PLoS Neglected Tropical Diseases
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A systematic review of alternative surveillance approaches for lymphatic filariasis in low prevalence settings: Implications for post-validation settings
Volume: 14, Issue: 5
DOI 10.1371/journal.pntd.0008289
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Abstract

Lymphatic filariasis (LF) is a mosquito-borne disease, which can result in complications including swelling affecting the limbs (lymphoedema) or scrotum (hydrocele). LF can be eliminated by mass drug administration (MDA) which involves whole communities taking drug treatment at regular intervals. After MDA programmes, country programmes conduct the Transmission Assessment Survey (TAS), which tests school children for LF. It is important to continue testing for LF after elimination because there can be a 10-year period between becoming infected and developing symptoms, but it is thought that the use of TAS in such settings is likely to be too expensive and also not sensitive enough to detect low-level infections. Our study assesses the results from 44 studies in areas of low LF prevalence that have investigated methods of surveillance for LF which differ from the standardised TAS approach. These include both human and mosquito studies. Results show that there is currently no standardised approach to testing, but that surveillance can be made more sensitive through the use of new diagnostic tests, such as antibody testing, and also by targeting higher risk populations. However, further research is needed to understand whether these approaches work in a range of settings and whether they are affordable on the ground.

Riches, Badia-Rius, Mzilahowa, Kelly-Hope, and Lammie: A systematic review of alternative surveillance approaches for lymphatic filariasis in low prevalence settings: Implications for post-validation settings

Introduction

Lymphatic filariasis (LF) is a mosquito-borne parasitic infection which is caused by three species of filarial worms: Wuchereria bancrofti, Brugia malayi and Brugia timori [1, 2]. It can damage the human lymphatic system, resulting in disabling complications including lymphoedema and hydrocele[1]. An estimated 886 million people live in areas at risk of LF infection and 36 million people are currently suffering from LF-related complications[2].

The Global Programme to Eliminate LF (GPELF) was established in 2000 with the intention of eliminating LF as a public health problem[3]. This has involved actions to interrupt transmission, through the systematic delivery of mass drug administration (MDA) at a population level, and to ensure that cases of morbidity linked to LF receive appropriate treatment[4].

Since 2010, demonstrating interruption of transmission has required three successful Transmission Assessment Surveys (TAS). These are school-based surveys which use rapid antigen tests (e.g. BinaxNOW) to sample a population of 6-7-year-old children at least 6 months after the final MDA[4, 5]. Successful delivery of these TASs allows a country to be validated as having eliminated LF as a public health problem.

By the end of 2018, 14 countries had been validated as having eliminated LF, with a further 59 requiring ongoing interventions and surveillance[2]. In the coming decade, many of these countries are expected to be validated as having achieved elimination status. This work is supported by the continued funding commitment from international donors and new drug regimens such as triple therapy which could be scaled up in challenging areas, including India which has the largest burden of disease[1, 6].

Following validation of elimination of LF as a public health problem, the WHO recommend that countries continue surveillance for LF to detect any possible recrudescence of infection but there are no clear recommendations on specific surveillance methods and thresholds to be used[4, 6]. It is acknowledged that the TAS methodology is resource-intensive and may also lack sensitivity in low-prevalence settings[5, 7]. Consequently, there is increasing interest in the appropriateness and effectiveness of alternative methods of LF surveillance, and whether these can be integrated within health systems in post-validation settings.

This review focuses on alternative (non-TAS) LF surveillance studies conducted in low-prevalence settings since 2000, including both human and mosquito studies. This cut-off represents the establishment of GPELF and the introduction of a more standardised approach to LF surveillance and the emergence of newer diagnostic tests. It aims to describe these studies in relation to factors including diagnostic tests, sampling methods and reported results, and to compare results with concurrent TAS outcomes where possible, in order to make recommendations to programme managers and highlight areas requiring further research.

Methods

Protocol and registration

This review was conducted and reported according to Preferred Reporting Items for Systematic Reviews and Meta-analyses Statement (PRISMA) guidelines (S1 File).

Search strategy

The following databases were searched for papers published from 2000 to November 2018: PubMed, Scopus and the Cochrane Database of Systematic Reviews. A combination of MeSH terms and text words were used to describe concepts relating to both LF and surveillance (S2 File). Any additional papers found to be relevant during this process were included.

Inclusion criteria

Studies were included in the systematic review if they (1) were a primary research study investigating methods of population-based LF surveillance other than routine TAS surveys; (2) included surveillance methods pertaining to either humans (reservoir) and/or mosquitoes (vector); and (3) were conducted in a low prevalence setting, either post-MDA or post-validation. The review was limited to English-language publications with full-text availability conducted after 2000, following the establishment of the GPELF. Studies describing diagnostic test studies were not included if their design did not include population-level sampling.

Study selection and data extraction

A two-stage process was followed for data selection. Firstly, titles and abstracts of all eligible studies were independently reviewed (co-authors NR and XBR). Any article deemed ‘potentially’ relevant then underwent independent full-text review (NR and XBR). Discrepant ratings for any papers at stage two were discussed until consensus was reached. A standardised data extraction form was developed, piloted and refined. Where papers reported on more than one study design, these were extracted separately. NR extracted from all the papers and XBR extracted from a sample of 10% of the total. No significant discrepancies were identified during this process.

Extraction focused on the core themes identified during scoping work: (1) location (WHO Region and country, predominant mosquito type); (2) programme context (number of MDA rounds, date of last MDA and elimination status; (3) study design; (4) sampling strategy (including sample size and sampling methods); (5) diagnostic tests used; (6) outcomes of surveillance activity, including comparison with TAS results where applicable; and (7) integration of surveillance with other disease programmes.

Risk of bias assessment

Risk of bias was assessed using a modified version of the Crowe and GATE validated appraisal tools. Scores of 0–2 were assigned for all studies based on study design (not stated, cross-sectional, longitudinal). Human sampling studies were further assessed in relation to sample size terciles (0-760/761-2,464/>2,464), method of sampling participants (not stated/non-random/random) and study population (not stated/children or adults/children and adults). Mosquito sampling studies also assigned scores according to sample size terciles (0–4,679/4,680–10,871/>10,871), catch-site sampling (not stated, non-random, random) and method of analysis (not stated/dissection/PCR analysis). It was decided not to include location sampling in the assessment since it may be preferable to use non-random methods in some scenarios (e.g. conducting surveillance activities in response to a suspected hotspot). Total risk of bias scores (marked out of 8) were calculated for each study and are presented in Tables 2 and 4. A full breakdown of scores for each study is listed in S1 Table.

Data synthesis and analysis

Details of publication details, programme context and study design are presented for all studies combined. This is followed by data on sampling strategy, diagnostic test usage and outcomes, split for human and mosquito surveillance studies separately. The impact of age and gender on diagnostic test performance in humans is explored. Analysis then included: (1) comparison between human and mosquito surveillance studies; (2) comparison with TAS results, where applicable; and (3) evidence of integration of surveillance methods within health systems. The analysis aims to determine factors which can increase the sensitivity (defined as the proportion of true positive cases identified by a diagnostic test) in low prevalence settings.

Results

Selected studies

Fig 1 highlights the PRISMA steps of identification, screening, eligibility and inclusion of papers. A total of 1,378 papers were identified from the initial search, once duplicates had been excluded. Of these, 71 were considered potentially relevant following title/abstract screening by two independent reviewers. 57 of these were labelled potentially relevant by at least one reviewer following full-text screening. When discrepant results were reviewed, this total reduced to 40 papers which then proceeded to data extraction. An additional four papers were identified during the peer review process. The 44 papers which met eligibility criteria comprised of 83 methodologically distinct study designs (Table 1). These studies are henceforth considered separately except for one paper which pooled results of school and community surveys.

PRISMA flow diagram.
Fig 1
PRISMA flow diagram.
Table 1
Characteristics of included studies.
DescriptionNo. of studies (%)
Study start date
2000–20046 (7.2%)
2005–200931 (37.3%)
2010–201419 (22.9%)
2015–201916 (19.3%)
Not stated11 (13.3%)
Study type
Cross-sectional61 (73.5%)
Longitudinal22 (26.5%)
Surveillance method
Community survey42 (47.2%)
School survey19 (21.3%)
Laboratory surveillance2 (2.2%)
Health centre surveillance1 (1.1%)
Active surveillance1 (1.1%)
Occupational surveillance1 (1.1%)
Xenomonitoring survey23 (25.8%)

A significant degree of heterogeneity was identified in the included studies. This included variation in study design, baseline endemicity, population sampled, use of diagnostic tests and reporting metrics. It was agreed that this variation precluded formal meta-analysis and instead required a narrative review structured according to the core themes identified.

Publication details

26 papers (59.1%) were published between 2015–2019, 15 (34.1%) were published between 2010–2014 and 3 (6.8%) between 2005–2009. 23 (52.3%) papers reported on human surveillance only, 9 (20.5%) on mosquito surveillance only and 12 (27.2%) reported on both human and mosquito surveillance together.

