Predictive risk mapping of lymphatic filariasis residual hotspots in American Samoa using demographic and environmental factors

In this study, we conducted a Bayesian geostatistical analysis of LF infection markers at the household level and produced predictive prevalence maps for American Samoa in 2016. In addition, this study examined potential sociodemographic and environmental factors that may influence the geographical distribution of LF in the territory. To our knowledge, this is the first time that the distributions of LF infection markers have been examined at such high spatial resolution to predict prevalence. Our results suggest that there are still areas with high prevalence of LF infection markers (including Ag) in American Samoa, particularly in the north-west of the main island of Tutuila. Also, we found that there are sociodemographic and environmental factors that may underly the geographical distribution of LF and potentially contribute to persistent transmission. These predicted prevalence estimates of LF infection markers may help maximise the effectiveness of post-intervention surveillance by contributing to the identification of areas with highest probability of residual transmission (7). The results showed that the predicted prevalence of Ag, Wb123, Bm14 and Bm33 Abs differed geographically across the territory. Areas around Fagali'i and Fagamalo villages in the north-west had the highest predicted prevalence of all infection markers. Also, in the south, high predicted prevalence, particularly for Bm33 Ab, were observed in localised areas in Vaitogi, Futiga and Tafuna villages. These findings concurred with the results of a previous research conducted in the territory that found significant spatial dependency for all infection markers, and confirmed the presence of LF clusters and hotspots in the north-west, south and central part of Tutuila (18). The high-risk area in the far north-west was also previously identified as potential hotspot of residual infection in cross-sectional surveys conducted in American Samoa in 2010, 2014 and 2016 (10, 11, 13). In this study, the cluster . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted September 26, 2022. ; https://doi.org/10.1101/2022.09.26.22280353doi: medRxiv preprint 30 sizes for all infection markers were larger compared to the previous findings (11, 18). This discrepancy in cluster size may be explained by the implementation of different spatial methods, and also by the incorporation of sociodemographic and environmental covariates into the geostatistical models (noting that the cluster size is in the residual component). These covariates may be associated with heterogeneous exposure to mosquito bites. In areas where the parasite is transmitted predominantly by night-biting mosquitos, clustering of infection around household locations can be expected and has been demonstrated (29, 32, 44). The

