After a bit of field testing, we finalized the outreach materials for our study households in February. After looking into different media and outreach strategies used by the WHO and Hesperian Foundation, we decided to use comics as an approach to deliver easy to interpret messages to pastoral communities with varying degrees of literacy, especially among our target sub-population of women and children. The final comic leaflet was created using images captured in Tanzania and converted to comic format in Adobe PS. Friends Danielle Knueppel and Howard Kombe provided the excellent translations into Kiswahili. We’ll roll out the comics as outreach materials as the HALI project enters a new phase with NIH and hopefully CRSP funding in the fall of 2010.
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This is a priceless excerpt from an article send out by the PROMED disease outbreak surveillance listserv discussing cholera outbreaks in Malawi and Angola, and an innovative community led total sanitation (CLTS) approach implemented in Mkanda. Kids and stones, what an amazing control mechanism…
Source: Inter Press Service, IPS News Agency [edited]
Defecating in the open, especially in the bush, has long been
standard practice in the rural areas of Malawi. Many people see the
construction of a toilet as a luxury they can do without in this poor
country, where up to 60 percent of the population lives below the
poverty line of USD 1.00 a day.
“Even now, you see that it is usual for many men around the country
to go by the roadside or under a tree to urinate. Some even come out
of cars just to urinate by the roadside. But for the people of
Mkanda, this sort of practice is frowned upon,” says Mchipha.
A neighbourhood watch, comprising both children and adults, patrols
the Mkanda area ensuring that no one defecates in the open. Natural
leaders — who have shown special enthusiasm for the concept during
orientation sessions run by extension workers — are charged with
coordinating efforts. “While patrolling, we are armed with catapults
which we use to stone anyone found defecating in the open,” Mkanda
resident Ganizo Kalaya told IPS.
Kalaya explains that children are engaged in the neighbourhood watch
because they are usually honest and have no hesitation sharing
information about who is still defecating in the open. He says an
emergency meeting is called if the patrol discovers any faeces in the
village grounds or bushes and that the entire neighbourhood is taken
to task to reveal the culprit.
“It gets embarrassing for the villagers to be put through such an
ordeal, as such the practice of defecating in the open is becoming a
very rare occurrence,” Kalaya says. Mkanda area has since been
declared “open defecation free” by the Mchinji district commission
and is being used as a model for the CLTS approach in the country.
Moving beyond the academics, it was time to get serious about disseminating an outreach and extension message to the pastoralists in Ruaha. Feedback is an integral component of research projects, an often overlooked and underappreciated component lost in the bliss of publication, graduation, etc. From an ethical perspective, sharing research results with study participants, along with integrating any study findings into an outreach/education message to enhance awareness and promote discussion is not only necessary, but fun.
Finally, you can move beyond the technical entrapment of scientific language and do something a bit more creative. I chose comics. My study area is home to a large rural and pastoral population with a high rate of illiteracy. Targeting these households required an image intensive approach. Comics seemed an ideal medium. Combined with disease reports informing the pastoralists if protozoal pathogens were detected in their calf herds with a simple frowning or smiling cow, a short 2 page comic on herd and household protection from infection and risk of transmission was created using the software ComicLife by Plasq.
I’ll post the finished Swahili comic here post production. In the meantime, enjoy the English version [download a PDF via “Outreach Message(Reduced)]…
Feel free to contact me with any details on either the disease reports of comics.
With the analysis completed (multiple times), articles written and rewritten, reviewed, and finally approved, I managed to squeak by the University of California Davis graduate studies deadline for the submission of theses/dissertations on November 25th. I have officially graduated, completing the study on calf diarrheal disease and its public health importance among pastoralists, and finalizing the outreach materials.
In the end, we focused on the use of calves as disease sentinels for rudimentary diarrheal disease surveillance. The concept of animal sentinels has a long history with the best example provided by canaries in coal mines: build-ups of CH4 and CO immediately effect the more fragile and susceptible birds who pass out and expire in cages alerting miners to the presence of a deadly substance. Likewise, in this study, calves were identified as highly susceptible to diarrheal disease, along with the diarrheal disease causing pathogens Giardia and Cryptosporidium; the presence of diarrhoeic animals in a pastoral herd can therefore be utilized as a sign for the presence of potentially infectious agents, alerting managers who may then implement a series of recommended counter measures to protect the remainder of the herd and household members from exposure.
