Point-of-Use Ruaha

Healthy Comics…

February 1, 2010 · Leave a Comment

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)]…

Outreach_Message(Reduced)

Feel free to contact me with any details on either the disease reports of comics.

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Canaries in Coal Mines and Calves in Bomas…

December 30, 2009 · Leave a Comment

Source: Linkian209

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!

HALI Project Blog post

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HALI Project Presentation from the GL-CRSP End of Program Conference

September 30, 2009 · Leave a Comment

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…

Questions

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.

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Map Skills…

September 29, 2009 · Leave a Comment

Remember these?

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…

Diarrhea and Protozoan HHs

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…

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Data Analysis Part 1: Database Management, Distributions, and Bivariates

September 26, 2009 · Leave a Comment

Binary Wrapping Paper available at “Think Geek!”

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.”

Get a better office.

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…

A typical UC Davis JMP user in the Population Biology Graduate Group…

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)

HRD_SIZE

image2

Quantiles

100.0% maximum 1328.0
99.5% 1328.0
97.5% 1186.3
90.0% 487.2
75.0% quartile 249.8
50.0% median 121.5
25.0% quartile 84.3
10.0% 48.0
2.5% 12.9
0.5% 9.0
0.0% minimum 9.0

Moments

Mean 221.90385
Std Dev 246.72103
Std Err Mean 34.21405
upper 95% Mean 290.59142
lower 95% Mean 153.21627
N 52

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

Count
Total %
Col %
Row %
0 1
1 6
11.54
25.00
22.22
21
40.38
75.00
77.78
27
51.92
2 18
34.62
75.00
72.00
7
13.46
25.00
28.00
25
48.08
24
46.15
28
53.85
52

Tests

N DF -LogLike RSquare (U)
52 1 6.7637548 0.1885
Test ChiSquare Prob>ChiSq
Likelihood Ratio 13.528 0.0002
Pearson 12.942 0.0003
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.

Who cares?

Next time: Analysis Part 2: Multivariate Modeling and the Beginning of the End….

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Ingredients for Tasty Publications

September 23, 2009 · Leave a Comment

I’ve been working on data analysis for the past month, and have completed all preliminary and bivariate analyses for the study, looking into associations between survey and sampling data and Cryptosporidium, Giardia, Cryptosporidium and Giardia together, Clinical Diarrhea, and households with herds shedding large quantities of oocysts and cysts of the protozoal pathogens.  I’m now moving towards the development of a model, which will allow us to determine with greater confidence the factors impacting infection in calf herds, factors that without a model cannot be looked at in combination with other elements.  I’ve put together a short list here of some of the statistically significant risk factors for infection for Cryptosporidium to illustrate what ingredients are considered in model design.  All statistics have been performed in the JMP statistical program, created by the SAS Institute.  I’ll post more on the process of analysis in a bit…

Model Recipe 1: Cryptosporidium a la mode…

Hypotheses/Assumptions. Cypto is a fecal-borne pathogen with water-borne capability.  Water sources in the area have been identified as contaminated with Crypto.  Water is a factor.  Feces is a factor.  Calves with more exposure to feces will be at higher risk.  Therefore, larger herds, more density, and managing manure will play a role.  My data shows that smaller herds, less density, and managing manure increase infection contradicting these claims.  A new management score will thus be developed to test for a combination/score/ranking significance to further flush out these relationships.

Source: NorCal Blogs.com Bullfight!

Significant Ingredients:

NEORATIO (p=0.0711): Prob C+ > with increasing NEORATIO
Birth/Herd Ratio (Ratio of neonates/young livestock to adults)
NEORATIOS range from 0 to 1.  A ratio of 1 implies one calf per cow.  A ratio of 0.01 implies 0.01 calves per cow, biologically intangible unless cows have died, been sold, or calves have been purchased, traded, or stolen.  A herd averages one calving season per year, and so the range should be closer to 1.  More C+ at a ratio closer to 1 could mean more births per year for each cow, could imply larger herds with more bulls and cows, or could indicate a loss of cows, all factors that may imply death/loss, or more potential for fecal generation and thereby transmission of Crypto.

DTH_GTS (p=0.0835): Prob C+ > with increasing death
Number of goat deaths in last 12 months
Small ruminants are expected to be a reservoir for Crypto and Giardia, and more deaths could imply greater disease burden among this population.  No sampling was conducted here, and so it remains a priority for disease surveillance.  A small ruminant variable capturing births, deaths, and total herd size as a SR household score could be a way to better explore the impact of SRs on calf infection.  Note: calves and SR are often kept in the same boma, and may explain the frequent significance of SR on outcome variables.

