Analysis of fecal sludges reveals common enteric pathogens in urban Maputo, Mozambique

Abstract

Sewage surveillance is increasingly used in public health applications: metabolites, biomarkers, and pathogens are detectable in wastewater and can provide useful information about community health. Work on this topic has been limited to wastewaters in mainly high-income settings, however. In low-income countries, where the burden of enteric infection is high, non-sewered sanitation predominates. In order to assess the utility of fecal sludge surveillance as a tool to identify the most prevalent enteric pathogens circulating among at-risk children, we collected 95 matched child stool and fecal sludge samples from household clusters sharing latrines in urban Maputo, Mozambique. We analyzed samples for 20 common enteric pathogens via multiplex real-time quantitative PCR. Among the 95 stools matched to fecal sludges, we detected the six most prevalent bacterial pathogens (Enteroaggregative E. coli, Shigella/Enteroinvasive E. coli, Enterotoxigenic E. coli, Enteropathogenic E. coli, shiga-toxin producing E. coli, Salmonella) and all three protozoan pathogens (Giardia duodenalis, Cryptosporidium parvum, Entamoeba histolytica) in the same rank order in both matrices. We did not observe the same trend for viral pathogens or soil-transmitted helminths, however. Our results suggest that sampling fecal sludges from onsite sanitation offers potential for localized pathogen surveillance in low-income settings where enteric pathogen prevalence is high.

Graphical Abstract

Introduction

Wastewater monitoring is increasingly used in community health surveillance: as a composite sample of a population’s fecal waste, sewage has been shown to provide useful community-level information on biomarkers of illicit drug use¹, antimicrobial resistance^2,3, and chronic disease.⁴ Sewage surveillance has yielded advanced warning of viral outbreaks^5,6 and has gained increased prominence in monitoring for SARS-CoV-2 in sewersheds^7–11, which is now being applied globally. Apart from poliovirus monitoring to complement eradication efforts^12–14, the method has not been widely used in low- and middle-income countries (LMICs), despite the need for better surveillance of enteric infections in high-burden settings.^15,16

Although wastewater is the focus of most efforts in this area, and even lends its name to the emerging field of “wastewater based epidemiology” (WBE),¹⁷ onsite sanitation systems predominate in the lowest income settings.¹⁸ Such systems serve at least 1.8 billion people in LMICs¹⁹, where sewerage has not kept pace with rapidly densifying cities.^20,21 Where sewers are absent, analysis of fecal sludges from onsite systems, including shared systems which serve 630 million people¹⁸, offers a compelling method for infection surveillance.

Direct examination of stool, serum, or other biological samples from individuals is commonly used to estimate community prevalence of infection or to identify key pathogens of interest in a given setting.^22,23 For enteric pathogens shed in feces, both wastewater and fecal sludge monitoring have advantages over biological samples from individuals in being non-invasive, lower cost, and logistically less complex, including the potential to be classified as non-human subjects research. Compared with fecal sludges, which remain sequestered until a pit is emptied, wastewater as a matrix generally includes waste from more individuals. Therefore, wastewater may be more representative of fecal wastes in a population and provide a more current snapshot of circulating pathogens. However, wastewater may also be more dilute²⁴ and farther downstream from specific sub-populations of interest, like residents of urban informal communities²⁵. Where these sub-populations are not covered by sewers, and may subsequently be at greater risk of enteric diseases^23,26, fecal sludge offers the opportunity to concentrate surveillance efforts in limited geographic areas.

As pathogens in fecal sludges indicate previous exposures among those contributing waste²⁷, using fecal sludges to identify the primary enteric pathogens circulating in a community may inform how best to control them. Public health interventions including improved water, sanitation, and hygiene (WASH)²⁸; mass drug administration (MDA) ²⁹; and vaccination³⁰ could thus be targeted to specific infections when and where they occur, according to their effectiveness in controlling specific pathogens.

