Evaluation of Survival of Cancer Patients Based on
Registry Data From Low or Medium Resource Countries 

Acta Universitatis Tamperensis No. 1724

By Swaminathan Rajaraman
July 2012
Tampere University Press
ISBN: 9789514487859
155 pages
$82.50 Paper original

Cancer is a growing global health issue and many low or medium resource countries are ill-prepared to deal with the ever-increasing cancer burden owing to lack of well-developed surveillance systems. This needs an inter-disciplinary approach through international collaborations between low, middle and high income countries. Systematic reporting of cancer incidence and to some extent, cancer mortality, has been done periodically for many decades now. Unlike in well-developed countries, cancer survival however, is not routinely reported from low or medium resource countries. It required special and concerted efforts from multiple quarters to get reliable survival statistics.

Cancer survival generally refers to the lifetime of a person after the diagnosis. Population-based cancer survival data are essential for evaluating the development and distribution of and accessibility to cancer health services like treatment or screening. Since data from low or medium resource countries are beginning to surface in intermittent intervals, so have comparisons between well-developed and less-developed countries. This dissertation provides a stepwise methodological evaluation right from the conduct of survival study to the estimation of survival probability through empirical data from more than 25 registries in several low or medium resource countries with variable gross national income values. This is inevitable for a balanced interpretation of survival differences. The main material for study came from the SURVCAN database of the multinational study by the International Agency for Research on Cancer, Lyon, France, and is supplemented by several materials from India and Thailand.

The impact of variation in patient follow-up on survival statistics is undisputed. It could be due to inappropriate methods employed for getting vital status information: lack of active methods of follow up in the presence of sub-optimal mortality ascertainment or high magnitude of loss to follow up by ineffective active follow up. In both instances, it is shown by empirical data that application of standard methodology results in systematic bias in the estimate of survival. If the losses are high and result in non-random censoring due to correlation with outcome, say death, it is a clear indicator to improve the follow up by vigorous active methods and to deviate from standard life table estimation of survival and resort to estimation of survival by differential loss-adjustment procedures explained through its determinants. The magnitude of bias varied between 1-4 percent units for population-based 5-year absolute survival and was larger between 2-7 percent units even for 3-year overall survival for hospital-based studies, for different cancers.

In a registry data environment that warranted the employment of active methods of follow up and the real losses to follow up did not exceed one in five cases, the bias induced in actuarial survival under different assumptions of vital status of cases due to inappropriate choice of follow up methods revealed the following: if only passive methods were employed, say for convenience or out of constraints, without any active follow up component, the bias induced in 5-year absolute survival estimates varied between 22-47 percent units for different cancers; when predominantly passive methods of follow up were employed with necessary active component, the bias ranged between 3-10 percent units; when follow up methods were totally by active methods but losses to follow up cases were excluded from analysis, the bias induced varied between 2-8 percent units for different cancers. This provides an objective index of bias resulting in over-estimation or under-estimation of survival in a low or medium resource country setting.

In these circumstances, age-standardized survival rates might adjust for the potential confounders and survival data by important prognostic factors like extent of disease may still appear plausible or consistent. But a systematic evaluation of bias in estimating survival due to methodological problems and its suitable correction are mandatory before survival differences could be attributed to the varied development of treatment resources and/or disease characteristics in low or medium resources settings.

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