Costs of Transmission Assessment Surveys to Provide Evidence for the Elimination of Lymphatic Filariasis

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Abstract

Background

To reach the global goal of elimination of lymphatic filariasis as a public health problem by 2020, national programs will have to implement a series of transmission assessment surveys (TAS) to determine prevalence of the disease by evaluation unit. It is expected that 4,671 surveys will be required by 2020. Planning in advance for the costs associated with these surveys is essential to ensure that the required resources are available for this essential program activity.

Methodology and Findings

Retrospective cost data was collected from reports from 13 countries which implemented a total of 105 TAS surveys following a standardized World Health Organization (WHO) protocol between 2012 and 2014. The median cost per survey was $21,170 (including the costs for rapid diagnostic tests [RDTs]) and $9,540 excluding those costs. Median cost per cluster sampled (without RDT costs) was $101. Analysis of costs (excluding RDTs) by category showed that the main cost drivers were personnel and travel.

Conclusion

Transmission assessment surveys are critical to collect evidence to validate elimination of LF as a public health problem. National programs and donors can use the costing results to adequately plan and forecast the resources required to undertake the necessary activities to conduct high-quality transmission assessment surveys.

Author Summary

As national programs are nearing elimination of lymphatic filariasis as a public health problem, questions have been raised about the cost of collecting the data necessary for documenting validation of elimination. A series of standardized population-based surveys is necessary to determine prevalence of infection in endemic areas. The authors retrospectively collected data on the costs of these surveys from 13 countries to determine median cost per survey and per cluster sampled. Costs were found to be comparable with other neglected tropical disease surveys. The major cost drivers were personnel and travel for supporting collection of data in the field. National programs and donors can use these results to plan and advocate more effectively for sufficient resources to support validation of elimination.

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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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Incremental Cost of Conducting Population-Based Prevalence Surveys for a Neglected Tropical Disease: The Example of Trachoma in 8 National Programs

Chaoqun Chen, Elizabeth A. Cromwell, Jonathan D King … (2011)

