• 1.

    Ozawa S, Evans D, Bessias S, Haynie D, Yemeke T, Laing S, Herrington J, 2018. Prevalence and Estimated Economic Burden of Substandard and Falsified Medicines in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis. JAMA Netw Open 1: e181662.

    • Search Google Scholar
    • Export Citation
  • 2.

    World Health Organization, 2017. A Study on the Public Health and Socioeconomic Impact of Substandard and Falsified Medical Products. Geneva, Switzerland: WHO.

    • Search Google Scholar
    • Export Citation
  • 3.

    Hall Z, Allan EL, van Schalkwyk DA, van Wyk A, Kaur H, 2016. Degradation of artemisinin-based combination therapies under tropical conditions. Am J Trop Med Hyg 94: 9931001.

    • Search Google Scholar
    • Export Citation
  • 4.

    Kelesidis T, Falagas ME, 2015. Substandard/counterfeit antimicrobial drugs. Clin Microbiol Rev 28: 443464.

  • 5.

    World Health Organizaiton, 1999. Counterfeit Drugs: Guidelines for the Development of Measures to Combat Counterfeit Drugs. Geneva, Switzerland: WHO.

    • Search Google Scholar
    • Export Citation
  • 6.

    Institute of Medicine, 2013. Buckley GJ, Gostin LO, eds. Countering the Problem of Falsified and Substandard Drugs. Washington, DC: The National Academies Press.

    • Search Google Scholar
    • Export Citation
  • 7.

    White N, 1999. Antimalarial drug resistance and combination chemotherapy. Philos Trans R Soc Lond B Biol Sci 354: 739749.

  • 8.

    White NJ, Pongtavornpinyo W, Maude RJ, Saralamba S, Aguas R, Stepniewska K, Lee SJ, Dondorp AM, White LJ, Day NPJ, 2009. Hyperparasitaemia and low dosing are an important source of anti-malarial drug resistance. Malaria J 8: 253.

    • Search Google Scholar
    • Export Citation
  • 9.

    Newton PN, Green MD, Fernandez FM, Day NP, White NJ, 2006. Counterfeit anti-infective drugs. Lancet Infect Dis 6: 602613.

  • 10.

    Newton PN et al. 2011. Poor quality vital anti-malarials in Africa—an urgent neglected public health priority. Malaria J 10: 352.

  • 11.

    Okeke IN, Lamikanra A, Edelman R, 1999. Socioeconomic and behavioral factors leading to acquired bacterial resistance to antibiotics in developing countries. Emerg Infect Dis 5: 1827.

    • Search Google Scholar
    • Export Citation
  • 12.

    Wirtz V, Hogerzeil H, Gray A, Bigdeli M, de Joncheere C, Ewen M, Gyansa-Lutterodt M, Jing S, Luiza V, Mbindyo R, Moller H, Moucheraud C, Pecoul B, Rago L, Rashidian A, Ross-Degnan D, Stephens P, Teerawattananon Y, ’t Hoen E, Wagner A, Yadav P, Reich M. Essential medicines for universal health coverage. Lancet 389: 403476.

    • Search Google Scholar
    • Export Citation
  • 13.

    World Health Organization, 2018. World Malaria Report 2018. Geneva, Switzerland: WHO.

  • 14.

    United States Agency for International Development, 2017. President’s Malaria Initiative Democratic Republic of the Congo Malaria Operational Plan FY 2017. Washington, DC: USAID.

    • Search Google Scholar
    • Export Citation
  • 15.

    World Health Organization, 2016. Global Health Observatory Data Repository. Geneva, Switzerland: WHO.

  • 16.

    ACTwatch Group Mpanya G, Tshefu A, Likwela JL, 2017. The malaria testing and treatment market in Kinshasa, Democratic Republic of the Congo, 2013. Malaria J 16: 94.

    • Search Google Scholar
    • Export Citation
  • 17.

    Programme National de Lutte contre le Paludisme (PNLP)/Mali, Institut National de la Statistique (INSTAT)/Mali, INFO-STAT/Mali, Institut National de la Recherche en Santé Publique (INRSP)/Mali, ICF International, 2016. République du Mali Enquête sur les Indicateurs du Paludisme (EIPM) 2015. Bamako, Mali: PNLP, INSTAT, INFO-STAT, INRSP, and ICF International.

    • Search Google Scholar
    • Export Citation
  • 18.

    Atemnkeng MA, De Cock K, Plaizier-Vercammen J, 2007. Quality control of active ingredients in artemisinin-derivative antimalarials within Kenya and DR Congo. Trop Med Int Health 12: 6874.

    • Search Google Scholar
    • Export Citation
  • 19.

    Atemnkeng MA, Chimanuka B, Plaizier-Vercammen J, 2007. Quality evaluation of chloroquine, quinine, sulfadoxine-pyrimethamine and proguanil formulations sold on the market in east Congo DR. J Clin Pharm Ther 32: 123132.

    • Search Google Scholar
    • Export Citation
  • 20.

    Newton PN, Hanson K, Goodman C, 2017. Do anti-malarials in Africa meet quality standards? The market penetration of non quality-assured artemisinin combination therapy in eight African countries. Malar J 16: 204.

    • Search Google Scholar
    • Export Citation
  • 21.

    Conte R, Paolucci M, 2014. On agent-based modeling and computational social science. Front Psychol 5: 668.

  • 22.

    Ministère du Plan et Suivi de la Mise en œuvre de la Révolution de la Modernité (MPSMRM)/Congo, Ministère de la Santé Publique (MSP)/Congo, ICF International, 2014. République Démocratique du Congo Enquête Démographique et de Santé (EDS-RDC) 2013–2014. Rockville, MD: MPSMRM, MSP, and ICF International.

    • Search Google Scholar
    • Export Citation
  • 23.

    World Health Organization, 2015. World Malaria Report 2015. Geneva, Switzerland: WHO.

  • 24.

    World Health Organizaiton, 2016. World Malaria Report 2016. Geneva, Switzerland: WHO.

  • 25.

    Malaria Atlas Project, 2018. The Malaria Atlas Project, Interactive Map Tool. Available at: https://map.ox.ac.uk/explorer/#/explorer. Accessed February 20, 2018.

    • Search Google Scholar
    • Export Citation
  • 26.

    ACTwatch Group and ASF, 2015. ACTwatch Study Reference Document: The Democratic Republic of the Congo Outlet Survey 2015. Washington, DC: PSI.

    • Search Google Scholar
    • Export Citation
  • 27.

    World Health Organization, 2015. Guidelines for the Treatment of Malaria, 3rd edition. Geneva, Switzerland: WHO.

  • 28.

    The Global Fund to Fight AIDS, Tuberculosis and Malaria, 2016. Global Fund Grants to the Democratic Republic of the Congo, Audit Report. Geneva, Switzerland: The Global Fund.

    • Search Google Scholar
    • Export Citation
  • 29.

    Kazadi WM, Vong S, Makina BN, Mantshumba JC, Kabuya W, Kebela BI, Ngimbi NP, 2003. Assessing the efficacy of chloroquine and sulfadoxine-pyrimethamine for treatment of uncomplicated Plasmodium falciparum malaria in the Democratic Republic of Congo. Trop Med Int Health 8: 868875.

    • Search Google Scholar
    • Export Citation
  • 30.

