Objective: To determine the bacterial etiology, clinical presentation and risk factors for outcome of serious community-acquired infections in young infants.Study Design: Infants younger than 60 days, admitted for severe pneumonia or suspected sepsis/meningitis were prospectively evaluated using complete blood count, blood culture, chest radiograph, cerebrospinal fluid (CSF) culture in suspected meningitis. w 2 or Fisher's exact test and stepwise logistic regression were used for analysis.Results: Thirty-four of 767 enrolled infants had a positive blood or CSF culture. Gram-negative bacteria were more frequent than Gram positive: overall (P ¼ 0.004), in those below 7 days of age (P ¼ 0.002) and among home deliveries (P ¼ 0.012). Case fatality rates were higher among infants below 1 week old (OR 4.14, P<0.001), those with dense (OR 2.92, P<0.001) or diffuse radiographic infiltrates (OR 2.79, P ¼ 0.003).Conclusions: Gram-negative enteric bacteria are the predominant causes of community-acquired infections in Filipino infants below 2 months old. Age below 7 days and radiographic pneumonia predicted death.
Pneumococcal conjugate vaccines (PCVs) have demonstrated efficacy against childhood pneumococcal disease in several regions globally. We demonstrate how spatial epidemiological analysis of a PCV trial can assist in developing vaccination strategies that target specific geographic subpopulations at greater risk for pneumococcal pneumonia. We conducted a secondary analysis of a randomized, placebo-controlled, double-blind vaccine trial that examined the efficacy of an 11-valent PCV among children less than 2 y of age in Bohol, Philippines. Trial data were linked to the residential location of each participant using a geographic information system. We use spatial interpolation methods to create smoothed surface maps of vaccination rates and local-level vaccine efficacy across the study area. We then measure the relationship between distance to the main study hospital and local-level vaccine efficacy, controlling for ecological factors, using spatial autoregressive models with spatial autoregressive disturbances. We find a significant amount of spatial variation in vaccination rates across the study area. For the primary study endpoint vaccine efficacy increased with distance from the main study hospital from −14% for children living less than 1.5 km from Bohol Regional Hospital (BRH) to 55% for children living greater than 8.5 km from BRH. Spatial regression models indicated that after adjustment for ecological factors, distance to the main study hospital was positively related to vaccine efficacy, increasing at a rate of 4.5% per kilometer distance. Because areas with poor access to care have significantly higher VE, targeted vaccination of children in these areas might allow for a more effective implementation of global programs.spatial epidemiology | spatial analysis | targeted intervention | randomized controlled trial | GIS
Summaryobjective To develop a clinical algorithm that can be used to identify pneumonia deaths in young infants in developing countries and estimate the disease burden in this population.patients and methods Infants younger than 60 days hospitalized with signs of severe pneumonia who underwent clinical, microbiologic and radiological evaluation were the subjects. Stepwise logistic regression and subtractive iterative process were used to derive the algorithm.results Three-hundred and one infants had either clinical or radiographic pneumonia. The case fatality rate for 185 infants with radiographic pneumonia was 21% vs. 5% for clinical pneumonia. Age below 7 days was associated with an increased risk of dying. Among 7-to 59-day-old infants, poor feeding, cyanosis and absence of crackles were predictors of death from pneumonia. Using logistic regression, an algorithm consisting of any one of three clinical signs (cyanosis, poor feeding and abnormally sleepy) was developed in infants aged 7-59 days; 80% of deaths and 50% of those with radiographic pneumonia have at least one of these signs. It performed better than both the WHO case management algorithm and the IMCI algorithm.
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