scholarly journals Recurrent Traumatic Brain Injury Surveillance Using Administrative Health Data: A Bayesian Latent Class Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Oliver Lasry ◽  
Nandini Dendukuri ◽  
Judith Marcoux ◽  
David L. Buckeridge
Keyword(s):  
Measurement Error ◽  
Latent Class ◽  
Severity Index ◽  
Health Data ◽  
Case Definition ◽  
Latent Class Models ◽  
Case Definitions ◽  
Epidemiological Research ◽  
Administrative Health Data ◽  
Injury Burden

Background: The initial injury burden from incident TBI is significantly amplified by recurrent TBI (rTBI). Unfortunately, research assessing the accuracy to conduct rTBI surveillance is not available. Accurate surveillance information on recurrent injuries is needed to justify the allocation of resources to rTBI prevention and to conduct high quality epidemiological research on interventions that mitigate this injury burden. This study evaluates the accuracy of administrative health data (AHD) surveillance case definitions for rTBI and estimates the 1-year rTBI incidence adjusted for measurement error.Methods: A 25% random sample of AHD for Montreal residents from 2000 to 2014 was used in this study. Four widely used TBI surveillance case definitions, based on the International Classification of Disease and on radiological exams of the head, were applied to ascertain suspected rTBI cases. Bayesian latent class models were used to estimate the accuracy of each case definition and the 1-year rTBI measurement-error-adjusted incidence without relying on a gold standard rTBI definition that does not exist, across children (<18 years), adults (18-64 years), and elderly (> =65 years).Results: The adjusted 1-year rTBI incidence was 4.48 (95% CrI 3.42, 6.20) per 100 person-years across all age groups, as opposed to a crude estimate of 8.03 (95% CrI 7.86, 8.21) per 100 person-years. Patients with higher severity index TBI had a significantly higher incidence of rTBI compared to patients with lower severity index TBI. The case definition that identified patients undergoing a radiological examination of the head in the context of any traumatic injury was the most sensitive across children [0.46 (95% CrI 0.33, 0.61)], adults [0.79 (95% CrI 0.64, 0.94)], and elderly [0.87 (95% CrI 0.78, 0.95)]. The most specific case definition was the discharge abstract database in children [0.99 (95% CrI 0.99, 1.00)], and emergency room visits claims in adults/elderly [0.99 (95% CrI 0.99, 0.99)]. Median time to rTBI was the shortest in adults (75 days) and the longest in children (120 days).Conclusion: Conducting accurate surveillance and valid epidemiological research for rTBI using AHD is feasible when measurement error is accounted for.

scholarly journals Using Linked Administrative Health Databases for An Obesity Case Definition

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Naomi C Hamm ◽  
Lin Yan ◽  
Lisa M Lix
Keyword(s):  
Primary Care ◽  
Chronic Conditions ◽  
Health Data ◽  
Case Definition ◽  
Income Quintile ◽  
Age Group ◽  
Case Definitions ◽  
Administrative Health Data ◽  
Case Ascertainment ◽  
Primary Care Data

IntroductionCapture of obesity using administrative health data is poor, with many cases being under coded within the data. Linking multiple health data sources may improve case ascertainment and facilitate the use of administrative health data for obesity research and surveillance. Objectives and ApproachThis research aims to determine if using individual-level linked data from multiple sources can improve case ascertainment for obesity in administrative health data. Data from between April 1, 2001 and March 31, 2015 were obtained from the Manitoba Population Data Repository. Eighteen obesity case definitions were developed with different observation times and combinations of diagnosis, procedure, and prescription codes from physician billing claims, hospitalization abstracts, and prescription drug records. Body mass index (BMI) records from primary care data and the Bone Mineral Density (BMD) registry were used for validation. Sensitivity, specificity, and Cohen’s kappa were calculated. ResultsIndividuals with a higher BMI class had more physician visits and were more likely to have comorbidities and obese codes in the administrative health data. A higher BMI class was associated with being in a lower income quintile and the age group 40-59. Overall, the case definitions for obesity had high specificity (0.98-0.99) and low sensitivity (0.005-0.19) when validated using primary care data. Case definitions with obesity codes from multiple databases 3 year prior to and including the index date had the highest sensitivity (0.06-0.19) and kappa (0.04-0.23). Results with the BMD data were similar (specificity: 0.97-0.99; sensitivity: 0.007-0.21). Stratified analyses found agreement measures improved slightly for females, those who had chronic conditions and a later index year, and the age group 40-59. Conclusion / ImplicationsWhen using multiple databases to build a case definition for obesity, sensitivity improves but remains low. Individuals with other chronic conditions and a higher BMI class were more likely to be accurately classified as an obese case.

