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Demographic, socioeconomic and disease knowledge factors, but not population mobility, associated with lymphatic filariasis infection in adult workers in American Samoa in 2014
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Abstract
Background
Prevalence of lymphatic filariasis (LF) antigen in American Samoa was 16.5% in 1999. Seven rounds of mass drug administration (MDA) programmes between 2000 and 2006 reduced antigen prevalence to 2.3%. The most efficient methods of surveillance after MDA are not clear, but testing specific at-risk groups such as adults may provide earlier warning of resurgence. The role of migration from LF endemic countries in maintaining transmission also needs investigation. Few studies have investigated knowledge about LF and how that relates to infection risk. This study aims to investigate associations between socio-demographics, population mobility, disease knowledge and LF infection risk.
Methods
In 2014, we surveyed 670 adults aged 16–68 years (62% female) at two worksites in American Samoa. Sera were tested for LF antigen and antibodies (Bm14 and Wb123) by rapid test and/or ELISA. Multivariate logistic regression was used to assess association between seromarkers and demographic factors, household socioeconomic status (SES), residence, travel history, and knowledge of LF.
Results
Overall, 1.8% of participants were positive for antigen, 11.8% for Bm14, 11.3% for Wb123 and 17.3% for at least one antibody. Recent travel outside American Samoa was not associated with positivity for any seromarker. Men had higher seroprevalence than women for all outcomes (any antibody: adjusted odds ratio (aOR) = 3.49 (95% CI: 2.21–5.49). Those aged over 35 years (compared to 15–24 years) had higher prevalence of Bm14 antibody (aOR = 3.75, 3.76 and 4.17 for ages 35–44, 45–54 and ≥ 55 years, respectively, P < 0.05). Lower SES was associated with seropositivity (antigen: aOR = 2.89, 95% CI: 1.09–7.69; either antibody: aOR = 1.51, 95% CI: 1.12–2.05). Those who knew that mosquitoes transmitted LF had lower Wb123 antibody prevalence (aOR = 0.55, 95% CI: 0.32–0.95).
Conclusions
Opportunistic sampling of adults at worksites provided an efficient and representative way to assess prevalence and risk factors for LF in American Samoa and in hindsight, foreshadowed the resurgence of transmission. Risk of LF infection, detected by one or more serological markers, was not related to recent travel history, but was strongly associated with male gender, older age, lower SES, and lack of knowledge about mosquito transmission. These results could guide future efforts to increase MDA participation.
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Most cited references30
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Detecting and confirming residual hotspots of lymphatic filariasis transmission in American Samoa 8 years after stopping mass drug administration
Colleen L. Lau, Sarah Sheridan, Stephanie Ryan … (2017)
The Global Programme to Eliminate Lymphatic Filariasis (LF) aims to eliminate the disease as a public health problem by 2020 by conducting mass drug administration (MDA) and controlling morbidity. Once elimination targets have been reached, surveillance is critical for ensuring that programmatic gains are sustained, and challenges include timely identification of residual areas of transmission. WHO guidelines encourage cost-efficient surveillance, such as integration with other population-based surveys. In American Samoa, where LF is caused by Wuchereria bancrofti, and Aedes polynesiensis is the main vector, the LF elimination program has made significant progress. Seven rounds of MDA (albendazole and diethycarbamazine) were completed from 2000 to 2006, and Transmission Assessment Surveys were passed in 2010/2011 and 2015. However, a seroprevalence study using an adult serum bank collected in 2010 detected two potential residual foci of transmission, with Og4C3 antigen (Ag) prevalence of 30.8% and 15.6%. We conducted a follow up study in 2014 to verify if transmission was truly occurring by comparing seroprevalence between residents of suspected hotspots and residents of other villages. In adults from non-hotspot villages (N = 602), seroprevalence of Ag (ICT or Og4C3), Bm14 antibody (Ab) and Wb123 Ab were 1.2% (95% CI 0.6–2.6%), 9.6% (95% CI 7.5%-12.3%), and 10.5% (95% CI 7.6–14.3%), respectively. Comparatively, adult residents of Fagali’i (N = 38) had significantly higher seroprevalence of Ag (26.9%, 95% CI 17.3–39.4%), Bm14 Ab (43.4%, 95% CI 32.4–55.0%), and Wb123 Ab 55.2% (95% CI 39.6–69.8%). Adult residents of Ili’ili/Vaitogi/Futiga (N = 113) also had higher prevalence of Ag and Ab, but differences were not statistically significant. The presence of transmission was demonstrated by 1.1% Ag prevalence (95% CI 0.2% to 3.1%) in 283 children aged 7–13 years who lived in one of the suspected hotspots; and microfilaraemia in four individuals, all of whom lived in the suspected hotspots, including a 9 year old child. Our results provide field evidence that integrating LF surveillance with other surveys is effective and feasible for identifying potential hotspots, and conducting surveillance at worksites provides an efficient method of sampling large populations of adults.
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Seroprevalence and Spatial Epidemiology of Lymphatic Filariasis in American Samoa after Successful Mass Drug Administration
Colleen L. Lau, Kimberly Y. Won, Luke Becker … (2014)
Introduction Lymphatic filariasis (LF) is a neglected tropical disease of global importance, with an estimated 1.4 billion people in 73 countries at risk of infection. Over 120 million people worldwide are currently affected by lymphatic filariasis and 40 million are disfigured and disabled [1]. Infection is transmitted by mosquito vectors including Anopheles, Aedes, Culex and Mansonia species. The Pacific Programme for Elimination of Lymphatic Filariasis (PacELF) was formed in 1999, and as part of the Global Programme to Eliminate LF (GPELF), aimed to eliminate the disease as a public health problem in 22 Pacific Island countries and territories (PICTs) by 2020 [2]. The Programme in the Pacific covers over 3000 islands and 8.6 million people, and consists of two strategies: firstly, to interrupt transmission through mass drug administration (MDA) using albendazole and diethycarbamazine (DEC) and secondly, to control morbidity and disability of affected persons [2]. Baseline surveys conducted in 1999 and 2000 determined that 11 PICTs were endemic for LF, five partially endemic, and six non-endemic [2]. Since then, variable progress has been made towards reducing prevalence and interrupting transmission on different islands [3], but significant success has been achieved in the Samoan Islands, particularly in American Samoa. Before the 1960s, both Samoa (formerly called Western Samoa) and American Samoa had high prevalence (∼20%) of lymphatic filariasis [4], [5]. Multiple rounds of MDA in the 1960s had considerable impact and reduced the prevalence of microfilaraemia to less than 2%, but neither Samoa nor American Samoa managed to achieve sustained interruption of transmission at that time [6]–[9]. By 1999, antigen prevalence of 16.5% (N = 3018) was recorded in American Samoa and 4.5% (N = 7006) in Samoa. In American Samoa, after seven rounds of MDA from 2000–2006, antigen prevalence dropped to 2.3% (N = 1881) in 2007 in a community cluster survey that involved all age groups [10]. Current WHO guidelines [11] recommend that in areas where W. bancrofti is endemic and Aedes is the principal vector, the target threshold for post-MDA transmission assessment surveys (TAS) is 128 Og4C3>32 Wb123 Bm14 N % N Prevalence OR p N Prevalence OR p N Prevalence OR p N Prevalence OR p Total samples 807 805 805 806 806 Total positive 6 0.7% 26 3.2% 65 8.1% 144 17.9% Gender Females 380 47.1% 0 0.0% - - 8 