators by day for individual weather stations

17. Climate and seasonality Distribution of disease incidence rates /100,000 by calendar week for US states before vaccine introduction Measles Polio Calendar week Calendar week

18. Eight cities along N-S gradient Portland, ME Boston, MA New York, NY Philadelphia, PA Baltimore, MD Richmond, VA Raleigh, NC Charleston, SC Brunswick, GA

19. North-South Gradient of Temperature Median of maximum temperatures per calendar day for 8 cities using daily data from 1900-2010 South Temperature max North Calendar day

20. North-South Gradient of measles ? Median incidence rates by calendar week for US cities using weekly data between 1906-1948 Measles incidence rate Calendar week Start epidemic cycle

21. Association epidemic start and climate Measles incidence rates for Boston: 1906-1948 Starting points of epidemic cycles identified (length of bar is week number) Measles incidence rate Start week new cycle Week

22. Measles incidence and relative humidity Weekly median cases by city (red) and relative humidity anomaly (value-mean) Relative humidity (value- mean) Measles incidence rate Week

23. Next steps • Fully integrate disease, demographic and climate data • Continue example analyses: • Climate and measles seasonality • Climate and polio seasonality • Explore additional climate indices • Birth rates and measles multi-annual seasonality • Direct linking between Tycho, climate and demographic databases (establish collaborations) • Open access to enhance opportunities for discovery

24. Acknowledgements Tycho database team Don Burke, Wilbert van Panhuis, John Grefenstette, Shawn Brown, Ernesto Marques, Bruce Lee, Derek Cummings, Vladimir Zadorozhny, Steve Wisniewski, Su Yon Jung, Nian Shong Chok, Heather Eng, Anne Cross, David Galloway, Suzanne Cake, Raaka Kumbhakar Dataverse team on climate and demography Patrick Manning, Dan Bain, Xi Zhang, Yongxu Huang, Erin Jenkings