Reducing the Antigen Prevalence Target Threshold for Stopping and Restarting Mass Drug Administration for Lymphatic Filariasis Elimination: A Model-Based Cost-effectiveness Simulation in Tanzania, India and Haiti.

BACKGROUND: The Global Programme to Eliminate Lymphatic Filariasis (GPELF) aims to reduce and maintain infection levels through mass drug administration (MDA), but there is evidence of ongoing transmission after MDA in areas where Culex mosquitoes are the main transmission vector, suggesting that a more stringent criterion is required for MDA decision making in these settings. METHODS: We use a transmission model to investigate how a lower prevalence threshold (<1% antigenemia [Ag] prevalence compared with <2% Ag prevalence) for MDA decision making would affect the probability of local elimination, health outcomes, the number of MDA rounds, including restarts, and program costs associated with MDA and surveys across different scenarios. To determine the cost-effectiveness of switching to a lower threshold, we simulated 65% and 80% MDA coverage of the total population for different willingness to pay per disability-adjusted life-year averted for India ($446.07), Tanzania ($389.83), and Haiti ($219.84). RESULTS: Our results suggest that with a lower Ag threshold, there is a small proportion of simulations where extra rounds are required to reach the target, but this also reduces the need to restart MDA later in the program. For 80% coverage, the lower threshold is cost-effective across all baseline prevalences for India, Tanzania, and Haiti. For 65% MDA coverage, the lower threshold is not cost-effective due to additional MDA rounds, although it increases the probability of local elimination. Valuing the benefits of elimination to align with the GPELF goals, we find that a willingness to pay per capita government expenditure of approximately $1000-$4000 for 1% increase in the probability of local elimination would be required to make a lower threshold cost-effective. CONCLUSIONS: Lower Ag thresholds for stopping MDAs generally mean a higher probability of local elimination, reducing long-term costs and health impacts. However, they may also lead to an increased number of MDA rounds required to reach the lower threshold and, therefore, increased short-term costs. Collectively, our analyses highlight that lower target Ag thresholds have the potential to assist programs in achieving lymphatic filariasis goals.

How Does the Proportion of Never Treatment Influence the Success of Mass Drug Administration Programs for the Elimination of Lymphatic Filariasis?

BACKGROUND: Mass drug administration (MDA) is the cornerstone for the elimination of lymphatic filariasis (LF). The proportion of the population that is never treated (NT) is a crucial determinant of whether this goal is achieved within reasonable time frames. METHODS: Using 2 individual-based stochastic LF transmission models, we assess the maximum permissible level of NT for which the 1% microfilaremia (mf) prevalence threshold can be achieved (with 90% probability) within 10 years under different scenarios of annual MDA coverage, drug combination and transmission setting. RESULTS: For Anopheles-transmission settings, we find that treating 80% of the eligible population annually with ivermectin + albendazole (IA) can achieve the 1% mf prevalence threshold within 10 years of annual treatment when baseline mf prevalence is 10%, as long as NT <10%. Higher proportions of NT are acceptable when more efficacious treatment regimens are used. For Culex-transmission settings with a low (5%) baseline mf prevalence and diethylcarbamazine + albendazole (DA) or ivermectin + diethylcarbamazine + albendazole (IDA) treatment, elimination can be reached if treatment coverage among eligibles is 80% or higher. For 10% baseline mf prevalence, the target can be achieved when the annual coverage is 80% and NT ≤15%. Higher infection prevalence or levels of NT would make achieving the target more difficult. CONCLUSIONS: The proportion of people never treated in MDA programmes for LF can strongly influence the achievement of elimination and the impact of NT is greater in high transmission areas. This study provides a starting point for further development of criteria for the evaluation of NT.

Source-Apportioned PM2.5 and Cardiorespiratory Emergency Department Visits: Accounting for Source Contribution Uncertainty.

