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The cost of mapping trachoma: Data from the Global Trachoma Mapping Project

  • Guillaume Trotignon ,

    Contributed equally to this work with: Guillaume Trotignon, Ellen Jones, Thomas Engels

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

    gtrotignon@sightsavers.org

    Affiliation Research Department, Sightsavers, Haywards Heath, United Kingdom

  • Ellen Jones ,

    Contributed equally to this work with: Guillaume Trotignon, Ellen Jones, Thomas Engels

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Neglected Tropical Diseases Department, Sightsavers, Haywards Heath, United Kingdom

  • Thomas Engels ,

    Contributed equally to this work with: Guillaume Trotignon, Ellen Jones, Thomas Engels

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Research Department, Sightsavers, Haywards Heath, United Kingdom

  • Elena Schmidt ,

    Roles Conceptualization, Methodology, Writing – original draft, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation Research Department, Sightsavers, Haywards Heath, United Kingdom

  • Deborah A. McFarland ,

    Roles Methodology, Writing – original draft, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation Rollins School of Public Health, Emory University, Atlanta, GA, United States of America

  • Colin K. Macleod ,

    Roles Data curation, Writing – original draft, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation Clinical Research Department, London School of Hygiene & Tropical Medicine, London, United Kingdom

  • Khaled Amer,

    Roles Writing – review & editing

    Affiliation Prevention of Blindness Programme, Ministry of Health, Cairo, Egypt

  • Amadou A. Bio,

    Roles Writing – review & editing

    Affiliation Programme National de Lutte Contre les Maladies Transmissibles, Ministère de la Santé, Cotonou, Benin

  • Ana Bakhtiari,

    Roles Data curation, Writing – review & editing

    Affiliation Task Force for Global Health, Atlanta, GA, United States of America

  • Sarah Bovill,

    Roles Conceptualization, Data curation, Writing – review & editing

    Affiliation Neglected Tropical Diseases Department, Sightsavers, Haywards Heath, United Kingdom

  • Amy H. Doherty,

    Roles Data curation, Writing – review & editing

    Affiliation RTI International, Washington, D.C., United States of America

  • Asad Aslam Khan,

    Roles Writing – review & editing

    Affiliation King Edward Medical University, Lahore, Pakistan

  • Mariamo Mbofana,

    Roles Writing – review & editing

    Affiliation Health Programa Nacional de Oftalmologia, Ministerio da Saude, Maputo, Moçambique

  • Siobhain McCullagh,

    Roles Conceptualization, Funding acquisition, Writing – review & editing

    Affiliation Neglected Tropical Diseases Department, Sightsavers, Haywards Heath, United Kingdom

  • Tom Millar,

    Roles Conceptualization, Funding acquisition, Writing – review & editing

    Affiliation Neglected Tropical Diseases Department, Sightsavers, Haywards Heath, United Kingdom

  • Consity Mwale,

    Roles Writing – review & editing

    Affiliation Provincial Health Office, Ndola, Zambia

  • Lisa A. Rotondo,

    Roles Data curation, Writing – review & editing

    Affiliation RTI International, Washington, D.C., United States of America

  • Angela Weaver,

    Roles Funding acquisition, Writing – review & editing

    Affiliation United States Agency for International Development, Washington, D.C., United States of America

  • Rebecca Willis,

    Roles Data curation, Writing – review & editing

    Affiliation Task Force for Global Health, Atlanta, GA, United States of America

  •  [ ... ],
  • Anthony W. Solomon

    Roles Methodology, Writing – original draft, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliations Clinical Research Department, London School of Hygiene & Tropical Medicine, London, United Kingdom, Department of Control of Neglected Tropical Diseases, World Health Organization, Geneva, Switzerland

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The cost of mapping trachoma: Data from the Global Trachoma Mapping Project

  • Guillaume Trotignon, 
  • Ellen Jones, 
  • Thomas Engels, 
  • Elena Schmidt, 
  • Deborah A. McFarland, 
  • Colin K. Macleod, 
  • Khaled Amer, 
  • Amadou A. Bio, 
  • Ana Bakhtiari, 
  • Sarah Bovill
PLOS
x

Abstract

Background

The Global Trachoma Mapping Project (GTMP) was implemented with the aim of completing the baseline map of trachoma globally. Over 2.6 million people were examined in 1,546 districts across 29 countries between December 2012 and January 2016. The aim of the analysis was to estimate the unit cost and to identify the key cost drivers of trachoma prevalence surveys conducted as part of GTMP.

Methodology and principal findings

In-country and global support costs were obtained using GTMP financial records. In-country expenditure was analysed for 1,164 districts across 17 countries. The mean survey cost was $13,113 per district [median: $11,675; IQR = $8,365-$14,618], $17,566 per evaluation unit [median: $15,839; IQR = $10,773-$19,915], $692 per cluster [median: $625; IQR = $452-$847] and $6.0 per person screened [median: $4.9; IQR = $3.7-$7.9]. Survey unit costs varied substantially across settings, and were driven by parameters such as geographic location, demographic characteristics, seasonal effects, and local operational constraints. Analysis by activities showed that fieldwork constituted the largest share of in-country survey costs (74%), followed by training of survey teams (11%). The main drivers of in-country survey costs were personnel (49%) and transportation (44%). Global support expenditure for all surveyed districts amounted to $5.1m, which included grant management, epidemiological support, and data stewardship.

Conclusion

This study provides the most extensive analysis of the cost of conducting trachoma prevalence surveys to date. The findings can aid planning and budgeting for future trachoma surveys required to measure the impact of trachoma elimination activities. Furthermore, the results of this study can also be used as a cost basis for other disease mapping programmes, where disease or context-specific survey cost data are not available.

