Measuring the Multidimensional Reproductive Concerns of Young Adult Male Cancer Survivors

Abstract

Purpose:

The aims of this study were to adapt the multidimensional Reproductive Concerns After Cancer (RCAC) scale for use with young adult (YA) male cancer survivors, defined as current age 18–35 years and at least 1 year postdiagnosis, and to examine the factor structure, reliability, and validity of the newly adapted RCAC-Male (RCAC-M) scale.

Methods:

We conducted cognitive interviews with 10 YA male cancer survivors to inform modifications to RCAC item wording. Online surveys were then completed by 170 YA male cancer survivors. We used confirmatory factor analysis to examine structural models of the RCAC-M scale, and assessed reliability and construct validity.

Results:

The oblique six-factor model of the RCAC-M scale provided the best model fit. Omega total and Revelle's omega total estimates for all six three-item subscales were in the nearly satisfactory to good range (0.69–0.88). As hypothesized, men who wanted to have a baby and men who believed that having a biological child was very important scored higher on at least one RCAC-M subscale. We observed correlations in the expected directions between the RCAC-M and measures of depression and health-related quality of life.

Conclusion:

The RCAC-M scale is a multidimensional scale that is best represented as a profile of subscale scores. This scale demonstrated good reliability and construct validity and can be used to inform tailored interventions to more effectively address men's reproductive concerns.

Background

Many young adult (YA) cancer survivors have concerns associated with becoming a parent after cancer that persist well into survivorship.^1–4 For YA males, malignant disease can influence gonadal function and cancer treatment can result in anatomical defect, hormonal insufficiency, damage or depletion of the germinal stem cells, or absence of sperm.^5,6 The degree of impairment depends on cancer and treatment type, dosage, and pretreatment fertility.^7,8 Male cancer survivors report stigma associated with infertility, compromised body image, have concerns about disclosure of infertility, question their sexuality and virility, and worry about ambiguity in their fertile status.^9–13

Several factors may contribute to YA male reproductive concerns, including access to fertility-related information, support, and care across the cancer continuum. Despite clinical guidelines¹⁴ affirming the importance of clinical communication about possible infertility, and recommendations to discuss fertility preservation as an option for reproductive-age survivors, rates of uptake remain low.^2,15,16 This may be partly due to poor patient/provider communication and low referral rates.^17,18 Additionally, YA survivors face significant cancer-related financial challenges, which introduce another barrier to fertility care, sperm banking, and related treatments.^19–21 Uptake of later semen analysis is also low, possibly because they are unaware that their fertility could recover.²²

Unaddressed reproductive concerns are associated with poorer quality-of-life outcomes.^23–27 However, the majority of research on reproductive concerns has involved female cancer survivors. Gaining knowledge of the specific reproductive concerns experienced by YA male cancer survivors is an important step toward identifying and addressing those concerns. We have previously developed a valid measure of reproductive concerns experienced by YA female survivors, the Reproductive Concerns After Cancer (RCAC) scale.^28,29 The RCAC scale is an 18-item self-report scale with six three-item subscales assessing the following areas of concern: fertility potential, partner disclosure of fertility status, (potential) child's health, personal health, acceptance of possible infertility, and achieving pregnancy. It was developed based on qualitative analysis of focus groups and individual interviews with YA female cancer survivors who were 18–35 years of age.^29,30 This was the first multidimensional scale developed to measure the variety of reproductive concerns that YA female survivors experience. The RCAC scale has been used in several studies to assess the presence and severity of female survivors' reproductive concerns.^31–35 The objective of the current study was to adapt and then psychometrically evaluate the scale for use with YA male survivors. We expect the scale will improve the capacity of investigators and clinicians to identify and address the range of reproductive concerns experienced by all YA survivors.

Methods

Phase I: cognitive interviews to adapt the RCAC for YA male survivors

In phase I, 10 YA male cancer survivors, ages 18–35 years, participated in a telephone-based cognitive interview. We recruited participants through social media outreach (e.g., young cancer survivor advocacy and support groups) and announcements and flyers at UC San Diego Moores Cancer Center. Participants were compensated $25 for their participation. Recruitment ended when data saturation had been reached (i.e., interviews provided no new information). Before the interview, participants received a version of the RCAC scale with language adapted for men by the research team. The male version of the RCAC scale included six domains of concern in parallel with the original RCAC scale for women. A trained interviewer asked participants to critically appraise each item using a verbal probing technique. Interviews also assessed overall structure of the survey, cultural sensitivity, relevance for YA male cancer survivors, and whether participants felt additional types of concerns should be included. Interviews were recorded and transcribed, and feedback was summarized. Results were used to further refine item wording. No new domains of concern were identified.

