Cardiovascular preventive pharmacotherapy stratified by predicted cardiovascular risk: a national data linkage study. (18th January 2021) - Mehta, Suneela; Zhao, Jinfeng; Poppe, Katrina; Kerr, Andrew J; Wells, Sue; Exeter, Dan J; Selak, Vanessa; Grey, Corina; Jackson, Rod
Abstract: Aims: Cardiovascular disease (CVD) risk management guided by predicted CVD risk is widely recommended internationally. This is the first study to examine CVD preventive pharmacotherapy in a whole-of-country primary prevention population, stratified by CVD risk. Methods and results: Anonymized individual-level linkage of New Zealand administrative health and non-health data identified 2 250 201 individuals without atherosclerotic CVD, alive, and aged 30–74 years on 31 March 2013. We identified individuals with ≥1 dispensing by community pharmacies of blood pressure lowering (BPL) and/or lipid-lowering (LL) medications at baseline (1 October 2012–31 March 2013) and in 6-month periods between 1 April 2013 and 31 March 2016. Individuals were stratified using 5-year CVD risk equations specifically developed for application in administrative datasets. One-quarter of individuals had ≥5% 5-year risk (the current New Zealand guideline threshold for discussing preventive medications) and 5% met the ≥15% risk threshold for recommended dual therapy. By study end, dual therapy was dispensed to 2%, 18%, 34%, and 49% of individuals with <5%, 5–9%, 10–14%, and ≥15% 5-year risk, respectively. Among those dispensed baseline dual therapy, 83–89% across risk strata were still treated after 3 years. Dual therapy initiation during follow-up occurred among only 13% of high-risk individuals untreated at baseline. People without diabetes and those aged ≥65 years were more likely to remain untreated. Conclusion: Cardiovascular disease primary preventive pharmacotherapy was strongly associated with predicted CVD risk and, once commenced, was generally continued. However, only half of high-risk individuals received recommended dual therapy and treatment initiation was modest. Individually linked administrative datasets can identify clinically relevant quality improvement opportunities for entire populations.