    Achan J, Talisuna AO, Erhart A, Yeka A, Tibenderana JK, Baliraine FN, Rosenthal PJ, D’Alessandro U, 2011. Quinine, an old anti-malarial drug in a modern world: role in the treatment of malaria. Malar J 10: 144.

    • Search Google Scholar
    • Export Citation
  • 31.

    Nosten F, White NJ, 2007. Artemisinin-based combination treatment of falciparum malaria. Am J Trop Med Hyg 77: 181192.

  • 32.

    Tshilumba PM, Amuri SB, Kaghowa ER, Mbikay DM, Impele AB, Duez P, Ndoumba JBK, 2015. Survey of counterfeit anti-infectives medicines sold in Lubumbashi [in French]. Pan Afr Med J 22: 318.

    • Search Google Scholar
    • Export Citation
  • 33.

    Ashley EA, White NJ, 2014. The duration of Plasmodium falciparum infections. Malar J 13: 500.

  • 34.

    Ilunga-Ilunga F, Leveque A, Okenge Ngongo L, Tshimungu Kandolo F, Dramaix M, 2014. Costs of treatment of children affected by severe malaria in reference hospitals of Kinshasa, Democratic Republic of Congo. J Infect Dev Ctries 8: 15741583.

    • Search Google Scholar
    • Export Citation
  • 35.

    Kim SY, Sweet S, Slichter D, Goldie SJ, 2010. Health and economic impact of rotavirus vaccination in GAVI-eligible countries. BMC Public Health 10: 253.

    • Search Google Scholar
    • Export Citation
  • 36.

    The World Bank, 2015. GDP Per Capita (Current US$). Available at: https://data.worldbank.org/indicator/ny.gdp.pcap.cd. Accessed April 17, 2018.

    • Search Google Scholar
    • Export Citation
  • 37.

    UNdata, 2014. Life Expectancy at Birth for Both Sexes Combined (Years). Available at: http://data.un.org/Data.aspx?d=PopDiv&f=variableID%3A68. Accessed April 17, 2018.

    • Search Google Scholar
    • Export Citation
  • 38.

    World Health Organization, 2018. Choosing Interventions that are Cost Effective (WHO-CHOICE), Purchasing Power Parity 2005. Geneva, Switzerland: WHO. Available at: https://www.who.int/choice/costs/ppp/en. Accessed April 17, 2018.

    • Search Google Scholar
    • Export Citation
  • 39.

    Dondorp AM et al. 2009. Artemisinin resistance in Plasmodium falciparum malaria. N Engl J Med 361: 455467.

  • 40.

    Mvumbi DM, Kayembe JM, Situakibanza H, Bobanga TL, Nsibu CN, Mvumbi GL, Melin P, De Mol P, Hayette MP, 2015. Falciparum malaria molecular drug resistance in the Democratic Republic of Congo: a systematic review. Malar J 14: 354.

    • Search Google Scholar
    • Export Citation
  • 41.

    Infectious Diseases Data Observatory, 2018. Medicine Quality Literature Report. Oxford, United Kingdom: Infectious Diseases Data Observatory.

    • Search Google Scholar
    • Export Citation
  • 42.

    Schiavetti B et al. 2018. The quality of medicines used in children and supplied by private pharmaceutical wholesalers in Kinshasa, Democratic Republic of Congo: a prospective survey. Am J Trop Med Hyg 98: 894903.

    • Search Google Scholar
    • Export Citation
  • 43.

    Lubell Y, Dondorp A, Guerin PJ, Drake T, Meek S, Ashley E, Day NP, White NJ, White LJ, 2014. Artemisinin resistance—modelling the potential human and economic costs. Malar J 13: 452.

    • Search Google Scholar
    • Export Citation
  • 44.

    Das D et al. 2013. Effect of high-dose or split-dose artesunate on parasite clearance in artemisinin-resistant falciparum malaria. Clin Infect Dis 56: e48e58.

    • Search Google Scholar
    • Export Citation
  • 45.

    O’Connell KA et al. 2011. Got ACTs? Availability, price, market share and provider knowledge of anti-malarial medicines in public and private sector outlets in six malaria-endemic countries. Malar J 10: 326.

    • Search Google Scholar
    • Export Citation
  • 46.

    Ntamabyaliro NY et al. 2018. Drug use in the management of uncomplicated malaria in public health facilities in the Democratic Republic of the Congo. Malar J 17: 189.

    • Search Google Scholar
    • Export Citation
  • 47.

    Mvumbi DM, Bobanga TL, Melin P, De Mol P, Kayembe JMN, Situakibanza HNT, Mvumbi GL, Nsibu CN, Umesumbu SE, Hayette MP, 2016. High prevalence of Plasmodium falciparum infection in asymptomatic individuals from the Democratic Republic of the Congo. Malar Res Treat 2016: 5405802.

    • Search Google Scholar
    • Export Citation
  • 48.

    Siddiqui MR, Willis A, Bil K, Singh J, Mukomena Sompwe E, Ariti C, 2015. Adherence to artemisinin combination therapy for the treatment of uncomplicated malaria in the Democratic Republic of the Congo. F1000Res 4: 51.

    • Search Google Scholar
    • Export Citation
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Modeling the Economic Impact of Substandard and Falsified Antimalarials in the Democratic Republic of the Congo

Sachiko OzawaDivision of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina;
Department of Maternal and Child Health, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina;

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Deson G. HaynieDepartment of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, Virginia;

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Sophia BessiasEnterprise Analytics and Data Sciences, University of North Carolina Health Care, Chapel Hill, North Carolina;

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Sarah K. LaingDivision of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina;

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Emery Ladi NgamasanaCarolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina;

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Tatenda T. YemekeDivision of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina;

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Daniel R. EvansDuke University School of Medicine, Durham, North Carolina

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Substandard and falsified medicines pose significant risks to global health, including increased deaths, prolonged treatments, and growing drug resistance. Antimalarials are one of the most common medications to be of poor quality in low- and middle-income countries. We assessed the health and economic impact of substandard and falsified antimalarials for children less than 5 years of age in the Democratic Republic of the Congo, which has one of the world’s highest malaria mortality rates. We developed an agent-based model to simulate patient care-seeking behavior and medicine supply chain processes to examine the impact of antimalarial quality in Kinshasa province and Katanga region. We simulated the impact of potential interventions to improve medicinal quality, reduce stockouts, or educate caregivers. We estimated that substandard and falsified antimalarials are responsible for $20.9 million (35% of $59.6 million; 95% CI: $20.7–$21.2 million) in malaria costs in Kinshasa province and $130 million (43% of $301 million; $129–$131 million) in malaria costs in the Katanga region annually. If drug resistance to artemisinin were to develop, total annual costs of malaria could increase by $17.9 million (30%; $17.7–$18.0 million) and $73 million (24%; $72.2–$72.8 million) in Kinshasa and Katanga, respectively. Replacing substandard and falsified antimalarials with good quality medicines had a larger impact than interventions that prevented stockouts or educated caregivers. The results highlight the importance of improving access to good quality antimalarials to reduce the burden of malaria and mitigate the development of antimalarial resistance.

