Determinants of trends in reported antibiotic use among sick children under five years of age across low-income and middle-income countries in 2005–17: A systematic analysis of user characteristics based on 132 national surveys from 73 countries

Abstract

This study aimed to analyze any reported antibiotic use for children aged <5 years with fever, diarrhea or cough with fast or difficult breathing (outcome) from low-income and middle-income countries (LMICs) during 2005-2017 by user characteristics: rural/urban residence, maternal education, household wealth, and healthcare source visited. Methods: Based on 132 demographic and health surveys and multiple indicator cluster surveys from 73 LMICs, the outcome by user characteristics for all country-years was estimated using a hierarchical Bayesian linear regression model. Results: Across LMICs during 2005-2017, the greatest relative increases in the outcome occurred in rural areas, poorest quintiles and least educated populations, particularly in low-income countries and South-East Asia. In low-income countries, rural areas had a 72% relative increase from 17.8% (Uncertainty Interval (UI): 5.2%-44.9%) in 2005 to 30.6% (11.7%-62.1%) in 2017, compared to a 29% relative increase in urban areas from 27.1% (8.7%-58.2%) in 2005 to 34.9% (13.3%-67.3%) in 2017. Despite these increases, the outcome was consistently highest in urban areas, wealthiest quintiles, and populations with the highest maternal education. Conclusion: These estimates suggest that the increasing reported antibiotic use for sick children aged <5 years in LMICs during 2005-2017 was driven by gains among groups often underserved by formal health services.

Antibiotic stories: a mixed-methods, multi-country analysis of household antibiotic use in Malawi, Uganda and Zimbabwe

“…Recognition of significant increases in antibiotic use in LMICs [2][3][4][5][6] and its implications for rising AMR has led to urgent calls to understand reasons for antibiotic (mis)use, especially beyond prescriber settings. 8 9 This article presents a mixed-methods, multi-country analysis of the patterns of and reasons behind household antibiotic use practices in a range of rural and urban settings in Malawi, Uganda and Zimbabwe.…”

“…To mitigate against the predicted health and economic impacts of antimicrobial resistance (AMR), the WHO's global action plan has called for improved surveillance of antibiotic use to inform antimicrobial stewardship strategies. 1 Many low-income and middle-income countries (LMICs) have experienced steep rises in antibiotic use in recent decades [2][3][4][5][6] and been identified as particular targets for interventions. Surveillance at national level has been challenging to implement, and has generally prioritised the collection of…”

“…Analyses of demographic and health surveys and multi-indicator cluster surveys suggest that the greatest rises in antibiotic use in LMICs have been among rural, the poorest and least educated populations, particularly in low-income countries and South-East Asia, although the greatest overall rates are seen in urban centres and among the most affluent and most educated demographics. 3 12 Studies have further sought to capture the extent of the misuse of antibiotics, undergirded by benchmarking ‘(ir)rational’ practice against indicators of expected clinical need. Findings indicate high rates of inappropriate use across LMICs, 13–16 and that non-prescription use or ‘self-medication’ can make up as much as 100% in some populations.…”

Antibiotic Use for Febrile Illness among Under-5 Children in Bangladesh: A Nationally Representative Sample Survey

“…We assessed the overall effect of age in model 2. Similarly, the household wealth index and parents' education affect children's antibiotic usage and have an interaction effect [41][42][43][44]. We used model 3 to account for the effect of the household wealth index and the parents' education level.…”

“…We investigated seven associated factors with antibiotic usage in children with febrile illnesses based on existing literature [13,26,31,32,[36][37][38][39][40][41][42][43][44]. Explanatory variables utilised in this study are defined in Table 3.…”