The survey was conducted as a subset of the ICBP survey of awareness and beliefs about cancer in adults aged ≥50 years in six countries [33]. For the present analyses, we used data from female respondents in Wales. Ethical approval was obtained from Cardiff University School of Medicine Research Ethics Committee. The survey was carried out by trained interviewers who introduced the study to eligible individuals and obtained verbal informed consent.

Inclusion/exclusion criteria

Respondents were women aged over 50 years who were resident in Wales and gave verbal consent. Women were excluded if they reported having had a personal diagnosis of ovarian cancer and/or had undergone oophorectomy.

Procedures

Random probability sampling was used to achieve a population-representative sample using electronic telephone directories as the sampling frame. The final two digits of each selected telephone number were replaced with two random numbers, to include numbers that were not publicly available. Households were eligible if one or more person was aged 50 or over and spoke English. Where more than one person was eligible, the Rizzo method was used to randomly select one person to be interviewed, thereby giving an equal chance of selection to all eligible people living in the household [34]. Survey data were collected during May to July 2011 using computer-assisted telephone interviews. At the end of the interview, participants were offered contact details of a local cancer support charity.

Sample size

Assuming a design effect of 1.2 (adjusting for the impact of the weighting scheme employed), a sub-sample of 1000 women was estimated to provide conservative 95% confidence intervals of +/−3.7%.

Measures

A survey instrument (ABC-O; Awareness and Beliefs about Cancer-Ovarian) was adapted from the internationally validated Awareness and Beliefs about Cancer measure ABC; [35], and the Cancer Awareness Measure CAM; [36] and its ovarian-specific version [37]. ABC-O questions were tested for comprehensibility using cognitive interviews (n = 10), for test-retest reliability (n = 100), and for content validity using expert ratings (n = 8) of relevance and representativeness. Anticipated time to presentation questions were placed ahead of the symptom recognition question, and the order of all other questions and response options was rotated randomly. Major news stories relating to cancer and cancer awareness campaigns were monitored two weeks prior to and during the survey data collection period. None observed during this period was related to ovarian cancer symptom awareness.

Ovarian cancer symptom awareness

Eleven statements about recognition of ovarian cancer symptoms were presented using the question “I’m now going to list some symptoms that may or may not be warning signs for ovarian cancer. For each one, can you tell me whether you think that it could be a warning sign for ovarian cancer?” The list of symptoms included persistent pain in the abdomen, persistent pain in the pelvis, vaginal bleeding after the menopause, persistent bloating, increased abdominal size, feeling full persistently, difficulty eating, passing more urine than usual, a change in bowel habits, extreme tiredness, and back pain (response options were yes, no, don’t know). Items were adapted from the validated ovarian CAM [37] and included less common symptoms (change in bowel habit, fatigue, back pain) to reflect the UK Department of Health’s ‘Key Messages’ on ovarian cancer for health professionals and the public [11, 15]. The number of symptoms endorsed was summed (total score range 0–11).

Anticipated delay

An open-ended question was used to assess anticipated time to symptomatic presentation: “If you had a symptom that you thought might be a sign of ovarian cancer, please tell me how long it would take you to go to the doctors from the time you first noticed the symptom.” Responses were coded according to a number of predefined categories (e.g., “I would go as soon as I noticed”, “up to one week”, “more than a month”). A dichotomous delay variable (< 3 weeks, > 3 weeks) was created to reflect guidelines regarding frequency and persistence of symptoms such as bloating and pain, and the three week symptom timeline currently used in the UK ovarian cancer awareness campaign [38]. Sensitivity analyses were used to test effects of using different delay thresholds (1 and 2 weeks).

Health beliefs

Health beliefs included perceived benefits of early symptomatic presentation, emotional barriers to presentation, practical barriers to presentation, perceived risk, and confidence in symptom detection. Perceived benefits included five items (e.g. “If ovarian cancer is diagnosed early, it can be treated more successfully”) rated from 1 (strongly disagree) to 4 (strongly agree) with a total possible score range of 5–20 (Cronbach’s α = 0.71). Four items measured emotional barriers (e.g. “I would be too scared”, score range 4–12, α = 0.68). Three items measured practical barriers (e.g. “I would be too busy to make time to go to the doctor”, score range 3–9, α = 0.60). Response options for the barriers items were 1 = yes, often, 2 = yes, sometimes, and 3 = no (reverse scored). Perceived risk was a single item adapted from previous research [39], with response options from 1 (much more likely to get it) to 5 (much less likely to get it) recoded so that a higher score indicated higher perceived risk. Confidence in symptom detection was measured by asking respondents “How confident, or not, are you that you would notice a symptom of ovarian cancer?” (1 = not at all confident and 4 = very confident).

Cancer worry

The Ovarian Cancer Worry Scale [40] included three items regarding the frequency of worry (“How often do you worry about getting ovarian cancer someday?”), and the impact of worry on mood (“How often, if at all, does your worry about getting ovarian cancer someday affect your mood?”) and functioning (“How often, if at all, does your worry about getting ovarian cancer someday affect your ability to perform your daily activities?”). Items were rated from 1 (not at all) to 5 (almost all the time), with a score range 1–15 (α = 0.69). Scores were log transformed due to non-normal distribution (floor effect).

Demographic variables included age, ethnicity, level of education, socioeconomic status (Welsh Index of Material Deprivation score), relationship status, and experience of ovarian cancer diagnosed in family members or friends.

Statistical analysis

Survey response rate was calculated using the American Association for Public Opinion Research (AAPOR) conventions, because the denominator of eligible people was unknown and therefore response rate could not be calculated in the usual way [41]. The ‘minimum response rate’ was conservatively calculated as the number of complete interviews divided by the number of all possible interviews (the number of interviews among eligible people plus the number of households where eligible people were known to live, but where the interview could not be completed (e.g. refusal, interview broken off) plus the number of all households of unknown eligibility). It represents the response rate assuming that all households that we could not assess for eligibility were eligible (equivalent to AAPOR response rate formula 1). It is likely to underestimate response rates because it is likely that many households were ineligible. We also calculated the ‘estimated response rate’ as the number of completed interviews divided by the estimated number of eligible individuals, based on the proportion of households that were eligible out of those assessed for eligibility (equivalent to AAPOR response rate formula 3).

Associations between demographic variables and ovarian symptom awareness were examined using appropriate univariate analyses. Preliminary associations between anticipated delay and demographic variables, symptom awareness, health beliefs and cancer worry were tested using chi square or independent t-tests, with variables significant at p ≤ 0.01 subsequently entered into a logistic regression model. Results are presented for both unadjusted data and data adjusted for sample non-representativeness in age, region, relationship status and education. Sensitivity analyses were undertaken at each stage to test for effects of under-representation of certain demographic groups.