INTRODUCTION

Substandard and falsified medicines are a substantial problem in low- and middle-income countries (LMICs), where a recent meta-analysis found 13.6% (95% CI: 11.0–16.3%) of essential medications including 19.1% (15.0-23.3%) of antimalarials to be of poor quality.1 The World Health Organization (WHO) defines substandard medicines as “authorized medical products that fail to meet their quality standards, specifications, or both.”2 Medical products that “deliberately and fraudulently misrepresent their identity, composition, or source” are classified as falsified.2 Inadequate supply chain and storage conditions, as well as poor quality or illegal manufacturing, can result in medications that contain no active pharmaceutical ingredients, improper ingredients, or subtherapeutic concentrations of the stated ingredients.35 Poor-quality medicines introduce risks of prolonged illness, disability, and even death due to treatment failure.2 Patients who would otherwise recover with proper treatment remain sick or could experience adverse effects after consuming substandard or falsified medicines. Beyond the individual patient, subtherapeutic doses of medicines increase the risk of drug resistance for entire communities.68 Falsified medicines with improper ingredients may trigger additional illnesses or create the need for more medical treatment. Over time, poor clinical outcomes associated with substandard and falsified medicines undermine trust in the healthcare system and its legitimate providers.6,9

Treatments for malaria are one of the most commonly found medications to be substandard or falsified in LMICs. These poor-quality malaria medications are estimated to result in between 31,000 and 116,000 additional deaths in sub-Saharan Africa annually, with the large range due to differences in malaria case estimates by the World Malaria Report and the Clinton Health Access Initiative.2 Poor-quality antimalarial medicines not only threaten the global progress made toward reducing malaria morbidity and mortality but also risk compromising the effectiveness of medications through antimicrobial resistance.10 This is particularly concerning in developing countries, where many antimalarials are available without a prescription from a variety of private vendors (e.g., formal and informal pharmacies and street vendors), or are not available in public facilities.11 In addition to the health effects, substandard and falsified antimalarials impose avertable economic costs on already poor patients who face increased morbidity and mortality, and who must use up their limited resources to pay for healthcare out-of-pocket.6,12 No studies to date have examined the country-level impact of substandard and falsified antimalarials or simulated the effects of potential interventions to improve malaria medication quality in LMICs.

The Democratic Republic of the Congo (DRC) has one of the world’s largest malaria burden with approximately 25 million cases and 46,000 deaths in 2017.13 This accounts for an estimated 12% of the malaria cases in sub-Saharan Africa.13 Most of these deaths are among children less than 5 years of age.15 Plasmodium falciparum is responsible for nearly 95% of malaria infections in the DRC.14 Although national guidelines endorsed by the Ministry of Public Health (MOPH) and the National Malaria Control Program recommend artemisinin-based combination therapies (ACTs) for first-line treatment of malaria, alternative treatments are known to comprise more than 50% of the market share in parts of the DRC.16,17 In addition, non–quality-assured ACTs and substandard and counterfeit artemisinin derivatives have been found in high prevalence in the DRC, particularly in the private marketplace.16,1820 Our study estimates the health and economic impact that substandard and falsified antimalarials have on children with malaria in the DRC, and examines the effects of potential interventions to inform policy.

MATERIALS AND METHODS

Development of an agent-based model (ABM).

We developed an ABM using the NetLogo simulation software (Wilensky, U. Evanston IL, version 6.0) to evaluate the impact of interventions to improve medicinal quality by reducing substandard and falsified medicines in the DRC. Agent-based models can model complex adaptive systems, such as patient care-seeking behavior and medicine supply chain processes that evolve over time.21 We simulated the use of antimalarials in two regions in the DRC, Kinshasa province and the region formerly encompassing Katanga province, where antimalarial market data were available from ACTwatch.16 At the time of our model development, data inputs were obtained from sources citing information from Katanga province, which in 2015 was reorganized into four separate provinces (Tanganyika, Haut-Lomami, Lualaba, and Haut-Katanga). We used epidemiological, demographic, and healthcare–seeking behavior data from the Malaria Atlas Project, the Demographic and Health Survey (DHS), the WHO World Malaria Report, ACTwatch, and other sources to inform key model inputs (Table 1).2225 ACTwatch provided data on antimalarial availability and costs.16,26

Table 1

Key model inputs, sources, and assumptions

Model inputsKinshasa provinceKatanga regionSource
Demographic and epidemiological data< 5 population at risk1,014,6321,624,934DHS 201422
Malaria incidence (6–59 months old)0.4060.923MAP25
Case fatality rate (overall)0.01520.0152WHO 201624
 With medications0.00380.0038Estimated
 No medications0.03040.0304Estimated
Probability of hospitalization Quality-assured ACTs0.0500.050Estimated
 Non–quality-assured ACTs/other treatments*0.0990.099WHO 201523
 No treatment0.1990.199Estimated
Probability of testing0.3910.162DHS 201422
Healthcare–seeking behaviorCare-seeking behavior (%)
 Public facilities5.2813.09ACTwatch 2015,26 DHS 201422
 Private practices6.0719.76
 Pharmacies/drug stores74.6849.89
 Street vendors/hawkers0.000.49
 Self/neighbors†6.864.94
 No treatment7.1111.82
Medication stock by facilityPublic facilitiesACTwatch 201716
 % Stock quality-assured ACTs33.6257.36
 % Stock non–quality-assured ACTs17.243.02
 % Stock other treatments50.0039.62
Private practices
 % Stock quality-assured ACTs15.6928.83
 % Stock non–quality-assured ACTs31.3717.12
 % Stock other treatments54.9054.05
Pharmacies/drug stores
 % Stock quality-assured ACTs1.3620.48
 % Stock non–quality-assured ACTs42.6528.32
 % Stock other treatments56.0051.20
Street vendors/hawkers
 % Stock quality-assured ACTs0.000.00
 % Stock non–quality-assured ACTs0.000.00
 % Stock other treatments100.00100.00
Self/neighbors
 % Stock quality-assured ACTs4.004.00Assumption
 % Stock non–quality-assured ACTs8.008.00Assumption
 % Stock other treatments88.0088.00Assumption
Medication in-stock probabilitiesAntimalarial in-stock probabilities (%)
 Public facilities0.9850.980ACTwatch 201526
 Private practices0.6340.721
 Pharmacies/drug stores00
 Street vendors/hawkers00
 Self/neighbor00
Medication effectivenessProbability of cure
 Quality-assured ACTs0.9500.950WHO 201523
 Non–quality-assured ACTs0.7660.700Estimated
 Other treatments0.6880.627Estimated
 Other treatments (no SF scenario)0.8540.851Estimated
 Other treatments (AMR scenario)0.5610.580Estimated
 No treatment0.0000.000Assumption
Medication costs by facility‡Public facilitiesACTwatch 201526
 Cost of quality-assured ACTs$0.56 ($0.00–$1.40)$0.56 ($0.00–$1.40)
 Cost of non–quality-assured ACTs$0.56 ($0.00–$1.40)$0.56 ($0.00–$1.40)
 Cost of other treatments$0.44 ($0.33–$0.55)$0.44 ($0.33–$0.55)
Private facilities
 Cost of quality-assured ACTs$0.00 ($0.00–$0.00)$0.00 ($0.00–$1.10)
 Cost of non–quality-assured ACTs$3.07 ($1.64–$4.17)$2.19 ($1.23–$4.38)
 Cost of other treatments$0.33 ($0.33–$0.55)$0.33 ($0.33–$0.55)
Pharmacies/drug stores
 Cost of quality-assured ACTs$6.47 ($4.93–$8.77)$1.64 ($1.10–$2.19)
 Cost of non–quality-assured ACTs$3.84 ($3.07–$4.93)$4.38 ($2.19–$4.93)
 Cost of other treatments$0.44 ($0.33–$0.55)$0.33 ($0.33–$0.55)
Street vendors/hawkers
 Cost of other treatments$0.44 ($0.33–$0.55)$0.33 ($0.33–$0.55)
Self/neighbors
 Cost of all treatments00Assumption
Non-medication costsCost per hospitalization§$150.53 ($10.83–$204.68)$150.53 ($10.83–$204.68)Ilunga-llunga 201434
Testing costs$1.10 ($0.55–$1.10)$1.10 ($0.55–$1.10)ACTwatch 201526
Average transportation costs$2.58$2.58Kim 201035
Productivity loss per sick day$1.25$1.25World Bank 201536
Productivity loss per death$11,518.47$11,518.47UN Data 201437

