Background: Fatalistic beliefs may be implicated in longer help-seeking intervals, and consequently, greater risk of advanced stage at cancer diagnosis.

Methods: We examined associations between fatalism and stage at diagnosis in a population-based cohort of 4,319 U.S. patients with newly diagnosed lung or colorectal cancer participating in the Cancer Care Outcomes and Research Surveillance (CanCORS) study. Fatalistic beliefs were assessed with an established measure. A fatalism score (range, 4–16) was created by summing Likert scale responses to four items. Cancer stage at diagnosis was abstracted from medical records by trained staff. Logistic regression was used to assess the association between fatalism score and advanced stage at diagnosis (IV vs. I–III), adjusting for sociodemographic and clinical characteristics.

Results: Overall, 917 (21%) patients had stage IV cancers (lung: 28%, colorectal: 16%). The mean fatalism score was 10.7 (median = 11; interquartile range, 9–12). In adjusted analyses, a higher fatalism score was associated with greater odds of stage IV diagnosis (OR per unit increase in fatalism = 1.05; 95% confidence interval 1.02–1.08; P = 0.003). Patients with the highest fatalism score had an adjusted 8.9% higher frequency of stage IV diagnosis compared with patients with the lowest score (25.4% vs. 16.5%).

Discussion: In this large and socioeconomically, geographically, and ethnically diverse population of patients with lung and colorectal cancer, fatalistic beliefs were associated with higher risk of advanced stage at diagnosis. Longitudinal studies are needed to confirm causation.

Impact: These findings support the value of incorporating information about the curability of early-stage cancers in public education campaigns. Cancer Epidemiol Biomarkers Prev; 24(4); 720–6. ©2015 AACR.

Many patients with cancer are diagnosed after a symptomatic presentation, because effective screening tests exist for few cancer sites and participation rates are suboptimal. For example, in England, more than 90% of all patients with cancer are diagnosed following symptomatic presentations (1). As stage at diagnosis is a key determinant of cancer survival, interventions to ensure that symptomatic patients are diagnosed at the earliest possible stage can help to reduce cancer mortality.

Psychosocial factors are important candidates for influencing the length of the period from symptom onset to presentation to a doctor (i.e., the “patient interval”), and, consequently, the stage at diagnosis (2, 3). These factors encompass both cognitive (e.g., awareness of potential associations of symptoms with cancer) and emotional processes (e.g., fear of cancer or embarrassment about symptoms; ref. 4).

Fatalism, whether it relates to a general belief that life events are predetermined and inevitable, or “cancer fatalism,” defined by Powe as “the belief that death is inevitable when cancer is present” (5), has been implicated in longer patient intervals in a study of intended help seeking for breast cancer among asymptomatic women (6). Individuals with more fatalistic beliefs may be both more fearful of a cancer diagnosis and more skeptical about the value of early detection of cancer, and therefore, may delay seeking medical help (7). Several studies have shown that cancer fatalism influences cancer screening uptake (8–10). Direct evidence that fatalism is associated with longer patient intervals in symptomatic patients is limited, although two small single-center studies including a few hundred patients each and primarily examining the potential determinants of ethnic disparities in cancer outcomes have shown that fatalistic beliefs were associated with advanced stage of diagnosis of breast and lung cancer (11, 12). Specifically, an association between endorsing the fatalistic belief “if it's meant to be, I will stay healthy” and advanced stage at diagnosis was reported in a sample of 540 women with breast cancer (11). Furthermore, in 357 patients with lung cancer, there was an independent association between one of three fatalism items (“bad things are meant to be”) and advanced stage at diagnosis (12). It is therefore important to extend the focus of prior inquiries about the potential influence of fatalism on stage at diagnosis to larger and more representative samples of patients and encompass cancers other than those previously studied.

Against this background, we hypothesized that patients with cancer with higher scores on measures of fatalistic beliefs will be more likely to be diagnosed with cancer at a more advanced stage. We have subsequently examined this hypothesis association in a large population-based survey of newly diagnosed patients with lung and colorectal cancer.

### Data

#### Study design and participants.

The CanCORS (Cancer Care Outcomes Research and Surveillance) study prospectively enrolled U.S. adults diagnosed with lung or colorectal cancer in 2003–2005 who lived in certain geographic areas (northern California; Los Angeles County, CA; North Carolina; Iowa; or Alabama) or received care in one of five health maintenance organizations (HMO), or fifteen Veterans Affairs medical centers (13, 14). Participants were representative of U.S. patients with these cancers (15). Patients were identified within weeks of diagnosis and interviews (with the patient or a surrogate, if the patient was deceased or too ill to participate) conducted approximately 4 to 6 months after diagnosis. Interviews were conducted by telephone; trained interviewers used computer-assisted telephone interviewing software to navigate complex skip patterns. The study was approved by human subjects committees at all participating institutions.

Among 9,732 CanCORS participants, we studied the 5,453 for whom interviews were conducted with the patients themselves. We then excluded 761 patients who did not respond to all four fatalism items and 373 patients for whom data on stage at diagnosis were not available, resulting in an analysis sample of 4,319 patients (Fig. 1).

Figure 1.

Analysis sample derivation.

Figure 1.

Analysis sample derivation.

Close modal

#### Data items.

A four-item measure assessing general fatalism that had been developed for the Americans' Changing Lives Panel Study (House JS, http://sodapop.pop.psu.edu/codebooks/hwboa/acl.pdf) and reported by Jacobson was included in the questionnaire for patients who completed the survey themselves (16). Patients were asked how strongly they agreed or disagreed with the following four statements:

• When bad things happen, we are not supposed to know why, we are just supposed to accept them.

• People die when it is their time to die and nothing can change that.

• Everything that happens is a part of God's plan.

• If bad things happen, it is because they were meant to be.

Responses were on a 4-point Likert scale (strongly agree/agree/disagree/strongly disagree; coded as 4, 3, 2, and 1). Jacobson reported results of a factor analysis showing that the four items formed a single scale with high internal reliability (α = 0.77; ref. 16), whereas in our analysis sample, we have observed a slightly higher value (α = 0.79).

Trained abstractors documented stage at diagnosis from medical records based on the criteria of the American Joint Committee on Cancer, 6th edition. Medical records were available for 87% of patients; for other patients, stage data were obtained from cancer registries. Information was also available on cancer site (colorectal or lung), age, sex, race/ethnicity, education, income, marital status, smoking status, count of self-reported co-morbid conditions using the Katz and colleagues questionnaire (17), self-reported health status a year before the interview (0–100 on a visual analogue scale, 100 = perfect health), symptom status at diagnosis (yes/no), and CanCORS study site. Variables were categorized as in Table 1.

Table 1.

Distribution of the fatalism scores (n = 4,319 patients who completed a postdiagnosis interview themselves, with fully observed information on fatalism score and stage at diagnosis)

Patient characteristicsNMedianMeanP
Sexa
Men 2372 11 10.5 <0.001
Women 1947 11 10.9
Age at diagnosisa
<55 950 11 10.8 <0.001
55–59 553 10 10.4
60–64 604 11 10.4
65–69 671 11 10.7
70–74 588 11 10.7
75–80 525 11 10.9
80+ 428 11 10.9
Race/ethnicity
White 3115 11 10.5 <0.001
Hispanic 310 12 11.3
Black 577 11 11.3
Asian 200 11 10.9
Other 117 11 11.3
Educational attainment
<High school 730 12 11.8 <0.001
High school/some college 2514 11 10.9
≥College degree 1070 9.5
Missing 12 12.4
Income in past year (US $) <20,000 1181 12 11.3 <0.001 20,000–39,999 1133 11 10.8 40,000–59,999 677 10 10.4 ≥60,000 1000 10 9.8 Missing 328 12 11.2 Study site 5 Integrated health-care delivery systems 606 10 10.3 <0.001 8 counties in Northern California 905 11 10.4 Los Angeles County 962 11 10.5 State of Alabama 486 12 11.4 22 counties in North Carolina 446 11 11.1 State of Iowa 409 11 11.0 15 Veterans Affairs Medical Centers 505 11 10.9 Married/living with partner Yes 2686 11 10.6 0.01 No 1630 11 10.8 Missing 9.7 Smoking status Never smoker 1242 11 10.8 <0.001 Former smoker 2583 11 10.6 Current smoker 487 11 11.0 Missing 11 11.1 Comorbidities (count of) 0b 1724 11 10.7 0.24 1 1441 11 10.6 2 718 11 10.8 ≥3 436 11 10.9 Symptom status at diagnosis “Yes, symptomatic” 3541 11 10.7 0.23 “No, diagnosed without symptoms” 777 11 10.6 Missing 12 12.0 Cancer sitea Colorectal 2396 11 10.6 0.06 Lung 1923 11 10.8 Stage at diagnosisa I 1266 11 10.7 0.007 II 849 11 10.5 III 1287 11 10.6 IV 917 11 10.9 Fatalism scoresa 4 98 5 47 6 108 7 151 8 443 9 505 10 544 11 664 12 923 13 322 14 179 15 133 16 202 Health status a year before interview Median 90 (inter-quartile range, 75–100) 4,286 Total 4,319 Patient characteristicsNMedianMeanP Sexa Men 2372 11 10.5 <0.001 Women 1947 11 10.9 Age at diagnosisa <55 950 11 10.8 <0.001 55–59 553 10 10.4 60–64 604 11 10.4 65–69 671 11 10.7 70–74 588 11 10.7 75–80 525 11 10.9 80+ 428 11 10.9 Race/ethnicity White 3115 11 10.5 <0.001 Hispanic 310 12 11.3 Black 577 11 11.3 Asian 200 11 10.9 Other 117 11 11.3 Educational attainment <High school 730 12 11.8 <0.001 High school/some college 2514 11 10.9 ≥College degree 1070 9.5 Missing 12 12.4 Income in past year (US$)
<20,000 1181 12 11.3 <0.001
20,000–39,999 1133 11 10.8
40,000–59,999 677 10 10.4
≥60,000 1000 10 9.8
Missing 328 12 11.2
Study site
5 Integrated health-care delivery systems 606 10 10.3 <0.001
8 counties in Northern California 905 11 10.4
Los Angeles County 962 11 10.5
State of Alabama 486 12 11.4
22 counties in North Carolina 446 11 11.1
State of Iowa 409 11 11.0
15 Veterans Affairs Medical Centers 505 11 10.9
Married/living with partner
Yes 2686 11 10.6 0.01
No 1630 11 10.8
Missing 9.7
Smoking status
Never smoker 1242 11 10.8 <0.001
Former smoker 2583 11 10.6
Current smoker 487 11 11.0
Missing 11 11.1
Comorbidities (count of)
0b 1724 11 10.7 0.24
1 1441 11 10.6
2 718 11 10.8
≥3 436 11 10.9
Symptom status at diagnosis
“Yes, symptomatic” 3541 11 10.7 0.23
“No, diagnosed without symptoms” 777 11 10.6
Missing 12 12.0
Cancer sitea
Colorectal 2396 11 10.6 0.06
Lung 1923 11 10.8
Stage at diagnosisa
I 1266 11 10.7 0.007
II 849 11 10.5
III 1287 11 10.6
IV 917 11 10.9
Fatalism scoresa
4 98
5 47
6 108
7 151
8 443
9 505
10 544
11 664
12 923
13 322
14 179
15 133
16 202
Health status a year before interview
Median 90 (inter-quartile range, 75–100) 4,286
Total 4,319

aInformation on these variables was fully observed (no “missing” values) in the analysis sample.

bIncluded missing observations.

### Statistical analysis

We compared patients included in the analysis with those excluded (because of not completing an interview themselves, missing responses to all four fatalism items or missing stage at diagnosis, see Fig. 1) using logistic regression. After examining Spearman rank correlation coefficients for pairwise associations between the four fatalism items, to maximize power, replies to each of the four items were summed and the total score was used in the main analysis; higher scores reflected higher levels of fatalistic beliefs. We categorized advanced stage at diagnosis as stage IV (vs. I–III) and examined crude and adjusted associations between advanced stage at diagnosis and fatalism using logistic regression. We initially tested whether there was evidence that the association between fatalism and stage at diagnosis varied by cancer site by an interaction term fatalism*cancer site, but found no such evidence (P = 0.40). We therefore included all patients in the same model, adjusting for cancer site, and sex, age, race/ethnicity, education, income, marital status, smoking status, number of comorbidities, health status a year before interview, and study site. Subsequently, to aid interpretation, we predicted the proportion of patients diagnosed in stage IV for each score of the fatalism scale, by direct standardization using the regression model (18). The fully observed dataset was used for sample descriptions (Table 1) and 5 imputed datasets, produced through multiple imputation as previously described (19), were used in the logistic regression models, and outputs were combined with the SAS MIANALYZE procedure. In the current analysis, imputed data only relate to the small proportion of records with missing income (8%) and education, marital, and smoking status information (<1% for all three), as all other variables were complete in the analysis sample (a priori restricted to patients with fully observed fatalism and stage at diagnosis, Fig. 1).

#### Sensitivity and supplementary analyses.

In sensitivity analyses we examined alternative parameterizations of stage at diagnosis and fatalism score, i.e., by categorizing advanced stage at diagnosis as stage III/IV vs. I/II; by analyzing scores for each fatalism item individually; and by excluding the religious beliefs (“God's plan”) item from the total fatalism score. In supplementary analysis, we also examined the potential moderating effect of symptom status at diagnosis by repeating the main analysis model, also adjusting for symptom presence at the time of diagnosis (yes/no) and an interaction between symptom presence and fatalism score. Finally, in a subgroup of patients with known grade and/or lung cancer tumor type (non–small cell/small cell), we additionally adjusted for tumor grade and/or type.

### Sample description

Compared with the final sample (Fig. 1), excluded patients (who were predominantly deceased, or too ill to participate at the time of the baseline survey and thus were not asked the fatalism items) were more likely to have had lung than colorectal cancer and to be male, older, Asian and smokers, and with lower educational attainment and income, higher count of comorbidities and more advanced stage at diagnosis (P < 0.01 for all; Supplementary Table S1). Among patients included in the analysis, the median age was 65 (interquartile range, 56–73) and there was a slight preponderance of colorectal cancer and male patients (56% and 55% of the sample, respectively). Just under three quarters of patients where White (72%) and 7%, 13%, and 5% were Hispanic, Black, and Asian, respectively. Most patients (82%) had symptoms at the time of diagnosis. Further sample details are provided in Table 1.

### Stage at diagnosis

Among all patients 1,266 (29%), 849 (20%), 1,287 (30%), and 917 (21%) patients were diagnosed in stages I to IV, respectively. The proportion diagnosed with stage IV cancers was greater among patients with lung than colorectal cancer (28% vs. 16%, P < 0.001).

The mean fatalism score was 10.7 (median score, 11; interquartile range, 9–12). Responses to the four individual items were moderately correlated (pairwise Spearman rank correlation coefficients range, 0.34–0.55; P < 0.001 for all). There were differences in fatalism scores by race/ethnicity, with mean scores of 10.5 for White, and 11.3, 11.3, and 10.9 for Hispanic, Black, and Asian patients, respectively (P < 0.001). On average, women had higher mean fatalism scores than men (10.9 vs. 10.5, P < 0.001) and the same was true for current smokers compared with former smokers and nonsmokers (11.0, 10.6, 10.8, respectively; P < 0.001). Patients with lower educational attainment had higher fatalism scores than patients with higher education (11.8, 10.9, and 9.5, respectively, for non-high school graduates, high school graduates, and college graduates; P < 0.001). Patients with lower income also had higher fatalism scores than patients with higher income (11.3 and 9.8, respectively, for patients with annual income <$20,000 and >60,000; P < 0.001). There were no notable associations of fatalism with count of comorbid conditions, symptomatic detection status, or cancer site. ### Crude associations In crude analysis (logistic regression), higher fatalism scores were associated with advanced stage at diagnosis [OR per unit increase in fatalism = 1.05, 95% confidence interval (CI): 1.02–1.08, P = 0.001]. Other factors associated with stage IV diagnosis included lung cancer, younger age, lower count of comorbidities, being a current smoker, and symptomatic detection (P < 0.01 for all, Table 2). Table 2. Factors associated with diagnosis of cancer at stage IVa VariableStage IV (%)Crude ORs for stage IV95% CIPaAdjusted ORs for stage IV95% CIPa Fatalism (per one unit increase 4–16 scale) 1.05 1.02–1.08 0.001 1.05 1.02–1.08 0.003 Sex Men 22.6 — 0.015 — <0.001 Women 19.6 0.83 0.72–0.97 0.73 0.61–0.86 Age at diagnosis <55 24.6 — <0.001 — <0.001 55–59 24.1 0.97 0.76–1.24 0.93 0.72–1.20 60–64 23.7 0.95 0.75–1.21 0.88 0.69–1.14 65–69 20.0 0.76 0.60–0.97 0.72 0.56–0.93 70–74 21.8 0.85 0.67–1.09 0.83 0.63–1.08 75–80 16.8 0.62 0.47–0.81 0.60 0.44–0.80 80 or more 13.3 0.47 0.34–0.64 0.48 0.34–0.67 Race/ethnicity White 20.3 — 0.204 — 0.87 Hispanic 23.6 1.20 0.91–1.59 1.18 0.86–1.62 Black 23.5 1.20 0.97–1.48 1.03 0.82–1.30 Asian 24.5 1.27 0.91–1.78 1.11 0.77–1.60 Other 23.9 1.24 0.80–1.91 1.09 0.70–1.72 Education <High school 20.4 — 0.019 — 0.099 High school/some college 22.6 1.14 0.93–1.40 1.23 0.98–1.53 ≥College degree 18.5 0.89 0.70–1.13 1.06 0.80–1.41 Income (US$)
<20,000 23.0 —  0.50 —  0.82
20,000–39,999 21.9 0.92 0.75–1.12  0.97 0.78–1.20
40,000–59,999 20.4 0.86 0.69–1.08  0.89 0.68–1.15
≥60,000 20.3 0.88 0.71–1.07  0.91 0.70–1.19
Study site
5 Integrated health-care delivery systems 20.1 —  0.001 —  0.134
8 counties in Northern California 23.1 1.19 0.93–1.53  1.20 0.93–1.56
Los Angeles County 20.6 1.03 0.80–1.32  0.97 0.74–1.26
State of Alabama 23.3 1.20 0.90–1.61  1.11 0.82–1.51
22 counties in North Carolina 13.5 0.62 0.44–0.86  0.85 0.59–1.21
State of Iowa 23.2 1.20 0.89–1.63  0.82 0.59–1.13
15 Veterans Affairs Medical Centers 23.8 1.24 0.93–1.65  1.00 0.73–1.37
Married/living with partner
Yes 20.7 —  0.28 —  0.134
No 22.1 1.09 0.94–1.26  1.13 0.95–1.34
Smoking status
Never smoker 18.8 —  <0.001 —  0.109
Ex-smoker 21.2 1.16 0.98–1.37  0.89 0.73–1.09
Current smoker 27.5 1.63 1.28–2.09  1.13 0.85–1.49
Comorbidities
0 23.9 —  0.004 —  0.003
1 20.2 0.81 0.68–0.97  0.78 0.65–0.94
2 18.8 0.74 0.59–0.92  0.70 0.56–0.89
≥3 17.9 0.69 0.53–0.90  0.65 0.48–0.87
Cancer site
Lung 27.6 —  <0.001 —  <0.001
Colorectal 16.2 0.51 0.44–0.59  0.44 0.36–0.52
Symptoms at diagnosis
Yes 23.4 2.33 1.85–2.94 <0.001
No 11.6 —
Health status a year before interview (0–100)  1.00 1.00–1.01 0.028 1.00 1.00–1.01 0.223
VariableStage IV (%)Crude ORs for stage IV95% CIPaAdjusted ORs for stage IV95% CIPa
Fatalism (per one unit increase 4–16 scale)  1.05 1.02–1.08 0.001 1.05 1.02–1.08 0.003
Sex
Men 22.6 —  0.015 —  <0.001
Women 19.6 0.83 0.72–0.97  0.73 0.61–0.86
Age at diagnosis
<55 24.6 —  <0.001 —  <0.001
55–59 24.1 0.97 0.76–1.24  0.93 0.72–1.20
60–64 23.7 0.95 0.75–1.21  0.88 0.69–1.14
65–69 20.0 0.76 0.60–0.97  0.72 0.56–0.93
70–74 21.8 0.85 0.67–1.09  0.83 0.63–1.08
75–80 16.8 0.62 0.47–0.81  0.60 0.44–0.80
80 or more 13.3 0.47 0.34–0.64  0.48 0.34–0.67
Race/ethnicity
White 20.3 —  0.204 —  0.87
Hispanic 23.6 1.20 0.91–1.59  1.18 0.86–1.62
Black 23.5 1.20 0.97–1.48  1.03 0.82–1.30
Asian 24.5 1.27 0.91–1.78  1.11 0.77–1.60
Other 23.9 1.24 0.80–1.91  1.09 0.70–1.72
Education
<High school 20.4 —  0.019 —  0.099
High school/some college 22.6 1.14 0.93–1.40  1.23 0.98–1.53
≥College degree 18.5 0.89 0.70–1.13  1.06 0.80–1.41
Income (US \$)
<20,000 23.0 —  0.50 —  0.82
20,000–39,999 21.9 0.92 0.75–1.12  0.97 0.78–1.20
40,000–59,999 20.4 0.86 0.69–1.08  0.89 0.68–1.15
≥60,000 20.3 0.88 0.71–1.07  0.91 0.70–1.19
Study site
5 Integrated health-care delivery systems 20.1 —  0.001 —  0.134
8 counties in Northern California 23.1 1.19 0.93–1.53  1.20 0.93–1.56
Los Angeles County 20.6 1.03 0.80–1.32  0.97 0.74–1.26
State of Alabama 23.3 1.20 0.90–1.61  1.11 0.82–1.51
22 counties in North Carolina 13.5 0.62 0.44–0.86  0.85 0.59–1.21
State of Iowa 23.2 1.20 0.89–1.63  0.82 0.59–1.13
15 Veterans Affairs Medical Centers 23.8 1.24 0.93–1.65  1.00 0.73–1.37
Married/living with partner
Yes 20.7 —  0.28 —  0.134
No 22.1 1.09 0.94–1.26  1.13 0.95–1.34
Smoking status
Never smoker 18.8 —  <0.001 —  0.109
Ex-smoker 21.2 1.16 0.98–1.37  0.89 0.73–1.09
Current smoker 27.5 1.63 1.28–2.09  1.13 0.85–1.49
Comorbidities
0 23.9 —  0.004 —  0.003
1 20.2 0.81 0.68–0.97  0.78 0.65–0.94
2 18.8 0.74 0.59–0.92  0.70 0.56–0.89
≥3 17.9 0.69 0.53–0.90  0.65 0.48–0.87
Cancer site
Lung 27.6 —  <0.001 —  <0.001
Colorectal 16.2 0.51 0.44–0.59  0.44 0.36–0.52
Symptoms at diagnosis
Yes 23.4 2.33 1.85–2.94 <0.001
No 11.6 —
Health status a year before interview (0–100)  1.00 1.00–1.01 0.028 1.00 1.00–1.01 0.223

Abbreviation: ORs, odds ratios.

aFrom univariable or multivariable regression, as applicable. Multivariable regression models adjusted for all variables in the table except symptom status at diagnosis.

Adjusted analysis (logistic regression) revealed a very similar association between fatalism and stage IV diagnosis (OR per unit increase in fatalism = 1.05, 95% CI, 1.02–1.08, P = 0.003, Table 2). Patients with the highest fatalism score (16) had an OR of 1.77 (95% CI, 1.21–2.57) for advanced stage at diagnosis compared with those with the lowest score (4), equivalent to an absolute adjusted difference of 8.9% in the proportion of patients diagnosed with stage IV cancers (Table 3). Similarly, patients with the highest score (16) had an OR of 1.34 (95% CI, 1.10–1.60) for stage IV diagnosis compared with those in mid-scale (10), equivalent to 4.7% increase in the proportion of patients diagnosed with stage IV cancers. There was also evidence that lung cancer, male sex, younger age, and lower count of comorbid conditions were associated with higher odds of stage IV diagnosis (P < 0.01 for all, Table 2).

Table 3.

Proportion of patients diagnosed with stage IV cancers (crude and case-mix adjusted)

98 18.4% 16.5%
47 14.9% 17.2%
108 18.5% 17.8%
151 17.9% 18.5%
443 16.7% 19.2%
505 21.4% 19.9%
10 544 23.7% 20.7%
11 664 21.8% 21.4%
12 923 19.2% 22.2%
13 322 27.0% 23.0%
14 179 16.2% 23.8%
15 133 27.8% 24.6%
16 202 29.2% 25.4%
98 18.4% 16.5%
47 14.9% 17.2%
108 18.5% 17.8%
151 17.9% 18.5%
443 16.7% 19.2%
505 21.4% 19.9%
10 544 23.7% 20.7%
11 664 21.8% 21.4%
12 923 19.2% 22.2%
13 322 27.0% 23.0%
14 179 16.2% 23.8%
15 133 27.8% 24.6%
16 202 29.2% 25.4%

aAdjusted proportions calculated from the logistic regression model—this is the expected percentage that would have been observed if the case-mix of patients in each fatalism score category was the same as in the total sample.

### Sensitivity analysis

When we examined the association of fatalism with stage III/IV diagnosis, the direction of association remained but its strength was attenuated, such that there was only weak evidence for an association (OR, 1.03; 95% CI, 1.00–1.05; P = 0.055; Supplementary Table S2). When running logistic regression models that included each individual fatalism item separately instead of the total score (four separate models) the observed findings were concordant with those of the main analysis, with respective OR >1.0 and some significant associations. Repeating the analysis using a total score but excluding the “God's plan” individual item also produced concordant results (OR, 1.06; 95% CI, 1.02–1.10; P = 0.004). Similarly, inclusion of symptomatic detection status and an interaction variable symptomatic detection*fatalism score produced concordant findings to those observed in the main analysis with no evidence for an interaction (P = 0.68). Adjustment for tumor grade and/or lung cancer type made no material difference to the findings of the main analysis model.

In a large population-based survey of patients with lung and colorectal cancer, we observed an independent association between higher fatalism scores and advanced stage at diagnosis of lung and colorectal cancer. Sensitivity analyses provided concordant findings.

Although previous research has documented associations between fatalism and lower participation in cancer screening (8–10), evidence linking fatalism with clinical outcomes such as stage at diagnosis is limited. Our inquiry is methodologically similar to two previous studies, and a smaller qualitative study of patients with breast cancer (11, 12, 20), but has a substantially larger and sociodemographically and geographically more diverse sample that includes patients with both lung and colorectal cancer.

Powe and Finnie suggest that the two factors that shape fatalism are angst, defined as the perceived collapse of meaning in the presence of despair about the future, and nihilism, defined as lived experiences of coping with feelings of meaningless, hopelessness, and despair (5). Accordingly, cancer fatalism takes shape as individuals experience others being diagnosed with cancer at an advanced stage, leading to poor outcomes and death. This can lead to skepticism about the value of cancer screening or prompt symptomatic presentation, leading to prolonged intervals to presentation and increasing the risk of advanced stage at diagnosis (21). In addition, cultural or religious values emphasizing acceptance may accentuate fatalistic attitudes among some groups (22). Modifying entrenched fatalistic beliefs is not likely to be easy, but evidence that an understanding of the value of prevention or early diagnosis can be held in parallel with fatalism suggests that exposure to culturally appropriate information on the value of early detection might be beneficial (23). If fatalistic beliefs can be modified, campaigns to promote symptom awareness are also likely to contribute to more rapid symptomatic presentation (24). In a recent development of a new scale, Shen and colleagues propose that fatalism is a multidimensional construct, encompassing “predestination,” “luck,” and “pessimism” (25). The Fatalism scale used in the current study more closely resembles the predestination dimension, and it will be important in future work to examine the differential effects of these three elements on screening and help-seeking behaviors.

Strengths of our study include its relatively large and diverse patient population and high data quality, including information on stage at diagnosis abstracted from medical records and the use of validated instruments for assessing fatalism. We were also able to adjust our analysis for a large number of potential confounders, including age, sex, race/ethnicity, education, income, comorbidity, smoking status, and prior health status.

Our study has two principal limitations. First, our analysis included only patients who were alive and able to complete the baseline survey, resulting in relative under representation of older and more comorbid patients and those with more advanced stage at diagnosis (Supplementary Table S1). However, sample attrition and nonresponse patterns are a necessary but not sufficient condition for nonresponse bias in estimates of associations. After appropriate adjustment for case-mix the effect of such bias in surveys with an appropriately defined sampling frame is small (26, 27). Achieving more representative patient samples might be possible if interviews are conducted sooner after diagnosis; however, this is rarely feasible in large population-based studies of patients with cancer.

Second, as for any cross-sectional study, the causal direction of the observed association cannot be proven. Fatalism could lead to advanced stage at diagnosis, but it is also possible that advanced stage at diagnosis could lead to higher fatalism scores, although Powe has reported unpublished data indicating that cancer fatalism has declined from pre- to postdiagnosis (28). Nevertheless, a causal association of fatalism with advanced stage at diagnosis is plausible. If people believe that cancer is inevitably fatal, then they might delay or avoid seeking medical help after symptom onset or participation in cancer screening, and thus have a higher risk of advanced cancer at diagnosis. Although we did not observe statistically significant differences in fatalism scores based on presence or absence of symptoms, patients may have differed in how long they waited to seek care after developing symptoms. Other studies of asymptomatic individuals have documented associations between fatalism and both intended help seeking and participation in cancer screening, further supporting a causal effect of fatalism on the length of the patient interval and stage at diagnosis (6–10). Prospective cohort studies involving fatalism measurement in individuals who are free of cancer at study entry would be ideal, but would require large populations and adequate follow-up to achieve sufficient numbers of cancer cases. Critically, new prospective studies would be associated with ethical challenges (i.e., leaving fatalistic beliefs about cancer curability unchallenged during follow-up could be considered unethical). This may explain why the few previous studies have also measured fatalism after diagnosis (11, 12).

When fatalism items form part of surveys of patients after a cancer diagnosis, there may be ethical concerns about use of cancer-specific fatalism items, and this was the rationale for using a general fatalism item in the CanCORS study. A qualitative study confirmed low acceptability of cancer fatalism questions among recently diagnosed patients with breast cancer (29). General (as opposed to cancer-specific) fatalism items may have both merits (e.g., less likely to be prone to bias resulting from knowledge of cancer diagnosis) and limitations (it makes the argument for public health interventions aimed at reducing “cancer fatalism” less direct). Subject to research ethics considerations, future surveys of patients with cancer may be able to investigate differences in results from use of general and cancer-specific fatalism items.

In conclusion, we identified an association between fatalism and advanced stage at diagnosis for patients with newly diagnosed lung and colorectal cancer, most of whom had presented with symptoms. The consistency of these findings with those from other qualitative and quantitative studies, the plausibility of the association and its large size suggest that the findings should be considered in the context of public health policy initiatives and education campaigns designed to shorten the patient interval of newly diagnosed cancer cases (30,31). Traditionally such strategies have focused on cognitive aspects (i.e., “awareness” of cancer symptoms) but including “antifatalism” components could have a powerful impact. Such approaches may include factual information about the high probability of long-term survival when the diagnosis occurs at a nonadvanced stage and the availability of effective treatments that offer good health-related quality of life.

No potential conflicts of interest were disclosed.

The views expressed in this publication are those of the authors and not necessarily those of the National Cancer Institute, the Department of Veterans Affairs, the NHS (National Health Service), the National Institute for Health Research (NIHR), the (UK) Department of Health, or any other funder.

Conception and design: G. Lyratzopoulos, M.P.-H. Liu, J. Wardle, N.L. Keating

Development of methodology: G. Lyratzopoulos, M.P.-H. Liu, N.L. Keating

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): N.L. Keating

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): M.P.-H. Liu, G.A. Abel, J. Wardle, N.L. Keating

Writing, review, and/or revision of the manuscript: G. Lyratzopoulos, M.P.-H. Liu, G.A. Abel, J. Wardle, N.L. Keating