Location (WHO Region and country)

Papers reported data from 22 countries in total; 21 (41.1%) came from the Western Pacific Region, 13 (25.5%) from the African Region, 12 (23.5%) from the South East Asian Region, 4 (7.8%) from the Eastern Mediterranean Region and 1 (2.0%) from the Region of the Americas. Fig 2 shows the geographical distribution of countries included in the review.

Map of countries reporting data.
Fig 2
For highly populated countries (e.g. Nigeria and India) where mapping was not nationally representative, the specific area/region being sampled is highlighted. These data were extracted from the Geoconnect website (https://www.geoconnect.org/).Map of countries reporting data.

Programme context

72 (86.7%) studies reported data from countries which had completed MDA but had not yet completed TAS. 11 studies (13.3%) were from countries validated as having eliminated LF, of which two studies described surveillance following successful completion of TAS. 36 (70.6%) studies reported on previous MDA activity, for which the median number of MDA rounds prior to surveillance was 5 (range 3 to 13).

Predominant mosquito type

Studies were conducted in areas with a range of different mosquito vector genera, most commonly Anopheles sp (n = 15, 29.4%) from the African and Western Pacific Regions, Culex sp (n = 13, 25.5%) from studies in the South East Asian and Eastern Mediterranean Regions, and Aedes sp (n = 12, 23.5%) from the Western Pacific Region. A further 12 studies (23.5%) were conducted in settings with more than one vector for LF according to the WHO Practical Entomology manual[8].

Study design

61 studies (73.5%) were cross-sectional in design and 22 (26.5%) were longitudinal studies. The most common study designs were community surveys (n = 42; 47.2%), xenomonitoring surveys (n = 23; 25.8%) and school surveys (n = 13; 14.6%).

Human surveillance studies

Table 2 summarises the characteristics of the 35 papers which reported data on human surveillance for lymphatic filariasis, comprising 60 distinct studies. Full results from these studies can be found in S2 Table.

Table 2
Human surveillance study characteristics.
Country
(last MDA)1
Reference
(Quality score)
Study dateContextStudy designAge criteriaTotal sample sizeTests performed
American Samoa (2007)Mladonicky et al. 2009 [9] (4/8)2006Post-MDACross-sectional community survey≥5 years579BinaxNOW, MF, Bm14 Ab
Coutts et al. 2017 [10] (6/8)2007Post-MDACross-sectional community survey≥2 years1,881BinaxNOW
Lau et al. 2014 [11] (5/8)2010Post-MDACross-sectional community survey≥18 years807Og4cC3 Ag>128 units, Og4cC3 Ag>32 units, Wb123 Ab, Bm14 Ab
Lau et al. 2017a [12] (4/8)2014Post-MDACross-sectional occupational survey≥15 years602BinaxNOW, Og4C3 Ag, Bm14 Ab, Wb123 Ab
Lau et al. 2017b [12] (4/8)2014Post-MDACross-sectional community survey≥2 years476BinaxNOW, Og4C3 Ag, Bm14 Ab, Wb123 Ab
Lau et al. 2017c [12] (4/8)2014Post-MDACross-sectional school survey7–13 years283BinaxNOW
Won et al. 2018 [13] (5/8)2015Post-MDALongitudinal school survey5–10 years1,134(TAS 1)
864(TAS 2)
BinaxNOW, Wb123 Ab, Bm14 Ab, Bm33 Ab
Sheel et al. 2018 [14] (7/8)2016Post-MDACross-sectional community survey≥8 years2,507MF, FTS (filarial test strips)
ChinaHuang et al. 2016a [15] (2/8)2002Post-validationCross-sectional school surveyChildren542Chinese filariasis IgG4 ELISA kit, MF
Huang et al. 2016b [15] (1/8)2003Post-validationCross-sectional community surveyNot stated436Chinese filariasis IgG4 ELISA kit
Huang et al. 2016c [15] (3/8)2004Post-validationCross-sectional community surveyNot stated5,787Chinese filariasis IgG4 ELISA kit
Huang et al. 2016d [15] (4/8)2002 and 2004Post-validationCross-sectional community surveyChildren and adults762Chinese filariasis IgG4 ELISA kit, MF
Huang et al. 2016e [15] (2/8)2002–2008Post-validationLongitudinal community surveyNot stated218Chinese filariasis IgG4 ELISA kit
Itoh et al. 2007 [16] (4/8)2004Post-validationCross-sectional school survey6 to 10 years (Yongjia)
5–15 years (Gaoan)
2,411 (Yongjia)
7,998 (Gaoan)
IgG4 ELISA (urinary)
Egypt
(2005)
Moustafa et al. 2014a [17] (4/8)2012Post-MDACross-sectional school survey6–7 years1,321BinaxNOW, Bm14Ab
Moustafa et al. 2014b [17] (3/8)2012Post-MDACross-sectional community survey16–60 years75BinaxNOW
Ramzy et al. 2006a [18] (7/8)Not statedPost-MDALongitudinal community survey≥4 years1,064 (Giza)
744 (Qalubiya)
BinaxNOW, MF
Ramzy et al. 2006b [18] (4/8)Not statedPost-MDALongitudinal school survey7 and 11 years1,653BinaxNOW, Bm14 Ab
French PolynesiaGass et al. 2011a [19] (5/8)2007–2008Post-MDACross-sectional school and community survey3–80 years1,383Bm14 Ab, PanLF, Urine SXP, ICT, Og4C3 Ag, MF, PCR
GambiaWon et al. 2018 [20] (6/8)2015Post-validationCross-sectional community survey≥1 year2,612Wb 123 Ab ELISA, Bm14 Ab ELISA
GhanaGass et al. 2011b [19] (5/8)2007–2008Post-MDACross-sectional school and community survey3–80 years1,466Bm14 Ab, ICT, Og4C3 Ag, MF, PCR
Owusu et al. 2015a [21] (4/8)2008Post-MDACross-sectional school survey6–7 and 10–11 years308BinaxNOW, Og4C3 Ag, Bm14 Ab, Wb123 Ab
Owusu et al. 2015b [21] (5/8)2008Post-MDACross-sectional community survey3–80 years653BinaxNOW, MF, Og4C3 Ag, Bm14 Ab, Wb123 Ab
HaitiGass et al. 2011c [19] (5/8)2007–2008Post-MDACross-sectional survey3–80 years1,322Bm14 Ab, PanLF, Urine SXP, ICT, Og4C3 Ag, MF, PCR
India
(2011)
(2007)
(2004)
Ramaiah et al. 2013 [22] (6/8)2005–2008Post-MDALongitudinalAdults and childrenApprox. 700MF, BinaxNOW
Swaminathan et al. 2012 [23] (6/8)2015–2017Post-MDACross-sectional community survey≥2 years35,582MF, Og4C3 Ag
Mehta et al. 2018 [24] (3/8)Study year not reportedPost-MDACross-sectional community survey≥5 years290BinaxNOW, MF
Madagascar
(2016)
Garchitorena et al. 2018 [25] (5/8)2016Post-MDACross-sectional community survey≥5 years545FTS
Mali
(2008)
Coulibaly et al. 2015 [26] (5/8)2007Post-MDALongitudinal community survey≥2 years760BinaxNOW, MF
Coulibaly et al. 2016a [27] (6/8)2009–2013Post-MDALongitudinal community survey6–7 years3,457BinaxNOW, MF (if BinaxNOW positive), Wb PCR, Wb123 Ab, Og4C3 Ag
Coulibaly et al. 2016b [27] (5/8)2009–2013Post-MDALongitudinal community survey≥8 years1,184BinaxNOW, MF (if BinaxNOW positive), Wb PCR, Wb123 Ab, Og4C3 Ag
Nigeria
(2009)
Richards et al. 2011 [28] (6/8)2009Post-MDALongitudinal community survey≥2 years1,720BinaxNOW, MF
Papua New GuineaMitja et al. 2011 [29] (6/8)2011Post-MDALongitudinal community surveyNot stated6,263BinaxNOW
Samoa
(2008)
Joseph et al. 2011A [30]2 (7/8)2007Post-MDACross-sectional community surveyAny age6,648BinaxNOW, MF (if BinaxNOW +ve), BM14 Ab (children aged 5–10 years only)
Joseph et al. 2011Ba [31]2 (7/8)2008Post-MDACross-sectional community survey≥2 years2,474BinaxNOW, MF, BM14 Ab
Solomon Islands
(N/A)
Harrington et al. 2013 [32] (4/8)2011Post-validationCross-sectional community surveyAdults and children307Og4C3Ag, MF (if ICT positive/borderline plus 10% of negative screens)
Sri Lanka
(2015)
Rao et al. 2016 [33] (7/8)2013Post-MDACross-sectional community survey2–70 years12,977MF
Gass et al. 2011d [19] (5/8)2007–2008Post-MDACross-sectional school and community survey3–80 years1,477PanLF, ICT, Og4C3 Ag, MF, PCR
Chandrasena et al. 2016a [34] (6/8)2009–2015Post-MDALongitudinal community survey4–80 years2,461MF
Chandrasena et al. 2016b [34] (2/8)2015Post-MDACross-sectional community survey7–12 years250Brugia Rapid
Rahman et al. 2018a [35] (4/8)Not statedPost-TASCross-sectional community survey5–84 years630MF, FTS
Rahman et al. 2018b [35] (4/8)Not statedPost-TASCross-sectional school survey5–13 years2,301IgG4 ELISA (urinary)
Rao et al. 2014a [36] (7/8)2011–2013Post-MDACross-sectional community survey≥10 years7,156BinaxNOW, MF
Rao et al. 2014b [36] (5/8)Not statedPost-MDACross-sectional school surveyGrade 1 and 217,000BinaxNOW, BM14 Ab
Rao et al. 2017a [37] (4/8)2015–2017Post-MDACross-sectional school survey6–8 years2,227BinaxNOW, MF if BinaxNOW +ve, BM14 Ab
Rao et al. 2017b [37] (7/8)2015–2017Post-MDACross-sectional community survey≥10 years3,123BinaxNOW, MF if BinaxNOW +ve
Rao et al. 2018a [38] (3/8)2015Post-MDACross-sectional school surveyFirst and second grade children401BinaxNOW, BM14 Ab, MF
Rao et al. 2018b [38] (5/8)2015Post-MDACross-sectional community survey10–70 years528BinaxNOW, MF
Rao et al. 2018c [38] (7/8)2015Post-MDACross-sectional community survey≥2 years16,927MF
Tanzania
(2014)
Gass et al. 2011e [19] (5/8)2007–2008Post-MDACross-sectional school and community survey3–80 years1,384Urine SXP, ICT, Og4C3 Ag, PCR
Jones et al. 2018 [29, 39] (6/8)2015Post-MDACross-sectional community survey10–79 years854BinaxNOW
Togo
(2009)
Budge et al. 2014a [40] (6/8)2006–2007Post-MDALongitudinal laboratory surveillance studyAdults6,509MF
Budge et al. 2014b [40] (5/8)2006–2007Post-MDACross-sectional community surveyAdults7,800BinaxNOW
Budge et al. 2014c [40] (6/8)2010–2011Post-MDALongitudinal health facility surveillance studyAdults2,880Og4C3 Ag, MF (if Ag +ve)
Mathieu et al. 2011 [41] (5/8)2006–2007Post-MDALongitudinal laboratory surveillance studyNot stated8,050MF
Dorkenoo et al. 2018A [42] (4/8)2010–2015Post-MDACross-sectional active surveillance of positive casesChildren and adults40MF, Og4c3 Ag, FTS
Tonga
(2005)
Joseph et al. 2011Bb [31] (4/8)2007Post-MDACross-sectional school survey5–6 years797BinaxNOW, MF (if ICT +ve), BM14 Ab
TuvaluGass et al. 2011f [19] (5/8)2007–2008Post-MDACross-sectional school and community survey3–80 years1,481PanLF, Urine SXP, ICT, Og4C3 Ag, MF, PCR
Vanuatu
(2005)
Joseph et al. 2011Bc [31] (5/8)2007Post-MDACross-sectional school survey5–6 years3,840BinaxNOW, MF (if ICT +ve), BM14 Ab
Allen at al. 2017 [43] (7/8)2005–2006Post-MDACross-sectional community survey≥1 year7,657BinaxNOW, MF (if ICT +ve
1 According to country or region-level, where stated in papers
2 MDA in Samoa was subsequently re-started, commencing in 2008

