Eleven-year trends in socioeconomic inequalities in the prevalence and incidence of pharmacologically treated type 2 diabetes in France, 2010-2020.

AIM: This study aimed to describe the association between socioeconomic inequalities and the prevalence and incidence of pharmacologically-treated type 2 diabetes in European France over the 2010-2020 period. METHODS: Diabetes cases were identified using a validated algorithm from the French National Health Data System. Analysis was restricted to adults aged 45 years and older to focus on type 2 diabetes. Socioeconomic inequalities were measured for all years in European France using the French deprivation index (FDep, 2015 version), which is an area-based deprivation indicator using population-weighted quintiles (Q1 corresponds to the least deprived municipalities). The relative risks of diabetes prevalence and incidence associated with FDep quintiles (Q1 as the reference) were estimated by sex using a log-linear Poisson model adjusted for year, age and French department. The study population was the French health consumers aged 45 years and over (from 24,228,526 in 2010 to 29,772,928 in 2020). RESULTS: A positive gradient was observed in the relative risks of type 2 diabetes prevalence and incidence by FDep quintiles over the study period. The strength of the estimated associations increased over the last decade for prevalence among men and women and for incidence among men in the two most deprived quintiles. CONCLUSION: Thus, type 2 diabetes prevention should include a proportionate universalism approach, proposing actions of greater intensity in the most deprived areas.

Cross-sectional study examining factors impacting on uptake of postpartum type 2 diabetes screening among women diagnosed with hyperglycaemia in pregnancy.

AIMS: Early postpartum glucose screening of women with hyperglycaemia in pregnancy (HIP) can identify women who have the highest risk of developing impaired glucose tolerance and T2DM. This study examines the association between demographics, events during pregnancy, socioeconomic status and postpartum T2DM screening. METHODS: Using the French National Health Data System, this cross-sectional study included all deliveries where the mother had HIP in France in 2015, (n = 76,862). The odds ratio (OR) for attending postpartum screening was calculated via multi-level logistic regression. RESULTS: T2DM screening uptake at six months postpartum was 42·9% [95 % Confidence Interval: 42·6-43·3]. Several characteristics were associated with lower uptake: living in the most deprived area(OR = 0·78[0·74-0·83]); being < 25 years-old (reference age group 25-29;≤17: 0.53 [0·31-0·90];18-24: 0.73[0·69-0·78]); smoking (0·65[0·62-0·68]); obesity (0·93[0·89-0·97]); caesarean delivery (0·95[0·92-0·99]). Factors associated with higher uptake included primiparity (1·30[1·26-1·34]); having followed the French recommendations for HIP screening (1·24[1·20-1·28]); insulin prescription (1·75[1·69-1·81]) and pre-eclampsia (1·30[1·19-1·42]). p < 0.01 is justified due to sample size. CONCLUSION: Improving identification of factors affecting postpartum T2DM screening uptake, such as demographics, socioeconomic context and events during pregnancy, may lead to development of target interventions to aide adherence to screening regime and thereby diagnosis of women with prediabetes or diabetes, for whom secondary and tertiary prevention is crucial.

Maternal and neonatal outcomes according to the timing of diagnosis of hyperglycaemia in pregnancy: a nationwide cross-sectional study of 695,912 deliveries in France in 2018.

