Using administrative health data to identify individuals with intellectual and developmental disabilities: a comparison of algorithms.
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 s...
| Publicado en: | Journal of Intellectual Disability Research Vol. 57; no. 5; pp. 462 - 478 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
May2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104268471&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104268471 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09642633 EUL jtl: Journal of Intellectual Disability Research issn: 09642633 maglogo: Y pubinfo: dt: May2013 vid: 57 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104268471 86728464 10.1111/jir.12002 NLM23116328 104268471 ppf: 462 ppct: 16 formats: fmt: @attributes: type: P tig: atl: Using administrative health data to identify individuals with intellectual and developmental disabilities: a comparison of algorithms. aug: au: Lin, E. Balogh, R. Cobigo, V. Ouellette-Kuntz, H. Wilton, A. S. Lunsky, Y. affil: Provincial System Support Program, Centre for Addiction and Mental Health sug: subj: Intellectual Disability Developmental Disabilities Resource Databases Algorithms Persons with Intellectual Disabilities Human Intellectual Disability Epidemiology Developmental Disabilities Epidemiology Prevalence Prospective Studies Effect Size Sample Size Descriptive Statistics Funding Source ab: 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 broad algorithm required only one IDD service contact across all available data and time periods, the intermediate algorithm added the restriction of a minimum of two physician visits while the narrow 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 broad, intermediate and narrow 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 narrow 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 intermediate algorithm. Conclusions The prevalence rate produced by the intermediate 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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