Introduction Substandard and falsified medications pose significant risks to global health. Nearly one in five antimalarials circulating in low-and middle-income countries are substandard or falsified. We assessed the health and economic impact of substandard and falsified antimalarials on children under five in Nigeria, where malaria is endemic and poor-quality medications are commonplace. Methods We developed a dynamic agent-based SAFARI (Substandard and Falsified Antimalarial Research Impact) model to capture the impact of antimalarial use in Nigeria. The model simulated children with background characteristics, malaria infections, patient care-seeking, disease progression, treatment outcomes, and incurred costs. Using scenario analyses, we simulated the impact of substandard and falsified medicines, antimalarial resistance, as well as possible interventions to improve the quality of treatment, reduce stock-outs, and educate caregivers about antimalarial quality.

BackgroundGlobal efforts to address the burden of malaria have stagnated in recent years with malaria cases beginning to rise. Substandard and falsified anti-malarial treatments contribute to this stagnation. Poor quality anti-malarials directly affect health outcomes by increasing malaria morbidity and mortality, as well as threaten the effectiveness of treatment by contributing to artemisinin resistance. Research to assess the scope and impact of poor quality anti-malarials is essential to raise awareness and allocate resources to improve the quality of treatment. A probabilistic agent-based model was developed to provide country-specific estimates of the health and economic impact of poor quality anti-malarials on paediatric malaria. This paper presents the methodology and case study of the Substandard and Falsified Antimalarial Research Impact (SAFARI) model developed and applied to Uganda.ResultsThe total annual economic impact of malaria in Ugandan children under age five was estimated at US$614 million. Among children who sought medical care, the total economic impact was estimated at $403 million, including $57.7 million in direct costs. Substandard and falsified anti-malarials were a significant contributor to this annual burden, accounting for $31 million (8% of care-seeking children) in total economic impact involving $5.2 million in direct costs. Further, 9% of malaria deaths relating to cases seeking treatment were attributable to poor quality anti-malarials. In the event of widespread artemisinin resistance in Uganda, we simulated a 12% yearly increase in costs associated with paediatric malaria cases that sought care, inflicting $48.5 million in additional economic impact annually.ConclusionsImproving the quality of treatment is essential to combat the burden of malaria and prevent the development of drug resistance. The SAFARI model provides country-specific estimates of the health and economic impact of substandard and falsified anti-malarials to inform governments, policy makers, donors and the malaria community about the threat posed by poor quality medicines. The model findings are useful to illustrate the significance of the issue and inform policy and interventions to improve medicinal quality.Electronic supplementary materialThe online version of this article (10.1186/s12936-018-2628-3) contains supplementary material, which is available to authorized users.

Substandard and falsified medications are a major threat to public health, directly increasing the risk of treatment failure, antimicrobial resistance, morbidity, mortality and health expenditures. While antimalarial medicines are one of the most common to be of poor quality in low- and middle-income countries, their distributional impact has not been examined. This study assessed the health equity impact of substandard and falsified antimalarials among children under five in Uganda. Using a probabilistic agent-based model of paediatric malaria infection (Substandard and Falsified Antimalarial Research Impact, SAFARI model), we examine the present day distribution of the burden of poor-quality antimalarials by socio-economic status and urban/rural settings, and simulate supply chain, policy and patient education interventions. Patients incur US$26.1 million (7.8%) of the estimated total annual economic burden of substandard and falsified antimalarials, including $2.3 million (9.1%) in direct costs and $23.8 million (7.7%) in productivity losses due to early death. Poor-quality antimalarials annually cost $2.9 million to the government. The burden of the health and economic impact of malaria and poor-quality antimalarials predominantly rests on the poor (concentration index −0.28) and rural populations (98%). The number of deaths among the poorest wealth quintile due to substandard and falsified antimalarials was 12.7 times that of the wealthiest quintile, and the poor paid 12.1 times as much per person in out-of-pocket payments. Rural populations experienced 97.9% of the deaths due to poor-quality antimalarials, and paid 10.7 times as much annually in out-of-pocket expenses compared with urban populations. Our simulations demonstrated that interventions to improve medicine quality could have the greatest impact at reducing inequities, and improving adherence to antimalarials could have the largest economic impact. Substandard and falsified antimalarials have a significant health and economic impact, with greater burden of deaths, disability and costs on poor and rural populations, contributing to health inequities in Uganda.

Objective To assess the importance of ensuring medicine quality in order to achieve universal health coverage (UHC). Methods We developed a systems map connecting medicines quality assurance systems with UHC goals to illustrate the ensuing impact of quality-assured medicines in the implementation of UHC. The association between UHC and medicine quality was further examined in the context of essential medicines in low-and middle-income countries (LMICs) by analyzing data on reported prevalence of substandard and falsified essential medicines and established indicators for UHC. Finally, we examined the health and economic savings of improving antimalarial quality in four countries in sub-Saharan Africa: the Democratic Republic of the Congo (DRC), Nigeria, Uganda, and Zambia. Findings A systems perspective demonstrates how quality assurance of medicines supports dimensions of UHC. Across 63 LMICs, the reported prevalence of substandard and falsified essential medicines was found to be negatively associated with both an indicator for coverage of essential services (p = 0.05) and with an indicator for government effectiveness (p = 0.04). We estimated that investing in improving the quality of antimalarials by 10% would

ABSTRACT. Substandard and falsified medicines are often reported jointly, making it difficult to recognize variations in medicine quality. This study characterized medicine quality based on active pharmaceutical ingredient (API) amounts reported among substandard and falsified essential medicines in low- and middle-income countries (LMICs). A systematic review and meta-analysis was conducted using PubMed, supplemented by results from a previous systematic review, and the Medicine Quality Scientific Literature Surveyor. Study quality was assessed using the Medicine Quality Assessment Reporting Guidelines (MEDQUARG). Random-effects models were used to estimate the prevalence of medicines with < 50% API. Among 95,520 medicine samples from 130 studies, 12.4% (95% confidence interval [CI]: 10.2–14.6%) of essential medicines tested in LMICs were considered substandard or falsified, having failed at least one type of quality analysis. We identified 99 studies that reported API content, where 1.8% (95% CI: 0.8–2.8%) of samples reported containing < 50% of stated API. Among all failed samples (N = 9,724), 25.9% (95% CI: 19.3–32.6%) reported having < 80% API. Nearly one in seven (13.8%, 95% CI: 9.0–18.6%) failed samples were likely to be falsified based on reported API amounts of < 50%, whereas the remaining six of seven samples were likely to be substandard. Furthermore, 12.5% (95% CI: 7.7–17.3%) of failed samples reported finding 0% API. Many studies did not present a breakdown of actual API amount of each tested sample. We offer suggested improved guidelines for reporting poor-quality medicines. Consistent data on substandard and falsified medicines and medicine-specific tailored interventions are needed to ensure medicine quality throughout the supply chain.