Assessing the prevalence of unmet need for genetic counseling in Canada and exploring associations with sociodemographic factors

Abstract

Understanding the prevalence and distribution of unmet need for genetic counseling (GC) can help inform health human resource planning. It is known that not all patients who could benefit from GC are currently accessing it, however, the prevalence of unmet need in Canada is unknown. Using a cross-sectional design, we surveyed 1160 Canadians to estimate the prevalence and distribution of unmet need for GC. The survey included measures of unmet need (NSGC Pathways Tool), personal utility (PrU), capability (ICECAP-A), distrust in healthcare (Revised Health Care System Distrust Scale) and demographic variables. A market research company (Leger Opinion Panel) was used for recruitment. We used descriptive statistics to estimate prevalence and multivariable regression to explore factors associated with unmet need. We found that 39% of respondents (457/1160) had unmet need for GC and 68% of this unmet need was unperceived. In the multivariable regression analysis, unmet need for GC was more likely in individuals who: had a mental health condition, were younger ($\le$45 yo), reported higher personal utility, and lower levels of capability (all p < 0.05 in multivariable analysis). There is a high prevalence of unmet need for GC in Canada and individuals experiencing other challenges to accessing healthcare may also be more likely to have unmet need for GC.

Introduction

Genetic counseling can be defined as a psychotherapeutic process that helps patients and families make meaning of genetic information and use it in a way that aligns with their values, wants, and needs, to manage their health in the context of uncertainty [1]. Although genetic counseling is often delivered in the context of genetic testing, it can also improve patient outcomes in the absence of genetic testing, by increasing empowerment, knowledge, and adaptation, improving family communication, and reducing anxiety and negative emotions resulting from genetic information [2, 3]. Additionally, while there is evidence suggesting that genetic information is not typically sufficient to change health behaviors, genetic counseling can lead to positive health behaviors [4–10]. Genetic counseling has previously been characterized as a psychotherapeutic intervention and can lead to changes in health behaviors through the development of a strong therapeutic alliance that can help assist patients in modifying their behaviors, cognitions, and emotions in accordance to their own personal values and goals [11, 12]. This conceptualization can apply to any population receiving genetic counseling, although the specific goals may vary depending on the clinical context.

Previous studies have shown that not everyone who could potentially benefit from genetic counseling receives it, suggesting the presence of unmet need for patients and families [13, 14]. This research has sought to describe unmet need for clinical genetic services in specific contexts by looking at rates of uptake and utilization and have found sociodemographic, psychological, and clinical factors associated with the uptake or utilization of genetic counseling and testing [13]. These studies report that education level, socioeconomic status, race, cancer-specific distress and personal diagnosis of cancer, degree of perceived benefit, understanding of reason for referral, and wait times were consistently predictors for the uptake of genetic services [13–17]. However, it is important to note that not all differences in uptake and utilization indicate unmet need. If patients make an autonomous and informed decision to not proceed with care that is accessible, available, and appropriate, then this is not considered “unmet need” [18]. Rather, unmet need occurs when differences in uptake and utilization of services are a result of access or awareness barriers or incomplete or inequitable referral patterns [19–21].

Providing healthcare on the basis of need is a key principle of the Canadian healthcare system, however there is no singular definition of “need” for healthcare [22, 23]. Generally, “need” is said to exist when someone is not at full health (measured by mortality, morbidity, and quality of life), a healthcare intervention exists that would improve health, and that this healthcare intervention is cost-effective in the context of resource constraints [24]. Applying these criteria to genetic counseling is very challenging, in part because of how “health” is defined narrowly [25]. As such, there is currently no consensus about how to define or evaluate need for genetic counseling. One of the ways to conceptualize need for genetic counseling is that need exists if someone has the capacity to receive direct health (e.g. improved quality of life or life expectancy) or indirect/non-health related benefits (e.g. improved empowerment, adaptation, psychological well-being) from care [25]. This broad definition captures the goals and possible impacts of genetic counseling for a diverse and heterogenous patient population.

Even though there is unmet need, there are also long wait times and high caseloads in tertiary genetic care in Canada [26, 27]. In response to this, many jurisdictions are implementing new service delivery models or engaging in workforce planning to meet the current levels of need for care. Many of these service delivery models are designed around maximizing efficiency of providing access to genetic testing [28–32]. Workforce planning for genetic counseling is typically based on provider-population ratios to determine the target workforce size (e.g. 1 GC for every 100,000 individuals); however, many of these target ratios are not evidence based [27, 33]. This focus on improving efficiency could increase the patient throughput in tertiary care settings, which in turn could lead to increased access to care. However, most tertiary care settings prioritize triaging patients who would be most likely to be eligible to receive genetic testing and many of these centers have restricted their eligibility criteria to decline or re-direct referrals for patients with multifactorial conditions (i.e.: patients with hypermobile Ehlers-Danlos syndrome, or psychiatric conditions), which could contribute to further unmet need for genetic counseling for patients and families [34].