Author summary

There are currently few data sets available to aid programmes in planning and budgeting for population-based surveys in low- and middle- income countries. With the objective of identifying cost drivers and key variables influencing prevalence survey costs, the authors collected expenses incurred during the Global Trachoma Mapping Project (GTMP) which surveyed 2.6 million people across 29 countries. Expenditure from surveying 1,164 districts in 17 countries was analysed. Our results showed that the majority of in-country expenditure was spent on personnel (per diems, accommodation, meals and beverages) (49%) and local transportation (44%) and that the median survey expenditure was US$11,675 per district (or US$15,839 per evaluation unit, US$625 per cluster and US$4.9 per person examined). There were large variations in survey unit costs across settings, based on local geographic, demographic, seasonal effects and local operational characteristics. In addition, the resources required for the global support and coordination of the GTMP were analysed and amounted to US$5.1m (US$3,318 per district or US$5,668 per EU). Global support expenses can be substantial for a large multi-country mapping exercise conducted in a limited period of time such as the GTMP. Findings from this study can be used to inform other disease mapping projects, and to inform planning and budgeting for the prevalence surveys that will assess the impact of trachoma elimination interventions.

Introduction

An estimated 285 million people worldwide live with visual impairment, including 39 million who are blind [1]. Trachoma is the leading infectious cause of avoidable blindness, responsible for the visual impairment of about 1.9 million people, of whom 0.5 million are irreversibly blind [2]. The disease is a public health problem in 42 countries, with just over 200 million people being at risk, the majority of whom are in Sub-Saharan Africa [3].

The World Health Organization (WHO) has called for global elimination of trachoma as a public health problem by 2020. To achieve it, WHO recommends the use of a strategy known as SAFE. This includes (i) Surgery to correct the advanced stage of the disease, known as trachomatous trichiasis (TT), when one or more eyelashes rub on the eyeball; (ii) Antibiotics to clear ocular Chlamydia trachomatis infection, and (iii) Facial cleanliness and (iv) Environmental improvement to reduce infection transmission. The “S” component targets individuals, while “A”, “F” and “E” are community-based interventions. WHO recommends that “A”, “F” and “E” are delivered for at least three years in districts in which ≥10% of children aged 1–9 years are estimated to have the sign trachomatous inflammation-follicular (TF), increasing to 5 years in districts in which ≥30% of children have TF. Data on the prevalence of TF and TT at district level (where “districts” generally contain populations of 100,000–250,000 people) are therefore vital for planning interventions and judging the subsequent success of those efforts [4].

The Global Trachoma Mapping Project (GTMP) was launched in 2012, with the aim of completing the baseline map of trachoma worldwide by estimating the prevalence of TF and TT in all suspected trachoma-endemic areas for which prevalence data were unavailable. Between December 2012 and January 2016 the GTMP examined more than 2.6 million people across 29 countries (Fig 1).

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Fig 1. Countries Surveyed as Part of the Global Trachoma Mapping Project.

Fig 1 was adapted from an open source map retrieved on Natural Earth Data at http://www.naturalearthdata.com/about/terms-of-use.

https://doi.org/10.1371/journal.pntd.0006023.g001

In addition to providing the evidence base for commencing district-wide implementation of the SAFE strategy, prevalence surveys are also used to evaluate the impact of trachoma elimination interventions. The need for such surveys will increase in the near future, as elimination efforts are ramped up globally. The cost of population-based surveys has been cited as a reason for delays in conducting impact and surveillance surveys, so providing data on actual costs is important to inform debate [5]. The existing literature on the economics of trachoma elimination is sparse, with only one published study addressing the incremental costs of prevalence surveys [5], and several other papers focusing on socioeconomic burden, and the cost-effectiveness of interventions [69].

Methods

Surveys were implemented by health ministries, supported by local nongovernmental organisation (NGO) partners. All surveys followed a standardised methodology, as described in detail elsewhere [4].

Survey design

Surveys were carried out in areas defined as evaluation units (EUs). Evaluation units were used to address two issues: (1) local definitions of a “district” vary greatly in terms of population size and geographical area, and (2) WHO encourages baseline trachoma prevalence surveys to be conducted at larger-than-district level, where trachoma is expected to be highly and widely endemic [10]. EUs (rather than districts) have been used as the principal unit of analysis in order to allow comparability of data on survey costs in this paper. However, cost per districts and clusters are also reported in the tables.

Each EU-level survey followed a multi-stage cluster random sampling methodology. First-level clusters were broadly synonymous with “villages”, or represented the lowest administrative unit covered by census data. In each first-level cluster, teams randomly selected a number of households for inclusion, corresponding to the number of households that a team could screen in one day. To estimate a 10% prevalence of TF with absolute precision of ±3% at the 95% confidence level, 1,019 children aged 1–9 years needed to be sampled, including a design effect of 2.65 to account for the clustered design; this was inflated by a factor of 1.2 to allow for non-response. The number of first-level clusters required varied based on the local demographical situation, with 20 set as a minimum.

Field teams comprised one trachoma grader and one data recorder, and in most cases a driver and a local guide. (In a small number of subprojects, due to local norms, one male grader plus one female grader were needed per team to grade male and female subjects, respectively.) Graders and recorders were recruited by the local health ministry and trained by GTMP-certified trainers, using a standardised training protocol and materials, over five days, consisting of intensive classroom sessions, field based training and testing [4]. In the field, the teams were supervised by an ophthalmologist or senior ophthalmic nurse, who was appointed for every seven to ten teams on average. In selected households, all residents over the age of one year were invited to participate. Consenting individuals were examined for the signs of TT, TF and trachomatous inflammation-intense (TI). Additional questions were asked about households’ access to water and sanitation (WASH), and observation was undertaken of latrine facilities, if present. In some surveys, data were also collected on other diseases of local importance.

