+ Site Statistics
References:
54,258,434
Abstracts:
29,560,870
PMIDs:
28,072,757
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

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



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



Plos One 10(7): E0131256



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. 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. 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. 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.

(PDF emailed within 0-6 h: $19.90)

Accession: 058427509

Download citation: RISBibTeXText

PMID: 26214851

DOI: 10.1371/journal.pone.0131256


Related references

Predicting future mortality from acute myocardial infarction in a Western society considering population shift, present trends and risk factor intervention. Journal of the American College of Cardiology 33(2 SUPPL A): 307A, 1999

Differential impact of admission C-reactive protein levels on 28-day mortality risk in patients with ST-elevation versus non-ST-elevation myocardial infarction (from the Monitoring Trends and Determinants on Cardiovascular Diseases [MONICA]/Cooperative Health Research in the Region of Augsburg [KORA] Augsburg Myocardial Infarction Registry). American Journal of Cardiology 102(9): 1125-1130, 2008

Prognostic impact of demographic factors and clinical features on the mode of death in high-risk patients after myocardial infarction--a combined analysis from multicenter trials. Clinical Cardiology 28(10): 471-478, 2005

Womens recovery during the first year after an acute myocardial infarction Trends in risk factor modification. European Heart Journal 19(ABST SUPPL ): 575, 1998

Impact of cardiovascular events on change in quality of life and utilities in patients after myocardial infarction: a VALIANT study (valsartan in acute myocardial infarction). Jacc. Heart Failure 2(2): 159-165, 2015

Impact of diabetes on long-term survival after acute myocardial infarction: comparability of risk with prior myocardial infarction. Diabetes Care 24(8): 1422-1427, 2001

Impact of early risk stratification on the length of hospitalization in patients with acute Q-wave myocardial infarction. 'The 60-minutes myocardial infarction project'. Cardiology 90(3): 212-219, 1999

In patients with acute myocardial infarction, the impact of hyperglycemia as a risk factor for mortality is not homogeneous across age-groups. Diabetes Care 35(1): 150-152, 2012

Risk stratification in patients after myocardial infarction using demographic risk factors A powerful prognostic indicator. Journal Of The American College Of Cardiology. 37(2 Supplement A): 331a-332a, Ruary, 2001

Impact of low levels of vascular endothelial growth factor after myocardial infarction on 6-month clinical outcome. Results from the Nagoya Acute Myocardial Infarction Study. Circulation Journal 76(6): 1509-1516, 2012

Demographic characteristics and trends in arteriosclerotic heart disease mortality: Sudden death and myocardial infarction. Circulation 52(6 Suppl): Iii1-Ii15, 1975

Duration of treatment with nonsteroidal anti-inflammatory drugs and impact on risk of death and recurrent myocardial infarction in patients with prior myocardial infarction: a nationwide cohort study. Circulation 123(20): 2226-2235, 2011

Renal dysfunction as a risk factor for painless myocardial infarction: results from Korea Acute Myocardial Infarction Registry. Clinical Research in Cardiology 101(10): 795-803, 2013

Previous myocardial infarction as a risk factor for in-hospital cardiovascular outcomes (from the National Registry of Myocardial Infarction 4 and 5). American Journal of Cardiology 111(12): 1694-1700, 2013