An exploratory study of community factors relevant for participatory malaria control on Rusinga Island, western Kenya

<p>Abstract</p> <p>Background</p> <p>Capacity strengthening of rural communities, and the various actors that support them, is needed to enable them to lead their own malaria control programmes. Here the existing capacity of a rural community in western Kenya was evaluated in preparation for a larger intervention.</p> <p>Methods</p> <p>Focus group discussions and semi-structured individual interviews were carried out in 1,451 households to determine (1) demographics of respondent and household; (2) socio-economic status of the household; (3) knowledge and beliefs about malaria (symptoms, prevention methods, mosquito life cycle); (4) typical practices used for malaria prevention; (5) the treatment-seeking behaviour and household expenditure for malaria treatment; and (6) the willingness to prepare and implement community-based vector control.</p> <p>Results</p> <p>Malaria was considered a major threat to life but relevant knowledge was a chimera of scientific knowledge and traditional beliefs, which combined with socio-economic circumstances, leads to ineffective malaria prevention. The actual malaria prevention behaviour practiced by community members differed significantly from methods known to the respondents. Beside bednet use, the major interventions implemented were bush clearing and various hygienic measures, even though these are ineffective for malaria prevention. Encouragingly, most respondents believed malaria could be controlled and were willing to contribute to a community-based malaria control program but felt they needed outside assistance.</p> <p>Conclusion</p> <p>Culturally sensitive but evidence-based education interventions, utilizing participatory tools, are urgently required which consider traditional beliefs and enable understanding of causal connections between mosquito ecology, parasite transmission and the diagnosis, treatment and prevention of disease. Community-based organizations and schools need to be equipped with knowledge through partnerships with national and international research and tertiary education institutions so that evidence-based research can be applied at the grassroots level.</p

Profile : the rusinga health and demographic surveillance system, western Kenya

The health and demographic surveillance system on Rusinga Island, Western Kenya, was initiated in 2012 to facilitate a malaria intervention trial: the SolarMal project. The project aims to eliminate malaria from Rusinga Island using the nationwide adopted strategy for malaria control (insecticide-treated bed nets and case management) augmented with mass trapping of anopheline mosquitoes. The main purpose of the health and demographic surveillance is to measure the effectiveness of the trial on clinical malaria incidence, and to monitor demographic, environmental and malaria-related data variables. At the end of 2014, the 44 km(2) island had a population of approximately 25 000 individuals living in 8746 residential structures. Three times per year, all individuals are followed up and surveyed for clinical malaria. Following each round of surveillance, a randomly selected cross-section of the population is subject to a rapid diagnostic test to measure malaria. Additionally, extensive monitoring of malaria vectors is performed. Data collection and management are conducted using the OpenHDS platform, with tablet computers and applications with advanced software connected to a centralized database. Besides the general demographic information, other health-related data are collected which can be used to facilitate a range of other studies within and outside the current project. Access to the core dataset can be obtained on request from the authors

Serological Surveillance Development for Tropical Infectious Diseases Using Simultaneous Microsphere-Based Multiplex Assays and Finite Mixture Models

