Propensity score weighting methods are often used in non-randomized studies to adjust for confounding and assess treatment effects. The most popular among them, the inverse probability weighting, assigns weights that are proportional to the inverse of the conditional probability of a specific treatment assignment, given observed covariates. A key requirement for inverse probability weighting estimation is the positivity assumption, i.e. the propensity score must be bounded away from 0 and 1. In practice, violations of the positivity assumption often manifest by the presence of limited overlap in the propensity score distributions between treatment groups. When these practical violations occur, a small number of highly influential inverse probability weights may lead to unstable inverse probability weighting estimators, with biased estimates and large variances. To mitigate these issues, a number of alternative methods have been proposed, including inverse probability weighting trimming, overlap weights, matching weights, and entropy weights. Because overlap weights, matching weights, and entropy weights target the population for whom there is equipoise (and with adequate overlap) and their estimands depend on the true propensity score, a common criticism is that these estimators may be more sensitive to misspecifications of the propensity score model. In this paper, we conduct extensive simulation studies to compare the performances of inverse probability weighting and inverse probability weighting trimming against those of overlap weights, matching weights, and entropy weights under limited overlap and misspecified propensity score models. Across the wide range of scenarios we considered, overlap weights, matching weights, and entropy weights consistently outperform inverse probability weighting in terms of bias, root mean squared error, and coverage probability.

IntroductionIn many malaria-endemic countries, the private retail sector is a major source of antimalarial drugs. However, the rarity of malaria diagnostic testing in the retail sector leads to overuse of the first-line class of antimalarial drugs known as artemisinin-combination therapies (ACTs). The goal of this study was to identify the combination of malaria rapid diagnostic test (RDT) and ACT subsidies that maximises the proportion of clients seeking care in a retail outlet that choose to purchase an RDT (RDT uptake) and use ACTs appropriately.Methods842 clients seeking care in 12 select retail outlets in western Kenya were recruited and randomised into 4 arms of different combinations of ACT and RDT subsidies, with ACT subsidies conditional on a positive RDT. The outcomes were RDT uptake (primary) and appropriate and targeted ACT use (secondary). Participants’ familiarity with RDTs and their confidence in test results were also evaluated.ResultsRDT uptake was high (over 96%) across the study arms. Testing uptake was 1.025 times higher (98% CI 1.002 to 1.049) in the RDT subsidised arms than in the unsubsidised groups. Over 98% of clients were aware of malaria testing, but only 35% had a previous experience with RDTs. Nonetheless, confidence in the accuracy of RDTs was high. We found high levels of appropriate use and targeting of ACTs, with 86% of RDT positives taking an ACT, and 93.4% of RDT negatives not taking an ACT. The conditional ACT subsidy did not affect the RDT test purchasing behaviour (risk ratio: 0.994; 98% CI 0.979 to 1.009).ConclusionTest dependent ACT subsidies may contribute to ACT targeting. However, in this context, high confidence in the accuracy of RDTs and reliable supplies of RDTs and ACTs likely played a greater role in testing uptake and adherence to test results.

Cellular differentiation is associated with the acquisition of a unique protein signature which is essential to attain the ultimate cellular function and activity of the differentiated cell. This is predicted to result in unique biosynthetic demands that arise during differentiation. Using a bioinformatic approach, we discovered osteoblast differentiation is associated with increased demand for the amino acid proline. When compared to other differentiated cells, osteoblast-associated proteins including RUNX2, OSX, OCN and COL1A1 are significantly enriched in proline. Using a genetic and metabolomic approach, we demonstrate that the neutral amino acid transporter SLC38A2 acts cell autonomously to provide proline to facilitate the efficient synthesis of proline-rich osteoblast proteins. Genetic ablation of SLC38A2 in osteoblasts limits both osteoblast differentiation and bone formation in mice. Mechanistically, proline is primarily incorporated into nascent protein with little metabolism observed. Collectively, these data highlight a requirement for proline in fulfilling the unique biosynthetic requirements that arise during osteoblast differentiation and bone formation.