All data were captured electronically, using a purpose-built Open Data Kit-based Android smartphone application, and uploaded to a cloud-based server for quality assurance checks (including Global Positioning System (GPS) localisation of households surveyed) and subsequent data cleaning and analysis.

Costing approach and inclusion criteria

The GTMP was implemented as a series of subprojects. A subproject was a group of surveys conducted in a defined geographical area, for which a unique budget was established and expenditure reports prepared. To ensure comparability across countries and surveys, a number of inclusion criteria were introduced for this study. Mapping subprojects were only included in the costing analysis if they 1) followed the standardised GTMP methodologies; 2) only collected data on trachoma and WASH, or were integrated surveys of trachoma and other diseases in which the trachoma- and WASH-data-related expenditure could be explicitly identified.

In this study, the cost of conducting baseline trachoma surveys included only financial expenditure covered by the GTMP. An activity-based costing accounting model was used to analyse survey expenditure. Data on in-kind contributions and expenditure from project partners or health ministries that would have been incurred in the absence of the mapping project were not collected, with the exception of vehicles. For example, any per diems received by health ministry staff during field work were included, but their base salaries were not. When vehicle time was donated by the health ministry or its partners, a cost estimate was included, based on the duration of mapping and the average daily cost of vehicle hire in previous programmes (mean cost of daily vehicle hire: Malawi MKW90,000, Nigeria NGN15,000, United Republic of Tanzania TZS150,000, Zambia ZMW341, Zimbabwe USD$180).

Finally, administrative costs charged by implementing partners were excluded from the expenditure analysis on the basis that rates were agreed contractually and did not necessarily reflect the actual overhead costs of implementing partners.

Data collection

All data were collected retrospectively between June 2015 and March 2016. Financial data were obtained via country-specific budgets and expenditure claim forms submitted by in-country implementing partners. Financial data were extracted from the GTMP financial reporting system (CLAIMS) or collected via Excel spreadsheets. Other data, such as the list of EUs, districts, and clusters mapped for each subproject, were provided by the GTMP data managers who cleaned and analysed all field data. Specific information on in-country surveys was also collected through narrative reports and email or phone queries to implementing partners.

Data analysis

GTMP expenditure was allocated to two categories of activity (in-country survey and global support expenditures) and further divided into five sub-categories as indicated in Table 1.

Global support expenditure was broken down into three activities: grant management, epidemiological support, and data stewardship and processing.

In-country survey expenditure was allocated to either training or survey implementation sub-categories. Since it was not possible to link training expenditure to specific subprojects (because training activities often involved groups of trainees destined to work in different subprojects), this expenditure has been allocated to each subproject in proportion to the number of EUs surveyed. Details of items included or excluded for each activity and sub-activity are provided in Table 2.

Data were collected and analyzed in Excel and Stata 12.0 (Stata Corp, TX, USA). Descriptive statistics were calculated including means, medians and interquartile ranges (IQRs) since in-country survey expenditure by country or subproject did not follow a normal distribution. The IQR includes all values between the 25th and 75th percentile, while outliers are defined as any values above the 95th percentile. It was not possible to allocate survey expenditure to specific EUs, districts or clusters, given that financial data were aggregated and reported by partners at subproject level. The mean unit cost of mapping was hence obtained by dividing the total expenditure by the number of districts, EUs and clusters surveyed in each subproject.

Currency and purchasing power parity

In-country survey and global operational expenditure were converted in US Dollars (USD) using the mean annual market exchange rates over the mapping period [11]. All USD expenses were then inflated by applying the United States Consumer Price Index, using 2015 as the base year [12]. In order to facilitate cross-country comparisons, the mean survey expenditure per cluster was also converted to International Dollars. An international dollar is a hypothetical currency adjusted by purchasing power parity conversion factors to compensate for price level differences between countries [13]. Therefore, each country’s mean expenditure has been divided by the latest Purchasing Power Parity conversion factors available for each country (PPP, private consumption) [14]. Figures in International Dollars (PPP) are provided in the results section.

Ethics statement

Ethics clearance for the costing study was granted by the London School of Hygiene & Tropical Medicine ethics committee (reference 11195). The research used routine financial data from the project and did not require additional primary data collection. The underlying epidemiological data were collected during the GTMP and ethics clearance was obtained from the London School of Hygiene & Tropical Medicine (references 6319 and 8355) and from relevant ethical review boards identified by the ministry of health in each country [4]. Informed verbal consent was sought for each survey participant or from the parent or legal guardian when the participant was minor. Verbal consent was preferred over written consent considering that surveys were generally conducted in rural areas with high illiteracy rates. Verbal consent was requested and recorded using the LINKS application (purpose-built application on Android smartphones used for data collection).

Results

Baseline trachoma mapping was completed for a total of 1,629 districts (993 EUs) in 32 countries [1540]. Global support expenditures cover only districts which were mapped using standardised GTMP methodologies: 1,546 districts (905 EUs) in 29 countries. In-country survey expenditures were calculated for 17 countries, including 1,164 districts (673 EUs) which met our study inclusion criteria (Fig 2).

Global support expenditure

The total cost of the global GTMP support package was $5,129,348 (Table 3). This covered centralised support functions for rolling out the GTMP programme in 29 countries, as well as discussions with a further 20 countries in which mapping, ultimately, was not taken forward for one of a variety of reasons, including excessive security risks or insufficient evidence to justify conducting formal population-based prevalence surveys. Global support expenditure included grant management (40%), epidemiological support (31%), and data stewardship and processing (29%).

In-country survey expenditures

Overall, 89% of in-country survey expenditure was spent on survey implementation and 11% on training. The mean training expenditure per mapping team was $2,357, ranging from $792 per team in Egypt to $8,513 in Pakistan. Survey implementation expenditures included fieldwork (74%), coordination & planning (9%), and supervision (6%). When breaking down in-country survey expenditure by inputs, personnel represented the highest share, accounting for 49% of total spend, followed by transportation (44%), supplies (4%), and other expenditure (3%) (Table 4).

