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Identifying optimal threshold statistics for elimination of hookworm using a stochastic simulation model

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      Abstract

      Background

      There is an increased focus on whether mass drug administration (MDA) programmes alone can interrupt the transmission of soil-transmitted helminths (STH). Mathematical models can be used to model these interventions and are increasingly being implemented to inform investigators about expected trial outcome and the choice of optimum study design. One key factor is the choice of threshold for detecting elimination. However, there are currently no thresholds defined for STH regarding breaking transmission.

      Methods

      We develop a simulation of an elimination study, based on the DeWorm3 project, using an individual-based stochastic disease transmission model in conjunction with models of MDA, sampling, diagnostics and the construction of study clusters. The simulation is then used to analyse the relationship between the study end-point elimination threshold and whether elimination is achieved in the long term within the model. We analyse the quality of a range of statistics in terms of the positive predictive values (PPV) and how they depend on a range of covariates, including threshold values, baseline prevalence, measurement time point and how clusters are constructed.

      Results

      End-point infection prevalence performs well in discriminating between villages that achieve interruption of transmission and those that do not, although the quality of the threshold is sensitive to baseline prevalence and threshold value. Optimal post-treatment prevalence threshold value for determining elimination is in the range 2% or less when the baseline prevalence range is broad. For multiple clusters of communities, both the probability of elimination and the ability of thresholds to detect it are strongly dependent on the size of the cluster and the size distribution of the constituent communities. Number of communities in a cluster is a key indicator of probability of elimination and PPV. Extending the time, post-study endpoint, at which the threshold statistic is measured improves PPV value in discriminating between eliminating clusters and those that bounce back.

      Conclusions

      The probability of elimination and PPV are very sensitive to baseline prevalence for individual communities. However, most studies and programmes are constructed on the basis of clusters. Since elimination occurs within smaller population sub-units, the construction of clusters introduces new sensitivities for elimination threshold values to cluster size and the underlying population structure. Study simulation offers an opportunity to investigate key sources of sensitivity for elimination studies and programme designs in advance and to tailor interventions to prevailing local or national conditions.

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      Most cited references 30

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      Global numbers of infection and disease burden of soil transmitted helminth infections in 2010

      Background Quantifying the burden of parasitic diseases in relation to other diseases and injuries requires reliable estimates of prevalence for each disease and an analytic framework within which to estimate attributable morbidity and mortality. Here we use data included in the Global Atlas of Helminth Infection to derive new global estimates of numbers infected with intestinal nematodes (soil-transmitted helminths, STH: Ascaris lumbricoides, Trichuris trichiura and the hookworms) and use disability-adjusted life years (DALYs) to estimate disease burden. Methods Prevalence data for 6,091 locations in 118 countries were sourced and used to estimate age-stratified mean prevalence for sub-national administrative units via a combination of model-based geostatistics (for sub-Saharan Africa) and empirical approaches (for all other regions). Geographical variation in infection prevalence within these units was approximated using modelled logit-normal distributions, and numbers of individuals with infection intensities above given thresholds estimated for each species using negative binomial distributions and age-specific worm/egg burden thresholds. Finally, age-stratified prevalence estimates for each level of infection intensity were incorporated into the Global Burden of Disease Study 2010 analytic framework to estimate the global burden of morbidity and mortality associated with each STH infection. Results Globally, an estimated 438.9 million people (95% Credible Interval (CI), 406.3 - 480.2 million) were infected with hookworm in 2010, 819.0 million (95% CI, 771.7 – 891.6 million) with A. lumbricoides and 464.6 million (95% CI, 429.6 – 508.0 million) with T. trichiura. Of the 4.98 million years lived with disability (YLDs) attributable to STH, 65% were attributable to hookworm, 22% to A. lumbricoides and the remaining 13% to T. trichiura. The vast majority of STH infections (67%) and YLDs (68%) occurred in Asia. When considering YLDs relative to total populations at risk however, the burden distribution varied more considerably within major global regions than between them. Conclusion Improvements in the cartography of helminth infection, combined with mathematical modelling approaches, have resulted in the most comprehensive contemporary estimates for the public health burden of STH. These numbers form an important benchmark upon which to evaluate future scale-up of major control efforts.
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        Sensitivity of diagnostic tests for human soil-transmitted helminth infections: a meta-analysis in the absence of a true gold standard