The Thumbnail Cover: “Calves as Sentinels for Diarrheal Disease in Households Practicing Traditional Livestock Husbandry”
Thanks also to friends at the HALI project for the kind posting on completing my degree!
The Co-Principal Investigators of the Health for Animals and Livelihood Improvement (HALI) project, the mothership to which my calf study belongs, gave a really nice overview of the HALI project’s background, goals, and preliminary results this summer at the Global Livestock CRSP‘s End of Program Conference “From Problem Models to Solutions” on June 17th 2009 in Naivasha Kenya. The Co-PIs, Professors Jon Erickson of the University of Vermont and Rudovick Kazwala of the Sokoine University of Agriculture, have been very helpful during the course of my study, and it was great to see them in Naivasha. As thanks (though Jon will probably kill me for it), I uploaded their slides and the audio I captured in Naivasha to slideshare as a SlideCast, a nice interactive tool allowing you to sit on your sofa at home with a cool glass of Chimay, and absorb the intrigue of academic lectures. Sweet memories of the university are nurutured by each click of the slideshow…. Plus, Kaz give me a shout-out on Slide 27: “…one master’s student from UC Davis, which is David here, [pointing at me as I acknowledge the audience and nod over the recording equipment],” so you gotta check it out!
Now, without further ado, allow Drs. Erickson and Kazwala to present:
“The One Health Approach to Solve Complex Problems and Improve Livelihoods at the Human-Livestock-Wildlife Interface”
Questions from the audience are almost unintelligable in the audio, so I listed them here below. The full presentation will be included as an article in the upcoming GL-CRSP End of Program Conference Proceedings, edited by yours truly, to be released in early 2010…
What did you mean by the environment, as to where the diseases come from? Why?
The environment largely concerns water, but also other vectors, like flies, wildlife and so on. As to why we are seeing a resurgence of disease due to a water scarcity, we need to consider the wetlands, which serve as a sponge. If the wetlands were not there, the water would flow out and dry up the entire ecosystem. During the dry season the water slowly trickles out of the wetlands and provides water for the ecosystem. And so one of the driving factors is the effect of grazing pressure on compromising these wetlands. After the removal of pastoralists from these areas, they’ve seen a rebound in wetlands and also in water provision during the dry season.
Given evidence for the linkages, how do you propose to tackle them?
We propose to tackle them through a One Medicine [One Health] approach. The concept is to create a bridge across the three populations: veterinary teams, medical teams, and other teams integrated to deal with the questions. The diseases [zoonotic diseases] in the lab are all the same diseases. Teams need to work in the same environment and in the same lab on the same diseases. We need better integration and common interest. This is the case of the One Health approach. Other things to look at are landscape and bio-regulatory function. Eco system services for example are very critical in this role, and water and health are very intertwined at the landscape scale.
Map Skills were intense in 3rd grade. Workbooks, worksheets, new worlds. Though I’ve colored-in many maps in my day, I never thought I’d have the skills to make my own. But alas, today I waved my nerd flag and created a series of maps for the study, complete with projections from the old Geographic Coordinate System (WGS 1984 World) to the UTM WGS 1984 36S projection specific to Central Tanzania. “Damn that’s dorky,” says my inner child, and yes indeed, working with ArcGIS is dorky. Perhaps the dorkiest. But it makes pretty maps, and ones that you can analyze. So next time you need to find out which shopping centers near you have registered cases of children’s diarrhea and are 100 yards from a public restroom, or other recipes conducive to shitting your pants, you just let me know…
An example map of households containing calves with clinical diarrhea and identified protozoan infection (Crypto and/or Giardia) – shitty resolution so ya’ll can’t steal it before it’s published. Shame on you Chris Bond…
This is part 1 of how to analyze data, for those who have never analyzed data…
Part 1: Database Management, Distributions (aka Descriptive Statistics), and Bivariate Analysis
If you have never created, organized, or utilized data from a database, analysis will truly suck for you. If you have experience working with and organizing data in Microsoft Excel or Access, or some other database program like FileMakerPro, then it will just suck.
