Contractual acrobatics: a configurational analysis of outcome specifications and payment in outcome-based contracts

Outcome-based contracting (OBC) seeks to improve public services by paying for service outcomes rather than service activities. This article explores the link between how outcomes are contractually specified and how much is paid for their achievement. Using fuzzy-set Qualitative Comparative Analysis, we test a framework for assessing the strength of outcome specifications in 34 UK-based social impact bonds, a particular form of OBC. Results show that contract features which define intended participant cohorts and include deadweight estimation approaches help constrain suppliers’ ability to appropriate value and thus reduce the likelihood that public managers pay for social outcomes of questionable value

Beyond “make” or “buy”: evaluating value‐for‐money in public service delivery

Outsourcing of public services is under heightened scrutiny. Public managers are asked to conduct thorough “make or buy” assessments to help assure deliverability, affordability, and value for money of public services. The naivety of this request dramatically overlooks the subtlety and challenge faced by public managers. In this paper we connect a range of differently configured contractual agreements to underlying components of “value for money”, namely, the pursuit of economy, efficiency and effectiveness. We set out a framework consisting of conceptual models and the corresponding decision tree to allow comparison across alternative sourcing strategies, considering both the associated transaction costs and transaction benefits. We also use simulation methods to capture uncertainty while establishing the practicality of the framework. This study advocates for moving beyond the polarized ‘make or buy’ debate with more instrumental considerations of “how to buy” from the perspective of the public manager

Metagenomic sequencing of clinical samples reveals a single widespread clone of Lawsonia intracellularis responsible for porcine proliferative enteropathy.

Lawsonia intracellularis is a Gram-negative obligate intracellular bacterium that is the aetiological agent of proliferative enteropathy (PE), a common intestinal disease of major economic importance in pigs and other animal species. To date, progress in understanding the biology of L. intracellularis for improved disease control has been hampered by the inability to culture the organism in vitro. In particular, our understanding of the genomic diversity and population structure of clinical L. intercellularis is very limited. Here, we utilized a metagenomic shotgun approach to directly sequence and assemble 21 L. intracellularis genomes from faecal and ileum samples of infected pigs and horses across three continents. Phylogenetic analysis revealed a genetically monomorphic clonal lineage responsible for infections in pigs, with distinct subtypes associated with infections in horses. The genome was highly conserved, with 94 % of genes shared by all isolates and a very small accessory genome made up of only 84 genes across all sequenced strains. In part, the accessory genome was represented by regions with a high density of SNPs, indicative of recombination events importing novel gene alleles. In summary, our analysis provides the first view of the population structure for L. intracellularis, revealing a single major lineage associated with disease of pigs. The limited diversity and broad geographical distribution suggest the recent emergence and clonal expansion of an important livestock pathogen

The 'analysis of gene expression and biomarkers for point-of-care decision support in Sepsis' study; temporal clinical parameter analysis and validation of early diagnostic biomarker signatures for severe inflammation andsepsis-SIRS discrimination

Introduction: Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. We describe identification of mRNA biomarkers from peripheral blood leukocytes, able to identify severe, systemic inflammation (irrespective of origin) and differentiate Sepsis from SIRS, in adult patients within a multi-center clinical study.Methods: Participants were recruited in Intensive Care Units (ICUs) from multiple UK hospitals, including fifty-nine patients with abdominal sepsis, eighty-four patients with pulmonary sepsis, forty-two SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), sampled at four time points, in addition to thirty healthy control donors. Multiple clinical parameters were measured, including SOFA score, with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools.Results: Nineteen high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed 'indicators of inflammation' (I°I), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty entities, termed 'SIRS or Sepsis' (S°S) biomarkers, were differentially expressed between sepsis and SIRS (FC>2·0, p-value<0.05).Discussion: The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758). The I°I and S°S signatures performed variably in other independent gene expression datasets, this may be due to technical variation in the study/assay platform

The 'analysis of gene expression and biomarkers for point-of-care decision support in Sepsis' study; temporal clinical parameter analysis and validation of early diagnostic biomarker signatures for severe inflammation andsepsis-SIRS discrimination

Introduction: Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. We describe identification of mRNA biomarkers from peripheral blood leukocytes, able to identify severe, systemic inflammation (irrespective of origin) and differentiate Sepsis from SIRS, in adult patients within a multi-center clinical study.Methods: Participants were recruited in Intensive Care Units (ICUs) from multiple UK hospitals, including fifty-nine patients with abdominal sepsis, eighty-four patients with pulmonary sepsis, forty-two SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), sampled at four time points, in addition to thirty healthy control donors. Multiple clinical parameters were measured, including SOFA score, with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools.Results: Nineteen high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed 'indicators of inflammation' (I°I), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty entities, termed 'SIRS or Sepsis' (S°S) biomarkers, were differentially expressed between sepsis and SIRS (FC>2·0, p-value<0.05).Discussion: The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758). The I°I and S°S signatures performed variably in other independent gene expression datasets, this may be due to technical variation in the study/assay platform

Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance

Purpose: Diagnosis of genetic disorders is hampered by large numbers of variants of uncertain significance (VUSs) identified through next-generation sequencing. Many such variants may disrupt normal RNA splicing. We examined effects on splicing of a large cohort of clinically identified variants and compared performance of bioinformatic splicing prediction tools commonly used in diagnostic laboratories. Methods: Two hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were obtained using Alamut, HSF 3.1, and SpliceAI software. Results: Eighty-five variants (33%) were associated with abnormal splicing. The most frequent abnormality was upstream exon skipping (39/85 variants), which was most often associated with splice donor region variants. SpliceAI had greatest accuracy in predicting splicing abnormalities (0.91) and outperformed other tools in sensitivity and specificity. Conclusion: Splicing analysis of blood RNA identifies diagnostically important splicing abnormalities and clarifies functional effects of a significant proportion of VUSs. Bioinformatic predictions are improving but still make significant errors. RNA analysis should therefore be routinely considered in genetic disease diagnostics.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.This research was funded by National Institute for Health Research (NIHR) and the NewLife Foundation. The Baralle lab is supported by NIHR Research Professorship to D.B. (RP-2016-07-011).published version, accepted version (6 month embargo), submitted versio

Confirmation of TNIP1 and IL23A as susceptibility loci for psoriatic arthritis

Objectives: To investigate a shared genetic aetiology for skin involvement in psoriasis and psoriatic arthritis (PsA) by genotyping single-nucleotide polymorphisms (SNPs), reported to be associated in genome-wide association studies of psoriasis, in patients with PsA. Methods: SNPs with reported evidence for association with psoriasis were genotyped in a PsA case and control collection from the UK and Ireland. Genotype and allele frequencies were compared between PsA cases and controls using the Armitage test for trend. Results: Seven SNPs mapping to the IL1RN, TNIP1, TNFAIP3, TSC1, IL23A, SMARCA4 and RNF114 genes were successfully genotyped. The IL23A and TNIP1 genes showed convincing evidence for association (rs2066808, p = 9.1 x 10 ?7 ; rs17728338, p = 3.5 x 10 ?5 , respectively) whilst SNPs mapping to the TNFAIP3, TSC1 and RNF114 genes showed nominal evidence for association (rs610604, p = 0.03; rs1076160, p = 0.03; rs495337, p = 0.0025). No evidence for association with IL1RN or SMARCA4 was found but the power to detect association was low. Conclusions: SNPs mapping to previously reported psoriasis loci show evidence for association to PSA, thus supporting the hypothesis that the genetic aetiology of skin involvement is the same in both PsA and psoriasi