MORT_RATIO (p=0.0325): Prob C+ > with increasing MORT_RATIO
Herd mortality ratio (number of deaths/number of animals in herd)
As MORT_RATIO approaches 1, herds begin to die off in entirety.  A low MORT_RATIO indicates less death in the past year, a potential indicator of herd health.  Higher MORT_RATIOS may indicate unhealthy herds, or those closer to predation, as disease and predation are the two major causes of death.  Both wildlife vectors, water scarcity, and nutrition may be factors at play in herds facing predation (closer to wildlife management areas (WMAs), further from villages), while disease death may indicate overall herd susceptibility to infection.

BOM_MOVE (p=0.0016; RR=7.737): Prob C+ > in HHs that MOVE
Do you move the boma?
While moving the boma was hypothesized as being protective, households that move bomas may be responding to a build-up of feces indicative of larger quantities of fecal matter, or to disease in the herd requiring the construction/relocation for herd health.  Survey responses indicate that HHs move bomas primarily when they are full of manure, as this allows predators to easily enter over the acacia thorn barriers, and when it rains, as water and manure make for excellent disease incubating conditions.  Therefore, moving the boma is a response to disease instead of a protective measure.

Giardia 3D Model (Source: Tjamrog)

GIARDIA (p=0.0231; RR=3.33): Prob C+ > if herds are G+
Simple positive infection of Giardia in calf herd
This is an expected association: herds with Crypto are expected to also be infected with Giardia, as they share similar transmission dynamics.

HighShedding_Giardia (p=0.024; RR=2.05): Prob C+ > if herds are HSG+
High or low cyst shedding calf herds based on histogram breakdown
This is also expected.  Herds with Crypto are expected to also be infected with Giardia, and herds with both Crypto and Giardia infections are expected to be higher shedding herds as they are more at risk, and most likely exposed to a variety of disease causing organisms.  These herds (C+, G+ HSC+ and HSG+) are definitely the most at-risk HHs in the study.  A closer examination of these herds is in order.

LS_H20_CLEANED (p=0.646 no Fischer; Kappa=0.016): Prob C+ > for non-SW users
Water sources (3 categories: surface, well/spring, and multiple)
Surface water was hypothesized to be a factor in disease transmission, and Crypto has been identified in several water sources in the area.  However, it is the non-surface water households that were identified with greater probability of infection.  These are a minority in the sample, and while it is possible that non-surface water sources are also contaminated, more characterization must be done.  Herds relying on well/spring water may be located closer to villages, may be more intensively managed, and therefore may be more prone to infection due to the fecal borne transmission route.  I must look into these elements in more detail.

Stocking Density (herd) (p=0.0931): Prob C+ < with increasing density
Ratio of total number of animals to primary boma area (animals/m2)
This is truly an odd finding; counter intuitive and contradictory to hypotheses.  Increasing density should increase exposure to feces and thereby transmission.  Low density however, indicates either smaller herds or larger bomas. Smaller herds can be an element of total income (another variable to look into), poverty, marginality, death, trade, or livestock exchange (no data on this).  It is possible that smaller herds and greater poverty are located in areas with poor forage and access to resources (Barabaig) and may be less healthy overall due to location (need to run spatial data in ArcGIS to test).  Larger bomas may indicate more space for boma expansion, or the existence of a once large herd that has diminished in size due to disease/predation/exchange.  Greater C+ in the lower density category therefore may be indicative of factors like poverty or livestock losses.  Less dense herds and livestock deaths should be investigated more closely, along with income and poverty variables to get at the question: why is density important?

Stocking Density (calves) (p=0.0912): Prob C+ < with increasing density
Ratio of total number of calves to calf boma area (calves/m2)
See above.  For calves, less density means less births or larger calf boma.  Less births may mean more unhealthy cows, cow losses, low milk production and higher calf mortality, or simply bigger calf bomas.  Larger calf bomas may in fact be due to larger herds of small ruminants, as the two are often housed together.

Area_Calf_Boma (p=0.0064): Prob C+ > with more area
Total area of calf boma (m2)
Larger calf bomas may in fact be due to larger herds of small ruminants, as the two are often housed together, or due to larger calf herds.  No density is in this variable, and though density trended opposite (less dense herds > risk of C+), it is possible that it was a low calving season, and the boma itself is a greater indicator of calf herd size.  Was it a low calving season due to drought other factors?  Damn lack of longitudinal data!