The objectives of this study were to i) estimate the prevalence of unmet need for genetic counseling in Canada and ii) determine if unmet need is associated with any sociodemographic characteristics at the population level. Additional evidence about the level of unmet need for genetic counseling at the population level may be useful for informing investments into health service delivery with the goal of increasing equitable access to genetics healthcare.

Materials and methods

We used a cross-sectional survey to sample 1300 Canadians to estimate the prevalence of unmet need for genetic counseling and evaluate associations between sociodemographic characteristics and unmet need. This study was approved by the University of British Columbia Research Ethics Board (Certificate H22-10827). We have followed the STROBE reporting guidelines for cross-sectional study designs [35]. (Supplementary Table 2).

Participants

The survey was deployed through a Canadian-based market research company (Leger Opinion Panel) with over 400,000 registered individuals who are compensated to complete surveys. We created sampling quotas that were representative of the Canadian population on the basis of province of residence and race using the 2016 Canadian Census categories. Data about race was collected according to guidance from the Canadian Institutes for Health Information [36]. These characteristics were chosen because healthcare is largely organized, funded, and delivered at the provincial level and race and ethnicity have previously been reported as being associated with unmet need for genetic counseling [14–16]. For analysis, racial groups were combined into two groups (i) minoritized racial group (including multiracial individuals) and (ii) non-minorized group (individuals who only selected “White” to self-describe their race). The survey took approximately 10 min to complete and participants were provided $3–$5 CAD to complete the survey. Leger Opinion Panel uses a point system for remuneration, thus the exact dollar amount varied between participants. The eligibility criteria were: (i) registered survey taker with Leger Opinion Panel; (ii) over the age of 18; and, (iii) ability to complete the survey in English or French. Data collection occurred from June 28, 2023 to July 7, 2023.

Survey instruments

The survey consisted of a combination of validated scales, previously published unvalidated scales, adaptations of existing scales, novel questions, and demographic questions. These tools were included to measure characteristics that could influence unmet need for genetic counseling.

Unmet need for genetic counseling was measured using the Pathways to Genetic Counseling Tool (Pathways Tool), a pragmatic tool developed using expert consensus of a workgroup established by the National Society of Genetic Counselors [37]. This scale underwent face and content validation during development. This tool was designed to allow individuals to self-identify if they had unmet healthcare needs that would be optimally addressed by seeing a genetic counselor working at the top of their scope of practice [38]. The Pathways Tool can identify individuals who have unmet cognitive or psychological needs about a condition that they have or that runs in their family. This is classified as self-reported unmet need according to clinician-validated reasons [18]. Unmet need for genetic counseling was a binary outcome (present/absent). Logic used to determine unmet need is in Table 1 and there are additional details in Supplementary Table 1.

Table 1.

Conditions for evaluating unmet need for genetic counseling.

Items on Pathways Tool (participants could select all that apply):
1	I have a diagnosis of a health condition that I have been told may be genetic, or that runs in my family (eg: cystic fibrosis, cancer, NF, sickle cell, thalassemia, depression, cardiovascular disease, diabetes, RP, Alzheimer’s, multiple sclerosis)
2	I have already had genetic testing
3	I have been offered genetic testing by a healthcare provider
4	I am trying to decide if genetic testing is right for me
5	I have concerns about a health condition in my family
6	I worry or have questions about what my condition might mean for me or others in my family
7	I have questions or worries about my health condition that haven’t been addressed by the healthcare system
8	I would like support from an expert about my health condition and what it means for me
9	I struggle with feelings of guilt or shame or feel confused or helpless about my condition
10	None of the above

Selections that indicated unmet need for genetic counseling:
4 or 5 or 6 or 9
(1, 2, or 7) and 8
2 and 7 or 8

Selections that indicated no unmet need for genetic counseling:
Only 1 or 2 or 3 or 7 or 8
Only 1 and 3
Only 2 and 3
Only 7 and 3
Only 8 and 3
10

When individuals were determined to have unmet need based on their responses to the Pathways Tool, we assessed if their unmet need was perceived or unperceived using two questions: (i) has there been a time that you felt you needed genetic counseling and were unable to access it (yes/no); and, (ii) I have not had genetic counseling, but I think I have a reason to have it (yes/no). If a participant responded affirmatively to either of these questions, then they were deemed to have perceived unmet need, and if they responded no to both of these questions, they had unperceived unmet need. Participants with perceived unmet need for genetic counseling were also asked about any barriers to care they experienced.