2.1% 1 11 2.9% 1 41 10.8% 1 Males 423 52.4% 6 1.4% - - 18 4.3% 2.1 0.1 54 12.8% 4.90 0.00 103 24.3% 2.7 0.00 Age (years) 70 36 4.5% 0 0.0% - - 1 2.8% 3.0 0.44 3 8.3% 3.1 0.18 9 25.0% 6.7 0.00 Years lived in AS Whole life 555 68.8% 4 0.7% 13 2.3% 39 7.0% 87 15.7% >10 years 718 89.0% 4 0.6% 1 19 2.6% 1 55 7.7% 1 129 18.0% 1 5 to 10 years 54 6.7% 0 0.0% - - 2 3.7% 1.4 0.65 5 9.3% 1.2 0.67 6 11.1% 0.6 0.21 $30,000 64 7.9% 0 0.0% - - 1 1.6% 0.5 0.45 2 3.1% 0.3 0.12 7 10.9% 0.48 0.08 Unknown 128 15.9% 0 0.0% - - 4 3.1% 0.9 0.89 10 7.8% 0.8 0.63 19 14.8% 0.68 0.18 Chi2/Fisher * Chi2/Fisher * Chi2/Fisher * Chi2/Fisher * Island of residence p p p p Tutuila 721 89.3% 6 0.7% 1.00 26 3.2% 0.10 57 7.1% 0.65 127 15.8% 0.62 Other islands 86 10.7% 0 0.0% 1.00 0 0.0% 0.10 8 1.0% 0.65 17 2.1% 0.62 Statistically significant results (p 128 units (positive according to manufacturer's instructions), and >32 units (positive and equivocal). The mean value recorded for the negative sera over 20 plates was 4.9 units. Wb123 antibody ELISA test The test detects antibody to the Wb123 antigen identified from a library generated from L3 larval stages of W.bancrofti [26]. The assay was performed in ELISA format using plates pre-coated with 10 µg/mL Wb123 antigen. Sera at 1∶50 dilution in PBS/1%BSA/0.05% Tween 20 (PBS/T) were added at 50 µL per well, 50 µL of a known positive control were added at 1∶500 (high positive) and 1∶5000 (low positive) while negative control serum was added at 1∶50. Plates were incubated for 30 minutes at room temperature, washed 6 times in PBS/T and 50 µL per well of HRP conjugated mouse anti-human IgG4 (Invitrogen A10654) at 1∶5000 dilution was added for 45 minutes incubation. After 6 washes TMB substrate was added at 50 µL per well and the reaction was stopped with 1 M HCl after 5 minutes in the dark. Plates were read at 450 nm. Samples were classed as positive if their average optical density (OD) ratio was 9 or more times the negative control (based on the average ratio of of the low positive 1∶5000 serum control to blank). The mean OD ratio of the negative control to blank over 19 plates was 1.0. Bm14 antibody ELISA test The test detects antibody to an antigen identified from a cDNA library screened using sera from microfilaria positive people [27]. Sera were tested at 1∶50 dilution using the method described by [27] and [12]. A known positive control from PNG (S19) and negative serum from lab members were included on each plate. A 7-point standard curve in duplicate using a known high positive serum from PNG (S200) starting at 1∶200 dilution (1000 arbitrary units) and then 2–fold serial dilutions in PBS/T was included on each plate. A 4-parameter calibration curve was used to estimate the units of Bm14 antibody per sample. The cutoff for positivity was 125 units determined empirically as described by [27] using known positive and negative serum panels. Statistical analysis Outcome measures used for statistical analyses were ELISA test results for each LF antigen and antibody. For Og4C3 antigen, statistical analyses were performed using two different cutoff points: >128 units (positive results) and >32 units (equivocal and positive results). Independent variables assessed included age, sex, years lived in American Samoa, occupation, household income, and island of residence. The number of years lived in American Samoa was categorized into 10 years (those who lived in Am Sam during all of the MDA activities). Occupation groups were categorized into those who worked i) predominantly indoors, ii) predominantly outdoors, iii) tuna cannery workers (the largest non-government employer in American Samoa; >90% of employees are migrant workers), and iv) others (including unemployed, unknown occupation, and those who have jobs that include both indoor and outdoor work). Data on household income was available in four categories. Island of residence was categorized into Tutuila and other islands. The serum bank consisted of samples and data on 807 participants. There was sufficient serum in 805 samples to perform ELISA for Og4C3 antigen, and in 806 samples for Wb123 and Bm14 antibodies. Data on gender were available for 803 participants, on age for 798, on years lived in American Samoa for 800, and on household income for 679. Island of residence and geo-locations of households were available for all participants. Chi-squared or Fisher exact tests were used to compare outcomes for categorical independent variables. Variables with p 128 units (positive result) were found in 0.75% (6 persons, 95% CI 0.3–1.6%) of participants, and levels of >32 units (equivocal plus positive results) in 3.2% (26 persons, 95% CI 0.6–4.7%). The seroprevalence of Wb123 and Bm14 antibodies were 8.1% (65 positives, 95% CI6.3–10.2%) and 17.9% (144 positives, 95% CI 15.3–20.7%) respectively. Factors associated with positive LF antigen and antibodies Table 1 provides a summary of the associations between demographic variables and the presence of LF antigen and antibodies. Our results show that both antigen and antibody prevalence were higher in males compared to females (Table 1). Figure 1 shows the age distribution of participants, and the prevalence of antigen (Og4C3>128 and Og4C3>32) and antibodies (Wb123 and Bm14) in each age group. Antigen-positive individuals were identified in all age groups, with no significant difference between ages. Prevalence of both Wb123 and Bm14 antibodies were higher in the older age groups. In participants aged 30 years and older, Bm14 prevalence was two to three times higher than Wb123 prevalence in all age groups. 10.1371/journal.pntd.0003297.g001 Figure 1 Prevalence of filarial antigen and antibodies by age groups, American Samoa 2010. Antibody and antigen prevalence were inversely associated with the number of years lived in American Samoa (Figure 2 and Table 1). Of all study participants, 68.8% (n = 555) had lived in American Samoa for all of their lives. Compared to individuals who had lived in American Samoa for over 10 years, new migrants who had lived there for 128 units, and odds ratio of 6.1 (95% CI: 1.9–19.4) of having Og4C3 antigen of >32 units (Table 1). New migrants also had higher prevalence of Wb123 and Bm14 antibodies compared to those who had lived in American Samoa for >10 years, but differences were not statistically significant. The prevalence of antibodies and antigen were higher in residents on the main island of Tutuila compared to those who lived in smaller islands, but differences were not statistically significant. Tuna cannery workers had significantly higher prevalence of Wb123 antibodies, but there were no other associations between occupational groups and seroprevalence. Our study did not find any association between income and seroprevalence. 10.1371/journal.pntd.0003297.g002 Figure 2 Prevalence of filarial antigen and antibodies by years lived in American Samoa. Geographical clustering of serological indicators For reference, a kernel density map of population distribution in American Samoa is shown in Figure 3 (reproduced from [23]). The household locations of individuals with positive and negative Bm14 and Wb123 antibodies are shown in Figure 4a and 4b, and positive/equivocal Og4C3 levels shown in Figures 5a and 5b. High resolution maps of the villages of Fagalii (Figure 6a) and Ili'ili (Figure 6b) show the locations of participants' households, those with positive/equivocal results for Og4C3, and the location of the elementary school where two ICT-positive children were identified during the 2011 Transmission Assessment Survey. 10.1371/journal.pntd.0003297.g003 Figure 3 Population distribution on the islands of American Samoa 2010 (Reproduced from Lau et al. (23). 10.1371/journal.pntd.0003297.g004 Figure 4 Household locations of individuals with positive and negative antibodies on Tutuila. A. Wb123, B. Bm14. 10.1371/journal.pntd.0003297.g005 Figure 5 Household locations of individuals with positive and negative antigen on Tutuila. A. Og4C3>128 units, B. Og4C3>32 units. 