Constructing a toolkit to evaluate quality of state and local administrative data

Seeskin, Zachary H.; Ugarte, Gabriel; Datta, A. Rupa

doi:10.23889/ijpds.v4i1.937

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The metrics we considered in comparing the synthetic and the original data cover all the dimensions for evaluating administrative data quality [ 6 , 23 ]. All analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC).…”

Section: Methodsmentioning

confidence: 99%

Generating synthetic data from administrative health records for drug safety and effectiveness studies

Ayilara,

Platt,

Dahl

et al. 2023

IJPDS

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IntroductionAdministrative health records (AHRs) are used to conduct population-based post-market drug safety and comparative effectiveness studies to inform healthcare decision making. However, the cost of data extraction, and the challenges associated with privacy and securing approvals can make it challenging for researchers to conduct methodological research in a timely manner using real data. Generating synthetic AHRs that reasonably represent the real-world data are beneficial for developing analytic methods and training analysts to rapidly implement study protocols. We generated synthetic AHRs using two methods and compared these synthetic AHRs to real-world AHRs. We described the challenges associated with using synthetic AHRs for real-world study. MethodsThe real-world AHRs comprised prescription drug records for individuals with healthcare insurance coverage in the Population Research Data Repository (PRDR) from Manitoba, Canada for the 10-year period from 2008 to 2017. Synthetic data were generated using the Observational Medical Dataset Simulator II (OSIM2) and a modification (ModOSIM). Synthetic and real-world data were described using frequencies and percentages. Agreement of prescription drug use measures in PRDR, OSIM2 and ModOSIM was estimated with the concordance coefficient. ResultsThe PRDR cohort included 169,586,633 drug records and 1,395 drug types for 1,604,734 individuals. Synthetic data for 1,000,000 individuals were generated using OSIM2 and ModOSIM. Sex and age group distributions were similar in the real-world and synthetic AHRs. However, there were significant differences in the number of drug records and number of unique drugs per person for OSIM2 and ModOSIM when compared with PRDR. For the average number of days of drug use, concordance with the PRDR was 16% (95% confidence interval [CI]: 12%-19%) for OSIM2 and 88% (95% CI: 87%-90%) for ModOSIM. ConclusionsModOSIM data were more similar to PRDR than OSIM2 data on many measures. Synthetic AHRs consistent with those found in real-world settings can be generated using ModOSIM. Synthetic data will benefit rapid implementation of methodological studies and data analyst training.

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Section: Methodsmentioning

confidence: 99%

Generating synthetic data from administrative health records for drug safety and effectiveness studies

Ayilara,

Platt,

Dahl

et al. 2023

IJPDS

View full text Add to dashboard Cite

show abstract

“…Há um crescente interesse no uso de dados administrativos em pesquisa, avaliação e vigilância em saúde, entretanto, seu uso deve ser precedido da avaliação de qualidade 16,17,18,19 . Estudos sobre a qualidade dos dados do SINASC para monitoramento de malformações congênitas foram realizados no Rio de Janeiro, em 2004 20,21 ; em Campinas (São Paulo), em 2004 e 2009 22,23 ; em outros municípios do Brasil, em 2004 e 2007 24 ; em São Carlos (São Paulo), entre 2003 e 2007 25 ; e no Recife (Pernambuco), entre 2013 e 2015 26 .…”

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Evolução da completude e consistência do registro de gastrosquise no Sistema de Informações sobre Nascidos Vivos no Brasil, de 2005 a 2020

et al. 2023

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O objetivo deste estudo foi avaliar a evolução da completude e da consistência do registro de gastrosquise no Sistema de Informações sobre Nascidos Vivos (SINASC) no Brasil. Trata-se de estudo de série temporal sobre a completude da variável “ocorrência de anomalia congênita” e a consistência do diagnóstico de gastrosquise no SINASC, nos biênios entre 2005 e 2020, para Unidades da Federação, região e Brasil. A consistência foi estimada pela razão entre óbitos por gastrosquise registrados no Sistema de Informações sobre Mortalidade (SIM) e o total de casos registrados no SINASC. A tendência temporal foi analisada por regressão joinpoint. No período, havia registro de 46.574.995 nascidos vivos e 10.024 casos de gastrosquise entre eles. Identificamos 5.632 óbitos infantis por gastrosquise. O percentual de incompletude diminuiu de 6,52% para 1,87%, com variação percentual anual (VPA) de -14,5%, e a completude atingiu a excelência (≤ 5% de incompletude), exceto no Centro-oeste do país. Razão óbito/caso acima de 1 foi encontrada nas regiões Norte e Nordeste e em alguns estados do Centro-oeste, mas houve diminuição, aproximando-se da mortalidade encontrada em estudos no Sul e Sudeste. Sua redução foi mais acentuada até 2009-2010 (VPA = -10,7%) e menor posteriormente (VPA = -4,4%). A qualidade do registro de gastrosquise reflete as diferenças regionais da qualidade geral do SINASC, configurando-se uma condição marcadora para malformações que demandam atenção neonatal complexa.

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Evaluating data quality for blended data using a data quality framework

Parker,

Mirel,

Lee

et al. 2024

SJI

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In 2020 the U.S. Federal Committee on Statistical Methodology (FCSM) released “A Framework for Data Quality”, organized by 11 dimensions of data quality grouped among three domains of quality (utility, objectivity, integrity). This paper addresses the use of the FCSM Framework for data quality assessments of blended data. The FCSM Framework applies to all types of data, however best practices for implementation have not been documented. We applied the FCSM Framework for three health-research related case studies. For each case study, assessments of data quality dimensions were performed to identify threats to quality, possible mitigations of those threats, and trade-offs among them. From these assessments the authors concluded: 1) data quality assessments are more complex in practice than anticipated and expert guidance and documentation are important; 2) each dimension may not be equally important for different data uses; 3) data quality assessments can be subjective and having a quantitative tool could help explain the results, however, quantitative assessments may be closely tied to the intended use of the dataset; 4) there are common trade-offs and mitigations for some threats to quality among dimensions. This paper is one of the first to apply the FCSM Framework to specific use-cases and illustrates a process for similar data uses.

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Constructing a toolkit to evaluate quality of state and local administrative data

Cited by 3 publications

References 14 publications

Generating synthetic data from administrative health records for drug safety and effectiveness studies

Generating synthetic data from administrative health records for drug safety and effectiveness studies

Evolução da completude e consistência do registro de gastrosquise no Sistema de Informações sobre Nascidos Vivos no Brasil, de 2005 a 2020

Evaluating data quality for blended data using a data quality framework

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