comparing self-reported and measured high blood pressure and high cholesterol status using data from...

394 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 2010 vol. 34 no. 4© 2010 The Authors. Journal Compilation © 2010 Public Health Association of Australia

Comparing self-reported and measured high blood

pressure and high cholesterol status using data from

a large representative cohort study

Abstract

Objective: To examine the relationship

between self-reported and clinical

measurements for high blood pressure

(HBP) and high cholesterol (HC) in a

random population sample.

Method: A representative population sample

of adults aged 18 years and over living in

the north-west region of Adelaide (n=1537)

were recruited to the biomedical cohort

study in 2002/03. In the initial cross-sectional

component of the study, self-reported HBP

status and HC status were collected over

the telephone. Clinical measures of blood

pressure were obtained and fasting blood

taken to determine cholesterol levels. In

addition, data from a continuous chronic

disease and risk factor surveillance system

were used to assess the consistency of self-

reported measures over time.

Result: Self-report of current HBP and

HC showed >98% specificity for both, but

sensitivity was low for HC (27.8%) and

moderate for HBP (49.0%). Agreement

between current self-report and clinical

measures was moderate (kappa 0.55)

for HBP and low (kappa 0.30) for HC.

Demographic differences were found with

younger people more likely to have lower

sensitivity rates. Self-reported estimates

for the surveillance system had not varied

significantly over time.

Conclusion: Although self-reported

measures are consistent over time

there are major differences between the

self-reported measures and the actual

clinical measurements. Technical aspects

associated with clinic measurements could

explain some of the difference.

Implications: Monitoring of these broad

population measures requires knowledge

of the differences and limitations in

population settings.

Key words: hypertension, cholesterol,

data collection, validity, cross sectional

survey.

Aust NZ J Public Health. 2010; 34:394-400

doi: 10.1111/j.1753-6405.2010.00572.x

Anne Taylor, Eleonora Dal Grande, Tiffany Gill Population Research and Outcomes Studies Unit, South Australia Health

Sandra PickeringHealth Observatory , Department of Medicine, University of Adelaide, The Queen Victoria Hospital, South Australia

Janet GrantPopulation Research and Outcomes Studies Unit, South Australia Health

Robert AdamsHealth Observatory , Department of Medicine, University of Adelaide, The Queen Victoria Hospital, South Australia

Patrick PhillipsEndocrine and Diabetes Service, University of Adelaide, The Queen Victoria Hospital, South Australia

Hy p e r t e n s i o n a n d h y p e r -

cholesterolemia are two of the most

important factors in the prevention

and control of diabetes, cardiovascular disease

and metabolic syndrome. Determining

population measures of high blood pressure

(HBP) and total high cholesterol (HC)

using clinical measurements is an expensive

and time-consuming undertaking. Most

commonly these measurements are gained,

at the population level, by surveys, with

respondents self-reporting their HBP/HC

status based on recall as to whether they

had been told by a doctor or nurse they had

high readings.

Many chronic disease and risk factor

surveillance systems rely on self-report as the

basis of the data collection.1-3 These systems

provide an important service in highlighting

prevalence estimates for health planners,

policy makers and health promotion experts.

Although acknowledgement is made that

Submitted: July 2009 Revision requested: November 2009 Accepted: February 2010Correspondence to:Dr Anne Taylor, Population Research and Outcomes Studies Unit, SA Dept of Health, Level 8, CitiCentre Building, 11 Hindmarsh Sq, Adelaide, South Australia 5000. Fax: (08) 8226 6244; e-mail: [email protected]

the values are only ‘estimates’ of the true

prevalence, they are important as monitoring

indicators when population studies based

on actual clinically controlled studies

are undertaken with considerable gaps

between years of collection. It is therefore

important that the self-reported estimates

are as accurate as possible, of the highest

standard, and the differences are known. One

way of assessing the appropriateness of the

self-reported data is by validity studies with

criterion validity studies the most highly

relevant.

Studies overseas have determined

the agreement between self-reported

hypertension and/or cholesterol against actual

measurements using three methodologies.

The first group have used administrative data

(use of health service or personal medical

records) to assess the presence of HBP or

HC against self-reported measures.1-5,9,10

Sensitivity (true positives), specificity (true

Chronic illnesses Article

2010 vol. 34 no. 4 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 395© 2010 The Authors. Journal Compilation © 2010 Public Health Association of Australia

negatives) and the predictive value of a positive or negative answer

were calculated to evaluate the utility of these data. Studies have

reported sensitivity rates for HBP varying between 73% and 83%

and specificity between 81% to 95%.5-7 For HC, sensitivity ranges

were lower and varied from 59% to 64% with specificity ranging

from 84% to 93%.5,9,10

Secondly, studies have assessed the overall agreement of these

two variables/indicators using self-completed questionnaires

followed by clinical examinations using non-random, convenience

samples or targeted racial/ethnic groups.11-14 The only study to

assess sensitivity reported 87% for persons aged between 18 and

74 years for HBP.14 Specificity was also high (96%).

