{'fraction_non_alphanumeric': 0.0553290623179965, 'fraction_numerical': 0.054746651135701804, 'mean_word_length': 5.028070175438597, 'pattern_counts': {'":': 0, '<': 1, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Severe acute malnutrition as a major predictor for poor nutritional outcome among adult HIV patients treated with FPB program: a case-control study

IntroductionDifferent studies had explained the treatment outcomes of the food by prescription (FBP) program among people living with HIV (PLHIV). This study aimed to assess factors affecting nutritional treatment outcome among PLHIV using FBP. MethodsAn institution-based unmatched case control study was conducted from August 1 to September 30, 2017 in public health facilities. A total of 566 samples (283 cases and 283 controls) was drawn using random sampling technique. Data were collected from FBP, ART/PRE-ART registers and client’s follow up charts. Bivariate and multivariate logistic regression analyses were conducted to estimate the relationship of the independent variables with the outcome variable, and a p-value <0.05 was considered as statistically significant at 95% confidence level. All statistical analysis was performed using SPSS version 23.ResultsIn this study severe acute malnutrition at admission (AOR=4.45. 95% CI: 2.63–7.55), moderate acute malnutrition at admission (AOR=3.8, 95% CI: 2.18–6.67), lack of regular follow up (AOR=3.81, 95% CI: 2.18–6.67), low CD4 count below 100 (AOR= 2.93, 95% CI: 1.19–7.20), hemoglobin value below 10mg/dL (AOR=2.72, 95% CI: 1.17, 6.35), and male sex (AOR=1.77, 95% CI: 1.19, 2.63) were found to be significant predictors for poor nutritional treatment outcome.Conclusions Severity of malnutrition was the major predictor for poor nutritional treatment outcome of patients who were on FBP program. The policy makers can use the information to improve HIV and nutrition interventions based on the result.