Factors associated with high heterogeneity of malaria at fine spatial scale in the Western Kenyan highlands.

BACKGROUND: The East African highlands are fringe regions between stable and unstable malaria transmission. What factors contribute to the heterogeneity of malaria exposure on different spatial scales within larger foci has not been extensively studied. In a comprehensive, community-based cross-sectional survey an attempt was made to identify factors that drive the macro- and micro epidemiology of malaria in a fringe region using parasitological and serological outcomes. METHODS: A large cross-sectional survey including 17,503 individuals was conducted across all age groups in a 100 km(2) area in the Western Kenyan highlands of Rachuonyo South district. Households were geo-located and prevalence of malaria parasites and malaria-specific antibodies were determined by PCR and ELISA. Household and individual risk-factors were recorded. Geographical characteristics of the study area were digitally derived using high-resolution satellite images. RESULTS: Malaria antibody prevalence strongly related to altitude (1350-1600 m, p < 0.001). A strong negative association with increasing altitude and PCR parasite prevalence was found. Parasite carriage was detected at all altitudes and in all age groups; 93.2 % (2481/2663) of malaria infections were apparently asymptomatic. Malaria parasite prevalence was associated with age, bed net use, house construction features, altitude and topographical wetness index. Antibody prevalence was associated with all these factors and distance to the nearest water body. CONCLUSION: Altitude was a major driver of malaria transmission in this study area, even across narrow altitude bands. The large proportion of asymptomatic parasite carriers at all altitudes and the age-dependent acquisition of malaria antibodies indicate stable malaria transmission; the strong correlation between current parasite carriage and serological markers of malaria exposure indicate temporal stability of spatially heterogeneous transmission

The Impact of Hotspot-Targeted Interventions on Malaria Transmission in Rachuonyo South District in the Western Kenyan Highlands: A Cluster-Randomized Controlled Trial.

BACKGROUND: Malaria transmission is highly heterogeneous, generating malaria hotspots that can fuel malaria transmission across a wider area. Targeting hotspots may represent an efficacious strategy for reducing malaria transmission. We determined the impact of interventions targeted to serologically defined malaria hotspots on malaria transmission both inside hotspots and in surrounding communities. METHODS AND FINDINGS: Twenty-seven serologically defined malaria hotspots were detected in a survey conducted from 24 June to 31 July 2011 that included 17,503 individuals from 3,213 compounds in a 100-km2 area in Rachuonyo South District, Kenya. In a cluster-randomized trial from 22 March to 15 April 2012, we randomly allocated five clusters to hotspot-targeted interventions with larviciding, distribution of long-lasting insecticide-treated nets, indoor residual spraying, and focal mass drug administration (2,082 individuals in 432 compounds); five control clusters received malaria control following Kenyan national policy (2,468 individuals in 512 compounds). Our primary outcome measure was parasite prevalence in evaluation zones up to 500 m outside hotspots, determined by nested PCR (nPCR) at baseline and 8 wk (16 June-6 July 2012) and 16 wk (21 August-10 September 2012) post-intervention by technicians blinded to the intervention arm. Secondary outcome measures were parasite prevalence inside hotpots, parasite prevalence in the evaluation zone as a function of distance from the hotspot boundary, Anopheles mosquito density, mosquito breeding site productivity, malaria incidence by passive case detection, and the safety and acceptability of the interventions. Intervention coverage exceeded 87% for all interventions. Hotspot-targeted interventions did not result in a change in nPCR parasite prevalence outside hotspot boundaries (p ≥ 0.187). We observed an average reduction in nPCR parasite prevalence of 10.2% (95% CI -1.3 to 21.7%) inside hotspots 8 wk post-intervention that was statistically significant after adjustment for covariates (p = 0.024), but not 16 wk post-intervention (p = 0.265). We observed no statistically significant trend in the effect of the intervention on nPCR parasite prevalence in the evaluation zone in relation to distance from the hotspot boundary 8 wk (p = 0.27) or 16 wk post-intervention (p = 0.75). Thirty-six patients with clinical malaria confirmed by rapid diagnostic test could be located to intervention or control clusters, with no apparent difference between the study arms. In intervention clusters we caught an average of 1.14 female anophelines inside hotspots and 0.47 in evaluation zones; in control clusters we caught an average of 0.90 female anophelines inside hotspots and 0.50 in evaluation zones, with no apparent difference between study arms. Our trial was not powered to detect subtle effects of hotspot-targeted interventions nor designed to detect effects of interventions over multiple transmission seasons. CONCLUSIONS: Despite high coverage, the impact of interventions targeting malaria vectors and human infections on nPCR parasite prevalence was modest, transient, and restricted to the targeted hotspot areas. Our findings suggest that transmission may not primarily occur from hotspots to the surrounding areas and that areas with highly heterogeneous but widespread malaria transmission may currently benefit most from an untargeted community-wide approach. Hotspot-targeted approaches may have more validity in settings where human settlement is more nuclear. TRIAL REGISTRATION: ClinicalTrials.gov NCT01575613

