Inclusion of marginalized populations in HPV vaccine modeling: A systematic review.

OBJECTIVE: Models simulating the potential impacts of Human Papillomavirus (HPV) vaccine have been used globally to guide vaccination policies and programs. We sought to understand how and why marginalized populations have been incorporated into HPV vaccine simulation models. METHODS: We conducted a systematic search of PubMed, CINAHL, Scopus, and Embase to identify studies using simulation models of HPV vaccination incorporating one or more marginalized population through stratification or subgroup analysis. We extracted data on study characteristics and described these overall and by included marginalized groups. RESULTS: We identified 36 studies that met inclusion criteria, which modeled vaccination in 21 countries. Models included men who have sex with men (MSM; k = 16), stratification by HIV status (k = 9), race/ethnicity (k = 6), poverty (k = 5), rurality (k = 4), and female sex workers (k = 1). When evaluating for a marginalized group (k = 10), HPV vaccination was generally found to be cost-effective, including for MSM, individuals living with HIV, and rural populations. In studies evaluating equity in cancer prevention (k = 9), HPV vaccination generally advanced equity, but this was sensitive to differences in HPV vaccine uptake and use of absolute or relative measures of inequities. Only one study assessed the impact of an intervention promoting HPV vaccine uptake. DISCUSSION: Incorporating marginalized populations into decision models can provide valuable insights to guide decision making and improve equity in cancer prevention. More research is needed to understand the equity impact of HPV vaccination on cancer outcomes among marginalized groups. Research should emphasize implementation - including identifying and evaluating specific interventions to increase HPV vaccine uptake.

Comparing the return on investment of technologies to detect substandard and falsified amoxicillin: A Kenya case study.

The prevalence of substandard and falsified medicines in low- and middle-income countries (LMICs) is a major global public health concern. Multiple screening technologies for post-market surveillance of medicine quality have been developed but there exists no clear guidance on which technology is optimal for LMICs. This study examined the return on investment (ROI) of implementing a select number of screening technologies for post-market surveillance of amoxicillin quality in a case study of Kenya. An agent-based model, Examining Screening Technologies using Economic Evaluations for Medicines (ESTEEM), was developed to estimate the costs, benefits, and ROI of implementing screening technologies for post-market surveillance of substandard and falsified amoxicillin for treatment of pediatric pneumonia in Kenya. The model simulated sampling, testing, and removal of substandard and falsified amoxicillin from the Kenyan market using five screening technologies: (1) Global Pharma Health Fund's GPHF-Minilab, (2) high-performance liquid chromatography (HPLC), (3) near-infrared spectroscopy (NIR), (4) paper analytical devices / antibiotic paper analytical devices (PADs/aPADs), and (5) Raman spectroscopy. The study team analyzed the population impact of utilizing amoxicillin for the treatment of pneumonia in children under age five in Kenya. We found that the GPHF-Minilab, NIR, and PADs/aPADs were similar in their abilities to rapidly screen for and remove substandard and falsified amoxicillin from the Kenyan market resulting in a higher ROI compared to HPLC. NIR and PADs/aPADs yielded the highest ROI at $21 (90% Uncertainty Range (UR) $5-$51) each, followed by GPHF-Minilab ($16, 90%UR $4 - $38), Raman ($9, 90%UR $2 - $21), and HPLC ($3, 90%UR $0 - $7). This study highlights screening technologies that can be used to reduce costs, speed up the removal of poor-quality medicines, and consequently improve health and economic outcomes in LMICs. National medicine regulatory authorities should adopt these fast, reliable, and low-cost screening technologies to better detect substandard and falsified medicines, reserving HPLC for confirmatory tests.

Modeling the Health and Economic Impact of Substandard and Falsified Medicines: A Review of Existing Models and Approaches.

