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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.

Lyme disease is the most common tick-borne disease in North America. Though human infection is mostly transmitted in a limited geography, the range has expanded in recent years. One notable area of recent expansion is in the mountainous region of southwestern Virginia. The ecological factors that facilitate or constrain the range of human Lyme disease in this region remain uncertain. To evaluate this further, we obtained ecological data, including remotely sensed data on forest structure and vegetation, weather data, and elevation. These data were aggregated within the census block groups of a 9,153 km2 area around the cities of Blacksburg and Roanoke, VA, an area with heterogeneous Lyme disease transmission. In this geographic area, 755 individuals were reported to have Lyme disease in the 10 yr from 2006 to 2015, and these cases were aggregated by block group. A zero-inflated negative binomial model was used to evaluate which environmental variables influenced the abundance of Lyme disease cases. Higher elevation and higher vegetation density had the greatest effect size on the abundance of Lyme disease. Measures of forest edge, forest integrity, temperature, and humidity were not associated with Lyme disease cases. Future southward expansion of Lyme disease into the southeastern states may be most likely in ecologically similar mountainous areas.

We introduce a Bayesian non-parametric spatial factor analysis model with spatial dependency induced through a prior on factor loadings. For each column of the loadings matrix, spatial dependency is encoded using a probit stick-breaking process (PSBP) and a multiplicative gamma process shrinkage prior is used across columns to adaptively determine the number of latent factors. By encoding spatial information into the loadings matrix, meaningful factors are learned that respect the observed neighborhood dependencies, making them useful for assessing rates over space. Furthermore, the spatial PSBP prior can be used for clustering temporal trends, allowing users to identify regions within the spatial domain with similar temporal trajectories, an important task in many applied settings. In the manuscript, we illustrate the model's performance in simulated data, but also in two real-world examples: longitudinal monitoring of glaucoma and malaria surveillance across the Peruvian Amazon. The R package spBFA, available on CRAN, implements the method., This is a preprint of an article submitted for publication in the Journal of the American Statistical Association. The article contains 35 pages, 5 figures and 2 tables

Drug overdoses are a national and global epidemic. However, while overdoses are inextricably linked to social, demographic, and geographical determinants, geospatial patterns of drug-related admissions and overdoses at the neighborhood level remain poorly studied. The objective of this paper is to investigate spatial distributions of patients admitted for drug-related admissions and overdoses from a large, urban, tertiary care center using electronic health record data. Additionally, these spatial distributions were adjusted for a validated socioeconomic index called the Area Deprivation Index (ADI). We showed spatial heterogeneity in patients admitted for opioid, amphetamine, and psychostimulant-related diagnoses and overdoses. While ADI was associated with drug-related admissions, it did not correct for spatial variations and could not account alone for this spatial heterogeneity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11524-020-00436-8) contains supplementary material, which is available to authorized users.

BACKGROUND AND AIM: Implementation science (IS) has emerged as an important approach to achieving scale-up and policy adoption of evidence-based research. The majority of IS approaches have been in...

Border regions have been implicated as important hot spots of malaria transmission, particularly in Latin America, where free movement rights mean that residents can cross borders using just a national ID. Additionally, rural livelihoods largely depend on short-term migrants traveling across borders via the Amazon&rsquo, s river networks to work in extractive industries, such as logging. As a result, there is likely considerable spillover across country borders, particularly along the border between Peru and Ecuador. This border region exhibits a steep gradient of transmission intensity, with Peru having a much higher incidence of malaria than Ecuador. In this paper, we integrate 13 years of weekly malaria surveillance data collected at the district level in Peru and the canton level in Ecuador, and leverage hierarchical Bayesian spatiotemporal regression models to identify the degree to which malaria transmission in Ecuador is influenced by transmission in Peru. We find that increased case incidence in Peruvian districts that border the Ecuadorian Amazon is associated with increased incidence in Ecuador. Our results highlight the importance of coordinated malaria control across borders.