Phase II: psychometric analysis

Participants

Participants were 170 English-speaking male survivors of any cancer type, ages 18–35, and at least 1-year postdiagnosis. Participants were recruited between April 2016 and December 2018. First, we recruited participants through social media posts and flyers placed in clinics at UC San Diego Moores Cancer Center. Those who completed the survey were eligible to receive a $20 gift card. This method yielded 157 potential participants. Nineteen were screened as ineligible and 97 were removed based on embedded data checks (e.g., inconsistency in responses), yielding 41 participants. An additional 129 participants were recruited through a Qualtrics research panel. Qualtrics uses purposive sampling to recruit samples from a variety of sources throughout the United States, including website interception, member referrals, targeted email lists, permission-based networks, and social media. Participants who are highly likely to qualify for a particular survey are randomly selected and sent an invitation to participate. Respondents received points for participation that were redeemable for incentives such as gift cards, airline miles, and app purchases. Of the 1655 individuals that Qualtrics invited, only 129 were eligible and completed the survey. Most were screened as ineligible (n = 1235). Others (n = 291) were removed based on embedded data checks.

All potential participants completed online screening questions to determine their eligibility. Eligible participants who gave informed consent were directed to complete a 20-minute, anonymous online survey. This study was approved by the Oregon State University Institutional Review Board (study number 8731) and the University of California San Diego Institutional Review Board (study number 181035).

Measurement

The survey collected self-report information on demographics, cancer characteristics and treatments, parenthood desires, reproductive history, reproductive concerns, and psychosocial health outcomes. This survey parallels that used in a prior study with female cancer survivors in the same age range.²⁹

The RCAC-Male (RCAC-M) scale⁷ measures multiple dimensions of reproductive concerns among men diagnosed with cancer and includes six subscale scores: fertility potential, partner disclosure of fertility status, (potential) child's health, personal health, acceptance of possible infertility, and achieving pregnancy. Each subscale contains three items, and subscale scores are the average scale responses across those items. Responses are captured on a five-point Likert scale ranging from 1 = “Strongly disagree” to 5 = “Strongly agree.” All subscale scores range from 1 to 5. Items 5, 10, and 15 (i.e., the items on the acceptance subscale) are reverse scored, such that higher scores indicate less acceptance of potential infertility. For all other subscales, higher scores indicate greater concerns within that domain.

Data analysis

We descriptively evaluated participant characteristics using frequencies for categorical data and mean and standard deviations (SDs) for continuous data. Following the same procedure as for the original RCAC scale,²⁸ we used confirmatory factor analysis (CFA) to examine standardized factor loadings (i.e., the relationship of the observed variables to the underlying latent variable), interfactor correlations, and the goodness of fit of three structural models of the RCAC-M scale: (1) one-factor, (2) higher-order with one second-order factor and six first-order factors, and (3) oblique six-factor. CFA is a hypothesis testing approach, used when we have an a priori idea of which items belong to each factor. All 18 observed items were indicated to 1 latent variable (RCAC) for the one-factor model. In the higher-order model, one second-order factor (RCAC) was indicated by six first-order factors (i.e., fertility potential, partner disclosure, child's health, personal health, acceptance, and achieving pregnancy) that were indicated by three observed items per factor. For the oblique six-factor model, each of the six latent variables was indicated by three observed variables and interfactor correlations were specified among all six of the latent variables.

We used recommendations by Bentler to assess model fit,³⁶ examining three indicators of model fit: (1) Root Mean Square Error of Approximation (RMSEA),³⁷ an absolute index of overall model fit; (2) the Standardized Root Mean Residual (SRMR),³⁸ also an absolute index of overall model fit; and (3) Comparative Fit Index (CFI),³⁹ a relative index of model fit compared with the null model. For the absolute indices of model fit, values <0.08 indicate acceptable model fit, and values <0.05 indicate good model fit. For the CFI, values >0.93 indicate acceptable model fit and values >0.95 indicate good fit. We reported the χ² value completeness but did not use it to assess model fit because it is not robust to sample size.⁴⁰ When at least two of the three descriptive fit indices met acceptable model fit criteria, model fit was determined to be acceptable. This is consistent with Bentler's and others' suggestions for using these indicators as the most stable fit indices.^36,41,42

We examined reliability using omega total and Revelle's omega total.⁴³ We did not assess reliability using Cronbach's coefficient alpha because the tau equivalence assumption was violated for several of the subscales. Omega total and Revelle's omega total provide more valid reliability estimates because they rely on fewer assumptions.⁴³ Data were analyzed with MPlus version 8,⁴⁴ SAS version 9.4,⁴⁵ and R in conjunction with RStudio version 1.⁴⁶

Based on our prior research,²⁹ we used the known groups' approach to evaluate differences in RCAC subscale scores between those who desired a child (yes vs. no and do not know) and those who said that having a biologic child was very important (yes vs. no or do not know). We hypothesized that those who reported “yes” would have higher scores on one or more of the RCAC-M subscale scores. We used analysis of variance to conduct exploratory analyses comparing RCAC-M subscale scores across categories of age, race/ethnicity, relationship status, and reproductive history. We also evaluated convergent validity by evaluating the relationship between RCAC-M subscale scores and scores from four validated instruments hypothesized to be associated.²⁹ We evaluated statistical associations between scores for each subscale of the RCAC-M and scores for the medical outcomes study Social Support Survey,⁴⁷ Satisfaction with Life Scale (SWLS),⁴⁸ Patient Health Questionnaire (PHQ-8, assessing depression),⁴⁹ and CDC Health-Related Quality of Life Assessment (HRQOL-4).⁵⁰ Data were analyzed using SAS version 9.4. For all statistical tests, a two-tailed test with alpha 0.05 was considered significant.