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Table 4. Percentage of in-country total survey expenditure by input and activity, in 2015 USD.

https://doi.org/10.1371/journal.pntd.0006023.t004

The median in-country survey cost was $15,839 per EU, ranging from $7,550 in Niger State, Nigeria to $43,580 in Papua New Guinea. The mean cost per EU was $17,566 (IQR $10,773–$19,915; SD = $9,470). As shown in Fig 3 on the “All regions” box plot, the mean unit cost was higher than the median value because of four outliers: Papua New Guinea ($43,580), Vanuatu ($42,795) and two regions in Ethiopia (Ethiopia Somali region $41,719; Gambella region, $40,767). Fig 3 also shows that subprojects in the Middle East and North Africa had the smallest variance in expenditures per EU, as demonstrated by their relatively low standard deviation (US$ 4,309) compared to the Sub-Saharan African countries (US$ 9,277) and the Pacific Islands and South Asia (US$ 12,777).

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Fig 3. In-country survey expenditure presented by world region (expenditure per EU, in 2015 USD).

Vertical boxes show the 25th and 75th percentile of the mean expenditure per EU. The median values are represented by the horizontal bar in each box. Upper and lower bars show extreme values and dots indicate outliers (outliers, defined with Tukey’s method, are values above 3rd quartile plus IQR times 1.5 and values below 1st quartile plus IQR times 1.5 [41]).

https://doi.org/10.1371/journal.pntd.0006023.g003

At cluster level, the median survey cost was $625, ranging from $290 in Zanzibar, United Republic of Tanzania, to $1,687 in Papua New Guinea (mean of unit costs $692; IQR $452–$847; SD = $350). Outlier values were Papua New Guinea ($1,687), Ethiopia Gambella region ($1,631) and Somali region ($1,558).

A summary of in-country survey expenditure by country and subproject is presented in Table 5. More detailed information is also available in S1 Dataset.

Discussion

This study presents the costs of conducting trachoma prevalence surveys in suspected endemic populations, with support from the GTMP. It is important to have setting-specific unit costs for conducting trachoma prevalence surveys against which future surveys can be benchmarked. Indeed, the evaluation of trachoma elimination efforts requires re-estimation of TF and TT prevalence following implementation of the SAFE strategy. It is thus anticipated that the need for surveys will increase in the near future as work is ramped up to meet the 2020 trachoma elimination target. We believe that our work’s value is not limited to future efforts to plan and budget for trachoma surveys. As one of the largest infectious disease mapping exercises ever conducted, the GTMP offers important information for mapping other diseases too.

We find that the mean cost for trachoma prevalence surveys across our sample was $17,566 per EU. However, our analysis also shows that the level of expenditure for trachoma prevalence surveys was substantially different between world regions, countries, and at sub-national level (Table 5). These variations can be attributed to a number of parameters, including i) geographic location (topography, transport infrastructure, etc.); ii) demographic characteristics of the population (population density, age groups, etc.); iii) seasonal effects (dry or wet season) and iv) operational constraints (timeframe, security, logistics, unexpected events, NGOs's travel policies etc.). These parameters had an influence on the means of transportation, travel distance, per diem rates and the duration of mapping, and consequently on transport and personnel expenditure, which represented nearly 85% of the total cost of the surveys. When comparing different parts of the world, these factors were key to accounting for unit cost differences observed between the groups of subprojects in Sub-Saharan Africa, Middle-East and North Africa, and Pacific Islands and South Asia.

In the Pacific Islands and South Asia, the survey unit cost and variance (mean = US$ 24,681 per EU, standard deviation = US$ 12,777) can be explained by low density of population, security risks and relatively high price levels for commodities and services in some countries. In Vanuatu, due to the island configuration and the low density of population, a high number of clusters had to be surveyed across 17 islands in order to reach the required sample size. In addition, in both Vanuatu and Papua New Guinea, survey teams had to travel by aircraft or boat, which incurred high transport costs and increased the vulnerability of the work to weather-related delay, such as tropical storms. For security reasons, training for the Pakistan teams had to be conducted by international staff in Nigeria, increasing the cost of transportation for training participants. Finally, the high prices of commodities and services in island nations such as Solomon Islands and Vanuatu further inflated survey costs there.

In Sub-Saharan Africa, unit cost variations between sub-projects can also be explained by local conditions (mean = $16,734 per EU, SD = $9,277). The lowest survey costs were in Zanzibar, United Republic of Tanzania (US$ 7,551 per EU) and Nigeria (US$ 8,577 per EU) because of relatively high population densities and hence shorter mapping duration. On the other hand, in some regions of Ethiopia (Gambella, Beneshangul Gumuz, Ethiopia Somali and Afar), more resources were required to overcome implementation challenges caused by low population densities, unpredictable environments and harsh climatic conditions, such as extreme temperatures, volcanic activity and flooding. In Afar, for example, temperatures exceeded 45°C in the afternoon, and fieldwork was only possible for half of each day. In addition, remoteness and poor road infrastructure in some districts increased the time and resources required to reach scattered clusters. As a result, the time required to map one cluster increased substantially; resulting in high survey costs for the Gambella (US$ 40,767 per EU) and Ethiopia Somali (US$ 41,719 per EU) regions compared to other subprojects.

In contrast, with the notable exception of Darfur, Sudan, the contexts in which we worked in the Middle East and North Africa group shared quite similar characteristics, in terms of geography and population density. This underlies both the low mean unit cost ($15,252 per EU) and low variance (SD = $4,309) observed there.

