Seroprevalence of HAV, HBV, HCV, and HEV among acute hepatitis patients at Kenyatta National Hospital in Nairobi, Kenya

Background: Acute viral hepatitis is most frequently caused by the hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV) and hepatitis E virus (HEV).Objectives: To determine seroprevalence of HAV, HBV, HCV and HEV among patients with acute hepatitis in Nairobi, Kenya, elucidate various risk factors for hepatitis viral infection and determine the co-infection rates with these viruses in the acute hepatitis patients.Design: Across sectional descriptive study.Setting: Kenyatta National Hospital, from November 2007 to April 2008.Subjects: One hundred patients were recruited by purposive sampling method and comprised of 57 males and 43 females.Results: Among the enrolled patients, twenty three tested positive for one or more markers of acute viral hepatitis, that is, HAV, HBV, HCV and HEV. No markers were detected in 77 patients, 2% tested positive for IgM anti-HAV; 11% for IgM anti-HBc; 3% for HBsAg; 5% for HCV RNA and 7% for IgM anti-HEV.Various risk factors associated with acute viral hepatitis were identified; poor sanitation, source of water, occupation, place of residence, level of education,household size, drug abuse and sexual behaviours. Co-infection rate with hepatitis Viruses was at 4%, IgM anti-HAV and IgM anti-HEV 1 % (n=1); IgM anti-HBc and IgM anti-HEV 1% (n=1); IgM anti-HBc and anti-HCV 2% (n=2).Three patients were positive for HBsAg; among this two were negative for IgM anti-HBc and this accounted for HBV carriage (2 %).Conclusion: Hepatitis viruses’ infections are common cause of hepatitis among patients with acute hepatitis at Kenyatta National Hospital. Co-infection with these viruses was also identified among these patients

The risks of malariainfection in Kenya in 2009

BACKGROUND: To design an effective strategy for the control of malaria requires a map of infection and disease risks to select appropriate suites of interventions. Advances in model based geo-statistics and malaria parasite prevalence data assemblies provide unique opportunities to redefine national Plasmodium falciparum risk distributions. Here we present a new map of malaria risk for Kenya in 2009. METHODS: Plasmodium falciparum parasite rate data were assembled from cross-sectional community based surveys undertaken from 1975 to 2009. Details recorded for each survey included the month and year of the survey, sample size, positivity and the age ranges of sampled population. Data were corrected to a standard age-range of two to less than 10 years (PfPR2-10) and each survey location was geo-positioned using national and on-line digital settlement maps. Ecological and climate covariates were matched to each PfPR2-10 survey location and examined separately and in combination for relationships to PfPR2-10. Significant covariates were then included in a Bayesian geostatistical spatial-temporal framework to predict continuous and categorical maps of mean PfPR2-10 at a 1 x 1 km resolution across Kenya for the year 2009. Model hold-out data were used to test the predictive accuracy of the mapped surfaces and distributions of the posterior uncertainty were mapped. RESULTS: A total of 2,682 estimates of PfPR2-10 from surveys undertaken at 2,095 sites between 1975 and 2009 were selected for inclusion in the geo-statistical modeling. The covariates selected for prediction were urbanization; maximum temperature; precipitation; enhanced vegetation index; and distance to main water bodies. The final Bayesian geo-statistical model had a high predictive accuracy with mean error of -0.15% PfPR2-10; mean absolute error of 0.38% PfPR2-10; and linear correlation between observed and predicted PfPR2-10 of 0.81. The majority of Kenya's 2009 population (35.2 million, 86.3%) reside in areas where predicted PfPR2-10 is less than 5%; conversely in 2009 only 4.3 million people (10.6%) lived in areas where PfPR2-10 was predicted to be > or =40% and were largely located around the shores of Lake Victoria. CONCLUSION: Model based geo-statistical methods can be used to interpolate malaria risks in Kenya with precision and our model shows that the majority of Kenyans live in areas of very low P. falciparum risk. As malaria interventions go to scale effectively tracking epidemiological changes of risk demands a rigorous effort to document infection prevalence in time and space to remodel risks and redefine intervention priorities over the next 10-15 years

Needs assessment to strengthen capacity in water and sanitation research in Africa:experiences of the African SNOWS consortium