ACTs = artemisinin-based combination therapies; AMR = antimicrobial resistance; DHS = Demographic and Health Survey; MAP = Malaria Atlas Project; SF = substandard or falsified; UN = United Nations; WHO = World Health Organization.

* Other treatments include artemisinin-containing monotherapies, sulfadoxine–pyrimethamine, and oral quinine.

† The self/neighbor category included medications obtained from friends, personal stores, or reported use of traditional medicines.

‡ All costs with ranges are median costs with the interquartile range. All other costs are means or values calculated from means. All costs are presented in 2017 US dollars.

§ Direct state hospital costs were utilized.

Figure 1 presents the structure of our ABM. A one-year time horizon was used for the analysis, divided into 52 one-week intervals. Each week, children could become ill with malaria based on the regional incidence for children less than 5 years of age.22 Those who fell ill or remained ill from previous weeks underwent simulated care-seeking for malaria treatment. Patients in the model either sought care at one of five types of locations (public facility, private practice, pharmacy/drug store, street vendor/hawker, or neighbor/home/family), or chose not to seek care, based on published data on care-seeking behavior specific to malaria in the DRC.22,26 Location of care-seeking is an important driver in the model, as pharmacies and informal drug stores (separate in our model from private sector health practices) play a large role in the distribution of substandard and falsified medicines in the DRC. The model was run for 1,000 modeled children less than 5 years of age in each region. Results are presented for all children less than 5 years of age in Kinshasa province and Katanga region.

Figure 1.
Figure 1.

The model structure depicting a week in a 52-week care-seeking cycle.

Citation: The American Journal of Tropical Medicine and Hygiene 100, 5; 10.4269/ajtmh.18-0334

Malaria treatment.

We simulated each location where children could obtain malaria treatment with stock of medications, including quality-assured ACTs, non–quality-assured ACTs, and other treatments. Other treatments included artemisinin-containing monotherapies, sulfadoxine–pyrimethamine (SP), and oral quinine. Based on the WHO recommendation that quality-assured ACTs should be the first-line treatment in all uncomplicated cases of malaria, we considered quality-assured ACTs as the gold standard.27 When children who sought care visited facilities, they received one of these medications or faced a stockout based on the probability of antimalarial availability at each location. The average probabilities of facilities facing stockouts in our model were based on data collected by the Global Fund to Fight AIDS, Tuberculosis and Malaria on the average number of stockout days for ACTs over a 15-month period across health centers and general reference hospitals in the DRC.28 For non–quality-assured ACTs and other treatments, the published effectiveness of each medication23,2931 was reduced by the prevalence of substandard and falsified medication from the literature, under the assumption that substandard and falsified medicines were ineffective. For other treatments, we estimated a combined effectiveness of artemisinin-containing monotherapies, SP, and oral quinine adjusted by market share and estimated to be substandard or falsified.26 The prevalence of substandard and falsified medicines was estimated at 19.1% based on a meta-analysis of antimalarial quality.18,32 Without medication, it was assumed that there is no parasite clearance and no spontaneous recovery.33

In our simulation, each modeled child faced a corresponding probability of recovering, remaining ill, or dying based on the type of medication they recieved.24 Reported overall probability of hospitalization was adjusted for each treatment. The probability of hospitalization with only quality-assured ACTs was assumed to be half the overall rate of hospitalization and the probability of hospitalization with no medication was set at twice the overall rate. The overall reported case fatality rate (CFR) was also adjusted by treatment, where we assumed the CFR without medication was double the average CFR, and with treatment was a quarter of the average CFR.

Economic impact.

Throughout each of these steps, the cost of medication and expected transportation costs, testing costs, and hospitalization costs were estimated using a cost-of-illness approach.26,34,35 We estimated productivity losses including short-term losses due to caretakers’ lost work time as well as long-term productivity losses due to child death from malaria, using the DRC’s gross domestic product (GDP) per capita from the World Bank.36 Short-term productivity losses estimated the loss of daily per capita GDP of caretakers, whereas long-term productivity losses projected economic productivity loss of deceased children from age 15 years until life expectancy, discounted to the present value at 3%.36,37 We took a patient perspective using data on out-of-pocket costs.16,34,35 Transportation costs were converted from 2005 international dollars using purchasing power parity conversions.38 All costs are presented in 2017 US$ and rounded to the nearest three digits.

Scenario analyses.

We examined two extreme scenarios to help describe the potential effects of SF antimalarials. First, we examined a scenario with no substandard and falsified medicines, in which there is no reduction in treatment efficacy for either ACTs or other treatments due to substandard and falsified medications. In another scenario, we examined the possibility where P. falciparum develops drug resistance to artemisinin-based antimalarials. To simulate this, we reduced the effectiveness of both quality-assured and non–quality-assured ACTs to that of other treatments, and made artemisinin monotherapies ineffective to examine the effects.

Finally, we conducted additional scenario analyses to assess the potential impact that various interventions to improve stockouts, antimalarial quality, caregiver education, and healthcare access could have in the DRC. These intervention scenarios included the following: 1) having no antimalarial stockouts in public facilities, 2) having no antimalarial stockouts in private facilities, 3) having no antimalarial stockouts in public and private facilities, 4) replacing other treatments with ACTs such that only ACTs are present in the health system, 5) educating caregivers to select quality-assured ACTs, 6) educating caregivers to select ACTs and reject other treatments if offered, 7) removing all substandard and falsified ACTs in the health system, and 8) getting everyone to seek care. The model was presented to various experts who have worked on malaria and/or in the DRC to help validate the structure, inputs, and assumptions.

RESULTS

We assessed the health and economic burden of malaria in two regions, Kinshasa province and Katanga region, in the DRC (Table 2). Annually, we estimated approximately 1.9 million cases of malaria in children less than 5 years of age in these two regions. This resulted in 260,000 hospitalizations, 25,000 deaths, and 3 million person-weeks of annual malaria illness. The burden of malaria was much higher in the Katanga region, with 3.6 times the number of estimated cases compared with Kinshasa province.

