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Disability-Related Questions for Administrative Datasets.

ABSTRACT: High rates of unemployment among people with disability are long-standing and persistent problems worldwide. For public policy, estimates of prevalence and population profiles are required for designing support schemes such as Australia's National Disability Insurance Scheme; for monitoring implementation of the United Nations Convention on Rights of Persons with Disabilities; and for monitoring service access, participation, and equity for people with disability in mainstream systems including employment. In the public sector, creating a succinct identifier for disability in administrative systems is a key challenge for public policy design and monitoring. This requires concise methods of identifying people with disability within systems, producing data comparable with population data to gauge accessibility and equity. We aimed to create disability-related questions of value to the purposes of an Australian state and contribute to literature on parsimonious and respectful disability identification for wider application. The research, completed in 2017, involved mapping and identification of key disability concepts for inclusion in new questions, focus groups to refine wording of new questions, and online surveys of employees evaluating two potential new question sets on the topic of disability and environment. Recommendations for new disability-related questions and possible new data collection processes are being considered and used by the leading state authority.

SUBMITTER: Madden RH

PROVIDER: S-EPMC7432011 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Disability-Related Questions for Administrative Datasets.

Madden Rosamond H RH Lukersmith Sue S Zhou Qingsheng Q Glasgow Melita M Johnston Scott S

International journal of environmental research and public health 20200728 15

High rates of unemployment among people with disability are long-standing and persistent problems worldwide. For public policy, estimates of prevalence and population profiles are required for designing support schemes such as Australia's National Disability Insurance Scheme; for monitoring implementation of the United Nations Convention on Rights of Persons with Disabilities; and for monitoring service access, participation, and equity for people with disability in mainstream systems including ...[more]

PMID: 32731541

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Project description:ObjectivesAccurately measuring disability is critical toward policy development, economic analyses, and determining individual-level effects of health interventions. Nationally representative population surveys such as the National Health and Nutrition Examination Survey provide key opportunities to measure disability constructs such as activity limitations. However, only very limited work has previously evaluated the item response properties of questions pertaining to limitations in National Health and Nutrition Examination Survey.MethodsThis was a cross-sectional study. We included participants ⩾20 years old for the 2013-2018 National Health and Nutrition Examination Survey cycles. Activity limitations, and a smaller number of body function impairments or participation restrictions, were determined from interview questions. We performed item response theory models (a two-parameter logistic and a graded response model) to characterize discriminating information along the latent continuum of activity limitation.ResultsWe included 17,057 participants. Although each particular limitation was somewhat rare (maximally 13%), 7214 (38%) responded having at least one limitation. We found a high amount of discriminating information at 1-2 standard deviations above average limitation, though essentially zero discrimination below that range. Items had substantial overlap in the range at which they provided information distinguishing individuals. The ordinal graded response model including 20 limitations provided greater information than the dichotomous two-parameter logistic model, though further omitting items from the graded response model led to loss of information.ConclusionNational Health and Nutrition Examination Survey disability-related questions, mostly specifically activity limitations, provided a high degree of information distinguishing individuals with higher than average limitations on the latent continuum, but essentially zero resolution to distinguish individuals with low or average limitations. Future work may focus on developing items which better distinguish individuals at the "lower" end of the limitation spectrum.

Project description:BackgroundAnaphylaxis events are increasing worldwide, based on studies of single administrative datasets including hospital admissions, emergency room presentations, and prescription and medical claims data. Linking multiple administrative datasets may provide better epidemiological estimates, by capturing a greater number of anaphylaxis events occurring at the individual level. In this linked data study in Western Australia, we combined 4 population-based datasets to identify anaphylaxis events, factors influencing occurrence, and change in event rates from 2002 to 2013.MethodsFour linked administrative datasets from the Western Australian Data Linkage System were used, representing ambulance attendances, emergency department presentations, hospital inpatient admissions and death registrations. An anaphylaxis cohort was identified using ICD-9-CM, ICD-10-AM and additional anaphylaxis diagnosis codes, with event rates calculated. We explored the impact of age, gender, cause, Indigenous status and socioeconomic index on event rates. Standard Poisson regression models were used to examine the significance of the change in anaphylaxis event rates over time.ResultsA total 12,637 individuals (mean age 31.8 years, 49.6% female) experienced 15,462 anaphylaxis events between 2002 and 2013 (97.5% in non-Indigenous patients and 59.5% residing in the area of greatest socioeconomic advantage). Anaphylaxis event rates increased from 15.4 to 82.5/105 population between 2002 and 2013. The greatest increase in anaphylaxis events was seen in those coded as unspecified anaphylaxis (all ages, males and females combined, p < 0.001), with the highest rates of unspecified anaphylaxis in males 0-4 years (171.9/105 population in 2013), and females 15-19 years (104.0/105 in 2013). The average annual percent increase (95% CI) for food-related anaphylaxis was 9.2% (6.6-12.0); for medication-related anaphylaxis was 5.8% (4.5-7.1); and for unspecified anaphylaxis was 10.4% (9.8-11.0); all p < 0.001. There was a significant increase in ambulance attendance, emergency presentations and inpatient admissions for anaphylaxis between 2002 and 2013, with emergency presentations (56.0/105 population), inpatient admissions (43.2/105), and ambulance attendance (21.6/105) highest in 2013. Only 25 anaphylaxis-related deaths were recorded in the mortality register with no significant change in rates over time.ConclusionUsing multiple linked administrative datasets, we identified significantly higher rates of total anaphylaxis than previously reported, with more than 5-fold increases in anaphylaxis events between 2002 and 2013. While the combination of 4 population-level datasets provides a more comprehensive capture of cases, even at the individual dataset level, admission rates for anaphylaxis in Western Australia are substantially higher than those previously reported for similar time periods, both in Australia and worldwide.