There are limited contemporary studies that have assessed

clinically measured HBP and/or HC against self-reported from

large random population samples. Natarajan et al.15 using

NHANES III (1988-1991) data, has reported sensitivity of 51%,

specificity of 89% (and Predictive Value Positive (PVP) 87% and

Predictive Value Negative (PVN) 55%) for HC. Vargas et al.16 used

the NHANES III (1988-1994) data set reported a sensitivity of

71%, specificity of 90%, for HBP with a PVP of 72% and PVN

of 89%. Giles et al.17 in a major regional US study, had similar

findings for HBP although there was some variation between

white and black respondents. In a 1993 assessment of BRFSS

respondents, values were overall lower for both HBP and HC

(sensitivity of 43% for HBP and 44% for HC, and specificity >

85% for both).18

To ascertain the agreement between these self-reported measures

in an Australian setting, data from the North West Adelaide

Health Study (NWAHS) were utilised. The data were also used

to undertake comparisons between different demographic groups.

The analysis was based on previous research assessing the

difference between self-reported and actual height and weight.19

In addition, data from the South Australian Monitoring and

Surveillance System (SAMSS), a continuous chronic disease and

risk factor system based on telephone interviews, were used to

assess the consistency of these self-reported measures over time.

MethodologySurvey samples

NWAHS is a representative biomedical cohort study of adults,

aged 18 years and over at the time of recruitment, living in the

north and west regions of Adelaide.20,21 The study is a collaboration

between SA Health, The Queen Elizabeth Hospital, the Lyell

McEwin Hospital, The University of Adelaide, the University

of South Australia and SA Pathology (formally the Institute

of Medical & Veterinary Science). Phase 1A of this study was

conducted primarily in 2000. Phase 1B of the study was conducted

between August 2002 and July 2003. Data used in these analyses

were obtained from Phase 1B. Details of the NWAHS design,

procedures and participants have been published elsewhere.20-22

Initially participants were recruited via a Computer Assisted

Telephone Interviewing (CATI) system based on a random

selection of telephone numbers in the Electronic White Pages

(EWP). The adult in the household with the last birthday was

selected for inclusion in the study. Interviews were conducted

in English. Prior to the recruitment telephone call, households

were sent an approach letter detailing the study. Respondents

successfully recruited into the study were sent a detailed

information pack and were asked to attend one of the two clinics in

the region at a time suitable to them. All protocols and procedures

were approved by the North Western Adelaide Health Service –

Ethics of Human Research Committee and the Aboriginal Health

Research Ethics Committee of South Australia.

Biomedical data were collected at the clinic. Participants were

asked if they were currently on medication for hypertension,

cholesterol or lipid lowering medication. These questions were

asked before blood pressure was measured and blood samples

drawn. Blood pressure was assessed by the average of two

measurements, recorded with a calibrated sphygmomanometer and

taken five to ten minutes apart while the participant was seated and

relaxed. HBP was defined as systolic blood pressure ≥140 mmHg

and/or diastolic blood pressure ≥90 mmHg.23,24

A fasting blood sample was drawn for glucose and lipid profile to

determine diabetes and cholesterol. HC was defined as total serum

plasma cholesterol ≥5.5 mmol/L.25 Other biomedical measures

and key findings are detailed elsewhere.26-29

Self-report data were collected in the initial recruitment

telephone call (including if they had ‘ever’ been told by a doctor

or nurse that they have HC or HBP, and if they still have HC or

HBP), and selected demographic information. Primary assessment

was made between clinical measurements and those reporting still

having HBP or HC. Additional comparisons were made between

those who self reported ‘ever’ having HBP or HC and the clinical

measurements.

To assess the consistency of the self-reported questions over

time, data from SAMSS (where the identical self-reported

questions on HBP and HC had been used since July 2002)

were used.30 All households in SA, with a number listed in the

EWP were eligible for selection in the SAMSS sample. A letter

introducing the surveillance system was sent to the household of

each selected telephone number. The letter informed people of the

purpose of the interview and indicated that they could expect a

telephone call within a certain time frame. Within each household,

the person who had their birthday last, aged zero years and over,

was selected for interview. Data were collected every month by

a contracted agency and interviews were conducted in English.