Capability is defined as “what people are able to be and do in their lives” and it was measured using the ICECAP-A, a 5-item tool, with each item rated on a 4-point Likert scale [39, 40]. The ICECAP-A measures capability across five areas (i) feeling settled and secure, (ii) love, friendship, and support, (iii) independence, (iv) achievement and progress, and (v) enjoyment and pleasure. The ICECAP-A has been validated across different populations and was scored according to UK-based tariffs, a scoring system that incorporates individuals’ preference weights, which is the most common way of scoring this instrument [41]. Tariff scores range from 0 to1, where higher scores indicate higher levels of capability. Capability has been proposed as an alternative method for measuring extra-benefits of genetic services for the purposes of economic evaluations and was included in this study to explore the relationship between need and the varied benefits of genetic counseling beyond mortality and morbidity [42, 43]. For analysis, we created four groups based on quartile cut points, (Q1 = ≤ 0.691, Q2 = 0.692–0.849, Q3 = 0.850–0.930, Q4 = ≥ 0.931). The group sizes vary slightly to be able to group all responses with the same value within the same category.

Personal utility is defined as a patient-centered construct to capture the non-medical perceived benefits associated with receiving genetic information, such as increased self-knowledge, coping, family communication, and altruism [44, 45]. The Personal Utility Scale (PrU) was used to assess levels of personal utility, which is defined as the perceived non-medical benefits resulting from receiving genetic information. This scale underwent face and content validation during its development. This scale has 14 items, rated on a 7-point Likert scale and was originally designed to be used post-genetic testing to assess how useful the genetic test results were for a patient [44, 45]. In this setting, we asked respondents to consider how useful certain aspects of receiving genetic information would be for them. The scores are calculated by adding the Likert scale responses for each item and thus total scores range from 14 to 98. For analysis, we created three groups based on PrU scores because this allowed for more meaningful interpretation of the regression model results. There are limited examples and guidance for scoring this scale, and to transform the PrU into a categorical variable we created three groups by dividing the possible scale scores into three groups (low = 14–41, moderate = 42–69, high = 70–98).

Healthcare distrust was evaluated using the Revised Healthcare System Distrust Scale (RHCSDS) [46]. We added the term “saving money” to questions that had the terms “making money” to make the scale more applicable to the Canadian healthcare system. The original RHCSDS contains 9 items each scored on a 5-point Likert scale; however, one item (“The Health Care System makes too many mistakes”) was omitted from our survey in error. Scores are calculated by summing the Likert scores for each question. Four of the items are reverse coded and the responses were transformed prior to totaling the score. In our survey, scores ranged from 8 to 40 with higher scores indicating higher levels of distrust. The original version of this scale has been previously validated.

The survey also included an open-ended question where participants could provide information about the specific conditions they were concerned about. The conditions of concern were grouped into categories based on the type of condition. We excluded survey responses that were completed in <4 min due to this being unrealistic for the length of the survey. Responses to the survey instruments were all required except for the demographic questions, which were optional. We included survey responses that had partially complete demographic characteristics.

The survey was piloted using cognitive interviews with five individuals who had not had genetic counseling or testing and who were largely naive to the subject matter. The cognitive interviews resulted in several changes to the survey that improved clarity and reduced redundancy. After the pilot interviews, the survey was translated into French. Two bilingual genetic counseling trainees translated the survey from English into French, they then discussed discrepancies and came to a consensus on the most appropriate translation.

Data analysis

Statistical measures

We used descriptive statistics to describe the sample and estimate the overall prevalence of unmet need for genetic counseling. Incomplete responses were included as long as the participants completed all of the questions assessing unmet need and at least some of the demographic information.

Multivariable regression

We conducted a multivariable logistic regression to assess which sociodemographic variables were most strongly associated with unmet need for genetic counseling. Variables were initially considered for inclusion if there was previous evidence that they may be associated with access to or outcomes of genetic services [14–16, 43, 47]. We used a stepwise selection of the variables to generate the final model [48]. Variables were first assessed using univariate regression and only considered for inclusion in the model if they were statistically significant at the univariate level (p < 0.05). Variables were added and removed (entry; p < 0.05, removal; p > 0.10) until only statistically significant variables remained. As gender and sex were highly correlated, gender was considered for inclusion because healthcare use is more reflective of a gendered behavior than influenced by biological sex.

The target sample size was determined a priori with the goal of having a large enough sample size to be adequately powered to accurately estimate the population prevalence with good precision. In order to detect a prevalence of 5% for unmet need for genetic counseling with a precision level of 1.5%, the minimum sample size required was n = 1000. We targeted a higher sample size to be able to increase the power for sub-group analyses [49]. All statistics were conducted using IBM SPSS Statistics (Version 29).

