Prevalence and incidence of young onset dementia and associations with comorbidities: A study of data from the French national health data system

Background Dementia onset in those aged <65 years (young onset dementia, YOD) has dramatic individual and societal consequences. In the context of population aging, data on YOD are of major importance to anticipate needs for planning and allocation of health and social resources. Few studies have provided precise frequency estimates of YOD. The aim of this study is to provide YOD prevalence and incidence estimates in France and to study the contribution of comorbidities to YOD incidence. Methods and findings Using data from the French national health data system (Système National des Données de Santé, SNDS) for 76% of the French population aged 40 to 64 years in 2016 (n = 16,665,795), we identified all persons with dementia based on at least 1 of 3 criteria: anti-Alzheimer drugs claims, hospitalization with the International Classification of Diseases-10th Revision (ICD-10) dementia code (F00 to F03, G30, G31.0, G31.1, or F05.1), or registration for free healthcare for dementia. We estimated prevalence rate (PR) and incidence rate (IR) and estimated the association of comorbidities with incident YOD. Sex differences were investigated. We identified 18,466 (PRstandardized = 109.7/100,000) and 4,074 incident (IRstandardized = 24.4/100,000 person-years) persons with prevalent and incident YOD, respectively. PR and IR sharply increased with age. Age-adjusted PR and IR were 33% (95% confidence interval (CI) = 29 to 37) and 39% (95% CI = 31 to 48) higher in men than women (p < 0.001 both for PR and IR). Cardio- and cerebrovascular, neurological, psychiatric diseases, and traumatic brain injury prevalence were associated with incident YOD (age- and sex-adjusted p-values <0.001 for all comorbidities examined, except p = 0.109 for antihypertensive drug therapy). Adjustment for all comorbidities explained more than 55% of the sex difference in YOD incidence. The lack of information regarding dementia subtypes is the main limitation of this study. Conclusions We estimated that there were approximately 24,000 and approximately 5,300 persons with prevalent and incident YOD, respectively, in France in 2016. The higher YOD frequency in men may be partly explained by higher prevalence of cardiovascular and neurovascular diseases, substance abuse disorders, and traumatic brain injury and warrants further investigation.

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Traumatic brain injury may increase risk of young onset dementia

Annals of Neurology ◽

10.1002/ana.24121 ◽

2014 ◽

Vol 75 (3) ◽

pp. 339-341 ◽

Cited By ~ 26

Author(s):

Raquel C. Gardner ◽

Kristine Yaffe

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Young Onset ◽

Increase Risk ◽

Young Onset Dementia

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Traumatic brain injury and young onset dementia: A nationwide cohort study

Annals of Neurology ◽

10.1002/ana.24101 ◽

2014 ◽

Vol 75 (3) ◽

pp. 374-381 ◽

Cited By ~ 105

Author(s):

Peter Nordström ◽

Karl Michaëlsson ◽

Yngve Gustafson ◽

Anna Nordström

Keyword(s):

Traumatic Brain Injury ◽

Cohort Study ◽

Brain Injury ◽

Young Onset ◽

Young Onset Dementia

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Multisite medical record review of emergency department visits for traumatic brain injury

Injury Prevention ◽

10.1136/injuryprev-2019-043510 ◽

2021 ◽

Vol 27 (S1) ◽

pp. i42-i48

Author(s):

Barbara A Gabella ◽

Jeanne E Hathaway ◽

Beth Hume ◽

Jewell Johnson ◽

Julia F Costich ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Emergency Department ◽

Brain Injury ◽

Skull Fracture ◽

Signs And Symptoms ◽

International Classification Of Diseases ◽

Emergency Department Visits ◽

Intracranial Injury ◽

Imaging Results ◽

Icd 10

BackgroundIn 2016, the CDC in the USA proposed codes from the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for identifying traumatic brain injury (TBI). This study estimated positive predictive value (PPV) of TBI for some of these codes.MethodsFour study sites used emergency department or trauma records from 2015 to 2018 to identify two random samples within each site selected by ICD-10-CM TBI codes for (1) intracranial injury (S06) or (2) skull fracture only (S02.0, S02.1-, S02.8-, S02.91) with no other TBI codes. Using common protocols, reviewers abstracted TBI signs and symptoms and head imaging results that were then used to assign certainty of TBI (none, low, medium, high) to each sampled record. PPVs were estimated as a percentage of records with medium-certainty or high-certainty for TBI and reported with 95% confidence interval (CI).ResultsPPVs for intracranial injury codes ranged from 82% to 92% across the four samples. PPVs for skull fracture codes were 57% and 61% in the two university/trauma hospitals in each of two states with clinical reviewers, and 82% and 85% in the two states with professional coders reviewing statewide or nearly statewide samples. Margins of error for the 95% CI for all PPVs were under 5%.DiscussionICD-10-CM codes for traumatic intracranial injury demonstrated high PPVs for capturing true TBI in different healthcare settings. The algorithm for TBI certainty may need refinement, because it yielded moderate-to-high PPVs for records with skull fracture codes that lacked intracranial injury codes.