10.1371/journal.pntd.0003297.g006 Figure 6 High resolution village maps of A. Fagali'I and B. Ili'ili, showing household locations of individuals with Og4C3 antigen of >128 units and >32 units, and school where two ICT-positive children identified in 2011 TAS. While the semivariograms for Wb123 and Bm14 antibodies did not reveal any significant small-scale spatial variation, the semivariograms for antigen (both Og4C3>128 units and Og4C3>32 units) showed considerable residual spatial variation (Figure 7 and Table 2). Our results indicate that the average size of a cluster for Og4C3>128 units was 1,242 metres and the proportion of the variation in Og4C3>128 units explained by geographical proximity was 85%. The average size of a cluster for Og4C3>32 units was 1,498 meters and the proportion of the variation in Og4C3>32 units explained by geographical proximity was 62%. Migrants who had lived in American Samoa for 128 units, B. Og4C3>32 units, C. Wb123 positive, D. Bm14 positive. 10.1371/journal.pntd.0003297.t002 Table 2 Spatial parameters of geographical clustering of Og4C3 antigen, and Wb123 and Bm14 antibodies. Spatial parameters Og4C3>128 Og4C3>32 Wb123 Bm14 Range (meters) 1,242 1,498 60 NA Partial sill 0.00965 0.0451 0.015 NA Nugget 0.00173 0.0281 0.075 NA Proportion of variance due to spatial dependence (%) 85 62 17 NA Discussion Our study demonstrates that high-risk populations for LF in American Samoa include adult males and recent migrants. The results also suggest the possible existence of residual foci of antigen-positive individuals in American Samoa. Although our findings do not provide conclusive evidence of recent transmission, further investigation is recommended to confirm (or otherwise) the possible high-risk populations and locations, and determine whether ongoing targeted surveillance of these groups is warranted, particularly in the Samoan Islands where there is a history of resurgence despite achieving very low prevalence [4]. The prevalence of Wb123 and Bm14 antibodies differed significantly, and further research is required to understand the role of each laboratory test in post-MDA surveillance. There was a sharp rise in Bm14 antibody prevalence from age 30–39 years, which was also observed by Mladonicky et al in 2006 in three villages of American Samoa [9]. We found that Wb123 antibody prevalence peaked in participants aged between 30 and 40 years, but at much lower prevalence than Bm14 antibody. Wb123 antibody is a relatively new assay, and the indicative cutoff point used in this study could have contributed to the differences between the prevalence of Wb123 and Bm14 antibodies. Neither Wb123 nor Bm14 antibody prevalence declined with age, but at present we cannot distinguish long-term persistence of antibodies from ongoing transmission. Other studies have noted persistence of Wb123 antibodies in adults for many years after MDA, although significant decline was observed in those who were antigen-negative [28]. Positive Og4C3 antigen was found in all age groups and did not show any age-specific patterns. The presence of Og4C3 is not necessarily associated with microfilaraemia and does not provide evidence of ongoing transmission. Antigen prevalence drops dramatically after MDA, but it is not possible to unequivocally distinguish between recent or past infection based on Og4C3 alone. However, for W. bancrofti areas, the WHO currently supports the use of circulating filarial antigen prevalence (measured by ICT card test) as an indicator of LF infection, and it is one of the options of diagnostic tests used for measuring the prevalence of infection at each stage of the elimination process (pre-MDA mapping, sentinel and spot check sites, and TAS). The Og4C3 antigen has also been used in a similar study in Haiti that investigated clustering of residual antigen-positive persons in low endemic areas [19]. In our study, three aspects of the Og4C3-positive individuals raised suspicion about the possibility of recent transmission. Firstly, one cluster of Og4C3-positive adults was located in very close proximity to the two ICT-positive children found during the 2011 TAS. Secondly, Og4C3 prevalence in our study was higher in migrants (mostly from Samoa) even though baseline antigen prevalence in 1999 was much lower in Samoa (4.5%) than American Samoa (16.5%). If positive Og4C3 in our sample predominantly reflected infections in the remote past, prevalence would be expected to be lower in the migrants. Thirdly, we found significant spatial clustering of Og4C3 antigen, but not of Wb123 or Bm14 antibodies. If the Og4C3-positive adults in our study were predominantly infected in the remote past, clustering would have been much less likely, as demonstrated by the absence of clustering of antibody-positive adults. Data on microfilaraemia would have helped determine the presence of ongoing transmission, but this was not possible with a serum bank. Despite this limitation, we believe that our seroprevalence study of adults provided valuable information about potential residual infections in American Samoa. Similar studies should be considered elsewhere for post-MDA surveillance and for identifying high-risk populations and/or locations that might warrant more intense targeted surveillance. Higher LF seroprevalence in males corroborates findings from some of the previous studies in Samoa [7], [18] and American Samoa [29], and could be explained by more time spent outdoors for work and recreation compared to females. Interestingly, LF prevalence was found to be equivalent in males and females in 1999 prior to MDA in American Samoa, but Liang et al. reported a shift toward higher prevalence in males in sentinel site surveys conducted during and after MDA [29]. Our study (using a much larger and more representative sample of the adult population) confirms the higher prevalence among males post-MDA in American Samoa. Our results also indicate higher antigen prevalence in new migrants, who were mostly from a neighbouring LF-endemic country where transmission is still occurring in some areas. This suggests that human movement could be an important pathway for parasite reintroduction and subsequent resurgence of LF in American Samoa. Visitors and migrants travel for family, work, and economic reasons and usually live and work in close proximity to local American Samoans. Prolonged visits and cross migration are also common, and further increase the chances of parasite reintroduction. In addition, American Samoans also travel frequently to Samoa and other neighbouring Pacific Islands, and could be at significant risk of infection if staying for extended periods in areas of high prevalence. In 2012, there were a total of 67,979 international arrivals to American Samoa (with a local population of ∼56,000). Of these 44,830 were citizens of other Pacific Islands, including 22,600 arrivals of returning citizens of American Samoa. A total of 20,082 arrivals were Samoan citizens, with 158 travelling for business, 4,158 for employment, 7,123 returning residents, and 8,757 visiting relatives [22]. Further research is required to improve understanding of the role of human movement in parasite reintroduction into American Samoa, and the consequent risk of resurgence based on travel patterns between Samoa and American Samoa, and LF prevalence at places of origin of visitors and new migrants. Cross-border strategies to coordinate efforts between Samoa and American Samoa for LF elimination and surveillance should also be considered. American Samoa's population mostly live on ancestral land, and most of the study participants had lived in the same village for most or all of their lives, thus providing an excellent opportunity to examine disease transmission patterns. Our results indicate that most of the spatial distribution of antigenaemia could be accounted for by geographical proximity of place of