Next Article in Journal
Utilization Trends of Novel Hormonal Agents in Metastatic Castration-Resistant Prostate Cancer in Quebec
Previous Article in Journal
A Robotic Completely Intercorporeal Jejunal Pouch Reconstruction after Gastrectomy
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Current Oncology
Volume 29
Issue 11
10.3390/curroncol29110679
curroncol-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

Clinical Delays and Comparative Outcomes in Younger and Older Adults with Colorectal Cancer: A Systematic Review

by
Matthew Castelo
1,2,
Colin Sue-Chue-Lam
1,2,
Lawrence Paszat
2,3,
Adena S. Scheer
1,2,4,
Bettina E. Hansen
2,
Teruko Kishibe
4 and
Nancy N. Baxter
1,2,4,5,*
1
Department of Surgery, University of Toronto, Toronto, ON M5T 1P5, Canada
2
Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 1P5, Canada
3
Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
4
Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, ON M5B 1W8, Canada
5
School of Population and Global Health, University of Melbourne, 207 Bouverie St. Level 5, Melbourne, VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Curr. Oncol. 2022, 29(11), 8609-8625; https://doi.org/10.3390/curroncol29110679
Submission received: 27 September 2022 / Revised: 3 November 2022 / Accepted: 10 November 2022 / Published: 12 November 2022
(This article belongs to the Section Gastrointestinal Oncology)
Browse Figures
Review Reports Versions Notes

Abstract

:
Outcome disparities between adults <50 with colorectal cancer (CRC) and older adults may be explained by clinical delays. This study synthesized the literature comparing delays and outcomes between younger and older adults with CRC. Databases were searched until December 2021. We included studies published after 1990 reporting delay in adults <50 that made comparisons to older adults. Comparisons were described narratively and stage between age groups was meta-analyzed. 39 studies were included representing 185,710 younger CRC patients and 1,422,062 older patients. Sixteen delay intervals were compared. Fourteen studies (36%) found significantly longer delays among younger adults, and nine (23%) found shorter delays among younger patients. Twelve studies compared time from symptom onset to diagnosis (N younger = 1538). Five showed significantly longer delays for younger adults. Adults <50 years also had higher odds of advanced stage (16 studies, pooled OR for Stage III/IV 1.76, 95% CI 1.52–2.03). Ten studies compared time from diagnosis to treatment (N younger = 171,726) with 4 showing significantly shorter delays for younger adults. All studies showing longer delays for younger adults examined pre-diagnostic intervals. Three studies compared the impact of delay on younger versus older adult. One showed longer delays were associated with advanced stage and worse survival in younger but not older adults. Longer delays among younger adults with CRC occur in pre-diagnostic intervals.

1. Introduction

The incidence of colorectal cancer (CRC) in younger adults (<50 years) has increased in a number of countries [1,2,3,4]. Further, CRC mortality among younger adults increased an average of 1.1% annually from 2005 to 2017 [5]. This stands in contrast to older adults, where mortality has decreased by 3% annually and incidence continues to fall [6].
One potential explanation for these disparities may be delays to diagnosis and treatment for younger adults. These delays may be the result of longer time to presentation on the part of the patient, misdiagnosis by physicians, and lack of access to colorectal cancer screening [7]. The US Preventative Services Task Force has recently recommended beginning colorectal cancer screening at age 45 [8], however younger patients are not eligible for screening. The majority of younger adults do not have family histories of CRC, and less than a quarter have a clear predisposing factor such as inflammatory bowel disease [7,9,10]. A previous review has suggested younger adults may take on average 6.2 months to present for initial evaluation after developing symptoms. [7] This evidence base is now dated, with all included studies being over 20 years old, and comparisons to older adults with CRC were not made, providing little insight into the emerging disparity between younger and older adults with CRC. Some authors have explicitly called for a reduction in the time from symptom onset to evaluation in an effort to improve CRC outcomes in younger adults [11], although previous studies have not clearly established a relationship between longer delay and adverse survival in adults <50 years [12,13]. Additionally, after adjustment for patient and disease characteristics, there is evidence younger adults may actually have improved survival compared to older adults [14].
Existing reviews do not examine differences in delay between younger and older patients with CRC [7,15,16], making it difficult to know whether delay might explain the observed divergence in CRC outcomes between these populations. Gaining a better understanding of patterns of delay between younger and older patients will inform efforts to address disparities in care. To address this knowledge gap, we have conducted a systematic review of all observational studies that compared delays between CRC patients <50 years and older adults. Among these studies we also compared the impact of delay on cancer outcomes, CRC stage at presentation, recurrence, and survival between younger and older patients.

2. Materials and Methods

We developed a systematic review protocol using the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines for protocols (PRISMA-P). [17] Our review was prospectively registered on PROSPERO (Prospective Register of Systematic Reviews—registration number CRD42020179707) and is reported according to the PRISMA guidelines (Supplementary Material Table S1) [18].

2.1. Information Sources

We developed a literature search strategy in collaboration with a senior information specialist. We limited the search to observational studies using a published search filter [19]. The electronic databases MEDLINE, Embase, and the Latin American and Caribbean Health Sciences Literature (LILACS) were searched from inception until 2 December 2021 (Supplementary Material Table S2). We limited results to studies published in English, French, Portuguese, and Spanish from 1990 to present. The search strategy was peer reviewed by a second information specialist following the Peer Review of Electronic Search Strategies (PRESS) checklist [20]. We performed a grey literature search according to the Canadian Agency—for Drugs and Technologies in Health Grey Matters checklist [21].

2.2. Eligibility Criteria

We included observational studies published after 1990 reporting any delay interval containing time points between symptom onset and treatment start. Studies must have reported delay measures in CRC patients <50 years of age, and additionally made comparisons to older adults with respect to these measures. We excluded studies meeting the following criteria: (i) results not reported for younger adults (age <50 or younger), (ii) majority of patients were pediatric (age <18), (iii) only reported delays to adjuvant therapy (e.g., time between surgery and chemotherapy), (iv) less than 10 younger patients included, (v) conference reports, published abstracts without accompanying complete articles, and (vi) papers that examined delays due to the impact of COVID-19 on health care systems.

2.3. Data Management

The DistillerSR (Ottawa, ON, Canada) software platform was used to store retrieved articles and perform the study selection process.

2.4. Study Selection

Screening was conducted in three stages. Two reviewers (MC and CS) independently screened titles, titles and abstracts, and finally full texts for results retrieved from the literature search. We resolved conflicts by discussion, with input from a third reviewer (NB) when required.

2.5. Data Collection Process

Two reviewers (MC and CS) independently abstracted study information, patient characteristics, tumor characteristics, delay measures, and cancer outcomes stratified by younger and older age. A piloted Microsoft Excel (Washington, DC, USA) data collection sheet was developed to record the data and risk of bias results. We resolved conflicts by discussion, with input from a third reviewer (NB) when required. Authors were contacted for data clarification as needed.

2.6. Outcomes and Definitions

The primary outcomes of interest were measures of delay. These included the magnitude and variability of any delay interval falling along the pathway to treatment (Supplementary Material Figure S3). [22] When reported in the studies we also compared stage at presentation, overall survival, disease-free/event-free survival, recurrence, and the impact of delay on these outcomes between younger and older adults. We defined advanced stage as Stage III or IV and early stage as Stage I or II.

2.7. Risk of Bias Assessment

Risk of bias was assessed independently by two reviewers (MC and CS). We resolved conflicts by discussion, with input from a third reviewer (NB) when required. The Newcastle-Ottawa Scale [23], and the Aarhus checklist [22] were used to assess the risk of bias for each study. The Aarhus checklist is an international consensus-derived 20-item tool specifically intended to assess the measurement of cancer delay intervals in observational research [22].