Proxy interviews were undertaken for respondents aged under 16

years. The response rate for SAMSS from July 2002 until June

2008 was between 65% and 70% each month. The Computer

Assisted Telephone Interview (CATI) system was used to conduct

the interviews. At least 10 call backs were made to the telephone

number selected to interview household members. Replacement

interviews for persons who could not be contacted or interviewed

was not permitted. Analysis for the comparison was limited to

SAMSS respondents aged 18+ years (n = 31,930).

Chronic illnesses Comparing self-reported and measured blood pressure and cholesterol


Statistical analysesThe NWAHS data were weighted to reflect region, age groups,

sex and probability of selection in the household to the Australian

Bureau of Statistic’s 2001 Census population.31 SAMSS data were

weighted by age, sex and area of residence to reflect the structure of

the population in South Australia to the latest Census or Estimated

Residential Population. Probability of selection in the household

is calculated based on the number of people in the household and

the number of listings in the White Pages. Weighting is used to

correct for disproportionality of the sample with respect to the

population of interest.

Data were analysed using SPSS version 15.0. The conventional

p value of 5% was used as the criteria for statistical significance.

Comparisons between self-reported current HBP and HC and

biomedically measured HBP or HC were made in terms of

sensitivity, specificity, positive predictive value (PPV), and

negative predictive value (NPV). These comparisons were made

for age groups and gender and other relevant variables.

For the SAMSS data, graphic interpretation was presented

by two-way fractional-polynomial prediction plots using time

in months as the X variable. In addition, yearly estimates were

obtained by combining the relevant 12 months of data.

ResultsThe overall response rate for Phase 1B of the NWAHS study

was 47.1% with n=1,537 people participating in both the telephone

questionnaire and attending the clinic for biomedical assessments.

Clinic data collected were missing for n=12 (0.8%) on blood

pressure and cholesterol. Analysis was undertaken only on those

in which both self-reported and clinical measured details were

available (n=1525; 99% of total sample).

Table 1 shows the demographic profile of the NWAHS

respondents used in the analyses. Previous analyses have compared

NWAHS study with ABS census data to assess bias.22

From the NWAHS CATI interview, 23.9% (95% CI 21.8 – 26.1)

of the respondents reported having ‘ever’ been told they had HBP

and 15.8% (95% CI 14.1 - 17.8) reported they still had HBP. In

addition, 1% of respondents reported not knowing if they ever had

HBP or had never had their blood pressure measured. Of those

who reported previously being told they had HBP, 6.1% reported

not knowing if they still had HBP. At the clinic, 30.6% (95% CI

28.3 - 32.9) had HBP and/or were currently taking medication for

their blood pressure.

Overall 23.8% (95% CI 21.8 – 26.0) reported having ‘ever’

been told they had HC and 12.3% (95% CI 10.8 – 14.1) reported

they still had HC. In addition, 3.3% of respondents reported not

knowing if they ever had HC or had never had their cholesterol

measured. Of those who reported previously being told they had

HC, 27.2% reported not knowing if they still had HC. At the clinic

42.8% (95% CI 40.4 – 45.3) had HC and/or were currently taking

medication for their cholesterol.

Table 2 shows the prevalence of self-reported (current) and

clinical measured HBP and HC by selected demographic and

health risk factors and detail the sensitivity, specificity, PPV and

NPV for these measures. The trend analysis highlighted in Figures

1 and 2 using SAMSS data shows the consistency of the self-report

current HBP and HC with little variation over the years and with no

significant increase or decrease over time. The comparable figures

for HBP and HC in 2003 (12 months of SAMSS data combined)

were 16.3% and 12.8%, a non-significant difference from the

NWAHS self-reported figures of 15.8% and 12.3% (2002/03).

When analysis was undertaken comparing the self-reported

‘ever’ diagnosed with the clinical measurements, sensitivity

and specificity were 49.3% and 91.6% for HBP and 48.5% and

94.6% for HC. Kappas were 0.54 (p<0.001) and 0.46 (p<0.001)

respectively.