residence. Geo-spatial analysis provided some evidence of possible micro-spatial clustering of antigen-positive adults at the neighborhood level at two villages. Clustering at the household level suggests that the home environment is important in transmission even though one of the major vectors is day-biting. The close proximity between the elementary school attended by the two ICT-positive young children identified during the 2011 TAS and one of the possible village clusters suggests possible ongoing transmission. Our results indicate an average cluster size of 1,200 meters to 1,500 meters for antigenaemia, and the estimate of cluster size provides important information for the design of further studies to identify local transmission foci. Our study demonstrates the potential value of geospatial databases in post-intervention surveillance, monitoring, and evaluation for identifying possible micro-spatial clusters that might not be captured by routine TAS alone. Early detection of such clusters could be essential for timely intervention to reduce the risk of resurgence. Geospatial analysis could therefore potentially be used as an additional tool for verifying elimination status and for confirming that transmission has been interrupted. Changes in the spatial distribution of serological markers over time would also potentially be useful for identifying focal transmission, but unfortunately results of previous surveys in American Samoa were only located to the village rather than household level, and not of sufficiently high spatial resolution for the types of analyses conducted in this study or for comparing changes over time. Further operational research could also explore the use of geospatial data for informing programme delivery (e.g. by identifying the size of clusters and delineating areas that might warrant targeted surveillance and monitoring); calculating the distance of influence on infection risk that antigen-positive persons have on their near neighbours; and determining transmission threshold targets that include a spatial component rather than just a simple average prevalence for an entire evaluation unit. The accuracy of prevalence estimations in evaluation units will also depend on spatial heterogeneity within the boundaries of the unit. Risk of LF and drivers of transmission are unlikely to be entirely uniform within any evaluation units, and be determined by many factors such as climatic conditions, population density, urban versus rural areas, MDA coverage, and vector species and density. The average prevalence in an evaluation unit could therefore mask focal areas of high prevalence (hotspots) if they are surrounded by large areas of low prevalence. Consequently, estimations of average prevalence in an area could vary greatly depending on how evaluation units were determined. Hotspots are more likely to be missed if they are small, in evaluation areas with greater spatial heterogeneity in risks and drivers, and when prevalence is very low such as in the post-MDA surveillance phase. Careful definition of evaluation units will therefore be crucial for optimising the probability of identifying any residual hotspots of transmission or early resurgence. One of the challenges pertaining to geospatial methods of cluster detection when utilising point location data is that such data are prone to random error and random variation in the presence of rare disease events and/or inadequate representation of the population at risk. We therefore used a robust geostatistical method to identify the presence of geographical clustering in our point location data by partitioning the variation in data that was due to random error and the variation that is due to spatial clustering. Semivariography (as utilized in this study) demonstrated that spatial clustering was present in the study area (Tutuila) but does not identify the location of clusters. The location of clusters could be further investigated by using model-based geostatistics that account for diagnostic uncertainty and variation in factors such as climate, population, and entomological parameters to produce predictive risk maps of LF. Spatial decision support systems are being used for malaria elimination programs, and similar tools could also be useful for LF [30]. A geospatial platform could also be used to integrate environmental and entomological data with human surveillance data, and used to explore possible environmental drivers of disease transmission, the impact of vector control on elimination programs, and the potential for using xenomonitoring to enhance post-MDA surveillance. This study also demonstrates the usefulness of high-quality serum banks for investigating multiple diseases (a dengue seroprevalence study was also conducted using the same serum bank [31]), and provides an example of successful collaboration between researchers of different diseases to improve the cost-effectiveness of field epidemiology investigations, which are often expensive and logistically challenging. We believe that the WHO's recommendations of integrating of LF surveillance activities with other population-based surveys are logistically feasible and practical. Our findings should be interpreted in light of potential limitations. First, the serum bank used for the study was collected for a leptospirosis study, and we could not ascertain whether participants had previously been diagnosed with or treated for LF, or participated in MDA in American Samoa or elsewhere. Only 28 participants (3.9%) were recent migrants (lived 128 units, and 26 participants with Og4C3 of >32 units, and small numbers could have affected the accuracy of spatial analyses. Small numbers generally reduce the likelihood of identifying statistically significant associations, but despite this, we found significant results using robust tests and geospatial analyses. Third, participants in the serum bank included adults of all ages, but did not include children or adolescents. Results of TAS conducted at about the same time provided antigen prevalence data in 6–7 year old children, but data on antigen and antibody levels in children of all ages would help improve understanding of the application of diagnostic tests for post-MDA surveillance. Finally, participants were geo-located to place of residence, but LF infection could occur elsewhere, particularly in the presence of efficient day-biting vectors. If vectors were predominantly night-biting, clustering of infections could potentially be even more readily defined around household locations. This study provides preliminary results to support the importance of further research designed to specifically focus on improving understanding of disease transmission at the last stages of elimination when prevalence is very low; answering operational questions in LF elimination programs, especially the role of migration; developing tools to enhance the effectiveness of post-MDA surveillance and monitoring; and providing an evidence base for elimination strategies and targets. Follow up studies are being conducted in American Samoa to determine whether hotspots truly exist, develop models to quantify the significance of migrants in LF elimination, and explore the use of molecular xenomonitoring in the Pacific Island setting. The study also highlights the importance of assessing locally relevant risks for infection, which could vary significantly between places depending on cultural, societal, and environmental factors, as well as filarial species and mosquito vectors. The approach and results of this study are specifically relevant for the Samoan islands, but could also provide insight into LF transmission in other LF-endemic areas, and be pertinent to other Pacific Islands with similar vectors, lifestyle, culture, climate, environmental conditions, and migration patterns. Supporting Information Checklist S1 STROBE Checklist for observational studies. (DOC) Click here for additional data file.