There is only one previously published study on the cost of trachoma mapping: by Chen et al. [5]. Their analysis is based on data from eight national programmes in Sub-Saharan Africa, covering a total of 165 districts and 1,203 clusters. Their quoted unit costs were lower than ours, with a median cost per cluster of US$344 compared to US$625 for the GTMP, when using the same base year (2015) [12]. However, caution is advised when comparing the overall cost estimates between these two studies since the survey methodology, countries and districts mapped, and the activities or expenditure included in the analysis, all diverged; in particular, Chen et al. excluded the cost of international support in their district- and cluster-level analyses. Despite these differences, both the previous work and ours found that transport and per diems during field work were the main cost drivers for trachoma prevalence surveys. Chen et al. also found large variations in survey unit costs; ranging from an average cost of US$ 1,511 per district in Ethiopia to US$ 25,409 for Ayod in Southern Sudan. Moreover, similar local factors affecting activity costs have also been observed on a published costing study concerning lymphatic filariasis [42]. Hence, it is important for funders and program managers to recognize the significant role that local factors play when planning and budgeting.

A specific and noteworthy example of this is that the level of supervision and epidemiological support required can vary greatly from one setting to another. Supervision and coordination activities can increase the overall survey expenditure in situations where local capacity is more limited; but are likely to reduce the risk of having to remap due to poor data quality. Similarly, international experts are essential for training where local expertise is not available.

In the GTMP, the massive scale, the short timeline for implementation, as well as the drive to produce objectively gold-standard data, required a significant level of investment in global support systems including international coordination, centralised epidemiological support, and single-source provision of data management services though a small, dedicated team. The global support expenditures amounted to US$3,318 on average per district (US$ 5,668 per EU). Although a global support expenditure of US$ 5,668 per EU is substantial when compared to in-country survey expenditure, some of these costs would otherwise have been covered in-country in duplicative fashion, for example, through local costs for grant management, data collection platform set-up, epidemiologist salaries, and creation of data processing systems. Undertaking the GTMP this way would have increased its overall cost.

Moreover, the GTMP was designed to be carried out quickly so that trachoma elimination activities could start, where needed, in time to meet the global 2020 elimination target [4]. The consequence is that 1,546 districts were surveyed over three years with full GTMP support, compared to 1,115 districts mapped in 12 years prior to 2012. It is possible that survey expenditure would have been lower had survey activities been implemented only when conditions in some areas were felt to be ideal: outside the rainy season, for example, or completely separated from periods of risk for political or civil unrest. However, the higher cost of mapping to the GTMP’s tight deadline is likely to be more than offset by accelerating the elimination of trachoma[43].

Our analysis is based on rigorous financial data obtained from the GTMP for 17 countries, but had some limitations. First, the unit costs presented do not fully encapsulate the total economic cost of conducting trachoma surveys, since they do not include donations and expenditure that were not covered by the GTMP itself, particularly base salaries for health ministry employees (outside of per diems covered by GTMP), apart from vehicles which costs have been estimated and added to the appropriate survey cost (in instances when vehicles were made available by the health ministry or partners in Malawi, Nigeria, United Republic of Tanzania, Zambia and Zimbabwe). Second, budgeting and financial reporting in the GTMP was done by subproject, and it was therefore not possible to allocate expenditure items to specific district-level surveys. Unit costs presented in this paper are hence the average expenditures for EUs, districts, and clusters within a subproject. Finally, in-country survey costs were estimated based on subprojects that met our study inclusion criteria; 382 of 1,546 districts surveyed with GTMP support were excluded.

Despite these limitations, this study provides data on the setting-specific costs of conducting trachoma prevalence surveys across 52 mapping subprojects and 17 countries, and adds considerably to previous work [5]. The information we provide here can be used by endemic countries and partners to help budget for future surveys. Data on the global distribution of infectious diseases is increasingly available [44]; but significant knowledge gaps remain, even for global priorities such as malaria, HIV and tuberculosis [4547]. We hope that our work can be used to guide studies that will start to close those gaps.

Supporting information

S1 File. List of GTMP partner organisations.

https://doi.org/10.1371/journal.pntd.0006023.s001

(PDF)

S1 Dataset. Detailed summary table of in-country expenditure per subproject.

https://doi.org/10.1371/journal.pntd.0006023.s002

(PDF)

Acknowledgments

We would like to thank Tawfik Al-Khatib, Bilghis Al-Kheir, Dézoumbé Djore, Jambi Garap, Arif Hussain, Iain Jones, Khumbo Kalua, Marie-Madeleine Kouakou Ilunga, Michael Masika, Nicholas Olobio, Fatma J. Omar, Isaac Phiri, Virginia Sarah, Oumer Shafi, Oliver Sokana and Fasihah Taleo for their support and comments on the manuscript.

