Page 168 - Contributed Paper Session (CPS) - Volume 3

CPS1982 Dmitri J. et al.
correction of old age mortality does not solve all problems (Jdanov et al.,
2008).
Evaluation of data quality at old ages was extensively discussed elsewhere
(Jdanov et al., 2008; Kannisto, 1994). The standard set of methods includes
tests on age overstatement (e.g. the ratio of the total person-years lived above
age 100 to the total person-years lived above age 80), precision of age
reporting with the UN age-sex accuracy index, age heaping with the Whipple's
Index of age accuracy. The comparison to other countries with reliable
statistics may be also used for evaluation of overall quality of mortality
estimates.

8. Conclusion
There is no perfect data in the world, but it is enough to have high quality
data. Data are of high quality if they are “Fit for Use” in their intended
operational, decision-making and other roles (Juran and Godfrey, 1999). This
is why the understanding of problems hidden in the data is important in any
demographic estimation, forecast or study. We discussed several approaches
which allow us to increase significantly utility of the data even if data quality
is problematic. Unfortunately, standard demographic methods which work
well with data from developing countries or historical data series are often not
applicable to problematic data from countries with functioning modern
statistical systems. Such data lead to new challenges and new problems. To
solve these problems more laborious approaches in combination with usage
of additional and alternative data sources are needed. Country-specific
approach should be combined with certain general principles that are applied
in all countries to ensure comparability of data series across time and space.

References
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D.A., Boe, C., Riffe, T., Grigoriev, P., Winant, C., 2015. Data Resource
Profile: The Human Mortality Database (HMD). Int. J. Epidemiol. 44,
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