ZOO_DIS (p=0.0438; RR=2.724): Prob C+ > if HHs believe diseases have zoonotic potential
Do you think bad things can be passed between animals and humans?
This is a purely qualitative variable with little assistance in disease risk factors for calves.  Plus, it seems that educated households have more infection.  Is this because more educated households are closer to villages, are studied more, are provided with more information?  Are the other HHs remote (Barabaig) or closer to the park (so more education)?  This can go many ways.  No candidate for the model, but interesting to touch on for the paper and extension.

Washing Udders! (Source: David B. Frankhauser)

UD_WSH (p=0.0386; RR=0.397): Prob C+ > non-udder washing HHs
Do you wash the udder of the cow?
Indeed.  Washing the udder of the cow is in fact protective, though the low RR says not by much.  Washing the udder may remove contaminants that would directly infect the calf through ingestion, as well as the HH through flow to milk container and contact with hands.  It is nice to see that this is working out here.  Add to model, combine with management score as well.

MLK_AMT_reclass (>1L) (p=0.0292; RR= infinity?, OR=0): Prob C+ > for herds with calves consuming more than 1L milk at a feeding
Categorical binomial breakdown of calf milk consumption (>1L; <1L)
Odds ratio indicates that all herds with calves consuming more than 1L are infected.  Why?  We know that this survey question was strange for pastoralists to answer as they measure the milk for themselves but allow calves to simply suckle.  However, such a variation in responses was obtained that perhaps there is something here.  Cows with greater access to milk may come from larger herds and be more exposed to feces.  More access to milk may mean less washing of udders (look into this).  More access to milk may mean less household members with milk demands, and less labor to manage bomas, attend to animals, etc.  Weak associations perhaps, but interesting to discuss.  Add to model?  Maybe.

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Risk at the Noxious Nexus of Unintended Consequences: Livestock, Wildlife, People, Disease, and Development

June 22, 2009 · 1 Comment

On June 19th I had the opportunity to participate in a panel at the Global Livestock CRSP End of Program Conference entitled “Risk at the Noxious Nexus: Livestock, Wildlife, People, Disease, and Development.”  Dr. Pete Coppolillo of the Wildlife Conservation Society’s Yellowstone Rockies program moderated the panel, which included Dr. Rudovick Kazwala of the Sokoine University of Agriculture, Tanzania, Dr. George Aning, of the University of Ghana, Legon, and myself.  As moderator, Pete prepared a conceptual diagram for the talk, based on the four pillars of zoonotic disease control and management: Mandate, Means, Motivation, and Knowledge (MMMK).  Quite an acronym.  I was asked to talk a little bit about knowledge and some conceptual linkages in zoonotic disease control and management, and in the preparations, put the following notes together.  I thought I’d share them here. Note: A Podcast from the panel will be posted on the GL-CRSP website, as soon as I have the time to edit it.

1

Source: Nature

Introductory Remarks.  Watching Dr. Kazwala’s slide show really brought an interesting image to mind.  I’m sure you’ve found that the past has a way of resurfacing and becoming relevant in new and very different ways as you age.   When I was a kid, I was really into old movies.  In particular Westerns.  I’ll always remember this one scene from a film set in a dusty rural outpost probably near the Badlands in the Dakotas, where a rough looking guy is on a plank board table in a dimly lit room in a clapboard house, held down by another guy with a beard looking quite like Michael Jacobs [Principal Investigator of the Afghan PEACE project for the GL-CRSP with trademark Montana 19th century beard] in a Civil War uniform.  He’s swigging a big gulp of whiskey from a dingy bottle when another guy, a medic or a doctor comes to the table with a tough looking saw.  They’re going to amputate his leg, and right when the doctor is about to start sawing, the guy on the table looks up and sees this picture on the wall. It’s an anatomically correct poster of a horse, and you see his face get this worried look as he asks the question:

“What kind of doctor are you anyway?”

The doctor looks down at the floor and under his breath replies:

“A veterinarian.”