References

  1. 1. Pascolini D. and Mariotti S.P., Global estimates of visual impairment: 2010. Br J Ophthalmol, 2012. 96(5): p. 614–8. pmid:22133988
  2. 2. Bourne R.R., Stevens G.A., White R.A., Smith J.L., Flaxman S.R., Price H., Jonas J.B., Keeffe J., Leasher J., Naidoo K., Pesudovs K., Resnikoff S., and Taylor H.R., Causes of vision loss worldwide, 1990–2010: a systematic analysis. Lancet Glob Health, 2013. 1(6): p. e339–49. pmid:25104599
  3. 3. World Health Organization. Trachoma: fact sheet. 2016 [cited 30 July 2016; Available from: http://www.who.int/mediacentre/factsheets/fs382/en/.
  4. 4. Solomon A.W., Pavluck A.L., Courtright P., Aboe A., Adamu L., Alemayehu W., Alemu M., Alexander N.D.E., Kello A.B., Bero B., Brooker S.J., Chu B.K., Dejene M., Emerson P.M., Flueckiger R.M., Gadisa S., Gass K., Gebre T., Habtamu Z., Harvey E., Haslam D., King J.D., Mesurier R.L., Lewallen S., Lietman T.M., MacArthur C., Mariotti S.P., Massey A., Mathieu E., Mekasha A., Millar T., Mpyet C., Muñoz B.E., Ngondi J., Ogden S., Pearce J., Sarah V., Sisay A., Smith J.L., Taylor H.R., Thomson J., West S.K., Willis R., Bush S., Haddad D., and Foster A., The Global Trachoma Mapping Project: Methodology of a 34-Country Population-Based Study. Ophthalmic Epidemiology, 2015. 22(3): p. 214–225. pmid:26158580
  5. 5. Chen C., Cromwell E.A., King J.D., Mosher A., Harding-Esch E.M., Ngondi J.M., and Emerson P.M., Incremental Cost of Conducting Population-Based Prevalence Surveys for a Neglected Tropical Disease: The Example of Trachoma in 8 National Programs. PLoS Neglected Tropical Diseases, 2011. 5(3): p. e979. pmid:21408130
  6. 6. Baltussen R.M., Sylla M., Frick K., and Mariotti S.P., Cost-effectiveness of trachoma control in seven world regions. Ophthalmic Epidemiol, 2005. 12(2): p. 91–101. pmid:16019692
  7. 7. Conteh L., Engels T., and Molyneux D.H., Socioeconomic aspects of neglected tropical diseases. The Lancet. 375(9710): p. 239–247.
  8. 8. Baltussen R. and Smith A., Cost effectiveness of strategies to combat vision and hearing loss in sub-Saharan Africa and South East Asia: mathematical modelling study. Bmj, 2012. 344: p. e615.
  9. 9. Solomon A.W., Mabey D.C., Gilbert C., Griffiths U., Mills A., Foster A., West S.K., Courtright P., Feczko J., Feczko J., Alemayehu W., Cross C., Lietman T.M., Haddad D., Harper C., Emerson P., Le Mesurier R., Engels D., and Mariotti S.P., Don't let misinformation derail the trachoma elimination programme. Bmj, 2012. 344: p. e2579; author reply e2588.
  10. 10. World Health Organization, Report of the 3rd global scientific meeting on trachoma, in 3rd global scientific meeting on trachoma. 2010, World Health Organization: Johns Hopkins University, Baltimore Maryland.
  11. 11. Central Intelligence Agency. The World Factbook. 2015 30.03.2016].
  12. 12. Inflation Data, Historical Consumer Price Index (CPI-U) Data. 2016, Inflation Data.
  13. 13. World Health Organization. Purchasing Power Parity 2005. 2016 [cited 01.07.2016; Available from: http://www.who.int/choice/costs/ppp/en/.
  14. 14. World Bank, International Comparison Program Database, W. Bank, Editor. 2016.
  15. 15. Kalua K., Phiri M., Kumwenda I., Masika M., Pavluck A.L., Willis R., Mpyet C., Lewallen S., Courtright P., and Solomon A.W., Baseline Trachoma Mapping in Malawi with the Global Trachoma Mapping Project (GTMP). Ophthalmic Epidemiol, 2015. 22(3): p. 176–83. pmid:26158575
  16. 16. Meng N., Seiha D., Thorn P., Willis R., Flueckiger R.M., Dejene M., Lewallen S., Courtright P., and Solomon A.W., Assessment of Trachoma in Cambodia: Trachoma Is Not a Public Health Problem. Ophthalmic Epidemiology, 2016. 23(sup1): p. 3–7. pmid:27726472
  17. 17. Mpyet C., Muhammad N., Adamu M.D., Muazu H., Umar M.M., Alada J., Onyebuchi U., Olamiju F., Isiyaku S., William A., Willis R., Flueckiger R.M., Pavluck A., Chu B.K., Mohammed D., Olobio N., and Solomon A.W., Trachoma Mapping in Gombe State, Nigeria: Results of 11 Local Government Area Surveys. Ophthalmic Epidemiol, 2016. 23(6): p. 406–411. pmid:27726459
  18. 18. Mpyet C., Muhammad N., Adamu M.D., Muazu H., Umar M.M., Abdull M., Alada J., Goyol M., Onyebuchi U., Olamiju F., Isiyaku S., William A., Nwobi B.C., Willis R., Flueckiger R.M., Pavluck A., Chu B.K., Olobio N., and Solomon A.W., Prevalence of Trachoma in Bauchi State, Nigeria: Results of 20 Local Government Area-Level Surveys. Ophthalmic Epidemiology, 2016. 23(sup1): p. 39–45. pmid:27846369
  19. 19. Mpyet C., Muhammad N., Adamu M.D., Muazu H., Umar M.M., Goyol M., Onyebuchi U., Chima I., Idris H., William A., Isiyaku S., Nwobi B., Flueckiger R.M., Willis R., Pavluck A., Chu B.K., Olobio N., and Solomon A.W., Prevalence of Trachoma in Katsina State, Nigeria: Results of 34 District-Level Surveys. Ophthalmic Epidemiol, 2016. 23(sup1): p. 55–62. pmid:27775463
