Humanitarian organizations (HOs) increasingly look to engage private sector supply chains in achieving outcomes. The right engagement approach may require knowledge of agents' preferences across multi-echelon supply chains to align private sector value creation with humanitarian outcomes. We propose a multi-attribute value analysis (MAVA) framework to elucidate such preferences. We formalize this approach and apply it in collaboration with a HO pilot aiming to facilitate better private sector availability of malaria rapid diagnostic tests in Uganda. We demonstrate how HOs could use criteria weights and value functions from MAVA for project evaluation; in the process, we reveal business model insights for importers, distributors, and retailers in the pilot. We also show how MAVA facilitates the impact assessment of hypothetical options (i.e., combinations of products, services, and subsidies) to guide HO resource deployment. It is important to note that specific insights and assessments, developed to illustrate applications of the approach, are drawn from a single case study and require further validation. This paper offers the first attempt, to our knowledge, to develop quantitative measures for economic and non-economic objectives involving all agents in a multiechelon supply chain, either humanitarian or commercial. We hope that this initial step stimulates further research to validate results and develop the framework proposed.

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Background: Human plasma contains a wide variety of circulating proteins. These proteins can be important clinical biomarkers in disease and also possible drug targets. Large scale genomics studies of circulating proteins can identify genetic variants that lead to relative protein abundance. Methods: We conducted a meta-analysis on genome-wide association studies of autosomal chromosomes in 22,997 individuals of primarily European ancestry across 12 cohorts to identify protein quantitative trait loci (pQTL) for 92 cardiometabolic associated plasma proteins. Results: We identified 503 (337 cis and 166 trans) conditionally independent pQTLs, including several novel variants not reported in the literature. We conducted a sex-stratified analysis and found that 118 (23.5%) of pQTLs demonstrated heterogeneity between sexes. The direction of effect was preserved but there were differences in effect size and significance. Additionally, we annotate trans-pQTLs with nearest genes and report plausible biological relationships. Using Mendelian randomization, we identified causal associations for 18 proteins across 19 phenotypes, of which 10 have additional genetic colocalization evidence. We highlight proteins associated with a constellation of cardiometabolic traits including angiopoietin-related protein 7 (ANGPTL7) and Semaphorin 3F (SEMA3F). Conclusion: Through large-scale analysis of protein quantitative trait loci, we provide a comprehensive overview of common variants associated with plasma proteins. We highlight possible biological relationships which may serve as a basis for further investigation into possible causal roles in cardiometabolic diseases.

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Introduction: Cardiovascular disease is the leading cause of maternal mortality. The hemodynamic changes that occur during pregnancy make this a particularly vulnerable time for women with heart disease. Additionally, it is known that social determinants have an effect on certain outcomes in pregnancy, although research to quantify this effect is limited. We compared demographics and outcomes for women in upper- and lower-income brackets based on zip codes. Methods: We performed a retrospective cohort study of high-risk pregnant patients with cardiac diagnoses between November 2010 and June 2019. Patients were stratified into upper- and lower-income based on median household income in their zip code (2018 U.S. census). Results: We studied 191 pregnancies. Patients were stratified by zip code into lower (<$118,201/yr, N = 95) and upper median household income (N = 96) groups (Table 1). Women in the lower income bracket had more antepartum hospitalizations (38.3% vs 17.9%), were younger (30.6 vs 33.9 years), Hispanic (42.1% vs 10.4%), and more likely to have public insurance (46.8% vs 21.3%). There was a difference in cardiac diagnoses between the two groups; those with lower income had more structural heart disease (41.1% vs 17.7%) and fewer arrhythmias (25.3% vs 39.6%). In the lower income group, there were 2 maternal deaths and 1 neonatal death before discharge, while in the upper income there was 1 neonatal death. Conclusions: Our study examined the relationship between median income per zip code and pregnancy outcomes, and demographics in women with heart disease. Our observations demonstrate a significant difference in maternal age, race, distribution of cardiac diagnoses, and antepartum hospitalizations. Despite all women being treated at the same facility, antepartum hospitalizations differed based on income bracket. Social determinants of health are important factors that impact outcomes in the cardiac-obstetric population and require further investigation.