Negative symptoms are a key feature of several psychiatric disorders. Difficulty identifying common neurobiological mechanisms that cut across diagnostic boundaries might result from equifinality (i.e., multiple mechanistic pathways to the same clinical profile), both within and across disorders. This study used a data-driven approach to identify unique subgroups of participants with distinct reward processing profiles to determine which profiles predicted negative symptoms.

Participants were a transdiagnostic sample of youth from a multisite study of psychosis risk, including 110 individuals at clinical high-risk for psychosis (CHR; meeting psychosis-risk syndrome criteria), 88 help-seeking participants who failed to meet CHR criteria and/or who presented with other psychiatric diagnoses, and a reference group of 66 healthy controls. Participants completed clinical interviews and behavioral tasks assessing four reward processing constructs indexed by the RDoC Positive Valence Systems: hedonic reactivity, reinforcement learning, value representation, and effort–cost computation.

k-means cluster analysis of clinical participants identified three subgroups with distinct reward processing profiles, primarily characterized by: a value representation deficit (54%), a generalized reward processing deficit (17%), and a hedonic reactivity deficit (29%). Clusters did not differ in rates of clinical group membership or psychiatric diagnoses. Elevated negative symptoms were only present in the generalized deficit cluster, which also displayed greater functional impairment and higher psychosis conversion probability scores.

Contrary to the equifinality hypothesis, results suggested one global reward processing deficit pathway to negative symptoms independent of diagnostic classification. Assessment of reward processing profiles may have utility for individualized clinical prediction and treatment.

Understanding patient preferences and the demand for healthcare interventions and technology is critical for health technology assessment (HTA). New health technologies have potential for savings and increased efficiency but even the most cost-effective and efficacious interventions can fail if patient preferences are not properly accounted for. Patient preferences in HTA are primarily limited to representation in appraisal committees; however, more robust methods are available and should be incorporated into the assessment of interventions.

Using data from three discrete choice experiments (DCEs), we reflect on the importance of patient preferences in the design of healthcare interventions. We draw insights from three studies which investigated preferences relating to HIV self-testing amongst long distance truck drivers in Kenya; differentiated antiretroviral therapy services amongst stable HIV patients in Zimbabwe; and tuberculosis preventive therapy for children in Eswatini.

We highlight three key findings. First, understanding patient preferences is crucial when designing services, and providers sometimes underestimate behavioural barriers and overestimate the extent to which people are motivated simply by health benefits. Optimism is often driven by evidence showing high acceptability, but when preference structures are incorporated in intervention design, there are important insights into how patients plan to utilize services. Second, trade-offs matter in determining which characteristics are perceived to be most important to patients – a key strength of the DCE methodology. Understanding of these trade-offs can help prioritize which characteristics of interventions to target. Finally, disentangling the effect of different characteristics of service delivery models on preferences is important for rethinking how interventions are delivered. If services are designed to better align with preferences, implementers can ensure new interventions have the desired effect on health and economic outcomes.

These findings highlight the value of behavioural economic approaches for investigating preferences for health interventions and providing insights into the demand for services, which must feed into the HTA analyses. Incorporating DCEs into HTA is inexpensive and provides robust data for improving HTA.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Surgical site infections (SSIs) are common surgical complications that lead to increased costs. Depending on payer type, however, they do not necessarily translate into deficits for every hospital.

We investigated how surgical site infections (SSIs) influence the contribution margin in 2 reimbursement systems based on diagnosis-related groups (DRGs).

This preplanned observational health cost analysis was nested within a Swiss multicenter randomized controlled trial on the timing of preoperative antibiotic prophylaxis in general surgery between February 2013 and August 2015. A simulation of cost and income in the National Health Service (NHS) England reimbursement system was conducted.

Of 5,175 patients initially enrolled, 4,556 had complete cost and income data as well as SSI status available for analysis. SSI occurred in 228 of 4,556 of patients (5%). Patients with SSIs were older, more often male, had higher BMIs, compulsory insurance, longer operations, and more frequent ICU admissions. SSIs led to higher hospital cost and income. The median contribution margin was negative in cases of SSI. In SSI cases, median contribution margin was Swiss francs (CHF) −2045 (IQR, −12,800 to 4,848) versus CHF 895 (IQR, −2,190 to 4,158) in non-SSI cases. Higher ASA class and private insurance were associated with higher contribution margins in SSI cases, and ICU admission led to greater deficits. Private insurance had a strong increasing effect on contribution margin at the 10th, 50th (median), and 90th percentiles of its distribution, leading to overall positive contribution margins for SSIs in Switzerland. The NHS England simulation with 3,893 patients revealed similar but less pronounced effects of SSI on contribution margin.

Depending on payer type, reimbursement systems with DRGs offer only minor financial incentives to the prevention of SSI.