Use Of Administrative Data Sources Research Articles

Autism spectrum disorder/Takiwātanga: An Integrated Data Infrastructure-based approach to autism spectrum disorder research in New Zealand.

New Zealand has few estimates of the prevalence of autism spectrum disorder and no national registry. The use of administrative data sources is expanding and could be useful in autism spectrum disorder research. However, the extent to which autism spectrum disorder can be captured in these data sources is unknown. In this study, we utilised three linked administrative health data sources from the Integrated Data Infrastructure to identify cases of autism spectrum disorder among New Zealand children and young people. We then investigated the extent to which a range of mental health, neurodevelopmental and related problems co-occur with autism spectrum disorder. In total, 9555 unique individuals aged 0–24 with autism spectrum disorder were identified. The identification rate for 8-year-olds was 1 in 102. Co-occurring mental health or related problems were noted in 68% of the autism spectrum disorder group. The most common co-occurring conditions were intellectual disability, disruptive behaviours and emotional problems. Although data from the Integrated Data Infrastructure may currently undercount cases of autism spectrum disorder, they could be useful for monitoring service and treatment-related trends, types of co-occurring conditions and for examining social outcomes. With further refinement, the Integrated Data Infrastructure could prove valuable for informing the national incidence and prevalence of autism spectrum disorder and the long-term effectiveness of clinical guidelines and interventions for this group.Lay abstractNew Zealand has few estimates of the prevalence autism spectrum disorder and no national registry or data set to identify and track cases. This hinders the ability to make informed, evidence-based decisions relating to autism spectrum disorder. In this study, we utilised linked health and non-health data to develop a method for identifying cases of autism spectrum disorder among children and young people in New Zealand. In addition, we examined rates of co-occurring mental health, neurodevelopmental and related conditions among this cohort and compared these to the general population. The method identified almost 10,000 children and young people with autism spectrum disorder in New Zealand. Co-occurring mental health or related problems were found in over 68% of this group (nearly seven times higher than the general population), and around half were identified with multiple co-occurring conditions. The most frequently identified conditions were intellectual disability, disruptive behaviours and emotional problems. We have developed a useful method for monitoring service and treatment-related trends, number and types of co-occurring conditions and examining social outcomes among individuals with autism spectrum disorder. While the method may underestimate the prevalence of autism spectrum disorder in New Zealand, it provides a significant step towards establishing a more comprehensive evidence base to inform autism spectrum disorder–related policy.

Quality measures for multisource statistics

The ESSnet on Quality of Multisource Statistics is part of the ESS.VIP Admin Project. The main objectives of that latter project are (i) to improve the use of administrative data sources and (ii) to support the quality assurance of the output produced using administrative sources. The ultimate aim of the ESSnet is to produce quality guidelines for National Statistics Institutes (NSIs) that are specific enough to be used in statistical production at those NSIs. The guidelines aim to cover the diversity of situations in which NSIs work as well as restrictions on data availability. The guidelines will list a variety of potential measures, indicate for each of them their applicability and in what situation it is preferred or not, and provide an ample set of examples of specific cases and decision-making processes. Work Package 3 (WP 3) of the ESSnet focuses on developing and testing quantitative measures for measuring the quality of output based on multiple data sources and on methods to compute such measures. In particular, WP 3 focuses on non-sampling errors. Well-known examples of such quality measures are bias and variance of the estimated output. Methods for computing these and other quality measures often depend on the specific data sources. Therefore, we have identified several basic data configurations for the use of administrative data sources in combination with other sources, for which we propose, revise and test quantitative measures for the accuracy and coherence of the output. In this article we discuss the identified basic data configurations, the approach taken in WP 3, and give some examples of quality measures and methods to compute those measures. We also point out some topics for future work.

COMPARISON OF CARDIAC SURGERY MORTALITY REPORTS USING RISK ADJUSTMENTS FROM ADMINISTRATIVE VERSUS CLINICAL DATA SOURCES

Quality of care outcomes for cardiac surgery are increasingly being publically reported. These reports employ regression models and use data from either administrative sources or clinical registries for risk-adjustment and to account for differences in case mix. However, the impact of the type of data used in risk-adjustment models on accuracy of outcomes reporting and perceived performance of a given operator has not been fully examined. In the same patient cohort, we compared the performance of two different risk-adjustment models using administrative or clinical data sources. The administrative-only risk-adjustment model which uses the International Classification of Diseases diagnosis codes (CIHI Cardiac Care Quality Indicator [CCQI]) and the Society of Thoracic Surgeons [STS] risk-adjustment model using clinical data were tested on patients undergoing cardiac surgery at a single centre between 2013-2016 (N=1,635). Comparing both risk-adjustment models, the STS model included more data on cardiac function/status prior to the procedure and clinical cardiovascular conditions while the CIHI model included more variables pertaining to non-cardiac comorbidities. The primary outcome was in-hospital mortality within 30 days of the operation. Model performance was established by comparing predicted and observed mortality, model calibration and handling of critical covariates. Observed mortality rate was 1.96% (95%CI: 1.40-2.75%) which was similar to STS predicted mortality (1.96%) but significantly higher than CCQI-predicted mortality (1.03%). Despite both models having similar c-statistics (0.756 CCQI; 0.758 STS), the CCQI model showed significant underestimation of probability of mortality at the higher end of the risk spectrum. There was significant miscalibration (underestimation in the CCQI model) of risk, which was largely driven by seven important pre-operative covariates: NYHA class IV; prior congestive heart failure; left ventricular ejection fraction <20%, prior atrial fibrillation; acute coronary insufficiency; cardiac compromise (defined by the presence of shock, myocardial infarction <24 hours, intra-aortic balloon pump in situ, cardiac resuscitation prior to surgery, or pre-procedure circulatory support); and creatinine concentration ≥100mg/dL. Together, these factors accounted for 84% of the variation in predicted mortality between the CCQI and STS models. Risk prediction using administrative data underestimated mortality risk compared to a validated clinical model. The use of administrative data sources for risk-adjusted mortality reporting may inflate observed to predicted mortality ratios at hospitals with patients who are more ill, more complex, or are at the higher end of the clinical risk spectrum. Caution is warranted when hospital outcomes reports of cardiac surgery are based on administrative data alone.

