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Identification of risk factors associated with carriage of resistant Escherichia coli in three culturally diverse ethnic groups in Tanzania: a biological and socioeconomic analysis

Summary Backgound Improved antimicrobial stewardship, sanitation, and hygiene are WHO-inspired priorities for restriction of the spread of antimicrobial resistance. Prioritisation among these objectives is essential, particularly in low-income and middle-income countries, but the factors contributing most to antimicrobial resistance are typically unknown and could vary substantially between and within countries. We aimed to identify the biological and socioeconomic risk factors associated with carriage of resistant Escherichia coli in three culturally diverse ethnic groups in northern Tanzania. Methods We developed a survey containing more than 200 items and administered it in randomly selected households in 13 Chagga, Arusha, or Maasai villages chosen on the basis of ethnic composition and distance to urban centres. Human stool samples were collected from a subset of households, as were liquid milk samples and swabs of milk containers. Samples were processed and plated onto MacConkey agar plates, then presumptive E coli isolates were identified on the basis of colony morphology. Susceptibility of isolates was then tested against a panel of nine antimicrobials (ampicillin, ceftazidime, chloramphenicol, ciprofloxacin, kanamycin, streptomycin, sulfamethoxazole, tetracycline, and trimethoprim) via a breakpoint assay. Susceptibility findings were matched with data across a wide range of household characteristics, including education, hygiene practices, wealth, livestock husbandry, and antibiotic use. Findings Between March 23, 2012, and July 30, 2015, we interviewed 391 households (118 Arusha, 100 Chagga, and 173 Maasai). Human stool samples were collected at 226 (58%) households across the 13 villages. 181 milk samples and 191 milk-container swabs were collected from 117 households across seven villages. 11 470 putative E coli samples were isolated from stool samples. Antimicrobial use in people and livestock was not associated with prevalence of resistance at the household level. Instead, the factors with the greatest predictive value involved exposure to bacteria, and were intimately connected with fundamental cultural differences across study groups. These factors included how different subsistence types (pastoralists vs farmers) access water sources and consumption of unboiled milk, reflecting increased exposure to resistant bacteria in milk. Interpretation When cultural and ecological conditions favour bacterial transmission, there is a high likelihood that people will harbour antimicrobial-resistant bacteria irrespective of antimicrobial use practices. Public health interventions to limit antimicrobial resistance need to be tailored to local practices that affect bacterial transmission. Funding US National Science Foundation; Biotechnology and Biological Sciences Research Council, UK Medical Research Council; and the Allen School.


3
. Map of study location with number of households sampled per village.

Collection and storage of fecal and milk samples
Fecal samples (N=1 per household) were collected from the floor or from the pit toilets and placed into sterile Whirl-Paks. In households with pit toilets (almost every house that had a toilet had drop toilets) a cotton tipped applicator was lowered into the pit. Milk and swab samples were only collected in the final year of survey given preliminary results indicating high levels of resistance in households that consumed large quantities of unboiled milk. A total of 272 milk (n=181) and swab (n = 91) samples were collected across Maasai (n=80) and Arusha households (n=36). Milk samples from cattle, sheep, and goats were collected by pouring milk from collection and storage containers into sterile 50-mL polypropylene tubes. Swab samples were collected by scrubbing the inside of milk containers using moist (~5 mL of water) cotton pads. Swabs were then placed into sterile 50 mL polypropylene tubes. Type of storage containers (calabash, metal, plastic), storage time (if known), and milk type (fresh/sour) were documented. Milk and swab samples were placed into a portable refrigerator that was kept below 5°C and transported to the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Arusha, Tanzania. Upon reception at NM-AIST, the fecal samples were diluted in approximately 9 parts of sterile water; glycerol was added (15% volume/volume final concentration) to a 1-mL aliquot of fecal slurries and were stored at -80˚C. The dried presumptive E. coli cultures from the 96-well assay plates were recovered at the WSU laboratory by adding 150 µL of LB broth into each well of the plates and incubating overnight at 37˚C. Duplicate working plates were prepared from the original plates; glycerol was added to the original plates and stored at -80˚C. The identity of n = 800 presumptive E. coli isolates was confirmed by their colony morphology on MacConkey agar (streaking those isolates on MAC plates) and by testing for the presence of uidA marker using a PCR assay 2 . As part of a related project, we later conducted whole-genome sequencing for 183 putative E. coli isolates from four Maasai households, of which 90·7% were E. coli and 7·7% were Enterobacter cloacae and for 25 putative E. coli isolates from waters, of which 92% were E. coli and 8% were Enterobacter cloacae. Milk samples transported to the NM-AIST laboratory where they were serially diluted (10-fold) by using sterile phosphate buffered saline; scrubbed swab samples were multiple times squeezed between fingers inside the Whirl-Paks. Milk samples (50 µl from 10 0 to 10 -2 dilutions) and undiluted swab extracts were plated on to MacConkey agar plates by using sterile glass-beads and then incubated overnight at 37°C in a stationary incubator. After 24 h the plates were examined for the presence of presumptive E. coli and/or other lactose-fermenting bacteria. When tested in this manner, the abundance of E. coli was limited in the milk samples and hence, the number of Gram-negative lactose-fermenting colonies was enumerated. The bacterial load (log10) was estimated per mL of milk. The undiluted milk and swab samples were stored at -80°C for future use. A related whole-genome sequencing project from four Maasai households indicated that milk isolates were composed of approximately 21·7% and 68·3% E. coli and E. cloacae, respectively, while swab isolates included 9·5% E. coli, 34·4% E. cloacae and 35·1% of Klebsiella sp.