Results

Sample characteristics

Phase II participants were YA male cancer survivors with a mean age of 28 years (SD = 4.9 years) and an average time since diagnosis of 5 years (SD = 5.1 years). The average age at diagnosis was 23 years (SD = 7.0 years). The most common cancer types reported were blood cancers/leukemia (19%) and gastrointestinal cancers (14%). Most participants were white (76%), non-Hispanic (81%), and were partnered at the time of the survey (52%). Most were employed (76%) and had health insurance (84%). About half were college graduates and 30% had children (Table 1).

Table 1.

Sample Characteristics (N = 170)

	N (%)
Age at survey, years^a	28.1 (4.9)
Race
White	129 (75.9)
Black	19 (11.2)
Asian	10 (5.9)
Other	12 (7.0)
Hispanic/Latino ethnicity	33 (19.4)
Partnered	89 (52.4)
College graduate	92 (54.1)
Employed	130 (76.5)
Has health insurance	143 (84.1)
Body mass index^a	25.7 (5.6)
Has biological child(ren)	50 (29.4)
Desires child(ren)	120 (74.1)
Cancer characteristics and treatment
Cancer type
Blood/hematologic	32 (18.8)
Gastrointestinal	24 (14.1)
Testicular	21 (12.4)
Other	93 (54.7)
Cancer stage
In situ/0	6 (3.5)
I	45 (26.5)
II	54 (31.8)
III	15 (8.8)
IV	8 (4.7)
Unknown	42 (24.7)
Age at diagnosis, years^a	23.4 (7.0)
Life stage at diagnosis
Childhood (≤14)	18 (10.6)
Adolescence (15–19)	29 (17.1)
Young adulthood (20–35)	123 (72.3)
Time since diagnosis, years^a	4.9 (5.1)
Surgery	88 (51.8)
Chemotherapy	87 (51.2)
Radiation	65 (38.2)
Endocrine therapy	8 (4.7)
Bone marrow or stem cell transplant	14 (8.2)
No treatment	7 (4.1)
Unknown	7 (4.1)
Other	8 (4.7)

Some percentages do not equal 100 due to missing data.

Mean (SD).

SD, standard deviation.

Psychometric evaluation

As in our prior CFA with the original version of the scale, we used CFA to examine three structural models of the RCAC scale: (1) one-factor, (2) higher-order model with one second-order factor and six first-order factors, and (3) oblique six-factor. The one-factor model did not fit the data well statistically or descriptively (χ² [135, N = 169] = 530.261, p < 0.001, CFI = 0.643, RMSEA = 0.132, SRMR = 0.112). The higher-order model with one second-order factor and six first-order factors did not fit the data well statistically (χ² [129, N = 169] = 224.733, p < 0.001), but it did fit well descriptively (CFI = 0.914, RMSEA = 0.066, SRMR = 0.075). The oblique six-factor model provided the best model fit; it did not fit well statistically (χ² [120, N = 169] = 171.282, p = 0.002), but it did fit well descriptively (CFI = 0.954, RMSEA = 0.050, SRMR = 0.048). All standardized factor loadings were generally large and statistically significant for all six of the factors (Table 2). All but 4 of the 15 interfactor correlations were statistically significant (Table 3).

Table 2.

Standardized Factor Loadings from the Six-Factor Reproductive Concerns After Cancer-Male Model

RCAC subscale/item	Factor loadings
Fertility potential
(1) I am afraid I won't be able to have any (more) children.	0.685^a
(8) I am worried about my ability to biologically father a child (again).	0.840^a
(17) I am concerned that I may not be able to have (more) children.	0.854^a
Partner disclosure
(3) I worry about telling my (potential) spouse/partner that I may be unable to have children.	0.793^a
(7) I am concerned that my (potential) spouse/partner will be disappointed if we can't get pregnant.	0.765^a
(16) The thought of telling my (potential) spouse/partner that I may be unable to have children makes me uncomfortable.	0.781^a
Child's health
(2) I am worried about passing on a genetic risk for cancer to my children.	0.802^a
(9) I am worried about how my family history might affect my children's health.	0.833^a
(18) I am afraid my children would have a high chance of getting cancer.	0.893^a
Personal health
(4) I am scared of not being around to take care of my children someday.	0.612^a
(11) Having (more) children will make me more nervous about getting cancer again.	0.632^a
(13) I am cautious about having (more) children because I might not be around to raise them.	0.718^a
Acceptance
(5) I can accept it if I'm unable to have (more) children.	0.714^a
(10) I will be happy with life whether or not I have (more) children someday.	0.704^a
(15) I will feel content if I do not have (more) children.	0.711^a
Achieving pregnancy
(6) The thought of trying to achieve a pregnancy (again) with my spouse/partner is overwhelming.	0.771^a
(12) I worry that achieving a pregnancy (again) would take too much time and effort.	0.632^a
(14) It is stressful to think about trying to achieve a pregnancy (again).	0.813^a

p < 0.01.

