Yet More Tableau Data Viz

More Data Viz

The Capitalism of Health

Here is a data journalism piece exposing the fact that massive spending on healthcare in the US does not equate to a healthy population;

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“Vox: Ways the American health care system is literally the worst: The United States comes in dead last in a new, international ranking of health care systems from a top health-care non-profit. A new Commonwealth Fund report looks at how the United States stacks up against other countries on things like access to doctors and quality of care. It pulls from three separate surveys conducted over the past three years: a 2011 survey of sicker patients, a 2012 survey of doctors and a 2013 survey of adults over 18. It also uses health outcome data from the OECD and World Health Organization.”

There are some nice data visualisations in this article;

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Where is the Sense?

I found this data viz online, and again, I just can’t stop thinking that it would make so much more sense to direct the (sickeningly huge amounts of) money spent on biomedical interventions (which don’t have much of an impact on health outcomes, in fact as little as 10%) towards improving living conditions and nutrition, thereby reducing the impact of disease and raising the general health of the population.

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After all, it’s all about the host. A healthy body is far more efficient at dealing with disease than an unhealthy one, so why aren’t we investing in health over “healthcare”?

Link to original article here:

World Bank Data

After spending some time on the World Bank website, it demonstrates a good range of data in relevant areas for this project, and also has a tool for creating data visualisations using the data provided. It has a relatively simple interface, but does not seem to be intelligent enough to suggest specifics for creating more readable data visualisations.

I will play around with this tool and see if I can create some usable data visualisations for the final piece of data journalism.

I have noticed that you can select as many variables as you like, which isn’t ideal, as it then creates unreadable or confusing data visualisations. Above all I want my data visualisations to be easily readable, or they fail at the most basic level.

Alcohol Consumption as a Measure of Social Degradation?

Using World Bank data, and their tool for creating data visualisations, I created a map using the same countries as I had found OECD data for in relation to environmental degradation,

The question is, can alcohol consumption be considered a good measure of social degradation? I’m sure that would an interesting (and somewhat controversial debate).

Most people would agree that excessive, compulsive, or irresponsible alcohol consumption could be a social issue; an expression of an unhappy and unfulfilled society, filling an emotional hole with the escapism of alcohol. The problem is that data doesn’t break this down for us, we can’t presume to know why people are drinking, or whether it is a problem for them or indeed for society at large. Unless we consider alcohol consumption in general to be negative for society (which I’m not sure we can) then this may not be an adequate or fair measure of social degradation or human health.

What is the Goal for Human Health?

Looking at data from The World Bank, it appears at the first look, that the focus on pharmaceutical (or biomedical) interventions to improve health have far less of an impact than lifestyle changes and access to healthy food, effective sanitation, and clean water.

In 2010, 66.8% of deaths (as percentage of total deaths) were from non-communicable diseases (heart-disease, stroke etc.). These diseases are lifestyle related. It is common knowledge that heart disease is exacerbated by smoking and stress for example, so would it not make more sense (if the goal was to improve human health outcomes in general) to focus on addressing these lifestyle related causes of disease and death?

In 2010, 24.103% of deaths worldwide (as percentage of total) were caused by communicable diseases and maternal, prenatal and nutrition conditions. So in fact, we cannot even see from the data displayed on this website the exact breakdown of deaths from communicable diseases (for which there are many pharmaceutical interventions) as distinct from maternal, prenatal and nutrition conditions. Again, it appears that the sensible interventions to prevent deaths and improve health outcomes would be in the areas of nutrition, water and sanitation. These improvements would all contribute to healthier people, who would then be less susceptible to communicable diseases.

It seems incredibly obvious when even having a cursory look at this data and the way it is presented, that there is no sense in trying to fix human health from the outside in, health simply does not work like that. The sensible thing to do is to ensure a sound basis of health with good nutrition, clean water, and sanitation. This foundation would then provide long-term protection from both communicable, non-communicable, and nutrition conditions

Making an Impact

My intentions for the finished artefact (of which this blog, and indeed this entire website are an intrinsic part) are that a selection of amazingly impactful and compelling data visualisations will combine with a powerful written narrative to bring to light the issues connecting human and earth health. In honesty, I’m hoping it will show a connection or a correlation, because it makes sense, but either way I will be able to explore the topic and investigate what the data is saying.

I have struggled with deciding on which data to use to express which measure, because there is such a wealth of data available, and so many different interpretations of what actually constitutes environmental degradation. I have decided that use of forestry resources and pollution are good and appropriate measures of environmental degradation, and possibly also CO2 emissions.

In terms of the human health aspect, or the social degradation aspect, I think that looking at mental health, health outcomes, and possibly crime or education would be good measures. I have been looking into data sets in this area, and will investigate further and create some more experimental data visualisations.