        1 Introduction Reliable, sensitive and practical diagnostic tests are an essential tool in disease control programmes, including those for neglected tropical diseases. The requirements and expectations for a diagnostic tool in terms of technical performance, feasibility and costs change as control programmes progress through different phases, from initially high levels of infections to the confirmation of absence of infections. More precisely, during initial mapping to identify priority areas for control, when infection levels are typically highest, a diagnostic test with moderate sensitivity is acceptable, although the chosen tool needs to be easy to use, cost-effective and allow for the high-throughput screening of large populations (McCarthy et al., 2012; Solomon et al., 2012). Since mapping data can also serve as a baseline for the monitoring and evaluation of programme impact, diagnostic tests must have sufficient performance to detect changes in the prevalence and intensity of infection (Solomon et al., 2012). In later stages of programmes, when infection prevalence and intensity have decreased significantly, more sensitive diagnostic tools are needed to establish an endpoint of treatment programmes. If test sensitivity is insufficient at this point, light infections might be missed and this runs the risk of stopping control programmes too early, before programme endpoints have been achieved. Highly sensitive tests are also required for surveillance once treatment has been stopped to detect the potential re-occurrence of infections (McCarthy et al., 2012; Solomon et al., 2012). Finally, diagnostic tests play an important role in the assessment of treatment efficacy (Albonico et al., 2012) and in patient management. For the detection of the human soil-transmitted helminth (STH) species, Ascaris lumbricoides, Trichuris trichiura and the hookworms (Necator americanus and Ancylostoma duodenale), The World Health Organization (WHO) currently recommends the use of the Kato-Katz method, based on duplicate slides (WHO, 2002). Other commonly used methods include direct smear microscopy, formol-ether concentration (FEC), McMaster, FLOTAC and Mini-FLOTAC. All of these techniques rely on visual examination of a small sample of stool to determine the presence and number of STH eggs (WHO, 1994). Due to intra- and inter-sample variation in egg counts (Booth et al., 2003; Krauth et al., 2012), microscopy-based techniques can have differing sensitivities, especially in low transmission settings. Moreover, diagnostic methods vary considerably in the quantification of egg counts, which is necessary to establish intensity of infection and to evaluate treatment effects (Knopp et al., 2011; Albonico et al., 2012; Levecke et al., 2014). In order to better understand the suitability of diagnostic tools for various transmission settings and stages of disease control programmes, we performed a meta-analysis of the most commonly used copro-microscopic STH diagnostic tests. Our main study objective was an independent and global assessment of the relative performance of commonly used diagnostic methods for STH, as well as factors associated with heterogeneity in test sensitivity. Previous evaluations of STH diagnostics have generally relied on comparisons with a combined reference standard (generated by adding the results of several compared tests or consecutively obtained samples), an approach which has been widely criticised (Enoe et al., 2000; Ihorst et al., 2007). Moreover, the absence of a common reference standard has been a major obstacle for combined evaluations of diagnostic tests in the form of a meta-analysis. We have addressed this problem by using Bayesian latent class analysis (LCA), which allows simultaneous estimation of the unknown true prevalence of infection and the sensitivities and specificities of compared diagnostic tests. This approach has been previously applied to the evaluation of imperfect diagnostic tests for Chagas disease, leishmaniasis and malaria (Menten et al., 2008; de Araujo Pereira et al., 2012; Goncalves et al., 2012), as well as specific studies evaluating STH diagnostic methods (Booth et al., 2003; Tarafder et al., 2010; Assefa et al., 2014; Knopp et al., 2014). The approach has also been used for the meta-analyses of diagnostic test performance (Ochola et al., 2006; Menten et al., 2008; Limmathurotsakul et al., 2012). The current paper presents a Bayesian meta-analysis of different diagnostic tests for the detection of STH species. 2 Materials and methods 2.1 Literature search A systematic literature search was performed to identify publications presenting the evaluation of diagnostic techniques for the human STH species, A. lumbricoides, T. trichiura and hookworms (N. americanus and A. duodenale). Systematic searches were performed (date of search 25th February 2014) using the electronic databases PubMed (http://www.ncbi.nlm.nih.gov/), MEDLINE and EMBASE (via OvidSP) (http://ovidsp.uk.ovid.com/) and the medical subject headings and search terms as detailed in Supplementary Data S1. Articles were considered if written in English, German, French or Spanish. The search was validated by verifying that a number of previously identified key readings were included in the retrieved search results. The titles of initially obtained search results were screened for suitable content and all abstracts mentioning studies on helminths were retrieved. The abstracts were subsequently screened for studies using more than one diagnostic test for the determination of infections, even if not directly mentioning a comparison of test performances. Full texts were read and information on test outcomes, egg counts, age-groups, countries of the studies and years of publication was extracted where results were presented in a suitable format as explained below. Reference lists were screened for additional publications. The literature selection process is outlined in Fig. 1. Data were collected separately for A. lumbricoides, T. trichiura and hookworms, and restricted to the most commonly used diagnostic methods for STH, namely Kato-Katz (Katz et al., 1972), direct microscopy (WHO, 1994), formol-ether concentration (FEC) (Ritchie, 1948), McMaster (Ministry of Agriculture Fisheries and Food, 1986), FLOTAC (Cringoli et al., 2010) and Mini-FLOTAC (Barda et al., 2013a). Other techniques such as midi-Parasep, Koga Agar Plate, Willis technique and Spontaneous tube sedimentation technique (SSTT) were not included due to a lack of suitable data. As performance during field surveys was the main interest, evaluations of diagnostic tests on samples from diagnostic laboratories of hospitals were excluded. Only data provided in the form of 2 × 2 comparisons (T1+T2+, T1+T2−, T1−T2+, T1−T2−, where T1 and T2 are the two diagnostic methods and + and − indicate the observed positive or negative results) were retained. This also included data for which these 2 × 2 comparisons could be created by transforming the original data provided, e.g. where comparisons were made against a combined ‘gold standard’ of two diagnostic methods. Additionally, data on egg counts obtained by the various techniques were retrieved, including those studies that did not provide data in a suitable format for the LCA. Arithmetic mean egg counts were the most commonly reported measures and therefore used for the analysis. For articles where data could not be directly extracted, corresponding authors were invited to contribute additional study results. Three authors replied and provided four datasets for the analysis; we were also able to contribute a further two datasets to the analysis. 2.2 Bayesian LCA A Bayesian latent class model was used to estimate the sensitivity of different diagnostic tests as described elsewhere (Dendukuri and Joseph, 2001; Branscum et al., 2005). LCA allows estimation of the sensitivity and specificity of imperfect diagnostic tests by assuming a probabilistic model for the relationship between five unobserved, or latent, parameters: true disease prevalence π k and the sensitivities S i , S j and specificities C i , C j of diagnostic methods i and j (Pepe and Janes, 2007). The model additionally incorporates the covariance terms covD ij + , covD ij - to account for conditional dependency between compared diagnostic tests amongst infected and non-infected individuals, which is necessary as the included diagnostic tests are based on the same biological principle (detection of eggs under a microscope) and therefore factors other than the true infection status are likely to influence both test outcomes simultaneously (Dendukuri and Joseph, 2001). Thus, the joint distribution of the results of a 2 × 2 table follows a multinomial distribution, ( X k + + , X k + - , X k - + , X k - - ) ∼ Multi ( p k + + , p k + - , p k - + , p k - - , N k ) with the multinomial probabilities calculated as follows: p k + + = P ( T i + , T j + | k th population ) = [ S i S j + covD ij + ] π k + [ ( 1 - C i ) ( 1 - C j ) + covD ij - ] ( 1 - π k ) p k + - = P ( T i + , T j - | k th population ) = [ S i ( S j - 1 ) - covD ij + ] π k + [ ( 1 - C i ) C j - covD ij - ] ( 1 - π k ) p k - + = P ( T i - , T j + | k th population ) = [ ( S i - 1 ) S j - covD ij + ] π k + [ C i ( 1 - C j ) - covD ij - ] ( 1 - π k ) p k - - = P ( T i - , T j - | k th population ) = [ ( S i - 1 ) ( S j - 1 ) + covD ij + ] π k + [ C i C j + covD ij - ] ( 1 - π k ) The conditional correlations between two test outcomes for infected and non-infected individuals were calculated as ρ D + = covD + S i ( 1 - S i ) S j ( 1 - S j ) and ρ D - = covD - C i ( 1 - C i ) C j ( 1 - C j ) , respectively. Uninformative prior information was provided for the sensitivity and underlying true prevalence (using a beta distribution with the shape parameters alpha and beta equal to 1). For the covariance terms, a uniform prior distribution was assumed with limits as described in Dendukuri and Joseph (2001) and Branscum et al. (2005) to ensure that probabilities are confined to values between 0 and 1. Specificity was included as a fixed term based on the most parsimonious, best-fitting model (i.e. that with the lowest deviance information criterion (DIC) value) and was assumed to be the same for all compared methods. This was justified on the dual assumption that false positives are rarely obtained by any type of copro-microscopic technique (Knopp et al., 2011; Levecke et al., 2011) and the necessity to restrict the number of estimated parameters for the identifiability of the model. The models, built separately for A. lumbricoides, T. trichiura and hookworms, were computed using WinBUGS software version 14 (Spiegelhalter, D., Thomas, A., Best, N., Gilks, W., 1996. BUGS: Bayesian Inference Using Gibbs Sampling. MRC Biostatistics Unit, Cambridge). Models were also developed separately for low and high intensity settings. Stratification was based on reported arithmetic mean egg counts (in eggs per gram of faeces, epg). Empirical cut-offs of 2500 epg, 400 epg and 165 epg average infection intensity were used for A. lumbricoides, T. trichiura and hookworms, respectively. These cut-offs were established based on the overall average infection intensity of studies included in the meta-analysis. Data with only geometric means reported were excluded from this analysis unless the geometric mean, which is lower than the average egg count, exceeded the cut-off value. Further details of model parameterisation, including handling of multiple slides, are provided in Supplementary Data S2. 