Applying the concept of fecal waste monitoring to onsite systems in LMICs requires initial testing and validation using fecal sludges, including comparison with community infection prevalence. In fact, apart from the concurrent collection of stools and wastewater to monitor polio vaccination campaigns in Atlanta, USA³¹ and Havana, Cuba³², wastewater surveillance methods have generally not been “ground truthed” by comparison with stool sample collection and analysis. Our study aim was to determine whether the most prevalent enteric pathogens in fecal sludges from shared onsite sanitation systems at compounds (clusters of multiple households sharing common outside space and sanitation) enrolled in the Maputo Sanitation (MapSan) trial^23,33 were also the most prevalent enteric pathogens in stool samples from children and infants living in these compounds. The MapSan trial was a controlled trial to evaluate the impact of an urban shared sanitation intervention on enteric infection and other health outcomes in children (Text S1). We sought to assess whether analysis of fecal sludges from shared latrines can reliably identify which enteric pathogens are most common among children living in households served by them.

Materials and Methods

Our study took place in MapSan intervention and control compounds in low-income neighborhoods of Maputo, Mozambique, 24-months after the implementation of the shared sanitation intervention.^23,33 In study neighborhoods the population density is high (>15,000 people per square km)³⁴, sanitary conditions are inadequate²³, and at study baseline 86% of all children tested positive for one or more enteric infection²³. We used convenience sampling to collect fecal sludges from 95 MapSan compounds 1–10 days following stool collection of the enrolled child (n=95) (October 2017-April 2018).^23,33 Methods for stool collection were previously described elsewhere.²³ We obtained children’s age and household socioeconomic characteristics from the MapSan 24-month survey dataset.³⁵

Because pit latrines produce thicker sludge than septic tanks, and because sludges in septic tanks separate into distinct layers of scum, liquids, and solids³⁶, we used a unique protocol for each system type. For sampling pit latrines, we adapted a Sludge Nabber (Nasco, Fort Atkinson, WI) with a plastic tubing cover and a 50 mL centrifuge tube (Figure S1, Text S2). For septic tanks, we used a modified Wheaton Sub-Surface Sampler I system (Fisher Scientific, Waltham, Massachusetts) with a plastic insert to hold a 50-mL centrifuge tube (Figure S1, Text S2). All fecal sludge samples were stored on ice for transport, aliquoted into 2-ml cryovials within 6 hours of collection and stored at −80°C at the Mozambican National Institute of Health. All samples were shipped from Maputo, Mozambique, to Atlanta, USA on dry ice (−80°C) with temperature monitoring for molecular analysis.

Sample processing

For total nucleic acid extraction from 100 mg of stools and fecal sludges (wet-weight), we followed a pre-treatment protocol validated for multiplex PCR (Text S3).^23,37,38 We proceeded with extraction following the manufacturer’s protocol for the QIAamp 96 Virus QIAcube HT Kit, which we automated on the QIAcube (Qiagen, Hilden, Germany). We included MS2 as an extraction control. To determine the solids content of fecal sludges, we adapted the oven drying method (Text S3).³⁹

Recent advances in multiplex PCR assays enable rapid and simultaneous detection of pathogens from a variety of samples types^23,37,40, providing a useful molecular method for pathogen surveillance. One such platform, the TaqMan array card (TAC), is a 384-well microfluidic card that can be customized using validated assays^40–42, and therefore is suited to detect a wide range of pathogens that may vary by setting²². We tested all samples using a custom TAC (ThermoFisher Scientific, Waltham, MA) that tested for 20 enteric pathogens, including ten bacteria, five viruses, three protozoa and two soil-transmitted helminths (STH) in duplicate (Text S4, Table S1, Table S2). We included a positive and negative control on each TAC (Text S4). We visually compared exponential curves and multicomponent plots with the positive control plots to validate positive amplification. Samples that exhibited positive amplification in either duplicate well before a quantification cycle (Cq) of 40 were considered positive (Table S3).

