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. Author manuscript; available in PMC: 2014 Jul 1.
Published in final edited form as: Psychooncology. 2012 Sep 11;22(7):1534–1542. doi: 10.1002/pon.3167

Survivor Typologies Predict Medical Surveillance Participation: The Childhood Cancer Survivor Study

Cheryl L Cox a, Liang Zhu b, Melissa M Hudson c, Brenda D Steen a, Leslie L Robison a, Kevin C Oeffinger d
PMCID: PMC3548022  NIHMSID: NIHMS406486  PMID: 22968964

Abstract

Objective

Adult survivors of childhood cancer adhere poorly to recommended medical surveillance. We sought to identify modifiable factors that contribute to non-adherence.

Methods

Latent class analysis categorized survivors (ages 18–52 years) at risk of cardiac, breast, or bone late sequelae on the basis of their health-related concerns, fears, and motivation. These classifications were compared at two time points for self-reported adherence to recommended echocardiography, mammography, and bone densitometry screening.

Results

Three classes (worried, collaborative, self-controlling) characterized survivors in each of the 3 risk groups: cardiac (N=564; BIC=10,824.66; LRMLRT P=.002), breast (N=584; BIC=11,779.97, LRMLRT P<.001), and bone (N=613; BIC=11,773.56; LMRLRT P=.028). Only 9% of at-risk survivors in the self-controlling class reported undergoing bone density screening in 2005, compared to 17.2% in the collaborative class (P=.034). Thirteen percent of the self-controlling, 24% of the collaborative (P=.025), and 34% of the worried (P=.010) classes reported undergoing bone densitometry in 2009. While 73% of at-risk survivors in the worried class reported having had an echocardiogram in 2009, only 57% of the collaborative (P=.040) and 43% of the self-controlling (P<.001) classes did. In 2005 and 2009, respectively, fewer survivors in the self-controlling class (37% and 53%) than in the collaborative (51%, P=.038 and 70%, P=.01) and worried (58%, P=0.002 and 69%, P=0.025) classes reported undergoing mammograms.

Conclusions

Modifiable intrapersonal characteristics associated with these 3 classes predict self-reported participation in medical surveillance. Continued observation and validation of these survivor profiles may inform tailored interventions to enhance survivors’ screening participation.

Keywords: childhood cancer, screening, late effects, pediatric oncology

INTRODUCTION

As childhood cancer survival rates rise,[1, 2] late treatment-related morbidity [36] is of increasing concern. Follow-up screening can modify the likelihood and severity of late effects [78], but many survivors do not adhere to these recommendations [915]. For example, despite female survivors’ increased risk of early breast cancer,[1618]recent studies found that only 41%– 55% of those with a history of chest radiation underwent mammography;[12, 19] among survivors <40 years, 47.3% had never had a mammogram, and only 52.6% of those 40–50 years were regularly screened (2 mammograms within 4 years)[19].

Treatment of childhood cancer with anthracyclines and/or chest radiation incurs a risk of late cardiotoxicity [6, 2024]. Thirty years after diagnosis, survivors of childhood cancer have a rate of cardiac death 7 times the age- and sex-matched national average [25]. However, only 28% of at-risk participants in the Childhood Cancer Survivor Study (CCSS) had received the recommended cardiac screening [12, 26]. Cranial radiotherapy, glucocorticoids, methotrexate, and prolonged corticosteroid therapy increase the risk of low bone density and osteonecrosis, [27] but only approximately 25% of survivors at greatest risk had recently undergone bone densitometry [28].

We recently identified 3 distinctive profiles that predicted adult survivors’ intent to undergo routine and/or cancer-related check-ups [29]. These profiles were defined by survivors’ health-related motivation, worry, and concern, -- established modifiable mediators and moderators of health outcomes [3036]. In this study, we replicated the distinctive survivor profiles in different samples, identified specific profile covariates, and used the profiles to predict self-reported participation in echocardiography, mammography, and bone densitometry screening at two different time points.