Table 1: Demographic profile of NWAHS respondents.

n %

Gender

Male 749 49.1

Female 776 50.9

Age groups

18 to 24 185 12.1

25 to 44 603 39.5

45 to 64 447 29.3

65 years and over 290 19.0

Country of birth

Australia 1,103 72.3

UK or Ireland 209 13.7

Europe 119 7.9

Asia 54 3.5

Other 29 1.9

Not stated 11 0.7

Marital status

Married or living with partner 958 62.8

Separated or divorced 128 8.4

Widowed 89 5.8

Never married 336 22.0

Not stated 14 0.9

Employment status

Full time employed 596 39.1

Part time / casual employment 306 20.0

Unemployed 48 3.1

Home duties 182 11.9

Retired 267 17.5

Student 71 4.6

Other 39 2.6

Not stated 16 1.1

Educational attainment

Secondary 633 41.5

Trade, Apprenticeship, Certificate, Diploma 621 40.7

Bachelor degree or higher 178 11.7

Other/Not stated 93 6.1

Gross household annual income

up to $20,000 309 20.2

$20,001-$40,000 352 23.1

$40,001-$60,000 322 21.1

$60,001+ 437 28.7

Not stated 105 6.9Total 1,525 100.0

Taylor et al. Article


DiscussionThe variation between current self-reported and clinically

measured HBP and HC seems considerable with the overall

difference between prevalence estimates of 14.8% for HBP and

30.5% for HC. There were some differences by sex, age group

and other key descriptors between those reporting current HBP/

HC and measured HBP/HC. Kappa values were generally in

the medium range. In terms of sensitivity, rates varied but were

generally about 50% (indicating that approximately 50% of true

cases were detected by self-report). Specificity was high (98+%)

indicating that most people who self-reported they did not have

current HBP or HC were found to not have a high reading in the

clinic environment. These analyses have shown that in a clinic

setting many more people are determined to have HBP or HC

than those reporting a current high measurement. These results are

similar to those reported for BRFSS18 but less than other similar

studies. Methodological, cultural, timing and guideline differences

make direct comparisons difficult. The SAMSS data show that the

self-report prevalence estimates are consistent and have not varied

considerably over time. Previous CATI field testing has shown

excellent reliability for both HBP and HC.32

These variations are less dramatic if the ‘ever’ estimates are

used with differences of 6.7% for HBP and 19.0% for HC. People

who self-report ‘no’ to current HBP/HC could be on medication

and rightly report that they currently did not have high readings.

In the clinic setting respondents who were on medication were

classified as having the condition.

There are many reasons for the differences between current

self-reported and clinic measurements besides the obvious

‘condition under-control’ scenario. Technical aspects associated

with measuring HBP could be a factor. Although the measurement

of BP within the NWAHS clinic was undertaken to best practice

Table 2: Proportion of participant with current high blood pressure from self-reported CATI recruitment interview and clinical measurements and sensitivity, specificity, positive, negative predictive values.

Prevalence Self-reported Measured Percentage Kappa P-value Sensitivity Specificity Positive Negative difference predictive predictive value value

Current high blood pressure Overall 15.8 30.6 -14.8 0.55 <0.001 49.0 98.8 94.6 81.5

Gender

Male 14.6 32.4 -17.8 0.47 <0.001 41.8 98.5 92.9 77.9

Female 17.0 28.8 -11.8 0.64 <0.001 56.9 99.1 96.1 85.0

Age groups

18 to 44 2.3 8.4 -6.1 0.26 <0.001 18.1 99.1 66.0 93.0

45 to 64 20.3 39.9 -19.6 0.49 <0.001 47.5 97.8 93.4 73.8

65 years and over 46.0 76.9 -30.9 0.40 <0.001 59.5 98.7 99.4 42.2

BMI categories

Normal/Underweight 8.3 15.7 -7.4 0.63 <0.001 51.2 99.6 96.2 91.7

Overweight 16.5 33.2 -16.7 0.54 <0.001 47.9 99.3 97.2 79.4

Obese 24.5 45.5 -21.0 0.47 <0.001 49.3 96.2 91.5 69.4

Smoking status

Non smoker 17.5 32.4 -14.9 0.58 <0.001 51.8 99.0 96.0 81.1

Ex-smoker 19.2 38.1 -18.9 0.54 <0.001 49.4 99.3 97.7 76.1 Current smoker 6.2 13.7 -7.5 0.38 <0.001 31.8 97.9 70.1 90.1

0

1020

3040

Pre

vale

nce

(%)

July

2003

Janu

ary 20

04

July

2004

Janu

ary 20

05

July

2005

Janu

ary 20

06

July

2006

Janu

ary 20

07

July

2007

Janu

ary 20

08

July

2008

Month

Actual Predicted 95% CI

010

2030

40

July

2003

Janu

ary 20

04

July

2004

Janu

ary 20

05

July

2005

Janu

ary 20

06

July

2006

Janu

ary 20

07

July

2007

Janu

ary 20

08

July

2008

July

2003

Janu

ary 20

04

July

2004

Janu

ary 20

05

July

2005

Janu

ary 20

06

July

2006

Janu

ary 20

07

July

2007

Janu

ary 20

08

July

2008

Male Female


Pre

vale

nce

(%)

