Abstract
BackgroundWe described the impact of different lengths of lookback window (LW), a retrospective time period to observe diagnoses in administrative data, on the prevalence and incidence of eight chronic diseases.MethodsOur study populations included people living with HIV (N = 5151) and 1:5 age-sex-matched HIV-negative individuals (N = 25,755) in British Columbia, Canada, with complete follow-up between 1996 and 2012. We measured period prevalence and incidence of diseases in 2012 using LWs ranging from 1 to 16 years. Cases were deemed prevalent if identified in 2012 or within a defined LW, and incident if newly identified in 2012 with no previous cases detected within a defined LW. Chronic disease cases were ascertained using published case-finding algorithms applied to population-based provincial administrative health datasets.ResultsOverall, using cases identified by the full 16-year LW as the reference, LWs ≥8 years and ≥ 4 years reduced the proportion of misclassified prevalent and incidence cases of most diseases to < 20%, respectively. The impact of LWs varied across diseases and populations.ConclusionsThis study underscored the importance of carefully choosing LWs and demonstrated data-driven approaches that may inform these choices. To improve comparability of prevalence and incidence estimates across different settings, we recommend transparent reporting of the rationale and limitations of chosen LWs.
Highlights
Large scale administrative health databases depicting claims related to healthcare utilization have become an indispensable data source in contemporary epidemiologicNanditha et al BMC Medical Research Methodology (2022) 22:1 relevant healthcare encounters must be searched within a specific retrospective period of time or a ‘lookback window’ (LW) [4, 5]
In summary, this study underlined the consequences of varying lengths of LW on the internal validity of prevalence and incidence estimates of eight common chronic diseases among people living with HIV (PLWH) and HIV-negative individuals
We demonstrated reproduceable datadriven approaches that quantified misclassification bias associated with shorter LWs to assist researchers in deciding an optimal study design, with additional considerations for studies comparing different populations
Summary
Large scale administrative health databases depicting claims related to healthcare utilization have become an indispensable data source in contemporary epidemiologicNanditha et al BMC Medical Research Methodology (2022) 22:1 relevant healthcare encounters must be searched within a specific retrospective period of time or a ‘lookback window’ (LW) [4, 5]. With people living with HIV (PLWH) living longer than ever before, administrative data are often used to calculate the prevalence and incidence of chronic diseases in this population, alone and in comparison to their HIV-negative counterparts [6,7,8,9,10]. Using data from a population-based cohort in British Columbia (BC), Canada, these analyses were conducted separately for PLWH and HIV-negative individuals, as their distinct healthcare utilization practices are likely to influence choices surrounding LWs. this study sought to propose replicable data-driven approaches to facilitate a more informed study design that would improve the validity of interpretations and comparisons of findings from administrative data studies. We described the impact of different lengths of lookback window (LW), a retrospective time period to observe diagnoses in administrative data, on the prevalence and incidence of eight chronic diseases
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