Sample size

The median sample size was 1,472 (range = 40 to 35,582; interquartile range = 596–3,207). The majority of studies (n = 36; 60.0%) included both children and adults in their study design. 15 studies (25.0%) focused on children only and five (8.3%) on adults only. In total, the studies reported data on 208,568 participants.

Sampling methods

Where stated (n = 42), the most common approach to selecting a sampling location involved non-random methods, such as purposive or convenience sampling (n = 30, 71.4%). In most cases surveillance was conducted in response to identification of a hotspot of infection. Other methods involved using random sampling methods (n = 7, 17.5%) while four studies described national surveillance studies [10, 11, 31]. Participants were then sampled using either non-random methods (n = 34, 69.3%) or random methods (n = 15, 30.6%).

Diagnostic tests

Included studies described results using 12 different diagnostic tests. 58 studies involved blood samples of which the majority were finger prick samples. The most common tests were microscopy for microfilaraemia (MF) (n = 38; 63.3%); BinaxNOW (n = 36; 60.0%); Bm14 Ab (n = 20; 33.3%), Og4C3 Ag (n = 17; 28.3%), Wb123 Ab (n = 9; 15.0%) and Wb PCR (8, 13.3%). Table 3 compares results where the same diagnostic test was used in the same population, allowing direct comparison of prevalence values within each study. Compared to Binax Now or Alere ICT (the most commonly used tests at the time of most of these surveys) as the index test, Table 3 shows that antibody tests produce a higher proportion of positive results. Bm14Ab and Wb123Ab values are, on average, 5.1 and 6.7 times higher respectively than the corresponding BinaxNOW values, based on the median value of this ratio across the selected studies. Og4C3Ag values are similar to BinaxNOW values in studies where both are used (median ratio = 0.95, range 0.2–1.6).

Table 3
Comparison of diagnostic test results when used for human surveillance in LF, using BinaxNOW as the index test.
CountryReferenceDiagnostic test prevalence
BinaxNOW
(Index test)
Bm14 AbOg4C3 Ag1Wb123 Ab
Prevalence (population tested)Ratio cf. index testPrevalence (population tested)Ratio cf. index testPrevalence (population tested)Ratio cf. index test
American SamoaLau et al. 2017a [12]1.3% (n = 602)11.7% (n = 598)9.01.2% (n = 598)0.910.9% (n = 598)8.4
Lau et al. 2017b2 [12]8.2% (n = 151)25.2% (n = 150)2.411.2% (n = 150)1.432.5% (n = 150)4.0
Mladonicky et al. 20092 [9]4.2% (n = 569)14.1% (n = 538)3.4----
Won et al. 2018 [13]0.2% (n = 937)6.8% (n = 1,112)34.0--1.0% (n = 1,112)5.0
Won et al. 2018 [13]0.1% (n = 768)3.0% (n = 836)30.0--3.6% (n = 836)36.0
EgyptMoustafa et al. 2014 [17]0.0% (n = 1,321)2.2% (n = 1,321)N/A----
French PolynesiaGass et al. (2011) [19]9.0% (n = 1,359)46.0% (n = 1,329)5.16.4% (1,355)0.7--
GhanaGass et al. (2011)6.7% (n = 1,372)9.9% (n = 1,159)1.58.9% (n = 1,355)1.3--
GhanaOwusu et al. 2015a [21]1.6% (n = 308)4.9% (n = 308)3.11.0% (n = 308)0.6--
Owusu et al. 2015a [21]7.8% (n = 653)12.9% (n = 653)1.712.2% (n = 653)1.6--
HaitiGass et al. (2011) [19]21.2% (n = 1,266)53.1% (n = 1,214)2.518.8% (n = 1,179)0.9--
SamoaJoseph et al. 20112 [31]7.7% (2,026)62.7% (n = 2,026)8.1----
Sri LankaGass et al. (2011) [19]3.0% (n = 1,449)--0.5% (n = 1,432)0.2--
Rao et al. 20172 [37]0.3% (n = 1,893)1.9% (n = 2,126)6.3----
Rao et al. 20142 [36]0.2% (n = 2,561)10.6% (n = 2,110)53----
Rao et al. 2014b [36]0.05% (n = 6,198)2.2% (n = 6,198)44----
Rao et al. 2018a [38]1.2% (n = 401)5.7% (n = 387)4.75----
TanzaniaGass et al. (2011) [19]8.1% (n = 1,316)--8.2% (n = 1,126)1.0--
TongaJoseph et al. 2011Bb [31]0% (n = 797)6.3% (n = 797)N/A----
TuvaluGass et al. (2011) [19]5.0% (n = 1,455)-4.9% (1,333)1.0--
VanuatuJoseph et al. 2011Bc [31]0% (n = 3,840)6.0% (n = 3,840)N/A----
1 A threshold value of >32 units was selected for Og4C3 Ag when multiple values were presented.
2 Weighted average of component studies
3 Standard TAS with the addition of antibody testing

Impact of age

Age-specific prevalence was extracted for twelve different LF diagnostic tests from studies which reported data allowing 10-year age bands to be calculated (Fig 3). A similar pattern is seen for each test, with rates generally increasing through childhood and adolescence before stabilising during adulthood and occasionally falling in older age.

Reported prevalence of LF tests according to age range.
Fig 3
Some studies reported decade age bands starting on an even year, e.g. 10–19, rather than 11–20. These data are included in the above table under the adjacent decade age band.Reported prevalence of LF tests according to age range.

