The rationale and cost-effectiveness of a confirmatory mapping tool for lymphatic filariasis: Examples from Ethiopia and Tanzania

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Abstract

Endemicity mapping is required to determining whether a district requires mass drug administration (MDA). Current guidelines for mapping LF require that two sites be selected per district and within each site a convenience sample of 100 adults be tested for antigenemia or microfilaremia. One or more confirmed positive tests in either site is interpreted as an indicator of potential transmission, prompting MDA at the district-level. While this mapping strategy has worked well in high-prevalence settings, imperfect diagnostics and the transmission potential of a single positive adult have raised concerns about the strategy’s use in low-prevalence settings. In response to these limitations, a statistically rigorous confirmatory mapping strategy was designed as a complement to the current strategy when LF endemicity is uncertain. Under the new strategy, schools are selected by either systematic or cluster sampling, depending on population size, and within each selected school, children 9–14 years are sampled systematically. All selected children are tested and the number of positive results is compared against a critical value to determine, with known probabilities of error, whether the average prevalence of LF infection is likely below a threshold of 2%. This confirmatory mapping strategy was applied to 45 districts in Ethiopia and 10 in Tanzania, where initial mapping results were considered uncertain. In 42 Ethiopian districts, and all 10 of the Tanzanian districts, the number of antigenemic children was below the critical cutoff, suggesting that these districts do not require MDA. Only three Ethiopian districts exceeded the critical cutoff of positive results. Whereas the current World Health Organization guidelines would have recommended MDA in all 55 districts, the present results suggest that only three of these districts requires MDA. By avoiding unnecessary MDA in 52 districts, the confirmatory mapping strategy is estimated to have saved a total of $9,293,219.

Author summary

Mapping is used by lymphatic filariasis (LF) elimination programs to determine if mass drug administration (MDA) is required. The current mapping approach, designed to be simple and practical, has worked well in high-prevalence settings but concerns about its reliability in low-prevalence settings have been raised. To address these concerns, a confirmatory mapping strategy was developed that utilizes probability-based sampling of school attending children to determine if the prevalence of LF antigenemia is below a 2% threshold. The confirmatory mapping strategy was implemented in 45 districts in Ethiopia and 10 in Tanzania where the need for MDA was uncertain. In 52 of the 55 districts, the number of LF antigen-positive children identified by the confirmatory mapping strategy was below the predetermined threshold and MDA was deemed unnecessary, while in three districts the number of positive children exceeded the threshold, suggesting that MDA is required. The use of this mapping strategy, to confirm whether MDA is required, is estimated to have saved the Ethiopian and Tanzanian programs $9,293,219 by avoiding unnecessary MDA in 52 districts.

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A Multicenter Evaluation of Diagnostic Tools to Define Endpoints for Programs to Eliminate Bancroftian Filariasis

Katherine Gass, Madsen Beau de Rochars, Daniel Boakye … (2012)

