Using linked records to improve national estimates of hospital admissions for coronary heart disease (CHD)

Research output: Contribution to conferenceAbstract

Abstract

Objectives
National statistics for hospital admissions for acute CHD based on unlinked administrative data are inflated because of inter/intra-hospital transfers or related readmissions for further investigations or procedures. Our objective was to estimate the inflation of CHD inpatient counts using multiple approaches based initially on Western Australian data that can be applied to future National studies.

Approach
We used a linked hospital morbidity dataset from the Western Australian Data Linkage System to determine hospitalisations for each CHD subcategory from 1990-2010. Transfers were defined as contiguous admissions separated by ≤1 day. Episodes-of-care (EOC) were defined as admissions (with/without transfers) that were within 28 days of the initial CHD admission. As the principal diagnosis may vary between hospitals involved in transfers or admissions within an EOC, we explored four approaches for allocating a diagnosis:i. Hierarchical diagnosis: selection of diagnosis based on clinical severity (ST-elevation myocardial infarction (STEMI)>non-STEMI>unstable angina>stable angina>other CHD>chest pain);ii. Hospital hierarchy: diagnosis based on highest hospital level (tertiary>private>other metropolitan non-tertiary>rural);iii/iv. Temporal order of diagnosis: diagnosis based on first or last record in transfer/EOC.

Results
The proportion of cases that were transferred varied according to disease severity and time: 13% (1990) to 27% (2010) for STEMI; 5% to 7% for stable angina and unchanged at 4% for chest pain. Compared to transfer-level data using the first approach, unlinked data overestimated STEMI counts by 3% (1990) to 11% (2010), stable angina by 3% to 5% and chest pain by 6% to 6%. Similarly for EOC-level data, the overestimates were 5% (1990) to 12% (2010) for STEMI, 13% to 19% for stable angina and 20% to 14% for chest pain. The four approaches for allocating a diagnosis produced differing counts with the difference being larger for more clinically severe diagnoses than for less clinically severe diagnoses. For example, using transfer-level data, the differences between approaches i and iv in 2010 were 12%, 2% and 1% for STEMI, stable angina and chest pain respectively.

Conclusion
There is a potential to overestimate counts of CHD in inpatient data if transfers and readmissions are not taken into account, and this inaccuracy can differ across disease subcategories and approach used. This has important implications where higher disease severity, such as myocardial infarction, is an indicator of population health. Transfer- or EOC-level data are more likely to reflect true CHD hospitalisation counts than unlinked-level data, and are more appropriate for epidemiological studies of CHD rates.
Original languageEnglish
DOIs
Publication statusPublished - 13 Apr 2017
EventInternational Population Data Linkage Conference - Swansea University Bay Campus, Swansea, United Kingdom
Duration: 24 Aug 201626 Aug 2016
https://www.ipdlnconference2016.org/

Conference

ConferenceInternational Population Data Linkage Conference
CountryUnited Kingdom
CitySwansea
Period24/08/1626/08/16
Internet address

Fingerprint

Coronary Disease
Episode of Care
Stable Angina
Chest Pain
Inpatients
Hospitalization
Information Storage and Retrieval
Unstable Angina
Economic Inflation
Information Systems
Tertiary Care Centers
Epidemiologic Studies
Heart Rate
Myocardial Infarction
ST Elevation Myocardial Infarction
Morbidity
Health
Population

