. 2023 Sep 14;20(9):e1004286. doi:10.1371/journal.pmed.1004286

Estimating the proportion of clinically suspected cholera cases that are trueVibrio cholerae infections: A systematic review and meta-analysis

Kirsten E Wiens^1,²,Hanmeng Xu¹,Kaiyue Zou¹,John Mwaba^3,^4,⁵,Justin Lessler^1,^6,⁷,Espoir Bwenge Malembaka^1,⁸,Maya N Demby¹,Godfrey Bwire⁹,Firdausi Qadri¹⁰,Elizabeth C Lee¹,Andrew S Azman^1,^11,^12,^*

¹Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America

²Department of Epidemiology and Biostatistics, College of Public Health, Temple University, Philadelphia, Pennsylvania, United States of America

³Centre for Infectious Disease Research in Zambia (CIDRZ), Lusaka, Zambia

⁴Department of Biomedical Sciences, School of Health Sciences, University of Zambia, Lusaka, Zambia

⁵Department of Pathology and Microbiology, University Teaching Hospital, Lusaka, Zambia

⁶Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America

⁷Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America

⁸Center for Tropical Diseases and Global Health (CTDGH), Université Catholique de Bukavu, Bukavu, Democratic Republic of the Congo

⁹Division of Public Health Emergency Preparedness and Response, Ministry of Health, Kampala, Uganda

¹⁰Infectious Diseases Division, International Centre for Diarrhoeal Disease Research Bangladesh (icddr,b), Dhaka, Bangladesh

¹¹Geneva Centre for Emerging Viral Diseases, Geneva University Hospitals, Geneva, Switzerland

¹²Division of Tropical and Humanitarian Medicine, Geneva University Hospitals, Geneva, Switzerland

JL is a paid statistical advisor for PLOS Medicine.

^✉

* E-mail:azman@jhu.edu

Roles

Kirsten E Wiens:Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing

Hanmeng Xu:Data curation, Investigation, Methodology, Project administration, Validation, Writing – original draft, Writing – review & editing

Kaiyue Zou:Data curation, Writing – review & editing

John Mwaba:Data curation, Writing – review & editing

Justin Lessler:Methodology, Writing – review & editing

Espoir Bwenge Malembaka:Writing – review & editing

Maya N Demby:Data curation, Writing – review & editing

Godfrey Bwire:Writing – review & editing

Firdausi Qadri:Writing – review & editing

Elizabeth C Lee:Data curation, Methodology, Writing – review & editing

Andrew S Azman:Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

Received 2022 Oct 25; Accepted 2023 Aug 25; Collection date 2023 Sep.

This is an open access article distributed under the terms of theCreative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PMC Copyright notice

PMCID: PMC10538743 PMID:37708235

Abstract

Background

Cholera surveillance relies on clinical diagnosis of acute watery diarrhea. Suspected cholera case definitions have high sensitivity but low specificity, challenging our ability to characterize cholera burden and epidemiology. Our objective was to estimate the proportion of clinically suspected cholera that are trueVibrio cholerae infections and identify factors that explain variation in positivity.

Methods and findings

We conducted a systematic review of studies that tested ≥10 suspected cholera cases forV.cholerae O1/O139 using culture, PCR, and/or a rapid diagnostic test. We searched PubMed, Embase, Scopus, and Google Scholar for studies that sampled at least one suspected case between January 1, 2000 and April 19, 2023, to reflect contemporary patterns inV.cholerae positivity. We estimated diagnostic test sensitivity and specificity using a latent class meta-analysis. We estimatedV.cholerae positivity using a random-effects meta-analysis, adjusting for test performance. We included 119 studies from 30 countries.V.cholerae positivity was lower in studies with representative sampling and in studies that set minimum ages in suspected case definitions. After adjusting for test performance, on average, 52% (95% credible interval (CrI): 24%, 80%) of suspected cases represented trueV.cholerae infections. After adjusting for test performance and study methodology, the odds of a suspected case having a true infection were 5.71 (odds ratio 95% CrI: 1.53, 15.43) times higher when surveillance was initiated in response to an outbreak than in non-outbreak settings. Variation across studies was high, and a limitation of our approach was that we were unable to explain all the heterogeneity with study-level attributes, including diagnostic test used, setting, and case definitions.

Conclusions

In this study, we found that burden estimates based on suspected cases alone may overestimate the incidence of medically attended cholera by 2-fold. However, accounting for cases missed by traditional clinical surveillance is key to unbiased cholera burden estimates. Given the substantial variability in positivity between settings, extrapolations from suspected to confirmed cases, which is necessary to estimate cholera incidence rates without exhaustive testing, should be based on local data.

Using combined data extracted from 119 studies from 30 countries, Kirsten E. Wiens and colleagues estimate the true burden Vibrio Cholerae positivity.

Author summary

Why was this study done?

Cholera surveillance typically relies on the clinical diagnosis of acute watery diarrhea (i.e., “suspected cholera”), but this definition has a low specificity for cholera.
Our goal was to estimate the proportion of suspected cholera cases that are trueVibrio cholerae infections and identify factors that contribute to variation in observed positivity.

What did the researchers do and find?

We conducted a systematic review of studies from 2000 to 2023 that tested suspected cholera cases forV.cholerae infection using one of 3 different laboratory tests.
We included 119 studies from 30 countries and found that, on average, half of suspected cholera cases represented trueV.cholerae infections, after accounting for laboratory test accuracy.
We also found high variability between studies and that the odds of a suspected case being a true infection were higher during outbreaks compared to non-outbreak settings.

What do these findings mean?

Our findings suggest that burden estimates based solely on suspected cases may overestimate the incidence of medically attended cholera by 2-fold.
The high variability across studies suggests also that local testing data should be used to inform assumptions about positivity when exhaustive testing is not feasible.
A limitation of our approach was that we could not account for cases missed by clinical surveillance, which is crucial for unbiased overall cholera burden estimates and an important area for future work.

Introduction

Current estimates of cholera burden rely on clinical diagnosis of individuals with acute watery diarrhea (i.e., suspected cholera cases) [1,2]. It is unclear how manyVibrio cholerae O1/O139 (serogroups that cause current epidemics) infections get missed due to mild symptoms and other barriers to care-seeking or how many get overcounted due to nonspecific suspected case definitions. In Bangladesh, previous studies estimated that asymptomatic and unreported infections account for at least half ofV.cholerae infections [3–5]. Meanwhile, the proportion of suspected cholera cases that represent laboratory-confirmed infections varies widely between studies, from 6% of those tested during routine surveillance in Bangladesh [6] to 72% of those tested during the initial phase of the 2017 outbreak in Yemen [7].

This wide variation in positivity may be caused by differences between sites inV.cholerae epidemiology [8], epidemiology of non-cholera diseases causing the same clinical symptoms [9–12], and variations in diagnostic tests and case definitions [13–15]. Typical suspected cholera case definitions have been shown to have high sensitivity but low specificity [14] for detecting true cholera and can vary by location across seasons [13]. Culture-based methods or polymerase chain reaction (PCR) are the gold standards to confirm cholera in clinical samples and generally have high specificity. Lateral flow rapid diagnostic tests (RDTs) may also be used and can be as sensitive as PCR [16]. Although recommended by the Global Task Force on Cholera Control (GTFCC) [17], systematic microbiological confirmation in surveillance is not always implemented, particularly during outbreaks when resources are limited [8]. To our knowledge—based on a literature review and discussion with experts—no study had yet systematically synthesized these data to estimate overallV.cholerae positivity and identify sources of this variation.

UnderstandingV.cholerae positivity among clinical cases could provide insights needed to improve laboratory testing strategies and allow for better estimates of cholera burden and risk, which are often used to allocate cholera resources, including oral cholera vaccines. Starting in 2023, the GTFCC has recommended using a combination of suspected cholera incidence, persistence, mortality, and cholera test positivity data across multiple years to identify priority areas for multisectoral interventions [18], which is particularly relevant in cholera endemic areas. As described above, theV.cholerae positivity data are often not available. We sought to address this knowledge gap by modeling the relationship between clinically suspected and laboratory confirmed cholera. Specifically, we aimed to estimate the proportion of suspected cholera cases that represent trueV.cholerae O1/O139 infections and identify factors that explain variability in positivity across settings.

Methods

Ethics

This study was approved by the Johns Hopkins University Institutional Review Board and Temple University Institutional Review Board.

Terminology

We focused onV.cholerae O1 and O139 because these are the serogroups that are responsible for the current seventh pandemic and the only ones known to lead to large outbreaks in humans [19]. These are also the serogroups that are targeted by each of the commonly usedV.cholerae diagnostic tests (culture, PCR, and RDT). Throughout this manuscript, we refer to the proportion of suspected cholera cases that represent trueV.cholerae O1/O139 infections as “V.cholerae positivity” or “cholera positivity.” In addition, since the available data did not allow us to evaluate the performance of multiple RDTs, we refer to RDT as any rapid diagnostic test forV.cholerae O1/O139 and do not distinguish between different RDT manufacturers or whether the RDT is enriched/direct swab RDT or stool RDT.

Systematic review

This study is reported as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline (S1 Checklist). The review was not preregistered, and a formal public protocol was not prepared, although all study methods can be found in the Methods below and the Methods inS1 Appendix.

We searched PubMed, Embase, Scopus, Google Scholar, andmedRxiv on October 16, 2021, using search provided in the Supplementary Methods inS1 Appendix. We updated PubMed, Embase, and Scopus searches on April 19, 2023. We included studies that (1) collected human samples; (2) reported the number of suspected and confirmed cholera cases in the sampling frame; (3) used culture, PCR, and/or RDT to test suspected cases for cholera; and (4) had at least one suspected case sample collected on or after January 1, 2000, to reflect contemporary patterns in cholera positivity. We excluded studies that (1) used a case definition not specific for suspected cholera (i.e., we accepted non-bloody watery diarrhea, acute watery diarrhea, or simply suspected cholera but not diarrhea, acute diarrhea, or acute gastroenteritis); (2) sampled only special populations (i.e., people living with HIV or cancer); (3) selected suspected cases based on epidemiological link to other cases or environmental sources; (4) tested fewer than 10 suspected cases; and (5) were reported in languages other than English, French, Spanish, and Chinese (languages our study team had proficiency in). We did not exclude studies based on study type or sampling method. Although we originally included preprints in our screening and extracted one preprint, we excluded this study at the time of the updated search because the published version of the manuscript no longer included positivity data.

Titles, abstracts, and full texts were uploaded to Covidence, a web-based screening tool (https://www.covidence.org/), and were assessed independently by two of the reviewers (ASA, ECL, HX, KEW, KZ, MND) for inclusion. Conflicts were resolved either by a third reviewer or through consensus/discussion. Data were extracted from included studies in a shared spreadsheet (S1 Data) by a single reviewer. The key extracted items included study timeframe and location, surveillance type (routine, outbreak, post-vaccination, or hybrid), case definition of suspected cholera (including age constraint and whether dehydrated or hospitalized, if provided), test method(s), sampling strategy for the test (all suspected cases, systematic or random sampling, convenience sampling, or unreported), number of tested and confirmed suspected cases, among other sample characteristics, if included. If only the proportion positive and total number tested were reported, the number of confirmed cholera cases was calculated by hand and rounded to the nearest whole number. If the surveillance contained multiple timeframes, tested samples with multiple tests, or reported stratified results, we extracted the data separately into different rows in the spreadsheet.

To identify overlapping samples, we manually reviewed all studies with overlapping timeframes by country. We excluded studies that had shorter timeframes, fewer suspected cases tested, less representative sampling methods, fewer confirmation tests, or reported positive results by 2 tests but did not disaggregate. Within studies, when suspected and confirmed cases were stratified multiple ways, we included the stratification by surveillance type if available, followed by age, antibiotic use, dehydration status, year, geography, or sex, in that order. When studies used multiple RDTs, we included results for Crystal VC (Arkray Healthcare, Gujarat, India) and direct rapid tests (as opposed to rapid tests performed after an enrichment step) because these were the most common.

To identify any mistakes and ensure quality of the extracted data, we performed data quality checks using a series of automated functions in R to identify implausible values (e.g., start date of study after end date, more cases positive than tested, lower age limit larger than upper age limit) and missing required data. If impossible or missing values were found, the entire extraction was double checked for accuracy and corrected by a single reviewer.

