Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Introduction: Timely, affordable, and sustained interventions reduce the risk of heart attack or Stroke in people with a high total risk of cardiovascular diseases (CVD). Risk prediction tools are available to estimate the cardiovascular risk using information on multiple variables. CVD risk charts prepared by the World Health Organization (WHO) has laboratory-based and non-laboratory-based charts with the latter meant for use in resource limited settings. We conducted a study to determine concordance between the laboratory- and non-laboratory risk charts and to estimate the prevalence of selected CVD risk factors in a rural Indian population.
Methods: A community-based cross-sectional study was conducted in rural area of Ballabgarh in district Faridabad, Haryana. Sample of 1,018 participants aged 30-69 years was selected randomly from study area. Information on CVDs risk factors was obtained using WHO STEPS questionnaire, anthropometry and laboratory investigation. Risk distribution among the study participants was observed. Concordance between laboratory- and non-laboratory-based WHO CVD risk charts was determined using agreement analysis.
Results: The mean age of the study participants was 43.9 (8.9) years and 55.6% participants were women. Among various CVD risk factors, hypertension (39.4%) was the major factor followed by overweight (34.1%) was found to be major factor, followed by current smoking (23.6%) and hypercholesterolemia (18.7%). The concordance between the two charts was 83.3% with kappa value of 0.64. Considering laboratory-based charts as the gold standard, the sensitivity and specificity of non-laboratory-based risk charts at 5% risk as cut-off was 86.5% and 90.3% respectively.
Conclusion: The study shows a good agreement between the laboratory-based and non-laboratory-based risk charts. Thus non-laboratory-based risk charts are suitable for risk estimation of CVDs for use in resource limited settings like India.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9438460 | PMC |
http://dx.doi.org/10.5334/gh.1148 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!