Informatics & Surveillance in Global Health: Informatics Capacity for Zika Outbreak

ObjeciveTo assess challenges of establishing surveillance and research study systems and present strategies for rapid deployment in global health for the outbreak response.IntroductionIn response to the February 2016 Zika virus (ZIKV) outbreak, an inter-agency agreement between the U.S. Centers for Disease Control and Prevention (CDC) and U.S. Agency for International Development (USAID) commissioned further research on the epidemiology, transmission, diagnosis, and birth defects associated with ZIKV. The surveillance and research activities conducted included ecology studies focusing on the transmission dynamics; pregnancy and infant cohort studies to look for birth defects, developmental outcomes and risk factors associated with ZIKV infection; and laboratory studies evaluating the usefulness of multiple Zika diagnostic platforms. These studies were established by either setting up new systems, or leveraging on existing surveillance systems to include ZIKV research specific data elements. Conducted using country-specific protocols, these research systems included key data elements for cross-site analysis. Challenges faced included collection of non-standardized data, differing functional requirements, varying security and confidentiality protocols and limitations of informatics infrastructure. These challenges highlight an opportunity to evaluate and present the informatics-based components necessary to rapidly deploy surveillance and research study activities during a global health emergency situation. We highlight the key challenges and presents strategies for setting up rapid surveillance and research study activities. Additional areas of focus also include system architecture, global partnerships, and workforce development.MethodsInformation systems used in the ZIKV ecology, pregnancy and diagnostic studies were evaluated in 12 countries in Asia, Africa and the Americas. The research data collection and enrolment for the studies started at different time points (between Feb 2017 and Aug 2017). A baseline survey (structured questionnaire) was administered to the 12 data points of contacts (POCs) in each country to identify existing or selected information systems for use, functional requirements (for data collection, hosting, analytics and integration), existing informatics and infrastructural capacity. Recommendations were made on the selection and configuration of information technology (IT) systems gaps identified in the baseline; with follow up visits to 5 selected sites for intervention implementation as part of CDC’s technical assistance. 6 key informant interviews were conducted with subject matter experts on the 6 proprietary(commercial/custom) and 12 semi-structured follow up interviews with data POCs to assess the implementation of the recommendations and interventions. Technical assistance impact was measured by averaging the number of informatics technical assistance requests monthly from the countries over approximately a one year period (Mar 2017- Apr-2018). The Delone and Mclean information system success model was used to measure success . Information quality was scored using completeness, format and timeliness; system quality was scored using availability, adaptability, integration and ease of use; and service quality was measured using reliability and user satisfaction ratings.Results13 (5 open source, 8 proprietary or custom systems) health information systems were identified; 9 exclusively for data collection while 4 had extended functionalities to include extract transform and load (ETL); and, analytics. Selection of these systems was based on awareness and popularity of information technologies in country. Open source systems included REDCap, EpiInfo, DHIS2, Kobo Toolbox, CommCare; while proprietary include University of Virginia’s Multi-Schema Information Capture (MuSIC), MS Access, SAMS (CDC’s Secure Access Management System) and 3 custom in-house systems. Two (2) Pregnancy study sites (Kenya and Guatemala) used REDCap to enroll and follow up over 1700, and 436 pregnant women respectively while 1 site(Thailand) used a custom web-based Visual Basic system for collecting data on 1000 pregnant women; Ecology studies in 3 sites( Brazil, Colombia and Peru) used smartphones installed with CommCare to collect data on 560 non-human subjects; diagnostic studies in 10 sites used existing Acute Febrile Illness (AFI) platforms running custom software, DHIS2, Kobo toolbox, Epi Info, REDCap and Ms Access systems data. Technical assistance (TA) requests were grouped into eight (8) core functional areas with systems design (21.9%), data transmission and synchronization (18.5%) and network configuration and diagnostics (13.2%) identified as key the top 3 areas of TA (n=820). TA requests to CDC ranged between an average of 4(mean=4, s.d =0.23) currently and 11(mean=11.25, s.d=0.16) requests per country per month at the beginning of the pregnancy cohort studies (n=3) in Kenya, Guatemala and Thailand. Diagnostic studies (n=10) ranged from 26.8 (s.d=14.6) to 7.5(s.d=4.3) while ecology averaged at 1.7 (N=3, s.d=1.5) requests per country per month. Mean scores of information quality, system quality and service quality were significantly different between sites, as well as between types of information systems (P&lt;0.05). Total mean scores of information quality, system quality and service quality for were 68.6, 59.8 and 61.4, respectively.ConclusionsRobust open source systems exist but their functioalities are not fully exploited. With rapidly changing contexts and outbreak type scenarios, surveillance and research systems must be flexible to rapidly adapt their functional requirements. With appropriate information systems selection guidelines and deliberate informatics technical capacity building we could greatly improve the ability to rapidly deploy systems for outbreak response and global health surveillance and research. Informatics capacity to incorporate design thinking and standardization in surveillance system design and implementation could help realize their potential to provide fast and accurate data for action especially in multi-site contexts. Data exchange and security policies across disparate systems in global health,need to be re-aligned with disease surveillance systems’ functional requirements.