AIMS/HYPOTHESIS: We aimed to assess maternal-fetal outcomes according to various subtypes of hyperglycaemia in pregnancy. METHODS: We used data from the French National Health Data System (Système National des Données de Santé), which links individual data from the hospital discharge database and the French National Health Insurance information system. We included all deliveries after 22 gestational weeks (GW) in women without pre-existing diabetes recorded in 2018. Women with hyperglycaemia were classified as having overt diabetes in pregnancy or gestational diabetes mellitus (GDM), then categorised into three subgroups according to their gestational age at the time of GDM diagnosis: before 22 GW (GDM<22); between 22 and 30 GW (GDM22-30); and after 30 GW (GDM>30). Adjusted prevalence ratios (95% CI) for the outcomes were estimated after adjusting for maternal age, gestational age and socioeconomic status. Due to the multiple tests, we considered an association to be statistically significant according to the Holm-Bonferroni procedure. To take into account the potential immortal time bias, we performed analyses on deliveries at ≥31 GW and deliveries at ≥37 GW. RESULTS: The study population of 695,912 women who gave birth in 2018 included 84,705 women (12.2%) with hyperglycaemia in pregnancy: overt diabetes in pregnancy, 0.4%; GDM<22, 36.8%; GDM22-30, 52.4%; and GDM>30, 10.4%. The following outcomes were statistically significant after Holm-Bonferroni adjustment for deliveries at ≥31 GW using GDM22-30 as the reference. Caesarean sections (1.54 [1.39, 1.72]), large-for-gestational-age (LGA) infants (2.00 [1.72, 2.32]), Erb's palsy or clavicle fracture (6.38 [2.42, 16.8]), preterm birth (1.84 [1.41, 2.40]) and neonatal hypoglycaemia (1.98 [1.39, 2.83]) were more frequent in women with overt diabetes. Similarly, LGA infants (1.10 [1.06, 1.14]) and Erb's palsy or clavicle fracture (1.55 [1.22, 1.99]) were more frequent in GDM<22. LGA infants (1.44 [1.37, 1.52]) were more frequent in GDM>30. Finally, women without hyperglycaemia in pregnancy were less likely to have preeclampsia or eclampsia (0.74 [0.69, 0.79]), Caesarean section (0.80 [0.79, 0.82]), pregnancy and postpartum haemorrhage (0.93 [0.89, 0.96]), LGA neonate (0.67 [0.65, 0.69]), premature neonate (0.80 [0.77, 0.83]) and neonate with neonatal hypoglycaemia (0.73 [0.66, 0.82]). Overall, the results were similar for deliveries at ≥37 GW. Although the estimation of the adjusted prevalence ratio of perinatal death was five times higher (5.06 [1.87, 13.7]) for women with overt diabetes, this result was non-significant after Holm-Bonferroni adjustment. CONCLUSIONS/INTERPRETATION: Compared with GDM22-30, overt diabetes, GDM<22 and, to a lesser extent, GDM>30 were associated with poorer maternal-fetal outcomes.

Need for improvement of cardiovascular health: a clustering method to identify cardiovascular health profiles.

BACKGROUND: In France, the overall trend in the incidence of cardiovascular disease is unfavourable, especially in young subjects. This highlights the need to promote cardiovascular health by targeting the main risk factors. Social marketing campaigns to improve cardiovascular health should identify unhealthy behaviour and understand the target audience. The objective of this study was to identify poor cardiovascular health profiles in the French population using a clustering method. METHODS: Subjects aged 18-74 years with no history of cardiovascular disease were included from the Esteban cross-sectional survey (2014-16). To evaluate cardiovascular health, seven items were considered as defined by the American Heart Association: blood glucose, blood cholesterol, blood pressure, body mass index, cigarette smoking, diet and physical activity. Cardiovascular health profiles were identified from these seven items by combining multiple correspondence analysis with hierarchical clustering and partitioning. RESULTS: A total of 1673 subjects were included in the main analysis. Five cardiovascular health profiles were identified: two profiles corresponded to subjects with poor cardiovascular health (mainly older men with a low socioeconomic status), two to subjects with intermediate cardiovascular health (one mainly comprised of young women with a low socioeconomic status and the other of young subjects with a high socioeconomic status) and one to subjects with good cardiovascular health (mainly older women). CONCLUSION: This description of cardiovascular health profiles, which led to the identification and characterization of target audiences for future population-based prevention campaigns, should be the starting point for improving cardiovascular health in the French population.

Relation of urinary bisphenol concentration and diabetes or prediabetes in French adults: A cross-sectional study.

