Mapping Snakebite Risk in Ghana and Rwanda

Overview

Snakebite causes approximately 138,000 deaths each year and non-fatal bites lead to considerable health burden, particularly in low-income tropical countries. Data on snakebite burden are lacking. Official data from health facilities are often either unavailable or underestimate cases, by excluding the many victims who do not attend formal health facilities; community surveys are useful for assessing burden, but they require significant resources to conduct. This study aims to understand both whether spatial analysis methods can help in assessing and predicting snakebite risk in different environments, and the value of current data collection methods for their contribution to this analysis approach. First, snakebite data already recorded in health facilities in Ghana and Rwanda will be extracted and analysed. Community surveys will be conducted in environmentally diverse areas of Ghana and Rwanda to collect information directly from randomly selected households about their experiences with snakebites. Using GPS to map household locations, geostatistical methods will be applied to the data to see if it can accurately predict areas at high risk of snakebite; the predictions will be used to generate risk maps. The study findings will build knowledge on geographical variation in snakebite risk and help develop an approach to mapping snakebite risk in sub-Saharan Africa. The risk maps generated will be compared with data on the distribution of antivenoms in each country. This will show if antivenoms are available in the places that need them most and help ensure antivenom supplies are better allocated in the future. It will also help identify high-risk areas so health officials can advocate for resources and develop treatment and prevention programmes.

Introduction Background Snakebite was declared a neglected tropical disease (NTD) by the World Health Organization (WHO) in 2017, reflecting its global public health impact. Over 95% of cases occur in tropical settings, predominantly in low- and middle-income countries (LMICs) in South Asia, sub-Saharan Africa and South America and the greatest burden falls upon the most vulnerable populations, including those living in less robust housing, agricultural workers on low incomes, and children. Snakebite envenoming is thought to cause up to 138,000 deaths per year and non-fatal bites lead to considerable health burden through physical disability, such as severe scarring and limb amputation, psychological impact, and social stigma, isolation and economic loss resulting from these consequences. To help address the public health impact of snakebite, WHO launched its strategy Snakebite Envenoming - A Strategy for Prevention and Control in 2019, with an ambitious aim to halve death and disability from the condition by 2030. Snakebite prevention and management is an evolving field. Significant work is needed to develop effective treatments against envenomation by the full range of medically important snake species at a cost that is affordable for the populations in need. Despite such challenges, approaches to help prevent and manage snakebite do exist and can be effective in reducing the burden of snakebite in high-risk communities. Prevention activities focus on building knowledge within communities on the dangers of snakebite envenoming, education regarding how to avoid snakes within the home and at work, including the use of protective footwear, and promotion of the importance of seeking effective medical treatment, if bitten, rather than relying upon traditional remedies. When a person experiences a snakebite, management of the victim may cover a range of measures including simple first aid techniques such as immobilisation and application of pressure bandages, supportive care in a medical facility, and, if needed and available, treatment with antivenom that is effective against the species of snake responsible. High quality data on snakebite burden are essential for ensuring that the resources for treatment and prevention are targeted towards the populations most at risk. However, such data are sparsely reported, with data availability especially limited across countries in sub-Saharan Africa (SSA). Most snakebite data from SSA come from health facility surveys and reporting. However, snakebite is not widely designated a notifiable condition and routine health facility data are often either not reported nationally or are done so inconsistently. Where health facility data are available, they are likely to underestimate snakebite burden as they do not capture the many snakebite victims either not reaching health facilities in time or seeking traditional treatment in the community. High-quality cross-sectional community surveys can help in the assessment of snakebite burden at a fine scale, however very few have been conducted in SSA to date, and as they require significant time and resources and may be difficult to undertake in areas with inaccessible terrain, weak healthcare infrastructure or political instability, they do not provide a feasible alternative for estimating burden at the scale needed. Better data are needed to characterise snakebite epidemiology to inform risk mitigation, prevention or elimination strategies and without robust surveillance systems, an alternative approach to effectively identifying high risk areas is needed. Problem statement and justification for the study High quality estimates of the geographical variation in snakebite burden in SSA are needed to advocate for resources for snakebite prevention and management, and to align these with the populations most at risk. Recognising the significant deficiencies in routine data and the challenges to relying upon bespoke data-collection methods as outlined above, the WHO's Road Map for Ending Neglected Tropical Diseases 2021-2030 states that better data are needed on snakebite risk distribution in low-resource settings, and to this end 'new approaches and mapping tools are necessary to obtain a