Data analysis

Predictors of the number of pathogens in stools and fecal sludges

To understand what variables were associated with the number of pathogens in stools and sludges, we investigated how children’s age, compound wealth, compound population, and the type of onsite sanitation predicted the number of pathogens in each matrix. Our response variables included the number of detected pathogenic bacteria (range: 0–10), viruses (0–5), protozoa (0–3), and STHs (0–2) in stools and fecal sludges. Exposure variables representing potential contributors to the number of pathogens were a one-quartile increase in wealth score⁴³, a 10-person increase in compound population, pour flush to septic tank sanitation compared to pit latrines, and specifically for stools we included a categorical variable for child’s age (1–23, 24–47, or 48–82 months). To account for missing data in the child’s age variable, we used multiple imputation with chained equations (Text S5).^44–46

We fit generalized linear models (GLM, Poisson regression with log link) to calculate unadjusted and adjusted prevalence ratios (PR, aPR) (Text S5). For this study, we define prevalence ratios as the number of pathogenic bacteria, viruses, protozoa, or STHs for an exposure variable compared to the reference. We fit models including all exposure variables simultaneously for stools and sludges, did not include any additional confounders in stool models (Figure S2), and included a sample’s log₁₀ transformed fecal sludge solids content as an additional covariate in fecal sludge models (Text S2, Figure S3). Recognizing our analysis generated multiple models, we applied a false discovery rate correction across taxa.⁴⁷ We analyzed data in R version 4.0.0 (R Foundation for Statistical Computing, Vienna, Austria).

Comparison of matched stools and fecal sludges

We used the presence of individual pathogens to compare stools and sludges. For matched samples, we used the Jaccard similarity coefficient⁴⁸ (e.g. the intersection of detections over the union), because it excludes instances of non-detects in both matrices which would bias the similarity between sludges and stools upwards for rare pathogens. For example, out of our 95 samples, if we were to detect an individual pathogen in both a stool and the matched fecal sludge sample 40 times, only in stool 10 times, only in sludge 30 times, and in neither sample 15 times, the Jaccard similarity coefficient is 50% (Equation 1).

$$J_X=40÷(40+10+30)=50$$ (1)

Results and Discussion

Pathogens detected in stools

We collected stools from 95 children who ranged in age from 1–82 months (median = 37 months, mean = 39 months, SD = 21 months). In stools, we most often detected pathogenic bacteria (96%, [91/95]), followed by protozoa (68%, [65/95]), STHs (53%, [50/95]) and viruses (28%, [27/95]) (Table S4, Table S5). Out of 20 pathogens, we detected a mean of 3.9 pathogens per stool on average (mean=3.9 out of 20, median=4.0, range=0–9), which is similar to children living in other LMICs²². Adjusted for wealth score, compound population, and the type of onsite sanitation, we found stools from the oldest children (48–82 months) had no difference in the number of pathogenic bacteria (aPR = 1.3, 95% CI [0.87, 1.9]) or protozoa (aPR = 1.3 [0.66, 2.6]), but observed a lower number of viruses (aPR = 0.17 [0.05, 0.57]), and a greater number of helminths (aPR = 4.3 [1.8, 10]) compared to the youngest children (1–23 months) (Table 1). This sub-sample of 95 stools collected from children in the MapSan cohort yielded consistent estimates of prevalence with the MapSan trial baseline (n = 759)²³ conducted 24–36 months earlier: we detected all pathogens in each taxa in the same rank order except for Campylobacter and STEC, which were infrequently detected at MapSan baseline (8% and 2% prevalence, respectively)²³.

Table 1.

Associations between variables and the number of detected pathogenic bacteria, viruses, protozoa, or STHs in stools and sludges.

