Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2015

Open Access 01-12-2015 | Research Article

Development and demonstration of a state model for the estimation of incidence of partly undetected chronic diseases

Authors: Ralph Brinks, Barbara H. Bardenheier, Annika Hoyer, Ji Lin, Sandra Landwehr, Edward W. Gregg

Published in: BMC Medical Research Methodology | Issue 1/2015

Login to get access

Abstract

Background

Estimation of incidence of the state of undiagnosed chronic disease provides a crucial missing link for the monitoring of chronic disease epidemics and determining the degree to which changes in prevalence are affected or biased by detection.

Methods

We developed a four-part compartment model for undiagnosed cases of irreversible chronic diseases with a preclinical state that precedes the diagnosis. Applicability of the model is tested in a simulation study of a hypothetical chronic disease and using diabetes data from the Health and Retirement Study (HRS).

Results

A two dimensional system of partial differential equations forms the basis for estimating incidence of the undiagnosed and diagnosed disease states from the prevalence of the associated states. In the simulation study we reach very good agreement between the estimates and the true values. Application to the HRS data demonstrates practical relevance of the methods.

Discussion

We have demonstrated the applicability of the modeling framework in a simulation study and in the analysis of the Health and Retirement Study. The model provides insight into the epidemiology of undiagnosed chronic diseases.
Appendix
Available only for authorised users
Literature
1.
Beagley J, Guariguata L, Weil C, Motala AA. Global estimates of undiagnosed diabetes in adults. Diabetes Res Clin Pract. 2014; 103(2):150–60.CrossRefPubMed
2.
Lao C, Brown C, Obertová Z, Edlin R, Rouse P, Hodgson F, et al. The costs of identifying undiagnosed prostate cancer in asymptomatic men in New Zealand general practice. Family practice. 2013; 30(6):641–47.CrossRefPubMed
3.
Hodgson N, Gitlin LN, Winter L, Czekanski K. Undiagnosed illness and neuropsychiatric behaviors in community-residing older adults with dementia. Alzheimer Dis Assoc Disord. 2011; 25(2):109.PubMedCentralCrossRefPubMed
4.
Harris M, Klein R, Welborn T, Knuiman M. Onset of NIDDM occurs at least 4–7 yr before clinical diagnosis. Diabetes Care. 1992; 15(7):815–9.CrossRefPubMed
5.
Porta M, Curletto G, Cipullo D, de la Longrais RR, Trento M, Passera P, et al. Estimating the delay between onset and diagnosis of type 2 diabetes from the time course of retinopathy prevalence. Diabetes Care. 2014; 37(6):1668–74.CrossRefPubMed
6.
Solomon PR, Murphy CA. Should we screen for Alzheimer’s disease? A review of the evidence for and against screening Alzheimer’s disease in primary care practice. Geriatrics. 2005; 60(11):26–31.PubMed
7.
Kahn R, Alperin P, Eddy D, Borch-Johnsen K, Buse J, Feigelman J, et al. Age at initiation and frequency of screening to detect type 2 diabetes: a cost-effectiveness analysis. Lancet. 2010; 375:1365–74.CrossRefPubMed
8.
Black J, Sharp S, Wareham N, Sandbaek A, Rutten G, Lauritzen T, et al. Does early intensive multifactorial therapy reduce modelled cardiovascular risk in individuals with screen-detected diabetes? Results from the ADDITION-Europe cluster randomized trial. Diabet Med. 2014; 31(6):647–56.PubMedCentralCrossRefPubMed
9.
10.
Geiss LS, Wang J, Cheng YJ, Thompson TJ, Barker L, Li Y, et al.Prevalence and incidence trends for diagnosed diabetes among adults aged 20 to 79 years, United States, 1980-2012. JAMA. 2014; 312(12):1218–26.CrossRefPubMed
11.
Brinks R, Landwehr S, Icks A, Koch M, Giani G. Deriving age-specific incidence from prevalence with an ordinary differential equation. Stat Med. 2013; 32(12):2070–78.CrossRefPubMed
12.
13.
Schenzle D. An age-structured model of pre-and post-vaccination measles transmission. Math Med Biol. 1984; 1(2):169–91.CrossRef
14.
Brinks R, Landwehr S. Age-and time-dependent model of the prevalence of non-communicable diseases and application to dementia in Germany. Theor Popul Biol. 2014; 92:62–8.CrossRefPubMed
15.
Kalbfleisch J, Prentice R. The Statistical Analysis of Failure Time Data, 2nd edn. Hoboken: John Wiley & Sons; 2002.CrossRef