Introduction In 2000 the Global Programme to Eliminate Lymphatic Filariasis (GPELF) was launched, providing antifilarial drugs to millions of people through mass drug administration (MDA) programs. During the GPELF's first nine years over 2.6 billion treatments of antifilarial drugs were given to people in 48 countries through MDA programs [1]. The success of the GPELF has led to dramatic reductions of both microfilaremia and antigenemia levels in countries that have completed multiple rounds of MDA [2]; the challenge now is to determine when it is most appropriate to stop MDA [3]. The decision to stop MDA is complicated and a variety of tools have been suggested to guide the decision [4]. The first step is to define the parameter(s) that will be measured and the best diagnostic tool for assessing it. At least seven diagnostic tests are currently available for detecting indicators of LF exposure and infection. Selection of the best diagnostic test for use in stopping-MDA decisions requires consideration of each test's accuracy, technical requirements, programmatic feasibility and reliability [5], as well as confidence in test performance, especially since there is no single gold standard test for LF (see Discussion). Next, following the selection of a preferred diagnostic tool for defining the end-point of MDA, the question of how best to sample the population must be resolved. In response to these and other challenges, this study was planned to evaluate diagnostic tools to assess MDA program success by standardizing the tools now available, comparing their effectiveness in demonstrating the interruption of LF transmission, and selecting the most effective for deciding when MDA can be suspended [6]. A large multi-country study was conducted in 2007–2008 to compare the effectiveness of seven available diagnostic tests for detecting evidence of Wuchereria bancrofti infection or transmission following multiple rounds of MDA, in settings where infection prevalence was likely to be low. The goal of the study was to select the best diagnostic tool(s) that would allow for definition of program end-points that will maximize the likelihood that LF transmission has been interrupted. Such a tool(s) would be the cornerstone of programmatic decision-making. Methods Site Selection Studies were performed in French Polynesia, Ghana, Haiti, Sri Lanka, Zanzibar (United Republic of Tanzania) and Tuvalu, representing a broad diversity of settings in which LF is present. The study sites were believed to have low residual microfilaremia prevalence rates in the range of 0.5–2% following at least five rounds of MDA [7]. Participant Selection One community survey and one school survey were performed in each country. Community surveys sampled residents of selected households between the ages of 3 and 80. School surveys were performed in primary schools that serve children in the same villages as those selected for the community surveys. First and fourth year students (approximately 6 and 10 years old, respectively) were selected for inclusion in the school surveys. Children from the school survey were excluded if they had already been included in the community survey. Since the primary objective of this first phase of research was not to assess program end-points in the specific study sites, but rather to compare test effectiveness in the same groups of individuals late in program activities, convenience sampling was used to select both communities and schools. However, selection of participants within each site was conducted randomly whenever possible. Standard Operating Procedures A workshop with all the investigators was held in Atlanta, GA to establish the study protocols and Standard Operating Procedures (SOP) [7] prior to the start of the study. For each country, a team of experienced investigators traveled to the study site to train the local team on data collection methods and laboratory procedures in accordance with the SOP. Demographic Data Collection All information on the participants was collected using handheld personal digital assistants (PDA) (Dell Axim X50 or X51) that eliminated the need for paper records. Unique identifiers were printed on labels which provided visual identification of the number as well as barcodes acquired by a Bluetooth® scanner (CHS 7p v.1, Socket Mobile) to facilitate specimen management. The PDAs were equipped with GPS devices (GlobalSat, City of Industry, CA, USA) and GPS coordinates were captured at each house and school visited. A questionnaire was administered to collect demographic information that included age, gender, bednet use, self-reported filarial disease status and compliance with the most recent MDA. Multiple teams could register households at the same time, and data collected could be synchronized in the field to create one master database. Each night all data were uploaded to a field laptop and a backup of the data was created on an external drive. Data were electronically transmitted in the form of encrypted excel files to the central analysis database at the Task Force for Global Health (Atlanta, GA). Blood and Urine Collection All field sample collections and field and laboratory tests were conducted according to the SOP. Blood and urine samples were collected 6–24 months following the last MDA. The periodicity of W. bancrofti required that blood collection in the community surveys be performed during the peak hours of microfilaremia (during daytime hours for French Polynesia and Tuvalu and between 10 pm and 2 am in the remaining countries where the parasite was nocturnally periodic). In the areas with nocturnal periodicity, collection teams had the option of registering households during the day or night. Teams that registered households during the day later returned in the evening to take the blood samples. Approximately 0.3–0.4 ml of blood was collected by finger prick from each participant into an EDTA coated blood collection tube and stored in coolers overnight before assays were performed the next day in the field laboratory. Up to six diagnostic assays were performed (with the exception of Ghana, which conducted up to five assays). Three of the assays were conducted in the field laboratory: blood smear (MF), ICT (Immunochromatographic test, Binax, Scarborough, ME), and the PanLF Rapid (MBDr, Selangor, Malaysia). The one exception to this was French Polynesia where the blood smear, ICT and PanLF assays were processed at the Institut Louis Malardé. The Bm14 antibody detection and Og4C3 antigen detection assays were conducted in five reference laboratories (see Table 1) and the PCR (Polymerase Chain Reaction) tests were conducted at Smith College in Northampton, MA, USA. 10.1371/journal.pntd.0001479.t001 Table 1 Laboratory locations of diagnostic tests. Bm14 PanLF Urine SXP ICT Og4C3 Blood Smear PCR* French Polynesia ILM ILM Aichi ILM ILM ILM Smith Ghana Noguchi – – Field lab Noguchi Field lab Smith Haiti CDC Field lab Aichi Field lab CDC Field lab Smith Sri Lanka Wash U. Field lab – Field lab Wash U. Field lab Wash U. Tuvalu Wash U. Field lab Aichi Field lab Smith Field lab Smith Zanzibar Smith Field lab Aichi Field lab Smith Field lab Smith Aichi = Aichi Medical University (Japan). CDC = Centers for Disease Control and Prevention (USA). Field Lab = in-country laboratory created, or in use, by field team. ILM = Institut Louis Malarde (French Polynesia). Noguchi = Noguchi Memorial Institute for Medical Research (Ghana). Smith = Smith College (USA). Wash U = Washington University in St. Louis, Missouri (USA). *Based on 10 µl blood specimen. For school participants, four diagnostic assays were performed: two conducted on site (ICT and PanLF) and two conducted in reference laboratories (Bm14 and Og4C3). Because microfilaremia levels were not assessed in the school surveys, blood collection occurred during the day at the time of registration. Urine cups were labeled and distributed at the time of enrollment, and each participant was asked to provide a urine sample (with the exception of those in Ghana and Sri Lanka). In the field laboratory, approximately 5 ml of urine was transferred into a smaller vial and sodium azide (0.1%) was added as a preservative [8]. Urine vials were shipped to Aichi Medical University (Nagoya, Japan) for anti-filarial antibody testing using the W. bancrofti SXP recombinant antigen. Table 2 summarizes the tests by: survey, specimen, test type, and target detected. 10.1371/journal.pntd.0001479.t002 Table 2 Diagnostic test characteristics. Test Name Surveys Used Specimen Type Test Type Target Detected Bm14 Community & School Bloodspot ELISA Antifilarial antibody PanLF Community & School Blood Rapid cassette test Antifilarial antibody Urine SXP Community & School Urine ELISA Antifilarial antibody ICT Community & School Blood Rapid card test Filarial-antigen Og4C3 Community & School Bloodspot ELISA Filarial-antigen Blood Smear Community 60 µl Blood Blood film Microfilariae PCR * Community 10 µl Bloodspot qPCR Microfilariae *Based on 10 µl blood specimen. Field Tests Blood films were used to determine MF levels in the communities. Sixty microliters of blood was streaked onto a glass slide (3 lines×20 µl), stained with Giemsa and read in the field laboratories. Filarial-antigen status was determined by ICT (Binax, Scarborough, ME, USA). EDTA anti-coagulated blood was used and the test was performed according to manufacturer's instructions. Antigen positive individuals were offered treatment with albendazole plus DEC or ivermectin. Anti-filarial antibody status was determined using the PanLF Rapid (MBDr, Selangor, Malaysia) cartridge test. EDTA anti-coagulated blood (35 µl) was placed on the sample pad and the test was performed according to manufacturer's instructions. The remaining blood was spotted onto two filter paper disks (TropBio, Townsville, Australia) (60 µl per disk), dried and stored until shipped to participating laboratories for further testing. Both the ICT and PanLF tests were conducted at the schools and blood was spotted onto filter paper disks. All field test results were entered into the PDA immediately and subsequently uploaded to the field laptop each night. Laboratory Tests Three laboratory assays were performed on the specimens, all of which were previously validated against non-endemic samples. One bloodspot (10 µl) was used for an enzyme linked immunosorbent assay (ELISA) to determine anti-filarial antibody reactivity to the recombinant antigen Bm14 (Cellabs, Sydney, Australia). Bloodspots were eluted overnight at 4°C and processed the following day according to the agreed SOP. Three dried bloodspots (3×10 µl) were used to measure quantitative filarial antigen levels by the Og4C3 ELISA (TropBio, Townsville, Australia). Bloodspots were eluted overnight at 4°C and boiled the next day. Boiled samples were centrifuged and supernatants were incubated overnight on a 96-well microtiter plate pre-coated with an Og4C3 monoclonal capture antibody. Plates were processed the next day. One bloodspot (10 µl) was used for PCR to detect the presence of parasite DNA. Bloodspots were pooled into groups of 10 individuals for initial testing. DNA was extracted using the QIAGEN DNeasy kit (Valencia, CA, USA) and analyzed by real-time PCR (qPCR) [9]. If a pool was positive, each sample that comprised the positive pool was tested individually using an additional 10 µl bloodspot. Results for all laboratory tests were entered into a standardized Microsoft Excel® spreadsheet and sent electronically to the Task Force for Global Health to be entered into the analysis database. Ethics Statement The research proposal was submitted by the principal investigators of each participating country to the local review board, or in certain cases an outside review board, as deemed most appropriate. All proposals were accepted by the respective review boards before research took place. The US-based laboratories analyzing results received an exemption from the IRBs, since all specimens and results were de-linked from personal identifiers. All subjects provided informed consent to participate in the study. More detailed information regarding the IRB institution for each country and the method for obtaining participant consent are described below. In French Polynesia, the Ethics Committee approved the French Polynesian study protocol and work. A consent form was read to all a subjects and written agreement of consent was required from subjects in order to participate in the study. Assent was obtained from children and a written consent was required from their parent or guardian. In addition to obtaining written consent from participants, interviewers documented receipt of consent for all participants using handheld PDA devices. For Ghana, the Noguchi Memorial Institute for Medical Research's Institutional Review Board approved the study protocol and work. Informed written consents were obtained from all individuals 18 years of age and above. For individuals aged 6–17 years informed assent was sought from all individuals, in addition to written consent of the parent or responsible adult. In addition to obtaining written consent from participants, interviewers documented receipt of consent for all participants using handheld PDA devices. The procedure was explained to all children 3–5 years of age, in addition to written consent of the parent or designated guardian. In Haiti the Centers for Disease Control IRB committee approved an amendment to a previously approved study protocol. Informed consent was obtained from each participant. The CDC IRB granted the team the right to obtain oral consent (assent for children of age 6 years or younger and consent of their parents) because most participants were unable to read and the research presented no more than minimal risk of harm to the subjects. Interviewers documented receipt of verbal consent for all participants using handheld PDA devices. In Sri Lanka both the Washington University IRB and the Sri Lanka Ministry of Health approved the study protocol and work. Both institutions considered the survey to be public health practice (evaluation of the national LF elimination program) and as a result did not require formal IRB submission; waiver letters were obtained. Field teams used consent scripts and obtained verbal consent (assent from children). Participation by children required consent from at least one parent plus assent from the child. The Washington University IRB and Sri Lanka Ministry of Health both approved the collection of verbal consent for the survey because the research was deemed to present no more than minimal risk of harm to the subjects. Interviewers documented receipt of verbal consent for all participants using handheld PDA devices. For Tuvalu the human research ethics committee at James Cook University approved the protocol and study. The ethical review committee at James Cook University granted the right to obtain verbal consent, as opposed to written consent, for this study, as the study was considered to present minimal risk of harm to the subjects. Assent was obtained from children, along with verbal consent from their parent or guardian. Interviewers documented receipt of verbal consent for all participants using handheld PDA devices. Finally, the Ethical Review Committee in Zanzibar (Zanzibar Health/Medical Task Force) approved the Zanzibar study protocol and work. For the community all participants were given consent forms to sign while for the school children parents/guardians of the children were informed of the study through School Committee meetings and an informed consent letter was handed over to them to be signed. In addition to obtaining written consent from participants, interviewers documented receipt of consent for all participants using handheld PDA devices. Analyses All data were compiled and managed using SQL server (2005, Microsoft Corporation®) and imported to SAS® v.9.2 (Statistical Analysis System; North Carolina) for analyses. Unless otherwise stated, all statistically significant associations were determined by setting the probability of a Type I error at 5% (α = 0.05). Univariate analyses of country, age, and gender were calculated for all specimens with results reading “positive”, “negative”, and “indeterminate” (Tables 2 and Table 3). For all remaining analyses results were limited to specimens testing “positive” or “negative.” 