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Accuracy of administrative databases in identifying patients with hypertension.

Tu K, Campbell NR, Chen ZL, Cauch-Dudek KJ, McAlister FA - Open Med (2007)

Bottom Line: Traditionally, the determination of the occurrence of hypertension in patients has relied on costly and time-consuming survey methods that do not allow patients to be followed over time.A case-definition algorithm employing 2 outpatient physician billing claims for hypertension over a 3-year period had a sensitivity of 73% (95% confidence interval [CI] 69%-77%), a specificity of 95% (CI 93%-96%), a positive predictive value of 87% (CI 84%-90%), and a negative predictive value of 88% (CI 86%-90%) for detecting hypertensive adults compared with physician-assigned diagnoses.Given that administrative data are already routinely collected, their use is likely to be substantially less expensive compared with serial cross-sectional or cohort studies for surveillance of hypertension occurrence and outcomes over time in a large population.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Traditionally, the determination of the occurrence of hypertension in patients has relied on costly and time-consuming survey methods that do not allow patients to be followed over time.

Objectives: To determine the accuracy of using administrative claims data to identify rates of hypertension in a large population living in a single-payer health care system.

Methods: Various definitions for hypertension using administrative claims databases were compared with 2 other reference standards: (1) data obtained from a random sample of primary care physician offices throughout the province, and (2) self-reported survey data from a national census.

Results: A case-definition algorithm employing 2 outpatient physician billing claims for hypertension over a 3-year period had a sensitivity of 73% (95% confidence interval [CI] 69%-77%), a specificity of 95% (CI 93%-96%), a positive predictive value of 87% (CI 84%-90%), and a negative predictive value of 88% (CI 86%-90%) for detecting hypertensive adults compared with physician-assigned diagnoses. Compared with self-reported survey data, the algorithm had a sensitivity of 64% (CI 63%-66%), a specificity of 94%(CI 93%-94%), a positive predictive value of 77% (76%-78%), and negative predictive value of 89% (CI 88%-89%). When this algorithm was applied to the entire province of Ontario, the age- and sex-standardized prevalence of hypertension in adults older than 35 years increased from 20% in 1994 to 29% in 2002.

Conclusions: It is possible to use administrative data to accurately identify from a population sample those patients who have been diagnosed with hypertension. Given that administrative data are already routinely collected, their use is likely to be substantially less expensive compared with serial cross-sectional or cohort studies for surveillance of hypertension occurrence and outcomes over time in a large population.

No MeSH data available.


Related in: MedlinePlus

Forest plots for validation of hypertension case-definition algorithms against self-report survey data for 22,087 adult patients (23% with self-reported diagnosis of hypertension)
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figure2: Forest plots for validation of hypertension case-definition algorithms against self-report survey data for 22,087 adult patients (23% with self-reported diagnosis of hypertension)

Mentions: Comparison to self-reported diagnoses of hypertension in the 2001 Canadian Community Health Survey (Figure 2) confirmed that the "2 physician billing claims in 3 years" or "2 physician billing claims or 1 hospital discharge in 3 years" case-definition algorithms for hypertension were also reasonably accurate in that dataset. Although the comparison to self-reported diagnosis had similar specificities, sensitivities were somewhat lower than those seen when the primary care chart diagnosis was used as the reference standard. These case-definition algorithms performed well in older patients from our primary care chart audit: 81% overall agreement, 78% sensitivity, 86% specificity, 90% positive predictive value, and 70% negative predictive value for "2 physician billing claims in 3 years".


Accuracy of administrative databases in identifying patients with hypertension.

Tu K, Campbell NR, Chen ZL, Cauch-Dudek KJ, McAlister FA - Open Med (2007)

Forest plots for validation of hypertension case-definition algorithms against self-report survey data for 22,087 adult patients (23% with self-reported diagnosis of hypertension)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2801913&req=5

figure2: Forest plots for validation of hypertension case-definition algorithms against self-report survey data for 22,087 adult patients (23% with self-reported diagnosis of hypertension)
Mentions: Comparison to self-reported diagnoses of hypertension in the 2001 Canadian Community Health Survey (Figure 2) confirmed that the "2 physician billing claims in 3 years" or "2 physician billing claims or 1 hospital discharge in 3 years" case-definition algorithms for hypertension were also reasonably accurate in that dataset. Although the comparison to self-reported diagnosis had similar specificities, sensitivities were somewhat lower than those seen when the primary care chart diagnosis was used as the reference standard. These case-definition algorithms performed well in older patients from our primary care chart audit: 81% overall agreement, 78% sensitivity, 86% specificity, 90% positive predictive value, and 70% negative predictive value for "2 physician billing claims in 3 years".

Bottom Line: Traditionally, the determination of the occurrence of hypertension in patients has relied on costly and time-consuming survey methods that do not allow patients to be followed over time.A case-definition algorithm employing 2 outpatient physician billing claims for hypertension over a 3-year period had a sensitivity of 73% (95% confidence interval [CI] 69%-77%), a specificity of 95% (CI 93%-96%), a positive predictive value of 87% (CI 84%-90%), and a negative predictive value of 88% (CI 86%-90%) for detecting hypertensive adults compared with physician-assigned diagnoses.Given that administrative data are already routinely collected, their use is likely to be substantially less expensive compared with serial cross-sectional or cohort studies for surveillance of hypertension occurrence and outcomes over time in a large population.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Traditionally, the determination of the occurrence of hypertension in patients has relied on costly and time-consuming survey methods that do not allow patients to be followed over time.

Objectives: To determine the accuracy of using administrative claims data to identify rates of hypertension in a large population living in a single-payer health care system.

Methods: Various definitions for hypertension using administrative claims databases were compared with 2 other reference standards: (1) data obtained from a random sample of primary care physician offices throughout the province, and (2) self-reported survey data from a national census.

Results: A case-definition algorithm employing 2 outpatient physician billing claims for hypertension over a 3-year period had a sensitivity of 73% (95% confidence interval [CI] 69%-77%), a specificity of 95% (CI 93%-96%), a positive predictive value of 87% (CI 84%-90%), and a negative predictive value of 88% (CI 86%-90%) for detecting hypertensive adults compared with physician-assigned diagnoses. Compared with self-reported survey data, the algorithm had a sensitivity of 64% (CI 63%-66%), a specificity of 94%(CI 93%-94%), a positive predictive value of 77% (76%-78%), and negative predictive value of 89% (CI 88%-89%). When this algorithm was applied to the entire province of Ontario, the age- and sex-standardized prevalence of hypertension in adults older than 35 years increased from 20% in 1994 to 29% in 2002.

Conclusions: It is possible to use administrative data to accurately identify from a population sample those patients who have been diagnosed with hypertension. Given that administrative data are already routinely collected, their use is likely to be substantially less expensive compared with serial cross-sectional or cohort studies for surveillance of hypertension occurrence and outcomes over time in a large population.

No MeSH data available.


Related in: MedlinePlus