2.8. Synthesis

Study characteristics and risk of bias results were described narratively. Each study was categorized according to the delay intervals measured. The number of studies and relevant sample sizes of younger and older adults were presented according to each interval. The differences in delay intervals between younger and older adults were described narratively. There was substantial heterogeneity in these reported comparisons, including intervals measured, age cut-offs and categorizations, statistical models, methods of adjustment, and patient populations. Therefore, quantitative synthesis was not possible.
Among studies that reported delays in younger adults, additional comparisons available included stage at presentation, survival, and recurrence, and the impact of delay on these outcomes. Formal meta-analysis was possible for the comparison of advanced stage at presentation between younger and older patients. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated from weights in a random effects model, treating advanced stage (Stage III/IV) as the outcome of interest. Subgroup analyses were performed between studies that examined colon and rectal cancer together, and each disease site separately. Two sensitivity analyses were performed. The first excluded the largest study, and the second defined advanced stage as only metastatic disease (Stage IV). Heterogeneity was quantified using I2 scores and Forest plots. I2 scores above 50% were considered high heterogeneity [24]. Similar to the delay comparison, heterogeneity precluded meta-analysis for survival outcomes.
Data were analyzed using R (R Foundation for Statistical Computing, Vienna, Austria), all statistical tests were two-sided, and p < 0.05 was considered statistically significant.

3. Results

3.1. Search Results

The database and grey literature searches returned 7421 non-duplicated citations (Figure 1). After screening titles and abstracts, we evaluated 464 full texts, and 39 studies [12,13,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61] met the inclusion criteria.

3.2. Study Characteristics

Table 1 presents the characteristics of 39 studies that reported a delay measure in younger (<50 years) adults with CRC, and made some comparison to delays in older adults. The most recent study was published in 2021, and the oldest in 1992. One study, Lima et al. [61], was published in Portuguese. All other studies were available in English. Three studies reported only an overall sample size without stratifying by younger and older adults (n = 14,296). [38,49,50] The remaining studies represented 1,607,772 patients with CRC, of whom 185,710 (11.6%) were classified as younger adults (as per individual study definitions). Most studies defined younger adults as age <50 (28/39; 72%), with the remaining 11 studies using lower age cut-offs (Table 1). Gabriel et al. [34] contributed the overwhelming majority of patients to both the younger and older cohorts, representing 75.5% of the overall sample size. The median number of younger patients among the 39 studies was 87 (IQR 53–565), and the median total sample size was 842 (IQR 308–7031). Seven studies (18%) had less than 50 younger CRC patients.
Studies were performed in sixteen different countries. Canada (8/39; 21%), the United Kingdom (7/39; 18%), and the United States (6/39; 15%) were the most common countries of study origin. The most common data source was primary data collection (22/39; 56%), followed by cancer registries or health administrative data (16/39; 41%). Most studies examined both colon and rectal cancer (32/39; 82%).

3.3. Risk of Bias Assessment

The Newcastle-Ottawa Scale [23] scores are presented in Supplementary Material Table S4A. Eighteen studies (46%) did not report on a truly representative cohort, being highly selected single-center studies. Twenty-two studies (56%) did not present comparisons adjusted for any potential confounders. Studies generally drew younger and older adults from the same community, and used secure records such as medical records to determine age. Twelve studies (31%) included statements or figures demonstrating loss to follow-up. The Aarhus Checklist was used to assess risk of bias specific to delay studies (Supplementary Material Table S4B) [22]. Of note, seven studies (18%) were published prior to the Aarhus Checklist’s availability. Studies reliably identified the beginning and end points of delay intervals, but few provided precise and repeatable definitions of how these time points were calculated. For studies that included the date of symptom onset, most (8/15; 53%) discussed the potential biases when measuring this time point. Eight studies (21%) explicitly referred to established definitions for delay intervals and referenced theoretical frameworks. Seven studies included data from questionnaires or patient interviews. Of these, five discussed the potential biases resulting from how and when the question is being asked, and the difficulty in determining patient-reported time points.

3.4. Delay Measures between Younger and Older Patients

While all studies made some comparison between delay in younger and older adults with CRC, there was substantial heterogeneity in reporting. Measures used to compare delay included medians, means, and proportions of patients reaching a delay cut-off. These comparisons were also heterogeneously reported, including odds ratios, risk ratios, differences in means/medians/90th percentiles, and additional days of delay. Sixteen unique delay intervals were compared among the 39 studies (Figure 2). Five interval measures were evaluated in only one study [13,42,60].

3.5. Interval from Symptom Onset to Diagnosis

Time between symptom onset and diagnosis was reported by the largest number of studies (n = 12), comparing 1538 younger adults to 13,543 older adults. Five of these studies found younger adults had significantly longer times between symptom onset and diagnosis, two found shorter delays for younger adults, and the remaining five showed no significant differences (Figure 2 and Supplementary Material Table S5). Zhu et al. [29], Kim et al. [12], and Majano et al. [60] were the largest studies reporting sample sizes of younger patients for time between symptom onset and diagnosis. Zhu et al. [29] compared 83 CRC patients under 30 to 4911 older patients with a mean age of 58.9 years, showing no significant difference in time from symptom onset to diagnosis (younger mean 4.6 months vs. older mean 6.2 months, p = 0.691). Kim et al. [12] reported significantly longer delays among 693 patients ≤45 years compared to patients aged 56–65 (younger mean 52.9 days vs. older mean 33.3 days, p = 0.001). Both comparisons were unadjusted. In an adjusted analysis of 131 CRC patients under 45 compared to 4705 older patients, Majano et al. [60] showed a non-significant difference in median time from symptom onset to diagnosis for rectal cancer (age <45 additional 59.0 days, 95% CI −8.5 to 126.5 vs. age 55–64) and colon cancer (age <45 additional 82.0 days, 95% CI −24.5 to 188.5 vs. age 55–64).

3.6. Advanced Stage at Presentation for Younger and Older Patients

Sixteen studies [12,25,29,31,32,34,36,38,40,43,44,47,48,53,54,57] compared stage at presentation between younger and older patients with CRC. Ten studies found significantly higher odds of advanced stage (Stage III or IV) among younger patients, and the remaining studies found no significant difference (Table 2 and Supplementary Material Table S6). None of the studies found lower odds of advanced stage among younger patients. A random-effects meta-analysis was performed, with very high heterogeneity (I2 = 97.0%; Figure 3). Younger age was associated with significantly higher odds of advanced stage at presentation (pooled OR 1.76, 95% CI 1.52–2.03). There was no significant subgroup difference by cancer site (colorectal vs. colon only vs. rectal only; p = 0.91; Figure 3). A sensitivity analysis excluding the large Gabriel et al. [34] study came to similar conclusions (Supplementary Material Figure S7), as did a sensitivity analysis examining metastatic disease versus non-metastatic disease (Supplementary Material Figure S8).

3.7. Interval from to Diagnosis to Treatment Start

Due to the study by Gabriel et al. [34], the largest comparison by sample size was time between diagnosis and treatment (Figure 2). Using National Cancer Database (NCDB) data, Gabriel et al. [34] reported 155,090 younger adults experienced a mean delay of 11.18 days for colon cancer compared to a mean delay of 13.18 days in 1,058,102 older adults. This difference was statistically significant (p < 0.001), but of questionable clinical relevance. The reported mean difference for rectal cancer patients was smaller (age <50 mean 22.02 days vs. age 60+ mean 22.48 days, p < 0.001). [34] Three additional studies reported this interval also found significantly shorter delays for younger adults [28,41,61]. No study found longer intervals between diagnosis and treatment for younger adults.

3.8. Remaining Delay Intervals

Three studies [25,45,46] examined the entire interval from symptom onset to treatment start, two of which showed longer delays for younger adults. Scott et al. [25] reported significantly longer delays among younger adults (younger median 217 days [n = 56] vs. older median 58 days [n = 56], p < 0.0001). Deng et al. [46] found mean time from symptoms onset to treatment was 120.3 days for younger adults compared to 74.4 days in older adults with colon cancer (p = 0.035). The same comparison was not significantly different for rectal cancer patients. [46] Esteva et al. [45] reported no significant difference in time between symptom onset and treatment for 45 younger adults compared to older adults in five regions in Spain (age <50 median 149.0 days vs. age 50–64 median 133.0 days, p = 0.20).
The interval from presentation to treatment was compared in three studies, [37,52,55] with all three finding significantly longer delays for younger adults (Figure 2). When studies were categorized by finding irrespective of interval studied, 14 studies (36%) found significantly longer delays among younger adults, nine (23%) found shorter delays among younger patients, two (5%) showed mixed findings, and the remaining 14 studies (36%) found no significant differences (Table 2). All studies that found longer delays among younger adults included pre-diagnostic time.