10.1371/journal.pntd.0001479.t003 Table 3 Demographic information by country and survey location. Location Measure French Polynesia Ghana Haiti Sri Lanka Tuvalu Zanzibar All Countries Community Age (median) 33 17 17 26 39 24 25 Age (IQR) 16–48 10–40 10–28 13–40 25–50 14–41 13–43 Percent Male 48 43 41 49 47 39 44 Total Tested 1018 1107 999 1167 1124 1028 6443 School Age (median) 7 7 7 7 9 9 7 Age (IQR) 7–10 6–10 6–10 6–10 7–10 6–10 6–10 Percent Male 50 49 49 63 48 48 51 Total Tested 365 359 323 310 357 356 2070 All Age (median) 20 12 12 19 29 16 17 Age (IQR) 9–42 8–30 7–23 8–36 10–46 10–36 9–37 Percent Male 48 44 43 52 47 41 46 Total Tested 1383 1466 1322 1477 1481 1384 8513 While five of the seven diagnostic tests provided qualitative (positive/negative) results, two provided quantitative results (Og4C3 and Bm14) in the form of unit values. In order to dichotomize these quantitative results, a cut-off value was defined for the Og4C3 and Bm14 tests, independently, such that all results with a unit value greater than the cut-off were considered “positive.” Receiver Operating Characteristic (ROC) curves were used to determine the best cut-off values, by plotting ‘sensitivity’ by ‘1-specificity’ at various signal to cut-off ratios using SAS®. ROC analysis requires identifying clearly positive and negative specimens whose assay values can be applied to the analysis, but since there is no true ‘gold standard’ for defining LF infection, operational criteria based on multiple tests were used to define these groups. This manuscript followed the Standards for the Reporting of Diagnostic accuracy studies (STARD) (Checklist S1). Results A total of 8513 people from the six countries participated in the study; 6443 through the community surveys and the remaining 2070 through the school surveys (Table 3). Specimens from these participants were used to conduct 47,110 diagnostic tests (Table 4). Of the 47,110 tests performed, 7481 test results (15.9%) were excluded from the subsequent analyses due to invalid or indeterminate test results (Table 5). Among the excluded results were all of the Bm14 tests for Sri Lanka, Tuvalu and Zanzibar (4006 tests) due to changes in the performance of the commercially manufactured kits. In addition to the Bm14, all of the PanLF and blood smear results from Zanzibar (a total of 2,329 tests) were excluded due to technical uncertainties affecting the quality of the results. Diagrams describing the process by which participant specimens were tested, excluded and classified for each of the antibody, antigen and microfilariae tests are available in the supplementary Texts S1, S2, and S3. 10.1371/journal.pntd.0001479.t004 Table 4 Specimens and tests performed by country of origin. Test Name French Polynesia Ghana Haiti Sri Lanka Tuvalu Zanzibar All Countries PanLF 1372 0 1269 1399 1448 1377 6865 Bm14 1329 1159 1214 1463 1245 1298 7708 Urine SXP 1268 0 1285 0 955 1366 4874 ICT 1359 1372 1266 1449 1455 1316 8217 Og4C3 1355 1355 1179 1432 1333 1126 7780 PCR * 1005 972 893 1161 1063 886 5980 Blood Smear 713 1081 882 1043 1015 952 5686 TOTAL 8401 5939 7988 7947 8514 8321 47110 *Based on 10 µl blood specimen. 10.1371/journal.pntd.0001479.t005 Table 5 Invalid or indeterminate test results by country (excluded from remaining analyses). Test French Polynesia Ghana Haiti Sri Lanka Tuvalu Zanzibar All Countries PanLF 66 – 48 435 382 1377 2308 Bm14 0 0 0 1463 1245 1298 4006 Urine SXP 0 – 0 – 0 0 0 ICT 25 119 33 1 7 30 215 Og4C3 0 0 0 0 0 0 0 PCR * 0 0 0 0 0 0 0 Blood Smear 0 0 0 0 0 952 952 TOTAL 91 119 81 1899 1634 4009 7481 Note: These test results make up 15.9% of the total (47,110) results. *Based on 10 µl blood specimen. ROC curves were used to determine the unit value cut-point to distinguish ‘positive’ and ‘negative’ results for the Og4C3 and Bm14 tests. For the Og4C3 antigen assessment true positives were defined as those individuals with positive specimens for either the blood smear (MF) test or PCR (parasite DNA). True negatives were defined as individuals with negative blood smears and PCR results (both negative or one negative and the other not assessed), plus a negative by ICT and a Bm14 antibody value 50 50.3 402 28.7 691 42.8 612 11.3 954 11.2 935 2.4 882 1.9 942 TOTAL 37.0 3702 17.5 5861 20.5 4874 8.6 8049 7.6 7780 1.6 5686 1.3 5980 *Based on 10 µl blood specimen. Though the overall levels of positivity were similar within targets of detection (antibody, antigen or microfilaremia), at the individual level the tests differed significantly. A comparison of the blood smear and PCR results using McNemar's test, matched on participant, found a significant difference between the two tests (p = 0.024). Likewise, a comparison of the ICT and Og4C3 results found the two antigen tests to be significantly different (p = 0.003). The prevalence of antifilarial antibodies differed significantly (p<0.0001) between Bm14, PanLF, and urine SXP tests. The results from all seven diagnostic tests indicated a significant age-prevalence trend of increasing positivity with age (p<0.0001) (Table 8). Of the diagnostic tests, the Bm14 and PanLF were found to be the most reactive in the youngest age groups. In the school studies, which focused on a comparison of 5–7 and 9–11 year olds, there were no significant differences in test results between the two age groups, and the results were subsequently pooled. The test concordance tables (Tables 9, 10, 11,12) record the pair-wise comparisons of test results within the school and community surveys. The resulting estimates can be considered the pair-wise sensitivity of the test. In the school survey, Og4C3 picked up 57% of the ICT positive results, whereas ICT picked up 51% of the Og4C3 positive results (Table 9). Among the antibody tests, Bm14 identified 90% of the positive PanLF results, whereas PanLF only identified 41% of the Bm14 results. These differences reflect the greater sensitivity of the ELISAs compared to the rapid tests. The urine SXP tests consistently identified about a quarter of the positive results from the remaining four tests. 10.1371/journal.pntd.0001479.t009 Table 9 Positive-to-positive concordance in school survey. COMPARISON TEST (Numerator) INDEX TEST (Denominator) BM14 PANLFBC URSXP ICT OG4C3 BM14 120/292 (41%) 38/283 (13%) 45/311 (14%) 53/310 (17%) PanLF 120/133 (90%) 33/136(24%) 44/145 (30%) 54/138 (39%) Urine SXP 38/42 (90%) 33/44 (75%) 16/69 (23%) 18/64 (28%) ICT 45/61 (73%) 44/63 (69%) 16/66 (24%) 42/74 (57%) Og4C3 53/63 (84%) 54/64 (84%) 18/77 (23%) 42/82 (51%) Note: Fractions represent the number of positive results for each test (numerator) out of those that were positive by the index test (denominator). The results are of the form: proportion (%). The number of positive index tests (denominator) changes by column because it only includes specimens with valid results by the comparison test (numerator). 10.1371/journal.pntd.0001479.t010 Table 10 Positive-to-positive concordance in community survey. COMPARISON TEST (Numerator) INDEX TEST (Denominator) BM14 PANLFBC URSXP ICT OG4C3 BLOOD SMEAR PCR* BM14 463/903 (51%) 386/905 (43%) 231/1015 (23%) 237/1033 (22%) 54/904 (6%) 55/1044 (5%) PanLF 463/516 (90%) 370/714 (52%) 216/869 (25%) 220/841 (26%) 47/783 (6%) 47/854 (5%) Urine SXP 386/428 (90%) 370/582 (64%) 181/878 (21%) 193/829 (23%) 36/572 (6%) 36/853 (4%) ICT 231/357 (65%) 216/384 (56%) 181/455 (40%) 299/560 (53%) 70/468 (15%) 60/571 (11%) Og4C3 237/323 (73%) 220/292 (75%) 193/367 (53%) 299/485 (62%) 76/397 (19%) 68/503 (14%) Blood Smear 54/75 (72%) 47/65 (72%) 36/64 (56%) 70/88 (80%) 76/87 (87%) 52/85 (61%) PCR * 55/62 (89%) 47/60 (78%) 36/66 (55%) 60/77 (78%) 68/75 (91%) 52/69 (75%) Note: Fractions represent the number of positive results for each test (numerator) out of those that were positive by the index test (denominator). The results are of the form: proportion (%). The number of positive index tests (denominator) changes by column because it only includes specimens with valid results by the comparison test (numerator). *Based on 10 µl blood specimen. 10.1371/journal.pntd.0001479.t011 Table 11 Negative-to-negative test concordance in school survey. COMPARISON TEST (Numerator) INDEX TEST (Denominator) BM14 PANLFBC URSXP ICT OG4C3 BM14 334/347 (96%) 338/342 (98%) 628/644 (98%) 645/655 (98%) PanLF 334/506 (66%) 566/577 (98%) 905/924 (98%) 880/890 (98%) Urine SXP 338/583 (57%) 566/669 (84%) 1012/1062 (95%) 921/980 (93%) ICT 628/894 (70%) 905/1006 (89%) 1012/1065 (95%) 1740/1780 (97%) Og4C3 645/902 (72%) 880/964 (91%) 921/967 (95%) 1740/1772 (98%) Note: Fractions represent the number of negative results for each test (numerator) out of those that were negative by the index test (denominator). The results are of the form: proportion (%). The number of negative index tests (denominator) changes by column because it only includes specimens with valid results by the comparison test (numerator). 10.1371/journal.pntd.0001479.t012 Table 12 Negative-to-negative test concordance in community survey. COMPARISON TEST (Numerator) INDEX TEST (Denominator) BM14 PANLFBC URSXP ICT OG4C3 BLOOD PCR* BM14 827/880 (94%) 825/867 (95%) 1394/1520 (91%) 1524/1610 (94%) 1404/1425 (98%) 1585/1592 (99%) PanLF 827/1267 (65%) 1401/1613 (86%) 2376/2544 (93%) 2432/2504 (97%) 2137/2155 (99%) 2512/2525 (99%) Urine SXP 825/1344 (61%) 1401/1745 (80%) 2380/2654 (89%) 2296/2470 (92%) 1595/1623 (98%) 2519/2549 (98%) ICT 1394/2178 (64%) 2376/3029 (78%) 2380/3077 (77%) 4903/5089 (96%) 3966/3984 (99%) 5149/5166 (99%) Og4C3 1524/2320 (65%) 2432/3053 (79%) 2296/2932 (78%) 4903/5164 (94%) 4051/4062 (99%) 5281/5288 (99%) Blood Smear 1404/2254 (62%) 2137/2873 (74%) 1595/2131 (74%) 3966/4364 (90%) 4051/4372 (92%) 4375/4392 (99%) PCR * 1585/2574 (61%) 2512/3319 (75%) 2519/3336 (75%) 5149/5660 (90%) 5281/5716 (92%) 4375/4408 (99%) Note: Fractions represent the number of negative results for each test (numerator) out of those that were negative by the index test (denominator). The results are of the form: proportion (%). The number of negative index tests (denominator) changes by column because it only includes specimens with valid results by the comparison test (numerator). *Based on 10 µl blood specimen. In the community survey, Og4C3 detected 87% and 91% of the blood smear and PCR positive results, respectively, while ICT detected 80% and 78% of the blood smear and PCR positive results, respectively (Table 10). The positive concordance between ICT and Og4C3 ranged from 53% (ICT positives testing positive by Og4C3) to 62% (Og4C3 positives testing positive by ICT). Of the microfilaremic individuals (positive by blood smear) only 61% were positive by a 10 µl PCR. Conversely 75% of PCR positive individuals were also positive by blood smear. Among the antibody tests, Bm14 identified 90% of individuals positive by PanLF or urine SXP. Negative test concordance in the school survey (Table 11) revealed that 98% of antibody negative individuals (by Bm14 or PanLF) also tested negative by the antigen tests (ICT or Og4C3) (i.e. few people had filarial antigenemia in the absence of a detected antibody response). Bm14 had the poorest negative concordance with the remaining tests in the school surveys; only 66–72% of those specimens negative by PanLF, urine SXP, ICT or Og4C3 were also negative by Bm14. However, since antibody tests are expected to be the most sensitive at detecting exposure to LF, it is possible that specimens negative for antigenemia would still be ‘true positive’ for Bm14 antibody. The negative concordance of the antigen tests with the antibody tests was somewhat less in the community survey compared to the school survey, with 90–97% of antibody negative specimens (by Bm14 or PanLF) also testing antigen negative (by ICT or Og4C3) (Table 12). The pair-wise specificity of Bm14 was similarly low in the community survey, as compared to the school survey, with Bm14 identifying as negative approximately two thirds of results that were negative by any of the remaining tests. Comparatively PanLF identified as negative 74–94% of results that were negative by the remaining six tests. In the absence of a true gold standard test for LF infection, operational definitions of positive and negative gold standards were used to calculate sensitivity and specificity. To measure sensitivity, ‘true positives’ were defined as being either blood smear or PCR positive. The sensitivity of the assays therefore relates to the sensitivity for detecting microfilaremic infections, a measure of justifiable interest to the global LF elimination program, since microfilariae are required to transmit infection. It is more difficult to define a gold standard for specificity of assays since it is recognized that exposure alone can convert individuals to positive-antibody status. Consequently, ‘true negatives’ for antibody tests cannot be defined based on the results of the antigen and parasite tests, making it impossible to calculate the specificity for the antibody tests. Specificity of the antigen tests can be assessed if one evaluates the ability of the antigen assays to identify individuals who are amicrofilaremic and have no antibody evidence of infection or exposure to infection. ‘True negatives’ for the antigen tests were therefore defined based on negative blood smear and PCR results (both negative or one negative and the other not assessed) as well as negative results for both Bm14 and PanLF. It is important to note that this was a conservative definition of antigen specificity, as only antibody-negative individuals were eligible to be considered ‘true negatives’ by the antigen tests (see Discussion). Sensitivity and specificity of test performance was calculated using the best-estimate gold standards as defined above. These calculations were limited to French Polynesia, Ghana, and Haiti due to missing values for Bm14 in the remaining countries. Overall, the ICT test was found to be 76% sensitive at detecting microfilaremic infections and 93% specific at identifying individuals negative for both microfilariae and antifilarial antibody (Table 13). Using the same gold standard estimates, Og4C3 was found to be 87% sensitive and 95% specific. Stratifying the results by country revealed a high degree of variability in these estimates. ICT sensitivity ranged from 61% in Ghana to 79% in Haiti and French Polynesia, while ICT specificity ranged from 89% in Haiti to 94% in Ghana. Similarly, the sensitivity of Og4C3 assays ranged from 72% in Ghana to 93% in French Polynesia, while Og4C3 specificity ranged from 92% in Ghana to 99% in French Polynesia. It is important to note that a portion of the variability is due to the relatively small sample sizes in the country-specific results, caused by the gold standard criteria. 