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Covariation of the incidence of type 1 diabetes with country characteristics available in public databases.

ABSTRACT: The incidence of Type 1 Diabetes (T1D) in children varies dramatically between countries. Part of the explanation must be sought in environmental factors. Increasingly, public databases provide information on country-to-country environmental differences.Information on the incidence of T1D and country characteristics were searched for in the 194 World Health Organization (WHO) member countries. T1D incidence was extracted from a systematic literature review of all papers published between 1975 and 2014, including the 2013 update from the International Diabetes Federation. The information on country characteristics was searched in public databases. We considered all indicators with a plausible relation with T1D and those previously reported as correlated with T1D, and for which there was less than 5% missing values. This yielded 77 indicators. Four domains were explored: Climate and environment, Demography, Economy, and Health Conditions. Bonferroni correction to correct false discovery rate (FDR) was used in bivariate analyses. Stepwise multiple regressions, served to identify independent predictors of the geographical variation of T1D.T1D incidence was estimated for 80 WHO countries. Forty-one significant correlations between T1D and the selected indicators were found. Stepwise Multiple Linear Regressions performed in the four explored domains indicated that the percentages of variance explained by the indicators were respectively 35% for Climate and environment, 33% for Demography, 45% for Economy, and 46% for Health conditions, and 51% in the Final model, where all variables selected by domain were considered. Significant environmental predictors of the country-to-country variation of T1D incidence included UV radiation, number of mobile cellular subscriptions in the country, health expenditure per capita, hepatitis B immunization and mean body mass index (BMI).The increasing availability of public databases providing information in all global environmental domains should allow new analyses to identify further geographical, behavioral, social and economic factors, or indicators that point to latent causal factors of T1D.

SUBMITTER: Diaz-Valencia PA

PROVIDER: S-EPMC4338253 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Covariation of the incidence of type 1 diabetes with country characteristics available in public databases.

Diaz-Valencia Paula Andrea PA Bougnères Pierre P Valleron Alain-Jacques AJ

PloS one 20150223 2

<h4>Background</h4>The incidence of Type 1 Diabetes (T1D) in children varies dramatically between countries. Part of the explanation must be sought in environmental factors. Increasingly, public databases provide information on country-to-country environmental differences.<h4>Methods</h4>Information on the incidence of T1D and country characteristics were searched for in the 194 World Health Organization (WHO) member countries. T1D incidence was extracted from a systematic literature review of a ...[more]

PMID: 25706995

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Project description:Background:Clinical inertia is related to the difficulty of achieving and maintaining optimal glycemic control. It has been extensively studied the delay of the period to insulin introduction in type 2 diabetes mellitus (T2DM) patients. This study aims to evaluate clinical inertia of insulin treatment intensification in a group of T2DM patients followed at a tertiary public Diabetes Center with limited pharmacologic armamentarium (Metformin, Sulphonylurea and Human Insulin). Methods:This is a real life retrospective record based study with T2DM patients. Demographic, clinical and laboratory characteristics were reviewed. Clinical inertia was considered when the patients did not achieve the individualized glycemic goals and there were no changes on insulin daily dose in the period. Results:We studied 323 T2DM patients on insulin therapy (plus Metformin and or Sulphonylurea) for a period of 2 years. The insulin daily dose did not change in the period and the glycated hemoglobin (A1c) ranged from 8.8?+?1.8% to 8.7?±?1.7% (basal vs 1st year; ns) and to 8.5?±?1.8% (basal vs 2nd year; p?=?0.035). The clinical inertia prevalence was 65.8% (basal), 61.9% (after 1 year) and 58.2% (after 2 years; basal vs 1st year vs 2nd year; ns). In a subgroup of 100 patients, we also studied the first 2 years after insulin introduction. The insulin daily dose ranged from 0.22?±?0.12 to 0.32?±?0.24 IU/kg of body weight/day (basal vs 1st year; p?<?0.001) and to 0.39?±?0.26 IU/kg of body weight/day (basal vs 2nd year; p?<?0.05). The A1c ranged from 9.6?+?2.1% to 8.6?+?2% (basal vs 1st year; p?<?0.001) and to 8.7?+?1.7% (1st year vs 2nd year; ns). The clinical inertia prevalence was 78.5% (at the moment of insulin therapy introduction), 56.2% (after 1 year; p?=?0.001) and 62.2% (after 2 years; ns). Conclusion:Clinical inertia prevalence ranged from 56.2 to 78.5% at different moments of the insulin therapy (first 2 years and long term) of T2DM patients followed at a tertiary public Diabetes Center from an upper-middle income country with limited pharmacologic armamentarium.

Project description:The objective of this study was to compare the effect of diabetes and pathologies potentially related to diabetes on the risk of infection and death from COVID-19 among people from Highly-Developed-Country (HDC), including Italians, and immigrants from the High-Migratory-Pressure-Countries (HMPC). Among the population with diabetes, whose prevalence is known to be higher among immigrants, we compared the effect of body mass index among HDC and HMPC populations. A population-based cohort study was conducted, using population registries and routinely collected surveillance data. The population was stratified into HDC and HMPC, according to the place of birth; moreover, a focus was set on the South Asiatic population. Analyses restricted to the population with type-2 diabetes were performed. We reported incidence (IRR) and mortality rate ratios (MRR) and hazard ratios (HR) with 95% confidence interval (CI) to estimate the effect of diabetes on SARS-CoV-2 infection and COVID-19 mortality. Overall, IRR of infection and MRR from COVID-19 comparing HMPC with HDC group were 0.84 (95% CI 0.82-0.87) and 0.67 (95% CI 0.46-0.99), respectively. The effect of diabetes on the risk of infection and death from COVID-19 was slightly higher in the HMPC population than in the HDC population (HRs for infection: 1.37 95% CI 1.22-1.53 vs. 1.20 95% CI 1.14-1.25; HRs for mortality: 3.96 95% CI 1.82-8.60 vs. 1.71 95% CI 1.50-1.95, respectively). No substantial difference in the strength of the association was observed between obesity or other comorbidities and SARS-CoV-2 infection. Similarly for COVID-19 mortality, HRs for obesity (HRs: 18.92 95% CI 4.48-79.87 vs. 3.91 95% CI 2.69-5.69) were larger in HMPC than in the HDC population, but differences could be due to chance. Among the population with diabetes, the HMPC group showed similar incidence (IRR: 0.99 95% CI: 0.88-1.12) and mortality (MRR: 0.89 95% CI: 0.49-1.61) to that of HDC individuals. The effect of obesity on incidence was similar in both HDC and HMPC populations (HRs: 1.73 95% CI 1.41-2.11 among HDC vs. 1.41 95% CI 0.63-3.17 among HMPC), although the estimates were very imprecise. Despite a higher prevalence of diabetes and a stronger effect of diabetes on COVID-19 mortality in HMPC than in the HDC population, our cohort did not show an overall excess risk of COVID-19 mortality in immigrants.

Dataset Information

Covariation of the incidence of type 1 diabetes with country characteristics available in public databases.

Publications

Covariation of the incidence of type 1 diabetes with country characteristics available in public databases.

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