Last fall I was in the field with the HALI project visiting some Maasai households out in the bush near a wildlife management area, also a dusty badlands looking place, watching the HALI team interact with pastoralists.  Often, I saw HALI vets and animal technicians interacting with household members, treating broken limbs, answering questions about respiratory issues and stomach problems, and essentially playing the role of the medic in the Western:  a multi-faceted medical resource for rural areas.  I didn’t witness any amputations, but I was impressed at both the trust and appreciation for the medical attention paid to the HALI team, and also the extreme realization for the challenges of providing medical prevention and treatment options out there.  Especially in areas where the health of livestock and  households are so intrinsically linked and connected.

I’m not a veterinarian, nor will I pretend to be an expert on zoonotic disease. In fact, I can look around the room today and see quite a few individuals with considerably more experience and knowledge in this area, notably David Bunn and Grace Marquis.  I even used one of Graces’ old observational public health studies from Peru as a model for a component of my current study.  I’m simply honored to be a participant in this panel, especially in the company of Drs. Kazwala and Aning.

What I can speak about and comment upon, however, is the complexity of zoonotic disease, the complexities in understanding and grappling with the challenges facing zoonotic disease control and management, and the severe difficulties in designing and implementing research targeting zoonotic disease and education.

Swine flu and vaccines. You know in Star Trek they seem to have these vaccinations for everything in the galaxy, yet space is still considered this dirty disease ridden place, kind of like an airport.  Yet when they visit a new alien planet they get a shot and are immune to everything.  It’s too bad things can’t be as simple as on TV.  Not to discount the idea of the vaccine, but I think all too often it’s considered a silver bullet, and frankly earth is not the Enterprise.  Zoonotic disease really catapulted on the scene again this spring with swine flu, and I found it interesting to observe the reactions of the global media.  In fact, I kind of always find it interesting to watch the reactions of the global media to what I’ll call “real” news, news that is exciting and fear really sells, just look at box office numbers for horribly written and produced horror films.   With swine flu there was of course such a frenzy.  Maybe because the pump is so primed thanks to Avain Flu, and other really exciting epidemics like Ebola and SARS, I think we are as a society really afraid of zoontoic disease, perhaps because we can’t see it, and don’t’ understand it, like a monster under the bed as a child.  It’s tough to fight something you can’t see and hardly understand.  And I think this is truly the challenge in working with zoonoses in the development context.  Not that we in developing countries understand it, but we don’t have to, because we as a public have these institutions and individuals who are trained to protect us, along with a government that ensures just that through funding and policies that treat health as a public good, at least when it comes to epidemics.  I think it’s widely considered now that Mexico did a tremendous job with swine flu response and control, and suffered the consequences.  Culling thousands of animals suspected as infected at huge cost to the government and pork industry.  While potentially not as rapid as some would have liked, their response seems to have come in good time considering the infrastructural challenges at hand.   Yet they intervened for both the national and global good, and will struggle with the stigma of having contaminated pork for probably years to come, and who knows what the impacts on livelihoods and human capital will be in the infected area.

This is just one of the complexities to the knowledge, management and control of zoonotic disease:  transparency.   After watching the “global pandemic” the Grim Reaper on the cover of the Economist, the studies on the cost of intervention at that magnitude.  I began to wonder that if there was no global media, would the Mexican government have responded as quickly?  If there was no limelight, no TV, how many would have died and could it have been another Spanish flu with Mexican pork continuing to be exported to global markets?

Things seem to keep coming back to the issue of power, and as Abdi Jama mentioned after the LINKS/LEWS team presentation, getting the information into the hands and channels that inform the public, that let the public own that knowledge is critical to control, to management, and to public health in general.  Publicizing the disease, the threat, is integral.  Ownership of the knowledge is really a crucial factor.