10.1371/journal.pntd.0001479.t013 Table 13 Sensitivity, specificity, and predictive values for antigen tests. ICT Og4C3 % (N) 95% Confidence Interval % (N) 95% Confidence Interval All Countries a Sensitivity 75.5 (94) (66.8, 84.2) 87.2 (94) (80.5, 94.0) Specificity 92.5 (1647) (91.2, 93.7) 94.6 (1647) (93.5, 95.7) Pos. Predictive Value 36.4 (195) (29.7, 43.2) 48.0 (171) (40.5, 55.4) Neg. Predictive Value 98.5 (1546) (97.9, 99.1) 99.2 (1570) (98.8, 99.7) French Polynesia Sensitivity 79.3 (29) (64.6, 94.1) 93.1 (29) (83.9, 100.0) Specificity 92.3 (517) (90.0, 94.6) 98.6 (517) (97.6, 99.6) Pos. Predictive Value 36.5 (63) (24.6, 48.4) 79.4 (34) (65.8, 93) Neg. Predictive Value 98.8 (483) (97.8, 99.8) 99.6 (512) (99.1, 100.0) Ghana Sensitivity 61.1 (18) (38.6, 83.6) 72.2 (18) (51.5, 92.9) Specificity 94.3 (754) (92.6, 96.0) 91.6 (754) (89.7, 93.6) Pos. Predictive Value 20.4 (54) (9.6, 31.1) 17.1 (76) (8.6, 25.6) Neg. Predictive Value 99.0 (718) (98.3, 99.7) 99.3 (696) (98.7, 99.9) Haiti Sensitivity 78.7 (47) (67.0, 90.4) 89.4 (47) (80.5, 98.2) Specificity 89.1 (376) (86.0, 92.3) 94.9 (376) (92.7, 97.2) Pos. Predictive Value 47.4 (78) (36.4, 58.5) 68.9 (61) (57.2, 80.5) Neg. Predictive Value 97.1 (345) (95.3, 98.9) 98.6 (362) (97.4, 99.8) Definition of antigen test accuracy. ‘True Positive’: Blood Smear or PCR (+). ‘True Negative’: Blood Smear and PCR not (+); Bm14 and PanLF not (+). a Includes French Polynesia, Ghana and Haiti only; others excluded due to missing values for Bm14. The sensitivity of the antibody tests at detecting microfilaremic individuals was 81% for Bm14, 73% for PanLF and 55% for SXP in urine (Table 14). Again, there was significant variability in these estimates at the country level, with Bm14 sensitivity estimates ranging from 50% in Ghana to 92% in French Polynesia. PanLF sensitivity ranged from 50% in Tuvalu to 77% in French Polynesia. Urine SXP sensitivity ranged from 32% in Haiti to 92% in French Polynesia. As with the antigen results, small sample size due to the limited number of microfilaremic individuals, is likely to account for some of the variability in the sensitivity estimates. 10.1371/journal.pntd.0001479.t014 Table 14 Sensitivity, specificity, and predictive values for antibody tests. PanLF Bm14 Urine SXP Rate 95% Confidence Interval Rate 95% Confidence Interval Rate 95% Confidence Interval All Countries Sensitivity 73.2 (82) (63.5, 82.8) 81.1 (74) (72.2, 90.0) 54.5 (77) (43.4, 65.7) Neg. Predictive Value 99.1 (2390) (98.7, 99.5) 98.2 (790) (97.3, 99.1) 97.7 (1522) (96.9, 98.5) French Polynesia Sensitivity 76.9 (26) (60.7, 93.1) 92.3 (26) (82.1, 100) 92.3 (26) (82.1, 102.6) Neg. Predictive Value 99.1 (675) (98.4, 99.8) 99.5 (438) (98.9, 100) 99.6 (539) (99.1, 100) Ghana Sensitivity – – 50.0 (16) (25.5, 74.5) – – Neg. Predictive Value – – 98.8 (680) (98.0, 99.6) – – Haiti Sensitivity 70.8 (48) (58.0, 83.7) 75.0 (48) (62.8, 87.2) 31.9 (47) (18.6, 45.2) Neg. Predictive Value 96.8 (447) (95.3, 98.5) 96.6 (336) (94.7, 98.5) 94.2 (554) (92.3, 96.2) Sri Lanka Sensitivity 66.7 (3) (13.3, 100) – – – – Neg. Predictive Value 99.9 (684) (99.6, 100) – – – – Tuvalu Sensitivity 50.0 (2) (0, 100) – – 50.0 (2) (0, 100) Neg. Predictive Value 99.8 (548) (99.5, 100) – – 99.7 (396) (99.3, 100) Zanzibar Sensitivity – – – – 42.9 (7) (6.2, 79.5) Neg. Predictive Value – – – – 99.3 (565) (98.6, 100) Definition of antibody test accuracy. ’True Positive’: Blood Smear or PCR (+). ‘True Negative’: Blood Smear and PCR not (+); ICT and Og4C3 not (+). Discussion Deciding whether or not to stop MDA will be expensive and laborious for countries because of both the sampling and testing requirements, so the selection of the diagnostic tool to use is of paramount importance. Accuracy, programmatic feasibility, testing requirements, time and cost must all be factored into the evaluation of the potential diagnostic tools [10]. The current study arose in response to this challenge. A summary of the features and performance of the seven diagnostic tests evaluated is presented in the supporting table at the end of this paper (Table S1). A common theme that emerges from this multi-country study is that the majority of the tests did not perform as well as expected, with regards to both accuracy and reliability. Though this finding is disappointing, it is important to note that the study represents an effectiveness trial, with the majority of the tests being conducted under varying conditions on-site or in field laboratories by local technicians. Though all the technicians were well-schooled, there were differences in adherence to established protocols. Indeed, the lessons learned with respect to test performance in this multi-country setting provide valuable insight and will hopefully lead to future test improvements. Some common areas identified for improvement across many of the tests include the need for thorough training of test-readers and lab technicians, along with simplification of logistical issues related to specimen storage, shipping and linking with test results. Another important concern identified was the need for improved standardization and rigorous quality control of commercially manufactured tests and kits, a problem noted particularly with variability in the lots of commercial kits measuring Bm14 antibodies (CELISA) and the TropBio Og4C3 antigen test. In addition, with an increasing reliance on laboratory tests for programmatic decision making, there is a critical need to provide laboratories with standard operating procedures and assay controls (e.g., samples for standard curves, positive and negative controls) that can be used across all labs. Both efforts are needed to guarantee that results generated across countries are comparable and can be used to make robust program decisions. Use of eluted filter paper blood spots rather than fresh serum in this study might have contributed to the sub-optimal performance of the Bm14 and Og4C3 ELISA tests. When this study was planned, all investigators on the project agreed that filter paper blood spots should be used for the ELISA tests. Multiple studies have described the equivalence of the blood spot and serum specimens for use in both the Bm14 and Og4C3 assays [11]–[14], but since this analysis was conducted, other studies have suggested that blood spots on filter paper might not perform as well as serum in the Bm14 ELISA, and there has been a call for additional studies to compare the two methods directly [15]. In the present study, project laboratories found that blood spot eluates sometimes produced variable and often high background OD values in the Bm14 ELISA, so that data from these countries had to be rejected (Table 5). When evaluating the best diagnostic tool for programmatic decision-making, the advantages of point-of-care tests are appreciable. In this study, the anticipated advantages of lab-based tests (i.e. better sensitivity and specificity) were outweighed by the convenience, comparable accuracy, and ability to standardize more easily the point-of-care tests. Given the challenges experienced with the lab-based tests (see Table S1) a point-of-care test appears to be most preferable for assessments leading to a decision on whether or not to stop MDA. Taking these aspects into consideration, we conclude that the ICT should be the primary tool recommended now for decision-making about stopping MDAs in areas with W. bancrofti infections. As a point-of-care card test, the ICT is relatively inexpensive, requires no laboratory equipment, and can be processed in 10 minutes, very consistent with programmatic use. As an antigen test, a “positive” ICT result is indicative of the presence of adult worms and the potential for ongoing transmission—arguably a more appropriate measure for establishing an end-point for MDA than antifilarial antibodies detecting exposure to infection. Additional research is needed to determine whether antibody tests are more appropriate for post-MDA surveillance. One concern with the ICT that arose from this study was the potential subjectivity involved in determining whether a weak-looking band indicates a positive or negative test. Fortunately, improvements to training and training materials can be expected to resolve some of this anxiety about the test's use. Indeed, with these improvements, the ICT appears as the diagnostic tool best suited for use even in low-resource settings to determine when the end-point for the MDA phase of the LF elimination program has been reached. This recommendation for the ICT test is not meant to undervalue the relatively good performance of the Og4C3 test, which was even more accurate than the ICT in identifying microfilaremic individuals in this study. However, as a laboratory-based assay, the Og4C3 test provided some additional challenges, including inconsistent product performance over time and quality control in the testing laboratories. The Og4C3 and other ELISA tests have performed well in research labs; our results and experience with quality control have illustrated the potential problems with translating these tools into an operational setting. The Og4C3 provides a satisfactory diagnostic alternative that may be appropriate in settings with well-equipped laboratories and the ability to adhere to a quality assurance strategy. Limitations and Areas of Future Research The absence of a true gold standard test for LF infection was a major limitation of this analysis. The need to define a best-estimate gold standard from the available tests further limited the analysis since tests used in the definition cannot be assessed by the same definition without entering into a tautology (an issue for both PCR and blood smear). To measure the sensitivity and specificity of the tests it was necessary to use the best-estimate gold standard to define “true positive” and “true negative” results and then limit the analysis to specimens falling within either category. Based on the criteria used, individuals who tested not positive by blood smear and PCR but positive by Bm14 or PanLF (n = 1737) were excluded from sensitivity and specificity calculations for antigen tests, as they were neither “true positive” nor “true negative”. It is important to note that such results are biologically plausible, as they may be indicative of individuals with increasing, but undetectable antigen levels, or they can represent individuals who are no longer infected but still have residual antifilarial antibodies. It is clear, though, that the definitions used to establish test sensitivity and specificity are imperfect because of the impossibility of defining a true gold standard of infection. The ROC analysis for determining Bm14 and Og4C3 cut-off levels was also contingent upon the best-estimate criteria. Therefore, any systematic errors resulting in misclassification of the tests used in the best estimate gold standard have the potential to influence this analysis. A sensitivity analysis was run, which evaluated the suspected ICT false positives, as well as false positive and false negative results with PCR and blood smear. The results from the sensitivity analysis indicate that the sensitivity and specificity of the tests, and conclusions drawn from this analysis, to be robust under various scenarios of misclassification (data not shown). For example, if all ICT-positive specimens with an Og4C3 quantitative result of “0” (N = 48) were considered “false positives” and recoded as ICT-negative, the sensitivity and specificity estimates would not change significantly. Finally, additional sources of error, common across many tests and countries, stemmed from external issues. Logistical constraints and risk of specimen contamination varied by country and is likely to have caused some of the variance in test performance. The possibility of reader error cannot be discounted. Some of this study's findings were unexpected and warrant future research and analysis. Though the overall prevalence of detection of antigen or antibody was similar for a given target, the distributions of the test results suggest that they are performing differently. Whether or not this difference is due to variability of test performance or to the tests' detecting different sub-populations of positive individuals is hard to determine. For example, the correlation between the ICT and Og4C3 antigen tests was much lower than expected (phi coefficient 0.53); however both tests identified similar overall prevalence of antigenemia. Part of the discordance may be explained by the cut-point selected for the Og4C3 test. Cut-points for Og4C3 were defined such that the only “true positive” specimens were those testing positive for microfilariae (blood smear or PCR). This is likely to have limited our ROC analysis to “strong positive” Og4C3 results (those with higher unit values), as previous studies have found Og4C3 unit values to be positively correlated with MF values [16]–[18]. Whether or not this biased our final cut-point is unclear. However, the poor correlation may also suggest that the ICT and Og4C3 test are capturing different aspects of antigenemia. A more controlled laboratory study would be needed to determine if this were the case. Next Steps The selection of the ICT as the best tool for establishing the MDA stopping criteria is a significant programmatic advance. However, further assessment is needed to develop the appropriate guidelines for country program managers eager to decide if they are ready to stop MDA. The selection of a diagnostic test is the first step, but it is necessary to define a “threshold” of positive results below which a country can safely discontinue its MDA program. With the less-than-perfect sensitivity and specificity of the diagnostic tools, such a threshold should be based on statistical criteria that can account for the level of error in the measurement with a 95% confidence interval [4]. Also integral to this assessment is the method by which the population will be sampled, as both sampling strategy and threshold will influence the sample size and power of the surveys used to determine if the stopping MDA criteria are met. Addressing these issues is the focus of ongoing research efforts. The global community has already made great progress on the path to elimination of lymphatic filariasis. The selection of the ICT test for defining the end-point of MDA, based on both the present study and earlier observations permits the WHO to develop appropriate guidelines that will allow many countries to move closer to stopping their MDA programs. Future studies to evaluate sampling strategies, ICT-based stopping thresholds, and long-term consequences of the stopping decision will increasingly strengthen the evidence base for the programmatic guidelines targeting LF elimination. Supporting Information Checklist S1 STARD Checklist. (DOC) Click here for additional data file. Table S1 A summary of the features and performance of the seven diagnostic tests evaluated. (DOC) Click here for additional data file. Flow Chart S1 STARD flow chart detailing the method for assessment of antibody diagnostic tests. (DOCX) Click here for additional data file. Flow Chart S2 STARD flow chart detailing the method for assessment of antigen diagnostic tests. (DOCX) Click here for additional data file. Flow Chart S3 STARD flow chart detailing the method for assessment of microfilariae diagnostic tests. (DOCX) Click here for additional data file.