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Molecular Xenomonitoring Using Mosquitoes to Map Lymphatic Filariasis after Mass Drug Administration in American Samoa
Mark A. Schmaedick, Amanda L. Koppel, Nils Pilotte … (2014)
Introduction Lymphatic filariasis (LF) caused by the diurnally subperiodic form of the mosquito-borne parasitic nematode Wuchereria bancrofti is endemic to American Samoa, a United States territory composed of the easternmost islands of the Samoan archipelago (Figure 1). LF is also endemic in the archipelago's western islands which comprise the independent nation of Samoa [1], [2]. In the Samoan archipelago, Aedes (Stegomyia) polynesiensis Marks and Aedes (Finlaya) samoanus (Grünberg) are the major vectors of LF [3], [4]. Natural infections have also been detected in Aedes (Stegomyia) upolensis Marks and Aedes (Finlaya) tutuilae Ramalingam and Belkin, but these species are not considered to be as epidemiologically important due to their relatively low abundances in human landing catches [5], [6]. Aedes polynesiensis is widespread in the South Pacific, inhabiting islands south of the equator from Tuvalu and Fiji eastward to the Marquesas and Pitcairn Island [7]. It breeds in a wide range of natural and artificial containers [8], [9], [10] and feeds primarily in the daytime [11], [6]. Aedes polynesiensis is believed to be a weak disperser, rarely traveling as far as 92 m [12], [11]. Aedes samoanus occurs only in American Samoa and Samoa, breeding primarily in water collecting in leaf axils of the forest climber Freycinetia reineckei in American Samoa, and in axils of F. reineckei and Pandanus spp. in Samoa [5], [13]. Aedes samoanus females feed at night [5], [6]. The dispersal capabilities of Ae. samoanus have not been investigated. Other mosquito species abundant in Samoa and American Samoa are Culex (Culex) quinquefasciatus Say, Culex (Culex) annulirostris Skuse, Culex (Culex) sitiens Wiedemann, Aedes (Stegomyia) aegypti (L.), Aedes (Finlaya) oceanicus Belkin, and Aedes (Aedimorphus) nocturnus (Theobald) [7], [14]; however, none of these species have been found to play a significant role in LF transmission in the Samoan islands [12], [5], [14], [15]. 10.1371/journal.pntd.0003087.g001 Figure 1 The study was conducted in American Samoa which is composed of the eastern islands of the Samoan Archipelago. (Swains Island and Rose Atoll not shown.) During the years 2000–2010, the American Samoa Department of Health undertook a campaign to eliminate LF through annual mass drug administration (MDA) using diethylcarbamazine and albendazole [16]. The campaign ran in conjunction with similar campaigns in other South Pacific countries and territories, including neighboring Samoa, under the Pacific Programme to Eliminate Lymphatic Filariasis [17]. Population coverage by MDA was 24–52% in the first three years and improved to 65–71% in the subsequent four years [16]. Infection prevalence before, during, and after MDA has been monitored primarily by an immunochromatographic (ICT) test, which detects circulating filarial antigen (CFA) released into the blood by adult W. bancrofti [18]. The testing was done across all age groups. Prevalence of CFA in a baseline survey in 1999 was 16.5% [19], and subsequent testing in four sentinel villages found CFA declining from 11.5% in 2001 to 0.95% in 2006 [20]. Prevalences in an additional four villages surveyed in 2006 were higher, ranging from 2.1% to 4.6% [20], [21], and a territory-wide serosurvey in 2007 found 2.3% CFA prevalence. Additional MDA activities took place during 2007–2010, but the level of MDA coverage during those years is unclear. Testing the human population for CFA can provide information about prevalence of W. bancrofti infection, and antibody testing can provide a sensitive indicator of levels of exposure to W. bancrofti [22]. In addition, one can sample the human population indirectly by sampling mosquito species known to feed on human blood. Molecular xenomonitoring (MX), the detection of parasite DNA or RNA in mosquitoes using the polymerase chain reaction (PCR), allows the testing of pools of mosquitoes and can be more efficient and more sensitive than dissections, especially when large numbers must be examined to detect evidence of W. bancrofti when prevalence is low [23], [24], [25]. The ability to test large numbers of mosquitoes also depends on the availability of efficient collection methods for local species. The development of the BG Sentinel trapping system has, for the first time, made trapping large numbers of Ae. polynesiensis over large geographic areas feasible in American Samoa [26]. It is important to recognize that MX cannot provide a direct measurement of ongoing transmission unless the PCR method used specifically targets the infective third stage larva (L3) of W. bancrofti [27]. Instead, it provides an indirect assessment of human infection. Fischer et al. [28] and Erickson et al. [29], studying Brugia malayi, found that parasite DNA could be detected in both vector and non-vector mosquito species long after ingestion of microfilariae, even when those microfilariae did not survive in the mosquito. Workers wishing to assess transmission directly still need to measure vector biting rates and use dissection or reverse transcriptase-PCR to specifically detect L3 in the vector mosquitoes. In 2006, a pilot study evaluated the use of MX and traditional xenomonitoring concurrently with serological testing of humans in three villages in American Samoa. Trapped mosquitoes were examined by PCR or dissection, and village residents were tested for CFA and antifilarial antibody [21]. (The Bm14 antibody test used is an indicator of infection or exposure and may give a positive result prior to development of patent infections [30], [31], [32].) The serological tests found 3.7–4.6% of residents of the three villages were positive for CFA and 12.5–14.9% positive for antifilarial IgG4 antibody to the recombinant Bm14 antigen [21]. Dissection of approximately half of the Ae. polynesiensis catch found infection prevalences of 0–0.23%, while PCR testing of the remainder gave estimates of 0.52–0.90% prevalence [25]. In summary, mosquito dissection proved relatively insensitive, while antigen and antibody testing and MX all gave similar results. All three indicated LF infections occurring at low levels in all three villages. In 2011, a territory-wide transmission assessment survey (TAS) was conducted according to the World Health Organization [18] guidelines for monitoring and assessment of MDA in LF elimination programs [33]. The TAS consisted of antigen and antibody testing of 6–7 year olds in the territory's elementary schools. Overall CFA prevalence in the survey was below the threshold at which the guidelines would recommend additional MDA [33]. The TAS results provide guidance to determine whether or not to restart MDA at the territory level. But if LF infection is uneven across subpopulations or across geographic areas, then some groups or areas may require additional MDA even though aggregate LF prevalence is below a level deemed necessary to sustain the infection in the population. The limited dispersal ability of the major LF vector Ae. polynesiensis and its susceptibility to the BG Sentinel trap suggested that MX using mosquitoes trapped from throughout American Samoa may be a useful adjunct to the school-based TAS for detecting areas of possible continuing LF transmission. We here describe the results of PCR testing for W. bancrofti DNA in mosquitoes captured from villages throughout American Samoa. Results of the TAS will be described elsewhere. Methods Study area The mosquito collections were conducted on the islands of Tutuila, Aunu'u, Ofu, Olosega, and Ta'u (Figure 1). These are the only islands in American Samoa that have been continuously inhabited in recent years. The five islands are located between 14° 9′ and 14° 22′S and 169° 25′ and 170° 51′W. The largest, Tutuila Island, comprises 68% of the territory's 199 km2 total land area and contains approximately 97% of its total population of 55,519 [34]. Aunu'u Island had 436 residents by the 2010 census [34]. Many of Aunu'u's residents commute by boat to nearby Tutuila for work or school. The more distant Ofu, Olosega, and Ta'u Islands, which together comprise the Manu'a group, had 176, 177, and 790 inhabitants, respectively, according to the 2010 census [34]. Much of the territory's land is forested, steep, and rugged, with about half the area having 70% or greater slope and over half covered