- Split View
-
Views
-
Cite
Cite
Jie W. Weiss, Michele Mouttapa, Lola Sablan-Santos, Jasmine DeGuzman Lacsamana, Lourdes Quitugua, Sora Park Tanjasiri, Decision making for Pap testing among Pacific Islander women, Health Education Research, Volume 31, Issue 6, December 2016, Pages 792–802, https://doi.org/10.1093/her/cyw044
- Share Icon Share
This study employed a Multi-Attribute Utility (MAU) model to examine the Pap test decision-making process among Pacific Islanders (PI) residing in Southern California. A total of 585 PI women were recruited through social networks from Samoan and Tongan churches, and Chamorro family clans. A questionnaire assessed Pap test knowledge, beliefs and past behaviour. The three MAU parameters of subjective value, subjective probability and momentary salience were measured for eight anticipated consequences of having a Pap test (e.g., feeling embarrassed, spending money). Logistic regression indicated that women who had a Pap test (Pap women) had higher total MAU utility scores compared to women who had not had a Pap test within the past three years (No Pap women) (adjusted Odds Ratio = 1.10). In particular, Pap women had higher utilities for the positive consequences ‘Detecting cervical cancer early, Peace of mind, and Protecting my family’, compared to No Pap women. It is concluded that the connection between utility and behaviour offers a promising pathway toward a better understanding of the decision to undergo Pap testing.
Introduction
In recent years, incidence and mortality rates for cervical cancer in the United States have decreased dramatically, in large part due to increased Pap testing [1]. However, those rates have tended to increase among Pacific Islanders [2]. Pacific Islanders (PIs) in the continental U.S. originate from Melanesia, Micronesia (e.g., Chamorros) and Polynesia (Samoans and Tongans). Age-adjusted incidence rates among selected PIs (12.3/100 000 Native Hawaiians; 15.1/100 000 Samoans) were higher than that of non-Hispanic whites (NHWs) (8.1/100 000) [3]. Stage of diagnosis is later for PIs than for NHWs; only about 40% of NHW cervical cancers are found at the regional or distant stage, compared to nearly 60% of the same cancers among Native Hawaiian and Samoan women.
Even with HPV testing and vaccines, Pap testing remains an important cervical cancer prevention and detection method. The U.S. Preventive Services Task Force (USPSTF) recommends women age 21–65 receive Pap testing every 3 years [4]. Unfortunately, the Pap test is severely underutilized among many ethnic/racial groups. PIs have low rates of Pap testing. Only 71% of Asian American and Pacific Islander (AAPI) women age 25+ years received a Pap test within the last 3 years (U.S. average= 82%). The USPSTF has called for increased attention to informed decision making, particularly in cross-cultural populations [5,6]. To answer this call, the current study aimed to examine the decision-making process among Pacific Islander women in Southern California regarding Pap testing.
Lack of knowledge and awareness have been identified as barriers for women obtaining cervical cancer screening [7,8]. In a study by Ideström et al. [9], knowledge and attitudes regarding Pap testing were shown to significantly predict compliance rates of cancer screening among women. However, evidence also suggests that increased knowledge alone is insufficient to initiate or sustain cancer screening and other health behaviours [10,11]. Accordingly, theorists have developed approaches that incorporate personal, interpersonal and community factors that influence people’s behaviour. These include, but are not limited to, Self-Regulation Theory [12], The Health Belief Model [13], The Theory of Reasoned Action [14], Protection-Motivation Theory [15], The Theory of Planned Behaviour [16], and Social-Cognitive Theory [17]. These theories involve sensible attempts to identify predictors using group data. They tend to focus on the risks of dangerous behaviours and the rewards of preventive behaviours, rather than considering the full range of consequences that a person thinks might attach to each possible course of action. Typically, interventions based on these theories attempt to increase knowledge and accuracy of beliefs.
Because getting tested is an individual’s prerogative, a research approach that focuses on decision making has the potential to expand our understanding of intrapersonal factors that influence women’s cancer screening behaviours. In this study, we adopt a descriptive version of the classical Multi-Attribute Utility Model (MAU) proposed by Keeney and Raiffa [18] to examine the decision- making process for Pap testing within the PI population. The key feature of the MAU model is that each decision option has a set of anticipated consequences attached to it. The consequences are what a decision maker (i.e., the woman) thinks might happen as the result of choosing an option. While all women desire good health, there are many other competing concerns that call for their attention and time. Deciding to go for a Pap test is a matter of managing these priorities. For the Pap test, the consequences can include short-term outcomes (such as pain, embarrassment, or inconvenience) as well as long-term outcomes (such as an extended lifespan). The MAU model also accommodates decisions that appear to be emotion-based (such as fear), in that affective consequences enter into the model in the same way that other consequences do [19].
According to the descriptive version of the MAU model [20], each decision option has a set of anticipated consequences attached to it, and the anticipated consequences of an option determine its attractiveness. The anticipated consequences constitute the ‘attributes’ of an option. They differ subjectively in 3 ways, giving rise to three model parameters attached to each consequence. These parameters are the components of the MAU model: (1) Subjective Value (SV), the perceived worth of the consequence, a quantity with either a positive or a negative sign; (2) Subjective Probability (SP), the perceived likelihood that the consequence will occur given the behavioural choice. Most knowledge is incorporated within this parameter, and (3) Momentary Salience (MS), the importance of that consequence to the person at the moment of decision. The third parameter was added to the classical model to account for variations over time and circumstances [21]. When a moment of decision arises, each of the options is evaluated using the three parameters in accordance with the expression: MAU = Σj SVj • SPj • MSjwhere j indexes the consequences anticipated by the decision maker if she chooses that option. The product of the three parameters for a consequence determines that consequence’s contribution to the total utility. The option with the highest utility, as expressed by the sum of the products across consequences, is chosen. The model is personalized via the parameters attached to each consequence. The parameters capture how people regard the behavioural options (e.g., to have the Pap test now or not). The number of consequences that play a role in the decision is incorporated into the model via the Momentary Salience parameter; a consequence that is not under consideration at the time the decision is made has zero momentary salience and therefore does not contribute to the sum.
Consequences might be expected to arise before, during, or after the test. These expectations can serve as barriers or as facilitators. It is important to note that subjective value alone does not determine whether a consequence will impact the decision. For example, a consequence such as feeling discomfort during the test might be viewed as negative in value and therefore could constitute a barrier. But if the patient were sufficiently reassured that discomfort was unlikely, the apparent barrier would be mitigated because of the multiplicative effect of the low subjective probability.
In this study, we employ the MAU model with the hope of expanding our understanding of the complex dynamics of the decision-making process in cancer screening. While we cannot claim that increased understanding will necessarily translate into improved clinical practice, we hope that identifying the tradeoffs inherent in this very personal decision will at least soften the attitudes of those who disparage people with whose choices they disagree
Methods
Study design
The data presented in this paper are from the initial phase of a five-year study that used Community-Based Participatory Research (CBPR) strategies involving staff from four community-based organizations, one university, and PI community leaders and cancer survivors who serve on a Community Advisory Board. The CBPR process and study recruitment approaches have been described elsewhere (Tanjasiri et al., in preparation).
Participants
We recruited adult PI women and their husbands/long-term partners from Samoan and Tongan churches and Chamorro family clans who reside in urban areas in Southern California. The leaders from each organization (church or clan) invited eligible couples to participate. Eligibility criteria for the women included being: (1) Chamorro, Samoan or Tongan and being a member of at least one of the organizations or clans enrolled this study, (2) between the ages of 21 and 65 and (3) married or in a long-term relationship with a man of any ethnicity for at least five years. For this study, 311 women who had received Pap tests within the last three years (Pap women) and 274 women who had not (No Pap women) completed the baseline survey. Hence, the analytic sample for this study was 585 women participants.
Procedures
Recruitment
We utilized an organizational sampling approach to recruit Samoan and Tongan churches and Chamorro clans. An official letter with contact information of the university and community partners, along with compensation of food or a cash equivalent of $200, was given to each church and clan as an act of good faith that they will be part of the project. The incentive was provided to show respect to the community organizations for their crucial role in the study, which the PI leaders indicated had been a concern regarding past studies carried out among this population. The project health educators spearheaded the recruitment process, which often involved multiple follow up calls, in-person meetings, and attendance at social events such as family parties, village fiestas and festival events, and church activities. Once the leader(s) of a given church or clan agreed to have their organization or clan participate the study, the health educator scheduled a meeting at the community’s site, to administer the baseline survey and implement the 2-h education among the eligible community participants.
Survey administration
The health educators administered the self-report instrument to the participants. The questionnaires were provided in both English and in the participants’ native language. Participants were instructed to complete one page at a time, and to wait until everyone had completed that page. Therefore, if any, questions came up, the health educator was able to address the questions with the entire group. After completing the survey, each participant received a small cultural gift and a $10 gift card. All procedures described here were approved by the University’s Institutional Review Board.
Survey translation
Survey translation from English into the native PI language was completed by a bilingual and bicultural translator, and then independently reviewed by a second translator checking for clarity, minimal use of jargon, utilization of conceptual equivalents of words and phrases rather than a literal translation, and use of appropriate words with consideration of the audience. Questionnaires were pilot tested on 18 community members who met all eligibility requirements of the study but were not participants.
Measures
The questionnaire included the following topics: demographics, knowledge of Pap tests and cervical cancer risks, knowledge and attitudes, decision-making utility and Pap screening behaviours.
Demographics
All participants were asked basic information about ethnicity, age, education level, health insurance status (including public and private, such as military insurance which is common among Chamorros), marital status, immigration status and English language ability.
Cervical cancer-related knowledge
Participants completed 11 true/false items which assessed participants’ knowledge about cervical cancer risk factors including sexual history, human papilloma virus infection, smoking, diet, oral contraceptives and family history. The items came from existing surveys of PI cancer control [22,23] and the National Health Interview Survey, Cancer Control Supplement [24].
Pap testing behaviour
Women were asked if they have ever had a Pap smear, and the date of their last Pap smear in the participants’ baseline survey: ‘When did you have your most recent Pap test?’ Based on the American Cancer Society recommendation that all women 21 and older receive Pap tests at least once every 3 years [25], we categorized women who answered ‘within the past 3 years’ as having had Pap testing.
Consequences list
Prior to finalizing the questionnaires, we conducted 4 focus groups drawn from the same pool of PI women in order to develop a list of consequences of Pap testing as perceived by this population. These elicited responses were tabulated, then pruned by the research team. The objective is to have a set of consequences that are independent (to yield proper weighting) and exhaustive (to ensure that the consequences deemed important by the focus groups are examined). Eight consequences emerged from this process: ‘Detecting cervical cancer early’, ‘Having peace of mind by getting a Pap test’, ‘Feeling discomfort during a Pap test’, ‘Spending money for getting a Pap test’, ‘Feeling embarrassed while having a Pap test’, ‘Protecting my family’, ‘Time spent getting a Pap test’, ‘Feeling scared of knowing my Pap test result’.
MAU model parameters
Questions in this section examined MAU with respect to the decision for Pap testing. The respondent was asked to provide three ratings, one for each of the three model parameters, for each anticipated consequence of the Pap test.
Each parameter was investigated on a separate page. For subjective value, the response options ranged from ‘extremely bad’ to ‘extremely good’. Each response was scored as a number between -3 (extremely bad) and +3 (extremely good). For subjective probability, the response options ranged from ‘completely unlikely’ to ‘completely likely’. These responses were scored as numbers between 0 (completely unlikely) and 5 (completely likely), and were then linearly transformed so their range was 0–1, so that they would resemble probabilities. For example, a score of 5 was transformed to 1 to reflect a 100% perceived likelihood that the consequence would occur if the participant received a Pap test. For momentary salience, the response options ranged from ‘not important at all’ to ‘extremely important’. These responses were also scored as numbers between 0 and 5 and were transformed in the same way as likelihood responses were. These rating scales have previously been used to measure utility parameters in studies in which the consequences were associated with smoking [26] and healthy eating [27].
Calculation of MAU parameters
We were interested in determining the extent to which the component utilities for individual consequences (e.g., peace of mind, protecting my family, etc.) were associated with Pap testing. An MAU score was calculated for each of the eight consequences. For each respondent, the subjective value for a consequence (which ranged from -3 to +3) was multiplied by the corresponding subjective probability and momentary salience (which had both rescaled to range between 0 and 1). Because the multipliers were positive quantities between zero and one, the products ranged between -3 and +3, carrying the signs of the values. These products were then averaged across respondents to form the utility score for each consequence. The averages were then summed over the eight consequences to create a total utility score.
Data analyses
Pearson’s chi-square tests were performed to compare Pap and No Pap women on the dichotomous demographic variables. ANOVAs were performed to examine differences between Pap and No Pap women on the number of knowledge questions answered correctly and years of marriage. Separate ANOVAs compared Pap and No Pap women on the total utility scores, the subjective value score, the subjective probability score, and momentary salience score, and scores for each of the eight consequences. Multiple logistic regression models were run for each of the MAU predictor variables in the ANOVA analyses, adjusting for the three demographic variables (Ethnicity, Insurance and Knowledge) that were found to be associated with Pap testing in the demographic analyses.
Results
Demographic characteristics of participants
Table 1 presents demographic characteristics of female participants by Pap testing status. Women with health insurance had Pap testing rates over twice as high (60.4%) as women without health insurance (26.6%, P = 0.000). Samoan and Chamorro participants (61.3%) had Pap testing rates that were nearly twice as high compared to Tongan women (31.4%, P = 0.000). Samoan and Chamorro women were combined because they did not significantly differ from each other on Pap testing status (58.2% and 65.7% , respectively, P = 0.116). Pap women had significantly lower average scores on the knowledge scale (5.62 ± 2.66) compared to No Pap women (4.72 ± 3.00).
. | No Pap test within 3 years (n = 274) . | Pap test within 3 years (n = 311) . | . | . | ||
---|---|---|---|---|---|---|
. | N . | % . | n . | % . | χ2 . | P . |
Demographic Characteristics | ||||||
Age- Under 40 years | 110 | 45.6 | 131 | 54.4 | 0.25 | 0.970 |
Age- 40+ years | 161 | 47.4 | 179 | 52.6 | ||
Tongans | 109 | 68.6 | 50 | 31.4 | 41.35 | 0.000 |
Samoans and Chamorros | 165 | 38.7 | 261 | 61.3 | ||
Have health insurance | 180 | 39.6 | 275 | 60.4 | 44.78 | 0.000 |
No insurance | 91 | 73.4 | 33 | 26.6 | ||
Other language preferred | 48 | 57.8 | 35 | 42.2 | 8.56 | 0.073 |
English or other language | 118 | 48.0 | 128 | 52.0 | ||
English language preferred | 108 | 42.9 | 144 | 57.1 | ||
M | SD | M | SD | F | P | |
Number of years in marriage/relationship | 17.42 | 12.50 | 16.81 | 11.72 | 0.549 | 0.359 |
Cervical cancer knowledge (out of 11) | 5.62 | 2.66 | 4.72 | 3.00 | 13.82 | 0.000 |
. | No Pap test within 3 years (n = 274) . | Pap test within 3 years (n = 311) . | . | . | ||
---|---|---|---|---|---|---|
. | N . | % . | n . | % . | χ2 . | P . |
Demographic Characteristics | ||||||
Age- Under 40 years | 110 | 45.6 | 131 | 54.4 | 0.25 | 0.970 |
Age- 40+ years | 161 | 47.4 | 179 | 52.6 | ||
Tongans | 109 | 68.6 | 50 | 31.4 | 41.35 | 0.000 |
Samoans and Chamorros | 165 | 38.7 | 261 | 61.3 | ||
Have health insurance | 180 | 39.6 | 275 | 60.4 | 44.78 | 0.000 |
No insurance | 91 | 73.4 | 33 | 26.6 | ||
Other language preferred | 48 | 57.8 | 35 | 42.2 | 8.56 | 0.073 |
English or other language | 118 | 48.0 | 128 | 52.0 | ||
English language preferred | 108 | 42.9 | 144 | 57.1 | ||
M | SD | M | SD | F | P | |
Number of years in marriage/relationship | 17.42 | 12.50 | 16.81 | 11.72 | 0.549 | 0.359 |
Cervical cancer knowledge (out of 11) | 5.62 | 2.66 | 4.72 | 3.00 | 13.82 | 0.000 |
. | No Pap test within 3 years (n = 274) . | Pap test within 3 years (n = 311) . | . | . | ||
---|---|---|---|---|---|---|
. | N . | % . | n . | % . | χ2 . | P . |
Demographic Characteristics | ||||||
Age- Under 40 years | 110 | 45.6 | 131 | 54.4 | 0.25 | 0.970 |
Age- 40+ years | 161 | 47.4 | 179 | 52.6 | ||
Tongans | 109 | 68.6 | 50 | 31.4 | 41.35 | 0.000 |
Samoans and Chamorros | 165 | 38.7 | 261 | 61.3 | ||
Have health insurance | 180 | 39.6 | 275 | 60.4 | 44.78 | 0.000 |
No insurance | 91 | 73.4 | 33 | 26.6 | ||
Other language preferred | 48 | 57.8 | 35 | 42.2 | 8.56 | 0.073 |
English or other language | 118 | 48.0 | 128 | 52.0 | ||
English language preferred | 108 | 42.9 | 144 | 57.1 | ||
M | SD | M | SD | F | P | |
Number of years in marriage/relationship | 17.42 | 12.50 | 16.81 | 11.72 | 0.549 | 0.359 |
Cervical cancer knowledge (out of 11) | 5.62 | 2.66 | 4.72 | 3.00 | 13.82 | 0.000 |
. | No Pap test within 3 years (n = 274) . | Pap test within 3 years (n = 311) . | . | . | ||
---|---|---|---|---|---|---|
. | N . | % . | n . | % . | χ2 . | P . |
Demographic Characteristics | ||||||
Age- Under 40 years | 110 | 45.6 | 131 | 54.4 | 0.25 | 0.970 |
Age- 40+ years | 161 | 47.4 | 179 | 52.6 | ||
Tongans | 109 | 68.6 | 50 | 31.4 | 41.35 | 0.000 |
Samoans and Chamorros | 165 | 38.7 | 261 | 61.3 | ||
Have health insurance | 180 | 39.6 | 275 | 60.4 | 44.78 | 0.000 |
No insurance | 91 | 73.4 | 33 | 26.6 | ||
Other language preferred | 48 | 57.8 | 35 | 42.2 | 8.56 | 0.073 |
English or other language | 118 | 48.0 | 128 | 52.0 | ||
English language preferred | 108 | 42.9 | 144 | 57.1 | ||
M | SD | M | SD | F | P | |
Number of years in marriage/relationship | 17.42 | 12.50 | 16.81 | 11.72 | 0.549 | 0.359 |
Cervical cancer knowledge (out of 11) | 5.62 | 2.66 | 4.72 | 3.00 | 13.82 | 0.000 |
Mean MAU scores by Pap test status
Table 2 presents average parameter scores for total utility scores of the three parameters (subjective value, subjective probability and momentary salience). Pap women had significantly higher means for total MAU scores (5.78 ± 3.05) and subjective values (0.93 ± 0.44) compared to No Pap women (4.54 ± 3.64 and 0.78 ± 0.51, respectively). These differences confirm that those who took the Pap test appreciated its virtues more than those who did not. Looking at utility scores for individual consequences, we found that Pap women had significantly higher scores for the positive consequences of Detecting cervical cancer early (1.04 ± 0.77), Peace of mind (1.39 ± 0.73), Protecting my family (1.57 ± 0.67), and were more tolerant of time spent (1.22 ± 0.77) compared to No Pap women (0.88 ± 0.77, 1.12 ± 0.75, 1.36± 0.78 and 0.88 ± 0.05, respectively).
. | No Pap in Past 3 years (n =274) . | Pap in Past 3 years (n = 311) . | F . | P . |
---|---|---|---|---|
. | M (SD) . | M (SD) . | . | . |
Total Utility Scorea | 4.54 (3.64) | 5.78 (3.05) | 18.59 | 0.000 |
Utility Scores for Individual Consequences | ||||
Detecting cervical cancer early | 0.89 (0.75) | 1.04 (0.77) | 5.54 | 0.019 |
Peace of mind | 1.12 (0.75) | 1.39 (0.74) | 19.10 | 0.000 |
Feeling discomfort | 0.05 (0.57) | 0.02 (0.50) | 0.62 | 0.433 |
Spending money | 0.42 (0.75) | 0.51 (0.74) | 2.168 | 0.141 |
Feeling embarrassed | 0.03 (0.56) | 0.03 (0.42) | 0.04 | 0.848 |
Protecting my family | 1.17 (0.81) | 1.49 (0.71) | 25.10 | 0.000 |
Time spent | 0.88 (0.83) | 1.22 (0.77) | 25.00 | 0.000 |
Feeling scared | 0.01 (0.87) | 0.04 (0.76) | 0.18 | 0.676 |
. | No Pap in Past 3 years (n =274) . | Pap in Past 3 years (n = 311) . | F . | P . |
---|---|---|---|---|
. | M (SD) . | M (SD) . | . | . |
Total Utility Scorea | 4.54 (3.64) | 5.78 (3.05) | 18.59 | 0.000 |
Utility Scores for Individual Consequences | ||||
Detecting cervical cancer early | 0.89 (0.75) | 1.04 (0.77) | 5.54 | 0.019 |
Peace of mind | 1.12 (0.75) | 1.39 (0.74) | 19.10 | 0.000 |
Feeling discomfort | 0.05 (0.57) | 0.02 (0.50) | 0.62 | 0.433 |
Spending money | 0.42 (0.75) | 0.51 (0.74) | 2.168 | 0.141 |
Feeling embarrassed | 0.03 (0.56) | 0.03 (0.42) | 0.04 | 0.848 |
Protecting my family | 1.17 (0.81) | 1.49 (0.71) | 25.10 | 0.000 |
Time spent | 0.88 (0.83) | 1.22 (0.77) | 25.00 | 0.000 |
Feeling scared | 0.01 (0.87) | 0.04 (0.76) | 0.18 | 0.676 |
aThe total utility score is the sum of the utility scores for the eight consequences.
. | No Pap in Past 3 years (n =274) . | Pap in Past 3 years (n = 311) . | F . | P . |
---|---|---|---|---|
. | M (SD) . | M (SD) . | . | . |
Total Utility Scorea | 4.54 (3.64) | 5.78 (3.05) | 18.59 | 0.000 |
Utility Scores for Individual Consequences | ||||
Detecting cervical cancer early | 0.89 (0.75) | 1.04 (0.77) | 5.54 | 0.019 |
Peace of mind | 1.12 (0.75) | 1.39 (0.74) | 19.10 | 0.000 |
Feeling discomfort | 0.05 (0.57) | 0.02 (0.50) | 0.62 | 0.433 |
Spending money | 0.42 (0.75) | 0.51 (0.74) | 2.168 | 0.141 |
Feeling embarrassed | 0.03 (0.56) | 0.03 (0.42) | 0.04 | 0.848 |
Protecting my family | 1.17 (0.81) | 1.49 (0.71) | 25.10 | 0.000 |
Time spent | 0.88 (0.83) | 1.22 (0.77) | 25.00 | 0.000 |
Feeling scared | 0.01 (0.87) | 0.04 (0.76) | 0.18 | 0.676 |
. | No Pap in Past 3 years (n =274) . | Pap in Past 3 years (n = 311) . | F . | P . |
---|---|---|---|---|
. | M (SD) . | M (SD) . | . | . |
Total Utility Scorea | 4.54 (3.64) | 5.78 (3.05) | 18.59 | 0.000 |
Utility Scores for Individual Consequences | ||||
Detecting cervical cancer early | 0.89 (0.75) | 1.04 (0.77) | 5.54 | 0.019 |
Peace of mind | 1.12 (0.75) | 1.39 (0.74) | 19.10 | 0.000 |
Feeling discomfort | 0.05 (0.57) | 0.02 (0.50) | 0.62 | 0.433 |
Spending money | 0.42 (0.75) | 0.51 (0.74) | 2.168 | 0.141 |
Feeling embarrassed | 0.03 (0.56) | 0.03 (0.42) | 0.04 | 0.848 |
Protecting my family | 1.17 (0.81) | 1.49 (0.71) | 25.10 | 0.000 |
Time spent | 0.88 (0.83) | 1.22 (0.77) | 25.00 | 0.000 |
Feeling scared | 0.01 (0.87) | 0.04 (0.76) | 0.18 | 0.676 |
aThe total utility score is the sum of the utility scores for the eight consequences.
Logistic regression of Pap status
We also conducted a logistic regression analysis of Pap test status, controlling for variables significantly associated with Pap testing as described in Table 1, specifically ethnicity, health insurance coverage and knowledge. The higher total utility was significantly associated with higher rates of Pap testing (AOR (Adjusted Odds Ratio) = 1.10, 95% CI= 1.04–1.17, P = 0.001)) . Thus, women for whom the utility of the procedure is higher are more likely to have been tested.
Discussion
This is perhaps the first study to use the MAU model to examine Pap testing decision-making process in an underserved minority population. The primary result is that women who got tested had higher utility for the test than those who did not. The importance of the finding is that the utility construct integrates the various reasons underlying a decision in a structured way. Examining individual consequences, we found that utilities for the three positive consequences of Pap testing (namely ‘Detecting cervical cancer early’, ‘Peace of mind’, and ‘Protecting my family’) were higher among women who had a Pap test in the past three years compared with those who had not. The effect of the latter consequence is consistent with previous research that has found family values to be a strong predictor of having a Pap test [28]. We also found that three purportedly negative consequences (Feeling discomfort, Feeling embarrassed, Feeling scared) have utilities near zero, suggesting that they contribute little to the decision.
The results showing the impact of utility on health-related behaviours are consistent with previous research showing that MAU for tobacco was on average higher among those who used tobacco than among those who did not [26] and MAU for healthy eating was higher for those who ate healthily than for those who did not [27]. The MAU analysis clarifies how perceived consequences influence behaviour. The main distinction between the groups is that those who took the test assigned higher value to the positive anticipated consequences of Pap testing, including ‘Detecting cervical cancer early’, ‘Peace of mind’, and ‘Protecting my family’.
Limitations
Use of the MAU model to understand everyday decision making entails compromises not confronted when the model is employed in controlled research settings. The customary caution regarding retrospective self-reports applies, although because the topic is not sensitive, there is little reason to expect intentional inaccuracy [29]. While we were not able to check the medical records, having had a Pap test is a salient event not likely to be forgotten. Similarly, we accept the responses that report parameters at face value, as there is no way to validate these internal quantities. Still, just as for smoking [26] and healthy eating [27], incorporating self-reported parameters into model-based utilities allows us to predict real-world behaviour.
An additional limitation imposed by a retrospective approach is that causality cannot be inferred. For example, although we found that PI women with higher MAU scores were more likely to have taken the Pap test in the past 3 years, we cannot be certain that higher MAU is a precursor to, rather than a result of, testing. In principle, a prospective study might allow stronger conclusions, and would also allow within-person comparisons of the MAU for testing vs. no testing. According to the model, each individual makes the decision after comparing MAU for the two options. It is experimentally challenging to elicit utilities for the no testing option. Furthermore, a prospective study may change the nature of the research. The deliberation invoked by prior elicitation could influence the subsequent choice. In that sense, a prospective examination would constitute an intervention rather than a description of the behavioural choice in the natural setting.
Another usual caveat regarding the specific sample applies to this study. The sample was limited to primarily church-going or clan-affiliated Pacific Islanders. It is possible that these findings may not generalize to other populations. Also, the sample was limited to women in long-term relationships, because we were interested in how support from a partner might influence the testing decision. A consequence such as ‘Protect my family’ would not be relevant to single women.
Conclusion
The findings of this study reinforce the wisdom underlying the call by the U.S. Preventive Services Task Force for increased attention to decision making, particularly within cross-cultural populations [6]. Our results suggest that possession of relevant information about risk factors is not what distinguishes those who receive a Pap test from those who do not. We interpret the data as suggesting that interventions whose focus is increased knowledge can be expected to have limited efficacy; current educational ventures are sufficient for understanding.
In contrast to results obtained previously [7–9], our respondents who chose to get tested actually exhibited less knowledge. A possible explanation for these conflicting outcomes is that knowledge can cut both ways. A more knowledgeable patient may determine her personal risk to be low, and thereby decide to forego testing. This suggests it might be worthwhile in future research to ask participants to report their views of their own risk factors.
What differentiates our groups is an appreciation of the positive emotional consequences of testing. Those who receive the test value the peace of mind that comes with a good test outcome, and accept that early detection will allow preservation of their family in the case of a bad test outcome. The results suggest that these are the consequences that practitioners would do well to emphasize.
Identifying the concerns that influence adherence to what health professionals view as routine recommendations may enable more effective implementation. The medical community tends to regard a problem as solved once an effective intervention or procedure has been developed [30], but there is another important step to consider. Personal decisions determine the ultimate success of the medical advance. Understanding the reasons that underlie those decisions promises to be more useful than merely identifying predictors. Decision-making theory holds the potential for expanding our understanding of how tailored interventions can influence women’s cancer screening behaviours by highlighting the needs and priorities of medically underserved consumers. More fine-grained analyses, such as comparing differences in utilities and behaviour across ethnic groups or across groups that differ according to age, economic status or other predictors, may offer a pathway toward realizing that potential.
Acknowledgements
We would like to recognize the following individuals for their contributions to the project: Mohammed Aftab, Alisa Chavarria, Laura Dandridge, Rine Fe’a, Peter Flores, Preciosa Flores, Mary Anne Foo, Ualani Ho`opai, Brian Hui, Genesis Lutu, Darlene March, Vanessa Tui’one May, Noelle Moananu, Ciara Paige, Peniamina Taito, Alisi Tulua, Lourdes Tuliau, Marina Tupua, Elenoa Vaikona, Dorothy Schmidt-Vaivao, Isileli Vunileva and Jennifer Xiong, Additionally, we would like to recognize the churches, clans and Community Advisory Board members who participated in this study. This project was funded by the National Cancer Institute’s Center to Reduce Cancer Health Disparities (grant number 5R01CA149324). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NCI CRCHD.
We dedicate this paper to the memory of Caroline Fe`a Pele, who we lost to cancer on July 4, 2015.
Funding
This project was supported by the National Cancer Institute’s Center to Reduce Cancer Health Disparities, grant number 5 R01CA149324-05. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the NCI CRCHD.
Conflict of interest statement
None declared.
References