Economic burden attributable to healthcare-associated infections at western China hospitals: 6 Year, prospective cohort study.

OBJECTIVES: Healthcare-associated infections (HAIs) represent a major threat to patient safety and are associated with significant economic burden. Calculating the costs attributable to HAIs is challenging given the various sources of bias. Although HAIs as a reasonably preventable medical harm should have been closely linked to medical insurance incentives, there was little linkage between HAIs and medicare in western China owing to the lack of economic evaluation data. The present study aimed to generate estimates of the attributable costs associated with HAIs and the magnitude of costs growth. METHODS: In this cohort study designed horizontally and vertically from 2016 to 2022, we compared outcomes of randomly sampling patients with HAIs and individually matched patients without HAIs in two cohorts at a 6-year interval at 34 hospitals in western China. The primary outcome was the direct medical cost for the entire hospital stay, converted to US dollars ($ for the benchmark year), discounted at 3% annually, and estimated separately in the full analysis set (FAS) and the per protocol set (PPS). We used multiple linear regression to adjust the discounted costs and to assess subgroups effects within each cohort. We nested a dynamic vertical comparison of costs attributable to HAIs between the front and rear cohorts. RESULTS: A total of 230 patients with HAIs in 2016 and 204 patients with HAIs in 2022 were enrolled. After a 1:1 match, all 431 pairs were recruited as FAS, of which 332 pairs as PPS met all matching restrictions. Compared to the 2016 cohort in FAS, the patients with HAIs in 2022 had a significantly older age (64.40 ± 16.45 years), higher repeat hospitalization rate (65 [32.02%] of 203), and lower immune function (69 [33.99%] of 203). The discounted costs and adjusted-discounted costs for patients with HAIs in the 2022 cohort were found to be significantly higher than those of patients without HAIs (discounted costs: $5484.60 [IQR 8426.03] vs $2554.04(4530.82), P < 0.001; adjusted-discounted costs: $5235.90 [3772.12] vs $3040.21(1823.36), P < 0.001, respectively), and also higher than those of patients with HAIs in the 2016 cohort (discounted costs: $5484.60 [8426.03] vs $3553.00 [6127.79], P < 0.001; adjusted-discounted costs: $5235.90 [3772.12] vs $3703.82 [3159.14], P < 0.001, respectively). In vertical comparison of PPS, the incremental costs of the 2022 cohort are 1.48 times higher than those of the 2016 cohort ($964.63(4076.15) vs $652.43 [2533.44], P = 0.084). CONCLUSIONS: This meticulously designed study in western China has successfully and accurately examined the economic burden attributable to HAIs. Their rapidly increasing tendency poses a serious challenge to patients, hospitals, and the medical insurance. A closer linkage between HAIs and ongoing motivating system changes is urgently needed in western China.

Prioritization of risk genes in multiple sclerosis by a refined Bayesian framework followed by tissue-specificity and cell type feature assessment.

BACKGROUND: Multiple sclerosis (MS) is a debilitating immune-mediated disease of the central nervous system that affects over 2 million people worldwide, resulting in a heavy burden to families and entire communities. Understanding the genetic basis underlying MS could help decipher the pathogenesis and shed light on MS treatment. We refined a recently developed Bayesian framework, Integrative Risk Gene Selector (iRIGS), to prioritize risk genes associated with MS by integrating the summary statistics from the largest GWAS to date (n = 115,803), various genomic features, and gene-gene closeness. RESULTS: We identified 163 MS-associated prioritized risk genes (MS-PRGenes) through the Bayesian framework. We replicated 35 MS-PRGenes through two-sample Mendelian randomization (2SMR) approach by integrating data from GWAS and Genotype-Tissue Expression (GTEx) expression quantitative trait loci (eQTL) of 19 tissues. We demonstrated that MS-PRGenes had more substantial deleterious effects and disease risk. Moreover, single-cell enrichment analysis indicated MS-PRGenes were more enriched in activated macrophages and microglia macrophages than non-activated ones in control samples. Biological and drug enrichment analyses highlighted inflammatory signaling pathways. CONCLUSIONS: In summary, we predicted and validated a high-confidence MS risk gene set from diverse genomic, epigenomic, eQTL, single-cell, and drug data. The MS-PRGenes could further serve as a benchmark of MS GWAS risk genes for future validation or genetic studies.