Quantitative bias analysis of prevalence under misclassification: evaluation indicators, calculation method and case analysis

Jin, Liu; Wang, Shiyuan; Shao, Fang

doi:10.1093/ije/dyac239

Cited by 2 publications

(4 citation statements)

References 26 publications

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“…We therefore conducted a quantitative bias analysis of potential misclassification of the conditions using the Rogan‐Gladen equation, 41 which uses bias parameters (e.g. sensitivity) and the observed prevalence to calculate a bias‐adjusted prevalence 42 . We conducted this bias analysis for each condition based on known bias parameters obtained from Ontario data 15–32 or approximated from similar algorithms in other regions 42 .…”

Section: Methodsmentioning

confidence: 99%

“…sensitivity) and the observed prevalence to calculate a bias-adjusted prevalence. 42 We conducted this bias analysis for each condition based on known bias parameters obtained from Ontario data [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] or approximated from similar algorithms in other regions. 42 Since there are no bias parameters for MCC specifically, we estimated these based on the weighted average of those for individual conditions.…”

Section: Quantitative Bias Analysismentioning

confidence: 99%

“…42 We conducted this bias analysis for each condition based on known bias parameters obtained from Ontario data [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] or approximated from similar algorithms in other regions. 42 Since there are no bias parameters for MCC specifically, we estimated these based on the weighted average of those for individual conditions. SAS version 9.4 and the R poLCA package 36 were used.…”

Section: Quantitative Bias Analysismentioning

confidence: 99%

“…substance-use disorders) may be underdiagnosed. After accounting for potential misclassification bias using a quantitative bias analysis, 41,42 the prevalence of MCC was approximately 18%. We could not assess the temporal relationship between conditions.…”

Section: Limitations Of the Datamentioning

confidence: 99%

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Patterns of multiple chronic conditions in pregnancy: Population‐based study using latent class analysis

Brown,

Fung,

Cohen

et al. 2023

Paediatric Perinatal Epid

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BackgroundAdults with multiple chronic conditions (MCC) are a heterogeneous population with elevated risk of future adverse health outcomes. Yet, despite the increasing prevalence of MCC globally, data about MCC in pregnancy are scarce.ObjectivesTo estimate the population prevalence of MCC in pregnancy and determine whether certain types of chronic conditions cluster together among pregnant women with MCC.MethodsWe conducted a population‐based cohort study in Ontario, Canada, of all 15–55‐year‐old women with a recognised pregnancy, from 2007 to 2020. MCC was assessed from a list of 22 conditions, identified using validated algorithms. We estimated the prevalence of MCC. Next, we used latent class analysis to identify classes of co‐occurring chronic conditions in women with MCC, with model selection based on parsimony, clinical interpretability and statistical fit.ResultsAmong 2,014,508 pregnancies, 324,735 had MCC (161.2 per 1000, 95% confidence interval [CI] 160.6, 161.8). Latent class analysis resulted in a five‐class solution. In four classes, mood and anxiety disorders were prominent and clustered with one additional condition, as follows: Class 1 (22.4% of women with MCC), osteoarthritis; Class 2 (23.7%), obesity; Class 3 (15.8%), substance use disorders; and Class 4 (22.1%), asthma. In Class 5 (16.1%), four physical conditions clustered together: obesity, asthma, chronic hypertension and diabetes mellitus.ConclusionsMCC is common in pregnancy, with sub‐types dominated by co‐occurring mental and physical health conditions. These data show the importance of preconception and perinatal interventions, particularly integrated care strategies, to optimise treatment and stabilisation of chronic conditions in women with MCC.

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Section: Methodsmentioning

confidence: 99%

Section: Quantitative Bias Analysismentioning

confidence: 99%

Section: Quantitative Bias Analysismentioning

confidence: 99%

Section: Limitations Of the Datamentioning

confidence: 99%

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Patterns of multiple chronic conditions in pregnancy: Population‐based study using latent class analysis

Brown,

Fung,

Cohen

et al. 2023

Paediatric Perinatal Epid

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Guidance of development, validation, and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD)

Wang,

Jin,

Liu

et al. 2024

Military Med Res

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Background In recent years, there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data (RCD). These studies rely on algorithms to identify specific health conditions (e.g. diabetes or sepsis) for statistical analyses. However, there has been substantial variation in the algorithm development and validation, leading to frequently suboptimal performance and posing a significant threat to the validity of study findings. Unfortunately, these issues are often overlooked. Methods We systematically developed guidance for the development, validation, and evaluation of algorithms designed to identify health status (DEVELOP-RCD). Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development, validation, and evaluation. Subsequently, we conducted an empirical study on an algorithm for identifying sepsis. Based on these findings, we formulated specific workflow and recommendations for algorithm development, validation, and evaluation within the guidance. Finally, the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize it. Results A standardized workflow for algorithm development, validation, and evaluation was established. Guided by specific health status considerations, the workflow comprises four integrated steps: assessing an existing algorithm’s suitability for the target health status; developing a new algorithm using recommended methods; validating the algorithm using prescribed performance measures; and evaluating the impact of the algorithm on study results. Additionally, 13 good practice recommendations were formulated with detailed explanations. Furthermore, a practical study on sepsis identification was included to demonstrate the application of this guidance. Conclusions The establishment of guidance is intended to aid researchers and clinicians in the appropriate and accurate development and application of algorithms for identifying health status from RCD. This guidance has the potential to enhance the credibility of findings from observational studies involving RCD.

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Quantitative bias analysis of prevalence under misclassification: evaluation indicators, calculation method and case analysis

Cited by 2 publications

References 26 publications

Patterns of multiple chronic conditions in pregnancy: Population‐based study using latent class analysis

Patterns of multiple chronic conditions in pregnancy: Population‐based study using latent class analysis

Guidance of development, validation, and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD)

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