Scientific
publications

AIMS: To investigate whether there were changes in the rate of prescribing of smoking cessation medications in the months leading up to, and after, the introduction of smoke-free legislation in England. DESIGN: Interrupted time-series analysis of prescribing rates using Autoregressive Integrated Moving Average (ARIMA) models. SETTING: A total of 350 general practices in England who contribute data to The Health Improvement Network (THIN) database. PARTICIPANTS: Patients in THIN aged 16+ identified from their medical records as smokers. MEASUREMENTS: Monthly rates of prescribing of nicotine replacement therapy (NRT), bupropion and varenicline were calculated from THIN from 2000 to 2009 for all smokers and for subgroups defined by patient sex, age group, history of chronic disease and quintile of the Townsend Index of Deprivation. ARIMA models were built to assess whether there were changes in prescribing before or after the introduction of smoke-free legislation over and above any long-term and seasonal trends. FINDINGS: There was a 6.4% (0.7-12.1) increase in prescribing of all smoking cessation medications in the 9 months before the introduction of smoke-free legislation and a 6.4% (1.1-11.7) reduction in the 9-month period afterwards. A 6.2% (1.4-11.0) increase in NRT prescribing and a 13.2% (4.3-22.2) increase in bupropion prescribing occurred in the 6- and 3-month periods, respectively, before smoke-free legislation was introduced, and a 5.5% (2.3-8.7) decline in NRT prescribing and a 13.7% (4.6-22.8) decline in bupropion prescribing in the 9 months post-legislation. The patterns of change in prescribing did not vary with patient demographics. CONCLUSIONS: Numbers of primary care prescriptions for smoking cessation medications increased prior to the introduction of smoke-free legislation but decreased afterwards, suggesting a temporal displacement in prescribing activity rather than a change in the overall volume of prescribing. Effects observed were consistent across all population subgroups, suggesting that the changes in prescribing will neither widen nor reduce smoking-related health inequalities.

BACKGROUND: Accurate and timely regional data on smoking trends allow tobacco control interventions to be targeted at the areas most in need and facilitate the evaluation of such interventions. Electronic primary care databases have the potential to provide a valuable source of such data due to their size, continuity and the availability of socio-demographic data. UK electronic primary care data on smoking prevalence from The Health Improvement Network (THIN) have previously been validated at the national level, but may be less representative at the regional level due to reduced sample sizes. We investigated whether this database provides valid regional data and whether it can be used to compare smoking prevalence in different UK regions. METHODS: Annual estimates of smoking prevalence by government office region (GOR) from THIN were compared with estimates of smoking prevalence from the General Lifestyle Survey (GLF) from 2000 to 2008. RESULTS: For all regions, THIN prevalence data were generally found to be highly comparable with GLF data from 2006 onwards. CONCLUSIONS: THIN primary care data could be used to monitor regional smoking prevalence and highlight regional differences in smoking in the UK.

OBJECTIVES: To determine the discrimination characteristics of a new algorithm and two existing symptom scoring systems for identification of patients with suspected colorectal cancer. DESIGN: Derivation of algorithm by a case-control study and assessment of discrimination characteristics using receiver operating characteristics (ROC) curves. Three colorectal cancer scoring systems were investigated. The Bristol-Birmingham (BB) equation, which we derived from a large primary care dataset; the CAPER score, previously derived from a primary care case-control study and a symptom score derived from National Institute of Clinical Excellence (NICE) guidance for urgent referral of symptomatic patients. Their discrimination characteristics were investigated in two datasets: the BB derivation dataset and the CAPER score derivation dataset. The main analyses were ROC curves and the areas under them for all three algorithms in both datasets. Setting Electronic primary care databases. Main outcome measures Diagnosis of colorectal cancer. RESULTS: In the BB dataset, areas under the curve were: BB equation 0.83 (95% CI 0.82 to 0.84); CAPER 0.79 (95% CI 0.79 to 0.80); the NICE guidelines 0.65 (95% CI 0.64 to 0.66). In the CAPER dataset, areas under the curve were: BB 0.92 (95% CI 0.91 to 0.94); CAPER 0.91 (95% CI 0.89 to 0.93); NICE guidelines 0.75 (95% CI 0.72 to 0.79). In subjects under 50 the discrimination characteristics of NICE referral guidelines were no better than chance. CONCLUSIONS: Both multivariable symptom scoring systems performed significantly better than NICE referral guidelines.

BACKGROUND: Both the incidence of myocardial infarction (MI) and short-term case fatality have declined in the UK. However, little is known about trends in longer-term survival following an MI. The aim of the study was to investigate trends in longer-term survival, alongside trends in medication prescribing in primary care. METHODS: Data came from 218 general practices contributing to the Health Improvement Network, a UK-wide primary care database. 3-year survival and medication use were determined for 6,586 men and 3,766 women who had an MI between 1991 and 2002 and had already survived 3 months. RESULTS: Adjusting for age and gender, the 3-year post-MI case-fatality rate among 3-month survivors fell by 28% (95% CI 13 to 40), from 83 deaths per 1000 person-years for MI occurring in 1991-2 to 61 deaths per 1000 person-years for MI in 2001-2. Relative declines in the case-fatality rate of 37% (20 to 50) and 14% (-11 to 34) were observed for men and women, respectively (p=0.06 for interaction). Prescribing in the 3 months following the MI of lipid-regulating drugs increased from 3% of patients in 1991 to 79% in 2002, prescribing of beta-blockers increased from 26% to 68%, prescribing of ACE inhibitors increased from 11% to 71% and prescribing of anti-platelet medication increased from 46% to 86%. CONCLUSION: There has been a moderate improvement in longer-term survival following an MI, distinct from improvements in short-term survival, although men may have benefited more than women. Increased medication prescribing in primary care may be a contributing factor.

BACKGROUND: The Health Improvement Network (THIN) database is a primary care electronic medical record database in the UK designed for pharmacoepidemiologic research. Matching on practice and calendar year often is used to account for secular trends in time and differences across practices. However, little is known about the consistency within practices across observation years and among practices within a given year, in THIN or other large medical record databases. METHODS: We analyzed mortality rates, cancer incidence rates, prescribing rates, and encounter rates across 415 practices from 2000 to 2007 using a practice-year as the unit of observation in separate random and fixed effects longitudinal Poisson regression models. Adjusted models accounted for aggregate practice-level characteristics (smoking, obesity, age, and Vision software experience). RESULTS: In adjusted models, subsequent calendar years were associated with lower reported mortality rates, increasing cancer reporting rates, increasing prescriptions per patient, and decreasing encounters per patient, with a corresponding linear trend (p < 0.001 for all analyses). For calendar year 2007, the ratio of the 75th percentile to the 25th percentile for crude rate of cancer, mortality, prescriptions, and encounters was 1.63, 1.63, 1.45, and 1.42, respectively. Adjusting for practice characteristics reduced the among-practice variation by approximately 40%. CONCLUSIONS: THIN data are characterized by secular trends and among-practice variation, both of which should be considered in the design of pharmacoepidemiology studies. Whether these are trends in data quality or true secular trends could not be definitively differentiated.