2.3 Comparison of quantitative performances To compare the various diagnostic tests in terms of their quantitative performance, we compared the arithmetic mean egg count obtained by various techniques. Statistical significance of differences was assessed using the non-parametric paired Wilcoxon signed-ranks test and the linearity of the relationship between counts was assessed by scatter plots of log-transformed (natural logarithm) average egg counts. Moreover, we evaluated the percentage of studies reporting egg counts of other techniques that were lower/higher than the Kato-Katz method, which currently forms the basis of the WHO defined intensity thresholds. To allow for a small variation in counts, egg counts were considered as lower or higher than the Kato-Katz method if these were lower or higher than the Kato-Katz egg count plus or minus 10%. Due to the limited availability of data and the fact that faecal egg counts do not vary significantly by the sampling effort for Kato-Katz analysis, all versions of Kato-Katz were combined (Levecke et al., 2014). 3 Results 3.1 Identification of diagnostic test comparisons The initial literature search identified 56 articles which were retrieved for full-text review. Of these, 32 studies fulfilled the inclusion criteria and 2 × 2 comparison data could be obtained for 20 studies (Table 1) (see Fig. 1 for an outline of literature selection steps). The number of extracted 2 × 2 comparisons by species and diagnostic methods is shown in Fig. 2. The included studies were published between 2003 and 2014 and conducted in 12 countries, primarily among school-aged children. The inclusion of only recent studies was somewhat surprising. Even though the original literature search had retrieved studies published since 1967, the non-availability of 2 × 2 data, the type of compared techniques and the evaluation of methods in laboratory or hospital samples led to their exclusion. The evaluation of diagnostic tests was mainly based on comparison with a combined reference-standard (14 of 20 studies); few studies used predicted estimates as a reference (1/20), an LCA approach (1/20) or a combination of the two (1/20). Three studies did not provide sensitivity estimates. The most widely applied method was the Kato-Katz method in 18 of 20 studies (mostly 1-slide or 2-slides on a single sample). The main characteristics of included studies are summarised in Table 1. 3.2 LCA of diagnostic test sensitivities (presence of infection) For all STH species, the models allowing for dependency between compared diagnostic tests showed a better fit, indicated by a lower DIC (not shown). Significant positive correlation between diagnostic test outcomes for infected individuals was observed, especially for comparisons of a 1-slide 1-sample Kato-Katz test with other diagnostic tests (details are provided in Supplementary Data S2). Taking this dependency into account, the sensitivities of selected diagnostic methods were estimated separately for A. lumbricoides, T. trichiura and hookworm and are provided in Table 2 and Fig. 3. Generally, sensitivities of all compared tests were higher for T. trichiuria (Fig. 3B) than for hookworm (Fig. 3C) and A. lumbricoides (Fig. 3A). The obtained sensitivities were highest overall for the FLOTAC method with 79.7% (95% Bayesian credible interval (BCI): 72.8–86.0%), 91.0% (95% BCI: 88.8–93.5%), and 92.4% (95% BCI: 87.6–96.2%) for A. lumbricoides, T. trichiura and hookworm, respectively (Table 2). The lowest sensitivity was observed for the direct microscopy method with 52.1% (95% BCI: 46.6–57.7%), 62.8% (95% BCI: 56.9–68.9%), and 42.8% (95% BCI: 38.3–48.4%), respectively. The estimated sensitivity of the 2-slide 1-sample Kato-Katz test for A. lumbricoides was 64.6% (95% BCI: 59.7–69.8%), for T. trichiura was 84.8% (95% BCI: 82.5–87.1%) and for hookworm was 63.0% (95% BCI: 59.8–66.4%). These estimates were only a slight improvement upon the sensitivities of a 1-slide 1-sample Kato-Katz test. However, increased sensitivities could be observed for 1-slide Kato-Katz performed on two consecutive samples. The sensitivity for Kato-Katz tests performed on three consecutive samples was only slightly further improved. Test specificities were not the main outcome and were fixed at 99.6% for A. lumbricoides, 97.5% for T. trichiura and 98.0% for hookworm, based upon model fit. 3.3 Effect of infection intensity on diagnostic test sensitivity The obtained sensitivity estimates by intensity group are presented in Table 3 and Fig. 4. For all tests and STH species evaluated in both intensity groups, sensitivity varied markedly and most strongly for the Kato-Katz method. For example, for A. lumbricoides the 1-slide Kato-Katz method had a sensitivity of 48.8% (95% BCI: 37.6–58.2%) in the low intensity group compared with 95.8% (95% BCI: 91.8–98.5%) in the high intensity group. Interestingly, in the low intensity group the sensitivity of Kato-Katz was improved markedly by performance of a second slide on the same sample. The sensitivity of the FLOTAC method was highest at 81.8% (95% BCI: 65.5–90.3%) at low intensity compared with 97.1% (95% BCI: 93.1–99.7%) at high intensity. 3.4 Comparison of quantitative test performances A total of 17, 16 and 27 comparisons of average Kato-Katz A. lumbricoides, T. trichiura and hookworm egg counts with other diagnostic methods were obtained from 11 articles (Table 1, analysis 2). The majority of comparisons were between versions of Kato-Katz and FLOTAC or McMaster techniques. Only a few studies compared egg counts between Kato-Katz and FEC or Mini-FLOTAC methods; none with direct microscopy. Table 4 shows that the FLOTAC method generally underestimates the average egg counts compared with Kato-Katz, even though the difference is not statistically significant for T. trichiura. The McMaster technique, however, resulted in a higher egg count for six of 11 comparisons (55%) for T. trichiura and four of 12 comparisons (33%) for hookworm whilst A. lumbricoides egg counts were significantly lower. The relationships between the logarithmic average measurements of Kato-Katz and FLOTAC or McMaster techniques followed a linear trend as shown by the scatter plots presented in Fig. 5. 4 Discussion A global assessment of STH diagnostic test sensitivities and their extent of variation is required to investigate the suitability of diagnostic tools for different transmission settings or stages of STH control programmes. Here we present, to our knowledge, the first meta-analysis of STH diagnostic method performance using a Bayesian LCA framework to overcome the absence of a true gold standard (Dendukuri and Joseph, 2001; Branscum et al., 2005). Our results demonstrate that sensitivities of evaluated diagnostic tests are low overall and cannot be generalised over different transmission settings. Sensitivity, overall and in both intensity groups, was highest for the FLOTAC method, but was comparable for Mini-FLOTAC and Kato-Katz methods. Test sensitivities are strongly influenced by intensity of infection and this variation needs to be taken into account for the choice of a diagnostic test in a specific setting. Moreover, reduced test sensitivity at low infection intensities is of increasing importance as ongoing control programmes reduce the prevalence and intensity of STH infections within endemic communities. The Kato-Katz method is the most widely used and reported diagnostic method, due to its simplicity and low cost (Katz et al., 1972), and is recommended by the WHO for the quantification of STH eggs in the human stool (WHO, 2002). Even though the overall sensitivity of the Kato-Katz method was low, the results of the stratified analysis suggest a high sensitivity of 74–95% when infection intensity is high, which is likely the case for mapping and baseline assessment. However, the test sensitivity dropped dramatically in low transmission settings, making the method a less valuable option in later stages of control programmes. This is likely a reflection of methodological problems specific to the Kato-Katz method, especially when diagnosing multiple STH species infections, as different helminth eggs have different clearing times (Bergquist et al., 2009). In high intensity settings, little value was added by performing a 2-slide test on the same sample, even though this is the currently recommended protocol; whereas in low intensity settings sensitivity was improved by performing a second slide. Sensitivity increased significantly when performing the Kato-Katz method on multiple consecutive samples, which