3.9. Impact of Delay on Outcomes between Younger and Older Patients

Three studies [12,13,36] evaluated the impact of delay on either stage at presentation or survival, stratified by younger and older adults. Table 3 presents the results for each study. Kim et al. [12] was the only study showing increased odds of advanced stage with longer delay for younger adults (symptom onset to diagnosis >3 months OR of Stage III/IV 6.33, 95% CI 3.05–13.12). In the same study, longer delay was not associated with advanced stage in older adults. Conversely, Chen et al. [36] found advanced stage was associated with a shorter interval from symptom onset to diagnosis for both younger and older adults. Girolamo et al. [13] similarly found lower odds of advanced stage with longer delays in older adults, and no significant association for younger adults.
Kim et al. [12] also reported stratified associations between delay and survival. In younger adults, longer delay was associated with worse survival only in the adjusted Cox model (symptom onset to diagnosis >3 months adjusted HR 2.57, 95% CI 1.34–4.94). Longer delay was not associated with worse survival in older adults. Girolamo et al. [13] reported higher 1-year survival among those with longer delays for both younger and older adults, although p-values were not provided.

3.10. Survival and Recurrence for Younger and Older Patients

Fifteen studies [12,13,25,26,28,32,34,40,41,43,44,48,53,54,55] compared survival between younger and older colorectal cancer patients. There was heterogeneity among outcomes (cancer-specific survival, overall survival, 30-day mortality), adjustment for covariates, follow-up time, and analysis strategy (time-to-event models versus binomial models). Formal meta-analysis was not possible. Two studies [12,53] (13%) found significantly worse survival among younger patients, 7 (47%) studies found worse survival in only unadjusted analyses or non-significant differences, and the remaining 6 studies (40%) showed significantly better survival among younger patients (Table 2 and Supplementary Material Table S9).
Five studies [12,32,43,48,54] (13%) examined disease recurrence or event-free/cancer-free survival (Table 2 and Supplementary Material Table S9). Two studies [12,32] showed worse outcomes for younger patients, and the remaining three studies [43,48,54] showed no significant differences.

4. Discussion

In this systematic review and meta-analysis of 39 studies comparing delay measures between adults <50 and older adults, we found a highly heterogeneous body of literature that examined sixteen distinct delay intervals and various patient populations, using differing age cut-offs, methods of adjustment, and sample sizes. However, these studies indicate that when younger adults do experience longer delays, the delay generally occurs before diagnosis. Indeed, studies finding longer delays in younger adults all compared intervals including pre-diagnostic time. However, the included studies reached contradictory conclusions regarding whether these pre-diagnostic delays lead to worse outcomes in younger adults compared to older adults. We found that younger patients were at higher risk of presenting with advanced stage at presentation (Stage III/IV); however there was no consistent relationship between survival and age at presentation.
Evaluating delay intervals in cancer research is complex, and numerous biases have been described when assessing time points along the pathway to treatment. [22] We were unable to perform a meta-analysis for delay interval comparisons between younger and older patients for reasons of heterogeneity. However, we are able to generalize based on the literature reviewed. Specifically, we found no evidence that younger adults experience longer delays from diagnosis to treatment. In fact, all four studies that found significant differences for this interval showed younger adults had shorter times from diagnosis to treatment. This interval tends to be widely reported in the literature, as health administrative databases and cancer registries are well positioned to measure cancer diagnosis dates and treatment dates [26,28,30,34,41,49,51,57,61,62,63].
Examining pre-diagnostic intervals are more challenging, especially date of symptom onset [22]. Health administrative databases are largely unsuitable for determining this date and patient questionnaires or detailed chart review must be used. Patient-reported dates may be subject to recall bias, and the quality of documentation in charts is variable [22]. Unsurprisingly, the sample sizes of comparisons using these time points were generally small. However, we did find several pre-diagnostic intervals with larger studies making comparisons to older adults. In 12 studies reporting a total of more than 1538 younger patients, five [12,33,36,44,56] found significantly longer times from symptom onset to diagnosis compared to older patients. Contained within this interval, two studies [27,31] reported time from referral to diagnosis for over 4000 younger patients and came to conflicting conclusions with regard to length of delay for younger versus older patients. Taken together, the interval between symptom onset and presentation (so-called patient delay) appears to be an important potential source of disparity between younger and older patients.
Younger patients may be at higher risk of experiencing delays from symptom onset to presentation [7,11,25] as they are not eligible for screening, and many initial symptoms of colorectal cancer are nonspecific or have easily offered alternative diagnoses such as hemorrhoidal disease. Experts have suggested patient education about the increasing risk of colorectal cancer among young people, increased awareness of alarm symptoms, and destigmatizing colorectal cancer as options to improve early detection [11]. However, the evidence base examining time from symptom onset to presentation is very weak. In our review, seven studies compared this interval between 545 younger and 1051 older adults, and only three demonstrated that younger patients have longer delays [25,36,53]. An additional study reported time from symptom onset to first investigation, with no significant difference between the age groups [60]. Interventions targeting the pre-diagnostic period are more attractive given these results, although there is a lack of research in this area, especially among younger adults. Research is needed to identify modifiable patient, clinician, and system factors that may be amenable to intervention.
However, the observed relationship in retrospective studies between delay and adverse outcomes in CRC is itself uncertain [15,16]. Large, methodologically robust studies of mostly older adults have come to differing conclusions [28,64,65,66]. While some have described worse survival for cancer patients with longer delays [65,67], few studies examine this association in younger adults with CRC [12,13]. Some have suggested fast-growing tumors may cause troublesome symptoms sooner and lead to shorter delay intervals [64]. These cancers also tend to be more aggressive and result in worse outcomes. Similarly, there is also not a clear relationship between increased delay and stage [15]. We were able to identify three studies that compared the impact of delays on stage at presentation or survival between younger and older adults [12,13,36]. Kim et al. [12] was the only study to show delay was associated with more advanced stage and worse survival in younger adults compared to older adults.
Although not the primary outcome of this review, we have shown younger patients present with more advanced colorectal cancer (pooled OR 1.76, 95% CI 1.52–2.03; I2 = 97.0%), and may have improved survival compared to older patients once adjusted for stage [14]. These comparisons were limited by our inclusion criteria of studies that reported delay measures among young adults, and high heterogeneity. There are several hypotheses regarding why younger patients present with more advanced disease, including adverse histology, intrinsic biological aggressiveness, and tumor location [68].
Strengths of this review include its size and scope—we endeavored to identify a global literature, including four languages in our search criteria. We categorized delay comparisons according to international consensus-derived definitions, referenced established theoretical frameworks for delay, and utilized a formal cancer delay quality tool (the Aarhus Checklist) [22,69]. Previous systematic reviews examining the relationships between delay, stage, and survival have not considered younger adults as a distinct population, and did not systematically classify delay intervals as we have done [15,16].
There are limitations to this review. There are important factors that influence delay that we were unable to incorporate into our analysis, including health care system differences, screening eligibility, co-morbidity, intrinsic biologic differences, and non-linear relationships with age. This was due to sparse reporting and the large number of distinct delay interval comparisons. The very large American study from Gabriel et al. [34] contributed a majority of patients from both the younger and older group, and may be overrepresented in our conclusions. We did not include studies reporting on care provided during the COVID-19 pandemic, and our results may not be applicable to delays experienced in this context. As discussed, comparisons of pre-diagnostic intervals include few younger adults and conclusions with respect to these intervals are likely limited by low power. Finally, all the studies included in this review were observational studies, many of which were single-centre studies of selected patient groups. These studies are subject to the biases of retrospective cohort studies, including selection bias and unmeasured confounding [70]. Despite these limitations, this review is the most comprehensive assessment of delays in younger versus older adults with CRC available [7].
While a large number of studies have examined the topic, our understanding of delay and its clinical impacts in younger adults with CRC remains incomplete. It appears younger patients may have longer pre-diagnostic delays but experience shorter delays from diagnosis to treatment compared to older adults. High-quality studies are needed to confirm these findings and, critically, examine the potential for reducing delays to improve survival or stage at presentation. Attempts to reduce clinical delays among younger adults with CRC should focus on the pre-diagnostic time period.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/curroncol29110679/s1, Table S1: PRISMA checklist; Table S2: Search strategy for Medline; Figure S3: The pathway to treatment; Table S4A: Scoring for the risk of bias tools including the Newcastle-Ottawa Scale for Cohort Studies; Table S4B: Scoring for the risk of bias tools including the Aarhus checklist; Table S5: Detailed comparison of delay measures between younger and older adults with colorectal cancer; Table S6: Advanced stage at diagnosis comparing older and younger patients; Figure S7: Random effects meta-analysis of advanced stage at diagnosis by age category sensitivity analysis excluding Gabriel et al. study; Figure S8: Random effects meta-analysis of advanced stage at diagnosis by age category sensitivity analysis of metastatic disease; Table S9: Survival and recurrence outcomes among younger and older patients.