METHODS

Data Source

The CCSS is an IRB-approved multi-institutional retrospective cohort study initiated in 1994 to examine late effects in survivors of pediatric cancers diagnosed and treated between 1970 and 1986. Eligible participants had survived 5 or more years after completion of treatment for a malignant disease diagnosed before age 21 years. Survivors completed a baseline questionnaire at study entry and they respond to follow-up questionnaires (available at http://ccss.stjude.org) at regular intervals [3738]. Participants provided informed consent for study procedures and release of their medical records.

Sample

Our study sample was drawn from CCSS participants who: a) had responded to two CCSS follow-up surveys (2005 and 2009) containing the screening outcome measures; b) participated in at least one of two CCSS ancillary studies containing the psychological latent class indicators (Health Care Barriers [HCB] in 2001, Mammogram Survey [MS]) in 2005; and c) were at high risk of treatment exposure–based late effects, including cardiac (anthracycline and/or chest or total body radiation), bone density (cranial radiotherapy, glucocorticoids, methotrexate, and/or prolonged corticosteroid use), and breast cancer (chest or spinal radiation) (Figure 1). The cardiac and bone risk groups had 387/808 survivors in common; the bone and breast-cancer risk groups shared 30/1226 survivors; the cardiac and breast-cancer risk groups shared 62/1141; and 28/1376 survivors belonged to all 3 groups. The overlap in the groups is related to multi-agent chemotherapy and/or multimodal therapy (e.g., chemotherapy, radiation, surgery). Each agent or modality confers excess risk for a spectrum of adverse treatment effects, some of which are overlapping. All survivors were age =18 years at the time of data collection.

Figure 1.

Figure 1

Description of study data sources

Measures

Class indicators (established by confirmatory factor analysis) were fears, health concerns, intrinsic motivation, and extrinsic motivation. These measures were identical and derived from either the Health Care Barriers (2001) or Mammogram (2005) ancillary CCSS studies and were antecedent to the surveys containing the screening outcomes. Three summed items defined survivors’ fears about future health, cancer recurrence, and discovery of a problem at a check-up visit (1=not at all; 5=extremely). Three items were summed to define survivors’ health concerns about general health, the chance of illness or health problems related to previous cancer, and the importance of a check-up visit (1=not at all; 5=extremely). Five summed items from the Multidimensional Health Locus of Control Scale [39] (e.g., “I am in control of my health”) defined survivors’ intrinsic motivation (1=moderately/strongly disagree; 4=moderately/strongly agree). Four summed items from the Multidimensional Health Locus of Control Scale [39] (e.g., “Health professionals control my health”) defined survivors’ extrinsic motivation (1=moderately/strongly disagree; 4=moderately/strongly agree).

Covariates included disease and treatment variables (diagnosis, age at follow-up, age at cancer diagnosis, years since cancer diagnosis), sex, education, race, total personal income, current health insurance status, perceived severity of late effects, and self-reported health status (1=excellent, 5=poor). Survivors indicated whether they had experienced chronic health problems lasting longer than 6 months and rated the severity of their main chronic health problem. These responses were categorized as moderate, severe, or life-threatening chronic problems vs. mild or no chronic problems.

Medical Surveillance

The Children’s Oncology Group has compiled risk-based, exposure-related guidelines for surveillance and management of late effects of treatment [7]. Echocardiography, mammography, and bone densitometry are recommended at specific intervals based on age at treatment, chemotherapy and/or radiation exposures, and clinical indications at the end of therapy. Recommended screening frequency ranges from as often as yearly to every 5 years based on the established criteria. Given the length of time between follow-up surveys, all patients would have been due for at least one of the three screenings. Survivors indicated the time of the most recent echocardiography (ultrasound or MUGA scan), bone densitometry (DEXA or CT scan), and mammography in both of the CCSS follow-up surveys (1=never, 5=5 or more years ago). Survivors who answered “don’t know” to any of these questions were excluded from analysis. We used a very conservative approach to code the medical surveillance outcome for the logistic regressions. For the echocardiogram and bone densitometry multinomial logistic regressions, “never” and “5 or more years ago” were re-coded as 0 (non-adherent); “more than 2 but less than 5 years ago”, “1–2 years ago” and “less than 1 year ago” were re-coded as 1 (adherent). In keeping with previous reports [19], for the mammogram logistic regressions, “more than 2 but less than 5 years ago” was considered non-adherent. Self-reports of medical screening have been established as valid study measures in the general population [4044]. Recent unpublished ancillary CCSS studies report 90% agreement between self-reports of screening and medical record review.