Importance of Communication. Listening to a lot of the talks over the past few days I couldn’t help but notice some similarities in solving development problems.  The listening and establishment of relationships and trust that leads to the understanding of the problems and definition of a problem model, and then the collaborations on training and capacity building that then enables the research, education and intervention efforts on a wide scale seems pretty standard for a lot of our successful projects: ENAM, PARIMA, AFS, and LEWS/LINKS to name a few.  Underlying this success, as Layne Coppock and a few others pointed out, is the time to initiate this process.  Time that groups like the can CRSP provide.  With zoonotic disease, this time element, the time that can enable the various sectoral collaborators to develop a working relationship to tackle the legal and policy challenges, along with the field-based capacity strengthening and development of appropriate diagnostic tools is especially critical.  One thing I’ve learned from HALI is how difficult it can be for a team of veterinarians to shake things up and get things moving along; both in terms of funding and understanding.  Communication and marketing I think are critical here, to gather the attention and really spread the message that there is a new and evolving role for veterinary science in development, away from increasing production, to a greater role in overall human health and development.   Like Michael and Catherine were talking about in Afghanistan, there is a great demand for trained professionals in the discipline, and the more integrated and multi-disciplined training that can be provided, the more impact may be obtained, for overall ecosystem health by combining outreach with education, information, and communication campaigns.  Yesterday during the group discussions, I thought the two modeling groups made a very excellent point: often it is the way ideas and information are packaged that truly impacts the way that information is used, or used at all.  The One Health campaign I think has been invaluable in this regard.   It has been especially difficult for the HALI team leaders to collaborate with medical professionals, and their relationships with the various government groups, especially Tanzanian National Parks were also a challenge.  But I think it is promising that veterinarians are taking the lead here, and I hope that the project has laid the groundwork as one type of model with promise for zoonotic disease management at the landscape level.

Complexities: What is emergence?  What are the threats? Pete has talked a bit about the changing nature of zoonotic disease in an era of rapid globalization and increasing physical linkages across landscapes.  And I think it’s important to note that often these changes brought about by modernization create opportunities not just for new emerging diseases, but also for the resurrection of older diseases, historically not recognized as a threat.  One of the pathogens we’re investigating with the HALI project is Cryptosporidium, a protozoal pathogen that is a major cause of morbidity and mortality in livestock and sometimes fatal in human cases as well.  Cryptosporidium has been around the block you could say, and has always impacted rural areas and areas where animals and humans share water sources and are in close proximity, but the infrastructural changes of urbanization, and piped water resources have really allowed it to flourish in remarkable ways.  The outbreak in Milwaukee US is an excellent example, where Crypto infiltrated the piped water network and caused a mass of infections, actually killing and hospitalizing scores of individuals, especially the immunosuppressed.  The point I suppose is that this idea of surveillance is especially difficult given the multiple transmission pathways, and sometimes at even the animal level.  There has been a lot of discussion about the potential to use the animal as sentinel, that if one can maintain an outbreak at the animal level, then the risk of transmission to humans can be surmounted.  But the challenge of both identifying and then containing this zoonosis, especially in rural areas that already suffer from a deficit of veterinary and medical services is really just enormous.   The HALI project has been sampling adult livestock for Crypto, along with water supplies in the Ruaha ecosystem, and while identifying it in water, did not have much success in finding the pathogen in livestock.  But then we started sampling the neonatal livestock, well over 25% of the calf herds were infected with Crypto, and over 60% of the calf herds with Giardia.  So surveillance must be targeted at just the right level to be effective, both within and across species, in livestock and in wildlife, and as Pete mentioned, the challenges of disease surveillance in wildlife are especially daunting.

Interventions and Rabies. I had the opportunity to work in Tanzania with the District Veterinary office and the WCS on a rabies education event in one of the villages near the WMA.  We walked around the village prior to the start of the event, with the district veterinary officer, and it was pretty apparent there was very little activity between that office and the village, either for livestock or for the intended purpose, rabies vaccinations.   At the end of the day, everyone seemed very excited about the rabies event.  They all in this case, seemed very informed as well.  And it was clear that education was not the missing link but simply the lack of resources to do vaccinations of village dogs.  This resource constraint is compounded with the lack of capacity to deliver the vaccine in terms of trained human resources, something the veterinary officer readily admitted.  Yet this is an issue that is fairly easily surmounted, by the injection of monetary resources and human energy, something that Dr. Kazwala and the HALI team intend to do this September.

Knowledge:  What are the conceptual linkages?  Do we know what the challenges are? I’m not sure we will ever fully know what challenges are in the realm of zoonotic disease.  The tricky thing in zoonoses is that it also involves the category of emerging diseases, diseases that may not be truly recognized or understood by science, by the health communities, and that may place society and communities, including the global community in a very vulnerable state.  I would also like to emphasize again the heavy burden born by the rural and urban poor who are usually at the front lines of the emerging disease threat especially in areas of land use change and intensifying use of limited resources.  I think a prime example here is HIV, another disease that impacted the rural areas for who knows how long before turning up in Los Angeles and across the US.  No one was prepared for HIV, it simply occurred, became endemic, and we are still reeling from the challenge that it instills in society, and in our communities, not just I the developing world, but also in the US, where there will always be considerable work to maintain the social structures behavioral practices that minimize infection and transmission.  But knowledge advancements are integral to the process of controlling and managing those zoonotic diseases that are both neglected and emerging, and the development of integrated systems, like those presented by Dr. Kazwala, those that coordinate between animal and human health sectors, are essential in protecting against the ill consequences of zoonotic disease over time.   I just think there must be strong emphasis, perhaps a boots on the ground approach, with trained community health workers and paravets working at the village level and continually reporting back to district offices with greater communication capacity.  LEWS/LINKS concept of an early warning system for disease could be a really effective tool here, especially to facilitate the information exchange from household to national levels, where intervention decisions can be made.  But again, with interventions, there needs to be resources to compensate producers for animals lost and to encourage reporting without fear of negative consequences.