BACKGROUND: International research has recently shown an association between exposure to bisphenol A (BPA) and the risk of diabetes, although limited results are available for exposure to bisphenol S (BPS) and bisphenol F (BPF). The aim of this study was to examine the relationships between impregnation with BPA, BPS, and BPF and the prevalence of diabetes or prediabetes in the French adult population. METHODS: Based on the Esteban cross-sectional study, 852 adults aged 18 to 74 years living in France were included. To assess the link between urinary concentration of BPA, BPS and BPF and a state of dysglycemia (diabetes or prediabetes), logistic regression multivariable models were performed and adjusted for known risk factors for diabetes and urine creatinine concentration. RESULTS: The percentage of included individuals with diabetes or prediabetes was 17.8% (95% CI = [15.3-20.4]). Urinary BPA concentration was significantly higher in people with diabetes or prediabetes, independent of the known risk factors for diabetes (OR for an increase of 0.1 units in log-transformed concentration of BPA (µg/L) = 1.12; 95%CI = [1.05-1.19], p < 0.001). However, we did not find any significant independent association between urinary BPS and BPF levels and the prevalence of diabetes or prediabetes. CONCLUSIONS: In this sample, considering the diabetes risk factors, diabetes or prediabetes was positively associated with higher urinary BPA concentration but not with urinary BPS and BPF concentrations. However, analysis of prospective longitudinal studies are still necessary to demonstrate a causal link between bisphenol exposure and the risk of diabetes or prediabetes.

Lifetime risk, life expectancy, and years of life lost to type 2 diabetes in 23 high-income jurisdictions: a multinational, population-based study.

BACKGROUND: Diabetes is a major public health issue. Because lifetime risk, life expectancy, and years of life lost are meaningful metrics for clinical decision making, we aimed to estimate these measures for type 2 diabetes in the high-income setting. METHODS: For this multinational, population-based study, we sourced data from 24 databases for 23 jurisdictions (either whole countries or regions of a country): Australia; Austria; Canada; Denmark; Finland; France; Germany; Hong Kong; Hungary; Israel; Italy; Japan; Latvia; Lithuania; the Netherlands; Norway; Scotland; Singapore; South Korea; Spain; Taiwan; the UK; and the USA. Our main outcomes were lifetime risk of type 2 diabetes, life expectancy in people with and without type 2 diabetes, and years of life lost to type 2 diabetes. We modelled the incidence and mortality of type 2 diabetes in people with and without type 2 diabetes in sex-stratified, age-adjusted, and calendar year-adjusted Poisson models for each jurisdiction. Using incidence and mortality, we constructed life tables for people of both sexes aged 20-100 years for each jurisdiction and at two timepoints 5 years apart in the period 2005-19 where possible. Life expectancy from a given age was computed as the area under the survival curves and lifetime lost was calculated as the difference between the expected lifetime of people with versus without type 2 diabetes at a given age. Lifetime risk was calculated as the proportion of each cohort who developed type 2 diabetes between the ages of 20 years and 100 years. We estimated 95% CIs using parametric bootstrapping. FINDINGS: Across all study cohorts from the 23 jurisdictions (total person-years 1 577 234 194), there were 5 119 585 incident cases of type 2 diabetes, 4 007 064 deaths in those with type 2 diabetes, and 11 854 043 deaths in those without type 2 diabetes. The lifetime risk of type 2 diabetes ranged from 16·3% (95% CI 15·6-17·0) for Scottish women to 59·6% (58·5-60·8) for Singaporean men. Lifetime risk declined with time in 11 of the 15 jurisdictions for which two timepoints were studied. Among people with type 2 diabetes, the highest life expectancies were found for both sexes in Japan in 2017-18, where life expectancy at age 20 years was 59·2 years (95% CI 59·2-59·3) for men and 64·1 years (64·0-64·2) for women. The lowest life expectancy at age 20 years with type 2 diabetes was observed in 2013-14 in Lithuania (43·7 years [42·7-44·6]) for men and in 2010-11 in Latvia (54·2 years [53·4-54·9]) for women. Life expectancy in people with type 2 diabetes increased with time for both sexes in all jurisdictions, except for Spain and Scotland. The life expectancy gap between those with and without type 2 diabetes declined substantially in Latvia from 2010-11 to 2015-16 and in the USA from 2009-10 to 2014-15. Years of life lost to type 2 diabetes ranged from 2·5 years (Latvia; 2015-16) to 12·9 years (Israel Clalit Health Services; 2015-16) for 20-year-old men and from 3·1 years (Finland; 2011-12) to 11·2 years (Israel Clalit Health Services; 2010-11 and 2015-16) for 20-year-old women. With time, the expected number of years of life lost to type 2 diabetes decreased in some jurisdictions and increased in others. The greatest decrease in years of life lost to type 2 diabetes occurred in the USA between 2009-10 and 2014-15 for 20-year-old men (a decrease of 2·7 years). INTERPRETATION: Despite declining lifetime risk and improvements in life expectancy for those with type 2 diabetes in many high-income jurisdictions, the burden of type 2 diabetes remains substantial. Public health strategies might benefit from tailored approaches to continue to improve health outcomes for people with diabetes. FUNDING: US Centers for Disease Control and Prevention and Diabetes Australia.

