Utilizing Social Determinants of Health Model to Understand Barriers to Medication Adherence in Patients with Ischemic Stroke: A Systematic Review

Suboptimal medication possession ratio is associated with recurrent ischemic stroke in a veteran population

OBJECTIVES

Recurrent stroke results in higher disability and mortality but might be mitigated through interventions that improve medication adherence. The medication possession ratio (MPR) is an objective proxy for adherence that may provide an individualized risk assessment for recurrent stroke.

METHODS

This is a retrospective, longitudinal cohort study of patients with recent ischemic stroke or TIA referred to a Veterans Affairs vascular neurology outpatient clinic between 2010 and 2016. We calculated average MPR quartile for four medication classes used for secondary stroke prevention by reviewing pharmacy and medical records following an incident cerebral ischemic event. Traditional stroke risk factors were quantified using the Framingham Stroke Risk Profile (FSRP) score. We hypothesized that lower average MPR would relate to higher recurrent stroke risk more than FSRP or stroke etiological classification.

RESULTS

For 255 patients with stroke or TIA, 57 (22.4 %) patients had recurrent stroke during a median follow-up period of 5.0 years (IQR 2.0). Compared to optimal average MPR, each quartile lower average MPR was associated with higher cumulative incidence of recurrent stroke (subhazard ratio 1.63, 95 %CI: 1.24 to 2.14, p<0.001) while accounting for the competing risk of death and covarying for FSRP. Neither FSRP nor stroke etiology were associated with recurrent stroke. However, higher baseline systolic blood pressure starting at 132 mm Hg was independently associated with stroke recurrence.

CONCLUSIONS

MPR is an effective proxy measurement to assess risk of recurrent stroke. Systems-based and individualized interventions to improve medication adherence are needed to reduce recurrent stroke rates in VA populations.

Potential benefits of incorporating social determinants of health screening on comprehensive medication management effectiveness

BACKGROUND

Increasingly, pharmacists are asked to incorporate social determinants of health (SDoH) identification and referral into clinical practice. However, to date, no studies have evaluated clinical changes from embedding SDoH screening into the delivery of comprehensive medication management (CMM) in patients with chronic conditions.

OBJECTIVE

To examine the clinical effectiveness of implementing a clinical pharmacist-led SDoH screening and referral process as part of CMM encounters across a network of 7 Federally Qualified Health Centers (FQHCs).

METHODS

We used a retrospective cohort design to evaluate the effectiveness of integrating SDoH screening into CMM across a network of 7 FQHCs. A difference-in-difference approach was used to compare the effectiveness of CMM between patients with and without SDoH needs on the probability of achieving clinical control for blood pressure (<140 systolic/90 diastolic mm Hg) and diabetes (<9% hemoglobin A1c).

RESULTS

Among 807 patients receiving CMM in 2023, 595 (74%) were screened for SDoH. 55.1% of patients screened had 1 or more SDoH, most commonly facing barriers related to insurance (22.0%), language (11.3%), transportation (9.1%), health behaviors (7.1%), income/employment (5.9%), and food insecurity (5.6%). Comparing patients with SDoH needs with those without, the proportion of patients controlled at baseline was 66.3% vs 72.3% for hypertension and 39.0% vs 75.4% for diabetes, respectively. Following a CMM encounter, the proportion of patients who achieved blood pressure control increased 7.6% more (P = 0.225) among patients with SDoH needs than in those without SDoH, whereas diabetes control rates increased 13.3% more (P = 0.143).

CONCLUSIONS

Although not statistically significant, the results of this pilot evaluation suggest the potential for meaningful clinical improvements from screening and referral of SDoH needs as a part of CMM encounters. These results should be corroborated using a larger, more robust study design.

Subjective Rationalities of Nonadherence to Treatment and Vaccination in Healthcare Decision-Making

Objective

In this short report contributing to the literature on treatment and vaccination adherence, nonadherence was examined from the perspective of decision-making (DM) practice in healthcare. The objective of this study was to survey the rationalities given for treatment nonadherence and their association with DM practice.

Methods

The Ottawa decision Support Framework was used as a theoretical background for the study. Multiple choice and open-text responses indicating nonadherence were drawn from vignette survey data. The results have been analyzed and reported as descriptive statistics and findings of data-driven content analysis. The number of observatory units was 1032 in the within-subject study design.

Results

DM practice was predominantly associated with nonadherence to vaccination, whereas nonadherence to treatment was consistently associated with attitudinal reasons independent of DM practice. Nonadherence to vaccination was most often rationalized by prior negative experiences in simple DM scenarios. After other DM practices, nonadherence was rationalized by uncertainty and criticism about the benefits of the recommended vaccine. Mistrust toward healthcare providers stood out, first in treatment nonadherence generally and, second, in vaccination nonadherence after simple DM where the final decision was left to the patient.

Conclusion

In medical DM, adherence to treatment and vaccination may be achieved through a recognition of patients' previous healthcare encounters and potential trust-related concerns, which could pose a risk for nonadherence. To be able to observe these risks, patient engagement and mutual trust should be priorities in decision support in healthcare.

Reimagining Medication Adherence: A Novel Holistic Model for Hypertension Therapy

Purpose

Patients' adherence to the prescribed therapy is influenced by several personal and social factors. However, existing studies have mostly focused on individual aspects. We took a holistic approach to develop a higher-level impact factor model.

Patients and Methods

In this independent, non-interventional, cross-sectional and anonymous study design the pharmacist recruited patients who entered the pharmacy and handed in a prescription for a blood pressure medication. The patients received a paper questionnaire with a stamped return envelope to volunteer participation. A total of 476 patients in Germany who reported having at least high normal blood pressure according to the Global Hypertension Practice Guidelines were surveyed. In this study, each patient received an average of 2.49 antihypertensive prescriptions and 7.9% of all patients received a fixed-dose combination. Partial least squares structural equation modeling was performed for model analytics since it enables robust analysis of complex relationships.

Results

Emotional attitude, behavioral control, and therapy satisfaction directly explained 65% of therapy adherence. The predictive power of the out-of-sample model for the Q2-statistic was significant. The patient's overall therapy satisfaction determined medication adherence. The medication scheme's complexity also influenced the adherence levels. Therapy satisfaction was significantly shaped by the complexity of the medication scheme, behavioral control, and emotional attitude. The results demonstrated the superior performance of fixed-dose combinations against combinations of mono-agents according to the adherence level. Additionally, patient-physician and patient-pharmacist relationships influenced behavioral control of medication therapy execution. According to the A14-scale to measure the level of adherence, 49.6% of patients were classified as adherent and the remainder as non-adherent.

Conclusion

The results enable healthcare stakeholders to target attractive variables for intervention to achieve maximum effectiveness. Moreover, the proven predictive power of the model framework enables clinicians to make predictions about the adherence levels of their hypertensive patients.