Substandard and falsified medicines are harmful to patients, causing prolonged illness, side effects, and preventable deaths. Moreover, they have an impact on the health system and society more broadly by leading to additional care, higher disease burden, productivity losses and loss of trust in health care. Models that estimate the health and economic impacts of substandard and falsified medicines can be useful for regulators to contextualize the problem and to make an economic case for solutions. Yet these models have not been systematically catalogued to date. We reviewed existing models that estimate the health and economic impact of substandard and falsified medicines to describe the varying modeling approaches and gaps in knowledge. We compared model characteristics, data sources, assumptions, and limitations. Seven models were identified. The models assessed the impact of antimalarial (n = 5) or antibiotic (n = 2) quality at a national (n = 4), regional (n = 2), or global (n = 1) level. Most models conducted uncertainty analysis and provided ranges around potential outcomes. We found that models are lacking for other medicines, few countries' data have been analyzed, and capturing population heterogeneity remains a challenge. Providing the best estimates of the impact of substandard and falsified medicines on a level that is actionable for decision-makers is important. To enable this, research on the impact of substandard and falsified medicines should be expanded to more medicine types and classes and tailored to more countries that are affected, with greater specificity.

Characterizing Medicine Quality by Active Pharmaceutical Ingredient Levels: A Systematic Review and Meta-Analysis across Low- and Middle-Income Countries.

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.

Educational and economic returns to cognitive ability in low- and middle-income countries: A systematic review.

There is growing interest to use early cognitive ability to predict schooling and employment outcomes in low- and middle-income countries (LMICs). Rather than using educational attainment and school enrollment as predictors of future economic growth or of improving an individual's earning potential, mounting evidence suggests that cognitive ability may be a better predictor. The relationship between cognitive ability, education, and employment are essential to predict future development in LMICs. We performed a systematic literature review and meta-analysis of the evidence regarding the relationship between cognitive ability and educational outcomes, and between cognitive ability and economic outcomes across LMICs. We searched peer-reviewed studies since 2000 that quantitatively measured these relationships. Based on an initial search of 3,766 records, we identified 14 studies, including 8 studies that examined the cognition-education link and 8 studies that assessed cognition-employment returns in LMICs. Identified studies showed that higher cognitive ability increased the probability of school enrollment, academic achievement, and educational attainment across LMICs. A meta-analysis of returns to wages from cognitive ability suggested that a standard deviation increase in cognitive test scores was associated with a 4.5% (95% CI 2.6%-9.6%) increase in wages. Investments into early cognitive development could play a critical role in improving educational and economic outcomes in LMICs. Further research should focus particularly in low-income countries with the least evidence, and examine the impact on education and economic outcomes by cognitive domains to provide more robust evidence for policy makers to take action.

Assessing the Impact of Substandard and Falsified Antimalarials in Benin.

Substandard and falsified antimalarials contribute to the global malaria burden by increasing the risk of treatment failures, adverse events, unnecessary health expenditures, and avertable deaths, yet no study has examined this impact in western francophone Africa to date. In Benin, where malaria remains endemic and is the leading cause of mortality among children younger than 5 years, there is a lack of robust data to combat the issue effectively and inform policy decisions. We adapted the Substandard and Falsified Antimalarial Research Impact model to assess the health and economic impact of poor-quality antimalarials in this population. The model simulates population characteristics, malaria infection, care-seeking behavior, disease progression, treatment outcomes, and associated costs of malaria. We estimated approximately 1.8 million cases of malaria in Benin among children younger than 5 years, which cost $193 million (95% CI, $192-$193 million) in treatment costs and productivity losses annually. Substandard and falsified antimalarials were responsible for 11% (n = 693) of deaths and nearly $20.8 million in annual costs. Moreover, we found that replacing all antimalarials with quality-ensured artemisinin combination therapies (ACTs) could result in $29.6 million in cost savings and prevent 1,038 deaths per year. These results highlight the value of improving access to quality-ensured artemisinin combination therapies for malaria treatment and increasing care-seeking in Benin. Policymakers and key stakeholders should use these findings to advocate for increased access to quality-ensured antimalarials, inform policies and interventions to improve health-care access and quality, and reduce the burden of malaria.