To assess whether different studies used methodologies that may have biased our results, we plotted cholera positivity in the raw data by (1) diagnostic test used; (2) sampling method quality; and (3) suspected cholera case definition. In addition, we plotted the relationship between cholera positivity in the raw data and (1) estimated suspected cholera incidence [2]; (2) the proportion of cases severely dehydrated; and (3) the proportion on antibiotics. We quantified the correlation between these variables using Spearman’s rank correlation coefficient using the spearman.ci function of the RVAideMemoire package in R [20]. Since these continuous variables were only available in a subset of studies, we did not adjust for them in final analyses. All data visualization was conducted using the ggplot2 package in R [21].

Data analysis

Estimating sensitivity and specificity of cholera confirmation tests

We constructed a latent-class model to assess sensitivity and specificity of culture, PCR, and RDT, assuming none had perfect performance. We fit a hierarchical conditional dependence model, similar to that proposed by Wang and colleagues, which takes into account potential pairwise dependence between the tests that could occur if the tests have reduced performance for similar reasons [22]. We performed inference in a Bayesian framework using Just Another Gibbs Sampler (JAGS) through the rjags package in R [23,24]. We pooled estimates across 4 published studies that reported cholera confirmation results for all 3 test methods [16,25–27].

We used flat prior distributions on sensitivity and specificity of each test with a lower bound set based on plausible values from the literature [15,16,25–27] (Table A inS1 Appendix). We assumed that culture had lower sensitivity than PCR and RDT because it depends on successful growth of viableV.cholerae in the laboratory. We assumed that RDT had lower specificity than culture and PCR because it may have cross-reactivity with other antigens in the stool or defects that lead to false positive results. For each prior, we selected a wider range than had been reported in previous studies to allow for greater variation. We ran 4 chains of 100,000 iterations and assessed convergence through visual inspection of traceplots and with the Gelman-Rubin R-hat statistic.

EstimatingV.cholerae positivity and sources of heterogeneity

We pooled estimates ofV.cholerae positivity across all studies using a generalized linear model with a study level random intercept, which allowed us to adjust for sensitivity and specificity of the diagnostic tests as well as examine the contributions of study methodology (i.e., whether the study used low- versus high-quality sampling, and whether or not the study set a minimum age in the suspected cholera case definition) and setting (whether surveillance was routine or post-vaccination versus initiated in response to an outbreak) on variation in positivity. To estimate the proportion positive, overall and by strata, we marginalized over study-level random effects. See Supplementary Methods inS1 Appendix for the full statistical model. We performed inference in a Bayesian framework using CmdStanR version 0.5.2 as an interface to Stan for R [24,28]. We additionally performed a sensitivity analysis where we shifted the prior set on the global intercept (see Methods inS1 Appendix). The odds of a suspected cholera case having a trueV.cholerae infection given each covariate were calculated as odds ratios by taking the mean and 95% credible interval (CrI) of 8,000 draws from the posterior distribution of each covariate’s exponentiated coefficient. Odds ratios with 95% CrIs that did not cross the value 1 were considered statistically significant.

To estimate the proportion of the variance in positivity attributable to true differences between studies, beyond simple sampling error, we calculated the I² statistic [29] as

I^{2} = \frac{τ^{2}}{τ^{2} + υ}

whereτ² was between-study heterogeneity or the variance of the random effect by observation. We calculated the within-study variance,υ, [30] as

υ = \frac{(k - 1) \sum_{1}^{i} ω_{i}}{(\sum_{1}^{i} ω_{i})^{2} - \sum_{1}^{i} ω_{i}^{2}}

wherek was the number of studies or observations included in the meta-analysis, andω_i = 1/v_i wherev_i was the variance of the proportion positive by culture, PCR, or RDT within each study/observation. When multiple tests were used in a study, we used the maximum variance estimate across the tests.

Results

Study characteristics

We identified 131 studies that met our inclusion criteria (Fig 1). Of these, 119 studies contained nonoverlapping samples and were included in our analysis dataset [6,7,9,10,12–14,16,25–27,31–131] and 12 were excluded from analysis due to overlaps [8,11,132–141] (Fig 1). Of the 119 studies included in our analysis dataset, one reported data for more than one sampling method [7], one for both outbreak and non-outbreak surveillance [37], and one for outbreak and non-outbreak surveillance in 6 different countries [13]. We defined each of these as separate entries in the dataset for a total of 132 observations. Extracted data including detailed individual study information can be found inS1 Data.

The nonoverlapping observations in our analysis dataset came from 30 countries and were reported at different geographic levels, including the country level (n = 16 observations) and first (n = 25), second (n = 66), and third administrative levels (n = 25) (Fig A inS1 Appendix). Twelve studies reported data for multiple administrative units, and 3 reported across multiple administrative divisions within a country; the numbers above reflect the largest administrative division reported per observation. Data were collected from 1992 through 2022 with most observations from studies that completed sampling during 2015 to 2022 (n = 53 observations), followed by 2010 to 2014 (n = 32), 2005 to 2009 (n = 21), and 1997 to 2004 (n = 17) (Fig B inS1 Appendix). Nine studies were missing sampling end dates. Most studies were conducted in South Asia and West, Central, and East Africa, with additional studies from Haiti, Yemen, Iraq, Iran, Laos, Vietnam, Papua New Guinea, Algeria, and the Philippines (Fig A inS1 Appendix).

Most of the observations were from surveillance studies (93/132, 70.5%), followed by diagnostic test accuracy studies (28/132, 21.2%) and vaccine effectiveness studies (10/132, 7.6%) (Table 1). High-quality sampling methods (i.e., tested all suspected cases, a random sample, or systematically selected every nth suspected case) were used in 28% (37/132) of observations, while the remaining 72% (95/132) used convenience sampling or did not report the sampling approach (Table 1). Even though most studies did not includeV.cholerae positivity disaggregated by individual-level characteristics, 24.2% (32/132) reported the proportion of suspected cases under age 5, 8.3% (11/132) reported the proportion severely dehydrated, 7.6% (10/132) reported the proportion on antibiotics, and one study reported all 3 (Table B inS1 Appendix).

Table 1. Study characteristics.

Number of observations included in the analysis dataset with each study characteristic. There is more than one observation per study when the study reported data for more than one sampling method, surveillance type, and/or country.

Category	Characteristic	Number (n = 132)	Percent
Study design	Surveillance	93	70.5
	Diagnostic test accuracy	28	21.2
	Vaccine effectiveness	10	7.6
	Randomized control trial	1	0.8
Sampling method quality	High	37	28.0
Sampling method quality	Low	95	72.0
Percent of suspected cases tested	0–4	12	9.1
	5–49	32	24.2
	50–95	27	20.5
	≥95	30	22.7
	Not reported	31	23.5
Number of tests used (of culture, PCR, and/or RDT)^*	1	106	80.3
	2	19	14.4
	≥3	7	5.3
Number of suspected cases tested	1–9^†	1	0.8
	10–99	37	28.0
	100–999	55	41.7
	≥1,000	39	29.5

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*PCR, polymerase chain reaction; RDT, rapid diagnostic test.

^†One multicountry surveillance study overall tested ≥10 suspected cholera cases forV.cholerae O1/O139 but reported fewer than 10 tested in one country.

V.cholerae positivity in unadjusted data

We found that reportedV.cholerae positivity varied greatly across studies with an interquartile range (IQR) of 30% to 60% (N = 165 observations of positivity; 25 of the 131 observations had positivity results for multiple tests) (Table 1). As expected, positivity varied by diagnostic test used with a median positivity of 36% by culture (IQR, 27% to 55%;N = 121), 37% by PCR (IQR, 34% to 55%;N = 11), and 49% by RDT (IQR, 38% to 67%;N = 33), with substantial overlap between distributions (Fig 2A). Positivity was higher across studies that used low-quality or convenience sampling methods (median of 43%;N = 117; IQR, 33% to 62%) compared to those that used high-quality or representative sampling (median of 35%; IQR, 14% to 51%) (Fig 2B). Positivity increased with higher minimum ages in suspected cholera case definitions (Fig 2C), and we found a modest negative correlation between positivity and the proportion of suspected cases under 5 years old (Spearmanr = −0.60; 95% confidence interval (CI): −0.81, −0.32;p < 0.001) (Fig Ca inS1 Appendix).

Fig 2 — Proportion of suspected cholera cases that were confirmed positive by(A) diagnostic test type,**(B)** quality of sampling methods, where “high” includes all suspected cases or a random or stratified sample and “low” includes convenience or unreported sampling methods,**(C)** age minimum in suspected case definition, where “0” indicates that no minimum age was set, and(D) whether surveillance was initiated in response to an outbreak or whether it was routine surveillance or non-outbreak. Each point is an observation included in the analysis dataset. There is more than one observation per study when the study reported data for more than one sampling method, surveillance type, and/or country. Boxes represent the median and IQR of positivity for each group. Lines extend from the top and bottom of box to the largest positivity value no further than 1.5 * IQR from the box. IQR, interquartile range; PCR, polymerase chain reaction; RDT, rapid diagnostic test.

Unadjusted positivity was higher when surveillance was initiated in response to an outbreak (median of 47%; IQR, 33% to 66%;N = 80) compared to situations where surveillance was routine or post-vaccination (median of 35%; IQR 17% to 49%;N = 85) (Fig 2D). We found limited evidence for differences in positivity by the 2010 to 2016 estimated mean annual suspected case incidence rate in countries where these estimates were available (Fig Cb inS1 Appendix; [2]).

We found a modest positive correlation between positivity and the proportion of suspected cases severely dehydrated (Spearmanr = 0.64; 95% CI: 0.22, 0.90;p = 0.001) (Fig Cc inS1 Appendix). While not statistically significant, we found a weak negative correlation between positivity and the proportion of suspected cases that had received antibiotics prior to testing (Spearmanr = −0.46; 95% CI: −0.83, 0.09;p = 0.07) (Fig Cd inS1 Appendix).

Adjusted underlyingV.cholerae positivity

Since different imperfect diagnostic tests were used to confirmV.cholerae O1/O139, we adjusted positivity estimates from each study to account for test performance. To estimate a median performance of each type of diagnostic test, we pooled estimates of sensitivity and specificity across 4 studies that reported detailed results for all 3 tests (seeMethods). This included data from Bangladesh [27], South Sudan [16], Kenya [25], and Zambia [26]. We estimated a median sensitivity of 82.0% (95% CrI: 37.5, 98.7) and specificity of 94.3% (95% Crl: 81.5, 99.6) for culture, a median sensitivity of 85.1% (95% CrI: 53.6%, 98.9%) and specificity of 94.2 (95% CrI: 81.8, 99.7) for PCR, and a median sensitivity of 90.4% (95% CrI: 55.2, 99.5) and specificity of 88.9% (95% CrI: 54.9, 99.4) for RDT (Fig 3A and Table C inS1 Appendix).

Fig 3 — **(A)** Posterior distributions of pooled percent sensitivity and specificity of culture (top), PCR (middle), and RDT (bottom) for detectingV.cholerae O1/O139 infections in suspected cholera cases. Dashed lines represent median values of each distribution.(B) The “Unadjusted” dot is meanV.cholerae positivity (lines represent 95% CrI) from random effects meta-analysis without adjustments for test performance. The “Adjusted for test performance” and “Stratum: …” dots are estimated meanV.cholerae positivity (lines represent 95% CrIs), adjusted for sensitivity/specificity of the tests. High-quality stratified estimates correspond to post-stratified estimates ofV.cholerae positivity for studies that use high quality sampling methods and whether an age minimum was set in the suspected case definition, as well as whether surveillance was initiated in response to an outbreak. CrI, credible interval; PCR, polymerase chain reaction; RDT, rapid diagnostic test.

After adjusting for diagnostic test performance, we estimated that 53% (95% CrI: 24%, 80%) of suspected cases tested were trueV.cholerae O1/O139 infections across all studies (Fig 3 and Fig D inS1 Appendix and Table D inS1 Appendix). These estimates remained similar in sensitivity analysis with an alternative prior distribution (Table D inS1 Appendix).