BACKGROUND: Despite evidence suggesting that air pollution-related health effects differ by emissions source, epidemiologic studies on fine particulate matter (PM2.5) infrequently differentiate between particles from different sources. Those that do rarely account for the uncertainty of source apportionment methods. METHODS: For each day in a 12-year period (1998-2010) in Atlanta, GA, we estimated daily PM2.5 source contributions from a Bayesian ensemble model that combined four source apportionment methods including chemical transport and receptor-based models. We fit Poisson generalized linear models to estimate associations between source-specific PM2.5 concentrations and cardiorespiratory emergency department visits (n = 1,598,117). We propagated uncertainty in the source contribution estimates through analyses using multiple imputation. RESULTS: Respiratory emergency department visits were positively associated with biomass burning and secondary organic carbon. For a 1 µg/m increase in PM2.5 from biomass burning during the past 3 days, the rate of visits for all respiratory outcomes increased by 0.4% (95% CI 0.0%, 0.7%). There was less evidence for associations between PM2.5 sources and cardiovascular outcomes, with the exception of ischemic stroke, which was positively associated with most PM2.5 sources. Accounting for the uncertainty of source apportionment estimates resulted, on average, in an 18% increase in the standard error for rate ratio estimates for all respiratory and cardiovascular emergency department visits, but inflation varied across specific sources and outcomes, ranging from 2% to 39%. CONCLUSIONS: This study provides evidence of associations between PM2.5 sources and some cardiorespiratory outcomes and quantifies the impact of accounting for variability in source apportionment approaches.

Ensemble-based source apportionment of fine particulate matter and emergency department visits for pediatric asthma.

Epidemiologic studies utilizing source apportionment (SA) of fine particulate matter have shown that particles from certain sources might be more detrimental to health than others; however, it is difficult to quantify the uncertainty associated with a given SA approach. In the present study, we examined associations between source contributions of fine particulate matter and emergency department visits for pediatric asthma in Atlanta, Georgia (2002-2010) using a novel ensemble-based SA technique. Six daily source contributions from 4 SA approaches were combined into an ensemble source contribution. To better account for exposure uncertainty, 10 source profiles were sampled from their posterior distributions, resulting in 10 time series with daily SA concentrations. For each of these time series, Poisson generalized linear models with varying lag structures were used to estimate the health associations for the 6 sources. The rate ratios for the source-specific health associations from the 10 imputed source contribution time series were combined, resulting in health associations with inflated confidence intervals to better account for exposure uncertainty. Adverse associations with pediatric asthma were observed for 8-day exposure to particles generated from diesel-fueled vehicles (rate ratio = 1.06, 95% confidence interval: 1.01, 1.10) and gasoline-fueled vehicles (rate ratio = 1.10, 95% confidence interval: 1.04, 1.17).

Associations between ambient air pollutant mixtures and pediatric asthma emergency department visits in three cities: a classification and regression tree approach.

BACKGROUND: Characterizing multipollutant health effects is challenging. We use classification and regression trees to identify multipollutant joint effects associated with pediatric asthma exacerbations and compare these results with those from a multipollutant regression model with continuous joint effects. METHODS: We investigate the joint effects of ozone, NO2 and PM2.5 on emergency department visits for pediatric asthma in Atlanta (1999-2009), Dallas (2006-2009) and St. Louis (2001-2007). Daily concentrations of each pollutant were categorized into four levels, resulting in 64 different combinations or "Day-Types" that can occur. Days when all pollutants were in the lowest level were withheld as the reference group. Separate regression trees were grown for each city, with partitioning based on Day-Type in a model with control for confounding. Day-Types that appeared together in the same terminal node in all three trees were considered to be mixtures of potential interest and were included as indicator variables in a three-city Poisson generalized linear model with confounding control and rate ratios calculated relative to the reference group. For comparison, we estimated analogous joint effects from a multipollutant Poisson model that included terms for each pollutant, with concentrations modeled continuously. RESULTS AND DISCUSSION: No single mixture emerged as the most harmful. Instead, the rate ratios for the mixtures suggest that all three pollutants drive the health association, and that the rate plateaus in the mixtures with the highest concentrations. In contrast, the results from the comparison model are dominated by an association with ozone and suggest that the rate increases with concentration. CONCLUSION: The use of classification and regression trees to identify joint effects may lead to different conclusions than multipollutant models with continuous joint effects and may serve as a complementary approach for understanding health effects of multipollutant mixtures.

Joint effects of ambient air pollutants on pediatric asthma emergency department visits in Atlanta, 1998-2004.