Statistical Analysis

Latent class analysis (LCA) enables the identification of a set of mutually exclusive typologies that account for the distribution of individuals in the population; typologies are created by cross-tabulation of observed discrete variables [45]. Unlike typical regression analysis, in which the population is assumed to be homogeneous and a single model holds for all cases, LCA can accommodate multiple populations (i.e., typologies) within the population. As recommended by Nylund and colleagues,[46] a combination of the Bayesian information criterion (BIC), Lo-Mendell-Rubin parametric likelihood ratio (LMRLRT), and bootstrap LMRLRT (BLMRT) tests were applied to determine the number of latent classes. Details regarding the interpretation of these parameters have been previously reported. [29] SAS 9.1 software (SAS Institute Inc, Cary, NC) was used to describe sample characteristics and MPlus Version 6.1 (Muthén and Muthén, Los Angeles, CA) was used to develop the latent class models.

Models that had the fewest substantively meaningful distinct classes and a BLMRT p ≤0.05 were accepted. Covariates that were significantly (p ≤.05) associated with the classes were included in the best-fitting latent class model. Binary outcomes (participation in echocardiography, mammography, and bone densitometry) were tested for equality of proportion to determine class-specific self-reported participation. All analyses were repeated for each risk group.

RESULTS

Latent Class Model Selection

Survivor classes were first identified within each risk group on the basis of our indicator variables, without controlling for covariates (Table 1). Five different models were tested for the cardiac and breast-cancer risk groups (1–5 classes) and 6 models (1–6 classes) for the bone risk group (Table 2). Both the 2- and 3-class models were tested for each risk group with covariates. Significant covariates included self-perceived health status and severity of late effects in all three models, health insurance status in the breast cancer risk group, race in the bone density risk group, and sex in the cardiac and bone density risk groups. Final fit statistics for the three models with covariates were: cardiac (N=564; BIC=10,824.66; LRMLRT P=0.002); bone (N=613; BIC=11,773.56; LMRLRT P=0.028); and breast (N=584; BIC=11, 779.97, LMRLRT P=<0.001). The final models with covariates demonstrated posterior probabilities (showing how well each participant fit the assigned class) of 85%-90%, supporting appropriate class assignment and model fit [47].

Table 1.

Descriptive summary of classes within risk groups before controlling for covariates

BONE RISK CARDIAC BREAST RISK

Total
n=624
Self-Control
n=163
Collab
n=353
Worried
n=108
P value Total
n=571
Self-Control
n=136
Collab
n=326
Worried
n=109
P value Total
n=632
Self-Control
n=110
Collab
n=330
Worried
N=192
P value

26% 56% 17% 24% 57% 19% 17% 52% 30%

Age at survey completion, 2003 .918 .219 .149
Mean 30.36 30.19 30.38 30.56 31.65 30.93 32.12 31.13 38.92 38.36 38.70 39.62
SD 7.2 6.8 7.3 7.3 7.5 7.1 7.5 7.95 6.18 6.09 6.35 5.89

Gender(%) <.001 <.001 NA
Female 52 33 57 67 51 38 51 66 100 100 100 100
Male 48 67 43 33 49 62 49 34

Race/Ethnicity (%) .004 .015 .859
 White 74 81 74 65 75 79 76 68 89 90 88 89
 Black 7 4 8 10 8 5 7 12 1 0 2 1
 Hispanic 12 4 12 20 12 6 13 16 4 3 5 4
 Other 7 10 6 5 6 10 4 5 6 7 6 6

Annual Personal Income (%) .005 .092 .834
 < $40,000 67 58 68 80 64 63 61 72 68 65 68 67
 >=$40,000 25 32 24 17 31 32 33 22 23 21 25 22
 Not reported 8 10 8 4 5 4 6 6 9 14 7 11