Estimating the bioavailability of oxytetracycline residues in milk
To determine if single boiling events can affect the biological activity of oxytetracycline residues in milk, we used a competition experiment between a resistant [SSuT-6; (22)] and an isogenic susceptible E. coli strain (the resistance genes were deleted). Briefly, milk (100 mL, pasteurized, homogenized) was transferred into sterile conical flasks and then supplemented with oxytetracycline [0.5 µg/mL final concentration before being serially diluted (2-fold) to attain 0·25 and 0·125 µg/mL concentrations]. Aliquots (5 ml) from each dilution were transferred into 15-mL polystyrene tubes and the remaining milk was heated until boiling for approximately 2-3 min. Boiled milk was cooled to room temperature and 5 mL aliquots were prepared. Drug-resistant or susceptible E. coli culture (isogenic strains) were added to every aliquot to a final concentration of approximately 6 log10 cfu/mL after which the milk samples were incubated at 37°C for 24 h. After incubation, the colony forming units of resistant and susceptible E. coli were enumerated on MacConkey agar plate with and without oxytetracycline (16 µg/mL). The colony forming units were log10 transformed and competitive indices were calculated using the following formula (23). Competitive Index (CI) = (X-Y) / (X+Y), where Xnumber of resistant and Ynumber of susceptible E. coli populations. Values >0 indicate that the resistant strain was numerically dominant while values <0 indicate that the susceptible strain dominated.

a. Lasso Models
The Lasso is a shrinkage and variable selection method. It minimizes the usual sum of squared errors bounding the sum of the absolute values of regression coefficients. Variable selection can be notoriously difficult for high dimensional data (the number of variables exceeds the sample size) or highly correlated variables with different magnitudes [3][4][5] . As a result, various regularization approaches have been widely used. In this case, the data are not high dimensional but some variables are highly correlated. To this end, we adopted the method originally proposed by Wang et. al. over a range of penalties 4 , shown to be effective in the low dimensional setting 6 .
Let N denote the number of isolates and P the number of variables. A number of bootstraps B and a range of penalty parameters Λ were chosen. We note that the choice of Λ is typically not influential on the results 4 and we chose the minimal value using a simple permutation test to ensure that any relationship between our variables and the response was stronger than if these relationships occurred by chance 7 . Our maximum penalty parameter was chosen such that at least one variable had a non-zero coefficient.
All continuous variables were standardized before all analyses. For each ethnic group separately, variables with a high proportions of missing values or with no variability were excluded before analysis. We chose a 5% cut 7 off threshold for missing data in order to retain as many variables as possible while minimizing exclusion of households.
The algorithm employed was as follows: For each , For the first bootstrap iteration (b = 1), Step 1 Draw a random subsample of size [N/2] without replacement 4 .
Step 1a Randomly select a subset of q variables such that q < P. We set coefficient estimates of those variables not selected equal to 0.
Step 1b For the selected q variables, obtain regression coefficients 1 using the R package glmmLasso 8 .
Step 2 Estimate the importance of each variable (p=1,…,P) 3 where importance is given by Step 2a Estimate selection probabilities of each variable, where the selection probability is given by Step 3 Draw a random subsample of size [N/2] without replacement.
Step 3a Select a subset of q variables such that q < P using selection probabilities ,. . The value of q was allowed to change at each bootstrap iteration. We set coefficient estimates of those variables not selected equal to 0.
Step 3b For the selected q variables, obtain penalised regression coefficients ̂.
Step 4 After B bootstraps, use a parametric test for variable selection (PSTVSboot) adopted by Park et al (5) to formally select variables. We set if variable p was included using regularisation λ and if variable p was not included using regularisation λ.
Step 5 For each p=1,…,P, inclusion probabilities were estimated as

