Integrated risk mapping and landscape characterisation of lymphatic filariasis and loiasis in South West Nigeria

Nigeria has the heaviest burden of lymphatic filariasis (LF) in sub-Saharan Africa, which is caused by the parasite Wuchereria bancrofti and transmitted by Anopheles mosquitoes. LF is targeted for elimination and the national programme is scaling up mass drug administration (MDA) across the country to interrupt transmission. However, in some regions the co-endemicity of the filarial parasite Loa loa (loiasis) is an impediment due to the risk of severe adverse events (SAEs) associated with the drug ivermectin. To better understand factors influencing LF elimination in loiasis areas, this study conducted a cross-sectional survey on the prevalence and co-distribution of the two infections, and the potential demographic, landscape, human movement, and intervention-related risk factors at a micro-level in the South West zone of Nigeria. In total, 870 participants from 10 communities on the fringe of a meso-endemic loiasis area of Osun State were selected. LF prevalence was measured by clinical assessment and using the rapid immunochromatographic test (ICT) to detect W. bancrofti antigen. Overall LF prevalence was low with ICT positivity ranging from 0 to 4.7%, with only 1 hydrocoele case identified. Males had significantly higher ICT positivity than females (3.2% vs 0.8%). Participants who did not sleep under a bed net had higher ICT positivity (4.0%) than those who did (1.3%). ICT positivity was also higher in communities with less tree coverage/canopy height (2.5–2.8%) than more forested areas with greater tree coverage/canopy height (0.9–1.0%). In comparison, loiasis was determined using the rapid assessment procedure for loiasis (RAPLOA), and found in all 10 communities with prevalence ranging from 1.4% to 11.2%. No significant difference was found by participants' age or sex. However, communities with predominately shrub land (10.4%) or forested land cover (6.2%) had higher prevalence than those with mosaic vegetation/croplands (2.5%). Satellite imagery showed denser forested areas in higher loiasis prevalence communities, and where low or no ICT positivity was found. Only one individual was found to be co-infected. GPS tracking of loiasis positive cases and controls also highlighted denser forested areas within higher loiasis risk communities and the sparser land cover in lower-risk communities. Mapping LF-loiasis distributions against landscape characteristics helped to highlight the micro-heterogeneity, identify potential SAE hotspots, and determine the safest and most appropriate treatment strategy

Epidemiological and Entomological Evaluations after Six Years or More of Mass Drug Administration for Lymphatic Filariasis Elimination in Nigeria

The current strategy for interrupting transmission of lymphatic filariasis (LF) is annual mass drug administration (MDA), at good coverage, for 6 or more years. We describe our programmatic experience delivering the MDA combination of ivermectin and albendazole in Plateau and Nasarawa states in central Nigeria, where LF is caused by anopheline transmitted Wuchereria bancrofti. Baseline LF mapping using rapid blood antigen detection tests showed mean local government area (LGA) prevalence of 23% (range 4–62%). MDA was launched in 2000 and by 2003 had been scaled up to full geographic coverage in all 30 LGAs in the two states; over 26 million cumulative directly observed treatments were provided by community drug distributors over the intervention period. Reported treatment coverage for each round was ≥85% of the treatment eligible population of 3.7 million, although a population-based coverage survey in 2003 showed lower coverage (72.2%; 95% CI 65.5–79.0%). To determine impact on transmission, we monitored three LF infection parameters (microfilaremia, antigenemia, and mosquito infection) in 10 sentinel villages (SVs) serially. The last monitoring was done in 2009, when SVs had been treated for 7–10 years. Microfilaremia in 2009 decreased by 83% from baseline (from 4.9% to 0.8%); antigenemia by 67% (from 21.6% to 7.2%); mosquito infection rate (all larval stages) by 86% (from 3.1% to 0.4%); and mosquito infectivity rate (L3 stages) by 76% (from 1.3% to 0.3%). All changes were statistically significant. Results suggest that LF transmission has been interrupted in 5 of the 10 SVs, based on 2009 finding of microfilaremia ≥1% and/or L3 stages in mosquitoes. Four of the five SVs where transmission persists had baseline antigenemia prevalence of >25%. Longer or additional interventions (e.g., more frequent MDA treatments, insecticidal bed nets) should be considered for ‘hot spots’ where transmission is ongoing

Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling

Schistosomiasis prevalence data for Nigeria were extracted from peer-reviewed journals and reports, geo-referenced and collated in a nationwide geographical information system database for the generation of point prevalence maps. This exercise revealed that the disease is endemic in 35 of the country's 36 states, including the federal capital territory of Abuja, and found in 462 unique locations out of 833 different survey locations. Schistosoma haematobium, the predominant species in Nigeria, was found in 368 locations (79.8%) covering 31 states, S. mansoni in 78 (16.7%) locations in 22 states and S. intercalatum in 17 (3.7%) locations in two states. S. haematobium and S. mansoni were found to be co-endemic in 22 states, while co-occurrence of all three species was only seen in one state (Rivers). The average prevalence for each species at each survey location varied between 0.5% and 100% for S. haematobium, 0.2% to 87% for S. mansoni and 1% to 10% for S. intercalatum. The estimated prevalence of S. haematobium, based on Bayesian geospatial predictive modelling with a set of bioclimatic variables, ranged from 0.2% to 75% with a mean prevalence of 23% for the country as a whole (95% confidence interval (CI): 22.8-23.1%). The model suggests that the mean temperature, annual precipitation and soil acidity significantly influence the spatial distribution. Prevalence estimates, adjusted for school-aged children in 2010, showed that the prevalence is >10% in most states with a few reaching as high as 50%. It was estimated that 11.3 million children require praziquantel annually (95% CI: 10.3-12.2 million)

LF prevalence data pre-MDA and post-MDA.

<p><b>a</b>. Pre-MDA CFA (n = 68). <b>b</b>. Post-MDA CFA (n = 66). <b>c</b>. Pre-MDA Mf (n = 124). <b>d</b>. Post-MDA Mf (n = 38). Note: Data source for CFA, MF prevalence available in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002416#pntd.0002416.s003" target="_blank">Table S1</a>.</p

Map of Nigeria and its geopolitical zones.

<p>North Central - Benue, FCT, Kogi, Kwara, Nasarawa, Niger, Plateau. North East - Adamawa, Bauchi, Borno, Gombe, Taraba, Yobe. North West - Kaduna, Katsina, Kano, Kebbi, Sokoto, Jigawa,, Zamfara. South East - Abia, Anambra, Ebonyi, Enugu, Imo. South - Akwa-Ibom, Bayelsa, Cross-River, Delta, Edo, Rivers. South West - Ekiti, Lagos, Osun, Ondo, Ogun, Oyo. Note: Elevation data based on ETOPO2 global 2-minute gridded resolution from National Oceanic and Atmospheric Administration (NOAA) available from ESRI Redland, CA.</p

LF prevalence data, endemicity status and disease data.

<p><b>a</b>. CFA and Mf data. <b>b</b>. LF endemicity. <b>c</b>. Disease data. Note: Data source for CFA, MF prevalence (2a) and disease (2c) data available in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002416#pntd.0002416.s003" target="_blank">Table S1</a>. LF endemicity map (2c) developed by FMoH.</p

LF prevalence and intervention distribution overlap.

<p><b>a</b>. CDTi treatment (ivermectin). <b>b</b>. CDTi and LF. <b>c</b>. CDTI and loaisis. <b>d</b>. LLIN coverage. <b>e</b>. LLINs and LF. <b>f</b>. LLINs and loiasis. Note: Data source for CDTi (5a) based on WHO-APOC Country profile – Nigeria <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002416#pntd.0002416-World9" target="_blank">[31]</a> (shaded grey) and for LLIN coverage (5d) based on Malaria Indicator Survey <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002416#pntd.0002416-National1" target="_blank">[40]</a> (shaded blue) to highlight the geographical overlap with CFA and Mf data prevalence points from <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002416#pntd.0002416.s003" target="_blank">Table S1</a>(5b and 5e) and loiasis map by Zouré et al. 2011 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002416#pntd.0002416-Zour1" target="_blank">[7]</a> (5c and 5f) respectively.</p

Summary of CFA prevalence by state.

<p>Note: All data included i.e. number of MDA rounds not taken into account.</p

LF prevalence overlapping loiasis areas.

<p><b>a</b>. CFA prevalence and loiasis. <b>b</b>. Mf prevalence and loiasis. <b>c</b>. LF endemicity and loiasis. <b>d</b>. Close up of LF and loiasis overlap. Note: Loiaisis endemicity based on eye worm history map determined from RAPLOA surveys published by Zouré et al. 2011 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002416#pntd.0002416-Zour1" target="_blank">[7]</a>. Three levels of loiasis shaded green <20%, yellow 20–40% and dark brown >40% highlight the extent of geographical overlap between CFA (4a) and MF (4b) prevalence data points available in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002416#pntd.0002416.s003" target="_blank">Table S1</a>. <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002416#pntd-0002416-g004" target="_blank">Figure 4c</a> shows loiasis overlap with LF endemicity map developed by the FMoH, and the medium to high risk loiasis areas (yellow and dark brown shading) of the south eastern region is shown close-up in 4d. The small localized high risk loiasis area (dark brown) geographically coincides with areas classified as LF non-endemic (solid green).</p

Summary of Mf prevalence by state.

<p>Note: All data included i.e. number of MDA rounds not taken into account.</p>*<p>Value for number of persons tested not available.</p><p>Confidence intervals calculated in Stata software (version 12, StataCorp, Texas, USA).</p