  20. 20. Mpyet C., Muhammad N., Adamu M.D., Muazu H., Umar M.M., Goyol M., Yahaya H.B., Onyebuchi U., Ogoshi C., Hussaini T., Isiyaku S., William A., Flueckiger R.M., Chu B.K., Willis R., Pavluck A.L., Olobio N., Phelan S., Macleod C., and Solomon A.W., Prevalence of Trachoma in Kano State, Nigeria: Results of 44 Local Government Area-Level Surveys. Ophthalmic Epidemiol, 2017. 24(3): p. 195–203. pmid:28276755
  21. 21. Kalua K., Chisambi A., Chinyanya D., Kamwendo Z., Masika M., Willis R., Flueckiger R.M., Pavluck A.L., and Solomon A.W., Completion of Baseline Trachoma Mapping in Malawi: Results of Eight Population-Based Prevalence Surveys Conducted with the Global Trachoma Mapping Project. Ophthalmic Epidemiology, 2016. 23(sup1): p. 32–38. pmid:27726469
  22. 22. Mwingira U.J., Kabona G., Kamugisha M., Kirumbi E., Kilembe B., Simon A., Nshala A., Damas D., Nanai A., Malecela M., Chikawe M., Mbise C., Mkocha H., Massae P., Mkali H.R., Rotondo L., Crowley K., Willis R., Solomon A.W., and Ngondi J.M., Progress of Trachoma Mapping in Mainland Tanzania: Results of Baseline Surveys from 2012 to 2014. Ophthalmic Epidemiol, 2016. 23(6): p. 373–380. pmid:27775455
  23. 23. Omar F.J., Kabona G., Abdalla K.M., Mohamed S.J., Ali S.M., Ame S.M., Ngwalle A., Mbise C., Rotondo L., Willis R., Flueckiger R.M., Massae P.A., Bakhtiari A., Solomon A.W., and Ngondi J.M., Baseline Trachoma Surveys in Kaskazini A and Micheweni Districts of Zanzibar: Results of Two Population-Based Prevalence Surveys Conducted with the Global Trachoma Mapping Project. Ophthalmic Epidemiol, 2016. 23(6): p. 412–417. pmid:27775454
  24. 24. Bero B., Macleod C., Alemayehu W., Gadisa S., Abajobir A., Adamu Y., Alemu M., Adamu L., Dejene M., Mekasha A., Habtamu Jemal Z., Yadeta D., Shafi O., Kiflu G., Willis R., Flueckiger R.M., Chu B.K., Pavluck A.L., and Solomon A.W., Prevalence of and Risk Factors for Trachoma in Oromia Regional State of Ethiopia: Results of 79 Population-Based Prevalence Surveys Conducted with the Global Trachoma Mapping Project. Ophthalmic Epidemiol, 2016. 23(6): p. 392–405. pmid:27820657
  25. 25. Southisombath K., Sisalermsak S., Chansan P., Akkhavong K., Phommala S., Lewallen S., Courtright P., and Solomon A.W., National Trachoma Assessment in the Lao People’s Democratic Republic in 2013–2014. Ophthalmic Epidemiology, 2016. 23(sup1): p. 8–14. pmid:27846362
  26. 26. Elshafie B.E., Osman K.H., Macleod C., Hassan A., Bush S., Dejene M., Willis R., Chu B., Courtright P., and Solomon A.W., The Epidemiology of Trachoma in Darfur States and Khartoum State, Sudan: Results of 32 Population-Based Prevalence Surveys. Ophthalmic Epidemiol, 2016. 23(6): p. 381–391. pmid:27841721
  27. 27. Ko R., Macleod C., Pahau D., Sokana O., Keys D., Burnett A., Willis R., Wabulembo G., Garap J., and Solomon A.W., Population-Based Trachoma Mapping in Six Evaluation Units of Papua New Guinea. Ophthalmic Epidemiol, 2016. 23(sup1): p. 22–31. pmid:27893297
  28. 28. Adamu Y., Macleod C., Adamu L., Fikru W., Kidu B., Abashawl A., Dejene M., Chu B.K., Flueckiger R.M., Willis R., Pavluck A.L., and Solomon A.W., Prevalence of Trachoma in Benishangul Gumuz Region, Ethiopia: Results of Seven Population-Based Surveys from the Global Trachoma Mapping Project. Ophthalmic Epidemiol, 2016. 23(sup1): p. 70–76. pmid:27918248
  29. 29. Sherief S.T., Macleod C., Gigar G., Godefay H., Abraha A., Dejene M., Kello A.B., Belete A., Assefa Y., Willis R., Chu B.K., and Solomon A.W., The Prevalence of Trachoma in Tigray Region, Northern Ethiopia: Results of 11 Population-Based Prevalence Surveys Completed as Part of the Global Trachoma Mapping Project. Ophthalmic Epidemiol, 2016. 23(sup1): p. 94–99. pmid:27918232
  30. 30. Adera T.H., Macleod C., Endriyas M., Dejene M., Willis R., Chu B.K., Letamo Y., Misganaw T., Mesele T., Mekonnen E., Sisay A., Mulugeta Y., Alemayehu W., Kalua K., Destu T.K., Adamu Y., Smith J.L., Beyene A., Tadesse A., and Solomon A.W., Prevalence of and Risk Factors for Trachoma in Southern Nations, Nationalities, and Peoples' Region, Ethiopia: Results of 40 Population-Based Prevalence Surveys Carried Out with the Global Trachoma Mapping Project. Ophthalmic Epidemiol, 2016. 23(sup1): p. 84–93. pmid:27918229
  31. 31. Muhammad N., Mpyet C., Adamu M.D., William A., Umar M.M., Goyol M., Muazu H., Onyebuchi U., Isiyaku S., Flueckiger R.M., Chu B.K., Willis R., Pavluck A.L., Alhassan A., Olobio N., Gordon B.A., and Solomon A.W., Mapping Trachoma in Kaduna State, Nigeria: Results of 23 Local Government Area-Level, Population-Based Prevalence Surveys. Ophthalmic Epidemiology, 2016. 23(sup1): p. 46–54. pmid:27918227
