Category Archives: disease modeling

Life Expectancy and Cause-Specific Mortality by Census Tract in King County, Washington

Some new research that I’m excited about came out last week: Variation in life expectancy and mortality by cause among neighborhoods in King County, WA, USA, 1990–2014: a census tract-level analysis for the Global Burden of Disease Study 2015. http://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(17)30165-2/fulltext

In some ways, it is very specific to Seattle and the surrounding county: https://vizhub.healthdata.org/subnational/usa/wa/king-county

But it is also a demonstration of the “fractal” nature of population health—the variation between life expectancy from country to country around the world is big! But it is around as big as the variation between life expectancy from county to county around the United States. And what this work shows is that even in the county where I live, the life expectancy varies between census tracts almost as much as from county to county or country to country. Inequality is happening at all scales.

Here is the data: http://ghdx.healthdata.org/record/united-states-king-county-washington-life-expectancy-and-cause-specific-mortality-census

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Using disease projections for workforce planning

Here is a short paper that cites my projections of ACHD cases [link] to recruit docs into the area: https://bjcardio.co.uk/2016/04/growing-need-for-trainees-in-adult-congenital-heart-disease-in-the-uk/

Cool, it’s just what I was hoping the results would be used for.

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Diabetes by County in USA

Diagnosed and Undiagnosed Diabetes Prevalence by County in the US, 1999–2012
Authors
Laura Dwyer-Lindgren, Johan P Mackenbach, Frank J van Lenthe, Abraham D Flaxman, Ali H Mokdad
Publication date
2016/9/1
Journal
Diabetes Care
Volume
39

http://care.diabetesjournals.org/content/39/9/1556.abstract

Cool maps:
http://vizhub.healthdata.org/us-health-map/

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Filed under disease modeling, global health

Recently published article from International Journal of Health Geographics to read:

Read some of the most recently published articles from International Journal of Health Geographics.

Identifying food deserts and swamps based on relative healthy food access: a spatio-temporal Bayesian approach

http://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-015-0030-8

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Brief survey on sequence classification

hi Abie,

It was great speaking with you. This is the paper I was talking about.

http://dl.acm.org/citation.cfm?id=1882478

Looking forward to know more about each other’s work.

Thanks,

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Filed under disease modeling, machine learning

Book’s got a cover!

An Integrative Metaregression Framework for Descriptive Epidemiology – http://www.amazon.com/Integrative-Metaregression-Descriptive-Epidemiology-Publications/dp/0295991844

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Scientific Constants

A fun pointer I picked up at the eScience institute recently is where to find constants in python: scipy.constants http://docs.scipy.org/doc/scipy/reference/constants.html

or, if you fancy units, astropy.constants http://docs.astropy.org/en/stable/constants/index.html

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