Table 2

Estimated annual health and economic impact of substandard and falsified antimalarials in Kinshasa and Katanga, the Democratic Republic of the Congo*

Burden of malariaDifference with better quality antimalarialsAdditional costs with antimicrobial resistance
Kinshasa provinceKatanga regionKinshasa province% DiffP-valueKatanga region% DiffP-valueKinshasa province% DiffP-valueKatanga region% DiffP-value
Health impactMalaria cases412,0001,500,0006800.65000.91−32000.98−10000.66
Malaria deaths3,90021,000−1,270−33< 0.01†−9,100−43< 0.01†1,18030< 0.01†5,10024< 0.01†
Malaria hospitalizations57,000206,000−35,800−63< 0.01†−128,000−62< 0.01†16,20028< 0.01†50,30024< 0.01†
Malaria days of illness4,310,0005,600,000−1,055,000−24< 0.01†−4,890,000−28< 0.01†1,250,00029< 0.01†4,260,00025< 0.01†
Malaria person-weeks616,0002,480,000−151,000−24< 0.01†−699,000−28< 0.01†178,00029< 0.01†608,00025< 0.01†
Economic impactMedication costs$981,000$2,490,000$464,00047< 0.01‡$741,00030< 0.01‡$282,00029< 0.01†$607,00024< 0.01†
Hospitalization costs$8,560,000$31,000,000−$5,380,000−63< 0.01†−$19,200,000−62< 0.01†$2,440,00028< 0.01†$7,600,00024< 0.01†
Healthcare transportation costs$181,000$2,110,000−$44,900−25< 0.01†−$598,000−28< 0.01†$51,30028< 0.01†$513,00024< 0.01†
Diagnostic costs$30,300$145,000−$7,800−26< 0.01†−$42,000−29< 0.01†$8,44028< 0.01†$35,30024< 0.01†
Short-term productivity losses$5,360,000$21,500,000−$1,310,000−24< 0.01†−$6,040,000−28< 0.01†$1,550,00029< 0.01†$5,280,00025< 0.01†
Long-term productivity losses$44,500,000$244,000,000−14,700,000−33< 0.01†−$105,000,000−43< 0.01†$13,500,00030< 0.01†$59,000,00024< 0.01†
All productivity losses$49,900,000$266,000,000−16,000,000−32< 0.01†−$111,000,000−42< 0.01†$15,100,00030< 0.01†$64,000,00024< 0.01†
Total costs$59,600,000$301,000,000−$20,900,000−35< 0.01†−$130,000,000−43< 0.01†$17,900,00030< 0.01†$73,000,00024< 0.01†

* All estimates are rounded to the nearest three digits. All costs are presented in 2017 US dollars.

P-value < 0.05.

‡ Additional medication costs are incurred with better quality antimalarials where artemisinin-based combination therapies are more costly.

The overall economic impact due to malaria was around $60 million (95% CI: $58.2–$61.0 million) in Kinshasa and $301 million (95% CI: $297–$306 million) in Katanga. Of the costs in Kinshasa, about $981,000 were attributable to medication costs, $8.56 million to hospitalization costs, $181,000 to transportation, and $30,000 to malaria diagnostic testing. The $301 million in costs in Katanga included $2.49 million for drug costs, $31 million for hospitalization costs, $2.11 million for transportation, and $145,000 for malaria diagnostic testing. Productivity losses due to malaria cases per year amounted to $49.9 million in Kinshasa and $266 million in Katanga.

Medicine quality and artemisinin resistance scenarios.

If all substandard and falsified antimalarials are replaced with good-quality medicines, the health and economic impact of malaria decreases significantly. Overall, having good-quality antimalarials resulted in 35,800 (63%) and 128,000 (62%) fewer hospitalizations in Kinshasa and Katanga, respectively. In Kinshasa, the total number of person-weeks of illness due to malaria decreased by 151,000 weeks (24%) and the number of deaths decreased by 1,270 (33%), with no substandard and falsified antimalarials. In Katanga, having better quality medicines yielded 699,000 (28%) less person-weeks of malaria and 9,100 (43%) less deaths due to malaria compared with baseline. The annual economic impact of substandard and falsified antimalarials was estimated at $20.9 million (95% CI: $20.7–$21.2 million; 35%) and $130 million (95% CI: $129–$131 million; 43%) in Kinshasa and Katanga, respectively (P < 0.01). Without poor-quality antimalarials, hospitalization costs could be reduced by $5.38 million (63%) and $19.2 million (62%) in Kinshasa and Katanga, respectively.

We estimated that the emergence of artemisinin resistance could result in 16,200 (28%) more hospitalizations, 178,000 (29%) more person-weeks of annual malaria illness, and 1180 (30%) more deaths in Kinshasa. In Katanga, 50,300 (24%) more hospitalizations, 608,000 more (25%) person-weeks of illness, and 5,100 (24%) more deaths were estimated with antimalarial resistance. Drug resistance increased the overall costs by $17.9 million (95% CI: $17.7–$18.0 million; 30%) in Kinshasa and by $73 million (95% CI: $72.2–$72.8 million; 24%) in Katanga compared with baseline. This included increased productivity losses of $15.1 million (30%) and $64 million (24%), as well as increased hospitalization costs of $2.44 million (28%) and $7.6 million (24%) in Kinshasa and Katanga, respectively.

Intervention scenarios.

Figure 2 presents the ABM model interface where simulations visually demonstrate the utilization of medications by care-seeking location and tally the health and economic impact. The switches on the left allow users to run various scenario analyses.

Figure 2.
Figure 2.

Model snapshot in Netlogo.

Citation: The American Journal of Tropical Medicine and Hygiene 100, 5; 10.4269/ajtmh.18-0334

Table 3 presents the impact that nine interventions which improve stockouts, antimalarial quality, caregiver education, and healthcare access can have on reducing deaths and overall costs. First, we found that removing substandard and falsified antimalarials has a substantial impact, second only to a scenario of everyone seeking care. Removing substandard and falsified ACTs makes up 55% and 65% of this impact in Kinshasa and Katanga, respectively ($11.5 million in Kinshasa and $85 million in Katanga), whereas the rest ($9.4 million in Kinshasa and $45 million in Katanga) comes from improving the quality of other treatments. If we were able to educate all caregivers to choose quality-assured ACTs or to choose ACTs over other treatments, this reduced the economic impact by $7.1–$9.9 million in Kinshasa and by $82.5–$97.2 million in Katanga. Following the WHO treatment guidelines of only using ACTs for treatment was estimated to reduce deaths by 759 in Kinshasa and 7,100 in Katanga annually, while decreasing costs by $7.4 million and $96.3 million in Kinshasa and Katanga, respectively. Improving stockouts at public and/or private facilities was less effective in reducing the burden of malaria, but still saved substantial costs, especially in Katanga.