Month

Graphs by sex

Figure 1: Overall and gender trends in proportion of adults (18+ years) with high blood pressure, 2003 to 2008

5

Figure 1: Overall and gender trends in proportion of adults (18+ years) with high blood pressure, 2003 to 2008.



standards, issues outside the control of the clinic staff could

affect measurement readings. These include the ‘white coat

syndrome’33,34 and the fact that an individual’s blood pressure

varies considerably across the day and from day to day.35 To

diagnose HBP, readings should be taken over more than one

visit; even two measurements taken five to 10 minutes apart does

not ensure a correct diagnosis with blood pressure measurement

obtained at a single point of time manifestly overestimating the true

prevalence.36 As argued by Marshall7 ‘measured blood pressure

is intrinsically variable because every cardiac cycle produces a

different blood pressure’. It is also argued that the diagnosis of

hypertension should be deferred for two years37; that 24-hour blood

pressure monitoring is required to properly diagnose HBP38; and

that there is difference depending upon which arm is used for the

measurement.39 For clinically meaningful diagnosis of HBP it

has also been argued that the use of a unified cuff size rather than

one tailored to BMI could produce differences in readings.14,40

Also compounding the self-report recall is the ‘arbitrary’ and

changeable cut-off points (e.g. 140/90) that has seen best practice

guidelines change over the years.38

For HC, confusion exists between the different components of

total cholesterol. Identification of cardiac risk factors including

HC and HBP, and subsequent effective management is relatively

infrequent in general practice.41 Identifying HC requires blood

testing, which may contribute to a relative under-diagnosis,

particularly in younger people where the level of cardio-metabolic

risk may not be as well appreciated by patients or clinicians.

Notwithstanding, the large differences between current self-

report and clinical measures, there are some good outcomes of this

study. The fact that both HBP and HC specificity levels were very

high (98%+) indicates that recall was not a problem. Self-report

failed to identify 18.5% of people found to have HBP in the clinic

010

2030

Pre

vale

nce

(%)

July

2003

Janu

ary 20

04

July

2004

Janu

ary 20

05

July

2005

Janu

ary 20

06

July

2006

Janu

ary 20

07

July

2007

Janu

ary 20

08

July

2008

Month


010

2030

July

2003

Janu

ary 20

04

July

2004

Janu

ary 20

05

July

2005

Janu

ary 20

06

July

2006

Janu

ary 20

07

July

2007

Janu

ary 20

08

July

2008

July

2003

Janu

ary 20

04

July

2004

Janu

ary 20

05

July

2005

Janu

ary 20

06

July

2006

Janu

ary 20

07

July

2007

Janu

ary 20

08

July

2008

Male Female


Pre

vale

nce

(%)

Month

Graphs by sex

Figure 2: Overall and gender trends in proportion of adults (18+ years) with high cholesterol, 2003 to 2008

6

Figure 2: Overall and gender trends in proportion of adults (18+ years) with high cholesterol, 2003 to 2008.

Table 3: Proportion of participant with current high cholesterol from self-reported CATI recruitment interview and clinical measurements and sensitivity, specificity, positive, negative predictive values.

Prevalence Self-reported Measured Percentage Kappa P-value Sensitivity Specificity Positive Negative difference predictive predictive value value

Current high cholesterol

Overall 12.3 42.8 -30.5 0.30 <0.001 27.8 99.2 96.4 64.7

Gender

Male 13.7 43.5 -29.8 0.32 <0.001 30.5 99.2 96.7 65.0

Female 11.0 42.2 -31.2 0.27 <0.001 25.0 99.3 96.1 64.5

Age groups

18 to 44 2.8 27.8 -25.0 0.12 <0.001 8.8 99.6 88.4 73.9

45 to 64 16.9 55.6 -38.7 0.24 <0.001 28.7 97.9 94.4 52.2

65 years and over 31.5 64.3 -32.8 0.41 <0.001 49.0 100.0 100.0 52.1

BMI categories

Normal/Underweight 7.6 30.0 -22.4 0.30 <0.001 23.8 99.4 94.5 75.3

Overweight 13.4 46.2 -32.8 0.30 <0.001 28.8 99.9 99.5 62.1

Obese 16.8 54.1 -37.3 0.25 <0.001 29.2 97.8 94.0 53.9

Smoking status

Non smoker 11.5 41.3 -29.8 0.28 <0.001 26.3 98.9 94.2 65.6

Ex-smoker 17.6 50.5 -32.9 0.34 <0.001 34.4 99.4 98.4 59.8 Current smoker 6.7 35.6 -28.9 0.22 <0.001 18.3 99.8 98.0 68.8