Introduction Trachoma is an eye disease, caused by infection with ocular Chlamydia trachomatis, which causes blindness. However, trachoma can be treated and prevented through the SAFE strategy, endorsed by the World Health Organization (WHO): Surgery for trichiasis; Antibiotic therapy through mass distribution; Facial cleanliness promotion through health education; and Environmental improvement with sanitation. Trachoma is endemic in 57 countries worldwide, with the burden of disease concentrated in sub-Saharan Africa and the Middle East[1]. The WHO estimates that over 80 million people currently have active trachoma and another 8 million suffer from trichiasis, with a potential productivity loss of $2.9 billion annually at the global scale[2]. The World Health Assembly has set 2020 as the target date for the elimination of blinding trachoma worldwide[3]. Where trachoma is suspected to be a public health problem, the WHO recommends that the prevalence of the clinical signs of the disease are estimated using a cluster random survey methodology at the district level[4]. There are two other less common methods used to assess the burden of trachoma disease: trachoma rapid assessments (TRA); and acceptance sampling trachoma rapid assessment (ASTRA)[5], [6]. As demonstrated in the literature[7], the population-based probability sampling (PBPS) method is the most epidemiologically robust method available to generalize the prevalence of clinical signs to the domain of interest. In brief, the PBPS method employs a multi-stage cluster random survey design to randomly select clusters, and households within the clusters. Once households are selected, all members of the household are examined for clinical signs of trachoma disease using the WHO Simplified Grading System[8]. Survey team members are trained to conduct trachoma grading and household selection before participating in survey field work. Most survey teams consist of pairs of trachoma examiners and recorders, with one or two pairs needed to survey a cluster. Upon completion, double entry of survey data and analysis are performed by temporary staff or non-governmental organizations and Ministry of Health personnel. Trachoma prevalence surveys provide an estimate of the burden of disease at the level of interest, usually the district. These data serve as the evidence base for determining how the SAFE strategy should be employed. For example, where the prevalence of the clinical grade TF (trachomatous inflammation, follicular) exceeds 10% in children aged 1–9 years, the WHO recommends district-wide mass treatment with antibiotics and facial cleanliness and environmental improvements—the “AFE” of SAFE. Prevalence survey data are also used to calculate annual intervention targets and ultimate intervention goals (UIGs), such as the number of people who require trichiasis surgery. These targets are used to plan annual activity budgets, forecast the need for donated pharmaceuticals and other supplies, and monitor progress towards the elimination of blinding trachoma. Although survey implementation may vary by location, there are currently no data on the cost of trachoma prevalence surveys in the peer-reviewed literature. There are examples in the literature where different survey methods were compared to determine the most cost-effective method to estimate immunization coverage[9], [10]. While comparisons such as these can be used to evaluate the cost-effectiveness of different survey methods, they do not provide sufficient data to generalize the cost of conducting these surveys at the regional or global level. In this paper, we present an analysis of costs incurred in the implementation of trachoma prevalence surveys across eight national trachoma control programs. The findings from this analysis will enable national trachoma program managers and international partners to budget for trachoma prevalence mapping appropriately. Methods Ethics Statement The analysis of prevalence survey cost data did not involve any research on human subjects. The prevalence surveys reviewed in this paper were conducted in accordance with the Declaration of Helsinki and reviewed by the Emory University Institutional Review Board or the London School of Hygiene and Tropical Medicine (LSHTM) Ethical Committee and each country's respective Ministry of Health. External funding for the prevalence surveys was as follows: LSHTM, The Gambia survey; Helen Keller International, Sikasso Region of Mali; The International Trachoma Initiative and The Carter Center, 18 districts in Ghana; The Carter Center, all other surveys. Data Collection A systematic review of trachoma prevalence surveys conducted in Ethiopia, Ghana, The Gambia, Mali, Niger, Nigeria, Sudan, and Southern Sudan was performed February through May 2010. This review of prevalence survey costs included surveys that employed a PBPS methodology to estimate trachoma prevalence at the district level, or the administrative unit equivalent to a district (administrative unit with population of approximately 100–250 thousand people: woreda in Ethiopia, region in The Gambia, local government area in Nigeria, locality in Sudan, and county in Southern Sudan). Included surveys were implemented from 2006–2010, and funded or co-funded by The Carter Center, LSHTM (The Gambia), The International Trachoma Initiative (Ghana), or Helen Keller International (Sikasso Region, Mali). All surveys were ‘cluster random surveys’ that used a two stage sampling process to select clusters (communities, villages, or enumeration areas) representative of the domain in the first stage and households within the cluster in the second. The numbers of clusters and households in the surveys was not constant between districts. A data collection tool was used to collect the actual costs incurred in local currency during survey activities from accounting records in the programs. The tool collected data for four cost activities: training, field work, supervision and data entry. Training included costs such as per diem of trainees and trainers, meeting facility and supplies, transportation to the practical exercise and any required overnight accommodation. Field work costs included per diems for survey personnel (trachoma grader and recorder), transportation of survey field team, accommodation and supplies such as tetracycline eye ointment and magnifying loupes. Supervision included any per diem, transport and accommodation paid to Ministry of Health or NGO personnel