by rainforest [35], [36]. Human settlement is mostly along the coastlines, with the exception of the Tafuna-Leone plains and the Aoloau-Aasu uplands areas in the southwest portion of Tutuila Island. Trapping was conducted within residential areas of all major villages of the four smaller islands and 34 randomly selected villages out of the 67 on Tutuila. These randomly selected villages contained approximately 57% of Tutuila's population and 52% of its land area [34]. In some cases, 2–4 adjacent selected villages on Tutuila Island were combined and treated as single village areas for trapping and analysis. In one case, leaders in a selected village were not available to assist during the trapping time, so a nearby village was used instead. In the TAS, only two children were identified as CFA positive [33]. These children both attended a school located in a village on Tutuila that was not among those randomly selected for mosquito trapping. As a result, additional trapping was conducted in and around the school grounds using the same procedures as in the selected villages. Because the school was not located in one of the selected villages, data from these traps were not included in the larger data set but are reported separately. Mosquito collections In each village (or group of contiguous smaller villages) ten BG-Sentinel traps baited with BG Lure (Biogents AG, Regensburg, Germany) were placed throughout the village and operated for approximately 24 or 48 h, depending on catch rate. Exceptions occurred in the combined area of Alega and Avaio villages where only six traps were placed, and Amaua village where four traps were placed. Traps were removed after 24 h if it appeared that the catch had reached a target of 200 Ae. polynesiensis females. The traps were placed on the ground in locations protected from direct sunlight and rain, often under eaves of houses or outbuildings such as unused open-sided traditional cookhouses. Placements were determined in consultation with village leaders and individual families while attempting to spread the traps evenly throughout the residential area of each village. Although village lands may be extensive, often spanning areas from the coast to the interior ridgetops, in most cases the residential areas are largely confined to lands near the coast or near major roads. Mosquitoes were removed from the traps twice per day at approximately 10:00 am and 6:30 pm following peak feeding times of the major vector Ae. polynesiensis [11], [6]. In one village (Vatia) the second trap check scheduled for 10:00 am had to be postponed to 4:30 pm due to a tsunami warning and village evacuation, so the Vatia traps ran for approximately 30.5 h rather than 24 or 48 h. Mosquitoes collected during the first day of trapping in Taputimu and Vailoatai villages were lost, so only the second day's catch was used from these two villages. In the laboratory, the mosquitoes were anaesthetized with carbon dioxide and identified on a tray resting on an ice pack under a stereomicroscope using the taxonomic keys of Ramalingam [14] and Huang [37]. The few mosquitoes that could not be identified due to damage or that were missing substantial parts of the head, thorax, or abdomen were not included in the analysis. Female mosquitoes were placed in pools of ≤20 (range 1–20) into microcentrifuge tubes separated by species, trap, location, and collection date and time. After freezing to ensure all mosquitoes were dead, the tubes were left open in an oven to dry at 75°C overnight, then closed and stored in a sealed plastic box with dessicant at 23°C until they were shipped for PCR analysis at Smith College, Massachusetts, USA. Trapping was conducted February 21–April 8, 2011 on Tutuila and Aunu'u and June 7–16, 2011 on the more remote Ofu, Olosega, and Ta'u islands. DNA extraction from mosquitoes DNA extraction was done using a modification of the commercial DNeasy kit protocol (Qiagen, Hilden, Germany) and methods adapted from Fischer et al. [38] and Laney et al. [27]. Briefly, a 4.5 mm zinc-plated bead and 180 µl phosphate-buffered saline (pH 7.2) were placed in each round-bottom 2-ml Eppendorf tube (Eppendorf North America, Hauppauge, NY, USA) containing up to 20 dried mosquitoes. The tube was capped and vortexed at high speed in a horizontal position for 15 min and again for an additional 5–10 min if necessary for complete maceration. The tube was centrifuged briefly before adding 20 µl proteinase K and 200 µl of Buffer AL. The mixture was vortexed gently for 3 sec, then incubated at 70°C for 10 min. After brief centrifugation, another 20 µl proteinase K was added and mixed with brief gentle vortexing before incubating for 1 h at 56°C. The mixture was then centrifuged at high speed, and the supernatant from each tube was added to a 1.5-ml Eppendorf tube containing 200 µl of 95–98% ethanol and mixed using the pipet. The entire mixture from each tube was then applied to a DNeasy kit column and centrifuged at 8,000 g for 1 min. The column was transferred to another 1.5-ml tube, and the DNA was washed twice with 500 µl of Buffer AW1, with each wash followed by a 1 min centrifugation at 8,000 g. The column was then transferred to another 1.5 ml tube, 500 µl Buffer AW2 was added, and the tube spun at 8,000 g for 3 min. The waste solution was discarded, and the column spun an additional 3 min at maximum speed to dry the column. The column was then transferred to a 1.5-ml microfuge tube and the DNA was eluted twice with 125 µl of Buffer AE followed by 2 min centrifugation, first at 8,000 g, and then at 10,000 g. The samples were held at 4°C until the qPCR was completed, then stored at −20°C. qPCR detection of W. bancrofti DNA Real-time PCR was done using a 7300 Real-Time PCR System (Applied Biosystems, Foster City, California, USA). Each reaction contained 1 µl of template DNA and 24 µl of qPCR master mix including 10 µM each of forward and reverse primers and taqman probe. The primers were designed to amplify a fragment of the “long dispersed repeat” of W. bancrofti (LDR; GenBank accession no. AY297458) [39]. The sequence of the primers and probe were as follows [39]: forward primer (Wb-LDR1) 5′-ATTTTGATCATCTGGGAACGTTAATA-3′, reverse primer (Wb-LDR2) 5′-CGACTGTCTAATCCATTCAGAGTGA-3′, and probe (Wb-LDR) 6FAM-ATCTGCCCATAGAAATAACTACGGTGGATCTCTG-TAMRA. The cycling conditions were 50°C for 2 min and 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 1 min. Four different controls were used: a negative extract control consisting of a DNA extract from 20 uninfected mosquitoes; positive PCR controls using 1 ng, 100 pg, or 10 pg DNA of W. bancrofti; a negative PCR control using the same ddH2O as used in the master mix; and a PCR inhibitor control comprised of 5 pg of W. bancrofti DNA added to 10 µl of negative extract control. The negative extract and PCR inhibitor controls were run periodically throughout the course of sample processing. Positive and negative PCR controls were run with every sample batch. Samples were run in duplicate, and qPCR results with Ct≥39 were checked by running two additional qPCR reactions on the same extract template. If the sample was positive at least once more, and all controls were as expected, then the sample was considered positive. If both verification reactions were negative, then the sample was considered negative. Statistical analysis Geographic coordinates were recorded for each trap location using a Trimble GeoXT 2005 Series Pocket PC handheld global positioning system (GPS) device (Trimble Navigation Ltd., Sunnyvale, California, USA). For 16 out of the 310 trap locations, the Trimble device was unable to record the positions due to topography, tree cover, weather conditions or satellite positions at the time, so a Garmin GPSmap 60CSx (Garmin International, Inc., Olathe, Kansas, USA) device was used instead for those locations. The positions were mapped using ArcGIS 10.1 software (Environmental Services Research Incorporated, Redlands, California, USA), and village boundaries were obtained from the 2010 U.S. Census Bureau's TIGER/Line “Places” shapefile for American Samoa [40]. In a few cases, traps were placed in locations which were inside the village boundaries as indicated by village leaders, but which fell outside the boundaries on the Census Bureau map. Point estimates and 95% confidence intervals for the percentage of mosquitoes containing W. bancrofti DNA were calculated for each mosquito species for the overall sample and for the most abundant species, Ae. polynesiensis, within each of the