Stool
		Bacteria		Viruses		Protozoa		STHs
Mean number detected (95% CI)		2.1 (1.9, 2.3)		0.36 (0.23, 0.49)		0.77 (0.65, 0.89)		0.74 (0.58, 0.90)
Exposure	Reference	PR (95% CI)	aPR* (95% CI)	PR (95% CI)	aPR* (95% CI)	PR (95% CI)	aPR* (95% CI)	PR (95% CI)	aPR* (95% CI)
Age (24–47 months)	1–23 months	1.2 (0.84, 1.8)	1.2 (0.82, 1.7)	0.39 (0.16, 0.90)	0.41 (0.17, 0.97)	1.8 (0.99, 3.4)	1.9 (1.0, 3.6)	3.8 (1.5, 9.4)	4.2 (1.7, 10)
Age (48–82 months)		1.3 (0.87, 1.9)	1.2 (0.82, 1.7)	0.17 (0.05, 0.57)	0.16 (0.05, 0.54)	1.2 (0.63, 2.4)	1.3 (0.66, 2.6)	4.3 (1.8, 11)	4.3 (1.8, 10)
Wealth score	1-quartile increase	0.94 (0.83, 1.1)	0.95 (0.84, 1.1)	1.2 (0.89, 1.6)	1.1 (0.83, 1.5)	0.98 (0.80, 1.2)	0.98 (0.79, 1.2)	0.96 (0.78, 1.2)	0.98 (0.79, 1.2)
Compound population	10-person increase	1.1 (0.95, 1.4)	1.1 (0.93, 1.4)	0.83 (0.48, 1.3)	0.95 (0.53, 1.7)	1.1 (0.78, 1.5)	0.99 (0.71, 1.4)	0.99 (0.70, 1.4)	0.91 (0.64, 1.3)
Onsite sanitation: pour-flush to septic tank system	Pit latrine	0.90 (0.68, 1.2)	0.86 (0.64, 1.2)	1.2 (0.60, 2.4)	1.1 (0.51, 2.2)	1.1 (0.71, 1.8)	1.1 (0.69, 1.8)	0.78 (0.49, 1.2)	0.89 (0.54, 1.4)
Sludge
		Bacteria		Viruses		Protozoa		STHs
Mean number detected (95% CI)		2.9 (2.6, 3.2)		2.2 (2.0, 2.5)		1.2 (1.1, 1.4)		1.5 (1.4, 1.7)
Exposure	Reference	PR (95% CI)	aPR† (95% CI)	PR (95% CI)	aPR† (95% CI)	PR (95% CI)	aPR† (95% CI)	PR (95% CI)	aPR† (95% CI)
Wealth score	1-quartile increase	1.0 (0.91, 1.1)	1.0 (0.91, 1.1)	0.98 (0.87, 1.1)	1.0 (0.89, 1.1)	0.99 (0.85, 1.2)	1.0 (0.86, 1.2)	0.95 (0.83, 1.1)	0.96 (0.83, 1.1)
Compound population	10-person increase	1.0 (0.85, 1.2)	1.1 (0.94, 1.3)	1.2 (1.0, 1.4)	1.2 (0.97, 1.4)	1.1 (0.88, 1.5)	1.1 (0.85, 1.4)	0.97 (0.77, 1.2)	0.95 (0.75, 1.2)
Onsite sanitation: pour- flush to septic tank system	Pit latrine	0.62 (0.49, 0.78)	0.66 (0.50, 0.86)	1.4 (1.1, 1.9)	1.3 (0.96, 1.8)	1.2 (0.84, 1.8)	1.2 (0.78, 1.8)	1.1 (0.81, 1.6)	1.2 (0.85, 1.8)
Fecal sludge solids content (mass dry solids / mass wet sludge)	Log10 increase	1.7 (1.2, 2.4)	1.4 (0.97, 2.1)	0.72 (0.49, 1.1)	0.88 (0.57, 1.3)	0.87 (0.52, 1.4)	0.98 (0.55, 1.7)	1.2 (0.76, 1.9)	1.3 (0.79, 2.2)

Note: Bold indicates p≤0.05 following false discovery rate correction for multiple comparisons across taxa. PR: prevalence ratio. aPR: adjusted prevalence ratio. STH: soil transmitted helminth. CI: confidence interval

^*The full model was run for each taxa, which included children’s age, wealth score, compound population, and sanitation system for a total of 4 stool models

^†The full model was run for each taxa, which included wealth score, compound population, sanitation system, and the log₁₀ transformed fecal sludge solids content for a total of 4 sludge models

Pathogens detected in fecal sludges

We collected 52 fecal sludge samples from pour-flush to septic tank sanitation systems and 43 from pit latrines. The mean number of people per compound was 15 (SD=7, median=13, range=4–38). In fecal sludges we commonly detected all types of pathogens (bacteria: 95%, [90/95], STHs: 95%, [90/95], viruses: 91%, [86/95], protozoa: 88%, [84/95]) and in high number (mean=7.9 out of 20, median=8.0, range=0–14). In addition, adjusted for wealth score, compound population, and fecal sludge solids content, we found septic tank systems were associated with a reduced number of bacterial pathogens (aPR = 0.66, 95% [0.50, 0.86]) compared to pit latrines (Table S6).