With additional adjustments for study methodology (i.e., sampling quality and whether an age minimum was set in suspected case definition), we estimated thatV.cholerae positivity for studies with high-quality sampling methods was 46% (95% CrI: 19%, 76%) when no age restriction was used and 68% (95% CrI: 33%, 98%) when a minimum age (typically 1 or 5 years old) was incorporated into the case definition (Fig 3, Table D inS1 Appendix). After adjusting for sampling quality and whether or not surveillance was initiated in response to a cholera outbreak, we estimated thatV.cholerae positivity for studies with high-quality sampling methods was 42% (95% CrI: 12%, 77%) in non-outbreak settings and 78% (95% CrI: 40%, 99%) in outbreak settings (Fig 3, Table D inS1 Appendix).

We found substantial heterogeneity between studies (I² = >99.99% (95% CrI: >99.99%, >99.99%;I² = 0.96 (95% CrI: 0.94, 0.98)) (Fig 4). Adjusted underlying positivity rates ranged from 0.008% (95% CrI: 0.0004%, 0.04%) for a high-quality study conducted during routine surveillance in Bangladesh to 99.8% (95% CrI: 98.7%, 100.0%) for a “low-quality” study conducted during a cholera outbreak in Uganda (Fig 4).

Factors associated with variation inV.cholerae positivity

We then examined factors that could explain variation inV.cholerae positivity. After adjusting for test performance, sampling quality, and outbreak setting, we found that setting any minimum age in the case definition (i.e., 1, 2, 5, or 10) was associated with 2.33 (95% CrI: 0.54, 6.40) times higher odds of a suspected cholera case having a true infection (Table E inS1 Appendix).

We estimated that the odds of a suspected cholera case having a trueV.cholerae O1/O139 infection were 5.71 (95% CrI: 1.53, 15.43) times higher when surveillance was initiated in response to a cholera outbreak compared to non-outbreak surveillance, after adjusting for test performance, sampling quality, and case definition (Table E inS1 Appendix).

Discussion

Here, we estimated that, on average, half of medically attended suspected cholera cases represent trueV.cholerae O1/O139 infections. We found thatV.cholerae positivity was higher when a minimum age was set in case definitions and when surveillance was initiated in response to an outbreak. Additionally, we found substantial heterogeneity inV.cholerae positivity between studies, so that simply multiplying the number of suspected cholera case counts by this global proportion positive to estimate the true number of cases will not be appropriate in most settings. To our knowledge, this is the first study to systematically synthesize data globally to estimate overallV.cholerae positivity and examine factors that contribute to variation in positivity.

A remaining question is why only about half of medically attended suspected cholera cases represent true infections. It is possible that we overestimated test sensitivity and have not fully accounted for false negatives; unfortunately, this is difficult to evaluate without a gold standard diagnostic test. A portion of the remaining suspected cases could also be infections with other enteric pathogens, especially those with similar transmission modes as cholera that may have outbreaks or high levels of endemic transmission concurrently. For example, in Uvira, Democratic Republic of the Congo, 36% of suspected cholera cases were positive for EnterotoxigenicEscherichia coli and 28% forCryptosporidium [10]. In rural Bangladesh, the majority of acute watery diarrhea in children under 18 months was attributable to rotavirus, while older children were more often infected withV.cholerae [12]. In Haiti, 64% of acute watery diarrhea cases tested positive forV.cholerae O1, 4% for rotavirus, and <1% for Shigella and Salmonella, though rotavirus positivity was higher among children under 5 [11]. Thus, the relative contribution of non-cholera watery diarrhea varies with age distribution and other location-specific drivers of enteric infections.

One of the limitations of this study was that we could not account for all potential drivers ofV.cholerae positivity, which contributed to the large heterogeneity we found between studies. In addition,V.cholerae positivity may be highest in the early stages of an outbreak [7,9,131], but we could not account for this, given the temporal resolution of our dataset. However, a strength of our approach is that we pooled estimates from studies across diverse geographies, time periods, and epidemiological contexts. A further potential limitation is that, without a gold standard diagnostic test, sensitivity and specificity estimates may be biased if the tests are less sensitive and/or specific for shared reasons. The hierarchical conditional dependence model we used accounted for this pairwise dependence and increased uncertainty around our estimates accordingly. This approach also allowed us to pool test performance estimates across studies from 4 countries. Thus, to our knowledge, we adjusted our estimates for test sensitivity and specificity using the best generic estimates available. Still, we likely overestimated sensitivity of culture for settings where samples had to be sent to a reference lab. Variation in the timing of tests in relation to when sample was taken could mean that one sensitivity and specificity estimate per diagnostic method is not appropriate. For example, a 2023 study in Haiti found that stool culture had a sensitivity of 33% during the waning phase of the 2018 to 2019 cholera outbreak [142], which is much lower than previous estimates. Overall, we have high confidence in our average estimates ofV.cholerae positivity, despite the difficulty of accurately estimating positivity in a new location/time/setting without confirmation tests.

These findings have several implications for cholera surveillance policy. The GTFCC defines suspected cholera in areas where an outbreak has not yet been reported as acute watery diarrhea and severe dehydration or death in individuals 2 years and older [17]. Our finding that setting any minimum age increases specificity for identifying a trueV.cholerae infection in suspected cases supports using an age restriction in this case definition. The February 2023 interim guidance from the GTFCC on cholera surveillance provides concrete recommendations for systematic and frequent testing of suspected cholera cases at the health facility or surveillance unit scale [17]. Our finding of high variability in positivity across settings and times lends support to these recommendations of systematically generating local data that can be used to scale suspected to true cholera. Our finding that high-quality sampling also increases specificity forV.cholerae suggests that systematically selecting cases to test is important for accurately evaluating endemic cholera. Finally, thatV.cholerae positivity was lower during non-outbreak surveillance suggests that systematic confirmation testing is additionally important for understanding cholera burden and epidemiology in endemic, non-outbreak settings where cocirculation of other enteric pathogens is common.

These estimates ofV.cholerae positivity address one part of the challenge in establishing the true burden of cholera: cases that are overcounted due to nonspecific suspected case definitions. A crucial next step will be to estimate missed cases due to care seeking and poor clinical surveillance. This could be done in part through systematically synthesizing data from studies of care seeking behavior for diarrheal symptoms (e.g., [143,144]), including where potential cholera cases seek care (e.g., at pharmacies, traditional healers, or hospitals). This could additionally be done through population representative surveys and active case finding, similar to studies conducted in Haiti [145] and Tanzania [146], respectively, which demonstrated higher mortality rates associated with cholera than had been reported through passive surveillance. Together, these studies will help to understand whether and to what degree missed cholera cases compensate for the biases described here in overcounting.

Ultimately, a better understanding ofV.cholerae positivity will help us move toward estimates of true cholera incidence and mortality. Given the large heterogeneity between studies, it will be important to do this in a way that accounts for variation inV.cholerae positivity between sites. Moreover, the proportion of suspected cholera cases missed because of milder symptoms or barriers to healthcare seeking needs to be estimated and accounted for. Such estimates will provide crucial information to guide the allocation of limited resources such as vaccines in a way that most effectively supports cholera prevention and control.

Supporting information

S1 Checklist. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Checklist.

(PDF)

Click here for additional data file.^{(123.8KB, pdf)}

S1 Appendix. Supporting information.

Detailed methods, including systematic review search terms and the full statistical model, as well as additional figures and tables.

(PDF)

Click here for additional data file.^{(941.2KB, pdf)}

S1 Data. Full dataset.

Excel sheet with the complete data extracted from all 131 studies that met the inclusion criteria (tab 1) as well as all variable descriptions (tab 2). Data extracted from the 119 nonoverlapping studies included in the main analysis dataset can be found by filtering for the values “1” in the column “Primary dataset.”

(XLSX)

Click here for additional data file.^{(296.8KB, xlsx)}

Acknowledgments

We thank Morgane Dominguez for feedback on this manuscript, Lori Rosman for assistance developing the literature search strategy, and Javier Perez-Saez for feedback on the analytical methods.

Abbreviations

CI: confidence interval
CrI: credible interval
GTFCC: Global Task Force on Cholera Control
IQR: interquartile range
JAGS: Just Another Gibbs Sampler
PCR: polymerase chain reaction
RDT: rapid diagnostic test

Data Availability

All input data and analytical code are available athttps://github.com/HopkinsIDD/cholera_positivity.

