Predictors of adherence to hormonal contraceptives in a female veteran population

The objective was to identify predictors of adherence to hormonal contraceptives in a female veteran population.

This was a retrospective cohort study of female veterans from the VA San Diego Healthcare System. The study period was April 1, 2010, to March 31, 2012. Each patient was followed for 1 year from the index date, defined as the date of first contraceptive prescription in the study time period. Adherence was defined as a medication possession ratio ≥ 0.9. Income was estimated using zip-code-based median household income and split into quintiles (quintile 1 being the lowest-earning group). Logistic regression was used to analyze the association between adherence and the independent variables.

A total of 805 patients were included in the final analysis. The majority of the population was white (62.2%) and receiving a 3-month supply of medication (87.6%). The following independent variables were predictive of increased adherence: 3-month supply versus 1-month supply [odds ratio (OR) 1.79, 95% confidence interval (CI) 1.03-3.13], age group 40-45 versus 18-24 (OR 2.57, 95% CI 1.16-5.70) and income quintiles 3 (OR 1.96, 95% CI 1.16-3.29), 4 (OR 1.77, 95% CI 1.06-2.98) and 5 (OR 1.75, 95% CI 1.03-2.98) each versus quintile 1 as reference group. The following were associated with decreased adherence: new start versus continuing user (OR 0.25, 95% CI 0.18-0.37), OB/GYN provider versus primary care provider (OR 0.60, 95% CI 0.38-0.95), and highest weight group versus lowest weight group (OR 0.40, 95% CI 0.17-0.94).

Hormonal contraceptive adherence in the veteran population is below optimal. Providing 3-month supplies of high-value therapies such as hormonal contraceptives is one strategy that may improve adherence. Initiatives to target lower socioeconomic status or new start populations to increase contraceptive adherence should also be considered.

Adherence to hormonal contraceptives is not as well studied in the literature as some other high-value therapies. Identifying predictive variables for adherence may have implications for establishing possible interventions, or refining benefit structures, in order to increase adherence.