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Transmission Assessment Surveys (TAS) to Define Endpoints for Lymphatic Filariasis Mass Drug Administration: A Multicenter Evaluation

Brian Chu, Michael S. Deming, Nana-Kwadwo Biritwum … (2013)

Introduction Lymphatic filariasis (LF) is a mosquito-borne parasitic disease endemic to 73 countries worldwide. An estimated 1.4 billion people are said to be at-risk of LF disease with approximately 120 million infected and 40 million suffering from the crippling and stigmatizing clinical manifestations of the disease, especially lymphoedema and hydrocele [1]. As such, LF is one of the leading causes of chronic disability worldwide. The primary focus for control and elimination of LF is the interruption of disease transmission through treatment of the entire at-risk population with repeated annual mass drug administration (MDA) using a single-dose combination of albendazole with either diethylcarbamazine (DEC) or ivermectin [2]. Since 2000, these efforts have been coordinated through the World Health Organization's (WHO) Global Programme to Eliminate Lymphatic Filariasis (GPELF), a collaborative public health program that has delivered to date nearly 4 billion drug treatments to over 950 million individuals in 53 countries [1]. This extraordinary achievement, made possible through the drug donations of manufacturers Merck (ivermectin) and GlaxoSmithKline (albendazole) has resulted in a marked reduction of infection prevalence in endemic areas, along with sizeable health and economic benefits to the affected populations [3], [4]. Essential to the Global Programme's success in combating LF is the important challenge of defining and confirming endpoints for MDA when disease transmission is presumed to have reached a level low enough that it cannot be sustained even in the absence of drug intervention. Given the biology and parasitic life cycle of LF, this threshold of infection is most likely to be reached following 4–6 annual MDA rounds with effective population coverage and a resulting microfilaria (mf) prevalence rate 1 so household enumeration was necessary. Following specimen collection, all blood-filled tubes were stored and transported via cold-chain to a nearby laboratory base to process and record ICT (or PanLF, Brugia Rapid) test results. Children testing positive were identified using the EDGE/ODK systems and individually followed up at night during peak mf hours to collect an additional blood sample for mf testing (10pm–2am except in American Samoa where W. bancrofti shows diurnal periodicity). The number of children absent on the survey date was recorded for all surveys. For community surveys, field teams made at least one revisit to the absent child's house before recording an official absence. The number of selected children without consent or refusing to participate was also captured in addition to invalid and incomplete tests due to malfunction or insufficient blood. Together, these absentees, refusals, and individuals with test errors were designated as TAS non-participators. Data Analysis All transmitted data were compiled into a central database at the Task Force for Global Health and exported into Microsoft Excel spreadsheets for final cleaning and approval by the collaborating principal investigators. Statistical analysis was done by importing the clean datasets into SAS v9.3 (SAS Institute). Summary statistics of test results and univariate analyses with regard to age, sex, and location were performed using the PROC UNIVARIATE function. Design effect calculations were conducted using the PROC SURVEYFREQ function. Results TAS Results For W. bancrofti countries , TAS-1 and TAS-2 results are presented in Table 3. All EUs passed TAS-1, meaning that the number of ICT positive children was no greater than the critical cutoff value. As recommended by the TAS, MDA was then stopped (or periodic post-MDA surveillance continued) in those specific EUs for approximately 24 months before conducting TAS-2. All W. bancrofti EUs (with the exception of American Samoa and Indonesia where follow-up assessments were not yet completed) also passed TAS-2, thereby corroborating the TAS-1 stop-MDA or post-MDA surveillance decision. Microfilaraemia (mf) tests were conducted on ICT positive children using the three-line blood smear (TAS-1 and TAS-2) and PCR (TAS-1) procedures. The proportion of mf-positive children among antigen-positive children identified in the TAS was low in the W. bancrofti countries. The positive blood smear to positive ICT proportion was 12.9% (4/31) for TAS-1 and 5.2% (1/19) for TAS-2, while the proportion of positive PCR to positive ICT was 22.6% (7/31). 10.1371/journal.pntd.0002584.t003 Table 3 ICT, blood smear, and PCR results for W. bancrofti countries. ICT (Ag) Blood smear (mf) PCR (mf) Country Critical cutoff value TAS-1 positive1 TAS-2 positive1 TAS-1 positive2 TAS-2 positive2 TAS-1 positive2 Am. Samoa3 6 2/949 n/a 0/2 (0.0%) n/a 0/2 (0.0%) Burkina Faso 18 13/1571 5/1591 2/13 (15.4%) 0/5 (0.0%) 5/13 (38.5%) Dom. Rep. 18 0/1609 3/1558 - 1/3 (33.3%) - Ghana 18 2/1557 0/1514 0/2 (0.0%) - 0/2 (0.0%) Indonesia4 18 6/1312 n/a4 0/6 (0.0%) n/a4 0/6 (0.0%) Philippines 18 2/1599 1/1656 0/2 (0.0%) 0/1 (0.0%) 0/2 (0.0%) Sri Lanka3 8 0/679 1/698 - 0/1 (0.0%) - Togo 18 2/1571 0/1550 1/2 (50.0%) - 1/2 (50.0%) Tanzania 18 10/1561 9/1588 1/10 (10.0%) 0/9 (0.0%) 1/9 (11.1%) Vanuatu 18, 195 0/933 2/954 - 0/2 (0.0%) - 1 % of total survey population. 2 % of ICT+ individuals; some individuals could not be retraced for mf testing. 3 Systematic sampling was used in American Samoa and Sri Lanka. 4 Indonesia EU of Alor+Pantar islands is endemic for both W. bancrofti and Brugia timori. TAS-2 ICT tests were not available due to logistic problems importing diagnostic tests into the country. 5 Census critical cutoff value is equal to .02N for EUs with Culex, Anopheles, or Mansonia as primary LF vector. For Brugia spp. countries, Indonesia passed TAS-1 and TAS-2 and only one mf positive was found across both surveys (Table 4). The number of PanLF positive children in Malaysia (Sabah), however, exceeded the critical cutoff value in TAS-1. MDA was, therefore, continued before re-testing in TAS-2, but for only one round in 8 IUs due to DEC supply problems. Results for TAS-2 using the Brugia Rapid test were still greater than the critical cutoff value so consequently, MDA has been recommended to continue in the EU for two more rounds before conducting another TAS evaluation. Mf results in Malaysia (Sabah, not peninsular Malaysia) confirmed a high likelihood of active transmission with a TAS-1 positive blood smear to positive PanLF proportion of 35.6% (32/90) and positive PCR to positive PanLF proportion of 52.2% (47/90). The TAS-2 positive blood smear to positive Brugia Rapid proportion decreased to 20.5% (15/73) following the additional rounds of MDA. 10.1371/journal.pntd.0002584.t004 Table 4 PanLF, Brugia Rapid, blood smear, and PCR results for Brugia spp. countries. PanLF or Brugia Rapid (Ab) Blood smear (mf) PCR (mf) Country Critical cutoff value TAS-1 (PanLF) positive1 TAS-2 (Brugia Rapid) positive1 TAS-1 positive2 TAS-2 positive2 TAS-1 positive2 Indonesia3 18 12/1353 14/1622 0/12 (0.0%) 1/14 (7.1%) 0/12 (0.0%) Malaysia 16 90/1429 73/1684 31/87 (35.6%) 15/73 (20.5%) 46/86 (53.4%) 1 % of total survey population. 2 % of PanLF(+) or Brugia Rapid(+) individuals; some individuals could not be retraced for mf testing. 3 Indonesia EU of Alor and Pantar islands is endemic for both W. bancrofti and Brugia timori. Population and Sampling Characteristics The proportions of male and female children sampled were very even across all school and community-based surveys in both TAS-1 and TAS-2 (Table 5). In addition, no one country in either survey had more than 54% male or female children in the sample. 10.1371/journal.pntd.0002584.t005 Table 5 TAS sample size by sex for school and community-based surveys. Sex School TAS (16 surveys) Community-based TAS (6 surveys) Total (22 surveys) Male 9,894 (50.2%) 4,752 (50.1%) 14,646 (50.2%) Female 9,798 (49.8%) 4,725 (49.9%) 14,523 (49.8%) Total1 19,692 (100.0%) 9,477 (100.0%) 29,169 (100.0%) 1 57 records were missing sex identification data. The target age group for TAS is 6 and 7 year old children, approximated by 1st and 2nd graders in school surveys. In W. bancrofti EUs, 84% of the total sample in school surveys was aged 6 and 7 and 95% between 6 and 10 years old (Table 6). The Brugia spp. EUs in Indonesia and Malaysia found a higher proportion of 8 year olds in the TAS sample due to 1st and 2nd grade in both countries primarily consisting of 7 and 8 year old children. No positive cases were detected outside the 6–10 year old range although one positive ICT test was associated with a child of unspecified age. 10.1371/journal.pntd.0002584.t006 Table 6 TAS results by age for school surveys in W. bancrofti and Brugia. spp. countries. W. bancrofti countries1 Brugia spp. countries Age (years) n (% of total) ICT+ (% of age) n (% of total) PanLF or Brugia Rapid+ (% of age) 10 37 (0.3%) 0 (0.0%) 2 (0.1%) 0 (0.0%) Total2 14,899 (100.0%) 17 (0.1%) 6,088 (100.0%) 189 (3.1%) 1 Includes TAS-1 ICT tests for Indonesia. 2 73 records were missing age data (including 1 ICT+). Table 7 is informative because it displays the target and actual sample sizes for TAS-1 and TAS-2 along with the number of clusters (schools or EAs) surveyed to achieve the total. The target sample size was mostly met in both surveys with a few notable exceptions. In American Samoa TAS-1, there was insufficient blood to perform the ICT test in a number of collected samples. Likewise in Indonesia TAS-1, ICT and PanLF tests were unavailable at the time of sampling; therefore, they were conducted retroactively using preserved serum and several samples did not have enough quantity to complete the test. For TAS-2 in Malaysia, the actual sample size greatly exceeded the target due to the random selection of several large schools in addition to a lower non-participation rate than initially estimated. 10.1371/journal.pntd.0002584.t007 Table 7 Comparison of target and actual sample sizes and number of clusters. Country Survey Target sample Actual sample1 % difference Original clusters selected Extra clusters needed Am. Samoa TAS-1 1,042 949 −8.9% 262 - TAS-2 - - - - - Burkina Faso TAS-1 1,556 1,571 1.0% 30 1 TAS-2 1,556 1,591 2.2% 30 8 Dom. Rep. TAS-1 1,532 1,609 5.0% 30 8 TAS-2 1,532 1,558 1.7% 40 0 Ghana TAS-1 1,556 1,557 0.1% 30 10 TAS-2 1,556 1,514 −2.7% 30 2 Indonesia TAS-1 1,548 1,353 −12.6% 30 13 TAS-2 1,548 1,622 4.8% 30 0 Malaysia TAS-1 1,368 1,429 4.5% 30 2 TAS-2 1,368 1,684 23.1% 33 0 Philippines TAS-1 1,552 1,599 3.0% 35 10 TAS-2 1,552 1,656 6.7% 35 0 Sri Lanka TAS-1 684 679 −0.7% 352 - TAS-2 684 698 2.0% 322 0 Togo TAS-1 1,548 1,571 1.5% 30 1 TAS-2 1,540 1,550 0.6% 39 0 Tanzania TAS-1 1,540 1,561 1.4% 51 18 TAS-2 1,540 1,588 3.1% 70 0 Vanuatu TAS-1 933 933 0.0% 63 0 TAS-2 954 954 0.0% 63 0 Total TAS-1 14,859 14,811 −0.3% 390 63 TAS-2 13,830 14,415 4.2% 402 10 1 Excluding invalid tests and specimens unable to be tested. 2 Systematic sampling; all eligible primary sampling units surveyed. Table 7 also presents the number of original clusters selected and the number of extra clusters needed to meet the sampling requirements. In TAS-1, a total of 63 extra clusters were required, most prominently in Ghana, Indonesia, Philippines, and Tanzania. In contrast, only 10 total extra clusters were required in TAS-2, primarily as a result of factoring the non- participation rates into the SSB survey design calculation. The non- participation rate includes children – enrolled in first and second grade (for school surveys) or residing in the selected house (for community-based surveys) – absent on the survey date and those refusing to participate or without consent. The rate was a combined 14.0% for TAS-1 and 10.2% for TAS-2 but varied by country and survey (Table 8). Non-participators also include invalid (i.e. malfunctioning) diagnostic tests or samples that were collected but had insufficient quantity or other barriers preventing completion of the test (e.g. blood clotting). These specific non- participation factors accounted for approximately 4% of total TAS-1 and 2% of total TAS-2 samples but were also dependent on country and survey. Some non- participation rates were not tracked or estimated in American Samoa (TAS-1), Burkina Faso (TAS-1), and Sri Lanka (TAS-1). 