16.
Woodward M. Epidemiology: Study Design and Data Analysis. Texts in statistical science. Boca Raton: Chapman & Hall/CRC; 2005.
17.
Rait G, Walters K, Bottomley C, Petersen I, Iliffe S, Nazareth I. Survival of people with clinical diagnosis of dementia in primary care: cohort study. BMJ. 2010; 341:3584. doi:10.​1136/​bmj.​c3584.CrossRef
18.
Ziegler U, Doblhammer G. Prävalenz und Inzidenz von Demenz in Deutschland–Eine Studie auf Basis von Daten der gesetzlichen Krankenversicherungen von 2002. Gesundheitswesen. 2009; 71(5):281–90.CrossRefPubMed
19.
Polyanin AD, Zaitsev VF, Moussiaux A. Handbook of First-Order Partial Differential Equations. Boca Raton: CRC Press; 2001.
20.
Draper NR, Smith H. Applied Regression Analysis. Wiley Series in Probability and Statistics. Hoboken: Wiley; 2014.
21.
22.
23.
Clair PS, Bugliari D, Campbell N, Chien S, Hayden O, Hurd M, et al.RAND HRS data documentation - Version L. Technical report, Labor & Population Program, RAND Center for the Study of Aging. 2011.
24.
Oakley JE, O’Hagan A. Probabilistic sensitivity analysis of complex models: a Bayesian approach. J R Stat Soc Ser B (Stat Methodol). 2004; 66(3):751–69.CrossRef
25.
Keiding N. Age-specific incidence and prevalence: a statistical perspective. J R Stat Soc A. 1991; 154:371–412.CrossRef
26.
Brunet RC, Struchiner CJ. A non-parametric method for the reconstruction of age-and time-dependent incidence from the prevalence data of irreversible diseases with differential mortality. Theor Popul Biol. 1999; 56(1):76–90.CrossRefPubMed
27.
Boyle JP, Thompson TJ, Gregg EW, Barker LE, Williamson DF. Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Popul Health Metrics. 2010; 8(1):29.CrossRef
28.
Hamers F, Phillips A. Diagnosed and undiagnosed HIV-infected populations in Europe. HIV Med. 2008; 9(s2):6–12.CrossRefPubMed
29.
Najafzadeh M, Marra CA, Lynd LD, Sadatsafavi M, FitzGerald JM, McManus B, Sin D. Future impact of various interventions on the burden of COPD in Canada: a dynamic population model. PloS one. 2012; 7(10):46746.CrossRef
30.
Manns B, Hemmelgarn B, Tonelli M, Au F, Chiasson TC, Dong J, et al.Population based screening for chronic kidney disease: cost effectiveness study. BMJ. 2010; 341:5869. doi:10.​1136/​bmj.​c5869.CrossRef
31.
Raffaelli M, Sessa L, Mingrone G, Bellantone R. Assessing the obese diabetic patient for bariatric surgery: which candidate do i choose?Diabetes, metabolic syndrome and obesity: targets and therapy. 2015; 8:255.CrossRef
32.
Gregg E, Chen H, Wagenknecht L, Clark J, Delahanty L, Bantle J, et al.Association of an intensive lifestyle intervention with remission of type 2 diabetes. JAMA. 2012; 308(23):2489–96.CrossRefPubMed
33.
Pournaras DJ, Osborne A, Hawkins SC, Vincent RP, Mahon D, Ewings P, et al.Remission of type 2 diabetes after gastric bypass and banding: mechanisms and 2 year outcomes. Ann Surg. 2010; 252(6):966–71.CrossRefPubMed
34.
Ardestani A, Rhoads D, Tavakkoli A. Insulin cessation and diabetes remission after bariatric surgery in insulin-treated type 2 diabetic adults. Diabetes care. 2015; 38(4):659–64.PubMed
Metadata
Title
Development and demonstration of a state model for the estimation of incidence of partly undetected chronic diseases
Authors
Ralph Brinks
Barbara H. Bardenheier
Annika Hoyer
Ji Lin
Sandra Landwehr
Edward W. Gregg
Publication date
01-12-2015
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2015
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-015-0094-y

Other articles of this Issue 1/2015

BMC Medical Research Methodology 1/2015 Go to the issue

Research article

Exploring physician specialist response rates to web-based surveys

Research article

Sample size calculations for skewed distributions

Research article

Development of the scale of hygıene behavıors for nursıng students

Research article

Comparing denominator degrees of freedom approximations for the generalized linear mixed model in analyzing binary outcome in small sample cluster-randomized trials

Research article

Optimal cut-point definition in biomarkers: the case of censored failure time outcome

Research article

Optimal likelihood-ratio multiple testing with application to Alzheimer’s disease and questionable dementia