Do we know what we are managing for? What is this “for”?  Is it management that limits zoonotic disease transmission?  Is it management to protect against global pandemics?  Is it management that is truly integrated to tackle the issues of neglected zoonotic diseases?  I think an interesting feature of the past couple of days has been this insistence on community and grass roots level focus in development.  I have heard several presenters talk about how their projects decided to focus at this level because they really did not have the capability to engage and interact for impact at higher levels.  Perhaps this is an issue with funding, with the position of outside researchers and power relationships in developing countries, and with what I would consider the limitations of research to enact higher-level policy change in the short term.  It really is no different with zoonotic disease.  Frankly, there is considerable and rapid progress that can be achieved with limited resources at the village and community levels, and at the district level, and perhaps the Economic Rates of Return (ERRs) here are much greater for a project, especially a CRSP project than at other levels.  And managing zoonoses, despite the complexities, can achieve results in the short term, across multiple levels.  I think we need only look to David Bunn and the Avian Flu School for evidence of success here.

But back to the question of “Do we know what we are managing for?” Yes, I think we do.  The problem is that we need to manage for different things at different levels.  We need to manage for animal health at the individual animal and herd levels.  We need to manage for human health at the individual and household level. And to achieve health, we need to manage for a host of other considerations, related to infrastructure, water quality, water quantity, and resource management, education, and capacity building (and now I sound like a grant proposal) but I think in the past several days, lots of us have sounded like proposals.

Revisiting Conflict: Battling Evolution. I’m still struggling with the complexities of zoonotic disease, and conceptually, these complexities I think will only expand the more we learn about the interactions and dynamics.  My study for example looked at one species, and within it, found that there are very different dynamics in terms of infection.  As microbes continue to evolve, to adapt to human methods of control, the challenge will continue.  It is not the type of battle that is won, but rather like was noted during the conflict presentations, a battle that is imperative to fight despite the potential futility, and knowing the enemy is critical.  In this case we may actually be battling evolution.  I didn’t really intend to frame this as a war.  I think we all realize that wars are rarely successful, instead, they tend to release a series of unpredictable and unintended consequences that actually make prevention and control more difficult and compromise the original goals and objectives. Just look at the War on Drugs.  Perhaps a race is a better analogy.  A race against evolution.

Question for the panel:  Surveillance. One of the questions that I’ve been struggling with in relation to zoonotic disease and development is the issue of “control” and surveillance.  There is a lot of focus on surveillance, and maybe to buck the trend, though I’m not moderator, I’d like to ask the panel a question myself:  What good is surveillance in areas where there is minimal capacity to respond and act?  I guess what I’m getting at is, if there are resources invested in surveillance in areas where an outbreak occurs, [and that outbreak is donor dependent, and donor intervention seems driven in zoonotic disease by fear, the fear of an outbreak spreading beyond the poor to developed countries] and there is no capacity to respond, then why invest in surveillance?  Could these resources be better allocated to other things to improve livelihoods and to work at interrelated issues that enhance and accentuate the risk factors of disease transmission?  Issues like water quality and sanitation?  Issues like those brought up in the conflict panel, which quite frankly resonated as a panel about power and power struggles between haves and have-nots?

Questions from the audience. If it’s OK, I’d like to hear some responses from the audience on what they think about zoonotic disease, and what are the important ways to address the challenges.  I’m particularly interested in what the donor community is thinking.  Again, zoonoses are nothing new.  They just seem to be increasingly popular as Pete said in this new era of climate change, globalization and media frenzy.

