Abstract
This study provides trends in obesity prevalence in adults from 2013 to 2016 in France. 63,582 men and women from independent samples upon inclusion from the Constances cohort were included. ...Anthropometrics were measured at Health Screening Centers and obesity defined as a Body mass index (BMI) ≥ 30 kg/m
2
; obesity classes according to BMI are as follows: class 1 30–34.9; class 2 35–39.9; class 3 ≥ 40 kg/m
2
. Linear trends across obesity classes by sex and age groups were examined in regression models and percentage point change from 2013 to 2016 for each age category calculated. All analyses accounted for sample weights for non-response, age and sex-calibrated to the French population. Prevalence of obesity ranged from 14.2 to 15.2% and from 14 to 15.3% in women and men respectively from 2013 to 2016. Class 1 obesity category prevalence was the only one to increase significantly across survey years in both men and women (
p
for linear trend = 0.04 and 0.01 in women and men respectively). The only significant increase for obesity was observed in the age group 18–29 y in both women and men (+ 2.71% and + 3.26% point increase respectively, equivalent to an approximate rise of 50% in women and 93% in men,
p
= 0.03 and 0.02 respectively). After adjustment for survey non-response and for age and sex distribution, the results show that class 1 obesity prevalence has significantly increased in both women and men from 2013 to 2016, and only in young adults in a representative sample of the French population aged 18–69 years old.
Using serum samples routinely collected in 9144 adults from a French general population-based cohort, we identified 353 participants with a positive anti-SARS-CoV-2 IgG test, among whom 13 were ...sampled between November 2019 and January 2020 and were confirmed by neutralizing antibodies testing. Investigations in 11 of these participants revealed experience of symptoms possibly related to a SARS-CoV-2 infection or situations at risk of potential SARS-CoV-2 exposure. This suggests early circulation of SARS-CoV-2 in Europe.
From an epidemiological standpoint, quantifying the individual and the combined effect of lifestyle factors on uncontrolled blood pressure (BP) deserves further evaluation. We aimed to examine the ...individual and combined associations between unhealthy behaviors and uncontrolled hypertension among treated hypertensive adults. Cross-sectional analysis was conducted using data from CONSTANCES, an ongoing French population-based cohort study. Uncontrolled BP was defined as mean systolic BP ≥140 mmHg and/or mean diastolic BP ≥90 mmHg. Unhealthy behaviors were considered as heavy alcohol consumption, low or medium adherence to dietary recommendations, sedentary physical activity level, and overweight. A total of 10,710 hypertensive treated volunteer participants were included and 56.1% had uncontrolled hypertension; of them, 2.0%, 24.5%, 54.0% and 19.5% exhibited 0, 1, 2 or ≥3 unhealthy behaviors respectively. In men, there was an increased odds of uncontrolled hypertension with heavy alcohol drinking compared to light-or-never (adjusted odds ratio 1.34, 95% CI 1.10-1.63), with low as well as with medium adherence to dietary recommendations compared to high (p < 0.05 for both), and with overweight or obesity compared to a normal body mass index (p ≤ 0.001 for both). In addition, men reporting a combination of ≥3 unhealthy behaviors compared to none, had an increased odds of hypertension of 1.67 (95% CI 1.09-2.53). Unhealthy behaviors described as, heavy alcohol consumption, non-adherence to dietary recommendations and overweight are associated with uncontrolled hypertension, at the individual and combined level, and particularly in men. Improvement of modifiable lifestyle factors could offer considerable benefits in the management of hypertension.
Breast cancer (BC) treatments and related symptoms may affect return to work (RTW). The objective of this study was to investigate the impact of BC care pathways (timing and sequence of treatments) ...and related symptoms on RTW.
