Using administrative health data to identify individuals with intellectual and developmental disabilities: a comparison of algorithms. (1st November 2012)

• Record Type: Journal Article
• Authors: Lin, E.; Balogh, R.; Cobigo, V.; Ouellette‐Kuntz, H.; Wilton, A. S.; Lunsky, Y.
• Is Part Of: Journal of intellectual disability research. Volume 57:Part 5(2013:May)
• Page Start: 462
• Page End: 477
• Publication Date: 2012-11-01
• Subjects: administrative data -- cohort algorithms -- intellectual disability -- standardised differences -- surveillance; Mental retardation -- Research -- Periodicals; 362.2
• DOI: 10.1111/jir.12002
• Languages: English
• ISSNs: 0964-2633

Abstract

Abstract: Background: Individuals with intellectual and developmental disabilities (IDD) experience high rates of physical and mental health problems; yet their health care is often inadequate. Information about their characteristics and health services needs is critical for planning efficient and equitable services. A logical source of such information is administrative health data; however, it can be difficult to identify cases with IDD in these data. The purpose of this study is to evaluate three algorithms for case finding of IDD in health administrative data. Methods: The three algorithms were created following existing approaches in the literature which ranged between maximising sensitivity versus balancing sensitivity and specificity. The algorithm required only one IDD service contact across all available data and time periods, the algorithm added the restriction of a minimum of two physician visits while the algorithm added a further restriction that the time period be limited to 2006 onward. The resulting three cohorts were compared according to socio‐demographic and clinical characteristics. Comparisons on different subgroups for a hypothetical population of 50 000 individuals with IDD were also carried out: this information may be relevant for planning specialised treatment or support programmes. Results: The prevalence rates of IDD per 100 were 0.80, 0.52 and 0.18 for the, and algorithms, respectively. Except for 'percentage with psychiatric co‐morbidity', the three cohorts had similar characteristics (standardised differences < 0.1). More stringent thresholds increased the percentage of psychiatric co‐morbidity and decreased the percentages of women and urban residents in the identified cohorts (standardised differences = 0.12 to 0.46). More concretely, using the algorithm to indirectly estimate the number of individuals with IDD, a practice not uncommon in planning and policy development, classified nearly 7000 more individuals with psychiatric co‐morbidities than using the algorithm. Conclusions: The prevalence rate produced by the algorithm most closely approximated the reported literature rate suggesting the value of imposing a two‐physician visit minimum but not restricting the time period covered. While the statistical differences among the algorithms were generally minor, differences in the numbers of individuals in specific population subgroups may be important particularly if they have specific service needs. Health administrative data can be useful for broad‐based service planning for individuals with IDD and for population level comparisons around their access and quality of care.