retained for supervision of field work activities. Data entry costs included per diem of data entry clerks, cost of computer rental and information technology support (if required) and supplies. For each cost activity, data were collected on the number of people paid, the daily rate and the number of days paid. Transportation costs included any vehicle rental, fuel expense and driver per diem. The data collected in this study captured the incremental cost of conducting prevalence surveys in the context of an existing national trachoma control program. Ministry of Health and NGO salaries and other associated costs were not included in the analysis. Integrated prevalence surveys (more than one disease measured) were excluded from this analysis. “Headquarters” expenses were not included in the primary analysis of prevalence survey costs. Although beneficial, consultant or other outside technical assistance is not required for a national program to conduct trachoma prevalence surveys. Furthermore, the cost of outside technical assistance is dependent on travel expense policies which are unique to each partner. The cost of Carter Center headquarter support for specific survey activities are reported in this review, but were not included in the district-level cost data, as these costs are organization-specific and cannot be generalized. Once completed, the cost data forms were verified against the financial reports from the Carter Center, Helen Keller International, LSHTM or the Ministries of Health. In Ghana, Ethiopia and Northern Sudan, exact data on distance traveled were not available; the data reported for these programs' distance traveled are estimates from the national programs. Data Analysis Data were converted to US dollars using the mean of the weighted average exchange rate from the World Bank (http://data/worldbank.org/indicator/PA.NUS.FCRF) for the years 2007–2009. Since most district-level prevalence surveys were conducted in groups (i.e. all districts in a region surveyed at the same time), costs were not reported for each individual district. Rather, each “grouping” of surveys that were financed at the same time was analyzed as the same observation. For example, in the Kayes Region of Mali, all 7 districts were surveyed using the same survey personnel within the same period of time. Funds were provided to the Ministry of Health to conduct the survey work for the entire region, which resulted in efficiencies gained by conducting one initial training and reducing the amount of transport required. Where data were reported in this fashion, the districts are treated as the same observation in the analysis. Based on these observations, the analysis generates the overall costs, the average survey costs per district and average costs per cluster for each observation. Data were first entered into Excel and then analyzed using STATA to generate descriptive statistics for each cost activity. Subsequently, a cost composition analysis was performed. The data were classified into activities as defined in the data collection tool to calculate the proportion of the total cost for each cost activity. Within each of the four activities (training, field work, supervision and data entry), four main cost categories were identified: personnel, transportation, supplies and other. The costs for each category were compared against the total cost for each activity to identify the main cost drivers of survey expenses. Normally distributed data are presented as the mean and standard deviation (SD). Not-normally distributed data is presented by the median and inter-quartile range (IQR). Results Survey Costs A total of 29 observations were collected from eight national trachoma control programs. The cost per district by observation is presented in Table 1. Overall, a total of 165 district-level surveys were included (Figure 1), representing a total of 3,203 clusters surveyed. The average costs per district were skewed to the right by an outlier (Ayod in Southern Sudan, $25,409) so are described by the median, $4,784 and IQR, $3,508–$6,650. The median cost per cluster was $311 (IQR = $119–$393) whilst the median cost per person screened was $3.50 (IQR = 1.94–4.16). (The mean cost per district, cluster and person was $5,849 (SD = $4,635), $324 (SD = $236), and $3.39 (SD = $2.02) respectively). The least expensive survey per district was in Ethiopia, approximately $1,511 per district. The number of districts, clusters and persons sampled per observation is presented in Table 1. 10.1371/journal.pntd.0000979.g001 Figure 1 Map of district-level trachoma prevalence surveys included in the cost analysis. 10.1371/journal.pntd.0000979.t001 Table 1 Summary of total costs, by observation. National program Observation Number of districts Number of clusters Number of households per cluster Number of people examined Total costs ($) Cost per district ($) Cost per cluster ($) Cost per person screened ($) Reference Ghana Northern & Upper West 18 720 30 74,225 72,249 4,014 100 0.97 Yayemain 2009 Mali Kidal 1 20 24 2,165 14,777 14,777 739 6.83 Bamani 2010 Kayes 7 140 24 13,576 13,593 1,942 97 1.00 Bamani 2010 Koulikoro 9 180 24 19,342 17,505 1,945 97 0.91 Bamani 2010 Sikasso 8 160 24 18,795 19,046 2,381 119 1.01 PNLCC Segou 8 160 24 16,471 18,553 2,319 116 1.13 PNLCC Nigeria Plateau & Nasarawa 13 260 16 21,606 24,036 1,849 92 1.11 King 2010 Southern Sudan Jonglei (Ayod County) 1 20 20 2,335 25,409 25,409 1,270 10.88 King 2008 Northern Sudan Kassala 10 132 30 10,576 35,308 3,531 267 3.34 FMOH GOS Blue Nile 4 45 20 5,166 18,799 4,700 418 3.64 FMOH GOS Gazeira 7 105 20 10,466 42,049 6,007 400 4.02 FMOH GOS White Nile 8 120 20 10,570 39,168 4,896 326 3.71 FMOH GOS Gadarif 10 150 20 13,682 47,839 4,784 319 3.50 FMOH GOS Sinnar 7 105 20 9,095 34,961 4,994 333 3.84 FMOH GOS River Nile 6 90 20 7,528 20,632 3,439 229 2.74 FMOH GOS Red Sea 10 150 20 9,918 40,680 4,068 271 4.10 FMOH GOS Northern 5 66 20 11,076 36,454 7,291 552 3.29 FMOH GOS North Kordofan 9 135 20 10,360 37,494 4,166 278 3.62 FMOH GOS South Kordofan 9 135 20 10,755 41,960 4,662 311 3.90 FMOH GOS Niger Magaria 1 20 24 1,789 7,884 7,884 394 4.41 PNLCC Niger Matameye 1 20 24 1,712 7,835 7,835 392 4.58 PNLCC Niger Nguigmi 1 20 24 1,659 7,866 7,866 393 4.74 PNLCC Niger Maine Soroa 1 20 24 1,867 7,866 7,866 393 4.21 PNLCC Niger Maradi Commune 1 20 24 2,393 6,132 6,132 307 2.56 PNLCC Niger Tessaoua 1 20 24 1,806 6,132 6,132 307 3.40 PNLCC Niger Gaya 1 20 24 2,036 6,650 6,650 