scholarly journals A population‐based study to develop juvenile arthritis case definitions for administrative health data using model‐based dynamic classification

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Allison Feely ◽  
Lily SH Lim ◽  
Depeng Jiang ◽  
Lisa M. Lix
Keyword(s):  
Population Based ◽  
Health Data ◽  
Case Definition ◽  
Deterministic Approach ◽  
Deterministic Case ◽  
Dynamic Approach ◽  
Case Definitions ◽  
Classification Approach ◽  
Administrative Health Data ◽  
Model Based

Abstract Background Previous research has shown that chronic disease case definitions constructed using population-based administrative health data may have low accuracy for ascertaining cases of episodic diseases such as rheumatoid arthritis, which are characterized by periods of good health followed by periods of illness. No studies have considered a dynamic approach that uses statistical (i.e., probability) models for repeated measures data to classify individuals into disease, non-disease, and indeterminate categories as an alternative to deterministic (i.e., non-probability) methods that use summary data for case ascertainment. The research objectives were to validate a model-based dynamic classification approach for ascertaining cases of juvenile arthritis (JA) from administrative data, and compare its performance with a deterministic approach for case ascertainment. Methods The study cohort was comprised of JA cases and non-JA controls 16 years or younger identified from a pediatric clinical registry in the Canadian province of Manitoba and born between 1980 and 2002. Registry data were linked to hospital records and physician billing claims up to 2018. Longitudinal discriminant analysis (LoDA) models and dynamic classification were applied to annual healthcare utilization measures. The deterministic case definition was based on JA diagnoses in healthcare use data anytime between birth and age 16 years; it required one hospitalization ever or two physician visits. Case definitions based on model-based dynamic classification and deterministic approaches were assessed on sensitivity, specificity, and positive and negative predictive values (PPV, NPV). Mean time to classification was also measured for the former. Results The cohort included 797 individuals; 386 (48.4 %) were JA cases. A model-based dynamic classification approach using an annual measure of any JA-related healthcare contact had sensitivity = 0.70 and PPV = 0.82. Mean classification time was 9.21 years. The deterministic case definition had sensitivity = 0.91 and PPV = 0.92. Conclusions A model-based dynamic classification approach had lower accuracy for ascertaining JA cases than a deterministic approach. However, the dynamic approach required a shorter duration of time to produce a case definition with acceptable PPV. The choice of methods to construct case definitions and their performance may depend on the characteristics of the chronic disease under investigation.

Development and validation of a case definition for epilepsy for use with administrative health data

2012 ◽  
Vol 102 (3) ◽  
pp. 173-179 ◽  
Author(s):  
Aylin Y. Reid ◽  
Christine St.Germaine-Smith ◽  
Mingfu Liu ◽  
Shahnaz Sadiq ◽  
Hude Quan ◽  
...  
Keyword(s):  
Health Data ◽  
Case Definition ◽  
Administrative Health Data ◽  
Development And Validation

scholarly journals SOCIAL ISOLATION AND LONELINESS: A LATENT CLASS APPROACH TO COSTING HEALTH SERVICE ENGAGEMENT IN AGING POPULATIONS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S535-S535
Author(s):  
Elaine Douglas ◽  
David Bell
Keyword(s):  
Health Service ◽  
Social Isolation ◽  
Latent Class ◽  
Health Data ◽  
Service Usage ◽  
Population Groups ◽  
Administrative Health Data ◽  
Service Engagement ◽  
Health Service Usage ◽  
The Impact