and 35.3% of those found to have HC as a result of their blood

test. These clinic measurements could be true under-diagnosis

where the respondents were unaware of his/her condition and, in

the case of blood pressure, the difference could be classified as

pre-hypertension.42,43 There is also the consideration that under

reporting could be related to misinterpretation, inadequate or

inappropriate communication between patient and doctor or

the changing of guidelines.44 In common with other Western

countries, around 60% of Australians have health literacy levels

that are not at the ‘minimum required for individuals to meet

the complex demands of everyday life and work in the emerging

knowledge-based economy’.45 Studies predominantly from the US

have shown that limited functional health literacy is associated

with less frequent preventive health behaviours and less active

self-management of chronic conditions.46,47 In addition, it should

be considered that older persons are more aware of their overall

health status and medical problems and are more likely to have

regular medical check-ups.

The strength of this study lies in its representative nature,

the large random sample, the high response rate, the clinical

operations and stringent measurement standards. Although the

response rate associated with the complete study involvement,

including obtaining blood and other bio-medical measurements,

was 49.6% (60% of people interviewed by telephone), this is

relatively high when compared to other recent, comparable

Australian studies.27 There is a trend towards lower response rates

in all types of population surveys as people protect their privacy,

or are overwhelmed by marketing telephone calls or mail outs.

The additional commitments associated with involvement in a

cohort study (including invasive tests such as the taking of blood

samples) add to respondent burden.

Weaknesses of the clinic measurements include the arbitrary

cut off point undertaken in our analysis, the dichotomy of the

diagnosis being made based on the average of the two readings and

the problems associated with determining clinical high readings

of these two measurements as detailed previously. In addition,

this analysis only assessed the total cholesterol measurement

and did not attempt to break down the levels of different types

of cholesterol. Questionnaire construction in self-report studies

could also have been expanded so that the self-report and clinical

measurements were exactly comparable especially in terms of

medication and control. In addition, although relatively high in

comparison to some other major studies, the low response rate

adds some responder bias. Extensive analysis has been undertaken

on the bias associated with this study19 and although the data are

weighted by age, sex, area and probability of selection, some

bias, as a result of under or over representation of some groups,

would be expected.

The study has shown major differences between estimates of

HBP and HC based on self-report estimates and those obtained

from clinic measures. However, people without high clinical

readings of blood pressure and cholesterol are not likely to report

they have HBP or HC. This suggests that self-report is more

likely to under- than to over-estimate the true prevalence of these

conditions. We conclude that self-reports of HBP and HC are

suitable for population monitoring, provided the limitations of

this information are acknowledged.

AcknowledgementsThe authors would like to thank the NWAHS participants and

the clinic staff.

References1. Department of Health [publication and report page on the Internet]. Perth

(AUST): Government of Western Australia; 2006 [cited 2007 Oct]. Population Surveys: Western Australian Health and Wellbeing Surveillance System. Available from: http://www.health.wa.gov.au/publications/pop_surveys.cfm

2. NSW Health [public health page on the Internet]. Sydney (AUST): State Government of New South Wales; 2006 [cited 2007 Oct]. New South Wales Health Survey Program. Available from: http://www.health.nsw.gov.au/public-health/survey/hsurvey.html

3. Taylor A. (2006) Chronic Disease Surveillance in South Australia. South Australia Public Health Bulletin [serial on the Internet]. 2006 [cited 2007 Oct 17]; 3:6-8. Available from: www.dh.sa.gov.au/pehs/publications/PHB-chron-disease-ed3-06.pdf

4. Muhajarine N, Mustard C, Roos LL, Young TK , Gelskey DE. Comparison of survey and physician claims data for detecting hypertension. J Clin Epidemiol. 1997;50(6):711-8.

5. Robinson JR, Young TK, Roos LL, Gelskey DE. Estimating the burden of disease: comparing administrative data and self-report. Med Care. 1997;35(9):932-47.

6. Klungel OH, de Boer A, Paes AH, Seidell JC, Bakker A. Cardiovascular diseases and risk factors in a population-based study in The Netherlands: agreement between questionnaire information and medical records. Neth J Med. 1999;55(4):177-83.

