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. 2022 Aug 20;11(1):106.
doi: 10.1186/s13756-022-01145-4.

Estimating the population-level prevalence of antimicrobial-resistant enteric bacteria from latrine samples

Sylvia Omulo  1   2   3 , Maina Mugoh  4 , Joshua Obiya  5 , Moshe Alando  6 , Douglas R Call  7   8
Affiliations

Estimating the population-level prevalence of antimicrobial-resistant enteric bacteria from latrine samples

Sylvia Omulo et al. Antimicrob Resist Infect Control. .

Abstract

Background: Logistical and economic barriers hamper community-level surveillance for antimicrobial-resistant bacteria in low-income countries. Latrines are commonly used in these settings and offer a low-cost source of surveillance samples. It is unclear, however, whether antimicrobial resistance prevalence estimates from latrine samples reflect estimates generated from randomly sampled people.

Methods: We compared the prevalence of antimicrobial-resistant enteric bacteria from stool samples of people residing in randomly selected households within Kibera-an informal urban settlement in Kenya-to estimates from latrine samples within the same community. Fecal samples were collected between November 2015 and Jan 2016. Presumptive Escherichia coli isolates were collected from each household stool sample (n = 24) and each latrine sample (n = 48), resulting in 8935 and 8210 isolates, respectively. Isolates were tested for resistance to nine antibiotics using the replica-plating technique. Correlation- and Kolmogorov-Smirnov (K-S) tests were used to compare results.

Results: Overall, the prevalence values obtained from latrine samples closely reflected those from stool samples, particularly for low-prevalence (< 15%) resistance phenotypes. Similarly, the distribution of resistance phenotypes was similar between latrine and household samples (r > 0.6; K-S p-values > 0.05).

Conclusions: Although latrine samples did not perfectly estimate household antimicrobial resistance prevalence, they were highly correlated and thus could be employed as low-cost samples to monitor trends in antimicrobial resistance, detect the emergence of new resistance phenotypes and assess the impact of community interventions.

Keywords: Antimicrobial resistance; Latrines; Prevalence estimates; Surveillance.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The general structure of a pit latrine, i.e., a hole dug into the ground for disposal of excreta (left) and the floor of a shallow concrete slab pit latrine in Kibera (right). Source: https://en.wikipedia.org/wiki/Pit_latrine
Fig. 2
Fig. 2
Prevalence estimates (mean and 95% CI) from latrine and stool samples over four rounds of sampling conducted two weeks apart (between rounds 1–2 and 2–3) and four weeks apart (rounds 3–4). Ampicillin (Amp), ceftazidime (Caz), chloramphenicol (Chl), ciprofloxacin (Cip), kanamycin (Kan), streptomycin (Str), sulfamethoxazole (Sul), tetracycline (Tet), trimethoprim (Tmp). Red diamonds indicate stool samples, while white diamonds indicate latrine samples
Fig. 3
Fig. 3
Aggregate proportions (mean and 95% CI) of the eight most abundant resistance profiles identified in latrine and stool sample isolates over the sampling period. The dotted line represents the proportion at which prevalence values in latrine and stool samples are equal. Points above this line have greater representation in latrine samples while points below this line have greater representation in stool samples
See this image and copyright information in PMC

References

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