Cite this

@conference{d23a44c84f474effa48e63c349b098f0,
title = "Using linked records to improve national estimates of hospital admissions for coronary heart disease (CHD)",
abstract = "ObjectivesNational statistics for hospital admissions for acute CHD based on unlinked administrative data are inflated because of inter/intra-hospital transfers or related readmissions for further investigations or procedures. Our objective was to estimate the inflation of CHD inpatient counts using multiple approaches based initially on Western Australian data that can be applied to future National studies.ApproachWe used a linked hospital morbidity dataset from the Western Australian Data Linkage System to determine hospitalisations for each CHD subcategory from 1990-2010. Transfers were defined as contiguous admissions separated by ≤1 day. Episodes-of-care (EOC) were defined as admissions (with/without transfers) that were within 28 days of the initial CHD admission. As the principal diagnosis may vary between hospitals involved in transfers or admissions within an EOC, we explored four approaches for allocating a diagnosis:i. Hierarchical diagnosis: selection of diagnosis based on clinical severity (ST-elevation myocardial infarction (STEMI)>non-STEMI>unstable angina>stable angina>other CHD>chest pain);ii. Hospital hierarchy: diagnosis based on highest hospital level (tertiary>private>other metropolitan non-tertiary>rural);iii/iv. Temporal order of diagnosis: diagnosis based on first or last record in transfer/EOC.ResultsThe proportion of cases that were transferred varied according to disease severity and time: 13{\%} (1990) to 27{\%} (2010) for STEMI; 5{\%} to 7{\%} for stable angina and unchanged at 4{\%} for chest pain. Compared to transfer-level data using the first approach, unlinked data overestimated STEMI counts by 3{\%} (1990) to 11{\%} (2010), stable angina by 3{\%} to 5{\%} and chest pain by 6{\%} to 6{\%}. Similarly for EOC-level data, the overestimates were 5{\%} (1990) to 12{\%} (2010) for STEMI, 13{\%} to 19{\%} for stable angina and 20{\%} to 14{\%} for chest pain. The four approaches for allocating a diagnosis produced differing counts with the difference being larger for more clinically severe diagnoses than for less clinically severe diagnoses. For example, using transfer-level data, the differences between approaches i and iv in 2010 were 12{\%}, 2{\%} and 1{\%} for STEMI, stable angina and chest pain respectively.ConclusionThere is a potential to overestimate counts of CHD in inpatient data if transfers and readmissions are not taken into account, and this inaccuracy can differ across disease subcategories and approach used. This has important implications where higher disease severity, such as myocardial infarction, is an indicator of population health. Transfer- or EOC-level data are more likely to reflect true CHD hospitalisation counts than unlinked-level data, and are more appropriate for epidemiological studies of CHD rates.",
author = "Derrick Lopez and Lee Nedkoff and Michael Hobbs and Tom Briffa and David Preen and Jane Heyworth and Frank Sanfilippo",
year = "2017",
month = "4",
day = "13",
doi = "10.23889/ijpds.v1i1.91",
language = "English",
note = "International Population Data Linkage Conference ; Conference date: 24-08-2016 Through 26-08-2016",
url = "https://www.ipdlnconference2016.org/",

}

Using linked records to improve national estimates of hospital admissions for coronary heart disease (CHD). / Lopez, Derrick; Nedkoff, Lee; Hobbs, Michael; Briffa, Tom; Preen, David; Heyworth, Jane; Sanfilippo, Frank.

2017. Abstract from International Population Data Linkage Conference, Swansea, United Kingdom.

Research output: Contribution to conferenceAbstract

TY - CONF

T1 - Using linked records to improve national estimates of hospital admissions for coronary heart disease (CHD)