is most likely explained by daily variations of egg excretions and the non-equal distribution of eggs in the faeces leading to substantial variation in egg numbers between stool samples from the same person (Booth et al., 2003; Krauth et al., 2012). For all investigated STH species, sensitivity was highest for the FLOTAC method, even when evaluated in low intensity settings, a finding which is consistent with previous evaluations (Utzinger et al., 2008; Knopp et al., 2009b; Glinz et al., 2010). However, despite its improved performance compared with other copro-microscopic methods, FLOTAC has several practical constraints including higher associated costs, necessity of a centrifuge and longer sample preparation time, decreasing its value as a universal diagnostic method (Knopp et al., 2009a). To enable its use in settings with limited facilities, the Mini-FLOTAC method, a simplified form of FLOTAC, was developed (Barda et al., 2013a). Our findings suggest that the sensitivity of Mini-FLOTAC is much lower than FLOTAC, and it does not outperform the less expensive Kato-Katz method according to a recent study in Kenya (Speich et al., 2010; Assefa et al., 2014). A recognised advantage of the Mini-FLOTAC method, however, is that it can be performed on fixed stools, enabling processing at a later date in a central laboratory. This can help to increase the quality control process and overcomes some of the logistical difficulties in examining fresh stool samples in the field on the day of collection (Barda et al., 2013a). The obtained Mini-FLOTAC sensitivity estimates have relatively high uncertainty, visible in the wide confidence intervals, probably due to the limited number of studies available for the analysis and their evaluation primarily in low transmission settings, where the number of positive individuals is very limited. The detection or failure of detection of a single individual therefore might have a large impact on the sensitivity estimate. In remote areas where microscopy is often unavailable, studies can also use FEC, which allows the fixation of stool samples for later examination (WHO, 1994); several authors have also suggested the use of the McMaster technique as it is easier to standardise than Kato-Katz (Levecke et al., 2011; Albonico et al., 2012). Overall, the observed relative performances of these diagnostic tests when compared with the Kato-Katz method are consistent with those presented in the literature: the performance of Kato-Katz and McMaster methods were comparable, although this did vary by setting (Levecke et al., 2011; Albonico et al., 2013). Similarly, even though FEC had predominantly lower sensitivity than Kato-Katz in included studies, the reported relative performance varies in the literature (Glinz et al., 2010; Speich et al., 2013). The sensitivity of direct microscopy was consistently lower than the Kato-Katz method. Other available methods which were not included in our meta-analysis due to limited data availability, such as the midi-Parasep, do not show any improved test performance in their previous evaluations (Funk et al., 2013). Although we present an improved approach for evaluating diagnostic test performances, accounting for the absence of a perfect gold standard by estimating the true unmeasured infection status and allowing for conditional dependency between the test outcomes, our analysis is subject to several limitations. The results presented here are limited by the low availability of comparable data for each diagnostic test, especially when performing the analysis stratified by intensity group. Direct microscopy was primarily evaluated in low intensity settings, which could have led to the lower observed sensitivity estimates, whereas the Kato-Katz method was evaluated in a full range of settings. The cut-off value to define high and low intensity groups of study populations was chosen based on the data included in the meta-analysis, but does not necessarily represent two main types of transmission settings. Nevertheless, the groupings demonstrate the substantial differences in test performance across varying infection intensities. As the investigated range of transmission settings was limited, further diagnostic test evaluations in specified transmission settings will be needed to provide concrete test performance estimates for each of the settings. To take into account the conditional dependency between compared diagnostic tests, we used a fixed effects model, assuming that conditional dependency is the same for all study settings. Different approaches allowing for varying correlations by using random effects to model sensitivities and specificities as a function of a latent subject-specific random variable could be explored further (Dendukuri and Joseph, 2001). Moreover, our findings might be biased towards results from studies comparing multiple diagnostic tests at the same time, as these are underpinned by a larger amount of data. Assumptions had to ensure identifiability of the model by limiting the number of parameters to be estimated. We focussed our analysis on the sensitivity of diagnostic tests, assuming that specificity of various methods do not differ largely, and therefore included the specificity of all single sample diagnostic tests as one fixed parameter. This assumption can be questioned, as for example Kato-Katz slides are more difficult to read than FLOTAC slides due to debris (Glinz et al., 2010); however, it is still an improvement on the assumption of 100% test specificity for all diagnostic tests as applied in previous publications (Booth et al., 2003; Knopp et al., 2011; Levecke et al., 2011). Using uninformative priors instead of fixed terms did not improve model fit and led to slightly wider BCIs. Importantly, the current model assumes that sensitivities are identical within all populations, which is not fulfilled if sensitivity varies by study setting (Toft et al., 2005). Indeed, the stratified analysis showed that sensitivity varied by infection intensity; however, there were not sufficient data to obtain good estimates for all tests in various transmission settings. Additionally, sensitivity in a specific study setting might be affected by other factors including stool consistency and diet, standardisation and adherence to protocols, equipment quality and human error (Bogoch et al., 2006; Bergquist et al., 2009; Levecke et al., 2011). To overcome the limited comparability of evaluations from different studies, purposeful evaluations of test sensitivity over a continuous range of infection intensities in comparable populations, for example before and after treatment rounds, are clearly necessary to better refine sensitivity estimates, and could be used to identify intensity categories within which sensitivity remains comparable. Results could then be transformed into recommendations for the use of diagnostic tests for different stages of disease control programmes. The performance of a diagnostic tool should not only be measured in terms of sensitivity, but also needs to consider the ability of the test to quantify faecal egg counts. Current infection and treatment effect indicators are based on the Kato-Katz method, and the question arises whether the increasing use of other methods will constitute a problem for standardised recommendations (WHO, 2002). The comparison of average egg counts obtained by Kato-Katz and FLOTAC methods shows a broad agreement with previous studies with generally higher Kato-Katz egg counts (Knopp et al., 2009b, 2011; Albonico et al., 2013). The quantitative performance of the McMaster technique, however, varied in comparison to the Kato-Katz method as higher McMaster average egg counts were observed in several studies, especially for T. trichiura and hookworms (Levecke et al., 2011; Albonico et al., 2012, 2013). The current analysis has focussed on copro-microscopic diagnostic tests, which are based on examination of stool samples. There is current interest in developing more sensitive assays that allow a high sample throughput for screening of large populations using other biological samples and the simultaneous detection of several parasite species in co-endemic settings (Bergquist et al., 2009; Knopp et al., 2014). Recently, assays based on PCR have been developed for the detection of STH (Verweij et al., 2007; Schar et al., 2013; Knopp et al., 2014); however, we did not include this method in our meta-analysis due to limited data availability from field settings. Nonetheless, a recent study showed that the sensitivity of PCR methods was comparable with the Kato-Katz method, especially in low endemicity settings (Knopp et al., 2014). In conclusion, we provide a first known meta-analysis of the sensitivity and quantitative performance of STH diagnostic methods most widely used in resource-limited settings. Our results show that the FLOTAC method had the highest sensitivity both overall and in low intensity settings; however this technique requires a centrifuge and has relatively low throughput. Our results further show that the sensitivities of the Kato-Katz and Mini-FLOTAC techniques were comparable and in high intensity settings both techniques provide a practical and reliable diagnostic method. A particular advantage of the Kato-Katz method is the ability to simultaneously detect STH and schistosome species at low cost; whereas the Mini-FLOTAC method has the advantage that it can be used on preserved samples. As control programmes reduce the intensity of infection, there is a need for diagnostic methods which are more sensitive than these currently used. In evaluating the performance of new diagnostic methods we recommend a standardised evaluation in multiple transmission settings, using the robust statistical methods presented here, as well as a consideration of the cost-effectiveness of alternative methods (Assefa et al., 2014).
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          How Effective Is School-Based Deworming for the Community-Wide Control of Soil-Transmitted Helminths?