Study supervision: N.L. Keating

This work of the Cancer Care Outcomes Research and Surveillance (CanCORS) Consortium was supported by grants from the NCI to the Statistical Coordinating Center (U01 CA093344) and the NCI-supported Primary Data Collection and Research Centers (Dana Farber Cancer Institute/Cancer Research Network U01 CA093332, Harvard Medical School/Northern California Cancer Center U01 CA093324, RAND/UCLA U01 CA093348, University of Alabama at Birmingham U01 CA093329, University of Iowa U01 CA093339, University of North Carolina U01 CA093326) and by a Department of Veterans Affairs grant to the Durham VA Medical Center CRS 02-164. N.L. Keating was supported by 1R01CA164021-01A1 and K24CA18151 from the NCI. J. Wardle was supported by Cancer Research UK Programme grant C1418/A14134. G. Lyratzopoulos is supported by a Post-Doctoral Fellowship award by the (UK) National Institute for Health Research (PDF-2011-04-047) 2012–2014 and a Cancer Research UK Clinician Scientist Fellowship award (A18180) from 2015.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Elliss-Brookes
L
,
McPhail
S
,
Ives
A
,
M
,
Shelton
J
,
Hiom
S
, et al
Routes to diagnosis for cancer - determining the patient journey using multiple routine data sets
.
Br J Cancer
2012
;
107
:
1220
6
.
2.
Lyratzopoulos
G
.
Measures and markers of timeliness of diagnosis in symptomatic patients. A conceptual framework and its implications
.
Cancer Epidemiol
2014
;
38
:
211
3
.
3.
Weller
D
,
Vedsted
P
,
Rubin
G
,
Walter
FM
,
Emery
J
,
Scott
S
, et al
The Aarhus statement: improving design and reporting of studies on early cancer diagnosis
.
Br J Cancer
2012
;
106
:
1262
7
.
4.
Simon
AE
,
Forbes
LJ
,
Boniface
D
,
Warburton
F
,
Brain
KE
,
Dessaix
A
, et al
An international measure of awareness and beliefs about cancer: development and testing of the ABC
.
BMJ Open
2012
;
2
pii
:
e001758
.
5.
Powe
BD
,
Finnie
R
.
Cancer fatalism: the state of the science
.
Cancer Nurs
2003
;
26
:
454
65
.
6.
Facione
NC
,
C
,
Dodd
MJ
,
Paul
SM
.
The self-reported likelihood of patient delay in breast cancer: new thoughts for early detection
.
Prev Med
2002
;
34
:
397
407
.
7.
Beeken
RJ
,
Simon
AE
,
von Wagner
C
,
Whitaker
KL
,
Wardle
J
.
Cancer fatalism: deterring early presentation and increasing social inequalities?
Cancer Epidemiol Biomarkers Prev
2011
;
20
:
2127
31
.
8.
Powe
BD
.
Fatalism among elderly African Americans. Effects on colorectal cancer screening
.
Cancer Nurs
1995
;
18
:
385
92
.
9.
Lostao
L
,
Joiner
TE
,
Pettit
JW
,
Chorot
P
,
Sandín
B
.
Health beliefs and illness attitudes as predictors of breast cancer screening attendance
.
Eur J Public Health
2001
;
11
:
274
9
.
10.
Peek
ME
,
JV
,
Markwardt
R
.
Fear, fatalism and breast cancer screening in low-income African-American women: the role of clinicians and the health care system
.
J Gen Intern Med
2008
;
23
:
1847
53
.
11.
Lannin
DR
,
Mathews
HF
,
Mitchell
J
,
Swanson
MS
,
Swanson
FH
,
Edwards
MS
.
Influence of socioeconomic and cultural factors on racial differences in late-stage presentation of breast cancer
.
JAMA
1998
;
279
:
1801
7
.
12.
Bergamo
C
,
Lin
JJ
,
Smith
C
,
Lurslurchachai
L
,
Halm
EA
,
Powell
CA
, et al
Evaluating beliefs associated with late-stage lung cancer presentation in minorities
.
J Thorac Oncol
2013
;
8
:
12
8
.
13.
Malin
JL
,
Ko
C
,
Ayanian
JZ
,
Harrington
D
,
Nerenz
DR
,
Kahn
KL
, et al
Understanding cancer patients' experience and outcomes: development and pilot study of the Cancer Care Outcomes Research and Surveillance patient survey
.
Support Care Cancer
2006
;
14
:
837
48
.
14.
Ayanian
JZ
,
Chrischilles
EA
,
Fletcher
RH
,
MN
,
Harrington
DP
,
Kahn
KL
, et al
Understanding cancer treatment and outcomes: the Cancer Care Outcomes Research and Surveillance Consortium
.
J Clin Oncol
2004
;
22
:
2992
6
.
15.
Catalano
PJ
,
Ayanian
JZ
,
Weeks
JC
,
Kahn
KL
,
Landrum
MB
,
Zaslavsky
AM
, et al
Representativeness of participants in the cancer care outcomes research and surveillance consortium relative to the surveillance, epidemiology, and end results program
.
Med Care
2013
;
51
:
e9
15
.
16.
Jacobson
CK
.
Denominational and racial and ethnic differences in fatalism
.
Rev Relig Res
1999
;
41
:
9
20
.
17.
Katz
JN
,
Chang
LC
,
Sangha
O
,
Fossel
AH
,
Bates
DW
.
Can comorbidity be measured by questionnaire rather than medical record review?
Med Care
1996
;
34
:
73
84
.
18.
Little
RJ
.
Direct standardization: a tool for teaching linear models for unbalanced data
.
Am Stat
1982
;
36
:
38
43
.
19.
He
Y
,
Zaslavsky
AM
,
Landrum
MB
,
Harrington
DP
,
Catalano
P
.
Multiple imputation in a large-scale complex survey: a practical guide
.
Stat Methods Med Res
2010
;
19
:
653
70
.
20.
Mohamed
IE
,
Skeel Williams
K
,
Tamburrino
M
,
Wryobeck
J
,
Carter
S
.
Understanding locally advanced breast cancer: what influences a woman's decision to delay treatment?
Prev Med
2005
;
41
:
399
405
.
21.
Cohen
M
.
Cancer fatalism. Attitudes toward screening and care
. 1st ed.
Carr
BI
,
Steel
J
,
editors
.
Springer US
:
Psychological Aspects of Cancer
;
2013
.
22.
Goss
PE
,
Strasser-Weippl
K
,
Lee-Bychkovsky
BL
,
Fan
L
,
Li
J
,
Chavarri-Guerra
Y
, et al
Challenges to effective cancer control in China, India, and Russia
.
Lancet Oncol
2014
;
15
:
489
538
.
23.
Keeley
B
,
Wright
L
,
Condit
CM
.
Functions of health fatalism: fatalistic talk as face saving, uncertainty management, stress relief and sense making
.
Sociol Health Illn
2009
;
31
:
734
47
.
24.
Quaife
SL
,
Forbes
LJ
,
Ramirez
AJ
,
Brain
KE
,
Donnelly
C
,
Simon
AE
, et al
Recognition of cancer warning signs and anticipated delay in help-seeking in a population sample of adults in the UK
.
Br J Cancer
2014
;
110
:
12
8
.
25.
Shen
L
,
Condit
CM
,
Wright
L
.
The psychometric property and validation of a fatalism scale
.
Psychol Health
2009
;
24
:
597
613
.
26.
Groves
R
.
Nonresponse rates and nonresponse bias in household surveys
.
Public Opin Q
2006
;
70
:
646
75
.
27.
Groves
R
,
Peytcheva
E
.
The impact of nonresponse rates on nonresponse bias: a meta-analysis
.
Public Opin Q
2008
;
72
:
167
89
.
28.
Powe
BD
.
Do recently diagnosed black breast cancer patients find questions about cancer fatalism acceptable? A preliminary report
.
J Cancer Educ
2011
;
26
:
3
4
29.
Sheppard
VB
,
Davis
K
,
Boisvert
M
,
Jennings
Y
,
Montalvo
B
.
Do recently diagnosed black breast cancer patients find questions about cancer fatalism acceptable? A preliminary report
.
J Cancer Educ
2011
;
26
:
5
10
.
30.
NHS England
.
Knowing the signs of cancer could save your life. Be clear on cancer
. http://www.nhs.uk/be-clear-on-cancer/.
31.
American Cancer Society
.
Signs and symptoms of cancer
. http://www.cancer.org/cancer/cancerbasics/signs-and-symptoms-of-cancer.