Modeling the cost effectiveness of malaria control interventions in the highlands of western Kenya

Tools that allow for in silico optimization of available malaria control strategies can assist the decision-making process for prioritizing interventions. The OpenMalaria stochastic simulation modeling platform can be applied to simulate the impact of interventions singly and in combination as implemented in Rachuonyo South District, western Kenya, to support this goal.; Combinations of malaria interventions were simulated using a previously-published, validated model of malaria epidemiology and control in the study area. An economic model of the costs of case management and malaria control interventions in Kenya was applied to simulation results and cost-effectiveness of each intervention combination compared to the corresponding simulated outputs of a scenario without interventions. Uncertainty was evaluated by varying health system and intervention delivery parameters.; The intervention strategy with the greatest simulated health impact employed long lasting insecticide treated net (LLIN) use by 80% of the population, 90% of households covered by indoor residual spraying (IRS) with deployment starting in April, and intermittent screen and treat (IST) of school children using Artemether lumefantrine (AL) with 80% coverage twice per term. However, the current malaria control strategy in the study area including LLIN use of 56% and IRS coverage of 70% was the most cost effective at reducing disability-adjusted life years (DALYs) over a five year period.; All the simulated intervention combinations can be considered cost effective in the context of available resources for health in Kenya. Increasing coverage of vector control interventions has a larger simulated impact compared to adding IST to the current implementation strategy, suggesting that transmission in the study area is not at a level to warrant replacing vector control to a school-based screen and treat program. These results have the potential to assist malaria control program managers in the study area in adding new or changing implementation of current interventions

Impact of metric and sample size on determining malaria hotspot boundaries

The spatial heterogeneity of malaria suggests that interventions may be targeted for maximum impact. It is unclear to what extent different metrics lead to consistent delineation of hotspot boundaries. Using data from a large community-based malaria survey in the western Kenyan highlands, we assessed the agreement between a model-based geostatistical (MBG) approach to detect hotspots using Plasmodium falciparum parasite prevalence and serological evidence for exposure. Malaria transmission was widespread and highly heterogeneous with one third of the total population living in hotspots regardless of metric tested. Moderate agreement (Kappa = 0.424) was observed between hotspots defined based on parasite prevalence by polymerase chain reaction (PCR)- and the prevalence of antibodies to two P. falciparum antigens (MSP-1, AMA-1). While numerous biologically plausible hotspots were identified, their detection strongly relied on the proportion of the population sampled. When only 3% of the population was sampled, no PCR derived hotspots were reliably detected and at least 21% of the population was needed for reliable results. Similar results were observed for hotspots of seroprevalence. Hotspot boundaries are driven by the malaria diagnostic and sample size used to inform the model. These findings warn against the simplistic use of spatial analysis on available data to target malaria interventions in areas where hotspot boundaries are uncertain

Impact of metric and sample size on determining malaria hotspot boundaries

The spatial heterogeneity of malaria suggests that interventions may be targeted for maximum impact. It is unclear to what extent different metrics lead to consistent delineation of hotspot boundaries. Using data from a large community-based malaria survey in the western Kenyan highlands, we assessed the agreement between a model-based geostatistical (MBG) approach to detect hotspots using Plasmodium falciparum parasite prevalence and serological evidence for exposure. Malaria transmission was widespread and highly heterogeneous with one third of the total population living in hotspots regardless of metric tested. Moderate agreement (Kappa = 0.424) was observed between hotspots defined based on parasite prevalence by polymerase chain reaction (PCR)- and the prevalence of antibodies to two P. falciparum antigens (MSP-1, AMA-1). While numerous biologically plausible hotspots were identified, their detection strongly relied on the proportion of the population sampled. When only 3% of the population was sampled, no PCR derived hotspots were reliably detected and at least 21% of the population was needed for reliable results. Similar results were observed for hotspots of seroprevalence. Hotspot boundaries are driven by the malaria diagnostic and sample size used to inform the model. These findings warn against the simplistic use of spatial analysis on available data to target malaria interventions in areas where hotspot boundaries are uncertain