Funding Statement

This work was supported by the Bill and Melinda Gates Foundation (https://www.gatesfoundation.org/) [grant number OPP1171700 to A.S.A.] and the National Institute of Allergy and Infectious Disease (https://www.niaid.nih.gov/) [grant number AI135115-01A1 to A.S.A.]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Ali M, Nelson AR, Lopez AL, Sack DA. Updated global burden of cholera in endemic countries.PLoS Negl Trop Dis. 2015;9:e0003832. doi: 10.1371/journal.pntd.0003832 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Lessler J, Moore SM, Luquero FJ, McKay HS, Grais R, Henkens M, et al. Mapping the burden of cholera in sub-Saharan Africa and implications for control: an analysis of data across geographical scales. Lancet. 2018;391:1908–1915. doi: 10.1016/S0140-6736(17)33050-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Azman AS, Lauer SA, Bhuiyan TR, Luquero FJ, Leung DT, Hegde ST, et al. Vibrio cholerae O1 transmission in Bangladesh: insights from a nationally representative serosurvey. Lancet Microbe. 2020;1:e336–e343. doi: 10.1016/S2666-5247(20)30141-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Mosley WH, Benenson AS, Barui R. A serological survey for cholera antibodies in rural East Pakistan. Bull World Health Organ. 1968;38:327–334. [PMC free article] [PubMed] [Google Scholar]
5.Weil AA, Begum Y, Chowdhury F, Khan AI, Leung DT, LaRocque RC, et al. Bacterial shedding in household contacts of cholera patients in Dhaka. Bangladesh Am J Trop Med Hyg. 2014;91:738–742. doi: 10.4269/ajtmh.14-0095 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Khan AI, Rashid MM, Islam MT, Afrad MH, Salimuzzaman M, Hegde ST, et al. Epidemiology of cholera in Bangladesh: findings from nationwide hospital-based surveillance, 2014–2018. Clin Infect Dis. 2020;71:1635–1642. doi: 10.1093/cid/ciz1075 [DOI] [PubMed] [Google Scholar]
7.Camacho A, Bouhenia M, Alyusfi R, Alkohlani A, Naji MAM, Radiguès X de, et al. Cholera epidemic in Yemen, 2016–18: an analysis of surveillance data. Lancet Glob Health. 2018;6: e680–e690. doi: 10.1016/S2214-109X(18)30230-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Sauvageot D, Njanpop-Lafourcade B-M, Akilimali L, Anne J-C, Bidjada P, Bompangue D, et al. Cholera incidence and mortality in sub-Saharan African sites during multi-country surveillance.PLoS Negl Trop Dis. 2016;10:e0004679. doi: 10.1371/journal.pntd.0004679 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Jameel SK, Shafek MA, Abdulmohsen AM, Mohamed NS, Naji SR, Mohammed TT. The isolation of Vibrio cholera and other enteric bacteria with molecular characterization of Vibrio cholera during the outbreak of Baghdad/Iraq in 2015.Adv Microbiol. 2016;6:699–715. doi: 10.4236/aim.2016.69069 [DOI] [Google Scholar]
10.Williams C, Cumming O, Grignard L, Rumedeka BB, Saidi JM, Grint D, et al. Prevalence and diversity of enteric pathogens among cholera treatment centre patients with acute diarrhea in Uvira, Democratic Republic of Congo.BMC Infect Dis.2020;20:741. doi: 10.1186/s12879-020-05454-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Steenland MW, Joseph GA, Lucien MAB, Freeman N, Hast M, Nygren BL, et al. Laboratory-confirmed cholera and rotavirus among patients with acute diarrhea in four hospitals in Haiti, 2012–2013. Am J Trop Med Hyg. 2013;89:641–646. doi: 10.4269/ajtmh.13-0307 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Siddique AK, Ahmed S, Iqbal A, Sobhan A, Poddar G, Azim T, et al. Epidemiology of rotavirus and cholera in children aged less than five years in rural Bangladesh.J Health Popul Nutr. 2011;29:1–8. doi: 10.3329/jhpn.v29i1.7560 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Nadri J, Sauvageot D, Njanpop-Lafourcade B-M, Baltazar CS, Banla Kere A, Bwire G, et al. Sensitivity, specificity, and public-health utility of clinical case definitions based on the signs and symptoms of cholera in Africa. Am J Trop Med Hyg. 2018;98:1021–1030. doi: 10.4269/ajtmh.16-0523 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Lucien MAB, Schaad N, Steenland MW, Mintz ED, Emmanuel R, Freeman N, et al. Identifying the most sensitive and specific sign and symptom combinations for cholera: results from an analysis of laboratory-based surveillance data from Haiti, 2012–2013. Am J Trop Med Hyg. 2015;92:758–764. doi: 10.4269/ajtmh.14-0429 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Muzembo BA, Kitahara K, Debnath A, Okamoto K, Miyoshi S-I. Accuracy of cholera rapid diagnostic tests: a systematic review and meta-analysis. Clin Microbiol Infect. 2022;28(2):155–162. doi: 10.1016/j.cmi.2021.08.027 [DOI] [PubMed] [Google Scholar]
16.Ontweka LN, Deng LO, Rauzier J, Debes AK, Tadesse F, Parker LA, et al. Cholera rapid test with enrichment step has diagnostic performance equivalent to culture.PLoS ONE. 2016;11:e0168257. doi: 10.1371/journal.pone.0168257 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Global Task Force on Cholera Control (GTFCC) Surveillance Working Group. Public health surveillance for cholera interim guidance. 2023. Available from:https://www.gtfcc.org/wp-content/uploads/2023/02/gtfcc-public-health-surveillance-for-cholera-interim-guidance.pdf
18.Global Task Force on Cholera Control. Identification of priority areas for multisectoral interventions (PAMIs) for cholera control. [cited 2023 May 16]. Available from:https://www.gtfcc.org/resources/identification-of-priority-areas-for-multisectoral-interventions-pamis-for-cholera-control/
19.Weill F-X, Domman D, Njamkepo E, Tarr C, Rauzier J, Fawal N, et al. Genomic history of the seventh pandemic of cholera in Africa. Science. 2017;358:785–789. doi: 10.1126/science.aad5901 [DOI] [PubMed] [Google Scholar]
20.Herve M.RVAideMemoire: testing and plotting procedures for biostatistics. 2023. Available from:https://cran.r-project.org/web/packages/RVAideMemoire/index.html [Google Scholar]
21.Wickham H, Chang W, Henry L, Pedersen TL, Takahashi K, Wilke C, et al. ggplot2: create elegant data visualisations using the grammar of graphics. 2023. Available from:https://cran.r-project.org/web/packages/ggplot2/index.html [Google Scholar]
22.Wang C, Lin X, Nelson KP. Bayesian hierarchical latent class models for estimating diagnostic accuracy.Stat Methods Med Res. 2020;29:1112–1128. doi: 10.1177/0962280219852649 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Plummer M, Stukalov A, Denwood M. rjags: Bayesian graphical models using MCMC.2022. Available from:https://CRAN.R-project.org/package=rjags [Google Scholar]
24.R Core Team.R: A language and environment for statistical computing.Vienna, Austria: R Foundation for Statistical Computing.http://wwwR-project.org/. 2016. [cited 2022 Aug 17]. Available from:https://cir.nii.ac.jp/crid/1574231874043578752 [Google Scholar]
25.Debes AK, Murt KN, Waswa E, Githinji G, Umuro M, Mbogori C, et al. Laboratory and field evaluation of the Crystal VC-O1 cholera rapid diagnostic test. Am J Trop Med Hyg. 2021;104:2017–2023. doi: 10.4269/ajtmh.20-1280 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Mwaba J, Ferreras E, Chizema-Kawesa E, Mwimbe D, Tafirenyika F, Rauzier J, et al. Evaluation of the SD bioline cholera rapid diagnostic test during the 2016 cholera outbreak in Lusaka, Zambia.Trop Med Int Health. 2018;23:834–840. doi: 10.1111/tmi.13084 [DOI] [PubMed] [Google Scholar]
27.Sayeed MA, Islam K, Hossain M, Akter NJ, Alam MN, Sultana N, et al. Development of a new dipstick (Cholkit) for rapid detection of Vibrio cholerae O1 in acute watery diarrheal stools.PLoS Negl Trop Dis.2018;12:e0006286. doi: 10.1371/journal.pntd.0006286 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Gabry J, Češnovar R, Bales B, Morris M, Popov M, Lawrence M, et al. R interface to CmdStan. 2022. [cited 2022 Aug 17]. Available from:https://mc-stan.org/cmdstanr/ [Google Scholar]
29.Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis.Stat Med. 2002;21:1539–1558. doi: 10.1002/sim.1186 [DOI] [PubMed] [Google Scholar]
30.Viechtbauer W.I2 for multilevel and multivariate models. 2022. [cited 2022 Aug 26]. Available from:https://www.metafor-project.org/doku.php/tips:i2_multilevel_multivariate [Google Scholar]
31.Abdullahi KN, Mutindin D, Kabugi W, Mowlid S. Epidemiological description of a protracted cholera outbreak in Hagadera refugee camp and the surrounding host community within Fafi Sub County and Garissa County in Kenya during march-September 2019.Epidemiol Open J. 2019;4:31–35. [Google Scholar]
32.Ahmed S, Afzal RK, Mian UA. A localized outbreak of cholera due to Vibrio cholerae 01, Ogawa resistant to tetracyclines.Pak Armed Forces Med J. 2015;65:595–599. [Google Scholar]
33.Alajo SO, Nakavuma J, Erume J. Cholera in endemic districts in Uganda during El Niño rains: 2002–2003.Afr Health Sci. 2006;6:93–97. doi: 10.5555/afhs.2006.6.2.93 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Alkassoum S, Djibo I, Amadou H, Bohari A, Issoufou H, Aka J, et al. The global burden of cholera outbreaks in Niger: an analysis of the national surveillance data, 2003–2015. Trans R Soc Trop Med Hyg. 2019;113:273–280. doi: 10.1093/trstmh/try145 [DOI] [PubMed] [Google Scholar]
35.Amadu DO, Abdullahi IN, Seibu E, Fadeyi A, Kamaldeen K, Akanbi AA, et al. Retrospective analysis of the serovars and antibiogram of Vibrio cholerae isolates of the 2017 Ilorin Cholera Outbreak.Nigeria Infect Chemother. 2021;53:368–373. doi: 10.3947/ic.2021.0001 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Anh DD, Lopez AL, Thiem VD, Grahek SL, Duong TN, Park JK, et al. Use of oral cholera vaccines in an outbreak in Vietnam: a case control study.PLoS Negl Trop Dis. 2011;5:e1006. doi: 10.1371/journal.pntd.0001006 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Baltazar CS, Langa JP, Baloi LD, Wood R, Ouedraogo I, Njanpop-Lafourcade B-M, et al. Multi-site cholera surveillance within the African Cholera Surveillance Network shows endemicity in Mozambique, 2011–2015.PLoS Negl Trop Dis. 2017;11:e0005941. doi: 10.1371/journal.pntd.0005941 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Bhattacharya MK, Dutta D, Ramamurthy T, Sarkar D, Singharoy A, Bhattacharya SK. Azithromycin in the treatment of cholera in children. Acta Paediatr. 2003;92:676–678. [PubMed] [Google Scholar]
39.Bhuiyan NA, Qadri F, Faruque ASG, Malek MA, Salam MA, Nato F, et al. Use of dipsticks for rapid diagnosis of cholera caused by Vibrio cholerae O1 and O139 from rectal swabs. J Clin Microbiol. 2003;41:3939–3941. doi: 10.1128/JCM.41.8.3939-3941.2003 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Bin-Hameed EA, Joban HA. Cholera outbreak in Hadhramout, Yemen: the epidemiological weeks 2019.Int J Epidemiol Res. 2021;8:40–46. [Google Scholar]
41.Brazilay E, Schaad N, Magloire R. Cholera surveillance during the Haiti epidemic-the first two years. N Engl J Med. 2013;368:599–609. [DOI] [PubMed] [Google Scholar]
42.Bukar AM, Goni HB, Bwala AB, Kolo FB, Isa A, Ibrahim A, et al. Determination of cholera outbreak among internally displaced persons (IDPs) in complex emergency settings within Maiduguri, Borno State-Nigeria.Int J Pure Appl Sci Res. 2020;12(2):28–36. [Google Scholar]
43.Bwire G, Malimbo M, Maskery B, Kim YE, Mogasale V, Levin A. The burden of cholera in Uganda.PLoS Negl Trop Dis. 2013;7:e2545. doi: 10.1371/journal.pntd.0002545 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Bwire G, Waniaye JB, Otim JS, Matseketse D, Kagirita A, Orach CG. Cholera risk in cities in Uganda: understanding cases and contacts centered strategy (3CS) for rapid cholera outbreak control.Pan Afr Med J. 2021;39:193. doi: 10.11604/pamj.2021.39.193.27794 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Chibwe I, Kasambara W, Kagoli M, Milala H, Gondwe C, Azman AS. Field evaluation of Cholkit rapid diagnostic test forVibrio cholerae O1 during a cholera outbreak in Malawi, 2018.Open Forum. Infect Dis Ther. 2020:7. doi: 10.1093/ofid/ofaa493 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Chirambo R, Mufunda J, Songolo P, Kachimba J, Vwalika B. Epidemiology of the 2016 cholera outbreak of Chibombo district, central Zambia.Med J Zambia.2016;43:61–63. [Google Scholar]
47.Chowdhury G, Senapati T, Das B, Kamath A, Pal D, Bose P, et al. Laboratory evaluation of the rapid diagnostic tests for the detection of Vibrio cholerae O1 using diarrheal samples.PLoS Negl Trop Dis. 2021;15:e0009521. doi: 10.1371/journal.pntd.0009521 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Das S, Gupta S. Diversity of Vibrio cholerae strains isolated in Delhi, India, during 1992–2000.J Health Popul Nutr. 2005;23:44–51. [PubMed] [Google Scholar]
