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Measuring distance through dense weighted networks: The case of hospital-associated pathogens.

ABSTRACT: Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the network. We determine the optimal way to measure the distance between hospitals within the network, by reconstructing the English hospital network based on shared patients in 2014-2015, and simulating the spread of a hospital-associated pathogen between hospitals, taking into consideration that each intermediate hospital conveys a delay in the further spread of the pathogen. While the risk of transferring a hospital-associated pathogen between directly neighbouring hospitals is a direct reflection of the number of shared patients, the distance between two hospitals far-away in the network is determined largely by the number of intermediate hospitals in the network. Because the network is dense, most long distance transmission chains in fact involve only few intermediate steps, spreading along the many weak links. The dense connectivity of hospital networks, together with a strong regional structure, causes hospital-associated pathogens to spread from the initial outbreak in a two-step process: first, the directly surrounding hospitals are affected through the strong connections, second all other hospitals receive introductions through the multitude of weaker links. Although the strong connections matter for local spread, weak links in the network can offer ideal routes for hospital-associated pathogens to travel further faster. This hold important implications for infection prevention and control efforts: if a local outbreak is not controlled in time, colonised patients will appear in other regions, irrespective of the distance to the initial outbreak, making import screening ever more difficult.

SUBMITTER: Donker T

PROVIDER: S-EPMC5542422 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Measuring distance through dense weighted networks: The case of hospital-associated pathogens.

Donker Tjibbe T Smieszek Timo T Henderson Katherine L KL Johnson Alan P AP Walker A Sarah AS Robotham Julie V JV

PLoS computational biology 20170803 8

Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the ne ...[more]

PMID: 28771581

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Project description:BackgroundMultiple sclerosis (MS) is a complex disease in which the immune system attacks the central nervous system. The molecular mechanisms contributing to the etiology of MS remain poorly understood. Genome-wide association studies (GWAS) of MS have identified a small number of genetic loci significant at the genome level, but they are mainly non-coding variants. Network-assisted analysis may help better interpret the functional roles of the variants with association signals and potential translational medicine application. The Dense Module Searching of GWAS tool (dmGWAS version 2.4) developed in our team is applied to 2 MS GWAS datasets (GeneMSA and IMSGC GWAS) using the human protein interactome as the reference network. A dual evaluation strategy is used to generate results with reproducibility.ResultsApproximately 7500 significant network modules were identified for each independent GWAS dataset, and 20 significant modules were identified from the dual evaluation. The top modules included GRB2, HDAC1, JAK2, MAPK1, and STAT3 as central genes. Top module genes were enriched with functional terms such as "regulation of glial cell differentiation" (adjusted p-value = 2.58 × 10- 3), "T-cell costimulation" (adjusted p-value = 2.11 × 10- 6) and "virus receptor activity" (adjusted p-value = 1.67 × 10- 3). Interestingly, top gene networks included several MS FDA approved drug target genes HDAC1, IL2RA, KEAP1, and RELA, CONCLUSIONS: Our dmGWAS network analyses highlighted several genes (GRB2, HDAC1, IL2RA, JAK2, KEAP1, MAPK1, RELA and STAT3) in top modules that are promising to interpret GWAS signals and link to MS drug targets. The genes enriched with glial cell differentiation are important for understanding neurodegenerative processes in MS and for remyelination therapy investigation. Importantly, our identified genetic signals enriched in T cell costimulation and viral receptor activity supported the viral infection onset hypothesis for MS.

Project description:ImportanceStudies of low-value care have focused on the prevalence of low-value care interventions but have rarely quantified downstream consequences of these interventions for patients or the health care system.ObjectiveTo measure immediate in-hospital harm associated with 7 low-value procedures.Design, setting, and participantsA cohort study with a descriptive analysis using hospital admission data from 225 public hospitals in New South Wales, Australia, was conducted from July 1, 2014, to June 30, 2017. All 9330 episodes involving 1 of 7 low-value procedures were evaluated, including endoscopy for dyspepsia in people younger than 55 years (3689 episodes); knee arthroscopy for osteoarthritis or meniscal tears (3963 episodes); colonoscopy for constipation in people younger than 50 years (665 episodes); endovascular repair of abdominal aortic aneurysm in asymptomatic, high-risk patients (508 episodes); carotid endarterectomy in asymptomatic, high-risk patients (273 episodes); renal artery angioplasty (176 episodes); and spinal fusion for uncomplicated low back pain (56 episodes). Sixteen hospital-acquired complications (HACs) were used as a measure of harm associated with low-value care.Main outcomes and measuresFor each low-value procedure, the percentage associated with any HAC and the difference in mean length of stay for patients receiving low-value care with and without HACs were calculated.ResultsAcross the 225 hospitals and 9330 episodes of low-value care, rates of HACs were low for low-value endoscopy (4 [0.1%] episodes; 95% CI, 0.02%-0.2%), knee arthroscopy (18 [0.5%] episodes; 95% CI, 0.2%-0.7%), and colonoscopy (2 [0.3%] episodes; 95% CI, 0.0%-0.9%) but higher for low-value spinal fusion (4 [7.1%] episodes; 95% CI, 2.2%-11.5%), endovascular repair of abdominal aortic aneurysm (76 [15.0%] episodes; 95% CI, 11.1%-19.7%), carotid endarterectomy (21 [7.7%] episodes; 95% CI, 5.2%-10.1%), and renal artery angioplasty (15 [8.5%] episodes; 95% CI, 5.8%-11.5%). For most procedures, the most common HAC was health care-associated infection, which accounted for 83 (26.3%) (95% CI, 21.8%-31.5%) of all HACs observed. The highest rate of health care-associated infection was 8.4% (95% CI, 5.2%-11.4%) for renal artery angioplasty. For all 7 low-value procedures, median length of stay for patients with an HAC was 2 times or more the median length of stay for patients without a complication. For example, median length of stay was 1 (interquartile range [IQR], 1-1) day for knee arthroscopy with no HACs but increased to 10.5 (IQR, 1.0-21.3) days for patients with an HAC.Conclusions and relevanceThese findings suggest that use of these 7 procedures in patients who probably should not receive them is harming some of those patients, consuming additional hospital resources, and potentially delaying care for other patients for whom the services would be appropriate. Although only some immediate consequences of just 7 low-value services were examined, harm related to all low-value procedures was noted, including high rates of harm for certain higher-risk procedures. The full burden of low-value care for patients and the health system is yet to be quantified.

Dataset Information

Measuring distance through dense weighted networks: The case of hospital-associated pathogens.

Publications

Measuring distance through dense weighted networks: The case of hospital-associated pathogens.

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