10.1371/journal.pntd.0002584.t008 Table 8 Non-participation rates observed in TAS-1 and TAS-2. Absent, refused, or no consent Invalid test or Unable to be tested Country Survey site TAS-1 TAS-2 TAS-1 TAS-2 Am. Samoa School - - 16.0% - Burkina Faso Community - 7.5% 0.9% 0.3% Dom. Rep. Community 12.6% 7.2% 0.6% 0.1% Ghana School 15.0% 15.0% 0.1% 2.9% Indonesia School 20.0% 10.0% 18.3% 9.5% Malaysia School 22.9% 20.4% 0.3% 0.5% Philippines School 4.0% 3.0% 4.0% 1.3% Sri Lanka School - 9.3% 0.0% 1.4% Togo School 12.0% 8.0% 0.0% 0.0% Tanzania Community 14.7% 5.7% 0.6% 1.1% Vanuatu School 10.7% 15.7% 0.0% 0.0% Total - 14.0% 10.2% 3.8% 1.9% Design effects for TAS-1 and TAS-2 cluster surveys are listed in Table 9. All W. bancrofti countries had design effects less than the TAS estimated value of 2 (for target populations >2400), indicating the required sample size was not underestimated. Conversely, Indonesia and Malaysia, both Brugia spp. EUs, had design effects larger than 2 that may be associated with the more sensitive detection of antibody versus antigenemia, and with the subsequently larger number of positive cases found, particularly in Malaysia. 10.1371/journal.pntd.0002584.t009 Table 9 Design effects calculated for TAS-1 and TAS-2 cluster surveys. Country TAS-1 TAS-2 Burkina Faso 1.3 0.8 Dom. Rep. - 1.6 Ghana 2.0 - Indonesia 2.5 2.2 Malaysia 7.9 7.0 Philippines 1.0 1.0 Togo 0.9 - Tanzania 1.1 1.1 Time and Costs of These Studies The overall average number of field days required for TAS was 26 in TAS-1 (range: 9–60) and 27 for TAS-2 (range: 12–50), using an average number of 4 field teams (range: 3–6) with 3–4 persons per team (Table 10). School surveys took 24–27 days on average versus 26–33 for community surveys but the overall survey length was highly dependent on country-specific factors including weather, distance, and other logistic delays, particularly in the Philippines, Dominican Republic, Indonesia, and Vanuatu. 10.1371/journal.pntd.0002584.t010 Table 10 Number of field days required to complete TAS-1 and TAS-2. Survey site Country Field days TAS-1 Field days TAS-2 Field teams TAS-1 and TAS-2 School Am. Samoa 9 - 6 Ghana 20 18 4 Indonesia 35 18 6 Malaysia 18 18 5 Philippines 60 50 3 Sri Lanka 26 32 3 Togo 14 12 3 Vanuatu 25 25 4 Average 27 24 4 Community Burkina Faso 19 18 3 Dom. Rep. 57 42 3 Tanzania 22 19 3 Average 33 26 3 All sites Average 26 27 4 The mean and median TAS costs in this operational research study were $25,500 and $24,900 with the largest proportion of costs allocated to personnel (33%) and transportation (24%) (Tables 11 and 12). Community surveys (mean $26,800, median $26,000) required slightly more resources than school surveys (average $24,900, median $23,800). Project cost was moderately correlated to the area of the EU (R2 = .39). It should be noted, however, that all costs referenced here reflect research budgets and objectives including training, foreign consultants, and extra specimen shipment and analysis; carried out for programmatic purposes, costs would be expected to be less. 10.1371/journal.pntd.0002584.t011 Table 11 Total TAS operational research costs for school and community-based surveys. Survey site Low High Mean Median School (n = 8) $16,200 $36,900 $24,900 $23,800 Community (n = 3) $17,500 $36,800 $26,800 $26,000 Total (n = 11) $16,200 $36,900 $25,500 $24,900 10.1371/journal.pntd.0002584.t012 Table 12 Allocation of TAS costs by spending category. Description % of total costs Personnel (per diems) 33% Transportation (fuel, vehicle hire) 24% Diagnostic tests (procurement, shipment, customs) 15% Consumable supplies (e.g. lancets, EDTA tubes) 14% Communication (e.g. printing, mobile phone data) 3% Other (e.g. training, consultants, sensitization, specimen shipment) 11% Total 100% Discussion LF elimination programs require a standardized methodology that is statistically robust and programmatically feasible in order to assure confidence in making stop-MDA and post-MDA surveillance decisions. In this regard, Transmission Assessment Surveys offer a more pragmatic approach than previous WHO guidelines and with 22 implementations of the TAS in 11 countries, this operational research study provides the first report of a large-scale rollout of the TAS at a programmatic level. Indeed, these field experiences in multiple geographic and epidemiological settings have offered a prime opportunity to evaluate the TAS protocol critically and identify both best practices for future implementation and important remaining research gaps. TAS Results and Sampling Strategy Consistent results were seen across TAS-1 and TAS-2. In the 10 EUs that passed TAS-1, the recommended decision to stop MDA was validated in TAS-2, as no resurgence of infection was observed above the critical cutoff value where active transmission is anticipated as likely to occur. This finding is extremely important from a programmatic perspective because if the TAS-2 result had differed from TAS-1, MDA might have needed to be restarted in the EU, which is not only a resource intensive process but one that could be politically and socially undesirable. A final TAS evaluation is recommended in these EUs after another 2–3 years to confirm the absence of reemerging transmission detectable by the TAS. The results were in-line with anticipated outcomes of the TAS survey design and sampling strategy. Design effects for W. bancrofti EUs fell within expected limits, and participant age and sex reflected distributions in the target population. One notable advantage of the TAS protocol is its inclusion of cluster surveys to reduce the number of survey sites and overall sample size. In this study, 8 of 11 countries used a cluster survey design although sampling efficiency differed from TAS-1 to TAS-2. For TAS-1, a total of 63 extra clusters had to be selected and surveyed in addition to the originally planned sample in order to fulfill the target sample size. Such a process proved burdensome to survey planning and resource allotment. In contrast, only 10 extra clusters were needed in TAS-2 to achieve the target objective. This vast improvement in TAS-2 is largely because of factoring in ‘non-participators’ (i.e. absent children and those refusing to participate or without consent) into the initial survey design calculation. Estimates of the non- participation rate, however, might be difficult to obtain or measure during TAS planning, as was the experience in several of the countries in TAS-2. In such cases, a 10–15% estimated non- participation rate can be recommended based on the results from this study (Table 9), although this rate may vary greatly by EU and survey location. Community-based surveys, in particular, may experience a larger non- participation rate than school surveys because of the unreliable availability of eligible children at specific times of the day. The amount of TAS pre-planning and school or community sensitization is also likely to influence non- participation rates considerably. Because the TAS uses a fixed sampling fraction within each cluster, the inclusion of an accurate non- participation rate into the survey design calculation is also necessary to achieve a more accurate sample size. More specifically, underestimating the non- participation rate would result in larger sampling intervals and, therefore, fewer children sampled per cluster than required given the number of clusters selected. Since the TAS presumes an equal probability sample, extra clusters would be needed to make up the sample size difference, as seen most notably in TAS-1. Despite best efforts to reach sample size targets efficiently using non- participation rates and extra clusters, our study found that discrepancies may persist because of outdated population or enrollment estimates, school closures, inclement weather, and other factors including the selection by chance of several large or small outlier schools. Non-participation is also not unprecedented in such types of surveys and because absentees were randomly spread out across clusters, sampling bias was likely not introduced. Furthermore, the inclusion of extra clusters improved sample robustness and reduced intraclass correlation between clusters. Probability proportional to estimated size (PPES) sampling has been investigated but preliminary assessment suggests the uncertainties of actual school size and number of smaller schools with target children below the fixed number needed would increase the average clusters required and likely offset benefits to standardizing the sample size [16]. Strategic approaches to harmonize the target and actual sample size will likely evolve as the TAS is further field tested and evaluated. Several improvements have already been made to the SSB tool including the input of an estimated non- participation rate and the automatic random selection of ‘backup clusters’ to survey in case the target sample size is not initially met. This study also validated the overall utility and convenience of the SSB tool with regards to simply determining the proper survey design, calculating sample sizes and sampling intervals, and randomizing cluster and child selection lists. Future TAS should continue using the SSB tool for survey planning. The TAS protocol identifies 6–7 year old children as the target age group. While no positive cases were found outside the 6–10 year age range, a narrower sampling frame of 6–7 year olds is believed to be more epidemiologically accurate and programmatically feasible to avoid larger sample sizes [16]. In school surveys, 6–7 year olds are approximated by 1st–2nd grade children. This approximation, however, proved ambiguous in countries where the target ages and grades did not effectively align. For example, in Ghana, children 8–10 years were frequent in 1st–2nd grade. In Malaysia and Indonesia, 1st–2nd grade typically corresponds to 7–8 year old children. Furthermore, some countries including Togo interpreted the guidelines as only including 6–7 year olds within 1st–2nd grade as the target population. Therefore, although the results show that 6–7 year old children still comprised the majority of all school surveys, the clarification of the age requirement in the TAS protocol is extremely important for planning and calculating an accurate survey design. To this end, the general guideline in the SSB tool has been revised for programs to specifically select the grade(s) in which 6–7 year old children are most likely to be found and then to use those grade(s) as the eligible target group for school surveys. This refined terminology was implemented successfully in the Vanuatu study and is likely to benefit and simplify future TAS implementations as well. Specimen Collection and Diagnostic Tests Specimen collection procedures were closely examined within the context of an operational research protocol that involved collecting blood into an EDTA-coated tube that would be transported and analyzed in a central laboratory, as opposed to directly conducting the ICT (or PanLF, Brugia Rapid) tests in the field. The perceived advantage of this method was to streamline blood collection in the field while being able to perform the diagnostic tests in a more controlled environment. This strategy proved adequate under operational research conditions to evaluate quality and consistency; however, it introduced logistic challenges in terms of transportation, time, and supplies. In addition, it was observed that field staff may be unfamiliar with drawing blood into EDTA tubes and basic pipetting techniques. This method was also more challenging