49.De Guzman A, de los Reyes VC, Sucaldito MN, Tayag E. Availability of safe drinking-water: the answer to cholera outbreak? Nabua, Camarines Sur, Philippines, 2012.Western Pac Surveill Response J. 2015;6:12–16. doi: 10.5365/WPSAR.2015.6.1.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Debes AK, Ateudjieu J, Guenou E, Ebile W, Sonkoua IT, Njimbia AC, et al. Clinical and environmental surveillance for Vibrio cholerae in resource constrained areas: application during a 1-year surveillance in the Far North Region of Cameroon. Am J Trop Med Hyg. 2016;94:537–543. doi: 10.4269/ajtmh.15-0496 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Dengo-Baloi LC, Semá-Baltazar CA, Manhique LV, Chitio JE, Inguane DL, Langa JP. Antibiotics resistance in El Tor Vibrio cholerae 01 isolated during cholera outbreaks in Mozambique from 2012 to 2015.PLoS ONE.2017;12:e0181496. doi: 10.1371/journal.pone.0181496 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Djomassi L, Gessner B, Andze G, Mballa G. Cholera epidemiology in Cameroon based on national surveillance data. J Infect Dis. 2013;208:S92–S97. [DOI] [PubMed] [Google Scholar]
53.Dutta BP, Kumar N, Meshram KC, Yadav R, Sodha SV, Gupta S. Cholera outbreak associated with contaminated water sources in paddy fields, Mandla District, Madhya Pradesh.India Indian J Public Health. 2021;65:S46–s50. doi: 10.4103/ijph.IJPH_1118_20 [DOI] [PubMed] [Google Scholar]
54.Dzotsi EK, Dongdem AZ, Boateng G, Antwi L, Owusu-Okyere G, Nartey DB, et al. Surveillance of bacterial pathogens of diarrhoea in two selected sub metros within the Accra metropolis.Ghana Med J. 2015;49:65–71. doi: 10.4314/gmj.v49i2.1 [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Eurien D, Mirembe BB, Musewa A, Kisaakye E, Kwesiga B, Ogole F, et al. Cholera Outbreak Caused by Drinking Unprotected Well Water Contaminated with Feces from an Open Storm Water Drainage—Kampala City, Uganda.January2019. 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Fouda AAB, Kollo B. Epidémie de choléra à Douala en 2011 épidémiologie, clinique et bactériologie Cholera outbreak in Douala in 2011 epidemiology, clinic and bacteriology. [Google Scholar]
57.Franke MF, Jerome JG, Matias WR, Ternier R, Hilaire IJ, Harris JB, et al. Comparison of two control groups for estimation of oral cholera vaccine effectiveness using a case-control study design. Vaccine. 2017;35:5819–5827. doi: 10.1016/j.vaccine.2017.09.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Fredrick T, Ponnaiah M, Murhekar MV, Jayaraman Y, David JK, Vadivoo S, et al. Cholera outbreak linked with lack of safe water supply following a tropical cyclone in Pondicherry, India, 2012.J Health Popul Nutr. 2015;33:31–38. [PMC free article] [PubMed] [Google Scholar]
59.George CM, Rashid MU, Sack DA, Bradley Sack R, Saif-Ur-Rahman KM, Azman AS, et al. Evaluation of enrichment method for the detection of Vibrio cholerae O1 using a rapid dipstick test in Bangladesh.Tropical Med Int Health. 2014;19:301–307. doi: 10.1111/tmi.12252 [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Grandesso F, Kasambara W, Page AL, Debes AK, et al. Effectiveness of oral cholera vaccine in preventing cholera among fishermen in Lake Chilwa, Malawi: a case-control study. Vaccine. 2019;37:3668–3676. doi: 10.1016/j.vaccine.2019.05.044 [DOI] [PubMed] [Google Scholar]
61.Guévart E, Noeske J, Sollé J, Mouangue A, Bikoti JM. Large-scale selective antibiotic prophylaxis during the 2004 cholera outbreak in Douala (Cameroon).Sante. 2007;17:63–68. [PubMed] [Google Scholar]
62.Gupta PK, Pant ND, Bhandari R, Shrestha P. Cholera outbreak caused by drug resistant Vibrio cholerae serogroup O1 biotype ElTor serotype Ogawa in Nepal; a cross-sectional study.Antimicrob Resist Infect Control. 2016;5:23. doi: 10.1186/s13756-016-0122-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Gupta S, Jhamb U, Uppal B, Chakraverti A, Mittal SK. Diagnosing cholera in the young: a review of W.H.O. criteria.JK Sci. 2007;9:137–139. [Google Scholar]
64.Haque F.Cholera outbreak in Netrokona Municipality, 2013.Health Sci Bull. 2014:12. [Google Scholar]
65.Haque F, Hossain MJ, Kundu SK, Naser AM, Rahman M, Luby SP. Cholera outbreaks in Urban Bangladesh in 2011.Epidemiology (Sunnyvale).2013:3. doi: 10.4172/2161-1165.1000126 [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Harris JR, Cavallaro EC, De Nóbrega AA, Dos S Barrado JC, Bopp C, Parsons MB, et al. Field evaluation of Crystal VC Rapid Dipstick test for cholera during a cholera outbreak in Guinea-Bissau.Trop Med Int Health. 2009;14: 1117–1121. doi: 10.1111/j.1365-3156.2009.02335.x [DOI] [PubMed] [Google Scholar]
67.Im J, Islam MT, Ahmmed F, Kim DR, Chon Y, Zaman K, et al. Use of oral cholera vaccine as a vaccine probe to determine the burden of culture-negative cholera. PLoS Negl Trop Dis. 2019;13:e0007179. doi: 10.1371/journal.pntd.0007179 [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Ingelbeen B, Hendrickx D, Miwanda B, van der Sande MAB, Mossoko M, Vochten H, et al. Recurrent cholera outbreaks, Democratic Republic of the Congo, 2008–2017. Emerg Infect Dis. 2019;25:856–864. doi: 10.3201/eid2505.181141 [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Islam MT, Khan AI, Sayeed MA, Amin J, Islam K, Alam N, et al. Field evaluation of a locally produced rapid diagnostic test for early detection of cholera in Bangladesh.PLoS Negl Trop Dis. 2019;13:e0007124. doi: 10.1371/journal.pntd.0007124 [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Issahaku GR, Asiedu-Bekoe F, Kwashie S, Broni F, Boateng P, Alomatu H, et al. Protracted cholera outbreak in the Central Region, Ghana, 2016.Ghana Med J. 2020;54:45–52. doi: 10.4314/gmj.v54i2s.8 [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Jain A, Choudhary S, Saroha E, Bhatnagar P, Harvey P. Cholera outbreak in an informal settlement at Shahpur huts, Panchkula District, Haryana State, India, 2019.Indian J Public Health. 2021;65:S51–s54. doi: 10.4103/ijph.IJPH_970_20 [DOI] [PMC free article] [PubMed] [Google Scholar]
72.Jeandron A, Cumming O, Rumedeka BB, Saidi JM, Cousens S. Confirmation of cholera by rapid diagnostic test amongst patients admitted to the cholera treatment centre in Uvira, Democratic Republic of the Congo.PLoS ONE.2018;13:e0201306. doi: 10.1371/journal.pone.0201306 [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Jones FK, Wamala JF, Rumunu J, Mawien PN, Kol MT, Wohl S, et al. Successive epidemic waves of cholera in South Sudan between 2014 and 2017: a descriptive epidemiological study. Lancet Planet Health. 2020;4:e577–e587. doi: 10.1016/S2542-5196(20)30255-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
74.Khatib A, Ali M, von Seidlein L, Kim D, Hashim R, Reyburn R. Direct and indirect effectiveness of an oral cholera vaccine in Zanzibar, East Africa: findings from a large mass vaccination campaign followed by an observational cohort study.Lancet Infect Dis.2012;12:837–844. [DOI] [PubMed] [Google Scholar]
75.Khazaei HA, Rezaei N, Bagheri GR, Moin AA. A six-year study on Vibrio cholerae in southeastern Iran. Jpn J Infect Dis. 2005;58:8–10. [PubMed] [Google Scholar]
76.Kisera N, Luxemburger C, Tornieporth N, Otieno G, Inda J. A descriptive cross-sectional study of cholera at Kakuma and Kalobeyei refugee camps, Kenya in 2018.Pan Afr Med J. 2020;37:197. doi: 10.11604/pamj.2020.37.197.24798 [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Koley H, Ray N, Chowdhury G, Barman S, Mitra S, Ramamurthy T, et al. Outbreak of cholera caused by Vibrio cholerae O1 El Tor variant strain in Bihar, India.Jpn J Infect Dis. 2014;67:221–226. doi: 10.7883/yoken.67.221 [DOI] [PubMed] [Google Scholar]
78.Kulkarni S, Chillarge C. Antibiotic susceptibility pattern of Vibrio cholerae causing diarrohea outbreaks in Bidar, North Karnataka, India.Int J Curr Microbiol App Sci. 2015;4:957–961. [Google Scholar]
79.Kuttiat VS, Lodha R, Das B, Kohli U. Prevalence of cholera in pediatric patients with acute dehydrating diarrhea. Indian J Pediatr. 2010;77:67–71. doi: 10.1007/s12098-010-0009-1 [DOI] [PubMed] [Google Scholar]
80.Kwesiga B, Pande G, Ario AR, Tumwesigye NM, Matovu JK, Zhu B-P. A prolonged, community-wide cholera outbreak associated with drinking water contaminated by sewage in Kasese District, western Uganda.BMC Public Health. 2018;18:1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
81.Landoh DE, Gessner BD, Badziklou K, Tamekloe T, Nassoury DI, Dagnra A, et al. National surveillance data on the epidemiology of cholera in Togo. J Infect Dis. 2013;208(Suppl 1):S115–S119. doi: 10.1093/infdis/jit244 [DOI] [PubMed] [Google Scholar]
82.Lenglet A, Khamphaphongphane B, Thebvongsa P, Vongprachanh P, Sithivong N, Chantavisouk C, et al. A cholera epidemic in Sekong Province, Lao People’s Democratic Republic, December 2007-January 2008.Jpn J Infect Dis. 2010;63:204–207. [PubMed] [Google Scholar]
83.Ley B, Khatib AM, Thriemer K, von Seidlein L, Deen J, Mukhopadyay A, et al. Evaluation of a rapid dipstick (Crystal VC) for the diagnosis of cholera in Zanzibar and a comparison with previous studies.PLoS ONE.2012;7:e36930. doi: 10.1371/journal.pone.0036930 [DOI] [PMC free article] [PubMed] [Google Scholar]
84.Llanes R, Lazo A, Somarriba L, Mas P. Sentinel surveillance detects low circulation of Vibrio cholerae serotype Inaba in Haiti, 2011–2012.MEDICC Rev.2015;17:43–46. doi: 10.37757/MR2015.V17.N3.9 [DOI] [PubMed] [Google Scholar]
85.Luquero FJ, Grout L, Ciglenecki I, Sakoba K, Traore B, Heile M, et al. Use of Vibrio cholerae vaccine in an outbreak in Guinea. N Engl J Med. 2014;370:2111–2120. doi: 10.1056/NEJMoa1312680 [DOI] [PubMed] [Google Scholar]
86.Mahamud AS, Ahmed JA, Nyoka R, Auko E, Kahi V, Ndirangu J, et al. Epidemic cholera in Kakuma Refugee Camp, Kenya, 2009: the importance of sanitation and soap.J Infect Dev Ctries. 2012;6:234–241. doi: 10.3855/jidc.1966 [DOI] [PubMed] [Google Scholar]
87.Matias WR, Cademil A, Julceus FE, Mayo-Smith LM, Franke MF, Harris JB, et al. Laboratory evaluation of immunochromatographic rapid diagnostic tests for cholera in Haiti. Am J Trop Med Hyg. 2015;93:569–569. [DOI] [PMC free article] [PubMed] [Google Scholar]
88.Mbala-Kingebeni P, Vogt F, Miwanda B, Sundika T, Mbula N, Pankwa I, et al. Sachet water consumption as a risk factor for cholera in urban settings: findings and implications from a case control study in Kinshasa, Democratic Republic of the Congo during the 2017–2018 outbreak.PLoS Negl Trop Dis.2021;15:e0009477. doi: 10.1371/journal.pntd.0009477 [DOI] [PMC free article] [PubMed] [Google Scholar]
89.Michel E, Gaudart J, Beaulieu S, Bulit G, Piarroux M, Boncy J, et al. Estimating effectiveness of case-area targeted response interventions against cholera in Haiti.elife. 2019:8. doi: 10.7554/eLife.50243 [DOI] [PMC free article] [PubMed] [Google Scholar]
90.Mishra A, Taneja N, Sharma M. Environmental and epidemiological surveillance of Vibrio cholerae in a cholera-endemic region in India with freshwater environs. J Appl Microbiol. 2012;112:225–237. doi: 10.1111/j.1365-2672.2011.05191.x [DOI] [PubMed] [Google Scholar]
91.Monje F, Ario AR, Musewa A, Bainomugisha K, Mirembe BB, Aliddeki DM, et al. A prolonged cholera outbreak caused by drinking contaminated stream water, Kyangwali refugee settlement, Hoima District, Western Uganda: 2018.Infect Dis Poverty. 2020;9:154. doi: 10.1186/s40249-020-00761-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
92.Mugoya I, Kariuki S, Galgalo T, Njuguna C, Omollo J, Njoroge J, et al. Rapid spread of Vibrio cholerae O1 throughout Kenya, 2005. Am J Trop Med Hyg. 2008;78:527–533. [PubMed] [Google Scholar]
93.Mukherjee P, Ghosh S, Ramamurthy T, Bhattacharya MK, Nandy RK, Takeda Y, et al. Evaluation of a rapid immunochromatographic dipstick kit for diagnosis of cholera emphasizes its outbreak utility. Jpn J Infect Dis. 2010;63:234–238. [PubMed] [Google Scholar]
94.Mwenda V, Niyomwungere A, Oyugi E, Githuku J, Obonyo M, Gura Z. Factors associated with cholera outbreaks, Nairobi County, July 2017: a case control study.bioRxiv. 2019;719641. [Google Scholar]
95.Ndugwa Kabwama S, Riolexus Ario A, Guwatudde D. Cholera outbreak caused by drinking lakeshore water contaminated by feces washed down from a hill-side residential area: Kaiso Village, Uganda.Pan Afr Med J Conference Proceedings.2017. [Google Scholar]
96.Noora CL, Issah K, Kenu E, Bachan EG, Nuoh RD, Nyarko KM, et al. Large cholera outbreak in Brong Ahafo Region.Ghana BMC Res Notes. 2017;10:389. doi: 10.1186/s13104-017-2728-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
97.Nsubuga F, Garang SC, Tut M, Oguttu D, Lubajo R, Lodiongo D, et al. Epidemiological description of a protracted cholera outbreak in Tonj East and Tonj North counties, former Warrap State, South Sudan, May-Oct 2017.BMC Infect Dis.2019;19:4. doi: 10.1186/s12879-018-3640-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
98.Okello PE, Bulage L, Riolexus AA, Kadobera D, Kwesiga B, Kajumbula H, et al. A cholera outbreak caused by drinking contaminated river water, Bulambuli District, Eastern Uganda, March 2016.BMC Infect Dis. 2019;19:516. doi: 10.1186/s12879-019-4036-x [DOI] [PMC free article] [PubMed] [Google Scholar]
99.Page AL, Alberti KP, Mondonge V, Rauzier J, Quilici ML, Guerin PJ. Evaluation of a rapid test for the diagnosis of cholera in the absence of a gold standard.PLoS ONE.2012;7:e37360. doi: 10.1371/journal.pone.0037360 [DOI] [PMC free article] [PubMed] [Google Scholar]