Author Contributions

M.C., L.P., A.S.S. and N.N.B. designed the study. M.C., T.K. and N.N.B. designed and reviewed the literature search. M.C. and C.S.-C.-L. performed the literature screen and extracted the data. M.C. and B.E.H. performed the analysis. A.S.S. and N.N.B. supervised the study. M.C. prepared the figures and tables, and wrote the main manuscript text. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by CIHR Foundation Grant (#148470).

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Bailey, C.E.; Hu, C.-Y.; You, Y.N.; Bednarski, B.K.; Rodriguez-Bigas, M.A.; Skibber, J.M.; Cantor, S.B.; Chang, G.J. Increasing disparities in the age-related incidences of colon and rectal cancers in the United States, 1975–2010. JAMA Surg. 2015, 150, 17–22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Crocetti, E.; Buzzoni, C.; Zappa, M. Colorectal cancer incidence rates have decreased in central Italy. Eur. J. Cancer Prev. Off. J. Eur. Cancer Prev. Organ. ECP 2010, 19, 424–425. [Google Scholar] [CrossRef] [PubMed]
  3. Haggar, F.A.; Preen, D.B.; Pereira, G.; Holman, C.D.J.; Einarsdottir, K. Cancer incidence and mortality trends in Australian adolescents and young adults, 1982–2007. BMC Cancer 2012, 12, 151. [Google Scholar] [CrossRef] [Green Version]
  4. Patel, P.; De, P. Trends in colorectal cancer incidence and related lifestyle risk factors in 15–49-year-olds in Canada, 1969–2010. Cancer Epidemiol. 2016, 42, 90–100. [Google Scholar] [CrossRef] [PubMed]
  5. Santucci, C.; Boffetta, P.; Levi, F.; La Vecchia, C.; Negri, E.; Malvezzi, M. Colorectal Cancer Mortality in Young Adults Is Rising in the United States, Canada, United Kingdom, and Australia but Not in Europe and Asia. Gastroenterology 2021, 160, 1860–1862.e2. [Google Scholar] [CrossRef] [PubMed]
  6. Siegel, R.L.; Miller, K.D.; Sauer, A.G.; Fedewa, S.A.; Butterly, L.F.; Anderson, J.C.; Cercek, A.; Smith, R.A.; Jemal, A. Colorectal cancer statistics, 2020. CA Cancer J. Clin. 2020, 70, 145–164. [Google Scholar] [CrossRef] [Green Version]
  7. O’Connell, J.B.; Maggard, M.A.; Livingston, E.H.; Yo, C.K. Colorectal cancer in the young. Am. J. Surg. 2004, 187, 343–348. [Google Scholar] [CrossRef]
  8. US Preventive Services Task Force; Davidson, K.W.; Barry, M.J.; Mangione, C.M.; Cabana, M.; Caughey, A.B.; Davis, E.M.; Donahue, K.E.; Doubeni, C.A.; Krist, A.H.; et al. Screening for Colorectal Cancer: US Preventive Services Task Force Recommendation Statement. JAMA 2021, 325, 1965–1977. [Google Scholar] [CrossRef]
  9. Clarke, W.T.; Feuerstein, J.D. Updates in colorectal cancer screening in inflammatory bowel disease. Curr. Opin. Gastroenterol. 2018, 34, 208–216. [Google Scholar] [CrossRef]
  10. Barrow, P.; Khan, M.; Lalloo, F.; Evans, D.G.; Hill, J. Systematic review of the impact of registration and screening on colorectal cancer incidence and mortality in familial adenomatous polyposis and Lynch syndrome. Br. J. Surg. 2013, 100, 1719–1731. [Google Scholar] [CrossRef]
  11. Siegel, R.L.; Jakubowski, C.D.; Fedewa, S.A.; Davis, A.; Azad, N.S. Colorectal Cancer in the Young: Epidemiology, Prevention, Management. Am. Soc. Clin. Oncol. Educ. Book Am. Soc. Clin. Oncol. Annu. Meet. 2020, 40, e75–e88. [Google Scholar] [CrossRef] [PubMed]
  12. Kim, T.J.; Kim, E.R.; Hong, S.N.; Chang, D.K.; Kim, Y.H. Long-Term Outcome and Prognostic Factors of Sporadic Colorectal Cancer in Young Patients: A Large Institutional-Based Retrospective Study. Medicine 2016, 95, e3641. [Google Scholar] [CrossRef] [PubMed]
  13. Di Girolamo, C.; Walters, S.; Gildea, C.; Benitez Majano, S.; Rachet, B.; Morris, M. Can we assess Cancer Waiting Time targets with cancer survival? A population-based study of individually linked data from the National Cancer Waiting Times monitoring dataset in England, 2009–2013. PLoS ONE. 2018, 13, e0201288. [Google Scholar] [CrossRef] [PubMed]
  14. Cheng, E.; Blackburn, H.N.; Ng, K.; Spiegelman, D.; Irwin, M.L.; Ma, X.; Gross, C.P.; Tabung, F.K.; Giovannucci, E.L.; Kunz, P.L.; et al. Analysis of Survival Among Adults With Early-Onset Colorectal Cancer in the National Cancer Database. JAMA Netw. Open 2021, 4, e2112539. [Google Scholar] [CrossRef]
  15. Ramos, M.; Esteva, M.; Cabeza, E.; Llobera, J.; Ruiz, A. Lack of association between diagnostic and therapeutic delay and stage of colorectal cancer. Eur. J. Cancer 2008, 44, 510–521. [Google Scholar] [CrossRef]
  16. Ramos, M.; Esteva, M.; Cabeza, E.; Campillo, C.; Llobera, J.; Aguiló, A. Relationship of diagnostic and therapeutic delay with survival in colorectal cancer: A review. Eur. J. Cancer 2007, 43, 2467–2478. [Google Scholar] [CrossRef]
  17. Moher, D.; Shamseer, L.; Clarke, M.; Ghersi, D.; Liberati, A.; Petticrew, M.; Shekelle, P.; Stewart, L.A. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev. 2015, 4, 1. [Google Scholar] [CrossRef] [Green Version]
  18. Liberati, A.; Altman, D.G.; Tetzlaff, J.; Mulrow, C.; Gøtzsche, P.C.; Ioannidis, J.P.A.; Clarke, M.; Devereaux, P.J.; Kleijnen, J.; Moher, D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. J. Clin. Epidemiol. 2009, 62, e1–e34. [Google Scholar] [CrossRef] [Green Version]
  19. Fraser, C.; Murray, A.; Burr, J. Identifying observational studies of surgical interventions in MEDLINE and EMBASE. BMC Med. Res. Methodol. 2006, 6, 41. [Google Scholar] [CrossRef] [Green Version]
  20. McGowan, J.; Sampson, M.; Salzwedel, D.M.; Cogo, E.; Foerster, V.; Lefebvre, C. PRESS Peer Review of Electronic Search Strategies: 2015 Guideline Statement. J. Clin. Epidemiol. 2016, 75, 40–46. [Google Scholar] [CrossRef]
  21. Grey Matters: A Practical Tool for Searching Health-Related Grey Literature. CADTH.ca. Published 29 July 2009. Available online: https://www.cadth.ca/resources/finding-evidence/grey-matters (accessed on 4 October 2019).
  22. Weller, D.; Vedsted, P.; Rubin, G.; Walter, F.M.; Emery, J.; Scott, S.; Campbell, C.; Andersen, R.S.; Hamilton, W.; Olesen, F.; et al. The Aarhus statement: Improving design and reporting of studies on early cancer diagnosis. Br. J. Cancer 2012, 106, 1262–1267. [Google Scholar] [CrossRef]
  23. Ottawa Hospital Research Institute. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses. Available online: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp (accessed on 13 March 2019).
  24. Cochrane Handbook for Systematic Reviews of Interventions. Available online: http://handbook-5-1.cochrane.org/ (accessed on 28 January 2019).
  25. Scott, R.B.; Rangel, L.E.; Osler, T.M.; Hyman, N.H. Rectal cancer in patients under the age of 50 years: The delayed diagnosis. Am. J. Surg. 2016, 211, 1014–1018. [Google Scholar] [CrossRef] [PubMed]