333 3.27 PNLCC Niger Loga 1 20 24 1,801 6,650 6,650 333 3.69 PNLCC Niger Ethiopia Amhara 5 90 10 5,762 7,556 1,511 84 1.31 Ngondi 2008 The Gambia Lower River & North Bank 2 60 25 2,990 7,815 3,908 130 2.61 Harding-Esch 2009 Total 165 3,203 301,552 672,897 Composition of Survey Costs When the costs for each survey activity were compared against the total cost (Table 2), the data showed that field work comprised on average 69.9% of the total cost of a survey. Among the observations, the proportion of total costs spent on field work ranged from 44.9% to 90.5%. Training costs ranged from 1.0% to 29.6% of total costs, supervision expenses were between 0.0% and 20.9% of the total, and data entry costs ranged from 0.0% to 25.0% across all observations. Within each survey activity, personnel costs were the most expensive, with personnel costs in field work accounting for 40.4% of the total survey costs reported by the national programs, followed by transportation during field work at 22.4%. 10.1371/journal.pntd.0000979.t002 Table 2 Average proportion of total survey costs attributed to cost categories and activities. Activities Training Field work Supervision Data entry Total Category Personnel 1.9% 40.4% 11.3% 10.9% 64.6% Transportation 1.6% 22.4% 1.7% 0.0% 25.7% Supplies 0.9% 5.3% 0.0% 0.0% 6.3% Others 1.4% 1.7% 0.3% 0.0% 3.3% Total 5.9% 69.9% 13.2% 10.9% 100.0% Training and data entry activity costs were reported by observation as the cost for each activity. These costs were not always directly related to the number of districts surveyed as some programs did not incur cash costs for these activities. The mean cost of training was $1,342 (SD $659) while the median was $1,791.50 (IQR = $588–$1,816). The mean cost of data entry was $2,548 (SD $3,493) and the median was $1,028 (IQR = $415–$4,431). Costs of ‘Headquarters’ Participation in Surveys Although the cost of outside technical assistance was not factored into the district or cluster level cost analysis, there were 9 observations that were surveyed with at least one representative from The Carter Center Headquarters (Atlanta, Georgia, USA) present, covering a total of 58 districts. The average cost for airfare, hotel, meals and incidentals per person-trip was $1,779 (n = 13, SD = $2,027) from 2006–2010. Discussion It is possible that trachoma control programs do not implement prevalence surveys due to a perception that the costs will be beyond the capacity of the program. However, the results of this analysis show that such surveys are not cost-prohibitive. The range of costs per district varied from $1,151–$25,409, in large part due to differences in accessibility and the number of clusters sampled in each survey. Of the 29 observations, only three surveys reported a cost per cluster exceeding $500: Ayod in Southern Sudan, Kidal in Mali and the Northern Region in Sudan. These surveys were characterized by both high transport and personnel costs. In Ayod County of Southern Sudan, where the average cost per cluster was $1,270 and average cost per person screened was $10.88, vast distances of water-logged and unforgiving terrain made vehicle transport impossible, requiring a chartered airplane to transport staff to airstrips from where they traveled to the clusters on foot over a period of days. These exceptional circumstances therefore required additional staff, working for a longer period of time, and transport by chartered aircraft. In Kidal Region (a desert region of Mali), the second most expensive survey per cluster ($739 per cluster, $6.83 per person screened), the sparse population (80,000) and low population density (less than one person per square kilometer) resulted in the national program treating the region as the domain, with the consequence that the distances between clusters was hundreds of kilometers. To conduct this survey, the program rented vehicles instead of using Ministry of Health and NGO transport due to security concerns in the area. The Northern Region of Sudan ($552 per cluster, $3.29 per person screened) is also on the edge of the Sahara with similar demands on transport and time. Least expensive, at under $100 per cluster, were the surveys conducted in the Amhara region of Ethiopia ($84 per cluster, $1.31 per person screened) and Plateau and Nasarawa States of Nigeria ($92 per cluster, $1.11 per person screened) where per diem rates were low and the population is relatively dense, reducing both the travel costs and time spent travelling between clusters. In total, 7 observations cost less than $125 per cluster and these also had the lowest cost per person screened ($0.91–$1.31). In these surveys, the relative proximity of clusters and low per diem rates contributed to lower costs in comparison to the more expensive surveys. Among the cost categories reported, the per diem of field staff and supervisors and the cost of transportation accounted for 73% of the total survey costs. In settings where distances between communities are great, trachoma control programs may consider reducing the number of clusters surveyed and increase the number of people screened per cluster to reduce costs but maintain an adequate sample size. However, the risks to accuracy and precision around the prevalence estimate should be considered. Cost savings on transport and accommodation costs can be achieved by planning the route of vehicles between clusters carefully. A route for two teams can often be planned in which the teams share one vehicle, work in the first and second clusters simultaneously (with the vehicle shuttling between as necessary) and then travel together to the next cluster where they camp for the night and sensitize the village population of the survey to be conducted the following day. Such transport sharing and camping has been both effective and enjoyable in most of the countries in this analysis. Per diem and allowance costs vary by national program, level of trained personnel recruited to serve as survey team members and local supervision requirements. Per diem costs in the surveys studied ranged from $6.21 per day for graders (junior health staff) to $250 a day for senior supervisors (an ophthalmology professor and National Coordinator). When designing surveys, due consideration should be given to assign roles and responsibilities consistent with the qualification and per diem given. Junior health staff who are comfortable with the climate, social circumstances and geography of the area to be surveyed make ideal