for follow-up testing or where there was insufficient blood quantity or clotting. As a result, it may be more efficient programmatically for teams to conduct diagnostic tests in the field, directly transferring blood from the finger prick to the ICT or Brugia Rapid card with a calibrated capillary tube. This process was carried out successfully in Vanuatu, Indonesia, and Malaysia because of logistic restrictions that are likely to be duplicated in other TAS-eligible EUs. However, because the rapid diagnostic tests are extremely time sensitive and require good lighting, it is highly recommended that one team member be specifically assigned to timing and reading the tests in an area with sufficient lighting. However, in community surveys where house-to-house visits are more time consuming and on-the-spot diagnostic testing is likely to exacerbate this constraint, especially when surveys are conducted in the afternoon or evening, lighting becomes more restricted and it might be preferable to collect blood in EDTA tubes for later analysis. The performance and reliability of the diagnostic tests used for the TAS are undoubtedly critical to the success of the survey. In TAS-1, all positive ICT tests were immediately followed-up with a repeat test to confirm the initial finding. In all 33 positive cases, the original and repeat ICT tests were both positive, indicating 100% positive concordance. Despite this limited sample size, repeat ICT tests are deemed unnecessary under current TAS programmatic guidelines. More importantly, however, the field experiences here showed that the quality and consistency of ICT results can be strongly improved with robust training and strict adherence to reading the cards after exactly ten minutes. A newer filariasis test strip with potential greater sensitivity and reduced susceptibility to heat will only improve the accuracy of TAS results although it may require the adjustment of critical cutoff values and sample sizes [17]. Mf tests using blood smear (TAS-1 and TAS-2) and PCR methods (TAS-1 only) were examined in this study and showed that positive concordance to antigen (W. bancrofti) and antibody (Brugia spp.) results were comparable to previous studies, albeit with much smaller sample sizes [14]. Programmatically, however, the ICT and Brugia Rapid tests remain more suitable as the primary TAS diagnostic tool given their convenience advantages. Mf tests may best be utilized as a positive-case follow-up tool to test for potential hotspots, focal transmission, or spatial clustering. School versus Community-Based TAS in Targeted EUs The community-based TAS studies in Burkina Faso and Tanzania highlighted several specific challenges; in particular, both had trouble finding children in the daytime and poor census and map accuracy led to difficulties estimating the target age group, enumerating houses, and defining EA boundaries. While not especially pronounced in these studies, non-participation rates, cost, and time can all be reasonably assumed to be higher in community TAS than in school TAS. Of note, the number of field days for school surveys was heavily skewed by the considerable time taken in the Philippines due to severe weather and poor accessibility to insecure areas in the EU. Moreover, the level of planning, training, sensitization, and field effort required for the community-based surveys in Burkina Faso and Tanzania were qualitatively much higher as reported by field staff and supervisors. Perhaps if more community-based TAS were conducted in this study and if time included the planning stage and was measured in person-hours rather than days, differences between school and community-based surveys would have been more evident. Community-based TAS are also limited by having to often sample eligible children on evenings or weekends outside of regular school hours. A more critical assessment of the 75% enrolment rate requirement for TAS school surveys could, therefore, have important implications if this threshold could be justifiably lowered. A comparison of school and community-based TAS is also important to disprove any selection bias that may occur by only sampling school children, namely that those not attending school may also not be attending MDAs and are at a higher risk for infection. Preliminary results from separate TAS studies appear to suggest there is no statistically significant difference or change in the TAS-recommended outcome for EUs with school primary enrolment rates as low as 59% [18]. Although the majority of TAS EUs are still likely to qualify for school surveys, validation of such results would greatly streamline the overall efficiency of the TAS sampling strategy if school surveys could be used on a wider or exclusive basis. The composition of the TAS EU requires careful consideration to ensure that uniform epidemiological conditions persist across the EU. Despite the TAS being designed to provide an accurate EU-wide assessment, an EU that is smaller in area would presumably be more likely to include a self-sustaining subpopulation in its cluster sample (if such a ‘hotspot’ existed), but it might also be more cost prohibitive at a regional or national scale. In contrast, combining multiple IUs into one larger EU is more cost-effective, but clusters are spread more thinly across the EU and may miss potential hotspots where infection may persist in a focal area despite the overall EU successfully passing the TAS. A simple linear regression analysis of the EUs in this study showed moderate correlation between the cost of the TAS and EU area size, although cost is dependent on the geographical setting (e.g. transportation costs in Vanuatu were understandably greater than in Togo and Ghana despite relatively similar EU area sizes). The maximum limit of 2 million people for an EU also requires evidence; however, as the average EU population here was approximately 250,000 with a maximum of 682,000, no information about the validity of extremely large EU populations can be ascertained from this study. Identification of cost and epidemiological appropriateness of EUs may also be aided by spatial modeling or related research to determine additional criteria that is pertinent to defining an ideal EU size or cost for TAS. Although there was no evidence of major differences between rural and non-rural clusters in our study, MDA coverage and compliance might differ considerably in both areas. Likewise, cross-border infection with high-endemic neighboring IUs or other countries may increase the risk of transmission into the TAS EU. In the Dominican Republic study, some evidence of cross-border infection from Haitian immigrants was described in bordering EAs. Other high-risk factors could persist in specific parts of an EU but not others. In the Philippines, a census evaluation of 533 TAS-eligible children was conducted in a sub-area of the EU where there is a high concentration of certain axillary plants known to support breeding of LF vectors and increase inhabitants' risk of exposure and infection. Though no positive cases or significant difference from the rest of the EU was detected in the high-risk area (unpublished data), such factors should be carefully examined and accounted for when classifying TAS EUs in order to maintain a fairly homogeneous EU so far as risk of LF infection can be assessed. Post-MDA Surveillance TAS is currently recommended for EUs in post-MDA surveillance mode using an identical methodology to EUs evaluating the decision to stop or continue MDA. The results in this study support the reliability of this strategy but because TAS is not powered to detect change or designed to identify hotspots, post-MDA surveillance would best be complemented in the short and long term with other, complementary diagnostic tests and surveillance methods. In particular, antibody testing using Bm14, Bm33, or Wb123 assays may be highly suitable for post-MDA surveillance because it is more sensitive than antigen testing and may be superior to TAS for early detection of residual or resurgent LF infection. Initial findings from American Samoa and Haiti comparing filarial antigen and antibody responses seem to indicate that the antibody responses may be early markers of infection and not just exposure [19], [20]. The development of multiplex tools for NTD surveillance further facilitates the ability to conveniently examine several parameters at once [21], [22]. Xenomonitoring may also be a useful complementary post-MDA surveillance strategy because advances in molecular technology give it the potential to identify low-level LF infection in vector mosquitoes while being ‘non-invasive’ to the human population. Particularly in the majority of countries where filariasis is transmitted by Culex mosquitoes, efficient collection techniques exist and early results have been promising [23]–[25]. Furthermore, preliminary analysis of mosquitoes collected in American Samoa and Sri Lanka, in conjunction with these TAS studies, shows that xenomonitoring may provide comparable transmission markers and offer a cost-effective addition to the periodic post-MDA surveys where appropriately trained entomology teams are available (unpublished data). Longer term, post-TAS surveillance may also best be met through passive surveillance strategies using appropriate sentinel groups for routine blood monitoring or through malaria- or other disease-surveillance efforts [12], [22], [26]. Utilizing the antibody-based critical cutoff values for Brugia spp. EUs remains a concern for the current TAS protocol. While successfully passing the TAS based on more conservative thresholds increases the confidence of the results, the antibody-based thresholds may be overly restrictive, compared to the antigen-based thresholds for W. bancrofti. Additionally, the design effects calculated in the two Brugia spp. TAS (Indonesia and Malaysia) were notably higher than those assumed for calculating TAS sample sizes. In Malaysia, the large design effect can be partially attributed to a greater number of positive cases found in the EU than normally presumed by TAS. In Indonesia, however, the sample size and number of positive cases were similar to Burkina Faso yet the design effect was 2–3 times greater. Such findings may be indicative of inherent epidemiological differences of the respective EUs, but also warrant further investigation of the implications of evaluating filarial antigen and antibody using the same decision criteria. Interruption of ongoing LF transmission and cessation of MDA in an LF endemic area are milestone achievements but ones that require careful determination and accurate assessment. TAS guidelines are currently in place for stopping MDA and post-MDA surveillance and can be carried out effectively and efficiently with recommendations and best practices identified through the operational research experiences here. While the general sampling strategy has proven to be robust and pragmatic, thresholds and sample sizes may need to be modified as new diagnostic tools become available and validated. The ability of the TAS, however, to detect recent or ongoing LF transmission in hotspots within an EU that passes the critical threshold is still untested and requires longer-term empirical evidence. Additional research into the composition of EUs and mechanisms for hotspot detection and post-MDA surveillance will only help evolve and strengthen the current guidelines. From a broader perspective, the survey design principle of the TAS can be realistically applied and adapted to other NTDs as they reach similar points in their programs. The TAS may also provide a very opportune platform and sampling strategy to integrate assessments for co-endemic NTDs such as onchocerciasis and STH. Continued deployment and refinement of the TAS, therefore, is essential not only for LF elimination programs but potentially to the wider NTD community as well. Supporting Information Checklist S1 STROBE checklist. (DOC) Click here for additional data file.