  26. Wanis, K.N.; Patel, S.V.B.; Brackstone, M. Do Moderate Surgical Treatment Delays Influence Survival in Colon Cancer? Dis. Colon Rectum 2017, 60, 1241–1249. [Google Scholar] [CrossRef] [PubMed]
  27. Pearson, C.; Fraser, J.; Peake, M.; Valori, R.; Poirier, V.; Coupland, V.H.; Hiom, S.; McPhail, S.; Moffat, J.; Lyratzopoulos, G.; et al. Establishing population-based surveillance of diagnostic timeliness using linked cancer registry and administrative data for patients with colorectal and lung cancer. Cancer Epidemiol. 2019, 61, 111–118. [Google Scholar] [CrossRef]
  28. Flemming, J.A.; Nanji, S.; Wei, X.; Webber, C.; Groome, P.; Booth, C.M. Association between the time to surgery and survival among patients with colon cancer: A population-based study. Eur. J. Surg. Oncol. J. Eur. Soc. Surg. Oncol. Br. Assoc. Surg. Oncol. 2017, 43, 1447–1455. [Google Scholar] [CrossRef] [PubMed]
  29. Zhu, C.; Ji, M.; Dai, W.; Ye, C.; Hu, Z.; Shi, J.; Zeng, X.; Lin, Y. Clinicopathological characteristics of chinese colorectal cancer patients under 30 years of age: Implication in diagnosis and therapy. Curr. Cancer Drug Targets 2015, 15, 27–34. [Google Scholar] [CrossRef]
  30. Roder, D.; Karapetis, C.; Olver, I.; Keefe, D.; Padbury, R.; Moore, J.; Joshi, R.; Wattchow, D.; Worthley, D.L.; Miller, C.L.; et al. Time from diagnosis to treatment of colorectal cancer in a South Australian clinical registry cohort: How it varies and relates to survival. BMJ Open 2019, 9, e031421. [Google Scholar] [CrossRef]
  31. Arhi, C.S.; Ziprin, P.; Bottle, A.; Burns, E.M.; Aylin, P.; Darzi, A. Colorectal cancer patients under the age of 50 experience delays in primary care leading to emergency diagnoses: A population-based study. Color. Dis. Off. J. Assoc. Coloproctol. Great Br. Irel. 2019, 21, 1270–1278. [Google Scholar] [CrossRef] [PubMed]
  32. Kaplan, M.A.; Ozaydin, S.; Yerlikaya, H.; Karaagac, M.; Gumus, M.; Cil, T.; Arslan, Y.; Ozdemir, N.; Sakin, A.; Bilici, M.; et al. Clinicopathologic and Prognostic Differences between Three Different Age Groups (Child/Adolescent, Young Adults, and Adults) of Colorectal Cancer Patients: A Multicentre Study. Oncol. Res. Treat. 2019, 42, 516–522. [Google Scholar] [CrossRef]
  33. Windner, Z.; Crengle, S.; de Graaf, B.; Samaranayaka, A.; Derrett, S. New Zealanders’ experiences and pathways to a diagnosis of bowel cancer: A cross-sectional descriptive study of a younger cohort. N. Z. Med. J. 2018, 131, 30–39. [Google Scholar]
  34. Gabriel, E.; Ostapoff, K.; Attwood, K.; Al-Sukhni, E.; Boland, P.; Nurkin, S. Disparities in the Age-Related Rates of Colorectal Cancer in the United States. Am. Surg. 2017, 83, 640–647. [Google Scholar] [CrossRef] [PubMed]
  35. Sikdar, K.C.; Dickinson, J.; Winget, M. Factors associated with mode of colorectal cancer detection and time to diagnosis: A population level study. BMC Health Serv. Res. 2017, 17, 7. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Chen, F.W.; Sundaram, V.; Chew, T.A.; Ladabaum, U. Advanced-Stage Colorectal Cancer in Persons Younger Than 50 Years Not Associated With Longer Duration of Symptoms or Time to Diagnosis. Clin. Gastroenterol. Hepatol. Off. Clin. Pract. J. Am. Gastroenterol. Assoc. 2017, 15, 728–737.e3. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Jones, L.A.; Ferrans, C.E.; Polite, B.N.; Brewer, K.C.; Maker, A.V.; Pauls, H.A.; Rauscher, G.H. Examining racial disparities in colon cancer clinical delay in the Colon Cancer Patterns of Care in Chicago study. Ann. Epidemiol. 2017, 27, 731–738.e1. [Google Scholar] [CrossRef] [PubMed]
  38. Pita-Fernández, S.; González-Sáez, L.; López-Calviño, B.; Seoane-Pillado, T.; Rodríguez-Camacho, E.; Pazos-Sierra, A.; González-Santamaría, P.; Pértega-Díaz, S. Effect of diagnostic delay on survival in patients with colorectal cancer: A retrospective cohort study. BMC Cancer 2016, 16, 664. [Google Scholar] [CrossRef] [Green Version]
  39. Zhang, J.-K.; Fang, L.-L.; Wu, X.-M.; Liu, J.-C.; Zhang, C.-D.; Dai, D.-Q. Factors associated with delaying medical assessment of patients and impacting the prognosis of rectal cancer. Eur. J. Cancer Prev. Off. J. Eur. Cancer Prev. Organ. ECP 2015, 24, 391–399. [Google Scholar] [CrossRef]
  40. Saluja, S.S.; Manipadam, J.M.; Mishra, P.K.; Sachdeva, S.; Solanki, N.; Shah, H. Young onset colorectal cancer: How does it differ from its older counterpart? Indian J. Cancer 2014, 51, 565–569. [Google Scholar] [CrossRef]
  41. Redaniel, M.T.; Martin, R.M.; Blazeby, J.M.; Wade, J.; Jeffreys, M. The association of time between diagnosis and major resection with poorer colorectal cancer survival: A retrospective cohort study. BMC Cancer 2014, 14, 642. [Google Scholar] [CrossRef] [Green Version]
  42. Gillis, A.; Dixon, M.; Smith, A.; Law, C.; Coburn, N.G. A patient-centred approach toward surgical wait times for colon cancer: A population-based analysis. Can. J. Surg. J. Can. Chir. 2014, 57, 94–100. [Google Scholar] [CrossRef] [Green Version]
  43. De Sousa, J.B.; Souza, C.S.; Fernandes, M.B.; Durães, L.D.C.; De Almeida, R.M.; Dos Santos, A.C.N.; Da Silva, E.F.; De Oliveira, P.G. Do young patients have different clinical presentation of colorectal cancer causing delay in diagnosis? Int. J. Color. Dis. 2014, 29, 519–527. [Google Scholar] [CrossRef]
  44. Ben-Ishay, O.; Brauner, E.; Peled, Z.; Othman, A.; Person, B.; Kluger, Y. Diagnosis of colon cancer differs in younger versus older patients despite similar complaints. Isr. Med. Assoc. J. IMAJ 2013, 15, 284–287. [Google Scholar]
  45. Esteva, M.; Leiva, A.; Ramos, M.; Pita-Fernández, S.; González-Luján, L.; Casamitjana, M.; Sánchez, M.A.; Pértega-Díaz, S.; Ruiz, A.; Gonzalez-Santamaría, P.; et al. Factors related with symptom duration until diagnosis and treatment of symptomatic colorectal cancer. BMC Cancer 2013, 13, 87. [Google Scholar] [CrossRef] [PubMed]
  46. Deng, S.X.; An, W.; Gao, J.; Yin, J.; Cai, Q.C.; Yang, M.; Hong, S.Y.; Fu, X.X.; Da Yu, E.; Xu, X.D.; et al. Factors influencing diagnosis of colorectal cancer: A hospital-based survey in China. J. Dig. Dis. 2012, 13, 517–524. [Google Scholar] [CrossRef] [PubMed]
  47. Chan, K.; Dassanayake, B.; Deen, R.; Wickramarachchi, R.; Kumarage, S.; Samita, S.; Deen, K. Young patients with colorectal cancer have poor survival in the first twenty months after operation and predictable survival in the medium and long-term: Analysis of survival and prognostic markers. World J. Surg. Oncol. 2010, 8, 82. [Google Scholar] [CrossRef] [Green Version]