Table 3

Impact of intervention scenarios to improve health and economic outcomes

InterventionsKinshasa provinceKatanga region
Deaths*Economic impact (in $ millions)Deaths*Economic impact (in $ millions)
No.P-valueEstimate95% CIP-valueNo.P-valueEstimate95% CIP-value
Base case3,860$59.6[58.2, 61.0]21,200$301[297, 306]
Difference from base case
 Drug resistance1,170< 0.01†$17.9[17.7, 18.0]< 0.01†5,100< 0.01†$73[72.2, 72.8]< 0.01†
 No public stockouts440.65‡$0.5[0.42, 0.49]0.66−1770.50−$2.3[−2.28, −2.41]0.44
 No public or private stockouts−422< 0.01†−$5.5[−5.34, −5.59]< 0.01†−5,030< 0.01†−$63.8[−63.3, −64.3]< 0.01†
 No private stockouts−487< 0.01†−$6.2[−6.36, −6.1]< 0.01†−4,750< 0.01†−$60.2[−59.7, −60.7]< 0.01†
 Only ACTs−759< 0.01†−$7.4[−7.29, −7.44]< 0.01†−7,100< 0.01†−$96.3[−95.5, −97.1]< 0.01†
 Reject substandard/falsified ACTs−468< 0.01†−$7.1[−7.05, −7.23]< 0.01†−6,120< 0.01†−$82.5[−81.9, −83.1]< 0.01†
 Reject other treatments−750< 0.01†−$9.9[−9.79, −9.98]< 0.01†−7,140< 0.01†−$97.2[−96.3, −98.0]< 0.01†
 No substandard/falsified ACTs−1,690< 0.01†−$11.5[−11.4, −11.7]< 0.01†−6,240< 0.01†−$85.0[−84.2, −85.9]< 0.01†
 No substandard/falsified antimalarials−1,270< 0.01†−$20.9[−20.7, −21.2]< 0.01†−9,120< 0.01†−$130.0[−129, −131]< 0.01†
 Everyone seeks care−578< 0.01†−$20.3[−20.0, −20.6]< 0.01†−13,500< 0.01†−$163.0[−161, −165]< 0.01†

ACTs = artemisinin-based combination therapies; CI = confidence intervals.

* Deaths are rounded to the nearest three digits.

P-value < 0.05.

‡ Because of the low probability of stockouts (1.5%) at public facilities in Kinshasa, we did not observe a statistically significant impact by removing public stockouts.

DISCUSSION

Substandard and falsified antimalarials threaten the progress made in combatting malaria, as seen in the significant health and economic impact of poor-quality antimalarials in the DRC. We found that providing access to quality-assured ACTs and quality antimalarial treatments could reduce the economic impact of malaria by about 35–43% (P < 0.01) and decrease deaths by 33–43% (P < 0.01). The emergence of artemisinin resistance, on the other hand, could increase both the number of deaths and overall costs by as much as 24–30% (P < 0.01). Such potential costs are important to consider, as resistance to artemisinin-based therapies has already been observed in Asia and could bring significant harm if it develops in parts of Africa, including in the DRC.39,40

Although we based the substandard and falsified antimalarial prevalence at 19.1% based on a meta-analysis specific to antimalarials,1 a number of studies have examined the quality of medicines in the DRC,41 including a recent study that found the prevalence of poor-quality antimalarials in Kinshasa to be as high as 62%.42 Our estimate on the impact of substandard and falsified antimalarials may therefore be conservative.

While direct comparisons with other models are difficult to make, our results are similar to estimates made by Lubell and others who modeled the global impact of artemisinin resistance on populations affected by P. falciparum malaria.43 Their results showed total direct medical costs for malaria treatment increased by 28% across malaria-endemic countries, whereas our model estimated increases in direct medical treatment costs between 24% in Katanga to 29% in Kinshasa. Although Lubell and others assumed 30% treatment failure based on a study on artesunate resistance in Cambodia,43,44 our model used a conservative estimate between 7% and 35% decreased treatment efficacy with artemisinin resistance.

Quality-assured ACTs are the WHO-recommended first-line regimen for treating P. falciparum malaria, and national guidelines in the DRC include ACTs (artesunate-amodiaquine) as first-line treatment options for uncomplicated malaria.13 Our results illustrate that greater focus should be given to increasing demand and reliable provision of quality-assured ACTs, where non–quality-assured ACTs comprised between 3% and 17% of antimalarials in public facilities, 17% and 31% in private practices, and 28% and 43% in pharmacy and drug stores in Katanga and Kinshasa, respectively.16 This presents a major opportunity for government and international stakeholders to increase access to quality-assured ACTs. Our results also support the idea that cost differences between quality-assured and non–quality-assured ACTs would likely be offset by the economic impact of having quality ACTs.

The MOPH of the DRC presently contracts out some services to private health facilities to help alleviate the burden on the public healthcare system. Previous studies have reported lower rates of private provider knowledge on appropriate treatment for malaria as compared with public providers.45 Our model scenarios in which caregivers are educated to choose quality-assured ACTs or to choose ACTs over other treatments illustrate that this could be effective in reducing the burden of malaria. In addition, our scenario analysis of improving care-seeking shows that educating caregivers of young children with malaria to seek care could significantly decrease the burden and costs of malaria. Although the scenario of getting everyone to seek care may be unrealistic, a large impact could still be observed by incrementally improving care-seeking rates.

Improving stockouts at public or private facilities provided some cost savings, but the benefits were much smaller than other interventions examined. This could be due to the low probability of stockouts at public facilities (1.5%) and low level of care-seeking at public facilities in our model, particularly in Kinshasa (5.28%).26 Records from ACTwatch illustrate that stockouts have improved in these regions of the DRC in recent years.26 However, stockouts due to ineffective stock management were noted as a problem in the past and the occurrence of a large stockout in the future could have much graver health and economic impacts than what these scenarios demonstrate.

This study is a novel first attempt at an ABM to examine the country-level impacts of substandard and falsified antimalarials in a LMIC. With this model, we are able to contribute a baseline assessment of the impact of substandard and falsified antimalarials in two geographic areas of the DRC. As new data become available and as interventions are carried out and evaluated, we will be able to provide updated analyses and better estimates of the impact. However, our model is presently limited by the quality of available data in the DRC, which had several implications. First, although we conducted a rigorous literature search to identify publications to inform our model inputs and also consulted multiple experts, nationally representative data were often unavailable for the DRC. Therefore, our findings represent two regions in the DRC and not for the entire country. Second, this model is not able to capture the heterogeneity in the population because of limited data availability. Each agent in the model has the same malaria incidence, care-seeking rate, and chance of hospitalization and death, where further analysis cannot be conducted to examine the variation within a heterogeneous population. Future research should examine the heterogenous impact within the population. Third, economic impact presented is from the patient perspective as we were unable to obtain costs for the health sector. We also assumed the same transportation costs for urban and rural areas because of limited data availability. In addition, the economic impact is likely a conservative estimate, as a high proportion of children in the DRC receive concomitant prescriptions with antimalarials, which was not accounted for in our model.46 Fourth, our disease model is based on symptomatic recovery rather than parasite clearance, which may affect transmission patterns and overall health outcomes.47 We were also not able to estimate the impact of treatment adherence, as there was little recent data available.48 Fifth, our analysis does not distinguish which drugs may have resulted from poor manufacturing versus from product degradation or improper cold-chain management. Rather, drug quality was factored into efficacy stated in the literature, where substandard and falsified antimalarials were assumed not to be effective. Finally, this analysis focused on the benefits of improving the quality of antimalarials and did not estimate the costs of interventions. Future research could examine the costs of these interventions to estimate the return on investment.