Abstract Social isolation and loneliness are associated with poorer health status and poorer health outcomes. Little is known the impact on health service usage, and its inherent cost, although it is considered to be higher. Latent class analysis (LCA) was used to determine profiles (population groups) of loneliness and social isolation in older people (aged 50+, n=1,057) using model-fit criteria. Loneliness was measured using the UCLA Loneliness Scale and social isolation used a measure of social networks and social contact. We then analysed the socio-demographic, perceived health, and health behaviour of these profiles using descriptive statistics and logistic regression. The survey data (HAGIS, 2016/17) were linked to retrospective administrative health data to investigate patterns of repeat prescription use (from 2009) and health service usage (from 2005) and their associated costs. Our results highlight the distinction and inter-relation between social isolation and loneliness (including associations with socio-demographic and health characteristics), and the variation in health service usage and costs between the population groups. LCA profiles may help focussed targeting of these groups for health interventions. Further, the data-driven approach of LCA may overcome some of the limitations of indices of social isolation and loneliness. As such, this will extend the existing methodological approaches to quantitative analyses of social isolation and loneliness and demonstrate the benefits of using linked administrative health data. Significantly, this study incorporates the social and financial cost of social isolation and loneliness on health and its implications for health services.

Systemic Autoimmune Rheumatic Disease Prevalence in Canada: Updated Analyses Across 7 Provinces

2014 ◽  
Vol 41 (4) ◽  
pp. 673-679 ◽  
Author(s):  
Laurel Broten ◽  
J. Antonio Aviña-Zubieta ◽  
Diane Lacaille ◽  
Lawrence Joseph ◽  
John G. Hanly ◽  
...  
Keyword(s):  
Rheumatic Disease ◽  
Lupus Erythematosus ◽  
Latent Class ◽  
Population Based ◽  
Case Definition ◽  
Case Definitions ◽  
Autoimmune Rheumatic Disease ◽  
Primary Sjögren Syndrome ◽  
Physician Billing ◽  
Systemic Autoimmune Rheumatic Disease

Objective.To estimate systemic autoimmune rheumatic disease (SARD) prevalence across 7 Canadian provinces using population-based administrative data evaluating both regional variations and the effects of age and sex.Methods.Using provincial physician billing and hospitalization data, cases of SARD (systemic lupus erythematosus, scleroderma, primary Sjögren syndrome, polymyositis/dermatomyositis) were ascertained. Three case definitions (rheumatology billing, 2-code physician billing, and hospital diagnosis) were combined to derive a SARD prevalence estimate for each province, categorized by age, sex, and rural/urban status. A hierarchical Bayesian latent class regression model was fit to account for the imperfect sensitivity and specificity of each case definition. The model also provided sensitivity estimates of different case definition approaches.Results.Prevalence estimates for overall SARD ranged between 2 and 5 cases per 1000 residents across provinces. Similar demographic trends were evident across provinces, with greater prevalence in women and in persons over 45 years old. SARD prevalence in women over 45 was close to 1%. Overall sensitivity was poor, but estimates for each of the 3 case definitions improved within older populations and were slightly higher for men compared to women.Conclusion.Our results are consistent with previous estimates and other North American findings, and provide results from coast to coast, as well as useful information about the degree of regional and demographic variations that can be seen within a single country. Our work demonstrates the usefulness of using multiple data sources, adjusting for the error in each, and providing estimates of the sensitivity of different case definition approaches.

scholarly journals Validation of laboratory tests for infectious diseases in wild mammals: review and recommendations