  48. Tohmé, C.; Labaki, M.; Hajj, G.; Abboud, B.; Noun, R.; Sarkis, R. Colorectal cancer in young patients: Presentation, clinicopathological characteristics and outcome. J. Med. Liban. 2008, 56, 208–214. [Google Scholar] [PubMed]
  49. Porter, G.A.; Inglis, K.M.; Wood, L.A.; Veugelers, P.J. Access to care and satisfaction in colorectal cancer patients. World J. Surg. 2005, 29, 1444–1451. [Google Scholar] [CrossRef]
  50. Neal, R.D.; Allgar, V.L. Sociodemographic factors and delays in the diagnosis of six cancers: Analysis of data from the ‘National Survey of NHS Patients: Cancer’. Br. J. Cancer. 2005, 92, 1971. [Google Scholar] [CrossRef] [PubMed]
  51. Johnston, G.M.; MacGarvie, V.L.; Elliott, D.; Dewar, R.A.D.; MacIntyre, M.M.; Nolan, M.C. Radiotherapy wait times for patients with a diagnosis of invasive cancer, 1992–2000. Clin. Investig. Med. Med. Clin. Exp. 2004, 27, 142–156. [Google Scholar]
  52. Robertson, R.; Campbell, N.C.; Smith, S.; Donnan, P.T.; Sullivan, F.; Duffy, R.; Ritchie, L.D.; Millar, D.; Cassidy, J.; Munro, A. Factors influencing time from presentation to treatment of colorectal and breast cancer in urban and rural areas. Br. J. Cancer 2004, 90, 1479–1485. [Google Scholar] [CrossRef] [Green Version]
  53. Marble, K.; Banerjee, S.; Greenwald, L. Colorectal carcinoma in young patients. J. Surg. Oncol. 1992, 51, 179–182. [Google Scholar] [CrossRef]
  54. Da Silva, F.M.M.; Duarte, R.P.; Leão, C.C.A.; Vissoci, C.M.; Alvarenga, A.L.A.T.; Ramos, A.B.S.; Goulart, A.E.C. Colorectal cancer in patients under age 50: A five-year experience. Rev. Colégio Bras. Cir. 2020, 47, e20202406. [Google Scholar] [CrossRef] [PubMed]
  55. Delisle, M.; Helewa, R.M.; Ward, M.A.R.; Hochman, D.J.; Park, J.; McKay, A. The Association Between Wait Times for Colorectal Cancer Treatment and Health Care Costs: A Population-Based Analysis. Dis. Colon Rectum 2020, 63, 160–171. [Google Scholar] [CrossRef]
  56. Di Leo, M.; Zuppardo, R.A.; Puzzono, M.; Ditonno, I.; Mannucci, A.; Antoci, G.; Raucci, A.R.; Patricelli, M.G.; Elmore, U.; Tamburini, A.M.; et al. Risk factors and clinical characteristics of early-onset colorectal cancer vs. late-onset colorectal cancer: A case-case study. Eur. J. Gastroenterol. Hepatol. 2020, 33, 1153–1160. [Google Scholar] [CrossRef] [PubMed]
  57. Galadima, H.I.; Adunlin, G.; Hughes, M.S.; Cropp, C.D.; Lucero, L.; Akpinar-Elci, M. Racial disparities and treatment trends among young-onset colorectal cancer patients: An analysis of a hospital cancer registry. Cancer Epidemiol. 2021, 72, 101911. [Google Scholar] [CrossRef] [PubMed]
  58. van Erp, N.F.; Helsper, C.W.; Olyhoek, S.M.; Janssen, R.R.T.; Winsveen, A.; Peeters, P.H.M.; de Wit, N.J. Potential for Reducing Time to Referral for Colorectal Cancer Patients in Primary Care. Ann. Fam. Med. 2019, 17, 419–427. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Webber, C.; Flemming, J.A.; Birtwhistle, R.; Rosenberg, M.; Groome, P.A. Colonoscopy resource availability and its association with the colorectal cancer diagnostic interval: A population-based cross-sectional study. Eur. J. Cancer Care 2020, 29, e13187. [Google Scholar] [CrossRef]
  60. Majano, S.B.; Rachet, B.; Lyratzopoulos, G.; Renzi, C.; de Wit, N.J. Do presenting symptoms, use of pre-diagnostic endoscopy and risk of emergency cancer diagnosis vary by comorbidity burden and type in patients with colorectal cancer? Br. J. Cancer 2021, 126, 652–663. [Google Scholar] [CrossRef]
  61. Lima, M.A.N.; Villela, D.A.M. Fatores sociodemográficos e clínicos associados ao tempo para o início do tratamento de câncer de cólon e reto no Brasil, 2006–2015. Cad. Saúde Pública Online 2021, 37, e00214919. [Google Scholar] [CrossRef]
  62. Eaglehouse, Y.L.; Georg, M.W.; Shriver, C.D.; Zhu, K. Racial Comparisons in Timeliness of Colon Cancer Treatment in an Equal-Access Health System. J. Natl. Cancer Inst. 2020, 112, 410–417. [Google Scholar] [CrossRef]
  63. Bergin, R.J.; Thomas, R.J.S.; Whitfield, K.; White, V. Concordance between Optimal Care Pathways and colorectal cancer care: Identifying opportunities to improve quality and reduce disparities. J. Eval. Clin. Pract. 2020, 26, 918–926. [Google Scholar] [CrossRef]
  64. Neal, R.D. Do diagnostic delays in cancer matter? Br. J. Cancer 2009, 101 (Suppl 2), S9–S12. [Google Scholar] [CrossRef]
  65. Kaltenmeier, C.; Shen, C.; Medich, D.S.; Geller, D.A.; Bartlett, D.L.; Tsung, A.; Tohme, S. Time to Surgery and Colon Cancer Survival in the United States. Ann. Surg. 2021, 274, 1025–1031. [Google Scholar] [CrossRef] [PubMed]
  66. Neal, R.D.; Tharmanathan, P.; France, B.; Din, N.U.; Cotton, S.; Fallon-Ferguson, J.; Hamilton, W.; Hendry, A.; Hendry, M.; Lewis, R.; et al. Is increased time to diagnosis and treatment in symptomatic cancer associated with poorer outcomes? Systematic review. Br. J. Cancer 2015, 112 (Suppl 1), S92–S107. [Google Scholar] [CrossRef] [PubMed]
  67. Bleicher, R.J. Timing and Delays in Breast Cancer Evaluation and Treatment. Ann. Surg. Oncol. 2018, 25, 2829–2838. [Google Scholar] [CrossRef] [PubMed]
  68. Connell, L.C.; Mota, J.M.; Braghiroli, M.I.; Hoff, P.M. The Rising Incidence of Younger Patients With Colorectal Cancer: Questions About Screening, Biology, and Treatment. Curr. Treat. Options Oncol. 2017, 18, 23. [Google Scholar] [CrossRef]
  69. Walter, F.; Webster, A.; Scott, S.; Emery, J. The Andersen Model of Total Patient Delay: A systematic review of its application in cancer diagnosis. J. Health Serv. Res. Policy 2012, 17, 110–118. [Google Scholar] [CrossRef] [Green Version]
  70. Bartlett, V.L.; Dhruva, S.S.; Shah, N.D.; Ryan, P.; Ross, J.S. Feasibility of Using Real-World Data to Replicate Clinical Trial Evidence. JAMA Netw. Open 2019, 2, e1912869. [Google Scholar] [CrossRef]
Curroncol 29 00679 g001 550
Figure 1. Preferred Reporting Items for Systematic Review and Meta-analysis flow diagram of included studies. Values indicate the number of studies meeting that criteria.
Figure 1. Preferred Reporting Items for Systematic Review and Meta-analysis flow diagram of included studies. Values indicate the number of studies meeting that criteria.
Curroncol 29 00679 g001
Curroncol 29 00679 g002 550
Figure 2. Reported comparisons of delay intervals between younger and older adults with colorectal cancer, grouped by interval on the pathway to treatment. Shown is the number of studies in each interval, and the sample size of younger and older adults. Studies that did not report sample sizes for both younger and older are not included in the number of patients. *Indicates study found significantly longer delay among younger adults with colorectal cancer and ^indicates study found significantly shorter delay among younger adults.