Results from this study should be used to help inform policymakers to ensure investments are made in the most effective and efficient ways to decrease the burden of malaria. Interventions may succeed in reducing the health and economic impact of malaria on Congolese families if they focus on providing good quality, efficacious antimalarial regimens in addition to encouraging people to seek good quality treatment and care. Donors and implementation agencies should continue to work with the local government on alleviating barriers to access quality-assured ACTs. The DRC government, including the MOPH, Department of Pharmacy and Medicine (DPM), the National Medicine Supply Program (PNAM), and donors working on reducing, eliminating, and eradicating malaria must invest in improving access to high-quality antimalarial medications. Novel approaches to engage the private sector and improve the quality of antimalarials in the market could help to alleviate malaria burden and associated costs. More data are needed to improve modeled estimates and examine the impact of substandard and falsified antimalarials for the entire country.

Acknowledgments:

We thank malaria and DRC experts who provided their feedback and insights on the model development including Peggy Bentley, Amanda Corbett, Karine Dube, Steve Meshnick, and Richard Steketee. We thank Jim Herrington for his support and Colleen Higgins for her contributions. We are grateful to Megan Littrell for her generosity in sharing data and her observations from the DRC We thank Narcisse Embeke and Jules Mwenze from Management Sciences for Health in the DRC, as well as Mame Niang and the DRC team of the President’s Malaria Initiative for their valuable comments. We also thank Marcel Lama for sharing his expertise on the private sector market.

REFERENCES

  • 1.

    Ozawa S, Evans D, Bessias S, Haynie D, Yemeke T, Laing S, Herrington J, 2018. Prevalence and Estimated Economic Burden of Substandard and Falsified Medicines in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis. JAMA Netw Open 1: e181662.

    • Search Google Scholar
    • Export Citation
  • 2.

    World Health Organization, 2017. A Study on the Public Health and Socioeconomic Impact of Substandard and Falsified Medical Products. Geneva, Switzerland: WHO.

    • Search Google Scholar
    • Export Citation
  • 3.

    Hall Z, Allan EL, van Schalkwyk DA, van Wyk A, Kaur H, 2016. Degradation of artemisinin-based combination therapies under tropical conditions. Am J Trop Med Hyg 94: 9931001.

    • Search Google Scholar
    • Export Citation
  • 4.

    Kelesidis T, Falagas ME, 2015. Substandard/counterfeit antimicrobial drugs. Clin Microbiol Rev 28: 443464.

  • 5.

    World Health Organizaiton, 1999. Counterfeit Drugs: Guidelines for the Development of Measures to Combat Counterfeit Drugs. Geneva, Switzerland: WHO.

    • Search Google Scholar
    • Export Citation
  • 6.

    Institute of Medicine, 2013. Buckley GJ, Gostin LO, eds. Countering the Problem of Falsified and Substandard Drugs. Washington, DC: The National Academies Press.

    • Search Google Scholar
    • Export Citation
  • 7.

    White N, 1999. Antimalarial drug resistance and combination chemotherapy. Philos Trans R Soc Lond B Biol Sci 354: 739749.

  • 8.

    White NJ, Pongtavornpinyo W, Maude RJ, Saralamba S, Aguas R, Stepniewska K, Lee SJ, Dondorp AM, White LJ, Day NPJ, 2009. Hyperparasitaemia and low dosing are an important source of anti-malarial drug resistance. Malaria J 8: 253.

    • Search Google Scholar
    • Export Citation
  • 9.

    Newton PN, Green MD, Fernandez FM, Day NP, White NJ, 2006. Counterfeit anti-infective drugs. Lancet Infect Dis 6: 602613.

  • 10.

    Newton PN et al. 2011. Poor quality vital anti-malarials in Africa—an urgent neglected public health priority. Malaria J 10: 352.

  • 11.

    Okeke IN, Lamikanra A, Edelman R, 1999. Socioeconomic and behavioral factors leading to acquired bacterial resistance to antibiotics in developing countries. Emerg Infect Dis 5: 1827.

    • Search Google Scholar
    • Export Citation
  • 12.

    Wirtz V, Hogerzeil H, Gray A, Bigdeli M, de Joncheere C, Ewen M, Gyansa-Lutterodt M, Jing S, Luiza V, Mbindyo R, Moller H, Moucheraud C, Pecoul B, Rago L, Rashidian A, Ross-Degnan D, Stephens P, Teerawattananon Y, ’t Hoen E, Wagner A, Yadav P, Reich M. Essential medicines for universal health coverage. Lancet 389: 403476.

    • Search Google Scholar
    • Export Citation
  • 13.

    World Health Organization, 2018. World Malaria Report 2018. Geneva, Switzerland: WHO.

  • 14.

    United States Agency for International Development, 2017. President’s Malaria Initiative Democratic Republic of the Congo Malaria Operational Plan FY 2017. Washington, DC: USAID.

    • Search Google Scholar
    • Export Citation
  • 15.

    World Health Organization, 2016. Global Health Observatory Data Repository. Geneva, Switzerland: WHO.

  • 16.

    ACTwatch Group Mpanya G, Tshefu A, Likwela JL, 2017. The malaria testing and treatment market in Kinshasa, Democratic Republic of the Congo, 2013. Malaria J 16: 94.

    • Search Google Scholar
    • Export Citation
  • 17.

    Programme National de Lutte contre le Paludisme (PNLP)/Mali, Institut National de la Statistique (INSTAT)/Mali, INFO-STAT/Mali, Institut National de la Recherche en Santé Publique (INRSP)/Mali, ICF International, 2016. République du Mali Enquête sur les Indicateurs du Paludisme (EIPM) 2015. Bamako, Mali: PNLP, INSTAT, INFO-STAT, INRSP, and ICF International.

    • Search Google Scholar
    • Export Citation
  • 18.

    Atemnkeng MA, De Cock K, Plaizier-Vercammen J, 2007. Quality control of active ingredients in artemisinin-derivative antimalarials within Kenya and DR Congo. Trop Med Int Health 12: 6874.

    • Search Google Scholar
    • Export Citation
  • 19.

    Atemnkeng MA, Chimanuka B, Plaizier-Vercammen J, 2007. Quality evaluation of chloroquine, quinine, sulfadoxine-pyrimethamine and proguanil formulations sold on the market in east Congo DR. J Clin Pharm Ther 32: 123132.

    • Search Google Scholar
    • Export Citation
  • 20.

    Newton PN, Hanson K, Goodman C, 2017. Do anti-malarials in Africa meet quality standards? The market penetration of non quality-assured artemisinin combination therapy in eight African countries. Malar J 16: 204.

    • Search Google Scholar
    • Export Citation
  • 21.

    Conte R, Paolucci M, 2014. On agent-based modeling and computational social science. Front Psychol 5: 668.

  • 22.

    Ministère du Plan et Suivi de la Mise en œuvre de la Révolution de la Modernité (MPSMRM)/Congo, Ministère de la Santé Publique (MSP)/Congo, ICF International, 2014. République Démocratique du Congo Enquête Démographique et de Santé (EDS-RDC) 2013–2014. Rockville, MD: MPSMRM, MSP, and ICF International.

    • Search Google Scholar
    • Export Citation
  • 23.

    World Health Organization, 2015. World Malaria Report 2015. Geneva, Switzerland: WHO.

  • 24.

    World Health Organizaiton, 2016. World Malaria Report 2016. Geneva, Switzerland: WHO.

  • 25.

