Constructing a Nurse-led Cardiovascular Disease Intervention in Rural Ghana: A Qualitative Analysis

Background: Cardiovascular disease (CVD) is a growing burden in low- and middle-income countries. Ghana seeks to address this problem by task-shifting CVD diagnosis and management to nurses. The Community-Based Health Planning and Services (CHPS) initiative offers maternal and pediatric health care throughout Ghana but faces barriers to providing CVD care. We employed in-depth interviews to identify solutions to constraints in CVD care to develop a nurse-led CVD intervention in two districts of Ghana’s Upper East Region. Objective: This study sought to identify non–physician-led interventions for the screening and treatment of cardiovascular disease to incorporate into Ghana’s current primary health care structure. Methods: Using a qualitative descriptive design, we conducted 31 semistructured interviews of community health officers (CHOs) and supervising subdistrict officers (SDOs) at CHPS community facilities. Summative content analysis revealed the most common intervention ideas and endorsements by the participants. Findings: Providers endorsed three interventions: increasing community CVD knowledge and engagement, increasing nonphysician prescribing abilities, and ensuring provider access to medical and transportation equipment. Providers suggested community leaders and volunteers should convey CVD knowledge, marshaling established gathering practices to educate communities and formulate action plans. Providers requested lectures paired with experiential learning to improve their prescribing confidence. Providers recommended revising reimbursement and equipment procurement processes for expediting access to necessary supplies. Conclusions: Frontline CHPS primary care providers believe CVD care is feasible. They recommended a three-pronged intervention that combines community outreach, provider training, and logistical support, thereby expanding task-shifting beyond hypertension to include other CVD risk factors. This model could be replicable elsewhere.