Digging into GBD 2010 Risk Factors

I have to make the old DisMod-III website disappear (is it still here?). It is beautiful, but it is not available, and so I have to turn away researchers who want to use it for their own work. Fortunately, I can send them to a GitHub repository of DisMod code that they can use. But recently, it was not really DisMod that the emailing researcher wanted. I think they were really interested in digging into the details behind a figure like this one:
rf

For that, there is a non-dead website I can offer: GBD Compare. Finding your way around it can be a bit of a challenge, though, so here is a link straight to the relative contribution of each nutritional risk factor for Germany and USA: http://ihmeuw.org/2c6t ; to see from which specific diseases the risk factor DALYs are attributed, you can use a different part of this tool, linked to here: http://ihmeuw.org/2c6v

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Age- and sex-specific death data

A colleague asked recently where to find different estimates of age- and sex-specific death counts for comparison purposes, and I told her that there should be some available on GapMinder. This was wrong (although you can compare child mortality rates there), so I did a little digging. Here are the results, in case they are useful for you, too:

If you want to explore the IHME data, here is a little notebook that you can use to get started.
Capture

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IHME Seminar: Tobacco 2025 Targets

The IHME weekly seminar kicked off for the quarter last week with Ver Bilano’s work on Estimation of recent trends in tobacco use and baseline projections to 2025. Ver used DisMod-MR extensively for this project, so I knew I was going to love it ahead of time.

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Summer’s End

2014-09-02 22.28.37There are new fellows running around, and classes are underway. I guess the summer is coming to an end. I wonder where it all went. No, I know where.

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MCMC in Python: sim and fit with same model

Here is a github issue and solution that I saw the other day. I think it’s a nice pattern.

def generate_model(values={'mu': true_param, 'm': None}):

    #prior
    mu = pymc.Uniform("mu", lower=-10, upper=10, value=values['mu'], 
        observed=(values['mu'] is not None))

    # likelihood function
    m = pymc.Normal("m", mu=mu, tau=tau, value=values['m'], 
        observed=(values['m'] is not None))

    return locals()

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MCMC in Python: Fit a non-linear function with PyMC

Here is a recent q&a on stack overflow that I did and liked.

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Tabular Data in Python: Getting just the columns I want from pandas.DataFrame.describe

The Python Pandas DataFrame object has become the mainstay of my data manipulation work over the last two years. One thing that I like about it is the `.describe()` method, that computes lots of interesting things about columns of a table. I often want those results stratified, and `.groupby(col)` + `.describe()` is a powerful combination for doing that.

*But* today, and many days, I don’t want all of the things that `.describe()` describes. And the ones that I do want, I want as columns. Here is the recipe for that:

import pandas as pd

df = pd.DataFrame({'A': [0,0,0,0,1,1],
                   'B': [1,2,3,4,5,6],
                   'C': [8,9,10,11,12,13]})

df.groupby('A').describe().unstack()\
    .loc[:,(slice(None),['count','mean']),]

and out comes just what I wanted:

       B            C
   count  mean  count  mean
A
0      4   2.5      4   9.5
1      2   5.5      2  12.5

It took me a while to figure this out, and these docs helped:

http://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-by-stacking-and-unstacking

http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-xs

Here it is as a ipython notebook.

(Note: this requires Pandas version at least 0.14.)

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