RCAC, Reproductive Concerns After Cancer.

Table 3.

Standardized Interfactor and Pearson Product-Moment Correlations Among the Six Latent Factors

	Fertility potential	Partner disclosure	Child's health	Personal health	Acceptance	Becoming pregnant
Fertility potential		0.729^a	0.524^a	0.413^a	0.215^b	0.515^a
Partner disclosure	0.733^a		0.743^a	0.525^a	0.211	0.588^a
Child's health	0.521^a	0.650^a		0.740^a	−0.010	0.472^a
Personal health	0.549^a	0.612^a	0.854^a		−0.099	0.457^a
Acceptance	0.257^b	0.221	−0.010	−0.137		−0.122
Achieving pregnancy	0.642^a	0.644^a	0.512^a	0.661^a	−0.158

Standardized interfactor correlations are reported below the diagonal. Pearson product-moment correlations are reported above the diagonal.

p < 0.01; ^bp < 0.05.

Composite reliability for each of the six three-item subscales were: fertility potential, Ω = 0.85, RΩ = 0.85; partner disclosure of fertility status, Ω = 0.83, RΩ = 0.83; child's health, Ω = 0.88, RΩ = 0.88; personal health, Ω = 0.69, RΩ = 0.70; acceptance of possible infertility, Ω = 0.76, RΩ = 0.76; and achieving pregnancy, Ω = 0.78, RΩ = 0.78, α = 0.78.

Table 4 shows mean RCAC-M subscale scores for the total sample and by characteristics. Mean scores for each subscale ranged from 2.60 (SD = 0.96) for concerns about achieving pregnancy to 3.33 (SD = 1.16) for concerns about (potential) child's health. In support of our a priori hypothesis, we found that men who wanted to have a baby and men who believed that having a biological child was important had significantly higher scores on at least one of the RCAC-M subscales. Partnered men had significantly lower scores for concerns about partner disclosure. Our exploratory analyses also revealed several significantly higher RCAC-M subscale scores for those reporting Hispanic/Latino ethnicity and non-white race.

Table 4.

Mean Subscale Scores by Parenthood Desires, Demographics, and Reproductive History

	Mean (SD)
	No. (%)	Fertility potential	Partner disclosure	Child health	Personal health	Acceptance^a	Achieving pregnancy
Mean scores	170 (100)	2.86 (1.18)	2.86 (1.13)	3.33 (1.16)	2.92 (0.99)	2.82 (1.00)	2.60 (0.96)
Parenthood desires					—
Wants a baby^b
Yes	120 (74.1)	3.08 (1.21) ^c	3.08 (1.16) ^c	3.46 (1.17) ^d	2.96 (1.02)	3.02 (0.98) ^c	2.60 (0.97)
No	42 (25.9)	2.35 (1.00)	2.25 (0.92)	3.01 (1.17)	2.82 (0.95)	2.37 (0.94)	2.56 (0.98)
Biological child important
Yes	138 (81.2)	2.94 (1.22)	2.97 (1.16) ^c	3.38 (1.17)	2.94 (0.99)	2.92 (1.02) ^c	2.58 (0.99)
No	32 (18.8)	2.54 (0.95)	2.40 (0.88)	3.09 (1.09)	2.84 (1.00)	2.40 (0.76)	2.68 (0.79)
Demographics
Current age (years)
18–29	92 (54.1)	2.72 (1.10)	2.89 (1.04)	3.31 (1.11)	2.80 (0.95)	2.81 (1.00)	2.59 (0.91)
30–35	78 (45.9)	3.03 (1.26)	2.83 (1.24)	3.35 (1.22)	3.06 (1.01)	2.84 (0.99)	2.61 (1.01)
Race
White	129 (75.9)	2.75 (1.16) ^d	2.80 (1.09)	3.32 (1.12)	2.89 (0.97)	2.76 (0.98)	2.59 (0.98)
Non-white	41 (24.1)	3.20 (1.19)	3.05 (1.25)	3.35 (1.30)	3.02 (1.02)	3.02 (1.04)	2.65 (.088)
Hispanic/Latino ethnicity
Yes	33 (19.4)	3.46 (1.08) ^c	3.38 (1.15) ^c	3.69 (1.00) ^d	3.01 (0.96)	2.99 (1.07)	2.87 (0.98)
No	137 (80.6)	2.72 (1.17)	2.74 (1.10)	3.24 (1.18)	2.90 (0.99)	2.78 (0.98)	2.54 (0.94)
Partnered relationship
Yes	89 (52.4)	2.80 (1.15)	2.68 (1.13) ^d	3.25 (1.21)	2.82 (1.02)	2.86 (1.00)	2.52 (0.90)
No	81 (47.6)	2.93 (1.23)	3.06 (1.11)	3.41 (1.11)	3.04 (0.94)	2.78 (1.00)	2.69 (1.01)
College graduate
Yes	92 (54.1)	2.87 (1.24)	2.92 (1.17)	3.40 (1.15)	2.91 (1.00)	2.92 (1.02)	2.56 (0.96)
No	78 (45.9)	2.85 (1.13)	2.79 (1.09)	3.25 (1.17)	2.94 (0.98)	2.71 (0.96)	2.65 (0.95)
Occupation status
Full time	100 (58.8)	2.83 (1.24)	2.90 (1.15)	3.38 (1.16)	2.91 (0.96)	2.82 (1.00)	2.65 (0.97)
Part time	30 (17.7)	2.89 (0.98)	2.96 (1.10)	3.44 (1.10)	3.06 (1.08)	2.88 (0.82)	2.60 (0.88)
Student	9 (5.3)	3.15 (1.14)	2.59 (1.09)	2.67 (1.08)	2.41 (0.85)	3.00 (1.28)	2.29 (0.97)
Other	31 (18.2)	2.85 (1.22)	2.72 (1.14)	3.26 (1.22)	2.98 (1.01)	2.72 (1.09)	2.54 (1.01)
Reproductive history
Has biological children
Yes	50 (29.4)	2.67 (1.22)	2.84 (1.14)	3.49 (1.14)	3.15 (0.94)	2.62 (0.96)	2.51 (0.90)
No	120 (70.6)	2.94 (1.16)	2.87 (1.13)	3.26 (1.17)	2.83 (0.99)	2.91 (1.00)	2.64 (0.98)