Education (%) .005 .320 .084
Less than HS 7 9 7 5 4 4 4 5 2 3 2 2
Completed HS 49 38 49 62 46 40 45 53 38 41 33 44
Completed College 42 50 42 31 49 54 50 40 53 48 58 47
Not reported 2 3 1 2 1 2 0 2 7 8 6 8

Chemotherapy (%) .520 .571 .312
Yes 93 91 93 94 89 92 89 89 75 81 73 76
No 7 9 7 6 11 8 11 11 24 19 26 24
Not reported 1 1

Radiation% .478 .499 NA
Yes 70 66 71 73 76 71 77 76 100 100 100 100
No 30 33 29 27 24 28 23 24
Not reported 0 1 0 0 0 1 0 0

Chemotherapy and radiation (%) .218 .924 .312
Yes 63 57 64 67 65 63 66 65 75 81 73 76
No 37 42 36 33 35 36 34 35 24 19 26 24
Not reported 0 1 0 0 0 1 0 0 1 1

Age at cancer diagnosis .723 .371 .213
Mean 9.02 9.31 8.95 8.81 10.11 9.65 10.41 9.77 11.54 10.78 11.56 11.95
SD 5.5 5.1 5.7 5.7 5.94 5.7 6.0 6.1 5.57 5.34 5.60 5.63

Years since cancer diagnosis .238 .577 .373
Mean 21.34 20.89 21.43 21.75 21.54 21.27 21.71 21.37 27.38 27.58 27.14 27.67
SD 4.4 4.2 4.4 4.3 4.5 4.3 4.7 4.3 4.46 4.53 4.42 4.50

Cancer type (%) .210 .786 .060
Leukemia 50 48 53 44 29 31 29 26 9 9 11 5
Central nervous system 9 12 7 9 1 1 1 1 3 4 4 2
Hodgkin lymphoma 11 10 10 17 23 18 23 30 54 46 52 63
Non-Hodgkin lymphoma 14 15 15 11 14 17 13 13 7 6 8 7
Wilms tumor 0 0 0 0 8 8 9 7 12 18 11 10
Neuroblastoma 0 0 0 2 5 5 5 3 5 2 6 6
Soft tissue sarcoma 7 7 7 7 8 10 6 7 4 6 5 3
Bone cancer 8 7 8 10 13 10 14 13 5 8 5 4

Chronic late effects (%) <.001 <.001 .003
Moderate, severe or life- threatening 20 7 20 38 22 10 21 41 57 47 55 67
Mild or none 79 93 78 58 77 90 78 56 43 53 45 53
Not reported 1 0 1 4 1 1 1 3

Self-rated health (%) <.001 <.001 <.001
Excellent/very good/good 84 96 88 55 83 96 86 59 78 83 83 68
Fair/poor 15 4 12 43 16 2 13 39 15 9 11 24
Not reported 1 1 1 3 1 1 1 2 7 8 6 8

Limited ability to participate in vigorous activities (%) .002 .014 .041
Yes 29 18 33 37 30 21 33 34 38 32 43 34
No 69 82 67 58 69 79 66 64 54 60 50 58
Not reported 1 0 1 5 1 0 1 2 7 8 6 8

Current health insurance (%) .381 .181 .003
Yes 87 83 88 89 90 90 90 87 87 79 89 88
No 12 17 11 10 10 10 10 11 6 13 4 5
Not reported 1 1 1 1 0 0 0 2 7 8 7 8

Abbreviations: SD, standard deviation; HS, high school; self-control, self-controlling; collab, collaborative

Table 2.