Measuring severe maternal morbidity: validation of potential measures

Both maternal mortality rate and severe maternal morbidity rate have risen significantly in the United Sates. Recently, the Centers for Disease Control and Prevention introduced International Classification of Diseases, 9th revision, criteria for defining severe maternal morbidity with the use of administrative data sources; however, those criteria have not been validated with the use of chart reviews. The primary aim of the current study was to validate theCenters for Disease Control and Prevention International Classificationof Diseases, 9th revision, criteria for the identification of severe maternal morbidity. This analysis initially required the development of a reproducible set of clinical conditions that were judged to be consistent with severe maternal morbidity to be used as the clinical gold standard for validation. Alternative criteria for severe maternal morbidity were also examined. The 67,468 deliveries that occurred during a 12-month period from 16 participating California hospitals were screened initially for severe maternal morbidity with the presence of any of 4 criteria: (1) Centers for Disease Control and Prevention International Classification of Diseases, 9th revision, diagnosis and procedure codes; (2) prolonged postpartum length of stay (>3 standard deviations beyond the mean length of stay for the California population); (3) any maternal intensive care unit admissions (with the use of hospital billing sources); and (4) the administration of any blood product (with the use of transfusion service data). Complete medical records for all screen-positive cases were examined to determine whether they satisfied the criteria for the clinical gold standard (determined by 4 rounds of a modified Delphi technique). Descriptive and statistical analyses that included area under the receiver operating characteristic curve and C-statistic were performed. The Centers for Disease Control and Prevention International Classification of Diseases, 9th revision, criteria had a reasonably highsensitivity of 0.77 and a positive predictive value of 0.44 with a C-statistic of 0.87. The most important source of false-positive cases weremothers whose only criterion was 1-2 units of blood products. The Centers for Disease Control and Prevention International Classification of Diseases, 9th revision, criteria screen rate ranged from 0.51-2.45% among hospitals. True positive severe maternal morbidity ranged from0.05-1.13%. When hospitals were grouped by their neonatal intensive care unit level of care, severe maternal morbidity rates were statistically lower at facilities with lower level neonatal intensive care units(P < .0001). The Centers for Disease Control and Prevention International Classification of Diseases, 9th revision, criteria can serve as a reasonable administrative metric for measuring severe maternal morbidity at population levels. Caution should be used with the use of these criteria for individual hospitals, because case-mix effects appear to be strong.

Measuring output quality for multisource statistics in official statistics: Some directions

Many statistical offices have been moving towards an increased use of administrative data sources for statistical pur- poses, both as a substitute and as a complement to survey data. Moreover, the emergence of big data constitutes a further increase in available sources. As a result, statistical output in official statistics is increasingly based on complex combinations of sources. The quality of such statistics depends on the quality of the primary sources and on the ways they are combined. This paper analyses the appropriateness of the current set of output quality measures for multiple source statistics, it explains the need for improvement and outlines directions for further work. The usual approach for measuring the quality of the statistical output is to assess quality through the measurement of the input and process quality. The paper argues that in multisource production environment this approach is not sufficient. It advocates measuring quality on the basis of the output itself - without analysing the details of the inputs and the production process - and proposes directions for further development.

Biographical Data of Social Insurance Agencies in Germany – Improving the Content of Administrative Data

The use of administrative data sources is getting more and more popular in various research topics. The majority of microeconomic evaluation studies in Europe, which is about 80 percent, are based on administrative data sources (Card /Kluve /Weber, 2010) and (Scioch /Oberschachtsiek, 2009). The main reason for using register based data is the multitude of information they provide. On the one hand they contain a high number of observed individuals, on the other hand precise information on characteristics like wages or employment. An advantage compared to survey data is that there is no existence of non-response or panel attrition (Kluve, 2006). Administrative data sources of the german social security system are generated mainly using two procedures: the notification of the social security and the internal processes of the respective agencies which collect the data.

Confidentiality in the use of administrative data sources

Confidentiality in the use of administrative data sources