field staff, and serve to lower per diem costs. It is appropriate for a National Coordinator or ophthalmology professor to spend a day or two testing the ability of the trained examiners before the survey starts, but costs can be reduced if that person does not spend many days in the field. The review of data entry costs also presents new findings for Ministries of Health. Although data entry was not an expense for all surveys reported, data entry accounted for an average of 11% of total survey expenses. In this sample, the incremental cost of data entry ranges from 0% in surveys where existing program staff conducted data entry on existing computers incurring no additional cash cost to 25% of the total cost of the survey where external contractors were hired to complete the work. Survey planners should consider the cost of data entry in their own country context to ensure that costs for double entry, analysis and preparation of printed reports are included in budgets. By design, we did not capture the cost of each Ministry of Health and NGO employee who contributed time to conduct survey work, the incremental cost effectiveness ratio is likely to be underestimated since these costs were not taken into account. This could be included in the analysis as an opportunity cost. However, since the implementation of prevalence surveys is recommended as the standard monitoring and evaluation framework for trachoma control programs by the WHO, these surveys were within the mandate of the Ministry of Health personnel who were engaged in field work and supervision. Salary costs were excluded as they were considered part of the functional trachoma control program and we sought to establish the incremental cost of conducting surveys in the presence of a program. We also did not include the cost of technical assistance (including travel) for ‘headquarters’ staff. Although the average cost of a person-trip from The Carter Center for technical assistance was $1,779 (SD = $2,027), we considered this to be a non-essential cost for a program, subject to considerable variation between supporting NGOs who have different travel policies, and likely to come from a different operating budget which would not have an incremental effect on the cost of a national program. The selection of a sample representative of the underlying population presents an opportunity to collect data on multiple conditions and this has been done for trachoma and malaria[11] and trachoma and urinary schistosomiasis[12]. Such integrated surveys were not included in this analysis since they were considered special cases and not what is typically done. However, the costs of adding indicators for additional diseases or conditions are the additional personnel, equipment and consumables required for that survey, with the other cost items such as transport and per diem of the drivers and assistants covered by the ‘parent’ survey. Although the data presented show costs from a variety of settings, there are a few limitations. The data in this analysis were reported retrospectively and therefore, it is possible that some costs may not have been captured. For some surveys (Ghana, Ethiopia and Northern Sudan) log book entries for distance travelled were not available and we relied on the local knowledge of the national program to calculate distance travelled. Each of these surveys was conducted in the presence of a functioning trachoma control program; there was no need to purchase new vehicles or make other large capital expenses. Survey work performed in the absence of this infrastructure would be more expensive. New country programs may find it necessary to rent vehicles and seek technical assistance for training survey staff, the costs of which would need to be considered in addition to the incremental costs of conducting a survey presented here. There are variations in the number of clusters surveyed among the different observations, based on the population of each survey domain, which may affect the comparability of the survey costs among different countries. However, the authors expected variation among national programs due to differences such as per diem rates, the level of qualified health professional involved in field work, and the capacity to complete data entry. The variation seen in these data illustrate the context-specific nature of planning survey activities. However, these limitations should not discourage program managers from using the data presented in this paper as benchmarks for determining funding needs. Twenty-six out of the 29 observations were conducted with external funding exclusively from The Carter Center, which may imply the cost estimates are limited to those surveys supported by this NGO. However, there are similarities between the cost per cluster from The Gambia, which was fully funded by LSHTM, districts in Mali supported by Helen Keller International, and districts in Ghana co-sponsored by the International Trachoma Initiative and The Carter Center. This suggests that our findings are not unique to the operating principles of one NGO. Since transport and per diem were identified as major cost drivers, it is possible to predict total survey costs for areas requiring surveys. It is also possible to use these data to project the cost of other survey methodologies by applying the average cost per cluster to the number of clusters required. Despite the potential limitations of this study, these data present the only summary of actual costs incurred during trachoma prevalence surveys in the peer-reviewed literature. For the goal of elimination of blinding trachoma worldwide by 2020 to be met, national programs will need to budget for impact evaluation at the district level. The cost of epidemiologically rigorous surveys should not been seen as a barrier to their implementation. With adequate baseline and impact evaluation data, national programs can maximize their limited programmatic resources. These data should inspire national trachoma program managers and ministry of health staff involved in other public health supervisory roles to consider implementation approaches that ensure surveys are designed in a cost-effective and efficient manner. These cost data will enable the international trachoma control community to create global estimates on the cost to complete trachoma prevalence mapping and estimate the financial needs to support impact assessments to measure progress towards the elimination of blinding trachoma.

