2013
DOI: 10.1136/jech-2013-202528
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Abstract: A diagnostic algorithm including EEG and selected treatment schedules is only moderately sensitive for the detection of epilepsy and seizures. These findings apply only to the Northern Italian scenario.

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Cited by 42 publications
(70 citation statements)
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References 17 publications
(9 reference statements)
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“…Despite the prevalence and seriousness of the condition, there is limited information on the quality of epilepsy care. This dearth results in large part from the limited availability of population‐wide databases and questions about the reliability of claims‐based algorithms …”
mentioning
confidence: 99%
“…Despite the prevalence and seriousness of the condition, there is limited information on the quality of epilepsy care. This dearth results in large part from the limited availability of population‐wide databases and questions about the reliability of claims‐based algorithms …”
mentioning
confidence: 99%
“…Nevertheless, it is possible that even after balancing measured confounders, the analysis might still be confounded by unmeasured variables. In our example, it is possible that differing degrees of generosity of any Medicare supplemental insurance (eg, patient cost-sharing, as determined by Medigap insurance) could affect the likelihood that patients receive the proper treatment 53. This is a major threat to using causal inference approaches, with patient cost-sharing levels (or overall insurance generosity) being an unmeasured confounder.…”
Section: Emulating the Target Trial Using Observational Datamentioning
confidence: 99%
“…As outlined in the “Assignment procedures” section, differences in care access are possible (eg, patient cost-sharing), which could affect the likelihood that patients receive proper treatment 53. Several approaches can evaluate the likelihood of this threat: 1) comparing 2011–2012 data to earlier periods, assessing the potential magnitude and impact of this bias, as described earlier; 2) using additional data, such as national surveys, to estimate the local area percentage use of specific insurance plans; 3) focusing on the subgroup of beneficiaries who have insurance plans that guarantee negligible cost-sharing; and 4) increasing the number of measured traits, eg, using a high-dimensional propensity score.…”
Section: Emulating the Target Trial Using Observational Datamentioning
confidence: 99%
“…[6][7][8][9] For instance, a systematic review 10 of ICD-9 code validation in Italian administrative databases reported that only a few regional databases have been validated, and just for a limited number of ICD-9 codes of diseases. [11][12][13][14][15][16][17][18] While non-clinical information in healthcare databases, such as demographic and prescription data, are highly accurate, 19 20 reliability of registered diagnoses and procedures is variable. 20 21 Determining the accuracy of the latter two categories of clinical information is important to all potential users, and involves confirming the consistency of information within the databases with the corresponding clinical records of patients.…”
Section: Introductionmentioning
confidence: 99%