Large scale historical data for public health: do climate and demographics explain disease

patterns?

Wilbert van PanhuisDan Bain

Erin Jenkins, Xi Zhang, Yongxu HuangPatrick Manning

1. Project Tycho: US disease data of past century

2. Use of integrating disease, climate and demographic

data

3. Compilation of demographic data

4. Climate data and seasonality of measles and polio

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

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.

Tycho Brahe

Digitization: 2 years, 200M keystrokes

35,000 files

Year 1 Year 2

Web interface in beta testing

www.tycho.pitt.edu

Measles, London

1944 1966

Measles, Pittsburgh

1906 1953

1. Project Tycho: US disease data of past century

2. Use of integrating disease, climate and

demographic data

3. Compilation of demographic data

4. Climate data and seasonality of measles and polio

Demographic drivers of disease patterns

Science, 01-28-2000 Science, 07-17-2009

Crude birth rate (/1000)

Tim

ing

of

peak

act

ivity

Climate drivers of disease patterns

Science, 01-28-2000

% polio cases per month by latitude: 1956-57, 1965-69

35-70ºN

10-35N

10ºN-10ºS

10-25ºS

25-55ºS

WHSQ,1979 AJE,1979

Integrating different data sets

Variable Disease data Climate data Demographic data

Location Cities/States Weather stations Cities, Counties, States

Time Week Day Decade, year

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

1. Project Tycho: US disease data of past century

2. Use of integrating disease, climate and demographic

data

3. Compilation of demographic data

4. Climate data and seasonality of measles and polio

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)

Interpolations of state population

Difference by yearDifference by state

Difference of linear interpolation and census interpolated data- Linear overestimates between 1940-1950- Similar variance across states

Census higher

Linear higher

Yearly birth and death rates for statesC

rude

birt

h ra

te

(/10

00)

Cru

de d

eath

rat

e (/

1000

)

1. Project Tycho: US disease data of past century

2. Use of integrating disease, climate and demographic

data

3. Compilation of demographic data

4. Climate data and seasonality of measles and polio

Climate data: sources

NCDC: climate indicators by day for individual weather stations

PRISM: data by month for weather stations

Climate and seasonality

Polio

Calendar week

Distribution of disease incidence rates /100,000 by calendar week for US states before vaccine introduction

Calendar week

Measles

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

North-South Gradient of Temperature

Calendar day

Tem

pera

ture

m

ax

Median of maximum temperatures per calendar day for 8 cities using daily data from 1900-2010

South

North

North-South Gradient of measles ?Median incidence rates by calendar week for US

cities using weekly data between 1906-1948

Mea

sles

inci

denc

e ra

te

Calendar week

Start epidemic cycle

Association epidemic start and climateMeasles incidence rates for Boston: 1906-1948

Starting points of epidemic cycles identified (length of bar is week number)

Mea

sles

inci

denc

e ra

te

Week

Start w

eek new cycle

Measles incidence and relative humidityWeekly median cases by city (red) and relative humidity anomaly (value-mean)

Rel

ativ

e hu

mid

ity (

valu

e- m

ean)

Week

Measles incidence rate

Next steps

1. Fully integrate disease, demographic and climate data

2. Continue example analyses:

a. Climate and measles seasonality

b. Climate and polio seasonality

c. Explore additional climate indices

d. Birth rates and measles multi-annual seasonality

3. Direct linking between Tycho, climate and demographic

databases (establish collaborations)

4. Open access to enhance opportunities for discovery

Acknowledgements

Tycho database teamDon 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 demographyPatrick Manning, Dan Bain, Xi Zhang, Yongxu Huang, Erin Jenkings