  32. 32. Adamu M.D., Mpyet C., Muhammad N., Umar M.M., Muazu H., Olamiju F., Isiyaku S., Onyebuchi U., Bosso U.A., William A., Nwobi B.C., Willis R., Flueckiger R.M., Pavluck A., Chu B.K., Olobio N., and Solomon A.W., Prevalence of Trachoma in Niger State, North Central Nigeria: Results of 25 Population-Based Prevalence Surveys Carried Out with the Global Trachoma Mapping Project. Ophthalmic Epidemiol, 2016. 23(sup1): p. 63–69. pmid:27918223
  33. 33. Abashawl A., Macleod C., Riang J., Mossisa F., Dejene M., Willis R., Flueckiger R.M., Pavluck A.L., Tadesse A., Adera T.H., and Solomon A.W., Prevalence of Trachoma in Gambella Region, Ethiopia: Results of Three Population-Based Prevalence Surveys Conducted with the Global Trachoma Mapping Project. Ophthalmic Epidemiology, 2016. 23(sup1): p. 77–83. pmid:27918222
  34. 34. Sokana O., Macleod C., Jack K., Butcher R., Marks M., Willis R., Chu B.K., Posala C., and Solomon A.W., Mapping Trachoma in the Solomon Islands: Results of Three Baseline Population-Based Prevalence Surveys Conducted with the Global Trachoma Mapping Project. Ophthalmic Epidemiology, 2016. 23(sup1): p. 15–21. pmid:27937043
  35. 35. Macleod C.K., Butcher R., Mudaliar U., Natutusau K., Pavluck A.L., Willis R., Alexander N., Mabey D.C., Cikamatana L., Kama M., Rafai E., Roberts C.H., and Solomon A.W., Low Prevalence of Ocular Chlamydia trachomatis Infection and Active Trachoma in the Western Division of Fiji. PLoS Negl Trop Dis, 2016. 10(7): p. e0004798. pmid:27404379
  36. 36. Bio A.A., Boko P.M., Dossou Y.A., Tougoue J.J., Kabore A., Sounouvou I., Biaou J.E., Nizigama L., Courtright P., Solomon A.W., Rotondo L., Batcho W., and Kinde-Gazard D., Prevalence of Trachoma in Northern Benin: Results from 11 Population-Based Prevalence Surveys Covering 26 Districts. Ophthalmic Epidemiol, 2017. 24(4): p. 265–273. pmid:28441120
  37. 37. Taleo F., Macleod C.K., Marks M., Sokana O., Last A., Willis R., Garae M., Bong A., Chu B.K., Courtright P., Kool J., Taleo G., Rory J.J., Solomon A.W., and Global Trachoma Mapping P., Integrated Mapping of Yaws and Trachoma in the Five Northern-Most Provinces of Vanuatu. PLoS Neglected Tropical Diseases, 2017. 11(1): p. e0005267. pmid:28118354
  38. 38. Abdala M., Singano C.C., Willis R., Macleod C.K., Backers S., Flueckiger R.M., Vilanculos A., Terefe D., Houane M., Bikele F., Kello A.B., Downs P., Bay Z., Senyonjo L., and Solomon A.W., The Epidemiology of Trachoma in Mozambique: Results of 96 Population-Based Prevalence Surveys. Ophthalmic Epidemiol, 2017: p. 1–10.
  39. 39. Kilangalanga J., Ndjemba J.M., Uvon P.A., Kibangala F.M., Mwandulo J.S.B., Mavula N., Ndombe M., Kazadi J., Limbaka H., Cohn D., Tougoue J.J., Kabore A., Rotondo L., Willis R., Bio A.A., Kadri B., Bakhtiari A., Ngondi J.M., and Solomon A.W., Trachoma in the Democratic Republic of the Congo: Results of 46 Baseline Prevalence Surveys Conducted with the Global Trachoma Mapping Project. Ophthalmic Epidemiol, 2017: p. 1–9.
  40. 40. Phiri I., Manangazira P., Macleod C.K., Mduluza T., Dhobbie T., Chaora S.G., Chigwena C., Katiyo J., Willis R., Bakhtiari A., Bare P., Courtright P., Macheka B., Midzi N., and Solomon A.W., The Burden of and Risk Factors for Trachoma in Selected Districts of Zimbabwe: Results of 16 Population-Based Prevalence Surveys. Ophthalmic Epidemiol, 2017: p. 1–11.
  41. 41. Cox N.J., Speaking Stata: Creating and varying box plots. The Stata Journal, 2009. 9(3): p. 478–496.
  42. 42. Brady M.A., Stelmach R., Davide-Smith M., Johnson J., Pou B., Koroma J., Frimpong K., and Weaver A., Costs of Transmission Assessment Surveys to Provide Evidence for the Elimination of Lymphatic Filariasis. PLOS Neglected Tropical Diseases, 2017. 11(2): p. e0005097. pmid:28146557
  43. 43. World Health Organization Alliance for the Global Elimination of Trachoma by 2020, Eliminating Trachoma: accelerating towards 2020. 2016, International Coalition for Trachoma Control: London.
  44. 44. Hay S.I., Battle K.E., Pigott D.M., Smith D.L., Moyes C.L., Bhatt S., Brownstein J.S., Collier N., Myers M.F., George D.B., and Gething P.W., Global mapping of infectious disease. Philos Trans R Soc Lond B Biol Sci, 2013. 368(1614).
  45. 45. Hay S.I. and Snow R.W., The Malaria Atlas Project: Developing Global Maps of Malaria Risk. PLoS Med, 2006. 3(12).
  46. 46. Hurlimann E., Schur N., Boutsika K., Stensgaard A.S., Laserna de Himpsl M., Ziegelbauer K., Laizer N., Camenzind L., Di Pasquale A., Ekpo U.F., Simoonga C., Mushinge G., Saarnak C.F., Utzinger J., Kristensen T.K., and Vounatsou P., Toward an open-access global database for mapping, control, and surveillance of neglected tropical diseases. PLoS Negl Trop Dis, 2011. 5(12): p. e1404. pmid:22180793
  47. 47. United Nations Programme on HIV and AIDS (UNAIDS). AIDSinfo. 2016 21.12.2016].