Primary care use of laboratory tests in Northern Ireland's Western Health and Social Care Trust: a cross-sectional study.

OBJECTIVES: To describe the laboratory test ordering patterns by general practitioners (GPs) in Northern Ireland Western Health and Social Care Trust (WHSCT) and explore demographic and socioeconomic associations with test requesting. DESIGN: Cross-sectional study. SETTING: WHSCT, Northern Ireland. : Particip ANTS: 55 WHSCT primary care medical practices that remained open throughout the study period 1 April 2011-31 March 2016. OUTCOMES: To identify the temporal patterns of laboratory test ordering behaviour for eight commonly requested clinical biochemistry tests/test groups in WHSCT. To analyse the extent of variations in laboratory test requests by GPs and to explore whether these variations can be accounted for by clinical outcomes or geographical, demographic and socioeconomic characteristics. RESULTS: The median number of adjusted test request rates over 5 consecutive years of the study period decreased by 45.7% for urine albumin/creatinine ratio (p<0.000001) and 19.4% for lipid profiles (p<0.000001) while a 60.6%, 36.6% and 29.5% increase was observed for HbA1c (p<0.000001), immunoglobulins (p=0.000007) and prostate-specific antigen (PSA) (p=0.0003), respectively. The between-practice variation in test ordering rates increased by 272% for immunoglobulins (p=0.008) and 500% for HbA1c (p=0.0001). No statistically significant relationship between ordering activity and either demographic (age and gender) and socioeconomic factors (deprivation) or Quality and Outcome Framework scores was observed. We found the rural-urban differences in between-practice variability in ordering rates for lipid profiles, thyroid profiles, PSA and immunoglobulins to be statistically significant at the Bonferroni-adjusted significance level p<0.01. CONCLUSIONS: We explored potential factors of the interpractice variability in the use of laboratory tests and found that differences in requesting activity appear unrelated to either demographic and socioeconomic characteristics of GP practices or clinical outcome indicators.

Prescriptive variability of drugs by general practitioners.

Prescription drug spending is growing faster than any other sector of healthcare. However, very little is known about patterns of prescribing and cost of prescribing between general practices. In this study, we examined variation in prescription rates and prescription costs through time for 55 GP surgeries in Northern Ireland Western Health and Social Care Trust. Temporal changes in variability of prescribing rates and costs were assessed using the Mann-Kendall test. Outlier practices contributing to between practice variation in prescribing rates were identified with the interquartile range outlier detection method. The relationship between rates and cost of prescribing was explored with Spearman's statistics. The differences in variability and mean number of prescribing rates associated with the practice setting and socioeconomic deprivation were tested using t-test and F-test respectively. The largest between-practice difference in prescribing rates was observed for Apr-Jun 2015, with the number of prescriptions ranging from 3.34 to 8.36 per patient. We showed that practices with outlier prescribing rates greatly contributed to between-practice variability. The largest difference in prescribing costs was reported for Apr-Jun 2014, with the prescription cost per patient ranging from £26.4 to £64.5. In addition, the temporal changes in variability of prescribing rates and costs were shown to undergo an upward trend. We demonstrated that practice setting and socio-economic deprivation accounted for some of the between-practice variation in prescribing. Rural practices had higher between practice variability than urban practices at all time points. Practices situated in more deprived areas had higher prescribing rates but lower variability than those located in less deprived areas. Further analysis is recommended to assess if variation in prescribing can be explained by demographic characteristics of patient population and practice features. Identification of other factors contributing to prescribing variability can help us better address potential inappropriateness of prescribing.