Sequentially partitioned population attributable fraction as an estimate of impact of tick presence on infection

he impact of Rhipicephalus appendiculatus tick presence (exposure) on Theileria parva infection seroprevalence (outcome) on a farm was assessed in a group of cattle using population attributable fractions (PAF). The analysis was based on a representative sample of 80 traditional smallholder mixed farms from Mbeere District, Kenya. The PAFs were estimated using sequentially partitioned PAF approach that estimated a PAF associated with the exposure after adjusting for any effect that the confounder (agro-ecological zone [AEZ]) may have had by influencing the prevalence of the exposure. The resultant PAF was compared with Bruzzi approach PAF that estimated the proportion of T. parva infection cases directly attributable to the exposure after controlling for confounding by AEZ. The estimated PAF on the Bruzzi approach was 26.4% [95% CI: 9.6%, 43.2%]) whereas the partitioned PAF was 15.5% [95% CI: 1.5%, 29.6%]) implying that about 11% of the estimated impacts was driven by AEZ effects. Both approaches were consistent in estimating a relatively low impact of farm vector tick presence with a relatively high level of uncertainty. Overall, the results suggested that under endemic instability in Mbeere District, (1) presence of R. appendiculatus was not a good indicator of T. parva infection occurrence on a farm, and (2) ecological variation could play a role in determining infection impacts. This study provides a preliminary basis for evaluating the potential value of estimating PAFs for variables amenable to control in tick-borne diseases epidemiological studies

Seroprevalence and risk factors of Coxiella burnetii (Q fever) infection among humans in Bura irrigation scheme, Tana River County, Kenya

Background: Coxiellosis (Q fever) is a zoonotic disease caused by the bacterium Coxiella burnetii. The bacteria is commonly found in various livestock and wildlife species such as cattle, sheep, goats, pigs, rodents, birds, dogs, cats, etc. In this study, we sought to estimate the prevalence of Coxiella burnetii IgG antibodies among humans and to identify risk factors for exposure. Methodology: Blood samples were collected from a total of 272 randomly selected subjects in Bura irrigation scheme and manyattas around the scheme. Serum were prepared from these samples in the study sites and transported to Nairobi for analysis. Screening was done using a commercial ELISA antibody test kit (SERION ELISA classic Coxiella burnetii Phase 1 IgG) for the detection of human antibodies in serum directed against Coxiella burnetii Phase 1. Results: The seroprevalence of coxiella burnetii in humans was 26.8%. This was higher in subjects sampled in the irrigation scheme (30.2% [95% CI]) relative to those sampled in the non-irrigated areas (16.4% [95% CI]). The seroprevalence for CB among adults, children and adolescents was 34.2% (95% CI), 26.8% (95% CI) and 23.2% (95% CI), respectively. There were marginal differences in the seroprevalence of Coxiella burnetii between males and females; these were estimated to be 28% and 26%, respectively. Risk factors associated with seropositivity in univariate analysis included occupation and age. Conclusion: Farmers have a high level of exposure to Coxiella burnetii compared to pastoralists. This is an unexpected observation and more analyses are being done to determine potential routes of exposure

Applications of ecological niche modelling for mapping the risk of Rift Valley fever in Kenya

Rift Valley Fever (RVF) is a viral zoonotic disease of economic importance caused by a virus of the Phlebovirus genus, Bunyaviridae family. The disease occurs cyclically between 5 to 15 years which is associated with El Nino/Southern Oscillation weather phenomenon. Various studies have been done to map RVF distribution using a variety of approaches including the use of disease occurrence maps, statistical models which uses presence and absence data such as logistic regression method, etc. However, acquiring correct absence data is not easy and hence maps generated from standard statistical models might not be a true representation of the disease distribution. In this study Ecological Niche Modeling was used to determine the distribution of RVF in Kenya using GARP algorithm which uses presence-only data. Occurrence of RVF data were obtained by geo-referencing all the known hotspots in the country based on historical data acquired from the Directorate of Veterinary Services (DVS). The environmental variables that were used as the input data included: land use, soil type, elevation, vegetation index acquired from MODIS satellite spanning from October 2006 to March 2007, rainfall and temperature for the same period of time as the satellite imagery. Of the sampled data 70% was used to train the model while 30% to test the model. The result mapped the actual distribution of RVF in Kenya with an AUC of 0.82. A model evaluation was done using Partial ROC which had a 1.77 indicating that the model predicted well. The results will be used to improve the already existing maps and for better planning of mitigation measures. It will also be used together with socio-economic variables to evaluate vulnerability indices in all the divisions across the country

Using ecological niche modelling for mapping the risk of Rift Valley fever in Kenya