The study population included working-age women with BC who were enrolled in the French CONSTANCES cohort from 2012 to 2018. BC treatments, antidepressant/anxiolytic and antalgic drug deliveries (used as proxies of depression and pain, respectively) and statutory sick pay (used to estimate RTW and time to RTW) were assessed monthly using data from the French national healthcare system database. BC care pathways were identified with the sequence analysis method. Cox models with time-dependent covariates were used to investigate the impact of BC care pathways and related symptoms on RTW and time to RTW, after adjusting for age and socioeconomic characteristics.
73.2% (231/303) of women returned to work within 2 years after BC diagnosis. Five BC care pathway patterns were identified: (i) BC surgery only, (ii) BC surgery and radiotherapy, (iii) BC surgery and chemotherapy, (iv) BC surgery and chemotherapy and radiotherapy, and (v) BC surgery and long-term alternative chemotherapy/radiotherapy. The hazards ratios of non-RTW were significantly higher for women who received BC surgery and long-term alternative chemotherapy/radiotherapy and for > 55-year-old women. Time to RTW was significantly longer in women who received chemotherapy (patterns iii to v) and in women with antidepressant/anxiolytic and antalgic drug deliveries.
This study highlights the value of considering the dynamic, cumulative and temporal features of BC care pathways and related symptoms to facilitate the RTW of women with BC.
Abstract
OBJECTIVES
We aimed to assess the hypertension (HTN) awareness and associated factors in France.
METHODS
We conducted a cross-sectional analysis using data from the CONSTANCES ...population-based cohort involving 87,808 volunteer participants included between 2012 and 2018. HTN was defined as average blood pressure (BP) over 140/90 or use of BP medication, awareness as self-reported HTN. Multivariable logistic regression models were used to identify the associated factors.
RESULTS
Overall, 27,160 hypertensive participants (men = 16,569) above 18 years old were analyzed. Hypertension awareness rate was 37.5%. In the multivariable regression model, awareness was predicted by female gender, age, prior cardiovascular disease (CVD), presence of diabetes mellitus (DM), presence of chronic kidney disease (CKD), level of education, and obesity or overweight. Older participants (P < 0.001), females (P < 0.001), participants with comorbidities (P < 0.001), were more likely to be aware when compared with younger participants, males and participants without comorbidities, respectively. The unawareness among participants without cardiometabolic factors (CMF, i.e., CVD, DM, CKD) was higher than participants with CMF (67% vs. 41%, respectively, P < 0.001). Moreover, some differences appeared in both genders in the association between awareness of HTN and health and lifestyle factors.
CONCLUSION
Our findings show that HTN awareness is low. Particular attention should be given to young men without comorbidities as these characteristics were predictors of poor awareness. Immediate action is required to improve HTN awareness in France.
ObjectivesTo determine whether the terrorist attacks occurring in Paris on November 2015 have changed benzodiazepine use in the French population.DesignInterrupted time series ...analysis.SettingNational population-based cohort.Participants90 258 individuals included in the population-based CONSTANCES cohort from 2012 to 2017.Outcome measuresBenzodiazepine use was evaluated according to two different indicators using objective data from administrative registries: weekly number of individuals with a benzodiazepine delivered prescriptions (BDP) and weekly number of defined daily dose (DDD). Two sets of analyses were performed according to sex and age (≤50 vs >50). Education, income and area of residence were additional stratification variables to search for at-risk subgroups.ResultsAmong women, those with younger age (incidence rate ratios (IRR)=1.18; 95% CI=1.05 to 1.32 for BDP; IRR=1.14; 95% CI=1.03 to 1.27 for DDD), higher education (IRR=1.23; 95% CI=1.03 to 1.46 for BDP; IRR=1.23; 95% CI=1.01 to 1.51 for DDD) and living in Paris (IRR=1.27; 95% CI=1.05 to 1.54 for BDP) presented increased risks for benzodiazepine use. Among participants under 50, an overall increase in benzodiazepine use was identified (IRR=1.14; 95% CI=1.02 to 1.28 for BDP and IRR=1.12; 95% CI=1.01 to 1.25 for DDD) and in several strata. In addition to women, those with higher education (IRR=1.22; 95% CI=1.02 to 1.47 for BDP), lower income (IRR=1.17; 95% CI=1.02 to 1.35 for BDP) and not Paris residents (IRR=1.13; 95% CI=1.02 to 1.26 for BDP and IRR=1.13; 95% CI=1.03 to 1.26 for DDD) presented increased risks for benzodiazepine use.ConclusionTerrorist attacks might increase benzodiazepine use at a population level, with at-risk subgroups being particularly concerned. Information and prevention strategies are needed to provide appropriate care after such events.