          Introduction In January 2012, a high-level meeting brought together 13 pharmaceutical companies and the global health community in London, UK to announce a new public-private partnership to eliminate or control the seven preventable neglected tropical diseases (NTDs) by 2020, based largely on a new NTD roadmap from the World Health Organization (WHO) [1]. The pledge by pharmaceutical companies to sustain and extend donation programmes facilitates a large portion of the necessary supply of drugs and other interventions to help achieve this goal [2]. Such commitments raise the question of how best to use these resources to induce maximum impact, given that many treatments for NTDs must be administered repeatedly to individuals living in endemic areas due to re-exposure to infection and the absence of fully protective acquired immunity. The most common NTDs worldwide are the soil-transmitted helminths (STH: Ascaris lumbricoides, Trichuris trichiura and the hookworms, Necator americanus and Ancylostoma duodenale), with an estimated 5.3 billion people worldwide, including 1.0 billion school-aged children, living in areas of stable transmission for at least one STH species [3]. STHs are easily treated with one of four drugs: albendazole and mebendazole, and to a lesser extent, levamisole, and pyrantel pamoate [4]–[5]. However, reinfection commonly occurs [6] due to the inability of the human host to mount protective immunity to reinfection by intestinal helminths [7]–[8], combined with inadequate hygiene and sanitation to restrict or eliminate re-exposure in environments continuously contaminated with the egg or larval free-living transmission stages of these parasitic worms [9]. Following treatment, average worm loads in the population return to their pre-treatment equilibiria in a monotonic manner. The exact dynamics will depend on a number of density-dependent processes that influence parasite reproduction, infection and mortality (in part related to the build-up of a degree of acquired immunity), plus the relatively long life expectancies of established worms in the human host (measured in years) [10]. It will also depend on the proportion of worms in the entire human community in a defined location which are exposed to treatment in a particular control programme. Deworming programmes for the STHs are often centred on school delivery because of the large burden of morbidity and concomitant developmental consequences for these children [11]–[13], as well as relative ease of access to children in poor rural areas through schools and the cost-effectiveness of school-based deworming [14]–[15]. A number of countries have programmes which additionally include adults, as part of lymphatic filariasis treatment campaigns providing mass treatment with albendazole and ivermectin (or diethylcarbamazine), with associated large impacts on transmission [16]. However, there are large areas where this STHs are not co-endemic for lymphatic filariasis and these areas the WHO-recommended treatment strategies prioritise school-aged children, but also recommend preventive chemotherapy of preschool children, women of childbearing age and adults at high risk [4]. A number of countries are currently implementing only school-based deworming [11]. The large donation of 600 million doses per year announced in the London Declaration almost completely covers the estimated 610 million school-aged children in need of preventive chemotherapy [1]–[2], [11], [17], but it does not cover pre-school children, women of child-bearing age or treatments more than once a year. This paper therefore examines the effectiveness of school-targeted programmes in restricting transmission within the larger community of pre-school children, school-aged children and adults. We use analytical methods deriving from the description of the transmission dynamics of these parasitic worms [18] and demographic plus school attendance information to calculate what fraction of the total population is treated. We also use mathematical models to discuss the impact of school-based programmes on transmission, including scenarios in which infective stages deposited by children are more likely to contribute to transmission than those from adults. We discuss how the impact of a treatment programme could affect the infection dynamics in the population as a whole depending on the, as yet unknown, details of between age-group mixing. Methods The effect of treating school-aged children on the overall transmission dynamics of the parasites depends on a number of factors. These include the fraction of the total worm population harboured by these age groups, the fraction of the eligible school-aged children who attend school and receive treatment, how other age groups are exposed to the eggs or infective larvae produced by school-aged children and vice versa, and drug efficacy. Data exist for the first two factors, which we discuss below. However there is limited evidence to facilitate the exploration of various assumptions such as random or non-random exposure of age groups to the infective stages produced by other age groups in a defined human community. Drug pharmacokinetics and efficacy are also reasonably well documented for the commonly used drugs for STHs and schistosomes (mebendazole [19], albendazole [20]–[22] and praziquantel [23]). In this analysis we estimate the fraction of worms among school-aged children and then discuss the likely impact of this level of coverage on transmission using a suite of infectious disease models. We first outline the parasitological measures and data on which these estimates will be based. Epidemiological Measures - Prevalence and Intensity Monitoring and evaluation of the impact of community-based preventive chemotherapy programmes is based on two epidemiological measures, the prevalence and the intensity of infection. Prevalence represents the fraction or percentage of the population infected and is typically stratified by factors such as age and gender. The intensity of infection or worm burden for STHs is typical measured indirectly by counts of eggs expelled in faeces (eggs per gram of faeces or EPG) and is similarly typically stratified by age and sex. Less commonly, worm burden may be measured by the worms expelled in total faecal output over a defined period post curative chemotherapy [24]–[26]. The two epidemiological measures are statistics of the probability distribution of worm numbers per person. These distributions are typically highly aggregated in form, with the variance exceeding the mean in value. They are well described by the negative binomial probability model [27]. For this distribution the relationship between prevalence (as a proportion) and mean intensity is given by: (1) Here, the negative binomial parameter varies inversely with the degree of parasite aggregation within the human population and for values in excess of five the distribution is approximately random in form with the variance approaching the mean in value. Some typical estimates of the magnitude of are recorded in Table 1. 10.1371/journal.pntd.0002027.t001 Table 1 Estimates for the negative binomial parasite aggregation parameter, . Parasite value or range Distribution measure Data source Ascaris lumbricoides 0.80 Worm numbers Elkins et al. (1986) [24] Ascaris lumbricoides 0.81 Worm numbers Croll et al. (1982) [34] Ascaris lumbricoides 0.44 Worm numbers Martin et al. (1983) [58] Ascaris lumbricoides 0.46 Worm numbers Thein-Hlaing et al. (1987) [33] Ascaris lumbricoides 0.36–0.54 Worm numbers Chai et al. (1985) [59] Ascaris lumbricoides 0.59 Worm numbers Bundy et al. (1987) [35] Necator americanus 0.34 Eggs per gram of faeces Bradley et al. (1993) [60] Necator americanus 0.33–0.61 Eggs per gram of faeces Quinnell et al. (1993) [36], [61] Trichuris trichiura 0.11–0.65 Eggs per gram of faeces Bundy et al. (1987) [35] Small indicates higher parasite aggregation. A plot of the relationship between and for the negative binomial is presented in Figure 1. It is clear from this figure that the prevalence is a very poor measure of the impact of community-based chemotherapy. Large changes in average worm load as a consequence of treatment will only have a small effect on prevalence unless the mean worm burden is low (i.e. when transmission is very low). For example, in a study in Myanmar two villages with mean EPGs of about 4000 and about 400, an order of magnitude difference in intensity, had almost no difference in prevalence (Figure 1B [28]) and so highly effective treatment of the high intensity village, reducing the burden by a factor of 10, might be viewed as a failed programme if only prevalence were monitored. 