| Literature DB >> 29890937 |
D Abreu1, P Sousa2,3, C Matias-Dias4, F J Pinto5,6.
Abstract
BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death around the world; however, many CVD events could be prevented if we focused on modification of the main risk factors. Increased salt consumption is estimated to have caused millions of deaths, mostly related to CVD, particularly stroke, which is the leading cause of death in Portugal. In our study, we aim to assess trends in the proportion of high blood pressure (HBP) in Acute Coronary Syndrome (ACS) patients as well as the trends in stroke and ACS in Portugal, especially after a set of public health initiatives were implemented to reduce salt intake.Entities:
Keywords: Cardiovascular disease; High blood pressure; Population wide-approach; Public health
Mesh:
Substances:
Year: 2018 PMID: 29890937 PMCID: PMC5996516 DOI: 10.1186/s12889-018-5634-z
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 3.295
Demographic characterization of the data analysed
| ACS (DRG data) | Stroke (DRG data) | HBP in ACS patients (NRACS data) | |
|---|---|---|---|
| Before regulation | |||
| | 26,00% | 49,43% | 35,33% |
| | 37,93% | 77,15% | 65,38% |
| After regulation | |||
| | 26,36% | 49,61% | 32,67% |
| | 37,85% | 78,26% | 64,29% |
Fig. 1Proportion of patients with HBP in the ACS population. Female/male ratio from years 2002 to 2015
Fig. 2Trends in overall monthly proportion of HBP in ACS patients from January 2002 to December 2015. Dashed line represents the beginning of 2013, year associated to the estimated decline in trends
Results from the three regressions applied, segmented multiple linear regression model, for the proportion of HBP patients, and both multiples linear regressions using standard methods for interrupted time series, for the ACS and stroke outcomes
| β (CI) | |||
|---|---|---|---|
| HBP proportion | |||
| Overalla | |||
| Pre-breakpoint trend (change per month) | 0.004 (0.003;0.006) | – | – |
| Change in trend (post-breakpoint vs pre-breakpoint) | −0.006 | – | < 0.001 |
| Post-breakpoint trend (change per month) | −0.002 (− 0.003;-0.001) | – | – |
| Malea | |||
| Pre-breakpoint trend (change per month) | − 0.003(− 0.022;0.022) | – | – |
| Change in trend (post-breakpoint vs pre-breakpoint) | − 0.016 | – | 0.215 |
| Post-breakpoint trend (change per month) | −0.019(− 0.061;0.023) | – | – |
| Femalea | |||
| Pre-breakpoint trend (change per month) | −0.003(− 0.028;0.022) | – | – |
| Change in trend (post-breakpoint vs pre-breakpoint) | − 0.016 | – | 0.215 |
| Post-breakpoint trend (change per month) | −0.018(− 0.052;0.014) | – | – |
| Age < 65a | |||
| Pre-breakpoint trend (change per month) | −0.022(− 0.798;0.754) | – | – |
| Change in trend (post-breakpoint vs pre-breakpoint) | 0.011 | – | > 0.05 |
| Post-breakpoint trend (change per month) | −0.011(− 0.039;0.017) | – | – |
| Age ≥ 65a | |||
| Pre-breakpoint trend (change per month) | 0.029(−0.017;0.075) | – | – |
| Change in trend (post-breakpoint vs pre-breakpoint) | −0.018 | – | > 0.05 |
| Post-breakpoint trend (change per month) | 0.011(−0.017;0.039) | – | – |
| ACS crude rates (per 100,000 adults) | |||
| Overalla | |||
| Time of the breakpoint | −0.112(− 2.041;1.817) | −0.114 | 0.909 |
| Time of the breakpointa time interaction | −0.031(− 0.09;0.036) | −0.903 | 0.368 |
| Malea | |||
| Time of the breakpoint | 0.289(− 2.269;2.848) | 0.222 | 0.825 |
| Time of the breakpointa time interaction | −0.018(− 0.108;0.072) | −0.393 | 0.695 |
| Femalea | |||
| Time of the breakpoint | −0.497(− 1.927;0.932) | −0.682 | 0.496 |
| Time of the breakpointa time interaction | 0.013(−0.034;0.060) | 0.560 | 0.576 |
| Age < 65a | |||
| Time of the breakpoint | 0.151(−0.723;1.024) | 0.338 | 0.736 |
| Time of the breakpointa time interaction | −0.015(− 0.044;0.015) | −0.986 | 0.325 |
| Age ≥ 65a | |||
| Time of the breakpoint | 0.151(− 5.159;6.721) | 0.258 | 0.7970 |
| Time of the breakpointa time interaction | −0.015(− 0.358;0.066) | −1.349 | 0.1792 |
| Stroke crude rates (per 100,000 adults) | |||
| Overalla | |||
| Time of the breakpoint | 0.265(−1.399;1.929) | 0.312 | 0.755 |
| Time of the breakpointa time interaction | −0.037(− 0.091;0.016) | −1.370 | 0.172 |
| Malea | |||
| Time of the breakpoint | 0.900(−0.743;2.543) | 1.074 | 0.284 |
| Time of the breakpointa time interaction | −0.022(− 0.074;0.030) | −0.822 | 0.412 |
| Femalea | |||
| Time of the breakpoint | 0.999(−1.204;3.202) | 0.888 | 0.376 |
| Time of the breakpointa time interaction | −0.047(− 0.121;0.027) | −1.244 | 0.215 |
| Age < 65a | |||
| Time of the breakpoint | 0.448(−0.261;1.157) | 1.238 | 0.217 |
| Time of the breakpointa time interaction | −0.014(− 0.037;0.009) | −1.207 | 0.229 |
| Age ≥ 65a | |||
| Time of the breakpoint | 2.548(− 5.351;10.446) | 0.632 | 0.528 |
| Time of the breakpointa time interaction | −0.238(− 0.502;0.026) | −1.764 | 0.079 |
Values not presented in the table (−) are not available for the type of regression
β represents the coefficients in the regression
HBP High blood pressure, ACS Acute coronary syndrome, CI confidence interval
aAll models were adjusted for seasonality
Fig. 3Trends in overall monthly crude rates of CV admissions from January 2002 to December 2016 per 100,000 adults. a) Trends for stroke crude rates admissions. b) Trends for ACS crude rates admissions. Dashed lines represent the beginning of 2013, year associated to the estimated decline in trends