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Background Scabies is known to be a public health problem in many settings but the majority of recent data is from rural settings in the Pacific. There is a need for high quality data from sub-Saharan Africa and peri-Urban settings to inform scale up of scabies control efforts. There have been anecdotal reports of scabies being a public health problem in Liberia but robust data are lacking. Methods We conducted a cross-sectional cluster-randomised prevalence survey for scabies in a peri-urban community in Monrovia, Liberia in February-March 2020. Participants underwent a standardised examination conducted by trained local health care workers. Health related quality of life (HRQoL) was assessed using age-appropriate dermatology life quality indices (DLQIs). Prevalence estimates were calculated accounting for clustering at community and household levels and associations with key demographic variables assessed through multivariable random-effects logistic regression. Results 1,318 participants from 477 households were surveyed. The prevalence of scabies prevalence was 9.3% (95% CI: 6.5-13.2%), across 75 (19.7%) households; impetigo or infected scabies prevalence was 0.8% (95% CI: 0.4-1.9%). The majority (52%) of scabies cases were classified as severe. Scabies prevalence was lower in females and higher in the youngest age group; no associations were found with other collected demographic or socio-economic variables. DLQI scores indicated a very or extremely large effect on HRQoL in 29% of adults and 18% of children diagnosed with scabies. Conclusions Our study indicates a substantial burden of scabies in this peri-Urban population in Liberia. This was associated with significant impact on quality of life, highlighting the need for action to control scabies in this population. Further work is needed to assess the impact of interventions in this context on both the prevalence of scabies and quality of life.

BackgroundOnchocerciasis (river blindness) is a filarial disease targeted for elimination of transmission. However, challenges exist to the implementation of effective diagnostic and surveillance strategies at various stages of elimination programs. To address these challenges, we used a network data analytics approach to identify optimal diagnostic scenarios for onchocerciasis elimination mapping (OEM).MethodsThe diagnostic network optimization (DNO) method was used to model the implementation of the old Ov16 rapid diagnostic test (RDT) and of new RDTs in development for OEM under different testing strategy scenarios with varying testing locations, test performance and disease prevalence. Environmental suitability scores (ESS) based on machine learning algorithms were developed to identify areas at risk of transmission and used to select sites for OEM in Bandundu region in the Democratic Republic of Congo (DRC) and Uige province in Angola. Test sensitivity and specificity ranges were obtained from the literature for the existing RDT, and from characteristics defined in the target product profile for the new RDTs. Sourcing and transportation policies were defined, and costing information was obtained from onchocerciasis programs. Various scenarios were created to test various state configurations. The actual demand scenarios represented the disease prevalence at IUs according to the ESS, while the counterfactual scenarios (conducted only in the DRC) are based on adapted prevalence estimates to generate prevalence close to the statistical decision thresholds (5% and 2%), to account for variability in field observations. The number of correctly classified implementation units (IUs) per scenario were estimated and key cost drivers were identified.ResultsIn both Bandundu and Uige, the sites selected based on ESS had high predicted onchocerciasis prevalence >10%. Thus, in the actual demand scenarios in both Bandundu and Uige, the old Ov16 RDT correctly classified all 13 and 11 IUs, respectively, as requiring CDTi. In the counterfactual scenarios in Bandundu, the new RDTs with higher specificity correctly classified IUs more cost effectively. The new RDT with highest specificity (99.8%) correctly classified all 13 IUs. However, very high specificity (e.g., 99.8%) when coupled with imperfect sensitivity, can result in many false negative results (missing decisions to start MDA) at the 5% statistical decision threshold (the decision rule to start MDA). This effect can be negated by reducing the statistical decision threshold to 2%. Across all scenarios, the need for second stage sampling significantly drove program costs upwards. The best performing testing strategies with new RDTs were more expensive than testing with existing tests due to need for second stage sampling, but this was offset by the cost of incorrect classification of IUs.ConclusionThe new RDTs modelled added most value in areas with variable disease prevalence, with most benefit in IUs that are near the statistical decision thresholds. Based on the evaluations in this study, DNO could be used to guide the development of new RDTs based on defined sensitivities and specificities. While test sensitivity is a minor driver of whether an IU is identified as positive, higher specificities are essential. Further, these models could be used to explore the development and optimization of new tools for other neglected tropical diseases.

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BackgroundTrichiasis is present when one or more eyelashes touches the eye. Uncorrected, it can cause blindness. Accurate estimates of numbers affected, and their geographical distribution, help guide resource allocation.MethodsWe obtained district-level trichiasis prevalence estimates for 44 endemic and previously-endemic countries. We used (1) the most recent data for a district, if more than one estimate was available; (2) age- and sex-standardized corrections of historic estimates, where raw data were available; (3) historic estimates adjusted using a mean adjustment factor for districts where raw data were unavailable; and (4) expert assessment of available data for districts for which no prevalence estimates were available.FindingsInternally age- and sex-standardized data represented 1,355 districts and contributed 662 thousand cases (95% confidence interval [CI] 324 thousand-1.1 million) to the global total. age- and sex-standardized district-level prevalence estimates differed from raw estimates by a mean factor of 0.45 (range 0.03-2.28). Previously non-standardized estimates for 398 districts, adjusted by ×0.45, contributed a further 411 thousand cases (95% CI 283-557 thousand). Eight countries retained previous estimates, contributing 848 thousand cases (95% CI 225 thousand-1.7 million). New expert assessments in 14 countries contributed 862 thousand cases (95% CI 228 thousand-1.7 million). The global trichiasis burden in 2016 was 2.8 million cases (95% CI 1.1-5.2 million).InterpretationThe 2016 estimate is lower than previous estimates, probably due to more and better data; scale-up of trichiasis management services; and reductions in incidence due to lower active trachoma prevalence.Author SummaryAs an individual with trichiasis blinks, the eyelashes abrade the cornea, which can lead to corneal opacity and blindness. Through high quality surgery, which involves correcting the position of the in-turned eyelid, it is possible to reduce the number of people with trichiasis. An accurate estimate of the number of persons with trichiasis and their geographical distribution are needed in order to effectively align resources for surgery and other necessary services. We obtained district-level trichiasis prevalence estimates for 44 endemic and previously-endemic countries. We used the most recently available data and expert assessments to estimate the global burden of trichiasis. We estimated that in 2016 the global burden was 2.8 million cases (95% CI 1.1-5.2 million).The 2016 estimate is lower than previous estimates, probably due to more and better data; scale-up of trichiasis management services; and reductions in incidence due to lower active trachoma prevalence.