Practical utility of general practice data capture and spatial analysis for understanding COPD and asthma

T Niyonsenga, N T Coffee, P Del Fante, S B Høj, M Daniel

Research output: Contribution to journalArticle

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Abstract

BACKGROUND: General practice-based (GP) healthcare data have promise, when systematically collected, to support estimating local rates of chronic obstructive pulmonary disease (COPD) and asthma, variations in burden of disease, risk factors and comorbid conditions, and disease management and quality of care. The use of GP information systems for health improvement has been limited, however, in the scope and quality of data. This study assessed the practical utility of de-identified clinical databases for estimating local rates of COPD and asthma. We compared COPD and asthma rates to national benchmarks, examined health related risk factors and co-morbidities as correlates of COPD and asthma, and assessed spatial patterns in prevalence estimates at the small-area level.

METHODS: Data were extracted from five GP databases in western Adelaide, South Australia, for active patients residing in the region between 2012 and 2014. Prevalence estimates were computed at the statistical area 1 (SA1) spatial unit level using the empirical Bayes estimation approach. Descriptive analyses included summary statistics, spatial indices and mapping of geographic patterns. Bivariate associations were assessed, and disease profiles investigated to ascertain multi-morbidities. Multilevel logistic regression models were fitted, accounting for individual covariates including the number of comorbid conditions to assess the influence of area-level socio-economic status (SES).

RESULTS: For 33,725 active patients, prevalence estimates were 3.4% for COPD and 10.3% for asthma, 0.8% higher and 0.5% lower for COPD and asthma, respectively, against 2014-15 National Health Survey (NHS) benchmarks. Age-specific comparisons showed discrepancies for COPD in the '64 years or less' and 'age 65 and up' age groups, and for asthma in the '15-25 years' and '75 years and up' age groups. Analyses confirmed associations with individual-level factors, co-morbid conditions, and area-level SES. Geographic aggregation was seen for COPD and asthma, with clustering around GP clinics and health care centres. Spatial patterns were inversely related to area-level SES.

CONCLUSION: GP-based data capture and analysis has a clear potential to support research for improved patient outcomes for COPD and asthma via knowledge of geographic variability and its correlates, and how local prevalence estimates differ from NHS benchmarks for vulnerable age-groups.

Original languageEnglish
Article number897
Pages (from-to)1-15
Number of pages15
JournalBMC Health Services Research
Volume18
Issue number1
DOIs
Publication statusPublished - 26 Nov 2018

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Spatial Analysis
General Practice
Chronic Obstructive Pulmonary Disease
Asthma
Benchmarking
Age Groups
Economics
Health Surveys
Logistic Models
Geographic Mapping
Databases
Morbidity
Delivery of Health Care
Health Information Systems
South Australia
Quality of Health Care
Disease Management
Cluster Analysis
Health

Cite this

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title = "Practical utility of general practice data capture and spatial analysis for understanding COPD and asthma",
abstract = "BACKGROUND: General practice-based (GP) healthcare data have promise, when systematically collected, to support estimating local rates of chronic obstructive pulmonary disease (COPD) and asthma, variations in burden of disease, risk factors and comorbid conditions, and disease management and quality of care. The use of GP information systems for health improvement has been limited, however, in the scope and quality of data. This study assessed the practical utility of de-identified clinical databases for estimating local rates of COPD and asthma. We compared COPD and asthma rates to national benchmarks, examined health related risk factors and co-morbidities as correlates of COPD and asthma, and assessed spatial patterns in prevalence estimates at the small-area level.METHODS: Data were extracted from five GP databases in western Adelaide, South Australia, for active patients residing in the region between 2012 and 2014. Prevalence estimates were computed at the statistical area 1 (SA1) spatial unit level using the empirical Bayes estimation approach. Descriptive analyses included summary statistics, spatial indices and mapping of geographic patterns. Bivariate associations were assessed, and disease profiles investigated to ascertain multi-morbidities. Multilevel logistic regression models were fitted, accounting for individual covariates including the number of comorbid conditions to assess the influence of area-level socio-economic status (SES).RESULTS: For 33,725 active patients, prevalence estimates were 3.4{\%} for COPD and 10.3{\%} for asthma, 0.8{\%} higher and 0.5{\%} lower for COPD and asthma, respectively, against 2014-15 National Health Survey (NHS) benchmarks. Age-specific comparisons showed discrepancies for COPD in the '64 years or less' and 'age 65 and up' age groups, and for asthma in the '15-25 years' and '75 years and up' age groups. Analyses confirmed associations with individual-level factors, co-morbid conditions, and area-level SES. Geographic aggregation was seen for COPD and asthma, with clustering around GP clinics and health care centres. Spatial patterns were inversely related to area-level SES.CONCLUSION: GP-based data capture and analysis has a clear potential to support research for improved patient outcomes for COPD and asthma via knowledge of geographic variability and its correlates, and how local prevalence estimates differ from NHS benchmarks for vulnerable age-groups.",
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Practical utility of general practice data capture and spatial analysis for understanding COPD and asthma. / Niyonsenga, T; Coffee, N T; Del Fante, P; Høj, S B; Daniel, M.