10.1371/journal.pntd.0002027.g001 Figure 1 The relationship between mean intensity and prevalence. A The relationship between the mean intensity of infection, , the prevalence of infection, , and the negative binomial aggregation parameter, as described by the relationship in equation 1. B Relationship between the prevalence and intensity of infection as observed in a study of A. lumbricoides [28]. The solid line is the predicted relationship between mean prevalence of infection and worm burden described in equation 1 and plotted in A fitted to estimate the aggregation parameter,  = 0.194. The cost and difficulty of monitoring intensity, as opposed to prevalence, is of course greater. However, if monitoring is to have any value, intensity must be measured in some fraction of the treated population. The issue of how best to sample to gain an accurate picture of the impact of treatment, while attempting to keep sample size and the concomitant costs low, requires careful thought. The underlying distribution of parasite numbers per host is central and given its heterogeneity, small sample sizes will not provide robust measures of trends [29]. One compromise is to monitor impact in a subset of age classes – one of children and one in the adult age groups, to see how the treatment of the school-aged children impacts on transmission to adults [30]. Surveillance and Epidemiological Databases The greatly expanded deworming programmes seen in many regions of the world in recent years have not been accompanied by systematic recording of treatments delivered and the associated impact on prevalence and intensity. Recently, however, some progress has been made on the generation of open access databases recording global and national spatial distributions of helminths based on estimates of the infection prevalence, as illustrated, by the Global Atlas of Helminth Infection (http://www.thiswormyworld.org [12]) and the Global Neglected Tropical Diseases database (http://www.gntd.org [31]). The Global Atlas of Helminth Infection will be expanded in the near future to include measures of the intensity of infection and treatments delivered. The website will also be extended to encourage the electronic deposition of data on STHs collected in association with the current expanded efforts on community-based control using mass or school-aged targeted anthelminthic treatment. Such data are collected by a number of excellent NTD programmes but is rarely subject to detailed analysis on trends in transmission [32]. The present absence of international databases on treatment of STH and impact of such treatment does make analysis of questions concerning the optimal delivery strategy for community-based programme somewhat challenging. We therefore base our analyses on a small number of available well-designed studies that record prevalence and intensity of infection, stratified by age and sex, before and after various treatment programmes. The age-profiles used are from studies of A. lumbricoides in Myanmar [33], India [24] and Iran [34]; T. trichiura in St Lucia [35]; hookworm in Uganda [36] and Vietman [37] and, for comparison, studies of Schistosoma mansoni in Uganda [38] and Brazil [39]. Fraction of the Total Worm Population in School-Aged Children (5 to 14 Years of Age) Epidemiological studies that record the mean intensity of infection stratified by age, when combined with demographic plus school enrolment data, provide information on the fraction of the total worm population exposed to treatment. Along with measures of drug efficacy, this in turn gives the fraction of the total worm population removed by school-aged targeted chemotherapy. The importance of this fraction to the overall transmission dynamics of the target parasites cannot be overstated – and in current control programmes it is an unmeasured parameter. If is the proportion of the total population in age class a, the proportion of the human population in the school age classes 5 to 14 years of age, is given by: (2) The proportion of the total worm population harboured at time by people between the ages of 5 to 14 years, , is then given by: (3) where is the mean worm burden in age-group at time . If the measure of intensity of infection is eggs per gram of faeces (EPG), then the proportion of egg output produced by school-aged children is: (4) Here is the density-dependent egg output function, which gives the expected egg output for an individual with mean worm burden for age and negative binomial aggregation parameter . In practice egg output is usually measured, rather than mean worm burden, so we can approximate this by (5) where the mean egg output in age-group at time . We calculate these fractions for some example datasets on parasite distributions, together with demographic and school enrollment data. Demographic Data Demographic data from various countries where STHs are endemic provides an initial template to assess these issues [40]. Table 2 records the fraction of various populations in the school ages of 5 up to 14 years. In general, within countries where helminth infections are endemic, the fraction of the total population in the school going groups is between 11% and 30%. 10.1371/journal.pntd.0002027.t002 Table 2 Percentage of population aged 5 to 14 years in 2011 [40]. Country Percentage of population 5–14 years of age Total population in millions Uganda 29.7 33.4 Nigeria 26.9 161.6 Rwanda 26.3 11.1 Kenya 26.3 41.9 Burundi 23.7 8.4 India 19.7 1190.0 Myanmar 18.4 54.0 Thailand 13.4 41.9 People's Republic of China 11.8 1340.0 United Kingdom 11.2 62.7 School Enrolment Data UNESCO and the World Bank provide data on school enrolment by sex, location (urban or rural) and country. Recent data are recorded in Figure 2 for rural and urban areas for a selection of countries [41] and in Table 6 for enrollement of female students. Over the past decade there has been a steady increase in school enrolment in most countries throughout the world. Progress has been less good in poor rural areas by comparison with urban districts in developing countries. Generally, the most recent data (2005 and beyond) suggest figures in the 80% to 90% range for most urban areas, but with a range of 20% to 60% in some sub-Saharan African countries in rural areas. There is often a gender bias in many poor countries, with attendance figures for females lower than those for males in the primary and secondary school enrolment data. Poor attendance could severely reduce the population-level impact of school-based deworming. Conversely however, there is anecdotal evidence that there may be higher attendance to schools for deworming days due to awareness of the health benefits. These effects have not yet been quantified, to our knowledge. 10.1371/journal.pntd.0002027.g002 Figure 2 School attendance for a selection of countries. This figure was generated by data published by UNICEF for 2005–2010 [41].. For each country there is net attendance rate at primary, in urban (open circles) and rural areas (closed circles) and a net attendance rate for secondary schools (filled squares). 