Big data sources represent an opportunity for diabetes research. One example is the French national health data system (SNDS), gathering information on medical claims of out-of-hospital health care ...and hospitalizations for the entire French population (66 million). Currently, a validated algorithm based on antidiabetic drug reimbursement is able to identify people with pharmacologically-treated diabetes in the SNDS. But it cannot distinguish type 1 from type 2 diabetes. Differentiating type 1 and type 2 diabetes is crucial in diabetes surveillance, because they carry differences in their prevention, populations at risk, disease natural history, pathophysiology, management and risk of complications.
This article investigates the development of a type 1/type 2 diabetes classification algorithm using artificial intelligence and its application to estimate the prevalence of type 1 and type 2 diabetes in France.
The final data set comprised all diabetes cases from the CONSTANCES cohort (n = 951). A supervised machine learning method based on eight steps was used: final data set selection, target definition (type 1), coding features, final data set splitting into training and testing data sets, feature selection and training and validation and selection of algorithms. The selected algorithm was applied to SNDS data to estimate the type 1 and type 2 diabetes prevalence among adults 18–70 years of age.
Among the 3481 SNDS features, 14 were selected to train the different algorithms. The final algorithm was a linear discriminant analysis model based on the number of reimbursements for fast-acting insulin, long-acting insulin and biguanides over the previous year (specificity 97% and sensitivity 100%). In 2016, after adjusting for algorithm performance, type 1 and type 2 diabetes prevalence in France was estimated to be 0.3% and 4.4%, respectively.
Our type 1/type 2 classification algorithm was found to perform well and to be applicable to any prescription or medical claims database from other countries. Artificial intelligence opens new possibilities for research and diabetes prevention.
Background The use of machine learning techniques is increasing in healthcare which allows to estimate and predict health outcomes from large administrative data sets more efficiently. The main ...objective of this study was to develop a generic machine learning (ML) algorithm to estimate the incidence of diabetes based on the number of reimbursements over the last 2 years. Methods We selected a final data set from a population-based epidemiological cohort (i.e., CONSTANCES) linked with French National Health Database (i.e., SNDS). To develop this algorithm, we adopted a supervised ML approach. Following steps were performed: i. selection of final data set, ii. target definition, iii. Coding variables for a given window of time, iv. split final data into training and test data sets, v. variables selection, vi. training model, vii. Validation of model with test data set and viii. Selection of the model. We used the area under the receiver operating characteristic curve (AUC) to select the best algorithm. Results The final data set used to develop the algorithm included 44,659 participants from CONSTANCES. Out of 3468 variables from SNDS linked to CONSTANCES cohort were coded, 23 variables were selected to train different algorithms. The final algorithm to estimate the incidence of diabetes was a Linear Discriminant Analysis model based on number of reimbursements of selected variables related to biological tests, drugs, medical acts and hospitalization without a procedure over the last 2 years. This algorithm has a sensitivity of 62%, a specificity of 67% and an accuracy of 67% 95% CI: 0.66-0.68. Conclusions Supervised ML is an innovative tool for the development of new methods to exploit large health administrative databases. In context of InfAct project, we have developed and applied the first time a generic ML-algorithm to estimate the incidence of diabetes for public health surveillance. The ML-algorithm we have developed, has a moderate performance. The next step is to apply this algorithm on SNDS to estimate the incidence of type 2 diabetes cases. More research is needed to apply various MLTs to estimate the incidence of various health conditions. Keywords: Artificial intelligence, Machine learning technique, Supervise learning, Health indicator, Incidence, Diabetes mellitus, Electronic health records and public health surveillance
Many patients report persistent symptoms after COVID-19. Our aim was to determine whether some of these symptoms were more associated with past SARS-CoV-2 infection compared to other conditions.
