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.