Time trend in excess mortality in children with type 1 diabetes from 1987 to 2016 in mainland France.

BACKGROUND: Mortality risk for children with type 1 diabetes (T1D) is unknown in France and their causes of death are not well documented. AIM: To determine the standardized mortality ratios (SMRs) and causes of death in children aged 1-14 years with T1D from 1987 to 2016. METHODS: The French Center for Epidemiology on Medical Causes of Death collected all death certificates in mainland France. SMRs, corrected SMRs (accounting for missing cases of deaths unrelated to diabetes), and 95% confidence intervals were calculated. RESULTS: Of 146 deaths with the contribution of diabetes, 97 were due to T1D. Mean age at death of the subjects with T1D was 8.8 ± 4.1 years (54% males). The cause of death was diabetic ketoacidosis (DKA) in 58% of the cases (70% in subjects 1-4 years), hypoglycemia or dead-in-bed syndrome in 4%, related to diabetes but not described in 24%, and unrelated to diabetes in 14%. The SMRs showed a significant decrease across the years, except for the 1-4 age group. In the last decade (2007-2016), the crude and corrected SMRs were significantly different from 1 in the 1-4 age group (5.4 [2.3; 10.7] and 6.1 [2.8; 11.5]), no longer significant in the 5-9 age group (1.7 [0.6; 4.0] and 2.1 [0.8; 4.5]) and borderline significant in the 10-14 age group (1.7 [0.8; 3.2] and 2.3 [1.2; 4.0]). CONCLUSIONS: Children with T1D aged 1-4 years still had a high mortality rate. Their needs for early recognition and safe management of diabetes are not being met.

Use of artificial intelligence for public health surveillance: a case study to develop a machine Learning-algorithm to estimate the incidence of diabetes mellitus in France.

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.

Trends in the incidence of diagnosed diabetes: a multicountry analysis of aggregate data from 22 million diagnoses in high-income and middle-income settings.

BACKGROUND: Diabetes prevalence is increasing in most places in the world, but prevalence is affected by both risk of developing diabetes and survival of those with diabetes. Diabetes incidence is a better metric to understand the trends in population risk of diabetes. Using a multicountry analysis, we aimed to ascertain whether the incidence of clinically diagnosed diabetes has changed over time. METHODS: In this multicountry data analysis, we assembled aggregated data describing trends in diagnosed total or type 2 diabetes incidence from 24 population-based data sources in 21 countries or jurisdictions. Data were from administrative sources, health insurance records, registries, and a health survey. We modelled incidence rates with Poisson regression, using age and calendar time (1995-2018) as variables, describing the effects with restricted cubic splines with six knots for age and calendar time. FINDINGS: Our data included about 22 million diabetes diagnoses from 5 billion person-years of follow-up. Data were from 19 high-income and two middle-income countries or jurisdictions. 23 data sources had data from 2010 onwards, among which 19 had a downward or stable trend, with an annual estimated change in incidence ranging from -1·1% to -10·8%. Among the four data sources with an increasing trend from 2010 onwards, the annual estimated change ranged from 0·9% to 5·6%. The findings were robust to sensitivity analyses excluding data sources in which the data quality was lower and were consistent in analyses stratified by different diabetes definitions. INTERPRETATION: The incidence of diagnosed diabetes is stabilising or declining in many high-income countries. The reasons for the declines in the incidence of diagnosed diabetes warrant further investigation with appropriate data sources. FUNDING: US Centers for Disease Control and Prevention, Diabetes Australia Research Program, and Victoria State Government Operational Infrastructure Support Program.

Prevalence of prediabetes and undiagnosed type 2 diabetes in France: Results from the national survey ESTEBAN, 2014-2016.