Bold values denote statistical significance at the p < 0.05 level.

Response scales range 1–5.

Reverse coded, higher score indicates lower acceptance of not having children.

Missing data (n = 8).

p < 0.01.

p < 0.05.

We observed significant low-to-moderate correlations in the expected direction between several RCAC-M subscale scores and measures of depression and quality of life, where several reproductive concern domains were significantly positively associated with depression and health-related quality of life (higher scores indicate poorer quality of life). Social support was not significantly correlated with any of the RCAC-M subscales. Partner disclosure concern scores were significantly positively associated with satisfaction with life (Table 5).

Table 5.

Pearson Product-Moment Correlations Between Reproductive Concerns After Cancer-Male Subscales and Related Scales

	Total score	Fertility potential	Achieving pregnancy	Acceptance	Personal health	Child health	Partner disclosure
Social support (MOS)	−0.14 (0.06)	−0.10 (0.20)	−0.11 (0.14)	−0.14 (0.08)	−0.12 (0.12)	−0.04 (0.58)	−0.11 (0.14)
Depression (PHQ-9)	0.34 (<0.0001)	0.30 (<0.0001)	0.14 (0.07)	0.15 (0.06)	0.29 (0.0002)	0.34 (<0.0001)	0.18 (0.02)
Satisfaction with life (SWLS)	0.03 (0.71)	0.01 (0.86)	0.01 (0.88)	0.08 (0.30)	−0.09 (0.24)	−0.08 (0.33)	0.18 (0.02)
HRQOL (CDC HRQOL-4)	0.25 (0.001)	0.29 (0.0002)	0.08 (0.28)	−0.03 (0.67)	0.25 (0.001)	0.29 (0.0001)	0.14 (0.08)

Bold values denote statistical significance at the p < 0.05 level.

HRQOL, Health-Related Quality of Life; PHQ, Patient Health Questionnaire; SWLS, Satisfaction with Life Scale; MOS, medical outcomes study.

Discussion

The results of this study provide evidence that the RCAC-M scale scores provide reliable and valid indicators of multiple dimensions of reproductive concerns experienced by YA male cancer survivors. Consistent with the conceptualization of the original RCAC scale,²⁹ the strongest model fit in the CFA was the six-factor model with intercorrelated factors. As with the original scale, this suggests that the RCAC-M scale may be best represented as a profile of subscale scores, rather than one summary score.²⁸ Revelle's omega total estimates for all six three-item subscales were in the nearly satisfactory to good range (0.69–0.88), which offers support for the reliability of the RCAC-M subscale scores. In line with our hypotheses, and consistent with prior research with women,²⁹ we observed higher levels of concerns across multiple domains for men who reported wanting a child and for men who believed having biological children was important. These findings support the construct validity of the scale. Each subscale indicates a particular domain of concern, and longitudinal research would help identify the best approaches to address these specific concerns.