100.Pal BB, Khuntia HK, Samal SK, Kerketta AS, Kar SK, Karmakar M, et al. Large outbreak of cholera caused by El Tor variant Vibrio cholerae O1 in the eastern coast of Odisha, India during 2009. Epidemiol Infect. 2013;141:2560–2567. doi: 10.1017/S0950268813000368 [DOI] [PMC free article] [PubMed] [Google Scholar]
101.Pal BB, Khuntia HK, Samal SK, Das SS, Chhotray GP. Emergence of Vibrio cholerae O1 biotype E1 Tor serotype Inaba causing outbreaks of cholera in Orissa, India.Jpn J Infect Dis. 2006;59:266. [PubMed] [Google Scholar]
102.Pande G, Kwesiga B, Bwire G, Kalyebi P, Riolexus A, Matovu JKB, et al. Cholera outbreak caused by drinking contaminated water from a lakeshore water-collection site, Kasese District, south-western Uganda, June-July 2015.PLoS ONE. 2018;13:e0198431. doi: 10.1371/journal.pone.0198431 [DOI] [PMC free article] [PubMed] [Google Scholar]
103.Phukan AC, Borah PK, Biswas D, Mahanta J. A cholera epidemic in a rural area of northeast India. Trans R Soc Trop Med Hyg. 2004;98:563–566. doi: 10.1016/j.trstmh.2004.01.002 [DOI] [PubMed] [Google Scholar]
104.Ramazanzadeh R, Rouhi S, Shakib P, Shahbazi B, Bidarpour F, Karimi M. Molecular characterization of Vibrio cholerae isolated from clinical samples in Kurdistan Province, Iran.Jundishapur J Microbiol. 2015;8:e18119. doi: 10.5812/jjm.8(5)2015.18119 [DOI] [PMC free article] [PubMed] [Google Scholar]
105.Rosewell A, Addy B, Komnapi L, Makanda F, Ropa B, Posanai E, et al. Cholera risk factors, Papua New Guinea, 2010.BMC Infect Dis.2012;12:287. doi: 10.1186/1471-2334-12-287 [DOI] [PMC free article] [PubMed] [Google Scholar]
106.Roskosky M, Acharya B, Shakya G, Karki K, Sekine K, Bajracharya D, et al. Feasibility of a comprehensive targeted cholera intervention in the Kathmandu Valley. Nepal Am J Trop Med Hyg. 2019;100:1088–1097. doi: 10.4269/ajtmh.18-0863 [DOI] [PMC free article] [PubMed] [Google Scholar]
107.Roy S, Parande MV, Mantur BG, Bhat S, Shinde R, Parande AM, et al. Multidrug-resistant Vibrio cholerae O1 in Belgaum, south India. J Med Microbiol. 2012;61:1574–1579. doi: 10.1099/jmm.0.049692-0 [DOI] [PubMed] [Google Scholar]
108.Sack RB, Siddique AK, Longini IM Jr, Nizam A, Yunus M, Islam MS, et al. A 4-year study of the epidemiology of Vibrio cholerae in four rural areas of Bangladesh. J Infect Dis. 2003;187:96–101. doi: 10.1086/345865 [DOI] [PubMed] [Google Scholar]
109.Saha R, Das S, Waghmare M, Ramachandran VG. Paradoxical reduction in prevalence of vibrio cholerae in its niche environment. Int J Pharm Bio Sci. 2013;4:B1099–B1107. [Google Scholar]
110.Sévère K, Rouzier V, Anglade SB, Bertil C, Joseph P, Deroncelay A, et al. Effectiveness of oral cholera vaccine in Haiti: 37-month follow-up. Am J Trop Med Hyg. 2016;94:1136–1142. doi: 10.4269/ajtmh.15-0700 [DOI] [PMC free article] [PubMed] [Google Scholar]
111.Shah WA, Shahina M, Ali N. First report of Vibrio cholerae infection from Andaman and Nicobar, India. J Commun Dis. 2002;34:270–275. [PubMed] [Google Scholar]
112.Sharma A, Dutta BS, Rasul ES, Barkataki D, Saikia A, Hazarika NK. Prevalence of Vibrio cholerae O1 serogroup in Assam, India: A hospital-based study. Indian J Med Res. 2017;146:401–408. doi: 10.4103/ijmr.IJMR_631_15 [DOI] [PMC free article] [PubMed] [Google Scholar]
113.Shikanga OT, Mutonga D, Abade M, Amwayi S, Ope M, Limo H, et al. High mortality in a cholera outbreak in western Kenya after post-election violence in 2008. Am J Trop Med Hyg. 2009;81:1085–1090. doi: 10.4269/ajtmh.2009.09-0400 [DOI] [PubMed] [Google Scholar]
114.Siddiqui FJ, Bhutto NS, von Seidlein L, Khurram I, Rasool S, Ali M, et al. Consecutive outbreaks of Vibrio cholerae O139 and V. cholerae O1 cholera in a fishing village near Karachi, Pakistan. Trans R Soc Trop Med Hyg. 2006;100:476–482. doi: 10.1016/j.trstmh.2005.07.019 [DOI] [PubMed] [Google Scholar]
115.Sinha A, Sengupta S, Ghosh S, Basu S, Sur D, Kanungo S, et al. Evaluation of a rapid dipstick test for identifying cholera cases during the outbreak. Indian J Med Res. 2012;135:523–528. [PMC free article] [PubMed] [Google Scholar]
116.Sreedhara H, Mohan N. Molecular epidemiology of vibrio cholerae causing outbreaks and sporadic cholera in and around Hassan district and its antibiotic susceptibility pattern. IP Int J Med Microbiol Trop Dis. 2019;5:41–46. [Google Scholar]
117.Sugunan AP, Ghosh AR, Roy S, Gupte MD, Sehgal SC. A cholera epidemic among the Nicobarese tribe of Nancowry, Andaman, and Nicobar, India. Am J Trop Med Hyg. 2004;71:822–827. [PubMed] [Google Scholar]
118.Sur D, Deen JL, Manna B, Niyogi SK, Deb AK, Kanungo S, et al. The burden of cholera in the slums of Kolkata, India: data from a prospective, community based study. Arch Dis Child. 2005;90:1175–1181. doi: 10.1136/adc.2004.071316 [DOI] [PMC free article] [PubMed] [Google Scholar]
119.Sur D, Sarkar BL, Manna B, Deen J, Datta S, Niyogi SK, et al. Epidemiological, microbiological & electron microscopic study of a cholera outbreak in a Kolkata slum community. Indian J Med Res. 2006;123:31–36. [PubMed] [Google Scholar]
120.Tamang M, Sharma N, Makaju R, Sarma A, Koju R, Nepali N, et al. An outbreak of El Tor cholera in Kavre district, Nepal.Kathmandu Univ Med J (KUMJ).2005;3:138–142. [PubMed] [Google Scholar]
121.Taneja N, Kaur J, Sharma K, Singh M, Kalra JK, Sharma NM, et al. A recent outbreak of cholera due to Vibrio cholerae O1 Ogawa in & around Chandigarh, North India. Indian J Med Res. 2003;117:243–246. [PubMed] [Google Scholar]
122.Thiem VD, Deen JL, von Seidlein L, Canh DG, Anh DD, Park JK, et al. Long-term effectiveness against cholera of oral killed whole-cell vaccine produced in Vietnam. Vaccine. 2006;24:4297–4303. doi: 10.1016/j.vaccine.2006.03.008 [DOI] [PubMed] [Google Scholar]
123.Torane V, Kuyare S, Nataraj G, Mehta P, Dutta S, Sarkar B. Phenotypic and antibiogram pattern of V. cholerae isolates from a tertiary care hospital in Mumbai during 2004–2013: a retrospective cross-sectional study. BMJ Open. 2016;6:e012638. doi: 10.1136/bmjopen-2016-012638 [DOI] [PMC free article] [PubMed] [Google Scholar]
124.Tripurari K, Deepak B, Kaur TA, Pushpa VV, Aakash S, Prakash NJ, et al. Vibrio cholerae outbreak in Batala town, Punjab, India 2012. J Commun Dis. 2017;49:35–40. doi: 10.24321/0019.5138.201705 [DOI] [Google Scholar]
125.Uthappa CK, Allam RR, Nalini C, Gunti D, Udaragudi PR, Tadi GP, et al. An outbreak of cholera in Medipally village, Andhra Pradesh, India, 2013.J Health Popul Nutr. 2015;33:7. doi: 10.1186/s41043-015-0021-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
126.Von Nguyen D, Sreenivasan N, Lam E, Ayers T, Kargbo D, Dafae F, et al. Cholera epidemic associated with consumption of unsafe drinking water and street-vended water-Eastern Freetown, Sierra Leone, 2012. Am J Trop Med Hyg. 2014;90:518–523. doi: 10.4269/ajtmh.13-0567 [DOI] [PMC free article] [PubMed] [Google Scholar]
127.Wang XY, Ansaruzzaman M, Vaz R, Mondlane C, Lucas ME, von Seidlein L, et al. Field evaluation of a rapid immunochromatographic dipstick test for the diagnosis of cholera in a high-risk population.BMC Infect Dis. 2006;6:17. doi: 10.1186/1471-2334-6-17 [DOI] [PMC free article] [PubMed] [Google Scholar]
128.Wierzba TF, Kar SK, Mogasale VV, Kerketta AS, You YA, Baral P, et al. Effectiveness of an oral cholera vaccine campaign to prevent clinically-significant cholera in Odisha State, India.Vaccine. 2015;33:2463–2469. doi: 10.1016/j.vaccine.2015.03.073 [DOI] [PubMed] [Google Scholar]
129.Zachariah R, Harries AD, Arendt V, Nchingula D, Chimtulo F, Courteille O, et al. Characteristics of a cholera outbreak, patterns of Vibrio cholerae and antibiotic susceptibility testing in rural Malawi. Trans R Soc Trop Med Hyg. 2002;96:39–40. doi: 10.1016/s0035-9203(02)90233-6 [DOI] [PubMed] [Google Scholar]
130.Zereen F, Akter S, Sobur MA, Hossain MT, Rahman MT. Molecular detection of Vibrio cholerae from human stool collected from SK Hospital, Mymensingh, and their antibiogram.J Adv Vet Anim Res. 2019;6:451–455. doi: 10.5455/javar.2019.f367 [DOI] [PMC free article] [PubMed] [Google Scholar]
131.Zgheir SM, Mustafa NM, Ali AA, Al-Diwan J. Cholera outbreak in Iraq, 2017.Indian J Public Health Res Dev. 2019;10:686. doi: 10.5958/0976-5506.2019.01654.1 [DOI] [Google Scholar]
132.Azman AS, Parker LA, Rumunu J, Tadesse F, Grandesso F, Deng LL, et al. Effectiveness of one dose of oral cholera vaccine in response to an outbreak: a case-cohort study. Lancet Glob Health. 2016;4:e856–e863. doi: 10.1016/S2214-109X(16)30211-X [DOI] [PubMed] [Google Scholar]
133.Blake A, Keita VS, Sauvageot D, Saliou M, Njanpop BM, Sory F, et al. Temporo-spatial dynamics and behavioural patterns of 2012 cholera epidemic in the African mega-city of Conakry, Guinea.Infect Dis Poverty. 2018;7:13. doi: 10.1186/s40249-018-0393-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
134.Boncy J, Rossignol E, Dahourou G, Hast M, Buteau J, Stanislas M, et al. Performance and utility of a rapid diagnostic test for cholera: notes from Haiti. Diagn Microbiol Infect Dis. 2013;76:521–523. doi: 10.1016/j.diagmicrobio.2013.03.010 [DOI] [PubMed] [Google Scholar]
135.Bwire G, Orach CG, Abdallah D, Debes AK, Kagirita A, Ram M, et al. Alkaline peptone water enrichment with a dipstick test to quickly detect and monitor cholera outbreaks.BMC Infect Dis. 2017;17:726. doi: 10.1186/s12879-017-2824-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
136.Ferreras E, Blake A, Chewe O, Mwaba J, Zulu G, Poncin M, et al. Alternative observational designs to estimate the effectiveness of one dose of oral cholera vaccine in Lusaka, Zambia.Epidemiol Infect. 2020;148:e78. doi: 10.1017/S095026882000062X [DOI] [PMC free article] [PubMed] [Google Scholar]
137.Franke MF, Ternier R, Jerome JG, Matias WR, Harris JB, Ivers LC. Long-term effectiveness of one and two doses of a killed, bivalent, whole-cell oral cholera vaccine in Haiti: an extended case-control study. Lancet Glob Health. 2018;6:e1028–e1035. doi: 10.1016/S2214-109X(18)30284-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
138.George CM, Monira S, Sack DA, Rashid M, Saif-Ur-Rahman KM, Mahmud T, et al. Randomized controlled trial of hospital-Bbased hygiene and water treatment intervention (CHoBI7) to reduce cholera.Emerg Infect Dis. 2016;22:233–241. doi: 10.3201/eid2202.151175 [DOI] [PMC free article] [PubMed] [Google Scholar]
139.Ivers LC, Hilaire IJ, Teng JE, Almazor CP, Jerome JG, Ternier R, et al. Effectiveness of reactive oral cholera vaccination in rural Haiti: a case-control study and bias-indicator analysis. Lancet Glob Health. 2015;3:e162–e168. doi: 10.1016/S2214-109X(14)70368-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
140.Lucas MES, Deen JL, von Seidlein L, Wang X-Y, Ampuero J, Puri M, et al. Effectiveness of mass oral cholera vaccination in Beira, Mozambique. N Engl J Med. 2005;352:757–767. doi: 10.1056/NEJMoa043323 [DOI] [PubMed] [Google Scholar]
141.Roy S, Dutta B, Ghosh AR, Sugunan AP, Nandy RK, Bhattacharya SK, et al. Molecular tracking of the lineage of strains of Vibrio cholerae O1 biotype El Tor associated with a cholera outbreak in Andaman and Nicobar Islands, India.Trop Med Int Health. 2005;10:604–611. doi: 10.1111/j.1365-3156.2005.01423.x [DOI] [PubMed] [Google Scholar]
142.Guillaume Y, Debela M, Slater D, Vissieres K, Ternier R, Franke M, et al. Poor sensitivity of stool culture compared to PCR in surveillance for V. cholerae in Haiti, 2018–2019.Open Forum. Infect Dis Ther.2023:ofad301. doi: 10.1093/ofid/ofad301 [DOI] [PMC free article] [PubMed] [Google Scholar]
143.Chowdhury F, Khan IA, Patel S, Siddiq AU, Saha NC, Khan AI, et al. Diarrheal illness and healthcare seeking behavior among a population at high risk for diarrhea in Dhaka, Bangladesh.PLoS ONE. 2015;10:e0130105. doi: 10.1371/journal.pone.0130105 [DOI] [PMC free article] [PubMed] [Google Scholar]
144.Fissehaye T, Damte A, Fantahun A, Gebrekirstos K. Health care seeking behaviour of mothers towards diarrheal disease of children less than 5 years in Mekelle city, North Ethiopia.BMC Res Notes.2018;11:749. doi: 10.1186/s13104-018-3850-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
145.Luquero FJ, Rondy M, Boncy J, Munger A, Mekaoui H, Rymshaw E, et al. Mortality rates during cholera epidemic, Haiti, 2010–2011.Emerg Infect Dis. 2016:22. doi: 10.3201/eid2203.141970 [DOI] [PMC free article] [PubMed] [Google Scholar]
146.McCrickard LS, Massay AE, Narra R, Mghamba J, Mohamed AA, Kishimba RS, et al. Cholera mortality during urban epidemic, Dar es Salaam, Tanzania, August 16, 2015–January 16, 2016.Emerg Infect Dis. 2017;23. doi: 10.3201/eid2313.170529 [DOI] [PMC free article] [PubMed] [Google Scholar]
147.Watts V.Confidence intervals for a population proportion. 2022. [cited 2023 Jul 10]. Available from:https://ecampusontario.pressbooks.pub/introstats/chapter/7-4-confidence-intervals-for-a-population-proportion/ [Google Scholar]

