A Model of Firms' Decisions to Export or Produce Abroad

This paper is a theoretical analysis of the factors influencing production location decisions by a multinational corporation. It starts with a simple model of optimization for a firm facing the choice between exporting and producing abroad a single differentiated final product and then develops the model to take account of production of intermediate as well as final products, the existence of scale economies, and finally, the effects of transport cost and of factors affecting the cost of production. The share of foreign output is shown to be related to the level of transport cost, to the size of host-country markets, to host-country wage levels relative to those of the home country, in combination with labor intensities of production. All of these relationships in turn are shown to interact in various ways with economies of scale in affecting the choice of production locations.

Patient‐Reported Outcomes From a Two‐Year Head‐to‐Head Comparison of Subcutaneous Abatacept and Adalimumab for Rheumatoid Arthritis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/122413/1/acr22763_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/122413/2/acr22763.pd

Reductions in disease activity in the AMPLE trial: clinical response by baseline disease duration

Objectives: To evaluate clinical response by baseline disease duration using 2-year data from the AMPLE trial. Methods: Patients were randomised to subcutaneous abatacept 125 mg weekly or adalimumab 40 mg biweekly, with background methotrexate. As part of a post hoc analysis, the achievement of validated definitions of remission (Clinical Disease Activity Index (CDAI) ≤2.8, Simplified Disease Activity Index (SDAI) ≤3.3, Routine Assessment of Patient Index Data 3 (RAPID3) ≤3.0, Boolean score ≤1), low disease activity (CDAI \u3c10, SDAI \u3c11, RAPID3 ≤6.0), Health Assessment Questionnaire-Disability Index response and American College of Rheumatology responses were evaluated by baseline disease duration (≤6 vs \u3e6 months). Disease Activity Score 28 (C-reactive protein) \u3c2.6 or ≤3.2 and radiographic non-progression in patients achieving remission were also evaluated. Results: A total of 646 patients were randomised and treated (abatacept, n=318; adalimumab, n=328). In both treatment groups, comparable responses were achieved in patients with early rheumatoid arthritis (≤6 months) and in those with later disease (\u3e6 months) across multiple clinical measures Conclusions: Abatacept or adalimumab with background methotrexate were associated with similar onset and sustainability of response over 2 years. Patients treated early or later in the disease course achieved comparable clinical responses

Pooled analysis of TNF inhibitor biosimilar studies comparing radiographic progression by disease activity states in rheumatoid arthritis

Objective: To evaluate the relationship between disease activity and radiographic progression in rheumatoid arthritis, three phase III studies of SB4, SB2 and SB5 (biosimilars of etanercept, infliximab and adalimumab) were pooled to assess radiographic progression by disease activity status. Methods: Patients from each study with radiographic data were pooled and grouped based on disease activity state (remission, low disease activity (LDA), moderate disease activity (MDA) and high disease activity (HDA)), determined by disease activity score based on 28-joint count (DAS28) per erythrocyte sedimentation rate, Simplified Disease Activity Index (SDAI) and Clinical Disease Activity Index (CDAI) at different time points. Mean change in modified Total Sharp Score (mTSS) and the proportion of radiographic non-progressors of higher disease activity groups (LDA, MDA and HDA) in reference to remission were summarised descriptively, with comparison of ORs using logistic models. Results: 1265 patients were included. In all treatments combined, the 1 year mean change in mTSS was 0.03, 0.4, 0.3 and 1.3 and proportion of radiographic non-progressors was 79.8%, 78.1%, 74.1% and 58.4% in the week 24/30 DAS28-determined remission, LDA, MDA and HDA groups, respectively. ORs (95% CIs) of the proportion of non-progressors were lowest in the HDA group in reference to remission (0.35 (0.23 to 0.54)), followed by MDA (0.72 (0.50 to 1.05)) and LDA (0.90 (0.55 to 1.48)) groups. Similar trends were observed when disease activity was assessed using SDAI or CDAI. Conclusion: A pooled analysis of radiographic assessment data from three biosimilar studies showed that radiographic progression is small overall but increases with worse disease activity. Trial registration numbers: NCT01895309, NCT01936181 and NCT0216713