Background:A strategy to combat infectious diseases, including neglected tropical diseases (NTDs), will depend on the development of reliable epidemiological surveillance methods. To establish a simple and practical seroprevalence detection system, we developed a microsphere-based multiplex immunoassay system and evaluated utility using samples obtained in Kenya.Methods:We developed a microsphere-based immuno-assay system to simultaneously measure the individual levels of plasma antibody (IgG) against 8 antigens derived from 6 pathogens: Entamoeba histolytica (C-IgL), Leishmania donovani (KRP42), Toxoplasma gondii (SAG1), Wuchereria bancrofti (SXP1), HIV (gag, gp120 and gp41), and Vibrio cholerae (cholera toxin). The assay system was validated using appropriate control samples. The assay system was applied for 3411 blood samples collected from the general population randomly selected from two health and demographic surveillance system (HDSS) cohorts in the coastal and western regions of Kenya. The immunoassay values distribution for each antigen was mathematically defined by a finite mixture model, and cut-off values were optimized.Findings:Sensitivities and specificities for each antigen ranged between 71 and 100%. Seroprevalences for each pathogen from the Kwale and Mbita HDSS sites (respectively) were as follows: HIV, 3.0% and 20.1%; L. donovani, 12.6% and 17.3%; E. histolytica, 12.8% and 16.6%; and T. gondii, 30.9% and 28.2%. Seroprevalences of W. bancrofti and V. cholerae showed relatively high figures, especially among children. The results might be affected by immunological cross reactions between W. bancrofti-SXP1 and other parasitic infections; and cholera toxin and the enterotoxigenic E. coli (ETEC), respectively.Interpretation:A microsphere-based multi-serological assay system can provide an opportunity to comprehensively grasp epidemiological features for NTDs. By adding pathogens and antigens of interest, optimized made-to-order high-quality programs can be established to utilize limited resources to effectively control NTDs in Africa

Innovative tools and OpenHDS for health and demographic surveillance on Rusinga Island, Kenya

Health in low and middle income countries is on one hand characterized by a high burden associated with preventable communicable diseases and on the other hand considered to be under-documented due to improper basic health and demographic record-keeping. health and demographic surveillance systems (HDSSs) have provided researchers, policy makers and governments with data about local population dynamics and health related information. In order for an HDSS to deliver high quality data, effective organization of data collection and management are vital. HDSSs impose a challenging logistical process typically characterized by door to door visits, poor navigational guidance, conducting interviews recorded on paper, error prone data entry, an extensive staff and marginal data quality management possibilities.; A large trial investigating the effect of odour-baited mosquito traps on malaria vector populations and malaria transmission on Rusinga Island, western Kenya, has deployed an HDSS. By means of computer tablets in combination with Open Data Kit and OpenHDS data collection and management software experiences with time efficiency, cost effectiveness and high data quality are illustrate. Step by step, a complete organization of the data management infrastructure is described, ranging from routine work in the field to the organization of the centralized data server.; Adopting innovative technological advancements has enabled the collection of demographic and malaria data quickly and effectively, with minimal margin for errors. Real-time data quality controls integrated within the system can lead to financial savings and a time efficient work flow.; This novel method of HDSS implementation demonstrates the feasibility of integrating electronic tools in large-scale health interventions

Spatially variable risk factors for malaria in a geographically heterogeneous landscape, Western Kenya : an explorative study

Large reductions in malaria transmission and mortality have been achieved over the last decade, and this has mainly been attributed to the scale-up of long-lasting insecticidal bed nets and indoor residual spraying with insecticides. Despite these gains considerable residual, spatially heterogeneous, transmission remains. To reduce transmission in these foci, researchers need to consider the local demographical, environmental and social context, and design an appropriate set of interventions. Exploring spatially variable risk factors for malaria can give insight into which human and environmental characteristics play important roles in sustaining malaria transmission.; On Rusinga Island, western Kenya, malaria infection was tested by rapid diagnostic tests during two cross-sectional surveys conducted 3 months apart in 3632 individuals from 790 households. For all households demographic data were collected by means of questionnaires. Environmental variables were derived using Quickbird satellite images. Analyses were performed on 81 project clusters constructed by a traveling salesman algorithm, each containing 50-51 households. A standard linear regression model was fitted containing multiple variables to determine how much of the spatial variation in malaria prevalence could be explained by the demographic and environmental data. Subsequently, a geographically-weighted regression (GWR) was performed assuming non-stationarity of risk factors. Special attention was taken to investigate the effect of residual spatial autocorrelation and local multicollinearity.; Combining the data from both surveys, overall malaria prevalence was 24 %. Scan statistics revealed two clusters which had significantly elevated numbers of malaria cases compared to the background prevalence across the rest of the study area. A multivariable linear model including environmental and household factors revealed that higher socioeconomic status, outdoor occupation and population density were associated with increased malaria risk. The local GWR model improved the model fit considerably and the relationship of malaria with risk factors was found to vary spatially over the island; in different areas of the island socio-economic status, outdoor occupation and population density were found to be positively or negatively associated with malaria prevalence.; Identification of risk factors for malaria that vary geographically can provide insight into the local epidemiology of malaria. Examining spatially variable relationships can be a helpful tool in exploring which set of targeted interventions could locally be implemented. Supplementary malaria control may be directed at areas, which are identified as at risk. For instance, areas with many people that work outdoors at night may need more focus in terms of vector control.; Trialregister.nl NTR3496-SolarMal, registered on 20 June 2012

