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

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.

Growth hormone gene polymorphism associated with selection for milk fat production in lines of cattle.

Fifty-eight calves of both sexes from lines of Red Danish dairy breed selected for high (n = 36) and low (n = 22) milk fat production, and 32 heifers from lines of Norwegian Red dairy breed selected for high (n = 16) and low (n = 16) milk fat yield were typed for two previously reported restriction fragment length polymorphisms (RFLPs) in the growth hormone gene. The RFLPs are consistent with: (1) an insertion(I)/deletion(D) of approximately 0.9 kb in the 3'-region of the growth hormone gene and (2) a polymorphic MspI(+/-) site in the third intron. A traditional RFLP procedure was used for typing the I/D polymorphism and a polymerase chain reaction (PCR) procedure was developed for typing the MspI polymorphism. Only the I-MspI(+) and D-MspI(-) haplotypes were found. In the Red Danish lines the frequency of D-MspI(-) haplotype was 0.28 in high line and 0.05 in low line calves, this difference was significant (P < 0.01). The corresponding frequencies in the Norwegian Red lines were 0.09 in the high line and 0.0 in the low line. Attempts to screen for RFLPs in the growth hormone receptor gene and in the insulin-like growth factor-I gene were unsuccessful.