Stools and fecal sludges comparison

Among all stools and fecal sludges, the six most frequently detected bacterial pathogens were the same in both matrices and were detected in the same rank order (Table 2). We did not observe the same pattern for the viral pathogens, and the prevalence of all viruses was much greater in fecal sludges than in stools. We detected all three protozoan pathogens in the same order of prevalence in stools and fecal sludges. We detected Trichuris more frequently than Ascaris in stools but detected Ascaris more frequently than Trichuris in fecal sludges. Stratifying our analysis by sanitation type (pit latrines and septic tanks), the six most frequently detected bacteria were different for pit latrines and septic tanks, but for each type of infrastructure the six most frequently detected bacterial pathogens were still detected in the same rank order as matched stools (Table S7).

Table 2.

Pathogens in stools and fecal sludges sorted by prevalence in stool (first column).

#	Pathogen	Stool (n = 95) (95% CI)	Sludge (n=95) (95% CI)	Jaccard similarity coefficient* (J)	Stool detections with a detection in the matched sludge (%)
	Bacteria
1	EAEC	67% (58%, 77%)	82% (74%, 90%)	65% (56/96)	88% (56/64)
2	Shigella/EIEC	51% (40%, 61%)	76% (67%, 84%)	45% (37/83)	77% (37/48)
3	ETEC (ST/LT)	38% (28%, 48%)	56% (46%, 66%)	33% (22/66)	63% (22/35)
4	EPEC	34% (24%, 43%)	39% (29%, 49%)	19% (11/57)	35% (11/31)
5	STEC (stx1/stx2)	6.3% (1.4%, 11%)	15% (7.6%, 22%)	0% (0/20)	0% (0/6)
6	Salmonella	6.3% (1.4%, 11%)	8.4% (2.8%, 14%)	0% (0/14)	0% (0/6)
7	Campylobacter jejuni/coli	5.3% (0.75%, 9.8%)	4.2% (0%, 8.3%)	0% (0/9)	0% (0/5)
8	C. difficile	3.4% (0%, 6.7%)	7.4% (2.1%, 13%)	0% (0/10)	0% (0/3)
9	Vibrio Cholerae	0%	1.1% (0%, 3.1%)	0% (0/1)
10	Yersinia spp.	0%	2.1% (0%, 5.0%)	0% (0/2)
	Viruses
1	Sapovirus I/II/IV/V	12% (5.1%, 18%)	47% (37%, 57%)	17% (8/48)	72% (8/11)
2	Norovirus GI/GII	11% (4.4%, 17%)	58% (48%, 68%)	8.3% (5/60)	50% (5/10)
3	Astrovirus	8.4% (2.8%, 14%)	63% (53%, 73%)	9.7% (6/62)	75% (6/8)
4	Adenovirus 40/41	4.2% (0%, 8.3%)	44% (34%, 54%)	7.0% (3/43)	75% (3/4)
5	Rotavirus A	1.1% (0%, 3.1%)	8.4% (2.8%, 14%)	13% (1/8)	100% (1/1)
	Protozoa
1	Giardia duodenalis	64% (55%, 74%)	86% (79%, 93%)	63% (55/88)	90% (55/61)
2	Cryptosporidium parvum	12% (5.1%, 18%)	24% (16%, 33%)	9.7% (3/31)	27% (3/11)
3	Entamoeba histolytica	1.1% (0%, 3.1%)	12% (5.1%, 18%)	9.1% (1/11)	100% (1/1)
	STHs
1	Trichuris trichiuria	42% (32%, 52%)	65% (56%, 75%)	42% (30/72)	75% (30/40)
2	Ascaris lumbricoides	32% (22%, 41%)	88% (82%, 95%)	31% (27/87)	90% (27/30)

Note: A bold number in the first column indicates the pathogen was detected in the same rank order in stools and fecal sludges. C. difficile: Clostridium difficile. EAEC: Enteroaggregative E. coli. EIEC: Enteroinvasive E. coli. ETEC: Enterotoxigenic E. coli. EPEC: Enteropathogenic E. coli. STEC: shiga-toxin producing E. coli.