BACKGROUND: Because ambient air pollution exposure occurs as mixtures, consideration of joint effects of multiple pollutants may advance our understanding of the health effects of air pollution. METHODS: We assessed the joint effect of air pollutants on pediatric asthma emergency department visits in Atlanta during 1998-2004. We selected combinations of pollutants that were representative of oxidant gases and secondary, traffic, power plant, and criteria pollutants, constructed using combinations of criteria pollutants and fine particulate matter (PM2.5) components. Joint effects were assessed using multipollutant Poisson generalized linear models controlling for time trends, meteorology, and daily nonasthma upper respiratory emergency department visit counts. Rate ratios (RRs) were calculated for the combined effect of an interquartile range increment in each pollutant's concentration. RESULTS: Increases in all of the selected pollutant combinations were associated with increases in warm-season pediatric asthma emergency department visits (eg, joint-effect RR = 1.13 [95% confidence interval = 1.06-1.21] for criteria pollutants, including ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, and PM2.5). Cold-season joint effects from models without nonlinear effects were generally weaker than warm-season effects. Joint-effect estimates from multipollutant models were often smaller than estimates based on single-pollutant models, due to control for confounding. Compared with models without interactions, joint-effect estimates from models including first-order pollutant interactions were largely similar. There was evidence of nonlinear cold-season effects. CONCLUSIONS: Our analyses illustrate how consideration of joint effects can add to our understanding of health effects of multipollutant exposures and also illustrate some of the complexities involved in calculating and interpreting joint effects of multiple pollutants.

Classification and regression trees for epidemiologic research: an air pollution example.

BACKGROUND: Identifying and characterizing how mixtures of exposures are associated with health endpoints is challenging. We demonstrate how classification and regression trees can be used to generate hypotheses regarding joint effects from exposure mixtures. METHODS: We illustrate the approach by investigating the joint effects of CO, NO2, O3, and PM2.5 on emergency department visits for pediatric asthma in Atlanta, Georgia. Pollutant concentrations were categorized as quartiles. Days when all pollutants were in the lowest quartile were held out as the referent group (n = 131) and the remaining 3,879 days were used to estimate the regression tree. Pollutants were parameterized as dichotomous variables representing each ordinal split of the quartiles (e.g. comparing CO quartile 1 vs. CO quartiles 2-4) and considered one at a time in a Poisson case-crossover model with control for confounding. The pollutant-split resulting in the smallest P-value was selected as the first split and the dataset was partitioned accordingly. This process repeated for each subset of the data until the P-values for the remaining splits were not below a given alpha, resulting in the formation of a "terminal node". We used the case-crossover model to estimate the adjusted risk ratio for each terminal node compared to the referent group, as well as the likelihood ratio test for the inclusion of the terminal nodes in the final model. RESULTS: The largest risk ratio corresponded to days when PM2.5 was in the highest quartile and NO2 was in the lowest two quartiles (RR: 1.10, 95% CI: 1.05, 1.16). A simultaneous Wald test for the inclusion of all terminal nodes in the model was significant, with a chi-square statistic of 34.3 (p = 0.001, with 13 degrees of freedom). CONCLUSIONS: Regression trees can be used to hypothesize about joint effects of exposure mixtures and may be particularly useful in the field of air pollution epidemiology for gaining a better understanding of complex multipollutant exposures.

How correlations between treatment access and surveillance inclusion impact neglected tropical disease monitoring and evaluation-A simulated study.

Neglected tropical diseases (NTDs) largely impact marginalised communities living in tropical and subtropical regions. Mass drug administration is the leading intervention method for five NTDs; however, it is known that there is lack of access to treatment for some populations and demographic groups. It is also likely that those individuals without access to treatment are excluded from surveillance. It is important to consider the impacts of this on the overall success, and monitoring and evaluation (M&E) of intervention programmes. We use a detailed individual-based model of the infection dynamics of lymphatic filariasis to investigate the impact of excluded, untreated, and therefore unobserved groups on the true versus observed infection dynamics and subsequent intervention success. We simulate surveillance in four groups-the whole population eligible to receive treatment, the whole eligible population with access to treatment, the TAS focus of six- and seven-year-olds, and finally in >20-year-olds. We show that the surveillance group under observation has a significant impact on perceived dynamics. Exclusion to treatment and surveillance negatively impacts the probability of reaching public health goals, though in populations that do reach these goals there are no signals to indicate excluded groups. Increasingly restricted surveillance groups over-estimate the efficacy of MDA. The presence of non-treated groups cannot be inferred when surveillance is only occurring in the group receiving treatment.