Impact of gender

Reported prevalence of LF tests are also known to generally be higher among men in comparison to women (Fig 4).

Reported prevalence of LF tests according to gender.
Fig 4
Reported prevalence of LF tests according to gender.

Mosquito surveillance studies

Table 4 summarises the characteristics of the 23 papers which reported data on mosquito surveillance for LF. Full results from these studies can be found in S3 Table.

Table 4
Mosquito diagnostic study characteristics
Country (last known MDA)1Main vectorReference (Quality score)Study dateContextStudy designCatch methodSample sizeAnalysis method
American Samoa (2007)Aedes spp.Schmaedick et al. 2014 [44] (6/8)2011Post-MDACross-sectional surveyBG-Sentinel traps21,861 mosquitoesPCR analysis
BangladeshCulex spp.Irish et al. 2018 [45] (6/8)2016Post-MDACross-sectional surveyCDC gravid traps5,926 mosquitoesPCR analysis
Egypt (2013)Culex spp.Ramzy et al. 2006 [18] (7/8)Not statedPost-MDALongitudinal surveyAspiration of indoor resting mosquitoes8,531 mosquitoesPCR analysis
Abdel-Shafi et al. 2016 [46] (5/8)2014–15Post-MDACross-sectional surveyLight traps Not statedPCR analysis
Moustafa et al. 2017 [47] (4/8)2014Post-MDACross-sectional surveyGravid traps7,970 mosquitoesPCR analysis
GhanaMultipleOwusu et al. 2015a [21] (5/8)2008Post-MDACross-sectional surveyPyrethrum knockdown method401 mosquitoesPCR analysis
Owusu et al. 2015b [21] (5/8)2008Post-MDACross-sectional surveyGravid trap4,099 mosquitoesPCR analysis
India (2011/ 2007/ 2004)MultipleRamaiah et al. 2013 [22] (4/8)2005–2010Post-MDALongitudinal surveyAspiration of indoor resting mosquitoes10,842 mosquitoesDissection
Subramanaian et al. 2017 [48] (8/8)2012Post-MDALongitudinal surveyCDC gravid traps 41,294 mosquitoesPCR analysis
Mehta et al. 2018 [24] (3/8)Not statedPost-MDACross-sectional surveyGravid trap2,429 mosquitoesDissection
MalaysiaMultipleBeng et al. 2016 [49] (3/8)Not statedPost-MDACross-sectional surveyBare leg catch and CDC light trap4,378 mosquitoesPCR analysis
Mali (2008)Anopheles spp.Coulibaly et al. 2015 [26] (4/8)2007Post-MDALongitudinal surveyHuman landing catch4,680 mosquitoesDissection
Coulibaly et al. 2016a [27] (5/8)2009–2013Post-MDALongitudinal surveyHuman landing catch14,424 mosquitoesDissection
Coulibaly et al. 2016b [27] (6/8)2012Post-MDALongitudinal surveyPyrethrum spray catch115 mosquitoesPCR analysis
Nigeria (2009)Anopheles spp.Richards et al. 2011 [28] (4/8)2009Post-MDALongitudinal surveyPyrethrum knockdown method4,398 mosquitoesDissection
Papua New Guinea (1998)Anopheles spp.Reimer et al. 2013 [50] (5/8)2007–2008Post-MDALongitudinal surveyHuman landing catch20,345 mosquitoesPCR analysis
South Korea (multiple)MultipleCho et al. 2012 [51] (4/8)2009Post-validationCross-sectional surveyLight trap (Black Hole)5,380 mosquitoesPCR analysis
Sri Lanka (2015)Culex spp.Rao et al. 2014c [36] (7/8)Not statedPost-MDACross-sectional surveyGravid traps69,680 mosquitoesPCR analysis
Rao et al. 2016 [33] (7/8)2013–2014Post-MDACross-sectional surveyCDC light trap28,717 mosquitoesPCR analysis
Rao et al. 2017c [37] (8/8)2011–2016Post-MDALongitudinal surveyCDC gravid traps48,301 mosquitoesPCR analysis
Rao et al. 2018d [38] (6/8)2015–2016Post-MDACross-sectional surveyCDC gravid traps7,750 mosquitoesPCR analysis
Tanzania (2014)MultipleJones et al. 2018 [29, 39] (4/8)2015Post-MDACross-sectional surveyCDC gravid traps and CDC light traps1,650 mosquitoesPCR analysis
Togo (2009)Anopheles spp.Dorkenoo et al. 2018B [52] (7/8)2015Post-MDACross-sectionalPyrethrum spray catch, Human landing catch and exit trap collection10,872 mosquitoesPCR analysis
1 According to country or region-level mentioned in papers

Sample size

The median number of mosquitoes collected was 7,860 per study (range 115–69,680, interquartile range 4,383–18,865).

Sampling methods

Similar to human surveillance studies, location sampling typically used non-random methods, following identification of a hotspot area by other methods. The majority of studies then described various methods for taking a random sample of households from which to sample mosquitoes, either indoors or outdoors. The most common mosquito sampling method was the gravid trap (n = 9; 39.1%) followed by various baited traps (n = 6; 26.1%), human landing collection (n = 5; 21.7%) and pyrethrum space spray catches (n = 4; 17.4%). The variation was partly due to the different species of mosquito being sampled.

Diagnostic tests

Most studies involved PCR analysis of mosquitoes (75.0%) rather than dissection (25.0%).

Comparison between human and mosquito surveillance results

Table 5 summarises studies which performed both human testing and xenomonitoring in the same geographical area. Overall, there was great variability in survey methods and results which limited comparisons. Interpretation is also limited by the fact that there are currently no recommended species-specific Mosquito Infectivity Rate (MIR) thresholds for LF[8, 53]. A number of studies reported similar results between human testing and xenomonitoring. For example, Rao et al 2018 (38) showed ICT rates of 3% and an MIR of 3%, but a similar pattern was not demonstrated in other Sri Lankan studies. There were also examples where human testing did not detect significant transmission but xenomonitoring did. For example, the study by Ramaiah et al. reported a mosquito infection rate of 4.7% of mosquitoes when a community survey performed concurrently found no evidence of human infection on ICT testing.