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Global programme to eliminate lymphatic filariasis: progress report, 2013.

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Patrick J. Lammie: 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): 1 February 2017

Publication date Collection: February 2017

Volume: 11

Issue: 2

Electronic Location Identifier: e0005097

Affiliations

[1 ]Global Health Department, RTI International, Washington, DC, United States of America

[2 ]Global Health, Population, and Nutrition (GHPN) Department, FHI360, Washington DC, United States of America

[3 ]Emerging Markets, Deloitte Consulting, Accra, Ghana

[4 ]Neglected Tropical Disease Program, United States Agency for International Development, Washington DC, United States of America

Task Force for Child Survival and Development for Global Health, UNITED STATES

Author notes

I have read the journal's policy and the authors of this manuscript have the following competing interests: Kingsley Frimpong is employed by a commercial company, Deloitte Consulting.

Conceptualization: MAB AW.
Data curation: RS.
Formal analysis: RS MAB.
Investigation: MDS JJ BP JK KF.
Methodology: MAB RS MDS.
Visualization: RS.
Writing – original draft: MAB.
Writing – review & editing: MAB RS MDS JJ BP JK KF AW.

* E-mail: mbrady@ 123456rti.org

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Molly A. Brady http://orcid.org/0000-0003-0113-9429

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Publisher ID: PNTD-D-16-01113

DOI: 10.1371/journal.pntd.0005097

PMC ID: 5287447

PubMed ID: 28146557

SO-VID: 2ddd9b59-2022-4d51-9e56-911b18fb52a9

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This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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Date received : 15 June 2016

Date accepted : 8 October 2016

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Figures: 3, Tables: 4, Pages: 11

Funding

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

Award ID: AID-OAA-A-11-00048

This work was made possible thanks to the generous support of the American People through the United States Agency for International Development ( https://www.usaid.gov/) and the ENVISION project led by RTI International (AID-OAA-A-11-00048). Additionally, Angela Weaver is employed by USAID. Her specific contributions are detailed in the 'author contributions' section. Via Angela Weaver, USAID contributed to study aims and review of the manuscript, but had no role in data collection and analysis. The authors’ views expressed in this publication do not necessarily reflect the views of the United States Agency for International Development or the United States Government.

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Costs of Transmission Assessment Surveys to Provide Evidence for the Elimination of Lymphatic Filariasis

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

Incremental Cost of Conducting Population-Based Prevalence Surveys for a Neglected Tropical Disease: The Example of Trachoma in 8 National Programs

Global programme to eliminate lymphatic filariasis: progress report, 2013.

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