Results

We received 1325 survey responses. Thirty-six were removed due to completing the survey in an unrealistic amount of time (<4 min), leaving 1289 responses. Individuals who reported that they previously had genetic counseling or had been referred for genetic counseling were excluded based on our definition of unmet need (n = 129). Our final sample included 1160 respondents and demographic characteristics are summarized in Table 2.

Table 2.

Demographic characteristics.

	All respondents n = 1160	Unmet need for genetic counseling n = 457	No unmet need for genetic counseling n = 703
Age	n = 1160 n (%)	n = 457 n (%)	n = 703 n (%)
≤45 years old	358 (30.8)	194 (42.5)	164 (23.3)
>45 years old	802 (69.2)	263 (57.5)	539 (76.7)
Sex	n = 1159 n (%)	n = 457 n (%)	n = 702 n (%)
Male	545 (47.0)	189 (41.4)	356 (50.7)
Female	606 (52.3)	267 (58.4)	339 (48.3)
Intersex	2 (0.2)	0	2 (0.3)
Prefer to self-describe	1 (0.1)	1 (0.2)	0
Prefer not to say	5 (0.4)	0	5 (0.7)
Gendera	n = 1159 n (%)	n = 456 n (%)	n = 703 n (%)
Man	542 (46.8)	187 (41.0)	355 (50.5)
Woman	602 (51.9)	263 (57.7)	339 (48.2)
Non-binary or agender	8 (0.7)	3 (0.7)	5 (0.7)
Prefer to self-describe	6 (0.5)	3 (0.7)	3 (0.4)
Prefer not to say	1 (0.1)	0	1 (0.1)
Transgender	n = 1157 n (%)	n = 456 n (%)	n = 701 n (%)
Yes	15 (1.3)	6 (1.3)	9 (1.3)
No	1138 (98.4)	450 (98.7)	688 (98.1)
Prefer not to say	4 (0.3)	0	4 (0.6)
Raceb	n = 1160 n (%)	n = 457 n (%)	n = 703 n (%)
South Asian	56 (4.8)	25 (5.5)	31 (4.4)
Chinese	59 (5.1)	23 (5.0)	36 (5.1)
Black	39 (3.4)	15 (3.3)	24 (3.4)
Filipino	27 (2.3)	17 (3.7)	10 (1.4)
Latin American	17 (1.5)	9 (2.0)	8 (1.1)
Arab	14 (1.2)	9 (2.0)	5 (0.7)
Southeast Asian	8 (0.7)	3 (0.7)	5 (0.7)
West Asian	9 (0.8)	5 (1.1)	4 (0.6)
Korean	6 (0.5)	3 (0.7)	3 (0.4)
Japanese	3 (0.3)	1 (0.2)	2 (0.3)
White	864 (74.5)	317 (69.4)	547 (77.8)
Indigenous	58 (5.0)	30 (6.6)	28 (4.0)
Province of residence	n = 1160 n (%)	n = 457 n (%)	n = 703 n (%)
British Columbia	162 (14.0)	59 (12.9)	66 (9.4)
Alberta	125 (10.8)	74 (16.2)	88 (12.5)
Saskatchewan	37 (3.2)	12 (2.6)	25 (3.6)
Manitoba	41 (3.5)	20 (4.4)	21 (3.0)
Ontario	447 (38.5)	182 (39.8)	265 (37.7)
Quebec	268 (23.1)	70 (15.3)	198 (28.2)
New Brunswick	21 (1.8)	10 (2.2)	11 (1.6)
Nova Scotia	33 (2.8)	16 (3.5)	17 (2.4)
Newfoundland & Labrador	18 (1.6)	8 (1.8)	10 (1.4)
Prince Edward Islandd	4 (0.3)	4 (0.9)	0
Territories (Yukon, NWT, Nunavut)	4 (0.3)	2 (0.4)	2 (0.3)
Income (CAD)	n = 1131 n (%)	n = 448 n (%)	n = 683 n (%)
Under 20,000	88 (7.8)	41 (9.2)	47 (6.9)
20,000–39,999	145 (12.8)	59 (13.2)	86 (12.6)
40,000–59,999	188 (16.6)	65 (14.5)	123 (18.0)
60,000–79,999	150 (13.3)	59 (13.2)	91 (13.3)
80,000–99,999	194 (17.2)	80 (17.9)	114 (16.7)
100,000–119,999	122 (10.8)	50 (11.2)	72 (10.5)
120,000–139,999	84 (7.4)	34 (7.6)	50 (7.3)
140,000–159,999	60 (5.3)	24 (5.4)	36 (5.3)
160,000–179,999	29 (2.6)	11 (2.5)	18 (2.8)
180,000–199,999	28 (2.5)	9 (2.0)	19 (2.8)
Over 200,000	43 (3.8)	16 (3.6)	27 (4.0)
Education	n = 1098 n (%)	n = 434 n (%)	n = 664 n (%)
Grade 12 or less	57 (5.2)	20 (4.6)	37 (5.6)
Completed high school	210 (19.1)	81 (18.7)	129 (19.4)
College diploma	240 (21.9)	100 (23.0)	140 (21.1)
Apprenticeship or other trades certificate	97 (8.8)	37 (8.5)	60 (9.0)
Associates degree	32 (2.9)	9 (2.1)	23 (3.5)
Bachelor’s degree	316 (28.8)	129 (29.7)	187 (28.2)
Master’s degree	103 (9.4)	187 (28.2)	64 (9.6)
Professional degree	28 (2.6)	14 (3.2)	14 (2.1)
Doctorate degree	15 (1.4)	5 (1.2)	10 (1.5)
Health Status	n = 1156 n (%)	n = 455 n (%)	n = 701 n (%)
Excellent	99 (8.6)	30 (6.6)	69 (9.8)
Very good	412 (35.6)	129 (28.4)	283 (40.4)
Good	429 (37.1)	177 (38.9)	252 (35.9)
Fair	173 (15.0)	93 (20.4)	80 (11.4)
Poor	43 (3.7)	26 (5.7)	17 (2.4)
Mental Health Statusc	n = 1159 n (%)	n = 456 n (%)	n = 703 n (%)
Self-diagnosed with mental health concerns	154 (13.3)	91 (20.0)	63 (9.0)
Formally diagnosed with mental illness	202 (17.4)	127 (27.9)	75 (10.7)
No mental health concerns	718 (61.9)	200 (43.9)	518 (73.7)
I don’t know	85 (7.3)	39 (8.3)	47 (6.7)
Capability ICECAP-A	n = 1160	n = 457	n = 703
Q1 (0–0.691)	295 (25.4)	174 (38.1)	121 (17.2)
Q2 (0.692–0.849)	350 (30.2)	149 (32.6)	201 (28.6)
Q3 (0.850–0.942)	255 (22.0)	72 (15.7)	183 (26.0)
Q4 (0.943–1)	260 (22.4)	62 (13.6)	198 (28.2)
Personal Utility PrU	n = 1160	n = 457	n = 703
Low (14–41)	236 (20.3)	29 (6.3)	207 (29.4)
Moderate (41–69)	573 (49.4)	207 (45.3)	366 (52.1)
High (70–98)	351 (30.3)	221 (48.4)	130 (18.5)
Healthcare distrust RHCSDS	n = 1160	n = 457	n = 703
Mean (SD)	22.6 (6.0)	23.4 (5.0)	22.1 (6.0)