villages. The program PoolScreen (version 2.0.3) was used to calculate maximum likelihood point estimates of prevalence, and confidence intervals were determined by the likelihood ratio method [41]. Results A total of 22,014 female mosquitoes were collected and sorted into 2,629 pools of ≤20 individuals each for PCR testing. PCR results for the most abundant species in the traps are shown in Table 1, and relative abundances of the three most numerous species having >1 positive pool are shown in Figure 2. Members of the Aedes (Finlaya) group of species occurring in American Samoa include Ae. oceanicus, Ae. samoanus, and Ae. tutuilae. They were difficult to distinguish due to their morphological similarity and the loss of scales in the traps, so were combined for PCR testing and analysis. Only one out of the 267 pools of Ae. (Finlaya) spp. was positive by PCR. Other species captured in lower numbers were Ae. nocturnus, Cx. annulirostris, and Cx sitiens. Wuchereria bancrofti DNA was not detected in these species (n = 68 pools). Aedes polynesiensis, Cx. quinquefasciatus, Ae. aegypti, and Ae. (Finlaya) group species all produced positive pools (Table 1). Estimated prevalence was highest in Ae. aegypti, although the 95% confidence interval for prevalence in this species overlapped with that for Ae. polynesiensis (Table 1). 10.1371/journal.pntd.0003087.g002 Figure 2 Catch of the three most numerous mosquito species which had >1 positive pool overall as a percentage of those three species' combined total in each village. The number above each bar is the combined total number captured of the three species. Ten traps were operated for 1–2 days in each village, except in Alega-Avaio and Amaua in which six and four traps were used, respectively. “Satala-Leloaloa Area” includes Satala, Anua, Atuu, and Leloaloa villages and “Leone Area” includes Auma, Leone, and Puapua villages. 10.1371/journal.pntd.0003087.t001 Table 1 Detection of W. bancrofti DNA in American Samoa mosquitoes by PCR. Species Females Pools1 Positive Pools Prevalence2 95% Confidence Interval3 Ae. polynesiensis 15,215 1,250 42 0.28% 0.20, 0.39% Cx. quinquefasciatus 4,413 585 5 0.11% 0.034, 0.27% Ae. aegypti 887 360 8 0.92% 0.37, 1.8% Ae. (Finlaya) spp.4 1,084 267 1 0.092% 0.0028, 0.48% Ae. upolensis 262 91 0 0% 0, 0.73% 1 Pools were comprised of ≤20 females. 2 Prevalence estimate by maximum likelihood. 3 Confidence intervals by likelihood ratio method. (One-sided when prevalence estimate is 0.). 4 May include Ae. oceanicus, Ae. samoanus, Ae. tutuilae. There were no positive pools of any species collected from the five major villages of the Manu'a Islands of Ofu, Olosega, and Ta'u. For Ae. polynesiensis, the most abundant species captured there, the upper limit for the one-sided 95% confidence interval estimate of prevalence across all three Manu'a Islands was 0.066% (n = 212 pools). On Tutuila and Aunu'u islands, 38 out of 260 total trap placements produced at least one positive pool. Positive mosquitoes were detected in the majority (16 out of 27) of the village areas sampled on these two islands. Areas producing positive mosquitoes on Tutuila Island were widely distributed throughout the island (Figure 3). Aedes polynesiensis was by far the most abundant mosquito species trapped overall, and prevalence estimates for Ae. polynesiensis from the villages are depicted in Figure 4. There was no evidence of a positive relationship between prevalence estimate and number of Ae. polynesiensis females or mean pool size (Figure 5), suggesting that the number of mosquitoes collected affected the breadth of confidence intervals as evident in Figure 4, but not prevalence point estimates. Nine traps which produced no positive pools of Ae. polynesiensis did produce positive pools of Cx. quinquefasciatus (5 traps), Ae. aegypti (6 traps), or Ae. (Finlaya) spp. (1 trap). At the village level, two villages with no positive Ae. polynesiensis catches had positive Cx. quinquefasciatus (Onenoa and Vailoatai) or Ae. aegypti (Vailoatai) pools. 10.1371/journal.pntd.0003087.g003 Figure 3 Mosquito trapping locations in villages on Tutuila and Aunu'u Islands, American Samoa. Filled circles represent traps which captured mosquitoes in which PCR testing detected W. bancrofti DNA. 10.1371/journal.pntd.0003087.g004 Figure 4 Estimated prevalence of Ae. polynesiensis females containing W. bancrofti DNA from trap catch in each village. Prevalences were estimated by maximum likelihood and confidence intervals by the likelihood ratio method [41]. The total number of Ae. polynesiensis is shown above each bar. “Satala-Leloaloa Area” includes Satala, Anua, Atuu, and Leloaloa villages and “Leone Area” includes Auma, Leone, and Puapua villages. 10.1371/journal.pntd.0003087.g005 Figure 5 Estimated prevalence of Ae. polynesiensis containing W. bancrofti DNA for each village versus total Ae. polynesiensis females tested (left) and mean pool size (right). Of the ten traps placed in and around the grounds of the elementary school attended by two children who tested positive for CFA in the TAS, five of the traps produced positive mosquito pools. Two of these traps had positive Ae. polynesiensis, two had positive Ae. aegypti, and one trap had both positive Ae. polynesiensis and positive Ae. aegypti. Prevalence estimates were 2.8% with a 95% confidence interval of (0.55–8.0%) (n = 107 females) for Ae. polynesiensis and 8.6% with a 95% confidence interval of (2.2–20.8%) (n = 55 females) for Ae. aegypti. Pools of the 84 Cx. quinquefasciatus and four Ae. (Finlaya) spp. females collected around the school were all negative. Discussion Molecular xenomonitoring of mosquitoes trapped from villages throughout American Samoa found evidence of low but widespread occurrence of W. bancrofti infections on Tutuila and Aunu'u islands which together are home to 98% of the territory's population. The study did not find evidence of infections on Ofu, Olosega, and Ta'u islands. The ability to detect very low W. bancrofti prevalences was limited, however, due to the low numbers of mosquitoes collected in many of the villages. This lack of sensitivity was reflected in the wide confidence intervals on prevalence estimates for many of the villages (Figure 4). Mosquito collection efforts and the number of pools that could be tested were limited by the resources available for the project. The type of mosquito collection method used may also have affected the sensitivity of xenomonitoring [42]. Female mosquitoes can contain W. bancrofti DNA only after they have completed at least one blood meal. The BG Sentinel traps used in this study are designed to capture host-seeking females, many of which may be nullipars seeking their first blood meal. Collections with gravid traps targeting ovipositing females [43], [44] can help ensure that a larger portion of the mosquitoes captured will have had at least one blood meal, but currently available gravid traps catch few Ae. polynesiensis (MAS unpublished data). For endophagic species, collection of resting mosquitoes in houses can also produce larger proportions of previously blood-fed females [45], [24]. Gravid traps and collection of resting mosquitoes in houses have been effective for Cx. quinquefasciatus xenomonitoring in areas where that species is the major LF vector. Culex quinquefasciatus does not appear to be an important LF vector in the Samoan islands [15], but it was the second most abundant species in our BG Sentinel traps and an estimated 0.11% contained W. bancrofti DNA. In villages where this species is abundant (Figure 2), use of gravid traps targeting Cx. quinquefasciatus in place of, or in addition to, BG Sentinel traps targeting Ae. polynesiensis might improve xenomonitoring efficiency by increasing both the capture rate and the proportion of the catch consisting of previously blood-fed individuals. This approach remains to be tested in American Samoa. The large proportion of traps which produced positive mosquitoes in the area of the school at which two children tested positive for CFA indicated possible ongoing transmission there. Examination of blood smears and PCR testing following the ICT failed to find evidence of microfilaremia in either child [33], suggesting they may not have been the sources of the W. bancrofti detected in the trapped mosquitoes. The two children came from different villages, and each lived approximately 1 km from the school. Because Ae. polynesiensis feeding times overlap with times when students are at school and at home [11], [6], transmission by this vector could