This ...prospective survey was nested in CONSTANCES, a randomly selected French population-based cohort, started in 2012. All participants being followed-up by internet completed 2 questionnaires during the first wave of the pandemic focusing on the acute symptoms of their COVID-19-like illness. Serological tests for SARS-CoV-2 were then performed (May-Nov 2020). Between December 2020 and January 2021, participants completed a third questionnaire about symptoms that had lasted more than 2 months. Participants were classified into four groups according to both European Center for Diseases Control (ECDC) criteria for COVID-19 (ECDC+ or ECDC-) and serological SARS-CoV-2 test results (Sero+ or Sero-). To compare the risk of each persistent symptom among the groups, logistic regression models were adjusted for age, sex, educational level, comorbidities, and the number of acute symptoms declared during the first wave of the epidemic. A mediation analysis was performed to estimate the direct effect of the infection on persistent symptoms and its indirect effect via the initial clinical presentation.
The analysis was performed in 25,910 participants. There was a higher risk of persistent dysgeusia/anosmia, dyspnea and asthenia in the ECDC+/Sero+ group than in the ECDC+/Sero- group (OR: 6.83 4.47–10.42, 1.69 1.07–2.6 and 1.48 1.05–2.07, respectively). Abdominal pain, sensory symptoms or sleep disorders were at lower risk in the ECDC+/Sero+ group than in the ECDC+/Sero- group (0.51 0.24–0.96, 0.40 0.16–0.85, and 0.69 0.49–0.95, respectively). The mediation analysis revealed that the association of the serological test results with each symptom was mainly mediated by ECDC symptoms (proportion mediated range 50–107%).
A greater risk of persistent dysgeusia/anosmia, dyspnea and asthenia was observed in SARS-CoV-2 infected people. The initial clinical presentation substantially drives the association of positive serological test results with persistent symptoms.
French National Research Agency
BackgroundIdentifying factors that predict the course of persistent symptoms that occurred during the COVID-19 pandemic is a public health issue. Modifiable factors could be targeted in therapeutic ...interventions.ObjectiveThis prospective study based on the population-based CONSTANCES cohort examined whether the psychological burden associated with incident persistent symptoms (ie, that first occurred from March 2020) would predict having ≥1 persistent symptom 6–10 months later.MethodsA total of 8424 participants (mean age=54.6 years (SD=12.6), 57.2% women) having ≥1 incident persistent symptom at baseline (ie, between December 2020 and February 2021) were included. The psychological burden associated with these persistent symptoms was assessed with the Somatic Symptom Disorder-B Criteria Scale (SSD-12). The outcome was having ≥1 persistent symptom at follow-up. Adjusted binary logistic regression models examined the association between the SSD-12 score and the outcome.FindingsAt follow-up, 1124 participants (13.3%) still had ≥1 persistent symptom. The SSD-12 score at baseline was associated with persistent symptoms at follow-up in both participants with (OR (95% CI) for one IQR increase: 1.42 (1.09 to 1.84)) and without SARS-CoV-2 infection prior to baseline (1.39 (1.25 to 1.55)). Female gender, older age, poorer self-rated health and infection prior to baseline were also associated with persistent symptoms at follow-up.ConclusionsThe psychological burden associated with persistent symptoms at baseline predicted the presence of ≥1 persistent symptom at follow-up regardless of infection prior to baseline.Clinical implicationsIntervention studies should test whether reducing the psychological burden associated with persistent symptoms could improve the course of these symptoms.
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