BACKGROUND On January 21, 2020, the World Health Organization reported the first case of severe acute respiratory syndrome coronavirus 2, which rapidly evolved to the COVID-19 pandemic. Since then, the virus has also rapidly spread among Latin American, Caribbean, and African countries. OBJECTIVE The first aim of this study is to identify new emerging COVID-19 clusters over time and space (from January 21 to mid-May 2020) in Latin American, Caribbean, and African regions, using a prospective space–time scan measurement approach. The second aim is to assess the impact of real-time population mobility patterns between January 21 and May 18, 2020, under the implemented government interventions, measurements, and policy restrictions on COVID-19 spread among those regions and worldwide. METHODS We created a global COVID-19 database, of 218 countries and territories, merging the World Health Organization daily case reports with other measures such as population density and country income levels for January 21 to May 18, 2020. A score of government policy interventions was created for low, intermediate, high, and very high interventions. The population’s mobility patterns at the country level were obtained from Google community mobility reports. The prospective space–time scan statistic method was applied in five time periods between January and May 2020, and a regression mixed model analysis was used. RESULTS We found that COVID-19 emerging clusters within these five periods of time increased from 7 emerging clusters to 28 by mid-May 2020. We also detected various increasing and decreasing relative risk estimates of COVID-19 spread among Latin American, Caribbean, and African countries within the period of analysis. Globally, population mobility to parks and similar leisure areas during at least a minimum of implemented intermediate-level control policies (when compared to low-level control policies) was related to accelerated COVID-19 spread. Results were almost consistent when regional stratified analysis was applied. In addition, worldwide population mobility due to working during high implemented control policies and very high implemented control policies, when compared to low-level control policies, was related to positive COVID-19 spread. CONCLUSIONS The prospective space–time scan is an approach that low-income and middle-income countries could use to detect emerging clusters in a timely manner and implement specific control policies and interventions to slow down COVID-19 transmission. In addition, real-time population mobility obtained from crowdsourced digital data could be useful for current and future targeted public health and mitigation policies at a global and regional level.

To reduce the risk of recurrence, women with a history of spontaneous preterm birth (PTB) are recommended to receive 17-hydroxyprogesterone caproate (17-P) injections starting by the 20th week of pregnancy. In women eligible for 17-P, we aimed to identify patient factors and geospatial locations associated with increased risk of presentation beyond 20 weeks gestation.We conducted a secondary analysis of a retrospective cohort study including all women meeting criteria for 17-P within a single academic medical center over a 2-year period. We compared early ( 20 6/7 weeks) with late ( 21 weeks) presenters via demographics, social history, and index pregnancy outcomes using standard and Bayesian statistical models. Geospatial mapping was performed to determine residential areas with high risk for late presentation.Geocoded address data was available for 351 women in whom the mean gestational age at first visit was 14.9 weeks, and 63 of whom were late presenters (17.9%). Younger maternal age, current smoking, and lack of health insurance were predictors of late presentation with greater than 95% probability. Hispanic ethnicity and black race were associated with higher odds of late presentation with 87 and 69% probability, respectively. The area with the latest gestational age at presentation was located within central Durham City and to the northeast.Our study identified patient-level risk factors and geographic locations associated with presentation beyond the recommend window for 17-P initiation. These findings suggest an urgent need for intervention to improve early prenatal care initiation and a target location where such interventions will be most impactful.

Surveillance and diagnosis of Plasmodium falciparum malaria relies predominantly on rapid diagnostic tests (RDT). However, false-negative (FN) RDT results are known to occur for a variety of reasons, including operator error, poor storage conditions, pfhrp2/3 gene deletions, poor performance of specific RDT brands and lots, and low-parasite density infections. We used RDT and microscopy results from 85 000 children enrolled in Demographic Health Surveys and Malaria Indicator Surveys from 2009 to 2015 across 19 countries to explore the distribution of and risk factors for FN-RDTs in sub-Saharan Africa, where malaria’s impact is greatest. We sought to (1) identify spatial and demographic patterns of FN-RDT results, defined as a negative RDT but positive gold standard microscopy test, and (2) estimate the percentage of infections missed within community-based malaria surveys due to FN-RDT results. Across all studies, 19.9% (95% CI 19.0% to 20.9%) of microscopy-positive subjects were negative by RDT. The distribution of FN-RDT results was spatially heterogeneous. The variance in FN-RDT results was best explained by the prevalence of malaria, with an increase in FN-RDT results observed at lower transmission intensities, among younger subjects, and in urban areas. The observed proportion of FN-RDT results was not predicted by differences in RDT brand or lot performance alone. These findings characterise how the probability of detection by RDTs varies in different transmission settings and emphasise the need for careful interpretation of prevalence estimates based on surveys employing RDTs alone. Further studies are needed to characterise the cost-effectiveness of improved malaria diagnostics (eg, PCR or highly sensitive RDTs) in community-based surveys, especially in regions of low transmission intensity or high urbanicity.