b. Variables entered into Lasso Models
A list of the 56 variables initially entered into each Lasso model is given in Table 3. Results of lasso selection are provided for all groups combined (Fig. 1a) and for each ethnic group separately (Fig1b-Fig1d). Odds ratios (OR) for the best fit models (assessed using the Akaike information criterion) are reported in Table 4a-Table 4c. urban Composite of whether the household had electricity (mainline or solar) and the number of items the household owned indicative of a more urban lifestyle, including radio, television, refrigerator, motorcycle/scooter, car truck and cell phones use_antimicrobial_illness_1_human A dichotomous variable indicating whether the most recent exotic medicine reported was an antimicrobial or nonantimicrobial.
vet_seek Self-report response to "Do you consult a veterinarian or other livestock care professional when your animals are sick?" Coded 1 Yes 0 = NO vaccine Self-report response on the number of different vaccinations (e.g. foot and mouth disease, East Coast Fever) waterhole_number Reported number of water holes used by household for livestock.

Share_water
Whether the household reported water sources that were shared (communal well, lake/ river water/seasonal stream) or not shared (stand pipe/private well/pay for delivery/neighbor stand pipe).
water_treatment Whether the household reported treating their drinking water. Treatments included boiling and chemical treatments.
withdrawal Whether the household reported observing withdrawal from antimicrobials. Options were using products after: the animal was healthy, 10 days after treatment, 3 days after treatment, and immediately after. Immediately was coded as not observing withdrawal (0) and all other options were recoded as observing withdrawal.
village_distance Distance from households to the nearest village total_dead Due to low livestock sizes in Chagga households, we combined dead_calves and kids_dead additively to create a variables which gives the reported total number of kids and calves born dead.

c. Scaling Veterinary Antimicrobial Use
We developed a scale of veterinary antibiotic use by combining self-report items and direct observation of antibiotics on-hand in the household (see Table 4). Self-reported indicators of lay veterinary antibiotic use included self-reported use, and ownership of syringes and needles for intramuscular or subcutaneous injections. In addition, we observed and inventoried antibiotics on hand in the household. We assumed that the combination of self-reported and direct observation items offers a robust scale with good sensitivity and specificity for veterinary antibiotic use, resulting in less complicated interpretation of P-values in regression models 9 .
We used a mixed data factor analysis 10 to explore the properties of scale. The first unrotated component was a weighted average of keeping needles and syringes and using veterinary antibiotics (abruse_comp1, Fig. S3, Table S3) and accounted for 66% of the variance. We interpreted abr_use_comp1 as households that administered livestock antibiotics in the most recent illness. The number of veterinary antibiotics kept at hand dominated the second unrotated component (abr_use_comp2) and so indicated high levels of antimicrobial usage and accounted for 23% of variation. Component 3 (ab_ruse_comp3) accounted for 11% of that variation and indicated households that used antibiotics in the most recent livestock illness but didn't keep needles and syringes (as so perhaps were less likely to self-administer).

