Mapping socioeconomic inequalities in malaria in Sub-Sahara African countries

Despite reductions in malaria incidence and mortality across Sub-Saharan (SSA) countries, malaria control and elimination efforts are currently facing multiple global challenges such as climate and land use change, invasive vectors, and disruptions in healthcare delivery. Although relationships between malaria risks and socioeconomic factors have been widely demonstrated, the strengths and variability of these associations have not been quantified across SSA. In this study, we used data from population-based malaria indicator surveys in SSA countries to assess spatial trends in relative and absolute socioeconomic inequalities, analyzed as social (mothers’ highest educational level—MHEL) and economic (wealth index—WI) inequalities in malaria prevalence. To capture spatial variations in socioeconomic (represented by both WI and MHEL) inequalities in malaria, we calculated both the Slope Index of Inequality (SII) and Relative Index of Inequality (RII) in each administrative region. We also conducted cluster analyses based on Local Indicator of Spatial Association (LISA) to consider the spatial auto-correlation in SII and RII across regions and countries. A total of 47,404 participants in 1874 Primary Sampling Units (PSU) were analyzed across the 13 SSA countries. Our multi-country assessment provides estimations of strong socioeconomic inequalities between and within SSA countries. Such within- and between- countries inequalities varied greatly according to the socioeconomic metric and the scale used. Countries located in Eastern Africa showed a higher median Slope Index of Inequality (SII) and Relative Index of Inequality (RII) in malaria prevalence relative to WI in comparison to countries in other locations across SSA. Pockets of high SII in malaria prevalence in relation to WI and MHEL were observed in the East part of Africa. This study was able to map this wide range of malaria inequality metrics at a very local scale and highlighted the spatial clustering patterns of pockets of high and low malaria inequality values

Revealing the air pollution burden associated with internal Migration in Peru

This study aims to quantify changes in outdoor (ambient) air pollution exposure from different migration patterns within Peru and quantify its effect on premature mortality. Data on ambient fine particulate matter (PM2.5) was obtained from the National Aeronautics and Space Administration (NASA). Census data was used to calculate rates of within-country migration at the district level. We calculated differences in PM2.5 exposure between "current" (2016-2017) and "origin" (2012) districts for each migration patterns. Using an exposure-response relationship for PM2.5 extracted from a meta-analysis, and mortality rates from the Peruvian Ministry of Health, we quantified premature mortality attributable to each migration pattern. Changes in outdoor PM2.5 exposure were observed between 2012 and 2016 with highest levels of PM2.5 in the Department of Lima. A strong spatial autocorrelation of outdoor PM2.5 values (Moran's I = 0.847, p-value=0.001) was observed. In Greater Lima, rural-to-urban and urban-to-urban migrants experienced 10-fold increases in outdoor PM2.5 exposure in comparison with non-migrants. Changes in outdoor PM2.5 exposure due to migration drove 137.1 (95%CI: 93.2, 179.4) premature deaths related to air pollution, with rural-urban producing the highest risk of mortality from exposure to higher levels of ambient air pollution. Our results demonstrate that the rural-urban and urban-urban migrant groups have higher rates of air pollution-related deaths

Author Correction: Revealing the air pollution burden associated with internal Migration in Peru.

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Travel Time to Health Facilities as a Marker of Geographical Accessibility Across Heterogeneous Land Coverage in Peru

To better estimate the travel time to the most proximate health care facility (HCF) and determine differences across heterogeneous land coverage types, this study explored the use of a novel cloud-based geospatial modeling approach. Geospatial data of 145,134 cities and villages and 8,067 HCF were gathered with land coverage types, roads and river networks, and digital elevation data to produce high-resolution (30 m) estimates of travel time to HCFs across Peru. This study estimated important variations in travel time to HCFs between urban and rural settings and major land coverage types in Peru. The median travel time to primary, secondary, and tertiary HCFs was 1.9-, 2.3-, and 2.2-fold higher in rural than urban settings, respectively. This study provides a new methodology to estimate the travel time to HCFs as a tool to enhance the understanding and characterization of the profiles of accessibility to HCFs in low- and middle-income countries

Spatio-temporal co-occurrence of hotspots of tuberculosis, poverty and air pollution in Lima, Peru.

Growing evidence suggests pollution and other environmental factors have a role in the development of tuberculosis (TB), however, such studies have never been conducted in Peru. Considering the association between air pollution and specific geographic areas, our objective was to determine the spatial distribution and clustering of TB incident cases in Lima and their co-occurrence with clusters of fine particulate matter (PM2.5) and poverty. We found co-occurrences of clusters of elevated concentrations of air pollutants such as PM2.5, high poverty indexes, and high TB incidence in Lima. These findings suggest an interplay of socio-economic and environmental in driving TB incidence

The Relative Role of Climate Variation and Control Interventions on Malaria Elimination Efforts in El Oro, Ecuador: A Modeling Study