Default mode network segregation and social deficits in autism spectrum disorder: Evidence from non-medicated children DMN in children with ASD

AbstractFunctional pathology of the default mode network is posited to be central to social-cognitive impairment in autism spectrum disorders (ASD). Altered functional connectivity of the default mode network's midline core may be a potential endophenotype for social deficits in ASD. Generalizability from prior studies is limited by inclusion of medicated participants and by methods favoring restricted examination of network function. This study measured resting-state functional connectivity in 22 8–13 year-old non-medicated children with ASD and 22 typically developing controls using seed-based and network segregation functional connectivity methods. Relative to controls the ASD group showed both under- and over-functional connectivity within default mode and non-default mode regions, respectively. ASD symptoms correlated negatively with the connection strength of the default mode midline core—medial prefrontal cortex–posterior cingulate cortex. Network segregation analysis with the participation coefficient showed a higher area under the curve for the ASD group. Our findings demonstrate that the default mode network in ASD shows a pattern of poor segregation with both functional connectivity metrics. This study confirms the potential for the functional connection of the midline core as an endophenotype for social deficits. Poor segregation of the default mode network is consistent with an excitation/inhibition imbalance model of ASD

The Influence of Polygenic Risk Scores on Heritability of Anti-CCP Level in RA

Objective: To study genetic factors that influence quantitative anti-cyclic citrullinated peptide (anti-CCP) antibody levels in RA patients. Methods: We carried out a genome wide association study (GWAS) meta-analysis using 1,975 anti-CCP+ RA patients from 3 large cohorts, the Brigham Rheumatoid Arthritis Sequential Study (BRASS), North American Rheumatoid Arthritis Consortium (NARAC), and the Epidemiological Investigation of RA (EIRA). We also carried out a genome-wide complex trait analysis (GCTA) to estimate the heritability of anti-CCP levels. Results: GWAS-meta analysis showed that anti-CCP levels were most strongly associated with the human leukocyte antigen (HLA) region with a p-value of 2×10−11 for rs1980493. There were 112 SNPs in this region that exceeded the genome-wide significance threshold of 5×10−8, and all were in linkage disequilibrium (LD) with the HLA- DRB1*03 allele with LD r2 in the range of 0.25-0.88. Suggestive novel associations outside of the HLA region were also observed for rs8063248 (near the GP2 gene) with a p-value of 3×10−7. None of the known RA risk alleles (~52 loci) were associated with anti-CCP level. Heritability analysis estimated that 44% of anti-CCP variation was attributable to genetic factors captured by GWAS variants. Conclusions: Anti-CCP level is a heritable trait. HLA-DR3 and GP2 are associated with lower anti-CCP levels

Automatic Prediction of Rheumatoid Arthritis Disease Activity from the Electronic Medical Records

Objective: We aimed to mine the data in the Electronic Medical Record to automatically discover patients' Rheumatoid Arthritis disease activity at discrete rheumatology clinic visits. We cast the problem as a document classification task where the feature space includes concepts from the clinical narrative and lab values as stored in the Electronic Medical Record. Materials and Methods The Training Set consisted of 2792 clinical notes and associated lab values. Test Set 1 included 1749 clinical notes and associated lab values. Test Set 2 included 344 clinical notes for which there were no associated lab values. The Apache clinical Text Analysis and Knowledge Extraction System was used to analyze the text and transform it into informative features to be combined with relevant lab values. Results: Experiments over a range of machine learning algorithms and features were conducted. The best performing combination was linear kernel Support Vector Machines with Unified Medical Language System Concept Unique Identifier features with feature selection and lab values. The Area Under the Receiver Operating Characteristic Curve (AUC) is 0.831 (σ = 0.0317), statistically significant as compared to two baselines (AUC = 0.758, σ = 0.0291). Algorithms demonstrated superior performance on cases clinically defined as extreme categories of disease activity (Remission and High) compared to those defined as intermediate categories (Moderate and Low) and included laboratory data on inflammatory markers. Conclusion: Automatic Rheumatoid Arthritis disease activity discovery from Electronic Medical Record data is a learnable task approximating human performance. As a result, this approach might have several research applications, such as the identification of patients for genome-wide pharmacogenetic studies that require large sample sizes with precise definitions of disease activity and response to therapies