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Clinical indicators of bacterial meningitis among neonates and young infants in rural Kenya

BACKGROUND: Meningitis is notoriously difficult to diagnose in infancy because its clinical features are non-specific. World Health Organization (WHO) guidelines suggest several indicative signs, based on limited data. We aimed to identify indicators of bacterial meningitis in young infants in Kenya, and compared their performance to the WHO guidelines. We also examined the feasibility of developing a scoring system for meningitis. METHODS: We studied all admissions aged < 60 days to Kilifi District Hospital, 2001 through 2005. We evaluated clinical indicators against microbiological findings using likelihood ratios. We prospectively validated our findings 2006 through 2007. RESULTS: We studied 2,411 and 1,512 young infants during the derivation and validation periods respectively. During derivation, 31/1,031 (3.0%) neonates aged < 7 days and 67/1,380 (4.8%) young infants aged 7-59 days (p < 0.001) had meningitis. 90% of cases could be diagnosed macroscopically (turbidity) or by microscopic leukocyte counting. Independent indicators of meningitis were: fever, convulsions, irritability, bulging fontanel and temperature >/= 39 degrees C. Areas under the receiver operating characteristic curve in the validation period were 0.62 [95%CI: 0.49-0.75] age < 7 days and 0.76 [95%CI: 0.68-0.85] thereafter (P = 0.07), and using the WHO signs, 0.50 [95%CI 0.35-0.65] age < 7 days and 0.82 [95%CI: 0.75-0.89] thereafter (P = 0.0001). The number needed to LP to identify one case was 21 [95%CI: 15-35] for our signs, and 28 [95%CI: 18-61] for WHO signs. With a scoring system, a cut-off of >/= 1 sign offered the best compromise on sensitivity and specificity. CONCLUSION: Simple clinical signs at admission identify two thirds of meningitis cases in neonates and young infants. Lumbar puncture is essential to diagnosis and avoidance of unnecessary treatment, and is worthwhile without CSF biochemistry or bacterial culture. The signs of Meningitis suggested by the WHO perform poorly in the first week of life. A scoring system for meningitis in this age group is not helpful.
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The plasma concentration of the B cell activating factor is increased in children with acute malaria

Malaria-specific antibody responses in children often appear to be short-lived but the mechanisms underlying this phenomenon are not well understood. In this study, we investigated the relationship between the B-cell activating factor (BAFF) and its receptors expressed on B cells with antibody responses during and after acute malaria in children. Our results demonstrate that BAFF plasma levels increased during acute malarial disease and reflected disease severity. The expression profiles for BAFF receptors on B cells agreed with rapid activation and differentiation of a proportion of B cells to plasma cells. However, BAFF receptor (BAFF-R) expression was reduced on all peripheral blood B cells during acute infection, but those children with the highest level of BAFF-R expression on B cells maintained schizont-specific immunoglobin G (IgG) over a period of 4 months, indicating that dysregulation of BAFF-R expression on B cells may contribute to short-lived antibody responses to malarial antigens in children. In summary, this study suggests a potential role for BAFF during malaria disease, both as a marker for disease severity and in shaping the differentiation pattern of antigen-specific B cells.
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Validating physician-certified verbal autopsy and probabilistic modeling (InterVA) approaches to verbal autopsy interpretation using hospital causes of adult deaths

BACKGROUND: The most common method for determining cause of death is certification by physicians based either on available medical records, or where such data are not available, through verbal autopsy (VA). The physician-certification approach is costly and inconvenient; however, recent work shows the potential of a computer-based probabilistic model (InterVA) to interpret verbal autopsy data in a more convenient, consistent, and rapid way. In this study we validate separately both physician-certified verbal autopsy (PCVA) and the InterVA probabilistic model against hospital cause of death (HCOD) in adults dying in a district hospital on the coast of Kenya. METHODS: Between March 2007 and June 2010, VA interviews were conducted for 145 adult deaths that occurred at Kilifi District Hospital. The VA data were reviewed by a physician and the cause of death established. A range of indicators (including age, gender, physical signs and symptoms, pregnancy status, medical history, and the circumstances of death) from the VA forms were included in the InterVA for interpretation. Cause-specific mortality fractions (CSMF), Cohen's kappa (kappa) statistic, receiver operating characteristic (ROC) curves, sensitivity, specificity, and positive predictive values were applied to compare agreement between PCVA, InterVA, and HCOD. RESULTS: HCOD, InterVA, and PCVA yielded the same top five underlying causes of adult deaths. The InterVA overestimated tuberculosis as a cause of death compared to the HCOD. On the other hand, PCVA overestimated diabetes. Overall, CSMF for the five major cause groups by the InterVA, PCVA, and HCOD were 70%, 65%, and 60%, respectively. PCVA versus HCOD yielded a higher kappa value (kappa = 0.52, 95% confidence interval [CI]: 0.48, 0.54) than the InterVA versus HCOD which yielded a kappa (kappa) value of 0.32 (95% CI: 0.30, 0.38). Overall, (kappa) agreement across the three methods was 0.41 (95% CI: 0.37, 0.48). The areas under the ROC curves were 0.82 for InterVA and 0.88 for PCVA. The observed sensitivities and specificities across the five major causes of death varied from 43% to 100% and 87% to 99%, respectively, for the InterVA/PCVA against the HCOD. CONCLUSION: Both the InterVA and PCVA compared well with the HCOD at a population level and determined the top five underlying causes of death in the rural community of Kilifi. We hope that our study, albeit small, provides new and useful data that will stimulate further definitive work on methods of interpreting VA data.
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