Figure 2. Reported comparisons of delay intervals between younger and older adults with colorectal cancer, grouped by interval on the pathway to treatment. Shown is the number of studies in each interval, and the sample size of younger and older adults. Studies that did not report sample sizes for both younger and older are not included in the number of patients. *Indicates study found significantly longer delay among younger adults with colorectal cancer and ^indicates study found significantly shorter delay among younger adults.
Curroncol 29 00679 g002
Curroncol 29 00679 g003 550
Figure 3. Random effects meta-analysis of advanced stage at diagnosis by age category. Subgroup analyses were performed by type of cancer studied. Advanced stage was defined as Stage III/IV and early stage was defined as Stage I/II. Squares indicate individual study effect sizes with arrows indicating confidence intervals. Black diamonds indicate pooled effect sizes and confidence intervals [12,29,31,32,36,40,43,47,48,53,54,57].
Figure 3. Random effects meta-analysis of advanced stage at diagnosis by age category. Subgroup analyses were performed by type of cancer studied. Advanced stage was defined as Stage III/IV and early stage was defined as Stage I/II. Squares indicate individual study effect sizes with arrows indicating confidence intervals. Black diamonds indicate pooled effect sizes and confidence intervals [12,29,31,32,36,40,43,47,48,53,54,57].
Curroncol 29 00679 g003
Table
Table 1. Characteristics of included studies (n = 39) [12,13,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61].
Table 1. Characteristics of included studies (n = 39) [12,13,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61].
StudyCharacteristic
Definition of YoungNo. YoungerNo. OlderCountryStudy TypeData SourceYears of StudyNumber of Sites
Colon and Rectal Cancer
Lima 2021 [61]<5014,67564,472BrazilRetrospective cohortCancer registry/health administrative data2006–2015Population-based
Majano 2021 [60]<451314705UKRetrospective cohortCancer registry/health administrative data2011–2015Population-based
Galadima 2021 [57]<505223983USARetrospective cohortCancer registry/health administrative data2008–2016Population-based
Delisle 2020 [55]<505196417CanadaRetrospective cohortCancer registry/health administrative data2004–2014Population-based
Di Leo 2020 [56]<5054494ItalyRetrospective cohortPrimary data collection2015–20181
Da Silva 2020 [54]<5039145BrazilRetrospective cohortPrimary data collection2013–20181
Webber 2020 [59]<50190222,059CanadaRetrospective cohortCancer registry/health administrative data2008–2012Population-based
Van Erp 2019 [58]<5035274NetherlandsRetrospective cohortCancer registry/health administrative data2007–2011Population-based
Roder 2019 [30]<50911584AustraliaRetrospective cohortCancer registry/health administrative data2000–20104
Arhi 2019 [31]<505086807UKRetrospective cohortCancer registry/health administrative data2006–2013Population-based
Kaplan 2019 [32]20–25141237TurkeyRetrospective cohortPrimary data collection2003–201520
Pearson 2019 [27]<50388660,072UKRetrospective cohortCancer registry/health administrative data2014–2015Population-based
Windner 2018 [33]<504155New ZealandSurvey studyPrimary data collection--
Girolamo 2018 [13]15–443542112,575UKRetrospective cohortCancer registry/health administrative data2009–2013Population-based
Gabriel 2017 [34]<50155,0901,058,102USARetrospective cohortCancer registry/health administrative data1998–2011Population-based
Sikdar 2017 [35]<508228804CanadaRetrospective cohortCancer registry/health administrative data2004–2010Population-based
Chen 2017 [36]<50253232USARetrospective cohortPrimary data collection2008–20141
Kim 2016 [12]≤456931823Republic of KoreaRetrospective cohortPrimary data collection2006–20111
Pita-Fernandez2016 [38]<50-942 younger and olderSpainRetrospective cohortPrimary data collection1994–20001
Zhu 2015 [29]<30834911ChinaRetrospective cohortPrimary data collection1995–20131
Saluja 2014 [40]<4066100IndiaRetrospective cohortPrimary data collection2003–20121
Redaniel 2014 [41]15–4492145,590UKRetrospective cohortCancer registry/health administrative data1996–2009Population-based
de Sousa 2014 [43]<5066149BrazilRetrospective cohortPrimary data collection2006–20101
Esteva 2013 [45]<5045732SpainCross-sectional studyPrimary data collection2006–20085 regions in Spain
Deng 2012 [46]<5075232ChinaProspective cohortPrimary data collection2008–20091
Chan 2010 [47]<405347Sri LankaRetrospective cohortPrimary data collection1996–20081
Tohme 2008 [48]<4543282LebanonRetrospective cohortPrimary data collection1995–20051
Porter 2005 [49]<50-110 younger and olderCanadaProspective cohortPrimary data collection20011
Neal 2005 [50]<45-13,244 younger and olderUKSurvey studyPrimary data collection2002Population-based
Johnston 2004 [51]25-5095503CanadaRetrospective cohortCancer registry/health administrative data1992–2000Population-based
Robertson 2004 [52]<50531018UKRetrospective cohortCancer registry/health administrative data1997–1998Population-based
Marble 1992 [53]<405050USARetrospective cohortPrimary data collection1935–19881
Colon cancer
Flemming 2017 [28]<502464080CanadaRetrospective cohortCancer registry/health administrative data and primary data collection2002–2008Population-based
Wanis 2017 [26]<5047861CanadaRetrospective cohortPrimary data collection2006–20151
Jones 2017 [37]<5074312USAProspective cohortPrimary data collection2010–20139
Gillis 2014 [42]<506959528CanadaProspective cohortCancer registry/health administrative data2002–2008Population-based
Ben-Ishay 2013 [44]<5031205IsraelRetrospective cohortPrimary data collection2000–20091
Rectal cancer
Scott 2016 [25]<505656USACase controlPrimary data collection1997–20071
Zhang 2015 [39]<5067566ChinaProspective cohortPrimary data collection2008–20091
Table
Table 2. Comparison of outcomes between older and younger colorectal cancer patients. Shown are delay measures (all intervals), stage at presentation, overall survival, and disease-free survival/recurrence.
Table 2. Comparison of outcomes between older and younger colorectal cancer patients. Shown are delay measures (all intervals), stage at presentation, overall survival, and disease-free survival/recurrence.
FindingStudies
Delay measures