Modernizing the design and analysis of prevalence surveys for neglected tropical diseases.

Current WHO guidelines set prevalence thresholds below which a neglected tropical disease can be considered to have been eliminated as a public health problem, and specify how surveys to assess whether elimination has been achieved should be designed and analysed, based on classical survey sampling methods. In this paper, we describe an alternative approach based on geospatial statistical modelling. We first show the gains in efficiency that can be obtained by exploiting any spatial correlation in the underlying prevalence. We then suggest that the current guidelines' implicit use of a significance testing argument is not appropriate; instead, we argue for a predictive inferential framework, leading to design criteria based on controlling the rates at which areas whose true prevalence lies above and below the elimination threshold are incorrectly classified. We describe how this approach naturally accommodates context-specific information in the form of georeferenced covariates that have been shown to be predictive of disease prevalence. Finally, we give a progress report of an ongoing collaboration with the Guyana Ministry of Health Neglected Tropical Disease programme on the design of an IDA (ivermectin, diethylcarbamazine and albendazole) Impact Survey of lymphatic filariasis to be conducted in Guyana in early 2023. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.

Correction: Onchocerciasis: Target product profiles of in vitro diagnostics to support onchocerciasis elimination mapping and mass drug administration stopping decisions.

[This corrects the article DOI: 10.1371/journal.pntd.0010682.].

How well do coverage surveys and programmatically reported mass drug administration coverage match? Results from 214 mass drug administration campaigns in 15 countries, 2008-2017.

INTRODUCTION: Delivering preventive chemotherapy through mass drug administration (MDA) is a central approach in controlling or eliminating several neglected tropical diseases (NTDs). Treatment coverage, a primary indicator of MDA performance, can be measured through routinely reported programmatic data or population-based coverage evaluation surveys. Reported coverage is often the easiest and least expensive way to estimate coverage; however, it is prone to inaccuracies due to errors in data compilation and imprecise denominators, and in some cases measures treatments offered as opposed to treatments swallowed. OBJECTIVE: Analyses presented here aimed to understand (1) how often coverage calculated using routinely reported data and survey data would lead programme managers to make the same programmatic decisions; (2) the magnitude and direction of the difference between these two estimates, and (3) whether there is meaningful variation by region, age group or country. METHODS: We analysed and compared reported and surveyed treatment coverage data from 214 MDAs implemented between 2008 and 2017 in 15 countries in Africa, Asia and the Caribbean. Routinely reported treatment coverage was compiled using data reported by national NTD programmes to donors, either directly or via NTD implementing partners, following the implementation of a district-level MDA campaign; coverage was calculated by dividing the number of individuals treated by a population value, which is typically based on national census projections and occasionally community registers. Surveyed treatment coverage came from post-MDA community-based coverage evaluation surveys, which were conducted as per standardised WHO recommended methodology. RESULTS: Coverage estimates using routine reporting and surveys gave the same result in terms of whether the minimum coverage threshold was reached in 72% of the MDAs surveyed in the Africa region and in 52% in the Asia region. The reported coverage value was within ±10 percentage points of the surveyed coverage value in 58/124 of the surveyed MDAs in the Africa region and 19/77 in the Asia region. Concordance between routinely reported and surveyed coverage estimates was 64% for the total population and 72% for school-age children. The study data showed variation across countries in the number of surveys conducted as well as the frequency with which there was concordance between the two coverage estimates. CONCLUSIONS: Programme managers must grapple with making decisions based on imperfect information, balancing needs for accuracy with cost and available capacity. The study shows that for many of the MDAs surveyed, based on the concordance with respect to reaching the minimum coverage thresholds, the routinely reported data were accurate enough to make programmatic decisions. Where coverage surveys do show a need to improve accuracy of routinely reported results, NTD programme managers should use various tools and approaches to strengthen data quality in order to use data for decision-making to achieve NTD control and elimination goals.

Sexual agreements in the partnerships of internet-using men who have sex with men.