Table 5
Comparison of human and mosquito surveillance study results.
Reference (Location)Human survey type (Age range)Human sampling results (95% confidence interval) [Sample size]Xenomonitoring results (95% confidence interval)
[Sample size]
Ramzy et al. 2006[18]
(Giza, Egypt)
Community survey (≥4 years)MF = 1.2% (0–2.6%); [n = 1064]MIR = 0.19% (0.08–0.38%)
[n = 4,273]
BinaxNOW = 4.8% (2.5–7.1%); [n = 1064]
School survey (7 years)BinaxNOW = 0.4%; [n=n.s.]
Bm14 Ab = 0.2% (0.0–0.5); [n = 896]
School survey (11 years)Bm14 Ab = 1.4% (0.3–2.6%); [n = 415]
Ramzy et al. 2006[18]
(Qalubyia, Egypt)
Community survey (≥4 years)BinaxNOW = 3.1% (1.2–4.9%); [n = 764]MIR = 0% (0.00–0.05%)
[n = 4,258]
MF = 1.2% (0–2.6%); [n = 764]
School survey (7 years)BinaxNOW = 0%; [n=n.s.]
Bm14 Ab = 0%; [n = 211]
School survey (11 years)Bm14 Ab = 0%; [n = 131]
Mehta et al. 2018[24]
(Pondicherry, India)
Community survey (≥5 years)MF = 0.69% (n.s.); [n = 290]MIR = 0.04% (n.s.)
ICT = 2.35% (n.s.); [n = 290]
Ramaiah et al. 2013[22] (Muppili, India)Community survey (15–45 years)ICT = 0.4% (n.s.); [n = 226]MIR = 0% (n.s.) [n = 366]
Ramaiah et al. 2013[22] (Thenber, India)Community survey (1–7 years)ICT = 0% (n.s.); [n = 50]MIR = 4.7% (n.s.) [n = 339)
Ramaiah et al. 2013[22] (Alagramam, India)Community survey (1–7 years)ICT = 4.6% (1–7 years); [n = 44]MIR = 2.2% (n.s.) [n = 361]
Community survey (15–45 years)ICT = 3.2% (15–45 years); [n = 95]
Coulibaly et al. 2016[27] (Sikasso District, Mali)
Community survey 2009 (6–7 years)ICT = 0% (0.00–1.64%); [n = 289]MIR = 0.05% (0.01–0.18%) [n = 4,375]
Community survey 2009 (≥8 years)ICT = 4.9% (3.53–6.67%); [n = 800]
Community survey 2011 (6–7 years)ICT = 2.7% (1.24–5.37); [n = 301]MIR = 0% (n.s.) [n = 2,803]
Community survey 2011 (≥8 years)ICT = 3.5% (2.40–5.12%); [n = 795]
Community survey 2012 (6–7 years)ICT = 3.9% (2.04–7.00%); [n = 285]MIR = 0% (n.s.) [n = 5,691]
Community survey 2012 (≥8 years)ICT = 2.8% (2.08–3.65%); [n = 1,812]
Coulibaly et al. 2015[26]
(Sikasso District, Mali)
Community survey (≥2 years)MF = 0% (n.s.); [n = 760]MIR = 0.02% (n.s.) [n = 4,680]
ICT = 7.2% (n.s.); [n = 760]
Richards et al. 2011[28]Community survey (≥2 years)MF = 0.9% (n.s.); [1,720]MIR = 0.4% (n.s.) [n = 4,398]
(Plateau/Nasarawa States, Nigeria)ICT = 7.4% (n.s.); [1,720]
Mitja et al. 2018[29] (Papua New Guinea)Community survey (10–79 years)BinaxNOW = 1.1% (0.6–2.0%)MIR = 0%
Rao et al. 2017[37]
(Colombo, Sri Lanka)
School survey (6–8 years)MF = 0% (0–1.0%); [n = 372]MIR = 0.34% (0.2–0.6)
[n = 4,000]
ICT = 0% (0–1.0%); [n = 372]
Bm14 Ab = 0% (0–1.0%); [n = 360]
Community survey (≥10 years)MF = 0% (0–0.7%); [n = 506]
ICT = 0% (0–0.7%); [n = 506]
Rao et al. 2017[37]
(Gampaha, Sri Lanka)
School survey (6–8 years)MF = 0% (0–1.0%); [n = 366]MIR = 0.23% (0.1 - 0.4%)
[n = 4,080]
ICT = 0.3% (0.5–1.5%); [n = 366]
Bm14 Ab = 0.6% (0.1–2.1); [n = 335]
Community survey (≥10 years)MF = 0% (0–0.7%); [n = 512]
ICT = 0.4% (0.1–1.4%) [n = 512]
Rao et al. 2017[37]
(Kalutara, Sri Lanka)
School survey (6–8 years)MF = 0% (0–1.0%); [n = 380]MIR = 0.26% (0.1 - 0.4%)
[n = 3,986]
ICT = 0% (0–1.0%); [n = 380]
Bm14 Ab = 2.4% (1.3–4.5%); [n = 378]
Community survey (≥10 years)MF = 0% (0–0.7%); [n = 528]
ICT = 0% (0–0.7%); [n = 528]
Rao et al. 2017[37]
(Ambalangoda, Galle, Sri
Lanka)
School survey (6–8 years)MF = 0% (0–1.0%); [n = 379]MIR = 1.17% (0.8–1.6%)
[n = 3,993]
ICT = 0.3% (0–1.5%); [n = 379]
Bm14 Ab = 2.3% (1.1–4.4%); [n = 353]
Community survey (≥10 years)MF = 0.2% (0.3–1.0%); [n = 520]
ICT = 1.0% (0.4–2.2%); [n = 520]
Rao et al. 2017[37]
(Unawatuna, Galle, Sri
Lanka)
School survey (6–8 years)MF = 0.3% (0–1.5%); [n = 359]MIR = 1.23% (0.8–1.7%)
[n = 4,002]
ICT = 1.1% (0.4–2.8%); [n = 359]
Bm14 Ab = 4.2% (2.5–7.0%); [n = 333]
Community survey (≥10 years)MF = 0.2% (0.0–1.0%); [n = 523]
ICT = 1.5% (0.8–2.9%); [n = 523]
Rao et al. 2017[37]
(Matara, Sri Lanka)
School survey (6–8 years)MF = 0% (0–1.0%); [n = 371]MIR = 1.09% (0.7–1.5%)
[n = 4,080]
ICT = 0% (0–1.0%); [n = 371]
Bm14 Ab = 2.2% (1.1–4.2%); [n = 367]
Community survey (≥10 years)MF = 0.2% (0.0–1.0%); [n = 525]
ICT = 0.2% (0–1.0%); [n = 525]
Rao et al. 2014[36]
(Sri Lanka)
Community survey (≥10 years)MF = 0–0.9%MIR = 0–1.56%
ICT = 0–3.4%
Rao et al. 2018[38] (Sri Lanka)Community survey (10–70 years)MF = 1.1% (0.5–2.5%)MIR (2015) = 5.2% (4.2–6.3%)
ICT = 3.0% (1.8–4.9%)MIR (2016) = 3.0% (2.3–3.8%)
Rao et al. 2016[33] (Sri Lanka)Community survey (2–70 years)MF = 0% (0.02–0.09%)MIR = 0.36% (0.29%-0.45%)

Comparison with TAS results

18 studies reported alternative surveillance methods which were performed concurrently with, or subsequent to, a TAS which was passed successfully. The comparative results are illustrated in Table 6 which shows that alternative surveillance methods can identify evidence to support ongoing transmission in areas which passed TAS. For example, Sheel et al. report LF prevalence (using Filarial Test Strips) of 6.2% in a community survey in an area, which had recently passed TAS[14]. In American Samoa, Lau et al. (2014) found levels of Og4C3Ag to be 3.2% and Wb123 Ab to be 8.1% in an area which had recently passed TAS. Xenomonitoring surveys also appear to have utility in identifying hotspots, as in the case of Rao et al. (2018) who detected a MIR of 5.2% in an area which had recently passed TAS[38].

Table 6
Results of alternative surveillance conducted in settings which underwent concurrent TAS.
CountryReferenceDate passed TASStudy dateStudy typeAgeSample sizeResults (95% C.I.s if stated)
American
Samoa
Lau et al. 2014 [11]20112010Community survey≥18 years807 participantsOg4cC3 Ag>32 units = 3.2% (0.6–4.7%);
Wb123 Ab = 8.1% (6.3–10.2%)
Bm14 Ab = 17.9% (15.3–20.7%)
Schmaedick et al. 2014 [44]20112011Xenomonitoring surveyN/A15,215 mosquitoesMIR rate = 0.28% (95% CI 0.20–0.39)
Sheel et al. 2018 [14]20152016Community survey≥8 years2,507 participantsFTS = 6.2% (4.5–8.6%)
MF = 22/86 +ve
Won et al. 2018 [13]20112011Enhanced TAS15–10 years1,134 participantsBinaxNOW = 0.2%
Wb123 Ab = 1.0%
Bm14 Ab = 6.8%
Bm33 Ab = 12.0%
20152015Enhanced TAS15–10 years864 participantsBinaxNOW = 0.1%
Wb123 Ab = 3.6%
Bm14 Ab = 3.0%
Bm33 Ab = 7.8%
BangladeshIrish et al. 2018 [45]20152016Xenomonitoring surveyN/A5,926 mosquitoesMIR = 0%
EgyptMoustafa 2014 [17]20122012Community survey≥18 years1,321 participantsBinaxNOW = 0%
Bm14 Ab = 2.2%
MadagascarGarchitorena et al. 2018 [25]20162016Community survey≥5 years545 participantsFTS = 15.78% (12.88–19.18%)
Sri LankaRao et al. 2014a [36]2012–132012–13Community survey≥10 years7,156 participantsMF = 0–0.9%
BinaxNOW = 0–3.4%
Rao et al. 2014b [36]2012–132012–13Enhanced TAS1
6–7 years17,000 participantsBm14 Ab = 0–6.9% across school sites
Rao et al. 2014c [36]2012–132012–13Xenomonitoring surveyN/A69,680 mosquitoes sampledMIR = 0% - 1.56%.
Rao et al. 2016 [33]2012–132014Xenomonitoring surveyN/A28,717 mosquitoesMIR = 0.36% (0.29–0.45%).
Rao et al. 2017c [37]20132015–17Community survey≥10 years3,123 participants (6 sites)BinaxNOW = 0–1.5%
MF = 0–0.2% (n.s.)
Rao et al. 2017c [37]20132015–17School survey6–8 years2,227 participants (6 sites)BinaxNOW = 0.0–1.1%
MF = 0–0.3%
Bm14 Ab = 0–4.2%
Rao et al. 2017c [37]20132015–16Xenomonitoring surveyN/A24,061 mosquitoes (6 sites)MIR = 0.23% (Peliyagoda) - 1.23% (Unawatuna)
Rao et al. 2018d [38]20132015–16Xenomonitoring surveyN/A2015: 4,000 mosquitoes2015: MIR = 5.2% (4.2–6.3%).
2016: 3,750 mosquitoes2016: MIR = 3.0% (2.3–3.8%).
Rao et al. 2018a [38]20132015School survey6–7 years401 participantsBinaxNOW = 1.2% (0.5–2.8%)
MF = 0.2% (0.0–1.4%)
Bm14 Ab = 5.7% (3.7–8.4%)
Rao et al. 2018b [38]20132015Community survey10–70 years528 participantsBinaxNOW = 3.0% (1.8–4.9%)
MF = 1.1% (0.5–2.5%)
Rao et al. 2018c [38]20132015Community survey≥2 years16,927 participantsMF = 0.6% (0.47–0.71%)
TogoDorkenoo et al. 2018B [52]20152015Xenomonitoring surveyN/A10,872 mosquitoesMIR = 0%.
1 Standard TAS with the addition of antibody testing

Integration of surveillance with other disease programmes

The WHO recommend integrating post-MDA surveillance strategies with other ongoing surveillance activities[4]. Only three papers reported on efforts to integrate LF surveillance with other activities. A study from American Samoa tested stored bloods from a leptospirosis survey for LF[10]. Two studies from Togo integrated LF testing (using either MF or Og4C3 Ag) within routine malaria investigations either at the point of the diagnostic test being taken in the healthcare facility, or when the blood film was being analysed in the laboratory[40, 42].