Malaria is a vector-borne disease of significant public health concern. Despite widespread success of many elimination initiatives, elimination efforts in some regions of the world have stalled. Barriers to malaria elimination include climate and land use changes, such as warming temperatures and urbanization, which can alter mosquito habitats. Socioeconomic factors, such as political instability and regional migration, also threaten elimination goals. This is particularly relevant in areas where local elimination has been achieved and consequently surveillance and control efforts are dwindling and are no longer a priority. Understanding how environmental change, impacts malaria elimination has important practical implications for vector control and disease surveillance strategies. It is important to consider climate change when monitoring the threat of malaria resurgence due to socioeconomic influences. However, there is limited assessment of how the combination of climate variation, interventions and socioeconomic pressures influence long-term trends in malaria transmission and elimination efforts. In this study, we used Bayesian hierarchical mixed models and malaria case data for a 29-year period to disentangle the impacts of climate variation and malaria control efforts on malaria risk in the Ecuadorian province of El Oro, which achieved local elimination in 2011. We found shifting patterns of malaria between rural and urban areas, with a relative increase of Plasmodium vivax in urbanized areas. Minimum temperature was an important driver of malaria seasonality and the association between warmer minimum temperatures and malaria incidence was greater for Plasmodium falciparum compared to P. vivax malaria. There was considerable heterogeneity in the impact of three chemical vector control measures on both P. falciparum and P. vivax malaria. We found statistically significant associations between two of the three measures [indoor residual spraying (IRS) and space spraying] and a reduction in malaria incidence, which varied between malaria type. We also found environmental suitability for malaria transmission is increasing in El Oro, which could limit future elimination efforts if malaria is allowed to re-establish. Our findings have important implications for understanding environmental obstacles to malaria elimination and highlights the importance of designing and sustaining elimination efforts in areas that remain vulnerable to resurgence

Use of open mobile mapping tool to assess human mobility traceability in rural offline populations with contrasting malaria dynamics

Infectious disease dynamics are affected by human mobility more powerfully than previously thought, and thus reliable traceability data are essential. In rural riverine settings, lack of infrastructure and dense tree coverage deter the implementation of cutting-edge technology to collect human mobility data. To overcome this challenge, this study proposed the use of a novel open mobile mapping tool, GeoODK. This study consists of a purposive sampling of 33 participants in six villages with contrasting patterns of malaria transmission that demonstrates a feasible approach to map human mobility. The self-reported traceability data allowed the construction of the first human mobility framework in rural riverine villages in the Peruvian Amazon. The mobility spectrum in these areas resulted in travel profiles ranging from 2 hours to 19 days; and distances between 10 to 167 km. Most Importantly, occupational-related mobility profiles with the highest displacements (in terms of time and distance) were observed in commercial, logging, and hunting activities. These data are consistent with malaria transmission studies in the area that show villages in watersheds with higher human movement are concurrently those with greater malaria risk. The approach we describe represents a potential tool to gather critical information that can facilitate malaria control activities

Continuous Supply of Plasmodium vivax Sporozoites from Colonized Anopheles darlingi in the Peruvian Amazon.

In vitro culture of Plasmodium vivax liver stages underlies key understandings of the fundamental biology of this parasite, particularly the latent, hyponozoite stage, toward drug and vaccine development. Here, we report systematic production of Plasmodium vivax sporozoites in colonized Anopheles darlingi mosquitoes in the Peruvian Amazon. Human subject-derived P. vivax-infected blood was fed to Anopheles darlingi females using standard membrane feedings assays. Optimizing A. darlingi infection and sporozoite production included replacement of infected patient donor serum with naïve donor serum, comparing anticoagulants in processing blood samples, and addition of penicillin-streptomycin and ATP to infectious blood meals. Replacement of donor serum by naïve serum in the P. vivax donor blood increased oocysts in the mosquito midgut, and heparin, as anticoagulant, was associated with the highest sporozoite yields. Maintaining blood-fed mosquitoes on penicillin-streptomycin in sugar significantly extended mosquito survival which enabled greater sporozoite yield. In this study, we have shown that a robust P. vivax sporozoite production is feasible in a malaria-endemic setting where infected subjects and a stable A. darlingi colony are brought together, with optimized laboratory conditions

Bovine Lactoferrin For the Prevention of Covid-19 infection in Health Care Personnel: a Double-Blinded Randomized Clinical Trial (Lf-Covid)

Lactoferrin (LF) has in vitro antiviral activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study aimed to determine the effect of bovine lactoferrin (bLF) in the prevention of SARS-CoV-2 infection in health care personnel. A randomized, double-blinded, placebo-controlled clinical trial was conducted in two tertiary hospitals that provide care to patients with SARS-CoV-2 infection in Lima, Peru. Daily supplementation with 600 mg of enteral bLF versus placebo for 90 days was compared. Participants were weekly screened for symptoms suggestive of SARS-CoV-2 infection and molecular testing was performed on suspected episodes. A serological test was obtained from all participants at the end of the intervention. The main outcome included symptomatic and asymptomatic cases. A sub-analysis explored the time to symptomatic infection. Secondary outcomes were the severity, frequency, and duration of symptomatic infection. The study was prematurely cancelled due to the availability of vaccines against SARS-CoV-2 in Peru. 209 participants were enrolled and randomized, 104 received bLF and 105 placebo. SARS-CoV-2 infection occurred in 11 (10.6%) participants assigned to bLF and in 9 (8.6%) participants assigned to placebo without significant differences (Incidence Rate Ratio = 1.23, 95%CI 0.51-3.06, p-value = 0.64). There was no significant effect of bLF on time to symptomatic infection (Hazard Ratio = 1.61, 95%CI 0.62-4.19, p-value = 0.3). There were no significant differences in secondary outcomes. A significant effect of bLF in preventing SARS-CoV-2 infection was not proven. Further studies are needed to assess the effect of bLF supplementation on SARS-CoV-2 infection.Clinical trial registration ClinicalTrials.gov Identifier: NCT04526821, https://clinicaltrials.gov/ct2/show/NCT04526821?term=LACTOFERRIN&cond=COVID-19&cntry=PE&city=Lima&draw=2&rank=1

Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance

Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Côte d’Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs