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

Area of Science:

• Public Health
• Epidemiology
• Health Services Research

Background:

• General practice (GP) data offer potential for estimating local chronic obstructive pulmonary disease (COPD) and asthma prevalence, risk factors, and care quality.
• Current GP information systems have limitations in data scope and quality, hindering health improvement efforts.
• This study evaluated the utility of de-identified GP clinical databases for local COPD and asthma rate estimation.

Purpose of the Study:

• To assess the practical utility of de-identified GP clinical databases for estimating local COPD and asthma prevalence.
• To compare local COPD and asthma rates with national benchmarks.
• To examine risk factors, comorbidities, and spatial patterns of COPD and asthma at a small-area level.

Main Methods:

• Extracted data from five GP databases in western Adelaide, South Australia (2012-2014).
• Calculated prevalence estimates at the Statistical Area 1 (SA1) level using empirical Bayes estimation.
• Employed descriptive analyses, spatial mapping, bivariate associations, and multilevel logistic regression models, including socio-economic status (SES).

Main Results:

• Prevalence estimates for 33,725 active patients were 3.4% for COPD and 10.3% for asthma.
• Local estimates showed discrepancies compared to National Health Survey (NHS) benchmarks, particularly in specific age groups.
• Confirmed associations with individual factors, comorbidities, and area-level SES; geographic clustering observed, inversely related to SES.

Conclusions:

• GP data capture and analysis show potential for COPD and asthma research, improving patient outcomes.
• Understanding geographic variability and correlates of COPD and asthma is crucial.
• Local prevalence estimates can highlight differences from NHS benchmarks for vulnerable populations.