Evaluating the relationship between ciprofloxacin prescription and non-susceptibility in Salmonella Typhi in Blantyre, Malawi: an observational study

Background Ciprofloxacin is the first-line drug for treating typhoid fever in many countries in Africa with a high disease burden, but the emergence of non-susceptibility poses a challenge to public health programmes. Through enhanced surveillance as part of vaccine evaluation, we investigated the occurrence and potential determinants of ciprofloxacin non-susceptibility in Blantyre, Malawi. Methods We conducted systematic surveillance of typhoid fever cases and antibiotic prescription in two health centres in Blantyre, Malawi, between Oct 1, 2016, and Oct 31, 2019, as part of the STRATAA and TyVAC studies. In addition, blood cultures were taken from eligible patients presenting at Queen Elizabeth Central Hospital, Blantyre, as part of routine diagnosis. Inclusion criteria were measured or reported fever, or clinical suspicion of sepsis. Microbiologically, we identified Salmonella enterica serotype Typhi (S Typhi) isolates with a ciprofloxacin non-susceptible phenotype from blood cultures, and used whole-genome sequencing to identify drug-resistance mutations and phylogenetic relationships. We constructed generalised linear regression models to investigate associations between the number of ciprofloxacin prescriptions given per month to study participants and the proportion of S Typhi isolates with quinolone resistance-determining region (QRDR) mutations in the following month. Findings From 46 989 blood cultures from Queen Elizabeth Central Hospital, 502 S Typhi isolates were obtained, 30 (6%) of which had either decreased ciprofloxacin susceptibility, or ciprofloxacin resistance. From 11 295 blood cultures from STRATAA and TyVAC studies, 241 microbiologically confirmed cases of typhoid fever were identified, and 198 isolates from 195 participants sequenced (mean age 12·8 years [SD 10·2], 53% female, 47% male). Between Oct 1, 2016, and Aug 31, 2019, of 177 typhoid fever cases confirmed by whole-genome sequencing, four (2%) were caused by S Typhi with QRDR mutations, compared with six (33%) of 18 cases between Sept 1 and Oct 31, 2019. This increase was associated with a preceding spike in ciprofloxacin prescriptions. Every additional prescription of ciprofloxacin given to study participants in the preceding month was associated with a 4·2% increase (95% CI 1·8–7·0) in the relative risk of isolating S Typhi with a QRDR mutation (p=0·0008). Phylogenetic analysis showed that S Typhi isolates with QRDR mutations from September and October, 2019, belonged to two distinct subclades encoding two different QRDR mutations, and were closely related (4–10 single-nucleotide polymorphisms) to susceptible S Typhi endemic to Blantyre. Interpretation We postulate a causal relationship between increased ciprofloxacin prescriptions and an increase in fluoroquinolone non-susceptibility in S Typhi. Decreasing ciprofloxacin use by improving typhoid diagnostics, and reducing typhoid fever cases through the use of an efficacious vaccine, could help to limit the emergence of resistance

A Bayesian approach for estimating typhoid fever incidence from large-scale facility-based passive surveillance data.

Funder: Public Health Research Programme; Id: http://dx.doi.org/10.13039/501100001921Decisions about typhoid fever prevention and control are based on estimates of typhoid incidence and their uncertainty. Lack of specific clinical diagnostic criteria, poorly sensitive diagnostic tests, and scarcity of accurate and complete datasets contribute to difficulties in calculating age-specific population-level typhoid incidence. Using data from the Strategic Typhoid Alliance across Africa and Asia program, we integrated demographic censuses, healthcare utilization surveys, facility-based surveillance, and serological surveillance from Malawi, Nepal, and Bangladesh to account for under-detection of cases. We developed a Bayesian approach that adjusts the count of reported blood-culture-positive cases for blood culture detection, blood culture collection, and healthcare seeking-and how these factors vary by age-while combining information from prior published studies. We validated the model using simulated data. The ratio of observed to adjusted incidence rates was 7.7 (95% credible interval [CrI]: 6.0-12.4) in Malawi, 14.4 (95% CrI: 9.3-24.9) in Nepal, and 7.0 (95% CrI: 5.6-9.2) in Bangladesh. The probability of blood culture collection led to the largest adjustment in Malawi, while the probability of seeking healthcare contributed the most in Nepal and Bangladesh; adjustment factors varied by age. Adjusted incidence rates were within or below the seroincidence rate limits of typhoid infection. Estimates of blood-culture-confirmed typhoid fever without these adjustments results in considerable underestimation of the true incidence of typhoid fever. Our approach allows each phase of the reporting process to be synthesized to estimate the adjusted incidence of typhoid fever while correctly characterizing uncertainty, which can inform decision-making for typhoid prevention and control

Direct inference and control of genetic population structure from RNA sequencing data.

Acknowledgements: We acknowledge the contributions of individuals and organizations who have arranged and taken part in the studies as well as the laboratory and field teams at the site, including the STRATAA Study Group and the Nepal Family Development Foundation team. We thank the Sanger sequencing teams. This research was funded in whole, or in part, by the Wellcome Trust [STRATAA, 106158/Z/14/Z and Sanger, 098051]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. This research was also funded by NHMRC [project grant APP1101728] and supported by core funding from the British Heart Foundation (RG/18/13/33946) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312)[*]. *The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. M.I. is supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312) [*]. M.I. was also supported by the UK Economic and Social Research 878 Council (ES/T013192/1). M.F. was supported by a Melbourne Research Scholarship from The University of Melbourne jointly funded by the Baker Heart and Diabetes Institute. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. This study was also supported by the Victorian Government’s Operational Infrastructure Support (OIS) program. *The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views expressed in this manuscript are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.Funder: Scottish Government Health and Social Care Directorate (SGHSC); doi: https://doi.org/10.13039/100011529Funder: State Government of Victoria (Victorian Government); doi: https://doi.org/10.13039/501100004752RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data