Project description:IntroductionThe American Hospital Association (AHA) has hospital-level data, while the Centers for Medicare & Medicaid Services (CMS) has patient-level data. Merging these with other distinct databases would permit analyses of hospital-based specialties, units, or departments, and patient outcomes. One distinct database is the National Emergency Department Inventory (NEDI), which contains information about all EDs in the United States. However, a challenge with merging these databases is that NEDI lists all US EDs individually, while the AHA and CMS group some EDs by hospital network. Consolidating data for this merge may be preferential to excluding grouped EDs. Our objectives were to consolidate ED data to enable linkage with administrative datasets and to determine the effect of excluding grouped EDs on ED-level summary results.MethodsUsing the 2014 NEDI-USA database, we surveyed all New England EDs. We individually matched NEDI EDs with corresponding EDs in the AHA and CMS. A "group match" was assigned when more than one NEDI ED was matched to a single AHA or CMS facility identification number. Within each group, we consolidated individual ED data to create a single observation based on sums or weighted averages of responses as appropriate.ResultsOf the 195 EDs in New England, 169 (87%) completed the NEDI survey. Among these, 130 (77%) EDs were individually listed in AHA and CMS, while 39 were part of groups consisting of 2-3 EDs but represented by one facility ID. Compared to the individually listed EDs, the 39 EDs included in a "group match" had a larger number of annual visits and beds, were more likely to be freestanding, and were less likely to be rural (all P<0.05). Two grouped EDs were excluded because the listed ED did not respond to the NEDI survey; the remaining 37 EDs were consolidated into 19 observations. Thus, the consolidated dataset contained 149 observations representing 171 EDs; this consolidated dataset yielded summary results that were similar to those of the 169 responding EDs.ConclusionExcluding grouped EDs would have resulted in a non-representative dataset. The original vs consolidated NEDI datasets yielded similar results and enabled linkage with large administrative datasets. This approach presents a novel opportunity to use characteristics of hospital-based specialties, units, and departments in studies of patient-level outcomes, to advance health services research.

Project description:Objective: We developed and internally validated an algorithm for disability status in multiple sclerosis (MS) using administrative data. Methods: We linked administrative data from Manitoba, Canada to a clinical dataset with Expanded Disability Status Scale (EDSS) scores for people with MS. Clinical EDSS scores constituted the reference standard. We created candidate indicators using the administrative data. These included indicators based on use of particular health care services (home care, long-term care, rehabilitation admission), use of specific diagnostic codes (such as spasticity, quadriplegia), and codes based on use of Employment and Income Insurance. We developed algorithms to predict severe disability (EDSS ≥6.0), and to predict disability as a continuous measure. We manually developed algorithms, and also employed regression approaches. After we selected our preferred algorithms for disability, we tested their association with health care use due to any cause and infection after potential confounders. Results: We linked clinical and administrative data for 1,767 persons with MS, most of whom were women living in urban areas. All individual indicators tested had specificities >90% for severe disability, and all but a diagnosis of visual disturbance had positive predictive values (PPV) >70%. The combination of home care or long-term care use or rehabilitation admission had a sensitivity of 61.9%, specificity of 90.76%, PPV of 70.06% and negative predictive of 87.21%. Based on regression modeling, the best-performing algorithm for predicting the EDSS as a continuous variable included age, home care use, long-term care admission, admission for rehabilitation, visual disturbance, other paralytic syndromes and spasticity. The mean difference between observed and predicted values of the EDSS was -0.0644 (95%CI -0.1632, 0.0304). Greater disability, whether measured using the clinical EDSS or either of the administrative data algorithms was similarly associated with increased hospitalization rates due to any cause and infection. Conclusion: We developed and internally validated an algorithm for disability in MS using administrative data that may support population-based studies that wish to account for disability status but do not have access to clinical data sources with this information. We also found that more severe disability is associated with increased health care use, including due to infection.

Dataset Information

Disability-Related Questions for Administrative Datasets.

Publications

Disability-Related Questions for Administrative Datasets.

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