Despite its contribution to global disease burden, diarrhoeal disease is still a relatively neglected area for research funding, especially in low-income country settings. The SNOWS consortium (Scientists Networked for Outcomes from Water and Sanitation) is funded by the Wellcome Trust under an initiative to build the necessary research skills in Africa. This paper focuses on the research training needs of the consortium as identified during the first three years of the project

Transcriptomes of <i>Trypanosoma brucei</i> rhodesiense from sleeping sickness patients, rodents and culture:Effects of strain, growth conditions and RNA preparation methods

All of our current knowledge of African trypanosome metabolism is based on results from trypanosomes grown in culture or in rodents. Drugs against sleeping sickness must however treat trypanosomes in humans. We here compare the transcriptomes of Trypanosoma brucei rhodesiense from the blood and cerebrospinal fluid of human patients with those of trypanosomes from culture and rodents. The data were aligned and analysed using new user-friendly applications designed for Kinetoplastid RNA-Seq data. The transcriptomes of trypanosomes from human blood and cerebrospinal fluid did not predict major metabolic differences that might affect drug susceptibility. Usefully, there were relatively few differences between the transcriptomes of trypanosomes from patients and those of similar trypanosomes grown in rats. Transcriptomes of monomorphic laboratory-adapted parasites grown in in vitro culture closely resembled those of the human parasites, but some differences were seen. In poly(A)-selected mRNA transcriptomes, mRNAs encoding some protein kinases and RNA-binding proteins were under-represented relative to mRNA that had not been poly(A) selected; further investigation revealed that the selection tends to result in loss of longer mRNAs

Leadership behaviours and entrepreneurial attitude as predictors of business outcomes within business incubators: A conceptual model

This conceptual paper proposes pathways through which transformational leadership and entrepreneurial attitude orientation could predict business outcomes within the business incubators context. Based on the literature review the paper argues that transformational leadership and entrepreneurial attitude orientation have both direct and indirect influence on business outcomes for business incubators. Furthermore, the paper proposes a conceptual framework useful in explaining the interplay between leadership, entrepreneurial attitude orientation and business outcomes. Finally, the paper outlines some steps to advance leadership theory within the business incubator context

The risks of malariainfection in Kenya in 2009

Abstract Background To design an effective strategy for the control of malaria requires a map of infection and disease risks to select appropriate suites of interventions. Advances in model based geo-statistics and malaria parasite prevalence data assemblies provide unique opportunities to redefine national Plasmodium falciparum risk distributions. Here we present a new map of malaria risk for Kenya in 2009. Methods Plasmodium falciparum parasite rate data were assembled from cross-sectional community based surveys undertaken from 1975 to 2009. Details recorded for each survey included the month and year of the survey, sample size, positivity and the age ranges of sampled population. Data were corrected to a standard age-range of two to less than 10 years (PfPR2-10) and each survey location was geo-positioned using national and on-line digital settlement maps. Ecological and climate covariates were matched to each PfPR2-10 survey location and examined separately and in combination for relationships to PfPR2-10. Significant covariates were then included in a Bayesian geostatistical spatial-temporal framework to predict continuous and categorical maps of mean PfPR2-10 at a 1 × 1 km resolution across Kenya for the year 2009. Model hold-out data were used to test the predictive accuracy of the mapped surfaces and distributions of the posterior uncertainty were mapped. Results A total of 2,682 estimates of PfPR2-10 from surveys undertaken at 2,095 sites between 1975 and 2009 were selected for inclusion in the geo-statistical modeling. The covariates selected for prediction were urbanization; maximum temperature; precipitation; enhanced vegetation index; and distance to main water bodies. The final Bayesian geo-statistical model had a high predictive accuracy with mean error of -0.15% PfPR2-10; mean absolute error of 0.38% PfPR2-10; and linear correlation between observed and predicted PfPR2-10 of 0.81. The majority of Kenya's 2009 population (35.2 million, 86.3%) reside in areas where predicted PfPR2-10 is less than 5%; conversely in 2009 only 4.3 million people (10.6%) lived in areas where PfPR2-10 was predicted to be ≥40% and were largely located around the shores of Lake Victoria. Conclusion Model based geo-statistical methods can be used to interpolate malaria risks in Kenya with precision and our model shows that the majority of Kenyans live in areas of very low P. falciparum risk. As malaria interventions go to scale effectively tracking epidemiological changes of risk demands a rigorous effort to document infection prevalence in time and space to remodel risks and redefine intervention priorities over the next 10-15 years.</p