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

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.