PLoS Med. doi:10.1371/journal.pmed.1004286.r001

Decision Letter 0

Philippa C Dodd

Roles

Philippa C Dodd:Senior Editor

PMC Copyright notice

27 Oct 2022

Dear Dr Azman,

Thank you for submitting your manuscript entitled "Towards estimating true cholera burden: a systematic review and meta-analysis of Vibrio cholerae positivity" for consideration by PLOS Medicine.

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PLOS Medicine

PLoS Med. doi:10.1371/journal.pmed.1004286.r002

Decision Letter 1

Philippa C Dodd

Roles

Philippa C Dodd:Senior Editor

PMC Copyright notice

17 Apr 2023

Dear Dr. Azman,

Thank you very much for submitting your manuscript "Towards estimating true cholera burden: a systematic review and meta-analysis of Vibrio cholerae positivity" (PMEDICINE-D-22-03502R1) for consideration at PLOS Medicine.

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We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here:http://journals.plos.org/plosmedicine/s/competing-interests.

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Please ensure that the paper adheres to the PLOS Data Availability Policy (seehttp://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Philippa Dodd, MBBS MRCP PhD

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

GENERAL

Please respond to all editor and reviewer comments detailed below in full.

* Please report your SR/MA according to the PRISMA guidelines provided at the EQUATOR site.

http://www.equator-network.org/reporting-guidelines/prisma/

- Please provide the completed PRISMA checklist. When completing the checklist, please use section and paragraph numbers, rather than page or line numbers, as these often change in the event of publication.

- Please add the following statement, or similar, to the Methods: "This study is reported as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline (S1 Checklist)."

** Was the study registered with PROSPERO? Please provide details.

*** Please update your search to the present time – PLOS Medicine requires that all systematic reviews are updated

to within 6 months of an anticipated publication date

COMMENTS FROM THE ACADEMIC EDITOR

Congrats on collating and synthesising these important data. Would your team be able to elaborate further on the utility of using suspect cholera case definitions in outbreak and non-outbreak settings? It seems the these case definitions have performed well during outbreaks (with some recommendations made in lines 258-270) but that laboratory confirmation may be needed for sporadic cases?

COMMENTS FROM THE EDITORS

ABSTRACT

Please report your abstract according to PRISMA for abstracts, following the PLOS Medicine abstract structure (Background, Methods and Findings, Conclusions)http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001419

Please provide the specific start and end dates of your search, data sources, types of study designs included, eligibility criteria, and synthesis/appraisal methods.

Please ensure that all numbers presented in the abstract are present and identical to numbers presented in the main manuscript text.

Please quantify the main results p values as well as with 95% CIs. Please report as p <0.001 or where higher as p=0.002, for example. Suggest separating upper and lower bounds of 95% CIs with commas instead of hyphens as these can be confused with negative values. For example, lines 56-57, suggest “…1.64 (95% Credible Interval: 1.06,2.52; p< or =)

Please include numerators and denominators used to derive percentages

Line 56 – do you present odds ratios here? Please clarify/define numerical values for the reader

Please include any important variables that are adjusted for in the analyses.

In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

Abstract Conclusions:

Please address the study implications without overreaching what can be concluded from the data; the phrase "In this study, we observed ..." may be useful.

Please interpret the study based on the results presented in the abstract, emphasizing what is new without overstating your conclusions.

Please avoid vague statements such as "these results have major implications for policy/clinical care". Mention only specific implications substantiated by the results.

AUTHOR SUMMARY

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The author summary should consist of 2-3 succinct bullet points under each of the following headings:

• Why Was This Study Done? Authors should reflect on what was known about the topic before the research was published and why the research was needed.

• What Did the Researchers Do and Find? Authors should briefly describe the study design that was used and the study’s major findings. Do include the headline numbers from the study, such as the sample size and key findings.

• What Do These Findings Mean? Authors should reflect on the new knowledge generated by the research and the implications for practice, research, policy, or public health. Authors should also consider how the interpretation of the study’s findings may be affected by the study limitations.

The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information:https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

INTRODUCTION

The editorial team agree that it would be helpful to justify/clarify how measuring the ‘burden’ of cholera is helpful considering it occurs most often in the context of sporadic outbreaks (peaks & troughs).

Please indicate whether your study is novel and how you determined that. If there has been a previous systematic review of the evidence, please refer to and reference that review and indicate whether it supports the need for your study.

METHODS and RESULTS

Please justify why your search start date was 2000

As above please ensure you update the search to the present time.

PLOS Medicine encourages inclusion of all non-English language studies. Please justify why studies reported in languages other than “...English, French, Spanish, and Chinese…” were excluded (line 117).

Studies reported on Medrix are not peer reviewed publications. It is not clear how many (if any) were included in the analyses. We suggest that these are excluded from the main analyses.

Please be reminded to update the manuscript accordingly including the PRISMA flowchart, figures, tables as necessary

As above, please include p values where you report 95% CIs. Please provide the statistical tests used to determine p values. Please report as p <0.001 or where higher as p=0.002, for example.

Please ensure consistency when reporting upper and lower 95% CI bounds in the abstract hyhens are used, here the word “to” when negative values are reported. We suggest the use of commas throughout.

When referring to ‘odds’ do you mean odds ration (OR)? Please clarify and define numerical values as necessary for the reader.

FIGURES

To make your figures more accessible to those with colour blindness, please consider avoiding the use of green and/or red.

Figure 1 – details here are inconsistent with those in the abstract and main manuscript text, please revise in line with the above comments.

Figure 2 - Please clearly indicate in the figure caption the meaning of the boxes and whiskers

Figure 3 – Please clearly indicate in the figure caption the meaning of the dots and lines

TABLES

Table 1 – please define PCR, RDT, please define the total number of studies as n= in the column header. Table 1 caption – in general this is a bit confusing “study-country-periods” is mentioned more than once but not in the table, what does it mean? Please revise/clarify for the reader such that the table contents are clearly defined without the need to refer to the manuscript text

There is 1 study defined as ‘other’ considering it is a single study would it be helpful to simply define it?

DISCUSSION

Please present and organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion. Please avoid the use of sub-headings such that discussion reads as a single continuous piece of prose.

REFERENCES

In the bibliography, please ensure that up to, but no more than, 6 author names are listed followed by et al, in the event that more that 6 authors contribute to an individual study. Journal name abbreviations should be those found in the National Center for Biotechnology Information (NCBI) databases.

Please see our website for other reference guidelineshttps://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references

SUPPLEMENTARY FILES

SUPP. METHODS

please ensure consistency with main manuscript text and figures

SUPP. FIGURES

Figure 1 & 2 – Please confirm that the appropriate usage rights apply to the use of this map. Please see our guidelines for map images:https://journals.plos.org/plosmedicine/s/figures#loc-maps

SUPP. TABLES

Please ensure all abbreviations are defined in the captions or an appropriate footnote (PCR/RDT, for example)

Please consider the use of commas to separate upper and lower bounds as hyphens can be confused with reporting of negative values.

Comments from the reviewers:

Reviewer #1: Thanks for the opportunity to review your manuscript. My role is as a statistical reviewer, so my review concentrates on the study design, data, and analysis that are presented. I have put general questions first, followed by queries relevant to a specific section of the manuscript (with a page/line reference).

This study is a systematic review and meta-analysis that examines test positivity for cholera. The systematic review is broad and includes a large pool of studies that met quality requirements. A Bayesian approach to test positivity is used, and includes an assessment of factors (surveillance, test type etc.) that may be associated with test positivity. This is out of my own usual working area, but I was convinced that an understanding of what affects test positivity rates is necessary background to make improvements to the quality of cholera testing. There were many overlapping studies (where the same patients contribute the same data to different studies), and this was dealt with by selecting the more desirable study (in terms of quality and size). While not perfect, this seems to be a reasonable way of dealing with the overlap. The analysis is Bayesian latent class model that takes into account the correlation of measures of test performance within the same study. The manuscript is clearly written, and I found the supplementary materials were excellent in helping me to understand the analysis (nice figures e.g. S4).

The methods sections mentions has JAGs being used for analysis, but in the supplementary methods Stan is also mentioned. To clarify, was JAGS used for the latent class part of the analysis and Stan for the hierarchical meta-analysis? This is what it looks like from the github site.

Are there terms to describe the types of cholera studies that met the inclusion criteria? E.g 'high quality' or 'population-based'?

The analysis uses beta(1,1,) priors, i.e. 'flat prior', where all values of prevalence, sensitivity, and specificity are equally likely with varying upper and lower bounds. I would argue that this is probably not strictly 'uninformative' and it might be better to describe them differently, e.g. flat prior. I can follow the truncation bounds for some of the priors, but not others. For example, I wasn't clear why the lower bound of truncation for specificity was different between PCR and RDT. Some explanation of why these truncation points were chose for each parameter would be helpful.

R-hat is checked for model convergence, where any other diagnostic checks from analysis (e.g. trace plots, divergence plots) performed?

Table S1 - should the last row 'Specificity of PCR uspec(RD)' actually be 'Specificity of RDT'?

Figure 2. With the number of studies contributing data I found it hard to get an understanding about differences in distribution of positivity according to the stratifying variables and quality of sampling. I wonder if something similar but with probability density plots (or violin or ridgeline plots) instead of box plots might allow more information to come out of this visualisation.

Reviewer #2:

Dear

General remark:

The study covers a relevant topic and is well written in most parts. However, the manuscript has some major limitations. This systematic review and meta-analysis does not fulfil the requirements of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines that may help to improve the manuscript. Please add a prisma cheklist as supplemantary file.

Reviewer #3: Thank you for considering me as a reviewer for this very interesting and timely analysis. Given the surge of cholera globally in the recent years, the study is contextual and adds to the evidence base. The key finding, of significantly higher odds of detecting cholera during outbreak-linked surveillance is intuitively expected, but pegging an estimated value will possibly help in understanding reported cases better (especially in outbreak-prone areas).

The discussion section lays out the shortcomings in the analysis, but it would be interesting if the authors could reflect on the policy implications of their findings, specifically, how can these findings be used by program managers and policymakers in cholera endemic and outbreak prone countries. This is especially important considering the rapid surge and spread of cholera in countries which have not seen a significant burden of cholera cases for several years (some even for decades).

The missed burden of cholera is significant, as in all the settings where cholera is a public health problem, healthcare services are often accessed through informal routes. Without an estimate of these cases, asymptomatic or mild-to-moderately symptomatic cases and missed cases, a more holistic understanding of the true burden will not be forthcoming. The authors recommend that these should be accounted for using local contexts/data. Would it be possible for the authors to provide a little bit more framing around this, so that policy implementers in countries with novel and extended cholera outbreaks can try to identify a closer, locally valid estimate?

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2023 Sep 14;20(9):e1004286. doi:10.1371/journal.pmed.1004286.r003

Author response to Decision Letter 1

Collection date 2023 Sep.

PMC Copyright notice

17 Jul 2023

Attachment

Submitted filename:Cholera_positivity_RTR.docx

Click here for additional data file.^{(364KB, docx)}

PLoS Med. doi:10.1371/journal.pmed.1004286.r004

Decision Letter 2

Philippa C Dodd

Roles

Philippa C Dodd:Senior Editor

PMC Copyright notice

12 Aug 2023

Dear Dr. Azman,

Thank you very much for re-submitting your manuscript "Towards estimating true cholera burden: a systematic review and meta-analysis of Vibrio cholerae positivity" (PMEDICINE-D-22-03502R2) for consideration at PLOS Medicine.

I have discussed the paper with our academic editor and it was also seen again by one reviewer. I am pleased to tell you that, provided the remaining editorial and production issues are fully dealt with, we expect to be able to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers athttp://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We hope to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately atplosmedicine@plos.org.

Please let me know f you have any questions, and we look forward to receiving the revised manuscript.

Sincerely,

Richard Turner PhD, for Philippa Dodd, MBBS MRCP PhD

Consulting Editor, PLOS Medicine

plosmedicine@plos.org

------------------------------------------------------------

Requests from Editors:

GENERAL

Thank you for your detailed responses to previous editor and reviewer comments. Please see below for further comments that we require you address in full prior to publication.

COMPETING INTERESTS

All authors must declare their relevant competing interests per the PLOS policy, which can be seen here:

https://journals.plos.org/plosmedicine/s/competing-interests

For authors with ties to industry, please indicate whether any of the interests has a financial stake in the results of the current study.

We ask you to revise the title to better match journal style and suggest: "Estimating the proportion of Vibrio cholerae infections among suspected cholera cases: A systematic review and meta-analysis”.

ABSTRACT

Please combine the methods and findings sections into one section, ‘Methods and findings’

Line 54 – ‘V. cholerae positivity was lower in studies with representative sampling and lower minimum ages in suspected case definitions.’ The use of the term lower (I think) in two different contexts is confusing, please revise for clarity.

Line 60 – please remove ‘and the resolution of the data’

Line 71 – please remove the funding statement and include only in the manuscript submission form.

In the main methods section (line 155) you state ‘…January 1, 2000 to reflect contemporary patterns in cholera positivity…’ please include the same or similar in the abstract to clearly justify your choice of search start date.

AUTHOR SUMMARY

Thank you for including an author summary, which reads very nicely.

Line 91 – please place as the final bullet point of this sub-section.

INTRODUCTION

Line 112 - please define ‘PCR’ at first use for the reader.

METHODS and RESULTS

Line 202 – please move this statement to the beginning of the methods section.

Currently, the ethics approval appears to be quoted both at the start and end of the Methods section: please de-duplicate.

TABLES

Please include a table which summaries the basic information of the studies utilized for your review – author, year of publication, country, study type/design, number of ppts, diagnostic test as a minimum.

Please include an assessment of study quality.

Table 1 – please provide an appropriate caption which clearly details the table content without the need to refer to the text. What do you mean by ‘Number of observations’ the ‘observations’ confuses me. Could it simply read number?

FIGURES

In the captions for example line 922 you refer to ‘observations’ here also. This term is not used in the text. Do you mean observed cases? Please clarify/amend and in consistency with table 1.

Figures 2, 3 and 4 please define all abbreviations in either a footnote or in the figure caption – PCR, RDT, IQR, V.cholera

Figure 3 – please detail the meaning of the dots and lines in the figure caption.

DISCUSSION

Lines 48 and 443 – please remove these statements from the end of the discussion and include only in the manuscript submission form they will be compiled as metadata.

SUPPORTING INFORMATION

Please cite, label and upload your Supporting Information as outlined here:https://journals.plos.org/plosmedicine/s/supporting-information

Please ensure that the reference format follows that of our guidance which can be found here.https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references

Figures – please define abbreviations RDT and PCR throughout in the caption or in a footnote.

Please use the form "PLoS" in the reference list; and revisit reference 88, which appears to be missing publication information.

SOCIAL MEDIA

To help us extend the reach of your research, please detail any Twitter handles you wish to be included when we tweet this paper (including your own, your coauthors’, your institution, funder, or lab) in the manuscript submission form when you re-submit the manuscript.

Comments from Reviewers:

*** Reviewer #1:

Thanks for the revised manuscript and replies to my original review.

With the addition of the new data, some of the STAN models needed modification. This sounds like a reasonable strategy to deal with the heterogeneity.

The manuscript looks good from my perspective, the figures are great (colour scheme looks fine to me).

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2023 Sep 14;20(9):e1004286. doi:10.1371/journal.pmed.1004286.r005

Author response to Decision Letter 2

Collection date 2023 Sep.

PMC Copyright notice

23 Aug 2023

Attachment

Submitted filename:Cholera_positivity_RTR_R2.docx

Click here for additional data file.^{(27.4KB, docx)}

PLoS Med. doi:10.1371/journal.pmed.1004286.r006

Decision Letter 3

Philippa C Dodd

Roles

Philippa C Dodd:Senior Editor

PMC Copyright notice

25 Aug 2023

Dear Dr Azman,

On behalf of my colleagues and the Academic Editor, Dr Amitabh Suthar, I am pleased to inform you that we have agreed to publish your manuscript "Estimating the proportion of clinically suspected cholera cases that are true Vibrio cholerae infections: A systematic review and meta-analysis" (PMEDICINE-D-22-03502R3) in PLOS Medicine.

Prior to publication we require that you make the following changes:

* Line 49 - should read ‘April 19, 2023’

* Line 250 - please remove the data availability statement from the main manuscript and include only in the manuscript submission form.

* Line 252 - sentence beginning ‘Extracted data…’ suggest moving this to an appropriate part of the results section. Line 263 perhaps?

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager athttp://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate withmedicinepress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visithttp://www.plos.org/about/media-inquiries/embargo-policy/.

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Philippa Dodd, MBBS MRCP PhD

PLOS Medicine

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Checklist. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Checklist.

(PDF)

Click here for additional data file.^{(123.8KB, pdf)}

S1 Appendix. Supporting information.

Detailed methods, including systematic review search terms and the full statistical model, as well as additional figures and tables.

(PDF)

Click here for additional data file.^{(941.2KB, pdf)}

S1 Data. Full dataset.

(XLSX)

Click here for additional data file.^{(296.8KB, xlsx)}

Attachment

Submitted filename:Cholera_positivity_RTR.docx

Click here for additional data file.^{(364KB, docx)}

Attachment

Submitted filename:Cholera_positivity_RTR_R2.docx

Click here for additional data file.^{(27.4KB, docx)}

Data Availability Statement

All input data and analytical code are available athttps://github.com/HopkinsIDD/cholera_positivity.

Movatterモバイル変換

PERMALINK

Estimating the proportion of clinically suspected cholera cases that are trueVibrio cholerae infections: A systematic review and meta-analysis

Kirsten E Wiens

Hanmeng Xu

Kaiyue Zou

John Mwaba

Justin Lessler

Espoir Bwenge Malembaka

Maya N Demby

Godfrey Bwire

Firdausi Qadri

Elizabeth C Lee

Andrew S Azman

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Ethics

Terminology

Systematic review

Data analysis

Estimating sensitivity and specificity of cholera confirmation tests

EstimatingV.cholerae positivity and sources of heterogeneity

Results

Study characteristics

Fig 1. PRISMA flow diagram.

Table 1. Study characteristics.

V.cholerae positivity in unadjusted data

Fig 2.V.cholerae positivity by study methodology and outbreak context.

Adjusted underlyingV.cholerae positivity

Fig 3. Estimated underlyingV.cholerae positivity.

Fig 4. Forest plot of study estimates and underlying positivity.

Factors associated with variation inV.cholerae positivity

Discussion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Philippa C Dodd

Roles

Decision Letter 1

Philippa C Dodd

Roles

Author response to Decision Letter 1

Decision Letter 2

Philippa C Dodd

Roles

Author response to Decision Letter 2

Decision Letter 3

Philippa C Dodd

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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