Our results suggest that reproductive concerns are experienced by YA male survivors, but they may be fewer and different from those experienced by YA female survivors.^4,51–53 Participants in this study had lower average subscale scores than those of same-aged cohort of female survivors from a prior study (2.8–3.3 for men compared with 2.7–3.7 for women).²⁹ Scores for men in the current study were lower than scores for women in our prior study across all subscales except acceptance. A recent series of studies in Sweden found that YA testicular cancer survivors had lower average RCAC subscale scores (2.2–2.5) than YA breast cancer survivors (2.4–3.6) at ∼2 years postdiagnosis.^35,54 One possible explanation is that the experience of infertility is fundamentally different for men and women, regardless of their cancer status.^51,55 Women facing infertility have more negative experiences related to identity, self-esteem, physical health, depression, stress, and anxiety,⁵⁶ while men are more concerned about aspects of control and their partner's response.⁵⁷ Interestingly, unlike our prior study with women, we found no significant correlation between men's reproductive concerns overall and satisfaction with life (r = 0.03) or social support (r = −0.14). We also found that men who were unpartnered were more concerned about disclosing possible infertility with a partner than those who were partnered. As in another recent study of YA survivors, participants also reported the highest level of concerns for their (potential) child's health.⁵⁴ Men and women appear equally concerned about passing on a genetic risk for cancer to their children.⁴ The RCAC-M scale can facilitate future research to identify possible gender differences in levels and types of reproductive concerns that YA survivors experience to better meet their medical and supportive care needs.

Our results suggest that the RCAC-M is a promising measurement tool, but there are limitations to our study. While participants in Phase I did not identify any additional domains of concern, they are not representative of all YA male cancer survivors and it is possible that other domains of concern exist. Our sample self-reported their cancer characteristics, and we had no way to verify this information. Future psychometric assessment should be completed with a sample having a medically confirmed cancer diagnosis, and include test/retest reliability and predictive validity in a prospective sample. The study sample is not representative of all YA male cancer survivors. Men self-selected to participate in this study, and we may have recruited men who were more interested in or concerned about fertility and family building. The sample for this study was diverse across several characteristics (e.g., age, education, life stage at diagnosis, cancer characteristics and treatment, duration of survivorship), but three quarters of the sample was white and only 20% reported Hispanic/Latino ethnicity. This limits our capacity to explore differences across characteristics as well as the generalizability of our findings. Our exploratory analyses did identify some notable differences in subscale scores across race and ethnicity, which could be partially explained by differences in access to health services, attitudes, and social cues.⁵⁸ These results point toward some disparities in reproductive concerns that merit exploration in a larger sample. Additional studies are needed to validate the RCAC-M scores for use with specific racial and ethnic groups.

Although more research has focused on YA female cancer survivors, YA male survivors also have reproductive concerns.^4,9,51,52,59 Reproductive concerns can persist in survivorship are associated with distress and poorer quality of life.^27,52 YA male survivors have expressed a need for better reproductive-related information and support.⁶⁰ The RCAC-M is a new scale to fill a gap in current measurement tools, allowing researchers and clinicians to comprehensively capture multiple dimensions of concerns experienced by YA male survivors. Our results suggest that future studies are needed to determine patterns and types of reproductive concerns across gender, race, ethnicity, and partnership status. Psychometric evidence to date suggests that the RCAC-M can facilitate such research. Important areas of future research include comparing patterns of reproductive concerns across demographic characteristics to identify higher risk groups, characterizing changes in reproductive concerns over time to optimize intervention timing, and delineating the relationship between reproductive health care, reproductive concerns, and long-term psychosocial and reproductive health outcomes to inform intervention strategies to improve outcomes. This knowledge can also support clinicians in tailoring their communication, referrals, and recommendations to more adequately address the reproductive concerns of YA survivors across the cancer continuum.

Footnotes

Acknowledgments

The authors would like to thank the men who participated in this study.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This study was supported by Oregon State University and UCSD Academy of Clinician Scholars.

References

Crawshaw

Psychosocial oncofertility issues faced by adolescents and young adults over their lifetime: a review of the research. Hum Fertil (Camb). 2013; 16(1):59–63.

Logan

, Perz

, Ussher

, et al. A systematic review of patient oncofertility support needs in reproductive cancer patients aged 14 to 45 years of age. Psychooncology. 2018; 27(2):401–9.

Geue

, Richter

, Schmidt

, et al. The desire for children and fertility issues among young German cancer survivors. J Adolesc Health. 2014; 54(5):527–35.

Benedict

, Shuk

, Ford

. Fertility issues in adolescent and young adult cancer survivors. J Adolesc Young Adult Oncol. 2016; 5(1):48–57.