2020 ◽  
Vol 32 (6) ◽  
pp. 776-792 ◽  
Author(s):  
Beibei Jia ◽  
Axel Colling ◽  
David E. Stallknecht ◽  
David Blehert ◽  
John Bingham ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Infectious Diseases ◽  
Latent Class ◽  
Case Definition ◽  
Latent Class Models ◽  
Test Validation ◽  
Test Accuracy ◽  
Wild Mammals ◽  
White Nose Syndrome ◽  
Free Ranging

Evaluation of the diagnostic sensitivity (DSe) and specificity (DSp) of tests for infectious diseases in wild animals is challenging, and some of the limitations may affect compliance with the OIE-recommended test validation pathway. We conducted a methodologic review of test validation studies for OIE-listed diseases in wild mammals published between 2008 and 2017 and focused on study design, statistical analysis, and reporting of results. Most published papers addressed Mycobacterium bovis infection in one or more wildlife species. Our review revealed limitations or missing information about sampled animals, identification criteria for positive and negative samples (case definition), representativeness of source and target populations, and species in the study, as well as information identifying animals sampled for calculations of DSe and DSp as naturally infected captive, free-ranging, or experimentally challenged animals. The deficiencies may have reflected omissions in reporting rather than design flaws, although lack of random sampling might have induced bias in estimates of DSe and DSp. We used case studies of validation of tests for hemorrhagic diseases in deer and white-nose syndrome in hibernating bats to demonstrate approaches for validation when new pathogen serotypes or genotypes are detected and diagnostic algorithms are changed, and how purposes of tests evolve together with the evolution of the pathogen after identification. We describe potential benefits of experimental challenge studies for obtaining DSe and DSp estimates, methods to maintain sample integrity, and Bayesian latent class models for statistical analysis. We make recommendations for improvements in future studies of detection test accuracy in wild mammals.

scholarly journals Model-based methods for case definitions from administrative health data: application to rheumatoid arthritis

BMJ Open ◽  
2017 ◽  
Vol 7 (6) ◽  
pp. e016173 ◽  
Author(s):  
Kristine Kroeker ◽  
Jessica Widdifield ◽  
Saman Muthukumarana ◽  
Depeng Jiang ◽  
Lisa M Lix
Keyword(s):  
Rheumatoid Arthritis ◽  
Health Data ◽  
Case Definitions ◽  
Administrative Health Data ◽  
Model Based ◽  
Data Application

A method to compare the performance of two molecular diagnostic tools in the absence of a gold standard

2017 ◽  
Vol 28 (2) ◽  
pp. 419-431 ◽  
Author(s):  
Xiaonan Xue ◽  
Maja Oktay ◽  
Sumanta Goswami ◽  
Mimi Y Kim
Keyword(s):  
Measurement Error ◽  
Tumor Cells ◽  
Gold Standard ◽  
Latent Class ◽  
Random Variable ◽  
Threshold Level ◽  
Latent Class Models ◽  
Molecular Diagnostic ◽  
Diagnostic Tools ◽  
Molecular Tests

The paper is motivated by the problem of comparing the accuracy of two molecular tests in detecting genetic mutations in tumor samples when there is no gold standard test. Commonly used sequencing methods require a large number of tumor cells in the tumor sample and the proportion of tumor cells with mutation positivity to be above a threshold level whereas new tests aim to reduce the requirement for number of tumor cells and the threshold level. A new latent class model is proposed to compare these two tests in which a random variable is used to represent the unobserved proportion of mutation positivity so that these two tests are conditionally dependent; furthermore, an independent random variable is included to address measurement error associated with the reading from each test, while existing latent class models often assume conditional independence and do not allow measurement error. In addition, methods for calculating the sample size for a study that is sufficiently powered to compare the accuracy of two molecular tests are proposed and compared. The proposed methods are then applied to a study which aims to compare two molecular tests for detecting EGFR mutations in lung cancer patients.

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