^*Size of the intersection of matched detections divided by the size of the union of detections. For example, we detected Giardia in both stool and the matched fecal sludge sample 55 times, only in stool 6 times, only in fecal sludge 27 times, and did not detect Giardia in either sample 7 times. E.g. ${\mathrm{J}}_{\mathit{Giardia}}=(55)/(55+6+27)=63\%$

Intra-compound stool and fecal sludge comparison

We detected every pathogen except Campylobacter more frequently in fecal sludges than in stools. Because we detected pathogens more frequently in sludges than in stools, the Jaccard similarity coefficients were highest among pathogens with the greatest prevalence in stool, lowest for pathogens with lowest prevalence in stools, and were zero for all bacterial pathogens detected in less than 10% of stools (Table 2). For bacteria, protozoa, and STHs matched detections in stools and fecal sludges tended to increase with increased prevalence in stool; in at least half of the instances, all viruses detected in stools were detected in matched fecal sludges (Table 2). Specifically, when EAEC, rotavirus A, Giardia duodenalis, Entamoeba hystolytica, and Ascaris lumbricoides were detected in a stool, they were also detected in the matched sludge at least 80% of the time. These observations suggest that, for these pathogens, sludge may be a useful proxy for prevalence of shedding among children in this setting.

Inter-compound vs. Intra-compound Interpretation

The same rank order of bacterial and protozoan pathogens in fecal sludges from pit latrines and septic tanks compared with pathogens in stools provides evidence that fecal sludges may be a reliable endpoint for enteric pathogen surveillance in low-income urban settings for at least some pathogens. However, the Jaccard similarity coefficients for 18 of the 20 pathogens assessed were less than 50%, indicating infrequent co-detection of pathogens in matched stools and sludges. Accordingly, surveillance of onsite sanitation systems may better provide a community-level snapshot of circulating pathogens, while being a poor predictor of individual prevalence.

While substantial overlap in gut microbiota has been observed among household members in previous studies,^49,50 suggesting that gut pathogen carriage may be comparable among those sharing living spaces, stool samples from any individual may not be representative of pathogens shared by others. We compared single stool samples from children with fecal sludges from latrines serving an average of 15 people, including adults. Of the 20 pathogens assessed, the number of detections in fecal sludges (n = 7.9) was twice that of stools (n = 3.9), reflective of the fact that sludge represents a composite of multiple individuals’ feces. Indeed, the total number of detects would necessarily increase unless pathogens were evenly distributed across all individuals. Furthermore, some children’s stools may not have been deposited in latrines.⁵¹ These factors may explain why we did not observe high Jaccard similarity coefficients between fecal sludges and individual stools.

Factors impacting pathogen detection

Variations in the detected order and prevalence between stools and sludges – especially among viral targets – suggests additional work is needed to interpret pathogen detection based on shedding rates in stool^52,53, fate and transport^53–55, biological and environmental factors^53–55, assay limits of detection, and potential differences in nucleic acid extraction kit efficiencies. Such factors may explain why we did not detect viruses and STHs in fecal sludge in the same rank order as in stool samples. Ascaris can persist longer in the environment than Trichuris⁵³ but MDA to treat helminthiasis is less effective for Trichuris than Ascaris.⁵⁶ Children enrolled in the MapSan trial received single-dose albendazole before the 24-month follow-up period, which may explain why we detected Trichuris more often in stools but Ascaris more often in sludges. While we detected bacterial pathogens in the same rank order in stools and sludges, environmental dynamics such as die-off and re-growth⁵⁷ could produce a different result in other settings. During viral gastroenteritis viruses are shed in high concentration (≤10¹⁰-10¹² per gram feces)^52,53 and viral infections were associated with symptomatic diarrhea in Maputo.¹¹ Watery diarrheal stools may result in greater spatial distribution inside a latrine compared to solid stools, and may explain why we often detected viruses in sludges despite a relatively lower prevalence in stools. As expected from a previous studies in Maputo²³ and Manhiça, Mozambique^22,58, we observed increasing age was associated with a reduced number of pathogenic viruses, and a greater number of pathogenic protozoa (though not after correcting for multiple comparisons) and STHs. As children age their mobility and consumption of food and drinking water increases, which increases infection risks. However, as children in LMICs begin walking on their own, their contact with other people may decrease, potentially decreasing their infection risk from viruses spread via person-to-person transmission⁵⁹. Considering sludges are a composite from individuals with a wider range of ages than the stools we measured, it is logical the rank order of viruses and STHs in sludges may not align with the rank order of stools from young children alone, since the pathogen shedding profile in infants and children may differ from others also contributing waste to the latrine. In addition, the difference in prevalence of the three pathogenic protozoa in stools was large and may have limited the potential for change in the rank order detection from stool to sludge.