granular view of disease epidemiology'. Spatial analysis and disease mapping techniques are increasingly recognised for their effectiveness in assessing the epidemiology of NTDs in areas in which the assessment and monitoring of disease burden face data availability challenges. Spatial analysis methods assist in the assessment of disease burden by utilising information on spatially varying explanatory variables, such as geographical, climatic, and population-based variables, and assessing their relationship with disease burden data collected at known locations. Such data is harnessed to develop geostatistical models to predict disease risk in unsampled locations. Snakebite envenoming risk is influenced by the geographic distributions of venomous snake species and human populations and by factors affecting the likelihood and nature of their interaction. As such, snakebite prevention and management programmes could benefit from the application of spatial analysis techniques. There is limited availability of accurate data on snake species distribution at fine scale. Detailed datasets of factors affecting snake habitat suitability, including geographical features such as altitude, climatic variables such as rainfall and temperature, and demographic variables such as population density, poverty and urban-rural distribution have become increasingly sophisticated and publicly available. Many of these datasets also include historical data, allowing for the assessment of temporal trends. The availability of such covariate data and the utility of spatial analysis methods provide a valuable opportunity for harnessing the limited snakebite incidence data to develop models for predicting variation in snakebite risk more widely. This project aims to understand whether spatial analysis and disease mapping techniques can be used to help understand geographical variation in snakebite risk and aid the prediction of risk in unsampled locations, thereby addressing some of the challenges generated by the poor availability of snakebite risk data in SSA and reducing the reliance upon traditional data collection approaches and the need for repeat surveys across the continent. It specifically aims to address the following research questions: 1\. To what extent can information from routinely available snakebite incidence data and spatially referenced covariates be used to predict geographical variation in snakebite risk? To enable the development of geostatistical models for predicting snakebite risk, sufficient data on snakebite incidence, and social and natural environmental data of the region of interest are needed. A limited number of existing datasets on snakebite burden, collected through previously conducted community and health facility studies have been identified and will be formally requested for analysis. Alongside this, there is an increasing availability of high-quality open-source datasets on spatially referenced potential explanatory variables, such as environmental (including land cover, altitude), climatic (including rainfall, temperature) and sociodemographic (including poverty and population density) factors, identified through literature review, are available. Analysing available historic data on snakebite incidence and potential explanatory variables will provide a better understanding of where to target available resources for effective primary data collection in order to validate or refine the geostatistical model. 2 To what extent can the combination of primary and secondary data be used to validate and refine the geostatistical analysis, and what is the relative utility of the different data sources available to the development of this geostatistical model? Further snakebite data at selected locations will be collected to validate or refine the assessment of the association between snakebite incidence and potential explanatory factors. Existing data collection platforms will be utilised to collect data on snakebite incidence. At this stage, a collaboration will be planned with the Global Health and Infectious Diseases research group at the Kumasi Centre for Collaborative Research in Tropical Medicine (KCCR), Kumasi, to conduct snakebite surveys alongside ongoing data collection in some of the selected study districts. There is also the potential to collaborate with the Rwanda Biomedical Centre in their upcoming village-level health assessment if feasible within the study period. In addition, stand-alone snakebite community surveys at selected complementary locations will be conducted. The collection of additional data will enable refinement of the geostatistical model and improve the assessment of the association between snakebite risk and spatially referenced explanatory factors. The aim is to contribute to the development of a predictive risk model for snakebite that can be applied within the study locations. In future work, and with both new or existing data, this would have the potential to be expanded to and applied in further locations across SSA. The outputs of these predictive risk models, in the form of snakebite risk maps, will provide much needed knowledge of geographical variation in snakebite risk so that resources for treatment and prevention can be effectively targeted to populations in need. Through this work, an assessment of the relative utility of more readily available data sources, such as health facility data and data collected through existing survey platforms, compared to a gold standard (data collected through community-based cross-sectional surveys) will be conducted, so that the value of these data sources in the prediction of snakebite risk can be better understood. 