Step 1: Filter and Clean your data. This is not a one-stop shop, but an iterative process that will evolve along with your understanding and appreciation for your data or lack thereof. All aspects of your field work, be they survey responses, laboratory results, observations, etc, must be evaluated to determine the most logical way of representing the information. Do not take advice from others on this process, unless these “others” have looked at your data and marginally begun the iterative process with you. Only you will ever look at your database, work with your database, and wish you had never created it. That said, do not expect to organize your information, say in tabular format, and then quickly proceed to analysis. It literally takes multiple attempts to develop a structure that logically extends into the analysis process. Binary wrapping paper (see above) is an example of how not to organize your data. I for example, assigned binary values to all variables prior to deeply analyzing the distribution of data and determining if in fact, a binary bivariate analysis of categorical data was what I needed. I have personally re-developed my database three times: binary; non-binary; and finally a combination of binary and non-binary variables determined by a deep analysis of variable distribution, logical assumption, and intended utilization. This third component was only possible after multiple phases of trial and error with the Bivariate analysis, Step 3. Each reorganization of the database was deeply painful. I have never walked aimlessly around a room out of sheer desperate boredom in my life. It is like prison and solitary confinement and can drive you mad unless you have interpersonal interaction. Do this process in the company of others to feel less crazy. Get exercise, try not to smoke. Keep lots of whiskey handy. Drink heavily after pushing “save.”
Step 2, Distributions. So you think your database is ready eh? Well OK. Let’s find out. Do you know statistics? If yes, good for you. Benjamin Disraeli was quoted once as saying statistics are a form of lying: “lies, damned lies, and statistics.” You will quickly learn why. But first, you will quickly learn why you don’t need to know statistics, and therefore why you have no idea what is happening inside your computer with your data. Thus, you won’t be able to explain your data mathematically, and will be forced to simply accept that your computer has done its job, and that your statistical results are not lies, though you have no idea why. Quite like a creationist’s explanation of Genesis: “The bible said so, so it is, I have dominion.” No explanation necessary. Yet science is not dogmatic, because we’re statistically less crazy than creationists.
Anyway, I use JMP Statistical Software from the SAS Institute. JMP is a menu driven program that is very visual and requires no previous experience with code languages. Why did I choose JMP? To maximize profit. JMP is cheap at UC Davis. Less than $30. It must be an incentive program to get young scientists addicted to JMP, so when they graduate they pay the full amount for a professional license. Like handing out crack for free on the corner, knowing they’ll pay premium once they’re hooked, like this guy…
Descriptive statistics are done for you by your program. If you want to know the theory, take a stats class or go to the library. It’s boring. Basically, a series of distributions are shown of your data. Depending on the type of data: nominal (categorical), ordinal, or continuous, your program will spit out some figures and charts and tables. You get to look at these and say: Thanks JMP! But then you have to remember that you’re moving towards Step 3: Bivariate Analysis, and so maybe you should be more thorough. For example, maybe the herd sizes in your study have a natural breaking point around 124 animals based on a histogram output shown by these descriptive statistics, where 52% of herds have less than 124 head of cattle, and 48% have more. You might decide that you want to develop a new variable called “HerdSize_Binomial” where 0 indicates small herds (<124 animals) and 1 indicates large herds (>124 animals). Take a look at Example 1 below….
Example 1: Distribution of Herd Size (HRD_SIZE)
|Std Err Mean||34.21405|
|upper 95% Mean||290.59142|
|lower 95% Mean||153.21627|
Here you can see several fun things. For instance, in the “Moments Table” we can see sample size (n=52 households) mean (u=221.9 animals), and some confidence intervals about the mean. Above in the Quantiles table we find the rationalization for the percentage breakdown. Our median (the number separating the higher half of the sample from the lower half) is 121.5 animals, meaning that about half of the households have more than 121.5 animals, and half have less. We could have used this as the breakdown point as well. It’s really up to the scientist, and that’s you remember!
