Currently, I am a part of a project looking at climate change impacts on the distribution of tree and grass pollens in the US and associations with allergy and asthma related emergency room visits
As part of that, we are collecting baseline data on symptomatic profiles of patients who are sensitive to tree and grass pollens and are currently undergoing immunotherapy in local clinics.
Our survey is two fold, the first a baseline survey of types of demographics, types of allergies, seasonal sensitivities, general symptoms and lifestyle impacts, the second a three week survey of sleep quality and allergy and asthma related events.
We hope to gather data to see how the ragweed season might impact general health and well being using a coarse raster of predicted pollen distribution.
The survey is being conducted at the University of Michigan Allergy Specialty Clinic and Food Allergy Clinic at Domino’s Farms and will include approximately 50 people.
At least that’s what we hope happens. Yesterday, I had the opportunity to join the Detroit Communities Reducing Energy and Water (use) project, focusing on Parkside, a subsidized housing community in Detroit, MI.
The project aims to help residents make changes to the electrical and plumbing infrastructure of their homes to reduce the energy costs. Residents in poor communities often live in housing that has old, inefficient and sometimes faulty electrical wiring, kitchen appliances and aging or damaged pipes, showers and toilets.
The University of Michigan School of Public Health has a community based participatory research project with the residents of Parkside, the Friends of Parkside, a local advocacy group.
We administered a survey on energy, housing conditions and health to about twenty residents who came to the event. Following the consumption of copious amounts of pizza, the goals of the study were explained to everyone in a group meeting and consent was obtained.
They then moved to another room and took the survey. Many of the residents were elderly, mostly women. All had interesting stories to tell about broken air conditioners, unresponsive maintenance crews, family, friends, kids…. everything you find in these kinds of surveys.
After they were done, they all got some ca$h and were provided with a temperature monitor so that we can better understand what they are experiencing in their homes during these hot summer months. We will then conduct a follow up survey to assess the impact of a home based educational program on energy use and health.
It had been a long time since I was involved in community and I was grateful to be a part of. Some people don’t like this kind of work, I really don’t understand what’s not to like about hanging out with survey respondents who feel invested in the project and their communities.
New chapter from myself in a Springer volume: “Access to Health Care in Sub-Saharan Africa: Challenges in a Changing Health Landscape in a Context of Development”
I wrote a chapter for “Health in Ecological Perspectives in the Anthropocene” edited by Watanabe Toru and Watanabe Chiho. I have no idea if they are related. Either way, my chapter “Access to Health Care in Sub-Saharan Africa: Challenges in a Changing Health Landscape in a Context of Development” occupies pages 95-106 in the volume.
Check it out, you can buy the book through Amazon for a cool $109, or just my chapter through the Springer site for $29 or you can simply write me and I’ll give you a synopsis.
Here’s the abstract for the book:
This book focuses on the emerging health issues due to climate change, particularly emphasizing the situation in developing countries. Thanks to recent development in the areas of remote sensing, GIS technology, and downscale modeling of climate, it has now become possible to depict and predict the relationship between environmental factors and health-related event data with a meaningful spatial and temporal scale. The chapters address new aspects of environment-health relationship relevant to this smaller scale analyses, including how considering people’s mobility changes the exposure profile to certain environmental factors, how considering behavioral characteristics is important in predicting diarrhea risks after urban flood, and how small-scale land use patterns will affect the risk of infection by certain parasites, and subtle topography of the land profile. Through the combination of reviews and case studies, the reader would be able to learn how the issues of health and climate/social changes can be addressed using available technology and datasets.
The post-2015 UN agenda has just put forward, and tremendous efforts have been started to develop and establish appropriate indicators to achieve the SDG goals. This book will also serve as a useful guide for creating such an indicator associated with health and planning, in line with the Ecohealth concept, the major tone of this book. With the increasing and pressing needs for adaptation to climate change, as well as societal change, this would be a very timely publication in this trans-disciplinary field.
I have nothing to say, I just want to see if this works
I found this post and wanted to see if it actually works (sometimes the code included in blog posts does not…actually, this code in this one did not. I had to make some modifications to get this to work.).
Apprently, I can include images, so I’ll include the most popular image on my site:
I can include R code
Which is great, because I do a lot of R work
So here’s some R code. You can see that it is formatted properly:
summary(mtcars) plot(mtcars$mpg, mtcars$cyl, main="myplot", xlab="mpg", ylab="cyl")
2. I can even include videos (I think), like this horrifying clip from Slithis Survival Kit:
Well, two packages, at least. Having not posted in well… forever… this is a decent move back into the world of blogging (which is far harder in 2018 than it was in, say, 2009.)
I have been working on Shiny based mapping apps recently and found the Zip Radius Package potentially convenient. I even made a map of zip codes and population within 100 miles of 48104.
The fieldRS package provides a convenient way of classifying and mapping remote sensing data, which will be extremly handy when doing the snake project, for example. An open question was how to access localized risk based on topography and landuse. I had no convenient way of assessing this at the time.
While other blog posts will do a much better job of explaining the Data Explorer package in R, it still seemed useful to mention it here.
A huge hurdle to data analysis is data cleaning, and to effectively develop a strategy to efficiently prepare data for analysis, a basic snapshot of the data is helpful.
Enter the Data Explorer package, a set of tools that can provide minimal descriptive information for not much effort at all. With a single command, you can take a raw dataset, and produce a useful report that you can use to start working on your plan of data cleaning attack.
I downloaded a portion of the Social Indicators Survey from Columbia University, and picked a small subset of variables.
Using this small set of code, I produced the report below.
sis_sm <- as.data.frame(with(sis, cbind(sex, race, educ_r, r_age, hispanic, pearn,
Data Profiling Report
The data is 34.8 Kb in size. There are 453 rows and 12 columns (features). Of all 12 columns, 9 are discrete, 3 are continuous, and 0 are all missing. There are 1,245 missing values out of 5,436 data points.
Data Structure (Text)
## 'data.frame': 453 obs. of 12 variables: ## $ sex : Factor w/ 2 levels "1","2": 2 1 2 1 2 2 1 2 2 1 ... ## $ race : Factor w/ 4 levels "1","2","3","4": 3 1 1 2 3 3 3 4 1 4 ... ## $ educ_r : Factor w/ 4 levels "1","2","3","4": 4 4 2 2 2 1 1 4 4 2 ... ## $ r_age : num 40 28 22 24 31 42 36 63 69 24 ... ## $ hispanic: Factor w/ 2 levels "0","1": 2 1 1 1 2 2 2 1 1 1 ... ## $ pearn : num 14400 14400 12000 15000 8000 9600 2400 9600 NA NA ... ## $ assets : num 5000 50000 4000 NA NA 6000 NA 1250 100000 NA ... ## $ poor : Factor w/ 2 levels "0","1": 1 1 1 2 2 2 2 2 2 2 ... ## $ read : Factor w/ 4 levels "1","2","3","4": NA NA NA NA NA NA NA NA NA NA ... ## $ homework: Factor w/ 4 levels "1","2","3","4": NA NA NA NA 4 1 1 NA NA NA ... ## $ black : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ... ## $ police : Factor w/ 2 levels "0","1": 2 2 1 1 2 2 1 NA 2 2 ...
Data Structure (Network Graph)
The following graph shows the distribution of missing values.
Continuous Features (Histogram)
Discrete Features (Bar Chart)