Population-based monitoring of cancer patient survival in situations with imperfect completeness of cancer registration

Abstract

Selective underascertainment of cases may bias estimates of cancer patient survival. We show that the magnitude of potential bias strongly depends on the time periods affected by underascertainment and on the type of survival analysis (cohort analysis vs period analysis). We outline strategies on how to minimise or overcome potential biases. Population-based monitoring of cancer patient survival is an important task of cancer registries (e.g. Berrino et al, 1995Berrino et al, , 1999Berrino et al, , 2003Dickman et al, 1999;Talbäck et al, 2003). As with other cancer statistics, the validity of population-based cancer survival estimates depends on the quality of the cancer registry data. Most obviously, a minimum requirement is reliable follow-up of patients with respect to vital status. The validity of survival estimates may also depend on completeness of cancer registration (Monnet et al, 1998;Prior et al, 1998). In particular, selective underascertainment of patients with a good prognosis may lead to underestimation of cancer patient survival, whereas an opposite effect could result from selective underascertainment of patients with poor prognosis. The aim of this paper is to assess the impact of various patterns of incompleteness of cancer registration on population-based estimates of cancer patient survival in a quantitative manner. MATERIAL AND METHODS DatabaseOur analysis is based on data from the nationwide Finnish Cancer Registry whose true completeness (in terms of ascertainment of both incident cases and follow-up status) is known to be very close to 100% (Teppo et al, 1994). We included patients, aged 15 years or older, with a first diagnosis of one of the six most common forms of cancer in Finland between 1990 and 1999. Statistical analysisThe impact of underascertainment of incident cases was assessed for 5-year relative survival rates (Ederer et al, 1961), which were derived using Hakulinen's (1982) method by two different approaches illustrated in Figure 1. With the first approach, 5-year survival rates were calculated for the cohort of patients diagnosed in 1990 -1994 and followed with respect to vital status until the end of 1999 (solid frame). The second approach is the so-called period analysis, which has first been proposed a few years ago to provide more up-to-date estimates of cancer patient survival Gefeller 1996, 1997). Here, 5-year relative survival estimates for the 1995 -1999 period are reported, which exclusively reflect the survival experience of patients during those years (dashed frame).To assess the impact of incompleteness of registration either in the earlier or in the more recent years of the database, we carried out both a cohort analysis for the 1990 -1994 cohort and a period analysis for the 1995 -1999 period, assuming underascertainment of the following cases either in 1990 -1994 or in 1995 -1999 in different scenarios: (a) all cases, (b) only cases dying within 5 years following diagnosis and (c) only cases still alive 5 years following diagnosis.Expected survival estimate...

Determinants and interpretation of death certificate only proportions in the initial years of newly established cancer registries

“…Their validity strongly depends on high-quality and completeness of incidence and follow-up data of population-based cancer registries. [1][2][3][4] A commonly used data quality indicator is the proportion of cases notified by death certificate only (DCO). 2,5,6 Such cases may arise from various sources, including incomplete registration during lifetime of patients dying from cancer.…”

Cancer survival analysis in Hamburg 1995–2003: Assessing the data quality within a population-based registry

“…According to an active trace back study in 2005, the vital status has been documented accurately in more than 95% of the cases, and the current migration ascertainment of registered patients will improve this aspect even more. With regard to the influence of vital status misclassification on survival estimates, a magnitude of 5% at the most might result in a deviation of approximately 1% in 5-year relative survival ratios [15]. A major proportion of histologically verified diagnoses and declining trends of DCO cases since the mid-nineties indicate a substantial and increasing degree of registration.…”

“…With regard to an exemplary and explorative analysis, four cancer sites and an appropriate period of diagnosis were selected according to the following criteria: Entities were considered among the ten most frequent malignancies in Hamburg, with at least 100 notified cases per sex and year, representing gender-specific occurrence vs. similar manifestation in both sexes and diverse degrees of completeness. A maximum allowance of 20% cases annually documented as diagnosed at death (DCO-notifications and clinical reports omitting date of diagnosis) was set to restrict the possible bias of survival estimates to a tolerable level [15].…”

Implications of incomplete registration of deaths on long‐term survival estimates from population‐based cancer registries

“…[1][2][3][4] Important prerequisites for obtaining valid survival estimates from population-based cancer registries are high quality and completeness of both incidence and mortality follow-up data. 5 Several articles have addressed the potential impact on cancer survival estimates of less than perfect completeness of incidence data, [6][7][8] but systematic quantitative investigation of less than perfect registration of deaths of cancer patients is sparse even though this very important issue has repeatedly come up in the interpretation of population-based cancer survival data. 9,10 The aim of this study was to assess and quantify the implications of incomplete registration of deaths on long-term survival estimates from population-based cancer registries.…”