Availability and promotion of alcohol across different outlet typologies and under different area-level socioeconomic status. / Disponibilidad y promoción de alcohol según la tipología de los locales y las condiciones socioeconómicas del área.

We aimed to characterize the availability and promotion of alcohol at alcohol outlets in Madrid and to compare them according to type of outlet and area-level socioeconomic status. We used the OHCITIES instrument to characterise the alcohol outlets in 42 census tracts of Madrid in 2016. We specified alcohol availability as the density of alcohol outlets and the number of alcohol outlets with extended opening hours (12 or more). We registered any type of promotion associated to alcohol outlets that could be perceived from outside the outlet. We calculated and compared proportions of availability and promotion by alcohol outlet (on- and off-premise) using chi-squared and Fisher Exact tests. We estimated the availability and promotion of alcohol densities per census tract according to area-level socioeconomic status. To assess statistical significance, we used Kruskal-Wallis tests. We recorded 324 alcohol outlets, 241 on-premise and 83 off-premise. Most of the outlets had extended opening hours (73.77%) and at least one sign promoting alcohol (89.51%). More on-premise outlets had extended opening hours and higher presence of alcohol promotion than off-premise (p < 0.001). Higher density of alcohol outlets, extended opening hours and presence of alcohol promotion were found in higher socioeconomic areas (all p < 0.001). These results were also observed for on-premise alcohol outlets. Alcohol availability and promotion were associated with alcohol outlets in Madrid. Future alcohol policies regulating the availability and promotion of alcohol should consider outlet types and area-level socioeconomic status.

El objetivo fue caracterizar la disponibilidad y promoción de alcohol asociados a los locales de venta y consumo de alcohol en Madrid, así como explorar las diferencias en su distribución en función de la tipología del local y las características socioeconómicas del área. Se utilizó el instrumento OHCITIES para caracterizar locales situados en 42 secciones censales de Madrid durante 2016. Se registró la densidad de locales y el número de locales con amplios horarios de apertura (12 o más horas). Se registró cualquier tipo de promoción asociada al local visible desde el exterior. Se compararon los porcentajes de características de disponibilidad y promoción asociada a los locales de consumo y venta de alcohol utilizando el test de chi cuadrado y la prueba exacta de Fisher. Se estimó la densidad de disponibilidad y promoción por sección censal y se exploró su distribución en función de las características socioeconómicas del área mediante el test de Kruskal-Wallis. Se registraron 324 locales, 241 de consumo y 83 de venta. La mayoría tenía un horario amplio de apertura (73,77%) y algún elemento promocional (89,51%). Los locales de consumo tenían horarios más amplios de apertura y más elementos promocionales que los de venta (p < 0,001). Se encontraron mayor densidad de locales, amplitud de horarios y elementos promocionales en áreas de nivel socioeconómico alto (todos p < 0,001). La disponibilidad y promoción estuvieron asociadas con los locales de venta y consumo de alcohol en Madrid. Futuras políticas cuyo objetivo sea la prevención del consumo de alcohol deben tener en cuenta la influencia de los tipos de locales y las características socioeconómicas del área en la distribución de la disponibilidad y promoción de alcohol.

Psycho-social factors related to obesity and their associations with socioeconomic characteristics: the RECORD study.

OBJECTIVES: We aimed to describe the main psycho-social factors related to obesity in an adult population and to develop a unified construct (psycho-social profiles), to explore the associations between socioeconomic characteristics and these psycho-social profiles. METHODS: In its second wave, the RECORD Study assessed 6460 participants aged 30-79 years living in the Paris region between 2011 and 2014. Factor analyses followed by cluster analysis were applied to identify psycho-social profiles related to obesity. The two psycho-social profiles were adverse profile-negative body image, underestimation of the impact of weight in quality of life, low weight-related self-efficacy, and weight-related external locus of control; and favorable profile-positive body image, high self-efficacy, and internal locus of control. The relationship between three socioeconomic dimensions-current socioeconomic status, childhood socioeconomic status, and neighborhood education status-and psycho-social profiles was assessed through binomial logistic regression adjusted for age, gender, depression, living alone, and weight status. RESULTS: Contrary to hypotheses, there were no associations between socioeconomic characteristics and obesity-related psycho-social profiles after adjustment for body mass index. Depressive symptoms (OR 2.21, 95% CI 2.70, 4.04) and being female (3.31, 95% CI 2.70, 4.40) were associated with an adverse psycho-social profile. CONCLUSIONS: Psycho-social profiles could help to understand the multifactorial nature of the determinants of obesity. LEVEL OF EVIDENCE: Level V, cross-sectional descriptive study.

Binge drinking and well-being in European older adults: do gender and region matter?

Background: We aimed to describe gender and region differences in the prevalence of binge drinking and in the association between binge drinking and well-being, among older adult Europeans. Methods: This is a cross-sectional study using the Survey of Health, Ageing and Retirement in Europe (SHARE) wave 4, conducted between 2011 and 2012, including 58 489 individuals aged 50 years or older. Sixteen European countries were grouped in four drinking culture regions: South, Central, North and East. We categorized drinking patterns as: never, former, no-binge and binge drinkers. We used the CASP-12 questionnaire to measure well-being. To assess the association between binge drinking and well-being, we fitted two-level mixed effects linear models. Results: The highest percentage of binge drinkers was found in Central Europe (17.25% in men and 5.05% in women) and the lowest in Southern Europe (9.74% in men and 2.34% in women). Former, never and binge drinkers had a significant negative association with well-being as compared with no-binge drinkers. There was a significant interaction in this association by gender and region. Overall, associations were generally stronger in women and in Southern and Eastern Europe. The negative association of binge drinking with well-being was especially strong in Southern European women (ß = -3.80, 95% CI: -5.16 to - 2.44, P value <0.001). Conclusion: In Southern and Eastern European countries the association between binge drinking and well-being is stronger, especially in women, compared with Northern and Central Europe. Cultural factors (such as tolerance to drunkenness) should be further explored.

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.

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.