| Title |
Lower educational level and unemployment increase the impact of cardiometabolic conditions on the quality of life: results of a population-based study in South Australia
|
|---|---|
| Published in |
Quality of Life Research, February 2017
|
| DOI | 10.1007/s11136-017-1503-y |
| Pubmed ID | |
| Authors |
David Alejandro González-Chica, Robert Adams, Eleonora Dal Grande, Jodie Avery, Phillipa Hay, Nigel Stocks |
| Abstract |
To investigate if sociodemographic characteristics increase the adverse effects of cardiovascular diseases (CVD) and cardiometabolic risk factors (CMRF) on health-related quality of life (HRQoL). Cross-sectional, face-to-face survey investigating 2379 adults living in South Australia in 2015 (57.1 ± 14 years; 51.7% females). Questions included diagnosis of CMRF (obesity, diabetes, hypertension, dyslipidaemia) and CVD. Physical and mental HRQoL were assessed using the SF-12v1 questionnaire. Multiple linear regression models including confounders (sociodemographic, lifestyle, use of preventive medication) and interaction terms between sociodemographic variables and cardiometabolic conditions were used in adjusted analysis. The prevalence of CMRF (one or more) was 54.6% and CVD was 13.0%. The physical HRQoL reduced from 50.8 (95%CI 50.2-51.4) in healthy individuals to 45.1 (95%CI 44.4-45.9) and 39.1 (95%CI 37.7-40.5) among those with CMRF and CVD, respectively. Adjustment for sociodemographic variables reduced these differences in 33%, remaining stable after controlling for lifestyle and use of preventive medications (p < 0.001). Differences in physical HRQoL according to cardiometabolic conditions were twice as high among those with lower educational level, or if they were not working. Among unemployed, having a CMRF or a CVD had the same impact on the physical HRQoL (9.7 lower score than healthy individuals). The inverse association between cardiometabolic conditions and mental HRQoL was subtle (p = 0.030), with no evidence of disparities due to sociodemographic variables. A lower educational level and unemployment increase the adverse effects of cardiometabolic conditions on the physical HRQoL. Targeted interventions for reducing CMRF and/or CVD in these groups are necessary to improve HRQoL. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 90 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 18 | 20% |
| Student > Master | 15 | 17% |
| Student > Ph. D. Student | 9 | 10% |
| Researcher | 6 | 7% |
| Student > Postgraduate | 5 | 6% |
| Other | 10 | 11% |
| Unknown | 27 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 26 | 29% |
| Nursing and Health Professions | 9 | 10% |
| Social Sciences | 6 | 7% |
| Psychology | 5 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 3% |
| Other | 11 | 12% |
| Unknown | 30 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 February 2017.
All research outputs
#18,531,724
of 22,953,506 outputs
Outputs from Quality of Life Research
#2,063
of 2,907 outputs
Outputs of similar age
#313,258
of 424,183 outputs
Outputs of similar age from Quality of Life Research
#45
of 66 outputs
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