Discussion

This review provides a timely collation of important information on alternative surveillance strategies for low prevalence and/or post-validation settings that will be useful to national programmes over the next decade as they seek to reduce LF incidence and meet the challenges of the NTD Roadmap 2030 [54]. However, the significant heterogeneity found in the study designs, population sampled, use of diagnostic tests and reporting metrics, highlights the need for more systematic methods and new WHO guidelines to be developed to supplement TAS.

This review has identified that the sensitivity of LF surveillance in selected low prevalence populations can be increased by changes to the diagnostic test and/or study population. TAS is an important programmatic tool to guide decisions on when to stop MDA but several studies report that it lacks sensitivity when used in low prevalence settings, such as a post-validation context[13, 17, 23, 38], and may not accurately describe the spatial distribution of LF at community-level[14]. This is important because evidence from countries that have recently eliminated LF indicates an increased risk of disease recrudescence, with ongoing hotspots of infection documented recently in both American Samoa and Sri Lanka[5, 12, 38]. The lag time between infection with LF and onset of symptoms may be 10 years or more, demonstrating the critical importance of maintaining surveillance programmes following elimination[41, 51]

Alternative diagnostic tests

The studies included in this review indicate that there may be benefit in moving from the conventional rapid antigen tests to antibody tests as they increase the proportion of positive results and, hence, the likelihood that residual hotspots will be detected. However, antibody tests are a measure of the host response to infection which can persist for some time after all antigenic material from the original infection has been eliminated. This means that antibody tests are associated with an increased false-positive rate and the detection of more historical cases, meaning there would be financial and logistical implications to switching to widespread antibody testing[13].

Antibody tests could be added to TAS without significant changes to study design[13]. Reported results suggest that testing Wb123 antibody (Ab) may have particular utility since it is thought to both become positive relatively soon after infection and decay faster following clearance, compared to Bm14 Ab[27, 55]. It also has been found to be significantly associated with molecular xenomonitoring results, suggesting it could act as an indicator of ongoing transmission[13, 55]. Urine ELISA may have greater acceptability than blood testing but requires further validation in LF-endemic regions[16, 35]. However, the current increased costs of antibody and ELISA tests may limit their widespread uptake and further research is needed to characterise the spatial distribution of antibody signals[13].

All methods of human surveillance are affected by the persistence of the marker (antibody or antigen) in circulation. This is of variable duration for different test types, meaning that their results are not directly comparable. It also means that results are not truly indicative of current infectivity and will therefore include cases of historical disease. By contrast, mosquito surveillance gives a snapshot indication of current infection, and could serve as a useful adjunct to human surveillance methods[27, 47]. However, mosquito surveillance requires entomology and laboratory capacity, which are both costly and time-consuming, meaning that it is typically only used in very defined areas, rather than for population-wide surveillance[27, 45].

Alternative approaches to sampling

Studies reported that LF tests typically report higher prevalence of infection in adults than in children[14, 31] and it is thought that adults (particularly adult men) may represent the majority of the reservoir of infection for LF[37]. As prevalence reduces it may therefore also be appropriate to target surveillance to focus on these high-risk populations. Methods that have been suggested include adopting a ‘test and treat’ approach for adult males, which could focus on settings in which they may be more likely to congregate, such as marketplaces[37].

Post-validation surveillance in Togo found positive cases in low-risk areas, highlighting the importance of developing surveillance systems with nationwide coverage[40, 42]. Areas with high levels of migration from endemic countries (e.g. border areas) may also require additional monitoring[24, 56]. Other recommended sampling methods include community-based methods targeting adults and children, school-based surveys with a wider age range and snowball sampling of positive cases[14].

Future research needs

In order to support countries to develop appropriate surveillance in low prevalence or post-validation settings, further research will be required to inform choices regarding the selection of diagnostic tests and appropriate sampling strategies. This will include work to determine the diagnostic performance and cost-effectiveness of novel tests in a range of different epidemiological settings and the identification of suitable threshold values for new LF diagnostic tests in humans[13]. Further research is also required to determine appropriate sample size and infection cut-off thresholds for surveillance in different mosquito species[18, 26, 44, 52].

There is a need to better understand the spatial and temporal dynamics of LF hotspots and their drivers, which will require more longitudinal studies to help inform future control and surveillance activities[12, 23]. Emerging evidence suggests that LF hotspots may be highly focal, increasing the likelihood that cluster-based methods will lack sensitivity to detect them[10, 12, 23, 57]. The risk of recrudescence of infection will depend on a range of factors including population density, baseline endemicity, uptake of MDA and concurrent vector control interventions. It may be appropriate to stratify the intensity of population-level surveillance based on assessment of these factors[58]. This must be supported by the development of data systems capable of continuously collecting, analysing and interpreting data in order to rapidly inform service planning and policy[6].

Further, there is a particular need to increase the evidence base in the African and South Asian Regions, which currently have the majority of ongoing transmission[1]. The evidence base supporting integration of surveillance activities with other health system processes must also be strengthened. Examples may include blood donation systems, surveillance for other co-endemic NTDs (e.g. onchocerciasis) or malaria and routine household surveys[24, 40, 41, 48]. Finally, post-validation surveillance programmes will require clear guidance on how to respond to the identification of new cases. Such interventions may include watchful waiting, vector control, resumption of MDA, treatment of cases only, or a combination of methods[36].

Limitations

It was not possible to conduct a meta-analysis of surveillance results which was largely due to the variation in study methods, but also because of the variation in the infectivity of different mosquito vectors and the influence of different environmental factors that are difficult to control for.

Regarding the study exclusion criteria, the decision to limit the analysis to the English language led to the exclusion of a small number of papers published in Spanish or Chinese, but we consider it unlikely that these results would have significantly changed the main outcomes of this review. The decision to limit the review to papers published after 2000 also excluded a small number of papers but it was considered that the results of more historical studies were likely to have limited transferability to current LF programmes. Finally, our search for unpublished data was limited. It is likely that some studies examining surveillance methods are conducted as part of routine LF programmatic activities and, hence, not published. If collected, such data could strengthen the evidence base in this area.

Conclusions

This is the first review to systematically investigate the evidence supporting alternative (non-TAS) approaches to LF surveillance in low prevalence and post-validation settings. The results demonstrate a need for a more standardised approach to LF surveillance in low prevalence and post-validation settings. Surveillance methods with greater sensitivity and more targeted sampling strategies to better detect residual hotspots than the current TAS methodology will be required. However, further research on the diagnostic performance and cost-effectiveness of new diagnostic tests, and how these can be integrated within routine health system activity, is needed to inform policy decisions over the next decade.

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10 Oct 2019

Dear Dr Riches:

Thank you very much for submitting your manuscript "A systematic review of alternative approaches to lymphatic filariasis post-elimination surveillance" (#PNTD-D-19-01432) for review by PLOS Neglected Tropical Diseases. Your manuscript was fully evaluated at the editorial level and by independent peer reviewers. The reviewers appreciated the attention to an important problem, but raised some substantial concerns about the manuscript as it currently stands. These issues must be addressed before we would be willing to consider a revised version of your study. We cannot, of course, promise publication at that time.

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Associate Editor

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Jennifer Keiser

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***********************

In preparing this manuscript for re-submission, it is important to address the concerns raised by all three reviewers.

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: The objective is to conduct a systematic review. This is not a hypothesis-based study although some hypotheses are implicit in the document (do adults have higher prevalence than children, for example). The study design is appropriate. Sample size depends on the studies reviewed and there are no ethical concerns.

The aim is stated to be to make recommendations to programme managers and highlight areas regarding further research. I don't think any clear recommendations (particularly to programme managers) come out of the review at present. I believe there may be useful summary observations that could be drawn out.

The paper contains a large amount of important information, and it is very useful to have this information collected together, but there are three main areas where improvement is needed to make this a more coherent and valuable contribution.

1. Terminology and organization. There are two relevant time periods for the studies reviewed. Post-MDA - after a country or area believes they can stop MDA, and post validation - after further time has elapsed and surveys have been conducted. The authors use the word 'elimination' usually to describe the stage of the process known as 'validation'. This is confusing and needs to be changed throughout. Since countries and areas are stopping at different times, it would help to show when each site reached these two milestones - i.e. present a table showing the country or area timelines of stopping MDA and validation (rather than just calendar dates of studies, as in Table 1) and then present results by the same timelines. Perhaps this might illuminate some insights about when it is best to do surveys (some are right after last MDA) or how things change over time since MDA or validation.

