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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Predictors of high healthcare resource utilization and liver disease progression among patients with chronic hepatitis C
|
|---|---|
| Published in |
Journal of Medical Economics, January 2016
|
| DOI | 10.3111/13696998.2015.1127252 |
| Pubmed ID | |
| Authors |
Joyce LaMori, Neeta Tandon, François Laliberté, Guillaume Germain, Dominic Pilon, Patrick Lefebvre, Avinash Prabhakar |
| Abstract |
Since hepatitis C virus therapy is typically prioritized for patients with more advanced disease, predicting which patients will progress could help direct scarce resources to those likely to benefit most. This study aims to identify demographics and clinical characteristics associated with high healthcare resource utilization (HRU) and liver disease progression among CHC patients. Using health insurance claims (01/2001-03/2013), adult patients with ≥2 CHC claims (ICD-9-CM: 070.44 or 070.54), and ≥6 months of continuous insurance coverage before and ≥36 months after the first CHC diagnosis were included. Patients with human immunodeficiency virus were excluded. Generalized estimating equations were used to identify the demographic and clinical characteristics of being in the 20% of patients with the highest HRU. Factors predicting liver disease progression were also identified. In the study population (N=4,898), liver disease severity and both CHC- and non-CHC-related comorbidities and conditions were strong predictors of high healthcare costs, with odds ratios (ORs; 95% confidence interval [CI]) for ≥2 CHC-related, and ≥2 non-CHC-related comorbidities/conditions 2.78 (2.48-3.12), and 2.19 (1.76-2.72), respectively. CHC- and non-CHC-related comorbidities and conditions were also strong predictors of liver disease progression with ORs (95% CI) for ≥2 CHC-related and ≥2 non-CHC-related comorbidities and conditions of 2.18 (1.83-2.60) and 1.50 (1.14-1.97), respectively. Potential inaccuracies in claims data, information or classification bias, and findings based on a privately insured population. This study suggests that CHC patients with high healthcare resource utilization have a high level of comorbidity at baseline and also that non-CHC comorbidities and conditions are strong predictors of high HRU. Non-cirrhotic CHC patients with one or more comorbidities are at high risk of progressing to cirrhosis or end-stage liver disease. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 7% |
| Canada | 1 | 3% |
| Unknown | 26 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 31% |
| Student > Master | 5 | 17% |
| Researcher | 3 | 10% |
| Professor > Associate Professor | 2 | 7% |
| Librarian | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 6 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 10 | 34% |
| Nursing and Health Professions | 3 | 10% |
| Economics, Econometrics and Finance | 3 | 10% |
| Agricultural and Biological Sciences | 1 | 3% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
| Other | 2 | 7% |
| Unknown | 9 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 January 2016.
All research outputs
#15,351,847
of 22,835,198 outputs
Outputs from Journal of Medical Economics
#823
of 1,473 outputs
Outputs of similar age
#231,688
of 394,937 outputs
Outputs of similar age from Journal of Medical Economics
#19
of 35 outputs
Altmetric has tracked 22,835,198 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,473 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 394,937 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.