Transcript mHealth Health and Infectious Disease Surveillance and - i-calQ

Part 2: mHealth and Infectious Disease
Surveillance and Notifiability
Joel Ehrenkranz MD
Chief Medical Officer
i-calQ LLC
Salt Lake City, Utah
USA
John Snow’s original 1854 Map of London Cholera Epidemic
• Smartphones
provide date and
time stamps and
GPS coordinates.
• Smartphones are
programmable
two-way
communication
devices
Smartphone Epidemiology
Snow’s Map Updated with Windows Geographic Cluster Analysis
mHealth represents the application of advances in electronics and computer science to health
and medical care.
A Loa loa “App” for Full Quantification and Mapping
• A snapshot of the assay will be taken with the camera of a smart
phone.
• A reader “app” will quantify the data on the spot and
simultaneously capture the GPS coordinates.
• The results and coordinates will be uploaded into a “Global
Database” for real-time mapping and monitoring of loiasis.
Biamonte M. and Baldwin3R.
2014. Personal Communication
Automated, Real Time Mapping of Disease Clusters
Nonstop and Connecting Flights to Munich in February,
2015 from Regions in which Malaria is Endemic.
mHealth Applications Can Automatically Upload
Patient Data to an Electronic Medical Record and
Automate Patient Follow-Up.
mHealth Can Monitor Population Health Over Time:
Micronutrient Malnutrition
Elimination of Iodine Deficiency in Thailand: 2003-2013
Data and mapping supplied by Pongsant Srijantr: Thailand MOPH
Year 2003
%IDD =
13.54
Year 2009
%IDD =
13.36
Year 2004
%IDD =
15.28
Year 2005
%IDD =
21.55
Year 2006
%IDD =
19.56
Year 2010
%IDD =
10.31
Year 2011
%IDD =
7.60
Year 2012
%IDD =
8.66
Year 2008
%IDD =
15.22
Year 2013
%IDD =
7.78
Risks of Geographic Information Systems:
Surrogate Marker Correlation Does Not Mean Causality.
Page 7
NIH-PA Author Manuscript
Measles epidemics in West
BhartiAfrica
et al.
cause a significant proportion of
vaccine-preventable childhood
mortality. Epidemics are strongly
seasonal, but the drivers of these
fluctuations are poorly understood,
which limits the predictability of
outbreaks and the dynamic response
to immunization. We show that
measles seasonality can be
explained by spatiotemporal changes
in population density, which we
measure by quantifying
anthropogenic light from satellite
imagery. We find that measles
transmission and population density
are highly correlated for three cities
in Niger. With dynamic epidemic
models, we demonstrate that
measures of population density are
essential for predicting epidemic
progression at the city level and
improving intervention strategies. In
addition to epidemiological
applications, the ability to measure
fine-scale changes in population
density has implications for public
health, crisis management, and
economic development.
Fig. 1.
NIH Public Access
NIH-P
NIH-PA Author Man
(A) Map of Africa, Niger in gray. (B) Three cities of Niger included in this study. ( C)
Average weekly annual rainfall for Niger (dark gray) and national weekly average of annual
measles cases, 1995–2004 (light gray). Shading gives 95% confidence intervals. ( D)
Relative transmission rates (number of infections per product of susceptible and infectious
individuals per 2 weeks) for Niamey, Maradi, Zinder by calendar day 1 to 365 ( x axis) (1).
Gray area indicates rainy season. (E) Relative brightness (cubic smoothing spline, df = 3) by
calendar day 1 to 365 (x axis) for each city. Gray area indicates rainy season; dashed line
Manuscript
indicates mean of brightness for each city (table S1). ( F) Author
Brightness
against relative
manuscript;
available
transmission rate for each city. Box indicates interquartileScience.
range, Author
whiskers
extend 1.5
times in PMC 201
the interquartile range. Width of boxes correlates to number
of
observations.
Published in final edited form as:
Science. 2011 December 9; 334(6061): 1424–1427. doi:
Technology drives medicine:
mHealth is the Future of Health Care
Smartphones can
• integrate subjective, physiological, and diagnostic data in real time
• increase access to diagnostics and decision support
• provide knowledge-based disease management
• automate data recording and medical record keeping
• assist with patient compliance
• improve outcomes
• save money
1816
1961
2011