2. Presentation and assessment of data. The authors state that they have not done and cannot do formal risk of bias assessment or formal metaanalysis. But there are tools available to do risk of bias assessment of such studies. An example is the Crowe Critical Appraisal tool or the Gate Frame. Studies can be ranked by sample size (small, med, large), method of sampling of sites (simple random, cluster, convenience, etc), number of sites (one village versus many), sampling of participants or mosquitoes (representativeness), methodology used for detecting infection, etc. At the very least, some key features of the studies and their quality should be reported in the tables rather than just summing up how many were random sampling (which ones?) and how many weren't. I am not sure if I am convinced that formal metanalysis could not have been done in some cases, but even if not, please at least borrow from the metananalysis field for data presentation, and use forest plots to present the prevalence estimates rather than solely tables. Trying to glean useful summary information from long tables such as Table 3 and 4, or 7 is very difficult for the reader. Showing forest plots graphically (ideally with CIs even if unadjusted), organised by strata such as time since MDA, age group or region/vector would be much more useful.

3. Discussion in context. Without some more effort to appraise the study quality or synthesize the results, even broadly, the true additional value of the paper is not clear. In the Discussion, a paper is supposed to show how the results from this paper add to the available literature or concepts. The authors revert back to citing individual studies to support their points rather than their own Results, which doesn't move us forward much. What can we conclude that is not already known? It leaves the impression that the Discussion could mostly have been written as a general review document before the systematic review was done. I think the hard work put into this deserves a more reflective, concise and nuanced Discussion than that.

Reviewer #2: This manuscript by Riches et al. is helpful in providing a list of published studies of interest to those who are tasked with determining which surveillance strategies would be most useful for defining the effectiveness of programs aimed at eliminating lymphatic filariasis. It does, however, lack so much important detail and conceptual context that it is unable to break much new ground or even to guide those not already very familiar with LF and its elimination in the most fruitful directions.

Principal problems with the manuscript:

1) Critical terms are poorly or not-at-all defined; these include TAS, enhanced TAS, elimination, post-elimination, post-MDA, elimination as a public health problem, elimination of transmission, transmission itself, validation, verification, and others

a. each of these terms is important, has a precise definition and relates in a very distinct way to the surveillance targets and challenges for LF

b. any review of approaches to surveillance for LF must include a clear identification of the goals being targeted at each stage of the program, and those goals demand clear definitions for these terms

2) The various ‘alternatives’ to TAS presented are a collection of studies with very different ‘context.’ The authors recognize this challenge and do not want to introduce bias, but just describing the studies and not adding some weighting of their importance based on context doesn’t much help in determining the most meaningful conclusions from the data

a. ‘Post-MDA’ assessment can imply any of a number of different time points, and the same can be said of ‘post-elimination’ assessments

b. sampling strategy, age-group sampled and diagnostic tools are variables that the authors do wrestle with, but the lack of uniformity in the studies makes many potentially important comparisons nearly impossible to make (especially those involving TAS comparisons).

3) In discussing the various diagnostic tests, higher positivity rates are considered to reflect greater sensitivity of the diagnostic. What is not known, however, is how diminished specificity affects the interpretation of these findings. Much is still unknown about the specificity, sensitivity and kinetics of antibody tests in particular.

Reviewer #3: (No Response)

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

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: Please see comment above regarding better presentation of study data as forest plots (available in RevMAN, STATA etc) to make the results clearer. Even if using tables, consider organizing or stratifying results by time post MDA and post validation, by type of sampling/quality of study, age group or other factor, rather than just by country and calendar year. Include at least minimal quality scores or quality features in descriptive tables or stratify by study type/quality in plots if possible.

Please be clearer about the term 'diagnostic yield'. e.g. in title of Table 4, line 207 and elsewhere. It is not a standard term. Perhaps just use 'estimated prevalence'? The authors need to clarify that tests for Mf, Ag (ICT, FTS, Og4C3) and Ab (various types) are testing for different things. The sensitivity or diagnostic accuracy of the diagnostic tests is not really being compared here.

Table 4 would be better to me if the different markers were separated out and presented by the different age groups. Perhaps simplify to just adults and children only, or 3 age groups only. Table 5 is even more obscure since it has both age and gender for one study. If prevalence by both age and gender are important and confounded, please separate out the studies that have both age and gender from just one or the other. Arrangement of the studies is not optimally logical for data synthesis or putting similar outcomes together.

Reviewer #2: see above

Reviewer #3: (No Response)

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

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: The paper's conclusions do not seem very well justified by the results, and are vague. They state need for more research and more standardized approaches, as well as for methods with greater diagnostic sensitivity and alternative sampling strategies, but don't give any suggestion about what these approaches should be. I am not convinced by the conclusion about diagnostic sensitivity. It is not the diagnostic methods, but when, whom and how you sample, what is measured, and what the thresholds are, that are more important. It might be good to have novel tests, but to solve what problem exactly? I think there are better and bolder conclusions that could be reached from the study.

Reviewer #2: (No Response)

Reviewer #3: (No Response)

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

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: I understand the reasonable limitations and the need for a date when the search had to stop, but there still seem to be some missing studies. Maybe there were reasons for exclusion.

for example

Post MDA studies in Lihir, island PNG (Mitja et al PLOS NTD 2011)

PNG: You have included the Tisch studies in 1990s which seem outside the time frame of publication starting 2000, but what about the later work by Riemer et al in the same area ?

Hapairai et al 2015 in Samoa (xenomoitoring) Parasites and Vectors 2015

French Polynesia: the Maupiti work, Esterre 2001 and 2005 (in English), maybe other areas in Fr Poly that stopped MDA?

Cook Islands: Ave et al 2018 Trop Med Health

Vanuatu: Taleo et al 2017, Trop Med Health

Gass et al 2012 (many countries)

An important paper not cited regarding xenomonitoring thresholds is by Pedersen et al 2009 Trends Parasitol

https://www.sciencedirect.com/science/article/pii/S1471492209001160?via%3Dihub

Minor error in Table 3 last line under Joseph et al: Vanuatu results are put under Tonga

Reviewer #2: see above

Reviewer #3: (No Response)

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

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: Generally this is a comprehensive study demonstrating thorough and apparently careful work and extraction of useful information from a large number of diverse studies that are useful to see together. To maximise its value the paper needs to present the data more clearly and logically to enable 'side by side' comparison of studies with similar outcomes or characteristics. It needs better organization and presentation in relation to MDA and validation timelines, some kind of critical appraisal of the studies, and effort to synthesize at least some combined broad interpretations which arise from the data in Tables or Figures and which are more available for the readers to assess themselves. The Discussion needs to specifically relate the findings in this paper to the overall status of LF elimination programmes, strategies and thresholds, rather than quoting individual studies again, and show how this study moves us forward with more specific recommendations about sampling strategies, methods or other improvements.

Reviewer #2: While the collection of studies reviewed in this manuscript will be helpful to those aiming to draw inferences and testable hypotheses from the information already available, it is unfortunate that these authors did not do the further analytic work to define many of these inferences and testable hypotheses themselves. It is clear from their other manuscripts that this group does have the technical and conceptual background to do such analytic work.

Reviewer #3: The authors have conducted an interesting literature review, dissecting various methods of LF surveillance in research studies after 2000.

It’s useful to see all that has been done and the results collated in one paper. However, other than categorizing and presenting results of any published non-TAS survey since 2000, it is not clear what question, if any is being asked. Can the authors articulate what would have the formal meta-analysis measured?

The review would be stronger if hypotheses could be tested and forest plots generated.

When referring to elimination of LF, please indicate elimination as a public health problem

Line 45: the review assesses the ‘results’ from 42 studies

Line 46-47: There is no standardized approach to testing ‘other than TAS’

Line 189. How does the variation in the sampling methods allow direct comparison of prevalence?

Line 204. Define noticeably. Was higher prevalence noticeable in all locations?

Line 208 second part of the sentence is a discussion point not a result.

Table 6 could the authors add the primary vector?

Line 237-238 define appear more sensitive. Should this interpretation be moved to the discussion?

Line 253-263 Indicates the main purpose of the paper was to conclude TAS lacks sensitivity. Where was this stated as an objective and how was this measured? Where was the evidence on size of evaluation units?

If revised, the statement starting in line 261 to 263 could be a conclusion of the review and would be ideal to put as the start of the discussion. It would be important for the authors to define sensitivity (or how they are using it) in the methods. More positive test results?

Line 284-286 – are the authors claiming that the results from the review suggest test-and treat strategy of males? Please clarify on which data this recommendation is based or remove.

Line 294 Future research needs – it is not clear whether these were derived from the studies reviewed. It would strengthen the paper if the authors noted / included in the results the various research needs identified in the included studies. As it is simply listed in the discussion it seems as the authors’ opinions of the needs.