occur in either setting. According to the 2010 census [46], approximately 21,196 of American Samoa's population attended school (pre-kindergarten – college) and 12,070 of the territory's 16,482 working population traveled more than 15 min from home to work. The mobility of the human population and the daytime feeding habits of Ae. polynesiensis suggest that W. bancrofti transmission likely occurs not only in residential areas of villages, but also at other locations, such as workplaces, bus stops, and schools. With the exception of the single school, this study did not sample these other potentially important locations. There were several similarities between the results of this study and the only other study to use MX in American Samoa [25]. Only one of the three villages sampled by Chambers et al. [25] was sampled again in the current study. Prevalence of W. bancrofti DNA in Ae. polynesiensis for Afao Village was estimated to be 0.82% in the 2006 study and 0.47% in the current one. The wide confidence intervals obtained in the two studies (Figure 4 here and Figure 4 of Chambers et al. [25]) indicate a much larger sample size would be required to evaluate the significance of a difference of this magnitude. The estimates for prevalence of W. bancrofti DNA in Ae. aegypti were higher than those for Ae. polynesiensis both in this study and in the 2006 study, although the 95% confidence intervals for the two species overlapped broadly in both cases. The high propensity of Ae. aegypti for feeding on human hosts is well documented (e.g., [47], [48]) and could result in a higher frequency of feeding on microfilaraemic individuals than would be the case for mosquito species with less affinity for humans. Aedes polynesiensis is known to feed on birds and mammals other than humans, but little is known about the frequency with which it feeds on the different hosts [11], [49], [5]. No W. bancrofti DNA was detected in the 262 Ae. upolensis collected from throughout the territory in the current study. A similar number of Ae. upolensis collected from three villages in the earlier study by Chambers et al. [25] produced one positive pool. The low incidence of W. bancrofti DNA in this species and the low numbers collected in villages support the suggestion that it is likely a minor vector of LF in American Samoa [14]. Positive PCR results for species not considered to be important LF vectors revealed evidence of W. bancrofti in some locations where results from Ae. polynesiensis collections did not. Only two of the six traps with positive pools of Ae. aegypti and only one of the five traps with positive Cx. quinquefasciatus also produced positive Ae. polynesiensis. At the village level, two villages (Onenoa and Vailoatai) produced positive Ae. aegypti or Cx. quinquefasciatus pools from multiple traps, but no positive Ae. polynesiensis pools. The discrepancies are likely due to behavioral differences and variation in relative abundance of the three species across trapping sites. Together they suggest that sampling multiple species—including non-vectors—with different feeding behaviors may provide a more complete assessment of W. bancrofti infections than sampling only a single important vector species. The three species exhibit important differences in feeding behavior [50], [7], [5]. Aedes aegypti, like Ae. polynesiensis, feeds primarily during the day, but is more endophilic than Ae. polynesiensis. Culex quinquefasciatus feeds mainly at night and feeds and rests both inside and outside houses. Differences in range of movement could also result in different exposures to W. bancrofti. Aedes aegypti and Ae. polynesiensis are believed to have limited dispersal ability [12], [11], [51], but Cx. quinquefasciatus may move longer distances [52], [53], [54], [55]. Finally, if multiple species are included in xenomonitoring, the reduced sensitivity resulting from a low catch rate for Ae. polynesiensis in some villages, as occurred in Vailoatai, might be partially compensated for by higher catches of other species (Figure 2). Xenomonitoring using multiple species, including non-vectors, is a departure from the approach of monitoring only a single vector species and comparing estimated prevalence in that species to model-based or empirical thresholds to assess progress in LF elimination programs [24], [42]. The latter approach is complicated in the Samoan islands due to the presence of an important secondary vector, Ae. samoanus, the lack of an effective trap for that species, and the difficulty in distinguishing it morphologically from a closely related non-vector species. Another complication is the spatial heterogeneity of LF prevalence and transmission [56], [57] which suggests that even when aggregate prevalence in mosquitoes captured over a large area may fall below a target threshold, some local prevalences may exceed it. In addition, earlier xenomonitoring efforts have revealed that W. bancrofti prevalence in Ae. polynesiensis collected at a single location can vary substantially over the course of a year or even between collection periods separated by as few as ten days [58], [25]. Together, these factors, along with the difficulty of collecting large numbers of vectors and the resulting wide confidence interval estimates, suggest that xenomonitoring currently has limited usefulness for quantifying the progress of LF elimination in American Samoa. Instead its operational value may lie in helping to map areas where human infections exist without the invasiveness of human blood collection. Even such presence-absence mapping, however, requires trapping sufficient mosquitoes at each location to provide a high probability of detecting positive mosquitoes in the locations where they occur—something that may be difficult to achieve in areas where prevalence and catch rates are low. In summary, the detection of W. bancrofti DNA in mosquitoes at many locations on Tutuila and Aunu'u islands suggests widespread occurrence of human infections on these islands, while the low overall prevalence estimate suggests a similarly low overall prevalence of human infections. But caution is required in making inferences about prevalence at more local levels due to small sample sizes in many villages. Currently xenomonitoring has little value for programmatic decision-making in American Samoa beyond its ability to identify areas where human infections may exist. Increasing its relevance to MDA decision-making will require additional research to develop more efficient mosquito collection methods and to improve understanding of the relationship between prevalence of W. bancrofti DNA in mosquitoes, infection rates in humans, and resulting transmission rates relative to critical thresholds.
Author and article information
Contributors
Patricia M. Graves:
ORCID: http://orcid.org/0000-0002-5215-3901
patricia.graves@jcu.edu.au
Sarah Sheridan: sarah.sheridan@unsw.edu.au
Saipale Fuimaono: sfuima6@yahoo.com
Colleen L. Lau: colleen.lau@anu.edu.au
Journal
Journal ID (nlm-ta): Parasit Vectors
Journal ID (iso-abbrev): Parasit Vectors
Title:
Parasites & Vectors
Publisher:
BioMed Central
(London
)
ISSN
(Electronic):
1756-3305
Publication date
(Electronic):
12
March
2020
Publication date PMC-release: 12
March
2020
Publication date Collection: 2020
Volume: 13
Electronic Location Identifier: 125
Affiliations
[1
]GRID grid.1011.1, ISNI 0000 0004 0474 1797, College of Public Health, Medical and Veterinary Sciences, , James Cook University, ; Cairns, QLD Australia
[2
]GRID grid.1011.1, ISNI 0000 0004 0474 1797, Australian Institute of Tropical Health and Medicine, , James Cook University, ; Cairns, QLD Australia
Author information
Patricia M. Graves
http://orcid.org/0000-0002-5215-3901
Article
Publisher ID: 3996DOI: 10.1186/s13071-020-3996-4
PMC ID: 7068921
PubMed ID: 32164780
SO-VID: 9b521e6f-95f0-4f18-a6a6-44b0027bb626
Copyright © © The Author(s) 2020
License:
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
History
Date
received
: 28
September
2019
Date
accepted
: 26
February
2020
Funding
Funded by: Australian Institute of Tropical Health and Medicine
Award ID: 13122014
Award Recipient
:
Patricia M. Graves
Funded by: University of Queensland
Award ID: 2127835
Award Recipient
:
Colleen L. Lau
Funded by: National Health and Medical Research Council, Australia
Award ID: 1109035
Award Recipient
:
Colleen L. Lau
Categories
Subject:
Research
Custom metadata
issue-copyright-statement © The Author(s) 2020
ScienceOpen disciplines: Parasitology
Data availability:
ScienceOpen disciplines: Parasitology