title: Characterising the extent of misreporting of high blood pressure, high cholesterol, and diabetes using the Australian Health Survey.
abstract: Measuring and monitoring the true prevalence of risk factors for chronic conditions is essential for evidence-based policy and health service planning. Understanding the prevalence of risk factors for cardiovascular disease (CVD) in Australia relies heavily on self-report measures from surveys, such as the triennial National Health Survey. However, international evidence suggests that self-reported data may substantially underestimate actual risk factor prevalence. This study sought to characterise the extent of misreporting in a large, nationally-representative health survey that included objective measures of clinical risk factors for CVD.
  
  
  
  This study employed a cross-sectional analysis of 7269 adults aged 18 years and over who provided fasting blood samples as part of the 2011-12 Australian Health Survey. Self-reported prevalence of high blood pressure, high cholesterol and diabetes was compared to measured prevalence, and univariate and multivariate logistic regression analyses identified socio-demographic characteristics associated with underreporting for each risk factor.
  
  
  
  Approximately 16 % of the total sample underreported high blood pressure (measured to be at high risk but didn't report a diagnosis), 33 % underreported high cholesterol, and 1.3 % underreported diabetes. Among those measured to be at high risk, 68 % did not report a diagnosis for high blood pressure, nor did 89 % of people with high cholesterol and 29 % of people with high fasting plasma glucose. Younger age was associated with underreporting high blood pressure and high cholesterol, while lower area-level disadvantage and higher income were associated with underreporting diabetes.
  
  
  
  Underreporting has important implications for CVD risk factor surveillance, policy planning and decisions, and clinical best-practice guidelines. This analysis highlights concerns about the reach of primary prevention efforts in certain groups and implications for patients who may be unaware of their disease risk status.
authors.affiliation: WHO Collaborating Centre for Obesity Prevention, Faculty of Health, Deakin University, Locked Bag 20001, Geelong, VIC, 3220, Australia. karen.peterson@deakin.edu.au.
authors.forename: Karen Louise
authors.initials: KL
authors.lastname: Peterson
authors.affiliation: WHO Collaborating Centre for Obesity Prevention, Faculty of Health, Deakin University, Locked Bag 20001, Geelong, VIC, 3220, Australia.
authors.forename: Jane Philippa
authors.initials: JP
authors.lastname: Jacobs
authors.affiliation: WHO Collaborating Centre for Obesity Prevention, Faculty of Health, Deakin University, Locked Bag 20001, Geelong, VIC, 3220, Australia.
authors.forename: Steven
authors.initials: S
authors.lastname: Allender
authors.affiliation: WHO Collaborating Centre for Obesity Prevention, Faculty of Health, Deakin University, Locked Bag 20001, Geelong, VIC, 3220, Australia.
authors.forename: Laura Veronica
authors.initials: LV
authors.lastname: Alston
authors.affiliation: WHO Collaborating Centre for Obesity Prevention, Faculty of Health, Deakin University, Locked Bag 20001, Geelong, VIC, 3220, Australia.
authors.forename: Melanie
authors.initials: M
authors.lastname: Nichols
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D001315:Australia / Q000453:epidemiology; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000209:etiology; D002784:Cholesterol; D003430:Cross-Sectional Studies; D003920:Diabetes Mellitus / Q000453:epidemiology*; D005260:Female; D006306:Health Surveys / Q000706:statistics & numerical data; D006801:Humans; D006937:Hypercholesterolemia / Q000453:epidemiology*; D006973:Hypertension / Q000453:epidemiology*; D007182:Income; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015995:Prevalence; D012307:Risk Factors; D057566:Self Report; D055815:Young Adult
chemical_list: D002784:Cholesterol
keywords: Cardiovascular disease/epidemiology; Diabetes mellitus/epidemiology; Health surveys; Hypercholesterolemia/epidemiology; Hypertension/epidemiology; Logistic models; Multivariate analysis; Odds ratio; Self disclosure