3\. Do changes in environmental variables such as climate, flood and land use (agriculture, roads, dams, building infrastructure) affect the incidence of snakebite? Environmental factors such as climate, floods, and land use may alter the habitat, distribution, and behaviour of snakes, leading to an increased likelihood of human-snake contacts in some areas. Other factors including humidity, temperature, elevation, rainfall, and normalized difference vegetation index (NDVI) have also been linked to geospatial variation in snakebite incidence, but the evidence is sparse in resource-limited settings, including Ghana and Rwanda. Using open source, spatially referenced natural and social environmental data, the study will assess the association between time-varying environmental data and snakebite occurrence to understand the temporal variations in snakebite incidence. Understanding the temporal variations in snakebite incidence is crucial as it could help predict seasons of heightened snakebite risk and inform the timing of preventive interventions. The results would therefore practically inform environmental management and risk mitigation measures in high-risk areas and be instrumental in forecasting future trends of the spatio-temporal variation in snakebite incidence across the study locations and other unsampled locations. 4\. How can epidemiological data assist in the appropriate distribution of antivenom? Within a model-based geostatistics framework, epidemiological data from community surveys can provide reliable estimates of the burden and risk of snakebite. By mapping both areas of increased snakebite incidence and predicting seasonal variation (periods of heightened risk), such epidemiological data could help identify seasonal variation in hotspots where they may be increased demand for antivenom. To answer this research question, available data on health facility locations and antivenom distribution will be used to generate a map of the current strategy for antivenom distribution. Snakebite risk maps will be compared with maps of the current antivenom distribution strategy. This information will guide the equitable allocation and distribution of antivenom to hospitals in high-risk zones to improve snakebite outcomes. Research Methodology Study design This study will employ a cross-sectional design. It will comprise an analytical observational study assessing quantitative data on snakebite epidemiology, spatial distribution of snakebite incidence, and antivenom distribution strategy collected through: 1. Community-based cross-sectional surveys 2. Assessment of snakebite incidence through an analysis of routine health facility data 3. Facility-level assessment of country-wide antivenom distribution Sample size and sampling method Ghana A multi-stage sampling method will be used to select household units for survey participation. This will be implemented within a four-level hierarchical design in descending order as follows: ecological zones, districts within ecological zones, enumeration areas (EAs) within districts, and households within EAs. Ghana is divided into five main ecological zones. The country is also divided into 16 geographical regions, which are further segmented into 261 districts (specifically, 144 ordinary districts, 111 municipal districts, and 6 metropolitan districts). For practical sampling purposes, all 6 metropolitan districts will be excluded from the sampling process as no snakebite is realistically expected in those areas. Each district is sub-divided into EAs-an EA is defined as the smallest geographical unit with distinct boundaries designated for census purposes. The 2021 population and housing census listed 51,917 EAs, with an average of approximately 199 EAs per district. Of the 255 remaining districts, 20 will be randomly selected to span the range of crude snakebite incidence within the five ecological zones. All districts will be categorized as high (≥100), mid-range (50-99) or low (\<50) based on the 2023 annual snakebite cases. Simple random sampling without replacement using a balloting technique will be used to select districts in each ecological zone. To ensure country-wide geographical spread, subsequent district selection after the first one will be subject to the condition that they are not contiguous to previously sampled districts; if this condition is not met, the district in question will be discarded. High case count districts will be oversampled to obtain more precise snakebite estimates. A total of 8 high, 6 mid-range, and 6 low case count districts will be included with the allocations for each ecological zone For each included district, 25 EAs will be randomly sampled using a random number generator, with the master list of EAs in the district as the sampling frame. Within each selected EA, 40 households will be selected for survey participation. The household list maintained by the Ghana Statistical Service will be used as the sampling frame. For each included EA, a computerised random number generator will be used to select 40 households for inclusion in the survey. Households included in the first random set that decline survey participation will be replaced with another household from a back-up list of randomly selected households not included in the first set. This sampling design is expected to yield a total of 20,000 households across all the 20 selected districts in Ghana. Rwanda For Rwanda, the multi-stage sampling method will be also implemented within a four-level hierarchical design in descending order as follows: ecological zones, districts within ecological zones, villages within sectors, and households within villages. Rwanda is divided into 10 ecological zones. The country is also divided into 5 provinces, which are further segmented into 30 districts. The districts are sub-divided into 416 sectors. However, due to the significant overlap of districts across the ecological zones, and for the purposes of obtaining adequate geographical spread, contiguity of some districts will be allowed. Each sector is sub-divided into cells, and further into villages. The 2022 population and housing census listed 14,837 villages. Twelve (12) of the 30 districts will be randomly selected to span the range of crude snakebite incidence across the ecological zones. All districts will be categorized as high (≥100), mid-range (50-99) or low (\<50) based on the 2022 annual snakebite