Statin Adherence, Utilisation, and Prescribing Patterns. Improving effectiveness of treatment

Statins are one of the most widely studied and evidence-based medications. Randomised controlled trials have provided convincing evidence on the benefits of statin therapy in preventing cardiovascular events. Despite proven benefits, low costs, and few adverse effects, everyday effectiveness of statins is limited, since adherence to statin therapy is poor. This thesis was conducted as four pharmacoepidemiological studies using register data on statin users in real clinical care. The main purpose of the study was to evaluate prescribing patterns and to discover the lifestyle factors predicting statin nonadherence and discontinuation. This knowledge is essential in order to help physicians to motivate the adherence of their patients to treatment. In Finland, from 1998 to 2004, the number of statin initiators nearly doubled. The discovered channelling of atorvastatin and simvastatin may have affected the treatment outcomes at the public health level. It is possible that money spent on statins in Finland in 1998‒2004 could have been used in a more cost-effective way. In 2015, the percentage of patients receiving reimbursement for statins was 12% of the total population. Thus, it is a major public health and economic challenge to improve statin effectiveness and allocate therapy correctly. Among the participants with cardiovascular comorbidities, risky alcohol use or clustering of lifestyle risks were predictors of nonadherence. In addition, the prevalence of nonadherence to statins increased after retirement among both men and women. This increase in post-retirement nonadherence was highest among those receiving statins for secondary prevention. Discontinuation of statin therapy was predicted by high patient co-payment, and in women, by risky alcohol use. Recognising the predictors of nonadherence to statins is important because nonadherence is associated with an increased risk of adverse cardiovascular outcomes and higher healthcare costs. In conclusion, optimal outcomes in medical therapy require both efficacious medications and adherence to those treatments. When prescribing statins to eligible patients, the physician’s clinical expertise in recognising patients at risk of statin discontinuation and nonadherence, as well as their ability to increase adherence, may have a great effect on public health.

Analyzing change in medication use - statistical approaches

The objective of this study was to gain an understanding of the effects of population heterogeneity, missing data, and causal relationships on parameter estimates from statistical models when analyzing change in medication use. From a public health perspective, two timely topics were addressed: the use and effects of statins in populations in primary prevention of cardiovascular disease and polypharmacy in older population. Growth mixture models were applied to characterize the accumulation of cardiovascular and diabetes medications among apparently healthy population of statin initiators. The causal effect of statin adherence on the incidence of acute cardiovascular events was estimated using marginal structural models in comparison with discrete-time hazards models. The impact of missing data on the growth estimates of evolution of polypharmacy was examined comparing statistical models under different assumptions for missing data mechanism. The data came from Finnish administrative registers and from the population-based Geriatric Multidisciplinary Strategy for the Good Care of the Elderly study conducted in Kuopio, Finland, during 2004–07. Five distinct patterns of accumulating medications emerged among the population of apparently healthy statin initiators during two years after statin initiation. Proper accounting for time-varying dependencies between adherence to statins and confounders using marginal structural models produced comparable estimation results with those from a discrete-time hazards model. Missing data mechanism was shown to be a key component when estimating the evolution of polypharmacy among older persons. In conclusion, population heterogeneity, missing data and causal relationships are important aspects in longitudinal studies that associate with the study question and should be critically assessed when performing statistical analyses. Analyses should be supplemented with sensitivity analyses towards model assumptions.