Recent studies among men who have sex with men (MSM) have found that the majority of HIV transmission results from sex with a main partner. One factor likely to affect the risk of transmission is the type of agreements the couple has regarding sexual behaviour within and outside the relationship. This study recruited 732 Internet-using MSM through Facebook banner ads. Participants completed an online questionnaire regarding demographic characteristics of the respondent and their main partner, the sexual behaviour of the couple, the existence of a sexual agreement, and the strength of investment in that agreement. The Pearson chi-square test was used to assess the association between sexual agreements (categorized as open, closed, or none) and the predictive variables. Respondents' investment in their sexual agreement was measured using the sexual agreement investment scale (a composite score ranging from 0 to 52). Ninety-one percent of respondents had some form of sexual agreement in place with their main partner. The presence and type of sexual agreement was found to be strongly associated with many characteristics of the individual and couple, including the respondent's HIV status, length of time with the main partner, having unprotected anal intercourse with a man other than their main partner, and happiness in the relationship. Increases in the strength of respondents' investment in their sexual agreement were found to be associated with newness of the relationship, relationship happiness, having a closed relationship, and decreases in risky sexual behaviour. This study offers further evidence of the important role that sexual agreements play in male couples. The overwhelming prevalence of sexual agreements and their association with relationship happiness and risky sexual behaviours has important implications for future HIV prevention and control strategies, including the implementation of couples voluntary counseling and testing.

Characterization of Ambient Air Pollution Measurement Error in a Time-Series Health Study using a Geostatistical Simulation Approach.

In recent years, geostatistical modeling has been used to inform air pollution health studies. In this study, distributions of daily ambient concentrations were modeled over space and time for 12 air pollutants. Simulated pollutant fields were produced for a 6-year time period over the 20-county metropolitan Atlanta area using the Stanford Geostatistical Modeling Software (SGeMS). These simulations incorporate the temporal and spatial autocorrelation structure of ambient pollutants, as well as season and day-of-week temporal and spatial trends; these fields were considered to be the true ambient pollutant fields for the purposes of the simulations that followed. Simulated monitor data at the locations of actual monitors were then generated that contain error representative of instrument imprecision. From the simulated monitor data, four exposure metrics were calculated: central monitor and unweighted, population-weighted, and area-weighted averages. For each metric, the amount and type of error relative to the simulated pollutant fields are characterized and the impact of error on an epidemiologic time-series analysis is predicted. The amount of error, as indicated by a lack of spatial autocorrelation, is greater for primary pollutants than for secondary pollutants and is only moderately reduced by averaging across monitors; more error will result in less statistical power in the epidemiologic analysis. The type of error, as indicated by the correlations of error with the monitor data and with the true ambient concentration, varies with exposure metric, with error in the central monitor metric more of the classical type (i.e., independent of the monitor data) and error in the spatial average metrics more of the Berkson type (i.e., independent of the true ambient concentration). Error type will affect the bias in the health risk estimate, with bias toward the null and away from the null predicted depending on the exposure metric; population-weighting yielded the least bias.

Potential use of antibodies to provide an earlier indication of lymphatic filariasis resurgence in post-mass drug ad ministration surveillance in American Samoa.

BACKGROUND: Under the Global Programme to Eliminate Lymphatic Filariasis (LF), American Samoa conducted 7 rounds of mass drug administration (MDA) between 2000 and 2006. The territory passed transmission assessment surveys (TASs) in 2011 (TAS-1) and 2015 (TAS-2). In 2016, the territory failed TAS-3, indicating resurgence. This study aims to determine if antibodies (Abs) may have provided a timelier indication of LF resurgence in American Samoa. METHODS: We examined school-level antigen (Ag) and Ab status (presence/absence of Ag- and Ab-positive children) and prevalence of single and combined Ab responses to Wb123, Bm14, and Bm33 Ags at each TAS. Pearson chi-square test and logistic regression were used to examine associations between school-level Ab prevalence in TAS-1 and TAS-2 and school-level Ag status in TAS-3. RESULTS: Schools with higher prevalence of Wb123 Ab in TAS-2 had higher odds of being Ag-positive in TAS-3 (odds ratio [OR] 24.5, 95% confidence interval [CI] 1.2-512.7). Schools that were Ab-positive for WB123 plus Bm14, Bm33, or both Bm14 and Bm33 in TAS-2 had higher odds of being Ag-positive in TAS-3 (OR 16.0-24.5). CONCLUSION: Abs could provide earlier signals of resurgence and enable a timelier response. The promising role of Abs in surveillance after MDA and decision making should be further investigated in other settings.

