Validity of Canadian discharge abstract data for hypertension and diabetes from 2002 to 2013

Jiang, Jason; Southern, Danielle; Beck, Cynthia A.; James, Matthew T.; Lu, Mingshan; Quan, Hude

doi:10.9778/cmajo.20160128

Cited by 19 publications

(16 citation statements)

References 21 publications

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“…We selected patients aged 40 years or older because, according to Spanish studies, the prevalence of T2DM is very low under this age (<1%) [41, 42]. Furthermore, even if the validity of diabetes as a discharge diagnosis had been demonstrated in previous investigations, the possibility of miscoding T1DM as T2DM is higher in younger groups [43, 44]…”

Section: Discussionmentioning

confidence: 99%

Type 2 diabetes and postoperative pneumonia: An observational, population-based study using the Spanish Hospital Discharge Database, 2001-2015

et al. 2019

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PurposeWe analyzed temporal trends, demographic and clinical characteristics and hospital mortality rates of postoperative pneumonia among type 2 diabetes mellitus (T2DM) patients in Spain from 2001 to 2015. We also compared the incidence, comorbidities and mortality between patients with and without T2DM suffering from postoperative pneumonia. Finally, we analyzed the factors involved in the prediction of in-hospital mortality among patients suffering postoperative pneumonia.MethodsWe used the Spanish National Hospital Discharge Database for the period 2001–2015. We analyzed patients aged 40 years or over who had been hospitalized for a surgical procedure and suffered pneumonia or ventilator-associated pneumonia during their hospital admission. We compared patients with and without T2DM. The main outcome measures were the type of surgical procedure, the presence of a comorbidity, the type of isolated pathogens, admission to the emergency room (ER) and in-hospital mortality (IHM).ResultsWe selected 117,665 hospitalized patients who suffered postoperative pneumonia (16.9% with T2DM). After multivariable adjustment, T2DM patients had a 21% higher incidence of postoperative pneumonia than nondiabetic patients (IRR 1.21, 95% CI 1.03–1.42). The IHM was approximately 31% in both groups. Predictors of IHM included age, the presence of comorbidities, treatment with a pleural drainage tube, dialysis, blood transfusion, mechanical ventilation and admission to the ER. From 2001 to 2015, the IHM decreased significantly in both populations. Suffering from T2DM was not a predictor of IHM (OR 0.99, 95% CI 0.96–1.03) in our investigation.ConclusionsT2DM patients have a higher incidence of postoperative pneumonia than those without this disease. The IHM decreased from 2001 to 2015, regardless of T2DM status. T2DM did not predict a higher IHM after suffering from postoperative pneumonia.

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Section: Discussionmentioning

confidence: 99%

Type 2 diabetes and postoperative pneumonia: An observational, population-based study using the Spanish Hospital Discharge Database, 2001-2015

et al. 2019

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“…Between 2002 and 2013, sensitivity, specificity, positive predictive value and negative predictive value ranged from 81% to 98% for diabetes and the corresponding kappa index scores ranged from 0.80 to 0.89. No significant differences in the validity of coding were found across age, gender or hospital location subgroups [ 47 ]. The only study conducted in Spain by Ribera et al found similar figures with a sensitivity of 55% and 97% specificity [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Type 2 diabetes and in-hospital complications after revision of total hip and knee arthroplasty

et al. 2017

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PurposeTo assess the effect of type 2 diabetes (T2DM) on hospital outcomes such as in hospital postoperative complications (IHPC), length of hospital stay (LOHS) and in-hospital mortality (IHM) after the revision of total hip arthroplasty (RHA) and total knee arthroplasty (RKA) and to identify factors associated with IHPC among T2DM patients undergoing these procedures.MethodsWe performed a retrospective study using the Spanish National Hospital Discharge Database, 2005–2014. We included patients who were ≥40 years old that had undergone RHA and RKA. For each T2DM patient, we selected a year-, gender-, age- and Charlson Comorbidity Index-matched non-diabetic patient.ResultsWe identified 44,055 and 39,938 patients who underwent RHA (12.72% with T2DM) and RKA (15.01% with T2DM). We matched 4,700 and 5,394 couples with RHA and RKA, respectively. Any IHPC was more frequent among patients with T2DM than among non-T2DM patients (19% vs. 15.64% in the RHA cohort and 12.94% vs. 11.09% in the RKA cohort, respectively). For patients who underwent RHA, postoperative infection (4.51% vs. 2.94%, p<0.001), acute post-hemorrhagic anemia (9.53% vs. 7.70%, p<0.001), mean LOHS and IHM were significantly higher in patients with T2DM. Among RKA patients, the incidence of acute posthemorrhagic anemia (7.21% vs. 5.62%; p = 0.001) and urinary tract infection (1.13% vs. 0.72%; p = 0.029) was significantly higher in patients with diabetes. Older age, obesity, infection due to internal joint prosthesis, myocardial infarction, congestive heart failure, mild liver disease and renal disease and emergency room admission were significantly associated with a higher risk of IHPC in T2DM patients. IHPC decreased over time only in T2DM patients who underwent RHA (OR 0.94, 95%CI 0.89–0.98).ConclusionsPatients with T2DM who underwent RHA and RKA procedures had more IHPC after controlling for the effects of possible confounders. LOHS and IHM were also higher among RHA patients with diabetes. Older age, comorbidity, obesity and emergency room admission were strong predictors of IHPC in diabetic patients.