Fossa

, Dahl

. Fertility and sexuality in young cancer survivors who have adult-onset malignancies. Hematol Oncol Clin North Am. 2008; 22(2):291–303, vii.

van Casteren

, Boellaard

, Romijn

, Dohle

. Gonadal dysfunction in male cancer patients before cytotoxic treatment. Int J Androl. 2010; 33(1):73–9.

Dohle

GR.

Male infertility in cancer patients: review of the literature. Int J Urol. 2010; 17(4):327–31.

Trottmann

, Becker

, Stadler

, et al. Semen quality in men with malignant diseases before and after therapy and the role of cryopreservation. Eur Urol. 2007; 52(2):355–67.

Crawshaw

Male coping with cancer-fertility issues: putting the “social” into biopsychosocial approaches. Reprod Biomed Online. 2013; 27(3):261–70.

10.

Crawshaw

, Glaser

, Hale

, Sloper

. Male and female experiences of having fertility matters raised alongside a cancer diagnosis during the teenage and young adult years. Eur J Cancer Care (Engl). 2009; 18(4):381–90.

11.

Peddie

, Porter

, Barbour

, et al. Factors affecting decision making about fertility preservation after cancer diagnosis: a qualitative study. BJOG. 2012; 119(9):1049–57.

12.

Carpentier

, Fortenberry

, Ott

, et al. Perceptions of masculinity and self-image in adolescent and young adult testicular cancer survivors: implications for romantic and sexual relationships. Psychooncology. 2011; 20(7):738–45.

13.

Carpentier

, Fortenberry

. Romantic and sexual relationships, body image, and fertility in adolescent and young adult testicular cancer survivors: a review of the literature. J Adolesc Health. 2010; 47(2):115–25.

14.

Loren

, Mangu

, Beck

, et al. Fertility preservation for patients with cancer: American Society of Clinical Oncology clinical practice guideline update. J Clin Oncol. 2013; 31(19):2500–10.

15.

Pacey

, Eiser

. The importance of fertility preservation in cancer patients. Expert Rev Anticancer Ther. 2014; 14(5):487–9.

16.

Petropanagos

, Campo-Engelstein

. Tough talk: discussing fertility preservation with adolescents and young adults with cancer. J Adolesc Young Adult Oncol. 2015; 4(3):96–9.

17.

Grover

, Deal

, Wood

, Mersereau

. Young men with cancer experience low referral rates for fertility counseling and sperm banking. J Oncol Pract. 2016; 12(5):465–71.

18.

Absolom

, Eiser

, Michel

, et al. Follow-up care for cancer survivors: views of the younger adult. Br J Cancer. 2009; 101(4):561–7.

19.

Huang

, Bhakta

, Brinkman

, et al. Determinants and consequences of financial hardship among adult survivors of childhood cancer: a report from the St. Jude Lifetime Cohort Study. J Natl Cancer Inst. 2019; 111(2):189–200.

20.

Ketterl

, Syrjala

, Casillas

, et al. Lasting effects of cancer and its treatment on employment and finances in adolescent and young adult cancer survivors. Cancer. 2019; 125(11):1908–17.

21.

Zheng

, Jemal

, Han

, et al. Medical financial hardship among cancer survivors in the United States. Cancer. 2019; 125(10):1737–47.

22.

Eiser

, Arden-Close

, Morris

, Pacey

. The legacy of sperm banking: how fertility monitoring and disposal of sperm are linked with views of cancer treatment. Hum Reprod. 2011; 26(10):2791–98.

23.

Kent

, Arora

, Rowland

, et al. Health information needs and health-related quality of life in a diverse population of long-term cancer survivors. Patient Educ Couns. 2012; 89(2):345–52.

24.

Canada

, Schover

. The psychosocial impact of interrupted childbearing in long-term female cancer survivors. Psychooncology. 2012; 21(2):134–43.

25.

Letourneau

, Ebbel

, Katz

, et al. Pretreatment fertility counseling and fertility preservation improve quality of life in reproductive age women with cancer. Cancer. 2012; 118(6):1710–7.

26.

Howard-Anderson

, Ganz

, Bower

, Stanton

. Quality of life, fertility concerns, and behavioral health outcomes in younger breast cancer survivors: a systematic review. J Natl Cancer Inst. 2012; 104(5):386–405.

27.

Logan

, Perz

, Ussher

, et al. Systematic review of fertility-related psychological distress in cancer patients: informing on an improved model of care. Psychooncology. 2019; 28(1):22–30.

28.

Gorman

, Pan-Weisz

, Drizin

, et al. Revisiting the reproductive concerns after cancer (RCAC) scale. Psychooncology. 2019; 28(7):1544–50.

29.

Gorman

, Su

, Pierce

, et al. A multidimensional scale to measure the reproductive concerns of young adult female cancer survivors. J Cancer Surviv. 2014; 8(2):218–28.

30.

Gorman

, Bailey

, Pierce

, Su

. How do you feel about fertility and parenthood? The voices of young female cancer survivors. J Cancer Surviv. 2012; 6(2):200–9.

31.