cases. Simple random sampling without replacement using a balloting technique will be used to select districts in each ecological zone, allowing for 4 contiguous pairs of districts overlapping different ecological zones, to achieve geographical spread. For each included district, 25 villages will be randomly sampled using a random number generator, with the master list of villages maintained by the National Institute of Statistics of Rwanda as the sampling frame. Within each selected village, 40 households will be selected for survey participation using a computerised random number generator, with the census household list as the sampling frame. Households included in the first random set that decline survey participation will be replaced with another household from a back-up list of randomly selected households not included in the first set. This sampling design is expected to yield a total of 12,000 households across all the 12 selected districts in Rwanda. Routine health facility data Routine health facility data from the health information management systems in both countries, covering the previous 5-10 years, will be obtained for secondary analysis where records are available. Sampling procedures Stand-alone community snakebite surveys will be conducted in separate locations and will use a multi-stage cluster-sampling approach, with the following components: Sampling units Districts in both countries will be randomly selected to span the range of snakebite incidence as estimated from available routine health data, as well as coverage of environmental and sociodemographic covariates of interest, identified through literature review and analysis of existing data. Primary Sampling Units (PSUs) PSUs within districts will be based on the smallest administrative units and will be selected using a random number generator, with number selected proportional to district population size. Population estimates and PSUs will be taken from the most recent population census data or more up-to-date estimates if these are available in-country. Census enumeration areas (EA) as set out in the Population and Housing Census will be used. Secondary Sampling Unit (SSUs) SSUs will be the household units. If household lists are uniformly available, households in each EA/EU will be randomly selected using a random number generator. If household lists are not uniformly available, compact segment sampling will be used, allowing for revisits to absent households to be conducted efficiently. A household unit will use the following definition : One or more people who live in the same compound (fenced or unfenced), are answerable to the same head, and share a common source of food and/or common household budget/income. A usual household member for the purposes of the household roster is defined as someone who is present in the household for more than 50% of the month, including domestic servants, lodgers or friends who usually live there. Revisits If the household head or responsible adult who can act on their behalf is not present, a revisit will be scheduled for later the same day. If it is feasible, within the logistical constraints of the survey, to revisit the following day if needed, this will be arranged. If a snakebite victim is identified but absent, a revisit will be arranged as above. If a revisit is not possible, the Snakebite Details Questionnaire will be asked of a responsible adult who is able to answer questions about the episode as far as possible. If potential participants would like longer to consider their participation, revisits may also be scheduled within the timeframes outlined above. Absent households If the household is not present at first attempt or revisit, it will be recorded as an absent household. Information on the number, age and sex of members of the absent household will be sought from the community guide (Community Health Volunteer), who holds information on the households within the villages they oversee, and this will be recorded for analysis of bias in data missingness. Household screening In existing demographic and health surveys, household screening questions will be embedded within the questionnaire used by the survey. These questions will screen all household members for a history of snakebite. A follow-up survey will be conducted with those reporting a history of snakebite. In stand-alone snakebite surveys, a separate Household Screening Questionnaire will be conducted to screen all household members for a snakebite and document additional household sociodemographic details. Questions will be asked, by trained data collectors, to the household head or, if unavailable, a responsible adult who is able to answer on behalf of the household head, at each household. Data will be collected using pre-programmed data collection forms developed on REDCap and mounted on tablets. Snakebite Details Questionnaire If a snakebite is reported, a Snakebite Details Questionnaire will be administered with the respective household member (or a responsible adult representative if the victim is a minor or deceased). In the context of existing health and demographic surveys, this questionnaire will be administered the same day, if feasible, or as part of a planned-follow up period with identified cases within the following one month. In stand-alone snakebite surveys, this questionnaire will immediately follow the Household Screening Questionnaire, unless the victim is absent, and a revisit is requested. Data quality assurance Data quality and consistency will be assured by training survey data collectors in standard survey procedures and questionnaire administration. Data collector competence will be reviewed during training during which they must demonstrate the ability to carry out standardised survey procedures prior to study commencement. When follow-up surveys are conducted with snakebite victims, this data will be reviewed regularly by the PI for quality and consistency. Survey forms will be programmed with pre-coded response selection to minimise free-text