Fit statistics of the risk LCA models, not controlled for covariates

Cardiac(N=571)
Classes Log-Likelihood BIC LMR Adjusted LRT P Value
1 −5597.15 11,245.07
2 −5489.75 11,062.02 P<.001
3 −5445.57 11,005.38 P=.01
4 −5431.99 11,009.97 P=.526
5 −5426.30 11,030.33 P=.332
Breast (N=632)
Classes Log-Likelihood BIC LMR Adjusted LRT P Value
1 −6482.38 13,016.36
2 −6372.91 12,829.65 P<.001
3 −6321.50 12,759.08 P<.001
4 −6301.62 12,751.56 P=.269
5 −6288.56 12,757.69 P=.234
Bone (N=624)
Classes Log-Likelihood BIC LMR Adjusted LRT P Value
1 −6157.20 12,365.88
2 −6027.78 12,139.23 P<.001
3 −5978.91 12,073.66 P =.166
4 −5962.22 12,072.46 P=.228
5 −5930.69 12,042.59 P=.352
6 −5915.41 12,043.22 P=.275

Abbreviations: BIC, Bayesian information criterion; LMRLRT, Lo-Mendell-Rubin parametric likelihood ratio test; BLMRT, bootstrap LMRLRT

Interpreting Class Profiles

Figure 2 and Table 3 show the class-specific means of the indicator variables. This estimate divided by the standard error (EST/SE) indicates the strength of the relationship between the indicator and the latent class variable. In each risk group, one class reported poor perceived health status and moderate to life-threatening chronic illness more frequently than did the other two classes; these survivors also reported the greatest worries and health concerns, the lowest level of intrinsic motivation, and the highest level of extrinsic motivation (Table 3). We labeled this group “worried.” This class was much larger in the breast-cancer risk group (30%) than in the bone (17%) or cardiac (19%) risk groups.

Figure 2.

Figure 2

Estimated mean values of the latent class indicators by risk group.

Table 3.

Latent class indicators within risk groups, controlled for significant covariates

Worried Self-Controlling Collaborative
Mean Estimate (95% CI) S.E. EST/SE P Mean Estimate (95% CI) S.E. EST/SE P Mean Estimate (95% CI) S.E. EST/SE P
Health Fears
Cardiac(n=564) 11.66 (10.50–12.83) 0.594 19.63 <.001 5.12 (4.67–5.58) 0.233 22.00 <.001 7.77 (7.23–8.30) 0.273 28.43 <.001
Breast(n=584) 11.68 (10.96–12.40) 0.366 31.92 <.001 5.37 (4.84–5.89) 0.267 20.12 <.001 7.01 (6.57–7.45) 0.227 30.93 <.001
Bone (n=613) 11.36 (10.32–12.41) 0.535 21.25 <.001 5.12 (4.69–5.55) 0.219 23.31 <.001 7.66 (7.10–8.22) 0.287 26.73 <.001
Health Concerns
Cardiac(n=564) 13.35 (12.88–13.83) 0.242 55.15 <.001 6.69 (5.92–7.45) 0.388 17.22 <.001 10.26 (9.70–10.82) 0.285 36.06 <.001
Breast(n=584) 13.65 (13.35–13.95) 0.152 89.97 <.001 7.58 (6.88–8.28) 0.358 21.16 <.001 11.64 (11.12–12.15) 0.261 44.54 <.001
Bone(n=613) 13.26 (12.66–13.87) 0.309 42.91 <.001 6.73 (5.99–7.47) 0.378 17.81 <.001 10.25 (9.65–10.85) 0.307 33.40 <.001
Extrinsic Motivation
Cardiac(n=564) 9.26 (8.36–10.17) 0.462 20.05 <.001 6.76 (6.25–7.27) 0.259 26.08 <.001 8.48 (8.04–8.93) 0.228 37.25 <.001
Breast(n=584) 12.92 (12.30–13.54) 0.317 40.73 <.001 10.68 (9.69–11.66) 0.503 21.22 <.001 12.64 (12.11–13.17) 0.269 46.99 <.001
Bone(n=613) 9.43 (8.29–10.57) 0.582 16.21 <.001 6.65 (6.21–7.09) 0.224 29.74 <.001 9.03 (8.41–9.66) 0.317 28.50 <.001
Intrinsic Motivation
Cardiac(n=564) 15.17 (14.05–16.29) 0.571 26.55 <.001 17.23 (16.67–17.78) 0.285 60.49 <.001 16.73 (16.29–17.17) 0.225 74.49 <.001
Breast(n=584) 22.36 (21.52–23.21) 0.433 51.70 <.001 23.42 (22.43–24.42) 0.509 46.02 <.001 23.99 (23.43–24.56) 0.288 83.29 <.001
Bone(n=613) 14.57 (13.13–15.78) 0.735 19.82 <.001 17.40 (16.98–17.83) 0.218 79.85 <.001 16.69 (16.22–17.16) 0.238 70.14 <.001