AU - Lopez, Derrick

AU - Nedkoff, Lee

AU - Hobbs, Michael

AU - Briffa, Tom

AU - Preen, David

AU - Heyworth, Jane

AU - Sanfilippo, Frank

PY - 2017/4/13

Y1 - 2017/4/13

N2 - ObjectivesNational statistics for hospital admissions for acute CHD based on unlinked administrative data are inflated because of inter/intra-hospital transfers or related readmissions for further investigations or procedures. Our objective was to estimate the inflation of CHD inpatient counts using multiple approaches based initially on Western Australian data that can be applied to future National studies.ApproachWe used a linked hospital morbidity dataset from the Western Australian Data Linkage System to determine hospitalisations for each CHD subcategory from 1990-2010. Transfers were defined as contiguous admissions separated by ≤1 day. Episodes-of-care (EOC) were defined as admissions (with/without transfers) that were within 28 days of the initial CHD admission. As the principal diagnosis may vary between hospitals involved in transfers or admissions within an EOC, we explored four approaches for allocating a diagnosis:i. Hierarchical diagnosis: selection of diagnosis based on clinical severity (ST-elevation myocardial infarction (STEMI)>non-STEMI>unstable angina>stable angina>other CHD>chest pain);ii. Hospital hierarchy: diagnosis based on highest hospital level (tertiary>private>other metropolitan non-tertiary>rural);iii/iv. Temporal order of diagnosis: diagnosis based on first or last record in transfer/EOC.ResultsThe proportion of cases that were transferred varied according to disease severity and time: 13% (1990) to 27% (2010) for STEMI; 5% to 7% for stable angina and unchanged at 4% for chest pain. Compared to transfer-level data using the first approach, unlinked data overestimated STEMI counts by 3% (1990) to 11% (2010), stable angina by 3% to 5% and chest pain by 6% to 6%. Similarly for EOC-level data, the overestimates were 5% (1990) to 12% (2010) for STEMI, 13% to 19% for stable angina and 20% to 14% for chest pain. The four approaches for allocating a diagnosis produced differing counts with the difference being larger for more clinically severe diagnoses than for less clinically severe diagnoses. For example, using transfer-level data, the differences between approaches i and iv in 2010 were 12%, 2% and 1% for STEMI, stable angina and chest pain respectively.ConclusionThere is a potential to overestimate counts of CHD in inpatient data if transfers and readmissions are not taken into account, and this inaccuracy can differ across disease subcategories and approach used. This has important implications where higher disease severity, such as myocardial infarction, is an indicator of population health. Transfer- or EOC-level data are more likely to reflect true CHD hospitalisation counts than unlinked-level data, and are more appropriate for epidemiological studies of CHD rates.

AB - ObjectivesNational statistics for hospital admissions for acute CHD based on unlinked administrative data are inflated because of inter/intra-hospital transfers or related readmissions for further investigations or procedures. Our objective was to estimate the inflation of CHD inpatient counts using multiple approaches based initially on Western Australian data that can be applied to future National studies.ApproachWe used a linked hospital morbidity dataset from the Western Australian Data Linkage System to determine hospitalisations for each CHD subcategory from 1990-2010. Transfers were defined as contiguous admissions separated by ≤1 day. Episodes-of-care (EOC) were defined as admissions (with/without transfers) that were within 28 days of the initial CHD admission. As the principal diagnosis may vary between hospitals involved in transfers or admissions within an EOC, we explored four approaches for allocating a diagnosis:i. Hierarchical diagnosis: selection of diagnosis based on clinical severity (ST-elevation myocardial infarction (STEMI)>non-STEMI>unstable angina>stable angina>other CHD>chest pain);ii. Hospital hierarchy: diagnosis based on highest hospital level (tertiary>private>other metropolitan non-tertiary>rural);iii/iv. Temporal order of diagnosis: diagnosis based on first or last record in transfer/EOC.ResultsThe proportion of cases that were transferred varied according to disease severity and time: 13% (1990) to 27% (2010) for STEMI; 5% to 7% for stable angina and unchanged at 4% for chest pain. Compared to transfer-level data using the first approach, unlinked data overestimated STEMI counts by 3% (1990) to 11% (2010), stable angina by 3% to 5% and chest pain by 6% to 6%. Similarly for EOC-level data, the overestimates were 5% (1990) to 12% (2010) for STEMI, 13% to 19% for stable angina and 20% to 14% for chest pain. The four approaches for allocating a diagnosis produced differing counts with the difference being larger for more clinically severe diagnoses than for less clinically severe diagnoses. For example, using transfer-level data, the differences between approaches i and iv in 2010 were 12%, 2% and 1% for STEMI, stable angina and chest pain respectively.ConclusionThere is a potential to overestimate counts of CHD in inpatient data if transfers and readmissions are not taken into account, and this inaccuracy can differ across disease subcategories and approach used. This has important implications where higher disease severity, such as myocardial infarction, is an indicator of population health. Transfer- or EOC-level data are more likely to reflect true CHD hospitalisation counts than unlinked-level data, and are more appropriate for epidemiological studies of CHD rates.

UR - https://ijpds.org/article/view/91

U2 - 10.23889/ijpds.v1i1.91

DO - 10.23889/ijpds.v1i1.91

M3 - Abstract

ER -