scholarly journals Syndemic profiles of people living with hepatitis C virus using population-level latent class analysis to optimize health services

2020 ◽  
Vol 100 ◽  
pp. 27-33
Author(s):  
Emilia Clementi ◽  
Sofia Bartlett ◽  
Michael Otterstatter ◽  
Jane A. Buxton ◽  
Stanley Wong ◽  
...  
Keyword(s):  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Health Services ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Population Level ◽  
Class Analysis ◽  
Optimize Health

Using latent class analysis to identify patterns of hepatitis C service provision in drug-free treatment programs in the U.S.

2006 ◽  
Vol 83 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Shiela M. Strauss ◽  
David M. Rindskopf ◽  
Janetta M. Astone-Twerell ◽  
Don C. Des Jarlais ◽  
Holly Hagan
Keyword(s):  
Hepatitis C ◽  
Latent Class Analysis ◽  
Service Provision ◽  
Latent Class ◽  
Treatment Programs ◽  
Class Analysis ◽  
Free Treatment ◽  
Drug Free ◽  
The U.S

Variation in Treatment Priorities for Chronic Hepatitis C: A Latent Class Analysis

2015 ◽  
Vol 9 (3) ◽  
pp. 241-249 ◽  
Author(s):  
Liana Fraenkel ◽  
Joseph Lim ◽  
Guadalupe Garcia-Tsao ◽  
Valerie Reyna ◽  
Alexander Monto ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis C ◽  
Latent Class Analysis ◽  
Chronic Hepatitis C ◽  
Latent Class ◽  
Class Analysis ◽  
Treatment Priorities

Chronic fatigue in a population sample: definitions and heterogeneity

2005 ◽  
Vol 35 (9) ◽  
pp. 1337-1348 ◽  
Author(s):  
PATRICK F. SULLIVAN ◽  
NANCY L. PEDERSEN ◽  
ANDREAS JACKS ◽  
BIRGITTA EVENGÅRD
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Population Sample ◽  
Empirical Support ◽  
Population Level ◽  
Chronic Fatigue ◽  
Case Definition ◽  
Class Analysis ◽  
Analytic Group ◽  
Fatigue Syndrome

Background. Numerous nosological decisions are made when moving from the common human symptom of unusual fatigue to the rare chronic fatigue syndrome (CFS). These decisions have infrequently been subjected to rigorous evaluation.Method. We obtained telephone interview data on fatiguing symptoms from 31406 individuals twins in the Swedish Twin Registry aged 42–64 years; 5330 subjects who endorsed fatigue and possessed no exclusionary condition formed the analytic group. We evaluated the definition and classification of CFS-like illness using graphical methods, regression models, and latent class analysis.Results. Our results raise fundamental questions about the 1994 Centers for Disease Control criteria as (1) there was no empirical support for the requirement of four of eight cardinal CFS symptoms; (2) these eight symptoms were not equivalent in their capacity to predict fatigue; and (3) no combination of symptoms was markedly more heritable. Critically, latent class analysis identified a syndrome strongly resembling CFS-like illness.Conclusions. Our data are consistent with the ‘existence’ of CFS-like illness although the dominant nosological approach captures population-level variation poorly. We suggest that studying a more parsimonious case definition – impairing chronic fatigue not due to a known cause – would represent a way forward.

scholarly journals Classifying Patients Seen at an Urban Healthcare for the Homeless Site: A Clinically-Driven Latent Class Analysis

2021 ◽  
Author(s):  
Matthew Mitchell ◽  
Brian Chan ◽  
Caroline King ◽  
Miles Sledd ◽  
David Dorr ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Population Level ◽  
Low Complexity ◽  
The United States ◽  
Urban Setting ◽  
Class Analysis ◽  
Feed Forward Neural Network ◽  
Final Model ◽  
Service Staff

Abstract Introduction: Federally qualified health centers (FQHC) provide care to over 28 million people in the United States, primarily serving people with low incomes who are underinsured or not insured. FQHC patients have different patterns of illness than non-FQHC populations, which may require tailored interventions to support at the population level. Model based segmentation methods can identify patterns of need but may generate spurious results without clinical context. Here, we used stakeholder feedback combined with machine learning methods to identify subgroups of patients seen by a large FQHC in an urban setting. Methods: We used electronic health record and administrative hospital utilization data to identify subgroups of patients seen at the FQHC in 2017 using latent class analysis (LCA). We designed an activity to gather feedback from physicians, social service staff, and administrators during model design. We trained a final model using a feed-forward neural network.Results: Using data from 5,985 primary care patients, we identified four candidate LCA models—with 26, 27, 28, and 29 classes—and integrated inter-professional feedback to develop a final model with 19 clinically meaningful classes. Three classes had greater medical complexity and older age, 15 classes were separated primarily by behavioral health diagnoses, and a final class had low complexity.Conclusions: Populations served by FQHCs are clinically heterogenous with varying levels of complexity. Use of LCA can provide insights into patterns among FQHC patients, which can be used to inform the development of interventions tailored to the needs of specific classes.

scholarly journals 86Segmenting persistently high-cost individuals into actionable groups

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Paul Ronksley ◽  
James Wick ◽  
Dave Campbell ◽  
Reed Beall ◽  
Brenda Hemmelgarn ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Population Level ◽  
Latent Class Models ◽  
Community Dwelling ◽  
Local Context ◽  
Medication Cost ◽  
Class Analysis ◽  
Targeted Interventions ◽  
Clinical Diagnoses

Abstract Background Despite growing evidence describing high cost patients, decision-makers struggle to implement effective strategies to improve care and curb spending in this population. Using a multi-phased approach, we aimed to classify high cost patients into homogeneous subgroups amenable to targeted interventions. Methods We linked population-level administrative health data in Alberta, Canada from 2012-2017. We defined “persistently high-cost” as those in the top 1% of cumulative inpatient, outpatient and medication cost in at least two consecutive years. We used latent class analysis to separate this persistent high-cost population into potentially actionable subgroups. Results Of the 3,795,067 adults residing in Alberta, 21,361 were ‘persistently high-cost’. Latent class models identified 10 high-cost subgroups: individuals with CKD (19.3% of persistent high-cost individuals), those undergoing joint surgery/replacement and rehabilitation (18.6%), individuals with IBD (11.6%), patients receiving biologics for autoimmune conditions (11.3%), patients receiving high cost drugs for other conditions (11.1%), community-dwelling individuals with multimorbid chronic conditions (9.0%), individuals with schizophrenia (6.8%), individuals with other mental health issues (6.2%), rural individuals with COPD (3.4%), and frail elderly in institutional settings (2.7%). Conclusions Latent class analysis was able to identify 10 persistently high-cost groups based on meaningful differences in health care spending, demographics, and clinical diagnoses. Key messages This taxonomy will inform the identification of interventions shown to improve care and reduce cost for each subgroup in addition to consultation with key stakeholders to identify and reflect on key barriers and facilitators to implementing identified interventions within the local context.

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