"Pain Special Issue" "Stakeholders' perspectives and requirements on pain self-management for adolescents living with HIV/AIDS in Malawi: a cross-sectional qualitative study".
Kennedy Bashan NkhomaGertrude Tiwonge MwalabuKatherine BristoweEdgar Arnold LunguRichard HardingPublished in: AIDS care (2021)
Malawi has one of the highest HIV prevalence rates (8.9%), and data suggest 27% pain prevalence among adolescents living with HIV (ALHIV) in Malawi. Pain among ALHIV is often under-reported and pain management is suboptimal. We aimed to explore stakeholders' perspectives and experiences on pain self-management for ALHIV and chronic pain in Malawi. We conducted cross-sectional in-depth qualitative interviews with adolescents/caregiver dyads and healthcare professionals working in HIV clinics. Data were audio-recorded, transcribed verbatim and translated (where applicable) then imported into NVivo version 12 software for framework analysis. We identified three main themes: (1) Experiencing "total pain": adolescents experienced physical, psychosocial, and spiritual pain which impacted their daily life activities. (2) Current self-management approaches: participants prefer group-based self-management approaches facilitated by healthcare professionals or peers at the clinic focussing on self-management of physical, psychosocial, and spiritual pain. (3) Current pain strategies: participants used prescribed drugs, traditional medicine, and non-pharmacological interventions, such as exercises to manage pain. A person-centred care approach to self-management of chronic pain among ALHIV is needed to mitigate the impact of pain on their daily activities. There is a need to integrate self-management approaches within the existing structures such as teen clubs in primary care.
Keyphrases
- chronic pain
- pain management
- primary care
- neuropathic pain
- hiv aids
- physical activity
- young adults
- mental health
- antiretroviral therapy
- palliative care
- machine learning
- cross sectional
- high resolution
- risk factors
- electronic health record
- quality improvement
- hiv testing
- deep learning
- data analysis
- hiv positive
- high intensity
- drug induced