A second class of survivors demonstrated class indicators and health status perceptions markedly opposite to those of the ‘worried’ class. Across all 3 risk groups, survivors in this class reported good/excellent health and little concern about health history or future health problems, and they placed little value on medical check-ups. While strongly intrinsically motivated for self-care, this group was minimally extrinsically motivated to involve health professionals in their long-term care management. We labeled this group “self-controlling.” In the cardiac and bone risk groups, this class was predominantly male; it had the lowest percentages of black and Hispanic survivors, the highest percentage of college graduates, and the highest reported incomes.

A third class, the largest class in each risk group, showed intermediate scores on the class indicators and on self-reported health status, was balanced in sex distribution, and endorsed moderate fears and health concerns. Members were highly intrinsically motivated to manage their health but also willing to work with health providers, as evidenced by their greater extrinsic motivation than the self-controlling group. We labeled this group “collaborative.”

Survivor Participation in Recommended Screening

Self-reported participation in echocardiography, bone densitometry, and mammography in two different follow-up surveys was compared within risk groups by class, controlling for significant covariates in each group (Table 4).

Table 4.

Comparison of participation in screening (2005 and 2009) according to class after adjustment for covariates

2005 2009
Class Worried vs. Self-Controlling Collaborative vs. Self-Controlling Worried vs. Collaborative Worried vs. Self-Controlling Collaborative vs. Self-Controlling Worried vs. Collaborative
Type of screening % (p value) % (p value) % (p value) % (p value) % (p value) % (p value)
Echocardiography (N=564) 54% vs. 40% (P=0.082) 50% vs. 40% (P=0.092) 54% vs. 50% (P=0.550) 73% vs. 43% (P=<0.001) 57% vs. 43% (P=0.036) 73% vs. 57% (P=0.040)
Mammography (N=584) 58% vs. 37% (P=0.002) 51% vs. 37% (P=0.038) 58% vs. 51% (P=0.243) 69% vs. 53% (P=0.025) 70% vs. 53% (P=0.013) 69% vs. 70% (P=0.861)
Bone densitometry(N=613) 17.4% vs. 9% (P=0.155) 17.2% vs. 9% (P=0.034) 17.4% vs.17.2% (P=0.976) 34% vs. 13% (P=0.010) 24% vs. 13% (P=0.025) 34% vs. 24% (P=0.232)

Comparison used chi-square equality tests of means across classes with posterior probability-based multiple imputations (2 degrees of freedom for the overall test and 1 for the pair wise tests).

(2005)

While the classes did not differ in echocardiography, a significantly larger proportion of survivors in the collaborative than in the self-controlling group underwent bone densitometry. Similarly, significantly larger proportions of the collaborative and worried classes than of the self-controlling class underwent recommended mammography (Table 4).

(2009)

A greater proportion of survivors reported echocardiography in 2009 than in 2005 across all 3 classes, but the proportions within each class differed significantly (Table 4). Similarly, the proportion of survivors participating in bone densitometry increased in all classes in 2009. A significantly larger proportion of survivors in the worried and collaborative classes than in the self-controlling class participated in both bone density and mammography.

DISCUSSION

We found 3 distinctive groups of survivors—worried, self-controlling, and collaborative—defined by indicators of health-related concerns, motivation, and affect, as in our previous work. This multiple nominal classification, rather than a single linear descriptive model, is supported by the fact that each survivor has a most likely class, the classes make up different proportions of the sample, and the classes have no ordered interrelation [48]. With the exception of the worried class in the breast-cancer risk group, the distribution of survivors across the 3 classes in each risk group was similar. The exclusively female sex of the breast-cancer risk group and the tendency toward female predominance in the worried class is likely to explain this difference.