Simulating the effect of evaluation unit size on eligibility to stop mass drug administration for lymphatic filariasis in Haiti.

BACKGROUND: The Transmission Assessment Survey (TAS) is a decision-making tool to determine when transmission of lymphatic filariasis is presumed to have reached a level low enough that it cannot be sustained even in the absence of mass drug administration. The survey is applied over geographic areas, called evaluation units (EUs); existing World Health Organization guidelines limit EU size to a population of no more than 2 million people. METHODOLOGY/PRINCIPAL FINDINGS: In 2015, TASs were conducted in 14 small EUs in Haiti. Simulations, using the observed TAS results, were performed to understand the potential programmatic impact had Haiti chosen to form larger EUs. Nine "combination-EUs" were formed by grouping adjacent EUs, and bootstrapping was used to simulate the expected TAS results. When the combination-EUs were comprised of at least one "passing" and one "failing" EU, the majority of these combination-EU would pass the TAS 79% - 100% of the time. Even in the case when both component EUs had failed, the combination-EU was expected to "pass" 11% of the time. Simulations of mini-TAS, a strategy with smaller power and hence smaller sample size than TAS, resulted in more conservative "passing" and "failing" when implemented in original EUs. CONCLUSIONS/SIGNIFICANCE: Our results demonstrate the high potential for misclassification when the average prevalence of lymphatic filariasis in the combined areas differs with regards to the TAS threshold. Of particular concern is the risk of "passing" larger EUs that include focal areas where prevalence is high enough to be potentially self-sustaining. Our results reaffirm the approach that Haiti took in forming smaller EUs. Where baseline or monitoring data show a high or heterogeneous prevalence, programs should leverage alternative strategies like mini-TAS in smaller EUs, or consider gathering additional data through spot check sites to advise EU formation.

Positive-case follow up for lymphatic filariasis after a transmission assessment survey in Haiti.

BACKGROUND: Lymphatic filariasis (LF) has been targeted for global elimination as a public health problem since 1997. The primary strategy to interrupt transmission is annual mass drug administration (MDA) for ≥5 years. The transmission assessment survey (TAS) was developed as a decision-making tool to measure LF antigenemia in children to determine when MDA in a region can be stopped. The objective of this study was to investigate potential sampling strategies for follow-up of LF-positive children identified in TAS to detect evidence of ongoing transmission. METHODOLOGY/PRINCIPLE FINDINGS: Nippes Department in Haiti passed TAS 1 with 2 positive cases and stopped MDA in 2015; however, 8 positive children were found during TAS 2 in 2017, which prompted a more thorough assessment of ongoing transmission. Purposive sampling was used to select the closest 50 households to each index case household, and systematic random sampling was used to select 20 households from each index case census enumeration area. All consenting household members aged ≥2 years were surveyed and tested for circulating filarial antigen (CFA) using the rapid filarial test strip and for Wb123-specific antibodies using the Filaria Detect IgG4 ELISA. Among 1,927 participants, 1.5% were CFA-positive and 4.5% were seropositive. CFA-positive individuals were identified for 6 of 8 index cases. Positivity ranged from 0.4-2.4%, with highest positivity in the urban commune Miragoane. Purposive sampling found the highest number of CFA-positives (17 vs. 9), and random sampling found a higher percent positive (2.4% vs. 1.4%). CONCLUSIONS/SIGNIFICANCE: Overall, both purposive and random sampling methods were reasonable and achievable methods of TAS follow-up in resource-limited settings. Both methods identified additional CFA-positives in close geographic proximity to LF-positive children found by TAS, and both identified strong signs of ongoing transmission in the large urban commune of Miragoane. These findings will help inform standardized guidelines for post-TAS surveillance.

Geostatistical modelling enables efficient safety assessment for mass drug administration with ivermectin in Loa loa endemic areas through a combined antibody and LoaScope testing strategy for elimination of onchocerciasis.