AIM: To assess the prevalence of prediabetes, undiagnosed diabetes and diagnosed diabetes in France, among adults between 2014 and 2016 using data from the nationwide Esteban survey. METHODS: National cross-sectional study conducted between 2014 and 2016 in metropolitan France. Individuals aged 18 to 74 were recruited with a 3-stage geographic sampling. They completed two face-to-face interviews, filled in a self-administered questionnaire and underwent a medical examination with the collection of biological samples. Their data were linked to the National Health Data System to identify anti-diabetic drugs reimbursement. Prediabetes and undiagnosed diabetes were defined as no diagnosis of diabetes and 6.1 mmol/l ≤ Fasting Plasma Glucose (FPG) < 7.0 mmol/l for prediabetes and FPG ≥ 7.0 mmol/l for undiagnosed diabetes. Non-pharmacologically treated diabetes and pharmacologically treated diabetes were defined as self-reported diabetes without or with self-reported or reimbursed antidiabetic medication, respectively. Estimated prevalence were weighted to take into account survey design and non-response. RESULTS: The ESTEBAN survey recruited 3476 adults, 2270 were included in this analysis. The weighted prevalence was 1.7% [1.1 - 2.4] for undiagnosed diabetes (men: 2.7%, women; 0.9%), 9.9% [8.3 - 11.5] for prediabetes (men: 13.2%, women: 7.0%), 5.7% [4.3 - 7.1] for diagnosed diabetes. Among the diagnosed cases, 79% were pharmacologically treated. Among all diabetes cases, 23% were undiagnosed. CONCLUSION: The prevalence of undiagnosed diabetes and prediabetes is increasing in France. Our results highlight the need to increase primary prevention and reinforce secondary prevention of diabetes.

Identifying diabetes cases in health administrative databases: a validation study based on a large French cohort.

OBJECTIVES: In the French national health insurance information system (SNDS) three diabetes case definition algorithms are applied to identify diabetic patients. The objective of this study was to validate those using data from a large cohort. METHODS: The CONSTANCES cohort (Cohorte des consultants des Centres d'examens de santé) comprises a randomly selected sample of adults living in France. Between 2012 and 2014, data from 45,739 participants recorded in a self-administrated questionnaire and in a medical examination were linked to the SNDS. Two gold standards were defined: known diabetes and pharmacologically treated diabetes. Sensitivity, specificity, positive and negative predictive values (PPV, NPV) and kappa coefficients (k) were estimated. RESULTS: All three algorithms had specificities and NPV over 99%. Their sensitivities ranged from 73 to 77% in algorithm A, to 86 and 97% in algorithm B and to 93 and 99% in algorithm C, when identifying known and pharmacologically treated diabetes, respectively. Algorithm C had the highest k when using known diabetes as the gold standard (0.95). Algorithm B had the highest k (0.98) when testing for pharmacologically treated diabetes. CONCLUSIONS: The SNDS is an excellent source for diabetes surveillance and studies on diabetes since the case definition algorithms applied have very good test performances.

Trends in childhood type 1 diabetes incidence in France, 2010-2015.

AIMS: To estimate type 1 diabetes incidence in children in France and its evolution between 2010 and 2015, based on comprehensive medico-administrative databases. METHODS: The algorithm built to identify new cases of type 1 diabetes selected children aged between 6 months and 14 years who had at least one hospital stay for diabetes, followed by their first insulin treatment, excluding children suffering from another form of diabetes. Age and sex specific annual incidence rates were estimated and time trend was analyzed using Poisson regression. RESULTS: A total of 12 067 children were identified as newly diagnosed with type 1 diabetes and the annual incidence rates increased between 2010 and 2015 (from 15.4 [95% Confidence Interval: 14.7; 16.1] to 19.1 [18.3; 19.9] per 100 000 person-years), among boys and girls, and in each age group (4 and under, 5-9, 10-14 year olds). The annual rate of increase was 4.0% [3.4; 4.6]. This trend was not significantly different between each gender, and each age group. CONCLUSIONS: Valid database information on disease incidence is essential for healthcare planning and provides a valuable resource for health research. An increase of the incidence rate of type 1 diabetes in children was highlighted in both sexes and in all age groups.