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Lymphatic filariasis: an infection of childhood.

Christopher Witt, Camilla Ottesen (2001)

Lymphatic filariasis (LF), already recognized as a widespread, seriously handicapping disease of adults, was generally thought to occur only sporadically in children. New, highly sensitive diagnostic tests (antigen detection, ultrasound examination) now reveal, however, that LF is first acquired in childhood, often with as many as one-third of children infected before age 5. Initial damage to the lymphatic system by the parasites generally remains subclinical for years or gives rise only to non-specific presentations of adenitis/adenopathy; however, especially after puberty the characteristic clinical features of the adult disease syndromes (lymphoedema, hydrocoele) manifest themselves. Recognizing that LF disease starts its development in childhood has immediate practical implications both for management and prevention of the disease in individual patients and for the broader public health efforts to overcome all childhood illnesses. For the new World Health Organization (WHO)-supported, public-/private-sector collaboration (Global Alliance) to eliminate LF through once-yearly drug treatment, this recognition means that children will be not only the principal beneficiaries of LF elimination but also a population particularly important to target in order for the programme to achieve its twin goals of interrupting transmission and preventing disease.

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Katherine M. Gass:

ORCID: http://orcid.org/0000-0002-2084-8039

Role: Formal analysisRole: MethodologyRole: SoftwareRole: Writing – original draft

Heven Sime: Role: InvestigationRole: Project administrationRole: SupervisionRole: Writing – review & editing

Upendo J. Mwingira: Role: InvestigationRole: Project administrationRole: Supervision

Andreas Nshala: Role: InvestigationRole: Project administrationRole: Supervision

Maria Chikawe: Role: InvestigationRole: Supervision

Sonia Pelletreau: Role: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – review & editing

Kira A. Barbre: Role: Formal analysisRole: Writing – review & editing

Michael S. Deming: Role: ConceptualizationRole: MethodologyRole: Writing – review & editing

Maria P. Rebollo: Role: ConceptualizationRole: Methodology

Charles H. King: Role: Editor

Journal

Journal ID (nlm-ta): PLoS Negl Trop Dis

Journal ID (iso-abbrev): PLoS Negl Trop Dis

Journal ID (publisher-id): plos

Journal ID (pmc): plosntds

Title: PLoS Neglected Tropical Diseases

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Print): 1935-2727

ISSN (Electronic): 1935-2735

Publication date (Electronic): 4 October 2017

Publication date Collection: October 2017

Volume: 11

Issue: 10

Electronic Location Identifier: e0005944

Affiliations

[1 ] Neglected Tropical Disease Support Center, Task Force for Global Health, Atlanta, United States of America

[2 ] Ethiopian Public Health Institute, Addis Ababa, Ethiopia

[3 ] Neglected Tropical Disease Control Program, Ministry of Health and Social Welfare, Dar es Salaam, Tanzania

[4 ] IMA World Health Tanzania, Dar es Salaam, Tanzania

[5 ] Children’s Investment Fund Foundation, London, United Kingdom

[6 ] Consultant, Neglected Tropical Diseases Support Center, Task Force for Global Health, Atlanta, United States of America

Case Western Reserve University School of Medicine, UNITED STATES

Author notes

I have read the journal's policy and the authors of this manuscript have the following competing interests: Ms. Sonia Pelletreau is currently employed by the Children's Investment Fund Foundation (CIFF), though her employment began after her involvement with this research project ended. Prior to her employment with CIFF, Ms. Pelletreau was a consultant paid by the NTD Support Center to lead the field work related to this manuscript. Dr. Gass is presently employed by the NTD Support Center at the Task Force for Global Health.

[¤]

Current address: Expanded Special Project for Elimination of Neglected Tropical Diseases, World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo

‡ KAB and MSD also contributed equally to this work.

* E-mail: kgass@ 123456taskforce.org

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Katherine M. Gass http://orcid.org/0000-0002-2084-8039

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Publisher ID: PNTD-D-17-00880

DOI: 10.1371/journal.pntd.0005944

PMC ID: 5643143

PubMed ID: 28976981

SO-VID: 6cf204ee-d1e6-42b3-b24c-e3b13d2d9aed

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

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Date received : 20 June 2017

Date accepted : 7 September 2017

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Figures: 1, Tables: 5, Pages: 14

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Funded by: funder-id http://dx.doi.org/10.13039/100000865, Bill and Melinda Gates Foundation;

Award ID: OPP1053230

Funded by: funder-id http://dx.doi.org/10.13039/100000200, United States Agency for International Development;

Award ID: AID-OAA-G-14-00008

This work received financial support from the Neglected Tropical Disease Support Center (NTDSC), which is funded at the Task Force for Global Health principally by grants from the Bill & Melinda Gates Foundation and United States Agency for International Development. The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Publication Update 2017-10-16

Data Availability All data are contained within the supporting information files.

The rationale and cost-effectiveness of a confirmatory mapping tool for lymphatic filariasis: Examples from Ethiopia and Tanzania

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A Multicenter Evaluation of Diagnostic Tools to Define Endpoints for Programs to Eliminate Bancroftian Filariasis

Transmission Assessment Surveys (TAS) to Define Endpoints for Lymphatic Filariasis Mass Drug Administration: A Multicenter Evaluation

Lymphatic filariasis: an infection of childhood.

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