The n varies by characteristic due to missing responses, the n for each variable is reported.

^a“Prefer to self-describe” and “prefer not to say” responses were excluded from regression analysis. The “prefer to self-describe” responses did not yield usable data.

^bRace was collapsed into two categories for the regression analysis (White and minoritized racial groups).

^c“I don’t know” responses were excluded from the regression analysis. The percentages may not add up to exactly 100% due to rounding.

^dAll respondents from PEI had unmet need for genetic counseling, these respondents were excluded from analysis. We also analyzed the province of residence by combining all maritime provinces (New Brunswick, Nova Scotia, Newfoundland & Labrador, and Prince Edward Island) and combining those currently within the catchment of a regional genetics clinic (the IWK Health Centre) (New Brunswick, Nova Scotia, and Prince Edward Island) and these groupings yielded the same statistical results.

Prevalence of unmet need for genetic counseling

The proportion of respondents who had unmet need for genetic counseling was 39% (457/1160) as measured by the Pathways Tool. Of the respondents with unmet need for genetic counseling, 32% (146/457) had perceived unmet need, meaning that they reported that they had a reason or need for genetic counseling. The remaining proportion 68% (311/457) had unperceived unmet need, meaning that they had unmet need detected by the Pathways Tool but did not report that they had a reason or need for genetic counseling.

Univariate results

Using univariate analyses, unmet need for genetic counseling was associated with sex, gender, mental health conditions, younger age group, province of residence, capability, medical distrust, and personal utility (p < 0.05). Unmet need for genetic counseling was not associated with education level, income, or city size. These results are described in Table 3.

Table 3.

Associations between variables and unmet need for genetic counseling.

