Variance models of the last age interval and their impact on life expectancy at subnational scales

Lo, Ernest; Vatnik, Dan; Benedetti, Andrea; Bourbeau, Robert

doi:10.4054/demres.2016.35.15

Cited by 11 publications

(9 citation statements)

References 33 publications

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“…Confidence intervals for the derived life expectancies were calculated using the Chiang II approach as advocated by Eayres & Williams [ 29 ]. This involves adding a correction term to the original Chiang variance to account for the under-estimation of the ‘true’ variance at the last age interval by assuming variance is zero rather than basing the estimate on length of survival [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

Immortal time bias for life-long conditions in retrospective observational studies using electronic health records

Tyrer

Bhaskaran

Rutherford

2022

BMC Med Res Methodol

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Background Immortal time bias is common in observational studies but is typically described for pharmacoepidemiology studies where there is a delay between cohort entry and treatment initiation. Methods This study used the Clinical Practice Research Datalink (CPRD) and linked national mortality data in England from 2000 to 2019 to investigate immortal time bias for a specific life-long condition, intellectual disability. Life expectancy (Chiang’s abridged life table approach) was compared for 33,867 exposed and 980,586 unexposed individuals aged 10+ years using five methods: (1) treating immortal time as observation time; (2) excluding time before date of first exposure diagnosis; (3) matching cohort entry to first exposure diagnosis; (4) excluding time before proxy date of inputting first exposure diagnosis (by the physician); and (5) treating exposure as a time-dependent measure. Results When not considered in the design or analysis (Method 1), immortal time bias led to disproportionately high life expectancy for the exposed population during the first calendar period (additional years expected to live: 2000–2004: 65.6 [95% CI: 63.6,67.6]) compared to the later calendar periods (2005–2009: 59.9 [58.8,60.9]; 2010–2014: 58.0 [57.1,58.9]; 2015–2019: 58.2 [56.8,59.7]). Date of entry of diagnosis (Method 4) was unreliable in this CPRD cohort. The final methods (Method 2, 3 and 5) appeared to solve the main theoretical problem but residual bias may have remained. Conclusions We conclude that immortal time bias is a significant issue for studies of life-long conditions that use electronic health record data and requires careful consideration of how clinical diagnoses are entered onto electronic health record systems.

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Section: Methodsmentioning

confidence: 99%

Immortal time bias for life-long conditions in retrospective observational studies using electronic health records

Tyrer

Bhaskaran

Rutherford

2022

BMC Med Res Methodol

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“…For the Chiang's abridged life table approach, con dence intervals were calculated using the adjusted Chiang approach advocated by Eayres & Williams [7] which involves adding a correction term to the original Chiang variance to incorporate length of survival in the last age group [25]. When calculating life expectancies in small areas, Chiang's methods with the adjusted variance are recommended over Silcocks' methods [7].…”

Section: Statistical Analysesmentioning

confidence: 99%

Flexible parametric methods for calculating life expectancy in small populations

Tyrer

Chudasama

Lambert

et al. 2022

Preprint

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BackgroundLife expectancy is a simple measure of assessing health differences between two or more populations but current life expectancy calculations are not reliable for small populations. A potential solution to this is to borrow strength from larger populations from the same source but this has not formally been investigated.MethodsUsing data on 451,222 individuals from the Clinical Practice Research Datalink (CPRD) on the presence/absence of intellectual disability and type 2 diabetes mellitus (T2DM), we compared stratified and combined flexible parametric models, and Chiang’s methods, for calculating life expectancy. Confidence intervals were calculated using the Delta method, Chiang’s adjusted life table approach and bootstrapping.ResultsThe flexible parametric models allowed calculation of life expectancy by exact age and beyond traditional life expectancy age thresholds. The combined model that fit age interaction effects as a spline term provided greater statistical precision for small covariate subgroups by borrowing strength from the larger subgroups. However, careful consideration of the distribution of events in the smallest group was needed.ConclusionsLife expectancy is a simple measure to compare health differences between populations. The use of combined flexible parametric methods to calculate life expectancy in small samples has shown promising results by allowing life expectancy to be modelled by exact age, greater statistical precision and prediction of different covariate patterns without stratification. We recommend further investigation of their application for both policymakers and researchers.

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“…We calculated variance in life expectancy via the Chiang methodology with no zero-death count substitution and adjusted Chiang variance for the final age interval. 6,[11][12][13] We considered substitution methods to compensate for the underestimation of the true variation due to zero death counts, including substitute values of 0.693 and 3.0, the Poisson means where the probability of observing zero deaths is 50% and 5%, respectively. However, this approach did not improve estimates, consistent with previous findings, 6,12 and, therefore, was not used.…”

Section: Life Expectancymentioning

confidence: 99%

Comparative Methodologic and Practical Considerations for Life Expectancy as a Public Health Mortality Measure

et al. 2021

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Introduction Life expectancy is a public health metric used to assess mortality. We describe life expectancy calculations for US counties and present methodologic considerations compared with years of potential life lost before age 75 (YPLL-75) and premature age-adjusted mortality (PAAM), 2 commonly used length-of-life metrics. Methods We used death data from the National Center for Health Statistics for 2015-2017 and other health measures from the 2019 County Health Rankings & Roadmaps. We calculated life expectancy from birth at the county level using an abridged life table and the Chiang method of variance. Studentized residuals identified counties with discordant life expectancy and YPLL-75 or PAAM values. Correlations tested associations of life expectancy with key health measures (eg, smoking, child poverty, uninsured). Results Among 3073 US counties, life expectancy ranged from 62.4 to 98.0 years, with a mean of 77.4 years. Life expectancy was strongly and negatively correlated with YPLL-75 ( r = −0.91) and PAAM ( r = −0.95) at the county level. Life expectancy was also associated with other key health metrics, such as smoking, employment, and education rates, where an improvement in the health factor indicated improvement in the respective length-of-life measure. Counties with discordant life expectancy and YPLL-75 or PAAM values had differing age structures. Practice Implications Commonly used length-of-life metrics in population health settings are differentiated by methodological matters, such as computation complexity, data availability, and differential risk among age groups, especially among the very old or very young. The choice of metric should consider these factors, in addition to practical concerns, such as the communication needs of the audience.

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Variance models of the last age interval and their impact on life expectancy at subnational scales

Cited by 11 publications

References 33 publications

Immortal time bias for life-long conditions in retrospective observational studies using electronic health records

Immortal time bias for life-long conditions in retrospective observational studies using electronic health records

Flexible parametric methods for calculating life expectancy in small populations

Comparative Methodologic and Practical Considerations for Life Expectancy as a Public Health Mortality Measure

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