As a cross-sectional study we were unable to assess the sensitivity of pathogen signals to changes in disease prevalence or incidence over time, though this is a logical next step for further research. Longitudinal studies of fecal sludges in LMICs are required to assess such changes over time, including utility in detecting disease outbreaks⁶⁰ and to assess if sludges are useful for health impact assessment in water, sanitation, and hygiene (WASH), vaccine, and MDA intervention trials. We observed the same rank order prevalence for bacterial and protozoan pathogens from both pit latrines and septic tanks, except we detected fewer bacterial pathogens in septic tank sludges, and the six most prevalent bacterial pathogens were different between the two systems. Stratification of data by onsite sanitation infrastructure type may provide helpful nuance to understanding circulating pathogens. This level of detail suggests that fecal sludge surveillance may offer greater insight into circulating pathogens at a finer spatial scale compared to dilute wastewater samples which may be more representative of an entire sewershed.

Future work is needed before fecal sludge surveillance can be scaled and used in LMICs. First, we collected one sample per sanitation system in a 50 mL centrifuge tube at a single point just below the surface of the solids. It is plausible the sludges we collected represented older feces than those at the solids surface and may offer a longer-term snapshot of community infections compared to wastewater. More work is needed to standardize sample collection techniques, which may include collecting and homogenizing a larger volume of sludge from each system. Second, quantitative work is needed to assess differences in pathogen concentration and nucleic acid recovery between stools and sludges and to reconcile potential differences in limits of detection. Such concentration data would be helpful for risk assessment modeling⁶¹, to assess the sensitivity of signals in sludges to the number of individuals contributing waste, and to monitor temporal changes. Third, decay experiments are needed to characterize pathogen persistence in fecal sludges, including for pathogen nucleic acids that would typical be detected via methods similar to those we used in this study. Finally, compared with PCR-based methods, metagenomics should be explored as it offers an unbiased approach to assess the microbial community in onsite sanitation systems, and may be desired for comparison with global wastewater surveillance efforts¹⁵. Applying these methods for tracking specific pathogens may be limited by comparatively high detection limits and a lack of capacity for analysis in high-burden settings, however.

The ability to capture the relative frequency of enteric pathogens in a community – without the logistical constraints and invasive nature of stool collection – offers the opportunity to rapidly gather novel and actionable information regarding community health. In urban settings with limited resources, surveillance of fecal sludges may be a cheap and scalable option to monitor emerging pathogens.¹¹ Applying the principles of wastewater-based surveillance to areas covered by onsite systems is promising, but future work is needed to standardize methods and better characterize and interpret observed pathogen signals. Advances in surveillance where data are limited may help develop and prioritize appropriate and effective strategies to reduce infection risk.

Supplementary Material

supplemental info

1. Figure S1. Photos of latrine samplers

2. Text S1. Description of MapSan trial

3. Text S2. Methodology for fecal sludge collection

4. Text S3. Methodology for total nucleic acid extraction from fecal sludge and stool.

5. Text S4. Custom TaqMan Array Card (TAC)

6. Table S1. TAC and GPP comparison

7. Table S2. Assays used on the custom TAC

8. Table S3. Interpretation of gene targets on the TAC

9. Text S5. Regression models

10. Figure S2. DAGs of stool models

11. Figure S3. DAGs of sludge models

12. Table S4. Summary of pathogens in stools and sludges

13. Table S5. Average Cq values

14. Table S6. Associations between variables and the number of detected pathogenic bacteria, viruses, protozoa, or STHs in stools and sludges.

15. Table S7. Pathogen detection data disaggregated by sanitation infrastructure (septic tanks and latrines)