7. Okura Y, Urban LH, Mahoney DW, Jacobsen SJ, Rodeheffer RJ. Agreement between self-report questionnaires and medical records data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin Epidemiol. 2004;57:1096-103.

8. Tormo MJ, Navarro C, Chirlaque MD, Barber X, and the EPIC group of Spain. Validation of self diagnosis of high blood pressure in a sample of the Spanish EPIC cohort: overall agreement and predictive values. J Epidemiol Community Health. 2002;54:221-6.

9. St Sauver JL, Hagen PT, Cha SS, Bagniewski SM, et al. Agreement between patient reports of cardiovascular disease and patient medical records. Mayo Clin Proc. 2005;80(2):203-10.

10. Martin LM, Leff M, Calonge N, Garrett C, Nelson DE. Validation of self-reported chronic conditions and health services in a managed care population. Am J Prev Med. 2000;18(3):215-18.

11. Fakiri FE, Bruijnzeels MA, Hoes AW. No evidence for marked ethnic differences in accuracy of self-reported diabetes, hypertension and hypercholesterolemia. J Clin Epidemiol. 2007;60:1271-9.

12. Alonso A, Beunza JJ, Delgado-Rodriguez M, Martinez-Gonzalez MA. Validation of self reported diagnosis of hypertension in a cohort of university graduates in Spain. BMC Public Health. 2005;5:94.

13. Perryman S, Beerman KA. Know your numbers: comparing participants and non-participants in a university health screening program. J Am Coll Health. 1997;46(2):87-91

14. Chen Y, Rennie DC, Lockinger LA, Dosman JA. Association between obesity and high blood pressure: reporting bias related to gender and age. Int J Obes [Lond]. 1998;22:771-7.

15. Natarajan S, Lipsitz SR, Nietert PJ. Self-report of high cholesterol – determinates of validly in US adults. Am J Prev Med. 2002;23(1):13-21.

16. Vargas CM, Burt VL, Gillum RF, Pamuk ER. Validity of self-reported hypertension in the National Health and Nutrition Examination Survey III, 1988-1991. Prev Med. 1997(26):678-85.

17. Giles WH, Croft JB, Keenan NL, Lane MJ, Wheeler FC. The validity of self-reported hypertension and correlates of hypertension awareness among blacks and whites within the stoke belt. Am J Prev Med. 1995;11(3):163-9.

18. Bowlin SJ, Morrill BD, Nafziger AN, Jenkins PL, Lewis C, Pearson TA. Validity of cardiovascular disease risk factors assessed by telephone survey: the behavioural Risk Factor Survey. J Clin Epidemiol. 1993;46(6):561-71.

19. Taylor AW, Dal Grande E, Gill TK, Chittleborough CR, Wilson DH, Adams RA, et al. How valid are self-reported height and weight? A comparison between CATI self-report and clinic measurements using a large representative cohort study. Aust NZ J Public Health. 2006;30:238-46.



20. Grant JF, Chittleborough CR, Taylor AW, Dal Grande E, Wilson DH, Phillips PJ, et al. The North West Adelaide Health Study: methodology and baseline segmentation of a cohort along a chronic disease continuum. Epidemiologic Perspectives & Innovations. 2006;3:4.

21. Taylor A, Dal Grande E, Chittleborough C, Cheek J, Wilson D, Phillips P, et al. The North West Adelaide Health Study - Key Biomedical Findings, Policy Implications and Research Recommendations. Adelaide (AUST): South Australian Department of Human Services; 2002.

22. Taylor AW, Dal Grande E, Gill T, Chittleborough CR, Wilson DH, Adams RJ, et al. Do people with risky behaviours participate in biomedical cohort studies? BMC Public Health. 2006;6:11.

23. Australian Institute of Health and Welfare [data online page on the Internet]. Canberra (AUST): AIHW; 2009 [cited Author: please supply year, month and day web page viewed]. Risk Factor Prevalence Study Survey 1989, Survey No. 3. Available from: http://www.aihw.gov.au/dataonline/riskfactors/index.cfm#RFPS

24. Chalmers J, et al. Guidelines for the Management of Hypertension. Geneva (CHE): WHO/ISH Hypertension Guidelines Committee, World Health Organisation; 1999.

25. National Stroke Foundation of Australia. Heart, Stroke and Vascular Diseases Australian Facts. Canberra (AUST): Australian Institute of Health and Welfare; 2001.

