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BackgroundThe rising level of antimicrobial resistance among bacterial pathogens is one of the most significant public health problems globally. While the antibiotic resistance of clinically important bacteria is closely tracked in many developed countries, the types and levels of resistance and multidrug resistance (MDR) among pathogens currently circulating in most countries of sub-Saharan Africa are virtually unknown.MethodsFrom December 2013 to April 2014, we collected 93 urine specimens from all outpatients showing symptoms of urinary tract infection (UTI) and 189 fomite swabs from a small hospital in Bo, Sierra Leone. Culture on chromogenic agar combined with biochemical and DNA sequence-based assays was used to detect and identify the bacterial isolates. Their antimicrobial susceptibilities were determined using a panel of 11 antibiotics or antibiotic combinations.ResultsThe 70 Enterobacteriaceae urine isolates were identified as Citrobacter freundii (n = 22), Klebsiella pneumoniae (n = 15), Enterobacter cloacae (n = 15), Escherichia coli (n = 13), Enterobacter sp./Leclercia sp. (n = 4) and Escherichia hermannii (n = 1). Antimicrobial susceptibility testing demonstrated that 85.7 % of these isolates were MDR while 64.3 % produced an extended-spectrum ß-lactamase (ESBL). The most notable observations included widespread resistance to sulphonamides (91.4 %), chloramphenicol (72.9 %), gentamycin (72.9 %), ampicillin with sulbactam (51.4 %) and ciprofloxacin (47.1 %) with C. freundii exhibiting the highest and E. coli the lowest prevalence of multidrug resistance. The environmental cultures resulted in only five Enterobacteriaceae isolates out of 189 collected with lower overall antibiotic resistance.ConclusionsThe surprisingly high proportion of C. freundii found in urine of patients with suspected UTI supports earlier findings of the growing role of this pathogen in UTIs in low-resource countries. The isolates of all analyzed species showed worryingly high levels of resistance to both first- and second-line antibiotics as well as a high frequency of MDR and ESBL phenotypes, which likely resulted from the lack of consistent antibiotic stewardship policies in Sierra Leone. Analysis of hospital environmental isolates however suggested that fomites in this naturally ventilated hospital were not a major reservoir for Enterobacteriaceae or antibiotic resistance determinants.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-016-1495-1) contains supplementary material, which is available to authorized users.

Background: Malaria continues to affect over 200 million individuals every year, especially children in Africa. Rapid and sensitive detection and identification of Plasmodium parasites is crucial for treating patients and monitoring of control efforts. Compared to traditional diagnostic methods such as microscopy and rapid diagnostic tests (RDTs), DNA based methods, such as polymerase chain reaction (PCR) offer significantly higher sensitivity, definitive discrimination of Plasmodium species, and detection of mixed infections. While PCR is not currently optimized for routine diagnostics, its role in epidemiological studies is increasing as the world moves closer toward regional and eventually global malaria elimination. This study demonstrates the field use of a novel, ambient temperature-stabilized, multiplexed PCR assay in a small hospital setting in Sierra Leone.Methods: Blood samples from 534 febrile individuals reporting to a hospital in Bo, Sierra Leone, were tested using three methods: a commercial RDT, microscopy, and a Multiplex Malaria Sample Ready (MMSR) PCR designed to detect a universal malaria marker and species-specific markers for Plasmodium falciparum and Plasmodium vivax. A separate PCR assay was used to identify species of Plasmodium in samples in which MMSR detected malaria, but was unable to identify the species.Results: MMSR detected the presence of any malaria marker in 50.2% of all tested samples with P. falciparum identified in 48.7% of the samples. Plasmodium vivax was not detected. Testing of MMSR P. falciparum-negative/universal malaria-positive specimens with a panel of species-specific PCRs revealed the presence of Plasmodium malariae (n = 2) and Plasmodium ovale (n = 2). The commercial RDT detected P. falciparum in 24.6% of all samples while microscopy was able to detect malaria in 12.8% of tested specimens.

BackgroundSpatial epidemiology is useful but difficult to apply in developing countries due to the low availability of digitized maps and address systems, accurate population distributions, and computational tools. A community-based mapping approach was used to demonstrate that participatory geographic information system (PGIS) techniques can provide information helpful for health and community development.ResultsThe PGIS process allowed for the rapid determination of sectional (neighborhood) boundaries within the city of Bo, Sierra Leone. When combined with data about hospital laboratory visits, a catchment area for one hospital in Bo could be established. A survey of households from within the catchment area determined that the average population per household (about 6 individuals) was similar to that found in the 2004 census. However, we also found that the average house was inhabited by more than one household, for an average of 17.5 inhabitants per residential building, which is critical information to know when estimating population size using remote imagery that can detect and enumerate buildings.ConclusionsThe methods developed in this paper serve as a model for the involvement of communities in the generation of municipal maps and their application to community and health concerns.

This study demonstrates the use of bootstrap methods to estimate the total population of urban and periurban areas using satellite imagery and limited survey data. We conducted complete household surveys in 20 neighborhoods in the city of Bo, Sierra Leone, which collectively were home to 25,954 persons living in 1,979 residential structures. For five of those twenty sections, we quantized the rooftop areas of structures extracted from satellite images. We used bootstrap statistical methods to estimate the total population of the pooled sections, including the associated uncertainty intervals, as a function of sample size. Evaluations based either on rooftop area per person or on the mean number of occupants per residence both converged on the true population size. We demonstrate with this simulation that demographic surveys of a relatively small proportion of residences can provide a foundation for accurately estimating the total population in conjunction with aerial photographs.