Introduction Rift valley fever (RVF) is a viral zoonotic disease of economic importance caused by a virus of the Phlebovirus genus, Bunyaviridae family. The disease occurs cyclically between 5 to 15 years which is associated with El Nino weather phenomenon. Various studies have been done to map RVF distribution using a variety of approaches including the use of disease occurrence maps, statistical models which uses presence and absence data such as logistic regression method, etc. However, acquiring correct absence data is not easy and hence maps generated from standard statistical models might not be a true representation of the disease distribution. Materials and Methods In this study Ecological Niche Modeling was used to determine the distribution of RVF in Kenya using GARP algorithm which uses presence-only data. RVF occurrence data were obtained by geo-referencing all the known hotspots in the country based on historical data acquired from the Directorate of Veterinary Services (DVS). The environmental variables that were used as the input data included: landuse, soil type, elevation, vegetation index acquired from MODIS satellite spanning from October 2006 to march 2007, rainfall and temperature for the same period of time as the satellite imagery. Of the sampled data 70% was used to train the model while 30% to test the model. Results The result mapped the actual distribution of RVF in Kenya with an AUC of 0.82. A model evaluation was done using Partial ROC which had a 1.74 indicating that the model predicted well. Conclusion and Recommendations The results will be used to improve the already existing maps and for better planning of mitigation measures. It will also be used together with socio-economic variables to evaluate vulnerability indices in all the divisions across the country

Features of dog ecology relevant to rabies spread in Machakos District, Kenya

A random sample of households in Machakos District of Kenya was surveyed using personal interviews to determine features of dog ecology relevant to the spread of rabies. A mean of 1,35 dogs/household, a dog to person ratio of 1:9,6 and a mean of 10,4 dogs/km² were estimated. The male to female ratio was 1:0,67 with 26% of the dog population being less than three months old. The dogs had a mean age of 1,8 years. The proportion of the dogs which fed on household leftovers and waste was 94,7%. Dogs were restricted in 19,4% of the households, while 69% of the dogs spent all of their time free outdoors. One-third of the dog population over three months old had been vaccinated against rabies. Considering the endemic status of rabies in Machakos District, methods which could be devised to control the disease are discussed.The articles have been scanned in colour with a HP Scanjet 5590; 600dpi. Adobe Acrobat XI Pro was used to OCR the text and also for the merging and conversion to the final presentation PDF-format.mn201

A hundred years of rabies in Kenya and the strategy for eliminating dog-mediated rabies by 2030

Background: Rabies causes an estimated 59,000 human deaths annually. In Kenya, rabies was first reported in a dog in 1912, with the first human case reported in 1928. Here we examine retrospective rabies data in Kenya for the period 1912 – 2017 and describe the spatial and temporal patterns of rabies occurrence in the country. Additionally, we detail Kenya’s strategy for the elimination of dog-mediated human rabies by 2030. Methods: Data on submitted samples and confirmed cases in humans, domestic animals and wildlife were obtained from Kenya’s Directorate of Veterinary Services. These data were associated with the geographical regions where the samples originated, and temporal and spatial trends examined. Results: Between 1912 and the mid 1970’s, rabies spread across Kenya gradually, with fewer than 50 cases reported per year and less than half of the 47 counties affected. Following an outbreak in the mid 1970’s, rabies spread rapidly to more than 85% of counties, with a 4 fold increase in the percent positivity of samples submitted and number of confirmed rabies cases. Since 1958, 7,584 samples from domestic animals (93%), wildlife (5%), and humans (2%) were tested. Over two-thirds of all rabies cases came from six counties, all in close proximity to veterinary diagnostic laboratories, highlighting a limitation of passive surveillance. Conclusions: Compulsory annual dog vaccinations between 1950’s and the early 1970’s slowed rabies spread. The rapid spread with peak rabies cases in the 1980’s coincided with implementation of structural adjustment programs privatizing the veterinary sector leading to breakdown of rabies control programs. To eliminate human deaths from rabies by 2030, Kenya is implementing a 15-year step-wise strategy based on three pillars: a) mass dog vaccination, b) provision of post-exposure prophylaxis and public awareness and c) improved surveillance for rabies in dogs and humans with prompt responses to rabies outbreaks

Modelling the potential importance of wildlife for rabies persistence in Machakos District, Kenya

The mechanisms by which the rabies virus persists in a host population are unclear. Mathematical models of rabies have difficulty in explaining how the rabies virus persists in the host population between epidemics. Simple deterministic models of rabies predict an unrealistically low number of infected hosts (i.e. fractions of infected hosts) between the first epidemic, following introduction of tile virus, and the second epidemic, after sufficient replenishment of susceptibles. It is in such inter-epidemic troughs, when the prevalence of the vine is at its lowest, that the chances of epidemic fadeout are greatest. Across East Africa, rabies is an important public health problem, and the domestic dog is regarded as the primary reservoir of disease and main vector of human rabies. Rabies cases in wildlife are said to represent spill-over infections which are not important in the long-term persistence of the virus. In Kenya, the rabies problem has been greatest in Machakos District where the disease has persisted, predominantly in domestic dogs although with regular wildlife cases, even when it was well controlled in the rest of the country". Here we investigate what contribution the infections in wildlife might play in the overall persistence of rabies in the domestic dog population of Machakos. We use simple deterministic models of rabies transmission in domestic dog and wild carnivore populations to examine the effects of inter-specific transmission (i.e. dog to wildlife and wildlife to dog) on the persistence of rabies between epidemic peaks