Time to diagnosis among children and adolescents with cancer in Quebec, Canada: a population-based study

This population-based study of nearly 3,000 children and adolescents in Quebec reveals that time to cancer diagnosis varies significantly by cancer type, age, and geographic location, with the longest delays for carcinomas and shortest for leukemias, while also noting that diagnostic intervals shortened for several cancer types in the post-pandemic period.

The Gist

Imagine the journey to a cancer diagnosis as a race to the finish line. The sooner a child crosses that line, the better their chances of winning the race against the disease. However, this race isn't run on a single, flat track; it's more like a maze where some paths are short and straight, while others are long, winding, and full of dead ends.

This study from Quebec, Canada, acted like a giant mapmaker. The researchers looked at nearly 3,000 children and teenagers (ages 0–19) who were diagnosed with cancer between 2010 and 2022. Their goal was to measure exactly how long it took for these young patients to go from their very first doctor's visit about a symptom to getting the official "it's cancer" diagnosis.

Here is what their map revealed, broken down into simple concepts:

1. The Shape of the Maze Depends on the Enemy

Not all cancers are the same, and the time it takes to find them varies wildly, much like different types of vehicles traveling through a city.

• The Speedsters (Leukemia): Finding leukemia was like spotting a bright red fire truck in a clear sky. It happened very fast, with a median time of just 8 days. The symptoms are often so obvious that the path to the finish line is short and direct.
• The Sneaky Hikers (Carcinomas): Finding carcinomas was like trying to find a specific hiker in a dense, foggy forest. It took much longer, with a median time of 100 days. These cancers can hide or mimic other minor illnesses, making the path to diagnosis long and confusing.

2. Geography Matters: The "City vs. Country" Detour

Where you live can change the length of your race.

• If a child lived in Montreal (the big city hub), the path was generally smoother.
• If a child lived in regional areas or other large cities outside Montreal, the path was often longer, especially for brain (CNS) and liver tumors. It's as if living in the countryside meant having to take a scenic, bumpy backroad to get to the specialist, whereas city dwellers had a direct highway.

3. The Pandemic Paradox: A Strange Shortcut

You might think the pandemic (2020–2022) would have made everything slower, like a traffic jam that never ends. Surprisingly, the study found the opposite for some cancers.

• After the initial chaos of the pandemic, diagnostic times actually got shorter for several types of cancer, especially brain tumors.
• The Analogy: Think of it like a school that was closed for a while. When it reopened, the principal realized, "We need to be super efficient!" So, they cleared the hallways, set up express lanes, and stopped wasting time on unnecessary meetings. The healthcare system likely streamlined its processes to catch up, creating "express lanes" for urgent cases that didn't exist before.

4. What Didn't Change the Race?

The researchers checked if things like a child's gender, how much money their family had, or their social status affected how long the wait was.

• The Result: Surprisingly, these factors didn't change the length of the race. Whether a child was rich or poor, a boy or a girl, the time to diagnosis was roughly the same. The main factors were simply what kind of cancer it was, how old the child was, and where they lived.

The Bottom Line

This study tells us that while we can't always control the "terrain" of the cancer (some are just harder to find), we can look at the "roads" we build. The fact that diagnosis times improved after the pandemic suggests that if we keep those efficient pathways open, we can help more children cross the finish line faster, giving them a better chance at a healthy future.

Technical Summary

1. Problem Statement

Timely diagnosis is a critical determinant of survival and treatment outcomes for pediatric and adolescent cancer. Despite its importance, significant disparities exist in diagnostic intervals (the time from symptom onset/first healthcare contact to definitive diagnosis) across different cancer types and healthcare settings. The study addresses the lack of comprehensive, population-level data quantifying these delays in Quebec, Canada, and seeks to identify specific patient, demographic, and system-level factors contributing to these variations.

2. Methodology

The researchers employed a retrospective population-based study design utilizing administrative health data from Quebec.

• Study Population: 2,927 children and adolescents (aged 0–19 years) diagnosed with one of 12 common pediatric cancers.
• Timeframe: Diagnosis dates ranged from 2010 to 2022.
• Primary Metric: The diagnostic interval, defined as the duration between the first cancer-related healthcare encounter and the confirmed diagnosis.
• Statistical Analysis:
  • Descriptive statistics (medians and interquartile ranges [IQR]) were calculated for overall intervals and stratified by cancer type.
  • Multivariable quantile regression was utilized to model the relationship between various factors and the diagnostic interval at the 25th, 50th (median), and 75th percentiles. This approach allowed the authors to assess how predictors affected the entire distribution of diagnostic times, rather than just the average.
• Variables Analyzed: Factors included cancer type, age, sex, geographic location (Montreal vs. regional areas vs. other large urban centers), material/social deprivation, and time period (pre- vs. post-pandemic).

3. Key Contributions

• Granular Quantification: The study provides the first comprehensive quantification of diagnostic delays across 12 specific cancer types within the Quebec pediatric population over a 12-year span.
• Advanced Statistical Modeling: By using quantile regression, the study moves beyond mean-based analyses to reveal how specific factors (like geography) disproportionately affect patients with the longest delays (75th percentile), offering a more nuanced view of health equity.
• Pandemic Impact Assessment: It uniquely isolates the effect of the COVID-19 pandemic (2020–2022) on diagnostic pathways, challenging the assumption that the pandemic universally delayed cancer diagnoses.

4. Key Results

• Variation by Cancer Type: Diagnostic intervals varied drastically depending on the malignancy:
  • Shortest: Leukemias (Median: 8 days; IQR 3–44).
  • Longest: Carcinomas (Median: 100 days; IQR 33–192).
• Geographic Disparities:
  • Children living in regional areas and other large urban centers (outside Montreal) experienced significantly longer diagnostic intervals compared to those in Montreal.
  • This disparity was most pronounced for hepatic and central nervous system (CNS) tumors, particularly at the 50th and 75th percentiles.
• Demographic Factors:
  • No significant association was found between diagnostic time and sex or material/social deprivation indices.
  • Age was a significant factor in interval variation.
• Temporal Trends (Post-Pandemic):
  • Contrary to expectations of delay, diagnostic intervals were shorter in the post-pandemic period (2020–2022) across several cancer sites.
  • CNS tumors showed a statistically significant reduction in diagnostic time across all quantiles (25th, 50th, and 75th) during this period.

5. Significance and Interpretation

The study highlights that while systemic delays are not uniform across all demographics (e.g., no disparity based on socioeconomic status), geographic location remains a critical barrier to timely care for specific high-risk tumors like CNS and hepatic cancers.

The finding of shorter diagnostic intervals during the post-pandemic period is particularly notable. The authors suggest that pandemic-related disruptions may have inadvertently streamlined care pathways, increased the urgency of referrals, or altered triage protocols, leading to expedited diagnoses for certain cancers. This challenges the narrative that the pandemic solely negatively impacted cancer care timelines and suggests that specific system-level adaptations can improve diagnostic efficiency.

These findings provide a evidence base for policymakers to target geographic inequities in pediatric oncology and to investigate which pandemic-era workflow changes can be institutionalized to maintain faster diagnostic timelines.