Identifying individual, household and environmental risk factors for malaria infection on Bioko Island to inform interventions.

BACKGROUND: Since 2004, malaria transmission on Bioko Island has declined significantly as a result of the scaling-up of control interventions. The aim of eliminating malaria from the Island remains elusive, however, underscoring the need to adapt control to the local context. Understanding the factors driving the risk of malaria infection is critical to inform optimal suits of interventions in this adaptive approach. METHODS: This study used individual and household-level data from the 2015 and 2018 annual malaria indicator surveys on Bioko Island, as well as remotely-sensed environmental data in multilevel logistic regression models to quantify the odds of malaria infection. The analyses were stratified by urban and rural settings and by survey year. RESULTS: Malaria prevalence was higher in 10-14-year-old children and similar between female and male individuals. After adjusting for demographic factors and other covariates, many of the variables investigated showed no significant association with malaria infection. The factor most strongly associated was history of travel to mainland Equatorial Guinea (mEG), which increased the odds significantly both in urban and rural settings (people who travelled had 4 times the odds of infection). Sleeping under a long-lasting insecticidal net decreased significantly the odds of malaria across urban and rural settings and survey years (net users had around 30% less odds of infection), highlighting their contribution to malaria control on the Island. Improved housing conditions indicated some protection, though this was not consistent across settings and survey year. CONCLUSIONS: Malaria risk on Bioko Island is heterogeneous and determined by a combination of factors interacting with local mosquito ecology. These interactions grant further investigation in order to better adapt control according to need. The single most important risk factor identified was travel to mEG, in line with previous investigations, and represents a great challenge for the success of malaria control on the Island.

Assessing IRS performance in a gender-integrated vector control programme on Bioko Island, Equatorial Guinea, 2010-2021.

BACKGROUND: Indoor residual spraying (IRS) is a common vector control strategy in countries with high malaria burden. Historically, social norms have prevented women from working in IRS programmes. The Bioko Island Malaria Elimination Project has actively sought to reduce gender inequality in malaria control operations for many years by promoting women's participation in IRS. METHODS: This study investigated the progress of female engagement and compared spray productivity by gender from 2010 to 2021, using inferential tests and multivariable regression. Spray productivity was measured by rooms sprayed by spray operator per day (RSOD), houses sprayed by spray operator per day (HSOD), and the daily productivity ratio (DPR), defined as the ratio of RSOD to HSOD, which standardized productivity by house size. RESULTS: The percentage of women participating in IRS has increased over time. The difference in DPR comparing male and female spray operators was only statistically significant (p < 0.05) for two rounds, where the value was higher for women compared to men. Regression analyses showed marginal, significant differences in DPR between men and women, but beta coefficients were extremely small and thus not indicative of a measurable effect of gender on operational performance. CONCLUSIONS: The quantitative analyses of spray productivity are counter to stigmatizing beliefs that women are less capable than male counterparts during IRS spray rounds. The findings from this research support the participation of women in IRS campaigns, and a renewed effort to implement equitable policies and practices that intentionally engage women in vector control activities.

Impact of a pioneer diabetes camp experience on glycemic control among children and adolescents living with type 1 diabetes in sub-Saharan Africa.

BACKGROUND: The metabolic impact of participating in a diabetes camp is little known among children and adolescents living with type 1 diabetes in sub-Saharan Africa. We aimed to assess the changes in glycemic control and insulin doses in a group of children and adolescents living with type 1 diabetes in Cameroon during and after camp attendance. METHODS: During a 5-day camp, we collected data on insulin doses, HbA1c, weight and blood glucose at least six times per day in a group of children and adolescents living with type 1 diabetes. We compared the evolution of these parameters 3 and 12 months after camp. RESULTS: Thirty-two campers completed the study. The mean age was 19 ± 2 years and the median duration of diabetes was 2 [IQR: 1.8-5] years. The mean HbA1c was 7.9 ± 2.2 % and the mean insulin dose was 49 ± 20 units/day upon arrival at camp. HbA1c dropped by 0.6 % after 12 months (p = 0.029). Despite the significant (p = 0.04) reduction in insulin dose from 49 ± 20 to 44 ± 18 units/day at the end of camp, hypoglycemic episodes occurred in 26 campers. However, the mean number of hypoglycemic episodes reduced from 1.32 (range: 0-4) on the first day, to 0.54 (range: 0-2) on the last day of camp (p = 0.006). Weight increased by 6 kg (p = 0.028) between 3 and 12 months after camp, but insulin doses remained unchanged. CONCLUSIONS: Attending camp for children and adolescents living with diabetes is associated with a significant decrease in HbA1c twelve months after camp without changes in insulin doses. Including camps as an integral part of type 1 diabetes management in children and adolescents in sub-Saharan Africa may yield some benefits. TRIAL REGISTRATION: ClinicalTrials.gov NCT02632032 . Registered 4 December 2015.

Impact of six-month COVID-19 travel moratorium on Plasmodium falciparum prevalence on Bioko Island, Equatorial Guinea.

Importation of malaria infections is a suspected driver of sustained malaria prevalence on areas of Bioko Island, Equatorial Guinea. Quantifying the impact of imported infections is difficult because of the dynamic nature of the disease and complexity of designing a randomized trial. We leverage a six-month travel moratorium in and out of Bioko Island during the initial COVID-19 pandemic response to evaluate the contribution of imported infections to malaria prevalence on Bioko Island. Using a difference in differences design and data from island wide household surveys conducted before (2019) and after (2020) the travel moratorium, we compare the change in prevalence between areas of low historical travel to those with high historical travel. Here, we report that in the absence of a travel moratorium, the prevalence of infection in high travel areas was expected to be 9% higher than observed, highlighting the importance of control measures that target imported infections.

Measuring the accuracy of gridded human population density surfaces: A case study in Bioko Island, Equatorial Guinea.

BACKGROUND: Geospatial datasets of population are becoming more common in models used for health policy. Publicly-available maps of human population make a consistent picture from inconsistent census data, and the techniques they use to impute data makes each population map unique. Each mapping model explains its methods, but it can be difficult to know which map is appropriate for which policy work. High quality census datasets, where available, are a unique opportunity to characterize maps by comparing them with truth. METHODS: We use census data from a bed-net mass-distribution campaign on Bioko Island, Equatorial Guinea, conducted by the Bioko Island Malaria Elimination Program as a gold standard to evaluate LandScan (LS), WorldPop Constrained (WP-C) and WorldPop Unconstrained (WP-U), Gridded Population of the World (GPW), and the High-Resolution Settlement Layer (HRSL). Each layer is compared to the gold-standard using statistical measures to evaluate distribution, error, and bias. We investigated how map choice affects burden estimates from a malaria prevalence model. RESULTS: Specific population layers were able to match the gold-standard distribution at different population densities. LandScan was able to most accurately capture highly urban distribution, HRSL and WP-C matched best at all other lower population densities. GPW and WP-U performed poorly everywhere. Correctly capturing empty pixels is key, and smaller pixel sizes (100 m vs 1 km) improve this. Normalizing areas based on known district populations increased performance. The use of differing population layers in a malaria model showed a disparity in results around transition points between endemicity levels. DISCUSSION: The metrics in this paper, some of them novel in this context, characterize how these population maps differ from the gold standard census and from each other. We show that the metrics help understand the performance of a population map within a malaria model. The closest match to the census data would combine LandScan within urban areas and the HRSL for rural areas. Researchers should prefer particular maps if health calculations have a strong dependency on knowing where people are not, or if it is important to categorize variation in density within a city.