Data from: The impact of hotspot-targeted interventions on malaria transmission in Rachuonyo south district in the western Kenyan highlands: a cluster-randomized controlled trial

Background: Malaria transmission is highly heterogeneous, generating malaria hotspots that can fuel malaria transmission across a wider area. Targeting hotspots may represent an efficacious strategy for reducing malaria transmission. We determined the impact of interventions targeted to serologically defined malaria hotspots on malaria transmission both inside hotspots and in surrounding communities. Methods and Findings: Twenty-seven serologically defined malaria hotspots were detected in a survey conducted from 24 June to 31 July 2011 that included 17,503 individuals from 3,213 compounds in a 100-km2 area in Rachuonyo South District, Kenya. In a cluster-randomized trial from 22 March to 15 April 2012, we randomly allocated five clusters to hotspot-targeted interventions with larviciding, distribution of long-lasting insecticide-treated nets, indoor residual spraying, and focal mass drug administration (2,082 individuals in 432 compounds); five control clusters received malaria control following Kenyan national policy (2,468 individuals in 512 compounds). Our primary outcome measure was parasite prevalence in evaluation zones up to 500 m outside hotspots, determined by nested PCR (nPCR) at baseline and 8 wk (16 June–6 July 2012) and 16 wk (21 August–10 September 2012) post-intervention by technicians blinded to the intervention arm. Secondary outcome measures were parasite prevalence inside hotpots, parasite prevalence in the evaluation zone as a function of distance from the hotspot boundary, Anopheles mosquito density, mosquito breeding site productivity, malaria incidence by passive case detection, and the safety and acceptability of the interventions. Intervention coverage exceeded 87% for all interventions. Hotspot-targeted interventions did not result in a change in nPCR parasite prevalence outside hotspot boundaries (p ≥ 0.187). We observed an average reduction in nPCR parasite prevalence of 10.2% (95% CI −1.3 to 21.7%) inside hotspots 8 wk post-intervention that was statistically significant after adjustment for covariates (p = 0.024), but not 16 wk post-intervention (p = 0.265). We observed no statistically significant trend in the effect of the intervention on nPCR parasite prevalence in the evaluation zone in relation to distance from the hotspot boundary 8 wk (p = 0.27) or 16 wk post-intervention (p = 0.75). Thirty-six patients with clinical malaria confirmed by rapid diagnostic test could be located to intervention or control clusters, with no apparent difference between the study arms. In intervention clusters we caught an average of 1.14 female anophelines inside hotspots and 0.47 in evaluation zones; in control clusters we caught an average of 0.90 female anophelines inside hotspots and 0.50 in evaluation zones, with no apparent difference between study arms. Our trial was not powered to detect subtle effects of hotspot-targeted interventions nor designed to detect effects of interventions over multiple transmission seasons. Conclusions: Despite high coverage, the impact of interventions targeting malaria vectors and human infections on nPCR parasite prevalence was modest, transient, and restricted to the targeted hotspot areas. Our findings suggest that transmission may not primarily occur from hotspots to the surrounding areas and that areas with highly heterogeneous but widespread malaria transmission may currently benefit most from an untargeted community-wide approach. Hotspot-targeted approaches may have more validity in settings where human settlement is more nuclear. Trial registration: ClinicalTrials.gov NCT01575613

Cost effectiveness planes.

<p>Simulated cumulative DALYs averted in a population of 100,000 individuals after five years compared to the no intervention scenario by net program costs for the intervention combinations with a better simulated health outcome than the currently implemented malaria control strategy, ranked in descending order of ACER. Black dots represent the mean simulation results across 14 model variants and five seeds. Circles represent the of simulated DALYs averted by net program costs with different assumptions of input costs of the case management system and malaria control interventions in the study area represented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107700#pone-0107700-t002" target="_blank">Table 2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107700#pone-0107700-t003" target="_blank">Table 3</a>. Dark blue circles are within the inter-quartile range of simulated DALYs averted and light blue circles are outside the range. Negative DALYs averted indicate simulated interventions that have a worse health outcome than the no intervention scenario. Negative net program costs indicate simulated interventions where the savings to the health system are greater than the delivery costs. Diagonal lines correspond to the ratios of mean (4.29 USD per DALY averted) ACER of the currently implemented intervention combination in the study area (LLIN use 56%, IRS coverage 70%).</p