Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015 Jul 27;10(7):e0131256.
doi: 10.1371/journal.pone.0131256. eCollection 2015.

Not Just the Demographic Change--The Impact of Trends in Risk Factor Prevalences on the Prediction of Future Cases of Myocardial Infarction

Wolfgang Hoffmann  1 , Jeanette Bahr  2 , Kerstin Weitmann  1 , Robert Herold  2 , Thomas Kohlmann  2 , Neeltje van den Berg  1
Affiliations

Not Just the Demographic Change--The Impact of Trends in Risk Factor Prevalences on the Prediction of Future Cases of Myocardial Infarction

Wolfgang Hoffmann et al. PLoS One. .

Abstract

Background: Previous predictions of population morbidity consider demographic changes only. To model future morbidity, however, changes in prevalences of risk factors should be considered. We calculated the number of incident cases of first myocardial infarction (MI) in Mecklenburg-Western Pomerania in 2017 considering the effects of demographic changes and trends in the prevalences of major risk factors simultaneously.

Methods: Data basis of the analysis were two population-based cohorts of the German Study of Health in Pomerania (SHIP-baseline [1997-2001] and the 5-year follow-up and SHIP-Trend-baseline [2008-2011] respectively). SHIP-baseline data were used to calculate the initial coefficients for major risk factors for MI with a Poisson regression model. The dependent variable was the number of incident cases of MI between SHIP-baseline and SHIP-5-year follow-up. Explanatory variables were sex, age, a validated diagnosis of hypertension and/or diabetes, smoking, waist circumference (WC), increased blood levels of triglycerides (TG) and low-density-lipoprotein cholesterol (LDL), and low blood levels of high-density-lipoprotein cholesterol (HDL). Applying the coefficients determined for SHIP baseline to risk factor prevalences, derived from the new cohort SHIP-Trend together with population forecast data, we calculated the projected number of incident cases of MI in 2017.

Results: Except for WC and smoking in females, prevalences of risk factors in SHIP-Trend-baseline were lower compared to SHIP-baseline. Based on demographic changes only, the calculated incidence of MI for 2017 compared to the reference year 2006 yields an increase of MI (males: +11.5%, females: +8.0%). However, a decrease of MI (males: -23.7%, females: -17.1%) is shown considering the changes in the prevalences of risk factors in the projection.

Conclusions: The predicted number of incident cases of MI shows large differences between models with and without considering changes in the prevalences of major risk factors. Hence, the prediction of incident MI should preferably not only be based on demographic changes.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flow diagram of the analysis strategy (Abbreviations: WC, waist circumference; TG, triglyceride; HDL, high-density-lipoprotein; LDL, low-density-lipoprotein).
Fig 2
Fig 2. Flow diagram of the data processing.
Fig 3
Fig 3. Relative changes of the prevalences of risk factors from SHIP-Trend compared to SHIP prevalences, age- and sex-standardized on the population of from Mecklenburg-Western Pomerania in 2006.
Abbreviations: WC, waist circumference; TG, triglyceride; HDL, high-density-lipoprotein; LDL, low-density-lipoprotein.
Fig 4
Fig 4. Absolute numbers of incident cases of MI in Mecklenburg-Western Pomerania in 2006 and 2017 with and without considering the changes in the prevalences of risk factors between 1997–2001 and 2008–2011.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Fendrich K, Hoffmann W. More than just aging societies: the demographic change has an impact on actual numbers of patients. J Public Health 2007;15: 345–351.
    1. Foerch C, Misselwitz B, Sitzer M, Steinmetz H, Neumann-Haefelin T. The projected burden of stroke in the German Federal State of Hesse up to the year 2050. Dtsch Arztebl Int. 2008; 105: 467–473. 10.3238/arztebl.2008.0467 - DOI - PMC - PubMed
    1. Siewert U, Fendrich K, Doblhammer-Reiter G, Scholz RD, Schuff-Werner P, Hoffmann W. Health care consequences of demographic changes in Mecklenburg-West Pomerania: projected case numbers for age-related diseases up to the year 2020, based on the Study of Health in Pomerania (SHIP). Dtsch Arztebl Int. 2010;107: 328–334. 10.3238/arztebl.2010.0328 - DOI - PMC - PubMed
    1. Schulz E, Leidl R, Konig HH. The impact of ageing on hospital care and long-term care—the example of Germany. Health Policy 2004;67: 57–74. - PubMed
    1. Statistische Ämter des Bundes und der Länder: Demografischer Wandel in Deutschland–Auswirkungen auf Krankenhausbehandlungen und Pflegebedürftige im Bund und in den Ländern (Heft 2). Wiesbaden: 2008.