input. The survey questionnaire will undergo a pilot test with a small group of respondents (15-20) to identify any issues, refine question wording, and address any ambiguities to improve clarity. Data collectors will be intensively trained on data collection procedures, including consenting process, survey administration, and handling of respondents' questions and concerns. The training will include role-playing sessions and mock interviews to ensure that data collectors fully understand the questions and follow standard survey practices. A dashboard will also be created to monitor progress with data collection and aid in identifying data quality issues for prompt resolution in near real-time. Data will be reviewed continuously for inconsistencies and errors during data collection. Queries about data inconsistencies, if any, will be sent to data collectors to resolve at the end of each day. Duplicate entries will be identified and removed, and any discrepancies or outliers will be carefully examined and corrected. Weekly team meetings will also be conducted to identify any challenges and discuss solutions to ensure overall quality of data collection activities. After data collection, verification procedures will be closed, and the database will be locked to prevent any further data entries or edits on the server. Data analysis Data will be analysed using R statistical software, mainly with the PrevMap package. A model-based geostatistical approach will be used. Exploratory analysis will first be conducted using standard linear and generalised linear regression to assess the associations between spatially referenced environmental and sociodemographic data and snakebite incidence. It will also be possible to utilise any individual covariate data collected as part of these surveys here, by including both individual level and cluster level effects. The spatial structure of residuals from this regression model will be assessed and the information will be used to build a predictive model that takes account of this spatial correlation that could not be removed through regression adjustments. Where possible, time varying risk factors will be factored in, for example, in assessment of health facility data. The addition of newly collected snakebite data from community and health facility surveys will require extension of the model-based geostatistical analysis to refine the model. Where comparable survey methodology and outcome measures are used, joint modelling may be conducted. Probability contour maps will also be produced incorporating likelihood estimates that indicate the probability of incidence exceeding a set threshold. The relative accuracy of each data source and data collection method will be compared for their value in assessing snakebite incidence, with the cluster-sampled surveys considered the gold standard, to better understand the most effective way in which these data sources can be utilised in the accurate prediction of snakebite risk. Model validation Internal validation of the predictive model will be conducted using simulated datasets to assess the predictive accuracy of the geostatistical model. The predictive model will be externally validated using data from newly sampled sentinel sites spanning the risk levels. Where data becomes available from studies outside of the surveyed region, it will be used to test model generalisability. Management of missing data Missing data will be analysed to assess whether it may have led to bias in terms of the demographic distribution of the survey sample. If details of absent household size and the age/gender of household members was available, this will be compared to the included sample. If this data is not available, the included sample will be compared to the demographic distribution of the county population as per the most recent census. Ethical and regulatory guidance This study will be conducted in accordance with the ethical principles set out in the Declaration of Helsinki and Good Clinical Practice (GCP) and all study investigators will be required to have up-to-date training in GCP. The study protocol will be subject to review by research ethics committees in the UK and study countries and approval will be sought before study commencement in the respective country. Research Ethics Committee (REC) review Before the start of the study, approval will be sought from the Liverpool School of Tropical Medicine (LSTM) Research Ethics Committee in the UK for the study protocol, informed consent forms and other study documents. We will provide the LSTM Sponsor and Research Ethics Committee with an annual report, due each year on the original approval date, and an end of study report once all activities are completed. Equivalent REC approval will also be sought in collaborating countries: In Ghana, ethical approval will be applied for through the Ghana Health Service Ethics Review Committee. One ethics application will be submitted to cover all data collection activities. In Rwanda, ethics approval will be applied for through the Rwanda National Ethics Committee. Protocol amendments We will ensure that the research does not deviate from the protocol described. If a protocol deviation does occur, we will submit amendments to LSTM and in-country authorities for approval. Dissemination and application of results On completion of the study, the data will be analysed, and a final study report prepared. Findings from the community surveys will be formally shared with research partners and local stakeholders (including the community leaders and healthcare representatives) for dissemination to the participant communities following completion of analysis through online or in-person workshops. Study results will be submitted in research papers to peer reviewed journals and for presentation at appropriate scientific conferences. The principal investigator will take responsibility for publication; publication will acknowledge the participants, personnel and funding body whose contribution enabled completion of the study.