Trends in risk on all causes of child mortality (ACCM) for non-net user children across long lasting insecticide nets (LLINs) density quartiles (A) and those across density quartiles of young people (B) for children who live in a house far from health facilities (Remote dataset^*).

<p>The best fit model was the 1000-meter radius model in trend analyses and the trend was 1.25 (95%CI 1.03–1.51) for LLIN density quartiles and 0.77 (95%CI 0.63–0.94) for density quartiles of young people. ^* Remote dataset: Dataset retrieved children of households located far from health facilities more than 3 kilometers from the district hospital or more than 1 kilometer from health centers and dispensaries in the area. Solid lines are point estimates of hazard ratios for Cox PH models within a radius from 100 meters to 3,000 meters and dotted vertical lines show the best fit model according to likelihood among models. Among the models both in (A) and (B), the best fit mode was the 1000-meter radius model (dotted vertical line). Gray bands indicate 95% confidence intervals for each point estimate.</p

Age distributions (in months) of children by sex and bed net type.

*<p>Whole dataset: the dataset of whole children.</p>**<p>Remote dataset: Dataset that excluded children whose house were located within three kilometers from the district hospital; or within one kilometer from health centers, dispensaries, and clinics to remove the effect of townships where lower child mortality was expected due to easy access to health facilities.</p

Adjusted hazard ratios (HRs) of densities of young population densities on all causes of child mortality (ACCM) for children sleeping without a bed net using the whole dataset ^* (A) for children limited to those who lived far from health facilities using the remote dataset^** (B). Reference is the lowest young population density quartile group of children.

<p>^* Whole dataset: Dataset covering the whole study area. ^** Remote dataset: Dataset retrieved children of households located far from health facilities more than 3 kilometers from the district hospital or more than 1 kilometer from health centers and dispensaries in the area. Solid lines are point estimates of hazard ratios for Cox PH models within a radius from 100 meters to 3,000 meters and dot vertical lines show the best fit model according to likelihood among models. Among the models in (A), the best fit model was the 900-meter radius model (dotted vertical line) and among the models in (B), it was the 2300-meter radius model (dotted vertical line). Gray bands indicate 95% confidence intervals for each point estimate.</p

Spatial distribution of death cases (A), population of children younger than 5 years old (B), and long lasting insecticide net distribution (C) in the study area across the study period.

<p>Kernel estimation of the probability density function for each grid was calculated in terms of death cases, child population and long lasting insecticide treated nets using the quartic kernel function with the fixed bandwidth of 1000 meter. For visualizing the estimated probabilities by the kernel estimation, the probabilities were classified into 20 groups and describes on the grid of the map. The 20 groups were colored by rainbow color: the lowest group in blue, the middle group in green, and the highest group in red. Black dots in (A) are observed death cases.</p

Bed net usage proportions during survey periods among individuals younger than 65 years (A) and among children younger than 5 years (B), according to the type of bed net used.

<p>Orange line, total bed nets; blue line, long lasting insecticide nets (LLIN); green line, untreated nets; dark red line, any bed net. Moving averages were calculated using five age groups to create graphs with smooth lines. Period I = October 14, 2008, to December 19, 2008; period II = May 11, 2009, to June 4, 2009; period III = January 7, 2010, to March 2, 2010; and period IV = September 22, 2010, to December 3, 2010.</p