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

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