Influence of Using Different Databases and 'Look Back' Intervals to Define Comorbidity Profiles for Patients with Newly Diagnosed Hypertension: Implications for Health Services Researchers.
To determine the data sources and 'look back' intervals to define comorbidities.Hospital discharge abstracts database (DAD), physician claims, population registry and death registry from April 1, 1994 to March 31, 2010 in Alberta, Canada.Newly-diagnosed hypertension cases from 1997 to 2008...
Main Authors: | , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2016-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC5008755?pdf=render |
id |
doaj-f0babad73c5b4e5ea706ac0808162074 |
---|---|
record_format |
Article |
spelling |
doaj-f0babad73c5b4e5ea706ac08081620742020-11-24T20:45:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01119e016207410.1371/journal.pone.0162074Influence of Using Different Databases and 'Look Back' Intervals to Define Comorbidity Profiles for Patients with Newly Diagnosed Hypertension: Implications for Health Services Researchers.Guanmin ChenLisa LixKaren TuBrenda R HemmelgarnNorm R C CampbellFinlay A McAlisterHude QuanHypertension Outcome and Surveillance TeamTo determine the data sources and 'look back' intervals to define comorbidities.Hospital discharge abstracts database (DAD), physician claims, population registry and death registry from April 1, 1994 to March 31, 2010 in Alberta, Canada.Newly-diagnosed hypertension cases from 1997 to 2008 fiscal years were identified and followed up to 12 years. We defined comorbidities using data sources and duration of retrospective observation (6 months, 1 year, 2 years, and 3 years). The C-statistics for logistic regression and concordance index (CI) for Cox model of mortality and cardiovascular disease hospitalization were used to evaluate discrimination performance for each approach of defining comorbidities.The comorbidities prevalence became higher with a longer duration. Using DAD alone underestimated the prevalence by about 75%, compared to using both DAD and physician claims. The C-statistic and CI were highest when both DAD and physician claims were used, and model performance improved when observation duration increased from 6 months to one year or longer.The comorbidities prevalence is greatly impacted by the data source and duration of retrospective observation. A combination of DAD and physicians claims with at least one year observation duration improves predictions for cardiovascular disease and one-year mortality outcome model performance.http://europepmc.org/articles/PMC5008755?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Guanmin Chen Lisa Lix Karen Tu Brenda R Hemmelgarn Norm R C Campbell Finlay A McAlister Hude Quan Hypertension Outcome and Surveillance Team |
spellingShingle |
Guanmin Chen Lisa Lix Karen Tu Brenda R Hemmelgarn Norm R C Campbell Finlay A McAlister Hude Quan Hypertension Outcome and Surveillance Team Influence of Using Different Databases and 'Look Back' Intervals to Define Comorbidity Profiles for Patients with Newly Diagnosed Hypertension: Implications for Health Services Researchers. PLoS ONE |
author_facet |
Guanmin Chen Lisa Lix Karen Tu Brenda R Hemmelgarn Norm R C Campbell Finlay A McAlister Hude Quan Hypertension Outcome and Surveillance Team |
author_sort |
Guanmin Chen |
title |
Influence of Using Different Databases and 'Look Back' Intervals to Define Comorbidity Profiles for Patients with Newly Diagnosed Hypertension: Implications for Health Services Researchers. |
title_short |
Influence of Using Different Databases and 'Look Back' Intervals to Define Comorbidity Profiles for Patients with Newly Diagnosed Hypertension: Implications for Health Services Researchers. |
title_full |
Influence of Using Different Databases and 'Look Back' Intervals to Define Comorbidity Profiles for Patients with Newly Diagnosed Hypertension: Implications for Health Services Researchers. |
title_fullStr |
Influence of Using Different Databases and 'Look Back' Intervals to Define Comorbidity Profiles for Patients with Newly Diagnosed Hypertension: Implications for Health Services Researchers. |
title_full_unstemmed |
Influence of Using Different Databases and 'Look Back' Intervals to Define Comorbidity Profiles for Patients with Newly Diagnosed Hypertension: Implications for Health Services Researchers. |
title_sort |
influence of using different databases and 'look back' intervals to define comorbidity profiles for patients with newly diagnosed hypertension: implications for health services researchers. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2016-01-01 |
description |
To determine the data sources and 'look back' intervals to define comorbidities.Hospital discharge abstracts database (DAD), physician claims, population registry and death registry from April 1, 1994 to March 31, 2010 in Alberta, Canada.Newly-diagnosed hypertension cases from 1997 to 2008 fiscal years were identified and followed up to 12 years. We defined comorbidities using data sources and duration of retrospective observation (6 months, 1 year, 2 years, and 3 years). The C-statistics for logistic regression and concordance index (CI) for Cox model of mortality and cardiovascular disease hospitalization were used to evaluate discrimination performance for each approach of defining comorbidities.The comorbidities prevalence became higher with a longer duration. Using DAD alone underestimated the prevalence by about 75%, compared to using both DAD and physician claims. The C-statistic and CI were highest when both DAD and physician claims were used, and model performance improved when observation duration increased from 6 months to one year or longer.The comorbidities prevalence is greatly impacted by the data source and duration of retrospective observation. A combination of DAD and physicians claims with at least one year observation duration improves predictions for cardiovascular disease and one-year mortality outcome model performance. |
url |
http://europepmc.org/articles/PMC5008755?pdf=render |
work_keys_str_mv |
AT guanminchen influenceofusingdifferentdatabasesandlookbackintervalstodefinecomorbidityprofilesforpatientswithnewlydiagnosedhypertensionimplicationsforhealthservicesresearchers AT lisalix influenceofusingdifferentdatabasesandlookbackintervalstodefinecomorbidityprofilesforpatientswithnewlydiagnosedhypertensionimplicationsforhealthservicesresearchers AT karentu influenceofusingdifferentdatabasesandlookbackintervalstodefinecomorbidityprofilesforpatientswithnewlydiagnosedhypertensionimplicationsforhealthservicesresearchers AT brendarhemmelgarn influenceofusingdifferentdatabasesandlookbackintervalstodefinecomorbidityprofilesforpatientswithnewlydiagnosedhypertensionimplicationsforhealthservicesresearchers AT normrccampbell influenceofusingdifferentdatabasesandlookbackintervalstodefinecomorbidityprofilesforpatientswithnewlydiagnosedhypertensionimplicationsforhealthservicesresearchers AT finlayamcalister influenceofusingdifferentdatabasesandlookbackintervalstodefinecomorbidityprofilesforpatientswithnewlydiagnosedhypertensionimplicationsforhealthservicesresearchers AT hudequan influenceofusingdifferentdatabasesandlookbackintervalstodefinecomorbidityprofilesforpatientswithnewlydiagnosedhypertensionimplicationsforhealthservicesresearchers AT hypertensionoutcomeandsurveillanceteam influenceofusingdifferentdatabasesandlookbackintervalstodefinecomorbidityprofilesforpatientswithnewlydiagnosedhypertensionimplicationsforhealthservicesresearchers |
_version_ |
1716814652288008192 |