Line 304 – do the authors mean recrudescence of infection or disease?

Conclusions

Line 334 – 335 the authors join two separate conclusions. Please revise to tease out the 2 points being made.

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

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No


23 Feb 2020

Dear Dr Riches,

Thank you very much for submitting your manuscript "A systematic review of alternative surveillance approaches for lymphatic filariasis in low prevalence settings: implications for post-validation" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

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Sincerely,

Patrick J. Lammie, Ph.D.

Associate Editor

PLOS Neglected Tropical Diseases

Jennifer Keiser

Deputy Editor

PLOS Neglected Tropical Diseases

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Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: The authors have been very responsive and addressed the main deficiences with the previous version. The presentaton is greatly improved and the inclusion of risk of bias/quality assessment is welcomed.

Post validation is now clear, but the definition of 'post MDA' is still not clear and a bit inconsistent. For example, in Table 2 you have included studies from countries that are not truly 'post-MDA'. For example Samoa in 2008 was after several MDAs, but the surveys by Joseph et al were done before (or around the same time as) the 2008 MDA, which was followed by MDAs in 2011 and later. Other countries like Nigeria, PNG, Haiti, Fr Poly and others are not 'post MDA'. American Samoa has restarted in 2018. I don't think you need to change which studies are included, but need to come up with a better term than 'post MDA' to describe them and why you included them. Solomons was not post -validaton becasue it did not need MDA.

Also please define 'enhanced TAS'.

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Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: Much clearer overall, but Table 6 is the exception. It's very obscure and hard to understand which surveys go together.

Please put the surveys side by side in Table 6.

Table S4 helps a bit - but requires effort for the reader to find. Maybe it should be in main paper if you are gonig to draw conclusions from it (as in abstract you do - but not in text).

The description of results and their interpretation from Table S4 and Table 6 are very skimpy . A table 7 is cited but not present.

You have made quite a bold statement in the abstract that MX is better at determining ongoing transmission. Where is the evidence for that from these tables? MX could also just be detecting MF in non competent vectors - the word 'mosquito transmission' in line 308 should be 'mosquito infection'. What is a 'successful' TAS? (line 259).

I am surprised you didn't include the paper by Lau et al 2016 in table S4 that directly compared the serology and MX results from American Samoa in roughly the same time frame as the MX and TAS in 2011 was done . It is is cited in the Won et al paper (13) in lines 300-303, but the actual comparison is in Lau et al 2016 PLOS NTD . If you are going to rely on this result so much for Wb123 please cite the original paper.

I like the trend lines in Table 4, but they sometimes don't seem to relate exactly to the data in the precending columns. Why no trend in the first row? For Lau et al 2014, there are 6 points on the line, but no data for the first two in the table. Same for Rao et al and Sheel et al missing 0-10 yrs.

Figure 3 with gender specific prevalence is great, but there seem to be some studies missing that reported gender . Lau et al 2014, 2017, maybe others.

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Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: Conclusion is much better and is anchored in the results/discussion. However still not very clear throughout on the definiton of diagnostic sensitivity and its difference from surveillance sensitivity. The Ag/Ab tests are detecting different things so it is not just differences in persistence or incubation period, as implied in Discusion.

Need to be very clear on sensitivity of surveillance methods versus sensitvity of tests. i.e. perhaps remove the word diagnostic line 364. Please check lines 135 and 136 as they do not seem correct either. What is the true positive here?

I am not clear on the definition of 'clinical effectiveness' - used in conclusion but also elsewhere. Please explain what you mean by this.

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Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: Please check use of 'paper/article', 'study' and 'study design' .

There are 44 articles and 83 studies. Lines 159 - 162 are confusing. Papers reported data from 22 countries?; 21 STUDIES came from WPRO etc,.

45 articles are mentioned line 168 when I think you mean 45 studies.

Legend to Fig 2 - I think you mean '60 distinct studies' not 'study designs'

There may be others.

Table S1 - Lau et al 2014 was actually simple random sample of households (not non-random). This will change the quality score.

Lau et al 2017 - What is definition of 'children'? line [3] was adult workers, only a few of whom were 16 -17 yrs old.

Please also check Joseph et al 2011 (nos 22 and 23). One of those was children only. Those are just the ones I know about.

Table 2 - please review 'Context' and consider a better classification. e.g. Just finishing MDA (doing a survey of all ages to check if can stop e.g. Joseph Samoa, Allen Vanuatu, Coutts Am Samoa); in a lull between MDAs and waiting for validation; doing a pre-TAS, never did MDA but checking, etc.

Table S2. same comments on Context column.You still have 'post-elimination' in the context here - do you mean post-validation?

Gass et al ref is 2012 not 2011 (study done 2011).

Mitja et al [30] is PNG not Tanzania.

Line 204 suggest change for clarity to

"Compared to Binax Now or Alere ICT (most commonly used tests at the time of most of these surveys) as the index test, Table 3 shows ..."

Line 308 suggest:

"Post validation surveillance in Togo found positive cases in low- risk areas" to cut repetition of 'found'

Fig 3 can you match the colours of % legends to the bars? It's unecessarily confusing right now.

Table S4 - define MIR

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Summary and General Comments

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Reviewer #1: Overall the aims are much clearer, the data presentation is greatly improved (with a few exceptions already mentioned earlier) and the paper is much more comprehensible and useful.

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13 Apr 2020

Dear Dr Riches,

We are pleased to inform you that your manuscript 'A systematic review of alternative surveillance approaches for lymphatic filariasis in low prevalence settings: implications for post-validation' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Patrick J. Lammie, Ph.D.

Associate Editor

PLOS Neglected Tropical Diseases

Jennifer Keiser

Deputy Editor

PLOS Neglected Tropical Diseases

***********************************************************

I appreciate the careful attention that the authors have focused on an important topic as well as their diligence in responding to reviewer suggestions. Nonetheless, I think that the manuscript would still benefit from a careful proof reading.

As examples:

Line 33: There is a loose “h” at the end of the line.

Table 4: The cells with the trend lines do not match up with the rows in all instances.

Line 256: The sentence ends with a double period.

Lines 257-258: The phrase “but interestingly not in other studies in Sri Lanka” requires further explanation.


23 Apr 2020

Dear Dr Riches,

We are delighted to inform you that your manuscript, "A systematic review of alternative surveillance approaches for lymphatic filariasis in low prevalence settings: Implications for post-validation settings," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. All authors will receive a confirmation email upon publication.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any scientific or type-setting errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Note: Proofs for Front Matter articles (Editorial, Viewpoint, Symposium, Review, etc...) are generated on a different schedule and may not be made available as quickly.

Soon after your final files are uploaded, the early version of your manuscript will be published online unless you opted out of this process. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Serap Aksoy

Editor-in-Chief

PLOS Neglected Tropical Diseases

Shaden Kamhawi

Editor-in-Chief

PLOS Neglected Tropical Diseases

https://www.researchpad.co/tools/openurl?pubtype=article&doi=10.1371/journal.pntd.0008289&title=A systematic review of alternative surveillance approaches for lymphatic filariasis in low prevalence settings: Implications for post-validation settings&author=Nicholas Riches,Xavier Badia-Rius,Themba Mzilahowa,Louise A. Kelly-Hope,Patrick J. Lammie,Patrick J. Lammie,Jennifer Keiser,Patrick J. Lammie,Jennifer Keiser,Patrick J. Lammie,Jennifer Keiser,Patrick J. Lammie,Jennifer Keiser,&keyword=&subject=Research Article,Medicine and Health Sciences,Infectious Diseases,Disease Vectors,Insect Vectors,Mosquitoes,Biology and Life Sciences,Species Interactions,Disease Vectors,Insect Vectors,Mosquitoes,Biology and Life Sciences,Organisms,Eukaryota,Animals,Invertebrates,Arthropoda,Insects,Mosquitoes,Medicine and Health Sciences,Epidemiology,Disease Surveillance,Infectious Disease Surveillance,Medicine and Health Sciences,Infectious Diseases,Infectious Disease Control,Infectious Disease Surveillance,Social Sciences,Sociology,Education,Schools,Research and Analysis Methods,Immunologic Techniques,Immunoassays,Enzyme-Linked Immunoassays,Medicine and Health Sciences,Epidemiology,Disease Surveillance,Biology and Life Sciences,Physiology,Immune Physiology,Antibodies,Medicine and Health Sciences,Physiology,Immune Physiology,Antibodies,Biology and Life Sciences,Immunology,Immune System Proteins,Antibodies,Medicine and Health Sciences,Immunology,Immune System Proteins,Antibodies,Biology and Life Sciences,Biochemistry,Proteins,Immune System Proteins,Antibodies,People and places,Geographical locations,Oceania,American Samoa,Medicine and Health Sciences,Parasitic Diseases,Helminth Infections,Filariasis,Lymphatic Filariasis,Medicine and Health Sciences,Tropical Diseases,Neglected Tropical Diseases,Lymphatic Filariasis,