A Review of Data Quality Assessment Methods for Public Health Information Systems

Chen, Hong; Hailey, David; Ning, Wang; Yu, Ping

doi:10.3390/ijerph110505170

Cited by 214 publications

(196 citation statements)

References 48 publications

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“…The chosen approach was guided by growing evidence supporting on-the-job training of healthcare workers that includes feedback and follow-up ( 14 ), which had previously been used successfully in Uganda ( 15 , 16 ). This article describes the initial implementation (November 2014–September 2016) and outcomes of Uganda’s national strategy to improve administrative vaccination data quality, defined by the dimensions of management; collection; data produced (accuracy, timeliness, completeness); analysis; and use ( 17 ). …”

mentioning

confidence: 99%

Enhancing Workforce Capacity to Improve Vaccination Data Quality, Uganda

Ward¹,

Mugenyi²,

Benke³

et al. 2017

Emerg. Infect. Dis.

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In Uganda, vaccine dose administration data are often not available or are of insufficient quality to optimally plan, monitor, and evaluate program performance. A collaboration of partners aimed to address these key issues by deploying data improvement teams (DITs) to improve data collection, management, analysis, and use in district health offices and health facilities. During November 2014–September 2016, DITs visited all districts and 89% of health facilities in Uganda. DITs identified gaps in awareness and processes, assessed accuracy of data, and provided on-the-job training to strengthen systems and improve healthcare workers’ knowledge and skills in data quality. Inaccurate data were observed primarily at the health facility level. Improvements in data management and collection practices were observed, although routine follow-up and accountability will be needed to sustain change. The DIT strategy offers a useful approach to enhancing the quality of health data.

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mentioning

confidence: 99%

Enhancing Workforce Capacity to Improve Vaccination Data Quality, Uganda

Ward¹,

Mugenyi²,

Benke³

et al. 2017

Emerg. Infect. Dis.

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“…19 Finally, a recent review by Chen et al of DQA methods for assessing public health information systems identified 49 distinct dimensions used when measuring DQ; completeness, accuracy, and timeliness being those most frequently described. 21 While there is a wide variety of DQ dimensions one can choose from when assessing a data source, there are equally as many DQA programs and processes.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Data Quality Assessment Checks in Six Data Sharing Networks

Callahan¹,

Bauck²,

Bertoch³

et al. 2017

eGEMs

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Objective: To compare rule-based data quality (DQ) assessment approaches across multiple national clinical data sharing organizations. Methods:Six organizations with established data quality assessment (DQA) programs provided documentation or source code describing current DQ checks. DQ checks were mapped to the categories within the data verification context of the harmonized DQA terminology. To ensure all DQ checks were consistently mapped, conventions were developed and four iterations of mapping performed. Difficultto-map DQ checks were discussed with research team members until consensus was achieved.Results: Participating organizations provided 11,026 DQ checks, of which 99.97 percent were successfully mapped to a DQA category. Of the mapped DQ checks (N=11,023), 214 (1.94 percent) mapped to multiple DQA categories. The majority of DQ checks mapped to Atemporal Plausibility (49.60 percent), Value Conformance (17.84 percent), and Atemporal Completeness (12.98 percent) categories. Discussion: Using the common DQA terminology, near-complete (99.97 percent) coverage across a wide range of DQA programs and specifications was reached. Comparing the distributions of mapped DQ checks revealed important differences between participating organizations. This variation may be related to the organization's stakeholder requirements, primary analytical focus, or maturity of their DQA program. Not within scope, mapping checks within the data validation context of the terminology may provide additional insights into DQA practice differences. Conclusion:A common DQA terminology provides a means to help organizations and researchers understand the coverage of their current DQA efforts as well as highlight potential areas for additional DQA development. Sharing DQ checks between organizations could help expand the scope of DQA across clinical data networks.

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“…The 27 remain ing twenty studies used cross -sectional data, which reflect only the time of data collection and are limited in their ability to draw valid conclusions about associations or possible causality [53]. Co mpared to other reviews of 1 COI studies on a specific single disease, this review on mult imorb idity included fewer cohort studies [54].…”

mentioning

confidence: 99%

A Systematic Review of Cost-of-Illness Studies of Multimorbidity

Wang

Liu

Cocker

et al. 2017

Appl Health Econ Health Policy

132

105

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1Objectives: The economic burden of mu ltimo rbid ity is considerable. This review analyzed the methods of cost-of-2 illness (COI) studies and summarized the economic outcomes of mu ltimo rbidity. 3Methods: A systematic review (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) was performed, which was registered with Prospero, reported according 4 to PRISMA, and used a quality checklist adapted for COI studies . The inclusion criteria were peer-reviewed COI 5 studies on multimo rbidity, whereas the exclusion criterion was studies focusing on an index d isease. Ext racted data 6 included the definition, measure, and prevalence of mult imorb idity; the number of included hea lth conditions; the 7 age of study population; the variables used in the COI methodology; the percentage of mu ltimorb idity vs. total costs; 8 and the average costs per capita. 9Results: Among the 26 included articles, 14 defined mult imorbid ity as a simple coun t of 2 or mo re conditions. 10Methodologies used to derive the costs were markedly d ifferent. Given different healthcare systems, OOP pay ments 11 of mult imorbid ity varied across countries. In the 17 and 12 studies with cut-offs of ≥2 and ≥3 condit ions, 12respectively, the ratios of multimo rbid ity to non-mult imorbid ity costs ranged from 2-16 and 2-10. A mong the 10 13 studies that provided cost breakdowns, studies with and without a societal perspective attributed the largest 14 percentage of mult imorb idity costs to social care and inpatient care/med icine, respectively. 15Conclusion: Multimorbid ity was associated with considerable economic burden . Synthesising the cost of 16 mu ltimo rbidity was challenging due to mu ltip le definitions of mult imorb idity and heterogeneity in COI methods. 17Count method was most popular to define mu ltimo rbid ity. There is consistent evidence that multimo rbidity was 18 associated with higher costs.

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A Review of Data Quality Assessment Methods for Public Health Information Systems

Cited by 214 publications

References 48 publications

Enhancing Workforce Capacity to Improve Vaccination Data Quality, Uganda

Enhancing Workforce Capacity to Improve Vaccination Data Quality, Uganda

A Comparison of Data Quality Assessment Checks in Six Data Sharing Networks

A Systematic Review of Cost-of-Illness Studies of Multimorbidity

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