Factors associated with mode of colorectal cancer detection and time to diagnosis: a population level study

Abstract Background Although it is well-known that early detection of colorectal cancer (CRC) is important for optimal patient survival, the relationship of patient and health system factors with delayed diagnosis are unclear. The purpose of this study was to identify the demographic, clinical and healthcare factors related to mode of CRC detection and length of the diagnostic interval. Methods All residents of Alberta, Canada diagnosed with first-ever incident CRC in years 2004–2010 were identified from the Alberta Cancer Registry. Population-based administrative health datasets, including hospital discharge abstract, ambulatory care classification system and physician billing data, were used to identify healthcare services related to CRC diagnosis. The time to diagnosis was defined as the time from the first CRC-related healthcare visit to the date of CRC diagnosis. Mode of CRC detection was classified into three groups: urgent, screen-detected and symptomatic. Quantile regression was performed to assess factors associated with time to diagnosis. Results 9626 patients were included in the study; 25% of patients presented as urgent, 32% were screen-detected and 43% were symptomatic. The median time to diagnosis for urgent, screen-detected and symptomatic patients were 6 days (interquartile range (IQR) 2–14 days), 74 days (IQR 36–183 days), 84 days (IQR 39–223 days), respectively. Time to diagnosis was greater than 6 months for 27% of non-urgent patients. Healthcare factors had the largest impact on time to diagnosis: 3 or more visits to a GP increased the median by 140 days whereas 2 or more visits to a GI-specialist increased it by 108 days compared to 0–1 visits to a GP or GI-specialist, respectively. Conclusion A large proportion of CRC patients required urgent work-up or had to wait more than 6 months for diagnosis. Actions are needed to reduce the frequency of urgent presentation as well as improve the timeliness of diagnosis. Findings suggest a need to improve coordination of care across multiple providers

Sample Size Calculation in Clinical Studies: Some Common Scenarios

Determining the optimal sample size is crucial for any scientific investigation. An optimal sample size provides adequate power to detect statistical significant difference between the comparison groups in a study and allows the researcher to control for the risk of reporting a false-negative finding (Type II error). A study with too large a sample is harder to conduct, expensive, time consuming and may expose an unnecessarily large number of subjects to potentially harmful or futile interventions. On the other hand, if the sample size is too small, a best conducted study may fail to answer a research question due to lack of sufficient power. To draw a valid and accurate conclusion, an appropriate sample size must be determined prior to start of any study. This paper covers the essentials in calculating sample size for some common study designs. Formulae along with some worked examples were demonstrated for potential applied health researchers. Although maximum power is desirable, this is not always possible given the resources available for a study. Researchers often needs to choose a sample size that makes a balance between what is desirable and what is feasible

Incidence of Pregnancy-Associated Cancer in Two Canadian Provinces: A Population-Based Study

Pregnancy-associated cancer—that is diagnosed in pregnancy or within 365 days after delivery—is increasingly common as cancer therapy evolves and survivorship increases. This study assessed the incidence and temporal trends of pregnancy-associated cancer in Alberta and Ontario—together accounting for 50% of Canada’s entire population. Linked data from the two provincial cancer registries and health administrative data were used to ascertain new diagnoses of cancer, livebirths, stillbirths and induced abortions among women aged 18–50 years, from 2003 to 2015. The annual crude incidence rate (IR) was calculated as the number of women with a pregnancy-associated cancer per 100,000 deliveries. A nonparametric test for trend assessed for any temporal trends. In Alberta, the crude IR of pregnancy-associated cancer was 156.2 per 100,000 deliveries (95% CI 145.8–166.7), and in Ontario, the IR was 149.4 per 100,000 deliveries (95% CI 143.3–155.4). While no statistically significant temporal trend in the IR of pregnancy-associated cancer was seen in Alberta, there was a rise in Ontario (p = 0.01). Pregnancy-associated cancer is common enough to warrant more detailed research on maternal, pregnancy and child outcomes, especially as cancer therapies continue to evolve.Medicine, Faculty ofNon UBCObstetrics and Gynaecology, Department ofReviewedFacult