Results of Lasso analyses pooled across groups and for each ethnic group.
Following standard approaches, variables with > 0 · 6 (Figs. S4a-S4d) 4 were used in an unpenalised regression to obtain reliable and interpretable regression coefficients (Tables S4a-S4d). For each antimicrobial, we set B=500 and present models including all variables with > 0 · 6(Lasso variables selection) and models with lowest AIC values (Best fit model selected via AIC minmization). The threshold value of 0·6 was chosen within a range of values suggested by Meinshausen 4 . We note the relationship between the expected number of falsely selected variables and the threshold value but our results showed little variability for threshold values greater than 0·6, as expected 4 .

Figure S4b. Chagga ethnic group risk factors selected by Lasso models for (Amp)icillin, (Tet)tracycline, (Tri)methoprime, (Sul)famethoxazole, (Str)eptomycin and multidrug resistant (MDR) phenotypes.
Variables selected more than 60% (black), 50% (red), 40% (dark pink) and 30% (light pink) of the time in random Lasso (section S3) from full set of variables (Table S2). The x-axis is the penalty parameter (see Section 3.1 above) and the y-axis is the number scales representing the variables in the model. Variables selected more than 60% (black), 50% (red), 40% (dark pink) and 30% (light pink) of the time in random Lasso (section S3) from full set of variables (Table S2). The x-axis is the penalty parameter (see Section 3.1 above) and the y-axis is the number scales representing the variables in the model. Variables selected more that 60% (black), 50% (red), 40% (dark pink) and 30% (light pink) of the time in random Lasso (section S3) from full set of variables (Table S2). The x-axis is the penalty parameter (see Section 3.1 above) and the y-axis is the a number scales representing the variables in the model. Table S4a. Risk factors for antimicrobial-resistant bacteria from Chagga households. Mixed model results for Chagga households using full set of variables included more than 60% in random Lasso (Fig S3b) Table 2 for variable descriptions.

Analyses of the prevalence of antimicrobial resistant lactose-fermenting Gram-negative bacteria in milk and swab samples
A total of 8,106 lactose-fermenting bacteria from milk (n = 5,131) and swabs (n = 2,975) were characterized for their antimicrobial resistance patterns. Overall, these isolates expressed similar resistance patterns as fecal resistant bacteria E. coli (Fig. 1, main manuscript) with the highest resistance for Amp, Tri, Sul, Tet and Str (Fig. 3A, main manuscript). Interestingly, Maasai milk and swab isolates harbored a higher prevalence of ciprofloxacin resistance (31·41% and 11·20%, respectively) compared to fecal isolates (Fig. 1, main manuscript) and Arusha milk isolates. Results from single-factor multivariate analysis of variance (MANOVA) shows that the Maasai milk harbored significantly higher levels of Sul (P = 0·003), Tet (P = 0·03) and Tri (P = 0·0007) resistant bacteria compared to the Arusha households (Fig. 3B). Except for Cip resistance, swab isolates from Maasai households (Fig. S5) and fecal isolates from Arusha and Maasai households (Fig. 1, main manuscript) showed similar resistance patterns. In addition, regression models comparing the average proportions of each antimicrobialresistant lactose-fermenting bacteria also showed a positive correlation between milk and fecal patterns (Fig. S7).

Oxytetracycline residues remained biologically active after boiling milk.
We examined the biological activity of oxytetracycline after milk is boiled by using a competitive growth study between antibiotic-resistant and an isogenic susceptible E. coli populations. Our findings showed that the resistant E. coli population had an advantage over susceptible population in the presence of oxytetracycline, and that boiling milk had no significant effect on oxytetracycline (P = 0·64, Fig. S8). (25)) and an isogenic strain (lacking streptomycin, sulfadiazine and tetracycline resistance genes) in milk (n = 4 independent replicates). Strong selection for the resistant population (values > 0) was evident in the presence of oxytetracycline in unboiled milk and after boiling milk samples that contained oxytetracycline. A two-factor ANOVA confirmed no differences between boiled and unboiled treatments (P = 0.64) while concentration was significant (P < 0.001; interactions P = 0.99). Error bars represent standard errors.