of this study support recent evidence that the home environment may be also an important area for exposure in LF-endemic regions where W. Bancrofti is transmitted by the day-biting mosquito, Ae. Polynesiensis. (45, 46). The cluster size also suggests that transmission may be occurring not only around households, but also in surrounding areas where the household members are likely to frequent (such as bus stops, schools and workplaces). In Samoa, a multilevel hierarchical modelling found that the intraclass correlation coefficients for Ag-positive individuals was higher at households (0.46) compared to primary sampling units (0.18) and regions (0.01) (46). The timely identification of these small pockets of residual infection can be used to prioritise further interventions to reduce the risk of LF recrudescence or resurgence in the territory. The predictive models developed for the different LF infection markers can help characterise the spatial patterns of serological responses to LF in American Samoa. In W. bancrofti endemic areas, WHO recommends the use of Ag testing to assess the impact of the MDA and determine when the elimination targets have been reached (47). However, there is increasing evidence that suggests that the use of Ag alone for post-MDA surveillance may not be sufficiently sensitive to detect residual infection (7, 48, 49). Therefore, antifilarial Ab testing is currently been examined as an alternative or complementary method of diagnosing LF in . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted September 26, 2022. ; https://doi.org/10.1101/2022.09.26.22280353doi: medRxiv preprint 31 post-MDA surveillance surveys (48-51). However, the dynamics of the Ab responses post- infection and post-treatment are still not well understood (52). In this study, the geographical distribution of the predicted prevalence of Wb123 and Bm14 Abs were more clustered compared to the widespread distribution of positive Bm33 Ab responses. This finding suggests that Bm33 Ab may not be the best indicator to identify areas of ongoing W. bancrofti transmission but may be used to provide information about levels of historical exposure and infection. Studies that have monitored the development of antifilarial immunity in LF endemic areas have shown that Bm33 Ab can be detected more than one year before the other Ab responses, and can decrease after MDA (50). Additional longitudinal studies are required to help monitor how the stage of the infection and magnitude of the immunological responses determine the spatial patterns of antifilarial Abs. Such information will have implications for the selection of the most suited LF diagnostic tools in low prevalence and post-MDA settings. There were consistent associations between the infection markers and the sociodemographic variables included in the models. The observed differences among females and males and the positive association with age is most likely to be exposure-related. These findings support what has been observed previously in the territory and in most LF-endemic areas (11, 45, 53). Males spend more time working outdoors compared to females. However, it has also been suggested that immunological and hormonal gender differences may account for the lower infection rates in females (54). There was a consistent positive association between all Abs and tuna cannery workers. Also, a positive association between Bm14 and Bm33 Abs and individuals working in outdoor locations. In 2013, An average of 17.6% of the total employed population in the territory worked in tuna cannery which is the largest non- government employer in American Samoa (55). The nature and time of day when this work . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted September 26, 2022. ; https://doi.org/10.1101/2022.09.26.22280353doi: medRxiv preprint 32 takes place may increase the risk of the exposure to mosquitoes. Higher prevalence of Wb123 Ab was also previously observed in tuna cannery workers in the territory but no associations between Ab responses has been identified with other occupational groups (11). The spatial models for all infection makers indicated that there was a positive association between the prevalence of LF and the extent of tree coverage in the 20 m buffers. This finding supports the hypothesis that the tree coverage may impact mosquito population dynamics and behaviours (56). Tree canopy may sustain W. Bancrofti life cycle in high temperature areas by facilitating the survival of mosquitos that move in response to food supply (14). Most of American Samoa is steep, with approximately half of the area covered by rainforest (14, 57). Trees primarily cover most areas in the northern part of the territory (Fig 2) where the highest prevalence of LF was observed. Rainfall has been shown to be associated with high prevalence of LF in several endemic countries where the infection is transmitted by different vectors (20, 23, 31, 58, 59). No associations between the prevalence of infection markers and rainfall were found in this study. This finding was unexpected and deserves further investigation. These findings raise the need for high-quality spatial environmental datasets that can be used in further studies to determine the association of LF and other potential environmental drivers. The strengths of this study include the availability of data at the household level that allowed us to assess the geographical distribution of LF in American Samoa at a small spatial scale. In this way, it was possible to explore the home environment as an exposure area of importance. The study also developed geostatistical models for different infection markers that may be used as baseline information to characterise the spatial patterns of the antifilarial Ab responses in the long-term. Besides the predicted prevalence maps, the spatial models developed here also . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted September 26, 2022. ; https://doi.org/10.1101/2022.09.26.22280353doi: medRxiv preprint 33 provided outputs to determine the associated uncertainty of the prevalence estimates (60). The maps of the SD (uncertainty) highlight the areas where predictions were imprecise and that need to be explored in future studies. The limitations of the study include the lack of high-quality spatial environmental datasets for the territory. As a result, it was not possible to include covariates such as temperature, that has consistently been associated positively with LF (20, 30, 31). Also, the rainfall data used in the study was only available in a spatial format for the year 2016. Based on the assessment presented in the supplementary files, data were found to be representative of the average rainfall estimates for the ten-year period prior the survey (most likely time period of potential exposure). Despite this limitation, we believe that our results provide valuable information about the potential sociodemographic and environmental factors that may be influencing the distribution of the infection in American Samoa. In this study, the Bayesian geostatistical models incorporating sociodemographic and environmental covariates showed that the predicted prevalence of LF was not homogeneous in American Samoa. Small-scale spatial variation in LF prevalence was observed which indicates that there is scope for further spatial analyses to help inform spatially-targeted interventions in American Samoa. Areas of priority for further study include the north and south-western part of the territory. Also, longitudinal monitoring of the prevalence of Ag, Wb123, Bm14 and Bm33 Abs would be useful to better understand the dynamics and potential use of different LF infection markers to inform and support the ongoing post-MDA surveillance efforts. Funding . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted September 26, 2022. ; https://doi.org/10.1101/2022.09.26.22280353doi: medRxiv preprint 34 This study was supported by the Coalition for Operational Research on Neglected Tropical Diseases (COR-NTD), which is funded at The Task Force for Global Health primarily by the Bill & Melinda Gates Foundation [OPP1053230], the United Kingdom Department for International Development, and by the United States Agency for International Development through its Neglected Tropical Diseases Program. CLL was supported by Australian National Health and Medical Research Council Fellowships (APP1193826). Author’s contributions AMC and CLL developed the study conception and design. Analyses were performed by AMCR and CLL. AMCR and CLL drafted the manuscript. All authors helped in the interpretation of results and critically reviewed the manuscript. Declaration of interests We declare no competing interests. Data sharing The data used in the present study are available from the corresponding author on reasonable request. . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted September 26, 2022. ; https://doi.org/10.1101/2022.09.26.22280353doi: medRxiv preprint 35

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The driest (August) and wettest (December) months in 2016 were representative of the average rainfall in the respective months in previous 20 years S2 Figure. Average annual rainfall (mm) for the period 2000-2020. The horizontal line indicates the average in the previous 10 years. Total rainfall in 2016 was representative of average rainfall in the previous 20 years. . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted September 26, 2022. ; https://doi.org/10.1101/2022.09.26.22280353doi: medRxiv preprint