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Management of pregnancy based on healthcare consumption of women who delivered in France in 2015: Contribution of the national health data system (SNDS)

Journal of Gynecology Obstetrics and Human Reproduction ◽

10.1016/j.jogoh.2018.05.014 ◽

2018 ◽

Vol 47 (7) ◽

pp. 299-307 ◽

Cited By ~ 1

Author(s):

Clara Piffaretti ◽

Vincent Looten ◽

Sylvie Rey ◽

Jeanne Fresson ◽

Anne Fagot-Campagna ◽

...

Keyword(s):

National Health ◽

Health Data ◽

Data System ◽

Healthcare Consumption

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26591 Characterization of flares in patients with generalized pustular psoriasis—A population-based study from the French National Health Data System database (SNDS)

Journal of the American Academy of Dermatology ◽

10.1016/j.jaad.2021.06.107 ◽

2021 ◽

Vol 85 (3) ◽

pp. AB20

Author(s):

Hervé Bachelez ◽

Jacques Massol ◽

Gérard de Pouvourville ◽

Tristan Gloede ◽

Patricia Medina ◽

...

Keyword(s):

National Health ◽

Population Based ◽

Health Data ◽

Data System ◽

Pustular Psoriasis ◽

Population Based Study ◽

System Database ◽

Generalized Pustular Psoriasis

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A decision tree framework for understanding blast-induced mild Traumatic Brain Injury in a military medical database

The Journal of Defense Modeling and Simulation Applications Methodology Technology ◽

10.1177/1548512916683841 ◽

2017 ◽

Vol 14 (4) ◽

pp. 389-398 ◽

Cited By ~ 2

Author(s):

Peter B Walker ◽

Melissa L Mehalick ◽

Amanda C Glueck ◽

Anna E Tschiffely ◽

Craig A Cunningham ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Personalized Medicine ◽

Decision Tree ◽

Mild Traumatic Brain Injury ◽

Health Data ◽

Decision Tree Classifier ◽

Tree Classifier ◽

Clustering Quality ◽

Over Time

Personalized medicine is a ubiquitous term that has come to be used to describe a medical model that proposes the customization of healthcare, such that decisions and/or treatments are tailored to each individual patient. Under this type of clinical practice model, diagnostic and prognostic decisions are often based upon selecting the most appropriate therapy based on a patient’s genetic, demographic, and/or other pertinent information. The primary aim of this paper is to use a personalized medicine framework to better understand the relationship between neuropsychological testing and the progression of symptoms in a blast-induced mild Traumatic Brain Injury (mTBI) patient population. In this paper, we extended our earlier work on Constrained Spectral Partitioning (CSP), a graph-based approach that incorporates additional information from separate graphs to help improve the clustering quality on both graphs simultaneously. While our previous work demonstrated the effectiveness of this algorithm in its ability to accurately classify whether symptoms improved or declined over time, that work did not provide any insights into the progression of symptoms. Therefore, this paper sought to identify, from a clinical perspective, whether symptoms increased/decreased over time. To accomplish this, we developed a decision tree classifier to classify symptom progression based on the outputs from our CSP algorithm. We present results from four separate decision tree classifiers that illustrate the adaptability of these algorithms for utilization as decision rules for the treatment of patients following blast-induced mTBI. Decision tree classifier models are useful in the healthcare setting because patient health data (e.g., diagnosis of a condition or a type of treatment) can be imput into the model and, based on the health data variables, a resulting outcome can be suggested, and providers can use this outcome as information to direct their clinical treatment.

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Analyzing Mortality Risk and Medical Burden among Patients with Traumatic Brain Injury and Subsequent Dementia

Journal of Clinical Medicine ◽

10.3390/jcm8050686 ◽

2019 ◽

Vol 8 (5) ◽

pp. 686

Author(s):

Dorji Harnod ◽

Tomor Harnod ◽

Cheng-Li Lin ◽

Chia-Hung Kao

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Mortality Risk ◽

International Classification Of Diseases ◽

Research Database ◽

Medical Visits ◽

Classification Of Diseases ◽

The Government ◽

Medical Burden ◽

Hospital Days

We used the National Health Insurance Research Database of Taiwan to determine whether patients with posttraumatic dementia (PTD) exhibit increased mortality and medical burden than those without it. Patients ≥20 years of age having head injury admission (per the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes 850–854, 959.01) between 2000 and 2012 were enrolled as traumatic brain injury (TBI) cohort. A PTD cohort (with ICD-9-CM codes 290, 294.1, 331.0) and a posttraumatic nondementia (PTN) cohort were established and compared in terms of age, sex, and comorbidities. We calculated adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) of all-cause mortality risk, number of hospital days, and frequency of medical visits in these cohorts. Patients with PTD had a higher mortality rate than did patients with TBI alone (rate per 1000 person-years: 12.00 vs. 6.32), with an aHR of 1.54 (95% CI: 1.32–1.80). Patients with PTD who were aged ≥65 years (aHR = 1.54, 95% CI: 1.31–1.80) or male (aHR = 1.78, 95% CI: 1.45–2.18) exhibited greatly increased risks of mortality. Furthermore, patients with PTD had 19.9 more hospital days and required medical visits 4.49 times more frequently compared with the PTN cohort. Taiwanese patients with PTD had increased mortality risk and medical burden compared with patients who had TBI only. Our findings provide crucial information for clinicians and the government to improve TBI and PTD outcomes.

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