The Democratic Republic of the Congo (DRC) has one of the lowest HIV prevalence in sub-Saharan Africa, estimated at 1.1% [0.9-1.3] of adults aged 15-49 in 2013 (UNAIDS). Within the 2 million km(2) country, however, there exists spatial variation in HIV prevalence, with the highest HIV prevalence observed in the large cities of Kinshasa and Lubumbashi. Globally, HIV is an increasingly rural disease, diffusing outwards from urban centers of high HIV prevalence to places where HIV was previously absent or present at very low levels. Utilizing data collected during Demographic and Health Surveillance (DHS) in 2007 and 2013 in the DRC, we sought to update the map of HIV prevalence in the DRC as well as to explore whether HIV in the DRC is an increasingly rural disease or remains confined to urban areas. Bayesian kriging and regression indicate that HIV prevalence in rural areas of the DRC is higher in 2013 than in 2007 and that increased distance to an urban area is no longer protective against HIV as it was in 2007. These findings suggest that HIV education, testing and prevention efforts need to diffuse from urban to rural areas just as HIV is doing.

Multilevel models have long been used by health geographers working on questions of space, place, and health. Similarly, health geographers have pursued interests in determining whether or not the effect of an exposure on a health outcome varies spatially. However, relatively little work has sought to use multilevel models to explore spatial variability in the effects of a contextual exposure on a health outcome. Methodologically, extending multilevel models to allow intercepts and slopes to vary spatially is straightforward. The purpose of this paper, therefore, is to show how multilevel spatial models can be extended to include spatially varying covariate effects. We provide an empirical example on the effect of agriculture on malaria risk in children under 5 years of age in the Democratic Republic of Congo.

Understanding how malaria parasites move between populations is important, particularly given the potential for malaria to be reintroduced into areas where it was previously eliminated. We examine the distribution of malaria genetics across seven sites within the Democratic Republic of Congo (DRC) and two nearby countries, Ghana and Kenya, in order to understand how the relatedness of malaria parasites varies across space, and whether there are barriers to the flow of malaria parasites within the DRC or across borders. Parasite DNA was retrieved from dried blood spots from 7 Demographic and Health Survey sample clusters in the DRC. Malaria genetic characteristics of parasites from Ghana and Kenya were also obtained. For each of 9 geographic sites (7 DRC, 1 Ghana and 1 Kenya), a pair-wise RST statistic was calculated, indicating the genetic distance between malaria parasites found in those locations. Mapping genetics across the spatial extent of the study area indicates a complex genetic landscape, where relatedness between two proximal sites may be relatively high (RST > 0.64) or low (RST < 0.05), and where distal sites also exhibit both high and low genetic similarity. Mantel’s tests suggest that malaria genetics differ as geographic distances increase. Principal Coordinate Analysis suggests that genetically related samples are not co-located. Barrier analysis reveals no significant barriers to gene flow between locations. Malaria genetics in the DRC have a complex and fragmented landscape. Limited exchange of genes across space is reflected in greater genetic distance between malaria parasites isolated at greater geographic distances. There is, however, evidence for close genetic ties between distally located sample locations, indicating that movement of malaria parasites and flow of genes is being driven by factors other than distance decay. This research demonstrates the contributions that spatial disease ecology and landscape genetics can make to understanding the evolutionary dynamics of infectious diseases.

Malaria surveillance is critical for control efforts, but diagnostic methods frequently disagree. Here we compare microscopy, PCR, and a Rapid Diagnostic Test in 7,137 samples from children in the Democratic Republic of the Congo using Latent Class Analysis. PCR had the highest sensitivity (94.6%) and microscopy had the lowest (76.7%).