    Malaria Atlas Project, 2018. The Malaria Atlas Project, Interactive Map Tool. Available at: https://map.ox.ac.uk/explorer/#/explorer. Accessed February 20, 2018.

    • Search Google Scholar
    • Export Citation
  • 26.

    ACTwatch Group and ASF, 2015. ACTwatch Study Reference Document: The Democratic Republic of the Congo Outlet Survey 2015. Washington, DC: PSI.

    • Search Google Scholar
    • Export Citation
  • 27.

    World Health Organization, 2015. Guidelines for the Treatment of Malaria, 3rd edition. Geneva, Switzerland: WHO.

  • 28.

    The Global Fund to Fight AIDS, Tuberculosis and Malaria, 2016. Global Fund Grants to the Democratic Republic of the Congo, Audit Report. Geneva, Switzerland: The Global Fund.

    • Search Google Scholar
    • Export Citation
  • 29.

    Kazadi WM, Vong S, Makina BN, Mantshumba JC, Kabuya W, Kebela BI, Ngimbi NP, 2003. Assessing the efficacy of chloroquine and sulfadoxine-pyrimethamine for treatment of uncomplicated Plasmodium falciparum malaria in the Democratic Republic of Congo. Trop Med Int Health 8: 868875.

    • Search Google Scholar
    • Export Citation
  • 30.

    Achan J, Talisuna AO, Erhart A, Yeka A, Tibenderana JK, Baliraine FN, Rosenthal PJ, D’Alessandro U, 2011. Quinine, an old anti-malarial drug in a modern world: role in the treatment of malaria. Malar J 10: 144.

    • Search Google Scholar
    • Export Citation
  • 31.

    Nosten F, White NJ, 2007. Artemisinin-based combination treatment of falciparum malaria. Am J Trop Med Hyg 77: 181192.

  • 32.

    Tshilumba PM, Amuri SB, Kaghowa ER, Mbikay DM, Impele AB, Duez P, Ndoumba JBK, 2015. Survey of counterfeit anti-infectives medicines sold in Lubumbashi [in French]. Pan Afr Med J 22: 318.

    • Search Google Scholar
    • Export Citation
  • 33.

    Ashley EA, White NJ, 2014. The duration of Plasmodium falciparum infections. Malar J 13: 500.

  • 34.

    Ilunga-Ilunga F, Leveque A, Okenge Ngongo L, Tshimungu Kandolo F, Dramaix M, 2014. Costs of treatment of children affected by severe malaria in reference hospitals of Kinshasa, Democratic Republic of Congo. J Infect Dev Ctries 8: 15741583.

    • Search Google Scholar
    • Export Citation
  • 35.

    Kim SY, Sweet S, Slichter D, Goldie SJ, 2010. Health and economic impact of rotavirus vaccination in GAVI-eligible countries. BMC Public Health 10: 253.

    • Search Google Scholar
    • Export Citation
  • 36.

    The World Bank, 2015. GDP Per Capita (Current US$). Available at: https://data.worldbank.org/indicator/ny.gdp.pcap.cd. Accessed April 17, 2018.

    • Search Google Scholar
    • Export Citation
  • 37.

    UNdata, 2014. Life Expectancy at Birth for Both Sexes Combined (Years). Available at: http://data.un.org/Data.aspx?d=PopDiv&f=variableID%3A68. Accessed April 17, 2018.

    • Search Google Scholar
    • Export Citation
  • 38.

    World Health Organization, 2018. Choosing Interventions that are Cost Effective (WHO-CHOICE), Purchasing Power Parity 2005. Geneva, Switzerland: WHO. Available at: https://www.who.int/choice/costs/ppp/en. Accessed April 17, 2018.

    • Search Google Scholar
    • Export Citation
  • 39.

    Dondorp AM et al. 2009. Artemisinin resistance in Plasmodium falciparum malaria. N Engl J Med 361: 455467.

  • 40.

    Mvumbi DM, Kayembe JM, Situakibanza H, Bobanga TL, Nsibu CN, Mvumbi GL, Melin P, De Mol P, Hayette MP, 2015. Falciparum malaria molecular drug resistance in the Democratic Republic of Congo: a systematic review. Malar J 14: 354.

    • Search Google Scholar
    • Export Citation
  • 41.

    Infectious Diseases Data Observatory, 2018. Medicine Quality Literature Report. Oxford, United Kingdom: Infectious Diseases Data Observatory.

    • Search Google Scholar
    • Export Citation
  • 42.

    Schiavetti B et al. 2018. The quality of medicines used in children and supplied by private pharmaceutical wholesalers in Kinshasa, Democratic Republic of Congo: a prospective survey. Am J Trop Med Hyg 98: 894903.

    • Search Google Scholar
    • Export Citation
  • 43.

    Lubell Y, Dondorp A, Guerin PJ, Drake T, Meek S, Ashley E, Day NP, White NJ, White LJ, 2014. Artemisinin resistance—modelling the potential human and economic costs. Malar J 13: 452.

    • Search Google Scholar
    • Export Citation
  • 44.

    Das D et al. 2013. Effect of high-dose or split-dose artesunate on parasite clearance in artemisinin-resistant falciparum malaria. Clin Infect Dis 56: e48e58.

    • Search Google Scholar
    • Export Citation
  • 45.

    O’Connell KA et al. 2011. Got ACTs? Availability, price, market share and provider knowledge of anti-malarial medicines in public and private sector outlets in six malaria-endemic countries. Malar J 10: 326.

    • Search Google Scholar
    • Export Citation
  • 46.

    Ntamabyaliro NY et al. 2018. Drug use in the management of uncomplicated malaria in public health facilities in the Democratic Republic of the Congo. Malar J 17: 189.

    • Search Google Scholar
    • Export Citation
  • 47.

    Mvumbi DM, Bobanga TL, Melin P, De Mol P, Kayembe JMN, Situakibanza HNT, Mvumbi GL, Nsibu CN, Umesumbu SE, Hayette MP, 2016. High prevalence of Plasmodium falciparum infection in asymptomatic individuals from the Democratic Republic of the Congo. Malar Res Treat 2016: 5405802.

    • Search Google Scholar
    • Export Citation
  • 48.

    Siddiqui MR, Willis A, Bil K, Singh J, Mukomena Sompwe E, Ariti C, 2015. Adherence to artemisinin combination therapy for the treatment of uncomplicated malaria in the Democratic Republic of the Congo. F1000Res 4: 51.

    • Search Google Scholar
    • Export Citation

Author Notes

Address correspondence to Sachiko Ozawa, Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, CB#7574, Beard Hall 115H, Chapel Hill, NC 27599. E-mail: ozawa@unc.edu

Authors’ addresses: Sachiko Ozawa, Sarah K. Laing, and Tatenda T. Yemeke, Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, E-mails: ozawa@unc.edu, tyemeke@email.unc.edu, and sklaing@unc.edu. Deson Haynie, Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, E-mail: dgh5xw@virginia.edu. Sophia Bessias, Enterprise Analytics and Data Sciences, University of North Carolina Health Care, Chapel Hill, NC, E-mail: sophia.bessias@unchealth.unc.edu. Emery Ladi Ngamasana, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, E-mail: elngams@email.unc.edu. Daniel R. Evans, Duke University School of Medicine, Durham, NC, Email: daniel.r.evans@duke.edu.

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