10.1371/journal.pntd.0002027.t006 Table 6 Percentage of female children who are enrolled in school [45]. Country Children enrolled in school, % Niger 45.5 Eritrea 49.9 Papua New Guinea 50.3 Djibouti 52.1 Chad 60.8 Central African Republic 61.2 Sudan 61.2 Cote d'Ivoire 63.7 Burkina Faso 65.9 Cook Islands 71.3 Effect of coverage on Programme Impact For a given coverage of a school-based programme, as defined by proportion of estimated treatment of a proportion of worms , , or egg output, , the impact on transmission will depend on the particular dynamics of the parasite. Here we outline general insights on the non-linear effects of limited coverage on transmission. We then use heterogeneous mixing models to investigate how different mixing patterns between adults and children will affect the impact of targeted programmes. The Basic Reproductive Number ( ) and Parasite Life Expectancy - Their Effects on the Impact of Community-Based Chemotherapy Simple theory provides some important general insights into the factors controlling helminth transmission and the impact of community-based chemotherapy [18]. For directly transmitted helminths with a free living larval or egg stage outside the human host, the basic dynamics of the system can be described by the following differential equation, (6) where 1/μ is human life expectancy, 1/μ1 is adult parasite life expectancy, M is the mean number of worms in the population, f(M) is the mean egg output per gram of stool, given a mean worm burden of M, the dispersal parameter, k, and fecundity coefficient, z: (7) The basic reproductive number defined as the average number of female worm offspring that survive to reproduce in the absence of density-dependent constraints (ignoring the complexities of mating probabilities and age structure in the human population) is given by equation 8 (8) Here, denotes the fraction of female worms, the per capita egg production rate, the proportion of female worms that survive in the human host to reproductive maturity, the fraction of eggs or larvae that survive to the infective state, is human life expectancy, is the adult worm life expectancy in the human host, is infective stage life expectancy and is the per capita transmission coefficient for the infective stage. Table 3 records published estimates of for various helminth species. 10.1371/journal.pntd.0002027.t003 Table 3 Published estimates of the basic reproductive number for various helminths. Parasite Basic reproductive number, Measure on which parameter estimated Data source Necator americanus 2.0 Re-infection post treatment Bradley et al. (1993) [60] Ascaris lumbricoides 4.3 Re-infection post treatment Croll et al. (1982) [34] Ascaris lumbricoides 1.7 Re-infection post treatment Thein-Hlaing et al. (1987) [33] Trichuris trichiura 4–6 Re-infection post treatment Bundy et al. (1987) [35] A rough approximation of the growth rate of the parasite population post extensive treatment, again ignoring the effects of density-dependence and worm mating probabilities, is given by: (9) Where ( ) is the parasite life expectancy in the human host. Equation 9 is based on the assumption that is much shorter than human life expectancy, which is true for all STHs (see Table 4). 10.1371/journal.pntd.0002027.t004 Table 4 Published estimates of parasite life expectancy, , in the human host [18], [27]. Parasite Life expectancy in years, Enterobius vermicularis 4, estimated values for particular parasites in Table 3) and is short (∼<1.2 years, values in Table 4) e.g.for A. lumbricoides and T. trichiura in high transmission settings. 10.1371/journal.pntd.0002027.g005 Figure 5 Critical fraction of the population to be treated. The predicted relationship between the critical fraction of the human population to be treated, , per annum with efficacy, , 0.9, and the basic reproductive number, , and parasite life expectancy, in years (from equation 11 in the main text). 10.1371/journal.pntd.0002027.g006 Figure 6 Impact of fraction treated on worm burden, prevalence and effective reproduction number. The impact of the fraction of the population treated, , on A the mean worm burden , B the prevalence of infection, and C the effective reproductive number , as described in equation 10. Parameter values are set for A. lumbricoides as follows:  = 0.81,  = 3,  = 1 yr,  = 0.967 and  = 0.95. Heterogeneity Between Age Classes in Contact with Infective Stages Further insight on the effect of targeting school-aged children can be gained by considering differential mixing patterns between children and the rest of the population, as outlined above. The results of example simulations are presented in Figures 7 and 8, where the worm burdens in school-aged children and other age groups (where applicable) and averaged across the community are presented for different modelled scenarios, helminths and treatment intervals. The columns of the figures correspond to the scenarios A (homogeneous population), B (homogeneous mixing) and C (heterogeneous mixing). The heterogeneous mixing (scenario C) results in a higher worm burden in the children than in the adults, as is seen in several settings (note this model does not include any immunity). All models have the same mid-range value of 3. Treatment in the homogeneous model is made comparable with the heterogeneous model by setting coverage to . We have simulated these scenarios for A. lumbricoides, with a life expectancy of 1 year (Figure 7) and hookworm with a life expectancy of 2.5 years (Figure 8). 10.1371/journal.pntd.0002027.g007 Figure 7 Effect of regular treatment on mean A. lumbricoides worm burden for different models. A homogeneous population (left column), B heterogeneous population with uniform transmission dynamics (central column) and C heterogeneous population with greater contribution from children (right column) as in the text. The two rows represent annual and half-yearly treatment respectively. For all runs, basic reproduction number is 3 and worm lifespan is 1 year. Other parameters (as defined for equations 6 and 7): μ2  = 5/yr, k = 0.7, z = 0.93. 10.1371/journal.pntd.0002027.g008 Figure 8 Effect of regular treatment on mean worm burden of hookworm for different models. As in Figure 7, the columns are A homogeneous model, B heterogeneous population with uniform transmission dynamics and C heterogeneous population with greater contribution from children, as in the text and different treatment intervals (rows). Simulations for basic reproduction number,  = 3, and worm lifespan is 2.5 years. Other parameters as in Figure 7. The two rows represent two-yearly and yearly treatment respectively. The most striking feature of Figure 7 and Figure 8 is the very modest impact of treatment of children on transmission, even at high levels of efficacy (95%) and coverage (85%). The treated children do have large benefits in terms of periods free of worms or with low worm burdens. However, the effect of treating children on worm burdens in the larger community is small. This reflects the proportion of the worm population actually reached by treatment, even though the chosen value (30%) is at the high end of school-attending fraction of the population (Table 5). Decreasing the treatment interval (bottom row in each figure) has only a moderate effect. The two group equal mixing model (scenario B) shows the direct effect of school-based treatment on school-aged children and also the indirect effect on adults through the reduction in infectious material in the community. The rate of bounce-back after treatment is slightly reduced in the heterogeneous model as compared to the homogeneous one (scenario B versus scenario A). This means that homogeneous descriptions (e.g. the homogeneous model, A) of non-uniform treatment regimens (targeted at some portion of the population) will always underestimate the time to recover to pre-treatment levels. Scenario C mimics what we believe to be a more realistic epidemiological scenario, with school-aged children contributing twice as much infectious material as other age groups and also being twice as exposed, resulting in higher worm burdens in school-aged children. This is more likely to be observed for A. lumbricoides (e.g. Figure 3) than for hookworm (e.g. Figure 4). As such, the effect of treatment on the school-aged group is quite pronounced, but the impact at the community level, sometimes termed the ‘herd impact’ is only marginally improved, due again to the small proportion of worms treated. These simulations highlight the importance of mixing patterns in determining the effectiveness of school-based treatment programmes. Discussion School-based approaches to deworming children have many advantages in terms of ease of access in urban and rural regions and the ability to link with other nutritional, health and education initiatives in order to try and minimize delivery and logistic costs. Advocacy for this approach to the control of STHs and the morbidity they induce has been made by