Dataset Information

Future prevalence of type 2 diabetes-A comparative analysis of chronic disease projection methods.

ABSTRACT:

Background

Accurate projections of the future number of people with chronic diseases are necessary for effective resource allocation and health care planning in response to changes in disease burden.

Aim

To introduce and compare different projection methods to estimate the number of people with diagnosed type 2 diabetes (T2D) in Germany in 2040.

Methods

We compare three methods to project the number of males with T2D in Germany in 2040. Method 1) simply combines the sex- and age-specific prevalence of T2D in 2010 with future population distributions projected by the German Federal Statistical Office (FSO). Methods 2) and 3) additionally account for the incidence of T2D and mortality rates using partial differential equations (PDEs). Method 2) models the prevalence of T2D employing a scalar PDE which incorporates incidence and mortality rates. Subsequently, the estimated prevalence is applied to the population projection of the FSO. Method 3) uses a two-dimensional system of PDEs and estimates future case numbers directly while future mortality of people with and without T2D is modelled independently from the projection of the FSO.

Results

Method 1) projects 3.6 million male people with diagnosed T2D in Germany in 2040. Compared to 2.8 million males in 2010, this equals an increase by 29%. Methods 2) and 3) project 5.9 million (+104% compared to 2010) and 6.0 million (+116%) male T2D patients, respectively.

Conclusions

The results of the three methods differ substantially. It appears that ignoring temporal trends in incidence and mortality may result in misleading projections of the future number of people with chronic diseases. Hence, it is essential to include these rates as is done by method 2) and 3).

SUBMITTER: Voeltz D

PROVIDER: S-EPMC8901066 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Future prevalence of type 2 diabetes-A comparative analysis of chronic disease projection methods.

Voeltz Dina D Tönnies Thaddäus T Brinks Ralph R Hoyer Annika A

PloS one 20220307 3

<h4>Background</h4>Accurate projections of the future number of people with chronic diseases are necessary for effective resource allocation and health care planning in response to changes in disease burden.<h4>Aim</h4>To introduce and compare different projection methods to estimate the number of people with diagnosed type 2 diabetes (T2D) in Germany in 2040.<h4>Methods</h4>We compare three methods to project the number of males with T2D in Germany in 2040. Method 1) simply combines the sex- an ...[more]

PMID: 35255104

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Project description:BackgroundChronic kidney disease (CKD) is a significant long-term complication of diabetes and is a primary contributor to end-stage kidney disease.ObjectiveThis study aimed to report comprehensive nationwide data on the prevalence, screening, and awareness rates of CKD in Chinese patients with type 2 diabetes, along with associated risk factors.MethodsBaseline data analysis of the ongoing prospective, observational IMPROVE study was conducted. The study cohort comprised patients who had been diagnosed with type 2 diabetes more than 12 months prior, received at least 1 hypoglycemic medication, and were aged ≥18 years. The participants completed questionnaires and underwent laboratory assessments, including blood and urine samples. The data encompassed patient demographics, medical history, concurrent medications, and comorbidities. Comprehensive evaluations involved physical examinations, urinary albumin-to-creatinine ratio (UACR), estimated glomerular filtration rate (eGFR), glycated hemoglobin (HbA1c), fasting blood glucose, 2-hour postprandial blood glucose, fasting blood lipid profile, and urinalysis. Descriptive statistics were applied for data interpretation, and logistic regression analyses were used to identify the CKD-associated risk factors in patients with type 2 diabetes.ResultsA national study from December 2021 to September 2022 enlisted 9672 participants with type 2 diabetes from 45 hospitals that had endocrinology departments. The enrollees were from diverse regions in China, as follows: central (n=1221), east (n=3269), south (n=1474), north (n=2219), and west (n=1489). The prevalence, screening, and awareness rates of CKD among patients with type 2 diabetes were 31% (2997/9672), 27% (810/2997), and 54.8% (5295/9672), respectively. Multivariate binary regression analysis revealed that the CKD risk factors were screening, awareness, smoking, age, diabetes duration, concurrent antihypertensive and microcirculation medications, diabetic complications (foot, retinopathy, and neuropathy), hypertension, elevated low-density lipoprotein (LDL) cholesterol, and suboptimal glycemic control. Subgroup analysis highlighted an increased CKD prevalence among older individuals, those with prolonged diabetes durations, and residents of fourth-tier cities. Residents of urban areas that had robust educational and economic development exhibited relatively high awareness and screening rates. Notably, 24.2% (1717/7107) of patients with an eGFR ≥90 mL/min/1.73 m2 had proteinuria, whereas 3.4% (234/6909) who had a UACR <30 mg/g presented with an eGFR <60 mL/min/1.73 m2. Compared with patients who were cognizant of CKD, those who were unaware of CKD had increased rates of HbA1c ≥7%, total cholesterol >5.18 μmol/L, LDL cholesterol >3.37 μmol/L, BMI ≥30 kg/m2, and hypertension.ConclusionsIn a Chinese population of adults with type 2 diabetes, the CKD prevalence was notable, at 31%, coupled with low screening and awareness rates. Multiple risk factors for CKD have been identified.Trial registrationClinicalTrials.gov NCT05047471; https://clinicaltrials.gov/study/NCT05047471.

Dataset Information

Future prevalence of type 2 diabetes-A comparative analysis of chronic disease projection methods.

Background

Aim

Methods

Results

Conclusions

Publications

Future prevalence of type 2 diabetes-A comparative analysis of chronic disease projection methods.

Similar Datasets

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