Antibiotic resistance poses a threat to public health and healthcare systems. Escherichia coli causes more bacteraemia episodes in England than any other bacterial species. This study aimed to estimate the burden of E. coli bacteraemia and associated antibiotic resistance in the secondary care setting.

Whole genome sequencing (WGS) has been used to investigate transmission of Neisseria gonorrhoeae, but to date, most studies have not combined genomic data with detailed information on sexual behaviour to define the extent of transmission across population risk groups (bridging). Here, through combined epidemiological and genomic analysis of 2,186N. gonorrhoeae isolates from Australia, we show widespread transmission of N. gonorrhoeae within and between population groups. We describe distinct transmission clusters associated with men who have sex with men (MSM) and heterosexuals, and men who have sex with men and women (MSMW) are identified as a possible bridging population between these groups. Further, the study identifies transmission of N. gonorrhoeae between HIV-positive and HIV-negative individuals receiving pre-exposure prophylaxis (PrEP). Our data highlight several groups that can be targeted for interventions aimed at improving gonorrhoea control, including returning travellers, sex workers, and PrEP users.

To inform integrated, person-centered interventions, this study aimed to determine the prevalence of having both a raised blood glucose and blood pressure (BP) in India, and its variation among states and population groups.

There is need for a coding system for categorizing the plastic surgery conditions to facilitate efficient data exchange, retrieval, research, time-series analysis, clinical audit, insurance and legal purposes. This is a pilot study to assess feasibility of newly proposed 5-D coding system in categorizing the plastic surgery conditions.

Prediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform for the development of sequence-based diagnostics. However, there has been no systematic evaluation of factors that may influence performance of such models, how they might apply to and vary across clinical populations, and what the implications might be in the clinical setting. Here, we performed a meta-analysis of seven large Neisseria gonorrhoeae datasets, as well as Klebsiella pneumoniae and Acinetobacter baumannii datasets, with whole genome sequence data and antibiotic susceptibility phenotypes using set covering machine classification, random forest classification, and random forest regression models to predict resistance phenotypes from genotype. We demonstrate how model performance varies by drug, dataset, resistance metric, and species, reflecting the complexities of generating clinically relevant conclusions from machine learning-derived models. Our findings underscore the importance of incorporating relevant biological and epidemiological knowledge into model design and assessment and suggest that doing so can inform tailored modeling for individual drugs, pathogens, and clinical populations. We further suggest that continued comprehensive sampling and incorporation of up-to-date whole genome sequence data, resistance phenotypes, and treatment outcome data into model training will be crucial to the clinical utility and sustainability of machine learning-based molecular diagnostics.

Insufficient or no health insurance creates financial access barriers to healthcare services, especially for vulnerable populations. The Green Card scheme, a non-contributory government-funded health insurance scheme for the poor in Turkey, was expanded in 2003-2006 and has provided citizens with extended benefits. We study the effects of this expansion of the Green Card scheme on out-of-pocket healthcare expenditures for low-income households.