many over the past decade [1], [30], [46]. However, with increased drug donations to support such programmes, it is now crucial to evaluate the benefits and disadvantages of such an approach. The limitations of this approach has already been implicitly acknowledged in the WHO recommendations to additionally target pre-school children, women of child-bearing age and high risk adults where possible [4] and previous identification of adults as a possible reservoir of infection [47]. However, there are many countries where only school-based deworming is currently under consideration [11]. Of particular importance in this context is the impact of school-based treatment on transmission of the parasites in the entire community, including the pre-school and adult age groups. In particular, sustained transmission (and thus production of infective stages) in other age groups will influence the frequency of treatment in school-aged children required to sustain infection at very low levels. A limited number of field-based studies of mass chemotherapy have suggested that adult age groups who do not have access to treatment still benefit from school-based deworming as a result of its impact on the overall intensity of transmission within the population [30], [33], [48]. The treatment of a few (heavily infected individuals) can impact on the effective reproductive number and therefore reduced exposure to infective stages in those untreated. This effect is analogous to the concept of herd immunity in community-based vaccination programmes where vaccination reduces transmission to those still susceptible to infection. In the context of worms and chemotherapy, the herd impact arises from the reduction in the output of infective stages in faeces that result in the contamination of the environment in which the community of all age groups lives. A similar indirect protection among untreated individuals is the impact upon older children and adults seen after age-targeted (1–10 year olds) large-scale treatment of azithromycin against trachoma [49]. We employed two approaches to examine the impact of school deworming programmes on infection within the entire community, the ‘herd impact’ of the treatment programme. The first was empirical and based on the calculation of the proportions of the population in the school attendance age groups, the fraction of these age groups who attend school and the fraction of the total worm population harboured by in these school-aged children. The results of a set of calculations are summarised in Table 5. It should be noted that the age-profile and population pyramids used here were not exactly matched, and therefore the calculations will not be precise for a particular point in time. It highlights the need for more recent data for these pathogens, as highlighted in recent articles [50]–[51]. In many of the low-income settings in which STHs are highly prevalent, the population pyramid is approximately exponential in shape, meaning that from 20–30% (Table 2, Figure 3A and Figure 4A) are of school age. However, for some STHs, such as A. lumbricoides, the highest intensity infections are in this age-group (Figure 3B) and therefore a large proportion of worms are targeted by school-aged treatment (up to 50% in our examples, Table 5). These predictions are similar to empirical findings by Bundy et al. [30] who evaluated the impact of age-target chemotherapy on community transmission on the island of Montserrat, West Indies, where T. trichiura, which has similar transmission dynamics to A. lumbricoides, was the predominant species and had a prevalence of 12% (A. lumbricoides and hookworm occurred at <2%). The authors found that 4-monthy treatment of 2–15 year olds had a subsidiary effect on intensity of untreated 16–25 year olds. In contrast to A. lumbricoides and T. trichiura which exhibit age-convexity in intensity, hookworm intensity tends to monotonically increase with age (e.g. Figure 4B), as has been seen in several studies [52]–[54], and therefore a smaller proportion of worms or egg output are targeted by school-aged treatment (<10% in one of our examples, Table 5). The different age profiles for these helminth species are a result of differing behaviour patterns, force of infection, heterogeneous exposure and, arguably, immunity and genetic pre-disposition. The details of the biology which generate these patterns do not need to be understood for the calculation of the proportion of worms treated. However, to estimate the impact of treating children on transmission in the larger community we need a combination of additional studies and novel modelling analyses. Our simulations (Figures 7 and 8) show the importance of understanding the nature of the interaction between school-aged children and the rest of the community in order to optimise treatment programmes in the longer term. It is possible that school-aged deworming will give little benefit outside the children being treated and that intensifying either the treatment coverage within this age-group and/or increasing the frequency of treatment (bottom rows in Figures 7 and 8) may not lead to the desired benefits in the larger community. In this case, other strategies will be needed to increase the potential for long-term control and, if the breakpoint can be crossed by combinations of interventions, eventual elimination of these helminths. As the schematic, Figure 9, shows, the impact of school-based treatment programmes on transmission in the larger community is diluted by a number of effects. The benefits of deworming for the affected children are many, but if we are to plan for long-term control and, in the longer term, elimination of these pathogens we need to consider strategies that will reduce transmission from year to year, as is already being discussed in some settings [55], particularly for schistosomiasis [56]. We also require an understanding of the species mix in each setting so as to tailor the design of interventions according to the underlying transmission dynamics. As this paper shows, there are many outstanding data gaps and needs for new modelling studies both to understand the dynamics of transmission under such programmes, and to design optimal treatment strategies for the future. 10.1371/journal.pntd.0002027.g009 Figure 9 Schematic illustration of the impact of school-based deworming on the transmission of parasites. The number of parasites affected by a school-based deworming programme is never 100%, it is reduced by the efficacy of the drug, the proportion of the population of school age and their intensity of infection, as well as school enrolment and attendance on a deworming day. The impact of such a treatment programme on transmission is less easily quantified and depends on the details of transmission between different age-groups in the population. For further details, see main text.
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            Affiliations
            [1 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, London Centre for Neglected Tropical Disease Research (LCNTDR), Department of Infectious Disease Epidemiology, , St. Mary’s Campus, Imperial College London, ; W2 1PG, London, UK
            [2 ]The DeWorm3 Project, The Natural History Museum of London, London, SW7 5BD UK
            [3 ]ISNI 0000 0004 1767 8969, GRID grid.11586.3b, Division of Gastrointestinal Sciences, , Christian Medical College, ; Vellore, 632004 India
            [4 ]ISNI 0000000122986657, GRID grid.34477.33, Department of Global Health, , University of Washington, ; Seattle, USA
            Contributors
            j.truscott@imperial.ac.uk
            m.werkman@imperial.ac.uk
            james.e.wright@imperial.ac.uk
            sam.farrell@imperial.ac.uk
            rsarkar@cmcvellore.ac.in
            kasbjorn@uw.edu
            roy.anderson@imperial.ac.uk
            Journal
            Parasit Vectors
            Parasit Vectors
            Parasites & Vectors
            BioMed Central (London )
            1756-3305
            30 June 2017
            30 June 2017
            2017
            : 10
            28666452 5493114 2256 10.1186/s13071-017-2256-8
            © The Author(s). 2017

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