Prevalence, incidence and mortality of diagnosed diabetes: evidence from an Italian population‐based study

Monesi, Lara; Baviera, Marta; Marzona, Irene; Avanzini, Fausto; G, Monesi; Nobili, Alessandro; Tettamanti, Mauro; Cortesi, Laura; Riva, Emma; Fortino, Ida; Bortolotti, Angela; Fontana, Giancarlo; Merlino, Luca; Roncaglioni, Maria Carla

doi:10.1111/j.1464-5491.2011.03446.x

Cited by 67 publications

(54 citation statements)

References 27 publications

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“…However, death rate even dominated incidence rate in determining prevalence in older adults. This is consistent with previous observations by Monesi et al [5]. Diabetes has been found to be the seventh leading cause of death among U.S. adults [22].…”

Section: Discussionsupporting

confidence: 83%

“…A model representing all major population mechanisms such as births, deaths, aging, migration, and diabetes incidence will help to assess relative sensitivities or strength of associations of each of these mechanisms to diabetes prevalence. Moreover, such a mathematical model also allows for estimating parameters of some critical mechanisms, for example, death rates [5]. The death rates associated with diseases are often estimated based on the information reported on death certificates.…”

Section: Introductionmentioning

confidence: 99%

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A mathematical model for determining age-specific diabetes incidence and prevalence using body mass index

Appuhamy

Kebreab

2013

Annals of Epidemiology

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a b s t r a c tPurpose: Few models have been developed specifically for the epidemiology of diabetes. Diabetes incidence is critical in predicting diabetes prevalence. However, reliable estimates of disease incidence rates are difficult to obtain. The aim of this study was to propose a mathematical framework for predicting diabetes prevalence using incidence rates estimated within the model using body mass index (BMI) data. Methods: A generic mechanistic model was proposed considering birth, death, migration, aging, and diabetes incidence dynamics. Diabetes incidence rates were determined within the model using their relationships with BMI represented by the Hill equation. The Hill equation parameters were estimated by fitting the model to National Health and Nutrition Examination Survey (NHANES) 1999e2010 data and used to predict diabetes prevalence pertaining to each NHANES survey year. The prevalences were also predicted using diabetes incidence rates calculated from the NHANES data themselves. The model was used to estimate death rate parameters and to quantify sensitivities of prevalence to each population dynamic. Results: The model using incidence rate estimates from the Hill equations successfully predicted diabetes prevalence of younger, middle-aged, and older adults (prediction error, 20.0%, 9.64%, and 7.58% respectively). Diabetes prevalence was positively associated with diabetes incidence in every age group, but the associations among younger adults were stronger. In contrast, diabetes prevalence was more sensitive to death rates in older adults than younger adults. Both diabetes incidence and prevalence were strongly sensitive to BMI at younger ages, but sensitivity gradually declined as age progressed. Younger and middle aged adults diagnosed with diabetes had at least a two-fold greater risk of death than their nondiabetic counterparts. Nondiabetic older adults were found to be under slightly higher death risk (0.079) than those diagnosed with diabetes (0.073). Conclusions: The proposed model predicts diagnosed diabetes incidence and prevalence reasonably well using the link between BMI and diabetes development risk. Ethnic group and gender-specific model parameter estimates could further improve predictions. Model prediction accuracy and applicability need to be comprehensively evaluated with independent data sets.Published by Elsevier Inc. IntroductionDiabetes prevalence is rising dramatically worldwide and is expected to rise from 366 million in 2011 to 552 million by 2030 [1]. More than 10% of world health care expenditure and about 14% of U.S. healthcare costs are attributable to diabetes [2]. Quantifying diabetes prevalence is important to allow rational planning of prevention programs and allocating resources for people affected by diabetes [2]. Mathematical models can be used effectively to estimate disease prevalence and help understand factors affecting disease development risk. The majority of diabetesrelated mathematical models explain clinical aspects of glucosee insulin dynamics, wh...

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Section: Discussionsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

A mathematical model for determining age-specific diabetes incidence and prevalence using body mass index

Appuhamy

Kebreab

2013

Annals of Epidemiology

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“…There have been several national reports on diabetes prevalence and incidence, with different findings. Some describe an increased prevalence of diabetes [8,[12][13][14][15][16][17]; the majority report a stable incidence [12,13,15,16,18], some an increasing incidence [19,20] and one a decreasing incidence [8]. Explanations for these discrepant findings could be related to the decade in which the studies were performed, the definitions applied, the type of register used and the ethnic composition of the population.…”

Section: Discussionmentioning

confidence: 74%

Incidence, prevalence and mortality of type 2 diabetes requiring glucose-lowering treatment, and associated risks of cardiovascular complications: a nationwide study in Sweden, 2006–2013

et al. 2016

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“…From a recent study published in 2012 the prevalence of diabetes in a region of Italy is shown to rise from 3.0% in 2000 to 4.2% in 2007 (40% more in only 7 years). The incidence shows a rate of 4 cases per 1000 per year (10). In other Italian regions higher prevalence values are published (11).…”

Section: Discussionmentioning

confidence: 96%

Smoking, diabetes, blood hypertension: possible etiologic role for Peyronie’s disease? Analysis in 279 patients with a control group in Sicily

Pavone¹,

D’Amato²,

Dispensa³

et al. 2015

Arch Ital Urol Androl

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Objective: To assess the proportion of patients with Peyronie's Disease (PD) and the possible association with its potential risk factors in the general population of the central and western Sicily in our weekly andrological outpatient clinic. Materials and methods: We recruited a sample of 279 consecutive patients consulting our andrological outpatient clinic. Two arms were created: the first one composed by PD patients (men with symptoms suggestive for PD), the second one composed by patients with other andrological diseases (control arm). For each patient we evaluated the age, cigarette smoking, diabetes, blood hypertension and erectile function. In the PD arm we administered validated questionnaires to determine the erectile function status by the International Index of Erectile Function 5 (IIEF-5) and the pain status during erection by the Visual Analogue Scale (VAS). A univariate analysis was conducted using R software. Results: We enrolled 279 consecutive patients. The number of PD patients was 97 (34,7%). The univariate analysis showed a correlation between PD and cigarette smoking (p = 0.0242), blood hypertension (p < 0.001), erectile dysfunction (p < 0.001). No significant association was observed between diabetes and PD (p = 0.358). The median age of PD arm was 60 years and the median age of the control arm was 63,5 years; therefore the median age of PD arm resulted lower than the median age of the control arm (p = 0,031). Conclusions: Peyronie's disease is more common than we might think; furthermore it can be diagnosed among young patients. According to our results, cigarette smoking and blood hypertension may be considered statically significant risk factors for developing PD. On the contrary diabetes seems not to be a risk factor for PD. According to our results PD should be sought also in young patients. Further studies are necessary to confirm that removing the indicated risk factors may reduce the incidence of PD. SummaryNo conflict of interest declared.

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Prevalence, incidence and mortality of diagnosed diabetes: evidence from an Italian population‐based study

Cited by 67 publications

References 27 publications

A mathematical model for determining age-specific diabetes incidence and prevalence using body mass index

A mathematical model for determining age-specific diabetes incidence and prevalence using body mass index

Incidence, prevalence and mortality of type 2 diabetes requiring glucose-lowering treatment, and associated risks of cardiovascular complications: a nationwide study in Sweden, 2006–2013

Smoking, diabetes, blood hypertension: possible etiologic role for Peyronie’s disease? Analysis in 279 patients with a control group in Sicily

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