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“…Studies have shown that case identification of chronic conditions (e.g. hypertension and diabetes) are valid and stable over time in Canadian administrative data when compared with clinical data sources (21)(22)(23)(24).…”

Section: Introductionmentioning

confidence: 99%

Identifying Cases of Sleep Disorders through International Classification of Diseases (ICD) Codes in Administrative Data

Jolley

Liang

Peng

et al. 2018

IJPDS

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ObjectivesPrevalence, and associated morbidity and mortality of chronic sleep disorders have been limited to small cohort studies, however, administrative data may be used to provide representation of larger population estimates of disease. With no guidelines to inform the identification of cases of sleep disorders in administrative data, the objective of this study was to develop and validate a set of ICD-codes used to define sleep disorders including narcolepsy, insomnia, and obstructive sleep apnea (OSA) in administrative data. MethodsA cohort of adult patients, with medical records reviewed by two independent board-certified sleep physicians from a sleep clinic in Calgary, Alberta between January 1, 2009 and December 31, 2011, was used as the reference standard. We developed a general ICD-coded case definition for sleep disorders which included conditions of narcolepsy, insomnia, and OSA using: 1) physician claims data, 2) inpatient visit data, 3) emergency department (ED) and ambulatory care data. We linked the reference standard data and administrative data to examine the validity of different case definitions, calculating estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). ResultsFrom a total of 1186 patients from the sleep clinic, 1045 (88.1%) were classified as sleep disorder positive, with 606 (51.1%) diagnosed with OSA, 407 (34.4%) with insomnia, and 59 (5.0%) with narcolepsy. The most frequently used ICD-9 codes were general codes of 307.4 (Nonorganic sleep disorder, unspecified), 780.5 (unspecified sleep disturbance) and ICD-10 codes of G47.8 (other sleep disorders), G47.9 (sleep disorder, unspecified). The best definition for identifying a sleep disorder was an ICD code (from physician claims) 2 years prior and 1 year post sleep clinic visit: sensitivity 79.2%, specificity 28.4%, PPV 89.1%, and NPV 15.6%. ICD codes from ED/ambulatory care data provided similar diagnostic performance when at least 2 codes appeared in a time period of 2 years prior and 1 year post sleep clinic visit: sensitivity 71.9%, specificity 54.6%, PPV 92.1%, and NPV 20.8%. The inpatient data yielded poor results in all tested ICD code combinations. ConclusionSleep disorders in administrative data can be identified mainly through physician claims data and with some being determined through outpatient/ambulatory care data ICD codes, however these are poorly coded within inpatient data sources. This may be a function of how sleep disorders are diagnosed and/or reported by physicians in inpatient and outpatient settings within medical records. Future work to optimize administrative data case definitions through data linkage are needed.

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Validity of Canadian discharge abstract data for hypertension and diabetes from 2002 to 2013

Cited by 19 publications

References 21 publications

Type 2 diabetes and postoperative pneumonia: An observational, population-based study using the Spanish Hospital Discharge Database, 2001-2015

Type 2 diabetes and postoperative pneumonia: An observational, population-based study using the Spanish Hospital Discharge Database, 2001-2015

Type 2 diabetes and in-hospital complications after revision of total hip and knee arthroplasty

Identifying Cases of Sleep Disorders through International Classification of Diseases (ICD) Codes in Administrative Data

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