Descriptive statistics are really fun to look at, especially if you’re interested in your study. If you’re not, they will suck. But why would we want to do this in the first place? Well, beyond having a lot of interesting informtion that will help us understand our data and write about it, they also help us move towards Step 3…
Step 3: Bivariate Analysis. You’ve just created a HerdSize_Binomial variable based on your Descriptive Statistical breakdown of data on the number of cattle per household. You have two scores: 0 and 1, for small and large herds respectively. If there is any point at all to your study, you also have a dependent variable, outcome variable, or something that you are investigating. Since I have been looking at diarrhea, and causes of diarrhea in calves, lets use diarrhea as an outcome. 0 means no diarrhea in the herd, and 1 means diarrhea. These values were obtained by observations of animals with diarrhea in the field. Now what we want to do, is determine if herd size is associated statistically with diarrhea, or whether the outcome variable is explained by the covariate (explanatory variable). If you want to know the theory, please go to the library and look up the statistics. If you’re in a hurry, just push a button on your stats program, or enable your vast working knowledge of code to run R or SAS software and “Fit Y by X” to compare outcome by covariate.
What happens, especially in the case of two nominal variables (0,1 vs. 0,1), is an output of contingency tables (2×2 tables) with subsequent Chi-Square values and their associated probabilities. If there is a statistically significant relationship, these probabilities will be less than 0.05, or 0.01 based on what you have determined to be statistically significant. I used 0.1 for the bivariates to look at a greater range of possible covariates to explain diarrhea for use in further more complex analytical procedures. There will be a Fischer’s Chi-Square test as well, which with the 2×2 tables, is more rigorous than any Likelihood Ratio or Pearsons’ tests run by your program.
Example 2: Bivariate Test of Clinical Diarrhea (CLN_DIAR) by Herd Size (Total_HerdSize_Rank_Binomial):
Contingency Table (2×2 Table)
Total_HerdSize_Rank_Binomial By CLN_DIAR
|Fisher’s Exact Test||Prob||Alternative Hypothesis|
|Left||0.0004||Prob(CLN_DIAR=1) is greater for Total_HerdSize_Rank_Binomial=1 than 2|
|Right||1.0000||Prob(CLN_DIAR=1) is greater for Total_HerdSize_Rank_Binomial=2 than 1|
|2-Tail||0.0007||Prob(CLN_DIAR=1) is different across Total_HerdSize_Rank_Binomial|
In this case, we can see that Clinical Diarrhea is indeed associated with herd size as there is a statistically significant relationship shown by the P-values (Prob>ChiSq) of 0.0002 and 0.0003 for the Likelihood Ratio and Pearson tests, and verified by the Fischer’s Exact Test (p=0.0004), where these values are less than 0.05, our level of significance. Therefore, we reject the null hypothesis: there is no relationship between the variables. However, this significance is between Clinical Diarrhea in calves, and small herds (See the Fischer’s Exact Test: left, and associated Alternative Hypothesis), as the probablity of diarrhea in calves is greater when the herd size value is 1 (small herd value). This requires logcially looking at your data, reviewing the distributions of herd size against other factors like location of these herds, water sources, management practices, numbers of small ruminants, and other factors that might be influencing diarrhea beyond just herd size. But just because it is significant in a way that is contrary to our expections, it is still worth reporting, and maybe worth analyzing in combination with other factors in a more complex multivariate model, the next phase of analysis.
Next Steps. You will conduct this procedure with every single covariate/variable in your data (though not all will be contingency tables as not all data will be categorical or dichotomous). You will then interpret the results, repeating Steps 1 and 2 of database management and descriptives to obtain more easily analyzable data (like changing number of animals to a herd size rank), and then re-analyzing, continually repeating this cycle until you have no idea what the file names are, where the outputs are stored, where you live, when you last showered, why your fiance is mad at you, and what your study objectives were in the first place.
At this point, it is generally a good idea to re-read your research proposal, remind yourself of why you conducted fieldwork and under what hypotheses, refresh the logical and theoretical assumptions and framework that influenced the data collection, and then revisit your analytical results. Otherwise, you are sure to attend a meeting where you present these bivariates, warranting a firm and authoritative “what’s the point of this study, and why are you showing me this?” response from your advisers and team members.
Next time: Analysis Part 2: Multivariate Modeling and the Beginning of the End….