	Univariate	Multivariable
	OR (95% CI)	OR (95% CI)
Sex (female) ref = male	1.48 (1.16, 1.88)
Gender ref = man
Woman	1.45 (1.15, 1.83)
Non-binary/agender	1.52 (0.40, 5.72)
Minoritized racial group (yes) ref = White	1.55 (1.19, 2.02)
Mental health concerns ref = none
Self-diagnosed mental health concerns	3.74 (2.60, 5.64)	2.55 (1.70, 3.81)
Clinically diagnosed mental health concerns	4.39 (3.16, 6.09)	2.63 (1.80, 3.82)
Health status (fair/poor) ref = good-excellent	2.21 (1.64, 2.98)
Income ref = <$20,000
$20,000–$39,999	0.79 (0.46, 1.34)
$40,000–$59,999	0.61 (0.36, 1.01)
$60,000–$79,999	0.74 (0.44, 1.27)
$80,000–$99,999	0.80 (0.48, 1.34)
$100,000–$119,999	0.80 (0.46, 1.38)
$120,000–$139,999	0.78 (0.43, 1.43)
$140,000–$159,999	0.76 (0.39, 1.49)
$160,000–$179,999	0.70 (0.30, 1.65)
$180,000–$199,999	0.54 (0.22, 1.33)
Over $200,000	0.68 (0.32, 1.43)
Age group (≤45 yo) ref = >45 yo	2.42 (1.88, 3.13)	1.47 (1.08, 1.99)
City size ref = very small (<50,000)
Small, 50k–100k	1.09 (0.69, 1.70)
Medium, 100k–250k	0.93 (0.63, 1.39)
Metro, 250k–1.5m	1.31 (0.94, 1.81)
Large metro, >1.5m	1.06 (0.75, 1.51)
Education ref = less than high school
Completed high school or equivalent	1.16 (0.63, 2.14)
College diploma	1.32 (0.72, 2,41)
Apprenticeship or other trades certificate	1.14 (0.58, 2.25)
Associates degree	0.72 (0.28, 1.86)
Bachelor’s degree	1.28 (0.71, 2.30)
Master’s degree	1.13 (0.56, 2.21)
Professional degree	1.85 (0.74, 4.64)
Doctorate degree	0.93 (0.28, 3.08)
Province ref = British Columbia
Alberta	1.08 (0.67, 1.73)
Saskatchewan	0.57 (0.27, 1.21)
Manitoba	1.13 (0.57, 1.25)
Quebec	0.42 (0.28, 0.63)
Ontario	0.82 (0.57, 1.17)
New Brunswick	1.08 (0.44, 2.69)
Nova Scotia	1.12 (0.53, 2.37)
Newfoundland & Labrador	0.95 (0.36, 2.53)
Prince Edward Island	excluded from analysesa
Territories	1.19 (0.16, 8.65)
Capability (ICECAP-A) ref = Q4
Capability (ICECAP-A) Q1	4.59 (3.18, 6.63)	3.17 (2.05, 4.91)
Capability (ICECAP-A) Q2	2.37 (1.66, 3.38)	1.95 (1.30, 2.94)
Capability (ICECAP-A) Q3	1.26 (0.85, 1.86)	1.18 (0.76, 1.84)
Healthcare distrust (RHCSDS)	1.04 (1.02, 1.06)
Personal utility (PrU) ref = low
Personal utility (PrU) (moderate)	4.04 (2.64, 6.17)	3.13 (1.98, 4.97)
Personal Utility (PrU) (high)	12.13 (7.78, 18.93)	9.54 (5.89, 15.45)

^aDue to small sample size (n = 4, 4/4 had unmet need for genetic counseling), Prince Edward Island was excluded from the analysis. The bolded items indicate statistical significance (p < 0.05).

Multivariable regression results

The variables that remained statistically significant in the multivariable regression model include personal utility, capability, mental health status, and younger age group and together, explain 31.0% of the variance in unmet need for genetic counseling. These results are summarized in Table 3.

Individuals with high or moderate levels of personal utility (PrU) were more likely to have unmet need for genetic counseling compared to those with low personal utility, (High: OR 9.54; 95% CI 5.89,15.45; Moderate: OR 3.13; 95% CI 1.98, 4.97). Individuals with lower levels of capability (ICECAP-A) were more likely to have unmet need for genetic counseling compared to those with higher capability. There was a consistent gradient of unmet need with lower quartiles of capability scores, Q1 and Q2 had significantly greater unmet need than Q4 (Q1: OR 3.17; 95% CI 2.05,4.91; Q2: OR 1.95; 95% CI 1.30, 2.94). The likelihood of experiencing unmet need for genetic counseling for individuals in Q3 was not significantly different from Q4, although the direction of the odds ratio was appropriate (Q3: OR 1.18; 95% CI 0.76, 1.84). Individuals with a self-diagnosed or formally diagnosed mental health condition were approximately 2.5 times as likely to have unmet need for genetic counseling compared to those with no mental health conditions (Self-diagnoses: OR 2.55; 95% CI 1.70, 3.81, Formal diagnosis: OR 2.63; 95% CI 1.80, 3.82). There was no difference in unmet need between those with a formal versus self-diagnosis of a mental health condition. Individuals who were in the younger age group $\left(\le 45\mathrm{years}\mathrm{old}\right)$ were 1.47 times as likely to have unmet need for genetic counseling compared to those who were older than 45 years old (OR 1.47; 95% CI 1.08, 1.99).

