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Routine data sources challenge international diabetes Federation extrapolations of national diabetes prevalence in Switzerland.

ABSTRACT: OBJECTIVE: Information on diabetes prevalence in the general population is scarce and often based on extrapolations. We evaluated whether prevalence could be estimated from routine data sources. RESEARCH DESIGN AND METHODS: The sources were 1) hospital discharges (2008, n = 828,171), 2) death registry (2007/2008, n = 118,659), and 3) Swiss Health Survey (SHS; 2007, n = 18,665). Persons without diabetes as underlying cause of death (death registry) or principal diagnosis (hospital discharges) were regarded as surrogate for a general population random sample. RESULTS: In those aged 20-84 years, 4.5% of men and 3% of women were expected to have diabetes. By source, estimations were 4.4 and 2.8% (hospital discharges), 3.8 and 3.1% (death registry), and 4.9 and 3.7% (SHS) for men and women, respectively. Among sources, age-sex patterns were similar. CONCLUSIONS: In countries with adequate data quality, combination of routine data may provide valid and reliable estimations of diabetes prevalence. Our figures suggest that International Diabetes Federation extrapolations substantially overestimate diabetes prevalence in Switzerland.

SUBMITTER: Bopp M

PROVIDER: S-EPMC3198273 | biostudies-other | 2011 Nov

REPOSITORIES: biostudies-other

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Routine data sources challenge international diabetes Federation extrapolations of national diabetes prevalence in Switzerland.

Bopp Matthias M Zellweger Ueli U Faeh David D

Diabetes care 20110916 11

<h4>Objective</h4>Information on diabetes prevalence in the general population is scarce and often based on extrapolations. We evaluated whether prevalence could be estimated from routine data sources.<h4>Research design and methods</h4>The sources were 1) hospital discharges (2008, n = 828,171), 2) death registry (2007/2008, n = 118,659), and 3) Swiss Health Survey (SHS; 2007, n = 18,665). Persons without diabetes as underlying cause of death (death registry) or principal diagnosis (hospital di ...[more]

PMID: 21926288

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Project description:ObjectiveTo determine the association between diabetes mellitus (DM) and marijuana use.DesignCross-sectional study.SettingData from the National Health and Nutrition Examination Survey (NHANES III, 1988-1994) conducted by the National Center for Health Statistics of the Centers for Disease Control and Prevention.ParticipantsThe study included participants of the NHANES III, a nationally representative sample of the US population. The total analytic sample was 10 896 adults. The study included four groups (n=10 896): non-marijuana users (61.0%), past marijuana users (30.7%), light (one to four times/month) (5.0%) and heavy (more than five times/month) current marijuana users (3.3%). DM was defined based on self-report or abnormal glycaemic parameters. We analysed data related to demographics, body mass index, smoking status, alcohol use, total serum cholesterol, high-density lipoprotein, triglyceride, serum 25-hydroxy vitamin D, plasma haemoglobin A1c, fasting plasma glucose level and the serum levels of C reactive protein and four additional inflammatory markers as related to marijuana use.Main outcome measuresOR for DM associated with marijuana use adjusted for potential confounding variables (ie, odds of DM in marijuana users compared with non-marijuana users).ResultsMarijuana users had a lower age-adjusted prevalence of DM compared to non-marijuana users (OR 0.42, 95% CI 0.33 to 0.55; p<0.0001). The prevalence of elevated C reactive protein (>0.5 mg/dl) was significantly higher (p<0.0001) among non-marijuana users (18.9%) than among past (12.7%) or current light (15.8%) or heavy (9.2%) users. In a robust multivariate model controlling for socio-demographic factors, laboratory values and comorbidity, the lower odds of DM among marijuana users was significant (adjusted OR 0.36, 95% CI 0.24 to 0.55; p<0.0001).ConclusionsMarijuana use was independently associated with a lower prevalence of DM. Further studies are needed to show a direct effect of marijuana on DM.

Project description:Background:Vaccine-preventable diseases among high-risk patients are a public health priority in high-income countries. Most national immunization programs have included vaccination recommendations for these population groups but they remain hard-to-reach and coverage data are poorly available. In a pilot study, we developed and tested an automated approach for identifying individuals with underlying medical conditions to feed an immunization information system (IIS). Methods:We reviewed published recommendations on medical conditions that indicate vaccination against influenza, pneumococcal disease, meningococcal disease, hepatitis A, and hepatitis B. For each medical condition, we identified the International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis and procedure codes, the user fee exempt codes and the Anatomical Therapeutic Chemical Classification System codes and we reported these data in correspondence tables. Using these tables, we extracted three lists of patients recorded in three current data sources between 2001 and 2010 in the Apulia region of Italy: the hospital discharge registry, the user fee exempt registry, and the drug prescription registry. Using a unique personal identification number, we linked these three lists of patients with the regional IIS (2012 database), obtaining a list of patients with chronic diseases eligible for vaccination. We tested completeness, sensitivity, and positive predictive value (PPV) of this approach by asking a sample of 28 general practitioners (GPs) to evaluate the matching between a sublist of patients with clinical recommendations for influenza vaccination and the GPs individual subjects medical records. Results:We included a total of 1,204,496 subjects with underlying medical conditions eligible to receive any of the aforementioned vaccinations. Of these, 9% were identified in all three data sources, 18% in two sources, and 73% in one source. The completeness of this automated process in identifying GPs high-risk patients eligible for influenza vaccination was 88.9% [95% confidence intervals (95% CI): 88.1-89.8%], with a sensitivity of 69.2% (95% CI: 67.7-70.6%) and a PPV of 85.7% (95% CI: 84.4-86.8%). Conclusion:The high completeness of the methodology used for identifying high-risk patients in current data sources encouraged us to apply this approach for feeding the regional IIS.

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Routine data sources challenge international diabetes Federation extrapolations of national diabetes prevalence in Switzerland.

Publications

Routine data sources challenge international diabetes Federation extrapolations of national diabetes prevalence in Switzerland.

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