Malaria surveillance is critical for control efforts, but diagnostic methods frequently disagree. Here we compare microscopy, PCR, and a Rapid Diagnostic Test in 7,137 samples from children in the Democratic Republic of the Congo using Latent Class Analysis. PCR had the highest sensitivity (94.6%) and microscopy had the lowest (76.7%).

Background Understanding the contribution of community-level long-lasting, insecticidal net (LLIN) coverage to malaria control is critical to planning and assessing intervention campaigns. The Democratic Republic of Congo (DRC), which has one of the highest burdens of malaria cases and deaths and has dramatically scaled up LLIN ownership in recent years thus it is an ideal setting to evaluate the effect of individual versus community-level use to prevent malaria among children under the age of 5. Results Data were derived from the 2013–2014 DRC Demographic and Health Survey. Community-level LLIN usage was significantly associated with protection against malaria, even when individual-level LLIN usage was included in the model. In stratified analysis, higher levels of community LLIN coverage enhanced the protective effect of individual LLIN usage, resulting in lower malaria prevalence among individuals who used a LLIN. A sub-analysis of individual LLIN usage by insecticide type revealed deltamethrin-treated nets were more protective than permethrin-treated nets, suggesting that mosquitoes in the DRC are more susceptible to deltamethrin. Conclusions This study examines the effects of individual and community-level LLIN usage in young children in an area of high ITN usage. Individual and community LLIN usage were significantly associated with protection against malaria in children under 5 in the DRC. Importantly, the protective effect of individual LLIN usage against malaria is enhanced when community LLIN coverage is higher, demonstrating the importance of increasing community-level LLIN usage. LLINs treated with deltamethrin were shown to be more protective against malaria than LLINs treated with permethrin. Demographic and Health Surveys are thus a novel and important means of surveillance for insecticide resistance.

Imported malaria threatens control and elimination efforts in countries that have low rates of transmission. In 2010, an outbreak of Plasmodium falciparum malaria was reported among United Nations peacekeeping soldiers from Guatemala who had recently returned from the Democratic Republic of the Congo (DRC). Epidemiologic evidence suggested that the soldiers were infected in the DRC, but local transmission could not be ruled out in all cases. We used population genetic analyses of neutral microsatellites to determine the outbreak source. Genetic relatedness was compared among parasites found in samples from the soldiers and parasite populations collected in the DRC and Guatemala; parasites identified in the soldiers were more closely related to those from the DRC. A phylogenetic clustering analysis confirms this identification with >99.9% confidence. Thus, results support the hypothesis that the soldiers likely imported malaria from the DRC. This study demonstrates the utility of molecular genotyping in outbreak investigations.

Background Rapid diagnostic tests (RDTs) account for more than two-thirds of malaria diagnoses in Africa. Deletions of the Plasmodium falciparum hrp2 (pfhrp2) gene cause false-negative RDT results and have never been investigated on a national level. Spread of pfhrp2-deleted P. falciparum mutants, resistant to detection by HRP2-based RDTs, would represent a serious threat to malaria elimination efforts. Methods Using a nationally representative cross-sectional study of 7,137 children under five years of age from the Democratic Republic of Congo (DRC), we tested 783 subjects with RDT-/PCR+ results using PCR assays to detect and confirm deletions of the pfhrp2 gene. Spatial and population genetic analyses were employed to examine the distribution and evolution of these parasites. Results We identified 149 pfhrp2-deleted parasites, representing 6.4% of all P. falciparum infections country-wide (95% confidence interval 5.1-8.0%). Bayesian spatial analyses identified statistically significant clustering of pfhrp2 deletions near Kinshasa and Kivu. Population genetic analysis revealed significant genetic differentiation between wild-type and pfhrp2-deleted parasite populations (GST = .046, p ≤ .00001). Conclusions Pfhrp2-deleted P. falciparum is a common cause of RDT-/PCR+ malaria among asymptomatic children in the DRC and appears to be clustered within select communities. Surveillance for these deletions is needed, and alternatives to HRP2-specific RDTs may be necessary.