Ingredients for Tasty Publications

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

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.

Off to Kenya…

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.

Christmas in May?

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…

My Labmate Enos is on the HALI Blog!

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!

The bulb blew out…

Shit!

I received a nice status update on the sampling analysis for the project some time ago from Tanzania:

“Am far from my note book now but I remember,I did about 186 fecal samples. The last day when I stained the slides to accomplish the examination of the sample number 186, it was a lucky day because after I completed examination of all the stained slides of that day; The slides went out, the reagents were completed, the microscope machine stoped from working(bulb problem) and I had a defficience of falcon tubes for processing the unprocesed fecal samples from the field.”

So, the bulb went out…the bulb for the fluorescence microscope that allows for observation of the Crypto and Giardia oocysts and cysts in the fecal samples.  Another one is now en-route from UC Davis with the HALI project coordinator, but the delay has caused another month delay.  Lab time now at 5 months and ticking for the analysis of 300 samples….

I’m beginning to think this project will never end.

The Method

Methods

Study population and sampling

The study was conducted over the course of six months, from August 2008 to January 2009, with the majority of fieldwork conducted between August and October of 2008.   The area sampled in the study covers two divisions within the Iringa District of Tanazania bordering the Ruaha National Park and Wildlife Management Areas: Pawaga and Idodi Divisions.  The Pawaga Division is comprised primarily of agro-pastoralist communities practicing subsistence living and traditional animal husbandry.  The Idodi Division is comprised of similar subsistence-based agricultural villages, but in general supports a more transient agro-pastoral and pastoralist community.  The Ruaha Landscape is located in the Southern Highlands of Tanzania; an area characterized by variable rainfall and prolonged periods of drought.

Study site roughly between Ruaha National Park and Iringa…

Households recruited for the study consisted of agro-pastoral and pastoralist households from the Barabaig, Maasai, and Sukuma tribes participating in a larger study on diseases that can be passed from animals to humans, as part of the Health for Animals and Livelihood Improvement (HALI) project, of which this study is a small component.  HALI selected study households in order to obtain an accurate representation of households in the larger region.  Criteria used in their selection process include ethnicity, socio-economic status, and geographic location.  Potential candidate households were then contacted to obtain consent, meaning the household members agree to participate in the study, and were informed that livestock would be sampled, and a series of interviews would take place primarily inquiring about their health, livestock and livestock health, their income and socio-economic activity and status.

The perfect sample…

In a scientific study, it is considered ideal to have a sample that is fairly uniform.  That is, a sample that shares certain traits and attributes in common, so that you can compare each household in your study to another regarding the research question, while minimizing the factors that may influence differences in results that are not controlled for in your study.  Loosely translated, this means that if I were studying the effects of eating cheeseburgers vs. beats on cholesterol, I would want a sample that was very similar: similar age, similar income level, similar ethnicity, etc.  A perfect sample would be Lego Men.  They’re all the same height, weight, color, and typically all have the same job and therefore income.  I could use them as a case-control group, and have 20 eat cheeseburgers for  month, while 20 others ate no cheeseburgers but only beats.  The effects would thereafter be measured, and we could be reasonable sure that all noticeable and measurable differences were due to diet.  A perfectly terrible example would be GI Joe men.  They’re all over the board.  Just read their “classfied cards” on the back of each package.  Let’s compare two:  Duke is a highly educated, Caucasian male who runs the entire show.  BBQ wears a mask, is a fireman (maybe even from another planet), and speaks a very strange dialect along with perfect English, maybe is mentally ill, and  probably follows a different nutritional regimen than Duke.   Any results of the cheeseburger/beat experiment on these two would not be so easily discernible, as BBQ may have variable cholesterol due to a host of factors ranging from genetics to on-the-job stress.  No Joe!

My study had to work with the HALI sample of 160 households, and select a sub-sample of 60.  I’ll get to why we chose 60 in a little bit, but first, let’s look at how I chose my households.  I needed to mimic the HALI sample and include a representative proportion of ethnicity, income, and livelihood, so my study needed Barabaig, Maasai, and Sukuma as well as pastoralists and agro-pastoralists living in both Pawaga and Idodi Divisions.  Tricky enough, but because my study wanted to look at diarrheal disease in calves, I also wanted to choose households that reported to HALI that diarrhea was a problem in their herds.  So I mined the HALI data, and created a list of households that reported diarrhea.  Then, in order to not be biased, I used a computer program called a random number generator (Random.org) to select 80 households from within the HALI sampling frame.  I needed 80 in case some households did not want to participate in the study, and so I had some alternate households to choose from.   We then selected households from this list, and because of conditions in the field, had to be pretty flexible in households we chose.  Sometimes, just like us, pastoralists are not at home.  But unlike us, pastoralists live far, far away, and driving there requires time, gas, patience, all of which equal money.  I didn’t have very much money, so we really had to work with households from the list that were around on the days we were in the field.

Sample size estimates

Within each household there is a livestock herd.  Within each livestock herd there is a ndama or calf herd.  So far, no one has studied diarrheal disease in these calf herds, but others have studied disease in calf herds in the Iringa area near villages (not pastoralist herds).  They found prevalence rates for Cryptosporidium of around 20% there.  That means 1 in 5 animals was infected with Cryptosporidium.  Since those studies were interested in infection rates at the animal level, and we were interested in rates of infection at the herd level, we had to choose our sample sizes a little differently.

Scientists use math to obtain a sample size.  If I were looking at infection at the animal level like the other study in Iringa, we would take their 20% prevalence rate as what we would expect to find, and use it obtain a sample size large enough to predict 20% prevalence in my study as well.  There is an equation you can use for this.  There are also tables printed in statistics books based on a series of equations run by others that you can reference for your sample size.

Most scientists use computer programs to run their sampling equations.  We decided on a sample size of 300 animals across our 60 households.  This allowed us to sample just enough animals to ensure that we reached a herd infection rate of 20%, meaning 1 in 5 herds belonging to the households were infected.  This was more important to us because we were interested in Cryptosporidum and Giardia infections that may be dangerous to people who closely interact with their animals.  Having just one animal nearby is enough to pose a threat to the health of people who watch and interact with that animal.

Another question in sample size was how many animals to sample at each household.  Since some households have small calf herds and others large calf herds, we had to vary the sample size at each household.  In small herds we sampled every animal, and in larger herds we tried to sample at least 50% of the animals.  In essence, this broke down to about 5 animals per household, as many herds were similar sizes.  In addition to the number of animals, you need to have a protocol for the selection of animals.  Typically we would randomly select an animal using a random number generator, and then target every other animal in the pen.  This makes sure that we aren’t unconsciously selecting animals that look ill, or that are easy to catch,  and that would bias our samples towards a certain type of animal (here we didn’t want Lego Men, but GI Joe, confusing I know…).  Furthermore, we had to make sure that animals we sampled met our criteria: they had to be calves (generally under 1 year of age in pastoralist herds).

Sampling Procedure (Livestock)

Yeah, we had to put on gloves and take poop out of calves’ buttholes.  Then we put the poop in little bags and mixed it with a buffered Formalin solution to preserve them like poop mummies.


Sampling Procedure (Household)

No poop here, well a lot less anyway.  Instead, we gave a 35 minute interview consisting of about 35 questions to members of the households.  The questions were about livestock management, livestock diseases, dangerous diseases and health of calves, and other questions about disease and health, and general livestock husbandry practices.  The interviews were done in Swahili since there is a high illiteracy rate, and questions were read to the household members by a field assistant and the answers recorded.  Then I measured things like the size of the livestock pens, stables, and inventoried the items in and around the property like water availability, forage, crops, property like bicycles and stuff, and how much environmental contamination (poop) was littered about the ground near to the household.