Reasons for unmet need for genetic counseling

Participants who had perceived unmet need for genetic counseling (n = 146) were asked if there was a specific reason they did not receive care and 56 provided a response to this question. Participants could select multiple responses from a list of reasons. Many participants did not seek care because of personal reasons like being too busy or not getting around to it (n = 37), or cost/funding barriers (n = 28). Other reasons included not knowing how to get referred (n = 18), their doctor said it wasn’t necessary (n = 14), concerns that genetic counseling would cause emotional distress (n = 12), and that the COVID-19 pandemic has made it too challenging to obtain healthcare (n = 11).

Conditions of concern

Using an open-text response box, participants could write the condition(s) of concern based on their personal or family history. Most participants described multifactorial conditions including cancer (n = 125), diabetes (n = 77), psychiatric conditions (n = 76), heart conditions (n = 59), and Alzheimer disease/dementia (n = 33). Some respondents reported inherited conditions such as cystic fibrosis, Tay Sachs, thalassemia, or chromosomal conditions such as Down syndrome, translocations, and sex chromosome aneuploidies.

Discussion

This study aimed to quantify the unmet need for genetic counseling in Canada and explore how unmet need is distributed in the population. Our study found that a large proportion of the Canadian population (39%) may have unmet need for genetic counseling as measured by the Pathways Tool, which was designed to detect patients who could potentially benefit from genetic counseling as conceptualized as a psychotherapeutic intervention [1]. We also found that unmet need for genetic counseling was more likely in those with mental health conditions, lower levels of capability, individuals who were younger, and those with higher levels of personal utility.

The intention of the Pathways Tool is to identify individuals who have unmet cognitive or psychological needs for a condition that they have or that runs in their family. Psychotherapy is defined as “the informed and intentional application of clinical methods and interpersonal stances derived from established psychological principles for the purpose of assisting people to modify their behaviors, cognitions, emotions, and/or other personal characteristics in directions that the participants deem desirable” [11]. Genetic counseling can be conceptualized as a psychotherapeutic intervention because the process of genetic counseling can lead to positive health behavior changes in alignment with patient values [12]. In healthcare systems with resource constraints, decisions are made to prioritize funding for healthcare interventions that lead to improvements in health outcomes rather than those that result in non-health benefits. Using the Pathways Tool to identify unmet need for genetic counseling likely identifies individuals who could receive health and/or non-health related benefits from care, so although there is a large proportion of the population that could receive some benefit from genetic counseling, the public healthcare system prioritizes funding of programs or treatments that could lead to health-related benefits (e.g. changes in screening or medication use, positive health behavior changes, improvements to quality of life). For patients where receipt of genetic counseling is unlikely to yield health-related benefits, a mixed reimbursement model could be considered, where these patients are still identified as having unmet need, but this care could be accessed outside of the public system (i.e.: out of pocket payment or through extended health insurance plans). There is evidence that high-quality psychotherapeutically oriented genetic counseling can lead to health-related benefits, however more research may be warranted to further identify patients for whom genetic counseling could lead to health benefits, and to better map the genetic counseling process and impacts to concrete health benefits. We also found that a substantial portion of the unmet need for genetic counseling is for multifactorial conditions for which no genetic testing would be clinically indicated. This means that using genetic test volume to inform health human resource planning for genetic counselors is likely to underestimate the workforce required to meet population needs.

The reasons for not receiving care include personal reasons (e.g. too busy), access barriers, limited availability of care, and lack of awareness. Most respondents had unmet need for genetic counseling for multifactorial conditions, where they needed support and education about the genetic contributions of disease for common, complex disorders like cancer, diabetes, and psychiatric conditions. For most of these individuals, genetic testing would not be clinically indicated, highlighting the importance of genetic counseling for addressing knowledge gaps and emotional distress in families. Additionally, in our regression model we found that unmet need for genetic counseling was associated with the lowest levels of capability and experiencing any mental health conditions, this highlights the need to use a combination of approaches to reach those who are likely experiencing the highest barriers to accessing genetics healthcare and not further perpetuate health inequities.

We found that unmet need was associated with some sociodemographic factors, many of which have been previously reported as contributing to differences in utilization and uptake in other studies. Individuals who are part of minoritized racial groups are less likely to receive genetic services (genetic counseling and testing), and this is well documented in the literature and was also associated with unmet need in our study in the univariate analysis but not the multivariable regression analysis [15, 16]. This could mean that in our population, race is not a driving factor for experiencing unmet need for genetic counseling. Willis et al., found that education level and socioeconomic status (comprised of income, employment status, class, and insurance status) were consistently associated with utilization level of genetic services for hereditary cancer; however, our study did not find an association between education or income level and unmet need for genetic counseling [14]. This could potentially be partially explained by the publicly funded healthcare system in Canada. In our study we also found that younger individuals (≤45 years old) were more likely to have unmet need for genetic counseling compared to those over the age of 45 years. This is in keeping with other research findings that demonstrate use of genetic services is generally more common in younger individuals [13].