Benedict

, Thom B

D N

F, et al. Young adult female cancer survivors' unmet information needs and reproductive concerns contribute to decisional conflict regarding posttreatment fertility preservation. Cancer. 2016; 122(13):2101–9.

32.

Benedict

, Thom

, Friedman

, et al. Fertility information needs and concerns post-treatment contribute to lowered quality of life among young adult female cancer survivors. Support Care Cancer. 2018; 26(7):2209–15.

33.

Gorman

, Su

, Roberts

, et al. Experiencing reproductive concerns as a female cancer survivor is associated with depression. Cancer. 2015; 121(6):935–42.

34.

Kim

, Mersereau

, Su

, et al. Young female cancer survivors' use of fertility care after completing cancer treatment. Support Care Cancer. 2016; 24(7):3191–9.

35.

Ljungman

, Ahlgren

, Petersson

, et al. Sexual dysfunction and reproductive concerns in young women with breast cancer: type, prevalence, and predictors of problems. Psychooncology. 2018; 27(12):2770–7.

36.

Bentler

PM.

On tests and indices for evaluating structural models. Pers Indiv Differ. 2007; 42(5):825–9.

37.

Steiger

JH.

Structural model evaluation and modification—an interval estimation approach. Multivar Behav Res. 1990; 25(2):173–80.

38.

, Bentler

. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling. 1999; 6(1):1–55.

39.

Bentler

PM.

Comparative fit indexes in structural models. Psychol Bull. 1990; 107(2):238–46.

40.

Kelloway

EK.

Structural equation modeling in perspective. J Organ Behav. 1995; 16(3):215–24.

41.

Beauducel

, Wittmann

. Simulation study on fit indexes in CFA based on data with slierhtly distorted simple structure. Struct Equ Modeling. 2005; 12(1):41–75.

42.

Marsh

HW.

In search of golden rules: comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler's (1999) findings. Struct Equ Modeling. 2004; 11(3):320–41.

43.

McNeish

Thanks coefficient alpha, we'll take it from here. Psychol Methods. 2018; 23(3):412–33.

44.

Muthén

, Muthén

. MPlus users' guide. 7th ed. Los Angeles, CA: Muthén & Muthén; 1998–2012.

45.

SAS

Institute

, Inc. SAS version 9.4. Cary, NC: SAS Institute, Inc.; 2018.

46.

R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2013.

47.

Sherbourne

, Stewart

. The MOS social support survey. Soc Sci Med. 1991; 32(6):705–14.

48.

Diener

, Emmons

, Larsen

, Griffin

. The satisfaction with life scale. J Pers Assess. 1985; 49(1):71–5.

49.

Kroenke

, Strine

, Spitzer

, et al. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009; 114(1–3):163–73.

50.

Centers for Disease Control and Prevention. Measuring healthy days. Atlanta, GA; 2000. Accessed March 31, 2020 from: https://www.cdc.gov/hrqol/pdfs/mhd.pdf.

51.

Gardino

, Rodriguez

, Campo-Engelstein

. Infertility, cancer, and changing gender norms. J Cancer Surviv. 2011; 5(2):152–57.

52.

Ussher

, Perz

. Infertility-related distress following cancer for women and men: a mixed method study. Psychooncology. 2019; 28(3):607–14.

53.

Ussher

, Perz J; Australian Cancer and Fertility Study Team (ACFST). Threat of biographical disruption: the gendered construction and experience of infertility following cancer for women and men. BMC Cancer. 2018; 18(1):250.

54.

Ljungman

, Eriksson

, Flynn

, et al. Sexual dysfunction and reproductive concerns in young men diagnosed with testicular cancer: an observational study. J Sex Med. 2019; 16(7):1049–59.

55.

Greil

, Slauson-Blevins

, McQuillan

. The experience of infertility: a review of recent literature. Sociol Health Illn. 2010; 32(1):140–62.

56.

Ying

, Wu

, Loke

. Gender differences in experiences with and adjustments to infertility: a literature review. Int J Nurs Stud. 2015; 52(10):1640–52.

57.

Hjelmstedt

, Andersson

, Skoog-Svanberg

, et al. Gender differences in psychological reactions to infertility among couples seeking IVF- and ICSI-treatment. Acta Obstet Gyn Scan. 1999; 78(1):42–8.

58.

Greil

, McQuillan

, Shreffler

, et al. Race-ethnicity and medical services for infertility: stratified reproduction in a population-based sample of U.S. women. J Health Soc Behav. 2011; 52(4):493–509.

59.

Crawshaw

, Sloper

. “Swimming against the tide”—the influence of fertility matters on the transition to adulthood or survivorship following adolescent cancer. Eur J Cancer Care (Engl). 2010; 19(5):610–20.

60.

Penrose

, Beatty

, Mattiske

, Koczwara

. Fertility and cancer—a qualitative study of Australian cancer survivors. Support Care Cancer. 2012; 20(6):1259–65.