26. Adams RJ, Wilson DH, Appleton S, Taylor A, Dal Grande E, Chittleborough CR, et al. Under-Diagnosed Asthma in South Australia. Thorax. 2003;58(10):846-50.

27. Gill T, Chittleborough C, Taylor A, Ruffin R, Wilson D, Phillips P. Body mass index, waist hip ratio, and waist circumference: which measure to classify obesity? Soz Praventivmed. 2003;48(3):191-200.

28. Appleton SL, Ruffin RE, Wilson DH, Taylor AW, Adams RJ. Asthma is associated with cardiovascular disease in a representative population sample. Obesity Research and Clinical Practice. 2008;2:91-9.

29. Adams RJ, Appleton S, Hill CL, Wilson DH, Taylor AW, Chittleborough C, et al. Independent association of HbA1c and incident cardiovascular disease in people without diabetes. Obesity. 2009;17(3):559-63.

30. Population Research and Outcome Studies Unit. The South Australian Monitoring and Surveillance System (SAMSS) 2002 - 20 [brief report]. Adelaide (AUST): South Australian Department of Health; 2002.

31. Australian Bureau of Statistics. 2015.4 - Census of Population and Housing Selected Social and Housing Characteristics for Statistical Local Areas, South Australia, 2001. Canberra (AUST): ABS; 2002.

32. Daly A, Taylor A. Field Testing 2 Report - Alcohol consumption, Cardiovascular Disease and Tobacco Consumption - January 2004. In: Population Health Monitoring and Surveillance, Question Development Field Testing Reports [report page on the Internet]. Melbourne (AUST): National Public Health Partnership; 2004 [cited Author: please supply year, month and day web page viewed]. Available from: http://www.nphp.gov.au/catitrg/

33. McGrath BP. Ambulatory blood pressure monitoring and ‘white coat’ hypertension: saving costs. Med J Aust. 2002;176:571-2.

34. Pierdomenico SD, Lapenna D, Di Mascio R, Cuccurullo F. Short- and long-term risk of cardiovascular events in white-coat hypertension. J Hum Hypertens. 2008;22(6):408-14.

35. Turner MJ, van Schalkwyk JM. Blood pressure variability causes identification of hypertension in clinical studies: a computer simulation study. Am J Hypertens. 2008;21(1):85-91.

36. Hajjar I, Kotchen JM, Kotchen TA. Hypertension: trends in prevalence, incidence and control. Annu Rev Public Health. 2006;27:465-90.

37. Marshall TP. Blood pressure Variability: the challenge of variation. Am J Hypertens. 2008;21(1):3-4.

38. Krum H, Jelinek MV, Stewart S, Sindone A, Atherton JJ, Hawkes AL, et al. Guidelines for the prevention, detection and management of people with chronic heart failure in Australia 2006. Med J Aust. 2006;185(10):549-56.

39. Agarwal R, Bunaye Z, Bekele DM. Prognostic significance of between-arm blood pressure differences. Hypertension. 2008;51:657-62.

40. Fiebach NH, Hebert PR, Stampfer MJ, Colditz GAS, Willett WC, Rosner B, et al. A prospective study of high blood pressure and cardiovascular disease in women. Am J Epidemiol. 1989;130(4):646-54.

41. Wan Q, Harris MF, Davies GP, et al. Cardiovascular risk management and its impact in Australian general practice patients with type 2 diabetes in urban and rural areas. Int J Clin Pract. 2008;62:53-8.

42. Kaplan NM. Treating pre-hypertension: a review of the evidence. Curr Hypertens Rep. 2008;10(4);326-9.

43. Pickering TG. The natural history of hypertension: pre-hypertension or masked hypertension? J Clin Hypertens (Greenwich). 2007;9(10):807-10.

44. Bays HE, Chapman RH, Fox KM, Grandy S, Shield Study Group. Comparison of self-reported survey (SHIELD) versus NHANES data in estimating prevalence of dyslipidemia. Current Medical Research and Opinions. 2008;24(4):1179-86.

45. Australian Bureau of Statistics. Adult Literacy and Life Skills Survey, Summary Results, Australia 2006. Canberra (AUST): ABS; 2007. Catalogue No.: 4228.0.

46. Berkman ND, et al. Literacy and Health Outcomes. Rockville (MD): Agency for Healthcare Research and Quality; 2004. AHRQ Publication No.: 04-E007-1.

47. De Walt DA, Boone RS, Pignone MP. Literacy and health outcomes: a systematic review of the literature. J Gen Intern Med. 2005;19:128-39.


comparing self-reported and measured high blood pressure and high cholesterol status using data from...

Documents