Predictors of Non-Communicable Disease Risk Factors Among People Living with HIV and HIV-Negative Patients in a Nigerian Tertiary Hospital.

Abstract

Purpose: In recent years, People Living with HIV (PLHIV) in sub-Saharan Africa have experienced a growing burden of non-communicable diseases (NCDs), straining already limited health systems. Identifying behavioural risk factors for NCDs in both PLHIV and HIV-negative individuals is essential for designing targeted interventions. This study aimed to identify common risk factors for NCDs in these groups and determine their predictors.

Patients and methods: A comparative cross-sectional study was conducted among 250 PLHIV and 250 age- and sex-matched HIV-negative individuals attending the Antiretroviral Therapy (ART) and General Out-Patient clinics of Jos University Teaching Hospital from March 1-12, 2021. Participants aged 18-65 years were enrolled. Data were collected using a semi-structured questionnaire and blood pressure measurements. Multivariable logistic regression was used to identify predictors of behavioural risk factors while adjusting for potential confounders.

Results: While both groups had similar age and gender distributions, significant differences were observed in education level, marital status, employment, residence, income, ethnicity, and household size (p<0.05). Among PLHIV, long-term ART use reduced smoking by 93%. In HIV-negative individuals, urban residence and non-harmful alcohol use reduced smoking by 94% and 99%, respectively. Problematic alcohol use was significantly lower in women and non-smokers among PLHIV and HIV-negative individuals, respectively. Low physical activity was associated with poor self-rated health status in both groups, with gender and age playing additional roles among PLHIV and HIV-negative participants, respectively. Among the HIV-negative, unhealthy diets were linked to lower income.

Conclusion: Predictors of behavioural risk factors among PLHIV included ART duration, gender, and self-rated health. In the HIV-negative group, age, gender, residence, income, and self-rated health were key predictors. The notable overlap between smoking and alcohol use underscores the need for integrated interventions targeting these behaviours in both populations.