The elimination of onchocerciasis through community-based Mass Drug Administration (MDA) of ivermectin (Mectizan) is hampered by co-endemicity of Loa loa, as individuals who are highly co-infected with Loa loa parasites can suffer serious and occasionally fatal neurological reactions from the drug. The test-and-not-treat strategy of testing all individuals participating in MDA has some operational constraints including the cost and limited availability of LoaScope diagnostic tools. As a result, a Loa loa Antibody (Ab) Rapid Test was developed to offer a complementary way of determining the prevalence of loiasis. We develop a joint geostatistical modelling framework for the analysis of Ab and Loascope data to delineate whether an area is safe for MDA. Our results support the use of a two-stage strategy, in which Ab testing is used to identify areas that, with acceptably high probability, are safe or unsafe for MDA, followed by Loascope testing in areas whose safety status is uncertain. This work therefore contributes to the global effort towards the elimination of onchocerciasis as a public health problem by potentially reducing the time and cost required to establish whether an area is safe for MDA.

Evaluating Molecular Xenomonitoring as a Tool for Lymphatic Filariasis Surveillance in Samoa, 2018-2019.

Molecular xenomonitoring (MX), the detection of filarial DNA in mosquitoes using molecular methods (PCR), is a potentially useful surveillance strategy for lymphatic filariasis (LF) elimination programs. Delay in filarial antigen (Ag) clearance post-treatment is a limitation of using human surveys to provide an early indicator of the impact of mass drug administration (MDA), and MX may be more useful in this setting. We compared prevalence of infected mosquitoes pre- and post-MDA (2018 and 2019) in 35 primary sampling units (PSUs) in Samoa, and investigated associations between the presence of PCR-positive mosquitoes and Ag-positive humans. We observed a statistically significant decline in estimated mosquito infection prevalence post-MDA at the national level (from 0.9% to 0.3%, OR 0.4) but no change in human Ag prevalence during this time. Ag prevalence in 2019 was higher in randomly selected PSUs where PCR-positive pools were detected (1.4% in ages 5-9; 4.8% in ages ≥10), compared to those where PCR-positive pools were not detected (0.2% in ages 5-9; 3.2% in ages ≥10). Our study provides promising evidence for MX as a complement to human surveys in post-MDA surveillance.

Onchocerciasis: Target product profiles of in vitro diagnostics to support onchocerciasis elimination mapping and mass drug administration stopping decisions.

In June 2021, the World Health Organization (WHO), recognizing the need for new diagnostics to support the control and elimination of onchocerciasis, published the target product profiles (TPPs) of new tests that would support the two most immediate needs: (a) mapping onchocerciasis in areas of low prevalence and (b) deciding when to stop mass drug administration programs. In both instances, the test should ideally detect an antigen specific for live, adult O. volvulus female worms. The preferred format is a field-deployable rapid test. For mapping, the test needs to be ≥ 60% sensitive and ≥ 99.8% specific, while to support stopping decisions, the test must be ≥ 89% sensitive and ≥ 99.8% specific. The requirement for extremely high specificity is dictated by the need to detect with sufficient statistical confidence the low seroprevalence threshold set by WHO. Surveys designed to detect a 1-2% prevalence of a given biomarker, as is the case here, cannot tolerate more than 0.2% of false-positives. Otherwise, the background noise would drown out the signal. It is recognized that reaching and demonstrating such a stringent specificity criterion will be challenging, but test developers can expect to be assisted by national governments and implementing partners for adequately powered field validation.

Proxy Responses for Mass Drug Administration Coverage Surveys: The Trends and Biases When Others are Allowed to Respond.

Coverage surveys for mass drug administration (MDA) rely on respondent recall and often permit proxy responses, whereby another household member is allowed to respond on behalf of an absent individual. In this secondary analysis of coverage surveys in Malawi, Burkina Faso, and Uganda, we explore the characteristics of individuals who require proxy responses and quantify the association between proxy responses and reported drug coverage. The adjusted logistic regression model found that men 11-39 years and women 11-18 years who were eligible for MDA had greater odds of requiring a proxy response compared with ineligible men and women in the same age groups. A hierarchical multivariable analysis found that proxy responses had 1.70 times the odds of reporting ingestion of MDA drugs compared with first-person responses, controlling for age and sex (95% CI: 1.17, 2.46). This finding is surprising, given that individuals absent during a coverage survey may also have been absent during the MDA, and suggests that proxy responses may be leading to an inflation of survey estimates of drug coverage. This study highlights the possibility for recall bias in proxy responses to MDA coverage; however, excluding absent individuals from coverage surveys would introduce a new bias. Further research is necessary to determine the best method for obtaining information on drug coverage when individuals are absent.