Importance of medicine quality in achieving universal health coverage.

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 result in annual savings of $8.3 million in Zambia, $14 million in Uganda, $79 million in two DRC regions, and $598 million in Nigeria, and was more impactful compared to other potential investments we examined. Costs of substandard and falsified antimalarials per malaria case ranged from $7 to $86, while costs per death due to poor-quality antimalarials ranged from $14,000 to $72,000. CONCLUSION: Medicines quality assurance systems play a critical role in reaching UHC goals. By ensuring the quality of essential medicines, they help deliver effective treatments that lead to less illness and result in health care savings that can be reinvested towards UHC.

Impact of substandard and falsified antimalarials in Zambia: application of the SAFARI model.

BACKGROUND: Many countries are striving to become malaria-free, but global reduction in case estimates has stagnated in recent years. Substandard and falsified medicines may contribute to this lack of progress. Zambia aims to eliminate their annual burden of 1.2 million pediatric malaria cases and 2500 child deaths due to malaria. We examined the health and economic impact of poor-quality antimalarials in Zambia. METHODS: An agent-based model, Substandard and Falsified Antimalarial Research Impact (SAFARI), was modified and applied to Zambia. The model was developed to simulate population characteristics, malaria incidence, patient care-seeking, disease progression, treatment outcomes, and associated costs of malaria for children under age five. Zambia-specific demographic, epidemiological, and cost inputs were extracted from the literature. Simulations were run to estimate the health and economic impact of poor-quality antimalarials, the effect of potential artemisinin resistance, and six additional malaria focused policy interventions. RESULTS: We simulated annual malaria cases among Zambian children under five. At baseline, we found 2610 deaths resulting in $141.5 million in annual economic burden of malaria. We estimated that elimination of substandard and falsified antimalarials would result in an 8.1% (n = 213) reduction in under-five deaths, prevent 937 hospitalizations, and realize $8.5 million in economic savings, annually. Potential artemisinin resistance could further increase deaths by 6.3% (n = 166) and cost an additional $9.7 million every year. CONCLUSIONS: Eliminating substandard and falsified antimalarials is an important step towards a malaria-free Zambia. Beyond the dissemination of insecticide-treated bed nets, indoor residual spraying, and other malaria control measures, attention must also be paid to assure the quality of antimalarial treatments.

Poor-quality antimalarials further health inequities in Uganda.

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.

The economic impact of substandard and falsified antimalarial medications in Nigeria.

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. RESULTS: We estimated that poor quality antimalarials are responsible for 12,300 deaths and $892 million ($890-$893 million) in costs annually in Nigeria. If antimalarial resistance develops, we simulated that current costs of malaria could increase by $839 million (11% increase, $837-$841 million). The northern regions of Nigeria have a greater burden as compared to the southern regions, with 9,700 deaths and $698 million ($697-$700 million) in total economic losses annually due to substandard and falsified antimalarials. Furthermore, our scenario analyses demonstrated that possible interventions-such as removing stock-outs in all facilities ($1.11 billion), having only ACTs available for treatment ($594 million), and 20% more patients seeking care ($469 million)-can save hundreds of millions in costs annually in Nigeria. CONCLUSIONS: The results highlight the significant health and economic burden of poor quality antimalarials in Nigeria, and the impact of potential interventions to counter them. In order to reduce the burden of malaria and prevent antimalarials from developing resistance, policymakers and donors must understand the problem and implement interventions to reduce the impact of ineffective and harmful antimalarials.

Development of an agent-based model to assess the impact of substandard and falsified anti-malarials: Uganda case study.

BACKGROUND: Global 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. RESULTS: The 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. CONCLUSIONS: Improving 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.