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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 primary care chart data for 1676 patients older than 35 years (32% with chart diagnosis of hypertension)
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figure1: Forest plots for validation of hypertension case-definition algorithms against primary care chart data for 1676 patients older than 35 years (32% with chart diagnosis of hypertension)

Mentions: Health card numbers were encrypted and converted into unique identifiers and linked to the Ontario Health Insurance Plan (OHIP) physician billing claims database and the Canadian Institute for Health Information (CIHI) hospital discharge abstracts database for each patient in the chart audit study. A variety of case-definition algorithms (see Figure 1) using hypertension codes (ICD-9-CM codes 401.x, 402.x, 403.x, 404.x, or 405.x and/or ICD-10 codes I10.x, I11.x, I12.x, I13.x, or I15.x in CIHI fiscal years 2002-2004) in physician billing claims alone and with hospital discharge records over various time frames were explored for percentage agreement, kappa score, sensitivity, specificity, positive/negative predictive values and area under the Receiver Operating Characteristic (ROC) curve. The chart audit diagnosis was used as the reference standard, and 95% confidence intervals were calculated using a binomial probability distribution. We defined sensitivity as the proportion of individuals with hypertension documented in their physician chart who were identified as having hypertension using administrative data, and we defined specificity as the proportion of individuals without hypertension documented in their physician chart who were identified as not having hypertension using the administrative data. We defined positive predictive value as the proportion of individuals identified as having hypertension in the administrative data whose diagnosis was confirmed by chart audit, and negative predictive value as the proportion of individuals identified as not having hypertension in the administrative data whose lack of a hypertension diagnosis was confirmed by chart audit.


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 primary care chart data for 1676 patients older than 35 years (32% with chart diagnosis of hypertension)
© Copyright Policy - open-access
Related In: Results  -  Collection

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

figure1: Forest plots for validation of hypertension case-definition algorithms against primary care chart data for 1676 patients older than 35 years (32% with chart diagnosis of hypertension)
Mentions: Health card numbers were encrypted and converted into unique identifiers and linked to the Ontario Health Insurance Plan (OHIP) physician billing claims database and the Canadian Institute for Health Information (CIHI) hospital discharge abstracts database for each patient in the chart audit study. A variety of case-definition algorithms (see Figure 1) using hypertension codes (ICD-9-CM codes 401.x, 402.x, 403.x, 404.x, or 405.x and/or ICD-10 codes I10.x, I11.x, I12.x, I13.x, or I15.x in CIHI fiscal years 2002-2004) in physician billing claims alone and with hospital discharge records over various time frames were explored for percentage agreement, kappa score, sensitivity, specificity, positive/negative predictive values and area under the Receiver Operating Characteristic (ROC) curve. The chart audit diagnosis was used as the reference standard, and 95% confidence intervals were calculated using a binomial probability distribution. We defined sensitivity as the proportion of individuals with hypertension documented in their physician chart who were identified as having hypertension using administrative data, and we defined specificity as the proportion of individuals without hypertension documented in their physician chart who were identified as not having hypertension using the administrative data. We defined positive predictive value as the proportion of individuals identified as having hypertension in the administrative data whose diagnosis was confirmed by chart audit, and negative predictive value as the proportion of individuals identified as not having hypertension in the administrative data whose lack of a hypertension diagnosis was confirmed by chart audit.

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