Because of their self-reported good health and low levels of health concerns and worries, survivors in the self-controlling group may see no need for cancer-related follow-up and screening [49]. Their high levels of intrinsic motivation and low levels of extrinsic motivation indicate that they are unlikely to initiate medical follow-up. This typology may reflect survivors’ unawareness of their long-term risks [50]. Based on our collective and cumulative clinical experience and on-going clinical trials, an optimal first intervention for this group might be distance-based strategies (e.g., web and/or print media detailing treatment-related risks and surveillance recommendations). Because of their strong intrinsic motivation, they may be more likely to initiate cancer-related follow-up and screening after being informed of their risks. Because these survivors report the least worry about their future health, the information provided should be detailed and graphic in explaining the probability and nature of their treatment-related risks [10, 50].

The worried group’s demographic profile suggests the most difficulty in obtaining risk-based health care (Table 1). Survivors who are African-American, older at interview, or uninsured were reported as less likely to receive risk-based, survivor-focused care [12]. The worried group was also distinguished by greater cancer-related fears. Fear and worry exert both positive and negative influences on health-related behaviors. For example worried or health-anxious survivors may avoid screening to moderate their fear of bad news [51, 52]. However, some excess worry can make the individual more sensitive to potential threats or more vigilant about lifestyle and surveillance behaviors [5156]. Misconceptions and lack of specific risk information can exacerbate fear or contribute to denial of the possibility of significant health problems [5762]. While our analysis does not specifically consider which particular worries were prominent in each risk group, it is highly likely that worries will differ from one diagnostic group to another, as well as across survivors with different co-morbidities and late effects experiences. Face-to-face encounters, where the clinician could identify specific worry targets, would likely provide the best care for this group; however, individualized print summaries together with supportive telephone interactions detailing long-term risks and ways to reduce those risks would potentially be useful. The worried group should be offered information in a manner that avoids exacerbating fears and concerns.

Survivors in the collaborative group were both receptive to professional care (extrinsically motivated) and highly intrinsically motivated to maintain their health. However, they may not fully understand their risks or the need for periodic screening, as reported among childhood cancer survivors in general [50, 5763]. Collaborators may be highly motivated to follow screening recommendations if they are provided risk-based information and specific recommendations. Print summaries of their treatment, including exposure risks and recommendations for medical follow-up and screening, would likely be sufficient to motivate collaborators to initiate care and work with their providers to minimize their risks.

In a previous report [29], the worried and collaborative classes differed markedly in that survivors in the worried class were more likely to obtain routine and cancer-related medical care. In this study, the two groups differed significantly only on echocardiography in the 2009 survey. While a larger proportion of the worried class than of the collaborative class participated in screening, statistical power may have been lacking in this small sample.

Limitations

While the CCSS population is a large and heterogeneous cohort of 5-year survivors, our results may not be generalizable to all childhood cancer survivors. Although observation of the same class structure in the risk groups could reflect non-independence of groups, the overlap across all 3 risk groups was only 2%, and the same class structure was previously determined in different samples [29]. Finally, there may be variables that differ across classes but were unavailable in our data set.

Conclusions

Our findings suggest that survivors’ participation in medical screening varies predictably across survivor typologies derived from personal endorsement of indicators of health-related concerns, motivation, and affect. Our current clinical trials are assessing the tailoring of interventions to these profiles to support physical activity among patients undergoing treatment and medical surveillance among adult survivors of childhood cancer. Future studies will examine to what extent these profiles predict other behavior-related health outcomes in other survivor samples.

Acknowledgments

Support: NIH grants R03 NR009203 (CL Cox, PI), U24 CA55727 (LL Robison, PI), and P30 CA 21765; the Robert Wood Johnson Generalist Physician Faculty Scholar award (Oeffinger KC, PI); and the American Lebanese Syrian Associated Charities (ALSAC).

Footnotes

The authors declare no conflicts of interest and no competing financial interests.

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