Probing all known drug-resistance loci using molecular inversion probes across the Democratic Republic of the Congo reveals prevalence, frequency, and geographical distribution of drug-resistant parasites, as well as spread of sulfadoxine-resistance mutations despite minimal official sulfadoxine use.

Human babesiosis caused by Babesia microti is an emerging tick-borne zoonosis of increasing importance due to rising incidence and expanding geographic range1. Infection with this organism, an intraerythrocytic parasite of the phylum Apicomplexa, causes a febrile syndrome similar to malaria2. Relapsing disease is common among immunocompromised and asplenic individuals3,4, and drug resistance has recently been reported5. To investigate the origin and genetic diversity of this parasite, we sequenced the complete genomes of 42 B. microti samples from around the world, including deep coverage of clinical infections at endemic sites in the continental United States. Samples from the continental US segregate into a Northeast lineage and a Midwest lineage, with subsequent divergence of subpopulations along geographic lines. We identify parasite variants that associate with relapsing disease, including amino acid substitutions in the atovaquone-binding regions of cytochrome b (cytb) and the azithromycin-binding region of ribosomal protein subunit L4 (rpl4). Our results shed light on the origin, diversity, and evolution of B. microti, suggest possible mechanisms for clinical relapse, and create the foundation for further research on this emerging pathogen.

Background: As our population ages at an increasing rate, the demand for nursing homes is rising. The challenge will be for nursing homes to maintain efficiency with limited resources while not compromising quality. This study aimed to review the nursing home efficiency literature to survey the application of efficiency methods and the measurements of inputs, outputs, facility characteristics and operational environment, with a special focus on quality measurement. Methods: We systematically searched three databases for eligible studies published in English between January 1995 and December 2018, supplemented by an exhaustive search of reference lists of included studies. The studies included were available in full text, their units of analysis were nursing homes, and the analytical methods and efficiency scores were clearly reported. Results: We identified 39 studies meeting the inclusion criteria, of which 31 accounted for quality measures. Standard efficiency measurement techniques, data envelopment analysis and stochastic frontier method, and their specifications (orientation, returns to scale, functional forms and error term assumptions) were adequately applied. Measurements of inputs, outputs and control variables were relatively homogenous while quality measures varied. Notably, most studies did not include all three quality dimensions (structure, process and outcome). One study claimed to include quality of life; however, it was not a well-validated and widely used measure. The impacts of quality on efficiency estimates were mixed. The effect of quality on the ranking of nursing home efficiency was rarely reported. Conclusions: When measuring nursing home efficiency, it is crucial to adjust for quality of care and resident’s quality of life because the ultimate output of nursing homes is quality-adjusted days living in the facility. Quality measures should reflect their multidimensionality and not be limited to quality of throughput (health-related events). More reliable estimation of nursing home efficiencies will require better routine data collection within the facility, where well-validated quality measures become an essential part of the minimum data requirement. It is also recommended that different efficiency methods and assumptions, and alternative measures of inputs, outputs and quality, are used for sensitivity analyses to ensure the robustness and validity of findings.

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