Note:  Jimmy Smith of the World Bank asked a pertinent question during the panel:  Should zoonotic disease surveillance, control and managment efforts be focused on “hot spots,” geographic areas featuring a series of risk factors ripe for the emergence of new zoonoses?  Or should efforts be more “impact” oriented, targeting areas where these zoonoses occur? [The map at the top of the post is a map of these very hot spots, identified in a recent paper in Nature]. The World Bank has invested considerable resoures focusing on a “hot spot” oriented approach to date.  After conisderable discussion, it was generally agreed that the so-called impact areas also areas of “hot spot” potential, and so the “hot spot” approach is a much more cost effective and efficient method for prevention and response.

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Off to Kenya…

June 11, 2009 · Leave a Comment

I am departing for Kenya tomorrow (June 12th) to attend the Global Livestock CRSP End of Program Conference “From Problem Models to Solutions,” held at the Sopa Lodge in Naivasha.  The conference is a capstone event of sorts for the GL-CRSP, the organization I have been working with since 2006, and will feature multiple presentations, key note addresses, and panel discussions on topics ranging from human health and nutrition, rangeland management and climate change, risk mitigation and pastoral development, and zoonotic disease and development.  I will be participating in the zoonotic disease and development panel, along with Pete Coppolillo of the Wildlife Conservation Society (WCS), Professor Rudovick Kazwala of the Sokoine University of Agriculture, and George Aning of the University of Ghana, Legon.

Pete Coppolillo demonstrating some raptor skills…

I am very lucky to be a part of such a commendable panel.  Dr. Coppolillo directed the Ruaha Landscape Program for the WCS, a program that enabled the Health for Animals and Livelihood Improvement (HALI) project of the GL-CRSP to really focus on zoonotic disease in the Ruaha ecosystem at the Wildlife-Livestock-Human interface.  Dr. Coppolillo is now the Director of the Yellowstone Rockies Program for WCS.  Dr. Kazwala is one of the world’s leading authorities on tuberculosis and bovine tuberculosis, working with multiple agencies including the World Health Organization and Food and Agriculture Organization on policies and programs for emerging zoonotic diseases.  I was fortunate to meet Dr. Kazwala through my research with HALI, and he has been of tremendous support and encouragement in helping HALI to thrive in their work on bovine tuberculosis and other zoonoses in Tanzania.  Both Dr. Coppolillo and Dr. Kazwala are Co-principal Investigators for the HALI project.  I am not familiar with Dr. Aning, but understand he is a veterinarian and an authority on avian influenza and poultry disease, especially village poultry.

Professor Kazwala at the Envirovet Institute in his swimmies!

I hope to record the panel discussion in order to post it on the blog as a podcast, and will be interacting with several individuals at the conference to capture video interviews, podcasts, and record presentations for the GL-CRSP website, which I will link to here.

I am returning to the US on the 22nd of June, just in time to start with all data analysis and development of the thesis, now that I have integrated all the data into a working database.  If possible, I’ll try to post a short article on the conference and events in Kenya, along with a synopsis of the zoonotic disease panel discussion.

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Christmas in May?

May 26, 2009 · Leave a Comment

Micro2BW

Where Enos rocks the mic…

Yesterday I received an email from Tanzania.

“Hi David.  Sorry I was a bit late to reply to you.  The work is over by today.  The attached file contains the data on what I did….Thank you David.  Enos”

It was from Enos Kamani.  Legendary scientist and soon to be veterinarian with honors from the Sokoine University of Agriculture in Morogoro, Tanzania.  The fecal samples are done and the data sheet delivered just two days shy of my 30th birthday.  It’s Christmas in May folks, and I’ll be unwrapping that glorious Excel file under a plum tree in San Rafael with a grin as wide as the Grinch and nervous fingers tip tapping away on old JMP.

It’s data analysis time!

Data Analysis Plan – Coming Soon…

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My Labmate Enos is on the HALI Blog!

May 26, 2009 · 1 Comment

My man Enos chillin’ in the lab…

It’s so exciting to see your friends online.  My lab mate, Enos Kamani, was featured in the Health for Animals and Livelihood Improvement (HALI) project blog for all of his hard work analyzing fecal and water samples.  Deana Clifford, HALI project coordinator and postdocotoral researcher at UC Davis’ Wildlife Health Center had the opportunitiy to interveiw Enos during her last trip to the Sokoine Unviersity Faculty of Veterinary Medicine in Morogoro, Tanzania, where Enos is pursuing his degree in veterinary medicine.

Check out Enos’ profile on the HALI blog here!

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