Study Design

This study followed a cross-sectional design.  Cross sectional designs are different from the case-control Lego Man design described above, because they are mainly suited to describing conditions at a certain time in a certain population.  Kind of like finding out the number of Lego Men with swords at castles in January.  What is the sword prevalence of the Lego Men at a distinct time and why?  Do Viking castles have a higher prevalence of sword?  What about Ninja castles?  That’s basically what we did.  How many calf herds had Crypto or Giardia at the time of interview/sampling (read August-January) in pastoralist herds in Iringa and why?  Do Maasai herds have higher prevalence?  What about herds closer to the wildlife management area?  What about larger herds?  You get the picture…  The interview helped us record some things we could use to try and find out what factors were related to infection so we could try and deduce what could be dangerous for the young livestock, and therefore help the households keep their animals healthy so they can stay healthy.

Sword Prevalence at about 42% here…

Case Definition

Case definitions are what scientists use to designate the intended outcome variable.  What is that?  Well, in our case for example, the outcome variable is a calf infected with Crypto or Giardia.  Our case definition is a calf infected with Crypto or Giardia, where infection is confirmed by laboratory analysis of its’ poop.

Laboratory Analysis

Laboratory analysis was done by Enos, a nice veterinary student at the Sokoine University of Agriculture in Tanzania.  Basically, Enos took my poop samples and prepared microscopic slides using a kit purchased from Waterborne Inc. The kit is called the A100FLK AquaGlo Giardia/Cryptosproidium Direct Comprehensive Kit.  In the preparation of the microscope slides, Enos applied a flourescein monoclonal antibody reagent to the poop solution.  This reagent is specially engineered to bond with Giardia and Crypto if it is in the poop.  Enos then would take the microscope slide and look at it under a fluorescence microscope, very very carefully.  I have a picture he sent me of a positive sample in some poop.  Check it out…

crypto-giardia

It’s not the greatest picture, but it shows how difficult it is to see Crypto in poop.  Poop is messy.  We all know this.  It’s not a surprise.  So you can see what Crypto does look like under the microscope, here’s another picture from the EPA website which isolates the oocysts, kind of a control slide…

That’s what Cryptosporidium oocysts look like in the microscope.  After finding some of these little blobs, Enos then starts to count them.  He counts them all over a certain area of the slide in order to generate a number of oocysts in the sample so we can determine just how infected the calf actually is.  Shedding oocysts sucks, and if a calf is really infected it will shed quite a few, especially during the height of the infection before it’s body starts to adapt to the pathogen.  From Enos’ calculations, we can do some calculations of our own, and find out how many oocysts per gram of poop the calf is spewing into the environment.  The more the merrier?  Not at all.  The more oocysts per gram of feces equates with a higher degree of environmental contamination from the pathogen, as well as the more risky that environment becomes for the people living and interacting with the animal.

Statistical Analysis

Once I have all of the laboratory information, then I start to analyze the data.  I do some statistical analysis.  I use my case definition and my outcome variable, and I start to look at how different things are associated with it.  I  use the questions and answers from my survey, questions like how many calves were born in the last year, and what is your calves water source, and I run what’s called a bivariate analysis.  This means I look at how closely water source and infection of a calf are associated statistically.  I do this in a computer program.  The computer program will give me some output and some graphs, and then I select the things that are most closely associated with infection, and some other things I think are logically associated with infection, and I make a statistical model.  It’s like cooking.  I add ingredients (things associated with infection) and watch it cook.  Then I taste it and see if it’s OK.  If it’s too salty I take out some ingredients and try it again.  If it’s too bland, I add some ingredients to it.  I do this until me and my computer program are happy with the dish.  Then we look at what the recipe is, and share it with the scientific community.  The ingredients in the recipe are now called “risk factors.”  The recipe is called “Prevalence of Cryptosporidium and associated risk factors in neonatal livestock in traditional pastoral livestock systems.”  That’s the bee’s knees.

I’ll post more about the statistical analysis and results next time.  It’ll take me awhile.  I started the bivariate analysis this week, getting familiar with all the ingredients.  As I still don’t have all the lab results, I have awhile to wait before we get really heavy with the data.  When I do, I’ll write all about it, I promise….