Longer delays among younger patients (N = 14)Webber 2020, Delisle 2020, Di Leo 2020, Windner 2018, Chen 2017, Jones 2017, Kim 2016, Scott 2016, Ben-Ishay 2013, Robertson 2004, Arhi 2019, Deng 2012, Tohme 2008, Marble 1992
No significant differences (N = 14)Majano 2021, Galadima 2021, Da Silva 2020, Van Erp 2019, Roder 2019, Kaplan 2019, Wanis 2017, Pita-Fernandez 2016, Zhu 2015, Saluja 2014, Esteva 2013, Chan 2010, Porter 2005, Johnston 2004
Mixed findings (N = 2)Neal 2005, Sikdar 2017
Shorter delays among younger patients (N = 9)Girolamo 2018, Gabriel 2017, Lima 2021, Pearson 2019, Flemming 2017, Zhang 2015, Redaniel 2014, Gillis 2014, de Sousa 2014
Stage at presentation
Worse stage at presentation among younger patients (N = 10)Galadima 2021, Da Silva 2020, Arhi 2019, Kaplan 2019, Gabriel 2017, Chen 2017, Kim 2016, Zhu 2015, Marble 1992, Pita-Fernandez 2016
No significant difference (N = 7)Pita-Fernandez 2016, Scott 2016, Saluja 2014, de Sousa 2014, Ben-Ishay 2013, Chan 2010, Tohme 2008
Survival
Worse survival among younger patients (N = 3)Kim 2016, Marble 1992, Kaplan 2019
No significant difference (N = 7)Da Silva 2020, Kaplan 2019, Scott 2016, Saluja 2014, de Sousa 2014, Ben-Ishay 2013, Tohme 2008
Better survival among younger patients (N = 6)Delisle 2020, Girolamo 2018, Gabriel 2017, Flemming 2017, Wanis 2017, Redaniel 2014
Recurrence and disease-free survival
Worse recurrence among younger patients (N = 2)Kaplan 2019, Kim 2016
No significant difference (N = 3)Da Silva 2020, de Sousa 2014, Tohme 2008
Table
Table 3. Associations between delay intervals and stage at presentation and survival between younger and older adults with colorectal cancer.
Table 3. Associations between delay intervals and stage at presentation and survival between younger and older adults with colorectal cancer.
StudyOutcomeFinding in Younger Adults *Finding in Older Adults *
Stage
Kim 2016 [12]Advanced stage
(Stage III/IV)		Longer delay associated with higher odds of advanced stage
Symptom onset to diagnosis
<1 month: Reference
1–3 months: OR 3.01 (1.77–5.12)
>3 months: OR 6.33 (3.05–13.12) 		Delay not associated with advanced stage
Symptom onset to diagnosis
<1 month: Reference
1–3 months: OR 1.28 (0.81–2.02)
>3 months: OR 1.46 (0.81–2.62)
Chen 2017 [36]Advanced stage
(Stage III/IV)		Advanced stage associated with shorter delays
Symptom onset to presentation
Stage III/IV vs. Stage I/II: median difference—30 days
Presentation to diagnosis
Stage III/IV vs. Stage I/II: median difference —0 days
Symptom onset to diagnosis
Stage III/IV vs. Stage I/II: median difference—40 days		Mixed findings
Symptom onset to presentation
Stage III/IV vs. Stage I/II: median difference +9 days
Presentation to diagnosis
Stage III/IV vs. Stage I/II: median difference—14 days
Symptom onset to diagnosis
Stage III/IV vs. Stage I/II: median difference—5 days
Girolamo 2018 [13] **Advanced stage
(Stage III/IV)		Delay not associated with advanced stage
Referral to specialist consultation
>2 weeks: OR 1.43 (0.65–3.52)
Decision to treat to treatment
>31 days: OR 0.76 (0.43–1.39)
Referral to treatment
>62 days: OR 1.03 (0.68–1.57)		Longed delay associated with lower odds of advanced stage for 2 intervals
Referral to specialist consultation
>2 weeks: OR 0.84 (0.76–0.92)
Decision to treat to treatment
>31 days: OR 0.66 (0.61–0.72)
Referral to treatment
>62 days: OR 0.99 (0.95–1.04)
Survival
Kim 2016 [12]Overall
survival		Longer delay associated with worse survival in adjusted analysis only
Symptom onset to diagnosis, unadjusted
<1 month: Reference
1–3 months: HR 1.41 (0.86–2.31)
>3 months: HR 1.69 (0.99–2.91)
Symptom onset to diagnosis, adjusted for sex and tumor differentiation
<1 month: Reference1–3 months: HR 1.62 (0.95–2.76)
>3 months: HR 2.57 (1.34–4.94)		Delay not associated with survival
Symptom onset to diagnosis, unadjusted
<1 month: Reference
1–3 months: HR 1.05 (0.59–1.87)
>3 months: HR 0.75 (0.41–1.35)
Girolamo 2018 [13]1-year overall survivalLonger delay associated with improved survival
Referral to specialist consultation
<2 weeks vs. >2 weeks
88.9% (86.6–91.2) vs. 90.1% (80.9–99.3)
Decision to treat to treatment
<31 days vs. >31 days
89.8% (88.8–90.8) vs. 94.8% (89.1–100.0)
Referral to treatment
<62 days vs. >62 days
90.7% (88.3–93.0) vs. 94.0% (90.1–97.9)		Longer delay associated with improved survival
Referral to specialist consultation
<2 weeks vs. >2 weeks
Age 45–54: 89.0% (87.9–90.1) vs. 89.4% (84.9–93.9)
Age 55–64: 86.0% (85.3–86.8) vs. 88.5% (85.5–91.5)
Age 65–74: 83.1% (82.4–83.7) vs. 86.5% (83.9–89.2)
Age 75+: 76.9% (76.3–77.6) vs. 79.7% (76.9–82.4)
Decision to treat to treatment
<31 days vs. >31 days
Age 45–54: 89.0% (87.9–90.1) vs. 91.0% (90.4–91.6)
Age 55–64: 86.0% (85.3–86.8) vs. 91.9% (91.6–92.3)
Age 65–74: 83.1% (82.4–83.7) vs. 91.0% (90.7–91.3)
Age 75+: 76.9% (76.3–77.6) vs. 85.7% (85.3–86.1)
Referral to treatment
<62 days vs. >62 days
Age 45–54: 91.6% (90.5–92.7) vs. 93.9% (92.1–95.8)
Age 55–64: 90.3% (89.6–91.1) vs. 91.5% (90.2–92.8)
Age 65–74: 89.4% (88.8–90.1) vs. 89.7% (88.7–90.8)
Age 75+: 85.4% (84.7–86.1) vs. 90.9% (89.9–91.8)
* Presented are effect estimates and 95% confidence intervals. Analyses are unadjusted unless otherwise specified. ** Values for older age groups were combined to calculate a single effect for older adults; OR—odds ratio, HR—hazard ratio.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Castelo, M.; Sue-Chue-Lam, C.; Paszat, L.; Scheer, A.S.; Hansen, B.E.; Kishibe, T.; Baxter, N.N. Clinical Delays and Comparative Outcomes in Younger and Older Adults with Colorectal Cancer: A Systematic Review. Curr. Oncol. 2022, 29, 8609-8625. https://doi.org/10.3390/curroncol29110679

AMA Style

Castelo M, Sue-Chue-Lam C, Paszat L, Scheer AS, Hansen BE, Kishibe T, Baxter NN. Clinical Delays and Comparative Outcomes in Younger and Older Adults with Colorectal Cancer: A Systematic Review. Current Oncology. 2022; 29(11):8609-8625. https://doi.org/10.3390/curroncol29110679

Chicago/Turabian Style

Castelo, Matthew, Colin Sue-Chue-Lam, Lawrence Paszat, Adena S. Scheer, Bettina E. Hansen, Teruko Kishibe, and Nancy N. Baxter. 2022. "Clinical Delays and Comparative Outcomes in Younger and Older Adults with Colorectal Cancer: A Systematic Review" Current Oncology 29, no. 11: 8609-8625. https://doi.org/10.3390/curroncol29110679

APA Style

Castelo, M., Sue-Chue-Lam, C., Paszat, L., Scheer, A. S., Hansen, B. E., Kishibe, T., & Baxter, N. N. (2022). Clinical Delays and Comparative Outcomes in Younger and Older Adults with Colorectal Cancer: A Systematic Review. Current Oncology, 29(11), 8609-8625. https://doi.org/10.3390/curroncol29110679

Article Metrics

Back to TopTop