In our regression model, personal utility was the strongest predictor of unmet need, where individuals who had moderate or higher levels on the PrU were 3.1 and 9.5 times more likely, respectively, than those with low PrU scores to have unmet need for genetic counseling. Perceived benefits have previously been associated with higher utilization and uptake of genetic services [14, 50]. Many of the items included in the PrU are aligned with non-health related benefits and this may be a useful tool in the future for triaging individuals according to potential benefits resulting from genetic counseling [44, 45]. Additionally, we found individuals with either a self-diagnosed or formally diagnosed mental health condition were more likely to have unmet need for genetic counseling. Previous research has demonstrated that patients receiving genetic counseling had a higher prevalence of psychiatric conditions compared to other healthcare users who did not receive genetic counseling, this is aligned with our finding that individuals with mental health conditions may have higher levels of need for genetic counseling [51].

Our study also found that most of the unmet need for genetic counseling (68%) was unperceived by the participants. This means that respondents had unmet need measured by the Pathways Tool but they did not self-report that they had a reason or had need for genetic counseling. One of the ways to increase equitable access to healthcare is by using a disaggregated approach to measuring sub-types of subjective unmet need as described by Allin et al., to be able to better understand the root causes of unmet need [18]. Individuals who have perceived unmet need are typically experiencing barriers to care such as challenges with the referral process or wait times and interventions that address barriers to care (e.g. funding, increased availability, increasing ease of referrals) would help address perceived unmet need. Individuals who have unperceived unmet need have a lack of awareness about genetic counseling and increasing public health education would have to be part of the strategy to address unperceived unmet need.

Limitations

Although distributing the survey through a market research company allowed representative sampling to occur, individuals who are registered survey takers may have characteristics that are different from the general population and our sample may not be fully representative. The survey was only available in English and French and individuals who do not speak either of the majority languages in Canada may have higher levels of unmet need, which would therefore not be captured in this study. This is the first study to estimate the population prevalence of unmet need for genetic counseling and the first applied use of the Pathways to Genetic Counseling tool for this purpose. Future research could be warranted to further validate the use of this tool in this context to explore the validity and accuracy of this measurement tool. We had omitted one item on the Revised Healthcare System Distrust Scale in error and it is unclear if this had any effect on our statistical results. The relationship between healthcare system distrust and unmet need for genetic counseling could be explored further in future research. Lastly, the PrU was designed for use in a population that received genetic information through sequencing and we used it to asses personal utility in a more hypothetical setting. Future research could further explore expanded use of this scale.

Conclusion

Our study found that unmet need for genetic counseling may be present in up to 39% of the Canadian population and emphasizes that there any many benefits patients and families can receive from genetic counseling. Unmet need for genetic counseling was associated with factors that may also reflect other barriers to healthcare, like mental health conditions and lowest levels of capability. This level and distribution of unmet need is not likely to be met through the current model of service delivery through tertiary care genetics clinics and opportunities exist to restructure the delivery of genetic counseling to improve equitable access to care.

Author contributions

Kennedy Borle: conceptualization, methodology, formal analysis, investigation, writing – original draft, writing – review & editing, project administration, funding acquisition. Jehannine Austin: conceptualization, methodology, formal analysis, resources, writing – review & editing, supervision, project administration, funding acquisition. Larry Lynd: conceptualization, methodology, formal analysis, resources, writing – review & editing, supervision, project administration, funding acquisition. All of the authors gave final approval of this version to be published and agree to be accountable for all aspects of the work.

Funding

Kennedy Borle was supported by a CIHR Banting and Best Doctoral Fellowship and received additional funding from the BCCHR Brain, Behaviour, and Development Trainee award and the UBC Public Scholar Initiative to complete this research. The funders had no role in review or approval of the manuscript.

Data availability

The data that support this study may be available on reasonable request from the corresponding author. Some data will not be made available to protect the privacy and confidentiality of participants.

Competing interests

KB, JA, and LL declare that they have no conflicts of interest to disclose. JA is the Editor-in-Chief of the Journal of Genetic Counseling and President of the International Society of Psychiatric Genetics.

Ethics

Informed consent was obtained from all participants in this research study. This study was approved by the University of British Columbia Research Ethics Board (Certificate H22-10827).

Supplementary information

The online version contains supplementary material available at 10.1038/s41431-025-01812-1.