In: BMC Health Services Research, Vol. 18, No. 1, 897, 26.11.2018, p. 1-15.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Practical utility of general practice data capture and spatial analysis for understanding COPD and asthma

AU - Niyonsenga, T

AU - Coffee, N T

AU - Del Fante, P

AU - Høj, S B

AU - Daniel, M

PY - 2018/11/26

Y1 - 2018/11/26

N2 - BACKGROUND: General practice-based (GP) healthcare data have promise, when systematically collected, to support estimating local rates of chronic obstructive pulmonary disease (COPD) and asthma, variations in burden of disease, risk factors and comorbid conditions, and disease management and quality of care. The use of GP information systems for health improvement has been limited, however, in the scope and quality of data. This study assessed the practical utility of de-identified clinical databases for estimating local rates of COPD and asthma. We compared COPD and asthma rates to national benchmarks, examined health related risk factors and co-morbidities as correlates of COPD and asthma, and assessed spatial patterns in prevalence estimates at the small-area level.METHODS: Data were extracted from five GP databases in western Adelaide, South Australia, for active patients residing in the region between 2012 and 2014. Prevalence estimates were computed at the statistical area 1 (SA1) spatial unit level using the empirical Bayes estimation approach. Descriptive analyses included summary statistics, spatial indices and mapping of geographic patterns. Bivariate associations were assessed, and disease profiles investigated to ascertain multi-morbidities. Multilevel logistic regression models were fitted, accounting for individual covariates including the number of comorbid conditions to assess the influence of area-level socio-economic status (SES).RESULTS: For 33,725 active patients, prevalence estimates were 3.4% for COPD and 10.3% for asthma, 0.8% higher and 0.5% lower for COPD and asthma, respectively, against 2014-15 National Health Survey (NHS) benchmarks. Age-specific comparisons showed discrepancies for COPD in the '64 years or less' and 'age 65 and up' age groups, and for asthma in the '15-25 years' and '75 years and up' age groups. Analyses confirmed associations with individual-level factors, co-morbid conditions, and area-level SES. Geographic aggregation was seen for COPD and asthma, with clustering around GP clinics and health care centres. Spatial patterns were inversely related to area-level SES.CONCLUSION: GP-based data capture and analysis has a clear potential to support research for improved patient outcomes for COPD and asthma via knowledge of geographic variability and its correlates, and how local prevalence estimates differ from NHS benchmarks for vulnerable age-groups.

AB - BACKGROUND: General practice-based (GP) healthcare data have promise, when systematically collected, to support estimating local rates of chronic obstructive pulmonary disease (COPD) and asthma, variations in burden of disease, risk factors and comorbid conditions, and disease management and quality of care. The use of GP information systems for health improvement has been limited, however, in the scope and quality of data. This study assessed the practical utility of de-identified clinical databases for estimating local rates of COPD and asthma. We compared COPD and asthma rates to national benchmarks, examined health related risk factors and co-morbidities as correlates of COPD and asthma, and assessed spatial patterns in prevalence estimates at the small-area level.METHODS: Data were extracted from five GP databases in western Adelaide, South Australia, for active patients residing in the region between 2012 and 2014. Prevalence estimates were computed at the statistical area 1 (SA1) spatial unit level using the empirical Bayes estimation approach. Descriptive analyses included summary statistics, spatial indices and mapping of geographic patterns. Bivariate associations were assessed, and disease profiles investigated to ascertain multi-morbidities. Multilevel logistic regression models were fitted, accounting for individual covariates including the number of comorbid conditions to assess the influence of area-level socio-economic status (SES).RESULTS: For 33,725 active patients, prevalence estimates were 3.4% for COPD and 10.3% for asthma, 0.8% higher and 0.5% lower for COPD and asthma, respectively, against 2014-15 National Health Survey (NHS) benchmarks. Age-specific comparisons showed discrepancies for COPD in the '64 years or less' and 'age 65 and up' age groups, and for asthma in the '15-25 years' and '75 years and up' age groups. Analyses confirmed associations with individual-level factors, co-morbid conditions, and area-level SES. Geographic aggregation was seen for COPD and asthma, with clustering around GP clinics and health care centres. Spatial patterns were inversely related to area-level SES.CONCLUSION: GP-based data capture and analysis has a clear potential to support research for improved patient outcomes for COPD and asthma via knowledge of geographic variability and its correlates, and how local prevalence estimates differ from NHS benchmarks for vulnerable age-groups.

KW - Adolescent

KW - Adult

KW - Aged

KW - Aged, 80 and over

KW - Asthma/epidemiology

KW - Bayes Theorem

KW - Child

KW - Child, Preschool

KW - Comorbidity

KW - Databases, Factual

KW - Female

KW - General Practice

KW - Health Surveys

KW - Humans

KW - Infant

KW - Logistic Models

KW - Male

KW - Middle Aged

KW - Prevalence

KW - Pulmonary Disease, Chronic Obstructive/epidemiology

KW - Risk Factors

KW - South Australia/epidemiology

KW - Spatial Analysis

KW - Young Adult

U2 - 10.1186/s12913-018-3714-5

DO - 10.1186/s12913-018-3714-5

M3 - Article

VL - 18

SP - 1

EP - 15

JO - BMC Health Services Research

JF - BMC Health Services Research

SN - 1472-6963

IS - 1

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ER -