Applying the Social-Ecological Approach to Evaluate Diabetes Medication Management in Older People

Adherence to treatment in pulmonary tuberculosis: Rodgers' evolutionary concept analysis

Background

Adherence to treatment is essential for the management of pulmonary tuberculosis. Nurses and healthcare professionals play a significant role in promoting adherence behavior among this population. Nevertheless, defining adherence to treatment within this particular population remains complex.

Objective

This study aimed to explore and clarify the concept of adherence to treatment among individuals with pulmonary tuberculosis.

Methods

Rodgers' evolutionary concept analysis was employed in this study. A literature search was conducted in the PubMed and Scopus databases to identify relevant studies published between July 2013 and July 2023.

Results

The attributes of adherence to treatment in pulmonary tuberculosis consist of multiple components: biological, individual, social, health service, and policy-making processes. Antecedents include various patient-related factors as well as factors associated with clinical conditions and patient-health professional engagement. Three consequences of the concept have emerged: enhanced treatment efficacy, increased commitment to tuberculosis treatment adherence, and improved health service quality.

Conclusion

This study provides a comprehensive operational definition of adherence to tuberculosis treatment, including its attributes, antecedents, and consequences. This framework will assist nurses in evaluating adherence more effectively. However, further research into the experiences of individuals adhering to tuberculosis treatment is needed to confirm and enhance these strategies.

Adherence-Promoting Design Features in Pediatric Neurostimulators for ADHD Patients

The emergence of remote health monitoring and increased at-home care emphasizes the importance of patient adherence outside the clinical setting. This is particularly pertinent in the treatment of Attention Deficit Hyperactivity Disorder (ADHD) in pediatric patients, as the population inherently has difficulty remembering and initiating treatment tasks. Neurostimulation is an emerging treatment modality for pediatric ADHD and requires strict adherence to a treatment regimen to be followed in an at-home setting. Thus, to achieve the desired therapeutic effect, careful attention must be paid to design features that can passively promote and effectively monitor therapeutic adherence. This work describes instrumentation designed to support a clinical trial protocol that tests whether choice of color, or color itself, can statistically significantly increase adherence rates in pediatric ADHD patients in an extraclinical environment. This is made possible through the development and application of an internet-of-things approach in a remote adherence monitoring technology that can be implemented in forthcoming neurostimulation devices for pediatric patient use. This instrumentation requires minimal input from the user, is durable and resistant to physical damage, and provides accurate adherence data to parents and physicians, increasing assurance that neurostimulation devices are effective for at-home care.

Cohort profile: a large EHR-based cohort with linked pharmacy refill and neighbourhood social determinants of health data to assess heart failure medication adherence

PURPOSE

Clinic-based or community-based interventions can improve adherence to guideline-directed medication therapies (GDMTs) among patients with heart failure (HF). However, opportunities for such interventions are frequently missed, as providers may be unable to recognise risk patterns for medication non-adherence. Machine learning algorithms can help in identifying patients with high likelihood of non-adherence. While a number of multilevel factors influence adherence, prior models predicting non-adherence have been limited by data availability. We have established an electronic health record (EHR)-based cohort with comprehensive data elements from multiple sources to improve on existing models. We linked EHR data with pharmacy refill data for real-time incorporation of prescription fills and with social determinants data to incorporate neighbourhood factors.

PARTICIPANTS

Patients seen at a large health system in New York City (NYC), who were >18 years old with diagnosis of HF or reduced ejection fraction (<40%) since 2017, had at least one clinical encounter between 1 April 2021 and 31 October 2022 and active prescriptions for any of the four GDMTs (beta-blocker, ACEi/angiotensin receptor blocker (ARB)/angiotensin receptor neprilysin inhibitor (ARNI), mineralocorticoid receptor antagonist (MRA) and sodium-glucose cotransporter 2 inhibitor (SGLT2i)) during the study period. Patients with non-geocodable address or outside the continental USA were excluded.

FINDINGS TO DATE

Among 39 963 patients in the cohort, the average age was 73±14 years old, 44% were female and 48% were current/former smokers. The common comorbid conditions were hypertension (77%), cardiac arrhythmias (56%), obesity (33%) and valvular disease (33%). During the study period, 33 606 (84%) patients had an active prescription of beta blocker, 32 626 (82%) had ACEi/ARB/ARNI, 11 611 (29%) MRA and 7472 (19%) SGLT2i. Ninety-nine per cent were from urban metropolitan areas.

FUTURE PLANS

We will use the established cohort to develop a machine learning model to predict medication adherence, and to support ancillary studies assessing associates of adherence. For external validation, we will include data from an additional hospital system in NYC.